FRACTIONAL INTEGRALS WITH VARIABLE KERNELS ON HARDY SPACES
ZhangPu; DingYong
2003-01-01
The fractional integral operators with variable kernels are discussed.It is proved that if the kernel satisfies the Dini-condition,then the fractional integral operators with variable kernels are bounded from Hp(Rn) into Lq(Rn) when 0
Parameter optimization in the regularized kernel minimum noise fraction transformation
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....
Nutrition Characters of Sweet Corns in Kernel Milky Maturity
WuMoucheng; ChenXiaoyi
2000-01-01
Three corn varieties,supper-sweet corn(S),standard-sweet corn(M),corn-non corn(C) were used for nutritional composition measurement during kernel milky maturity.The variations of protein,vitamin,total sugar showed as parabola.Mineral elements and fiber increase and reducing sugar decreased gradually.Total sugar,protein and fat in the sweet corn were much richer than those in common corn.VE and VC were very plentiful,and lysine was high.Proper harvest time of sweet corn M and S were DAP (days after pollination)19-21,and DAP 18-21 respectively.
Nutrition Characters of Sweet Corns in Kernel Milky Maturity
Wu Moucheng; Chen Xiaoyi
2000-01-01
Three corn varieties, supper-sweet corn (S), standard-sweet corn (M), common corn(C) were used for nutritional composition measurement during kernel milky maturity.The variations of protein, vitamin, total sugar showed as parabola. Mineral elements and fiber increase and reducing sugar decreased gradually. Total sugar, protein and fat in the sweet corn were much richer than those in common corn. VE and Vc were very plentiful, and lysine was high. Proper harvest time of sweet corn M and S were DAP (days after pollination)19-21 ,and DAP 18-21 respectively.
Generalized fractional integral transform with Whittaker's kernel
Rodrigues, M. Manuela
2013-10-01
In this work we present two generalizations (see the operators I0+α,ρ,σ and I-α,ρ,σ defined below) of the classical Liouville fractional integrals. We study their boundedness as operators mapping the space Lv,r into the spaces Lv+2+Re(α)/2-2Re(ρ),r and Lv+1+-Re(ρ),r. In the end, we will apply our generalization to some particular functions.
Reproducing Kernel Method for Fractional Riccati Differential Equations
X. Y. Li
2014-01-01
Full Text Available This paper is devoted to a new numerical method for fractional Riccati differential equations. The method combines the reproducing kernel method and the quasilinearization technique. Its main advantage is that it can produce good approximations in a larger interval, rather than a local vicinity of the initial position. Numerical results are compared with some existing methods to show the accuracy and effectiveness of the present method.
Gabor-based kernel PCA with fractional power polynomial models for face recognition.
Liu, Chengjun
2004-05-01
This paper presents a novel Gabor-based kernel Principal Component Analysis (PCA) method by integrating the Gabor wavelet representation of face images and the kernel PCA method for face recognition. Gabor wavelets first derive desirable facial features characterized by spatial frequency, spatial locality, and orientation selectivity to cope with the variations due to illumination and facial expression changes. The kernel PCA method is then extended to include fractional power polynomial models for enhanced face recognition performance. A fractional power polynomial, however, does not necessarily define a kernel function, as it might not define a positive semidefinite Gram matrix. Note that the sigmoid kernels, one of the three classes of widely used kernel functions (polynomial kernels, Gaussian kernels, and sigmoid kernels), do not actually define a positive semidefinite Gram matrix either. Nevertheless, the sigmoid kernels have been successfully used in practice, such as in building support vector machines. In order to derive real kernel PCA features, we apply only those kernel PCA eigenvectors that are associated with positive eigenvalues. The feasibility of the Gabor-based kernel PCA method with fractional power polynomial models has been successfully tested on both frontal and pose-angled face recognition, using two data sets from the FERET database and the CMU PIE database, respectively. The FERET data set contains 600 frontal face images of 200 subjects, while the PIE data set consists of 680 images across five poses (left and right profiles, left and right half profiles, and frontal view) with two different facial expressions (neutral and smiling) of 68 subjects. The effectiveness of the Gabor-based kernel PCA method with fractional power polynomial models is shown in terms of both absolute performance indices and comparative performance against the PCA method, the kernel PCA method with polynomial kernels, the kernel PCA method with fractional power
Does Illumination of Non-Mature Cereal Kernels During Drying Affect the Germination Ability?
Małuszyńska Elżbieta
2016-06-01
the germination ability of non-mature kernels depends on all studied factors: lighting during drying, terms of harvesting and the interaction light * term;non mature kernelsare more sensitive to drying conditions;lighting during seeds drying can have a positive effect on ability to germination;for breeding practice it would be better to harvest kernels at 23 DAF and dry them at room conditions under incandescent lamp.
The water activity (aw) and moisture content (KMC) of individual peanut kernels representing five different maturity stages were measured during a period of late-season drought stress leading up to normal harvest time. Curves were generated describing the relationship between aw and KMC for yellow 1...
Kernel maximum autocorrelation factor and minimum noise fraction transformations
Nielsen, Allan Aasbjerg
2010-01-01
) dimensional feature space via the kernel function and then performing a linear analysis in that space. Three examples show the very successful application of kernel MAF/MNF analysis to 1) change detection in DLR 3K camera data recorded 0.7 seconds apart over a busy motorway, 2) change detection...
Relationship between processing score and kernel-fraction particle size in whole-plant corn silage.
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
Michelitsch, Thomas; Riascos, Alejandro; Nowakowski, Andrzej F; Nicolleau, Franck C G A
2016-01-01
We introduce positive elastic potentials in the harmonic approximation leading by Hamilton's variational principle to fractional Laplacian matrices having the forms of power law matrix functions of the simple local Bornvon Karman Laplacian. The fractional Laplacian matrices are well defined on periodic and infinite lattices in $n=1,2,3,..$ dimensions. The present approach generalizes the central symmetric second differenceoperator (Born von Karman Laplacian) to its fractional central symmetric counterpart (Fractional Laplacian matrix).For non-integer powers of the Born von Karman Laplacian, the fractional Laplacian matrix is nondiagonal with nonzero matrix elements everywhere, corresponding to nonlocal behavior: For large lattices the matrix elements far from the diagonal expose power law asymptotics leading to continuum limit kernels of Riesz fractional derivative type. We present explicit results for the fractional Laplacian matrix in 1D for finite periodic and infinite linear chains and their Riesz fractio...
Atangana Abdon
2016-01-01
Full Text Available In this manuscript we proposed a new fractional derivative with non-local and no-singular kernel. We presented some useful properties of the new derivative and applied it to solve the fractional heat transfer model.
Samia Bushnaq
2014-01-01
Full Text Available We present a new version of the reproducing kernel Hilbert space method (RKHSM for the solution of systems of fractional integrodifferential equations. In this approach, the solution is obtained as a convergent series with easily computable components. Several illustrative examples are given to demonstrate the effectiveness of the present method. The method described in this paper is expected to be further employed to solve similar nonlinear problems in fractional calculus.
Space-time fractional diffusion equation using a derivative with nonsingular and regular kernel
Gómez-Aguilar, J. F.
2017-01-01
In this paper, using the fractional operators with Mittag-Leffler kernel in Caputo and Riemann-Liouville sense the space-time fractional diffusion equation is modified, the fractional equation will be examined separately; with fractional spatial derivative and fractional temporal derivative. For the study cases, the order considered is 0 < β , γ ≤ 1 respectively. In this alternative representation we introduce the appropriate fractional dimensional parameters which characterize consistently the existence of the fractional space-time derivatives into the fractional diffusion equation, these parameters related to equation results in a fractal space-time geometry provide a new family of solutions for the diffusive processes. The proposed mathematical representation can be useful to understand electrochemical phenomena, propagation of energy in dissipative systems, viscoelastic materials, material heterogeneities and media with different scales.
A Lyapunov type inequality for fractional operators with nonsingular Mittag-Leffler kernel.
Abdeljawad, Thabet
2017-01-01
In this article, we extend fractional operators with nonsingular Mittag-Leffler kernels, a study initiated recently by Atangana and Baleanu, from order [Formula: see text] to higher arbitrary order and we formulate their correspondent integral operators. We prove existence and uniqueness theorems for the Caputo ([Formula: see text]) and Riemann ([Formula: see text]) type initial value problems by using the Banach contraction theorem. Then we prove a Lyapunov type inequality for the Riemann type fractional boundary value problems of order [Formula: see text] in the frame of Mittag-Leffler kernels. Illustrative examples are analyzed and an application as regards the Sturm-Liouville eigenvalue problem in the sense of this fractional calculus is given as well.
Analysis of the Keller–Segel Model with a Fractional Derivative without Singular Kernel
Abdon Atangana
2015-06-01
Full Text Available Using some investigations based on information theory, the model proposed by Keller and Segel was extended to the concept of fractional derivative using the derivative with fractional order without singular kernel recently proposed by Caputo and Fabrizio. We present in detail the existence of the coupled-solutions using the fixed-point theorem. A detailed analysis of the uniqueness of the coupled-solutions is also presented. Using an iterative approach, we derive special coupled-solutions of the modified system and we present some numerical simulations to see the effect of the fractional order.
FRACTIONAL INTEGRALS WITH VARIABLE KERNELS ON HARDY SPACES%变量核分数次积分在Hardy空间的有界性
张璞; 丁勇
2003-01-01
The fractional integral operators with variable kernels are discussed.It is proved that if the kernel satisfies the Dini-condition,then the fractional integral operators with variable kernels are bounded from ≤1 and 1/ q =1/ p-α/n .The results in this paper improve the results obtained by Ding,Chen and Fan in 2002.
Zarei, Mohammad; Ghanbari, Rahele; Tajabadi, Naser; Abdul-Hamid, Azizah; Bakar, Fatimah Abu; Saari, Nazamid
2016-02-01
Palm kernel cake protein was hydrolyzed with different proteases namely papain, bromelain, subtilisin, flavourzyme, trypsin, chymotrypsin, and pepsin to generate different protein hydrolysates. Peptide content and iron-chelating activity of each hydrolysate were evaluated using O-phthaldialdehyde-based spectrophotometric method and ferrozine-based colorimetric assay, respectively. The results revealed a positive correlation between peptide contents and iron-chelating activities of the protein hydrolysates. Protein hydrolysate generated by papain exhibited the highest peptide content of 10.5 mM and highest iron-chelating activity of 64.8% compared with the other hydrolysates. Profiling of the papain-generated hydrolysate by reverse phase high performance liquid chromatography fractionation indicated a direct association between peptide content and iron-chelating activity in most of the fractions. Further fractionation using isoelectric focusing also revealed that protein hydrolysate with basic and neutral isoelectric point (pI) had the highest iron-chelating activity, although a few fractions in the acidic range also exhibited good metal chelating potential. After identification and synthesis of papain-generated peptides, GGIF and YLLLK showed among the highest iron-chelating activities of 56% and 53%, whereas their IC50 were 1.4 and 0.2 μM, respectively.
Yu-xin Zhao
2014-01-01
Full Text Available This paper presents a novel wavelet kernel neural network (WKNN with wavelet kernel function. It is applicable in online learning with adaptive parameters and is applied on parameters tuning of fractional-order PID (FOPID controller, which could handle time delay problem of the complex control system. Combining the wavelet function and the kernel function, the wavelet kernel function is adopted and validated the availability for neural network. Compared to the conservative wavelet neural network, the most innovative character of the WKNN is its rapid convergence and high precision in parameters updating process. Furthermore, the integrated pressurized water reactor (IPWR system is established by RELAP5, and a novel control strategy combining WKNN and fuzzy logic rule is proposed for shortening controlling time and utilizing the experiential knowledge sufficiently. Finally, experiment results verify that the control strategy and controller proposed have the practicability and reliability in actual complicated system.
Jing Shao
2014-01-01
Full Text Available Some new integral inequalities with weakly singular kernel for discontinuous functions are established using the method of successive iteration and properties of Mittag-Leffler function, which can be used in the qualitative analysis of the solutions to certain impulsive fractional differential systems.
Blending of mango kernel fat and palm oil mid-fraction to obtain cocoa butter equivalent.
Sonwai, Sopark; Kaphueakngam, Phimnipha; Flood, Adrian
2014-10-01
Cocoa butter equivalent (CBE) was produced from a blend of mango kernel fat (MKF) and palm oil mid-fraction (PMF). Five fat blends with different ratios of MKF/PMF (90/10, 80/20, 70/30, 60/40 and 50/50 (%wt)) and pure MKF, PMF and cocoa butter (CB) were characterized. Similar to CB, all fat blends contained palmitic (P), stearic (S) and oleic (O) acids as the main fatty acid components. The triglyceride compositions of all blends were significantly different from CB. However, blend 80/20, which contained higher content of SOS, similar content of POP and lower content of POS compared to CB, exhibited a slip melting point, crystallization and melting behavior most similar to CB and hence it was recommended as CBE. The chosen CBE was then mixed with CB in a ratio of 1:5.64 (wt), mimicking that of typical dark chocolate where 5 % of CBE is added to the finished product. The crystallization behavior, the crystal morphology and bloom behavior of the mixture was investigated and was found to be not significantly different from CB.
Abdulhameed, M.; Vieru, D.; Roslan, R.
2017-10-01
This paper investigates the electro-magneto-hydrodynamic flow of the non-Newtonian behavior of biofluids, with heat transfer, through a cylindrical microchannel. The fluid is acted by an arbitrary time-dependent pressure gradient, an external electric field and an external magnetic field. The governing equations are considered as fractional partial differential equations based on the Caputo-Fabrizio time-fractional derivatives without singular kernel. The usefulness of fractional calculus to study fluid flows or heat and mass transfer phenomena was proven. Several experimental measurements led to conclusion that, in such problems, the models described by fractional differential equations are more suitable. The most common time-fractional derivative used in Continuum Mechanics is Caputo derivative. However, two disadvantages appear when this derivative is used. First, the definition kernel is a singular function and, secondly, the analytical expressions of the problem solutions are expressed by generalized functions (Mittag-Leffler, Lorenzo-Hartley, Robotnov, etc.) which, generally, are not adequate to numerical calculations. The new time-fractional derivative Caputo-Fabrizio, without singular kernel, is more suitable to solve various theoretical and practical problems which involve fractional differential equations. Using the Caputo-Fabrizio derivative, calculations are simpler and, the obtained solutions are expressed by elementary functions. Analytical solutions of the biofluid velocity and thermal transport are obtained by means of the Laplace and finite Hankel transforms. The influence of the fractional parameter, Eckert number and Joule heating parameter on the biofluid velocity and thermal transport are numerically analyzed and graphic presented. This fact can be an important in Biochip technology, thus making it possible to use this analysis technique extremely effective to control bioliquid samples of nanovolumes in microfluidic devices used for biological
Li, Jun-Bao
2012-09-01
This paper presents Gabor filter based optical image recognition using Fractional Power Polynomial model based Common Kernel Discriminant Locality Preserving Projection. This method tends to solve the nonlinear classification problem endured by optical image recognition owing to the complex illumination condition in practical applications, such as face recognition. The first step is to apply Gabor filter to extract desirable textural features characterized by spatial frequency, spatial locality and orientation selectivity to cope with the variations in illumination. In the second step we propose Class-wise Locality Preserving Projection through creating the nearest neighbor graph guided by the class labels for the textural features reduction. Finally we present Common Kernel Discriminant Vector with Fractional Power Polynomial model to reduce the dimensions of the textural features for recognition. For the performance evaluation on optical image recognition, we test the proposed method on a challenging optical image recognition problem, face recognition.
Azad Saei
2014-02-01
Full Text Available Fully mature dendritic cells (DCs play pivotal role in inducing immune responses and converting naïve T lymphocytes into functional Th1 cells. We aimed to evaluate Listeria Monocytogenes-derived protein fractions to induce DC maturation and stimulating T helper (Th1 immune responses.In the present study, we fractionated Listeria Monocytogenes-derived proteins by adding of ammonium sulfate in a stepwise manner. DCs were also generated from C57BL/6 mice bone marrow precursor cells. Then, the effects of protein fractions on bone marrow derived DC (BMDC maturation were evaluated. In addition, we assessed the capacity of activated DCs to induce cytokine production and proliferation of lymphocytes.Listeria-derived protein fractions induced fully mature DCs expressing high costimulatory molecules such as CD80, CD86 and CD40. DCs that were activated by selected F3 fraction had low capacity to uptake exogenous antigens while secreted high levels of Interleukine (IL-12. Moreover, lymphocytes cultured with activated BMDCs produced high amounts of IFN-γ and showed higher proliferation than control. Listeria derived protein fractions differently influenced DC maturation.In conclusion, Listeria protein activated-BMDCs can be used as a cell based vaccine to induce anti-tumor immune responses.
Biorefinery methods for separation of protein and oil fractions from rubber seed kernel
Widyarani, R.; Ratnaningsih, E.; Sanders, J.P.M.; Bruins, M.E.
2014-01-01
Biorefinery of rubber seeds can generate additional income for farmers, who already grow rubber trees for latex production. The aim of this study was to find the best method for protein and oil production from rubber seed kernel, with focus on protein recovery. Different pre-treatments and oil separ
Andrea Sorzia
2016-01-01
Full Text Available A tensile test until breakage and a creep and relaxation test on a polypropylene fibre are carried out and the resulting creep and stress relaxation curves are fit by a model adopting a fraction-exponential kernel in the viscoelastic operator. The models using fraction-exponential functions are simpler than the complex ones obtained from combination of dashpots and springs and, furthermore, are suitable for fitting experimental data with good approximation allowing, at the same time, obtaining inverse Laplace transform in closed form. Therefore, the viscoelastic response of polypropylene fibres can be modelled straightforwardly through analytical methods. Addition of polypropylene fibres greatly improves the tensile strength of composite materials with concrete matrix. The proposed analytical model can be employed for simulating the mechanical behaviour of composite materials with embedded viscoelastic fibres.
