Systematic dependence on the slowing down environment, of nuclear lifetime measurements by DSAM

International Nuclear Information System (INIS)

Toulemonde, M.; Haas, F.

1976-01-01

The meanlife of the 22 Ne 3.34MeV level measured by DSAM (Doppler Shift Attenuation Method) at an average velocity of 0.009 c, shows large fluctuations with different slowing down materials ranging from Li to Pb. These fluctuations are correlated with a linear dependence of the 'apparent' meanlife tau on the electronic slowing down time

Investigating the critical slowing down of QCD simulations

International Nuclear Information System (INIS)

Schaefer, Stefan

2009-12-01

Simulations of QCD are known to suffer from serious critical slowing down towards the continuum limit. This is particularly prominent in the topological charge. We investigate the severeness of the problem in the range of lattice spacings used in contemporary simulations and propose a method to give more reliable error estimates. (orig.)

Light slow-down in semiconductor waveguides due to population pulsations

DEFF Research Database (Denmark)

Mørk, Jesper; Kjær, Rasmus; Poel, Mike van der

2005-01-01

This study theoretically analyzes the prospect of inducing light-slow down in a semiconductor waveguide based on coherent population oscillation. Experimental observations of the effect are also presented....

Study of the neutron slowing-down in graphite; Etude du ralentissement des neutrons dans le graphite

Energy Technology Data Exchange (ETDEWEB)

Martelly, J [Commissariat a l' Energie Atomique, Saclay (France). Centre d' Etudes Nucleaires; Duggal, V P [Commission a l' Energie atomique indienne (India)

1955-07-01

Study of the slowing-down age of neutrons in graphite. In the aim to eliminate the effect of resonance neutrons on the detector, neutrons captured by cadmium are studied with a classic method consisting of calculating the difference between the activity measured with and without screens. This method is described and screening properties of cadmium and gadolinium are compared. The experimental parameters and detectors details are described. The radioactive source is Ra-{alpha}-Be. The experimental results are given and the experimental distribution is compared with theoretical formula. In a second part, the spatial distribution of resonance neutrons in indium is discussed. Finally, the neutron slowing-down between the indium resonance and the thermal equilibrium is discussed as well as the research for the effective value of slowing-down age. The slowing-down age definition is given before calculating its effective value. It compared the slowing-down law with the experiment and the Gurney theory. (M.P.)

Slowing down modernity: A critique : A critique

OpenAIRE

Vostal , Filip

2017-01-01

International audience; The connection between modernization and social acceleration is now a prominent theme in critical social analysis. Taking a cue from these debates, I explore attempts that aim to 'slow down modernity' by resisting the dynamic tempo of various social processes and experiences. The issue of slowdown now accounts for a largely unquestioned measure, expected to deliver unhasty tempo conditioning good and ethical life, mental well-being and accountable democracy. In princip...

Lead Slowing Down Spectrometer Status Report

International Nuclear Information System (INIS)

Warren, Glen A.; Anderson, Kevin K.; Bonebrake, Eric; Casella, Andrew M.; Danon, Yaron; Devlin, M.; Gavron, Victor A.; Haight, R.C.; Imel, G.R.; Kulisek, Jonathan A.; O'Donnell, J.M.; Weltz, Adam

2012-01-01

This report documents the progress that has been completed in the first half of FY2012 in the MPACT-funded Lead Slowing Down Spectrometer project. Significant progress has been made on the algorithm development. We have an improve understanding of the experimental responses in LSDS for fuel-related material. The calibration of the ultra-depleted uranium foils was completed, but the results are inconsistent from measurement to measurement. Future work includes developing a conceptual model of an LSDS system to assay plutonium in used fuel, improving agreement between simulations and measurement, design of a thorium fission chamber, and evaluation of additional detector techniques.

Physical condition for the slowing down of cosmic acceleration

Science.gov (United States)

Zhang, Ming-Jian; Xia, Jun-Qing

2018-04-01

The possible slowing down of cosmic acceleration was widely studied. However, judgment on this effect in different dark energy parameterizations was very ambiguous. Moreover, the reason of generating these uncertainties was still unknown. In the present paper, we analyze the derivative of deceleration parameter q‧ (z) using the Gaussian processes. This model-independent reconstruction suggests that no slowing down of acceleration is presented within 95% C.L. from the Union2.1 and JLA supernova data. However, q‧ (z) from the observational H (z) data is a little smaller than zero at 95% C.L., which indicates that future H (z) data may have a potential to test this effect. From the evolution of q‧ (z), we present an interesting constraint on the dark energy and observational data. The physical constraint clearly solves the problem of why some dark energy models cannot produce this effect in previous work. Comparison between the constraint and observational data also shows that most of current data are not in the allowed regions. This implies a reason of why current data cannot convincingly measure this effect.

Development of lead slowing down spectrometer for isotopic fissile assay

International Nuclear Information System (INIS)

Lee, Yong Deok; Park, Chang Je; Ahn, Sang Joon; Kim, Ho Dong

2014-01-01

A lead slowing down spectrometer (LSDS) is under development for analysis of isotopic fissile material contents in pyro-processed material, or spent fuel. Many current commercial fissile assay technologies have a limitation in accurate and direct assay of fissile content. However, LSDS is very sensitive in distinguishing fissile fission signals from each isotope. A neutron spectrum analysis was conducted in the spectrometer and the energy resolution was investigated from 0.1eV to 100keV. The spectrum was well shaped in the slowing down energy. The resolution was enough to obtain each fissile from 0.2eV to 1keV. The detector existence in the lead will disturb the source neutron spectrum. It causes a change in resolution and peak amplitude. The intense source neutron production was designed for ∼E12 n's/sec to overcome spent fuel background. The detection sensitivity of U238 and Th232 fission chamber was investigated. The first and second layer detectors increase detection efficiency. Thorium also has a threshold property to detect the fast fission neutrons from fissile fission. However, the detection of Th232 is about 76% of that of U238. A linear detection model was set up over the slowing down neutron energy to obtain each fissile material content. The isotopic fissile assay using LSDS is applicable for the optimum design of spent fuel storage to maximize burnup credit and quality assurance of the recycled nuclear material for safety and economics. LSDS technology will contribute to the transparency and credibility of pyro-process using spent fuel, as internationally demanded.

Physical condition for the slowing down of cosmic acceleration

Directory of Open Access Journals (Sweden)

Ming-Jian Zhang

2018-04-01

Full Text Available The possible slowing down of cosmic acceleration was widely studied. However, judgment on this effect in different dark energy parameterizations was very ambiguous. Moreover, the reason of generating these uncertainties was still unknown. In the present paper, we analyze the derivative of deceleration parameter q′(z using the Gaussian processes. This model-independent reconstruction suggests that no slowing down of acceleration is presented within 95% C.L. from the Union2.1 and JLA supernova data. However, q′(z from the observational H(z data is a little smaller than zero at 95% C.L., which indicates that future H(z data may have a potential to test this effect. From the evolution of q′(z, we present an interesting constraint on the dark energy and observational data. The physical constraint clearly solves the problem of why some dark energy models cannot produce this effect in previous work. Comparison between the constraint and observational data also shows that most of current data are not in the allowed regions. This implies a reason of why current data cannot convincingly measure this effect.

Slowing down of test particles in a plasma (1961)

International Nuclear Information System (INIS)

Belayche, P.; Chavy, P.; Dupoux, M.; Salmon, J.

1961-01-01

Numerical solution of the Fokker-Planck equation applied to the slowing down of tritons in a deuterium plasma. After the equations and the boundary conditions have been written, some attention is paid to the numerical tricks used to run the problem on a high speed electronic computer. The numerical results thus obtained are then analyzed and as far as possible, mathematically explained. (authors) [fr

Measurement of the Slowing-Down and Thermalization Time of Neutrons in Water

Energy Technology Data Exchange (ETDEWEB)

Moeller, E [AB Atomenergi, Nykoeping (Sweden); Sjoestrand, N G [Chalmers Univ. of Technology, Goeteborg (Sweden)

1963-11-15

The experimental equipment for the study of the time behaviour of neutrons during slowing-down and thermalization in a moderator by the use of a pulsed van de Graaff accelerator as a neutron source is described. Information on the change with time of the neutron spectrum is obtained from its reaction with spectrum indicators, the reaction rate being observed by the detection of capture gamma rays. The time resolution may be chosen in the range 0.01 to 5 {mu}s. Measurements have been made for water with cadmium, gadolinium and samarium as indicators dissolved in the medium. A slowing- down time to 0.2 eV of 2.7 {+-} 0.4 {mu}s and a total thermalization time of 25 - 30 {mu}s were obtained. From 9 {mu}s after the injection, the results are well described by the assumption of the flux as a Maxwell distribution cooling down to the moderator temperature with a thermalization time constant of 4.1 {+-} 0.4 {mu}s.

Energy spectra from coupled electron-photon slowing down

International Nuclear Information System (INIS)

Beck, H.L.

1976-08-01

A coupled electron-photon slowing down calculation for determining electron and photon track length in uniform homogeneous media is described. The method also provides fluxes for uniformly distributed isotropic sources. Source energies ranging from 10 keV to over 10 GeV are allowed and all major interactions are treated. The calculational technique and related cross sections are described in detail and sample calculations are discussed. A listing of the Fortran IV computer code used for the calculations is also included. 4 tables, 7 figures, 16 references

7Li neutron-induced elastic scattering cross section measurement using a slowing-down spectrometer

Directory of Open Access Journals (Sweden)

Heusch M.

2010-10-01

Full Text Available A new integral measurement of the 7Li neutron induced elastic scattering cross section was determined in a wide neutron energy range. The measurement was performed on the LPSC-PEREN experimental facility using a heterogeneous graphite-LiF slowing-down time spectrometer coupled with an intense pulsed neutron generator (GENEPI-2. This method allows the measurement of the integral elastic scattering cross section in a slowing-down neutron spectrum. A Bayesian approach coupled to Monte Carlo calculations was applied to extract naturalC, 19F and 7Li elastic scattering cross sections.

Slowing down and straggling of protons and heavy ions in matter

International Nuclear Information System (INIS)

Aernsbergen, L.M. van.

1986-01-01

The Doppler Shift Attenuation (DSA) method is widely used to measure lifetimes of nuclear states. However, many of the lifetimes resulting from DSA measurements display large variations which are caused by an insufficient knowledge of slowing down processes of nucleus recoils. The measurement of 'ranges' is an often used method to study these slowing down processes. In this kind of measurement the distributions of implanted ions are determined for example by the method of Rutherford backscattering or from the yield curve of a resonant nuclear reaction. In this thesis, research on energy-loss processes of protons and Si ions in aluminium is presented. The so-called Resonance Shift method has been improved for the measurements on the protons themselves. This method has only been used occasionally before. A new method has been developed, which is called the Transmission Doppler Shift Attenuation (TDSA) method, for the measurement on Si ions. (Auth.)

Qsub(N) approximation for slowing-down in fast reactors

International Nuclear Information System (INIS)

Rocca-Volmerange, Brigitte.

1976-05-01

An accurate and simple determination of the neutron energy spectra in fast reactors poses several problems. The slowing-down models (Fermi, Wigner, Goertzel-Greuling...) which are different forms of the approximation with order N=0 may prove inaccurate, in spite of recent improvements. A new method of approximation is presented which turns out to be a method of higher order: the Qsub(N) method. It is characterized by a rapid convergence with respect to the order N, by the use of some global parameters to represent the slowing-down and by the expression of the Boltzmann integral equation in a differential formalism. Numerous test verify that, for the order N=2 or 3, the method gives precision equivalent to that of the multigroup numerical integration for the spectra with greatly reduced calculational effort. Furthermore, since the Qsub(N) expressions are a kind of synthesis method, they allow calculation of the spatial Green's function, or the use of collision probabilities to find the flux. Both possibilities have been introduced into existing reactor codes: EXCALIBUR, TRALOR, RE MINEUR... Some applications to multi-zone media (core, blanket, reflector of Masurca pile and exponential slabs) are presented in the isotropic collision approximation. The case of linearly anisotropic collisions is theoretically resolved [fr

Measurement of the Neutron Slowing-Down Time Distribution at 1.46 eV and its Space Dependence in Water

Energy Technology Data Exchange (ETDEWEB)

Moeller, E

1965-12-15

The use of the time dependent reaction rate method for the measurement of neutron slowing-down time distributions in hydrogen has been analyzed and applied to the case of sloping down in water. Neutrons with energies of about 1 MeV were slowed down, and the time-dependent neutron density at 1.46 eV and its space dependence was measured with a time resolution of 0.042 {mu}s. The results confirm the well known theory for time-dependent slowing down in hydrogen. The space dependence of the distributions is well described by the P{sub 1}-calculations by Claesson.

Measurement of the Neutron Slowing-Down Time Distribution at 1.46 eV and its Space Dependence in Water

International Nuclear Information System (INIS)

Moeller, E.

1965-12-01

The use of the time dependent reaction rate method for the measurement of neutron slowing-down time distributions in hydrogen has been analyzed and applied to the case of sloping down in water. Neutrons with energies of about 1 MeV were slowed down, and the time-dependent neutron density at 1.46 eV and its space dependence was measured with a time resolution of 0.042 μs. The results confirm the well known theory for time-dependent slowing down in hydrogen. The space dependence of the distributions is well described by the P 1 -calculations by Claesson

PN solutions for the slowing-down and the cell calculation problems in plane geometry

International Nuclear Information System (INIS)

Caldeira, Alexandre David

1999-01-01

In this work P N solutions for the slowing-down and cell problems in slab geometry are developed. To highlight the main contributions of this development, one can mention: the new particular solution developed for the P N method applied to the slowing-down problem in the multigroup model, originating a new class of polynomials denominated Chandrasekhar generalized polynomials; the treatment of a specific situation, known as a degeneracy, arising from a particularity in the group constants and the first application of the P N method, for arbitrary N, in criticality calculations at the cell level reported in literature. (author)

Measurements with the high flux lead slowing-down spectrometer at LANL

International Nuclear Information System (INIS)

Danon, Y.; Romano, C.; Thompson, J.; Watson, T.; Haight, R.C.; Wender, S.A.; Vieira, D.J.; Bond, E.; Wilhelmy, J.B.; O'Donnell, J.M.; Michaudon, A.; Bredeweg, T.A.; Schurman, T.; Rochman, D.; Granier, T.; Ethvignot, T.; Taieb, J.; Becker, J.A.

2007-01-01

A Lead Slowing-Down Spectrometer (LSDS) was recently installed at LANL [D. Rochman, R.C. Haight, J.M. O'Donnell, A. Michaudon, S.A. Wender, D.J. Vieira, E.M. Bond, T.A. Bredeweg, A. Kronenberg, J.B. Wilhelmy, T. Ethvignot, T. Granier, M. Petit, Y. Danon, Characteristics of a lead slowing-down spectrometer coupled to the LANSCE accelerator, Nucl. Instr. and Meth. A 550 (2005) 397]. The LSDS is comprised of a cube of pure lead 1.2 m on the side, with a spallation pulsed neutron source in its center. The LSDS is driven by 800 MeV protons with a time-averaged current of up to 1 μA, pulse widths of 0.05-0.25 μs and a repetition rate of 20-40 Hz. Spallation neutrons are created by directing the proton beam into an air-cooled tungsten target in the center of the lead cube. The neutrons slow down by scattering interactions with the lead and thus enable measurements of neutron-induced reaction rates as a function of the slowing-down time, which correlates to neutron energy. The advantage of an LSDS as a neutron spectrometer is that the neutron flux is 3-4 orders of magnitude higher than a standard time-of-flight experiment at the equivalent flight path, 5.6 m. The effective energy range is 0.1 eV to 100 keV with a typical energy resolution of 30% from 1 eV to 10 keV. The average neutron flux between 1 and 10 keV is about 1.7 x 10 9 n/cm 2 /s/μA. This high flux makes the LSDS an important tool for neutron-induced cross section measurements of ultra-small samples (nanograms) or of samples with very low cross sections. The LSDS at LANL was initially built in order to measure the fission cross section of the short-lived metastable isotope of U-235, however it can also be used to measure (n, α) and (n, p) reactions. Fission cross section measurements were made with samples of 235 U, 236 U, 238 U and 239 Pu. The smallest sample measured was 10 ng of 239 Pu. Measurement of (n, α) cross section with 760 ng of Li-6 was also demonstrated. Possible future cross section

On the neutron slowing-down in moderators

Energy Technology Data Exchange (ETDEWEB)

Caldeira, Alexandre D., E-mail: alexdc@ieav.cta.br [Instituto de Estudos Avançados (IEAV), São José dos Campos, SP (Brazil). Divisão de Energia Nuclear

2017-07-01

Neutron slowing-down is a very important subject to be considered in several areas of nuclear energy application, such as thermal nuclear reactors, nuclear medicine, radiological protection, detectors design and so on. Moderator materials are the responsible to perform this task and among the neutron induced cross sections, the elastic scattering cross section is the main nuclear interaction in this case. At thermal neutron energies, the moderator molecular or crystalline structure become important and dependent on the moderator phase, gas, liquid, or solid, its cross sections and, consequently, the angular and energy distributions of the scattered neutron are affected. The procedures used for generating correctly moderators cross sections at thermal neutron energies from evaluated nuclear data files utilizing the NJOY system are addressed. (author)

Particle-in-cell studies of fast-ion slowing-down rates in cool tenuous magnetized plasma

Science.gov (United States)

Evans, Eugene S.; Cohen, Samuel A.; Welch, Dale R.

2018-04-01

We report on 3D-3V particle-in-cell simulations of fast-ion energy-loss rates in a cold, weakly-magnetized, weakly-coupled plasma where the electron gyroradius, ρe, is comparable to or less than the Debye length, λDe, and the fast-ion velocity exceeds the electron thermal velocity, a regime in which the electron response may be impeded. These simulations use explicit algorithms, spatially resolve ρe and λDe, and temporally resolve the electron cyclotron and plasma frequencies. For mono-energetic dilute fast ions with isotropic velocity distributions, these scaling studies of the slowing-down time, τs, versus fast-ion charge are in agreement with unmagnetized slowing-down theory; with an applied magnetic field, no consistent anisotropy between τs in the cross-field and field-parallel directions could be resolved. Scaling the fast-ion charge is confirmed as a viable way to reduce the required computational time for each simulation. The implications of these slowing down processes are described for one magnetic-confinement fusion concept, the small, advanced-fuel, field-reversed configuration device.

Numerical studies of fast ion slowing down rates in cool magnetized plasma using LSP

Science.gov (United States)

Evans, Eugene S.; Kolmes, Elijah; Cohen, Samuel A.; Rognlien, Tom; Cohen, Bruce; Meier, Eric; Welch, Dale R.

2016-10-01

In MFE devices, rapid transport of fusion products from the core into the scrape-off layer (SOL) could perform the dual roles of energy and ash removal. The first-orbit trajectories of most fusion products from small field-reversed configuration (FRC) devices will traverse the SOL, allowing those particles to deposit their energy in the SOL and be exhausted along the open field lines. Thus, the fast ion slowing-down time should affect the energy balance of an FRC reactor and its neutron emissions. However, the dynamics of fast ion energy loss processes under the conditions expected in the FRC SOL (with ρe code, to examine the effects of SOL density and background B-field on the slowing-down time of fast ions in a cool plasma. As we use explicit algorithms, these simulations must spatially resolve both ρe and λDe, as well as temporally resolve both Ωe and ωpe, increasing computation time. Scaling studies of the fast ion charge (Z) and background plasma density are in good agreement with unmagnetized slowing down theory. Notably, Z-scaling represents a viable way to dramatically reduce the required CPU time for each simulation. This work was supported, in part, by DOE Contract Number DE-AC02-09CH11466.

Topology and slowing down of high energy ion orbits

Energy Technology Data Exchange (ETDEWEB)

Eriksson, L G [Commission of the European Communities, Abingdon (United Kingdom). JET Joint Undertaking; Porcelli, F [Politecnico di Torino, Turin (Italy); Berk, H L [Texas Univ., Austin, TX (United States). Inst. for Fusion Studies

1994-07-01

An analysis of nonstandard guiding centre orbits is presented, which is relevant to MeV ions in a Tokamak. The orbit equation has been simplified from the start, allowing to present an analytic classification of the possible orbits. The topological transitions of the orbits during collisional slowing down are described. In particular, the characteristic equations reveal the existence of a single fixed point in the relevant phase plane, and the presence of a bifurcation curve corresponding to the locus of the pinch orbits. A significant particle inward pinch has been discovered. (authors). 7 figs.

An Exact Solution of The Neutron Slowing Down Equation

Energy Technology Data Exchange (ETDEWEB)

Stefanovic, D [Boris Kidric Vinca Institute of Nuclear Sciences, Vinca, Belgrade (Yugoslavia)

1970-07-01

The slowing down equation for an infinite homogeneous monoatomic medium is solved exactly. The cross sections depend on neutron energy. The solution is given in analytical form within each of the lethargy intervals. This analytical form is the sum of probabilities which are given by the Green functions. The calculated collision density is compared with the one obtained by Bednarz and also with an approximate Wigner formula for the case of a resonance not wider than one collision interval. For the special case of hydrogen, the present solution reduces to Bethe's solution. (author)

Slowing-down of heavy ions in a fusible D-3He mixture

International Nuclear Information System (INIS)

Cocu, Francis; Uzureau, Jose; Lachkar, Jean.

1982-01-01

First experimental results connected with the study of the slowing-down of heavy ions ( 16 O, 63 Cu, 109 Ag) at energies of approximately 1 MeV/A in a fusible mixture of D- 3 He indicate that the higher is the projectile mass the greater is the fusion reaction rate [fr

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

Measurement of the Time Dependence of Neutron Slowing-Down and Therma in Heavy Water

Energy Technology Data Exchange (ETDEWEB)

Moeller, E

1966-03-15

The behaviour of neutrons during their slowing-down and thermalization in heavy water has been followed on the time scale by measurements of the time-dependent rate of reaction between the flux and the three spectrum indicators indium, cadmium and gadolinium. The space dependence of the reaction rate curves has also been studied. The time-dependent density at 1.46 eV is well reproduced by a function, given by von Dardel, and a time for the maximum density of 7.1 {+-} 0.3 {mu}s has been obtained for this energy in deuterium gas in agreement with the theoretical value of 7.2 {mu}s. The spatial variation of this time is in accord with the calculations by Claesson. The slowing- down time to 0.2 eV has been found to be 16.3 {+-}2.4 {mu}s. The approach to the equilibrium spectrum takes place with a time constant of 33 {+-}4 {mu}s, and the equilibrium has been established after about 200 {mu}s. Comparison of the measured curves for cadmium and gadolinium with multigroup calculations of the time-dependent flux and reaction rate show the superiority of the scattering models for heavy water of Butler and of Brown and St. John over the mass 2 gas model. The experiment has been supplemented with Monte Carlo calculations of the slowing down time.

Measurement of the Time Dependence of Neutron Slowing-Down and Therma in Heavy Water

International Nuclear Information System (INIS)

Moeller, E.

1966-03-01

The behaviour of neutrons during their slowing-down and thermalization in heavy water has been followed on the time scale by measurements of the time-dependent rate of reaction between the flux and the three spectrum indicators indium, cadmium and gadolinium. The space dependence of the reaction rate curves has also been studied. The time-dependent density at 1.46 eV is well reproduced by a function, given by von Dardel, and a time for the maximum density of 7.1 ± 0.3 μs has been obtained for this energy in deuterium gas in agreement with the theoretical value of 7.2 μs. The spatial variation of this time is in accord with the calculations by Claesson. The slowing- down time to 0.2 eV has been found to be 16.3 ±2.4 μs. The approach to the equilibrium spectrum takes place with a time constant of 33 ±4 μs, and the equilibrium has been established after about 200 μs. Comparison of the measured curves for cadmium and gadolinium with multigroup calculations of the time-dependent flux and reaction rate show the superiority of the scattering models for heavy water of Butler and of Brown and St. John over the mass 2 gas model. The experiment has been supplemented with Monte Carlo calculations of the slowing down time

Ballooning mode instability due to slowed-down ALPHA -particles and associated transport

International Nuclear Information System (INIS)

Itoh, Sanae; Itoh, Kimitaka; Tuda, Takashi; Tokuda, Shinji.

1982-01-01

The microscopic stability of tokamak plasma, which contains slowed-down alpha-particles and the anomalous fluxes enhanced by the fluctuation, was studied. The local maxwellian distribution with the density inhomogeneity as the equilibrium distribution of electrons, ions and alpha-particles was closen. In the zero-beta limit, two branches of eigenmodes, which are electrostatic, were obtained. The electrostatic ballooning mode became unstable by the grad B drift of particles in the toroidal plasma. It should be noted that there was no critical alpha-particle density and no critical beta-value for the onset of the instability in toroidal plasma even in the presence of the magnetic shear. When the beta-value exceeded the critical beta-value of the MHD ballooning mode, the growth rate approached to that of the MHD mode, and the mode sturcture became very close to that of the MHD mode. The unstable mode in toroidal plasma was the ballooning mode, and was unstable for all plasma parameters. The associated cross-field transport by the ballooning mode is considered. It was found that if the distribution function was assumed to be the birth distribution, the loss rate was very slow and slower than the slowing down time. The effect of alpha-particles on the large scale MHD activity of plasma is discussed. (Kato, T.)

