LOFT gamma densitometer background fluxes

International Nuclear Information System (INIS)

Grimesey, R.A.; McCracken, R.T.

1978-01-01

Background gamma-ray fluxes were calculated at the location of the γ densitometers without integral shielding at both the hot-leg and cold-leg primary piping locations. The principal sources for background radiation at the γ densitometers are 16 N activity from the primary piping H 2 O and γ radiation from reactor internal sources. The background radiation was calculated by the point-kernel codes QAD-BSA and QAD-P5A. Reasonable assumptions were required to convert the response functions calculated by point-kernel procedures into the gamma-ray spectrum from reactor internal sources. A brief summary of point-kernel equations and theory is included

MARMER, a flexible point-kernel shielding code

International Nuclear Information System (INIS)

Kloosterman, J.L.; Hoogenboom, J.E.

1990-01-01

A point-kernel shielding code entitled MARMER is described. It has several options with respect to geometry input, source description and detector point description which extend the flexibility and usefulness of the code, and which are especially useful in spent fuel shielding. MARMER has been validated using the TN12 spent fuel shipping cask benchmark. (author)

MARMER, a flexible point-kernel shielding code

Energy Technology Data Exchange (ETDEWEB)

Kloosterman, J.L.; Hoogenboom, J.E. (Interuniversitair Reactor Inst., Delft (Netherlands))

1990-01-01

A point-kernel shielding code entitled MARMER is described. It has several options with respect to geometry input, source description and detector point description which extend the flexibility and usefulness of the code, and which are especially useful in spent fuel shielding. MARMER has been validated using the TN12 spent fuel shipping cask benchmark. (author).

Calculation of dose point kernels for five radionuclides used in radio-immunotherapy

International Nuclear Information System (INIS)

Okigaki, S.; Ito, A.; Uchida, I.; Tomaru, T.

1994-01-01

With the recent interest in radioimmunotherapy, attention has been given to calculation of dose distribution from beta rays and monoenergetic electrons in tissue. Dose distribution around a point source of a beta ray emitting radioisotope is referred to as a beta dose point kernel. Beta dose point kernels for five radionuclides such as 131 I, 186 Re, 32 P, 188 Re, and 90 Y appropriate for radioimmunotherapy are calculated by Monte Carlo method using the EGS4 code system. Present results were compared with the published data of experiments and other calculations. Accuracy and precisions of beta dose point kernels are discussed. (author)

Spent Fuel Pool Dose Rate Calculations Using Point Kernel and Hybrid Deterministic-Stochastic Shielding Methods

International Nuclear Information System (INIS)

Matijevic, M.; Grgic, D.; Jecmenica, R.

2016-01-01

This paper presents comparison of the Krsko Power Plant simplified Spent Fuel Pool (SFP) dose rates using different computational shielding methodologies. The analysis was performed to estimate limiting gamma dose rates on wall mounted level instrumentation in case of significant loss of cooling water. The SFP was represented with simple homogenized cylinders (point kernel and Monte Carlo (MC)) or cuboids (MC) using uranium, iron, water, and dry-air as bulk region materials. The pool is divided on the old and new section where the old one has three additional subsections representing fuel assemblies (FAs) with different burnup/cooling time (60 days, 1 year and 5 years). The new section represents the FAs with the cooling time of 10 years. The time dependent fuel assembly isotopic composition was calculated using ORIGEN2 code applied to the depletion of one of the fuel assemblies present in the pool (AC-29). The source used in Microshield calculation is based on imported isotopic activities. The time dependent photon spectra with total source intensity from Microshield multigroup point kernel calculations was then prepared for two hybrid deterministic-stochastic sequences. One is based on SCALE/MAVRIC (Monaco and Denovo) methodology and another uses Monte Carlo code MCNP6.1.1b and ADVANTG3.0.1. code. Even though this model is a fairly simple one, the layers of shielding materials are thick enough to pose a significant shielding problem for MC method without the use of effective variance reduction (VR) technique. For that purpose the ADVANTG code was used to generate VR parameters (SB cards in SDEF and WWINP file) for MCNP fixed-source calculation using continuous energy transport. ADVATNG employs a deterministic forward-adjoint transport solver Denovo which implements CADIS/FW-CADIS methodology. Denovo implements a structured, Cartesian-grid SN solver based on the Koch-Baker-Alcouffe parallel transport sweep algorithm across x-y domain blocks. This was first

Considerations on absorbed dose estimates based on different β-dose point kernels in internal dosimetry

International Nuclear Information System (INIS)

Uchida, Isao; Yamada, Yasuhiko; Yamashita, Takashi; Okigaki, Shigeyasu; Oyamada, Hiyoshimaru; Ito, Akira.

1995-01-01

In radiotherapy with radiopharmaceuticals, more accurate estimates of the three-dimensional (3-D) distribution of absorbed dose is important in specifying the activity to be administered to patients to deliver a prescribed absorbed dose to target volumes without exceeding the toxicity limit of normal tissues in the body. A calculation algorithm for the purpose has already been developed by the authors. An accurate 3-D distribution of absorbed dose based on the algorithm is given by convolution of the 3-D dose matrix for a unit cubic voxel containing unit cumulated activity, which is obtained by transforming a dose point kernel into a 3-D cubic dose matrix, with the 3-D cumulated activity distribution given by the same voxel size. However, beta-dose point kernels affecting accurate estimates of the 3-D absorbed dose distribution have been different among the investigators. The purpose of this study is to elucidate how different beta-dose point kernels in water influence on the estimates of the absorbed dose distribution due to the dose point kernel convolution method by the authors. Computer simulations were performed using the MIRD thyroid and lung phantoms under assumption of uniform activity distribution of 32 P. Using beta-dose point kernels derived from Monte Carlo simulations (EGS-4 or ACCEPT computer code), the differences among their point kernels gave little differences for the mean and maximum absorbed dose estimates for the MIRD phantoms used. In the estimates of mean and maximum absorbed doses calculated using different cubic voxel sizes (4x4x4 mm and 8x8x8 mm) for the MIRD thyroid phantom, the maximum absorbed doses for the 4x4x4 mm-voxel were estimated approximately 7% greater than the cases of the 8x8x8 mm-voxel. They were found in every beta-dose point kernel used in this study. On the other hand, the percentage difference of the mean absorbed doses in the both voxel sizes for each beta-dose point kernel was less than approximately 0.6%. (author)

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

Science.gov (United States)

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

2015-07-07

The aim of this work was to investigate how dose distributions calculated with the collapsed cone (CC) algorithm depend on the size of the water phantom used in deriving the point kernel for multiple scatter. A research version of the CC algorithm equipped with a set of selectable point kernels for multiple-scatter dose that had initially been derived in water phantoms of various dimensions was used. The new point kernels were generated using EGSnrc in spherical water phantoms of radii 5 cm, 7.5 cm, 10 cm, 15 cm, 20 cm, 30 cm and 50 cm. Dose distributions derived with CC in water phantoms of different dimensions and in a CT-based clinical breast geometry were compared to Monte Carlo (MC) simulations using the Geant4-based brachytherapy specific MC code Algebra. Agreement with MC within 1% was obtained when the dimensions of the phantom used to derive the multiple-scatter kernel were similar to those of the calculation phantom. Doses are overestimated at phantom edges when kernels are derived in larger phantoms and underestimated when derived in smaller phantoms (by around 2% to 7% depending on distance from source and phantom dimensions). CC agrees well with MC in the high dose region of a breast implant and is superior to TG43 in determining skin doses for all multiple-scatter point kernel sizes. Increased agreement between CC and MC is achieved when the point kernel is comparable to breast dimensions. The investigated approximation in multiple scatter dose depends on the choice of point kernel in relation to phantom size and yields a significant fraction of the total dose only at distances of several centimeters from a source/implant which correspond to volumes of low doses. The current implementation of the CC algorithm utilizes a point kernel derived in a comparatively large (radius 20 cm) water phantom. A fixed point kernel leads to predictable behaviour of the algorithm with the worst case being a source/implant located well within a patient

Generic primal-dual interior point methods based on a new kernel function

NARCIS (Netherlands)

EL Ghami, M.; Roos, C.

2008-01-01

In this paper we present a generic primal-dual interior point methods (IPMs) for linear optimization in which the search direction depends on a univariate kernel function which is also used as proximity measure in the analysis of the algorithm. The proposed kernel function does not satisfy all the

PERCEVAL v4.0: a new PC code for gamma radiation studies based upon most recent development about point kernel

Energy Technology Data Exchange (ETDEWEB)

Bindel, Laurent; Clouet, Laurent; Castanier, Eric; Bonnet, Jerome; Fleury, Guillaume; Vermuse, Manuel; Gamess, Andre; Lejeune, Eric [Societe Generale pour les techniques Nouvelles, Saint Quentin en Yvelines (France)

2000-03-01

The present paper presents the capabilities of a new code named PERCEVAL v4.0 and based on the new point kernel described in an attached issue. Two linked codes named SPECTRE{sub G} and GRAPH{sub 3}D are part of the code package in order to establish the energetic source term and visualize the tri-dimensional scene respectively. (author)

Characterization of gamma irradiated peanut kernels stored one year under ambient and frozen conditions

International Nuclear Information System (INIS)

Chiou, R.Y.Y.; Shyu, S.L.; Tsai, C.L.

1991-01-01

Peanut kernels were gamma irradiated at 0, 2.5, 5.0, 10, and 20 KGy, and stored 1 yr at ambient and frozen (-14 degrees C) conditions. Irradiated peanuts lost germination capabilities during storage. Molds were detected only on peanuts irradiated with 2.5 KGy and stored at ambient temperature. Peanut oil in kernels stored at -14 degrees C was comparatively more stable than that in peanuts stored at ambient temperature. Oxidation of oil was not significantly changed by irradiation. Changes in fatty acid content varied slightly with exception of linoleic and linolenic acids which decreased with increased radiation depending on storage temperature. The SDS-PAGE protein patterns of peanuts revealed no noticeable variation of protein subunits resulting from irradiation and storage

Validation of a dose-point kernel convolution technique for internal dosimetry

International Nuclear Information System (INIS)

Giap, H.B.; Macey, D.J.; Bayouth, J.E.; Boyer, A.L.

1995-01-01

The objective of this study was to validate a dose-point kernel convolution technique that provides a three-dimensional (3D) distribution of absorbed dose from a 3D distribution of the radionuclide 131 I. A dose-point kernel for the penetrating radiations was calculated by a Monte Carlo simulation and cast in a 3D rectangular matrix. This matrix was convolved with the 3D activity map furnished by quantitative single-photon-emission computed tomography (SPECT) to provide a 3D distribution of absorbed dose. The convolution calculation was performed using a 3D fast Fourier transform (FFT) technique, which takes less than 40 s for a 128 x 128 x 16 matrix on an Intel 486 DX2 (66 MHz) personal computer. The calculated photon absorbed dose was compared with values measured by thermoluminescent dosimeters (TLDS) inserted along the diameter of a 22 cm diameter annular source of 131 I. The mean and standard deviation of the percentage difference between the measurements and the calculations were equal to -1% and 3.6% respectively. This convolution method was also used to calculate the 3D dose distribution in an Alderson abdominal phantom containing a liver, a spleen, and a spherical tumour volume loaded with various concentrations of 131 I. By averaging the dose calculated throughout the liver, spleen, and tumour the dose-point kernel approach was compared with values derived using the MIRD formalism, and found to agree to better than 15%. (author)

Neuronal model with distributed delay: analysis and simulation study for gamma distribution memory kernel.

Science.gov (United States)

Karmeshu; Gupta, Varun; Kadambari, K V

2011-06-01

A single neuronal model incorporating distributed delay (memory)is proposed. The stochastic model has been formulated as a Stochastic Integro-Differential Equation (SIDE) which results in the underlying process being non-Markovian. A detailed analysis of the model when the distributed delay kernel has exponential form (weak delay) has been carried out. The selection of exponential kernel has enabled the transformation of the non-Markovian model to a Markovian model in an extended state space. For the study of First Passage Time (FPT) with exponential delay kernel, the model has been transformed to a system of coupled Stochastic Differential Equations (SDEs) in two-dimensional state space. Simulation studies of the SDEs provide insight into the effect of weak delay kernel on the Inter-Spike Interval(ISI) distribution. A measure based on Jensen-Shannon divergence is proposed which can be used to make a choice between two competing models viz. distributed delay model vis-á-vis LIF model. An interesting feature of the model is that the behavior of (CV(t))((ISI)) (Coefficient of Variation) of the ISI distribution with respect to memory kernel time constant parameter η reveals that neuron can switch from a bursting state to non-bursting state as the noise intensity parameter changes. The membrane potential exhibits decaying auto-correlation structure with or without damped oscillatory behavior depending on the choice of parameters. This behavior is in agreement with empirically observed pattern of spike count in a fixed time window. The power spectral density derived from the auto-correlation function is found to exhibit single and double peaks. The model is also examined for the case of strong delay with memory kernel having the form of Gamma distribution. In contrast to fast decay of damped oscillations of the ISI distribution for the model with weak delay kernel, the decay of damped oscillations is found to be slower for the model with strong delay kernel.

Development of Point Kernel Shielding Analysis Computer Program Implementing Recent Nuclear Data and Graphic User Interfaces

International Nuclear Information System (INIS)

Kang, Sang Ho; Lee, Seung Gi; Chung, Chan Young; Lee, Choon Sik; Lee, Jai Ki

2001-01-01

In order to comply with revised national regulationson radiological protection and to implement recent nuclear data and dose conversion factors, KOPEC developed a new point kernel gamma and beta ray shielding analysis computer program. This new code, named VisualShield, adopted mass attenuation coefficient and buildup factors from recent ANSI/ANS standards and flux-to-dose conversion factors from the International Commission on Radiological Protection (ICRP) Publication 74 for estimation of effective/equivalent dose recommended in ICRP 60. VisualShield utilizes graphical user interfaces and 3-D visualization of the geometric configuration for preparing input data sets and analyzing results, which leads users to error free processing with visual effects. Code validation and data analysis were performed by comparing the results of various calculations to the data outputs of previous programs such as MCNP 4B, ISOSHLD-II, QAD-CGGP, etc

SU-E-T-209: Independent Dose Calculation in FFF Modulated Fields with Pencil Beam Kernels Obtained by Deconvolution

International Nuclear Information System (INIS)

Azcona, J; Burguete, J

2014-01-01

Purpose: To obtain the pencil beam kernels that characterize a megavoltage photon beam generated in a FFF linac by experimental measurements, and to apply them for dose calculation in modulated fields. Methods: Several Kodak EDR2 radiographic films were irradiated with a 10 MV FFF photon beam from a Varian True Beam (Varian Medical Systems, Palo Alto, CA) linac, at the depths of 5, 10, 15, and 20cm in polystyrene (RW3 water equivalent phantom, PTW Freiburg, Germany). The irradiation field was a 50 mm diameter circular field, collimated with a lead block. Measured dose leads to the kernel characterization, assuming that the energy fluence exiting the linac head and further collimated is originated on a point source. The three-dimensional kernel was obtained by deconvolution at each depth using the Hankel transform. A correction on the low dose part of the kernel was performed to reproduce accurately the experimental output factors. The kernels were used to calculate modulated dose distributions in six modulated fields and compared through the gamma index to their absolute dose measured by film in the RW3 phantom. Results: The resulting kernels properly characterize the global beam penumbra. The output factor-based correction was carried out adding the amount of signal necessary to reproduce the experimental output factor in steps of 2mm, starting at a radius of 4mm. There the kernel signal was in all cases below 10% of its maximum value. With this correction, the number of points that pass the gamma index criteria (3%, 3mm) in the modulated fields for all cases are at least 99.6% of the total number of points. Conclusion: A system for independent dose calculations in modulated fields from FFF beams has been developed. Pencil beam kernels were obtained and their ability to accurately calculate dose in homogeneous media was demonstrated

Suitability of point kernel dose calculation techniques in brachytherapy treatment planning

Directory of Open Access Journals (Sweden)

Lakshminarayanan Thilagam

2010-01-01

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

Comparison of electron dose-point kernels in water generated by the Monte Carlo codes, PENELOPE, GEANT4, MCNPX, and ETRAN.

Science.gov (United States)

Uusijärvi, Helena; Chouin, Nicolas; Bernhardt, Peter; Ferrer, Ludovic; Bardiès, Manuel; Forssell-Aronsson, Eva

2009-08-01

Point kernels describe the energy deposited at a certain distance from an isotropic point source and are useful for nuclear medicine dosimetry. They can be used for absorbed-dose calculations for sources of various shapes and are also a useful tool when comparing different Monte Carlo (MC) codes. The aim of this study was to compare point kernels calculated by using the mixed MC code, PENELOPE (v. 2006), with point kernels calculated by using the condensed-history MC codes, ETRAN, GEANT4 (v. 8.2), and MCNPX (v. 2.5.0). Point kernels for electrons with initial energies of 10, 100, 500, and 1 MeV were simulated with PENELOPE. Spherical shells were placed around an isotropic point source at distances from 0 to 1.2 times the continuous-slowing-down-approximation range (R(CSDA)). Detailed (event-by-event) simulations were performed for electrons with initial energies of less than 1 MeV. For 1-MeV electrons, multiple scattering was included for energy losses less than 10 keV. Energy losses greater than 10 keV were simulated in a detailed way. The point kernels generated were used to calculate cellular S-values for monoenergetic electron sources. The point kernels obtained by using PENELOPE and ETRAN were also used to calculate cellular S-values for the high-energy beta-emitter, 90Y, the medium-energy beta-emitter, 177Lu, and the low-energy electron emitter, 103mRh. These S-values were also compared with the Medical Internal Radiation Dose (MIRD) cellular S-values. The greatest differences between the point kernels (mean difference calculated for distances, electrons was 1.4%, 2.5%, and 6.9% for ETRAN, GEANT4, and MCNPX, respectively, compared to PENELOPE, if omitting the S-values when the activity was distributed on the cell surface for 10-keV electrons. The largest difference between the cellular S-values for the radionuclides, between PENELOPE and ETRAN, was seen for 177Lu (1.2%). There were large differences between the MIRD cellular S-values and those obtained from

BrachyTPS -Interactive point kernel code package for brachytherapy treatment planning of gynaecological cancers

International Nuclear Information System (INIS)

Thilagam, L.; Subbaiah, K.V.

2008-01-01

Brachytherapy treatment planning systems (TPS) are always recommended to account for the effect of tissue, applicator and shielding material heterogeneities exist in Intracavitary brachytherapy (ICBT) applicators. Most of the commercially available brachytherapy TPS softwares estimate the absorbed dose at a point, only taking care of the contributions of individual sources and the source distribution, neglecting the dose perturbations arising from the applicator design and construction. So the doses estimated by them are not much accurate under realistic clinical conditions. In this regard, interactive point kernel rode (BrachyTPS) has been developed to perform independent dose calculations by taking into account the effect of these heterogeneities, using two regions build up factors, proposed by Kalos. As primary input data, the code takes patients' planning data including the source specifications, dwell positions, dwell times and it computes the doses at reference points by dose point kernel formalisms, with multi-layer shield build-up factors accounting for the contributions from scattered radiation. In addition to performing dose distribution calculations, this code package is capable of displaying an isodose distribution curve into the patient anatomy images. The primary aim of this study is to validate the developed point kernel code integrated with treatment planning systems against the other tools which are available in the market. In the present work, three brachytherapy applicators commonly used in the treatment of uterine cervical carcinoma, Board of Radiation Isotope and Technology (BRIT) made low dose rate (LDR) applicator, Fletcher Green type LDR applicator and Fletcher Williamson high dose rate (HDR) applicator were studied to test the accuracy of the software

Gamma ray shielding: a web based interactive program

International Nuclear Information System (INIS)

Subbaiah, K.V.; Senthi Kumar, C.; Sarangapani, R.

2005-01-01

A web based interactive computing program is developed using java for quick assessment of Gamma Ray shielding problems. The program addresses usually encountered source geometries like POINT, LINE, CYLINDRICAL, ANNULAR, SPHERICAL, BOX, followed by 'SLAB' shield configurations. The calculation is based on point kernel technique. The source points are randomly sampled within the source volume. From each source point, optical path traversed in the source and shield media up to the detector location is estimated to calculate geometrical and material attenuations, and then corresponding buildup factor is obtained, which accounts for scattered contribution. Finally, the dose rate for entire source is obtained by summing over all sampled points. The application allows the user to select one of the seven regular geometrical bodies and provision exist to give source details such as emission energies, intensities, physical dimensions and material composition. Similar provision is provided to specify shield slab details. To aid the user, atomic numbers, densities, standard build factor materials and isotope list with respective emission energies and intensity for ready reference are given in dropdown combo boxes. Typical results obtained from this program are validated against existing point kernel gamma ray shielding codes. Additional facility is provided to compute fission product gamma ray source strengths based on the fuel type, burn up and cooling time. Plots of Fission product gamma ray source strengths, Gamma ray cross-sections and buildup factors can be optionally obtained, which enable the user to draw inference on the computed results. It is expected that this tool will be handy to all health physicists and radiological safety officers as it will be available on the internet. (author)

NARMER-1: a photon point-kernel code with build-up factors

Science.gov (United States)

Visonneau, Thierry; Pangault, Laurence; Malouch, Fadhel; Malvagi, Fausto; Dolci, Florence

2017-09-01

This paper presents an overview of NARMER-1, the new generation of photon point-kernel code developed by the Reactor Studies and Applied Mathematics Unit (SERMA) at CEA Saclay Center. After a short introduction giving some history points and the current context of development of the code, the paper exposes the principles implemented in the calculation, the physical quantities computed and surveys the generic features: programming language, computer platforms, geometry package, sources description, etc. Moreover, specific and recent features are also detailed: exclusion sphere, tetrahedral meshes, parallel operations. Then some points about verification and validation are presented. Finally we present some tools that can help the user for operations like visualization and pre-treatment.

Biasing anisotropic scattering kernels for deep-penetration Monte Carlo calculations

International Nuclear Information System (INIS)

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

1983-01-01

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

Calculation of gamma-rays and fast neutrons fluxes with the program Mercure-4

International Nuclear Information System (INIS)

Baur, A.; Dupont, C.; Totth, B.

1978-01-01

The program MERCURE-4 evaluates gamma ray or fast neutron attenuation, through laminated or bulky three-dimensionnal shields. The method used is that of line of sight point attenuation kernel, the scattered rays being taken into account by means of build-up factors for γ and removal cross sections for fast neutrons. The integration of the point kernel over the range of sources distributed in space and energy, is performed by the Monte-Carlo method, with an automatic adjustment of the importance functions. Since it is operationnal the program MERCURE-4 has been intensively used for many various problems, for example: - the calculation of gamma heating in reactor cores, control rods and shielding screens, as well as in experimental devices and irradiation loops; - the evaluation of fast neutron fluxes and corresponding damage in structural materials of reactors (vessel steels...); - the estimation of gamma dose rates on nuclear instrumentation in the reactors, around the reactor circuits and around spent fuel shipping casks

GRAYSKY-A new gamma-ray skyshine code

International Nuclear Information System (INIS)

Witts, D.J.; Twardowski, T.; Watmough, M.H.

1993-01-01

This paper describes a new prototype gamma-ray skyshine code GRAYSKY (Gamma-RAY SKYshine) that has been developed at BNFL, as part of an industrially based master of science course, to overcome the problems encountered with SKYSHINEII and RANKERN. GRAYSKY is a point kernel code based on the use of a skyshine response function. The scattering within source or shield materials is accounted for by the use of buildup factors. This is an approximate method of solution but one that has been shown to produce results that are acceptable for dose rate predictions on operating plants. The novel features of GRAYSKY are as follows: 1. The code is fully integrated with a semianalytical point kernel shielding code, currently under development at BNFL, which offers powerful solid-body modeling capabilities. 2. The geometry modeling also allows the skyshine response function to be used in a manner that accounts for the shielding of air-scattered radiation. 3. Skyshine buildup factors calculated using the skyshine response function have been used as well as dose buildup factors

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

International Nuclear Information System (INIS)

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

2011-01-01

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

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

Directory of Open Access Journals (Sweden)

Chenchao Zhao

2018-01-01

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

Assessing opportunities for physical activity in the built environment of children: interrelation between kernel density and neighborhood scale.

Science.gov (United States)

Buck, Christoph; Kneib, Thomas; Tkaczick, Tobias; Konstabel, Kenn; Pigeot, Iris

2015-12-22

Built environment studies provide broad evidence that urban characteristics influence physical activity (PA). However, findings are still difficult to compare, due to inconsistent measures assessing urban point characteristics and varying definitions of spatial scale. Both were found to influence the strength of the association between the built environment and PA. We simultaneously evaluated the effect of kernel approaches and network-distances to investigate the association between urban characteristics and physical activity depending on spatial scale and intensity measure. We assessed urban measures of point characteristics such as intersections, public transit stations, and public open spaces in ego-centered network-dependent neighborhoods based on geographical data of one German study region of the IDEFICS study. We calculated point intensities using the simple intensity and kernel approaches based on fixed bandwidths, cross-validated bandwidths including isotropic and anisotropic kernel functions and considering adaptive bandwidths that adjust for residential density. We distinguished six network-distances from 500 m up to 2 km to calculate each intensity measure. A log-gamma regression model was used to investigate the effect of each urban measure on moderate-to-vigorous physical activity (MVPA) of 400 2- to 9.9-year old children who participated in the IDEFICS study. Models were stratified by sex and age groups, i.e. pre-school children (2 to kernel approaches. Smallest variation in effect estimates over network-distances was found for kernel intensity measures based on isotropic and anisotropic cross-validated bandwidth selection. We found a strong variation in the association between the built environment and PA of children based on the choice of intensity measure and network-distance. Kernel intensity measures provided stable results over various scales and improved the assessment compared to the simple intensity measure. Considering different spatial

SFAK, Unscattered Gamma Self-Absorption from Regular Fuel Rod Assemblies

International Nuclear Information System (INIS)

Wand, H.

1982-01-01

1 - Description of problem or function: Calculation of the self- absorption of unscattered (gamma-) radiation from fuel assemblies which contain a regular arrangement of identical fuel rods. 2 - Method of solution: The point-kernel is integrated over the radiation sources, i.e. the fuel rods. A uniform mesh of integration points is used for each of the fuel rods. 3 - Restrictions on the complexity of the problem: Number of fuel rods is dynamically allocated

Semi-Supervised Kernel PCA

DEFF Research Database (Denmark)

Walder, Christian; Henao, Ricardo; Mørup, Morten

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

Global Polynomial Kernel Hazard Estimation

DEFF Research Database (Denmark)

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

2015-01-01

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

An Approximate Approach to Automatic Kernel Selection.