Non-ablative fractional laser provides long-term improvement of mature burn scars
Taudorf, Elisabeth H; Danielsen, Patricia L; Paulsen, Ida F
2015-01-01
BACKGROUND AND OBJECTIVES: Non-ablative fractional laser-treatment is evolving for burn scars. The objective of this study was to evaluate clinical and histological long-term outcome of 1,540 nm fractional Erbium: Glass laser, targeting superficial, and deep components of mature burn scars......-Patient-and-Observer-Scar-Assessment-Scale (mPOSAS, 1 = "normal skin", 10 = "worst imaginable scar"). Secondary outcomes included histology, patient satisfaction (0-10), patient-assessed improvement, and safety. RESULTS: Study was completed by 17 of 20 randomized patients with normotrophic (n = 11), hypertrophic (n = 5) or atrophic (n = 1...... of scar-appearance. CONCLUSIONS: Combined superficial and deep non-ablative fractional laser-treatments induce long-term clinical and histological improvement of mature burn scars....
Wong, Yoke-Ming; Brigham, Christopher J; Rha, ChoKyun; Sinskey, Anthony J; Sudesh, Kumar
2012-10-01
The potential of plant oils as sole carbon sources for production of P(3HB-co-3HHx) copolymer containing a high 3HHx monomer fraction using the recombinant Cupriavidus necator strain Re2160/pCB113 has been investigated. Various types and concentrations of plant oils were evaluated for efficient conversion of P(3HB-co-3HHx) copolymer. Crude palm kernel oil (CPKO) at a concentration of 2.5 g/L was found to be most suitable for production of copolymer with a 3HHx content of approximately 70 mol%. The time profile of these cells was also examined in order to study the trend of 3HHx monomer incorporation, PHA production and PHA synthase activity. (1)H NMR and (13)C NMR analyses confirmed the presence of P(3HB-co-3HHx) copolymer containing a high 3HHx monomer fraction, in which monomers were not randomly distributed. The results of various characterization analyses revealed that the copolymers containing a high 3HHx monomer fraction demonstrated soft and flexible mechanical properties. Copyright © 2012 Elsevier Ltd. All rights reserved.
J. F. Gómez-Aguilar
2016-01-01
Full Text Available We present an alternative representation of integer and fractional electrical elements in the Laplace domain for modeling electrochemical systems represented by equivalent electrical circuits. The fractional derivatives considered are of Caputo and Caputo-Fabrizio type. This representation includes distributed elements of the Cole model type. In addition to maintaining consistency in adjusted electrical parameters, a detailed methodology is proposed to build the equivalent circuits. Illustrative examples are given and the Nyquist and Bode graphs are obtained from the numerical simulation of the corresponding transfer functions using arbitrary electrical parameters in order to illustrate the methodology. The advantage of our representation appears according to the comparison between our model and models presented in the paper, which are not physically acceptable due to the dimensional incompatibility. The Markovian nature of the models is recovered when the order of the fractional derivatives is equal to 1.
Distribution of carotenoids in endosperm, germ, and aleurone fractions of cereal grain kernels.
Ndolo, Victoria U; Beta, Trust
2013-08-15
To compare the distribution of carotenoids across the grain, non-corn and corn cereals were hand dissected into endosperm, germ and aleurone fractions. Total carotenoid content (TCC) and carotenoid composition were analysed using spectrophotometry and HPLC. Cereal carotenoid composition was similar; however, concentrations varied significantly (paleurone layer had zeaxanthin levels 2- to 5-fold higher than lutein among the cereals. Positive significant correlations (paleurone layer. Our findings suggest that the aleurone of wheat, oat, corn and germ of barley have significantly enhanced carotenoid levels.
Busakorn Mahisanunt
2017-01-01
Full Text Available The present study performed isothermal (25 °C solvent fractionation of rambutan (Nephelium lappaceum L. kernel fat (RKF to obtain the fat fraction that had melting properties comparable to a commercial hydrogenated solid fat. The effect of two fractionation parameters, holding time (12, 18 and 24 h and solvent types (acetone and ethanol, on the properties of fractionated fat were investigated. The results showed that a fractionation time increase caused an increased yield and decreased iodine value for the high melting or stearin fractions. The thermal behaviors and solid fat index (SFI of these stearin fractions were different from the original fat, especially for stearin from acetone fractionation. The major fatty acid in this stearin fraction was arachidic acid (C20:0 consisting of more than 90%. Overall, we demonstrated that acetone fractionation of RKF at 25 °C for 24 h is effective for producing a solid fat fraction, which has comparable crystallizing and melting properties to commercial hydrogenated fat. The fractionated rambutan fat obtained by this process may lead to its potential use in specific food products.
Space Weathering in the Fine Size Fractions of Lunar Soils: Soil Maturity Effects
Keller, L. P.; Wentworth, S. J.; McKay, D. S.; Taylor, L. A.; Pieters, C.; Morris, R. V.
1999-01-01
The effects of space weathering on the optical properties of lunar materials have been well documented. These effects include a reddened continuum slope, lowered albedo, and attenuated absorption features in reflectance spectra of lunar soils as compared to finely comminuted rocks from the same Apollo sites. However, the regolith processes that cause these effects are not well known, nor is the petrographic setting of the products of these processes fully understood. A Lunar Soil Characterization Consortium has been formed with the purpose of systematically integrating chemical and mineralogical data with the optical properties of lunar soils. Understanding space-weathering effects is critical in order to fully integrate the lunar sample collection with remotely-sensed data from recent robotic missions (e.g., Lunar Prospector, Clementine, and Galileo) We have shown that depositional processes (condensation of impact-derived vapors, sputter deposits, accreted impact material, e.g., splash glass, spherules, etc.) are a major factor in the modification of the optical surfaces of lunar regolith materials. In mature soils, it is the size and distribution of the nanophase metal in the soil grains that has the major effect on optical properties. In this report, we compare and contrast the space-weathering effects in an immature and a mature soil with similar elemental compositions. For this study, we analyzed effects). The nanophase Fe in these rims probably accounts for a significant fraction of the increase in Is/FeO measured in these size fractions. In addition to the rims, the majority of particles also show abundant accreted material in the form of glass splashes and spherules that also contain nanophase Fe. In stark contrast, the surfaces of the mineral grains in the 71061 sample are relatively prisitine, as only about 14% of the mineral grains in the sample exhibited amorphous rims. Furthermore, the mineral particles are more angular and show greater surface
Yang, Zhong; Zhang, Maomao; Xin, Donglin; Wang, Jingfeng; Zhang, Junhua
2014-12-01
The production of fermentable sugars from different fractions of bamboo shoots and mature bamboos (Phyllostachys heterocycla var. pubescens) by cellulase and/or xylanase was investigated. Aqueous ammonia pretreatment exhibited high but different delignification capacities for different bamboo fractions. Supplementation of cellulases with xylanase synergistically improved the glucose and xylose yields of mature bamboo fractions. High hydrolyzability was observed in the hydrolysis of both non-pretreated and pretreated bamboo shoot fractions, suggesting pretreatment was not necessary for the hydrolysis of bamboo shoots. High hydrolyzability together with the advantages of low lignin content, fast growth, and widely distribution demonstrated that bamboo shoots were excellent lignocellulosic materials for the production of bioethanol and other biochemicals.
Lorenzatto, Karina R; Kim, Kyunggon; Ntai, Ioanna; Paludo, Gabriela P; Camargo de Lima, Jeferson; Thomas, Paul M; Kelleher, Neil L; Ferreira, Henrique B
2015-11-01
Echinococcus granulosus is the causative agent of cystic hydatid disease, a neglected zoonosis responsible for high morbidity and mortality. Several molecular mechanisms underlying parasite biology remain poorly understood. Here, E. granulosus subcellular fractions were analyzed by top down and bottom up proteomics for protein identification and characterization of co-translational and post-translational modifications (CTMs and PTMs, respectively). Nuclear and cytosolic extracts of E. granulosus protoscoleces were fractionated by 10% GELFrEE and proteins under 30 kDa were analyzed by LC-MS/MS. By top down analysis, 186 proteins and 207 proteoforms were identified, of which 122 and 52 proteoforms were exclusively detected in nuclear and cytosolic fractions, respectively. CTMs were evident as 71% of the proteoforms had methionine excised and 47% were N-terminal acetylated. In addition, in silico internal acetylation prediction coupled with top down MS allowed the characterization of 9 proteins differentially acetylated, including histones. Bottom up analysis increased the overall number of identified proteins in nuclear and cytosolic fractions to 154 and 112, respectively. Overall, our results provided the first description of the low mass proteome of E. granulosus subcellular fractions and highlighted proteoforms with CTMs and PTMS whose characterization may lead to another level of understanding about molecular mechanisms controlling parasitic flatworm biology.
Kernel versions of some orthogonal transformations
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...
Corn stover fractions as a function of hybrid, maturity, site and year
Lizotte, P.L. [Laval Univ., Quebec City, PQ (Canada). Dept. des sols et de genie agroalimentaire; Savoie, P. [Agriculture and Agri-Food Canada, Quebec City, PQ (Canada); Lefsrud, M. [McGill Univ., Macdonald College, Ste-Anne-de-Bellevue, PQ (Canada). Dept. of Biosystems Engineering
2010-07-01
Corn stover is usually left on the ground following corn harvest so that it can be incorporated into the soil as organic matter and to protect against erosion. Part of the corn stover is oxidized in the atmosphere. Corn stover represents between 40 and 50 per cent of the dry matter (DM) contained in the aerial biomass of corn plants. Recent studies have shown that half of the corn stover could be harvested sustainably on low-sloping land under no-till practice. In Quebec, where 400,000 ha of corn are planted each year, corn stover could provide one million t DM of currently neglected biomass. Various hybrids of corn were monitored from early September to late November at 4 different sites during 2007, 2008 and 2009. Whole plants cut at 100 mm above the ground were collected weekly and separated into 7 fractions, notably the grain, the cob, the husk, the stalk below the ear, the stalk above the ear, the leaves below the ear and the leaves above the ear. In 2007, corn ears on average, were at 0.96 m above the ground at a site with low crop heat units (CHU). Hybrids grown in a warmer site were taller and their ears were 1.21 m above the ground. The DM partitioned in 7 components was 54 per cent grain, 14 per cent bottom stalk, 6 per cent top stalk, 5 per cent bottom leaves, 7 per cent top leaves, 5 per cent husk and 9 per cent cob. The total mass of fibre during harvest decreased from 8.9 to 6.6 t DM/ha for a low CHU hybrid and from 9.3 to 8.3 t DM/ha for a high CHU hybrid. Grain yield increased in 2008 from 3.8 to 7.6 t DM/ha over a 12-week period.
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...... analysis, looking at the class of subsampled realised kernels and we derive the limit theory for this class of estimators. We find that subsampling is highly advantageous for estimators based on discontinuous kernels, such as the truncated kernel. For kinked kernels, such as the Bartlett kernel, we show...... 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...
Glombitza, Clemens; Mangelsdorf, Kai; Horsfield, Brian
2016-01-01
differences in the fatty acid composition between bitumen and kerogen-bound acids, both in the short (fatty acid range. The compositions of these two acid fractions changed independently as a function of maturation. This points to the long and short chain fatty acids in bitumen......Oxygen-bearing functional groups, in particular the carboxylic groups of acids and esters, are mainly responsible for the chemical reactivity of sedimentary organic matter. We have studied kerogen and bitumen fractions from a coalification series from the New Zealand Coal Band covering the rank...... range from 0.28% to 0.80% vitrinite reflectance. We investigated the composition of fatty acids separated from the bitumen, and compared this to the distribution of kerogen-bound fatty acids (esters) obtained after selective chemical degradation of the macromolecular organic matter. We found remarkable...
Kernel based orthogonalization for change detection in hyperspectral images
Nielsen, Allan Aasbjerg
Kernel versions of principal component analysis (PCA) and minimum noise fraction (MNF) analysis are applied to change detection in hyperspectral image (HyMap) data. The kernel versions are based on so-called Q-mode analysis in which the data enter into the analysis via inner products in the Gram...... the kernel function and then performing a linear analysis in that space. An example shows the successful application of (kernel PCA and) kernel MNF analysis to change detection in HyMap data covering a small agricultural area near Lake Waging-Taching, Bavaria, in Southern Germany. In the change detection...
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...
2008-08-01
Berg et al., 1984] has been used in a machine learning context by Cuturi and Vert [2005]. Definition 26 Let (X ,+) be a semigroup .2 A function ϕ : X...R is called pd (in the semigroup sense) if k : X × X → R, defined as k(x, y) = ϕ(x + y), is a pd kernel. Likewise, ϕ is called nd if k is a nd...kernel. Accordingly, these are called semigroup kernels. 7.3 Jensen-Shannon and Tsallis kernels The basic result that allows deriving pd kernels based on
Vormstein, Svendja; Kaiser, Michael; Ludwig, Bernard
2017-04-01
Forest top- and subsoil account for approximately 70 % of the organic C (OC) globally stored in soil reasoning their large importance for terrestrial ecosystem services such as the mitigation of climate change. In contrast to forest topsoil, there is much less information about the decomposition and stabilization of organic matter (OM) in subsoil. Therefore, we sampled the pedogenetic horizons of five soils under mature beech forest developed on different parent material (i.e. Tertiary Sand, Loess, Basalt, Lime Stone, Red Sandstone) down to the bedrock. The bulk soil samples were characterized for texture, oxalate and dithionite soluble Fe and Al, pH, OC, microbial biomass C and basal respiration (cumulative CO2 emission after 7 and 14 days). Furthermore, we analyzed aggregate size fractions separated by wet-sieving (i.e. >1000 µm, 1000-250 µm, 250-53 µm, 1000 µm). In contrast, the major part of the topsoil OC on Basalt and Tertiary Sand was found in the smaller macro-aggregates (1000-250 µm). For the topsoil samples, we found that the basal respiration as well as the microbial biomass C were positively correlated (p ≤0.05) with the OC amounts associated with the free and occluded light fraction and with the macro-aggregates (1000-250 µm) and micro-aggregates (250-53 µm) suggesting these fractions to store the major part of the easily decomposable OM. The OC amount associated with the heavy fraction and the fraction important for the OM stabilization in forest topsoil. In the subsoil (horizons below the Ah), the contribution of the OC associated with the aggregate size fractions 53 µm were positively correlated with basal respiration and the microbial biomass C. This suggests, in contrast to the topsoil, the easily decomposable OM to be distributed more homogeneously among fractions. Only the OC content of the relevant stabilization mechanisms. The results point toward similar OM stabilization mechanisms in the analysed forest top- and subsoils but
Approximate kernel competitive learning.
Wu, Jian-Sheng; Zheng, Wei-Shi; Lai, Jian-Huang
2015-03-01
Kernel competitive learning has been successfully used to achieve robust clustering. However, kernel competitive learning (KCL) is not scalable for large scale data processing, because (1) it has to calculate and store the full kernel matrix that is too large to be calculated and kept in the memory and (2) it cannot be computed in parallel. In this paper we develop a framework of approximate kernel competitive learning for processing large scale dataset. The proposed framework consists of two parts. First, it derives an approximate kernel competitive learning (AKCL), which learns kernel competitive learning in a subspace via sampling. We provide solid theoretical analysis on why the proposed approximation modelling would work for kernel competitive learning, and furthermore, we show that the computational complexity of AKCL is largely reduced. Second, we propose a pseudo-parallelled approximate kernel competitive learning (PAKCL) based on a set-based kernel competitive learning strategy, which overcomes the obstacle of using parallel programming in kernel competitive learning and significantly accelerates the approximate kernel competitive learning for large scale clustering. The empirical evaluation on publicly available datasets shows that the proposed AKCL and PAKCL can perform comparably as KCL, with a large reduction on computational cost. Also, the proposed methods achieve more effective clustering performance in terms of clustering precision against related approximate clustering approaches.
LaChance, L.E.; Graham, C.K. (Department of Agriculture, Fargo, ND (USA))
1984-06-01
Males of 4 species of insects: Musca domestica L. (housefly) (Diptera), Oncopeltus fasciatus (Dallas) (milkweed bug) (Hemiptera), Anagasta kuhniella (Zeller) (mealmoth) (Lepidoptera) and Heliothis virescens (Fab.) (tobacco budworm) (Lepidoptera) were irradiated as adults. Dose-response curves for the induction of dominant lethal mutations in the mature sperm were constructed. The curves were analyzed mathematically and compared with theoretical computer simulated curves requiring 1, 2, 4, 8 and 16 'hits' for the induction of a dominant lethal mutation. The 4 species belonging to 3 different orders of insects showed a wide range in radiation sensitivity and vastly different dose-response curves. When the data were analyzed by several mathematical models the authors found that a logistic response curve gave reasonably good fit with vastly different parameters for the 4 species. Dose-fractionation experiments showed no reduction in the frequency of lethal mutations induced in any species when an acute dose was fractionated into 2 equal exposures separated by an 8-h period.
Resolvent kernel for the Kohn Laplacian on Heisenberg groups
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.
Optimized Kernel Entropy Components.
Izquierdo-Verdiguier, Emma; Laparra, Valero; Jenssen, Robert; Gomez-Chova, Luis; Camps-Valls, Gustau
2016-02-25
This brief addresses two main issues of the standard kernel entropy component analysis (KECA) algorithm: the optimization of the kernel decomposition and the optimization of the Gaussian kernel parameter. KECA roughly reduces to a sorting of the importance of kernel eigenvectors by entropy instead of variance, as in the kernel principal components analysis. In this brief, we propose an extension of the KECA method, named optimized KECA (OKECA), that directly extracts the optimal features retaining most of the data entropy by means of compacting the information in very few features (often in just one or two). The proposed method produces features which have higher expressive power. In particular, it is based on the independent component analysis framework, and introduces an extra rotation to the eigen decomposition, which is optimized via gradient-ascent search. This maximum entropy preservation suggests that OKECA features are more efficient than KECA features for density estimation. In addition, a critical issue in both the methods is the selection of the kernel parameter, since it critically affects the resulting performance. Here, we analyze the most common kernel length-scale selection criteria. The results of both the methods are illustrated in different synthetic and real problems. Results show that OKECA returns projections with more expressive power than KECA, the most successful rule for estimating the kernel parameter is based on maximum likelihood, and OKECA is more robust to the selection of the length-scale parameter in kernel density estimation.