Slowing down of alpha particles in ICF DT plasmas

Science.gov (United States)

He, Bin; Wang, Zhi-Gang; Wang, Jian-Guo

2018-01-01

With the effects of the projectile recoil and plasma polarization considered, the slowing down of 3.54 MeV alpha particles is studied in inertial confinement fusion DT plasmas within the plasma density range from 1024 to 1026 cm-3 and the temperature range from 100 eV to 200 keV. It includes the rate of the energy change and range of the projectile, and the partition fraction of its energy deposition to the deuteron and triton. The comparison with other models is made and the reason for their difference is explored. It is found that the plasmas will not be heated by the alpha particle in its slowing down the process once the projectile energy becomes close to or less than the temperature of the electron or the deuteron and triton in the plasmas. This leads to less energy deposition to the deuteron and triton than that if the recoil of the projectile is neglected when the temperature is close to or higher than 100 keV. Our model is found to be able to provide relevant, reliable data in the large range of the density and temperature mentioned above, even if the density is around 1026 cm-3 while the deuteron and triton temperature is below 500 eV. Meanwhile, the two important models [Phys. Rev. 126, 1 (1962) and Phys. Rev. E 86, 016406 (2012)] are found not to work in this case. Some unreliable data are found in the last model, which include the range of alpha particles and the electron-ion energy partition fraction when the electron is much hotter than the deuteron and triton in the plasmas.

Contribution to analytical solution of neutron slowing down problem in homogeneous and heterogeneous media; Prilog analitickom resavanju problema usporavanja neutrona u homogenim i heterogenim sredinama

Energy Technology Data Exchange (ETDEWEB)

Stefanovic, D B [Institute of Nuclear Sciences Boris Kidric, Vinca, Beograd (Yugoslavia)

1970-07-01

The objective of this work is to describe the new analytical solution of the neutron slowing down equation for infinite monoatomic media with arbitrary energy dependence of cross section. The solution is obtained by introducing Green slowing down functions instead of starting from slowing down equations directly. The previously used methods for calculation of fission neutron spectra in the reactor cell were numerical. The proposed analytical method was used for calculating the space-energy distribution of fast neutrons and number of neutron reactions in a thermal reactor cell. The role of analytical method in solving the neutron slowing down in reactor physics is to enable understating of the slowing down process and neutron transport. The obtained results could be used as standards for testing the accuracy od approximative and practical methods.

Critical slowing down in driven-dissipative Bose-Hubbard lattices

Science.gov (United States)

Vicentini, Filippo; Minganti, Fabrizio; Rota, Riccardo; Orso, Giuliano; Ciuti, Cristiano

2018-01-01

We explore theoretically the dynamical properties of a first-order dissipative phase transition in coherently driven Bose-Hubbard systems, describing, e.g., lattices of coupled nonlinear optical cavities. Via stochastic trajectory calculations based on the truncated Wigner approximation, we investigate the dynamical behavior as a function of system size for one-dimensional (1D) and 2D square lattices in the regime where mean-field theory predicts nonlinear bistability. We show that a critical slowing down emerges for increasing number of sites in 2D square lattices, while it is absent in 1D arrays. We characterize the peculiar properties of the collective phases in the critical region.

First test experiment to produce the slowed-down RI beam with the momentum-compression mode at RIBF

Energy Technology Data Exchange (ETDEWEB)

Sumikama, T., E-mail: sumikama@ribf.riken.jp [RIKEN Nishina Center, 2-1 Hirosawa, Wako, Saitama 351-0198 (Japan); Department of Physics, Tohoku University, Aoba, Sendai 980-8578 (Japan); Ahn, D.S.; Fukuda, N.; Inabe, N.; Kubo, T.; Shimizu, Y.; Suzuki, H.; Takeda, H. [RIKEN Nishina Center, 2-1 Hirosawa, Wako, Saitama 351-0198 (Japan); Aoi, N. [Research Center for Nuclear Physics, Osaka University, Ibaraki, Osaka 567-0047 (Japan); Beaumel, D. [Institut de Physique Nucléaire d’Orsay (IPNO), CNRS/IN2P3, 91405 Orsay (France); Hasegawa, K. [Department of Physics, Tohoku University, Aoba, Sendai 980-8578 (Japan); Ideguchi, E. [Research Center for Nuclear Physics, Osaka University, Ibaraki, Osaka 567-0047 (Japan); Imai, N. [Center for Nuclear Study, University of Tokyo, RIKEN Campus, 2-1 Hirosawa, Wako, Saitama 351-0298 (Japan); Kobayashi, T. [Department of Physics, Tohoku University, Aoba, Sendai 980-8578 (Japan); Matsushita, M.; Michimasa, S. [Center for Nuclear Study, University of Tokyo, RIKEN Campus, 2-1 Hirosawa, Wako, Saitama 351-0298 (Japan); Otsu, H. [RIKEN Nishina Center, 2-1 Hirosawa, Wako, Saitama 351-0198 (Japan); Shimoura, S. [Center for Nuclear Study, University of Tokyo, RIKEN Campus, 2-1 Hirosawa, Wako, Saitama 351-0298 (Japan); Teranishi, T. [Department of Physics, Kyushu University, 6-10-1 Hakozaki, Fukuoka 812-8581 (Japan)

2016-06-01

The {sup 82}Ge beam has been produced by the in-flight fission reaction of the {sup 238}U primary beam with 345 MeV/u at the RIKEN RI beam factory, and slowed down to about 15 MeV/u using the energy degraders. The momentum-compression mode was applied to the second stage of the BigRIPS separator to reduce the momentum spread. The energy was successfully reduced down to 13 ± 2.5 MeV/u as expected. The focus was not optimized at the end of the second stage, therefore the beam size was larger than the expectation. The transmission of the second stage was half of the simulated value mainly due to out of focus. The two-stage separation worked very well for the slowed-down beam with the momentum-compression mode.

On the group approximation errors in description of neutron slowing-down at large distances from a source. Diffusion approach

International Nuclear Information System (INIS)

Kulakovskij, M.Ya.; Savitskij, V.I.

1981-01-01

The errors of multigroup calculating the neutron flux spatial and energy distribution in the fast reactor shield caused by using group and age approximations are considered. It is shown that at small distances from a source the age theory rather well describes the distribution of the slowing-down density. With the distance increase the age approximation leads to underestimating the neutron fluxes, and the error quickly increases at that. At small distances from the source (up to 15 lengths of free path in graphite) the multigroup diffusion approximation describes the distribution of slowing down density quite satisfactorily and at that the results almost do not depend on the number of groups. With the distance increase the multigroup diffusion calculations lead to considerable overestimating of the slowing-down density. The conclusion is drawn that the group approximation proper errors are opposite in sign to the error introduced by the age approximation and to some extent compensate each other

Development for fissile assay in recycled fuel using lead slowing down spectrometer

International Nuclear Information System (INIS)

Lee, Yong Deok; Je Park, C.; Kim, Ho-Dong; Song, Kee Chan

2013-01-01

A future nuclear energy system is under development to turn spent fuels produced by PWRs into fuels for a SFR (Sodium Fast Reactor) through the pyrochemical process. The knowledge of the isotopic fissile content of the new fuel is very important for fuel safety. A lead slowing down spectrometer (LSDS) is under development to analyze the fissile material content (Pu 239 , Pu 241 and U 235 ) of the fuel. The LSDS requires a neutron source, the neutrons will be slowed down through their passage in a lead medium and will finally enter the fuel and will induce fission reactions that will be analysed and the isotopic content of the fuel will be then determined. The issue is that the spent fuel emits intense gamma rays and neutrons by spontaneous fission. The threshold fission detector screens the prompt fast fission neutrons and as a result the LSDS is not influenced by the high level radiation background. The energy resolution of LSDS is good in the range 0.1 eV to 1 keV. It is also the range in which the fission reaction is the most discriminating for the considered fissile isotopes. An electron accelerator has been chosen to produce neutrons with an adequate target through (e - ,γ)(γ,n) reactions

Simple and efficient importance sampling scheme for a tandem queue with server slow-down

NARCIS (Netherlands)

Miretskiy, D.I.; Scheinhardt, W.R.W.; Mandjes, M.R.H.

2008-01-01

This paper considers importance sampling as a tool for rare-event simulation. The system at hand is a so-called tandem queue with slow-down, which essentially means that the server of the first queue (or: upstream queue) switches to a lower speed when the second queue (downstream queue) exceeds some

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 considering of the slowing down effect in the formalism of probability tables. Application to the effective cross section calculation

International Nuclear Information System (INIS)

Bouhelal, O.K.A.

1990-01-01

The exact determination of the effective multigroup cross sections imposes the numerical solution of the slowing down equation on a very fine energy mesh. Given the complexity of these calculations, different approximation methods have been developed but without a satisfactory treatment of the slowing-down effect. The usual methods are essentially based on interpolations using precalculated tables. The models that use the probability tables allow to reduce the amount of data and the computational effort. A variety of methods proposed by Soviets, then by Americans, and finally the French method, based on the ''moments of a probability distribution'' are incontestably valid within the framework of the statistical hypothesis. This stipulates that the collision densities do not depend on cross section and there is no ambiguity in the effective cross section calculation. The objective of our work is to show that the non statistical phenomena, such as the slowing-down effect which is taken into account, can be described by probability tables which are able to represent the neutronic values and collision densities. The formalism involved in the statistical hypothesis, is based on the Gauss quadrature of the cross sections moments. In the non-statistical hypothesis we introduce the crossed probability tables using the quadratures of double integrals of cross sections, comments. Moreover, a mathematical formalism allowing to establish a relationship between the crossed probability tables and the collision densities was developed. This method was applied on uranium-238 in the range of resolved resonances where the slowing down effect is significant. Validity of the method and the analysis of the obtained results are studied through a reference calculation based on a solution of a discretized slowing down equation using a very fine mesh in which each microgroup can be correctly defined via the statistical probability tables. 42 figs., 32 tabs., 49 refs. (author)

Utilizing the slowing-down-time technique for benchmarking neutron thermalization in graphite

International Nuclear Information System (INIS)

Zhou, T.; Hawari, A. I.; Wehring, B. W.

2007-01-01

Graphite is the moderator/reflector in the Very High Temperature Reactor (VHTR) concept of Generation IV reactors. As a thermal reactor, the prediction of the thermal neutron spectrum in the VHTR is directly dependent on the accuracy of the thermal neutron scattering libraries of graphite. In recent years, work has been on-going to benchmark and validate neutron thermalization in 'reactor grade' graphite. Monte Carlo simulations using the MCNP5 code were used to design a pulsed neutron slowing-down-time experiment and to investigate neutron slowing down and thermalization in graphite at temperatures relevant to VHTR operation. The unique aspect of this experiment is its ability to observe the behavior of neutrons throughout an energy range extending from the source energy to energies below 0.1 eV. In its current form, the experiment is designed and implemented at the Oak Ridge Electron Linear Accelerator (ORELA). Consequently, ORELA neutron pulses are injected into a 70 cm x 70 cm x 70 cm graphite pile. A furnace system that surrounds the pile and is capable of heating the graphite to a centerline temperature of 1200 K has been designed and built. A system based on U-235 fission chambers and Li-6 scintillation detectors surrounds the pile. This system is coupled to multichannel scaling instrumentation and is designed for the detection of leakage neutrons as a function of the slowing-down-time (i.e., time after the pulse). To ensure the accuracy of the experiment, careful assessment was performed of the impact of background noise (due to room return neutrons) and pulse-to-pulse overlap on the measurement. Therefore, the entire setup is surrounded by borated polyethylene shields and the experiment is performed using a source pulse frequency of nearly 130 Hz. As the basis for the benchmark, the calculated time dependent reaction rates in the detectors (using the MCNP code and its associated ENDF-B/VI thermal neutron scattering libraries) are compared to measured

Lack of Critical Slowing Down Suggests that Financial Meltdowns Are Not Critical Transitions, yet Rising Variability Could Signal Systemic Risk

Science.gov (United States)

Hoarau, Quentin

2016-01-01

Complex systems inspired analysis suggests a hypothesis that financial meltdowns are abrupt critical transitions that occur when the system reaches a tipping point. Theoretical and empirical studies on climatic and ecological dynamical systems have shown that approach to tipping points is preceded by a generic phenomenon called critical slowing down, i.e. an increasingly slow response of the system to perturbations. Therefore, it has been suggested that critical slowing down may be used as an early warning signal of imminent critical transitions. Whether financial markets exhibit critical slowing down prior to meltdowns remains unclear. Here, our analysis reveals that three major US (Dow Jones Index, S&P 500 and NASDAQ) and two European markets (DAX and FTSE) did not exhibit critical slowing down prior to major financial crashes over the last century. However, all markets showed strong trends of rising variability, quantified by time series variance and spectral function at low frequencies, prior to crashes. These results suggest that financial crashes are not critical transitions that occur in the vicinity of a tipping point. Using a simple model, we argue that financial crashes are likely to be stochastic transitions which can occur even when the system is far away from the tipping point. Specifically, we show that a gradually increasing strength of stochastic perturbations may have caused to abrupt transitions in the financial markets. Broadly, our results highlight the importance of stochastically driven abrupt transitions in real world scenarios. Our study offers rising variability as a precursor of financial meltdowns albeit with a limitation that they may signal false alarms. PMID:26761792

Exact solutions of the neutron slowing down equation

International Nuclear Information System (INIS)

Dawn, T.Y.; Yang, C.N.

1976-01-01

The problem of finding the exact analytic closed-form solution for the neutron slowing down equation in an infinite homogeneous medium is studied in some detail. The existence and unique properties of the solution of this equation for both the time-dependent and the time-independent cases are studied. A direct method is used to determine the solution of the stationary problem. The final result is given in terms of a sum of indefinite multiple integrals by which solutions of some special cases and the Placzek-type oscillation are examined. The same method can be applied to the time-dependent problem with the aid of the Laplace transformation technique, but the inverse transform is, in general, laborious. However, the solutions of two special cases are obtained explicitly. Results are compared with previously reported works in a variety of cases. The time moments for the positive integral n are evaluated, and the conditions for the existence of the negative moments are discussed

Analytical calculations of neutron slowing down and transport in the constant-cross-section problem

International Nuclear Information System (INIS)

Cacuci, D.G.

1978-01-01

Some aspects of the problem of neutron slowing down and transport in an infinite medium consisting of a single nuclide that scatters elastically and isotropically and has energy-independent cross sections were investigated. The method of singular eigenfunctions was applied to the Boltzmann equation governing the Laplace transform (with respect to the lethargy variable) of the neutron flux. A new sufficient condition for the convergence of the coefficients of the expansion of the scattering kernel in Legendre polynomials was rigorously derived for this energy-dependent problem. Formulas were obtained for the lethargy-dependent spatial moments of the scalar flux that are valid for medium to large lethargies. In deriving these formulas, use was made of the well-known connection between the spatial moments of the Laplace-transformed scalar flux and the moments of the flux in the ''eigenvalue space.'' The calculations were greatly aided by the construction of a closed general expression for these ''eigenvalue space'' moments. Extensive use was also made of the methods of combinatorial analysis and of computer evaluation, via FORMAC, of complicated sequences of manipulations. For the case of no absorption it was possible to obtain for materials of any atomic weight explicit corrections to the age-theory formulas for the spatial moments M/sub 2n/(u) of the scalar flux that are valid through terms of the order of u -5 . The evaluation of the coefficients of the powers of n, as explicit functions of the nuclear mass, is one of the end products of this investigation. In addition, an exact expression for the second spatial moment, M 2 (u), valid for arbitrary (constant) absorption, was derived. It is now possible to calculate analytically and rigorously the ''age'' for the constant-cross-section problem for arbitrary (constant) absorption and nuclear mass. 5 figures, 1 table

Analytical calculations of neutron slowing down and transport in the constant-cross-section problem

International Nuclear Information System (INIS)

Cacuci, D.G.

1978-04-01

Aspects of the problem of neutron slowing down and transport in an infinite medium consisting of a single nuclide that scatters elastically and isotropically and has energy-independent cross sections were investigated. The method of singular eigenfunctions was applied to the Boltzmann Equation governing the Laplace transform (with respect to the lethargy variable) of the neutron flux. A new sufficient condition for the convergence of the coefficients of the expansion of the scattering kernel in Legendre polynomials was rigorously derived for this energy-dependent problem. Formulas were obtained for the lethargy-dependent spatial moments of the scalar flux that are valid for medium to large lethargies. Use was made of the well-known connection between the spatial moments of the Laplace-transformed scalar flux and the moments of the flux in the ''eigenvalue space.'' The calculations were aided by the construction of a closed general expression for these ''eigenvalue space'' moments. Extensive use was also made of the methods of combinatorial analysis and of computer evaluation of complicated sequences of manipulations. For the case of no absorption it was possible to obtain for materials of any atomic weight explicit corrections to the age-theory formulas for the spatial moments M/sub 2n/(u) of the scalar flux that are valid through terms of the order of u -5 . The evaluation of the coefficients of the powers of n, as explicit functions of the nuclear mass, represent one of the end products of this investigation. In addition, an exact expression for the second spatial moment, M 2 (u), valid for arbitrary (constant) absorption, was derived. It is now possible to calculate analytically and rigorously the ''age'' for the constant-cross-section problem for arbitrary (constant) absorption and nuclear mass. 5 figures, 1 table

Analytical calculations of neutron slowing down and transport in the constant-cross-section problem

Energy Technology Data Exchange (ETDEWEB)

Cacuci, D.G.

1978-04-01

Aspects of the problem of neutron slowing down and transport in an infinite medium consisting of a single nuclide that scatters elastically and isotropically and has energy-independent cross sections were investigated. The method of singular eigenfunctions was applied to the Boltzmann Equation governing the Laplace transform (with respect to the lethargy variable) of the neutron flux. A new sufficient condition for the convergence of the coefficients of the expansion of the scattering kernel in Legendre polynomials was rigorously derived for this energy-dependent problem. Formulas were obtained for the lethargy-dependent spatial moments of the scalar flux that are valid for medium to large lethargies. Use was made of the well-known connection between the spatial moments of the Laplace-transformed scalar flux and the moments of the flux in the ''eigenvalue space.'' The calculations were aided by the construction of a closed general expression for these ''eigenvalue space'' moments. Extensive use was also made of the methods of combinatorial analysis and of computer evaluation of complicated sequences of manipulations. For the case of no absorption it was possible to obtain for materials of any atomic weight explicit corrections to the age-theory formulas for the spatial moments M/sub 2n/(u) of the scalar flux that are valid through terms of the order of u/sup -5/. The evaluation of the coefficients of the powers of n, as explicit functions of the nuclear mass, represent one of the end products of this investigation. In addition, an exact expression for the second spatial moment, M/sub 2/(u), valid for arbitrary (constant) absorption, was derived. It is now possible to calculate analytically and rigorously the ''age'' for the constant-cross-section problem for arbitrary (constant) absorption and nuclear mass. 5 figures, 1 table.

Design optimization of radiation shielding structure for lead slowing-down spectrometer system

OpenAIRE

Kim, Jeong Dong; Ahn, Sangjoon; Lee, Yong Deok; Park, Chang Je

2015-01-01

A lead slowing-down spectrometer (LSDS) system is a promising nondestructive assay technique that enables a quantitative measurement of the isotopic contents of major fissile isotopes in spent nuclear fuel and its pyroprocessing counterparts, such as 235U, 239Pu, 241Pu, and, potentially, minor actinides. The LSDS system currently under development at the Korea Atomic Energy Research Institute (Daejeon, Korea) is planned to utilize a high-flux (>1012 n/cm2·s) neutron source comprised of a high...

Slowing-down of non-relativistic ions in a hot dense plasma

International Nuclear Information System (INIS)

Maynard, G.

1982-01-01

The parameter γ (action of the free-electrons of the plasma) was investigated: calculation of the mean value of γ for a great number of monokinetic incident ions and of the dispersion about this mean value, using the random phase approximation; and calculation of the dielectric function. The contribution of the plasma ions to the stopping power was studied and the description of the ion-plasma interaction improved. The slowing-down of an ion at large distance by the bound electrons of an atom was calculated. This study is applied to the ion-plasma interaction in the ion-beam inertial confinement [fr

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

International Nuclear Information System (INIS)

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

2000-01-01

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

Critical slowing down and error analysis in lattice QCD simulations

Energy Technology Data Exchange (ETDEWEB)

Virotta, Francesco

2012-02-21

In this work we investigate the critical slowing down of lattice QCD simulations. We perform a preliminary study in the quenched approximation where we find that our estimate of the exponential auto-correlation time scales as {tau}{sub exp}(a){proportional_to}a{sup -5}, where a is the lattice spacing. In unquenched simulations with O(a) improved Wilson fermions we do not obtain a scaling law but find results compatible with the behavior that we find in the pure gauge theory. The discussion is supported by a large set of ensembles both in pure gauge and in the theory with two degenerate sea quarks. We have moreover investigated the effect of slow algorithmic modes in the error analysis of the expectation value of typical lattice QCD observables (hadronic matrix elements and masses). In the context of simulations affected by slow modes we propose and test a method to obtain reliable estimates of statistical errors. The method is supposed to help in the typical algorithmic setup of lattice QCD, namely when the total statistics collected is of O(10){tau}{sub exp}. This is the typical case when simulating close to the continuum limit where the computational costs for producing two independent data points can be extremely large. We finally discuss the scale setting in N{sub f}=2 simulations using the Kaon decay constant f{sub K} as physical input. The method is explained together with a thorough discussion of the error analysis employed. A description of the publicly available code used for the error analysis is included.

Critical slowing down and error analysis in lattice QCD simulations

International Nuclear Information System (INIS)

Virotta, Francesco

2012-01-01

In this work we investigate the critical slowing down of lattice QCD simulations. We perform a preliminary study in the quenched approximation where we find that our estimate of the exponential auto-correlation time scales as τ exp (a)∝a -5 , where a is the lattice spacing. In unquenched simulations with O(a) improved Wilson fermions we do not obtain a scaling law but find results compatible with the behavior that we find in the pure gauge theory. The discussion is supported by a large set of ensembles both in pure gauge and in the theory with two degenerate sea quarks. We have moreover investigated the effect of slow algorithmic modes in the error analysis of the expectation value of typical lattice QCD observables (hadronic matrix elements and masses). In the context of simulations affected by slow modes we propose and test a method to obtain reliable estimates of statistical errors. The method is supposed to help in the typical algorithmic setup of lattice QCD, namely when the total statistics collected is of O(10)τ exp . This is the typical case when simulating close to the continuum limit where the computational costs for producing two independent data points can be extremely large. We finally discuss the scale setting in N f =2 simulations using the Kaon decay constant f K as physical input. The method is explained together with a thorough discussion of the error analysis employed. A description of the publicly available code used for the error analysis is included.

Critical slowing down associated with regime shifts in the US housing market

Science.gov (United States)

Tan, James Peng Lung; Cheong, Siew Siew Ann

2014-02-01

Complex systems are described by a large number of variables with strong and nonlinear interactions. Such systems frequently undergo regime shifts. Combining insights from bifurcation theory in nonlinear dynamics and the theory of critical transitions in statistical physics, we know that critical slowing down and critical fluctuations occur close to such regime shifts. In this paper, we show how universal precursors expected from such critical transitions can be used to forecast regime shifts in the US housing market. In the housing permit, volume of homes sold and percentage of homes sold for gain data, we detected strong early warning signals associated with a sequence of coupled regime shifts, starting from a Subprime Mortgage Loans transition in 2003-2004 and ending with the Subprime Crisis in 2007-2008. Weaker signals of critical slowing down were also detected in the US housing market data during the 1997-1998 Asian Financial Crisis and the 2000-2001 Technology Bubble Crisis. Backed by various macroeconomic data, we propose a scenario whereby hot money flowing back into the US during the Asian Financial Crisis fueled the Technology Bubble. When the Technology Bubble collapsed in 2000-2001, the hot money then flowed into the US housing market, triggering the Subprime Mortgage Loans transition in 2003-2004 and an ensuing sequence of transitions. We showed how this sequence of couple transitions unfolded in space and in time over the whole of US.

ROLAIDS-CPM, 1-D Slowing-Down by Collision Problems Method

International Nuclear Information System (INIS)

De Kruijf, W.J.M.