Science.gov (United States)

Ding, Lizhong; Liao, Shizhong

2016-02-02

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

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

NARCIS (Netherlands)

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

2016-01-01

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

Optimal kernel shape and bandwidth for atomistic support of continuum stress

International Nuclear Information System (INIS)

Ulz, Manfred H; Moran, Sean J

2013-01-01

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

Robust Kernel (Cross-) Covariance Operators in Reproducing Kernel Hilbert Space toward Kernel Methods

OpenAIRE

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

2016-01-01

To the best of our knowledge, there are no general well-founded robust methods for statistical unsupervised learning. Most of the unsupervised methods explicitly or implicitly depend on the kernel covariance operator (kernel CO) or kernel cross-covariance operator (kernel CCO). They are sensitive to contaminated data, even when using bounded positive definite kernels. First, we propose robust kernel covariance operator (robust kernel CO) and robust kernel crosscovariance operator (robust kern...

Kernel principal component analysis for change detection

DEFF Research Database (Denmark)

Nielsen, Allan Aasbjerg; Morton, J.C.

2008-01-01

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

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

Directory of Open Access Journals (Sweden)

Mohammed D. ABDULMALIK

2008-06-01

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

Kernel bundle EPDiff

DEFF Research Database (Denmark)

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

2011-01-01

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

Convergence of barycentric coordinates to barycentric kernels

KAUST Repository

Kosinka, Jiří

2016-02-12

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

Convergence of barycentric coordinates to barycentric kernels

KAUST Repository

Kosinka, Jiří ; Barton, Michael

2016-01-01

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

Swift pointing and gravitational-wave bursts from gamma-ray burst events

International Nuclear Information System (INIS)

Sutton, Patrick J; Finn, Lee Samuel; Krishnan, Badri

2003-01-01

The currently accepted model for gamma-ray burst phenomena involves the violent formation of a rapidly rotating solar-mass black hole. Gravitational waves should be associated with the black-hole formation, and their detection would permit this model to be tested. Even upper limits on the gravitational-wave strength associated with gamma-ray bursts could constrain the gamma-ray burst model. This requires joint observations of gamma-ray burst events with gravitational and gamma-ray detectors. Here we examine how the quality of an upper limit on the gravitational-wave strength associated with gamma-ray bursts depends on the relative orientation of the gamma-ray-burst and gravitational-wave detectors, and apply our results to the particular case of the Swift Burst-Alert Telescope (BAT) and the LIGO gravitational-wave detectors. A result of this investigation is a science-based 'figure of merit' that can be used, together with other mission constraints, to optimize the pointing of the Swift telescope for the detection of gravitational waves associated with gamma-ray bursts

Delayed Fission Product Gamma-Ray Transmission Through Low Enriched UO₂ Fuel Pin Lattices in Air

Energy Technology Data Exchange (ETDEWEB)

Trumbull, TH [Rensselaer Polytechnic Inst., Troy, NY (United States)

2004-10-18

The transmission of delayed fission-product gamma rays through various arrangements of low-enriched UO2 fuel pin lattices in an air medium was studied. Experimental measurements, point-kernel and Monte Carlo photon transport calculations were performed to demonstrate the shielding effect of ordered lattices of fuel pins on the resulting gamma-ray dose to a detector outside the lattice. The variation of the gamma-ray dose on the outside of the lattice as a function of radial position, the so-called “channeling” effect, was analyzed. Techniques for performing experimental measurements and data reduction at Rensselaer Polytechnic Institute’s Reactor Critical Facility (RCF) were derived. An experimental apparatus was constructed to hold the arrangements of fuel pins for the measurements. A gamma-ray spectroscopy system consisting of a sodium-iodide scintillation detector was used to collect data. Measurements were made with and without a collimator installed. A point-kernel transport code was developed to map the radial dependence of the gamma-ray flux. Input files for the Monte Carlo code, MCNP, were also developed to accurately model the experimental measurements. The results of the calculations were compared to the experimental measurements. In order to determine the delayed fission-product gamma-ray source for the calculations, a technique was developed using a previously written code, DELBG and the reactor state-point data obtained during the experimental measurements. Calculations were performed demonstrating the effects of material homogenization on the gamma-ray transmission through the fuel pin lattice.Homogeneous and heterogeneous calculations were performed for all RCF fuel pin lattices as well as for a typical commercial pressurized water reactor fuel bundle. The results of the study demonstrated the effectiveness of the experimental measurements to isolate the channeling effect of delayed fission-product gamma-rays through lattices of RCF fuel pins

Shielding Factors for Gamma Radiation from Activity Deposited on Structures and Ground Surfaces

DEFF Research Database (Denmark)

Jensen, Per Hedemann

1985-01-01

A computer model DEPSHIELD for the calculation of shielding factors for gamma radiation at indoor residences in multistorey and single-family houses has been developed. The model is based on the exponential point kernel that links the radiation flux density at a given detector point to a point...... it possible to determine the dose reduction effect from a decontamination of the different surfaces. The model has been used in a study of the consequences of land contamination of Danish territory after hypothetical core-melt accidents at the Barseback nuclear power plant in Sweden. The model has also been...

New buildup factor data for point kernel calculations

International Nuclear Information System (INIS)

Trubey, D.K.; Harima, Y.

1986-01-01

An American Nuclear Society Standards Committee Working Group, identified as ANS-6.4.3, is developing a set of evaluated gamma-ray isotropic point-source buildup factors and attenuation coefficients for a standard reference data base. As a first step, a largely unpublished set of buildup factors calculated with the moments method has been evaluated by recalculating key values with Monte Carlo, integral transport, and discrete ordinates methods. Attention is being given to frequently-neglected processes such as bremsstrahlung and the effect of introducing a tissue phantom behind the shield. The proposed standard contains data for a source energy range from 15 keV to 15 MeV and for approximately 19 elements and 3 mixtures (water, air, and concrete). The data will also be represented as coefficients for the G-P fitting function. The 1985 data base was released as part of the CCC-493B/QAD-CGGP code package available from the Radiation Shielding Information Center (RSIC)

Shielding calculations and collective dose estimations with the point-kernel-code VISIPLAN registered for the example of the project ZENT

International Nuclear Information System (INIS)

Boehlke, S.; Niegoth, H.

2012-01-01

In the nuclear power plant Leibstadt (KKL) during the next year large components will be dismantled and stored for final disposal within the interim storage facility ZENT at the NPP site. Before construction of ZENT appropriate estimations of the local dose rate inside and outside the building and the collective dose for the normal operation have to be performed. The shielding calculations are based on the properties of the stored components and radiation sources and on the concepts for working place requirements. The installation of control and monitoring areas will depend on these calculations. For the determination of the shielding potential of concrete walls and steel doors with the defined boundary conditions point-kernel codes like MICROSHIELd registered are used. Complex problems cannot be modeled with this code. Therefore the point-kernel code VISIPLAN registered was developed for the determination of the local dose distribution functions in 3D models. The possibility of motion sequence inputs allows an optimization of collective dose estimations for the operational phases of a nuclear facility.

Primary and scattering contributions to beta scaled dose point kernels by means of Monte Carlo simulations; Contribuicoes primaria e espalhada para dosimetria beta calculadas pelo dose point kernels empregando simulacoes pelo Metodo Monte Carlo

Energy Technology Data Exchange (ETDEWEB)

Valente, Mauro [CONICET - Consejo Nacional de Investigaciones Cientificas y Tecnicas de La Republica Argentina (Conicet), Buenos Aires, AR (Brazil); Botta, Francesca; Pedroli, Guido [European Institute of Oncology, Milan (Italy). Medical Physics Department; Perez, Pedro, E-mail: valente@famaf.unc.edu.ar [Universidad Nacional de Cordoba, Cordoba (Argentina). Fac. de Matematica, Astronomia y Fisica (FaMAF)

2012-07-01

Beta-emitters have proved to be appropriate for radioimmunotherapy. The dosimetric characterization of each radionuclide has to be carefully investigated. One usual and practical dosimetric approach is the calculation of dose distribution from a unit point source emitting particles according to any radionuclide of interest, which is known as dose point kernel. Absorbed dose distributions are due to primary and radiation scattering contributions. This work presented a method capable of performing dose distributions for nuclear medicine dosimetry by means of Monte Carlo methods. Dedicated subroutines have been developed in order to separately compute primary and scattering contributions to the total absorbed dose, performing particle transport up to 1 keV or least. Preliminarily, the suitability of the calculation method has been satisfactory, being tested for monoenergetic sources, and it was further applied to the characterization of different beta-minus radionuclides of nuclear medicine interests for radioimmunotherapy. (author)

Gamma Rays from the Inner Milky Way: Dark Matter or Point Sources?

CERN Multimedia

CERN. Geneva

2015-01-01

Studies of data from the Fermi Gamma-Ray Space Telescope have revealed bright gamma-ray emission from the central regions of our galaxy, with a spatial and spectral profile consistent with annihilating dark matter. I will present a new model-independent analysis that suggests that rather than originating from dark matter, the GeV excess may arise from a surprising new population of as-yet-unresolved gamma-ray point sources in the heart of the Milky Way.

Kernel-based tests for joint independence

DEFF Research Database (Denmark)

Pfister, Niklas; Bühlmann, Peter; Schölkopf, Bernhard

2018-01-01

if the $d$ variables are jointly independent, as long as the kernel is characteristic. Based on an empirical estimate of dHSIC, we define three different non-parametric hypothesis tests: a permutation test, a bootstrap test and a test based on a Gamma approximation. We prove that the permutation test......We investigate the problem of testing whether $d$ random variables, which may or may not be continuous, are jointly (or mutually) independent. Our method builds on ideas of the two variable Hilbert-Schmidt independence criterion (HSIC) but allows for an arbitrary number of variables. We embed...... the $d$-dimensional joint distribution and the product of the marginals into a reproducing kernel Hilbert space and define the $d$-variable Hilbert-Schmidt independence criterion (dHSIC) as the squared distance between the embeddings. In the population case, the value of dHSIC is zero if and only...

Statistical analysis of the influence of wheat black point kernels on selected indicators of wheat flour quality

Directory of Open Access Journals (Sweden)

Petrov Verica D.

2011-01-01

Full Text Available The influence of wheat black point kernels on selected indicators of wheat flour quality - farinograph and extensograph indicators, amylolytic activity, wet gluten and flour ash content, were examined in this study. The examinations were conducted on samples of wheat harvested in the years 2007 and 2008 from the area of Central Banat in four treatments-control (without black point flour and with 2, 4 and 10% of black point flour which was added as a replacement for a part of the control sample. Statistically significant differences between treatments were observed on the dough stability, falling number and extensibility. The samples with 10% of black point flour had the lowest dough stability and the highest amylolytic activity and extensibility. There was a trend of the increasing 15 min drop and water absorption with the increased share of black point flour. Extensograph area, resistance and ratio resistance to extensibility decreased with the addition of black point flour, but not properly. Mahalanobis distance indicates that the addition of 10% black point flour had the greatest influence on the observed quality indicators, thus proving that black point contributes to the technological quality of wheat, i.e .flour.

Nomogram for Determining Shield Thickness for Point and Line Sources of Gamma Rays

Energy Technology Data Exchange (ETDEWEB)

Joenemalm, C; Malen, K

1966-10-15

A set of nomograms is given for the determination of the required shield thickness against gamma radiation. The sources handled are point and infinite line sources with shields of Pb, Fe, magnetite concrete (p = 3.6), ordinary concrete (p = 2.3) or water. The gamma energy range covered is 0.5 - 10 MeV. The nomograms are directly applicable for source and dose points on the surfaces of the shield. They can easily be extended to source and dose points in other positions by applying a geometrical correction. Also included are data for calculation of the source strength for the most common materials and for fission product sources.

Nomogram for Determining Shield Thickness for Point and Line Sources of Gamma Rays

International Nuclear Information System (INIS)

Joenemalm, C.; Malen, K

1966-10-01

A set of nomograms is given for the determination of the required shield thickness against gamma radiation. The sources handled are point and infinite line sources with shields of Pb, Fe, magnetite concrete (p = 3.6), ordinary concrete (p = 2.3) or water. The gamma energy range covered is 0.5 - 10 MeV. The nomograms are directly applicable for source and dose points on the surfaces of the shield. They can easily be extended to source and dose points in other positions by applying a geometrical correction. Also included are data for calculation of the source strength for the most common materials and for fission product sources

A simplified computer code based on point Kernel theory for calculating radiation dose in packages of radioactive material

International Nuclear Information System (INIS)

1986-03-01

A study on radiation dose control in packages of radioactive waste from nuclear facilities, hospitals and industries, such as sources of Ra-226, Co-60, Ir-192 and Cs-137, is presented. The MAPA and MAPAM computer codes, based on point Kernel theory for calculating doses of several source-shielding type configurations, aiming to assure the safe transport conditions for these sources, was developed. The validation of the code for point sources, using the values provided by NCRP, for the thickness of lead and concrete shieldings, limiting the dose at 100 Mrem/hr for several distances from the source to the detector, was carried out. The validation for non point sources was carried out, measuring experimentally radiation dose from packages developed by Brazilian CNEN/S.P. for removing the sources. (M.C.K.) [pt

GAMMA-CLOUD: a computer code for calculating gamma-exposure due to a radioactive cloud released from a point source

Energy Technology Data Exchange (ETDEWEB)

Sugimoto, O [Chugoku Electric Power Co. Inc., Hiroshima (Japan); Sawaguchi, Y; Kaneko, M

1979-03-01

A computer code, designated GAMMA-CLOUD, has been developed by specialists of electric power companies to meet requests from the companies to have a unified means of calculating annual external doses from routine releases of radioactive gaseous effluents from nuclear power plants, based on the Japan Atomic Energy Commission's guides for environmental dose evaluation. GAMMA-CLOUD is written in FORTRAN language and its required capacity is less than 100 kilobytes. The average ..gamma..-exposure at an observation point can be calculated within a few minutes with comparable precision to other existing codes.

B{yields}V{gamma} decays at NNLO in SCET

Energy Technology Data Exchange (ETDEWEB)

Ali, A.; Pecjak, B.D. [Deutsches Elektronen-Synchrotron (DESY), Hamburg (Germany); Greub, C. [Bern Univ. (Switzerland). Inst. fuer Theoretische Physik

2007-09-15

We compute NNLO (O({alpha}{sup 2}{sub s})) corrections to the hard-scattering kernels entering the QCD factorization formula for B {yields} V{gamma} decays, where V is a light vector meson. We give complete NNLO results for the dipole operators Q{sub 7} and Q{sub 8}, and partial results for Q{sub 1} valid in the large-{beta}{sub 0} limit and neglecting the NNLO correction from hard spectator scattering. Large perturbative logarithms in the hard-scattering kernels are identified and resummed using soft-collinear effective theory. We use our results to estimate the branching fractions for B {yields} K{sup *}{gamma} and B{sub s} {yields} {phi}{gamma} decays at NNLO and compare them with the current experimental data. (orig.)

Locally linear approximation for Kernel methods : the Railway Kernel

OpenAIRE

Muñoz, Alberto; González, Javier

2008-01-01

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

Code system BCG for gamma-ray skyshine calculation

International Nuclear Information System (INIS)

Ryufuku, Hiroshi; Numakunai, Takao; Miyasaka, Shun-ichi; Minami, Kazuyoshi.

1979-03-01

A code system BCG has been developed for calculating conveniently and efficiently gamma-ray skyshine doses using the transport calculation codes ANISN and DOT and the point-kernel calculation codes G-33 and SPAN. To simplify the input forms to the system, the forms for these codes are unified, twelve geometric patterns are introduced to give material regions, and standard data are available as a library. To treat complex arrangements of source and shield, it is further possible to use successively the code such that the results from one code may be used as input data to the same or other code. (author)

Formalism for neutron cross section covariances in the resonance region using kernel approximation

Energy Technology Data Exchange (ETDEWEB)

Oblozinsky, P.; Cho,Y-S.; Matoon,C.M.; Mughabghab,S.F.

2010-04-09

We describe analytical formalism for estimating neutron radiative capture and elastic scattering cross section covariances in the resolved resonance region. We use capture and scattering kernels as the starting point and show how to get average cross sections in broader energy bins, derive analytical expressions for cross section sensitivities, and deduce cross section covariances from the resonance parameter uncertainties in the recently published Atlas of Neutron Resonances. The formalism elucidates the role of resonance parameter correlations which become important if several strong resonances are located in one energy group. Importance of potential scattering uncertainty as well as correlation between potential scattering and resonance scattering is also examined. Practical application of the formalism is illustrated on {sup 55}Mn(n,{gamma}) and {sup 55}Mn(n,el).

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

Directory of Open Access Journals (Sweden)

Senyue Zhang

2016-01-01

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

Computer program SCAP-BR for gamma-ray streaming through multi-legged ducts

International Nuclear Information System (INIS)

Byoun, T.Y.; Babel, P.J.; Dajani, A.T.

1977-01-01

A computer program, SCAP-BR, has been developed at Burns and Roe for the gamma-ray streaming analysis through multi-legged ducts. SCAP-BR is a modified version of the single scattering code, SCAP, incorporating capabilities of handling multiple scattering and volumetric source geometries. It utilizes the point kernel integration method to calculate both the line-of-sight and scattered gamma dose rates by employing the ray tracing technique through complex shield geometries. The multiple scattering is handled by a repeated process of the single scatter method through each successive scatter region and collapsed pseudo source meshes constructed on the relative coordinate systems. The SCAP-BR results have been compared with experimental data for a Z-type (three-legged) concrete duct with a Co-60 source placed at the duct entrance point. The SCAP-BR dose rate predictions along the duct axis demonstrate an excellent agreement with the measured values

Kernel Machine SNP-set Testing under Multiple Candidate Kernels

Science.gov (United States)

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

2013-01-01

Joint testing for the cumulative effect of multiple single nucleotide polymorphisms grouped on the basis of prior biological knowledge has become a popular and powerful strategy for the analysis of large scale genetic association studies. The kernel machine (KM) testing framework is a useful approach that has been proposed for testing associations between multiple genetic variants and many different types of complex traits by comparing pairwise similarity in phenotype between subjects to pairwise similarity in genotype, with similarity in genotype defined via a kernel function. An advantage of the KM framework is its flexibility: choosing different kernel functions allows for different assumptions concerning the underlying model and can allow for improved power. In practice, it is difficult to know which kernel to use a priori since this depends on the unknown underlying trait architecture and selecting the kernel which gives the lowest p-value can lead to inflated type I error. Therefore, we propose practical strategies for KM testing when multiple candidate kernels are present based on constructing composite kernels and based on efficient perturbation procedures. We demonstrate through simulations and real data applications that the procedures protect the type I error rate and can lead to substantially improved power over poor choices of kernels and only modest differences in power versus using the best candidate kernel. PMID:23471868

Variable Kernel Density Estimation

OpenAIRE

Terrell, George R.; Scott, David W.

1992-01-01

We investigate some of the possibilities for improvement of univariate and multivariate kernel density estimates by varying the window over the domain of estimation, pointwise and globally. Two general approaches are to vary the window width by the point of estimation and by point of the sample observation. The first possibility is shown to be of little efficacy in one variable. In particular, nearest-neighbor estimators in all versions perform poorly in one and two dimensions, but begin to b...

Primary and scattering contributions to beta scaled dose point kernels by means of Monte Carlo simulations

International Nuclear Information System (INIS)

Valente, Mauro; Botta, Francesca; Pedroli, Guido

2012-01-01

Beta-emitters have proved to be appropriate for radioimmunotherapy. The dosimetric characterization of each radionuclide has to be carefully investigated. One usual and practical dosimetric approach is the calculation of dose distribution from a unit point source emitting particles according to any radionuclide of interest, which is known as dose point kernel. Absorbed dose distributions are due to primary and radiation scattering contributions. This work presented a method capable of performing dose distributions for nuclear medicine dosimetry by means of Monte Carlo methods. Dedicated subroutines have been developed in order to separately compute primary and scattering contributions to the total absorbed dose, performing particle transport up to 1 keV or least. Preliminarily, the suitability of the calculation method has been satisfactory, being tested for monoenergetic sources, and it was further applied to the characterization of different beta-minus radionuclides of nuclear medicine interests for radioimmunotherapy. (author)

Selection and properties of alternative forming fluids for TRISO fuel kernel production

Energy Technology Data Exchange (ETDEWEB)

Baker, M.P. [Colorado School of Mines, 1500 Illinois St., Golden, CO 80401 (United States); King, J.C., E-mail: kingjc@mines.edu [Colorado School of Mines, 1500 Illinois St., Golden, CO 80401 (United States); Gorman, B.P. [Colorado School of Mines, 1500 Illinois St., Golden, CO 80401 (United States); Marshall, D.W. [Idaho National Laboratory, 2525 N. Fremont Avenue, P.O. Box 1625, Idaho Falls, ID 83415 (United States)

2013-01-15

Highlights: Black-Right-Pointing-Pointer Forming fluid selection criteria developed for TRISO kernel production. Black-Right-Pointing-Pointer Ten candidates selected for further study. Black-Right-Pointing-Pointer Density, viscosity, and surface tension measured for first time. Black-Right-Pointing-Pointer Settling velocity and heat transfer rates calculated. Black-Right-Pointing-Pointer Three fluids recommended for kernel production testing. - Abstract: Current Very High Temperature Reactor (VHTR) designs incorporate TRi-structural ISOtropic (TRISO) fuel, which consists of a spherical fissile fuel kernel surrounded by layers of pyrolytic carbon and silicon carbide. An internal sol-gel process forms the fuel kernel using wet chemistry to produce uranium oxyhydroxide gel spheres by dropping a cold precursor solution into a hot column of trichloroethylene (TCE). Over time, gelation byproducts inhibit complete gelation, and the TCE must be purified or discarded. The resulting TCE waste stream contains both radioactive and hazardous materials and is thus considered a mixed hazardous waste. Changing the forming fluid to a non-hazardous alternative could greatly improve the economics of TRISO fuel kernel production. Selection criteria for a replacement forming fluid narrowed a list of {approx}10,800 chemicals to yield ten potential replacement forming fluids: 1-bromododecane, 1-bromotetradecane, 1-bromoundecane, 1-chlorooctadecane, 1-chlorotetradecane, 1-iododecane, 1-iodododecane, 1-iodohexadecane, 1-iodooctadecane, and squalane. The density, viscosity, and surface tension for each potential replacement forming fluid were measured as a function of temperature between 25 Degree-Sign C and 80 Degree-Sign C. Calculated settling velocities and heat transfer rates give an overall column height approximation. 1-bromotetradecane, 1-chlorooctadecane, and 1-iodododecane show the greatest promise as replacements, and future tests will verify their ability to form satisfactory

ATP-gamma-S shifts the operating point of outer hair cell transduction towards scala tympani.

Science.gov (United States)

Bobbin, Richard P; Salt, Alec N

2005-07-01

ATP receptor agonists and antagonists alter cochlear mechanics as measured by changes in distortion product otoacoustic emissions (DPOAE). Some of the effects on DPOAEs are consistent with the hypothesis that ATP affects mechano-electrical transduction and the operating point of the outer hair cells (OHCs). This hypothesis was tested by monitoring the effect of ATP-gamma-S on the operating point of the OHCs. Guinea pigs anesthetized with urethane and with sectioned middle ear muscles were used. The cochlear microphonic (CM) was recorded differentially (scala vestibuli referenced to scala tympani) across the basal turn before and after perfusion (20 min) of the perilymph compartment with artificial perilymph (AP) and ATP-gamma-S dissolved in AP. The operating point was derived from the cochlear microphonics (CM) recorded in response low frequency (200 Hz) tones at high level (106, 112 and 118 dB SPL). The analysis procedure used a Boltzmann function to simulate the CM waveform and the Boltzmann parameters were adjusted to best-fit the calculated waveform to the CM. Compared to the initial perfusion with AP, ATP-gamma-S (333 microM) enhanced peak clipping of the positive peak of the CM (that occurs during organ of Corti displacements towards scala tympani), which was in keeping with ATP-induced displacement of the transducer towards scala tympani. CM waveform analysis quantified the degree of displacement and showed that the changes were consistent with the stimulus being centered on a different region of the transducer curve. The change of operating point meant that the stimulus was applied to a region of the transducer curve where there was greater saturation of the output on excursions towards scala tympani and less saturation towards scala vestibuli. A significant degree of recovery of the operating point was observed after washing with AP. Dose response curves generated by perfusing ATP-gamma-S (333 microM) in a cumulative manner yielded an EC(50) of 19.8 micro

Data-variant kernel analysis

CERN Document Server

Motai, Yuichi

2015-01-01

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

Compactly Supported Basis Functions as Support Vector Kernels for Classification.

Science.gov (United States)

Wittek, Peter; Tan, Chew Lim

2011-10-01

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

Acoustic phonons in the hexagonal perovskite CsNiCl3 around the Gamma-point

DEFF Research Database (Denmark)

Visser, D.; Monteith, A.R.; Rønnow, H.M.

2000-01-01

The acoustic phonon dispersion curves of the hexagonal perovskite CsNiCl3 were measured at room temperature in the vicinity of the Gamma-point along the [0 0 1] and [1 1 0] directions. The derived velocity of sound values for the longitudinal and transverse acoustic phonons are compared with the ......The acoustic phonon dispersion curves of the hexagonal perovskite CsNiCl3 were measured at room temperature in the vicinity of the Gamma-point along the [0 0 1] and [1 1 0] directions. The derived velocity of sound values for the longitudinal and transverse acoustic phonons are compared...

Point source search techniques in ultra high energy gamma ray astronomy

International Nuclear Information System (INIS)

Alexandreas, D.E.; Biller, S.; Dion, G.M.; Lu, X.Q.; Yodh, G.B.; Berley, D.; Goodman, J.A.; Haines, T.J.; Hoffman, C.M.; Horch, E.; Sinnis, C.; Zhang, W.

1993-01-01

Searches for point astrophysical sources of ultra high energy (UHE) gamma rays are plagued by large numbers of background events from isotropic cosmic rays. Some of the methods that have been used to estimate the expected number of background events coming from the direction of a possible source are found to contain biases. Search techniques that avoid this problem are described. There is also a discussion of how to optimize the sensitivity of a search to emission from a point source. (orig.)