Tapiero, Charles S.; Vallois, Pierre
2016-11-01
The premise of this paper is that a fractional probability distribution is based on fractional operators and the fractional (Hurst) index used that alters the classical setting of random variables. For example, a random variable defined by its density function might not have a fractional density function defined in its conventional sense. Practically, it implies that a distribution's granularity defined by a fractional kernel may have properties that differ due to the fractional index used and the fractional calculus applied to define it. The purpose of this paper is to consider an application of fractional calculus to define the fractional density function of a random variable. In addition, we provide and prove a number of results, defining the functional forms of these distributions as well as their existence. In particular, we define fractional probability distributions for increasing and decreasing functions that are right continuous. Examples are used to motivate the usefulness of a statistical approach to fractional calculus and its application to economic and financial problems. In conclusion, this paper is a preliminary attempt to construct statistical fractional models. Due to the breadth and the extent of such problems, this paper may be considered as an initial attempt to do so.
Regularization in kernel learning
Mendelson, Shahar; 10.1214/09-AOS728
2010-01-01
Under mild assumptions on the kernel, we obtain the best known error rates in a regularized learning scenario taking place in the corresponding reproducing kernel Hilbert space (RKHS). The main novelty in the analysis is a proof that one can use a regularization term that grows significantly slower than the standard quadratic growth in the RKHS norm.
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`.
Kernel Affine Projection Algorithms
José C. Príncipe
2008-05-01
Full Text Available The combination of the famed kernel trick and affine projection algorithms (APAs yields powerful nonlinear extensions, named collectively here, KAPA. This paper is a follow-up study of the recently introduced kernel least-mean-square algorithm (KLMS. KAPA inherits the simplicity and online nature of KLMS while reducing its gradient noise, boosting performance. More interestingly, it provides a unifying model for several neural network techniques, including kernel least-mean-square algorithms, kernel adaline, sliding-window kernel recursive-least squares (KRLS, and regularization networks. Therefore, many insights can be gained into the basic relations among them and the tradeoff between computation complexity and performance. Several simulations illustrate its wide applicability.
Kernel Affine Projection Algorithms
Liu, Weifeng; Príncipe, José C.
2008-12-01
The combination of the famed kernel trick and affine projection algorithms (APAs) yields powerful nonlinear extensions, named collectively here, KAPA. This paper is a follow-up study of the recently introduced kernel least-mean-square algorithm (KLMS). KAPA inherits the simplicity and online nature of KLMS while reducing its gradient noise, boosting performance. More interestingly, it provides a unifying model for several neural network techniques, including kernel least-mean-square algorithms, kernel adaline, sliding-window kernel recursive-least squares (KRLS), and regularization networks. Therefore, many insights can be gained into the basic relations among them and the tradeoff between computation complexity and performance. Several simulations illustrate its wide applicability.
Composition Formulas of Bessel-Struve Kernel Function
K. S. Nisar
2016-01-01
Full Text Available The object of this paper is to study and develop the generalized fractional calculus operators involving Appell’s function F3(· due to Marichev-Saigo-Maeda. Here, we establish the generalized fractional calculus formulas involving Bessel-Struve kernel function Sαλz, λ,z∈C to obtain the results in terms of generalized Wright functions. The representations of Bessel-Struve kernel function in terms of exponential function and its relation with Bessel and Struve function are also discussed. The pathway integral representations of Bessel-Struve kernel function are also given in this study.
Winning, H.; Viereck, N.; Wollenweber, B.
2009-01-01
at terminal spikelet, during grain-filling or at both stages. Principal component trajectories of the total protein content and the protein fractions of flour as well as the H-1 NMR spectra of single wheat kernels, wheat flour, and wheat methanol extracts were analysed to elucidate the metabolic development...... during grain-filling. The results from both the H-1 NMR spectra of methanol extracts and the H-1 HR-MAS NMR of single kernels showed that a single drought event during the generative stage had as strong an influence on protein metabolism as two consecutive events of drought. By contrast, a drought event...... indicating that protein metabolism is influenced by multiple drought events, the H-1 NMR spectra of the methanol extracts of flour from mature grains revealed that the amount of fumaric acid is particularly sensitive to water deficits....
R. Lakshmi
2014-06-01
Full Text Available A kernel $J$ of a digraph $D$ is an independent set of vertices of $D$ such that for every vertex $w,in,V(D,setminus,J$ there exists an arc from $w$ to a vertex in $J.$ In this paper, among other results, a characterization of $2$-regular circulant digraph having a kernel is obtained. This characterization is a partial solution to the following problem: Characterize circulant digraphs which have kernels; it appeared in the book {it Digraphs - theory, algorithms and applications}, Second Edition, Springer-Verlag, 2009, by J. Bang-Jensen and G. Gutin.
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
Joel Irineu Fahl
1994-01-01
Full Text Available Neste trabalho, avaliou-se a utilização da linha do leite ("milk line" como meio visual de monitorar a maturação do grão de milho, para determinar o melhor momento de aplicação de dessecantes, visando antecipar a colheita sem ocasionar decréscimos na produção. O experimento foi instalado em 1993 em uma cultura de milho híbrido HT 8551 da Zeneca Sementes Ltda, no município de Holambra (SP, em um podzólico vermelho-amarelo. A maturação foi monitorada pelo movimento da linha do leite no grão, e comparada com a perda de umidade, acúmulo de massa seca e desenvolvimento da camada preta ("black layer". O dessecante foi aplicado em seis épocas, espaçadas de sete dias, consistindo em pulverizações da planta toda com paraquat (íon 1,1-dimetil-4,4-bipiridílio dicloreto na dose de 400 g/ha. A linha do leite foi uma característica facilmente visível para acompanhar a maturação e estimar o teor de umidade do grão. Não houve mais acúmulo significativo de massa no grão quando a linha do leite se posicionava na metade do grão, que apresentava 35% de umidade. Todas as aplicações com o dessecante paraquat, efetuadas quando os grãos apresentavam umidade inferior a 42% (linha do leite posicionada no terço superior do grão, não alteraram significativamente o decréscimo na umidade que ocorre durante a maturação. A produção de grãos não foi alterada pelos tratamentos com dessecante efetuados 127 dias após o plantio (linha do leite posicionada na metade do grão. Nas aplicações anteriores, ocorreram decréscimos na produção. A porcentagem de germinação das sementes não foi prejudicada pelas aplicações com paraquat.In order to determine the time of application of desiccant to antecipate corn harvest without yield loss, the kernel milk line was used as a mean of visual monitoring of grain maturity in maize. The experiment was carried out in 1993 on a Red-yellow Podzolic soil, using HT 8551 corn hybrid (Zeneca Sementes
Locally linear approximation for Kernel methods : the Railway Kernel
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 capability of the pr...
Kroah-Hartman, Greg
2009-01-01
Linux Kernel in a Nutshell covers the entire range of kernel tasks, starting with downloading the source and making sure that the kernel is in sync with the versions of the tools you need. In addition to configuration and installation steps, the book offers reference material and discussions of related topics such as control of kernel options at runtime.
吴云频; 陶祥兴
2011-01-01
证明带有粗糙核分数次积分算子的多线性算子TΩa^A,B（f）（x）=∫R^n P2（A;x,y）P2（B;x,y）/｜x-y｜^n-a＋2 Ω（x-y）f（y）dy的（H^1（R^n）,L^n/（n-a）∞（R^n））有界性，其中0〈a〈n,S^n-1表示R^n上的单位球面，Ω∈L^s（S^n-1）（S≥1）,且Ω是R^n上的零次齐次函数，A和B是R^n上函数，且P2（A；x，y），P2（B;x,y）是A和B分别在X点关于Y的二阶Taylor展式的余项，即P2（A；x，y）=A（x）-A（y）-△A（y）（x-y）,P2（B;x,y）=B（x）-B（y）-△B（y）（x-y）,这里△A，△B∈BMO（R^n）.%We prove the （H^1（R^n）,L^n/（n-a）∞（R^n）） boundednessTΩa^A,B（f）（x）=∫R^n P2（A;x,y）P2（B;x,y）/｜x-y｜^n-a＋2 Ω（x-y）f（y） dy of multilinear fractional integral operator with rough kernel, where 0〈a〈n,S^n-1 is the unit sphere in R^nΩ∈L^s（S^n-1）（S≥1）and .Ω is homogeneous of degree zero in R^n, A and B are function in R^n,P2（A;x,y）, P2（B;x,y） are the 2-th order remainder of the Taylor series of A, B at x about y, which hold the following relations：P2（A;x,y）=A（x）-A（y）-△A（y）（x-y）,P2（B;x,y）=B（x）-B（y）-△B（y）（x-y）,where △A,△B∈BMO（R^n）.
Influence of wheat kernel physical properties on the pulverizing process.
Dziki, Dariusz; Cacak-Pietrzak, Grażyna; Miś, Antoni; Jończyk, Krzysztof; Gawlik-Dziki, Urszula
2014-10-01
The physical properties of wheat kernel were determined and related to pulverizing performance by correlation analysis. Nineteen samples of wheat cultivars about similar level of protein content (11.2-12.8 % w.b.) and obtained from organic farming system were used for analysis. The kernel (moisture content 10 % w.b.) was pulverized by using the laboratory hammer mill equipped with round holes 1.0 mm screen. The specific grinding energy ranged from 120 kJkg(-1) to 159 kJkg(-1). On the basis of data obtained many of significant correlations (p 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.
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
Mixture Density Mercer Kernels
National Aeronautics and Space Administration — We present a method of generating Mercer Kernels from an ensemble of probabilistic mixture models, where each mixture model is generated from a Bayesian mixture...
Johno, Hisashi; Nakamoto, Kazunori; Saigo, Tatsuhiko
2015-01-01
Kernel Bayes' rule has been proposed as a nonparametric kernel-based method to realize Bayesian inference in reproducing kernel Hilbert spaces. However, we demonstrate both theoretically and experimentally that the prediction result by kernel Bayes' rule is in some cases unnatural. We consider that this phenomenon is in part due to the fact that the assumptions in kernel Bayes' rule do not hold in general.
Linearized Kernel Dictionary Learning
Golts, Alona; Elad, Michael
2016-06-01
In this paper we present a new approach of incorporating kernels into dictionary learning. The kernel K-SVD algorithm (KKSVD), which has been introduced recently, shows an improvement in classification performance, with relation to its linear counterpart K-SVD. However, this algorithm requires the storage and handling of a very large kernel matrix, which leads to high computational cost, while also limiting its use to setups with small number of training examples. We address these problems by combining two ideas: first we approximate the kernel matrix using a cleverly sampled subset of its columns using the Nystr\\"{o}m method; secondly, as we wish to avoid using this matrix altogether, we decompose it by SVD to form new "virtual samples," on which any linear dictionary learning can be employed. Our method, termed "Linearized Kernel Dictionary Learning" (LKDL) can be seamlessly applied as a pre-processing stage on top of any efficient off-the-shelf dictionary learning scheme, effectively "kernelizing" it. We demonstrate the effectiveness of our method on several tasks of both supervised and unsupervised classification and show the efficiency of the proposed scheme, its easy integration and performance boosting properties.
Freetly, H C; Kuehn, L A; Cundiff, L V
2011-08-01
The objective of this study was to evaluate the growth curves of females to determine if mature size and relative rates of maturation among breeds differed. Body weight and hip height data were fitted to the nonlinear function BW = f(age) = A - Be(k×age), where A is an estimate of mature BW and k determines the rate that BW or height moves from B to A. Cows represented progeny from 28 Hereford, 38 Angus, 25 Belgian Blue, 34 Brahman, 8 Boran, and 9 Tuli sires. Bulls from these breeds were mated by AI to Angus, Hereford, and MARC III composite (1/4 Angus, 1/4 Hereford, 1/4 Red Poll, and 1/4 Pinzgauer) cows to produce calves in 1992, 1993, and 1994. These matings resulted in 516 mature cows whose growth curves were subsequently evaluated. Hereford-sired cows tended to have heavier mature BW, as estimated by parameter A, than Angus- (P=0.09) and Brahman-sired cows (P=0.06), and were heavier than the other breeds (P Brahman-sired cows (P=0.94). Brahman-sired cows had a heavier mature BW than Boran- (P Brahman (P Brahman-sired cows took longer to mature than Boran- (P=0.03) or Belgian Blue-sired cows (P=0.003). Belgian Blue-sired cows were faster maturing than Tuli-sired cows (P=0.02). Brahman-sired cows had reached a greater proportion of their mature BW at puberty than had Hereford- (P < 0.001), Tuli- (P=0.003), and Belgian Blue-sired cows (P=0.001). Boran-sired cows tended to have reached a greater proportion of their mature BW at puberty than had Angus-sired cows (P=0.09), and had reached a greater proportion of their mature BW at puberty than had Hereford- (P < 0.001), Tuli- (P < 0.001), and Belgian Blue-sired cows (P < 0.001). Within species of cattle, the relative range in proportion of mature BW at puberty (Bos taurus 0.56 through 0.58, and Bos indicus 0.60) was highly conserved, suggesting that proportion of mature BW is a more robust predictor of age at puberty across breeds than is absolute weight or age. © 2011 American Society of Animal Science. All rights
Contingent kernel density estimation.
Scott Fortmann-Roe
Full Text Available Kernel density estimation is a widely used method for estimating a distribution based on a sample of points drawn from that distribution. Generally, in practice some form of error contaminates the sample of observed points. Such error can be the result of imprecise measurements or observation bias. Often this error is negligible and may be disregarded in analysis. In cases where the error is non-negligible, estimation methods should be adjusted to reduce resulting bias. Several modifications of kernel density estimation have been developed to address specific forms of errors. One form of error that has not yet been addressed is the case where observations are nominally placed at the centers of areas from which the points are assumed to have been drawn, where these areas are of varying sizes. In this scenario, the bias arises because the size of the error can vary among points and some subset of points can be known to have smaller error than another subset or the form of the error may change among points. This paper proposes a "contingent kernel density estimation" technique to address this form of error. This new technique adjusts the standard kernel on a point-by-point basis in an adaptive response to changing structure and magnitude of error. In this paper, equations for our contingent kernel technique are derived, the technique is validated using numerical simulations, and an example using the geographic locations of social networking users is worked to demonstrate the utility of the method.
BOERSMA, ER; OFFRINGA, PJ; MUSKIET, FAJ; CHASE, WM; SIMMONS, IJ
1991-01-01
Triglycerides, cholesterol, fatty acid composition, and tocopherols were determined in colostrum, transitional milk, and mature milk in St Lucia. With progress of lactation, triglycerides and percentage medium-chain fatty acids increased whereas tocopherols, cholesterol, and percentage longchain pol
Relaxation and diffusion models with non-singular kernels
Sun, HongGuang; Hao, Xiaoxiao; Zhang, Yong; Baleanu, Dumitru
2017-02-01
Anomalous relaxation and diffusion processes have been widely quantified by fractional derivative models, where the definition of the fractional-order derivative remains a historical debate due to its limitation in describing different kinds of non-exponential decays (e.g. stretched exponential decay). Meanwhile, many efforts by mathematicians and engineers have been made to overcome the singularity of power function kernel in its definition. This study first explores physical properties of relaxation and diffusion models where the temporal derivative was defined recently using an exponential kernel. Analytical analysis shows that the Caputo type derivative model with an exponential kernel cannot characterize non-exponential dynamics well-documented in anomalous relaxation and diffusion. A legitimate extension of the previous derivative is then proposed by replacing the exponential kernel with a stretched exponential kernel. Numerical tests show that the Caputo type derivative model with the stretched exponential kernel can describe a much wider range of anomalous diffusion than the exponential kernel, implying the potential applicability of the new derivative in quantifying real-world, anomalous relaxation and diffusion processes.
Explicit signal to noise ratio in reproducing kernel Hilbert spaces
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...
Multidimensional kernel estimation
Milosevic, Vukasin
2015-01-01
Kernel estimation is one of the non-parametric methods used for estimation of probability density function. Its first ROOT implementation, as part of RooFit package, has one major issue, its evaluation time is extremely slow making in almost unusable. The goal of this project was to create a new class (TKNDTree) which will follow the original idea of kernel estimation, greatly improve the evaluation time (using the TKTree class for storing the data and creating different user-controlled modes of evaluation) and add the interpolation option, for 2D case, with the help of the new Delaunnay2D class.
for palm kernel oil extraction
user
OEE), ... designed (CRD) experimental approach with 4 factor levels and 2 replications was used to determine the effect of kernel .... palm kernels in either a continuous or batch mode ... are fed through the hopper; the screw conveys, crushes,.
Silage quality of Piata palisadegrass with palm kernel cake
Rângelis de Sousa Figueredo
2014-02-01
Full Text Available This study was developed to evaluate silage quality of Piata palisadegrass with palm kernel cake (Elaeis guineensis Jacq.. The experiment was carried out at the Federal Institute of Goiás State, Campus Rio Verde, in a completely randomized design with four treatments and five repetitions. The treatments consisted of Piata palisadegrass ensiled with palm kernel in the levels of 0, 5, 10 and 15% on a natural basis of the Piata palisadegrass. The material was minced, mixed, packed into experimental silos and opened after 60 days of fermentation. The palm kernel cake is an agro-industrial by-product that can enrich the silage, increasing its nutritional value.The addition of palm kernel cake improved the fermentative and bromatological parameters of the silage, increasing the dry matter, crude protein, ether extract, and total digestible nutrients, with a reduction in the fiber fraction, values of pH, ammonia nitrogen, and titratable acidity. The use of palm kernel cake in Piata palisadegrass silage increase the fractions A, B1, B2 and in vitro dry matter digestibility, and decrease the fractions B3 and C. For achieving the best quality silage it is recommended the addition of 15% palm kernel cake.