2002-01-01

1 - Description of program or function: ROLAIDS-CPM is based on the AMPX-module ROLAIDS (PSR-315). CPM stands for Collision Probability Method. ROLAIDS is a one-dimensional slowing-down code which uses the interface currents method. ROLAIDS-CPM does not need the assumption of cosine currents at the interface of the zones. Extensions: collision probability method for slab and cylinder geometry; different temperatures for a nuclide can be used; flat lethargy source can be modelled. 2 - Method of solution: Collision probabilities in cylindrical geometry are calculated according to the Carlvik Method. This means that a Gauss integration is used. 3 - Restrictions on the complexity of the problem: The maximum number of zones is 30 for the collision probability method

The Solution of a Velocity-Dependent Slowing-Down Problem Using Case's Eigenfunction Expansion

Energy Technology Data Exchange (ETDEWEB)

Claesson, A

1964-11-15

The slowing-down of neutrons in a hydrogenous moderator is calculated assuming a plane source of monoenergetic neutrons. The scattering is regarded as spherically symmetric, but it is shown that a generalization to anisotropy is possible. The cross-section is assumed to be constant. The virgin neutrons are separated out, and it follows that the distribution of the remaining neutrons can be obtained by using an expansion in the eigenfunctions given by Case for the velocity-independent problem.

Slowing down of relativistic heavy ions and new applications

International Nuclear Information System (INIS)

Geissel, H.; Scheidenberger, C.

1997-10-01

New precision experiments using powerful accelerator facilities and high-resolution spectrometers have contributed to a better understanding of the atomic and nuclear interactions of relativistic heavy ions with matter. Experimental results on stopping power and energy-loss straggling of bare heavy projectiles demonstrate large systematic deviations from theories based on first order perturbation. The energy-loss straggling is more than a factor of two enhanced for the heaviest projectiles compared to the relativistic Bohr formula. The interaction of cooled relativistic heavy ions with crystals opens up new fields for basic research and applications, i. e., for the first time resonant coherent excitations of both atomic and nuclear levels can be measured at the first harmonic. The spatial monoisotopic separation of exotic nuclei with in-flight separators and the tumor therapy with heavy ions are new applications based on a precise knowledge of slowing down. (orig.)

Development of Cold Neutron Scattering Kernels for Advanced Moderators

International Nuclear Information System (INIS)

Granada, J. R.; Cantargi, F.

2010-01-01

The development of scattering kernels for a number of molecular systems was performed, including a set of hydrogeneous methylated aromatics such as toluene, mesitylene, and mixtures of those. In order to partially validate those new libraries, we compared predicted total cross sections with experimental data obtained in our laboratory. In addition, we have introduced a new model to describe the interaction of slow neutrons with solid methane in phase II (stable phase below T = 20.4 K, atmospheric pressure). Very recently, a new scattering kernel to describe the interaction of slow neutrons with solid Deuterium was also developed. The main dynamical characteristics of that system are contained in the formalism, the elastic processes involving coherent and incoherent contributions are fully described, as well as the spin-correlation effects.

Geant4-DNA simulation of electron slowing-down spectra in liquid water

Energy Technology Data Exchange (ETDEWEB)

Incerti, S., E-mail: sebastien.incerti@tdt.edu.vn [Division of Nuclear Physics, Ton Duc Thang University, Tan Phong Ward, District 7, Ho Chi Minh City (Viet Nam); Faculty of Applied Sciences, Ton Duc Thang University, Tan Phong Ward, District 7, Ho Chi Minh City (Viet Nam); Univ. Bordeaux, CENBG, UMR 5797, F-33170, Gradignan (France); CNRS, IN2P3, CENBG, UMR 5797, F-33170 Gradignan (France); Kyriakou, I. [Medical Physics Laboratory, University of Ioannina Medical School, 45110 Ioannina (Greece); Tran, H.N. [Division of Nuclear Physics, Ton Duc Thang University, Tan Phong Ward, District 7, Ho Chi Minh City (Viet Nam); Faculty of Applied Sciences, Ton Duc Thang University, Tan Phong Ward, District 7, Ho Chi Minh City (Viet Nam)

2017-04-15

This work presents the simulation of monoenergetic electron slowing-down spectra in liquid water by the Geant4-DNA extension of the Geant4 Monte Carlo toolkit (release 10.2p01). These spectra are simulated for several incident energies using the most recent Geant4-DNA physics models, and they are compared to literature data. The influence of Auger electron production is discussed. For the first time, a dedicated Geant4-DNA example allowing such simulations is described and is provided to Geant4 users, allowing further verification of Geant4-DNA track structure simulation capabilities.

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...

New resonance cross section calculational algorithms

International Nuclear Information System (INIS)

Mathews, D.R.

1978-01-01

Improved resonance cross section calculational algorithms were developed and tested for inclusion in a fast reactor version of the MICROX code. The resonance energy portion of the MICROX code solves the neutron slowing-down equations for a two-region lattice cell on a very detailed energy grid (about 14,500 energies). In the MICROX algorithms, the exact P 0 elastic scattering kernels are replaced by synthetic (approximate) elastic scattering kernels which permit the use of an efficient and numerically stable recursion relation solution of the slowing-down equation. In the work described here, the MICROX algorithms were modified as follows: an additional delta function term was included in the P 0 synthetic scattering kernel. The additional delta function term allows one more moments of the exact elastic scattering kernel to be preserved without much extra computational effort. With the improved synthetic scattering kernel, the flux returns more closely to the exact flux below a resonance than with the original MICROX kernel. The slowing-down calculation was extended to a true B 1 hyperfine energy grid calculatn in each region by using P 1 synthetic scattering kernels and tranport-corrected P 0 collision probabilities to couple the two regions. 1 figure, 6 tables

Some comments on cold hydrogenous moderators, simple synthetic kernels and benchmark calculations

International Nuclear Information System (INIS)

Dorning, J.

1997-09-01

The author comments on three general subjects which are not directly related, but which in his opinion are very relevant to the objectives of the workshop. The first of these is parahydrogen moderators, about which recurring questions have been raised during the Workshop. The second topic is related to the use of simple synthetic scattering kernels in conjunction with the neutron transport equation to carry out elementary mathematical analyses and simple computational analyses in order to understand the gross physics of time-dependent neutron transport initiated by pulsed sources in cold moderators. The third subject is that of 'simple' benchmark calculations by which is meant calculations that are simple compared to the very large scale combined spallation, slowing-down, thermalization calculations using MCNP and other large Monte Carlo codes. Such benchmark problems can be created so that they are closely related to both the geometric configuration and material composition of cold moderators of interest and still can be solved using steady-state deterministic transport codes to calculate the asymptotic time-decay constant, and the time-asymptotic energy spectrum of neutrons in the cold moderator and the spectrum of the cold neutrons leaking from it (neither of which should be expected to be Maxwellian in these small leakage-dominated systems). These would provide rather precise benchmark solutions against which the results of the large scale calculations carried out for the whole spallation, slowing-down, thermalization system -- for the same decoupled cold moderator -- could be compared.

Early warning of climate tipping points from critical slowing down: comparing methods to improve robustness

Science.gov (United States)

Lenton, T. M.; Livina, V. N.; Dakos, V.; Van Nes, E. H.; Scheffer, M.

2012-01-01

We address whether robust early warning signals can, in principle, be provided before a climate tipping point is reached, focusing on methods that seek to detect critical slowing down as a precursor of bifurcation. As a test bed, six previously analysed datasets are reconsidered, three palaeoclimate records approaching abrupt transitions at the end of the last ice age and three models of varying complexity forced through a collapse of the Atlantic thermohaline circulation. Approaches based on examining the lag-1 autocorrelation function or on detrended fluctuation analysis are applied together and compared. The effects of aggregating the data, detrending method, sliding window length and filtering bandwidth are examined. Robust indicators of critical slowing down are found prior to the abrupt warming event at the end of the Younger Dryas, but the indicators are less clear prior to the Bølling-Allerød warming, or glacial termination in Antarctica. Early warnings of thermohaline circulation collapse can be masked by inter-annual variability driven by atmospheric dynamics. However, rapidly decaying modes can be successfully filtered out by using a long bandwidth or by aggregating data. The two methods have complementary strengths and weaknesses and we recommend applying them together to improve the robustness of early warnings. PMID:22291229

The Widom-Rowlinson mixture on a sphere: elimination of exponential slowing down at first-order phase transitions

International Nuclear Information System (INIS)

Fischer, T; Vink, R L C

2010-01-01

Computer simulations of first-order phase transitions using 'standard' toroidal boundary conditions are generally hampered by exponential slowing down. This is partly due to interface formation, and partly due to shape transitions. The latter occur when droplets become large such that they self-interact through the periodic boundaries. On a spherical simulation topology, however, shape transitions are absent. We expect that by using an appropriate bias function, exponential slowing down can be largely eliminated. In this work, these ideas are applied to the two-dimensional Widom-Rowlinson mixture confined to the surface of a sphere. Indeed, on the sphere, we find that the number of Monte Carlo steps needed to sample a first-order phase transition does not increase exponentially with system size, but rather as a power law τ∝V α , with α∼2.5, and V the system area. This is remarkably close to a random walk for which α RW = 2. The benefit of this improved scaling behavior for biased sampling methods, such as the Wang-Landau algorithm, is investigated in detail.

ACTIV, Sandwich Detector Activity from In-Pile Slowing-Down Spectra Experiment

International Nuclear Information System (INIS)

Bozzi, L. and others

1978-01-01

1 - Nature of physical problem solved: Calculates the activities of a sandwich detector, to be used for in-pile measurements in slowing-down spectra below a few keV. The effect of scattering with energy degradation in the filter and in the detectors has been included to a first approximation. 2 - Method of solution: An iterative procedure is used: the calculation starts with a flux guess in which one assumes that each measured reactivity difference depends on the principal resonance only. The secondary resonance contribution is computed through the iterative process. For self-shielded cross-section calculations the model of Pearlstein and Weinstock (ref. 3) is used. The neutron spectrum can optionally be constant or 1/E inside each finite energy group relative to the resonance considered. 3 - Restrictions on the complexity of the problem: Maximum number of energy groups : 60

Evaluation of Shielding Wall Optimization in Lead Slowing Down Spectrometer System

Energy Technology Data Exchange (ETDEWEB)

Jeon, Ju Young; Kim, Jeong Dong; Lee, Yong Deok [Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of)

2015-10-15

A Lead Slowing Down Spectrometer (LSDS) system is nondestructive technology for analyzing isotope fissile content in spent fuel and pyro processed material, in real time and directly. The high intensity neutron and gamma ray were generated from a nuclear material (Pyro, Spent nuclear fuel), electron beam-target reaction and fission of fissile material. Therefore, shielding analysis of LSDS system should be carried out. In this study, Borax, B{sub 4}C, Li{sub 2}Co{sub 3}, Resin were chosen for shielding analysis. The radiation dose limit (<0.1 μSv/hr) was adopted conservatively at the outer wall surface. The covering could be able to reduce the concrete wall thickness from 5cm to 15cm. The optimized shielding walls evaluation will be used as an important data for future real LSDS facility design and shielding door assessment.

LEAD SLOWING DOWN SPECTROSCOPY FOR DIRECT Pu MASS MEASUREMENTS

International Nuclear Information System (INIS)

Ressler, Jennifer J.; Smith, Leon E.; Anderson, Kevin K.

2008-01-01

The direct measurement of Pu in previously irradiated fuel assemblies is a recognized need in the international safeguards community. A suitable technology could support more timely and independent material control and accounting (MC and A) measurements at nuclear fuel storage areas, the head-end of reprocessing facilities, and at the product-end of recycled fuel fabrication. Lead slowing down spectroscopy (LSDS) may be a viable solution for directly measuring not only the mass of 239Pu in fuel assemblies, but also the masses of other fissile isotopes such as 235U and 241Pu. To assess the potential viability of LSDS, an LSDS spectrometer was modeled in MCNP5 and 'virtual assays' of nominal PWR assemblies ranging from 0 to 60 GWd/MTU burnup were completed. Signal extraction methods, including the incorporation of nonlinear fitting to account for self-shielding effects in strong resonance regions, are described. Quantitative estimates of Pu uncertainty are given for simplistic and more realistic fuel isotopic inventories calculated using ORIGEN. A discussion of additional signal-perturbing effects that will be addressed in future work, and potential signal extraction approaches that could improve Pu mass uncertainties, are also discussed

Interference effects in angular and spectral distributions of X-ray Transition Radiation from Relativistic Heavy Ions crossing a radiator: Influence of absorption and slowing-down

Energy Technology Data Exchange (ETDEWEB)

Fiks, E.I.; Pivovarov, Yu.L.

2015-07-15

Theoretical analysis and representative calculations of angular and spectral distributions of X-ray Transition Radiation (XTR) by Relativistic Heavy Ions (RHI) crossing a radiator are presented taking into account both XTR absorption and RHI slowing-down. The calculations are performed for RHI energies of GSI, FAIR, CERN SPS and LHC and demonstrate the influence of XTR photon absorption as well as RHI slowing-down in a radiator on the appearance/disappearance of interference effects in both angular and spectral distributions of XTR.

A New Method for Predicting the Penetration and Slowing-Down of Neutrons in Reactor Shields

Energy Technology Data Exchange (ETDEWEB)

Hjaerne, L; Leimdoerfer, M

1965-05-15

A new approach is presented in the formulation of removal-diffusion theory. The 'removal cross-section' is redefined and the slowing-down between the multigroup diffusion equations is treated with a complete energy transfer matrix rather than in an age theory approximation. The method, based on the new approach contains an adjustable parameter. Examples of neutron spectra and thermal flux penetrations are given in a number of differing shield configurations and the results compare favorably with experiments and Moments Method calculations.

A New Method for Predicting the Penetration and Slowing-Down of Neutrons in Reactor Shields

International Nuclear Information System (INIS)

Hjaerne, L.; Leimdoerfer, M.

1965-05-01

A new approach is presented in the formulation of removal-diffusion theory. The 'removal cross-section' is redefined and the slowing-down between the multigroup diffusion equations is treated with a complete energy transfer matrix rather than in an age theory approximation. The method, based on the new approach contains an adjustable parameter. Examples of neutron spectra and thermal flux penetrations are given in a number of differing shield configurations and the results compare favorably with experiments and Moments Method calculations

Plasma heating and confinement in toroidal magnetic bottle by means of microwave slowing-down structure

International Nuclear Information System (INIS)

Datlov, J.; Klima, R.; Kopecky, V.; Musil, J.; Zacek, F.

1977-01-01

An invention is described concerning high-frequency plasma heating and confinement in toroidal magnetic vessels. Microwave energy is applied to the plasma via one or more slowing-down structures exciting low phase velocity waves whose energy may be efficiently absorbed by plasma electrons. The wave momentum transfer results in a toroidal electrical current whose magnetic field together with an external magnetic field ensure plasma confinement. The low-frequency modulation of microwave energy may also be used for heating the ion plasma component. (J.U.)

Comparison of analytical transport and stochastic solutions for neutron slowing down in an infinite medium

International Nuclear Information System (INIS)

Jahshan, S.N.; Wemple, C.A.; Ganapol, B.D.

1993-01-01

A comparison of the numerical solutions of the transport equation describing the steady neutron slowing down in an infinite medium with constant cross sections is made with stochastic solutions obtained from tracking successive neutron histories in the same medium. The transport equation solution is obtained using a numerical Laplace transform inversion algorithm. The basis for the algorithm is an evaluation of the Bromwich integral without analytical continuation. Neither the transport nor the stochastic solution is limited in the number of scattering species allowed. The medium may contain an absorption component as well. (orig.)

Critical slowing down of spin fluctuations in BiFeO3

International Nuclear Information System (INIS)

Scott, J F; Singh, M K; Katiyar, R S

2008-01-01

In earlier work we reported the discovery of phase transitions in BiFeO 3 evidenced by divergences in the magnon light-scattering cross-sections at 140 and 201 K (Singh et al 2008 J. Phys.: Condens. Matter 20 252203) and fitted these intensity data to critical exponents α = 0.06 and α' = 0.10 (Scott et al 2008 J. Phys.: Condens. Matter 20 322203), under the assumption that the transitions are strongly magnetoelastic (Redfern et al 2008 at press) and couple to strain divergences through the Pippard relationship (Pippard 1956 Phil. Mag. 1 473). In the present paper we extend those criticality studies to examine the magnon linewidths, which exhibit critical slowing down (and hence linewidth narrowing) of spin fluctuations. The linewidth data near the two transitions are qualitatively different and we cannot reliably extract a critical exponent ν, although the mean field value ν = 1/2 gives a good fit near the lower transition.

Third generation of lead slowing-down spectrometers: First results and prospects

International Nuclear Information System (INIS)

Alexeev, A.A.; Belousov, Yu.V.; Bergman, A.A.; Volkov, A.N.; Goncharenko, O.N.; Grachev, M.N.; Kazarnovsky, M.V.; Matushkov, V.L.; Mostovoy, V.I.; Novikov, A.V.; Novoselov, S.A.; Ryabov, Yu.V.; Stavissky, Yu.Ya.; Gledenov, Yu.M.; Parzhitski, S.S.; Popov, Yu.P.

1999-01-01

At the same neutron-source intensity S, lead slowing-down spectrometers (LSDS) for neutrons possess luminosity 10 3 -10 4 times as great as that of time-of-flight spectrometers with the same energy resolution ΔE/E(bar sign) (for the former, ΔE/E(bar sign)≅35-45% at a mean energy E(bar sign) less than 50 keV). In combination with high-current proton accelerators, third-generation LSDSs can operate at S values exceeding those acceptable for second-generation LSDSs coupled to electron accelerators by a factor of 10 2 to 10 3 . At the Institute for Nuclear Research (Russian Academy of Sciences, Moscow), the first third-generation LSDS facility called PITON (about 15 tons of lead) has been operating successfully and a large LSDS (more than 100 tons of lead) is now under construction. The results of the first experiments at the PITON facility are presented, and the experimental programs for both facilities is outlined

Analysis of spent fuel assay with a lead slowing down spectrometer

International Nuclear Information System (INIS)

Gavron, A.; Smith, L. Eric; Ressler, Jennifer J.

2009-01-01

Assay of fissile materials in spent fuel that are produced or depleted during the operation of a reactor, is of paramount importance to nuclear materials accounting, verification of the reactor operation history, as well as for criticality considerations for storage. In order to prevent future proliferation following the spread of nuclear energy, we must develop accurate methods to assay large quantities of nuclear fuels. We analyze the potential of using a Lead Slowing Down Spectrometer for assaying spent fuel. We conclude that it possible to design a system that will provide around 1% statistical precision in the determination of the 239 Pu, 241 Pu and 235 U concentrations in a PWR spent-fuel assembly, for intermediate-to-high burnup levels, using commercial neutron sources, and a system of 238 U threshold fission detectors. Pending further analysis of systematic errors, it is possible that missing pins can be detected, as can asymmetry in the fuel bundle. (author)

"You can save time if…"-A qualitative study on internal factors slowing down clinical trials in Sub-Saharan Africa.

Directory of Open Access Journals (Sweden)

Nerina Vischer

Full Text Available The costs, complexity, legal requirements and number of amendments associated with clinical trials are rising constantly, which negatively affects the efficient conduct of trials. In Sub-Saharan Africa, this situation is exacerbated by capacity and funding limitations, which further increase the workload of clinical trialists. At the same time, trials are critically important for improving public health in these settings. The aim of this study was to identify the internal factors that slow down clinical trials in Sub-Saharan Africa. Here, factors are limited to those that exclusively relate to clinical trial teams and sponsors. These factors may be influenced independently of external conditions and may significantly increase trial efficiency if addressed by the respective teams.We conducted sixty key informant interviews with clinical trial staff working in different positions in two clinical research centres in Kenya, Ghana, Burkina Faso and Senegal. The study covered English- and French-speaking, and Eastern and Western parts of Sub-Saharan Africa. We performed thematic analysis of the interview transcripts.We found various internal factors associated with slowing down clinical trials; these were summarised into two broad themes, "planning" and "site organisation". These themes were consistently mentioned across positions and countries. "Planning" factors related to budget feasibility, clear project ideas, realistic deadlines, understanding of trial processes, adaptation to the local context and involvement of site staff in planning. "Site organisation" factors covered staff turnover, employment conditions, career paths, workload, delegation and management.We found that internal factors slowing down clinical trials are of high importance to trial staff. Our data suggest that adequate and coherent planning, careful assessment of the setting, clear task allocation and management capacity strengthening may help to overcome the identified

The effect of straggling on the slowing down of neutrons in radiation protection

International Nuclear Information System (INIS)

Mostacci, D.; Molinari, V.; Teodori, F.; Pesic, M.

1999-01-01

All those techniques developed to describe neutron transport that rely on the flux isotropy conditions prevailing within the reactor core can be of no help in the study of neutron beams. Two main problems must be solved in investigating beams: determining the relevant cross-sections and solving the transport equation. Often in addressing neutron radiation protection problems, the available cross-section data are extremely detailed whereas the transport equations used are rather unrefined, making wide use of the continuous slowing down approximation to calculate stopping powers (e.g., Bethe's expressions). In this paper a simple approach to calculating stopping power and range is presented, that takes into account the effect of neutron energy straggling. Comparison with MCNP results is also presented. (author)

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...

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.

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.

Reaction-rate coefficients, high-energy ions slowing-down, and power balance in a tokamak fusion reactor plasma

International Nuclear Information System (INIS)

Tone, Tatsuzo

1978-07-01

Described are the reactivity coefficient of D-T fusion reaction, slowing-down processes of deuterons injected with high energy and 3.52 MeV alpha particles generated in D-T reaction, and the power balance in a Tokamak reactor plasma. Most of the results were obtained in the first preliminary design of JAERI Experimental Fusion Reactor (JXFR) driven with stationary neutral beam injection. A manual of numerical computation program ''BALTOK'' developed for the calculations is given in the appendix. (auth.)

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

Experimental and theoretical study of heavy ion slowing down in solid targets

International Nuclear Information System (INIS)

Mehana, A.

1993-06-01

Heavy ion energy losses in C, Al, Cu, Ag, Ta and Au solid targets have been measured at high energy (0.2 to 5 MeV/u), using the backward secondary ion technique, and at low energy (0.1 to 0.25 MeV/u) for the C, N and O ions, using the particle backscatter method. A brief review of the various matter-induced charged particle slowing down theories, and especially the Lindhard dielectric theory, is first presented. Then, the various models for the evaluation of the effective charge and of the high order correction, are discussed and compared. Experimental techniques and data processing methods are described, and the experimental results are compared to calculations derived from the dielectric theory. In particular, the effective charges and the high order corrections (Barkas-Bloch) are examined and compared to the models for the determination of the z 3 and z 4 terms for heavy ions

Detector Response to Neutrons Slowed Down in Media Containing Cadmium

Energy Technology Data Exchange (ETDEWEB)

Broda, E.

1943-07-01

This report was written by E. Broda, H. Hereward and L. Kowarski at the Cavendish Laboratory (Cambridge) in September 1943 and is about the detector response to neutrons slowed down in media containing cadmium. The following measurement description and the corresponding results can be found in this report: B, Mn, In, I, Dy and Ir detectors were activated, with and without a Cd shield, near the source in a vessel containing 7 litres of water or solutions of CdSO{sub 4} ranging between 0.1 and 2.8 mols per litre. Numerical data on observed activities are discussed in two different ways and the following conclusions can be drawn: The capture cross-section of dysprosium decreases quicker than 1/v and this discrepancy becomes noticeable well within the limits of the C-group. This imposes obvious limitations on the use of Dy as a detector of thermal neutrons. Cadmium differences of manganese seem to be a reliable 1/v detector for the whole C-group. Indium and iridium show definite signs of an increase of vσ in the upper regions of the C-group. Deviations shown by iodine are due to the imperfections of the technique rather than to a definite departure from the 1/v law. (nowak)

Exponential discontinuous numerical scheme for electron transport in the continuous slowing down approximation

International Nuclear Information System (INIS)

Prinja, A.K.