KARAKTERISTIK MINYAK CAMPURAN RED PALM OIL DENGAN PALM KERNEL OLEIN (Characteristics of Oil Blends from Red Palm Oil and Palm Kernel Olein

Directory of Open Access Journals (Sweden)

Maria Ulfah

2016-10-01

Full Text Available Characteristics of oil blends has been produced from red palm oil (RPO and palm kernel olein (PKOo with seven ratios with a total of 100, namely A (0:100, B (25:75, C (40:60, D (50:50, E (60:40, F (75:25 and G (100:0 v/v investigated with randomized complete block design. The result showed that different of ratio levels RPO and PKOo have some effects on peroxide value, saponification value, melting point, cloud point and β-carotene content from RPO-PKOo oil blends, but has not effect on free fatty acid content. Higher level of PKOo content on formulas oil blends were decreased of saponification value and melting point, but was increased of cloud point. The best of RPOPKOo oil blends has been obtained at ratio 50:50 (v/v, with 459.52 ppm β-carotene, 1.35 meq/kg peroxide value, 0.09 % free fatty acid, 202.60 saponification value, 24.15 oC melting point and 7.15 oC cloud point. Fatty acids composition were 1.24 % capric acid, 29.00 % lauric acid, 10.09 % miristic acid, 23.10 % palmitic acid, 5.84 linoleic acid, 27.30 % oleic acid and 3.43 % stearic acid. Keywords: Red palm oil, palm kernel olein, oil blends, chemical and physical properties ABSTRAK Sifat-sifat minyak campuran yang dihasilkan dari red palm oil (RPO dan palm kernel olein (PKOo dengan tujuh tingkat rasio yang totalnya 100, yaitu A (0:100, B (25:75, C (40:60, D (50:50, E (60:40, F (75:25 dan G (100:0 (v/v dikaji menggunakan rancangan acak lengkap kelompok. Hasil penelitian menunjukkan bahwa rasio RPO:PKOo mempengaruhi angka peroksida, angka penyabunan, melting point, cloud point dan kadar β-karoten dari minyak campuran RPO-PKOo yang dihasilkan, namun tidak mempengaruhi kadar asam lemak bebas. Peningkatan jumlah PKOo yang ditambahkan dalam minyak campuran RPO-PKOo, akan menurunkan angka penyabunan dan melting point, namun akan menaikkan cloud point. Produk minyak campuran RPO-PKOo terbaik diperoleh pada rasio 50:50 (v/v, dengan kadar β-karoten 459,52 ppm, angka peroksida 1,35 meq

Calculation of point isotropic buildup factors of gamma rays for water and lead

Directory of Open Access Journals (Sweden)

A. S. H.

2001-12-01

Full Text Available   Exposure buildup factors for water and lead have been calculated by the Monte-Carlo method for an isotropic point source in an infinite homogeneous medium, using the latest cross secions available on the Internet. The types of interactions considered are ,photoelectric effect, incoherent (or bound-electron Compton. Scattering, coherent (or Rayleigh scattering and pair production. Fluorescence radiations have also been taken into acount for lead. For each material, calculations were made at 10 gamma ray energies in the 40 keV to 10 MeV range and up to penetration depths of 10 mean free paths at each energy point. The results presented in this paper can be considered as modified gamma ray exposure buildup factors and be used in radiation shielding designs.

Approximate kernel competitive learning.

Science.gov (United States)

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

2015-03-01

Kernel competitive learning has been successfully used to achieve robust clustering. However, kernel competitive learning (KCL) is not scalable for large scale data processing, because (1) it has to calculate and store the full kernel matrix that is too large to be calculated and kept in the memory and (2) it cannot be computed in parallel. In this paper we develop a framework of approximate kernel competitive learning for processing large scale dataset. The proposed framework consists of two parts. First, it derives an approximate kernel competitive learning (AKCL), which learns kernel competitive learning in a subspace via sampling. We provide solid theoretical analysis on why the proposed approximation modelling would work for kernel competitive learning, and furthermore, we show that the computational complexity of AKCL is largely reduced. Second, we propose a pseudo-parallelled approximate kernel competitive learning (PAKCL) based on a set-based kernel competitive learning strategy, which overcomes the obstacle of using parallel programming in kernel competitive learning and significantly accelerates the approximate kernel competitive learning for large scale clustering. The empirical evaluation on publicly available datasets shows that the proposed AKCL and PAKCL can perform comparably as KCL, with a large reduction on computational cost. Also, the proposed methods achieve more effective clustering performance in terms of clustering precision against related approximate clustering approaches. Copyright © 2014 Elsevier Ltd. All rights reserved.

Calculation of electron and isotopes dose point kernels with FLUKA Monte Carlo code for dosimetry in nuclear medicine therapy

CERN Document Server

Mairani, A; Valente, M; Battistoni, G; Botta, F; Pedroli, G; Ferrari, A; Cremonesi, M; Di Dia, A; Ferrari, M; Fasso, A

2011-01-01

Purpose: The calculation of patient-specific dose distribution can be achieved by Monte Carlo simulations or by analytical methods. In this study, FLUKA Monte Carlo code has been considered for use in nuclear medicine dosimetry. Up to now, FLUKA has mainly been dedicated to other fields, namely high energy physics, radiation protection, and hadrontherapy. When first employing a Monte Carlo code for nuclear medicine dosimetry, its results concerning electron transport at energies typical of nuclear medicine applications need to be verified. This is commonly achieved by means of calculation of a representative parameter and comparison with reference data. Dose point kernel (DPK), quantifying the energy deposition all around a point isotropic source, is often the one. Methods: FLUKA DPKS have been calculated in both water and compact bone for monoenergetic electrons (10-3 MeV) and for beta emitting isotopes commonly used for therapy ((89)Sr, (90)Y, (131)I, (153)Sm, (177)Lu, (186)Re, and (188)Re). Point isotropic...

Classification With Truncated Distance Kernel.

Science.gov (United States)

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

2018-05-01

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

Proteome analysis of the almond kernel (Prunus dulcis).

Science.gov (United States)

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

2016-08-01

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

Optimizing Multiple Kernel Learning for the Classification of UAV Data

Directory of Open Access Journals (Sweden)

Caroline M. Gevaert

2016-12-01

Full Text Available Unmanned Aerial Vehicles (UAVs are capable of providing high-quality orthoimagery and 3D information in the form of point clouds at a relatively low cost. Their increasing popularity stresses the necessity of understanding which algorithms are especially suited for processing the data obtained from UAVs. The features that are extracted from the point cloud and imagery have different statistical characteristics and can be considered as heterogeneous, which motivates the use of Multiple Kernel Learning (MKL for classification problems. In this paper, we illustrate the utility of applying MKL for the classification of heterogeneous features obtained from UAV data through a case study of an informal settlement in Kigali, Rwanda. Results indicate that MKL can achieve a classification accuracy of 90.6%, a 5.2% increase over a standard single-kernel Support Vector Machine (SVM. A comparison of seven MKL methods indicates that linearly-weighted kernel combinations based on simple heuristics are competitive with respect to computationally-complex, non-linear kernel combination methods. We further underline the importance of utilizing appropriate feature grouping strategies for MKL, which has not been directly addressed in the literature, and we propose a novel, automated feature grouping method that achieves a high classification accuracy for various MKL methods.

SU-F-SPS-09: Parallel MC Kernel Calculations for VMAT Plan Improvement

International Nuclear Information System (INIS)

Chamberlain, S; French, S; Nazareth, D

2016-01-01

Purpose: Adding kernels (small perturbations in leaf positions) to the existing apertures of VMAT control points may improve plan quality. We investigate the calculation of kernel doses using a parallelized Monte Carlo (MC) method. Methods: A clinical prostate VMAT DICOM plan was exported from Eclipse. An arbitrary control point and leaf were chosen, and a modified MLC file was created, corresponding to the leaf position offset by 0.5cm. The additional dose produced by this 0.5 cm × 0.5 cm kernel was calculated using the DOSXYZnrc component module of BEAMnrc. A range of particle history counts were run (varying from 3 × 10"6 to 3 × 10"7); each job was split among 1, 10, or 100 parallel processes. A particle count of 3 × 10"6 was established as the lower range because it provided the minimal accuracy level. Results: As expected, an increase in particle counts linearly increases run time. For the lowest particle count, the time varied from 30 hours for the single-processor run, to 0.30 hours for the 100-processor run. Conclusion: Parallel processing of MC calculations in the EGS framework significantly decreases time necessary for each kernel dose calculation. Particle counts lower than 1 × 10"6 have too large of an error to output accurate dose for a Monte Carlo kernel calculation. Future work will investigate increasing the number of parallel processes and optimizing run times for multiple kernel calculations.

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

Science.gov (United States)

Tzortzis, Grigorios F; Likas, Aristidis C

2009-07-01

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

Pareto-path multitask multiple kernel learning.

Science.gov (United States)

Li, Cong; Georgiopoulos, Michael; Anagnostopoulos, Georgios C

2015-01-01

A traditional and intuitively appealing Multitask Multiple Kernel Learning (MT-MKL) method is to optimize the sum (thus, the average) of objective functions with (partially) shared kernel function, which allows information sharing among the tasks. We point out that the obtained solution corresponds to a single point on the Pareto Front (PF) of a multiobjective optimization problem, which considers the concurrent optimization of all task objectives involved in the Multitask Learning (MTL) problem. Motivated by this last observation and arguing that the former approach is heuristic, we propose a novel support vector machine MT-MKL framework that considers an implicitly defined set of conic combinations of task objectives. We show that solving our framework produces solutions along a path on the aforementioned PF and that it subsumes the optimization of the average of objective functions as a special case. Using the algorithms we derived, we demonstrate through a series of experimental results that the framework is capable of achieving a better classification performance, when compared with other similar MTL approaches.

Support vector machines for prediction and analysis of beta and gamma-turns in proteins.

Science.gov (United States)

Pham, Tho Hoan; Satou, Kenji; Ho, Tu Bao

2005-04-01

Tight turns have long been recognized as one of the three important features of proteins, together with alpha-helix and beta-sheet. Tight turns play an important role in globular proteins from both the structural and functional points of view. More than 90% tight turns are beta-turns and most of the rest are gamma-turns. Analysis and prediction of beta-turns and gamma-turns is very useful for design of new molecules such as drugs, pesticides, and antigens. In this paper we investigated two aspects of applying support vector machine (SVM), a promising machine learning method for bioinformatics, to prediction and analysis of beta-turns and gamma-turns. First, we developed two SVM-based methods, called BTSVM and GTSVM, which predict beta-turns and gamma-turns in a protein from its sequence. When compared with other methods, BTSVM has a superior performance and GTSVM is competitive. Second, we used SVMs with a linear kernel to estimate the support of amino acids for the formation of beta-turns and gamma-turns depending on their position in a protein. Our analysis results are more comprehensive and easier to use than the previous results in designing turns in proteins.

Subsampling Realised Kernels

DEFF Research Database (Denmark)

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

2011-01-01

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

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

Science.gov (United States)

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

2017-06-21

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

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

International Nuclear Information System (INIS)

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

1986-01-01

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

Optimized Kernel Entropy Components.

Science.gov (United States)

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

2017-06-01

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

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

Science.gov (United States)

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

2018-02-01

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

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

NARCIS (Netherlands)

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

2012-01-01

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

MERCURE-3, Gamma Attenuation by Line-of-Flight in 3-D Heterogeneous Geometry

International Nuclear Information System (INIS)

Devillers, C.; Szondi, Egon J.

1995-01-01

1 - Nature of physical problem solved: MERCURE-3/PC performs three- dimensional gamma shielding calculations around extended gamma sources. These may be even non-homogeneous. 2 - Method of solution: The algorithm of the code is the point kernel integration model combined with buildup calculations. The geometry can be described analytically using linear and quadratic surfaces of any type. A standard gamma cross section library is provided with the code. 3 - Restrictions on the complexity of the problem: To make possible the solution of large problems, all the problem size-dependent arrays are declared using symbolic constants. The default values are sufficient for most of the practical problems (e.g. 100 space elements; 100 equations; 300 surface elements; 40 materials; 25 energy groups, etc.); the code uses in this case only 200-250 Kbytes of memory, depending on the compiler used. If the problem is very large, the symbolic constants have to be modified, and the code has to be compiled using the Lahey F77L3 (protected mode) compiler

Sample selection based on kernel-subclustering for the signal reconstruction of multifunctional sensors

International Nuclear Information System (INIS)

Wang, Xin; Wei, Guo; Sun, Jinwei

2013-01-01

The signal reconstruction methods based on inverse modeling for the signal reconstruction of multifunctional sensors have been widely studied in recent years. To improve the accuracy, the reconstruction methods have become more and more complicated because of the increase in the model parameters and sample points. However, there is another factor that affects the reconstruction accuracy, the position of the sample points, which has not been studied. A reasonable selection of the sample points could improve the signal reconstruction quality in at least two ways: improved accuracy with the same number of sample points or the same accuracy obtained with a smaller number of sample points. Both ways are valuable for improving the accuracy and decreasing the workload, especially for large batches of multifunctional sensors. In this paper, we propose a sample selection method based on kernel-subclustering distill groupings of the sample data and produce the representation of the data set for inverse modeling. The method calculates the distance between two data points based on the kernel-induced distance instead of the conventional distance. The kernel function is a generalization of the distance metric by mapping the data that are non-separable in the original space into homogeneous groups in the high-dimensional space. The method obtained the best results compared with the other three methods in the simulation. (paper)

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

Science.gov (United States)

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

2017-12-15

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

7 CFR 981.7 - Edible kernel.

Science.gov (United States)

2010-01-01

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

The effects of food irradiation on quality of pine nut kernels

International Nuclear Information System (INIS)

Goelge, Evren; Ova, Guelden

2008-01-01

Pine nuts (Pinus pinae) undergo gamma irradiation process with the doses 0.5, 1.0, 3.0, and 5.0 kGy. The changes in chemical, physical and sensory attributes were observed in the following 3 months of storage period. The data obtained from the experiments showed the peroxide values of the pine nut kernels increased proportionally to the dose. On contrary, irradiation process has no effect on the physical quality such as texture and color, fatty acid composition and sensory attributes

Kernel versions of some orthogonal transformations

DEFF Research Database (Denmark)

Nielsen, Allan Aasbjerg

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

Model Selection in Kernel Ridge Regression

DEFF Research Database (Denmark)

Exterkate, Peter

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

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

OpenAIRE

Sitompul, Monica Angelina

2015-01-01

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

Validation of Born Traveltime Kernels

Science.gov (United States)

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

2001-12-01

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

7 CFR 981.8 - Inedible kernel.

Science.gov (United States)

2010-01-01

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

7 CFR 981.408 - Inedible kernel.

Science.gov (United States)

2010-01-01

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

Matrix kernels for MEG and EEG source localization and imaging

International Nuclear Information System (INIS)

Mosher, J.C.; Lewis, P.S.; Leahy, R.M.

1994-01-01

The most widely used model for electroencephalography (EEG) and magnetoencephalography (MEG) assumes a quasi-static approximation of Maxwell's equations and a piecewise homogeneous conductor model. Both models contain an incremental field element that linearly relates an incremental source element (current dipole) to the field or voltage at a distant point. The explicit form of the field element is dependent on the head modeling assumptions and sensor configuration. Proper characterization of this incremental element is crucial to the inverse problem. The field element can be partitioned into the product of a vector dependent on sensor characteristics and a matrix kernel dependent only on head modeling assumptions. We present here the matrix kernels for the general boundary element model (BEM) and for MEG spherical models. We show how these kernels are easily interchanged in a linear algebraic framework that includes sensor specifics such as orientation and gradiometer configuration. We then describe how this kernel is easily applied to ''gain'' or ''transfer'' matrices used in multiple dipole and source imaging models

Model selection in kernel ridge regression

DEFF Research Database (Denmark)

Exterkate, Peter

2013-01-01

Kernel ridge regression is a technique to perform ridge regression with a potentially infinite number of nonlinear transformations of the independent variables as regressors. This method is gaining popularity as a data-rich nonlinear forecasting tool, which is applicable in many different contexts....... The influence of the choice of kernel and the setting of tuning parameters on forecast accuracy is investigated. Several popular kernels are reviewed, including polynomial kernels, the Gaussian kernel, and the Sinc kernel. The latter two kernels are interpreted in terms of their smoothing properties......, and the tuning parameters associated to all these kernels are related to smoothness measures of the prediction function and to the signal-to-noise ratio. Based on these interpretations, guidelines are provided for selecting the tuning parameters from small grids using cross-validation. A Monte Carlo study...

Dose calculation methods in photon beam therapy using energy deposition kernels

International Nuclear Information System (INIS)

Ahnesjoe, A.

1991-01-01

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

LZW-Kernel: fast kernel utilizing variable length code blocks from LZW compressors for protein sequence classification.

Science.gov (United States)

Filatov, Gleb; Bauwens, Bruno; Kertész-Farkas, Attila

2018-05-07

Bioinformatics studies often rely on similarity measures between sequence pairs, which often pose a bottleneck in large-scale sequence analysis. Here, we present a new convolutional kernel function for protein sequences called the LZW-Kernel. It is based on code words identified with the Lempel-Ziv-Welch (LZW) universal text compressor. The LZW-Kernel is an alignment-free method, it is always symmetric, is positive, always provides 1.0 for self-similarity and it can directly be used with Support Vector Machines (SVMs) in classification problems, contrary to normalized compression distance (NCD), which often violates the distance metric properties in practice and requires further techniques to be used with SVMs. The LZW-Kernel is a one-pass algorithm, which makes it particularly plausible for big data applications. Our experimental studies on remote protein homology detection and protein classification tasks reveal that the LZW-Kernel closely approaches the performance of the Local Alignment Kernel (LAK) and the SVM-pairwise method combined with Smith-Waterman (SW) scoring at a fraction of the time. Moreover, the LZW-Kernel outperforms the SVM-pairwise method when combined with BLAST scores, which indicates that the LZW code words might be a better basis for similarity measures than local alignment approximations found with BLAST. In addition, the LZW-Kernel outperforms n-gram based mismatch kernels, hidden Markov model based SAM and Fisher kernel, and protein family based PSI-BLAST, among others. Further advantages include the LZW-Kernel's reliance on a simple idea, its ease of implementation, and its high speed, three times faster than BLAST and several magnitudes faster than SW or LAK in our tests. LZW-Kernel is implemented as a standalone C code and is a free open-source program distributed under GPLv3 license and can be downloaded from https://github.com/kfattila/LZW-Kernel. akerteszfarkas@hse.ru. Supplementary data are available at Bioinformatics Online.

Viscosity kernel of molecular fluids

DEFF Research Database (Denmark)

Puscasu, Ruslan; Todd, Billy; Daivis, Peter

2010-01-01

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

Kernel learning algorithms for face recognition

CERN Document Server

Li, Jun-Bao; Pan, Jeng-Shyang

2013-01-01

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

Introducing etch kernels for efficient pattern sampling and etch bias prediction

Science.gov (United States)

Weisbuch, François; Lutich, Andrey; Schatz, Jirka

2018-01-01

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

Lévy based Cox point processes

DEFF Research Database (Denmark)

Hellmund, Gunnar; Prokesová, Michaela; Jensen, Eva Bjørn Vedel

2008-01-01

In this paper we introduce Lévy-driven Cox point processes (LCPs) as Cox point processes with driving intensity function Λ defined by a kernel smoothing of a Lévy basis (an independently scattered, infinitely divisible random measure). We also consider log Lévy-driven Cox point processes (LLCPs......) with Λ equal to the exponential of such a kernel smoothing. Special cases are shot noise Cox processes, log Gaussian Cox processes, and log shot noise Cox processes. We study the theoretical properties of Lévy-based Cox processes, including moment properties described by nth-order product densities...

Low-energy electron dose-point kernel simulations using new physics models implemented in Geant4-DNA

Energy Technology Data Exchange (ETDEWEB)

Bordes, Julien, E-mail: julien.bordes@inserm.fr [CRCT, UMR 1037 INSERM, Université Paul Sabatier, F-31037 Toulouse (France); UMR 1037, CRCT, Université Toulouse III-Paul Sabatier, F-31037 (France); Incerti, Sébastien, E-mail: incerti@cenbg.in2p3.fr [Université de Bordeaux, CENBG, UMR 5797, F-33170 Gradignan (France); CNRS, IN2P3, CENBG, UMR 5797, F-33170 Gradignan (France); Lampe, Nathanael, E-mail: nathanael.lampe@gmail.com [Université de Bordeaux, CENBG, UMR 5797, F-33170 Gradignan (France); CNRS, IN2P3, CENBG, UMR 5797, F-33170 Gradignan (France); Bardiès, Manuel, E-mail: manuel.bardies@inserm.fr [CRCT, UMR 1037 INSERM, Université Paul Sabatier, F-31037 Toulouse (France); UMR 1037, CRCT, Université Toulouse III-Paul Sabatier, F-31037 (France); Bordage, Marie-Claude, E-mail: marie-claude.bordage@inserm.fr [CRCT, UMR 1037 INSERM, Université Paul Sabatier, F-31037 Toulouse (France); UMR 1037, CRCT, Université Toulouse III-Paul Sabatier, F-31037 (France)

2017-05-01

When low-energy electrons, such as Auger electrons, interact with liquid water, they induce highly localized ionizing energy depositions over ranges comparable to cell diameters. Monte Carlo track structure (MCTS) codes are suitable tools for performing dosimetry at this level. One of the main MCTS codes, Geant4-DNA, is equipped with only two sets of cross section models for low-energy electron interactions in liquid water (“option 2” and its improved version, “option 4”). To provide Geant4-DNA users with new alternative physics models, a set of cross sections, extracted from CPA100 MCTS code, have been added to Geant4-DNA. This new version is hereafter referred to as “Geant4-DNA-CPA100”. In this study, “Geant4-DNA-CPA100” was used to calculate low-energy electron dose-point kernels (DPKs) between 1 keV and 200 keV. Such kernels represent the radial energy deposited by an isotropic point source, a parameter that is useful for dosimetry calculations in nuclear medicine. In order to assess the influence of different physics models on DPK calculations, DPKs were calculated using the existing Geant4-DNA models (“option 2” and “option 4”), newly integrated CPA100 models, and the PENELOPE Monte Carlo code used in step-by-step mode for monoenergetic electrons. Additionally, a comparison was performed of two sets of DPKs that were simulated with “Geant4-DNA-CPA100” – the first set using Geant4′s default settings, and the second using CPA100′s original code default settings. A maximum difference of 9.4% was found between the Geant4-DNA-CPA100 and PENELOPE DPKs. Between the two Geant4-DNA existing models, slight differences, between 1 keV and 10 keV were observed. It was highlighted that the DPKs simulated with the two Geant4-DNA’s existing models were always broader than those generated with “Geant4-DNA-CPA100”. The discrepancies observed between the DPKs generated using Geant4-DNA’s existing models and “Geant4-DNA-CPA100” were

Generation of point isotropic source dose buildup factor data for the PFBR special concretes in a form compatible for usage in point kernel computer code QAD-CGGP

International Nuclear Information System (INIS)

Radhakrishnan, G.

2003-01-01

Full text: Around the PFBR (Prototype Fast Breeder Reactor) reactor assembly, in the peripheral shields special concretes of density 2.4 g/cm 3 and 3.6 g/cm 3 are to be used in complex geometrical shapes. Point-kernel computer code like QAD-CGGP, written for complex shield geometry comes in handy for the shield design optimization of peripheral shields. QAD-CGGP requires data base for the buildup factor data and it contains only ordinary concrete of density 2.3 g/cm 3 . In order to extend the data base for the PFBR special concretes, point isotropic source dose buildup factors have been generated by Monte Carlo method using the computer code MCNP-4A. For the above mentioned special concretes, buildup factor data have been generated in the energy range 0.5 MeV to 10.0 MeV with the thickness ranging from 1 mean free paths (mfp) to 40 mfp. Capo's formula fit of the buildup factor data compatible with QAD-CGGP has been attempted

Analytic properties of the Virasoro modular kernel

Energy Technology Data Exchange (ETDEWEB)

Nemkov, Nikita [Moscow Institute of Physics and Technology (MIPT), Dolgoprudny (Russian Federation); Institute for Theoretical and Experimental Physics (ITEP), Moscow (Russian Federation); National University of Science and Technology MISIS, The Laboratory of Superconducting metamaterials, Moscow (Russian Federation)

2017-06-15

On the space of generic conformal blocks the modular transformation of the underlying surface is realized as a linear integral transformation. We show that the analytic properties of conformal block implied by Zamolodchikov's formula are shared by the kernel of the modular transformation and illustrate this by explicit computation in the case of the one-point toric conformal block. (orig.)

Partial Deconvolution with Inaccurate Blur Kernel.

Science.gov (United States)

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

2017-10-17

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

Kernel methods for deep learning

OpenAIRE

Cho, Youngmin

2012-01-01

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

Correction of measured Gamma-Knife output factors for angular dependence of diode detectors and PinPoint ionization chamber.

Science.gov (United States)

Hršak, Hrvoje; Majer, Marija; Grego, Timor; Bibić, Juraj; Heinrich, Zdravko

2014-12-01

Dosimetry for Gamma-Knife requires detectors with high spatial resolution and minimal angular dependence of response. Angular dependence and end effect time for p-type silicon detectors (PTW Diode P and Diode E) and PTW PinPoint ionization chamber were measured with Gamma-Knife beams. Weighted angular dependence correction factors were calculated for each detector. The Gamma-Knife output factors were corrected for angular dependence and end effect time. For Gamma-Knife beams angle range of 84°-54°. Diode P shows considerable angular dependence of 9% and 8% for the 18 mm and 14, 8, 4 mm collimator, respectively. For Diode E this dependence is about 4% for all collimators. PinPoint ionization chamber shows angular dependence of less than 3% for 18, 14 and 8 mm helmet and 10% for 4 mm collimator due to volumetric averaging effect in a small photon beam. Corrected output factors for 14 mm helmet are in very good agreement (within ±0.3%) with published data and values recommended by vendor (Elekta AB, Stockholm, Sweden). For the 8 mm collimator diodes are still in good agreement with recommended values (within ±0.6%), while PinPoint gives 3% less value. For the 4 mm helmet Diodes P and E show over-response of 2.8% and 1.8%, respectively. For PinPoint chamber output factor of 4 mm collimator is 25% lower than Elekta value which is generally not consequence of angular dependence, but of volumetric averaging effect and lack of lateral electronic equilibrium. Diodes P and E represent good choice for Gamma-Knife dosimetry. Copyright © 2014 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.

7 CFR 981.9 - Kernel weight.

Science.gov (United States)

2010-01-01

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

Veto-Consensus Multiple Kernel Learning

NARCIS (Netherlands)

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

2016-01-01

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

7 CFR 51.2295 - Half kernel.

Science.gov (United States)

2010-01-01

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

Iterative software kernels

Energy Technology Data Exchange (ETDEWEB)

Duff, I.

1994-12-31

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

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

Science.gov (United States)

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

2018-05-01

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

A kernel version of spatial factor analysis

DEFF Research Database (Denmark)

Nielsen, Allan Aasbjerg

2009-01-01

. Schölkopf et al. introduce kernel PCA. Shawe-Taylor and Cristianini is an excellent reference for kernel methods in general. Bishop and Press et al. describe kernel methods among many other subjects. Nielsen and Canty use kernel PCA to detect change in univariate airborne digital camera images. The kernel...... version of PCA handles nonlinearities by implicitly transforming data into high (even infinite) dimensional feature space via the kernel function and then performing a linear analysis in that space. In this paper we shall apply kernel versions of PCA, maximum autocorrelation factor (MAF) analysis...