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...
Barndorff-Nielsen, Ole Eiler; Hansen, Peter Reinhard; Lunde, Asger
2011-01-01
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 error of certain types and can also handle non-synchronous trading. It is the first estimator...
Adaptive metric kernel regression
Goutte, Cyril; Larsen, Jan
2000-01-01
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...
Adaptive Metric Kernel Regression
Goutte, Cyril; Larsen, Jan
1998-01-01
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...
Viscosity kernel of molecular fluids
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...
Multiple Kernel Point Set Registration.
Nguyen, Thanh Minh; Wu, Q M Jonathan
2016-06-01
The finite Gaussian mixture model with kernel correlation is a flexible tool that has recently received attention for point set registration. While there are many algorithms for point set registration presented in the literature, an important issue arising from these studies concerns the mapping of data with nonlinear relationships and the ability to select a suitable kernel. Kernel selection is crucial for effective point set registration. We focus here on multiple kernel point set registration. We make several contributions in this paper. First, each observation is modeled using the Student's t-distribution, which is heavily tailed and more robust than the Gaussian distribution. Second, by automatically adjusting the kernel weights, the proposed method allows us to prune the ineffective kernels. This makes the choice of kernels less crucial. After parameter learning, the kernel saliencies of the irrelevant kernels go to zero. Thus, the choice of kernels is less crucial and it is easy to include other kinds of kernels. Finally, we show empirically that our model outperforms state-of-the-art methods recently proposed in the literature.
Linear and kernel methods for multivariate change detection
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...
Green's Theorem for Generalized Fractional Derivatives
Odzijewicz, Tatiana; Torres, Delfim F M
2012-01-01
We study three types of generalized partial fractional operators. An extension of Green's theorem, by considering partial fractional derivatives with more general kernels, is proved. New results are obtained, even in the particular case when the generalized operators are reduced to the standard partial fractional derivatives and fractional integrals in the sense of Riemann-Liouville or Caputo.
Testing Monotonicity of Pricing Kernels
Timofeev, Roman
2007-01-01
In this master thesis a mechanism to test mononicity of empirical pricing kernels (EPK) is presented. By testing monotonicity of pricing kernel we can determine whether utility function is concave or not. Strictly decreasing pricing kernel corresponds to concave utility function while non-decreasing EPK means that utility function contains some non-concave regions. Risk averse behavior is usually described by concave utility function and considered to be a cornerstone of classical behavioral ...
7 CFR 51.1415 - Inedible kernels.
2010-01-01
... 7 Agriculture 2 2010-01-01 2010-01-01 false Inedible kernels. 51.1415 Section 51.1415 Agriculture... Standards for Grades of Pecans in the Shell 1 Definitions § 51.1415 Inedible kernels. Inedible kernels means that the kernel or pieces of kernels are rancid, moldy, decayed, injured by insects or...
7 CFR 981.8 - Inedible kernel.
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,...
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....
7 CFR 981.408 - Inedible kernel.
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...
Clustering via Kernel Decomposition
Have, Anna Szynkowiak; Girolami, Mark A.; Larsen, Jan
2006-01-01
Methods for spectral clustering have been proposed recently which rely on the eigenvalue decomposition of an affinity matrix. In this work it is proposed that the affinity matrix is created based on the elements of a non-parametric density estimator. This matrix is then decomposed to obtain...... posterior probabilities of class membership using an appropriate form of nonnegative matrix factorization. The troublesome selection of hyperparameters such as kernel width and number of clusters can be obtained using standard cross-validation methods as is demonstrated on a number of diverse data sets....
Starch and Free Sugars during Kernel Development of Bomi Barley and its High-Lysine Mutant 1508
Kreis, Michael
1978-01-01
At maturity the high-lysine barley (Hordeum vulgare L.) Ris0 mutants 1508, 527 and 29 kernels contained about 20% less starch and twice as much free sugars as the parent varieties Bomi and Carlsberg II. An enhanched effect on starch reduction and free sugar accumulation was observed during kernel...
Kernel Phase and Kernel Amplitude in Fizeau Imaging
Pope, Benjamin J S
2016-01-01
Kernel phase interferometry is an approach to high angular resolution imaging which enhances the performance of speckle imaging with adaptive optics. Kernel phases are self-calibrating observables that generalize the idea of closure phases from non-redundant arrays to telescopes with arbitrarily shaped pupils, by considering a matrix-based approximation to the diffraction problem. In this paper I discuss the recent history of kernel phase, in particular in the matrix-based study of sparse arrays, and propose an analogous generalization of the closure amplitude to kernel amplitudes. This new approach can self-calibrate throughput and scintillation errors in optical imaging, which extends the power of kernel phase-like methods to symmetric targets where amplitude and not phase calibration can be a significant limitation, and will enable further developments in high angular resolution astronomy.
A Fast Reduced Kernel Extreme Learning Machine.
Deng, Wan-Yu; Ong, Yew-Soon; Zheng, Qing-Hua
2016-04-01
In this paper, we present a fast and accurate kernel-based supervised algorithm referred to as the Reduced Kernel Extreme Learning Machine (RKELM). In contrast to the work on Support Vector Machine (SVM) or Least Square SVM (LS-SVM), which identifies the support vectors or weight vectors iteratively, the proposed RKELM randomly selects a subset of the available data samples as support vectors (or mapping samples). By avoiding the iterative steps of SVM, significant cost savings in the training process can be readily attained, especially on Big datasets. RKELM is established based on the rigorous proof of universal learning involving reduced kernel-based SLFN. In particular, we prove that RKELM can approximate any nonlinear functions accurately under the condition of support vectors sufficiency. Experimental results on a wide variety of real world small instance size and large instance size applications in the context of binary classification, multi-class problem and regression are then reported to show that RKELM can perform at competitive level of generalized performance as the SVM/LS-SVM at only a fraction of the computational effort incurred.
Global Polynomial Kernel Hazard Estimation
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...
Global Polynomial Kernel Hazard Estimation
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...
Graph kernels between point clouds
Bach, Francis
2007-01-01
Point clouds are sets of points in two or three dimensions. Most kernel methods for learning on sets of points have not yet dealt with the specific geometrical invariances and practical constraints associated with point clouds in computer vision and graphics. In this paper, we present extensions of graph kernels for point clouds, which allow to use kernel methods for such ob jects as shapes, line drawings, or any three-dimensional point clouds. In order to design rich and numerically efficient kernels with as few free parameters as possible, we use kernels between covariance matrices and their factorizations on graphical models. We derive polynomial time dynamic programming recursions and present applications to recognition of handwritten digits and Chinese characters from few training examples.
Kernel Generalized Noise Clustering Algorithm
WU Xiao-hong; ZHOU Jian-jiang
2007-01-01
To deal with the nonlinear separable problem, the generalized noise clustering (GNC) algorithm is extended to a kernel generalized noise clustering (KGNC) model. Different from the fuzzy c-means (FCM) model and the GNC model which are based on Euclidean distance, the presented model is based on kernel-induced distance by using kernel method. By kernel method the input data are nonlinearly and implicitly mapped into a high-dimensional feature space, where the nonlinear pattern appears linear and the GNC algorithm is performed. It is unnecessary to calculate in high-dimensional feature space because the kernel function can do itjust in input space. The effectiveness of the proposed algorithm is verified by experiments on three data sets. It is concluded that the KGNC algorithm has better clustering accuracy than FCM and GNC in clustering data sets containing noisy data.
Bruemmer, David J.
2009-11-17
A robot platform includes perceptors, locomotors, and a system controller. The system controller executes a robot intelligence kernel (RIK) that includes a multi-level architecture and a dynamic autonomy structure. The multi-level architecture includes a robot behavior level for defining robot behaviors, that incorporate robot attributes and a cognitive level for defining conduct modules that blend an adaptive interaction between predefined decision functions and the robot behaviors. The dynamic autonomy structure is configured for modifying a transaction capacity between an operator intervention and a robot initiative and may include multiple levels with at least a teleoperation mode configured to maximize the operator intervention and minimize the robot initiative and an autonomous mode configured to minimize the operator intervention and maximize the robot initiative. Within the RIK at least the cognitive level includes the dynamic autonomy structure.
Nowicki, Dimitri; Siegelmann, Hava
2010-06-11
This paper introduces a new model of associative memory, capable of both binary and continuous-valued inputs. Based on kernel theory, the memory model is on one hand a generalization of Radial Basis Function networks and, on the other, is in feature space, analogous to a Hopfield network. Attractors can be added, deleted, and updated on-line simply, without harming existing memories, and the number of attractors is independent of input dimension. Input vectors do not have to adhere to a fixed or bounded dimensionality; they can increase and decrease it without relearning previous memories. A memory consolidation process enables the network to generalize concepts and form clusters of input data, which outperforms many unsupervised clustering techniques; this process is demonstrated on handwritten digits from MNIST. Another process, reminiscent of memory reconsolidation is introduced, in which existing memories are refreshed and tuned with new inputs; this process is demonstrated on series of morphed faces.
Dimitri Nowicki
Full Text Available This paper introduces a new model of associative memory, capable of both binary and continuous-valued inputs. Based on kernel theory, the memory model is on one hand a generalization of Radial Basis Function networks and, on the other, is in feature space, analogous to a Hopfield network. Attractors can be added, deleted, and updated on-line simply, without harming existing memories, and the number of attractors is independent of input dimension. Input vectors do not have to adhere to a fixed or bounded dimensionality; they can increase and decrease it without relearning previous memories. A memory consolidation process enables the network to generalize concepts and form clusters of input data, which outperforms many unsupervised clustering techniques; this process is demonstrated on handwritten digits from MNIST. Another process, reminiscent of memory reconsolidation is introduced, in which existing memories are refreshed and tuned with new inputs; this process is demonstrated on series of morphed faces.
Mixture Density Mercer Kernels: A Method to Learn Kernels
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...
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,...
(Pre)kernel catchers for cooperative games
Chang, Chih; Driessen, Theo
1995-01-01
The paper provides a new (pre)kernel catcher in that the relevant set always contains the (pre)kernel. This new (pre)kernel catcher gives rise to a better lower bound ɛ*** such that the kernel is included in strong ɛ-cores for all real numbers ɛ not smaller than the relevant bound ɛ***.
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....
A kernel version of spatial factor analysis
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...
无
2002-01-01
The methods for computing the kemel consistency-based diagnoses and the kernel abductive diagnoses are only suited for the situation where part of the fault behavioral modes of the components are known. The characterization of the kernel model-based diagnosis based on the general causal theory is proposed, which can break through the limitation of the above methods when all behavioral modes of each component are known. Using this method, when observation subsets deduced logically are respectively assigned to the empty or the whole observation set, the kernel consistency-based diagnoses and the kernel abductive diagnoses can deal with all situations. The direct relationship between this diagnostic procedure and the prime implicants/implicates is proved, thus linking theoretical result with implementation.
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....
Kernel Rootkits Implement and Detection
LI Xianghe; ZHANG Liancheng; LI Shuo
2006-01-01
Rootkits, which unnoticeably reside in your computer, stealthily carry on remote control and software eavesdropping, are a great threat to network and computer security. It' time to acquaint ourselves with their implement and detection. This article pays more attention to kernel rootkits, because they are more difficult to compose and to be identified than useland rootkits. The latest technologies used to write and detect kernel rootkits, along with their advantages and disadvantages, are present in this article.
A multi-resolution approach to heat kernels on discrete surfaces
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.
An Approximate Approach to Automatic Kernel Selection.
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.
Model Selection in Kernel Ridge Regression
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...
Generalized Langevin equation with tempered memory kernel
Liemert, André; Sandev, Trifce; Kantz, Holger
2017-01-01
We study a generalized Langevin equation for a free particle in presence of a truncated power-law and Mittag-Leffler memory kernel. It is shown that in presence of truncation, the particle from subdiffusive behavior in the short time limit, turns to normal diffusion in the long time limit. The case of harmonic oscillator is considered as well, and the relaxation functions and the normalized displacement correlation function are represented in an exact form. By considering external time-dependent periodic force we obtain resonant behavior even in case of a free particle due to the influence of the environment on the particle movement. Additionally, the double-peak phenomenon in the imaginary part of the complex susceptibility is observed. It is obtained that the truncation parameter has a huge influence on the behavior of these quantities, and it is shown how the truncation parameter changes the critical frequencies. The normalized displacement correlation function for a fractional generalized Langevin equation is investigated as well. All the results are exact and given in terms of the three parameter Mittag-Leffler function and the Prabhakar generalized integral operator, which in the kernel contains a three parameter Mittag-Leffler function. Such kind of truncated Langevin equation motion can be of high relevance for the description of lateral diffusion of lipids and proteins in cell membranes.
Integral equations with contrasting kernels
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.
Reduction of Aflatoxins in Apricot Kernels by Electronic and Manual Color Sorting
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.
Reduction of Aflatoxins in Apricot Kernels by Electronic and Manual Color Sorting.
Zivoli, Rosanna; Gambacorta, Lucia; Piemontese, Luca; Solfrizzo, Michele
2016-01-19
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.
Model selection for Gaussian kernel PCA denoising
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...
Kernel learning algorithms for face recognition
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
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....
Congruence Kernels of Orthoimplication Algebras
I. Chajda
2007-10-01
Full Text Available Abstracting from certain properties of the implication operation in Boolean algebras leads to so-called orthoimplication algebras. These are in a natural one-to-one correspondence with families of compatible orthomodular lattices. It is proved that congruence kernels of orthoimplication algebras are in a natural one-to-one correspondence with families of compatible p-filters on the corresponding orthomodular lattices. Finally, it is proved that the lattice of all congruence kernels of an orthoimplication algebra is relatively pseudocomplemented and a simple description of the relative pseudocomplement is given.
Transcriptional Profiles Uncover Aspergillus flavus-Induced Resistance in Maize Kernels
Zhi-Yuan Chen
2011-06-01
Full Text Available Aflatoxin contamination caused by the opportunistic pathogen A. flavus is a major concern in maize production prior to harvest and through storage. Previous studies have highlighted the constitutive production of proteins involved in maize kernel resistance against A. flavus’ infection. However, little is known about induced resistance nor about defense gene expression and regulation in kernels. In this study, maize oligonucleotide arrays and a pair of closely-related maize lines varying in aflatoxin accumulation were used to reveal the gene expression network in imbibed mature kernels in response to A. flavus’ challenge. Inoculated kernels were incubated 72 h via the laboratory-based Kernel Screening Assay (KSA, which highlights kernel responses to fungal challenge. Gene expression profiling detected 6955 genes in resistant and 6565 genes in susceptible controls; 214 genes induced in resistant and 2159 genes induced in susceptible inoculated kernels. Defense related and regulation related genes were identified in both treatments. Comparisons between the resistant and susceptible lines indicate differences in the gene expression network which may enhance our understanding of the maize-A. flavus interaction.
Model selection in kernel ridge regression
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...
Bergman kernel on generalized exceptional Hua domain
YIN; weipng(殷慰萍); ZHAO; zhengang(赵振刚)
2002-01-01
We have computed the Bergman kernel functions explicitly for two types of generalized exceptional Hua domains, and also studied the asymptotic behavior of the Bergman kernel function of exceptional Hua domain near boundary points, based on Appell's multivariable hypergeometric function.
A kernel version of multivariate alteration detection
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....
Random Feature Maps for Dot Product Kernels
Kar, Purushottam; Karnick, Harish
2012-01-01
Approximating non-linear kernels using feature maps has gained a lot of interest in recent years due to applications in reducing training and testing times of SVM classifiers and other kernel based learning algorithms. We extend this line of work and present low distortion embeddings for dot product kernels into linear Euclidean spaces. We base our results on a classical result in harmonic analysis characterizing all dot product kernels and use it to define randomized feature maps into explic...
ks: Kernel Density Estimation and Kernel Discriminant Analysis for Multivariate Data in R
Tarn Duong
2007-09-01
Full Text Available Kernel smoothing is one of the most widely used non-parametric data smoothing techniques. We introduce a new R package ks for multivariate kernel smoothing. Currently it contains functionality for kernel density estimation and kernel discriminant analysis. It is a comprehensive package for bandwidth matrix selection, implementing a wide range of data-driven diagonal and unconstrained bandwidth selectors.
On the Diamond Bessel Heat Kernel
Wanchak Satsanit
2011-01-01
Full Text Available We study the heat equation in n dimensional by Diamond Bessel operator. We find the solution by method of convolution and Fourier transform in distribution theory and also obtain an interesting kernel related to the spectrum and the kernel which is called Bessel heat kernel.
Local Observed-Score Kernel Equating
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…
Computations of Bergman Kernels on Hua Domains
殷慰萍; 王安; 赵振刚; 赵晓霞; 管冰辛
2001-01-01
@@The Bergman kernel function plays an important ro1e in several complex variables.There exists the Bergman kernel function on any bounded domain in Cn. But we can get the Bergman kernel functions in explicit formulas for a few types of domains only,for example:the bounded homogeneous domains and the egg domain in some cases.
Veto-Consensus Multiple Kernel Learning
Y. Zhou; N. Hu; C.J. Spanos
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 propose
Accelerating the Original Profile Kernel.
Tobias Hamp
Full Text Available One of the most accurate multi-class protein classification systems continues to be the profile-based SVM kernel introduced by the Leslie group. Unfortunately, its CPU requirements render it too slow for practical applications of large-scale classification tasks. Here, we introduce several software improvements that enable significant acceleration. Using various non-redundant data sets, we demonstrate that our new implementation reaches a maximal speed-up as high as 14-fold for calculating the same kernel matrix. Some predictions are over 200 times faster and render the kernel as possibly the top contender in a low ratio of speed/performance. Additionally, we explain how to parallelize various computations and provide an integrative program that reduces creating a production-quality classifier to a single program call. The new implementation is available as a Debian package under a free academic license and does not depend on commercial software. For non-Debian based distributions, the source package ships with a traditional Makefile-based installer. Download and installation instructions can be found at https://rostlab.org/owiki/index.php/Fast_Profile_Kernel. Bugs and other issues may be reported at https://rostlab.org/bugzilla3/enter_bug.cgi?product=fastprofkernel.