1997-01-01

A nonlinear discretization scheme in space and energy, based on the recently developed exponential discontinuous method, is applied to continuous slowing down dominated electron transport (i.e., in the absence of scattering.) Numerical results for dose and charge deposition are obtained and compared against results from the ONELD and ONEBFP codes, and against exact results from an adjoint Monte Carlo code. It is found that although the exponential discontinuous scheme yields strictly positive and monotonic solutions, the dose profile is considerably straggled when compared to results from the linear codes. On the other hand, the linear schemes produce negative results which, furthermore, do not damp effectively in some cases. A general conclusion is that while yielding strictly positive solutions, the exponential discontinuous method does not show the crude cell accuracy for charged particle transport as was apparent for neutral particle transport problems

Fission cross section measurement of Am-242m using lead slowing-down spectrometer

Energy Technology Data Exchange (ETDEWEB)

Kai, Tetsuya; Kobayashi, Katsuhei; Yamamoto, Shuji; Fujita, Yoshiaki [Kyoto Univ., Kumatori, Osaka (Japan). Research Reactor Inst.; Kimura, Itsuro; Ohkawachi, Yasushi; Wakabayashi, Toshio

1998-03-01

By making use of double fission chamber and lead slowing-down spectrometer coupled to an electron linear accelerator, fission cross section for the {sup 242m}Am(n,f) reaction has been measured relative to that for the {sup 235}U(n,f) reaction in the energy range from 0.1 eV to 10 keV. The measured result was compared with the evaluated nuclear data appeared in ENDF/B-VI and JENDL-3.2, of which evaluated data were broadened by the energy resolution function of the spectrometer. Although the JENDL-3.2 data seem to be a little smaller than the present measurement, good agreement can be seen in the general shape and the absolute values. The ENDF/B-VI data are larger more than 50 % than the present values above 3 eV. (author)

Lead Slowing-Down Spectrometry for Spent Fuel Assay: FY11 Status Report

International Nuclear Information System (INIS)

Warren, Glen A.; Casella, Andrew M.; Haight, R.C.; Anderson, Kevin K.; Danon, Yaron; Hatchett, D.; Becker, Bjorn; Devlin, M.; Imel, G.R.; Beller, D.; Gavron, A.; Kulisek, Jonathan A.; Bowyer, Sonya M.; Gesh, Christopher J.; O'Donnell, J.M.

2011-01-01

Executive Summary Developing a method for the accurate, direct, and independent assay of the fissile isotopes in bulk materials (such as used fuel) from next-generation domestic nuclear fuel cycles is a goal of the Office of Nuclear Energy, Fuel Cycle R and D, Material Protection and Control Technology (MPACT) Campaign. To meet this goal, MPACT supports a multi-institutional collaboration to study the feasibility of Lead Slowing Down Spectroscopy (LSDS). This technique is an active nondestructive assay method that has the potential to provide independent, direct measurement of Pu and U isotopic masses in used fuel with an uncertainty considerably lower than the approximately 10% typical of today's confirmatory assay methods. This document is a progress report for FY2011 collaboration activities. Progress made by the collaboration in FY2011 continues to indicate the promise of LSDS techniques applied to used fuel. PNNL developed an empirical model based on calibration of the LSDS to responses generated from well-characterized used fuel. The empirical model demonstrated the potential for the direct and independent assay of the sum of the masses of 239Pu and 241Pu to within approximately 3% over a wide used fuel parameter space. Similar results were obtained using a perturbation approach developed by LANL. Benchmark measurements have been successfully conducted at LANL and at RPI using their respective LSDS instruments. The ISU and UNLV collaborative effort is focused on the fabrication and testing of prototype fission chambers lined with ultra-depleted 238U and 232Th, and uranium deposition on a stainless steel disc using spiked U3O8 from room temperature ionic liquid was successful, with improving thickness obtained. In FY2012, the collaboration plans a broad array of activities. PNNL will focus on optimizing its empirical model and minimizing its reliance on calibration data, as well continuing efforts on developing an analytical model. Additional measurements are

ADVANCEMENTS IN TIME-SPECTRA ANALYSIS METHODS FOR LEAD SLOWING-DOWN SPECTROSCOPY

International Nuclear Information System (INIS)

Smith, Leon E.; Anderson, Kevin K.; Gesh, Christopher J.; Shaver, Mark W.

2010-01-01

Direct measurement of Pu in spent nuclear fuel remains a key challenge for safeguarding nuclear fuel cycles of today and tomorrow. Lead slowing-down spectroscopy (LSDS) is an active nondestructive assay method that has the potential to provide independent, direct measurement of Pu and U isotopic mass with an uncertainty lower than the approximately 10 percent typical of today's confirmatory assay methods. Pacific Northwest National Laboratory's (PNNL) previous work to assess the viability of LSDS for the assay of pressurized water reactor (PWR) assemblies indicated that the method could provide direct assay of Pu-239 and U-235 (and possibly Pu-240 and Pu-241) with uncertainties less than a few percent, assuming suitably efficient instrumentation, an intense pulsed neutron source, and improvements in the time-spectra analysis methods used to extract isotopic information from a complex LSDS signal. This previous simulation-based evaluation used relatively simple PWR fuel assembly definitions (e.g. constant burnup across the assembly) and a constant initial enrichment and cooling time. The time-spectra analysis method was founded on a preliminary analytical model of self-shielding intended to correct for assay-signal nonlinearities introduced by attenuation of the interrogating neutron flux within the assembly.

Slowing down tail enhanced, neoclassical and classical alpha particle fluxes in tokamak reactors

International Nuclear Information System (INIS)

Catto, P.J.; Tessarotto, M.

1988-01-01

The classical and neoclassical particle and energy fluxes associated with a slowing down tail, alpha particle distribution function are evaluated for arbitrary aspect ratio ε -1 , cross section, and poloidal magnetic field. The retention of both electron and ion drag and pitch angle scattering by the background ions results in a large diffusive neoclassical heat flux in the plasma core. This flux remains substantial at larger radii only if the characteristic speed associated with pitch angle scattering, v/sub b/, is close enough to the alpha birth speed v 0 so that ε(v 0 /v/sub b/) 3 remains less than some order unity critical value which is not determined by the methods herein. The enhanced neoclassical losses would only have a serious impact on ignition if the critical value of ε(v 0 /v/sub b/) 3 is found to be somewhat larger than unity

THE SLOWING DOWN OF THE CORROSION OF ELEMENTS OF THE EQUIPMENT OF HEAVY MET-ALS AT ELEVATED TEMPERATURES

OpenAIRE

Носачова, Юлія Вікторівна; Ярошенко, М. М.; Корзун, А. О.; КОРОВЧЕНКО, К. С.

2017-01-01

In this article examined the heavy metals ions and their ability to slow down the corrosion process also the impact of ambient temperature on their effectiveness. Solving the problem of corrosion will reduce the impact of large industrial enterprises on the environment and minimize the economic costs. To do this, plants should create a system without a discharge of waste water that is closed recycling systems, which result is a significant reduction in intake of fresh water from natural sourc...

Slowing down Presentation of Facial Movements and Vocal Sounds Enhances Facial Expression Recognition and Induces Facial-Vocal Imitation in Children with Autism

Science.gov (United States)

Tardif, Carole; Laine, France; Rodriguez, Melissa; Gepner, Bruno

2007-01-01

This study examined the effects of slowing down presentation of facial expressions and their corresponding vocal sounds on facial expression recognition and facial and/or vocal imitation in children with autism. Twelve autistic children and twenty-four normal control children were presented with emotional and non-emotional facial expressions on…

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

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

Slow-light pulses in moving media

International Nuclear Information System (INIS)

Fiurasek, J.; Leonhardt, U.; Parentani, R.

2002-01-01

Slow light in moving media reaches a counterintuitive regime when the flow speed of the medium approaches the group velocity of light. Pulses can penetrate a region where a counterpropagating flow exceeds the group velocity. When the counterflow slows down, pulses are reflected

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.

Detailed resonance absorption calculations with the Monte Carlo code MCNP and collision probability version of the slowing down code ROLAIDS

International Nuclear Information System (INIS)

Kruijf, W.J.M. de; Janssen, A.J.

1994-01-01

Very accurate Mote Carlo calculations with Monte Carlo Code have been performed to serve as reference for benchmark calculations on resonance absorption by U 238 in a typical PWR pin-cell geometry. Calculations with the energy-pointwise slowing down code calculates the resonance absorption accurately. Calculations with the multigroup discrete ordinates code XSDRN show that accurate results can only be achieved with a very fine energy mesh. (authors). 9 refs., 5 figs., 2 tabs

A Visual Approach to Investigating Shared and Global Memory Behavior of CUDA Kernels

KAUST Repository

Rosen, Paul

2013-01-01

We present an approach to investigate the memory behavior of a parallel kernel executing on thousands of threads simultaneously within the CUDA architecture. Our top-down approach allows for quickly identifying any significant differences between the execution of the many blocks and warps. As interesting warps are identified, we allow further investigation of memory behavior by visualizing the shared memory bank conflicts and global memory coalescence, first with an overview of a single warp with many operations and, subsequently, with a detailed view of a single warp and a single operation. We demonstrate the strength of our approach in the context of a parallel matrix transpose kernel and a parallel 1D Haar Wavelet transform kernel. © 2013 The Author(s) Computer Graphics Forum © 2013 The Eurographics Association and Blackwell Publishing Ltd.

A Visual Approach to Investigating Shared and Global Memory Behavior of CUDA Kernels

KAUST Repository

Rosen, Paul

2013-06-01

We present an approach to investigate the memory behavior of a parallel kernel executing on thousands of threads simultaneously within the CUDA architecture. Our top-down approach allows for quickly identifying any significant differences between the execution of the many blocks and warps. As interesting warps are identified, we allow further investigation of memory behavior by visualizing the shared memory bank conflicts and global memory coalescence, first with an overview of a single warp with many operations and, subsequently, with a detailed view of a single warp and a single operation. We demonstrate the strength of our approach in the context of a parallel matrix transpose kernel and a parallel 1D Haar Wavelet transform kernel. © 2013 The Author(s) Computer Graphics Forum © 2013 The Eurographics Association and Blackwell Publishing Ltd.

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.

A Histological Study of Aspergillus flavus Colonization of Wound Inoculated Maize Kernels of Resistant and Susceptible Maize Hybrids in the Field

Directory of Open Access Journals (Sweden)

Gary L. Windham

2018-04-01

Full Text Available Aspergillus flavus colonization in developing kernels of maize single-cross hybrids resistant (Mp313E × Mp717 and susceptible (GA209 × T173 to aflatoxin accumulation was determined in the field over three growing seasons (2012–2014. Plants were hand pollinated, and individual kernels were inoculated with a needle dipped in a suspension of A. flavus conidia 21 days after pollination. Kernels were harvested at 1- to 2-day intervals from 1 to 21 days after inoculation (DAI. Kernels were placed in FAA fixative, dehydrated, embedded in paraffin, sectioned, and stained with toluidine blue. Kernels were also collected additional kernels for aflatoxin analyses in 2013 and 2014. At 2 DAI, A. flavus hyphae were observed among endosperm cells in the susceptible hybrid, but colonization of the endosperm in the resistant hybrid was limited to the wound site of the resistant hybrid. Sections of the scutellum of the susceptible hybrid were colonized by A. flavus by 5 DAI. Fungal growth was slower in the resistant hybrid compared to the susceptible hybrid. By 10 DAI, A. flavus had colonized a large section of the embryo in the susceptible hybrid; whereas in the resistant hybrid, approximately half of the endosperm had been colonized and very few cells in the embryo were colonized. Fungal colonization in some of the kernels of the resistant hybrid was slowed in the aleurone layer or at the endosperm-scutellum interface. In wounded kernels with intact aleurone layers, the fungus spread around the kernel between the pericarp and aleurone layer with minimal colonization of the endosperm. Aflatoxin B1 was first detected in susceptible kernel tissues 8 DAI in 2013 (14 μg/kg and 2014 (18 μg/kg. The resistant hybrid had significantly lower levels of aflatoxin accumulation compared to the susceptible hybrid at harvests 10, 21, and 28 DAI in 2013, and 20 and 24 DAI in 2014. Our study found differential A. flavus colonization of susceptible and resistant kernel

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.

Stochastic dynamic modeling of regular and slow earthquakes

Science.gov (United States)

Aso, N.; Ando, R.; Ide, S.

2017-12-01

Both regular and slow earthquakes are slip phenomena on plate boundaries and are simulated by a (quasi-)dynamic modeling [Liu and Rice, 2005]. In these numerical simulations, spatial heterogeneity is usually considered not only for explaining real physical properties but also for evaluating the stability of the calculations or the sensitivity of the results on the condition. However, even though we discretize the model space with small grids, heterogeneity at smaller scales than the grid size is not considered in the models with deterministic governing equations. To evaluate the effect of heterogeneity at the smaller scales we need to consider stochastic interactions between slip and stress in a dynamic modeling. Tidal stress is known to trigger or affect both regular and slow earthquakes [Yabe et al., 2015; Ide et al., 2016], and such an external force with fluctuation can also be considered as a stochastic external force. A healing process of faults may also be stochastic, so we introduce stochastic friction law. In the present study, we propose a stochastic dynamic model to explain both regular and slow earthquakes. We solve mode III problem, which corresponds to the rupture propagation along the strike direction. We use BIEM (boundary integral equation method) scheme to simulate slip evolution, but we add stochastic perturbations in the governing equations, which is usually written in a deterministic manner. As the simplest type of perturbations, we adopt Gaussian deviations in the formulation of the slip-stress kernel, external force, and friction. By increasing the amplitude of perturbations of the slip-stress kernel, we reproduce complicated rupture process of regular earthquakes including unilateral and bilateral ruptures. By perturbing external force, we reproduce slow rupture propagation at a scale of km/day. The slow propagation generated by a combination of fast interaction at S-wave velocity is analogous to the kinetic theory of gasses: thermal

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...

Episodic memory deficits slow down the dynamics of cognitive procedural learning in normal ageing

Science.gov (United States)

Beaunieux, Hélène; Hubert, Valérie; Pitel, Anne Lise; Desgranges, Béatrice; Eustache, Francis

2009-01-01

Cognitive procedural learning is characterized by three phases, each involving distinct processes. Considering the implication of the episodic memory in the first cognitive stage, the impairment of this memory system might be responsible for a slowing down of the cognitive procedural learning dynamics in the course of aging. Performances of massed cognitive procedural learning were evaluated in older and younger participants using the Tower of Toronto task. Nonverbal intelligence and psychomotor abilities were used to analyze procedural dynamics, while episodic memory and working memory were assessed to measure their respective contributions to learning strategies. This experiment showed that older participants did not spontaneously invoke episodic memory and presented a slowdown in the cognitive procedural learning associated with a late involvement of working memory. These findings suggest that the slowdown in the cognitive procedural learning may be linked with the implementation of different learning strategies less involving episodic memory in older subjects. PMID:18654928

Measurement of fission cross section with pure Am-243 sample using lead slowing-down spectrometer

Energy Technology Data Exchange (ETDEWEB)

Kobayashi, Katsuhei; Yamamoto, Shuji; Kai, T.; Fujita, Yoshiaki; Yamamoto, Hideki; Kimura, Itsuro [Kyoto Univ. (Japan); Shinohara, Nobuo

1997-03-01

By making use of back-to-back type double fission chambers and a lead slowing-down spectrometer coupled to an electron linear accelerator, the fission cross section for the {sup 243}Am(n,f) reaction has been measured relative to that for the {sup 235}U(n,f) reaction in the energy range from 0.1 eV to 10 keV. The measured result was compared with the evaluated nuclear data appeared in ENDF/B-VI and JENDL-3.2, whose evaluated data were broadened by the energy resolution function of the spectrometer. General agreement was seen between the evaluated data and the measurement except that the ENDF/B-VI data were lower in the range from 15 to 60 eV and that the JENDL-3.2 data seemed to be lower above 100 eV. (author)

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.

Slow Light at High Frequencies in an Amplifying Semiconductor Waveguide

DEFF Research Database (Denmark)

Öhman, Filip; Yvind, Kresten; Mørk, Jesper

2006-01-01

We demonstrate slow-down of a modulated light signal in a semiconductor waveguide. Concatenated amplifying and absorbing sections simultaneously achieve both amplification and a controllable time delay at 15 GHz.......We demonstrate slow-down of a modulated light signal in a semiconductor waveguide. Concatenated amplifying and absorbing sections simultaneously achieve both amplification and a controllable time delay at 15 GHz....

The construction of a two-dimensional reproducing kernel function and its application in a biomedical model.

Science.gov (United States)

Guo, Qi; Shen, Shu-Ting

2016-04-29

There are two major classes of cardiac tissue models: the ionic model and the FitzHugh-Nagumo model. During computer simulation, each model entails solving a system of complex ordinary differential equations and a partial differential equation with non-flux boundary conditions. The reproducing kernel method possesses significant applications in solving partial differential equations. The derivative of the reproducing kernel function is a wavelet function, which has local properties and sensitivities to singularity. Therefore, study on the application of reproducing kernel would be advantageous. Applying new mathematical theory to the numerical solution of the ventricular muscle model so as to improve its precision in comparison with other methods at present. A two-dimensional reproducing kernel function inspace is constructed and applied in computing the solution of two-dimensional cardiac tissue model by means of the difference method through time and the reproducing kernel method through space. Compared with other methods, this method holds several advantages such as high accuracy in computing solutions, insensitivity to different time steps and a slow propagation speed of error. It is suitable for disorderly scattered node systems without meshing, and can arbitrarily change the location and density of the solution on different time layers. The reproducing kernel method has higher solution accuracy and stability in the solutions of the two-dimensional cardiac tissue model.

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

Directory of Open Access Journals (Sweden)

Hongzhi Wang

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

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

Science.gov (United States)

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

2016-01-01

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

Oligotrophication and Metabolic Slowing-Down of a NW Mediterranean Coastal Ecosystem

Directory of Open Access Journals (Sweden)

Susana Agusti

2017-12-01

Full Text Available Increased oligotrophication is expected for oligotrophic areas as a consequence of ocean warming, which reduces diffusive vertical nutrient supply due to strengthened stratification. Evidence of ocean oligotrophication has been, thus far, reported for the open ocean. Here we reported oligotrophication and associated changes in plankton community metabolism with warming in a pristine, oligotrophic Mediterranean coastal area (Cap Salines, Mallorca Island, Spain during a 10 years time series. As a temperate area, there were seasonal patterns associated to changes in the broad temperature range (12.0–28.4°C, with a primary phytoplankton bloom in late winter and a secondary one in the fall. Community respiration (R rates peaked during summers and showed higher rates relative to gross primary production (GPP with a prevalence of heterotrophic metabolism (2/3's of net community production (NCP estimates. Chlorophyll a concentration significantly decreased with increasing water temperature in the coastal site at a rate of 0.014 ± 0.003 μg Chla L−1 °C−1 (P < 0.0001. The study revealed a significant decrease with time in Chlorophyll a concentration and nutrients concentration, indicating oligotrophication during the last decade. Community productivity consistently decreased with time as both GPP and R showed a significant decline. Warming of the Mediterranean Sea is expected to increase plankton metabolic rates, but the results indicated that the associated oligotrophication must lead to a slowing down of the community metabolism.

Oligotrophication and Metabolic Slowing-Down of a NW Mediterranean Coastal Ecosystem

KAUST Repository

Agusti, Susana

2017-12-22

Increased oligotrophication is expected for oligotrophic areas as a consequence of ocean warming, which reduces diffusive vertical nutrient supply due to strengthened stratification. Evidence of ocean oligotrophication has been, thus far, reported for the open ocean. Here we reported oligotrophication and associated changes in plankton community metabolism with warming in a pristine, oligotrophic Mediterranean coastal area (Cap Salines, Mallorca Island, Spain) during a 10 years time series. As a temperate area, there were seasonal patterns associated to changes in the broad temperature range (12.0–28.4°C), with a primary phytoplankton bloom in late winter and a secondary one in the fall. Community respiration (R) rates peaked during summers and showed higher rates relative to gross primary production (GPP) with a prevalence of heterotrophic metabolism (2/3\\'s of net community production (NCP) estimates). Chlorophyll a concentration significantly decreased with increasing water temperature in the coastal site at a rate of 0.014 ± 0.003 μg Chla L−1 °C−1 (P < 0.0001). The study revealed a significant decrease with time in Chlorophyll a concentration and nutrients concentration, indicating oligotrophication during the last decade. Community productivity consistently decreased with time as both GPP and R showed a significant decline. Warming of the Mediterranean Sea is expected to increase plankton metabolic rates, but the results indicated that the associated oligotrophication must lead to a slowing down of the community metabolism.

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

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] ...

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...

Improved age-diffusion model for low-energy electron transport in solids. I. Theory

International Nuclear Information System (INIS)

Devooght, J.; Dubus, A.; Dehaes, J.C.

1987-01-01

We have developed in this paper a semianalytical electron transport model designed for parametric studies of secondary-electron emission induced by low-energy electrons (keV range) and by fast light ions (100 keV range). The primary-particle transport is assumed to be known and to give rise to an internal electron source. The importance of the nearly isotropic elastic scattering in the secondary-electron energy range (50 eV) and the slowing-down process strongly reduce the influence of the anisotropy of the internal electron source, and the internal electron flux is nearly isotropic as is evidenced by the experimental results. The differential energy behavior of the inelastic scattering kernel is very complicated and the real kernel is replaced by a synthetic scattering kernel of which parameters are obtained by energy and angle moments conservation. Through a P 1 approximation and the use of the synthetic scattering kernel, the Boltzmann equation is approximated by a diffusion--slowing-down equation for the isotropic part of the internal electron flux. The energy-dependent partial reflection boundary condition reduces to a Neumann-Dirichlet boundary condition. An analytical expression for the Green's function of the diffusion--slowing-down equation with the surface boundary condition is obtained by means of approximations close to the age-diffusion theory and the model allows for transient conditions. Independently from the ''improved age-diffusion'' model, a correction formula is developed in order to take into account the backscattering of primary electrons for an incident-electron problem

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

OpenAIRE

Sitompul, Monica Angelina

2015-01-01

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

7 CFR 981.8 - Inedible kernel.

Science.gov (United States)

2010-01-01

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

7 CFR 981.408 - Inedible kernel.

Science.gov (United States)

2010-01-01

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

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...

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.

Convergence of high order memory kernels in the Nakajima-Zwanzig generalized master equation and rate constants: Case study of the spin-boson model

Science.gov (United States)

Xu, Meng; Yan, Yaming; Liu, Yanying; Shi, Qiang

2018-04-01

The Nakajima-Zwanzig generalized master equation provides a formally exact framework to simulate quantum dynamics in condensed phases. Yet, the exact memory kernel is hard to obtain and calculations based on perturbative expansions are often employed. By using the spin-boson model as an example, we assess the convergence of high order memory kernels in the Nakajima-Zwanzig generalized master equation. The exact memory kernels are calculated by combining the hierarchical equation of motion approach and the Dyson expansion of the exact memory kernel. High order expansions of the memory kernels are obtained by extending our previous work to calculate perturbative expansions of open system quantum dynamics [M. Xu et al., J. Chem. Phys. 146, 064102 (2017)]. It is found that the high order expansions do not necessarily converge in certain parameter regimes where the exact kernel show a long memory time, especially in cases of slow bath, weak system-bath coupling, and low temperature. Effectiveness of the Padé and Landau-Zener resummation approaches is tested, and the convergence of higher order rate constants beyond Fermi's golden rule is investigated.

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

Therapeutic dosages of aspirin counteract the IL-6 induced pro-tumorigenic effects by slowing down the ribosome biogenesis rate.

Science.gov (United States)

Brighenti, Elisa; Giannone, Ferdinando Antonino; Fornari, Francesca; Onofrillo, Carmine; Govoni, Marzia; Montanaro, Lorenzo; Treré, Davide; Derenzini, Massimo

2016-09-27

Chronic inflammation is a risk factor for the onset of cancer and the regular use of aspirin reduces the risk of cancer development. Here we showed that therapeutic dosages of aspirin counteract the pro-tumorigenic effects of the inflammatory cytokine interleukin(IL)-6 in cancer and non-cancer cell lines, and in mouse liver in vivo. We found that therapeutic dosages of aspirin prevented IL-6 from inducing the down-regulation of p53 expression and the acquisition of the epithelial mesenchymal transition (EMT) phenotypic changes in the cell lines. This was the result of a reduction in c-Myc mRNA transcription which was responsible for a down-regulation of the ribosomal protein S6 expression which, in turn, slowed down the rRNA maturation process, thus reducing the ribosome biogenesis rate. The perturbation of ribosome biogenesis hindered the Mdm2-mediated proteasomal degradation of p53, throughout the ribosomal protein-Mdm2-p53 pathway. P53 stabilization hindered the IL-6 induction of the EMT changes. The same effects were observed in livers from mice stimulated with IL-6 and treated with aspirin. It is worth noting that aspirin down-regulated ribosome biogenesis, stabilized p53 and up-regulated E-cadherin expression in unstimulated control cells also. In conclusion, these data showed that therapeutic dosages of aspirin increase the p53-mediated tumor-suppressor activity of the cells thus being in this way able to reduce the risk of cancer onset, either or not linked to chronic inflammatory processes.