Blow-up in multidimensional aggregation equations with mildly singular interaction kernels

International Nuclear Information System (INIS)

Bertozzi, Andrea L; Laurent, Thomas; Carrillo, José A

2009-01-01

We consider the multidimensional aggregation equation u t − ∇· (u∇K * u) = 0 in which the radially symmetric attractive interaction kernel has a mild singularity at the origin (Lipschitz or better). In the case of bounded initial data, finite time singularity has been proved for kernels with a Lipschitz point at the origin (Bertozzi and Laurent 2007 Commun. Math. Sci. 274 717–35), whereas for C 2 kernels there is no finite-time blow-up. We prove, under mild monotonicity assumptions on the kernel K, that the Osgood condition for well-posedness of the ODE characteristics determines global in time well-posedness of the PDE with compactly supported bounded nonnegative initial data. When the Osgood condition is violated, we present a new proof of finite time blow-up that extends previous results, requiring radially symmetric data, to general bounded, compactly supported nonnegative initial data without symmetry. We also present a new analysis of radially symmetric solutions under less strict monotonicity conditions. Finally, we conclude with a discussion of similarity solutions for the case K(x) = |x| and some open problems

Regularized Pre-image Estimation for Kernel PCA De-noising

DEFF Research Database (Denmark)

Abrahamsen, Trine Julie; Hansen, Lars Kai

2011-01-01

The main challenge in de-noising by kernel Principal Component Analysis (PCA) is the mapping of de-noised feature space points back into input space, also referred to as “the pre-image problem”. Since the feature space mapping is typically not bijective, pre-image estimation is inherently illposed...

7 CFR 51.1441 - Half-kernel.

Science.gov (United States)

2010-01-01

... 7 Agriculture 2 2010-01-01 2010-01-01 false Half-kernel. 51.1441 Section 51.1441 Agriculture... Standards for Grades of Shelled Pecans Definitions § 51.1441 Half-kernel. Half-kernel means one of the separated halves of an entire pecan kernel with not more than one-eighth of its original volume missing...

Modelling microwave heating of discrete samples of oil palm kernels

International Nuclear Information System (INIS)

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

2016-01-01

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

Analysis of nonlocal neural fields for both general and gamma-distributed connectivities

Science.gov (United States)

Hutt, Axel; Atay, Fatihcan M.

2005-04-01

This work studies the stability of equilibria in spatially extended neuronal ensembles. We first derive the model equation from statistical properties of the neuron population. The obtained integro-differential equation includes synaptic and space-dependent transmission delay for both general and gamma-distributed synaptic connectivities. The latter connectivity type reveals infinite, finite, and vanishing self-connectivities. The work derives conditions for stationary and nonstationary instabilities for both kernel types. In addition, a nonlinear analysis for general kernels yields the order parameter equation of the Turing instability. To compare the results to findings for partial differential equations (PDEs), two typical PDE-types are derived from the examined model equation, namely the general reaction-diffusion equation and the Swift-Hohenberg equation. Hence, the discussed integro-differential equation generalizes these PDEs. In the case of the gamma-distributed kernels, the stability conditions are formulated in terms of the mean excitatory and inhibitory interaction ranges. As a novel finding, we obtain Turing instabilities in fields with local inhibition-lateral excitation, while wave instabilities occur in fields with local excitation and lateral inhibition. Numerical simulations support the analytical results.

Blending of palm oil, palm stearin and palm kernel oil in the preparation of table and pastry margarine.

Science.gov (United States)

Norlida, H M; Md Ali, A R; Muhadhir, I

1996-01-01

Palm oil (PO ; iodin value = 52), palm stearin (POs1; i.v. = 32 and POs2; i.v. = 40) and palm kernel oil (PKO; i.v. = 17) were blended in ternary systems. The blends were then studied for their physical properties such as melting point (m.p.), solid fat content (SFC), and cooling curve. Results showed that palm stearin increased the blends melting point while palm kernel oil reduced it. To produce table margarine with melting point (m.p.) below 40 degrees C, the POs1 should be added at level of pastry margarine.

Estimation of gamma dose rate from hulls and shield design for the hull transport cask of Fuel Reprocessing Plant (FRP)

International Nuclear Information System (INIS)

Chandrasekaran, S.; Rajagopal, V.; Jose, M.T.; Venkatraman, B.

2012-01-01

In Fuel Reprocessing Plant (FRP), un-dissolved clad of fuel pins known as hulls are the major sources of high level solid waste. Safe handling, transport and disposal require the estimation of radioactivity as a consequent of gamma dose rate from hulls in fast reactor fuel reprocessing plant in comparison with thermal reactor fuel. Due to long irradiation time and low cooling of spent fuel, the evolution of activation products 51 Cr, 58 Co, 54 Mn and 59 Fe present as impurities in the fuel clad are the major sources of gamma radiation. Gamma dose rate from hull container with hulls from Fuel Sub Assembly (FSA) and Radial Sub Assembly (RSA) of Fuel Reprocessing Plant (FRP) was estimated in order to design the hull transport cask. Shielding computations were done using point kernel code, IGSHIELD. This paper describes the details of source terms, estimation of dose rate and shielding design of hull transport cask in detail. (author)

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

Science.gov (United States)

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

2018-02-01

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

Local Observed-Score Kernel Equating

Science.gov (United States)

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

2014-01-01

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

Credit scoring analysis using kernel discriminant

Science.gov (United States)

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

2018-05-01

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

Development of fitting methods using geometric progression formulae of gamma-ray buildup factors

International Nuclear Information System (INIS)

Yoshida, Yoshitaka

2006-01-01

The gamma ray buildup factors are represented by an approximation method to speed up calculation using the point attenuation kernel method. The fitting parameters obtained by the GP formula and Taylor's formula are compiled in ANSI/ANS 6.4.3, available without any limitation. The GP formula featured high accuracy but required a high-level fitting technique. Thus the GP formula was divided into a curved line and a part representing the base values and used to develop the a fitting method and X k fitting method. As a result, this methodology showed that (1) when the fitting ranges were identical, there was no change in standard deviation when the unit penetration depth was varied; (2) even with fitting up to 300 mfp, the average standard deviation of 26 materials was 2.9% and acceptable GP parameters were extracted; (3) when the same end points of the fitting were selected and the starting points of fitting were identical with the unit penetration depth, the deviation became smaller with increasing unit penetration depth; and (4) even with the deviation adjusted to the positive side from 0.5 mfp to 300 mfp, the average standard deviation of 26 materials was 5.6%, which was an acceptable value. However, the GP parameters obtained by this methodology cannot be used for direct interpolation using gamma ray energy or materials. (author)

Kernel parameter dependence in spatial factor analysis

DEFF Research Database (Denmark)

Nielsen, Allan Aasbjerg

2010-01-01

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

Extraction of Point Source Gamma Signals from Aerial Survey Data Taken over a Las Vegas Nevada, Residential Area

International Nuclear Information System (INIS)

Thane J. Hendricks

2007-01-01

Detection of point-source gamma signals from aerial measurements is complicated by widely varying terrestrial gamma backgrounds, since these variations frequently resemble signals from point-sources. Spectral stripping techniques have been very useful in separating man-made and natural radiation contributions which exist on Energy Research and Development Administration (ERDA) plant sites and other like facilities. However, these facilities are generally situated in desert areas or otherwise flat terrain with few man-made structures to disturb the natural background. It is of great interest to determine if the stripping technique can be successfully applied in populated areas where numerous man-made disturbances (houses, streets, yards, vehicles, etc.) exist

Multiple Kernel Learning with Data Augmentation

Science.gov (United States)

2016-11-22

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

OS X and iOS Kernel Programming

CERN Document Server

Halvorsen, Ole Henry

2011-01-01

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

Model selection for Gaussian kernel PCA denoising

DEFF Research Database (Denmark)

Jørgensen, Kasper Winther; Hansen, Lars Kai

2012-01-01

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

Nonparametric evaluation of dynamic disease risk: a spatio-temporal kernel approach.

Directory of Open Access Journals (Sweden)

Zhijie Zhang

Full Text Available Quantifying the distributions of disease risk in space and time jointly is a key element for understanding spatio-temporal phenomena while also having the potential to enhance our understanding of epidemiologic trajectories. However, most studies to date have neglected time dimension and focus instead on the "average" spatial pattern of disease risk, thereby masking time trajectories of disease risk. In this study we propose a new idea titled "spatio-temporal kernel density estimation (stKDE" that employs hybrid kernel (i.e., weight functions to evaluate the spatio-temporal disease risks. This approach not only can make full use of sample data but also "borrows" information in a particular manner from neighboring points both in space and time via appropriate choice of kernel functions. Monte Carlo simulations show that the proposed method performs substantially better than the traditional (i.e., frequency-based kernel density estimation (trKDE which has been used in applied settings while two illustrative examples demonstrate that the proposed approach can yield superior results compared to the popular trKDE approach. In addition, there exist various possibilities for improving and extending this method.

A framework for multiple kernel support vector regression and its applications to siRNA efficacy prediction.

Science.gov (United States)

Qiu, Shibin; Lane, Terran

2009-01-01

The cell defense mechanism of RNA interference has applications in gene function analysis and promising potentials in human disease therapy. To effectively silence a target gene, it is desirable to select appropriate initiator siRNA molecules having satisfactory silencing capabilities. Computational prediction for silencing efficacy of siRNAs can assist this screening process before using them in biological experiments. String kernel functions, which operate directly on the string objects representing siRNAs and target mRNAs, have been applied to support vector regression for the prediction and improved accuracy over numerical kernels in multidimensional vector spaces constructed from descriptors of siRNA design rules. To fully utilize information provided by string and numerical data, we propose to unify the two in a kernel feature space by devising a multiple kernel regression framework where a linear combination of the kernels is used. We formulate the multiple kernel learning into a quadratically constrained quadratic programming (QCQP) problem, which although yields global optimal solution, is computationally demanding and requires a commercial solver package. We further propose three heuristics based on the principle of kernel-target alignment and predictive accuracy. Empirical results demonstrate that multiple kernel regression can improve accuracy, decrease model complexity by reducing the number of support vectors, and speed up computational performance dramatically. In addition, multiple kernel regression evaluates the importance of constituent kernels, which for the siRNA efficacy prediction problem, compares the relative significance of the design rules. Finally, we give insights into the multiple kernel regression mechanism and point out possible extensions.

Paramecium: An Extensible Object-Based Kernel

NARCIS (Netherlands)

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

1995-01-01

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

Theory of reproducing kernels and applications

CERN Document Server

Saitoh, Saburou

2016-01-01

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

Kernels for structured data

CERN Document Server

Gärtner, Thomas

2009-01-01

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

7 CFR 981.401 - Adjusted kernel weight.

Science.gov (United States)

2010-01-01

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

Formal truncations of connected kernel equations

International Nuclear Information System (INIS)

Dixon, R.M.

1977-01-01

The Connected Kernel Equations (CKE) of Alt, Grassberger and Sandhas (AGS); Kouri, Levin and Tobocman (KLT); and Bencze, Redish and Sloan (BRS) are compared against reaction theory criteria after formal channel space and/or operator truncations have been introduced. The Channel Coupling Class concept is used to study the structure of these CKE's. The related wave function formalism of Sandhas, of L'Huillier, Redish and Tandy and of Kouri, Krueger and Levin are also presented. New N-body connected kernel equations which are generalizations of the Lovelace three-body equations are derived. A method for systematically constructing fewer body models from the N-body BRS and generalized Lovelace (GL) equations is developed. The formally truncated AGS, BRS, KLT and GL equations are analyzed by employing the criteria of reciprocity and two-cluster unitarity. Reciprocity considerations suggest that formal truncations of BRS, KLT and GL equations can lead to reciprocity-violating results. This study suggests that atomic problems should employ three-cluster connected truncations and that the two-cluster connected truncations should be a useful starting point for nuclear systems

Testing Infrastructure for Operating System Kernel Development

DEFF Research Database (Denmark)

Walter, Maxwell; Karlsson, Sven

2014-01-01

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

7 CFR 51.1403 - Kernel color classification.

Science.gov (United States)

2010-01-01

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

Calculation of electron and isotopes dose point kernels with FLUKA Monte Carlo code for dosimetry in nuclear medicine therapy.

Science.gov (United States)

Botta, F; Mairani, A; Battistoni, G; Cremonesi, M; Di Dia, A; Fassò, A; Ferrari, A; Ferrari, M; Paganelli, G; Pedroli, G; Valente, M

2011-07-01

The calculation of patient-specific dose distribution can be achieved by Monte Carlo simulations or by analytical methods. In this study, FLUKA Monte Carlo code has been considered for use in nuclear medicine dosimetry. Up to now, FLUKA has mainly been dedicated to other fields, namely high energy physics, radiation protection, and hadrontherapy. When first employing a Monte Carlo code for nuclear medicine dosimetry, its results concerning electron transport at energies typical of nuclear medicine applications need to be verified. This is commonly achieved by means of calculation of a representative parameter and comparison with reference data. Dose point kernel (DPK), quantifying the energy deposition all around a point isotropic source, is often the one. FLUKA DPKS have been calculated in both water and compact bone for monoenergetic electrons (10-3 MeV) and for beta emitting isotopes commonly used for therapy (89Sr, 90Y, 131I 153Sm, 177Lu, 186Re, and 188Re). Point isotropic sources have been simulated at the center of a water (bone) sphere, and deposed energy has been tallied in concentric shells. FLUKA outcomes have been compared to PENELOPE v.2008 results, calculated in this study as well. Moreover, in case of monoenergetic electrons in water, comparison with the data from the literature (ETRAN, GEANT4, MCNPX) has been done. Maximum percentage differences within 0.8.RCSDA and 0.9.RCSDA for monoenergetic electrons (RCSDA being the continuous slowing down approximation range) and within 0.8.X90 and 0.9.X90 for isotopes (X90 being the radius of the sphere in which 90% of the emitted energy is absorbed) have been computed, together with the average percentage difference within 0.9.RCSDA and 0.9.X90 for electrons and isotopes, respectively. Concerning monoenergetic electrons, within 0.8.RCSDA (where 90%-97% of the particle energy is deposed), FLUKA and PENELOPE agree mostly within 7%, except for 10 and 20 keV electrons (12% in water, 8.3% in bone). The

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

Science.gov (United States)

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

2014-09-01

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

Airborne gamma-ray spectrometer and magnetometer survey, Point Lay Quadrangle, Alaska. Volume I. Final report

International Nuclear Information System (INIS)

1981-02-01

The results obtained from an airborne high sensitivity gamma-ray spectrometer and magnetometer survey over the Point Lay map area of Alaska are presented. Based on the criteria outlined in the general section on interpretation, a total of six uranium anomalies have been indicated on the interpretation map. All six are only weakly to moderately anomalous in either uranium or the uranium ratios. None of these are thought to be of any economic significance. No follow-up work is recommended for the Point Lay Quadrangle

The Kernel Mixture Network: A Nonparametric Method for Conditional Density Estimation of Continuous Random Variables

OpenAIRE

Ambrogioni, Luca; Güçlü, Umut; van Gerven, Marcel A. J.; Maris, Eric

2017-01-01

This paper introduces the kernel mixture network, a new method for nonparametric estimation of conditional probability densities using neural networks. We model arbitrarily complex conditional densities as linear combinations of a family of kernel functions centered at a subset of training points. The weights are determined by the outer layer of a deep neural network, trained by minimizing the negative log likelihood. This generalizes the popular quantized softmax approach, which can be seen ...

The definition of kernel Oz

OpenAIRE

Smolka, Gert

1994-01-01

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

Fabrication of Uranium Oxycarbide Kernels for HTR Fuel

International Nuclear Information System (INIS)

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

2010-01-01

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

Multiple kernel SVR based on the MRE for remote sensing water depth fusion detection

Science.gov (United States)

Wang, Jinjin; Ma, Yi; Zhang, Jingyu

2018-03-01

Remote sensing has an important means of water depth detection in coastal shallow waters and reefs. Support vector regression (SVR) is a machine learning method which is widely used in data regression. In this paper, SVR is used to remote sensing multispectral bathymetry. Aiming at the problem that the single-kernel SVR method has a large error in shallow water depth inversion, the mean relative error (MRE) of different water depth is retrieved as a decision fusion factor with single kernel SVR method, a multi kernel SVR fusion method based on the MRE is put forward. And taking the North Island of the Xisha Islands in China as an experimentation area, the comparison experiments with the single kernel SVR method and the traditional multi-bands bathymetric method are carried out. The results show that: 1) In range of 0 to 25 meters, the mean absolute error(MAE)of the multi kernel SVR fusion method is 1.5m,the MRE is 13.2%; 2) Compared to the 4 single kernel SVR method, the MRE of the fusion method reduced 1.2% (1.9%) 3.4% (1.8%), and compared to traditional multi-bands method, the MRE reduced 1.9%; 3) In 0-5m depth section, compared to the single kernel method and the multi-bands method, the MRE of fusion method reduced 13.5% to 44.4%, and the distribution of points is more concentrated relative to y=x.

Anisotropic hydrodynamics with a scalar collisional kernel

Science.gov (United States)

Almaalol, Dekrayat; Strickland, Michael

2018-04-01

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

Object classification and detection with context kernel descriptors

DEFF Research Database (Denmark)

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

2014-01-01

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

Mapping correlation of a simulated dark matter source and a point source in the gamma-ray sky - Oral Presentation

Energy Technology Data Exchange (ETDEWEB)

Gibson, Alexander [SLAC National Accelerator Lab., Menlo Park, CA (United States)

2015-08-23

In my research, I analyzed how two gamma-ray source models interact with one another when optimizing to fit data. This is important because it becomes hard to distinguish between the two point sources when they are close together or looking at low energy photons. The reason for the first is obvious, the reason why they become harder to distinguish at lower photon energies is the resolving power of the Fermi Gamma-Ray Space Telescope gets worse at lower energies. When the two point sources are highly correlated (hard to distinguish between), we need to change our method of statistical analysis. What I did was show that highly correlated sources have larger uncertainties associated with them, caused by an optimizer not knowing which point source’s parameters to optimize. I also mapped out where their is high correlation for 2 different theoretical mass dark matter point sources so that people analyzing them in the future knew where they had to use more sophisticated statistical analysis.

Ranking Support Vector Machine with Kernel Approximation.

Science.gov (United States)

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

2017-01-01

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

Ranking Support Vector Machine with Kernel Approximation

Directory of Open Access Journals (Sweden)

Kai Chen

2017-01-01

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

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

Science.gov (United States)

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

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

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

Science.gov (United States)

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

2016-04-01

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

Wigner functions defined with Laplace transform kernels.

Science.gov (United States)

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

2011-10-24

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

Metabolic network prediction through pairwise rational kernels.

Science.gov (United States)

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

2014-09-26

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

A Precise Drunk Driving Detection Using Weighted Kernel Based on Electrocardiogram.

Science.gov (United States)

Wu, Chung Kit; Tsang, Kim Fung; Chi, Hao Ran; Hung, Faan Hei

2016-05-09

Globally, 1.2 million people die and 50 million people are injured annually due to traffic accidents. These traffic accidents cost $500 billion dollars. Drunk drivers are found in 40% of the traffic crashes. Existing drunk driving detection (DDD) systems do not provide accurate detection and pre-warning concurrently. Electrocardiogram (ECG) is a proven biosignal that accurately and simultaneously reflects human's biological status. In this letter, a classifier for DDD based on ECG is investigated in an attempt to reduce traffic accidents caused by drunk drivers. At this point, it appears that there is no known research or literature found on ECG classifier for DDD. To identify drunk syndromes, the ECG signals from drunk drivers are studied and analyzed. As such, a precise ECG-based DDD (ECG-DDD) using a weighted kernel is developed. From the measurements, 10 key features of ECG signals were identified. To incorporate the important features, the feature vectors are weighted in the customization of kernel functions. Four commonly adopted kernel functions are studied. Results reveal that weighted feature vectors improve the accuracy by 11% compared to the computation using the prime kernel. Evaluation shows that ECG-DDD improved the accuracy by 8% to 18% compared to prevailing methods.

A Precise Drunk Driving Detection Using Weighted Kernel Based on Electrocardiogram

Directory of Open Access Journals (Sweden)

Chung Kit Wu

2016-05-01

Full Text Available Globally, 1.2 million people die and 50 million people are injured annually due to traffic accidents. These traffic accidents cost $500 billion dollars. Drunk drivers are found in 40% of the traffic crashes. Existing drunk driving detection (DDD systems do not provide accurate detection and pre-warning concurrently. Electrocardiogram (ECG is a proven biosignal that accurately and simultaneously reflects human’s biological status. In this letter, a classifier for DDD based on ECG is investigated in an attempt to reduce traffic accidents caused by drunk drivers. At this point, it appears that there is no known research or literature found on ECG classifier for DDD. To identify drunk syndromes, the ECG signals from drunk drivers are studied and analyzed. As such, a precise ECG-based DDD (ECG-DDD using a weighted kernel is developed. From the measurements, 10 key features of ECG signals were identified. To incorporate the important features, the feature vectors are weighted in the customization of kernel functions. Four commonly adopted kernel functions are studied. Results reveal that weighted feature vectors improve the accuracy by 11% compared to the computation using the prime kernel. Evaluation shows that ECG-DDD improved the accuracy by 8% to 18% compared to prevailing methods.

Influence Function and Robust Variant of Kernel Canonical Correlation Analysis

OpenAIRE

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

2017-01-01

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

The Linux kernel as flexible product-line architecture

NARCIS (Netherlands)

M. de Jonge (Merijn)

2002-01-01

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

Traveltime sensitivity kernels for wave equation tomography using the unwrapped phase

KAUST Repository

Djebbi, Ramzi

2014-02-18

Wave equation tomography attempts to improve on traveltime tomography, by better adhering to the requirements of our finite-frequency data. Conventional wave equation tomography, based on the first-order Born approximation followed by cross-correlation traveltime lag measurement, or on the Rytov approximation for the phase, yields the popular hollow banana sensitivity kernel indicating that the measured traveltime at a point is insensitive to perturbations along the ray theoretical path at certain finite frequencies. Using the instantaneous traveltime, which is able to unwrap the phase of the signal, instead of the cross-correlation lag, we derive new finite-frequency traveltime sensitivity kernels. The kernel reflects more the model-data dependency, we typically encounter in full waveform inversion. This result confirms that the hollow banana shape is borne of the cross-correlation lag measurement, which exposes the Born approximations weakness in representing transmitted waves. The instantaneous traveltime can thus mitigate the additional component of nonlinearity introduced by the hollow banana sensitivity kernels in finite-frequency traveltime tomography. The instantaneous traveltime simply represents the unwrapped phase of Rytov approximation, and thus is a good alternative to Born and Rytov to compute the misfit function for wave equation tomography. We show the limitations of the cross-correlation associated with Born approximation for traveltime lag measurement when the source signatures of the measured and modelled data are different. The instantaneous traveltime is proven to be less sensitive to the distortions in the data signature. The unwrapped phase full banana shape of the sensitivity kernels shows smoother update compared to the banana–doughnut kernels. The measurement of the traveltime delay caused by a small spherical anomaly, embedded into a 3-D homogeneous model, supports the full banana sensitivity assertion for the unwrapped phase.

Traveltime sensitivity kernels for wave equation tomography using the unwrapped phase

KAUST Repository

Djebbi, Ramzi; Alkhalifah, Tariq Ali

2014-01-01

Wave equation tomography attempts to improve on traveltime tomography, by better adhering to the requirements of our finite-frequency data. Conventional wave equation tomography, based on the first-order Born approximation followed by cross-correlation traveltime lag measurement, or on the Rytov approximation for the phase, yields the popular hollow banana sensitivity kernel indicating that the measured traveltime at a point is insensitive to perturbations along the ray theoretical path at certain finite frequencies. Using the instantaneous traveltime, which is able to unwrap the phase of the signal, instead of the cross-correlation lag, we derive new finite-frequency traveltime sensitivity kernels. The kernel reflects more the model-data dependency, we typically encounter in full waveform inversion. This result confirms that the hollow banana shape is borne of the cross-correlation lag measurement, which exposes the Born approximations weakness in representing transmitted waves. The instantaneous traveltime can thus mitigate the additional component of nonlinearity introduced by the hollow banana sensitivity kernels in finite-frequency traveltime tomography. The instantaneous traveltime simply represents the unwrapped phase of Rytov approximation, and thus is a good alternative to Born and Rytov to compute the misfit function for wave equation tomography. We show the limitations of the cross-correlation associated with Born approximation for traveltime lag measurement when the source signatures of the measured and modelled data are different. The instantaneous traveltime is proven to be less sensitive to the distortions in the data signature. The unwrapped phase full banana shape of the sensitivity kernels shows smoother update compared to the banana–doughnut kernels. The measurement of the traveltime delay caused by a small spherical anomaly, embedded into a 3-D homogeneous model, supports the full banana sensitivity assertion for the unwrapped phase.

Exploiting graph kernels for high performance biomedical relation extraction.

Science.gov (United States)

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

2018-01-30

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

Approximation of the breast height diameter distribution of two-cohort stands by mixture models III Kernel density estimators vs mixture models

Science.gov (United States)

Rafal Podlaski; Francis A. Roesch

2014-01-01

Two-component mixtures of either the Weibull distribution or the gamma distribution and the kernel density estimator were used for describing the diameter at breast height (dbh) empirical distributions of two-cohort stands. The data consisted of study plots from the Å wietokrzyski National Park (central Poland) and areas close to and including the North Carolina section...

Applications of Monte Carlo codes to a study of gamma-ray buildup factors, skyshine and duct streaming

Energy Technology Data Exchange (ETDEWEB)

Hirayama, H. [High Energy Accelerator Research Organization (KEK), Ibaraki (Japan)

2001-07-01

Many shielding calculations for gamma-rays have continued to rely on point-kernel methods incorporating buildup factor data. Line beam or conical beam response functions, which are calculated using a Monte Carlo code, for skyshine problems are useful to estimate the skyshine dose from various facilities. A simple calculation method for duct streaming was proposed using the parameters calculated by the Monte Carlo code. It is therefore important to study, improve and produce basic parameters related to old, but still important, problems in the fields of radiation shielding using the Monte Carlo code. In this paper, these studies performed by several groups in Japan as applications of the Monte Carlo method are discussed. (orig.)

The Kernel Estimation in Biosystems Engineering

Directory of Open Access Journals (Sweden)

Esperanza Ayuga Téllez

2008-04-01

Full Text Available In many fields of biosystems engineering, it is common to find works in which statistical information is analysed that violates the basic hypotheses necessary for the conventional forecasting methods. For those situations, it is necessary to find alternative methods that allow the statistical analysis considering those infringements. Non-parametric function estimation includes methods that fit a target function locally, using data from a small neighbourhood of the point. Weak assumptions, such as continuity and differentiability of the target function, are rather used than "a priori" assumption of the global target function shape (e.g., linear or quadratic. In this paper a few basic rules of decision are enunciated, for the application of the non-parametric estimation method. These statistical rules set up the first step to build an interface usermethod for the consistent application of kernel estimation for not expert users. To reach this aim, univariate and multivariate estimation methods and density function were analysed, as well as regression estimators. In some cases the models to be applied in different situations, based on simulations, were defined. Different biosystems engineering applications of the kernel estimation are also analysed in this review.

GRIM : Leveraging GPUs for Kernel integrity monitoring

NARCIS (Netherlands)

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

2016-01-01

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

7 CFR 51.2296 - Three-fourths half kernel.

Science.gov (United States)

2010-01-01

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

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

Science.gov (United States)

Cid, Jaime A; von Davier, Alina A

2015-05-01

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

Adaptive Kernel in Meshsize Boosting Algorithm in KDE ...

African Journals Online (AJOL)

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

A kernel version of multivariate alteration detection

DEFF Research Database (Denmark)

Nielsen, Allan Aasbjerg; Vestergaard, Jacob Schack

2013-01-01

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

Implementing Kernel Methods Incrementally by Incremental Nonlinear Projection Trick.