Adaptive wiener image restoration kernel
Yuan, Ding
2007-06-05
A method and device for restoration of electro-optical image data using an adaptive Wiener filter begins with constructing imaging system Optical Transfer Function, and the Fourier Transformations of the noise and the image. A spatial representation of the imaged object is restored by spatial convolution of the image using a Wiener restoration kernel.
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.
Huan Yang; Yanling Shi; Renchao Xu; Dalei Lu; Weiping Lu
2016-01-01
Understanding the effects of shading after pollination on kernel filling and physicochemical properties of waxy maize could improve kernel quality. Experiments were conducted to investigate the effects of shading (30% and 50% light deprivation, taken plants without shading as control) on kernel weight, size, and physicochemical properties during kernel development in 2013 and 2014 using two waxy maize varieties (Suyunuo 5 and Yunuo 7). Results indicated that shading reduced kernel filling rate and decreased kernel size and weight, and the influence was large under severe light deprivation conditions. The large reduction in kernel weight and volume of Suyunuo 5 in response to shading indicated that it was more sensitive to shading than Yunuo 7. Starch content was reduced and protein content was increased by shading, especially under severe shading after 22 days after pollination (DAP). The iodine binding capacity of Yunuo 7 was not affected by shading at fresh and maturity stages but was gradually decreased by shading at the newly formed stage, while the values for Suyunuo 5 were decreased at 7 and 40 DAP only by moderate shading and were similar among three treatments at 22 DAP. Severe shading decreased crystallinity at all kernel development stages, whereas moderate shading decreased crystallinity at fresh stage and increased it at mature stage for Suyunuo 5. Crystallinity in Yunuo 7 was increased by shading at 7 DAP and decreased by shading at 40 DAP, whereas the value at 22 DAP was increased by moderate shading and reduced by severe shading, respectively. The average gelatinization temperatures at different stages were decreased by shading and showed no difference between two shading levels. The retrogradation percentage at 7 DAP for both varieties was increased by shading. The value at 22 DAP was increased by moderate shading for Suyunuo 5 and decreased by severe shading for Yunuo 7, respectively. The retrogradation percentage at 40 DAP was decreased by shading
Huan Yang; Yanling Shi; Renchao Xu; Dalei Lu; Weiping Lu
2016-01-01
Understanding the effects of shading after pollination on kernel filling and physicochemical properties of waxy maize could improve kernel quality.Experiments were conducted to investigate the effects of shading(30% and 50% light deprivation,taken plants without shading as control) on kernel weight,size,and physicochemical properties during kernel development in 2013 and 2014 using two waxy maize varieties(Suyunuo 5 and Yunuo 7).Results indicated that shading reduced kernel filling rate and decreased kernel size and weight,and the influence was large under severe light deprivation conditions.The large reduction in kernel weight and volume of Suyunuo 5 in response to shading indicated that it was more sensitive to shading than Yunuo 7.Starch content was reduced and protein content was increased by shading,especially under severe shading after 22 days after pollination(DAP).The iodine binding capacity of Yunuo 7 was not affected by shading at fresh and maturity stages but was gradually decreased by shading at the newly formed stage,while the values for Suyunuo 5were decreased at 7 and 40 DAP only by moderate shading and were similar among three treatments at 22 DAP.Severe shading decreased crystallinity at all kernel development stages,whereas moderate shading decreased crystallinity at fresh stage and increased it at mature stage for Suyunuo 5.Crystallinity in Yunuo 7 was increased by shading at 7 DAP and decreased by shading at 40 DAP,whereas the value at 22 DAP was increased by moderate shading and reduced by severe shading,respectively.The average gelatinization temperatures at different stages were decreased by shading and showed no difference between two shading levels.The retrogradation percentage at 7 DAP for both varieties was increased by shading.The value at 22 DAP was increased by moderate shading for Suyunuo 5 and decreased by severe shading for Yunuo 7,respectively.The retrogradation percentage at 40 DAP was decreased by shading treatments for Suyunuo 5
Huan Yang
2016-06-01
Full Text Available Understanding the effects of shading after pollination on kernel filling and physicochemical properties of waxy maize could improve kernel quality. Experiments were conducted to investigate the effects of shading (30% and 50% light deprivation, taken plants without shading as control on kernel weight, size, and physicochemical properties during kernel development in 2013 and 2014 using two waxy maize varieties (Suyunuo 5 and Yunuo 7. Results indicated that shading reduced kernel filling rate and decreased kernel size and weight, and the influence was large under severe light deprivation conditions. The large reduction in kernel weight and volume of Suyunuo 5 in response to shading indicated that it was more sensitive to shading than Yunuo 7. Starch content was reduced and protein content was increased by shading, especially under severe shading after 22 days after pollination (DAP. The iodine binding capacity of Yunuo 7 was not affected by shading at fresh and maturity stages but was gradually decreased by shading at the newly formed stage, while the values for Suyunuo 5 were decreased at 7 and 40 DAP only by moderate shading and were similar among three treatments at 22 DAP. Severe shading decreased crystallinity at all kernel development stages, whereas moderate shading decreased crystallinity at fresh stage and increased it at mature stage for Suyunuo 5. Crystallinity in Yunuo 7 was increased by shading at 7 DAP and decreased by shading at 40 DAP, whereas the value at 22 DAP was increased by moderate shading and reduced by severe shading, respectively. The average gelatinization temperatures at different stages were decreased by shading and showed no difference between two shading levels. The retrogradation percentage at 7 DAP for both varieties was increased by shading. The value at 22 DAP was increased by moderate shading for Suyunuo 5 and decreased by severe shading for Yunuo 7, respectively. The retrogradation percentage at 40 DAP was
Random Feature Maps for Dot Product Kernels
Kar, Purushottam
2012-01-01
Approximating non-linear kernels using feature maps has gained a lot of interest in recent years due to applications in reducing training and testing times of SVM classifiers and other kernel based learning algorithms. We extend this line of work and present low distortion embeddings for dot product kernels into linear Euclidean spaces. We base our results on a classical result in harmonic analysis characterizing all dot product kernels and use it to define randomized feature maps into explicit low dimensional Euclidean spaces in which the native dot product provides an approximation to the dot product kernel with high confidence.
Testing Infrastructure for Operating System Kernel Development
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....
Speech Enhancement Using Kernel and Normalized Kernel Affine Projection Algorithm
Bolimera Ravi
2013-08-01
Full Text Available The goal of this paper is to investigate the speech signal enhancement using Kernel Affine ProjectionAlgorithm (KAPA and Normalized KAPA. The removal of background noise is very important in manyapplications like speech recognition, telephone conversations, hearing aids, forensic, etc. Kernel adaptivefilters shown good performance for removal of noise. If the evaluation of background noise is more slowlythan the speech, i.e., noise signal is more stationary than the speech, we can easily estimate the noiseduring the pauses in speech. Otherwise it is more difficult to estimate the noise which results indegradation of speech. In order to improve the quality and intelligibility of speech, unlike time andfrequency domains, we can process the signal in new domain like Reproducing Kernel Hilbert Space(RKHS for high dimensional to yield more powerful nonlinear extensions. For experiments, we have usedthe database of noisy speech corpus (NOIZEUS. From the results, we observed the removal noise in RKHShas great performance in signal to noise ratio values in comparison with conventional adaptive filters.
Weighted Inequalities for Fractional Type Operators with Some Homogeneous Kernels
María Silvina RIVEROS; Marta URCIUOLO
2013-01-01
In this paper, we study integral operators of the form Tαf(x) = ∫Rn ∣x - A1y∣-α1 …∣x - Amy∣-αm f(y)dy, where Ai are certain invertible matrices, αi > 0, 1 ≤ i ≤ m, α1 + …+ αm = n - α ,0 ≦ α ≦ n. For 1/q = 1/p - α/n, we obtain the Lp(Rn,wp) - Lq(Rn,wq) boundedness for weights w in A(p,q) satisfying that there exists c > 0 such that w(Aix) ≤ cw(x), a.e. x ∈ Rn, 1 ≤ i ≤ m. Moreover, we obtain the appropriate weighted BMO and weak type estimates for certain weights satisfying the above inequality. We also give a Coifman type estimate for these operators.
Li, Yinian; Wang, Jun; Xie, Weizhong; Lu, Daxin; Ding, Weimin
2013-10-01
Physicochemical properties of wheat grains with largest kernel thickness always was lowest than the other sections, examination of microstructure of wheat grains can help us understand this phenomena. Two varieties of wheat, soft white winter wheat Yangmai 11 and hard white winter wheat Zhengmai 9023, were fractionated into five sections by kernel thickness. Then the fractionated wheat grains in 2.7-3.0 mm section were separated into three fractions by kernel specific density sequentially. Microstructure of the fractured surface were evaluated at different scale level to two varieties wheat with different kernel thickness and specific density by using environmental scanning electron microscopy. Compactness and size of endosperm cell tended to decrease with decreasing wheat kernel thickness and specific density. Protein matrix tended to increase with decreasing wheat kernel thickness and specific density. Size of starch granules and proportion for different type starch granules also varied with different wheat kernel thickness and specific density. Those microstructure properties of the fractured surface, formation of endosperm cells, protein matrix and starch granules were close related to rheological properties and pasting properties of wheat grains.
Nonlinear Deep Kernel Learning for Image Annotation.
Jiu, Mingyuan; Sahbi, Hichem
2017-02-08
Multiple kernel learning (MKL) is a widely used technique for kernel design. Its principle consists in learning, for a given support vector classifier, the most suitable convex (or sparse) linear combination of standard elementary kernels. However, these combinations are shallow and often powerless to capture the actual similarity between highly semantic data, especially for challenging classification tasks such as image annotation. In this paper, we redefine multiple kernels using deep multi-layer networks. In this new contribution, a deep multiple kernel is recursively defined as a multi-layered combination of nonlinear activation functions, each one involves a combination of several elementary or intermediate kernels, and results into a positive semi-definite deep kernel. We propose four different frameworks in order to learn the weights of these networks: supervised, unsupervised, kernel-based semisupervised and Laplacian-based semi-supervised. When plugged into support vector machines (SVMs), the resulting deep kernel networks show clear gain, compared to several shallow kernels for the task of image annotation. Extensive experiments and analysis on the challenging ImageCLEF photo annotation benchmark, the COREL5k database and the Banana dataset validate the effectiveness of the proposed method.
Nonlinear projection trick in kernel methods: an alternative to the kernel trick.
Kwak, Nojun
2013-12-01
In kernel methods such as kernel principal component analysis (PCA) and support vector machines, the so called kernel trick is used to avoid direct calculations in a high (virtually infinite) dimensional kernel space. In this brief, based on the fact that the effective dimensionality of a kernel space is less than the number of training samples, we propose an alternative to the kernel trick that explicitly maps the input data into a reduced dimensional kernel space. This is easily obtained by the eigenvalue decomposition of the kernel matrix. The proposed method is named as the nonlinear projection trick in contrast to the kernel trick. With this technique, the applicability of the kernel methods is widened to arbitrary algorithms that do not use the dot product. The equivalence between the kernel trick and the nonlinear projection trick is shown for several conventional kernel methods. In addition, we extend PCA-L1, which uses L1-norm instead of L2-norm (or dot product), into a kernel version and show the effectiveness of the proposed approach.
Theory of reproducing kernels and applications
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...
Filters, reproducing kernel, and adaptive meshfree method
You, Y.; Chen, J.-S.; Lu, H.
Reproducing kernel, with its intrinsic feature of moving averaging, can be utilized as a low-pass filter with scale decomposition capability. The discrete convolution of two nth order reproducing kernels with arbitrary support size in each kernel results in a filtered reproducing kernel function that has the same reproducing order. This property is utilized to separate the numerical solution into an unfiltered lower order portion and a filtered higher order portion. As such, the corresponding high-pass filter of this reproducing kernel filter can be used to identify the locations of high gradient, and consequently serves as an operator for error indication in meshfree analysis. In conjunction with the naturally conforming property of the reproducing kernel approximation, a meshfree adaptivity method is also proposed.
Anthocyanins and antioxidant activity in coloured waxy corn at different maturation stages
Concentrations of anthocyanins, phenolic compounds and antioxidant activities in kernels of 20 genotypes of waxy corn were investigated at two maturation stages, namely milky and mature. The levels of anthocyanins increased throughout the development of each genotype of corn, while phenolic compound...
Kernel principal component analysis for change detection
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....
Tame Kernels of Pure Cubic Fields
Xiao Yun CHENG
2012-01-01
In this paper,we study the p-rank of the tame kernels of pure cubic fields.In particular,we prove that for a fixed positive integer m,there exist infinitely many pure cubic fields whose 3-rank of the tame kernel equal to m.As an application,we determine the 3-rank of their tame kernels for some special pure cubic fields.
Kernel Factor Analysis Algorithm with Varimax
Xia Guoen; Jin Weidong; Zhang Gexiang
2006-01-01
Kernal factor analysis (KFA) with varimax was proposed by using Mercer kernel function which can map the data in the original space to a high-dimensional feature space, and was compared with the kernel principle component analysis (KPCA). The results show that the best error rate in handwritten digit recognition by kernel factor analysis with varimax (4.2%) was superior to KPCA (4.4%). The KFA with varimax could more accurately image handwritten digit recognition.
Convergence of barycentric coordinates to barycentric kernels
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.
Efficient classification for additive kernel SVMs.
Maji, Subhransu; Berg, Alexander C; Malik, Jitendra
2013-01-01
We show that a class of nonlinear kernel SVMs admits approximate classifiers with runtime and memory complexity that is independent of the number of support vectors. This class of kernels, which we refer to as additive kernels, includes widely used kernels for histogram-based image comparison like intersection and chi-squared kernels. Additive kernel SVMs can offer significant improvements in accuracy over linear SVMs on a wide variety of tasks while having the same runtime, making them practical for large-scale recognition or real-time detection tasks. We present experiments on a variety of datasets, including the INRIA person, Daimler-Chrysler pedestrians, UIUC Cars, Caltech-101, MNIST, and USPS digits, to demonstrate the effectiveness of our method for efficient evaluation of SVMs with additive kernels. Since its introduction, our method has become integral to various state-of-the-art systems for PASCAL VOC object detection/image classification, ImageNet Challenge, TRECVID, etc. The techniques we propose can also be applied to settings where evaluation of weighted additive kernels is required, which include kernelized versions of PCA, LDA, regression, k-means, as well as speeding up the inner loop of SVM classifier training algorithms.
Molecular hydrodynamics from memory kernels
Lesnicki, Dominika; Carof, Antoine; Rotenberg, Benjamin
2016-01-01
The memory kernel for a tagged particle in a fluid, computed from molecular dynamics simulations, decays algebraically as $t^{-3/2}$. We show how the hydrodynamic Basset-Boussinesq force naturally emerges from this long-time tail and generalize the concept of hydrodynamic added mass. This mass term is negative in the present case of a molecular solute, at odds with incompressible hydrodynamics predictions. We finally discuss the various contributions to the friction, the associated time scales and the cross-over between the molecular and hydrodynamic regimes upon increasing the solute radius.
Hilbertian kernels and spline functions
Atteia, M
1992-01-01
In this monograph, which is an extensive study of Hilbertian approximation, the emphasis is placed on spline functions theory. The origin of the book was an effort to show that spline theory parallels Hilbertian Kernel theory, not only for splines derived from minimization of a quadratic functional but more generally for splines considered as piecewise functions type. Being as far as possible self-contained, the book may be used as a reference, with information about developments in linear approximation, convex optimization, mechanics and partial differential equations.
Distribution of aflatoxins in corn fractions visually segregated for defects
Piedade Fabiana Segatti
2002-01-01
Full Text Available The aflatoxin distribution in corn fractions obtained after visual segregation for defects in 30 samples, known to be contaminated, was studied. Each sample was passed through a 5.0 mm round holes sieve, graded for defects and then segregated in sound kernels (regular kernels and non-sound kernels (injured, germinated, fermented, moldy, heated, insect damaged, immature, broken, hollow, fermented up to ¼, discolored, extraneous materials, and injured by other causes, as defined by the Brazilian Official Grading rules for corn. The non-sound kernels showed the highest contamination levels in all samples. The contamination levels of non-sound kernels (20% of total weight ranged from 23 to 1,365 µg/kg of aflatoxins (B1, B2, G1 and G2 and were higher than sound kernels (p<1% ranging from not detected (ND to 126 µg/kg and in 87% of these the aflatoxin contents were lower than 20 µg/kg. Statistically significant correlation indexes were found among the percentage of defective groups like fermented, heated and sprouted kernels or the total injured kernels, and the estimated contamination levels for the sound and non sound fractions. It was concluded that the non-sound kernels fraction, even being small in weight, has contributed with 84% of the estimated contamination of the samples. The segregation of the non-sound kernels would favor a reduction in the contamination of corn lots. The poorer quality corn types (types 3 and Bellow Standart have predominated among samples of the experiment.
Commutators of Integral Operators with Variable Kernels on Hardy Spaces
Pu Zhang; Kai Zhao
2005-11-01
Let $T_{,}(0≤ < n)$ be the singular and fractional integrals with variable kernel $(x,z)$, and $[b, T_{,}]$ be the commutator generated by $T_{,}$ and a Lipschitz function . In this paper, the authors study the boundedness of $[b, T_{,}]$ on the Hardy spaces, under some assumptions such as the $L^r$-Dini condition. Similar results and the weak type estimates at the end-point cases are also given for the homogeneous convolution operators $T_{\\overline{},}(0≤ < n)$. The smoothness conditions imposed on $\\overline{}$ are weaker than the corresponding known results.