Therapeutic dosages of aspirin counteract the IL-6 induced pro-tumorigenic effects by slowing down the ribosome biogenesis rate

Science.gov (United States)

Brighenti, Elisa; Giannone, Ferdinando Antonino; Fornari, Francesca; Onofrillo, Carmine; Govoni, Marzia; Montanaro, Lorenzo; Treré, Davide; Derenzini, Massimo

2016-01-01

Chronic inflammation is a risk factor for the onset of cancer and the regular use of aspirin reduces the risk of cancer development. Here we showed that therapeutic dosages of aspirin counteract the pro-tumorigenic effects of the inflammatory cytokine interleukin(IL)-6 in cancer and non-cancer cell lines, and in mouse liver in vivo. We found that therapeutic dosages of aspirin prevented IL-6 from inducing the down-regulation of p53 expression and the acquisition of the epithelial mesenchymal transition (EMT) phenotypic changes in the cell lines. This was the result of a reduction in c-Myc mRNA transcription which was responsible for a down-regulation of the ribosomal protein S6 expression which, in turn, slowed down the rRNA maturation process, thus reducing the ribosome biogenesis rate. The perturbation of ribosome biogenesis hindered the Mdm2-mediated proteasomal degradation of p53, throughout the ribosomal protein-Mdm2-p53 pathway. P53 stabilization hindered the IL-6 induction of the EMT changes. The same effects were observed in livers from mice stimulated with IL-6 and treated with aspirin. It is worth noting that aspirin down-regulated ribosome biogenesis, stabilized p53 and up-regulated E-cadherin expression in unstimulated control cells also. In conclusion, these data showed that therapeutic dosages of aspirin increase the p53-mediated tumor-suppressor activity of the cells thus being in this way able to reduce the risk of cancer onset, either or not linked to chronic inflammatory processes. PMID:27557515

Aligning Biomolecular Networks Using Modular Graph Kernels

Science.gov (United States)

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

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

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

Slowing down and stretching DNA with an electrically tunable nanopore in a p–n semiconductor membrane

International Nuclear Information System (INIS)

Melnikov, Dmitriy V; Gracheva, Maria E; Leburton, Jean-Pierre

2012-01-01

We have studied single-stranded DNA translocation through a semiconductor membrane consisting of doped p and n layers of Si forming a p–n-junction. Using Brownian dynamics simulations of the biomolecule in the self-consistent membrane–electrolyte potential obtained from the Poisson–Nernst–Planck model, we show that while polymer length is extended more than when its motion is constricted only by the physical confinement of the nanopore. The biomolecule elongation is particularly dramatic on the n-side of the membrane where the lateral membrane electric field restricts (focuses) the biomolecule motion more than on the p-side. The latter effect makes our membrane a solid-state analog of the α-hemolysin biochannel. The results indicate that the tunable local electric field inside the membrane can effectively control dynamics of a DNA in the channel to either momentarily trap, slow down or allow the biomolecule to translocate at will. (paper)

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...

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. ...

Assaying Used Nuclear Fuel Assemblies Using Lead Slowing-Down Spectroscopy and Singular Value Decomposition

International Nuclear Information System (INIS)

Kulisek, Jonathan A.; Anderson, Kevin K.; Casella, Andrew M.; Gesh, Christopher J.; Warren, Glen A.

2013-01-01

This study investigates the use of a Lead Slowing-Down Spectrometer (LSDS) for the direct and independent measurement of fissile isotopes in light-water nuclear reactor fuel assemblies. The current study applies MCNPX, a Monte Carlo radiation transport code, to simulate the measurement of the assay of the used nuclear fuel assemblies in the LSDS. An empirical model has been developed based on the calibration of the LSDS to responses generated from the simulated assay of six well-characterized fuel assemblies. The effects of self-shielding are taken into account by using empirical basis vectors calculated from the singular value decomposition (SVD) of a matrix containing the self-shielding functions from the assay of assemblies in the calibration set. The performance of the empirical algorithm was tested on version 1 of the Next-Generation Safeguards Initiative (NGSI) used fuel library consisting of 64 assemblies, as well as a set of 27 diversion assemblies, both of which were developed by Los Alamos National Laboratory. The potential for direct and independent assay of the sum of the masses of Pu-239 and Pu-241 to within 2%, on average, has been demonstrated

An Approximate Approach to Automatic Kernel Selection.

Science.gov (United States)

Ding, Lizhong; Liao, Shizhong

2016-02-02

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

Iterative software kernels

Energy Technology Data Exchange (ETDEWEB)

Duff, I.

1994-12-31

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

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

Science.gov (United States)

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

2018-05-01

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

A kernel version of spatial factor analysis

DEFF Research Database (Denmark)

Nielsen, Allan Aasbjerg

2009-01-01

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

Nuclear lifetimes and the slowing down of heavy ions in solids

International Nuclear Information System (INIS)

Scherpenzeel, D.E.C.

1981-01-01

Nuclear lifetime measurements by means of the Doppler Shift Attenuation (DSA) method at low recoil velocities (β approximately less than 0.01) are notoriously difficult due to the observed strong dependence of the extracted lifetimes on the slowing-down material at low initial velocities. This is mainly caused by the lack of reliable stopping power data for these velocities and the absence of an adequate theory to compensate for that. This problem of the determination of the correct mean life for the lowest Jsup(π) = 4 + state of 22 Ne is solved by measurements with the coincident high-velocity DSA method. Excited nuclei of high initial velocity [β(0) approximately 0.05] are generated by the bombardment of light targets, such as 1 H, 2 H, 3 H and 4 He, with beams of heavy ions. The combination of high initial velocity and coincidence restriction offers many advantages over the conventional techniques. The coincident high-velocity DSA method is also used to determine mean lives of low-lying excited states of the silicon isotopes 28 29 30 Si. The observed Doppler patterns are analyzed with experimental stopping powers and the resulting mean lives range from about 25 fs to 4 ps. The mean lives of the first excited state of 18 O and some low-lying levels of 35 S are determined from Doppler patterns analyzed with experimental stopping powers. The present stopping results for O, Si and S ions in Mg are also analyzed in terms of the effective charge concept. It is concluded that at the present level of accuracy of about 5 % the obtained results are consistent with this concept. (Auth.)

Slowing down of test particles in a plasma (1961); Ralentissement de particules test dans un plasma (1961)

Energy Technology Data Exchange (ETDEWEB)

Belayche, P; Chavy, P; Dupoux, M; Salmon, J [Commissariat a l' Energie Atomique, Saclay (France). Centre d' Etudes Nucleaires

1961-07-01

Numerical solution of the Fokker-Planck equation applied to the slowing down of tritons in a deuterium plasma. After the equations and the boundary conditions have been written, some attention is paid to the numerical tricks used to run the problem on a high speed electronic computer. The numerical results thus obtained are then analyzed and as far as possible, mathematically explained. (authors) [French] Resolution numerique de l'equation de Fokker-Planck appliquee au ralentissement de tritons dans un plasma de deuterium. Apres avoir rappele les equations, les conditions aux limites, l'accent est mis sur les artifices numeriques utilises pour traiter le probleme sur une calculatrice a grande vitesse. Les resultats numeriques obtenus sont ensuite analyses et si possible expliques mathematiquement. En particulier ils peuvent se rattacher a ceux obtenus par application directe de la formule de Spitzer. (auteurs)

Slow Images and Entangled Photons

International Nuclear Information System (INIS)

Swordy, Simon

2007-01-01

I will discuss some recent experiments using slow light and entangled photons. We recently showed that it was possible to map a two dimensional image onto very low light level signals, slow them down in a hot atomic vapor while preserving the amplitude and phase of the images. If time remains, I will discuss some of our recent work with time-energy entangled photons for quantum cryptography. We were able to show that we could have a measurable state space of over 1000 states for a single pair of entangled photons in fiber.

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...

Influence of many-particle interactions on slow light phenomena in quantum dots

DEFF Research Database (Denmark)

Houmark-Nielsen, Jakob; Jauho, Antti-Pekka; Nielsen, Torben Roland

2008-01-01

We investigate the impact of many-particle interactions on group-velocity slowdown achieved via Electromagnetically Induced Transparency (EIT) in quantum dots. Using a ladder scheme we find in the steady-state an increase in maximum slow-down as compared to the non-interacting case, which can...... be attributed to Coulomb interaction effects. The necessary pump power at which maximum slow down is obtained EIT remains, however....

Measurement and Analysis Plan for Investigation of Spent-Fuel Assay Using Lead Slowing-Down Spectroscopy

International Nuclear Information System (INIS)

Smith, Leon E.; Haas, Derek A.; Gavron, Victor A.; Imel, G.R.; Ressler, Jennifer J.; Bowyer, Sonya M.; Danon, Y.; Beller, D.

2009-01-01

Under funding from the Department of Energy Office of Nuclear Energy's Materials, Protection, Accounting, and Control for Transmutation (MPACT) program (formerly the Advanced Fuel Cycle Initiative Safeguards Campaign), Pacific Northwest National Laboratory (PNNL) and Los Alamos National Laboratory (LANL) are collaborating to study the viability of lead slowing-down spectroscopy (LSDS) for spent-fuel assay. Based on the results of previous simulation studies conducted by PNNL and LANL to estimate potential LSDS performance, a more comprehensive study of LSDS viability has been defined. That study includes benchmarking measurements, development and testing of key enabling instrumentation, and continued study of time-spectra analysis methods. This report satisfies the requirements for a PNNL/LANL deliverable that describes the objectives, plans and contributing organizations for a comprehensive three-year study of LSDS for spent-fuel assay. This deliverable was generated largely during the LSDS workshop held on August 25-26, 2009 at Rensselaer Polytechnic Institute (RPI). The workshop itself was a prominent milestone in the FY09 MPACT project and is also described within this report.

Timing of the Crab pulsar III. The slowing down and the nature of the random process

International Nuclear Information System (INIS)

Groth, E.J.

1975-01-01

The Crab pulsar arrival times are analyzed. The data are found to be consistent with a smooth slowing down with a braking index of 2.515+-0.005. Superposed on the smooth slowdown is a random process which has the same second moments as a random walk in the frequency. The strength of the random process is R 2 >=0.53 (+0.24, -0.12) x10 -22 Hz 2 s -1 , where R is the mean rate of steps and 2 > is the second moment of the step amplitude distribution. Neither the braking index nor the strength of the random process shows evidence of statistically significant time variations, although small fluctuations in the braking index and rather large fluctuations in the noise strength cannot be ruled out. There is a possibility that the random process contains a small component with the same second moments as a random walk in the phase. If so, a time scale of 3.5 days is indicated

FOXO/DAF-16 Activation Slows Down Turnover of the Majority of Proteins in C. elegans

Directory of Open Access Journals (Sweden)

Ineke Dhondt

2016-09-01

Full Text Available Most aging hypotheses assume the accumulation of damage, resulting in gradual physiological decline and, ultimately, death. Avoiding protein damage accumulation by enhanced turnover should slow down the aging process and extend the lifespan. However, lowering translational efficiency extends rather than shortens the lifespan in C. elegans. We studied turnover of individual proteins in the long-lived daf-2 mutant by combining SILeNCe (stable isotope labeling by nitrogen in Caenorhabditis elegans and mass spectrometry. Intriguingly, the majority of proteins displayed prolonged half-lives in daf-2, whereas others remained unchanged, signifying that longevity is not supported by high protein turnover. This slowdown was most prominent for translation-related and mitochondrial proteins. In contrast, the high turnover of lysosomal hydrolases and very low turnover of cytoskeletal proteins remained largely unchanged. The slowdown of protein dynamics and decreased abundance of the translational machinery may point to the importance of anabolic attenuation in lifespan extension, as suggested by the hyperfunction theory.

The Fourier transform method for infinite medium resonance absorption problems

International Nuclear Information System (INIS)

Menon, S.V.G.; Sahni, D.C.

1978-01-01

A new method, using Fourier transforms, is developed for solving the integral equation of slowing down of neutrons in the resonance region. The transformations replace the slowing down equation with a discontinuous kernel by an integral equation with a continuous kernel over the interval (-infinity, infinity). Further the Doppler broadened line shape functions have simple analytical representations in the transform variable. In the limit of zero temperature, the integral equation reduces to a second order differential equation. Accurate expressions for the zero temperature resonance integrals are derived, using the WKB method. In general, the integral equation is seen to be amenable to solution by Ganss-Hermite quadrature formule. Doppler coefficients of 238 U resonances are given and compared with Monte Carlo calculations. The method is extended to include the effect of interference between neighbouring resonances of an absorber. For the case of two interfering resonances the slowing down equation is transformed to the coupled integral equations that are amenable to solution by methods indicated earlier. Numerical results presented for the low lying thorium-232 doublet show that the Doppler coefficients of the resonances are reduced considerably because of the overlap between them. (author)

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.

Status on Establishing the Feasibility of Lead Slowing Down Spectroscopy for Direct Measurement of Plutonium in Used Fuel

Energy Technology Data Exchange (ETDEWEB)

Kulisek, Jonathan A.; Anderson, Kevin K.; Casella, Andrew M.; Gesh, Christopher J.; Warren, Glen A.; Gavron, Victor A.; Devlin, M.; Haight, R. C.; O' Donnell, J. M.; Danon, Yaron; Weltz, Adam; Bonebrake, Eric; Imel, G. R.; Harris, Jason; Beller, Dennis; Hatchett, D.; Droessler, J.

2012-08-30

Developing a method for the accurate, direct, and independent assay of the fissile isotopes in bulk materials (such as used fuel) from next-generation domestic nuclear fuel cycles is a goal of the Office of Nuclear Energy, Fuel Cycle R&D, Material Protection and Control Technology (MPACT) Campaign. To meet this goal, MPACT supports a multi-institutional collaboration to study the feasibility of Lead Slowing Down Spectroscopy. This technique is an active nondestructive assay method that has the potential to provide independent, direct measurement of Pu and U isotopic masses in used fuel with an uncertainty considerably lower than the approximately 10% typical of today’s confirmatory assay methods. This paper will present efforts on the development of time-spectral analysis algorithms, fast neutron detector advances, and validation and testing measurements.

Comparison Algorithm Kernels on Support Vector Machine (SVM To Compare The Trend Curves with Curves Online Forex Trading

Directory of Open Access Journals (Sweden)

irfan abbas

2017-01-01

Full Text Available At this time, the players Forex Trading generally still use the data exchange in the form of a Forex Trading figures from different sources. Thus they only receive or know the data rate of a Forex Trading prevailing at the time just so difficult to analyze or predict exchange rate movements future. Forex players usually use the indicators to enable them to analyze and memperdiksi future value. Indicator is a decision making tool. Trading forex is trading currency of a country, the other country's currency. Trading took place globally between the financial centers of the world with the involvement of the world's major banks as the major transaction. Trading Forex offers profitable investment type with a small capital and high profit, with relatively small capital can earn profits doubled. This is due to the forex trading systems exist leverage which the invested capital will be doubled if the predicted results of buy / sell is accurate, but Trading Forex having high risk level, but by knowing the right time to trade (buy or sell, the losses can be avoided. Traders who invest in the foreign exchange market is expected to have the ability to analyze the circumstances and situations in predicting the difference in currency exchange rates. Forex price movements that form the pattern (curve up and down greatly assist traders in making decisions. The movement of the curve used as an indicator in the decision to purchase (buy or sell (sell. This study compares (Comparation type algorithm kernel on Support Vector Machine (SVM to predict the movement of the curve in live time trading forex using the data GBPUSD, 1H. Results of research on the study of the results and discussion can be concluded that the Kernel Dot, Kernel Multiquaric, Kernel Neural inappropriately used for data is non-linear in the case of data forex to follow the pattern of trend curves, because curves generated curved linear (straight and then to type of kernel is the closest curve

Designing an Educational Application of Parental-Mediated Intervention and Its Effectiveness to Promote Reading Skills Among Slow-Paced Students with Down Syndrome

Directory of Open Access Journals (Sweden)

Kosar Bereyhi

2017-02-01

Full Text Available Objectives This study aimed to design an educational application of parental-mediated intervention and its effectiveness to promote reading skills in students with Down syndrome. Methods This applied semi-experimental study is a pre-test- and post-test project, follow-up with the test and control groups which was conducted on twenty slow-paced students with Down syndrome in the range of 5 to 12 years old. Patients were randomly selected and classify into two groups; test and control. Wechsler IQ test, TOLD test and peabody picture vocabulary test (PPVT were performed for students in the pre-test however; TOLD test was conducted as the post-test and a half month at 15-day after follow-up stage. Results results showed α > 0.001 for reading skills between test and control groups; however the difference is remained sustainable in follow-up stage. Conclusions Education with new educational technologies that focused on software may be helpful for children with Down syndrome and should be seriously considered. Family- centered parental-mediated intervention in order to promote reading skills application can be used for teaching children, families and educators.

Characterization and fine mapping of qkc7.03: a major locus for kernel cracking in maize.

Science.gov (United States)

Yang, Mingtao; Chen, Lin; Wu, Xun; Gao, Xing; Li, Chunhui; Song, Yanchun; Zhang, Dengfeng; Shi, Yunsu; Li, Yu; Li, Yong-Xiang; Wang, Tianyu

2018-02-01

A major locus conferring kernel cracking in maize was characterized and fine mapped to an interval of 416.27 kb. Meanwhile, combining the results of transcriptomic analysis, the candidate gene was inferred. Seed development requires a proper structural and physiological balance between the maternal tissues and the internal structures of the seeds. In maize, kernel cracking is a disorder in this balance that seriously limits quality and yield and is characterized by a cracked pericarp at the kernel top and endosperm everting. This study elucidated the genetic basis and characterization of kernel cracking. Primarily, a near isogenic line (NIL) with a B73 background exhibited steady kernel cracking across environments. Therefore, deprived mapping populations were developed from this NIL and its recurrent parent B73. A major locus on chromosome 7, qkc7.03, was identified to be associated with the cracking performance. According to a progeny test of recombination events, qkc7.03 was fine mapped to a physical interval of 416.27 kb. In addition, obvious differences were observed in embryo development and starch granule arrangement within the endosperm between the NIL and its recurrent parent upon the occurrence of kernel cracking. Moreover, compared to its recurrent parent, the transcriptome of the NIL showed a significantly down-regulated expression of genes related to zeins, carbohydrate synthesis and MADS-domain transcription factors. The transcriptomic analysis revealed ten annotated genes within the target region of qkc7.03, and only GRMZM5G899476 was differently expressed between the NIL and its recurrent parent, indicating that this gene might be a candidate gene for kernel cracking. The results of this study facilitate the understanding of the potential mechanism underlying kernel cracking in maize.

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....

MicroRNA-124 slows down the progression of Huntington′s disease by promoting neurogenesis in the striatum

Directory of Open Access Journals (Sweden)

Tian Liu

2015-01-01

Full Text Available MicroRNA-124 contributes to neurogenesis through regulating its targets, but its expression both in the brain of Huntington′s disease mouse models and patients is decreased. However, the effects of microRNA-124 on the progression of Huntington′s disease have not been reported. Results from this study showed that microRNA-124 increased the latency to fall for each R6/2 Huntington′s disease transgenic mouse in the rotarod test. 5-Bromo-2′-deoxyuridine (BrdU staining of the striatum shows an increase in neurogenesis. In addition, brain-derived neurotrophic factor and peroxisome proliferator-activated receptor gamma coactivator 1-alpha protein levels in the striatum were increased and SRY-related HMG box transcription factor 9 protein level was decreased. These findings suggest that microRNA-124 slows down the progression of Huntington′s disease possibly through its important role in neuronal differentiation and survival.

Spectrum Evolution of Accelerating or Slowing down Soliton at its Propagation in a Medium with Gold Nanorods

Science.gov (United States)

Trofimov, Vyacheslav A.; Lysak, Tatiana M.

2018-04-01

We investigate both numerically and analytically the spectrum evolution of a novel type soliton - nonlinear chirped accelerating or decelerating soliton - at a femtosecond pulse propagation in a medium containing noble nanoparticles. In our consideration, we take into account one- or two-photon absorption of laser radiation by nanorods, and time-dependent nanorod aspect ratio changing due to their melting or reshaping because of laser energy absorption. The chirped solitons are formed due to the trapping of laser radiation by the nanorods reshaping fronts, if a positive or negative phase-amplitude grating is induced by laser radiation. Accelerating or slowing down chirped soliton formation is accompanied by the soliton spectrum blue or red shift. To prove our numerical results, we derived the approximate analytical law for the spectrum maximum intensity evolution along the propagation coordinate, based on earlier developed approximate analytical solutions for accelerating and decelerating solitons.

Analyzing kernel matrices for the identification of differentially expressed genes.

Directory of Open Access Journals (Sweden)

Xiao-Lei Xia

Full Text Available One of the most important applications of microarray data is the class prediction of biological samples. For this purpose, statistical tests have often been applied to identify the differentially expressed genes (DEGs, followed by the employment of the state-of-the-art learning machines including the Support Vector Machines (SVM in particular. The SVM is a typical sample-based classifier whose performance comes down to how discriminant samples are. However, DEGs identified by statistical tests are not guaranteed to result in a training dataset composed of discriminant samples. To tackle this problem, a novel gene ranking method namely the Kernel Matrix Gene Selection (KMGS is proposed. The rationale of the method, which roots in the fundamental ideas of the SVM algorithm, is described. The notion of ''the separability of a sample'' which is estimated by performing [Formula: see text]-like statistics on each column of the kernel matrix, is first introduced. The separability of a classification problem is then measured, from which the significance of a specific gene is deduced. Also described is a method of Kernel Matrix Sequential Forward Selection (KMSFS which shares the KMGS method's essential ideas but proceeds in a greedy manner. On three public microarray datasets, our proposed algorithms achieved noticeably competitive performance in terms of the B.632+ error rate.

Confinement of ripple-trapped slowing-down ions by a radial electric field

International Nuclear Information System (INIS)

Herrmann, W.

1998-03-01

Weakly collisional ions trapped in the toroidal field ripples at the outer plasma edge can be prevented to escape the plasma due to grad B-drift by a counteracting radial electric field. This leads to an increase in the density of ripple-trapped ions, which can be monitored by the analysis of charge exchange neutrals. The minimum radial electric field E r necessary to confine ions with energy E and charge q (q=-1: charge of the electron) is E r = -E/(q * R), where R is the major radius at the measuring point. Slowing-down ions from neutral injection are usually in the right energy range to be sufficiently collisionless in the plasma edge and show the confinement by radial electric fields in the range of tens of kV/m. The density of banana ions is almost unaffected by the radial electric field. Neither in L/H- nor in H/L-transitions does the density of ripple-trapped ions and, hence, the neutral particle fluxes, show jumps in times shorter than 1 ms. According to [1,2] the response time of the density and the fluxes to a sudden jump in the radial electric field is less than 200 μs, if the halfwidth of the electric field is larger or about 2 cm. This would exclude rapid jumps in the radial electric field at the transition. Whether the halfwidth of the electric field is that large during transition cannot be decided from the measurement of the fluxes alone. (orig.)

Multiple Kernel Learning with Data Augmentation

Science.gov (United States)

2016-11-22

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

OS X and iOS Kernel Programming

CERN Document Server

Halvorsen, Ole Henry

2011-01-01

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

Model selection for Gaussian kernel PCA denoising

DEFF Research Database (Denmark)

Jørgensen, Kasper Winther; Hansen, Lars Kai

2012-01-01

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

Paramecium: An Extensible Object-Based Kernel

NARCIS (Netherlands)

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

1995-01-01

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

Theory of reproducing kernels and applications

CERN Document Server

Saitoh, Saburou

2016-01-01

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

Loan Growth Slowing down in 1st 7 Months

Institute of Scientific and Technical Information of China (English)

无

2004-01-01

@@ The growth of loans significantly slowed during the first seven months of this year, which indicates the government's economic macro control measures are taking effect, the People's Bank of China (PBOC) said. By the end of July, outstanding loans of all financial institutions stood at RMB18.1 trillion (US$2.18 trillion),up 15.9% on a year-on-year basis. The growth rate compared to 23.2% registered during the same period in 2003,the central bank said.

Kernels for structured data

CERN Document Server

Gärtner, Thomas

2009-01-01

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

7 CFR 981.401 - Adjusted kernel weight.

Science.gov (United States)

2010-01-01

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

Testing Infrastructure for Operating System Kernel Development

DEFF Research Database (Denmark)

Walter, Maxwell; Karlsson, Sven

2014-01-01

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

7 CFR 51.1403 - Kernel color classification.

Science.gov (United States)

2010-01-01

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

FOXO/DAF-16 Activation Slows Down Turnover of the Majority of Proteins in C. elegans.

Science.gov (United States)

Dhondt, Ineke; Petyuk, Vladislav A; Cai, Huaihan; Vandemeulebroucke, Lieselot; Vierstraete, Andy; Smith, Richard D; Depuydt, Geert; Braeckman, Bart P

2016-09-13

Most aging hypotheses assume the accumulation of damage, resulting in gradual physiological decline and, ultimately, death. Avoiding protein damage accumulation by enhanced turnover should slow down the aging process and extend the lifespan. However, lowering translational efficiency extends rather than shortens the lifespan in C. elegans. We studied turnover of individual proteins in the long-lived daf-2 mutant by combining SILeNCe (stable isotope labeling by nitrogen in Caenorhabditiselegans) and mass spectrometry. Intriguingly, the majority of proteins displayed prolonged half-lives in daf-2, whereas others remained unchanged, signifying that longevity is not supported by high protein turnover. This slowdown was most prominent for translation-related and mitochondrial proteins. In contrast, the high turnover of lysosomal hydrolases and very low turnover of cytoskeletal proteins remained largely unchanged. The slowdown of protein dynamics and decreased abundance of the translational machinery may point to the importance of anabolic attenuation in lifespan extension, as suggested by the hyperfunction theory. Copyright © 2016 The Author(s). Published by Elsevier Inc. All rights reserved.