Science.gov (United States)

Kwak, Nojun

2016-05-20

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

Uranium kernel formation via internal gelation

International Nuclear Information System (INIS)

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

2004-01-01

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

Promising mutant variety of rice evolved through gamma irradiation

International Nuclear Information System (INIS)

Prasad, S.C.; Sinha, S.K.

1980-01-01

Rice occupies a major share in crop production in the Chotanagpur plateau of Bihar State. Uplands are roughly 40% in area where traditional low yielding rice, known as ''gora'' is cultivated as directly sown crop. Despite introduction of high yielding rice varieties, gora group of rices continue to prevail. It is therefore desired to increase the productivity level of the gora rice by mutation breeding. One such mutant known as ''gora mutant'' was obtained through gamma irradiation (10 kR) of variety Brown gora. The maturity of both parent and mutant remaining constant (ie. 100 days), there is some improvement in other characteristics like plant height, tillering capacity and kernel character. The parent being tall, shy in tillering and red bold kernel, the mutant has dwarfish characteristics, profuse tillering habit and white kernel with fine grains. The yielding capacity of mutant derivative is 30-40% higher than the parent Brown gora. This variety is in pre-release stage, and the farmers have taken great liking for it. (author)

Kernel learning at the first level of inference.

Science.gov (United States)

Cawley, Gavin C; Talbot, Nicola L C

2014-05-01

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

Quantum tomography, phase-space observables and generalized Markov kernels

International Nuclear Information System (INIS)

Pellonpaeae, Juha-Pekka

2009-01-01

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

A Experimental Study of the Growth of Laser Spark and Electric Spark Ignited Flame Kernels.

Science.gov (United States)

Ho, Chi Ming

1995-01-01

Better ignition sources are constantly in demand for enhancing the spark ignition in practical applications such as automotive and liquid rocket engines. In response to this practical challenge, the present experimental study was conducted with the major objective to obtain a better understanding on how spark formation and hence spark characteristics affect the flame kernel growth. Two laser sparks and one electric spark were studied in air, propane-air, propane -air-nitrogen, methane-air, and methane-oxygen mixtures that were initially at ambient pressure and temperature. The growth of the kernels was monitored by imaging the kernels with shadowgraph systems, and by imaging the planar laser -induced fluorescence of the hydroxyl radicals inside the kernels. Characteristic dimensions and kernel structures were obtained from these images. Since different energy transfer mechanisms are involved in the formation of a laser spark as compared to that of an electric spark; a laser spark is insensitive to changes in mixture ratio and mixture type, while an electric spark is sensitive to changes in both. The detailed structures of the kernels in air and propane-air mixtures primarily depend on the spark characteristics. But the combustion heat released rapidly in methane-oxygen mixtures significantly modifies the kernel structure. Uneven spark energy distribution causes remarkably asymmetric kernel structure. The breakdown energy of a spark creates a blast wave that shows good agreement with the numerical point blast solution, and a succeeding complex spark-induced flow that agrees reasonably well with a simple puff model. The transient growth rates of the propane-air, propane-air -nitrogen, and methane-air flame kernels can be interpreted in terms of spark effects, flame stretch, and preferential diffusion. For a given mixture, a spark with higher breakdown energy produces a greater and longer-lasting enhancing effect on the kernel growth rate. By comparing the growth

Single pass kernel k-means clustering method

Indian Academy of Sciences (India)

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

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

KAUST Repository

Malas, Tareq

2011-07-01

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

Dose point kernel simulation for monoenergetic electrons and radionuclides using Monte Carlo techniques.

Science.gov (United States)

Wu, J; Liu, Y L; Chang, S J; Chao, M M; Tsai, S Y; Huang, D E

2012-11-01

Monte Carlo (MC) simulation has been commonly used in the dose evaluation of radiation accidents and for medical purposes. The accuracy of simulated results is affected by the particle-tracking algorithm, cross-sectional database, random number generator and statistical error. The differences among MC simulation software packages must be validated. This study simulated the dose point kernel (DPK) and the cellular S-values of monoenergetic electrons ranging from 0.01 to 2 MeV and the radionuclides of (90)Y, (177)Lu and (103 m)Rh, using Fluktuierende Kaskade (FLUKA) and MC N-Particle Transport Code Version 5 (MCNP5). A 6-μm-radius cell model consisting of the cell surface, cytoplasm and cell nucleus was constructed for cellular S-value calculation. The mean absolute percentage errors (MAPEs) of the scaled DPKs, simulated using FLUKA and MCNP5, were 7.92, 9.64, 4.62, 3.71 and 3.84 % for 0.01, 0.1, 0.5, 1 and 2 MeV, respectively. For the three radionuclides, the MAPEs of the scaled DPKs were within 5 %. The maximum deviations of S(N←N), S(N←Cy) and S(N←CS) for the electron energy larger than 10 keV were 6.63, 6.77 and 5.24 %, respectively. The deviations for the self-absorbed S-values and cross-dose S-values of the three radionuclides were within 4 %. On the basis of the results of this study, it was concluded that the simulation results are consistent between FLUKA and MCNP5. However, there is a minor inconsistency for low energy range. The DPK and the cellular S-value should be used as the quality assurance tools before the MC simulation results are adopted as the gold standard.

Relationship between attenuation coefficients and dose-spread kernels

International Nuclear Information System (INIS)

Boyer, A.L.

1988-01-01

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

Mixture Density Mercer Kernels: A Method to Learn Kernels

Data.gov (United States)

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

Integral equations with contrasting kernels

Directory of Open Access Journals (Sweden)

Theodore Burton

2008-01-01

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

Kernel methods in orthogonalization of multi- and hypervariate data

DEFF Research Database (Denmark)

Nielsen, Allan Aasbjerg

2009-01-01

A kernel version of maximum autocorrelation factor (MAF) analysis is described very briefly and applied to change detection in remotely sensed hyperspectral image (HyMap) data. The kernel version is based on a dual formulation also termed Q-mode analysis in which the data enter into the analysis...... via inner products in the Gram matrix only. In the kernel version the inner products are replaced by inner products between nonlinear mappings into higher dimensional feature space of the original data. Via kernel substitution also known as the kernel trick these inner products between the mappings...... are in turn replaced by a kernel function and all quantities needed in the analysis are expressed in terms of this kernel function. This means that we need not know the nonlinear mappings explicitly. Kernel PCA and MAF analysis handle nonlinearities by implicitly transforming data into high (even infinite...

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

Directory of Open Access Journals (Sweden)

Omar Abu Arqub

2012-01-01

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

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

DEFF Research Database (Denmark)

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

2009-01-01

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

Sparse Event Modeling with Hierarchical Bayesian Kernel Methods

Science.gov (United States)

2016-01-05

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

The Classification of Diabetes Mellitus Using Kernel k-means

Science.gov (United States)

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

2018-01-01

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

Parsimonious Wavelet Kernel Extreme Learning Machine

Directory of Open Access Journals (Sweden)

Wang Qin

2015-11-01

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

Measurement of gamma quantum interaction point in plastic scintillator with WLS strips

Energy Technology Data Exchange (ETDEWEB)

Smyrski, J., E-mail: smyrski@if.uj.edu.pl [Faculty of Physics, Astronomy and Applied Computer Science, Jagiellonian University, S. Łojasiewicza 11, 30-348 Cracow (Poland); Alfs, D.; Bednarski, T.; Białas, P.; Czerwiński, E.; Dulski, K.; Gajos, A.; Głowacz, B.; Gupta-Sharma, N. [Faculty of Physics, Astronomy and Applied Computer Science, Jagiellonian University, S. Łojasiewicza 11, 30-348 Cracow (Poland); Gorgol, M.; Jasińska, B. [Department of Nuclear Methods, Institute of Physics, Maria Curie-Sklodowska University, 20-031 Lublin (Poland); Kajetanowicz, M.; Kamińska, D.; Korcyl, G. [Faculty of Physics, Astronomy and Applied Computer Science, Jagiellonian University, S. Łojasiewicza 11, 30-348 Cracow (Poland); Kowalski, P. [Świerk Computing Centre, National Centre for Nuclear Research, 05-400 Otwock-Świerk (Poland); Krzemień, W. [High Energy Department, National Centre for Nuclear Research, 05-400 Otwock-Świerk (Poland); Krawczyk, N.; Kubicz, E.; Mohammed, M.; Niedźwiecki, Sz. [Faculty of Physics, Astronomy and Applied Computer Science, Jagiellonian University, S. Łojasiewicza 11, 30-348 Cracow (Poland); and others

2017-04-11

The feasibility of measuring the aśxial coordinate of a gamma quantum interaction point in a plastic scintillator bar via the detection of scintillation photons escaping from the scintillator with an array of wavelength-shifting (WLS) strips is demonstrated. Using a test set-up comprising a BC-420 scintillator bar and an array of sixteen BC-482A WLS strips we achieved a spatial resolution of 5 mm (σ) for annihilation photons from a {sup 22}Na isotope. The studied method can be used to improve the spatial resolution of a plastic-scintillator-based PET scanner which is being developed by the J-PET collaboration.

Deletion mutagenesis identifies a haploinsufficient role for gamma-zein in opaque-2 endosperm modification

Science.gov (United States)

Quality Protein Maize (QPM) is a hard kernel variant of the high-lysine mutant, opaque-2. Using gamma irradiation, we created opaque QPM variants to identify opaque-2 modifier genes and to investigate deletion mutagenesis combined with Illumina sequencing as a maize functional genomics tool. A K0326...

Difference between standard and quasi-conformal BFKL kernels

International Nuclear Information System (INIS)

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

2012-01-01

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

A laser optical method for detecting corn kernel defects

Energy Technology Data Exchange (ETDEWEB)

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

1984-01-01

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

Coronary Stent Artifact Reduction with an Edge-Enhancing Reconstruction Kernel - A Prospective Cross-Sectional Study with 256-Slice CT.

Science.gov (United States)

Tan, Stéphanie; Soulez, Gilles; Diez Martinez, Patricia; Larrivée, Sandra; Stevens, Louis-Mathieu; Goussard, Yves; Mansour, Samer; Chartrand-Lefebvre, Carl

2016-01-01

Metallic artifacts can result in an artificial thickening of the coronary stent wall which can significantly impair computed tomography (CT) imaging in patients with coronary stents. The objective of this study is to assess in vivo visualization of coronary stent wall and lumen with an edge-enhancing CT reconstruction kernel, as compared to a standard kernel. This is a prospective cross-sectional study involving the assessment of 71 coronary stents (24 patients), with blinded observers. After 256-slice CT angiography, image reconstruction was done with medium-smooth and edge-enhancing kernels. Stent wall thickness was measured with both orthogonal and circumference methods, averaging thickness from diameter and circumference measurements, respectively. Image quality was assessed quantitatively using objective parameters (noise, signal to noise (SNR) and contrast to noise (CNR) ratios), as well as visually using a 5-point Likert scale. Stent wall thickness was decreased with the edge-enhancing kernel in comparison to the standard kernel, either with the orthogonal (0.97 ± 0.02 versus 1.09 ± 0.03 mm, respectively; pkernel generated less overestimation from nominal thickness compared to the standard kernel, both with the orthogonal (0.89 ± 0.19 versus 1.00 ± 0.26 mm, respectively; pkernel was associated with lower SNR and CNR, as well as higher background noise (all p kernel. Stent visual scores were higher with the edge-enhancing kernel (pkernel generates thinner stent walls, less overestimation from nominal thickness, and better image quality scores than the standard kernel.

Kernel maximum autocorrelation factor and minimum noise fraction transformations

DEFF Research Database (Denmark)

Nielsen, Allan Aasbjerg

2010-01-01

in hyperspectral HyMap scanner data covering a small agricultural area, and 3) maize kernel inspection. In the cases shown, the kernel MAF/MNF transformation performs better than its linear counterpart as well as linear and kernel PCA. The leading kernel MAF/MNF variates seem to possess the ability to adapt...

Identification of Fusarium damaged wheat kernels using image analysis

Directory of Open Access Journals (Sweden)

Ondřej Jirsa

2011-01-01

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

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

Science.gov (United States)

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

2015-05-01

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

Digital signal processing with kernel methods

CERN Document Server

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

2018-01-01

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

Higher-Order Hybrid Gaussian Kernel in Meshsize Boosting Algorithm

African Journals Online (AJOL)

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

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

African Journals Online (AJOL)

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

Delimiting areas of endemism through kernel interpolation.

Science.gov (United States)

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

2015-01-01

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

Delimiting areas of endemism through kernel interpolation.

Directory of Open Access Journals (Sweden)

Ubirajara Oliveira

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

Generalization Performance of Regularized Ranking With Multiscale Kernels.

Science.gov (United States)

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

2016-05-01

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

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

Science.gov (United States)

Tezuka, Taro

2018-06-01

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

An analysis of 1-D smoothed particle hydrodynamics kernels

International Nuclear Information System (INIS)

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

1996-01-01

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

Putting Priors in Mixture Density Mercer Kernels

Science.gov (United States)

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

2004-01-01

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

NLO corrections to the Kernel of the BKP-equations

Energy Technology Data Exchange (ETDEWEB)

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

2012-10-02

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

A Fast and Simple Graph Kernel for RDF

NARCIS (Netherlands)

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

2013-01-01

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

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

Energy Technology Data Exchange (ETDEWEB)

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

2017-04-20

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

An SVM model with hybrid kernels for hydrological time series

Science.gov (United States)

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

2017-12-01

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

Kernel based eigenvalue-decomposition methods for analysing ham

DEFF Research Database (Denmark)

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

2010-01-01

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

Reduced multiple empirical kernel learning machine.

Science.gov (United States)

Wang, Zhe; Lu, MingZhe; Gao, Daqi

2015-02-01

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

Enhanced gluten properties in soft kernel durum wheat

Science.gov (United States)

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

7 CFR 981.61 - Redetermination of kernel weight.

Science.gov (United States)

2010-01-01

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

Adaptive metric kernel regression

DEFF Research Database (Denmark)

Goutte, Cyril; Larsen, Jan

2000-01-01

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

Consistent Estimation of Pricing Kernels from Noisy Price Data

OpenAIRE

Vladislav Kargin

2003-01-01

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

Stable Kernel Representations as Nonlinear Left Coprime Factorizations

NARCIS (Netherlands)

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

1994-01-01

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

7 CFR 981.60 - Determination of kernel weight.

Science.gov (United States)

2010-01-01

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

End-use quality of soft kernel durum wheat

Science.gov (United States)

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

Per-Sample Multiple Kernel Approach for Visual Concept Learning

Directory of Open Access Journals (Sweden)

Ling-Yu Duan

2010-01-01

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

Per-Sample Multiple Kernel Approach for Visual Concept Learning

Directory of Open Access Journals (Sweden)

Tian Yonghong

2010-01-01

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

Discrete non-parametric kernel estimation for global sensitivity analysis

International Nuclear Information System (INIS)

Senga Kiessé, Tristan; Ventura, Anne

2016-01-01

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

Deep Restricted Kernel Machines Using Conjugate Feature Duality.

Science.gov (United States)

Suykens, Johan A K

2017-08-01

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

Improved modeling of clinical data with kernel methods.

Science.gov (United States)

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

2012-02-01

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

Proximate Nutritional Evaluation of Gamma Irradiated Black Rice (Oryza sativa L. cv. Cempo ireng)

Science.gov (United States)

Riyatun; Suharyana; Ramelan, A. H.; Sutarno; Saputra, O. A.; Suryanti, V.

2018-03-01

Black rice is a type of pigmented rice with black bran covering the endosperm of the rice kernel. The main objective of the present study was to provide details information on the proximate composition of third generation of gamma irradiated black rice (Oryza sativa L. cv. Cempo ireng). In respect to the control, generally speaking, there were no significant changes of moisture, lipids, proteins, carbohydrates and fibers contents have been observed for the both gamma irradiated black rice. However, the 200-BR has slightly better nutritional value than that of 300-BR and the control. The mineral contents of 200-BR increased significantly of about 35% than the non-gamma irradiated black rice.

Guaranteed Unresolved Point Source Emission and the Gamma-ray Background

International Nuclear Information System (INIS)

Pavlidou, Vasiliki; Siegal-Gaskins, Jennifer M.; Brown, Carolyn; Fields, Brian D.; Olinto, Angela V.

2007-01-01

The large majority of EGRET point sources remain without an identified low-energy counterpart, and a large fraction of these sources are most likely extragalactic. Whatever the nature of the extragalactic EGRET unidentified sources, faint unresolved objects of the same class must have a contribution to the diffuse extragalactic gamma-ray background (EGRB). Understanding this component of the EGRB, along with other guaranteed contributions from known sources (blazars and normal galaxies), is essential if we are to use this emission to constrain exotic high-energy physics. Here, we follow an empirical approach to estimate whether the contribution of unresolved unidentified sources to the EGRB is likely to be important. Additionally, we discuss how upcoming GLAST observations of EGRET unidentified sources, their fainter counterparts, and the Galactic and extragalactic diffuse backgrounds, will shed light on the nature of the EGRET unidentified sources even without any positional association of such sources with low-energy counterparts

Linear and kernel methods for multi- and hypervariate change detection

DEFF Research Database (Denmark)

Nielsen, Allan Aasbjerg; Canty, Morton J.

2010-01-01

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

Kernel based orthogonalization for change detection in hyperspectral images

DEFF Research Database (Denmark)

Nielsen, Allan Aasbjerg

function and all quantities needed in the analysis are expressed in terms of this kernel function. This means that we need not know the nonlinear mappings explicitly. Kernel PCA and MNF analyses handle nonlinearities by implicitly transforming data into high (even infinite) dimensional feature space via...... analysis all 126 spectral bands of the HyMap are included. Changes on the ground are most likely due to harvest having taken place between the two acquisitions and solar effects (both solar elevation and azimuth have changed). Both types of kernel analysis emphasize change and unlike kernel PCA, kernel MNF...

Mitigation of artifacts in rtm with migration kernel decomposition

KAUST Repository

Zhan, Ge; Schuster, Gerard T.

2012-01-01

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

Adaptive Metric Kernel Regression

DEFF Research Database (Denmark)

Goutte, Cyril; Larsen, Jan

1998-01-01

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

Improved estimates of external gamma dose rates in the environs of Hinkley Point Power Station

International Nuclear Information System (INIS)

Macdonald, H.F.; Thompson, I.M.G.

1988-07-01

The dominant source of external gamma dose rates at centres of population within a few kilometres of Hinkley Point Power Station is the routine discharge of 41-Ar from the 'A' station magnox reactors. Earlier estimates of the 41-Ar radiation dose rates were based upon measured discharge rates, combined with calculations using standard plume dispersion and cloud-gamma integration models. This report presents improved dose estimates derived from environmental gamma dose rate measurements made at distances up to about 1 km from the site, thus minimising the degree of extrapolation introduced in estimating dose rates at locations up to a few kilometres from the site. In addition, results from associated chemical tracer measurements and wind tunnel simulations covering distances up to about 4 km from the station are outlined. These provide information on the spatial distribution of the 41-Ar plume during the initial stages of its dispersion, including effects due to plume buoyancy and momentum and behaviour under light wind conditions. In addition to supporting the methodology used for the 41-Ar dose calculations, this information is also of generic interest in the treatment of a range of operational and accidental releases from nuclear power station sites and will assist in the development and validation of existing environmental models. (author)

21 CFR 176.350 - Tamarind seed kernel powder.

Science.gov (United States)

2010-04-01

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

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

African Journals Online (AJOL)

ADOWIE PERE

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

Multivariate and semiparametric kernel regression

OpenAIRE

Härdle, Wolfgang; Müller, Marlene

1997-01-01

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

MeV gamma-ray observation with a well-defined point spread function based on electron tracking

Science.gov (United States)

Takada, A.; Tanimori, T.; Kubo, H.; Mizumoto, T.; Mizumura, Y.; Komura, S.; Kishimoto, T.; Takemura, T.; Yoshikawa, K.; Nakamasu, Y.; Matsuoka, Y.; Oda, M.; Miyamoto, S.; Sonoda, S.; Tomono, D.; Miuchi, K.; Kurosawa, S.; Sawano, T.

2016-07-01

The field of MeV gamma-ray astronomy has not opened up until recently owing to imaging difficulties. Compton telescopes and coded-aperture imaging cameras are used as conventional MeV gamma-ray telescopes; however their observations are obstructed by huge background, leading to uncertainty of the point spread function (PSF). Conventional MeV gamma-ray telescopes imaging utilize optimizing algorithms such as the ML-EM method, making it difficult to define the correct PSF, which is the uncertainty of a gamma-ray image on the celestial sphere. Recently, we have defined and evaluated the PSF of an electron-tracking Compton camera (ETCC) and a conventional Compton telescope, and thereby obtained an important result: The PSF strongly depends on the precision of the recoil direction of electron (scatter plane deviation, SPD) and is not equal to the angular resolution measure (ARM). Now, we are constructing a 30 cm-cubic ETCC for a second balloon experiment, Sub-MeV gamma ray Imaging Loaded-on-balloon Experiment: SMILE-II. The current ETCC has an effective area of 1 cm2 at 300 keV, a PSF of 10° at FWHM for 662 keV, and a large field of view of 3 sr. We will upgrade this ETCC to have an effective area of several cm2 and a PSF of 5° using a CF4-based gas. Using the upgraded ETCC, our observation plan for SMILE-II is to map of the electron-positron annihilation line and the 1.8 MeV line from 26Al. In this paper, we will report on the current performance of the ETCC and on our observation plan.

Residual analysis for spatial point processes

DEFF Research Database (Denmark)

Baddeley, A.; Turner, R.; Møller, Jesper

We define residuals for point process models fitted to spatial point pattern data, and propose diagnostic plots based on these residuals. The techniques apply to any Gibbs point process model, which may exhibit spatial heterogeneity, interpoint interaction and dependence on spatial covariates. Ou...... or covariate effects. Q-Q plots of the residuals are effective in diagnosing interpoint interaction. Some existing ad hoc statistics of point patterns (quadrat counts, scan statistic, kernel smoothed intensity, Berman's diagnostic) are recovered as special cases....

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

International Nuclear Information System (INIS)

Drozdowicz, K.

1995-01-01

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

Hadamard Kernel SVM with applications for breast cancer outcome predictions.

Science.gov (United States)

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

2017-12-21

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

Aflatoxin contamination of developing corn kernels.

Science.gov (United States)

Amer, M A

2005-01-01

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

Shielding calculations and collective dose estimations with the point-kernel-code VISIPLAN {sup registered} for the example of the project ZENT; Abschirmberechnungen und Kollektivdosisabschaetzungen mit dem Punkt-Kern-Code VISIPLAN {sup registered} am Beispiel des Projektes ZENT

Energy Technology Data Exchange (ETDEWEB)

Boehlke, S.; Niegoth, H. [STEAG Energy Services GmbH, Essen (Germany). Nuclear Technologies; Stalder, I. [Kernkraftwerk Leibstadt AG, Leibstadt (Switzerland)

2012-11-01

In the nuclear power plant Leibstadt (KKL) during the next year large components will be dismantled and stored for final disposal within the interim storage facility ZENT at the NPP site. Before construction of ZENT appropriate estimations of the local dose rate inside and outside the building and the collective dose for the normal operation have to be performed. The shielding calculations are based on the properties of the stored components and radiation sources and on the concepts for working place requirements. The installation of control and monitoring areas will depend on these calculations. For the determination of the shielding potential of concrete walls and steel doors with the defined boundary conditions point-kernel codes like MICROSHIELd {sup registered} are used. Complex problems cannot be modeled with this code. Therefore the point-kernel code VISIPLAN {sup registered} was developed for the determination of the local dose distribution functions in 3D models. The possibility of motion sequence inputs allows an optimization of collective dose estimations for the operational phases of a nuclear facility.

Kernel Korner : The Linux keyboard driver

NARCIS (Netherlands)

Brouwer, A.E.

1995-01-01

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

Radiation transport in earth for neutron and gamma ray point sources above an air-ground interface

International Nuclear Information System (INIS)

Lillie, R.A.; Santoro, R.T.

1979-03-01

Two-dimensional discrete ordinates methods were used to calculate the instantaneous dose rate in silicon and neutron and gamma ray fluences as a function of depth in earth from point sources at various heights (1.0, 61.3, and 731.5 meters) above an air--ground interface. The radiation incident on the earth's surface was transported through an earth-only and an earth--concrete model containing 0.9 meters of borated concrete beginning 0.5 meters below the earth's surface to obtain fluence distributions to a depth of 3.0 meters. The inclusion of borated concrete did not significantly reduce the total instantaneous dose rate in silicon and, in all cases, the secondary gamma ray fluence and corresponding dose are substantially larger than the primary neutron fluence and corresponding dose for depths greater than 0.6 meter. 4 figures, 4 tables

Radiation transport in earth for neutron and gamma-ray point sources above an air-ground interface

International Nuclear Information System (INIS)

Lillie, R.A.; Santoro, R.T.

1980-01-01

Two-dimensional discrete-ordinates methods have been used to calculate the instantaneous dose rate in silicon and neutron and gamma-ray fluences as a function of depth in earth from point sources at various heights (1.0, 61.3, and 731.5 m) above an air-ground interface. The radiation incident on the earth's surface was transported through an earth-only and an earth-concrete model containing 0.9 m of borated concrete beginning 0.5 m below the earth's surface to obtain fluence distributions to a depth of 3.0 m. The inclusion of borated concrete did not significantly reduce the total instantaneous dose rate in silicon, and in all cases, the secondary gamma-ray fluence and corresponding dose are substantially larger than the primary neutron fluence and corresponding dose for depths > 0.6 m

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

International Nuclear Information System (INIS)

Damunir; Sri Rinanti Susilowati; Ariyani Kusuma Dewi

2015-01-01

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

Searching for fixed point combinators by using automated theorem proving: A preliminary report

International Nuclear Information System (INIS)

Wos, L.; McCune, W.

1988-09-01

In this report, we establish that the use of an automated theorem- proving program to study deep questions from mathematics and logic is indeed an excellent move. Among such problems, we focus mainly on that concerning the construction of fixed point combinators---a problem considered by logicians to be significant and difficult to solve, and often computationally intensive and arduous. To be a fixed point combinator, Θ must satisfy the equation Θx = x(Θx) for all combinators x. The specific questions on which we focus most heavily ask, for each chosen set of combinators, whether a fixed point combinator can be constructed from the members of that set. For answering questions of this type, we present a new, sound, and efficient method, called the kernel method, which can be applied quite easily by hand and very easily by an automated theorem-proving program. For the application of the kernel method by a theorem-proving program, we illustrate the vital role that is played by both paramodulation and demodulation---two of the powerful features frequently offered by an automated theorem-proving program for treating equality as if it is ''understood.'' We also state a conjecture that, if proved, establishes the completeness of the kernel method. From what we can ascertain, this method---which relies on the introduced concepts of kernel and superkernel---offers the first systematic approach for searching for fixed point combinators. We successfully apply the new kernel method to various sets of combinators and, for the set consisting of the combinators B and W, construct an infinite set of fixed point combinators such that no two of the combinators are equal even in the presence of extensionality---a law that asserts that two combinators are equal if they behave the same. 18 refs

Searching for fixed point combinators by using automated theorem proving: A preliminary report

Energy Technology Data Exchange (ETDEWEB)

Wos, L.; McCune, W.