Differentiable Kernels in Generalized Matrix Learning Vector Quantization
Kästner, M.; Nebel, D.; Riedel, M.; Biehl, M.; Villmann, T.
2013-01-01
In the present paper we investigate the application of differentiable kernel for generalized matrix learning vector quantization as an alternative kernel-based classifier, which additionally provides classification dependent data visualization. We show that the concept of differentiable kernels allo
Kernel current source density method.
Potworowski, Jan; Jakuczun, Wit; Lȩski, Szymon; Wójcik, Daniel
2012-02-01
Local field potentials (LFP), the low-frequency part of extracellular electrical recordings, are a measure of the neural activity reflecting dendritic processing of synaptic inputs to neuronal populations. To localize synaptic dynamics, it is convenient, whenever possible, to estimate the density of transmembrane current sources (CSD) generating the LFP. In this work, we propose a new framework, the kernel current source density method (kCSD), for nonparametric estimation of CSD from LFP recorded from arbitrarily distributed electrodes using kernel methods. We test specific implementations of this framework on model data measured with one-, two-, and three-dimensional multielectrode setups. We compare these methods with the traditional approach through numerical approximation of the Laplacian and with the recently developed inverse current source density methods (iCSD). We show that iCSD is a special case of kCSD. The proposed method opens up new experimental possibilities for CSD analysis from existing or new recordings on arbitrarily distributed electrodes (not necessarily on a grid), which can be obtained in extracellular recordings of single unit activity with multiple electrodes.
Filtering algorithms using shiftable kernels
Chaudhury, Kunal Narayan
2011-01-01
It was recently demonstrated in [4][arxiv:1105.4204] that the non-linear bilateral filter \\cite{Tomasi} can be efficiently implemented using an O(1) or constant-time algorithm. At the heart of this algorithm was the idea of approximating the Gaussian range kernel of the bilateral filter using trigonometric functions. In this letter, we explain how the idea in [4] can be extended to few other linear and non-linear filters [18,21,2]. While some of these filters have received a lot of attention in recent years, they are known to be computationally intensive. To extend the idea in \\cite{Chaudhury2011}, we identify a central property of trigonometric functions, called shiftability, that allows us to exploit the redundancy inherent in the filtering operations. In particular, using shiftable kernels, we show how certain complex filtering can be reduced to simply that of computing the moving sum of a stack of images. Each image in the stack is obtained through an elementary pointwise transform of the input image. Thi...
From fractional Fourier transformation to quantum mechanical fractional squeezing transformation
吕翠红; 范洪义; 李东韡
2015-01-01
By converting the triangular functions in the integration kernel of the fractional Fourier transformation to the hy-perbolic function, i.e., tanα→tanhα, sinα→sinhα, we find quantum mechanical fractional squeezing transformation (FrST) which satisfies additivity. By virtue of the integration technique within ordered product of operators (IWOP) wederive the unitary operator responsible for the FrST, which is composite and is made of eiπa†a/2 and exp[ iα2 (a2+a†2)]. The FrST may be implemented in combinations of quadratic nonlinear crystals with different phase mismatches.
Kim, Hun; Woloshuk, C P
2008-06-01
Fumonisin B1 (FB(1)) biosynthesis is repressed in cultures containing ammonium as the nitrogen source and when grown on blister kernels, the earliest stages of kernel development. In this study AREA, a regulator of nitrogen metabolism, was disrupted in Fusarium verticilliodes. The mutant (DeltaareA) grew poorly on mature maize kernels, but grew similar to wild type (WT) with the addition of ammonium phosphate. FB(1) was not produced by DeltaareA under any condition or by the WT with added ammonium phosphate. Constitutive expression of AREA (strain AREA-CE) rescued the growth and FB(1) defects in DeltaareA. Growth of WT, DeltaareA, and AREA-CE on blister-stage kernels was similar. After 7 days of growth, none of the strains produced FB(1) and the pH of the kernel tissues was 8.0. Addition of amylopectin to the blister kernels resulted in a pH near 6.6 and FB(1) production by WT and AREA-CE. The results support the hypothesis that FB(1) biosynthesis is regulated by AREA. Also the failure to produce FB(1) in blister kernels is due to high pH conditions generated because of an unfavorable carbon/nitrogen environment.
Kernel parameter dependence in spatial factor analysis
Nielsen, Allan Aasbjerg
2010-01-01
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...
Improving the Bandwidth Selection in Kernel Equating
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…
Ranking Support Vector Machine with Kernel Approximation.
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.
Generalized Derivative Based Kernelized Learning Vector Quantization
Schleif, Frank-Michael; Villmann, Thomas; Hammer, Barbara; Schneider, Petra; Biehl, Michael; Fyfe, Colin; Tino, Peter; Charles, Darryl; Garcia-Osoro, Cesar; Yin, Hujun
2010-01-01
We derive a novel derivative based version of kernelized Generalized Learning Vector Quantization (KGLVQ) as an effective, easy to interpret, prototype based and kernelized classifier. It is called D-KGLVQ and we provide generalization error bounds, experimental results on real world data, showing t
PALM KERNEL SHELL AS AGGREGATE FOR LIGHT
of cement, sand, gravel andpalm kernel shells respectively gave the highest compressive strength of ... Keywords: Aggregate, Cement, Concrete, Sand, Palm Kernel Shell. ... delivered to the jOb Slte in a plastic ... structures, breakwaters, piers and docks .... related to cement content at a .... sheet and the summary is shown.
Panel data specifications in nonparametric kernel regression
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...
无
2006-01-01
A kernel-based discriminant analysis method called kernel direct discriminant analysis is employed, which combines the merit of direct linear discriminant analysis with that of kernel trick. In order to demonstrate its better robustness to the complex and nonlinear variations of real face images, such as illumination, facial expression, scale and pose variations, experiments are carried out on the Olivetti Research Laboratory, Yale and self-built face databases. The results indicate that in contrast to kernel principal component analysis and kernel linear discriminant analysis, the method can achieve lower (7%) error rate using only a very small set of features. Furthermore, a new corrected kernel model is proposed to improve the recognition performance. Experimental results confirm its superiority (1% in terms of recognition rate) to other polynomial kernel models.
Parameter-Free Spectral Kernel Learning
Mao, Qi
2012-01-01
Due to the growing ubiquity of unlabeled data, learning with unlabeled data is attracting increasing attention in machine learning. In this paper, we propose a novel semi-supervised kernel learning method which can seamlessly combine manifold structure of unlabeled data and Regularized Least-Squares (RLS) to learn a new kernel. Interestingly, the new kernel matrix can be obtained analytically with the use of spectral decomposition of graph Laplacian matrix. Hence, the proposed algorithm does not require any numerical optimization solvers. Moreover, by maximizing kernel target alignment on labeled data, we can also learn model parameters automatically with a closed-form solution. For a given graph Laplacian matrix, our proposed method does not need to tune any model parameter including the tradeoff parameter in RLS and the balance parameter for unlabeled data. Extensive experiments on ten benchmark datasets show that our proposed two-stage parameter-free spectral kernel learning algorithm can obtain comparable...
Resistant-starch formation in high-amylose maize starch during Kernel development.
Jiang, Hongxin; Lio, Junyi; Blanco, Mike; Campbell, Mark; Jane, Jay-Lin
2010-07-14
The objective of this study was to understand the resistant-starch (RS) formation during kernel development of a high-amylose maize, GEMS-0067 line. The RS content of the starch, determined using AOAC method 991.43 for total dietary fiber, increased with kernel maturation and increase in the amylose/intermediate component (IC) content of the starch. Gelatinization of the native starches showed a major thermal transition with peak temperature at 76.6-81.0 degrees C. An additional peak ( approximately 97.1 degrees C) first appeared 20 days after pollination and then developed into a significant peak on later dates. After removal of lipids from the starch, this peak disappeared, but the conclusion gelatinization temperature remained the same. The proportion of the enthalpy change of the thermal transition above 95 degrees C, calculated from the thermogram of the defatted starch, increased with kernel maturation and was significantly correlated with the RS content of the starch (r = 0.98). These results showed that the increase in crystallites of amylose/IC long-chain double helices in the starch resulted in the increase in the RS content of the starch during kernel development.
Xyloglucans from flaxseed kernel cell wall: Structural and conformational characterisation.
Ding, Huihuang H; Cui, Steve W; Goff, H Douglas; Chen, Jie; Guo, Qingbin; Wang, Qi
2016-10-20
The structure of ethanol precipitated fraction from 1M KOH extracted flaxseed kernel polysaccharides (KPI-EPF) was studied for better understanding the molecular structures of flaxseed kernel cell wall polysaccharides. Based on methylation/GC-MS, NMR spectroscopy, and MALDI-TOF-MS analysis, the dominate sugar residues of KPI-EPF fraction comprised of (1,4,6)-linked-β-d-glucopyranose (24.1mol%), terminal α-d-xylopyranose (16.2mol%), (1,2)-α-d-linked-xylopyranose (10.7mol%), (1,4)-β-d-linked-glucopyranose (10.7mol%), and terminal β-d-galactopyranose (8.5mol%). KPI-EPF was proposed as xyloglucans: The substitution rate of the backbone is 69.3%; R1 could be T-α-d-Xylp-(1→, or none; R2 could be T-α-d-Xylp-(1→, T-β-d-Galp-(1→2)-α-d-Xylp-(1→, or T-α-l-Araf-(1→2)-α-d-Xylp-(1→; R3 could be T-α-d-Xylp-(1→, T-β-d-Galp-(1→2)-α-d-Xylp-(1→, T-α-l-Fucp-(1→2)-β-d-Galp-(1→2)-α-d-Xylp-(1→, or none. The Mw of KPI-EPF was calculated to be 1506kDa by static light scattering (SLS). The structure-sensitive parameter (ρ) of KPI-EPF was calculated as 1.44, which confirmed the highly branched structure of extracted xyloglucans. This new findings on flaxseed kernel xyloglucans will be helpful for understanding its fermentation properties and potential applications. Crown Copyright © 2016. Published by Elsevier Ltd. All rights reserved.
Heat-kernel approach for scattering
Li, Wen-Du
2015-01-01
An approach for solving scattering problems, based on two quantum field theory methods, the heat kernel method and the scattering spectral method, is constructed. This approach has a special advantage: it is not only one single approach; it is indeed a set of approaches for solving scattering problems. Concretely, we build a bridge between a scattering problem and the heat kernel method, so that each method of calculating heat kernels can be converted into a method of solving a scattering problem. As applications, we construct two approaches for solving scattering problems based on two heat-kernel expansions: the Seeley-DeWitt expansion and the covariant perturbation theory. In order to apply the heat kernel method to scattering problems, we also calculate two off-diagonal heat-kernel expansions in the frames of the Seeley-DeWitt expansion and the covariant perturbation theory, respectively. Moreover, as an alternative application of the relation between heat kernels and partial-wave phase shifts presented in...
Ideal regularization for learning kernels from labels.
Pan, Binbin; Lai, Jianhuang; Shen, Lixin
2014-08-01
In this paper, we propose a new form of regularization that is able to utilize the label information of a data set for learning kernels. The proposed regularization, referred to as ideal regularization, is a linear function of the kernel matrix to be learned. The ideal regularization allows us to develop efficient algorithms to exploit labels. Three applications of the ideal regularization are considered. Firstly, we use the ideal regularization to incorporate the labels into a standard kernel, making the resulting kernel more appropriate for learning tasks. Next, we employ the ideal regularization to learn a data-dependent kernel matrix from an initial kernel matrix (which contains prior similarity information, geometric structures, and labels of the data). Finally, we incorporate the ideal regularization to some state-of-the-art kernel learning problems. With this regularization, these learning problems can be formulated as simpler ones which permit more efficient solvers. Empirical results show that the ideal regularization exploits the labels effectively and efficiently.
Kernel score statistic for dependent data.
Malzahn, Dörthe; Friedrichs, Stefanie; Rosenberger, Albert; Bickeböller, Heike
2014-01-01
The kernel score statistic is a global covariance component test over a set of genetic markers. It provides a flexible modeling framework and does not collapse marker information. We generalize the kernel score statistic to allow for familial dependencies and to adjust for random confounder effects. With this extension, we adjust our analysis of real and simulated baseline systolic blood pressure for polygenic familial background. We find that the kernel score test gains appreciably in power through the use of sequencing compared to tag-single-nucleotide polymorphisms for very rare single nucleotide polymorphisms with <1% minor allele frequency.
Kernel-based Maximum Entropy Clustering
JIANG Wei; QU Jiao; LI Benxi
2007-01-01
With the development of Support Vector Machine (SVM),the "kernel method" has been studied in a general way.In this paper,we present a novel Kernel-based Maximum Entropy Clustering algorithm (KMEC).By using mercer kernel functions,the proposed algorithm is firstly map the data from their original space to high dimensional space where the data are expected to be more separable,then perform MEC clustering in the feature space.The experimental results show that the proposed method has better performance in the non-hyperspherical and complex data structure.
Kernel adaptive filtering a comprehensive introduction
Liu, Weifeng; Haykin, Simon
2010-01-01
Online learning from a signal processing perspective There is increased interest in kernel learning algorithms in neural networks and a growing need for nonlinear adaptive algorithms in advanced signal processing, communications, and controls. Kernel Adaptive Filtering is the first book to present a comprehensive, unifying introduction to online learning algorithms in reproducing kernel Hilbert spaces. Based on research being conducted in the Computational Neuro-Engineering Laboratory at the University of Florida and in the Cognitive Systems Laboratory at McMaster University, O
Multiple Operator-valued Kernel Learning
Kadri, Hachem; Bach, Francis; Preux, Philippe
2012-01-01
This paper addresses the problem of learning a finite linear combination of operator-valued kernels. We study this problem in the case of kernel ridge regression for functional responses with a lr-norm constraint on the combination coefficients. We propose a multiple operator-valued kernel learning algorithm based on solving a system of linear operator equations by using a block coordinate descent procedure. We experimentally validate our approach on a functional regression task in the context of finger movement prediction in Brain-Computer Interface (BCI).
Polynomial Kernelizations for $\\MINF_1$ and $\\MNP$
Kratsch, Stefan
2009-01-01
The relation of constant-factor approximability to fixed-parameter tractability and kernelization is a long-standing open question. We prove that two large classes of constant-factor approximable problems, namely $\\MINF_1$ and $\\MNP$, including the well-known subclass $\\MSNP$, admit polynomial kernelizations for their natural decision versions. This extends results of Cai and Chen (JCSS 1997), stating that the standard parameterizations of problems in $\\MSNP$ and $\\MINF_1$ are fixed-parameter tractable, and complements recent research on problems that do not admit polynomial kernelizations (Bodlaender et al. ICALP 2008).
Approximating W projection as a separable kernel
Merry, Bruce
2015-01-01
W projection is a commonly-used approach to allow interferometric imaging to be accelerated by Fast Fourier Transforms (FFTs), but it can require a huge amount of storage for convolution kernels. The kernels are not separable, but we show that they can be closely approximated by separable kernels. The error scales with the fourth power of the field of view, and so is small enough to be ignored at mid to high frequencies. We also show that hybrid imaging algorithms combining W projection with ...
Approximating W projection as a separable kernel
Merry, Bruce
2016-02-01
W projection is a commonly used approach to allow interferometric imaging to be accelerated by fast Fourier transforms, but it can require a huge amount of storage for convolution kernels. The kernels are not separable, but we show that they can be closely approximated by separable kernels. The error scales with the fourth power of the field of view, and so is small enough to be ignored at mid- to high frequencies. We also show that hybrid imaging algorithms combining W projection with either faceting, snapshotting, or W stacking allow the error to be made arbitrarily small, making the approximation suitable even for high-resolution wide-field instruments.
Defective Kernel Mutants of Maize II. Morphological and Embryo Culture Studies.
Sheridan, W F; Neuffer, M G
1980-08-01
This report presents the initial results of our study of the immature kernel stage of 150 defective kernel maize mutants. They are single gene, recessive mutants that map throughout the genome, defective in both endosperm and embryo development and, for the most part, lethal (Neuffer and Sheridan 1980). All can be distinguished on immature ears, and 85% of them reveal a mutant phenotype within 11 to 17 days post-pollination. Most have immature kernels that are smaller and lighter in color than their normal counterparts. Forty of the mutants suffer from their defects early in kernel development and are blocked in embryogenesis before their primordia differentiate, or, if primordia are formed, they are unable to germinate when cultured as immature embryos or tested at maturity; a few begin embryo degeneration prior to the time that mutant kernels became visually distinguishable. The others express the associated lesion later in kernel development and form at least one leaf primordium by the time kernels are distinguishable and will germinate when cultured or tested at maturity. In most cases, on a fresh weight basis, the mutants have embryos that are more severely defective than the endosperm; their embryos usually are no more than one-half to two-thirds the size, and lag behind by one or two developmental stages. in comparison with embryos in normal kernels from the same ear. One hundred and two mutants were examined by culturing embryos on basal and enriched media; 21 simply enlarged or completely failed to grow on any of the media tested; and 81 produced shoots and roots on at least one medium. Many grew equally well on basal and enriched media; 16 grew at a faster rate on basal medium and 23 displayed a superior growth on enriched medium. Among the latter group, 10 may be auxotrophs. One of these mutants and another mutant isolated by E. H. Coe are proline-requiring mutants, allelic to pro-1. Considering their diversity of expression as evidenced by their
Extension of Wirtinger's Calculus in Reproducing Kernel Hilbert Spaces and the Complex Kernel LMS
Bouboulis, Pantelis
2010-01-01
Over the last decade, kernel methods for nonlinear processing have successfully been used in the machine learning community. The primary mathematical tool employed in these methods is the notion of the Reproducing Kernel Hilbert Space. However, so far, the emphasis has been on batch techniques. It is only recently, that online techniques have been considered in the context of adaptive signal processing tasks. Moreover, these efforts have only been focussed on and real valued data sequences. To the best of our knowledge, no kernel-based strategy has been developed, so far, that is able to deal with complex valued signals. In this paper, we present a general framework to attack the problem of adaptive filtering of complex signals, using either real reproducing kernels, taking advantage of a technique called \\textit{complexification} of real RKHSs, or complex reproducing kernels, highlighting the use of the complex gaussian kernel. In order to derive gradients of operators that need to be defined on the associat...