Potential energy landscape signatures of slow dynamics in glass forming liquids

DEFF Research Database (Denmark)

Sastry, S.; Debenedetti, P. G.; Stillinger, F. H.

1999-01-01

We study the properties of local potential energy minima (‘inherent structures’) sampled by liquids at low temperatures as an approach to elucidating the mechanisms of the observed dynamical slowing down observed as the glass transition temperature is approached. This onset of slow dynamics...

PENGARUH PERLAKUAN PENDAHULUAN SEBELUM PENGERINGAN TERHADAP INSTAN JAGUNG MUDA [Effects of Pre-treatments Prior Drying on Young Corn Kernel Instant (YCKI

Directory of Open Access Journals (Sweden)

Marleni Limonu1

2008-12-01

Full Text Available The objective of this research was to study the effects of pre-gelatinization and freezing processes on physico-chemical characteristics of young corn kernel instant. The results showed that pre-gelatinization and slow freezing processes significantly affected bulk density, rehidration capacity, hardness and cooking time of young corn kernel instant. The study of water sorption isothermic showed that the product had a sigmoid curve. Based on this curve, shelf life of the product had been calculated. The YCKI waxy, YCKI Flint, and YCKI Sweet products packed in alufo had shelf life of 7.2, 12.1 and 13.8 months respectively.

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.

How Accurately Can We Calculate Neutrons Slowing Down In Water ?

International Nuclear Information System (INIS)

Cullen, D E; Blomquist, R; Greene, M; Lent, E; MacFarlane, R; McKinley, S; Plechaty, E; Sublet, J C

2006-01-01

We have compared the results produced by a variety of currently available Monte Carlo neutron transport codes for the relatively simple problem of a fast source of neutrons slowing down and thermalizing in water. Initial comparisons showed rather large differences in the calculated flux; up to 80% differences. By working together we iterated to improve the results by: (1) insuring that all codes were using the same data, (2) improving the models used by the codes, and (3) correcting errors in the codes; no code is perfect. Even after a number of iterations we still found differences, demonstrating that our Monte Carlo and supporting codes are far from perfect; in particularly we found that the often overlooked nuclear data processing codes can be the weakest link in our systems of codes. The results presented here represent the today's state-of-the-art, in the sense that all of the Monte Carlo codes are modern, widely available and used codes. They all use the most up-to-date nuclear data, and the results are very recent, weeks or at most a few months old; these are the results that current users of these codes should expect to obtain from them. As such, the accuracy and limitations of the codes presented here should serve as guidelines to code users in interpreting their results for similar problems. We avoid crystal ball gazing, in the sense that we limit the scope of this report to what is available to code users today, and we avoid predicting future improvements that may or may not actual come to pass. An exception that we make is in presenting results for an improved thermal scattering model currently being testing using advanced versions of NJOY and MCNP that are not currently available to users, but are planned for release in the not too distant future. The other exception is to show comparisons between experimentally measured water cross sections and preliminary ENDF/B-VII thermal scattering law, S(α,β) data; although these data are strictly preliminary

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...

Ranking Support Vector Machine with Kernel Approximation.

Science.gov (United States)

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

2017-01-01

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

Ranking Support Vector Machine with Kernel Approximation

Directory of Open Access Journals (Sweden)

Kai Chen

2017-01-01

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

Dose point kernels for beta-emitting radioisotopes

International Nuclear Information System (INIS)

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

1986-01-01

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

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

Science.gov (United States)

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

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

Wigner functions defined with Laplace transform kernels.

Science.gov (United States)

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

2011-10-24

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

Metabolic network prediction through pairwise rational kernels.

Science.gov (United States)

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

2014-09-26

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

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

Fast and slow spindles during the sleep slow oscillation: disparate coalescence and engagement in memory processing.

Science.gov (United States)

Mölle, Matthias; Bergmann, Til O; Marshall, Lisa; Born, Jan

2011-10-01

Thalamo-cortical spindles driven by the up-state of neocortical slow (memory consolidation during sleep. We examined interactions between SOs and spindles in human slow wave sleep, focusing on the presumed existence of 2 kinds of spindles, i.e., slow frontocortical and fast centro-parietal spindles. Two experiments were performed in healthy humans (24.5 ± 0.9 y) investigating undisturbed sleep (Experiment I) and the effects of prior learning (word paired associates) vs. non-learning (Experiment II) on multichannel EEG recordings during sleep. Only fast spindles (12-15 Hz) were synchronized to the depolarizing SO up-state. Slow spindles (9-12 Hz) occurred preferentially at the transition into the SO down-state, i.e., during waning depolarization. Slow spindles also revealed a higher probability to follow rather than precede fast spindles. For sequences of individual SOs, fast spindle activity was largest for "initial" SOs, whereas SO amplitude and slow spindle activity were largest for succeeding SOs. Prior learning enhanced this pattern. The finding that fast and slow spindles occur at different times of the SO cycle points to disparate generating mechanisms for the 2 kinds of spindles. The reported temporal relationships during SO sequences suggest that fast spindles, driven by the SO up-state feed back to enhance the likelihood of succeeding SOs together with slow spindles. By enforcing such SO-spindle cycles, particularly after prior learning, fast spindles possibly play a key role in sleep-dependent memory processing.

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

Spectral history correction of microscopic cross sections for the PBR using the slowing down balance

International Nuclear Information System (INIS)

Hudson, N.; Rahnema, F.; Ougouag, A. M.; Gougar, H. D.

2006-01-01

A method has been formulated to account for depletion effects on microscopic cross sections within a Pebble Bed Reactor (PBR) spectral zone without resorting to calls to the spectrum (cross section generation) code or relying upon table interpolation between data at different values of burnup. In this method, infinite medium microscopic cross sections, fine group fission spectra, and modulation factors are pre-computed at selected isotopic states. This fine group information is used with the local spectral zone nuclide densities to generate new cross sections for each spectral zone. The local spectrum used to generate these microscopic cross sections is estimated through the solution to the cell-homogenized, infinite medium slowing down balance equation during the flux calculation. This technique is known as Spectral History Correction (SHC), and it is formulated to specifically account for burnup within a spectral zone. It was found that the SHC technique accurately calculates local broad group microscopic cross sections with local burnup information. Good agreement is obtained with cross sections generated directly by the cross section generator. Encouraging results include improvement in the converged fuel cycle eigenvalue, the power peaking factor, and the flux. It was also found that the method compared favorably to the benchmark problem in terms of the computational speed. (authors)

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 ...

Scientific Computing Kernels on the Cell Processor

Energy Technology Data Exchange (ETDEWEB)

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

2007-04-04

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

Does Physiological Stress Slow Down Wound Healing in Patients With Diabetes?

Science.gov (United States)

Razjouyan, Javad; Grewal, Gurtej Singh; Talal, Talal K; Armstrong, David G; Mills, Joseph L; Najafi, Bijan

2017-07-01

Poor healing is an important contributing factor to amputation among patients with diabetic foot ulcers (DFUs). Physiological stress may slow wound healing and increase susceptibility to infection. The objective was to examine the association between heart rate variability (HRV) as an indicator of physiological stress response and healing speed (Heal Speed ) among outpatients with active DFUs. Ambulatory patients with diabetes with DFUs (n = 25, age: 59.3 ± 8.3 years) were recruited. HRV during pre-wound dressing was measured using a wearable sensor attached to participants' chest. HRVs were quantified in both time and frequency domains to assess physiological stress response and vagal tone (relaxation). Change in wound size between two consecutive visits was used to estimate Heal Speed . Participants were then categorized into slow healing and fast healing groups. Between the two groups, comparisons were performed for demographic, clinical, and HRV derived parameters. Associations between different descriptors of HRV and Heal Speed were also assessed. Heal Speed was significantly correlated with both vagal tone ( r = -.705, P = .001) and stress response ( r = .713, P = .001) extracted from frequency domain. No between-group differences were observed except those from HRV-derived parameters. Models based on HRVs were the highest predictors of slow/fast Heal Speed (AUC > 0.90), while models based on demographic and clinical information had poor classification performance (AUC = 0.44). This study confirms an association between stress/vagal tone and wound healing in patients with DFUs. In particular, it highlights the importance of vagal tone (relaxation) in expediting wound healing. It also demonstrates the feasibility of assessing physiological stress responses using wearable technology in outpatient clinic during routine clinic visits.

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.)

Kernel learning at the first level of inference.

Science.gov (United States)

Cawley, Gavin C; Talbot, Nicola L C

2014-05-01

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

Global Polynomial Kernel Hazard Estimation

DEFF Research Database (Denmark)

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

2015-01-01

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

Quantum tomography, phase-space observables and generalized Markov kernels

International Nuclear Information System (INIS)

Pellonpaeae, Juha-Pekka

2009-01-01

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

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

Directory of Open Access Journals (Sweden)

Xin Zhao

2017-01-01

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

New Model to describe the interaction of slow neutrons with solid deuterium

International Nuclear Information System (INIS)

Granada, J.R

2009-01-01

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

Single pass kernel k-means clustering method

Indian Academy of Sciences (India)

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

Relationship between attenuation coefficients and dose-spread kernels

International Nuclear Information System (INIS)

Boyer, A.L.

1988-01-01

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

Mixture Density Mercer Kernels: A Method to Learn Kernels

Data.gov (United States)

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

Isotopic fissile assay of spent fuel in a lead slowing-down spectrometer system

Energy Technology Data Exchange (ETDEWEB)

Lee, Yong Deok; Jeon, Ju Young [Dept. of Fuel Cycle Technology, Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of); Park, Chang Je [Dept. of Nuclear Engineering, Sejong University, Seoul (Korea, Republic of)

2017-04-15

A lead slowing-down spectrometer (LSDS) system is under development to analyze isotopic fissile content that is applicable to spent fuel and recycled material. The source neutron mechanism for efficient and effective generation was also determined. The source neutron interacts with a lead medium and produces continuous neutron energy, and this energy generates dominant fission at each fissile, below the unresolved resonance region. From the relationship between the induced fissile fission and the fast fission neutron detection, a mathematical assay model for an isotopic fissile material was set up. The assay model can be expanded for all fissile materials. The correction factor for self-shielding was defined in the fuel assay area. The corrected fission signature provides well-defined fission properties with an increase in the fissile content. The assay procedure was also established. The assay energy range is very important to take into account the prominent fission structure of each fissile material. Fission detection occurred according to the change of the Pu239 weight percent (wt%), but the content of U235 and Pu241 was fixed at 1 wt%. The assay result was obtained with 2∼3% uncertainty for Pu239, depending on the amount of Pu239 in the fuel. The results show that LSDS is a very powerful technique to assay the isotopic fissile content in spent fuel and recycled materials for the reuse of fissile materials. Additionally, a LSDS is applicable during the optimum design of spent fuel storage facilities and their management. The isotopic fissile content assay will increase the transparency and credibility of spent fuel storage.

Integral equations with contrasting kernels

Directory of Open Access Journals (Sweden)

Theodore Burton

2008-01-01

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

Kernel methods in orthogonalization of multi- and hypervariate data

DEFF Research Database (Denmark)

Nielsen, Allan Aasbjerg

2009-01-01

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

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

DEFF Research Database (Denmark)

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

2009-01-01

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

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

Slow light enhancement and limitations in periodic media

DEFF Research Database (Denmark)

Grgic, Jure

in the vicinity of the band edge. The minimum attainable group velocity will depend on the amount of imperfections. Since imperfections are inherited as part of any periodic structure it is necessary to take them into account when we are interested in slow light applications. Slowly propagating light gives rise......Properties of periodic dielectric media have attracted a big interest in the last two decades due to numerous exciting physical phenomena that cannot occur in homogeneous media. Due to their strong dispersive properties, the speed of light can be significantly slowed down in periodic structures....... When light velocity is much smaller than the speed of light in a vacuum, we describe this phenomena as slow light. In this thesis, we analyze important properties of slow light enhancement and limitations in periodic structures. We analyze quantitatively and qualitatively different technologies...

The Classification of Diabetes Mellitus Using Kernel k-means

Science.gov (United States)

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

2018-01-01

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

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

NARCIS (Netherlands)

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

2016-01-01

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

Parsimonious Wavelet Kernel Extreme Learning Machine

Directory of Open Access Journals (Sweden)

Wang Qin

2015-11-01

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

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)

Group-index limitations in slow-light photonic crystals

DEFF Research Database (Denmark)

Grgic, Jure; Pedersen, Jesper Goor; Xiao, Sanshui

2010-01-01

radiation, and in-plane leakage. Often, the different mechanisms are playing in concert, leading to attenuation and scattering of electromagnetic modes. The very same broadening mechanisms also limit the attainable slow-down which we mimic by including a small imaginary part to the otherwise real...

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 ...

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

Directory of Open Access Journals (Sweden)

Mohammed D. ABDULMALIK

2008-06-01

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

Generalization Performance of Regularized Ranking With Multiscale Kernels.

Science.gov (United States)

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

2016-05-01

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

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%.

Capillary waves in slow motion

International Nuclear Information System (INIS)

Seydel, Tilo; Tolan, Metin; Press, Werner; Madsen, Anders; Gruebel, Gerhard

2001-01-01

Capillary wave dynamics on glycerol surfaces has been investigated by means of x-ray photon correlation spectroscopy performed at grazing angles. The measurements show that thermally activated capillary wave motion is slowed down exponentially when the sample is cooled below 273 K. This finding directly reflects the freezing of the surface waves. The wave-number dependence of the measured time constants is in quantitative agreement with theoretical predictions for overdamped capillary waves

Experiments on the thermalization of slow neutrons by liquid hydrogen (1962)

International Nuclear Information System (INIS)

Cribier, D.; Jacrot, B.; Lacaze, A.; Roubeau, P.

1962-01-01

In order to increase the flux of neutrons of long wave-length (λ > 4 A) emerging from a channel in the EL-3, a liquid hydrogen device was introduced into a channel of the reactor (Channel H 1 ). The principle of the device is simple. A volume of liquid hydrogen is introduced as close as possible to the reactor core into a region of intense isotropic flux. This hydrogen slows down the slow neutrons; because of the very small mean free diffusion path of slow in hydrogen, this slowing down is considerable even in a small volume of liquid hydrogen, and the spectrum temperature of neutrons emerging from the volume of liquid hydrogen can therefore be shifted. The intensity gain for neutrons with a wave length λ, is a G (λ) function which, for perfect thermalization and ignoring capture, is expressed by: G (λ) = 225 exp (- 45.3/λ 2 ), assuming a temperature of 300 deg. K for the neutrons before cooling and is 20 deg. K after cooling. For a wave-length of 5 A, the theoretical maximum gain of thus about 37. (authors) [fr

Design optimization of radiation shielding structure for lead slowing-down spectrometer system

Energy Technology Data Exchange (ETDEWEB)

Kim, Jeong Dong; Ahn, Sang Joon; Lee, Yong Deok [Nonproliferation System Research Division, Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of); Park, Chang Je [Dept. of Nuclear Engineering, Sejong University, Seoul (Korea, Republic of)

2015-04-15

A lead slowing-down spectrometer (LSDS) system is a promising nondestructive assay technique that enables a quantitative measurement of the isotopic contents of major fissile isotopes in spent nuclear fuel and its pyroprocessing counterparts, such as 235U, 239Pu, 241Pu, and, potentially, minor actinides. The LSDS system currently under development at the Korea Atomic Energy Research Institute (Daejeon, Korea) is planned to utilize a high-flux (>101{sup 2n}/cm{sup 2}·s) neutron source comprised of a high-energy (30 MeV)/high-current (∼2 A) electron beam and a heavy metal target, which results in a very intense and complex radiation field for the facility, thus demanding structural shielding to guarantee the safety. Optimization of the structural shielding design was conducted using MCNPX for neutron dose rate evaluation of several representative hypothetical designs. In order to satisfy the construction cost and neutron attenuation capability of the facility, while simultaneously achieving the aimed dose rate limit (<0.06 μSv/h), a few shielding materials [high-density polyethylene (HDPE)–Borax, B{sub 4}C, and Li{sub 2}CO{sub 3}] were considered for the main neutron absorber layer, which is encapsulated within the double-sided concrete wall. The MCNP simulation indicated that HDPE-Borax is the most efficient among the aforementioned candidate materials, and the combined thickness of the shielding layers should exceed 100 cm to satisfy the dose limit on the outside surface of the shielding wall of the facility when limiting the thickness of the HDPE-Borax intermediate layer to below 5 cm. However, the shielding wall must include the instrumentation and installation holes for the LSDS system. The radiation leakage through the holes was substantially mitigated by adopting a zigzag-shape with concrete covers on both sides. The suggested optimized design of the shielding structure satisfies the dose rate limit and can be used for the construction of a facility in

Design optimization of radiation shielding structure for lead slowing-down spectrometer system

International Nuclear Information System (INIS)

Kim, Jeong Dong; Ahn, Sang Joon; Lee, Yong Deok; Park, Chang Je

2015-01-01

A lead slowing-down spectrometer (LSDS) system is a promising nondestructive assay technique that enables a quantitative measurement of the isotopic contents of major fissile isotopes in spent nuclear fuel and its pyroprocessing counterparts, such as 235U, 239Pu, 241Pu, and, potentially, minor actinides. The LSDS system currently under development at the Korea Atomic Energy Research Institute (Daejeon, Korea) is planned to utilize a high-flux (>101 2n /cm 2 ·s) neutron source comprised of a high-energy (30 MeV)/high-current (∼2 A) electron beam and a heavy metal target, which results in a very intense and complex radiation field for the facility, thus demanding structural shielding to guarantee the safety. Optimization of the structural shielding design was conducted using MCNPX for neutron dose rate evaluation of several representative hypothetical designs. In order to satisfy the construction cost and neutron attenuation capability of the facility, while simultaneously achieving the aimed dose rate limit (<0.06 μSv/h), a few shielding materials [high-density polyethylene (HDPE)–Borax, B 4 C, and Li 2 CO 3 ] were considered for the main neutron absorber layer, which is encapsulated within the double-sided concrete wall. The MCNP simulation indicated that HDPE-Borax is the most efficient among the aforementioned candidate materials, and the combined thickness of the shielding layers should exceed 100 cm to satisfy the dose limit on the outside surface of the shielding wall of the facility when limiting the thickness of the HDPE-Borax intermediate layer to below 5 cm. However, the shielding wall must include the instrumentation and installation holes for the LSDS system. The radiation leakage through the holes was substantially mitigated by adopting a zigzag-shape with concrete covers on both sides. The suggested optimized design of the shielding structure satisfies the dose rate limit and can be used for the construction of a facility in the near future.

Design optimization of radiation shielding structure for lead slowing-down spectrometer system

Directory of Open Access Journals (Sweden)

Jeong Dong Kim

2015-04-01

Full Text Available A lead slowing-down spectrometer (LSDS system is a promising nondestructive assay technique that enables a quantitative measurement of the isotopic contents of major fissile isotopes in spent nuclear fuel and its pyroprocessing counterparts, such as 235U, 239Pu, 241Pu, and, potentially, minor actinides. The LSDS system currently under development at the Korea Atomic Energy Research Institute (Daejeon, Korea is planned to utilize a high-flux (>1012 n/cm2·s neutron source comprised of a high-energy (30 MeV/high-current (∼2 A electron beam and a heavy metal target, which results in a very intense and complex radiation field for the facility, thus demanding structural shielding to guarantee the safety. Optimization of the structural shielding design was conducted using MCNPX for neutron dose rate evaluation of several representative hypothetical designs. In order to satisfy the construction cost and neutron attenuation capability of the facility, while simultaneously achieving the aimed dose rate limit (<0.06 μSv/h, a few shielding materials [high-density polyethylene (HDPE–Borax, B4C, and Li2CO3] were considered for the main neutron absorber layer, which is encapsulated within the double-sided concrete wall. The MCNP simulation indicated that HDPE-Borax is the most efficient among the aforementioned candidate materials, and the combined thickness of the shielding layers should exceed 100 cm to satisfy the dose limit on the outside surface of the shielding wall of the facility when limiting the thickness of the HDPE-Borax intermediate layer to below 5 cm. However, the shielding wall must include the instrumentation and installation holes for the LSDS system. The radiation leakage through the holes was substantially mitigated by adopting a zigzag-shape with concrete covers on both sides. The suggested optimized design of the shielding structure satisfies the dose rate limit and can be used for the construction of a facility in the near

NLO corrections to the Kernel of the BKP-equations

Energy Technology Data Exchange (ETDEWEB)

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

2012-10-02

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

A Fast and Simple Graph Kernel for RDF

NARCIS (Netherlands)

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

2013-01-01

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

An SVM model with hybrid kernels for hydrological time series

Science.gov (United States)

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

2017-12-01

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

Kernel based eigenvalue-decomposition methods for analysing ham

DEFF Research Database (Denmark)

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

2010-01-01

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

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

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....

Finite-difference solution of the space-angle-lethargy-dependent slowing-down transport equation

Energy Technology Data Exchange (ETDEWEB)

Matausek, M V [Boris Kidric Vinca Institute of Nuclear Sciences, Vinca, Belgrade (Yugoslavia)

1972-07-01

A procedure has been developed for solving the slowing-down transport equation for a cylindrically symmetric reactor system. The anisotropy of the resonance neutron flux is treated by the spherical harmonics formalism, which reduces the space-angle-Iethargy-dependent transport equation to a matrix integro-differential equation in space and lethargy. Replacing further the lethargy transfer integral by a finite-difference form, a set of matrix ordinary differential equations is obtained, with lethargy-and space dependent coefficients. If the lethargy pivotal points are chosen dense enough so that the difference correction term can be ignored, this set assumes a lower block triangular form and can be solved directly by forward block substitution. As in each step of the finite-difference procedure a boundary value problem has to be solved for a non-homogeneous system of ordinary differential equations with space-dependent coefficients, application of any standard numerical procedure, for example, the finite-difference method or the method of adjoint equations, is too cumbersome and would make the whole procedure practically inapplicable. A simple and efficient approximation is proposed here, allowing analytical solution for the space dependence of the spherical-harmonics flux moments, and hence the derivation of the recurrence relations between the flux moments at successive lethargy pivotal points. According to the procedure indicated above a computer code has been developed for the CDC -3600 computer, which uses the KEDAK nuclear data file. The space and lethargy distribution of the resonance neutrons can be computed in such a detailed fashion as the neutron cross-sections are known for the reactor materials considered. The computing time is relatively short so that the code can be efficiently used, either autonomously, or as part of some complex modular scheme. Typical results will be presented and discussed in order to prove and illustrate the applicability of the

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

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...

Neutron slowing down and transport in a medium of constant cross section. I. Spatial moments

International Nuclear Information System (INIS)

Cacuci, D.G.; Goldstein, H.

1977-01-01

Some aspects of the problem of neutron slowing down and transport have been investigated in an infinite medium consisting of a single nuclide scattering elastically and isotropically without absorption and with energy-independent cross sections. The method of singular eigenfunctions has been applied to the Boltzmann equation governing the Laplace transform (with respect to the lethargy variable) of the neutron flux. Formulas have been obtained for the lethargy dependent spatial moments of the scalar flux applicable in the limit of large lethargy. In deriving these formulas, use has been made of the well-known connection between the spatial moments of the Laplace-transformed scalar flux and the moments of the flux in the ''eigenvalue space.'' The calculations have been greatly aided by the construction of a closed general expression for these ''eigenvalue space'' moments. Extensive use has also been made of the methods of combinatorial analysis and of computer evaluation, via FORMAC, of complicated sequences of manipulations. It has been possible to obtain for materials of any atomic weight explicit corrections to the age theory formulas for the spatial moments M/sub 2n/(u), of the scalar flux, valid through terms of order of u -5 . Higher order correction terms could be obtained at the expense of additional computer time. The evaluation of the coefficients of the powers of n, as explicit functions of the nuclear mass, represent the end product of this investigation

Mitigation of artifacts in rtm with migration kernel decomposition

KAUST Repository

Zhan, Ge; Schuster, Gerard T.

2012-01-01

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

Semi-Supervised Kernel PCA

DEFF Research Database (Denmark)

Walder, Christian; Henao, Ricardo; Mørup, Morten

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

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...

Slow-light enhanced absorption in a hollow-core fiber

DEFF Research Database (Denmark)

Grgic, Jure; Xiao, Sanshui; Mørk, Jesper

2010-01-01

Light traversing a hollow-core photonic band-gap fiber may experience multiple reflections and thereby a slow-down and enhanced optical path length. This offers a technologically interesting way of increasing the optical absorption of an otherwise weakly absorbing material which can infiltrate...

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.