1988-09-01

In this report, we establish that the use of an automated theorem- proving program to study deep questions from mathematics and logic is indeed an excellent move. Among such problems, we focus mainly on that concerning the construction of fixed point combinators---a problem considered by logicians to be significant and difficult to solve, and often computationally intensive and arduous. To be a fixed point combinator, THETA must satisfy the equation THETAx = x(THETAx) for all combinators x. The specific questions on which we focus most heavily ask, for each chosen set of combinators, whether a fixed point combinator can be constructed from the members of that set. For answering questions of this type, we present a new, sound, and efficient method, called the kernel method, which can be applied quite easily by hand and very easily by an automated theorem-proving program. For the application of the kernel method by a theorem-proving program, we illustrate the vital role that is played by both paramodulation and demodulation---two of the powerful features frequently offered by an automated theorem-proving program for treating equality as if it is ''understood.'' We also state a conjecture that, if proved, establishes the completeness of the kernel method. From what we can ascertain, this method---which relies on the introduced concepts of kernel and superkernel---offers the first systematic approach for searching for fixed point combinators. We successfully apply the new kernel method to various sets of combinators and, for the set consisting of the combinators B and W, construct an infinite set of fixed point combinators such that no two of the combinators are equal even in the presence of extensionality---a law that asserts that two combinators are equal if they behave the same. 18 refs.

Analysis of the traveltime sensitivity kernels for an acoustic transversely isotropic medium with a vertical axis of symmetry

KAUST Repository

Djebbi, Ramzi

2016-02-05

In anisotropic media, several parameters govern the propagation of the compressional waves. To correctly invert surface recorded seismic data in anisotropic media, a multi-parameter inversion is required. However, a tradeoff between parameters exists because several models can explain the same dataset. To understand these tradeoffs, diffraction/reflection and transmission-type sensitivity-kernels analyses are carried out. Such analyses can help us to choose the appropriate parameterization for inversion. In tomography, the sensitivity kernels represent the effect of a parameter along the wave path between a source and a receiver. At a given illumination angle, similarities between sensitivity kernels highlight the tradeoff between the parameters. To discuss the parameterization choice in the context of finite-frequency tomography, we compute the sensitivity kernels of the instantaneous traveltimes derived from the seismic data traces. We consider the transmission case with no encounter of an interface between a source and a receiver; with surface seismic data, this corresponds to a diving wave path. We also consider the diffraction/reflection case when the wave path is formed by two parts: one from the source to a sub-surface point and the other from the sub-surface point to the receiver. We illustrate the different parameter sensitivities for an acoustic transversely isotropic medium with a vertical axis of symmetry. The sensitivity kernels depend on the parameterization choice. By comparing different parameterizations, we explain why the parameterization with the normal moveout velocity, the anellipitic parameter η, and the δ parameter is attractive when we invert diving and reflected events recorded in an active surface seismic experiment. © 2016 European Association of Geoscientists & Engineers.

SU-E-T-423: Fast Photon Convolution Calculation with a 3D-Ideal Kernel On the GPU

Energy Technology Data Exchange (ETDEWEB)

Moriya, S; Sato, M [Komazawa University, Setagaya, Tokyo (Japan); Tachibana, H [National Cancer Center Hospital East, Kashiwa, Chiba (Japan)

2015-06-15

Purpose: The calculation time is a trade-off for improving the accuracy of convolution dose calculation with fine calculation spacing of the KERMA kernel. We investigated to accelerate the convolution calculation using an ideal kernel on the Graphic Processing Units (GPU). Methods: The calculation was performed on the AMD graphics hardware of Dual FirePro D700 and our algorithm was implemented using the Aparapi that convert Java bytecode to OpenCL. The process of dose calculation was separated with the TERMA and KERMA steps. The dose deposited at the coordinate (x, y, z) was determined in the process. In the dose calculation running on the central processing unit (CPU) of Intel Xeon E5, the calculation loops were performed for all calculation points. On the GPU computation, all of the calculation processes for the points were sent to the GPU and the multi-thread computation was done. In this study, the dose calculation was performed in a water equivalent homogeneous phantom with 150{sup 3} voxels (2 mm calculation grid) and the calculation speed on the GPU to that on the CPU and the accuracy of PDD were compared. Results: The calculation time for the GPU and the CPU were 3.3 sec and 4.4 hour, respectively. The calculation speed for the GPU was 4800 times faster than that for the CPU. The PDD curve for the GPU was perfectly matched to that for the CPU. Conclusion: The convolution calculation with the ideal kernel on the GPU was clinically acceptable for time and may be more accurate in an inhomogeneous region. Intensity modulated arc therapy needs dose calculations for different gantry angles at many control points. Thus, it would be more practical that the kernel uses a coarse spacing technique if the calculation is faster while keeping the similar accuracy to a current treatment planning system.

Mitigation of artifacts in rtm with migration kernel decomposition

KAUST Repository

Zhan, Ge

2012-01-01

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

Development of point Kernel radiation shielding analysis computer program implementing recent nuclear data and graphic user interfaces

International Nuclear Information System (INIS)

Kang, S.; Lee, S.; Chung, C.

2002-01-01

There is an increasing demand for safe and efficient use of radiation and radioactive work activity along with shielding analysis as a result the number of nuclear and conventional facilities using radiation or radioisotope rises. Most Korean industries and research institutes including Korea Power Engineering Company (KOPEC) have been using foreign computer programs for radiation shielding analysis. Korean nuclear regulations have introduced new laws regarding the dose limits and radiological guides as prescribed in the ICRP 60. Thus, the radiation facilities should be designed and operated to comply with these new regulations. In addition, the previous point kernel shielding computer code utilizes antiquated nuclear data (mass attenuation coefficient, buildup factor, etc) which were developed in 1950∼1960. Subsequently, the various nuclear data such mass attenuation coefficient, buildup factor, etc. have been updated during the past few decades. KOPEC's strategic directive is to become a self-sufficient and independent nuclear design technology company, thus KOPEC decided to develop a new radiation shielding computer program that included the latest regulatory requirements and updated nuclear data. This new code was designed by KOPEC with developmental cooperation with Hanyang University, Department of Nuclear Engineering. VisualShield is designed with a graphical user interface to allow even users unfamiliar to radiation shielding theory to proficiently prepare input data sets and analyzing output results

Realized kernels in practice

DEFF Research Database (Denmark)

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

2009-01-01

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

The effect of STDP temporal kernel structure on the learning dynamics of single excitatory and inhibitory synapses.

Directory of Open Access Journals (Sweden)

Yotam Luz

Full Text Available Spike-Timing Dependent Plasticity (STDP is characterized by a wide range of temporal kernels. However, much of the theoretical work has focused on a specific kernel - the "temporally asymmetric Hebbian" learning rules. Previous studies linked excitatory STDP to positive feedback that can account for the emergence of response selectivity. Inhibitory plasticity was associated with negative feedback that can balance the excitatory and inhibitory inputs. Here we study the possible computational role of the temporal structure of the STDP. We represent the STDP as a superposition of two processes: potentiation and depression. This allows us to model a wide range of experimentally observed STDP kernels, from Hebbian to anti-Hebbian, by varying a single parameter. We investigate STDP dynamics of a single excitatory or inhibitory synapse in purely feed-forward architecture. We derive a mean-field-Fokker-Planck dynamics for the synaptic weight and analyze the effect of STDP structure on the fixed points of the mean field dynamics. We find a phase transition along the Hebbian to anti-Hebbian parameter from a phase that is characterized by a unimodal distribution of the synaptic weight, in which the STDP dynamics is governed by negative feedback, to a phase with positive feedback characterized by a bimodal distribution. The critical point of this transition depends on general properties of the STDP dynamics and not on the fine details. Namely, the dynamics is affected by the pre-post correlations only via a single number that quantifies its overlap with the STDP kernel. We find that by manipulating the STDP temporal kernel, negative feedback can be induced in excitatory synapses and positive feedback in inhibitory. Moreover, there is an exact symmetry between inhibitory and excitatory plasticity, i.e., for every STDP rule of inhibitory synapse there exists an STDP rule for excitatory synapse, such that their dynamics is identical.

Anatomically-aided PET reconstruction using the kernel method.

Science.gov (United States)

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

2016-09-21

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

Embedded real-time operating system micro kernel design

Science.gov (United States)

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

2005-12-01

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

Kernel Temporal Differences for Neural Decoding

Science.gov (United States)

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

2015-01-01

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

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

International Nuclear Information System (INIS)

Zielinska, S.

1985-03-01

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

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

KAUST Repository

Malas, Tareq Majed Yasin

2012-05-21

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

Determinantal point process models on the sphere

DEFF Research Database (Denmark)

Møller, Jesper; Nielsen, Morten; Porcu, Emilio

defined on Sd × Sd . We review the appealing properties of such processes, including their specific moment properties, density expressions and simulation procedures. Particularly, we characterize and construct isotropic DPPs models on Sd , where it becomes essential to specify the eigenvalues......We consider determinantal point processes on the d-dimensional unit sphere Sd . These are finite point processes exhibiting repulsiveness and with moment properties determined by a certain determinant whose entries are specified by a so-called kernel which we assume is a complex covariance function...... and eigenfunctions in a spectral representation for the kernel, and we figure out how repulsive isotropic DPPs can be. Moreover, we discuss the shortcomings of adapting existing models for isotropic covariance functions and consider strategies for developing new models, including a useful spectral approach....

Classification of maize kernels using NIR hyperspectral imaging

DEFF Research Database (Denmark)

Williams, Paul; Kucheryavskiy, Sergey V.

2016-01-01

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

Influence of wheat kernel physical properties on the pulverizing process.

Science.gov (United States)

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

2014-10-01

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

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

International Nuclear Information System (INIS)

Li Heng; Mohan, Radhe; Zhu, X Ronald

2008-01-01

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

Implementation of a revised numerical integration technique into QAD

International Nuclear Information System (INIS)

De Gangi, N.L.

1983-01-01

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

Evolution kernel for the Dirac field

International Nuclear Information System (INIS)

Baaquie, B.E.

1982-06-01

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

Gradient-based adaptation of general gaussian kernels.

Science.gov (United States)

Glasmachers, Tobias; Igel, Christian

2005-10-01

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

Analog forecasting with dynamics-adapted kernels

Science.gov (United States)

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.

Open Problem: Kernel methods on manifolds and metric spaces

DEFF Research Database (Denmark)

Feragen, Aasa; Hauberg, Søren

2016-01-01

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

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

Science.gov (United States)

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

2016-02-01

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

Kernel-based noise filtering of neutron detector signals

International Nuclear Information System (INIS)

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

2007-01-01

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

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

Science.gov (United States)

Jiang, Wenhao; Chung, Fu-lai

2014-01-01

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

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

African Journals Online (AJOL)

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

Heat kernel analysis for Bessel operators on symmetric cones

DEFF Research Database (Denmark)

Möllers, Jan

2014-01-01

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

A multi-scale kernel bundle for LDDMM

DEFF Research Database (Denmark)

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

2011-01-01

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

Training Lp norm multiple kernel learning in the primal.

Science.gov (United States)

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

2013-10-01

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

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

Science.gov (United States)

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

2018-02-13

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

Assessment of the microbiological safety of edible roasted nut kernels on retail sale in England, with a focus on Salmonella.

Science.gov (United States)

Little, C L; Jemmott, W; Surman-Lee, S; Hucklesby, L; de Pinnal, E

2009-04-01

There is little published information on the prevalence of Salmonella in edible nut kernels. A study in early 2008 of edible roasted nut kernels on retail sale in England was undertaken to assess the microbiological safety of this product. A total of 727 nut kernel samples of different varieties were examined. Overall, Salmonella and Escherichia coli were detected from 0.2 and 0.4% of edible roasted nut kernels. Of the nut varieties examined, Salmonella Havana was detected from 1 (4.0%) sample of pistachio nuts, indicating a risk to health. The United Kingdom Food Standards Agency was immediately informed, and full investigations were undertaken. Further examination established the contamination to be associated with the pistachio kernels and not the partly opened shells. Salmonella was not detected in other varieties tested (almonds, Brazils, cashews, hazelnuts, macadamia, peanuts, pecans, pine nuts, and walnuts). E. coli was found at low levels (range of 3.6 to 4/g) in walnuts (1.4%), almonds (1.2%), and Brazils (0.5%). The presence of Salmonella is unacceptable in edible nut kernels. Prevention of microbial contamination in these products lies in the application of good agricultural, manufacturing, and storage practices together with a hazard analysis and critical control points system that encompass all stages of production, processing, and distribution.

Advanced HEDL gamma scan system

International Nuclear Information System (INIS)

Smith, F.C.; Olson, R.N.

1983-01-01

The design of an advanced state-of-the-art gamma scan system built for the purpose of measuring the point-by-point gamma activity of irradiated fuel rods is described. The emphasis of the system design was to achieve the highest rate of throughput with the minimum per rod cost while maintaining system accuracy and reliability. Preliminary tests demonstrate that all system requirements were met or exceeded. The system provides improved throughput, precision, automation, flexibility, and data processing capability over previous gamma scan systems

Stochastic subset selection for learning with kernel machines.

Science.gov (United States)

Rhinelander, Jason; Liu, Xiaoping P

2012-06-01

Kernel machines have gained much popularity in applications of machine learning. Support vector machines (SVMs) are a subset of kernel machines and generalize well for classification, regression, and anomaly detection tasks. The training procedure for traditional SVMs involves solving a quadratic programming (QP) problem. The QP problem scales super linearly in computational effort with the number of training samples and is often used for the offline batch processing of data. Kernel machines operate by retaining a subset of observed data during training. The data vectors contained within this subset are referred to as support vectors (SVs). The work presented in this paper introduces a subset selection method for the use of kernel machines in online, changing environments. Our algorithm works by using a stochastic indexing technique when selecting a subset of SVs when computing the kernel expansion. The work described here is novel because it separates the selection of kernel basis functions from the training algorithm used. The subset selection algorithm presented here can be used in conjunction with any online training technique. It is important for online kernel machines to be computationally efficient due to the real-time requirements of online environments. Our algorithm is an important contribution because it scales linearly with the number of training samples and is compatible with current training techniques. Our algorithm outperforms standard techniques in terms of computational efficiency and provides increased recognition accuracy in our experiments. We provide results from experiments using both simulated and real-world data sets to verify our algorithm.

RTOS kernel in portable electrocardiograph

Science.gov (United States)

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

2011-12-01

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

RTOS kernel in portable electrocardiograph

International Nuclear Information System (INIS)

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

2011-01-01

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

RKRD: Runtime Kernel Rootkit Detection

Science.gov (United States)

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

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

Denoising by semi-supervised kernel PCA preimaging

DEFF Research Database (Denmark)

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

2014-01-01

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

Sentiment classification with interpolated information diffusion kernels

NARCIS (Netherlands)

Raaijmakers, S.

2007-01-01

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

Linear and kernel methods for multivariate change detection

DEFF Research Database (Denmark)

Canty, Morton J.; Nielsen, Allan Aasbjerg

2012-01-01

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

Wave equation tomography using the unwrapped phase - Analysis of the traveltime sensitivity kernels

KAUST Repository

Djebbi, Ramzi

2013-01-01

Full waveform inversion suffers from the high non-linearity in the misfit function, which causes the convergence to a local minimum. In the other hand, traveltime tomography has a quasi-linear misfit function but yields low- resolution models. Wave equation tomography (WET) tries to improve on traveltime tomography, by better adhering to the requirements of our finite-frequency data. However, conventional (WET), based on the crosscorelaion lag, yields the popular hallow banana sensitivity kernel indicating that the measured wavefield at a point is insensitive to perturbations along the ray theoretical path at certain finite frequencies. Using the instantaneous traveltime, the sensitivity kernel reflects more the model-data dependency we grown accustom to in seismic inversion (even phase inversion). Demonstrations on synthetic and the Mamousi model support such assertions.

Absorbed dose evaluation of Auger electron-emitting radionuclides: impact of input decay spectra on dose point kernels and S-values.

Science.gov (United States)

Falzone, Nadia; Lee, Boon Q; Fernández-Varea, José M; Kartsonaki, Christiana; Stuchbery, Andrew E; Kibédi, Tibor; Vallis, Katherine A

2017-03-21

The aim of this study was to investigate the impact of decay data provided by the newly developed stochastic atomic relaxation model BrIccEmis on dose point kernels (DPKs - radial dose distribution around a unit point source) and S-values (absorbed dose per unit cumulated activity) of 14 Auger electron (AE) emitting radionuclides, namely 67 Ga, 80m Br, 89 Zr, 90 Nb, 99m Tc, 111 In, 117m Sn, 119 Sb, 123 I, 124 I, 125 I, 135 La, 195m Pt and 201 Tl. Radiation spectra were based on the nuclear decay data from the medical internal radiation dose (MIRD) RADTABS program and the BrIccEmis code, assuming both an isolated-atom and condensed-phase approach. DPKs were simulated with the PENELOPE Monte Carlo (MC) code using event-by-event electron and photon transport. S-values for concentric spherical cells of various sizes were derived from these DPKs using appropriate geometric reduction factors. The number of Auger and Coster-Kronig (CK) electrons and x-ray photons released per nuclear decay (yield) from MIRD-RADTABS were consistently higher than those calculated using BrIccEmis. DPKs for the electron spectra from BrIccEmis were considerably different from MIRD-RADTABS in the first few hundred nanometres from a point source where most of the Auger electrons are stopped. S-values were, however, not significantly impacted as the differences in DPKs in the sub-micrometre dimension were quickly diminished in larger dimensions. Overestimation in the total AE energy output by MIRD-RADTABS leads to higher predicted energy deposition by AE emitting radionuclides, especially in the immediate vicinity of the decaying radionuclides. This should be taken into account when MIRD-RADTABS data are used to simulate biological damage at nanoscale dimensions.

Panel data specifications in nonparametric kernel regression

DEFF Research Database (Denmark)

Czekaj, Tomasz Gerard; Henningsen, Arne

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

Scuba: scalable kernel-based gene prioritization.

Science.gov (United States)

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

2018-01-25

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

MULTITASKER, Multitasking Kernel for C and FORTRAN Under UNIX

International Nuclear Information System (INIS)

Brooks, E.D. III

1988-01-01

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

Multiple kernel boosting framework based on information measure for classification

International Nuclear Information System (INIS)

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

2016-01-01

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

Kernel Methods for Mining Instance Data in Ontologies

Science.gov (United States)

Bloehdorn, Stephan; Sure, York

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

A kernel adaptive algorithm for quaternion-valued inputs.

Science.gov (United States)

Paul, Thomas K; Ogunfunmi, Tokunbo

2015-10-01

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

Improving the Bandwidth Selection in Kernel Equating

Science.gov (United States)

Andersson, Björn; von Davier, Alina A.

2014-01-01

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

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

Science.gov (United States)

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

2013-05-01

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

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

Indian Academy of Sciences (India)

2011-12-02

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

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

Science.gov (United States)

Zhang, Xueying; Song, Qinbao

2015-01-01

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

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

International Nuclear Information System (INIS)

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

1988-01-01

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

The Relationship between Black Point and Fungi Species and Effects of Black Point on Seed Germination Properties in Bread Wheat

OpenAIRE

TOKLU, Faruk; AKGÜL, Davut Soner; BİÇİCİ, Mehmet; KARAKÖY, Tolga

2014-01-01

This study was undertaken to investigate the relationship between some fungi species and black point incidence and the effect of black point on seed weight, germination percentage, seedling emergence, seedling establishment, number of embryonic roots, and coleoptile length under field conditions in bread wheat. In this research, black-pointed and black point-free kernel samples of 5 bread wheat cultivars, namely Ceyhan-99, Doğankent-1, Yüreğir-89, Seyhan-95, and Adana-99 - commonly grown unde...

On the maximum and minimum of two modified Gamma-Gamma variates with applications

KAUST Repository

Al-Quwaiee, Hessa; Ansari, Imran Shafique; Alouini, Mohamed-Slim

2014-01-01

on these new results to present the performance analysis of (i) a dual-branch free-space optical selection combining diversity undergoing independent but not necessarily identically distributed Gamma-Gamma fading under the impact of pointing errors and of (ii

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

Science.gov (United States)

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

Evaluation of the Lubricating Properties of Palm Kernel Oil

Directory of Open Access Journals (Sweden)

John J MUSA

2009-07-01

Full Text Available The search for renewable energy resources continues to attract attention in recent times as fossil fuels such as petroleum, coal and natural gas, which are been used to meet the energy needs of man are associated with negative environmental impacts such as global warming. Biodiesel offered reduced exhaust emissions, improved biodegradability, reduced toxicity and higher carotene rating which can improve performance and clean up emissions. Standard methods were used to determine the physical and chemical properties of the oil, which includes the Density, Viscosity, flash/fire point, carbon residue, volatility and Specific Gravity were determined by chemical experimental analysis. The flash/fire points of the Heavy duty oil (SAE 40 and Light duty oil (SAE 30 is 260/300(°C and 243/290(°C respectively while the pour points of the samples are 22°C for palm kernel oil while 9°C and 21°C for SAE 40and SAE 30 respectively.

Comparison of gamma ray and electron beam irradiation on extraction yield, morphological and antioxidant properties of polysaccharides from tamarind seed

Energy Technology Data Exchange (ETDEWEB)

Choi, Jong-il [Advanced Radiation Technology Institute, Korea Atomic Energy Research Institute, Jeongeup 580-185 (Korea, Republic of); Kim, Jae-Kyung [Advanced Radiation Technology Institute, Korea Atomic Energy Research Institute, Jeongeup 580-185 (Korea, Republic of); Graduate school of Food and Biotechnology, Korea University, Seoul 146-701 (Korea, Republic of); Srinivasan, Periasamy; Kim, Jae-Hun [Advanced Radiation Technology Institute, Korea Atomic Energy Research Institute, Jeongeup 580-185 (Korea, Republic of); Park, Hyun-Jin [Graduate school of Food and Biotechnology, Korea University, Seoul 146-701 (Korea, Republic of); Byun, Myung-Woo [Advanced Radiation Technology Institute, Korea Atomic Energy Research Institute, Jeongeup 580-185 (Korea, Republic of); Lee, Ju-Woon [Advanced Radiation Technology Institute, Korea Atomic Energy Research Institute, Jeongeup 580-185 (Korea, Republic of)], E-mail: sjwlee@kaeri.re.kr

2009-07-15

Tamarind (Tamarindus indica L) seed polysaccharide (TSP) is of great important due to its various biological activities. The present investigation was carried out to compare extraction yield, morphological characteristics, average molecular weights and antioxidant activities of TSP from gamma- and electron beam (EB)-irradiated tamarind kernel powder. The tamarind kernel powder was irradiated with 0, 5 and 10 kGy by gamma ray (GR) and electron beam, respectively. The extraction yield of TSP was increased significantly by EB and GR irradiation, but there was no significant difference between irradiation types. Morphological studies by scanning electron microscope showed that TSP from GR-irradiated tamarind seed had a fibrous structure, different from that of EB irradiated with a particle structures. The average molecular weight of TSP was decreased by the irradiation, and EB treatment degraded more severely than GR. Superoxide radical scavenging ability and total antioxidant capacity of EB-treated TSP showed higher than those of GR-treated TSP.

Protein fold recognition using geometric kernel data fusion.

Science.gov (United States)

Zakeri, Pooya; Jeuris, Ben; Vandebril, Raf; Moreau, Yves

2014-07-01

Various approaches based on features extracted from protein sequences and often machine learning methods have been used in the prediction of protein folds. Finding an efficient technique for integrating these different protein features has received increasing attention. In particular, kernel methods are an interesting class of techniques for integrating heterogeneous data. Various methods have been proposed to fuse multiple kernels. Most techniques for multiple kernel learning focus on learning a convex linear combination of base kernels. In addition to the limitation of linear combinations, working with such approaches could cause a loss of potentially useful information. We design several techniques to combine kernel matrices by taking more involved, geometry inspired means of these matrices instead of convex linear combinations. We consider various sequence-based protein features including information extracted directly from position-specific scoring matrices and local sequence alignment. We evaluate our methods for classification on the SCOP PDB-40D benchmark dataset for protein fold recognition. The best overall accuracy on the protein fold recognition test set obtained by our methods is ∼ 86.7%. This is an improvement over the results of the best existing approach. Moreover, our computational model has been developed by incorporating the functional domain composition of proteins through a hybridization model. It is observed that by using our proposed hybridization model, the protein fold recognition accuracy is further improved to 89.30%. Furthermore, we investigate the performance of our approach on the protein remote homology detection problem by fusing multiple string kernels. The MATLAB code used for our proposed geometric kernel fusion frameworks are publicly available at http://people.cs.kuleuven.be/∼raf.vandebril/homepage/software/geomean.php?menu=5/. © The Author 2014. Published by Oxford University Press.

Unsupervised multiple kernel learning for heterogeneous data integration.

Science.gov (United States)

Mariette, Jérôme; Villa-Vialaneix, Nathalie

2018-03-15

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

Localized Multiple Kernel Learning Via Sample-Wise Alternating Optimization.

Science.gov (United States)

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

2014-01-01

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

Observing integrals of heat kernels from a distance

DEFF Research Database (Denmark)

Heat kernels have integrals such as Brownian motion mean exit time, potential capacity, and torsional rigidity. We show how to obtain bounds on these values - essentially by observing their behaviour in terms of the distance function from a point and then comparing with corresponding values in ta...... and discussed as test cases. The talk is based on joint work with Vicente Palmer....... in tailor-made warped product spaces. The results will be illustrated by applications to the so-called 'type' problem: How to decide if a given manifold or surface is transient (hyperbolic) or recurrent (parabolic). Specific examples of minimal surfaces and constant pressure dry foams will be shown...

Control Transfer in Operating System Kernels

Science.gov (United States)

1994-05-13

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

Bivariate discrete beta Kernel graduation of mortality data.

Science.gov (United States)

Mazza, Angelo; Punzo, Antonio

2015-07-01

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

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

Science.gov (United States)

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

2015-04-01

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

3-D waveform tomography sensitivity kernels for anisotropic media

KAUST Repository

Djebbi, Ramzi

2014-01-01

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

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

International Nuclear Information System (INIS)

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

2011-01-01

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

Resummed memory kernels in generalized system-bath master equations

International Nuclear Information System (INIS)

Mavros, Michael G.; Van Voorhis, Troy

2014-01-01

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

The dipole form of the gluon part of the BFKL kernel

International Nuclear Information System (INIS)

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

2007-01-01

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

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

Science.gov (United States)

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

2018-02-19

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

A Class of Large-Update and Small-Update Primal-Dual Interior-Point Algorithms for Linear Optimization

NARCIS (Netherlands)

Bai, Y.Q.; Lesaja, G.; Roos, C.; Wang, G.Q.; El Ghami, M.