Kernel map compression for speeding the execution of kernel-based methods.
Arif, Omar; Vela, Patricio A
2011-06-01
The use of Mercer kernel methods in statistical learning theory provides for strong learning capabilities, as seen in kernel principal component analysis and support vector machines. Unfortunately, after learning, the computational complexity of execution through a kernel is of the order of the size of the training set, which is quite large for many applications. This paper proposes a two-step procedure for arriving at a compact and computationally efficient execution procedure. After learning in the kernel space, the proposed extension exploits the universal approximation capabilities of generalized radial basis function neural networks to efficiently approximate and replace the projections onto the empirical kernel map used during execution. Sample applications demonstrate significant compression of the kernel representation with graceful performance loss.
GPR Surveying in the kernel area of Grove Mountains, Antarctica
WANG Zemin; TAN Zhi; AI Songtao; LIU Haiyan; CHE Guowei
2014-01-01
The Grove Mountains, located between the Zhongshan Station and Dome A, are a very important area in inland Antarctic research. China has organized ifve investigations of the Grove Mountains, encompassing the geological structure, ancient climate, meteorites, ice-movement monitoring, basic mapping, meteorological observations, and other multi-disciplinary observational studies. During the 26th Chinese National Antarctic Research Expedition in 2010, the Grove Mountains investigation team applied specialized ground-penetrating radar (GPR) to survey subglacial topography in the eastern kernel area of the Grove Mountains. In this paper, we processed GPS and GPR data gathered in the ifeld and drew, for the ifrst time, two subglacial topographic maps of the Grove Mountains kernel area using professional graphics software. The preliminary results reveal the mystery of the nunatak landform of this area, give an exploratory sense of the real bedrock landforms, and indicate a possible sedimentary basin under the Pliocene epoch fossil ice in the Grove Mountains area. Additionally, it has been proven from cross-sectional analysis between Mount Harding and the Zakharoff ridge that the box-valley shape between two nunataks has already matured.
The Linux kernel as flexible product-line architecture
Jonge, M. de
2002-01-01
The 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 component they real
7 CFR 51.2296 - Three-fourths half kernel.
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...
7 CFR 981.401 - Adjusted kernel weight.
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...
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...
7 CFR 51.1403 - Kernel color classification.
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...
NLO corrections to the Kernel of the BKP-equations
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.
Relative n-widths of periodic convolution classes with NCVD-kernel and B-kernel
无
2010-01-01
In this paper,we consider the relative n-widths of two kinds of periodic convolution classes,Kp(K) and Bp(G),whose convolution kernels are NCVD-kernel K and B-kernel G. The asymptotic estimations of Kn(Kp(K),Kp(K))q and Kn(Bp(G),Bp(G))q are obtained for p=1 and ∞,1≤ q≤∞.
Reproducing Kernel for D2(Ω, ρ) and Metric Induced by Reproducing Kernel
ZHAO Zhen Gang
2009-01-01
An important property of the reproducing kernel of D2(Ω, ρ) is obtained and the reproducing kernels for D2(Ω, ρ) are calculated when Ω = Bn × Bn and ρ are some special functions. A reproducing kernel is used to construct a semi-positive definite matrix and a distance function defined on Ω×Ω. An inequality is obtained about the distance function and the pseudodistance induced by the matrix.
ARKCoS: Artifact-Suppressed Accelerated Radial Kernel Convolution on the Sphere
Elsner, Franz
2011-01-01
We describe a hybrid Fourier/direct space convolution algorithm for compact radial (azimuthally symmetric) kernels on the sphere. For high resolution maps covering a large fraction of the sky, our implementation takes advantage of the inexpensive massive parallelism afforded by consumer graphics processing units (GPUs). Applications involve modeling of instrumental beam shapes in terms of compact kernels, computation of fine-scale wavelet transformations, and optimal filtering for the detection of point sources. Our algorithm works for any pixelization where pixels are grouped into isolatitude rings. Even for kernels that are not bandwidth limited, ringing features are completely absent on an ECP grid. We demonstrate that they can be highly suppressed on the popular HEALPix pixelization, for which we develop a freely available implementation of the algorithm. As an example application, we show that running on a high-end consumer graphics card our method speeds up beam convolution for simulations of a characte...
Fractional Radon Transform and Transform of Wigner Operator
FAN Hong-Yi; CHEN Jun-Hua
2003-01-01
Based on the Radon transform and fractional Fourier transform we introduce the fractional Radon trans-formation (FRT). We identify the transform kernel for FRT. The FRT of Wigner operator is derived, which naturallyreduces to the projector of eigenvector of the rotated quadrature in the usual Radon transform case.
Optimizing Arteriovenous Fistula Maturation
2009-01-01
Autogenous arteriovenous fistulas are the preferred vascular access in patients undergoing hemodialysis. Increasing fistula prevalence depends on increasing fistula placement, improving the maturation of fistula that fail to mature and enhancing the long-term patency of mature fistula. Percutaneous methods for optimizing arteriovenous fistula maturation will be reviewed.
Discriminant Kernel Assignment for Image Coding.
Deng, Yue; Zhao, Yanyu; Ren, Zhiquan; Kong, Youyong; Bao, Feng; Dai, Qionghai
2017-06-01
This paper proposes discriminant kernel assignment (DKA) in the bag-of-features framework for image representation. DKA slightly modifies existing kernel assignment to learn width-variant Gaussian kernel functions to perform discriminant local feature assignment. When directly applying gradient-descent method to solve DKA, the optimization may contain multiple time-consuming reassignment implementations in iterations. Accordingly, we introduce a more practical way to locally linearize the DKA objective and the difficult task is cast as a sequence of easier ones. Since DKA only focuses on the feature assignment part, it seamlessly collaborates with other discriminative learning approaches, e.g., discriminant dictionary learning or multiple kernel learning, for even better performances. Experimental evaluations on multiple benchmark datasets verify that DKA outperforms other image assignment approaches and exhibits significant efficiency in feature coding.
Multiple Kernel Spectral Regression for Dimensionality Reduction
Bing Liu
2013-01-01
Full Text Available Traditional manifold learning algorithms, such as locally linear embedding, Isomap, and Laplacian eigenmap, only provide the embedding results of the training samples. To solve the out-of-sample extension problem, spectral regression (SR solves the problem of learning an embedding function by establishing a regression framework, which can avoid eigen-decomposition of dense matrices. Motivated by the effectiveness of SR, we incorporate multiple kernel learning (MKL into SR for dimensionality reduction. The proposed approach (termed MKL-SR seeks an embedding function in the Reproducing Kernel Hilbert Space (RKHS induced by the multiple base kernels. An MKL-SR algorithm is proposed to improve the performance of kernel-based SR (KSR further. Furthermore, the proposed MKL-SR algorithm can be performed in the supervised, unsupervised, and semi-supervised situation. Experimental results on supervised classification and semi-supervised classification demonstrate the effectiveness and efficiency of our algorithm.
Quantum kernel applications in medicinal chemistry.
Huang, Lulu; Massa, Lou
2012-07-01
Progress in the quantum mechanics of biological molecules is being driven by computational advances. The notion of quantum kernels can be introduced to simplify the formalism of quantum mechanics, making it especially suitable for parallel computation of very large biological molecules. The essential idea is to mathematically break large biological molecules into smaller kernels that are calculationally tractable, and then to represent the full molecule by a summation over the kernels. The accuracy of the kernel energy method (KEM) is shown by systematic application to a great variety of molecular types found in biology. These include peptides, proteins, DNA and RNA. Examples are given that explore the KEM across a variety of chemical models, and to the outer limits of energy accuracy and molecular size. KEM represents an advance in quantum biology applicable to problems in medicine and drug design.
Kernel method-based fuzzy clustering algorithm
Wu Zhongdong; Gao Xinbo; Xie Weixin; Yu Jianping
2005-01-01
The fuzzy C-means clustering algorithm(FCM) to the fuzzy kernel C-means clustering algorithm(FKCM) to effectively perform cluster analysis on the diversiform structures are extended, such as non-hyperspherical data, data with noise, data with mixture of heterogeneous cluster prototypes, asymmetric data, etc. Based on the Mercer kernel, FKCM clustering algorithm is derived from FCM algorithm united with kernel method. The results of experiments with the synthetic and real data show that the FKCM clustering algorithm is universality and can effectively unsupervised analyze datasets with variform structures in contrast to FCM algorithm. It is can be imagined that kernel-based clustering algorithm is one of important research direction of fuzzy clustering analysis.
Kernel representations for behaviors over finite rings
Kuijper, M.; Pinto, R.; Polderman, J.W.; Yamamoto, Y.
2006-01-01
In this paper we consider dynamical systems finite rings. The rings that we study are the integers modulo a power of a given prime. We study the theory of representations for such systems, in particular kernel representations.
Ensemble Approach to Building Mercer Kernels
National Aeronautics and Space Administration — 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...
Convolution kernels for multi-wavelength imaging
Boucaud, Alexandre; Bocchio, Marco; Abergel, Alain; Orieux, François; Dole, Hervé; Hadj-Youcef, Mohamed Amine
2016-01-01
.... Given the knowledge of the PSF in each band, a straightforward way of processing images is to homogenise them all to a target PSF using convolution kernels, so that they appear as if they had been...
Difference image analysis: Automatic kernel design using information criteria
Bramich, D M; Alsubai, K A; Bachelet, E; Mislis, D; Parley, N
2015-01-01
We present a selection of methods for automatically constructing an optimal kernel model for difference image analysis which require very few external parameters to control the kernel design. Each method consists of two components; namely, a kernel design algorithm to generate a set of candidate kernel models, and a model selection criterion to select the simplest kernel model from the candidate models that provides a sufficiently good fit to the target image. We restricted our attention to the case of solving for a spatially-invariant convolution kernel composed of delta basis functions, and we considered 19 different kernel solution methods including six employing kernel regularisation. We tested these kernel solution methods by performing a comprehensive set of image simulations and investigating how their performance in terms of model error, fit quality, and photometric accuracy depends on the properties of the reference and target images. We find that the irregular kernel design algorithm employing unreg...
Preparing UO2 kernels by gelcasting
GUO Wenli; LIANG Tongxiang; ZHAO Xingyu; HAO Shaochang; LI Chengliang
2009-01-01
A process named gel-casting has been developed for the production of dense UO2 kernels for the high-ten-temperature gas-cooled reactor. Compared with the sol-gel process, the green microspheres can be got by dispersing the U3O8 slurry in gelcasting process, which means that gelcasting is a more facilitative process with less waste in fabricating UO2 kernels. The heat treatment.
The Bergman kernel functions on Hua domains
无
2001-01-01
We get the Bergman kernel functions in explicit formulas on four types of Hua domain.There are two key steps: First, we give the holomorphic automorphism groups of four types of Hua domain; second, we introduce the concept of semi-Reinhardt domain and give their complete orthonormal systems. Based on these two aspects we obtain the Bergman kernel function in explicit formulas on Hua domains.
Fractal Weyl law for Linux Kernel Architecture
Ermann, L; Shepelyansky, D L
2010-01-01
We study the properties of spectrum and eigenstates of the Google matrix of a directed network formed by the procedure calls in the Linux Kernel. Our results obtained for various versions of the Linux Kernel show that the spectrum is characterized by the fractal Weyl law established recently for systems of quantum chaotic scattering and the Perron-Frobenius operators of dynamical maps. The fractal Weyl exponent is found to be $\
Varying kernel density estimation on ℝ+
Mnatsakanov, Robert; Sarkisian, Khachatur
2015-01-01
In this article a new nonparametric density estimator based on the sequence of asymmetric kernels is proposed. This method is natural when estimating an unknown density function of a positive random variable. The rates of Mean Squared Error, Mean Integrated Squared Error, and the L1-consistency are investigated. Simulation studies are conducted to compare a new estimator and its modified version with traditional kernel density construction. PMID:26740729
Adaptively Learning the Crowd Kernel
Tamuz, Omer; Belongie, Serge; Shamir, Ohad; Kalai, Adam Tauman
2011-01-01
We introduce an algorithm that, given n objects, learns a similarity matrix over all n^2 pairs, from crowdsourced data alone. The algorithm samples responses to adaptively chosen triplet-based relative-similarity queries. Each query has the form "is object 'a' more similar to 'b' or to 'c'?" and is chosen to be maximally informative given the preceding responses. The output is an embedding of the objects into Euclidean space (like MDS); we refer to this as the "crowd kernel." The runtime (empirically observed to be linear) and cost (about $0.15 per object) of the algorithm are small enough to permit its application to databases of thousands of objects. The distance matrix provided by the algorithm allows for the development of an intuitive and powerful sequential, interactive search algorithm which we demonstrate for a variety of visual stimuli. We present quantitative results that demonstrate the benefit in cost and time of our approach compared to a nonadaptive approach. We also show the ability of our appr...
Evaluating the Gradient of the Thin Wire Kernel
Wilton, Donald R.; Champagne, Nathan J.
2008-01-01
Recently, a formulation for evaluating the thin wire kernel was developed that employed a change of variable to smooth the kernel integrand, canceling the singularity in the integrand. Hence, the typical expansion of the wire kernel in a series for use in the potential integrals is avoided. The new expression for the kernel is exact and may be used directly to determine the gradient of the wire kernel, which consists of components that are parallel and radial to the wire axis.
On the Inclusion Relation of Reproducing Kernel Hilbert Spaces
Zhang, Haizhang; Zhao, Liang
2011-01-01
To help understand various reproducing kernels used in applied sciences, we investigate the inclusion relation of two reproducing kernel Hilbert spaces. Characterizations in terms of feature maps of the corresponding reproducing kernels are established. A full table of inclusion relations among widely-used translation invariant kernels is given. Concrete examples for Hilbert-Schmidt kernels are presented as well. We also discuss the preservation of such a relation under various operations of ...
Clauer, N.; Lewan, M. D.; Dolan, M. P.; Chaudhuri, S.; Curtis, J. B.
2014-04-01
Progressive maturation of the Eocene Kreyenhagen Shale from the San Joaquin Basin of California was studied by combining mineralogical and chemical analyses with K-Ar dating of whole rocks and bearing minerals of the rocks. The Al/K ratio of the naturally matured rocks is essentially constant for the entire depth sequence, indicating that there is no detectable variation in the crystallo-chemical organization of the K-bearing alumino-silicates with depth. No supply of K from outside of the rock volumes occurred, which indicates a closed-system behavior for it. Conversely, the content of the total organic carbon (TOC) content decreases significantly with burial, based on the progressive increasing Al/TOC ratio of the whole rocks. The initial varied mineralogy and chemistry of the rocks and their sources and depositional settings give scattered results that homogenize progressively during burial due to increased authigenesis, and concomitant increased alteration of the detrital material. Hydrous pyrolysis was intended to alleviate the problem of mineral and chemical variations in initially deposited rocks of naturally matured sequences. However, experiments on aliquots from thermally immature Kreyenhagen Shale outcrop sample did not mimic the results from naturally buried samples. Experiments conducted for 72 h at temperatures from 270 to 365 °C did not induce significant changes at temperatures above 310 °C in the mineralogical composition and K-Ar ages of the rock and heated the most at 365 °C for 216 h. This slight decrease in age outlines some loss of radiogenic 40Ar, together with losses of organic matter as oil, gas, and aqueous organic species. Large amounts of smectite layers in the illite-smectite mixed layers of the pyrolyzed outcrop source rock, the pore system is no longer wetted by water and smectite to illite conversion ceases. Experimental attempts to evaluate the smectite conversion to illite should preferentially use low-TOC rocks to avoid
Ariana Vieira Alves
Full Text Available Insect consumption as food is culturally practiced in various regions of the world. In Brazil, there are more than 130 species of edible insects registered, from nine orders, among which stands out the Coleoptera. The larva of the beetle Pachymerus nucleorum Fabricius, 1792, grows into the bocaiuva fruit (Acrocomia aculeata (Jacq. Lodd. Ex Mart., 1845, which has proven nutritional quality. The aim of this work was to evaluate the nutritional potential of P. nucleorum larvae compared to bocaiuva kernels for human consumption. Proteins were the second largest portion of the larvae nutritional composition (33.13%, with percentage higher than the bocaiuva kernels (14.21%. The larval lipid content (37.87% was also high, very close to the kernels (44.96%. The fraction corresponding to fatty acids in the oil extracted from the larvae was 40.17% for the saturated and 46.52% for the unsaturated. The antioxidant activity value was 24.3 uM trolox/g of oil extracted from larvae. The larvae tryptic activity was 0.032±0.006 nmol BAPNA/min. Both the larvae and the bocaiuva kernel presented absence of anti-nutritional factors. These results favor the use of P. nucleorum larvae as food, which are a great protein and lipid sources with considerable concentrations of unsaturated fatty acids compared to the bocaiuva kernel.
João Pedro Velho
2008-02-01
Full Text Available Objetivou-se avaliar o efeito da maturidade dos grãos de milho e do tempo de exposição ao ar antes da ensilagem sobre o pH e os componentes nitrogenados de silagens de milho "safrinha". O experimento foi conduzido em delineamento completamente casualizado com arranjo fatorial 2 x 4, no qual foram testados dois estádios de maturidade (MA ao corte (grão completamente leitoso (GL ou grão ½ leitoso ½ farináceo (GF e quatro tempos de exposição ao ar (EX, sem compactação (0, 12, 24 e 36 horas antes da ensilagem em minissilos. Houve efeito significativo (PThis experiment was aimed at evaluating the effect of corn grain maturity and air exposure time prior to ensiling on the pH and nitrogenous fractions of corn silage from corn crop planted late in the season. A completely randomized 2 x 4 factorial arrangement was used to test the effect of two maturity stages at harvest (GL, milky kernel and GF, ½ milky ½ dough kernel of maize crops and the effects of four periods of air exposure of chopped materials (zero, 12, 24 and 36 hours previous to packing in mini-silos, on pH and N fractions of silages. There was a significant effect (P<0.05 related to stage of maturity and period of air exposure concerning the contents of crude protein and neutral detergent insoluble nitrogen, as well as an interaction between the main effects for ammonia nitrogen and acid detergent insoluble nitrogen. Due to the interaction, the maize silage harvested at the milky kernel stage resulted in higher pH value and ammonia nitrogen contents. A delayed packing procedure increases the insoluble nitrogen fractions and may affect negatively protein digestibility.