The energy deposition of slowing down particles in heterogeneous media

International Nuclear Information System (INIS)

Prinja, A.K.; Williams, M.M.R.

1980-01-01

Energy deposition by atomic particles in adjacent semi-infinite, amorphous media is described using the forward form of the Boltzmann transport equation. A transport approximation to the scattering kernel, developed elsewhere, incorporating realistic energy transfer is employed to assess the validity of the commonly used isotropic-scattering and straight-ahead approximations. Results are presented for integral energy deposition rates due to a plane, isotropic and monoenergetic source in one half-space for a range of mass ratios between 0.1 and 5.0. Integral profiles for infinite and semi-infinite media are considered and the influence of reflection for different mass ratios is evaluated. The dissimilar scattering properties of the two media induce a discontinuity at the interface in the energy deposition rate the magnitude of which is sensitive to the source position relative to the interface. A comprehensive evaluation of the total energy deposited in the source free medium is presented for a range of mass ratios and source positions. An interesting minimum occurs for off-interface source locations as a function of the source-medium mass ratio, the position of which varies with the source position but is insensitive to the other mass ratio. As a special case, energy reflection and escape coefficients for semi-infinite media are obtained which demonstrates that the effect of a vacuum interface is insignificant for deep source locations except for large mass ratios when reflection becomes dominant. (author)

Multivariate and semiparametric kernel regression

OpenAIRE

Härdle, Wolfgang; Müller, Marlene

1997-01-01

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

Notes on the gamma kernel

DEFF Research Database (Denmark)

Barndorff-Nielsen, Ole E.

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

Convergence of barycentric coordinates to barycentric kernels

KAUST Repository

Kosinka, Jiří

2016-02-12

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

Convergence of barycentric coordinates to barycentric kernels

KAUST Repository

Kosinka, Jiří ; Barton, Michael

2016-01-01

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

Hadamard Kernel SVM with applications for breast cancer outcome predictions.

Science.gov (United States)

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

2017-12-21

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

Aflatoxin contamination of developing corn kernels.

Science.gov (United States)

Amer, M A

2005-01-01

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

Kernel Korner : The Linux keyboard driver

NARCIS (Netherlands)

Brouwer, A.E.

1995-01-01

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

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

International Nuclear Information System (INIS)

Damunir; Sri Rinanti Susilowati; Ariyani Kusuma Dewi

2015-01-01

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

Mitigation of artifacts in rtm with migration kernel decomposition

KAUST Repository

Zhan, Ge

2012-01-01

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

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.

Slow and fast light effects in semiconductor optical amplifiers for applications in microwave photonics

DEFF Research Database (Denmark)

Xue, Weiqi

This thesis analyzes semiconductor optical amplifiers based slow and fast light effects with particular focus on the applications in microwave photonics. We conceive novel ideas and demonstrate a great enhancement of light slow down. Furthermore, by cascading several slow light stages, >360 degree...... microwave phase shifts over a bandwidth of several tens of gigahertz are achieved. These also satisfy the basic requirements of microwave photonic systems. As an application demonstration, a tunable microwave notch filter is realized, where slow light based phase shifters provide 100% fractional tuning over...

When high-capacity readers slow down and low-capacity readers speed up: Working memory and locality effects

Directory of Open Access Journals (Sweden)

Bruno eNicenboim

2016-03-01

Full Text Available We examined the effects of argument-head distance in SVO and SOV languages (Spanish and German, while taking into account readers’ working memory capacity and controlling for expectation (Levy, 2008 and other factors. We predicted only locality effects, that is, a slow-down produced by increased dependency distance (Gibson, 2000; Lewis & Vasishth, 2005. Furthermore, we expected stronger locality effects for readers with low working memory capacity. Contrary to our predictions, low-capacity readers showed faster reading with increased distance, while high-capacity readers showed locality effects. We suggest that while the locality effects are compatible with memory-based explanations, the speedup of low-capacity readers can be explained by an increased probability of retrieval failure. We present a computational model based on ACT-R built under the previous assumptions, which is able to give a qualitative account for the present data and can be tested in future research. Our results suggest that in some cases, interpreting longer RTs as indexing increased processing difficulty and shorter RTs as facilitation may be too simplistic: The same increase in processing difficulty may lead to slowdowns in high-capacity readers and speedups in low-capacity ones. Ignoring individual level capacity differences when investigating locality effects may lead to misleading conclusions.

Charging of insulators by multiply-charged-ion impact probed by slowing down of fast binary-encounter electrons

Science.gov (United States)

de Filippo, E.; Lanzanó, G.; Amorini, F.; Cardella, G.; Geraci, E.; Grassi, L.; La Guidara, E.; Lombardo, I.; Politi, G.; Rizzo, F.; Russotto, P.; Volant, C.; Hagmann, S.; Rothard, H.

2010-12-01

The interaction of ion beams with insulators leads to charging-up phenomena, which at present are under investigation in connection with guiding phenomena in nanocapillaries with possible application in nanofocused beams. We studied the charging dynamics of insulating foil targets [Mylar, polypropylene (PP)] irradiated with swift ion beams (C, O, Ag, and Xe at 40, 23, 40, and 30 MeV/u, respectively) via the measurement of the slowing down of fast binary-encounter electrons. Also, sandwich targets (Mylar covered with a thin Au layer on both surfaces) and Mylar with Au on only one surface were used. Fast-electron spectra were measured by the time-of-flight method at the superconducting cyclotron of Laboratori Nazionali del Sud (LNS) Catania. The charge buildup leads to target-material-dependent potentials of the order of 6.0 kV for Mylar and 2.8 kV for PP. The sandwich targets, surprisingly, show the same behavior as the insulating targets, whereas a single Au layer on the electron and ion exit side strongly suppresses the charging phenomenon. The accumulated number of projectiles needed for charging up is inversely proportional to electronic energy loss. Thus, the charging up is directly related to emission of secondary electrons.

Charging of insulators by multiply-charged-ion impact probed by slowing down of fast binary-encounter electrons

International Nuclear Information System (INIS)

De Filippo, E.; Lanzano, G.; Cardella, G.; Amorini, F.; Geraci, E.; Grassi, L.; Politi, G.; La Guidara, E.; Lombardo, I.; Rizzo, F.; Russotto, P.; Volant, C.; Hagmann, S.; Rothard, H.

2010-01-01

The interaction of ion beams with insulators leads to charging-up phenomena, which at present are under investigation in connection with guiding phenomena in nanocapillaries with possible application in nanofocused beams. We studied the charging dynamics of insulating foil targets [Mylar, polypropylene (PP)] irradiated with swift ion beams (C, O, Ag, and Xe at 40, 23, 40, and 30 MeV/u, respectively) via the measurement of the slowing down of fast binary-encounter electrons. Also, sandwich targets (Mylar covered with a thin Au layer on both surfaces) and Mylar with Au on only one surface were used. Fast-electron spectra were measured by the time-of-flight method at the superconducting cyclotron of Laboratori Nazionali del Sud (LNS) Catania. The charge buildup leads to target-material-dependent potentials of the order of 6.0 kV for Mylar and 2.8 kV for PP. The sandwich targets, surprisingly, show the same behavior as the insulating targets, whereas a single Au layer on the electron and ion exit side strongly suppresses the charging phenomenon. The accumulated number of projectiles needed for charging up is inversely proportional to electronic energy loss. Thus, the charging up is directly related to emission of secondary electrons.

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.

Revealing the Formation Mechanism of CsPbBr3 Perovskite Nanocrystals Produced via a Slowed-Down Microwave-Assisted Synthesis.

Science.gov (United States)

Li, Yanxiu; Huang, He; Xiong, Yuan; Kershaw, Stephen V; Rogach, Andrey L

2018-03-24

We developed a microwave-assisted slowed-down synthesis of CsPbBr 3 perovskite nanocrystals, which retards the reaction and allows us to gather useful insights into the formation mechanism of these nanoparticles, by examining the intermediate stages of their growth. The trends in the decay of the emission intensity of CsPbBr 3 nanocrystals under light exposure are well correlated with their stability against decomposition in TEM under electron beam. The results show the change of the crystal structure of CsPbBr 3 nanocrystals from a deficient and easier to be destroyed lattice to a well crystallized one. Conversely the shift in the ease of degradation sheds light on the formation mechanism, indicating first the formation of a bromoplumbate ionic scaffold, with Cs-ion infilling lagging a little behind. Increasing the cation to halide ratio towards the stoichiometric level may account for the improved radiative recombination rates observed in the longer reaction time materials. © 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Kernel Temporal Differences for Neural Decoding

Science.gov (United States)

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

2015-01-01

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

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

International Nuclear Information System (INIS)

Zielinska, S.

1985-03-01

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

Classification of maize kernels using NIR hyperspectral imaging

DEFF Research Database (Denmark)

Williams, Paul; Kucheryavskiy, Sergey V.

2016-01-01

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

Influence of wheat kernel physical properties on the pulverizing process.

Science.gov (United States)

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

2014-10-01

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

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....

Experimental Demonstration and Theoretical Analysis of Slow Light in a Semiconductor Waveguide at GHz Frequencies

DEFF Research Database (Denmark)

Mørk, Jesper; Kjær, Rasmus; Poel, Mike van der

2005-01-01

Experimental demonstration and theoretical analysis of slow light in a semiconductor waveguide at GHz frequencies slow-down of light by a factor of two in a semiconductor waveguide at room temperature with a bandwidth of 16.7 GHz using the effect of coherent pulsations of the carrier density...

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...

Schrodinger cat state generation using a slow light

International Nuclear Information System (INIS)

Ham, B. S.; Kim, M. S.

2003-01-01

We show a practical application of giant Kerr nonlinearity to quantum information processing based on superposition of two distinct macroscopic states- Schrodinger cat state. The giant Kerr nonlinearity can be achieved by using electromagnetically induced transparency, in which light propagation should be slowed down so that a pi-phase shift can be easily obtained owing to increased interaction time.

Proton energy dependence of slow neutron intensity

International Nuclear Information System (INIS)

Teshigawara, Makoto; Harada, Masahide; Watanabe, Noboru; Kai, Tetsuya; Sakata, Hideaki; Ikeda, Yujiro

2001-01-01

The choice of the proton energy is an important issue for the design of an intense-pulsed-spallation source. The optimal proton beam energy is rather unique from a viewpoint of the leakage neutron intensity but no yet clear from the slow-neutron intensity view point. It also depends on an accelerator type. Since it is also important to know the proton energy dependence of slow-neutrons from the moderators in a realistic target-moderator-reflector assembly (TMRA). We studied on the TMRA proposed for Japan Spallation Neutron Source. The slow-neutron intensities from the moderators per unit proton beam power (MW) exhibit the maximum at about 1-2 GeV. At higher proton energies the intensity per MW goes down; at 3 and 50 GeV about 0.91 and 0.47 times as low as that at 1 GeV. The proton energy dependence of slow-neutron intensities was found to be almost the same as that of total neutron yield (leakage neutrons) from the same bare target. It was also found that proton energy dependence was almost the same for the coupled and decoupled moderators, regardless the different moderator type, geometry and coupling scheme. (author)

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.

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

GORGON - a computer code for the calculation of energy deposition and the slowing down of ions in cold materials and hot dense plasmas

International Nuclear Information System (INIS)

Long, K.A.; Moritz, N.; Tahir, N.A.

1983-11-01

The computer code GORGON, which calculates the energy deposition and slowing down of ions in cold materials and hot plasmas is described, and analyzed in this report. This code is in a state of continuous development but an intermediate stage has been reached where it is considered useful to document the 'state of the art' at the present time. The GORGON code is an improved version of a code developed by Zinamon et al. as part of a more complex program system for studying the hydrodynamic motion of plane metal targets irradiated by intense beams of protons. The improvements made in the code were necessary to improve its usefulness for problems related to the design and burn of heavy ion beam driven inertial confinement fusion targets. (orig./GG) [de

Linear and kernel methods for multivariate change detection

DEFF Research Database (Denmark)

Canty, Morton J.; Nielsen, Allan Aasbjerg

2012-01-01

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

Panel data specifications in nonparametric kernel regression

DEFF Research Database (Denmark)

Czekaj, Tomasz Gerard; Henningsen, Arne

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

Scuba: scalable kernel-based gene prioritization.

Science.gov (United States)

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

2018-01-25

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

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.

Analysis and investigation of temperature and hydrostatic pressure effects on optical characteristics of multiple quantum well slow light devices.

Science.gov (United States)

Abdolhosseini, Saeed; Kohandani, Reza; Kaatuzian, Hassan

2017-09-10

This paper represents the influences of temperature and hydrostatic pressure variations on GaAs/AlGaAs multiple quantum well slow light systems based on coherence population oscillations. An analytical model in non-integer dimension space is used to study the considerable effects of these parameters on optical properties of the slow light apparatus. Exciton oscillator strength and fractional dimension constants have special roles on the analytical model in fractional dimension. Hence, the impacts of hydrostatic pressure and temperature on exciton oscillator strength and fractional dimension quantity are investigated theoretically in this paper. Based on the achieved results, temperature and hydrostatic pressure play key roles on optical parameters of the slow light systems, such as the slow down factor and central energy of the device. It is found that the slope and value of the refractive index real part change with alterations of temperature and hydrostatic pressure in the range of 5-40 deg of Kelvin and 1 bar to 2 kbar, respectively. Thus, the peak value of the slow down factor can be adjusted by altering these parameters. Moreover, the central energy of the device shifts when the hydrostatic pressure is applied to the slow light device or temperature is varied. In comparison with previous reported experimental results, our simulations follow them successfully. It is shown that the maximum value of the slow down factor is estimated close to 5.5×10 4 with a fine adjustment of temperature and hydrostatic pressure. Meanwhile, the central energy shift of the slow light device rises up to 27 meV, which provides an appropriate basis for different optical devices in which multiple quantum well slow light is one of their essential subsections. This multiple quantum well slow light device has potential applications for use as a tunable optical buffer and pressure/temperature sensors.

FROM SLOW FOOD TO SLOW TOURISM

Directory of Open Access Journals (Sweden)

Bac Dorin Paul

2014-12-01

Full Text Available One of the effects of globalization is the faster pace of our lives. This rhythm can be noticed in all aspects of life: travel, work, shopping, etc. and it has serious negative effects. It has become common knowledge that stress and speed generate serious medical issues. Food and eating habits in the modern world have taken their toll on our health. However, some people took a stand and argued for a new kind of lifestyle. It all started in the field of gastronomy, where a new movement emerged – Slow Food, based on the ideas and philosophy of Carlo Petrini. Slow Food represents an important adversary to the concept of fast food, and is promoting local products, enjoyable meals and healthy food. The philosophy of the Slow Food movement developed in several directions: Cittaslow, slow travel and tourism, slow religion and slow money etc. The present paper will account the evolution of the concept and its development during the most recent years. We will present how the philosophy of slow food was applied in all the other fields it reached and some critical points of view. Also we will focus on the presence of the slow movement in Romania, although it is in a very early stage of development. The main objectives of the present paper are: to present the chronological and ideological evolution of the slow movement; to establish a clear separation of slow travel and slow tourism, as many mistake on for the other; to review the presence of the slow movement in Romania. Regarding the research methodology, information was gathered from relevant academic papers and books and also from interviews and discussions with local entrepreneurs. The research is mostly theoretical and empirical, as slow food and slow tourism are emerging research themes in academic circles.

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.

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

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

Energy Technology Data Exchange (ETDEWEB)

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

2003-12-01

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

Monte-Carlo method for studying the slowing down of neutrons in a thin plate of hydrogenated matter; Methode de Monte-Carlo pour l'etude du ralentissement des neutrons dans une plaque mince de matiere hydrogenee

Energy Technology Data Exchange (ETDEWEB)

Ribon, P; Michaudon, A [Commissariat a l' Energie Atomique, Saclay (France). Centre d' Etudes Nucleaires

1965-07-01

The studies of interaction of slow neutrons with atomic nuclei by means of the time of flight methods are made with a pulsed neutron source with a broad energy spectrum. The measurement accuracy needs a high intensity and an output time as short as possible and well defined. If the neutrons source is a target bombarded by the beam of a pulsed accelerator, it is usually required to slow down the neutrons to obtain a sufficient intensity at low energies. The purpose of the Monte-Carlo method which is described in this paper is to study the slowing down properties, mainly the intensity and the output time distribution of the slowed-down neutrons. The choice of the method and parameters studied is explained as well as the principles, some calculations and the program organization. A few results given as examples were obtained in the line of this program, the limits of which are principally due to simplifying physical hypotheses. (author) [French] l'etude de l'interaction des neutrons lents avec les noyaux atomiques par la methode du temps de vol s'effectue avec une source pulsee de neutrons dont le spectre en energie est assez etendu. La precision des mesures demande que la source soit intense et que la duree d'emission des neutrons soit breve et bien definie. Si la source est une cible bombardee par le faisceau de particules d'un accelerateur pulse, il est generalement indispensable de ralentir les neutrons pour avoir une intensite suffisante a basse energie. Nous presentons ici une methode de Monte-Carlo pour l'etude detaillee de ce ralentissement, notamment l'intensite et la distribution des temps de sortie des neutrons ralentis. Cette presentation comprend: la justification du choix de la methode de Monte-Carlo, les principes generaux, les differentes etapes du calcul et du programme ecrit pour le calculateur electronique IBM 7090. Nous indiquons aussi les restrictions qui sont apportees au domaine d'application de ce programme et qui proviennent surtout des

Leaf litter traits of invasive species slow down decomposition compared to Spanish natives: a broad phylogenetic comparison.

Science.gov (United States)

Godoy, Oscar; Castro-Díez, Pilar; Van Logtestijn, Richard S P; Cornelissen, Johannes H C; Valladares, Fernando

2010-03-01

Leaf traits related to the performance of invasive alien species can influence nutrient cycling through litter decomposition. However, there is no consensus yet about whether there are consistent differences in functional leaf traits between invasive and native species that also manifest themselves through their "after life" effects on litter decomposition. When addressing this question it is important to avoid confounding effects of other plant traits related to early phylogenetic divergences and to understand the mechanism underlying the observed results to predict which invasive species will exert larger effects on nutrient cycling. We compared initial leaf litter traits, and their effect on decomposability as tested in standardized incubations, in 19 invasive-native pairs of co-familial species from Spain. They included 12 woody and seven herbaceous alien species representative of the Spanish invasive flora. The predictive power of leaf litter decomposition rates followed the order: growth form > family > status (invasive vs. native) > leaf type. Within species pairs litter decomposition tended to be slower and more dependent on N and P in invaders than in natives. This difference was likely driven by the higher lignin content of invader leaves. Although our study has the limitation of not representing the natural conditions from each invaded community, it suggests a potential slowing down of the nutrient cycle at ecosystem scale upon invasion.

A kernel adaptive algorithm for quaternion-valued inputs.

Science.gov (United States)

Paul, Thomas K; Ogunfunmi, Tokunbo

2015-10-01

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

Improving the Bandwidth Selection in Kernel Equating

Science.gov (United States)

Andersson, Björn; von Davier, Alina A.

2014-01-01

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

Point kernels and superposition methods for scatter dose calculations in brachytherapy

International Nuclear Information System (INIS)

Carlsson, A.K.

2000-01-01

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

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

Science.gov (United States)

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

2013-05-01

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

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

Indian Academy of Sciences (India)

2011-12-02

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

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

Science.gov (United States)

Zhang, Xueying; Song, Qinbao

2015-01-01

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

Epigenomic maintenance through dietary intervention can facilitate DNA repair process to slow down the progress of premature aging.

Science.gov (United States)

Ghosh, Shampa; Sinha, Jitendra Kumar; Raghunath, Manchala

2016-09-01

DNA damage caused by various sources remains one of the most researched topics in the area of aging and neurodegeneration. Increased DNA damage causes premature aging. Aging is plastic and is characterised by the decline in the ability of a cell/organism to maintain genomic stability. Lifespan can be modulated by various interventions like calorie restriction, a balanced diet of macro and micronutrients or supplementation with nutrients/nutrient formulations such as Amalaki rasayana, docosahexaenoic acid, resveratrol, curcumin, etc. Increased levels of DNA damage in the form of double stranded and single stranded breaks are associated with decreased longevity in animal models like WNIN/Ob obese rats. Erroneous DNA repair can result in accumulation of DNA damage products, which in turn result in premature aging disorders such as Hutchinson-Gilford progeria syndrome. Epigenomic studies of the aging process have opened a completely new arena for research and development of drugs and therapeutic agents. We propose here that agents or interventions that can maintain epigenomic stability and facilitate the DNA repair process can slow down the progress of premature aging, if not completely prevent it. © 2016 IUBMB Life, 68(9):717-721, 2016. © 2016 International Union of Biochemistry and Molecular Biology.

Slow-light solitons in atomic media and doped optical fibers

International Nuclear Information System (INIS)

Korolkova, N.; Sinclair, G.F.; Leonhardt, U.

2005-01-01

Full text: We show how to generate optical solitons in atomic media that can be slowed down or accelerated at will. Such slow-light soliton is a polarization structure propagating with a speed that is proportional to the total intensity of the incident light. Ultimately, this method will allow the storage, retrieval and possibly the manipulation of the quantum information in atomic media. Solitons with controllable speed are constructed generalizing the theory of slow-light propagation to an integrable regime of nonlinear dynamics. For the first time, the inverse scattering method for slow-light solitons is developed. In contrast to the pioneering experimental demonstrations of slow light, we consider strong spin modulations where the non-linear dynamics of light and atoms creates polarization solitons. We also analyze how this scheme can be implemented in optical fibers doped with Lambda-atoms. In quantum-information applications, such slow-light solitons could complement the use of quantum solitons in fibres with the advantage of storing quantum information in media and complement methods for quantum memory with the advantages of non-linear dynamics, in particular the intrinsic stability of solitons. (author)

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

Sound asleep: processing and retention of slow oscillation phase-targeted stimuli.

Science.gov (United States)

Cox, Roy; Korjoukov, Ilia; de Boer, Marieke; Talamini, Lucia M

2014-01-01

The sleeping brain retains some residual information processing capacity. Although direct evidence is scarce, a substantial literature suggests the phase of slow oscillations during deep sleep to be an important determinant for stimulus processing. Here, we introduce an algorithm for predicting slow oscillations in real-time. Using this approach to present stimuli directed at both oscillatory up and down states, we show neural stimulus processing depends importantly on the slow oscillation phase. During ensuing wakefulness, however, we did not observe differential brain or behavioral responses to these stimulus categories, suggesting no enduring memories were formed. We speculate that while simpler forms of learning may occur during sleep, neocortically based memories are not readily established during deep sleep.

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.

Kernel bundle EPDiff

DEFF Research Database (Denmark)

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

2011-01-01

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

Proteome analysis of the almond kernel (Prunus dulcis).

Science.gov (United States)

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

2016-08-01

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

Localized Multiple Kernel Learning Via Sample-Wise Alternating Optimization.

Science.gov (United States)

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

2014-01-01

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

Control Transfer in Operating System Kernels

Science.gov (United States)

1994-05-13

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

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.

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

The Potential of/for 'Slow': Slow Tourists and Slow Destinations

Directory of Open Access Journals (Sweden)

J. Guiver

2016-05-01

Full Text Available Slow tourism practices are nothing new; in fact, they were once the norm and still are for millions of people whose annual holiday is spent camping, staying in caravans, rented accommodation, with friends and relations or perhaps in a second home, who immerse themselves in their holiday environment, eat local food, drink local wine and walk or cycle around the area. So why a special edition about slow tourism? Like many aspects of life once considered normal (such as organic farming or free-range eggs, the emergence of new practices has highlighted differences and prompted a re-evaluation of once accepted practices and values. In this way, the concept of ‘slow tourism’ has recently appeared as a type of tourism that contrasts with many contemporary mainstream tourism practices. It has also been associated with similar trends already ‘branded’ slow: slow food and cittaslow (slow towns and concepts such as mindfulness, savouring and well-being.

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.

Slow oscillations orchestrating fast oscillations and memory consolidation.

Science.gov (United States)

Mölle, Matthias; Born, Jan

2011-01-01

Slow-wave sleep (SWS) facilitates the consolidation of hippocampus-dependent declarative memory. Based on the standard two-stage memory model, we propose that memory consolidation during SWS represents a process of system consolidation which is orchestrated by the neocortical memory. The slow oscillations temporally group neuronal activity into up-states of strongly enhanced neuronal activity and down-states of neuronal silence. In a feed-forward efferent action, this grouping is induced not only in the neocortex but also in other structures relevant to consolidation, namely the thalamus generating 10-15Hz spindles, and the hippocampus generating sharp wave-ripples, with the latter well known to accompany a replay of newly encoded memories taking place in hippocampal circuitries. The feed-forward synchronizing effect of the slow oscillation enables the formation of spindle-ripple events where ripples and accompanying reactivated hippocampal memory information become nested into the single troughs of spindles. Spindle-ripple events thus enable reactivated memory-related hippocampal information to be fed back to neocortical networks in the excitable slow oscillation up-state where they can induce enduring plastic synaptic changes underlying the effective formation of long-term memories. Copyright © 2011 Elsevier B.V. All rights reserved.