2008-01-01

In this paper we present a class of polynomial primal-dual interior-point algorithms for linear optimization based on a new class of kernel functions. This class is fairly general and includes the classical logarithmic function, the prototype self-regular function, and non-self-regular kernel

Pressure Prediction of Coal Slurry Transportation Pipeline Based on Particle Swarm Optimization Kernel Function Extreme Learning Machine

Directory of Open Access Journals (Sweden)

Xue-cun Yang

2015-01-01

Full Text Available For coal slurry pipeline blockage prediction problem, through the analysis of actual scene, it is determined that the pressure prediction from each measuring point is the premise of pipeline blockage prediction. Kernel function of support vector machine is introduced into extreme learning machine, the parameters are optimized by particle swarm algorithm, and blockage prediction method based on particle swarm optimization kernel function extreme learning machine (PSOKELM is put forward. The actual test data from HuangLing coal gangue power plant are used for simulation experiments and compared with support vector machine prediction model optimized by particle swarm algorithm (PSOSVM and kernel function extreme learning machine prediction model (KELM. The results prove that mean square error (MSE for the prediction model based on PSOKELM is 0.0038 and the correlation coefficient is 0.9955, which is superior to prediction model based on PSOSVM in speed and accuracy and superior to KELM prediction model in accuracy.

Observation of a 3D Magnetic Null Point

Energy Technology Data Exchange (ETDEWEB)

Romano, P.; Falco, M. [INAF—Osservatorio Astrofisico di Catania, Via S. Sofia 78, I-95123 Catania (Italy); Guglielmino, S. L.; Murabito, M., E-mail: prom@oact.inaf.it [Dipartimento di Fisica e Astronomia—Sezione Astrofisica, Università di Catania, Via S. Sofia 78, I-95123 Catania (Italy)

2017-03-10

We describe high-resolution observations of a GOES B-class flare characterized by a circular ribbon at the chromospheric level, corresponding to the network at the photospheric level. We interpret the flare as a consequence of a magnetic reconnection event that occurred at a three-dimensional (3D) coronal null point located above the supergranular cell. The potential field extrapolation of the photospheric magnetic field indicates that the circular chromospheric ribbon is cospatial with the fan footpoints, while the ribbons of the inner and outer spines look like compact kernels. We found new interesting observational aspects that need to be explained by models: (1) a loop corresponding to the outer spine became brighter a few minutes before the onset of the flare; (2) the circular ribbon was formed by several adjacent compact kernels characterized by a size of 1″–2″; (3) the kernels with a stronger intensity emission were located at the outer footpoint of the darker filaments, departing radially from the center of the supergranular cell; (4) these kernels started to brighten sequentially in clockwise direction; and (5) the site of the 3D null point and the shape of the outer spine were detected by RHESSI in the low-energy channel between 6.0 and 12.0 keV. Taking into account all these features and the length scales of the magnetic systems involved in the event, we argue that the low intensity of the flare may be ascribed to the low amount of magnetic flux and to its symmetric configuration.

A new discrete dipole kernel for quantitative susceptibility mapping.

Science.gov (United States)

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

2018-09-01

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

Genetic Analysis of Kernel Traits in Maize-Teosinte Introgression Populations

Directory of Open Access Journals (Sweden)

Zhengbin Liu

2016-08-01

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

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

DEFF Research Database (Denmark)

Petersen, Annette

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

Kernel Function Tuning for Single-Layer Neural Networks

Czech Academy of Sciences Publication Activity Database

Vidnerová, Petra; Neruda, Roman

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

Ensuring microbiological safety and extending the shelf life of shelled sweet corn kernels using hurdle technology

International Nuclear Information System (INIS)

Kumar, Sanjeev; Gautam, Satyendra

2015-01-01

Shelled sweet corn kernels are prone to microbial contaminations due to high moisture and nutrient contents. Post harvest handling further aggravates the condition and makes the product highly perishable and unsafe. In freshly shelled kernels total aerobic plate count, yeast mold count and presumptive coliforms were found to be ∼ 8, 7, and 4 log cfu/g, respectively. IMViC analysis confirmed presence of opportunistic pathogens like Escherichia coli and Enterobacteraerogenes in these samples. Besides, occurrence of mycotoxin such as ochratoxin A (OTA), classified as a possible carcinogenic compound, was found to be high in sweet corn samples spiked with toxigenic strain. To address this issue, a combination process including NaOCl washing (200 ppm for 5 min), hot water blanching (60℃ for 5 min), air drying, LDPE packaging, and finally gamma radiation (5 kGy) treatment was developed. The developed combination process was found to reduce microbial load to below detectable level and quite effectively inactivated Aspergillusochraceus spores as well as pre-formed toxin. These treatments were not found to affect the contents of biochemical constituents such as total and reducing sugars, proteins, phenolics, and flavonoids during storage. Prophylactic properties in terms of antioxidant capacity and potential to suppress chemical induced mutagenesis were not affected in these samples. Physical properties and sensory qualities were also found to be similar to fresh (control). Thus, the developed combination process ensured microbiological safety and extended shelf life of sweet corn kernels up to 30 days at 4℃ . (author)

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

Science.gov (United States)

Saleh, Mohammed; Meullenet, Jean-Francois

2013-05-01

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

A new mutant gene su-1 in corn obtained by irradiation with low doses of gamma rays

International Nuclear Information System (INIS)

Diaconu, P.

1993-01-01

This paper provides a description of a sugar corn mutant obtained by irradiation of wetted kernels of Romanesc de Studina variety with low doses of gamma rays (300 R). This mutant influences the structure of the endosperm similarly to the su-1 genes developed spontaneously which resulted in the corn variety Zea mays saccharata thousands of years ago. Although the mutant is a multiple allele of the su-1 locus in chromosome IV it differs widely from the spontaneous mutant. The length of the ears is much reduced, varying between 4 and 6 cm, with numbers of kernels per ear varying between 45 and 72. Attempts to improve the cob size and the number of kernels by breeding and propagation in an insulated area led to no result. Crossing the mutants with the sugar hybrid Delicious resulted in sugar type progeny which confirms the common position of the mutant gene induced by irradiation and the spontaneous su-1 gene. The progenies of sugar mutant x Delicious are 38-43 % lower in cob vigor and 36-46% lower in kernel number. (author). 2 figs, 2 tab., 16 refs

Local coding based matching kernel method for image classification.

Directory of Open Access Journals (Sweden)

Yan Song

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

Prediction of protein subcellular localization using support vector machine with the choice of proper kernel

Directory of Open Access Journals (Sweden)

Al Mehedi Hasan

2017-07-01

Full Text Available The prediction of subcellular locations of proteins can provide useful hints for revealing their functions as well as for understanding the mechanisms of some diseases and, finally, for developing novel drugs. As the number of newly discovered proteins has been growing exponentially, laboratory-based experiments to determine the location of an uncharacterized protein in a living cell have become both expensive and time-consuming. Consequently, to tackle these challenges, computational methods are being developed as an alternative to help biologists in selecting target proteins and designing related experiments. However, the success of protein subcellular localization prediction is still a complicated and challenging problem, particularly when query proteins may have multi-label characteristics, i.e. their simultaneous existence in more than one subcellular location, or if they move between two or more different subcellular locations as well. At this point, to get rid of this problem, several types of subcellular localization prediction methods with different levels of accuracy have been proposed. The support vector machine (SVM has been employed to provide potential solutions for problems connected with the prediction of protein subcellular localization. However, the practicability of SVM is affected by difficulties in selecting its appropriate kernel as well as in selecting the parameters of that selected kernel. The literature survey has shown that most researchers apply the radial basis function (RBF kernel to build a SVM based subcellular localization prediction system. Surprisingly, there are still many other kernel functions which have not yet been applied in the prediction of protein subcellular localization. However, the nature of this classification problem requires the application of different kernels for SVM to ensure an optimal result. From this viewpoint, this paper presents the work to apply different kernels for SVM in protein

Design and Performance of the GAMMA-400 Gamma-Ray Telescope for Dark Matter Searches

Science.gov (United States)

Galper, A. M.; Adriani, O.; Aptekar, R. L.; Arkhangelskaja, I. V.; Arkhangelskiy, A. I.; Boezio, M.; Bonvicini, V.; Boyarchuk, K. A.; Fradkin, M. I.; Gusakov, Yu V.;

Factors affecting cadmium absorbed by pistachio kernel in calcareous soils, southeast of Iran.

Science.gov (United States)

Shirani, H; Hosseinifard, S J; Hashemipour, H

2018-03-01

Cadmium (Cd) which does not have a biological role is one of the most toxic heavy metals for organisms. This metal enters environment through industrial processes and fertilizers. The main objective of this study was to determine the relationships between absorbed Cd by pistachio kernel and some of soil physical and chemical characteristics using modeling by stepwise regression and Artificial Neural Network (ANN), in calcareous soils in Rafsanjan region, southeast of Iran. For these purposes, 220 pistachio orchards were selected, and soil samples were taken from two depths of 0-40 and 40-80cm. Besides, fruit and leaf samples from branches with and without fruit were taken in each sampling point. The results showed that affecting factors on absorbed Cd by pistachio kernel which were obtained by regression method (pH and clay percent) were not interpretable, and considering unsuitable vales of determinant coefficient (R 2 ) and Root Mean Squares Error (RMSE), the model did not have sufficient validity. However, ANN modeling was highly accurate and reliable. Based on its results, soil available P and Zn and soil salinity were the most important factors affecting the concentration of Cd in pistachio kernel in pistachio growing areas of Rafsanjan. Copyright © 2017 Elsevier B.V. All rights reserved.

The (n, $\\gamma$) reaction in the s-process branching point $^{59}$Ni

CERN Multimedia

We propose to measure the $^{59}$Ni(n,$\\gamma$)$^{56}$Fe cross section at the neutron time of flight (n TOF) facility with a dedicated chemical vapor deposition (CVD) diamond detector. The (n, ) reaction in the radioactive $^{59}$Ni is of relevance in nuclear astrophysics as it can be seen as a rst branching point in the astrophysical s-process. Its relevance in nuclear technology is especially related to material embrittlement in stainless steel. There is a strong discrepancy between available experimental data and the evaluated nuclear data les for this isotope. The aim of the measurement is to clarify this disagreement. The clear energy separation of the reaction products of neutron induced reactions in $^{59}$Ni makes it a very suitable candidate for a rst cross section measurement with the CVD diamond detector, which should serve in the future for similar measurements at n_TOF.

Studies on non-traditional oils: l. Detailed studies on different lipid profiles of some Rosaceae kernel oils

Directory of Open Access Journals (Sweden)

Hassanein, Minar M. M.

1999-10-01

Full Text Available Kernel oils obtained from apricot, peach and plum, produced as by-products from food canning industry, were analyzed by capillary GC and HPLC. The fatty acid composition of the fractionated mono-, di- and triacylglycerol was elucidated. Oleic acid ranged from 64 to 72% in triacylglycerol (TAG, whereas linoleic acid ranged from 17% to 27%. The TAG profile showed the presence of 0 0 0 (35-42%, LOO (22-28%, LLO (7-16%, LOP and LLS (6-7%, OOP (6-10.4%. Campesterol-, 5-stigmasterol-, β-sitosterol-. isofucosterol, 7-stigmasterol and avenasterol were detected in the three analyzed oils. Sterylglycosides were also determined and showed some characteristic profiles. The three kernel oils contained appreciable amounts of tocopherols in which gamma-tocopherol was the predominating one. Alpha-and delta-tocopherols were also present in smaller quantities. Results obtained can be used to characterize these kernel oils and facilitate their differentiation from the other oils.

Aceites obtenidos de las semillas de albaricoque, melocotón y ciruela, producidos como subproductos de la industria alimenticia conservera, se analizaron mediante cromatografía gaseosa capilar y cromatografía líquida de alta resolución. Se estableció la composición en ácidos grasos de los mono-, di- y triacilgliceroles fraccionados. El ácido oleico osciló desde el 64% al 72% en triacilgliceroles (TAG, mientras que el ácido linoleico lo hizo entre el 17% y el 27%. El perfil de TAG mostró la presencia de OOO (35-42%, LOO (22-28%, LLO (7-16%, LOP y LLS (6-7%, OOP (6-10.4%. En los tres aceites analizados se detectaron campesterol-, 5-estigmasterol, β-sitosterol-, isofucosterol, 7-estigmasterol y avenasterol. También se determinaron glicósidos de esteroles, mostrando perfiles característicos. El contenido en tocoferoles fue apreciable en todos ellos, siendo el gamma-tocoferol el predominante. Los alfa- y delta-tocoferoles se detectaron en pequeñas cantidades. Los

Multivariate realised kernels

DEFF Research Database (Denmark)

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

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

Process for producing metal oxide kernels and kernels so obtained

International Nuclear Information System (INIS)

Lelievre, Bernard; Feugier, Andre.

1974-01-01

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

Ideal Gas Resonance Scattering Kernel Routine for the NJOY Code

International Nuclear Information System (INIS)

Rothenstein, W.

1999-01-01

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

Geodesic exponential kernels: When Curvature and Linearity Conflict

DEFF Research Database (Denmark)

Feragen, Aase; Lauze, François; Hauberg, Søren

2015-01-01

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

Real time kernel performance monitoring with SystemTap

CERN Multimedia

CERN. Geneva

2018-01-01

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

Comparative Analysis of Kernel Methods for Statistical Shape Learning

National Research Council Canada - National Science Library

Rathi, Yogesh; Dambreville, Samuel; Tannenbaum, Allen

2006-01-01

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

Semi-supervised learning for ordinal Kernel Discriminant Analysis.

Science.gov (United States)

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

2016-12-01

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

One Point Isometric Matching with the Heat Kernel

KAUST Repository

Ovsjanikov, Maks

2010-09-21

A common operation in many geometry processing algorithms consists of finding correspondences between pairs of shapes by finding structure-preserving maps between them. A particularly useful case of such maps is isometries, which preserve geodesic distances between points on each shape. Although several algorithms have been proposed to find approximately isometric maps between a pair of shapes, the structure of the space of isometries is not well understood. In this paper, we show that under mild genericity conditions, a single correspondence can be used to recover an isometry defined on entire shapes, and thus the space of all isometries can be parameterized by one correspondence between a pair of points. Perhaps surprisingly, this result is general, and does not depend on the dimensionality or the genus, and is valid for compact manifolds in any dimension. Moreover, we show that both the initial correspondence and the isometry can be recovered efficiently in practice. This allows us to devise an algorithm to find intrinsic symmetries of shapes, match shapes undergoing isometric deformations, as well as match partial and incomplete models efficiently. Journal compilation © 2010 The Eurographics Association and Blackwell Publishing Ltd.

Point Defect Properties of Cd(Zn)Te and TlBr for Room-Temperature Gamma Radiation Detectors

Science.gov (United States)

Lordi, Vincenzo

2013-03-01

The effects of various crystal defects in CdTe, Cd1-xZnxTe (CZT), and TlBr are critical for their performance as room-temperature gamma radiation detectors. We use predictive first principles theoretical methods to provide fundamental, atomic scale understanding of the defect properties of these materials to enable design of optimal growth and processing conditions, such as doping, annealing, and stoichiometry. Several recent cases will be reviewed, including (i) accurate calculations of the thermodynamic and electronic properties of native point defects and point defect complexes in CdTe and CZT; (ii) the effects of Zn alloying on the native point defect properties of CZT; (iii) point defect diffusion and binding related to Te clustering in Cd(Zn)Te; (iv) the profound effect of native point defects--principally vacancies--on the intrinsic material properties of TlBr, particularly electronic and ionic conductivity; (v) tailored doping of TlBr to independently control the electronic and ionic conductivity; and (vi) the effects of metal impurities on the electronic properties and device performance of TlBr detectors. Prepared by LLNL under Contract DE-AC52-07NA27344 with support from the National Nuclear Security Administration Office of Nonproliferation and Verification Research and Development NA-22.

Ideal gas scattering kernel for energy dependent cross-sections

International Nuclear Information System (INIS)

Rothenstein, W.; Dagan, R.

1998-01-01

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

Parameter optimization in the regularized kernel minimum noise fraction transformation

DEFF Research Database (Denmark)

Nielsen, Allan Aasbjerg; Vestergaard, Jacob Schack

2012-01-01

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

On flame kernel formation and propagation in premixed gases

Energy Technology Data Exchange (ETDEWEB)

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

2010-12-15

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

Insights from Classifying Visual Concepts with Multiple Kernel Learning

Science.gov (United States)

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

2012-01-01

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

Examination of the gamma equilibrium point hypothesis when applied to single degree of freedom movements performed with different inertial loads.

Science.gov (United States)

Bellomo, A; Inbar, G

1997-01-01

One of the theories of human motor control is the gamma Equilibrium Point Hypothesis. It is an attractive theory since it offers an easy control scheme where the planned trajectory shifts monotionically from an initial to a final equilibrium state. The feasibility of this model was tested by reconstructing the virtual trajectory and the stiffness profiles for movements performed with different inertial loads and examining them. Three types of movements were tested: passive movements, targeted movements, and repetitive movements. Each of the movements was performed with five different inertial loads. Plausible virtual trajectories and stiffness profiles were reconstructed based on the gamma Equilibrium Point Hypothesis for the three different types of movements performed with different inertial loads. However, the simple control strategy supported by the model, where the planned trajectory shifts monotonically from an initial to a final equilibrium state, could not be supported for targeted movements performed with added inertial load. To test the feasibility of the model further we must examine the probability that the human motor control system would choose a trajectory more complicated than the actual trajectory to control.

Molecular analysis of point mutations in a barley genome exposed to MNU and gamma rays

Energy Technology Data Exchange (ETDEWEB)

Kurowska, Marzena, E-mail: mkurowsk@us.edu.pl [Department of Genetics, Faculty of Biology and Environmental Protection, University of Silesia, Jagiellonska 28, 40-032 Katowice (Poland); Labocha-Pawlowska, Anna; Gnizda, Dominika; Maluszynski, Miroslaw; Szarejko, Iwona [Department of Genetics, Faculty of Biology and Environmental Protection, University of Silesia, Jagiellonska 28, 40-032 Katowice (Poland)

2012-10-15

We present studies aimed at determining the types and frequencies of mutations induced in the barley genome after treatment with chemical (N-methyl-N-nitrosourea, MNU) and physical (gamma rays) mutagens. We created M{sub 2} populations of a doubled haploid line and used them for the analysis of mutations in targeted DNA sequences and over an entire barley genome using TILLING (Targeting Induced Local Lesions in Genomes) and AFLP (Amplified Fragment Length Polymorphism) technique, respectively. Based on the TILLING analysis of the total DNA sequence of 4,537,117 bp in the MNU population, the average mutation density was estimated as 1/504 kb. Only one nucleotide change was found after an analysis of 3,207,444 bp derived from the highest dose of gamma rays applied. MNU was clearly a more efficient mutagen than gamma rays in inducing point mutations in barley. The majority (63.6%) of the MNU-induced nucleotide changes were transitions, with a similar number of G > A and C > T substitutions. The similar share of G > A and C > T transitions indicates a lack of bias in the repair of O{sup 6}-methylguanine lesions between DNA strands. There was, however, a strong specificity of the nucleotide surrounding the O{sup 6}-meG at the -1 position. Purines formed 81% of nucleotides observed at the -1 site. Scanning the barley genome with AFLP markers revealed ca. a three times higher level of AFLP polymorphism in MNU-treated as compared to the gamma-irradiated population. In order to check whether AFLP markers can really scan the whole barley genome for mutagen-induced polymorphism, 114 different AFLP products, were cloned and sequenced. 94% of bands were heterogenic, with some bands containing up to 8 different amplicons. The polymorphic AFLP products were characterised in terms of their similarity to the records deposited in a GenBank database. The types of sequences present in the polymorphic bands reflected the organisation of the barley genome.

The rise in the positron fraction. Distance limits on positron point sources from cosmic ray arrival directions and diffuse gamma-rays

Energy Technology Data Exchange (ETDEWEB)

Gebauer, Iris; Bentele, Rosemarie [Karlsruhe Institute of Technology, Karlsruhe (Germany)

2016-07-01

The rise in the positron fraction as observed by AMS and previously by PAMELA, cannot be explained by the standard paradigm of cosmic ray transport in which positrons are produced by cosmic-ray-gas interactions in the interstellar medium. Possible explanations are pulsars, which produce energetic electron-positron pairs in their rotating magnetic fields, or the annihilation of dark matter. Here we assume that these positrons originate from a single close-by point source, producing equal amounts of electrons and positrons. The propagation and energy losses of these electrons and positrons are calculated numerically using the DRAGON code, the source properties are optimized to best describe the AMS data. Using the FERMI-LAT limits on a possible dipole anisotropy in electron and positron arrival directions, we put a limit on the minimum distance of such a point source. The energy losses that these energetic electrons and positrons suffer on their way through the galaxy create gamma ray photons through bremsstrahlung and Inverse Compton scattering. Using the measurement of diffuse gamma rays from Fermi-LAT we put a limit on the maximum distance of such a point source. We find that a single electron positron point source powerful enough to explain the locally observed positron fraction must reside between 225 pc and 3.7 kpc distance from the sun and compare to known pulsars.

Time-dependent integral transport equation kernels, leakage rates and collision rates for plane and spherical geometry

International Nuclear Information System (INIS)

Henderson, D.L.

1987-01-01

Time-dependent integral transport equation flux and current kernels for plane and spherical geometry are derived for homogeneous media. Using the multiple collision formalism, isotropic sources that are delta distributions in time are considered for four different problems. The plane geometry flux kernel is applied to a uniformly distributed source within an infinite medium and to a surface source in a semi-infinite medium. The spherical flux kernel is applied to a point source in an infinite medium and to a point source at the origin of a finite sphere. The time-dependent first-flight leakage rates corresponding to the existing steady state first-flight escape probabilities are computed by the Laplace transform technique assuming a delta distribution source in time. The case of a constant source emitting neutrons over a time interval, Δt, for a spatially uniform source is obtained for a slab and a sphere. Time-dependent first-flight leakage rates are also determined for the general two region spherical medium problem for isotropic sources with a delta distribution in time uniformly distributed throughout both the inner and outer regions. The time-dependent collision rates due to the uncollided neutrons are computed for a slab and a sphere using the time-dependent first-flight leakage rates and the time-dependent continuity equation. The case of a constant source emitting neutrons over a time interval, Δt, is also considered

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

Science.gov (United States)

Dougherty, Andrew W.

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

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

International Nuclear Information System (INIS)

Lelievre Bernard; Feugier, Andre.

1973-01-01

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

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

Science.gov (United States)

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

2017-10-02

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

Towards a Holistic Cortical Thickness Descriptor: Heat Kernel-Based Grey Matter Morphology Signatures.

Science.gov (United States)

Wang, Gang; Wang, Yalin

2017-02-15

In this paper, we propose a heat kernel based regional shape descriptor that may be capable of better exploiting volumetric morphological information than other available methods, thereby improving statistical power on brain magnetic resonance imaging (MRI) analysis. The mechanism of our analysis is driven by the graph spectrum and the heat kernel theory, to capture the volumetric geometry information in the constructed tetrahedral meshes. In order to capture profound brain grey matter shape changes, we first use the volumetric Laplace-Beltrami operator to determine the point pair correspondence between white-grey matter and CSF-grey matter boundary surfaces by computing the streamlines in a tetrahedral mesh. Secondly, we propose multi-scale grey matter morphology signatures to describe the transition probability by random walk between the point pairs, which reflects the inherent geometric characteristics. Thirdly, a point distribution model is applied to reduce the dimensionality of the grey matter morphology signatures and generate the internal structure features. With the sparse linear discriminant analysis, we select a concise morphology feature set with improved classification accuracies. In our experiments, the proposed work outperformed the cortical thickness features computed by FreeSurfer software in the classification of Alzheimer's disease and its prodromal stage, i.e., mild cognitive impairment, on publicly available data from the Alzheimer's Disease Neuroimaging Initiative. The multi-scale and physics based volumetric structure feature may bring stronger statistical power than some traditional methods for MRI-based grey matter morphology analysis. Copyright © 2016 Elsevier Inc. All rights reserved.

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

Directory of Open Access Journals (Sweden)

Hjalmar Rosengren

2006-12-01

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

A multi-resolution approach to heat kernels on discrete surfaces

KAUST Repository

Vaxman, Amir

2010-07-26

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

Development of new iraqi wheat varieties induced by gamma rays

International Nuclear Information System (INIS)

Ibrahim, I.F.; Al-Janabi, K.K.; Al-Maaroof, E.M.; Al-Aubaidi, M.O.; Mahmoud, A.H.; Al-Janabi, A.A.

1991-01-01

The aim of the present investigation is to study agronomic traits of three wheat mutants induced by gamma rays and compared with their origin 'Saber Beg' during M 8 - M 11 generations. These mutants showed a moderate resistance to leaf rust and lodging, while the origin was susceptible. Also, these mutants surpassed their origin in seed weight of 100 spikes, weight of 1000 kernels and protein yield per unit area. Chemical and physical analyses of mutant flours indicated that it could be used for bread making successfully.2 fig.,4 tab

Analysis of the cable equation with non-local and non-singular kernel fractional derivative

Science.gov (United States)

Karaagac, Berat

2018-02-01

Recently a new concept of differentiation was introduced in the literature where the kernel was converted from non-local singular to non-local and non-singular. One of the great advantages of this new kernel is its ability to portray fading memory and also well defined memory of the system under investigation. In this paper the cable equation which is used to develop mathematical models of signal decay in submarine or underwater telegraphic cables will be analysed using the Atangana-Baleanu fractional derivative due to the ability of the new fractional derivative to describe non-local fading memory. The existence and uniqueness of the more generalized model is presented in detail via the fixed point theorem. A new numerical scheme is used to solve the new equation. In addition, stability, convergence and numerical simulations are presented.

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

Science.gov (United States)

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

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

DEFF Research Database (Denmark)

Li, Gang; Guan, Wei; Sierszecki, Krzysztof

2012-01-01

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

On the change points of mean residual life and failure rate functions for some generalized gamma type distributions

Directory of Open Access Journals (Sweden)

Parsa M.

2014-01-01

Full Text Available Mean residual life and failure rate functions are ubiquitously employed in reliability analysis. The term of useful period of lifetime distributions of bathtub-shaped failure rate functions is referred to the flat rigion of this function and has attracted authors and researchers in reliability, actuary, and survival analysis. In recent years, considering the change points of mean residual life and failure rate functions has been extensively utelized in determining the optimum burn-in time. In this paper we investigate the difference between the change points of failure rate and mean residual life functions of some generalized gamma type distributions due to the capability of these distributions in modeling various bathtub-shaped failure rate functions.

Generalized synthetic kernel approximation for elastic moderation of fast neutrons

International Nuclear Information System (INIS)

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

1975-01-01

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

Effect of Palm Kernel Cake Replacement and Enzyme ...