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.

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

Energy Technology Data Exchange (ETDEWEB)

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

2015-06-15

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

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

International Nuclear Information System (INIS)

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

2015-01-01

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

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/

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.

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.

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.

The condition for classical slow rolling in new inflation

International Nuclear Information System (INIS)

Sasaki, Misao; Nambu, Yasusada; Nakao, Ken-ichi.

1988-02-01

By means of the stochastic description of inflation, we investigate the dynamics of a fixed comoving domain in a continuously inflating universe on the global scale, both analytically and numerically. A particular attention is paid to the condition for a domain to enter the classical slow rolling phase. New inflationary universe models with the potential form, V(φ) ∼ V 0 - cφ 2n at φ ∼ 0 are considered. The critical value of the scalar field beyond which the field slowly rolls down the potential hill is estimated. We find, for all models under consideration, the condition for classical slow rolling is a sufficient condition for the expected amplitude of density perturbations to be smaller than unity. In other words, the density perturbation amplitude at the later Friedmann stage is always smaller than unity if the universe experienced the classical slow roll-over phase. (author)

The condition for classical slow rolling in new inflation

International Nuclear Information System (INIS)

Sasaki, Misao; Nambu, Yasusada; Nakao, Ken-ichi

1988-01-01

By means of the stochastic description of inflation we investigate the dynamics of a fixed comoving domain in a continuously inflating universe on a global scale, both analytically and numerically. Particular attention is paid to the condition for a domain to enter the classical slow rolling phase. New inflationary universe models with the potential form V(φ) ≅ V 0 -cφ 2n at φ ≅ 0 are considered. The critical value of the scalar field beyond which the field slowly rolls down the potential hill is estimated. We find that for all models under consideration, the condition for classical slow rolling is a sufficient condition for the expected amplitude of density perturbations to be smaller than unity. In other words, the density perturbation amplitude at the later Friedmann stage is always smaller than unity if the universe experienced the classical slow roll-over phase. (orig.)

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....

Sound asleep: processing and retention of slow oscillation phase-targeted stimuli.

Directory of Open Access Journals (Sweden)

Roy Cox

Full Text Available The sleeping brain retains some residual information processing capacity. Although direct evidence is scarce, a substantial literature suggests the phase of slow oscillations during deep sleep to be an important determinant for stimulus processing. Here, we introduce an algorithm for predicting slow oscillations in real-time. Using this approach to present stimuli directed at both oscillatory up and down states, we show neural stimulus processing depends importantly on the slow oscillation phase. During ensuing wakefulness, however, we did not observe differential brain or behavioral responses to these stimulus categories, suggesting no enduring memories were formed. We speculate that while simpler forms of learning may occur during sleep, neocortically based memories are not readily established during deep sleep.

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

The up and down of sleep: From molecules to electrophysiology.

Science.gov (United States)

Navarro-Lobato, Irene; Genzel, Lisa

2018-03-12

Alternations of up and down can be seen across many different levels during sleep. Neural firing-rates, synaptic markers, molecular pathways, and gene expression all show differential up and down regulation across brain areas and sleep stages. And also the hallmarks of sleep - sleep stage specific oscillations - are characterized themselves by up and down as seen within the slow oscillation or theta cycles. In this review, we summarize the up and down of sleep covering molecules to electrophysiology and present different theories how this up and down could be regulated by the up and down of sleep oscillations. Further, we propose a tentative theory how this differential up and down could contribute to various outcomes of sleep related memory consolidation: enhancement of hippocampal representations of very novel memories and cortical consolidation of memories congruent with previous knowledge-networks. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

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

International Nuclear Information System (INIS)

Lelievre Bernard; Feugier, Andre.

1973-01-01

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

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

Science.gov (United States)

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

2017-06-21

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

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

Science.gov (United States)

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

2017-10-02

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

Optimal 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)

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

Directory of Open Access Journals (Sweden)

Hjalmar Rosengren

2006-12-01

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

A multi-resolution approach to heat kernels on discrete surfaces

KAUST Repository

Vaxman, Amir

2010-07-26

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

Compactly Supported Basis Functions as Support Vector Kernels for Classification.

Science.gov (United States)

Wittek, Peter; Tan, Chew Lim

2011-10-01

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

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

Science.gov (United States)

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

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

DEFF Research Database (Denmark)

Li, Gang; Guan, Wei; Sierszecki, Krzysztof

2012-01-01

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

Generalized synthetic kernel approximation for elastic moderation of fast neutrons

International Nuclear Information System (INIS)

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

1975-01-01

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

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 ...

Results from ORNL Characterization of Nominal 350 (micro)m LEUCO Kernels (LEU03) from the BWXT G73V-20-69303 Composite

International Nuclear Information System (INIS)

Kercher, Andrew K.; Hunn, John D.

2006-01-01

kernels could roll down and drop through a slot in the pan, separating them from less round kernels, fragments and debris. A total of 0.37 g was removed in this manner from the 510 g composite. This can be expected to only produce a slight improvement in the ORNL aspect ratio acceptance value. The estimated change in the 95% confidence critical limit due to hand tabling, which was equivalent to removing 2-3 high aspect ratios kernels from the 3847 kernels in the measured sample, would be an increase from <0.48% to approximately <0.57% of the kernels in the composite predicted to have an aspect ratio greater than 1.05.

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

NARCIS (Netherlands)

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

2012-01-01

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

Flour quality and kernel hardness connection in winter wheat

Directory of Open Access Journals (Sweden)

Szabó B. P.

2016-12-01

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

Deep kernel learning method for SAR image target recognition

Science.gov (United States)

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

2017-10-01

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

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

African Journals Online (AJOL)

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

A framework for dense triangular matrix kernels on various manycore architectures

KAUST Repository

Charara, Ali

2017-06-06

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

A framework for dense triangular matrix kernels on various manycore architectures

KAUST Repository

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

2017-01-01

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

PERI - auto-tuning memory-intensive kernels for multicore

International Nuclear Information System (INIS)

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

2008-01-01

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

PERI - Auto-tuning Memory Intensive Kernels for Multicore

Energy Technology Data Exchange (ETDEWEB)

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

2008-06-24

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

Kernel Bayesian ART and ARTMAP.

Science.gov (United States)

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

2018-02-01

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

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

Directory of Open Access Journals (Sweden)

Yi-Hung Liu

2014-01-01

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

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

African Journals Online (AJOL)

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

Variable kernel density estimation in high-dimensional feature spaces

CSIR Research Space (South Africa)

Van der Walt, Christiaan M

2017-02-01

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

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.

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.

Slowing-down calculation for charged particles, application to the calculation of the (alpha, neutron) reaction yield in UO2 - PuO2 fuel

International Nuclear Information System (INIS)

Dulieu, P.

1967-11-01

There are no complete theory nor experimental data sufficient to predict exactly, in a systemic way, the slowing down power of any medium for any ion with any energy. However, in each case, the energy range can be divided in three areas, the low energiy range where the de/dx is an ascending energy function, the intermediate energy region where de/dx has a maximum, the high energy region where de/dx is a descending energy function. In practice, the code Irma 3 allows to obtain with a good precision de/dx for the protons, neutrons, tritons, alphas in any medium. For particles heavier than alpha it is better to use specific methods. In the case of calculating the yield of the (alpha, neutron) reaction in a UO 2 -PuO 2 fuel cell, the divergences of experimental origin, between the existing data lead to adopt a range a factor 1.7 on the yields [fr

Dose calculation methods in photon beam therapy using energy deposition kernels

International Nuclear Information System (INIS)

Ahnesjoe, A.

1991-01-01

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

Boundary singularity of Poisson and harmonic Bergman kernels

Czech Academy of Sciences Publication Activity Database

Engliš, Miroslav

2015-01-01

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

Optimal Bandwidth Selection in Observed-Score Kernel Equating

Science.gov (United States)

Häggström, Jenny; Wiberg, Marie

2014-01-01

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

Spin-down of neutron stars by neutrino emission

International Nuclear Information System (INIS)

Dvornikov, Maxim; Dib, Claudio

2010-01-01

We study the spin-down of a neutron star during its early stages due to the neutrino emission. The mechanism we consider is the subsequent collisions of the produced neutrinos with the outer shells of the star. We find that this mechanism can indeed slow down the star rotation but only in the first tens of seconds of the core formation, which is when the appropriate conditions of flux and collision rate are met. We find that this mechanism can extract less than 1% of the star angular momentum, a result which is much less than previously estimated by other authors.

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...

The slow oscillation in cortical and thalamic networks: mechanisms and functions

Directory of Open Access Journals (Sweden)

Garrett T. Neske

2016-01-01

Full Text Available During even the most quiescent behavioral periods, the cortex and thalamus express rich spontaneous activity in the form of slow (<1 Hz, synchronous network state transitions. Throughout this so-called slow oscillation, cortical and thalamic neurons fluctuate between periods of intense synaptic activity (Up states and almost complete silence (Down states. The two decades since the original characterization of the slow oscillation in the cortex and thalamus have seen considerable advances in deciphering the cellular and network mechanisms associated with this pervasive phenomenon. There are, nevertheless, many questions regarding the slow oscillation that await more thorough illumination, particularly the mechanisms by which Up states initiate and terminate, the functional role of the rhythmic activity cycles in unconscious or minimally conscious states, and the precise relation between Up states and the activated states associated with waking behavior. Given the substantial advances in multineuronal recording and imaging methods in both in vivo and in vitro preparations, the time is ripe to take stock of our current understanding of the slow oscillation and pave the way for future investigations of its mechanisms and functions. My aim in this Review is to provide a comprehensive account of the mechanisms and functions of the slow oscillation, and to suggest avenues for further exploration.

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

NARCIS (Netherlands)

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

2010-01-01

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

Reproducing Kernels and Coherent States on Julia Sets

Energy Technology Data Exchange (ETDEWEB)

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

2007-11-15

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

Reproducing Kernels and Coherent States on Julia Sets

International Nuclear Information System (INIS)

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

2007-01-01

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

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

Science.gov (United States)

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

2017-10-01

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

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

Science.gov (United States)

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

2016-07-07

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

Mixed kernel function support vector regression for global sensitivity analysis

Science.gov (United States)

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

2017-11-01

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

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

Science.gov (United States)

Zeng, Hong; Cheung, Yiu-ming

2011-08-01

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

Semisupervised kernel marginal Fisher analysis for face recognition.

Science.gov (United States)

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

2013-01-01

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

Weighted Feature Gaussian Kernel SVM for Emotion Recognition.

Science.gov (United States)

Wei, Wei; Jia, Qingxuan

2016-01-01

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

Structure and thermal evolution of spinning-down neutron stars

International Nuclear Information System (INIS)

Negreiros, R.; Schramm, S.; Weber, F.

2011-01-01

In this paper we address the effects of spin-down on the cooling of neutron stars. During its evolution, stellar composition and structure might be substantially altered, as a result of spin-down and the consequent density increase. Since the timescale of cooling might be comparable to to that of the spin-evolution, the modifications to the structure/composition might have important effects on the thermal evolution of the object. We show that the direct Urca process might be delayed or supressed, when spin-down is taken into account. This leads to neutron stars with slow cooling, as opposed to enhanced cooling as would be the case if a "froze-in" structure and composition were considered. In conclusion we demonstrate that the inclusion of spin-down effects on the cooling of neutron stars have far-reaching implications for the interpretation of pulsars. (author)

Einstein versus the Simple Pendulum Formula: Does Gravity Slow All Clocks?

Science.gov (United States)

Puri, Avinash

2015-01-01

According to the Newtonian formula for a simple pendulum, the period of a pendulum is inversely proportional to the square root of "g", the gravitational field strength. Einstein's theory of general relativity leads to the result that time slows down where gravity is intense. The two claims look contradictory and can muddle student and…

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.

Slowing down the speed of light using an electromagnetically-induced-transparency mechanism in a modified reservoir

Science.gov (United States)

Liu, Ronggang; Liu, Tong; Wang, Yingying; Li, Yujie; Gai, Bingzheng

2017-11-01

We propose an effective method to achieve extremely slow light by using both the mechanism of electromagnetically induced transparency (EIT) and the localization of a coupled cavity waveguide (CCW). Based on quantum mechanics theory and the dispersion relation of a CCW, we derive a group-velocity formula that reveals both the effects of the EIT and CCW. Results show that ultralow light velocity at the order of several meters per second or even static light, could be obtained feasibly. In comparison with the EIT mechanism in a background of vacuum, this proposed method is more effective and realistic to achieve extremely slow light. And it exhibits potential values in the field of light storage.

Modelling microwave heating of discrete samples of oil palm kernels

International Nuclear Information System (INIS)

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

2016-01-01

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

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

KAUST Repository

Djebbi, Ramzi

2013-08-19

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

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

KAUST Repository

Djebbi, Ramzi; Alkhalifah, Tariq Ali

2013-01-01

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

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

International Nuclear Information System (INIS)

Khanymova, T.; Poloni, E.

1980-01-01

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

Treatment in patients with osteoarthritis at different sites: Place of slow-acting drugs

Directory of Open Access Journals (Sweden)

N. V. Chichasova

2015-01-01

Full Text Available The main goal of osteoarthritis (OA treatment is to perform rational analgesic and anti-inflammatory therapy, to slow down the progression of the disease, and to preserve quality of life in patients. The performance of analgesic therapy in the elderly is impeded by the presence of a concomitant disease, primarily that of the cardiovascular system and gastrointestinal tract. A group of experts has elaborated the algorithm for managing OA patients, which tracks a careful approach to using nonsteroidal anti-inflammatory drugs and confirms the efficacy of slow-acting agents (chondroitin sulfate (CS and glucosamine and intraarticular hyaluronate. The experts have concluded that the use of symptomatic slow-acting drugs for the treatment of OA (SYSADOA, if need be, in combination with short-term paracetanol cycles as basic therapy for this condition is safer and more effective. The 2003 EULAR guidelines identify CS and glucosamine as chondroprotectors. Many studies have shown that CS and glucosamine have a moderate or significant effect on joint pain syndrome and functional mobility in OA; they are safe and characterized by minimal side effects. Long-term qualitative randomized controlled trials have demonstrated that CS and glucosamine are able to slow down the progression of joint space narrowing in OA. It is also shown that the use of a combination of glucosamine and CS allows cartilage loss to be prevented.

Investigation and realization of a slow-positron beam

International Nuclear Information System (INIS)

Ruiz, Nicolas

2011-01-01

This research thesis first proposes a presentation of the GBAR project (Gravitational Behaviour of Anti-hydrogen at Rest) within which this research took place, and which aims at performing the first direct test of the Weak Equivalence Principle on anti-matter by studying the free fall of anti-hydrogen atoms in the Earth gravitational field. The author presents different aspects of this project: scientific objective, experiment principle and structure, detailed structure (positron beam, positron trap, positron/positronium conversion, anti-proton beam, trapping, slowing down and neutralisation of anti-hydrogen ions). The author then reports the design of the positron beam: study of source technology, studies related to the fast positron source, design of the low positron line (approach, functions, simulations, technology). The two last chapters report the construction and the characterization of the slow-positron line [fr

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.

Experimental assessment of the performance of a proposed lead slowing-down spectrometer at WNR/PSR [Weapons Neutron Research/Proton Storage Ring

International Nuclear Information System (INIS)

Moore, M.S.; Koehler, P.E.; Michaudon, A.; Schelberg, A.; Danon, Y.; Block, R.C.; Slovacek, R.E.; Hoff, R.W.; Lougheed, R.W.

1990-01-01

In November 1989, we carried out a measurement of the fission cross section of 247 Cm, 250 Cf, and 254 Es on the Rensselaer Intense Neutron Source (RINS) at Rensselaer Polytechnic Institute (RPI). In July 1990, we carried out a second measurement, using the same fission chamber and electronics, in beam geometry at the Los Alamos Neutron Scattering Center (LANSCE) facility. Using the relative count rates observed in the two experiments, and the flux-enhancement factors determined by the RPI group for a lead slowing-down spectrometer compared to beam geometry, we can assess the performance of a spectrometer similar to RINS, driven by the Proton Storage Ring (PSR) at the Los Alamos National Laboratory. With such a spectrometer, we find that is is feasible to make measurements with samples of 1 ng for fission 1 μg for capture, and of isotopes with half-lives of tens of minutes. It is important to note that, while a significant amount of information can be obtained from the low resolution RINS measurement, a definitive determination of average properties, including the level density, requires that the resonance structure be resolved. 12 refs., 5 figs., 3 tabs

Experimental assessment of the performance of a proposed lead slowing-down spectrometer at WNR/PSR (Weapons Neutron Research/Proton Storage Ring)

Energy Technology Data Exchange (ETDEWEB)

Moore, M.S.; Koehler, P.E.; Michaudon, A.; Schelberg, A. (Los Alamos National Lab., NM (USA)); Danon, Y.; Block, R.C.; Slovacek, R.E. (Rensselaer Polytechnic Inst., Troy, NY (USA)); Hoff, R.W.; Lougheed, R.W. (Lawrence Livermore National Lab., CA (USA))

1990-01-01

In November 1989, we carried out a measurement of the fission cross section of {sup 247}Cm, {sup 250}Cf, and {sup 254}Es on the Rensselaer Intense Neutron Source (RINS) at Rensselaer Polytechnic Institute (RPI). In July 1990, we carried out a second measurement, using the same fission chamber and electronics, in beam geometry at the Los Alamos Neutron Scattering Center (LANSCE) facility. Using the relative count rates observed in the two experiments, and the flux-enhancement factors determined by the RPI group for a lead slowing-down spectrometer compared to beam geometry, we can assess the performance of a spectrometer similar to RINS, driven by the Proton Storage Ring (PSR) at the Los Alamos National Laboratory. With such a spectrometer, we find that is is feasible to make measurements with samples of 1 ng for fission 1 {mu}g for capture, and of isotopes with half-lives of tens of minutes. It is important to note that, while a significant amount of information can be obtained from the low resolution RINS measurement, a definitive determination of average properties, including the level density, requires that the resonance structure be resolved. 12 refs., 5 figs., 3 tabs.

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.

Exocytosis from chromaffin cells: hydrostatic pressure slows vesicle fusion

Science.gov (United States)

Stühmer, Walter

2015-01-01

Pressure affects reaction kinetics because chemical transitions involve changes in volume, and therefore pressure is a standard thermodynamic parameter to measure these volume changes. Many organisms live in environments at external pressures other than one atmosphere (0.1 MPa). Marine animals have adapted to live at depths of over 7000 m (at pressures over 70 MPa), and microorganisms living in trenches at over 110 MPa have been retrieved. Here, kinetic changes in secretion from chromaffin cells, measured as capacitance changes using the patch-clamp technique at pressures of up to 20 MPa are presented. It is known that these high pressures drastically slow down physiological functions. High hydrostatic pressure also affects the kinetics of ion channel gating and the amount of current carried by them, and it drastically slows down synaptic transmission. The results presented here indicate a similar change in volume (activation volume) of 390 ± 57 Å3 for large dense-core vesicles undergoing fusion in chromaffin cells and for degranulation of mast cells. It is significantly larger than activation volumes of voltage-gated ion channels in chromaffin cells. This information will be useful in finding possible protein conformational changes during the reactions involved in vesicle fusion and in testing possible molecular dynamic models of secretory processes. PMID:26009771

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.

Event-Related Potentials for Post-Error and Post-Conflict Slowing

Science.gov (United States)

Chang, Andrew; Chen, Chien-Chung; Li, Hsin-Hung; Li, Chiang-Shan R.

2014-01-01

In a reaction time task, people typically slow down following an error or conflict, each called post-error slowing (PES) and post-conflict slowing (PCS). Despite many studies of the cognitive mechanisms, the neural responses of PES and PCS continue to be debated. In this study, we combined high-density array EEG and a stop-signal task to examine event-related potentials of PES and PCS in sixteen young adult participants. The results showed that the amplitude of N2 is greater during PES but not PCS. In contrast, the peak latency of N2 is longer for PCS but not PES. Furthermore, error-positivity (Pe) but not error-related negativity (ERN) was greater in the stop error trials preceding PES than non-PES trials, suggesting that PES is related to participants' awareness of the error. Together, these findings extend earlier work of cognitive control by specifying the neural correlates of PES and PCS in the stop signal task. PMID:24932780

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

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.

On methods to increase the security of the Linux kernel

International Nuclear Information System (INIS)

Matvejchikov, I.V.

2014-01-01

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

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

Science.gov (United States)

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

2016-04-01

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

Structured Kernel Dictionary Learning with Correlation Constraint for Object Recognition.

Science.gov (United States)

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

2017-06-21

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

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

Significant change in the construction of a door to a room with slowed down neutron field by means of commonly used inexpensive protective materials

International Nuclear Information System (INIS)

Konefal, Adam; Laciak, Marcin; Dawidowska, Anna; Osewski, Wojciech

2014-01-01

The detailed analysis of nuclear reactions occurring in materials of the door is presented for the typical construction of an entrance door to a room with a slowed down neutron field. The changes in the construction of the door were determined to reduce effectively the level of neutron and gamma radiation in the vicinity of the door in a room adjoining the neutron field room. Optimisation of the door construction was performed with the use of Monte Carlo calculations (GEANT4). The construction proposed in this paper bases on the commonly used inexpensive protective materials such as borax (13.4 cm), lead (4 cm) and stainless steel (0.1 and 0.5 cm on the side of the neutron field room and of the adjoining room, respectively). The improved construction of the door, worked out in the presented studies, can be an effective protection against neutrons with energies up to 1 MeV (authors)

Effect of the size of experimental channels of the lead slowing-down spectrometer SVZ-100 (Institute for Nuclear Research, Moscow) on the moderation constant

Energy Technology Data Exchange (ETDEWEB)

Latysheva, L. N.; Bergman, A. A.; Sobolevsky, N. M., E-mail: sobolevs@inr.ru [Russian Academy of Sciences, Institute for Nuclear Research (Russian Federation); Ilic, R. D. [Vinca Institute of Nuclear Sciences (Serbia)

2013-04-15

Lead slowing-down (LSD) spectrometers have a low energy resolution (about 30%), but their luminosity is 10{sup 3} to 10{sup 4} times higher than that of time-of-flight (TOF) spectrometers. A high luminosity of LSD spectrometers makes it possible to use them to measure neutron cross section for samples of mass about several micrograms. These features specify a niche for the application of LSD spectrometers in measuring neutron cross sections for elements hardly available in macroscopic amounts-in particular, for actinides. A mathematical simulation of the parameters of SVZ-100 LSD spectrometer of the Institute for Nuclear Research (INR, Moscow) is performed in the present study on the basis of the MCNPX code. It is found that the moderation constant, which is the main parameter of LSD spectrometers, is highly sensitive to the size and shape of detecting volumes in calculations and, hence, to the real size of experimental channels of the LSD spectrometer.

Effect of the size of experimental channels of the lead slowing-down spectrometer SVZ-100 (Institute for Nuclear Research, Moscow) on the moderation constant

International Nuclear Information System (INIS)

Latysheva, L. N.; Bergman, A. A.; Sobolevsky, N. M.; Ilić, R. D.

2013-01-01

Lead slowing-down (LSD) spectrometers have a low energy resolution (about 30%), but their luminosity is 10 3 to 10 4 times higher than that of time-of-flight (TOF) spectrometers. A high luminosity of LSD spectrometers makes it possible to use them to measure neutron cross section for samples of mass about several micrograms. These features specify a niche for the application of LSD spectrometers in measuring neutron cross sections for elements hardly available in macroscopic amounts—in particular, for actinides. A mathematical simulation of the parameters of SVZ-100 LSD spectrometer of the Institute for Nuclear Research (INR, Moscow) is performed in the present study on the basis of the MCNPX code. It is found that the moderation constant, which is the main parameter of LSD spectrometers, is highly sensitive to the size and shape of detecting volumes in calculations and, hence, to the real size of experimental channels of the LSD spectrometer.

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.

Slow decay of magnetic fields in open Friedmann universes

International Nuclear Information System (INIS)

Barrow, John D.; Tsagas, Christos G.

2008-01-01

Magnetic fields in Friedmann universes can experience superadiabatic growth without departing from conventional electromagnetism. The reason is the relativistic coupling between vector fields and spacetime geometry, which slows down the decay of large-scale magnetic fields in open universes, compared to that seen in perfectly flat models. The result is a large relative gain in magnetic strength that can lead to astrophysically interesting B fields, even if our Universe is only marginally open today

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...

Explicit signal to noise ratio in reproducing kernel Hilbert spaces

DEFF Research Database (Denmark)

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

2011-01-01

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

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

Directory of Open Access Journals (Sweden)

Rosanna Zivoli

2016-01-01

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

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

African Journals Online (AJOL)

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

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