African Journals Online (AJOL)

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

Data-based diffraction kernels for surface waves from convolution and correlation processes through active seismic interferometry

Science.gov (United States)

Chmiel, Malgorzata; Roux, Philippe; Herrmann, Philippe; Rondeleux, Baptiste; Wathelet, Marc

2018-05-01

We investigated the construction of diffraction kernels for surface waves using two-point convolution and/or correlation from land active seismic data recorded in the context of exploration geophysics. The high density of controlled sources and receivers, combined with the application of the reciprocity principle, allows us to retrieve two-dimensional phase-oscillation diffraction kernels (DKs) of surface waves between any two source or receiver points in the medium at each frequency (up to 15 Hz, at least). These DKs are purely data-based as no model calculations and no synthetic data are needed. They naturally emerge from the interference patterns of the recorded wavefields projected on the dense array of sources and/or receivers. The DKs are used to obtain multi-mode dispersion relations of Rayleigh waves, from which near-surface shear velocity can be extracted. Using convolution versus correlation with a grid of active sources is an important step in understanding the physics of the retrieval of surface wave Green's functions. This provides the foundation for future studies based on noise sources or active sources with a sparse spatial distribution.

On the maximum and minimum of two modified Gamma-Gamma variates with applications

KAUST Repository

Al-Quwaiee, Hessa

2014-04-01

In this work, we derive the statistical characteristics of the maximum and the minimum of two modified1 Gamma-Gamma variates in closed-form in terms of Meijer\\'s G-function and the extended generalized bivariate Meijer\\'s G-function. Then, we rely on these new results to present the performance analysis of (i) a dual-branch free-space optical selection combining diversity undergoing independent but not necessarily identically distributed Gamma-Gamma fading under the impact of pointing errors and of (ii) a dual-hop free-space optical relay transmission system. Computer-based Monte-Carlo simulations verify our new analytical results.

Quantitative interpretation of nuclear logging data by adopting point-by-point spectrum striping deconvolution technology

International Nuclear Information System (INIS)

Tang Bin; Liu Ling; Zhou Shumin; Zhou Rongsheng

2006-01-01

The paper discusses the gamma-ray spectrum interpretation technology on nuclear logging. The principles of familiar quantitative interpretation methods, including the average content method and the traditional spectrum striping method, are introduced, and their limitation of determining the contents of radioactive elements on unsaturated ledges (where radioactive elements distribute unevenly) is presented. On the basis of the intensity gamma-logging quantitative interpretation technology by using the deconvolution method, a new quantitative interpretation method of separating radioactive elements is presented for interpreting the gamma spectrum logging. This is a point-by-point spectrum striping deconvolution technology which can give the logging data a quantitative interpretation. (authors)

StreamMap: Smooth Dynamic Visualization of High-Density Streaming Points.

Science.gov (United States)

Li, Chenhui; Baciu, George; Han, Yu

2018-03-01

Interactive visualization of streaming points for real-time scatterplots and linear blending of correlation patterns is increasingly becoming the dominant mode of visual analytics for both big data and streaming data from active sensors and broadcasting media. To better visualize and interact with inter-stream patterns, it is generally necessary to smooth out gaps or distortions in the streaming data. Previous approaches either animate the points directly or present a sampled static heat-map. We propose a new approach, called StreamMap, to smoothly blend high-density streaming points and create a visual flow that emphasizes the density pattern distributions. In essence, we present three new contributions for the visualization of high-density streaming points. The first contribution is a density-based method called super kernel density estimation that aggregates streaming points using an adaptive kernel to solve the overlapping problem. The second contribution is a robust density morphing algorithm that generates several smooth intermediate frames for a given pair of frames. The third contribution is a trend representation design that can help convey the flow directions of the streaming points. The experimental results on three datasets demonstrate the effectiveness of StreamMap when dynamic visualization and visual analysis of trend patterns on streaming points are required.

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

NARCIS (Netherlands)

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

2012-01-01

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

Flour quality and kernel hardness connection in winter wheat

Directory of Open Access Journals (Sweden)

Szabó B. P.

2016-12-01

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

Deep kernel learning method for SAR image target recognition

Science.gov (United States)

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

2017-10-01

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

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

African Journals Online (AJOL)

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

Multi-template Scale-Adaptive Kernelized Correlation Filters

KAUST Repository

Bibi, Adel Aamer

2015-12-07

This paper identifies the major drawbacks of a very computationally efficient and state-of-the-art-tracker known as the Kernelized Correlation Filter (KCF) tracker. These drawbacks include an assumed fixed scale of the target in every frame, as well as, a heuristic update strategy of the filter taps to incorporate historical tracking information (i.e. simple linear combination of taps from the previous frame). In our approach, we update the scale of the tracker by maximizing over the posterior distribution of a grid of scales. As for the filter update, we prove and show that it is possible to use all previous training examples to update the filter taps very efficiently using fixed-point optimization. We validate the efficacy of our approach on two tracking datasets, VOT2014 and VOT2015.

Multi-template Scale-Adaptive Kernelized Correlation Filters

KAUST Repository

Bibi, Adel Aamer; Ghanem, Bernard

2015-01-01

This paper identifies the major drawbacks of a very computationally efficient and state-of-the-art-tracker known as the Kernelized Correlation Filter (KCF) tracker. These drawbacks include an assumed fixed scale of the target in every frame, as well as, a heuristic update strategy of the filter taps to incorporate historical tracking information (i.e. simple linear combination of taps from the previous frame). In our approach, we update the scale of the tracker by maximizing over the posterior distribution of a grid of scales. As for the filter update, we prove and show that it is possible to use all previous training examples to update the filter taps very efficiently using fixed-point optimization. We validate the efficacy of our approach on two tracking datasets, VOT2014 and VOT2015.

Computational techniques in gamma-ray skyshine analysis

International Nuclear Information System (INIS)

George, D.L.

1988-12-01

Two computer codes were developed to analyze gamma-ray skyshine, the scattering of gamma photons by air molecules. A review of previous gamma-ray skyshine studies discusses several Monte Carlo codes, programs using a single-scatter model, and the MicroSkyshine program for microcomputers. A benchmark gamma-ray skyshine experiment performed at Kansas State University is also described. A single-scatter numerical model was presented which traces photons from the source to their first scatter, then applies a buildup factor along a direct path from the scattering point to a detector. The FORTRAN code SKY, developed with this model before the present study, was modified to use Gauss quadrature, recent photon attenuation data and a more accurate buildup approximation. The resulting code, SILOGP, computes response from a point photon source on the axis of a silo, with and without concrete shielding over the opening. Another program, WALLGP, was developed using the same model to compute response from a point gamma source behind a perfectly absorbing wall, with and without shielding overhead. 29 refs., 48 figs., 13 tabs

A framework for dense triangular matrix kernels on various manycore architectures

KAUST Repository

Charara, Ali

2017-06-06

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

A framework for dense triangular matrix kernels on various manycore architectures

KAUST Repository

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

2017-01-01

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

PERI - auto-tuning memory-intensive kernels for multicore

International Nuclear Information System (INIS)

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

2008-01-01

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

PERI - Auto-tuning Memory Intensive Kernels for Multicore

Energy Technology Data Exchange (ETDEWEB)

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

2008-06-24

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

Kernel Bayesian ART and ARTMAP.

Science.gov (United States)

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

2018-02-01

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

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

Directory of Open Access Journals (Sweden)

Yi-Hung Liu

2014-01-01

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

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

African Journals Online (AJOL)

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

Variable kernel density estimation in high-dimensional feature spaces

CSIR Research Space (South Africa)

Van der Walt, Christiaan M

2017-02-01

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

Heat Kernel Asymptotics of Zaremba Boundary Value Problem

Energy Technology Data Exchange (ETDEWEB)

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

2004-03-15

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

Graphical analyses of connected-kernel scattering equations

International Nuclear Information System (INIS)

Picklesimer, A.

1982-10-01

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

An Ensemble Approach to Building Mercer Kernels with Prior Information

Science.gov (United States)

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

2005-01-01

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

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

Directory of Open Access Journals (Sweden)

Shashi Kumar C.

2016-12-01

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

Using kernel density estimates to investigate lymphatic filariasis in northeast Brazil

Science.gov (United States)

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

2012-01-01

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

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

Science.gov (United States)

Sumarjaya, I. W.

2017-10-01

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

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

International Nuclear Information System (INIS)

Foerthmann, R.

1979-10-01

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

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

Science.gov (United States)

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

2017-01-01

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

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

Science.gov (United States)

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

2017-01-01

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

Effective and efficient Grassfinch kernel for SVM classification and its application to recognition based on image set

International Nuclear Information System (INIS)

Du, Genyuan; Tian, Shengli; Qiu, Yingyu; Xu, Chunyan

2016-01-01

This paper presents an effective and efficient kernel approach to recognize image set which is represented as a point on extended Grassmannian manifold. Several recent studies focus on the applicability of discriminant analysis on Grassmannian manifold and suffer from not obtaining the inherent nonlinear structure of the data itself. Therefore, we propose an extension of Grassmannian manifold to address this issue. Instead of using a linear data embedding with PCA, we develop a non-linear data embedding of such manifold using kernel PCA. This paper mainly consider three folds: 1) introduce a non-linear data embedding of extended Grassmannian manifold, 2) derive a distance metric of Grassmannian manifold, 3) develop an effective and efficient Grassmannian kernel for SVM classification. The extended Grassmannian manifold naturally arises in the application to recognition based on image set, such as face and object recognition. Experiments on several standard databases show better classification accuracy. Furthermore, experimental results indicate that our proposed approach significantly reduces time complexity in comparison to graph embedding discriminant analysis.

Boundary singularity of Poisson and harmonic Bergman kernels

Czech Academy of Sciences Publication Activity Database

Engliš, Miroslav

2015-01-01

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

Optimal Bandwidth Selection in Observed-Score Kernel Equating

Science.gov (United States)

Häggström, Jenny; Wiberg, Marie

2014-01-01

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

Kernel structures for Clouds

Science.gov (United States)

Spafford, Eugene H.; Mckendry, Martin S.

1986-01-01

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

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

Indian Academy of Sciences (India)

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

Oven-drying reduces ruminal starch degradation in maize kernels

NARCIS (Netherlands)

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

2014-01-01

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

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

International Nuclear Information System (INIS)

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

2011-01-01

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

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

Science.gov (United States)

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

Kernel Methods for Machine Learning with Life Science Applications

DEFF Research Database (Denmark)

Abrahamsen, Trine Julie

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

Nonlinear Forecasting With Many Predictors Using Kernel Ridge Regression

DEFF Research Database (Denmark)

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

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

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

NARCIS (Netherlands)

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

2010-01-01

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

Reproducing Kernels and Coherent States on Julia Sets

Energy Technology Data Exchange (ETDEWEB)

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

2007-11-15

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

Reproducing Kernels and Coherent States on Julia Sets

International Nuclear Information System (INIS)

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

2007-01-01

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

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

Science.gov (United States)

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

2017-10-01

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

Automatically detect and track infrared small targets with kernel Fukunaga-Koontz transform and Kalman prediction

Science.gov (United States)

Liu, Ruiming; Liu, Erqi; Yang, Jie; Zeng, Yong; Wang, Fanglin; Cao, Yuan

2007-11-01

Fukunaga-Koontz transform (FKT), stemming from principal component analysis (PCA), is used in many pattern recognition and image-processing fields. It cannot capture the higher-order statistical property of natural images, so its detection performance is not satisfying. PCA has been extended into kernel PCA in order to capture the higher-order statistics. However, thus far there have been no researchers who have definitely proposed kernel FKT (KFKT) and researched its detection performance. For accurately detecting potential small targets from infrared images, we first extend FKT into KFKT to capture the higher-order statistical properties of images. Then a framework based on Kalman prediction and KFKT, which can automatically detect and track small targets, is developed. Results of experiments show that KFKT outperforms FKT and the proposed framework is competent to automatically detect and track infrared point targets.

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

Science.gov (United States)

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

2016-07-07

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

Mixed kernel function support vector regression for global sensitivity analysis

Science.gov (United States)

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

2017-11-01

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

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

Science.gov (United States)

Zeng, Hong; Cheung, Yiu-ming

2011-08-01

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

Semisupervised kernel marginal Fisher analysis for face recognition.

Science.gov (United States)

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

2013-01-01

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

Weighted Feature Gaussian Kernel SVM for Emotion Recognition.

Science.gov (United States)

Wei, Wei; Jia, Qingxuan

2016-01-01

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

On the logarithmic-singularity correction in the kernel function method of subsonic lifting-surface theory

Science.gov (United States)

Lan, C. E.; Lamar, J. E.

1977-01-01

A logarithmic-singularity correction factor is derived for use in kernel function methods associated with Multhopp's subsonic lifting-surface theory. Because of the form of the factor, a relation was formulated between the numbers of chordwise and spanwise control points needed for good accuracy. This formulation is developed and discussed. Numerical results are given to show the improvement of the computation with the new correction factor.

Comparative Histological and Transcriptional Analysis of Maize Kernels Infected with Aspergillus flavus and Fusarium verticillioides

Directory of Open Access Journals (Sweden)

Xiaomei Shu

2017-12-01

Full Text Available Aspergillus flavus and Fusarium verticillioides infect maize kernels and contaminate them with the mycotoxins aflatoxin, and fumonisin, respectively. Genetic resistance in maize to these fungi and to mycotoxin contamination has been difficult to achieve due to lack of identified resistance genes. The objective of this study was to identify new candidate resistance genes by characterizing their temporal expression in response to infection and comparing expression of these genes with genes known to be associated with plant defense. Fungal colonization and transcriptional changes in kernels inoculated with each fungus were monitored at 4, 12, 24, 48, and 72 h post inoculation (hpi. Maize kernels responded by differential gene expression to each fungus within 4 hpi, before the fungi could be observed visually, but more genes were differentially expressed between 48 and 72 hpi, when fungal colonization was more extensive. Two-way hierarchal clustering analysis grouped the temporal expression profiles of the 5,863 differentially expressed maize genes over all time points into 12 clusters. Many clusters were enriched for genes previously associated with defense responses to either A. flavus or F. verticillioides. Also within these expression clusters were genes that lacked either annotation or assignment to functional categories. This study provided a comprehensive analysis of gene expression of each A. flavus and F. verticillioides during infection of maize kernels, it identified genes expressed early and late in the infection process, and it provided a grouping of genes of unknown function with similarly expressed defense related genes that could inform selection of new genes as targets in breeding strategies.

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

Indian Academy of Sciences (India)

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

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

Science.gov (United States)

Binol, Hamidullah; Bal, Abdullah; Cukur, Huseyin

2015-10-01

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

Fast dose kernel interpolation using Fourier transform with application to permanent prostate brachytherapy dosimetry.

Science.gov (United States)

Liu, Derek; Sloboda, Ron S

2014-05-01

Boyer and Mok proposed a fast calculation method employing the Fourier transform (FT), for which calculation time is independent of the number of seeds but seed placement is restricted to calculation grid points. Here an interpolation method is described enabling unrestricted seed placement while preserving the computational efficiency of the original method. The Iodine-125 seed dose kernel was sampled and selected values were modified to optimize interpolation accuracy for clinically relevant doses. For each seed, the kernel was shifted to the nearest grid point via convolution with a unit impulse, implemented in the Fourier domain. The remaining fractional shift was performed using a piecewise third-order Lagrange filter. Implementation of the interpolation method greatly improved FT-based dose calculation accuracy. The dose distribution was accurate to within 2% beyond 3 mm from each seed. Isodose contours were indistinguishable from explicit TG-43 calculation. Dose-volume metric errors were negligible. Computation time for the FT interpolation method was essentially the same as Boyer's method. A FT interpolation method for permanent prostate brachytherapy TG-43 dose calculation was developed which expands upon Boyer's original method and enables unrestricted seed placement. The proposed method substantially improves the clinically relevant dose accuracy with negligible additional computation cost, preserving the efficiency of the original method.

Biologically-Inspired Spike-Based Automatic Speech Recognition of Isolated Digits Over a Reproducing Kernel Hilbert Space

Directory of Open Access Journals (Sweden)

Kan Li

2018-04-01

Full Text Available This paper presents a novel real-time dynamic framework for quantifying time-series structure in spoken words using spikes. Audio signals are converted into multi-channel spike trains using a biologically-inspired leaky integrate-and-fire (LIF spike generator. These spike trains are mapped into a function space of infinite dimension, i.e., a Reproducing Kernel Hilbert Space (RKHS using point-process kernels, where a state-space model learns the dynamics of the multidimensional spike input using gradient descent learning. This kernelized recurrent system is very parsimonious and achieves the necessary memory depth via feedback of its internal states when trained discriminatively, utilizing the full context of the phoneme sequence. A main advantage of modeling nonlinear dynamics using state-space trajectories in the RKHS is that it imposes no restriction on the relationship between the exogenous input and its internal state. We are free to choose the input representation with an appropriate kernel, and changing the kernel does not impact the system nor the learning algorithm. Moreover, we show that this novel framework can outperform both traditional hidden Markov model (HMM speech processing as well as neuromorphic implementations based on spiking neural network (SNN, yielding accurate and ultra-low power word spotters. As a proof of concept, we demonstrate its capabilities using the benchmark TI-46 digit corpus for isolated-word automatic speech recognition (ASR or keyword spotting. Compared to HMM using Mel-frequency cepstral coefficient (MFCC front-end without time-derivatives, our MFCC-KAARMA offered improved performance. For spike-train front-end, spike-KAARMA also outperformed state-of-the-art SNN solutions. Furthermore, compared to MFCCs, spike trains provided enhanced noise robustness in certain low signal-to-noise ratio (SNR regime.

Biologically-Inspired Spike-Based Automatic Speech Recognition of Isolated Digits Over a Reproducing Kernel Hilbert Space.

Science.gov (United States)

Li, Kan; Príncipe, José C

2018-01-01

This paper presents a novel real-time dynamic framework for quantifying time-series structure in spoken words using spikes. Audio signals are converted into multi-channel spike trains using a biologically-inspired leaky integrate-and-fire (LIF) spike generator. These spike trains are mapped into a function space of infinite dimension, i.e., a Reproducing Kernel Hilbert Space (RKHS) using point-process kernels, where a state-space model learns the dynamics of the multidimensional spike input using gradient descent learning. This kernelized recurrent system is very parsimonious and achieves the necessary memory depth via feedback of its internal states when trained discriminatively, utilizing the full context of the phoneme sequence. A main advantage of modeling nonlinear dynamics using state-space trajectories in the RKHS is that it imposes no restriction on the relationship between the exogenous input and its internal state. We are free to choose the input representation with an appropriate kernel, and changing the kernel does not impact the system nor the learning algorithm. Moreover, we show that this novel framework can outperform both traditional hidden Markov model (HMM) speech processing as well as neuromorphic implementations based on spiking neural network (SNN), yielding accurate and ultra-low power word spotters. As a proof of concept, we demonstrate its capabilities using the benchmark TI-46 digit corpus for isolated-word automatic speech recognition (ASR) or keyword spotting. Compared to HMM using Mel-frequency cepstral coefficient (MFCC) front-end without time-derivatives, our MFCC-KAARMA offered improved performance. For spike-train front-end, spike-KAARMA also outperformed state-of-the-art SNN solutions. Furthermore, compared to MFCCs, spike trains provided enhanced noise robustness in certain low signal-to-noise ratio (SNR) regime.

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

KAUST Repository

Djebbi, Ramzi

2013-08-19

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

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

KAUST Repository

Djebbi, Ramzi; Alkhalifah, Tariq Ali

2013-01-01

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

Capturing option anomalies with a variance-dependent pricing kernel

NARCIS (Netherlands)

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

2013-01-01

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

Resolvent kernel for the Kohn Laplacian on Heisenberg groups

Directory of Open Access Journals (Sweden)

Neur Eddine Askour

2002-07-01

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

SU-E-T-442: Geometric Calibration and Verification of a GammaPod Breast SBRT System

Energy Technology Data Exchange (ETDEWEB)

Yu, C [Univ Maryland School of Medicine, Baltimore, MD (United States); Xcision Medical Systems, Columbia, MD (United States); Niu, Y; Maton, P; Hoban, P [Xcision Medical Systems, Columbia, MD (United States); Mutaf, Y [Univ Maryland School of Medicine, Baltimore, MD (United States)

2015-06-15

Purpose: The first GammaPod™ unit for prone stereotactic treatment of early stage breast cancer has recently been installed and calibrated. Thirty-six rotating circular Co-60 beams focus dose at an isocenter that traverses throughout a breast target via continuous motion of the treatment table. The breast is immobilized and localized using a vacuum-assisted stereotactic cup system that is fixed to the table during treatment. Here we report on system calibration and on verification of geometric and dosimetric accuracy. Methods: Spatial calibration involves setting the origin of each table translational axis within the treatment control system such that the relationship between beam isocenter and table geometry is consistent with that assumed by the treatment planning system. A polyethylene QA breast phantom inserted into an aperture in the patient couch is used for calibration and verification. The comparison is performed via fiducial-based registration of measured single-isocenter dose profiles (radiochromic film) with kernel dose profiles. With the table calibrations applied, measured relative dose distributions were compared with TPS calculations for single-isocenter and dynamic (many-isocenter) treatment plans. Further, table motion accuracy and linearity was tested via comparison of planned control points with independent encoder readouts. Results: After table calibration, comparison of measured and calculated single-isocenter dose profiles show agreement to within 0.5 mm for each axis. Gamma analysis of measured vs calculated profiles with 3%/2mm criteria yields a passing rate of >99% and >98% for single-isocenter and dynamic plans respectively. This also validates the relative dose distributions produced by the TPS. Measured table motion accuracy was within 0.05 mm for all translational axes. Conclusion: GammaPod table coordinate calibration is a straightforward process that yields very good agreement between planned and measured relative dose distributions

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

Science.gov (United States)

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

2017-07-01

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

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

DEFF Research Database (Denmark)

Li, Gang; Guan, Wei; Sierszecki, Krzysztof

2012-01-01

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

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

Science.gov (United States)

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

2009-08-01

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

Neutron shielding point kernel integral calculation code for personal computer: PKN-pc

International Nuclear Information System (INIS)

Kotegawa, Hiroshi; Sakamoto, Yukio; Nakane, Yoshihiro; Tomita, Ken-ichi; Kurosawa, Naohiro.

1994-07-01

A personal computer version of PKN code, PKN-pc, has been developed to calculate neutron and secondary gamma-ray 1cm depth dose equivalents in water, ordinary concrete and iron for neutron source. Characteristics of PKN code are, to able to calculate dose equivalents in multi-layer three-dimensional system, which are described with two-dimensional surface, for monoenergetic neutron source from 0.01 to 14.9 MeV, 252 Cf fission and 241 Am-Be neutron source quick and easily. In addition to these features, the PKN-pc is possible to process interactive input and to get graphical system configuration and graphical results easily. (author)

Collimatorless imaging of gamma rays with help of gamma-ray tracking

CERN Document Server

Marel, J V D

2001-01-01

In many gamma-ray detector systems that are built for imaging purposes Compton scattered photons are suppressed as much as possible. However, the information from photons that scattered inside a detector system can be used to reconstruct the tracks of the photons with help of gamma-ray tracking. Estimates of the incident directions of the photons can be made and an image can be created. Examples of potential applications for this technique are the use as a gamma-camera in medical imaging (e.g. SPECT) or as a detector for PET. Due to the omission of collimators, much higher detection efficiencies can be achieved, reducing the doses required for an image. A gamma-ray tracking method, called backtracking, has been developed for nuclear spectroscopy. The method tracks gamma-rays originating from a point source in the center of a spherical detector system consisting of position-sensitive germanium detectors. This method can also be used as a tracking technique for imaging of an unknown source distribution. With he...

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

Directory of Open Access Journals (Sweden)

Ujjwal Maulik

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

Graphical analyses of connected-kernel scattering equations

International Nuclear Information System (INIS)

Picklesimer, A.

1983-01-01

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

Modelling lateral beam quality variations in pencil kernel based photon dose calculations

International Nuclear Information System (INIS)

Nyholm, T; Olofsson, J; Ahnesjoe, A; Karlsson, M

2006-01-01

Standard treatment machines for external radiotherapy are designed to yield flat dose distributions at a representative treatment depth. The common method to reach this goal is to use a flattening filter to decrease the fluence in the centre of the beam. A side effect of this filtering is that the average energy of the beam is generally lower at a distance from the central axis, a phenomenon commonly referred to as off-axis softening. The off-axis softening results in a relative change in beam quality that is almost independent of machine brand and model. Central axis dose calculations using pencil beam kernels show no drastic loss in accuracy when the off-axis beam quality variations are neglected. However, for dose calculated at off-axis positions the effect should be considered, otherwise errors of several per cent can be introduced. This work proposes a method to explicitly include the effect of off-axis softening in pencil kernel based photon dose calculations for arbitrary positions in a radiation field. Variations of pencil kernel values are modelled through a generic relation between half value layer (HVL) thickness and off-axis position for standard treatment machines. The pencil kernel integration for dose calculation is performed through sampling of energy fluence and beam quality in sectors of concentric circles around the calculation point. The method is fully based on generic data and therefore does not require any specific measurements for characterization of the off-axis softening effect, provided that the machine performance is in agreement with the assumed HVL variations. The model is verified versus profile measurements at different depths and through a model self-consistency check, using the dose calculation model to estimate HVL values at off-axis positions. A comparison between calculated and measured profiles at different depths showed a maximum relative error of 4% without explicit modelling of off-axis softening. The maximum relative error

On methods to increase the security of the Linux kernel

International Nuclear Information System (INIS)

Matvejchikov, I.V.

2014-01-01

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

Structured Kernel Dictionary Learning with Correlation Constraint for Object Recognition.

Science.gov (United States)

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

2017-06-21

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

TOWARDS FINDING A NEW KERNELIZED FUZZY C-MEANS CLUSTERING ALGORITHM

Directory of Open Access Journals (Sweden)

Samarjit Das

2014-04-01

Full Text Available Kernelized Fuzzy C-Means clustering technique is an attempt to improve the performance of the conventional Fuzzy C-Means clustering technique. Recently this technique where a kernel-induced distance function is used as a similarity measure instead of a Euclidean distance which is used in the conventional Fuzzy C-Means clustering technique, has earned popularity among research community. Like the conventional Fuzzy C-Means clustering technique this technique also suffers from inconsistency in its performance due to the fact that here also the initial centroids are obtained based on the randomly initialized membership values of the objects. Our present work proposes a new method where we have applied the Subtractive clustering technique of Chiu as a preprocessor to Kernelized Fuzzy CMeans clustering technique. With this new method we have tried not only to remove the inconsistency of Kernelized Fuzzy C-Means clustering technique but also to deal with the situations where the number of clusters is not predetermined. We have also provided a comparison of our method with the Subtractive clustering technique of Chiu and Kernelized Fuzzy C-Means clustering technique using two validity measures namely Partition Coefficient and Clustering Entropy.

A relationship between Gel'fand-Levitan and Marchenko kernels

International Nuclear Information System (INIS)

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

1989-01-01

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

Migration of ThO2 kernels under the influence of a temperature gradient

International Nuclear Information System (INIS)

Smith, C.L.

1976-11-01

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

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

Science.gov (United States)

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

2018-02-01

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

Visualization of nonlinear kernel models in neuroimaging by sensitivity maps

DEFF Research Database (Denmark)

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

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