Hanft, J M; Jones, R J
1986-06-01
Kernels cultured in vitro were induced to abort by high temperature (35 degrees C) and by culturing six kernels/cob piece. Aborting kernels failed to enter a linear phase of dry mass accumulation and had a final mass that was less than 6% of nonaborting field-grown kernels. Kernels induced to abort by high temperature failed to synthesize starch in the endosperm and had elevated sucrose concentrations and low fructose and glucose concentrations in the pedicel during early growth compared to nonaborting kernels. Kernels induced to abort by high temperature also had much lower pedicel soluble acid invertase activities than did nonaborting kernels. These results suggest that high temperature during the lag phase of kernel growth may impair the process of sucrose unloading in the pedicel by indirectly inhibiting soluble acid invertase activity and prevent starch synthesis in the endosperm. Kernels induced to abort by culturing six kernels/cob piece had reduced pedicel fructose, glucose, and sucrose concentrations compared to kernels from field-grown ears. These aborting kernels also had a lower pedicel soluble acid invertase activity compared to nonaborting kernels from the same cob piece and from field-grown ears. The low invertase activity in pedicel tissue of the aborting kernels was probably caused by a lack of substrate (sucrose) for the invertase to cleave due to the intense competition for available assimilates. In contrast to kernels cultured at 35 degrees C, aborting kernels from cob pieces containing all six kernels accumulated starch in a linear fashion. These results indicate that kernels cultured six/cob piece abort because of an inadequate supply of sugar and are similar to apical kernels from field-grown ears that often abort prior to the onset of linear growth.
Single pass kernel -means clustering method
T Hitendra Sarma; P Viswanath; B Eswara Reddy
2013-06-01
In unsupervised classiﬁcation, kernel -means clustering method has been shown to perform better than conventional -means clustering method in identifying non-isotropic clusters in a data set. The space and time requirements of this method are $O(n^2)$, where is the data set size. Because of this quadratic time complexity, the kernel -means method is not applicable to work with large data sets. The paper proposes a simple and faster version of the kernel -means clustering method, called single pass kernel k-means clustering method. The proposed method works as follows. First, a random sample $\\mathcal{S}$ is selected from the data set $\\mathcal{D}$. A partition $\\Pi_{\\mathcal{S}}$ is obtained by applying the conventional kernel -means method on the random sample $\\mathcal{S}$. The novelty of the paper is, for each cluster in $\\Pi_{\\mathcal{S}}$, the exact cluster center in the input space is obtained using the gradient descent approach. Finally, each unsampled pattern is assigned to its closest exact cluster center to get a partition of the entire data set. The proposed method needs to scan the data set only once and it is much faster than the conventional kernel -means method. The time complexity of this method is $O(s^2+t+nk)$ where is the size of the random sample $\\mathcal{S}$, is the number of clusters required, and is the time taken by the gradient descent method (to ﬁnd exact cluster centers). The space complexity of the method is $O(s^2)$. The proposed method can be easily implemented and is suitable for large data sets, like those in data mining applications. Experimental results show that, with a small loss of quality, the proposed method can signiﬁcantly reduce the time taken than the conventional kernel -means clustering method. The proposed method is also compared with other recent similar methods.
Kernel-Based Reconstruction of Graph Signals
Romero, Daniel; Ma, Meng; Giannakis, Georgios B.
2017-02-01
A number of applications in engineering, social sciences, physics, and biology involve inference over networks. In this context, graph signals are widely encountered as descriptors of vertex attributes or features in graph-structured data. Estimating such signals in all vertices given noisy observations of their values on a subset of vertices has been extensively analyzed in the literature of signal processing on graphs (SPoG). This paper advocates kernel regression as a framework generalizing popular SPoG modeling and reconstruction and expanding their capabilities. Formulating signal reconstruction as a regression task on reproducing kernel Hilbert spaces of graph signals permeates benefits from statistical learning, offers fresh insights, and allows for estimators to leverage richer forms of prior information than existing alternatives. A number of SPoG notions such as bandlimitedness, graph filters, and the graph Fourier transform are naturally accommodated in the kernel framework. Additionally, this paper capitalizes on the so-called representer theorem to devise simpler versions of existing Thikhonov regularized estimators, and offers a novel probabilistic interpretation of kernel methods on graphs based on graphical models. Motivated by the challenges of selecting the bandwidth parameter in SPoG estimators or the kernel map in kernel-based methods, the present paper further proposes two multi-kernel approaches with complementary strengths. Whereas the first enables estimation of the unknown bandwidth of bandlimited signals, the second allows for efficient graph filter selection. Numerical tests with synthetic as well as real data demonstrate the merits of the proposed methods relative to state-of-the-art alternatives.
Karras, G; Billard, F; Lavorel, B; Siour, G; Hartmann, J -M; Faucher, O; Gershnabel, Erez; Prior, Yehiam; Averbukh, Ilya Sh
2016-01-01
We report the observation of fractional echoes in a double-pulse excited nonlinear system. Unlike standard echoes which appear periodically at delays which are integer multiple of the delay between the two exciting pulses, the fractional echoes appear at rational fractions of this delay. We discuss the mechanism leading to this phenomenon, and provide the first experimental demonstration of fractional echoes by measuring third harmonic generation in a thermal gas of CO2 molecules excited by a pair of femtosecond laser pulses.
Maria Klimikova
2010-01-01
Understanding the reasons of the present financial problems lies In understanding the substance of fractional reserve banking. The substance of fractional banking is in lending more money than the bankers have. Banking of partial reserves is an alternative form which links deposit banking and credit banking. Fractional banking is causing many unfavorable economic impacts in the worldwide system, specifically an inflation.
Maria Klimikova
2010-01-01
Understanding the reasons of the present financial problems lies In understanding the substance of fractional reserve banking. The substance of fractional banking is in lending more money than the bankers have. Banking of partial reserves is an alternative form which links deposit banking and credit banking. Fractional banking is causing many unfavorable economic impacts in the worldwide system, specifically an inflation.
A new Mercer sigmoid kernel for clinical data classification.
Carrington, André M; Fieguth, Paul W; Chen, Helen H
2014-01-01
In classification with Support Vector Machines, only Mercer kernels, i.e. valid kernels, such as the Gaussian RBF kernel, are widely accepted and thus suitable for clinical data. Practitioners would also like to use the sigmoid kernel, a non-Mercer kernel, but its range of validity is difficult to determine, and even within range its validity is in dispute. Despite these shortcomings the sigmoid kernel is used by some, and two kernels in the literature attempt to emulate and improve upon it. We propose the first Mercer sigmoid kernel, that is therefore trustworthy for the classification of clinical data. We show the similarity between the Mercer sigmoid kernel and the sigmoid kernel and, in the process, identify a normalization technique that improves the classification accuracy of the latter. The Mercer sigmoid kernel achieves the best mean accuracy on three clinical data sets, detecting melanoma in skin lesions better than the most popular kernels; while with non-clinical data sets it has no significant difference in median accuracy as compared with the Gaussian RBF kernel. It consistently classifies some points correctly that the Gaussian RBF kernel does not and vice versa.
Alvarez Prado, Santiago; Sadras, Víctor O; Borrás, Lucas
2014-08-01
Maize kernel weight (KW) is associated with the duration of the grain-filling period (GFD) and the rate of kernel biomass accumulation (KGR). It is also related to the dynamics of water and hence is physiologically linked to the maximum kernel water content (MWC), kernel desiccation rate (KDR), and moisture concentration at physiological maturity (MCPM). This work proposed that principles of phenotypic plasticity can help to consolidated the understanding of the environmental modulation and genetic control of these traits. For that purpose, a maize population of 245 recombinant inbred lines (RILs) was grown under different environmental conditions. Trait plasticity was calculated as the ratio of the variance of each RIL to the overall phenotypic variance of the population of RILs. This work found a hierarchy of plasticities: KDR ≈ GFD > MCPM > KGR > KW > MWC. There was no phenotypic and genetic correlation between traits per se and trait plasticities. MWC, the trait with the lowest plasticity, was the exception because common quantitative trait loci were found for the trait and its plasticity. Independent genetic control of a trait per se and genetic control of its plasticity is a condition for the independent evolution of traits and their plasticities. This allows breeders potentially to select for high or low plasticity in combination with high or low values of economically relevant traits.
Characterization of myocardial motion patterns by unsupervised multiple kernel learning.
Sanchez-Martinez, Sergio; Duchateau, Nicolas; Erdei, Tamas; Fraser, Alan G; Bijnens, Bart H; Piella, Gemma
2017-01-01
We propose an independent objective method to characterize different patterns of functional responses to stress in the heart failure with preserved ejection fraction (HFPEF) syndrome by combining multiple temporally-aligned myocardial velocity traces at rest and during exercise, together with temporal information on the occurrence of cardiac events (valves openings/closures and atrial activation). The method builds upon multiple kernel learning, a machine learning technique that allows the combination of data of different nature and the reduction of their dimensionality towards a meaningful representation (output space). The learning process is kept unsupervised, to study the variability of the input traces without being conditioned by data labels. To enhance the physiological interpretation of the output space, the variability that it encodes is analyzed in the space of input signals after reconstructing the velocity traces via multiscale kernel regression. The methodology was applied to 2D sequences from a stress echocardiography protocol from 55 subjects (22 healthy, 19 HFPEF and 14 breathless subjects). The results confirm that characterization of the myocardial functional response to stress in the HFPEF syndrome may be improved by the joint analysis of multiple relevant features.
Maturity and maturity models in lean construction
Claus Nesensohn
2014-03-01
Full Text Available In recent years there has been an increasing interest in maturity models in management-related disciplines; which reflects a growing recognition that becoming more mature and having a model to guide the route to maturity can help organisations in managing major transformational change. Lean Construction (LC is an increasingly important improvement approach that organisations seek to embed. This study explores how to apply the maturity models to LC. Hence the attitudes, opinions and experiences of key industry informants with high levels of knowledge of LC were investigated. To achieve this, a review of maturity models was conducted, and data for the analysis was collected through a sequential process involving three methods. First a group interview with seven key informants. Second a follow up discussion with the same individuals to investigate some of the issues raised in more depth. Third an online discussion held via LinkedIn in which members shared their views on some of the results. Overall, we found that there is a lack of common understanding as to what maturity means in LC, though there is general agreement that the concept of maturity is a suitable one to reflect the path of evolution for LC within organisations.
Maturity and maturity models in lean construction
Claus Nesensohn
2014-03-01
Full Text Available In recent years there has been an increasing interest in maturity models in management-related disciplines; which reflects a growing recognition that becoming more mature and having a model to guide the route to maturity can help organisations in managing major transformational change. Lean Construction (LC is an increasingly important improvement approach that organisations seek to embed. This study explores how to apply the maturity models to LC. Hence the attitudes, opinions and experiences of key industry informants with high levels of knowledge of LC were investigated. To achieve this, a review of maturity models was conducted, and data for the analysis was collected through a sequential process involving three methods. First a group interview with seven key informants. Second a follow up discussion with the same individuals to investigate some of the issues raised in more depth. Third an online discussion held via LinkedIn in which members shared their views on some of the results. Overall, we found that there is a lack of common understanding as to what maturity means in LC, though there is general agreement that the concept of maturity is a suitable one to reflect the path of evolution for LC within organisations.
Pattern Classification of Signals Using Fisher Kernels
Yashodhan Athavale
2012-01-01
Full Text Available The intention of this study is to gauge the performance of Fisher kernels for dimension simplification and classification of time-series signals. Our research work has indicated that Fisher kernels have shown substantial improvement in signal classification by enabling clearer pattern visualization in three-dimensional space. In this paper, we will exhibit the performance of Fisher kernels for two domains: financial and biomedical. The financial domain study involves identifying the possibility of collapse or survival of a company trading in the stock market. For assessing the fate of each company, we have collected financial time-series composed of weekly closing stock prices in a common time frame, using Thomson Datastream software. The biomedical domain study involves knee signals collected using the vibration arthrometry technique. This study uses the severity of cartilage degeneration for classifying normal and abnormal knee joints. In both studies, we apply Fisher Kernels incorporated with a Gaussian mixture model (GMM for dimension transformation into feature space, which is created as a three-dimensional plot for visualization and for further classification using support vector machines. From our experiments we observe that Fisher Kernel usage fits really well for both kinds of signals, with low classification error rates.
Analog forecasting with dynamics-adapted kernels
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.
Object classification and detection with context kernel descriptors
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...
OS X and iOS Kernel Programming
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
Povstenko, Yuriy
2015-01-01
This book is devoted to fractional thermoelasticity, i.e. thermoelasticity based on the heat conduction equation with differential operators of fractional order. Readers will discover how time-fractional differential operators describe memory effects and space-fractional differential operators deal with the long-range interaction. Fractional calculus, generalized Fourier law, axisymmetric and central symmetric problems and many relevant equations are featured in the book. The latest developments in the field are included and the reader is brought up to date with current research. The book contains a large number of figures, to show the characteristic features of temperature and stress distributions and to represent the whole spectrum of order of fractional operators. This work presents a picture of the state-of-the-art of fractional thermoelasticity and is suitable for specialists in applied mathematics, physics, geophysics, elasticity, thermoelasticity and engineering sciences. Corresponding sections of ...
Tan, Yen Nee; Ayob, Mohd Khan; Wan Yaacob, Wan Ahmad
2013-01-01
Palm kernel cake (PKC), the most useful by-product resulted from palm kernel oil production. In this study, PKC-derived protein product was found suitable for use as an antimicrobial agent with potent antibacterial activity, particularly against Bacillus species, after enzymatic hydrolysis with alcalase. The hydrolysate was further purified by gel filtration chromatography. The purified fraction was found to have 14.63±0.70% (w/w) protein, a molecular mass of 2.4kDa and low hemolytic activity (antibacterial effect of purified PKC fraction. This study suggests that the antibacterial PKC compound may be not a pure peptide but instead a peptide-containing compound high in lauric acid derivative.
The scalar field kernel in cosmological spaces
Koksma, Jurjen F; Prokopec, Tomislav [Institute for Theoretical Physics (ITP) and Spinoza Institute, Utrecht University, Postbus 80195, 3508 TD Utrecht (Netherlands); Rigopoulos, Gerasimos I [Helsinki Institute of Physics, University of Helsinki, PO Box 64, FIN-00014 (Finland)], E-mail: J.F.Koksma@phys.uu.nl, E-mail: T.Prokopec@phys.uu.nl, E-mail: gerasimos.rigopoulos@helsinki.fi
2008-06-21
We construct the quantum-mechanical evolution operator in the functional Schroedinger picture-the kernel-for a scalar field in spatially homogeneous FLRW spacetimes when the field is (a) free and (b) coupled to a spacetime-dependent source term. The essential element in the construction is the causal propagator, linked to the commutator of two Heisenberg picture scalar fields. We show that the kernels can be expressed solely in terms of the causal propagator and derivatives of the causal propagator. Furthermore, we show that our kernel reveals the standard light cone structure in FLRW spacetimes. We finally apply the result to Minkowski spacetime, to de Sitter spacetime and calculate the forward time evolution of the vacuum in a general FLRW spacetime.
Robust Visual Tracking via Fuzzy Kernel Representation
Zhiqiang Wen
2013-05-01
Full Text Available A robust visual kernel tracking approach is presented for solving the problem of existing background pixels in object model. At first, after definition of fuzzy set on image is given, a fuzzy factor is embedded into object model to form the fuzzy kernel representation. Secondly, a fuzzy membership functions are generated by center-surround approach and log likelihood ratio of feature distributions. Thirdly, details about fuzzy kernel tracking algorithm is provided. After that, methods of parameter selection and performance evaluation for tracking algorithm are proposed. At last, a mass of experimental results are done to show our method can reduce the influence of the incomplete representation of object model via integrating both color features and background features.
Fractal Weyl law for Linux Kernel architecture
Ermann, L.; Chepelianskii, A. D.; Shepelyansky, D. L.
2011-01-01
We study the properties of spectrum and eigenstates of the Google matrix of a directed network formed by the procedure calls in the Linux Kernel. Our results obtained for various versions of the Linux Kernel show that the spectrum is characterized by the fractal Weyl law established recently for systems of quantum chaotic scattering and the Perron-Frobenius operators of dynamical maps. The fractal Weyl exponent is found to be ν ≈ 0.65 that corresponds to the fractal dimension of the network d ≈ 1.3. An independent computation of the fractal dimension by the cluster growing method, generalized for directed networks, gives a close value d ≈ 1.4. The eigenmodes of the Google matrix of Linux Kernel are localized on certain principal nodes. We argue that the fractal Weyl law should be generic for directed networks with the fractal dimension d < 2.
Optoacoustic inversion via Volterra kernel reconstruction
Melchert, O; Roth, B
2016-01-01
In this letter we address the numeric inversion of optoacoustic signals to initial stress profiles. Therefore we put under scrutiny the optoacoustic kernel reconstruction problem in the paraxial approximation of the underlying wave-equation. We apply a Fourier-series expansion of the optoacoustic Volterra kernel and obtain the respective expansion coefficients for a given "apparative" setup by performing a gauge procedure using synthetic input data. The resulting effective kernel is subsequently used to solve the optoacoustic source reconstruction problem for general signals. We verify the validity of the proposed inversion protocol for synthetic signals and explore the feasibility of our approach to also account for the diffraction transformation of signals beyond the paraxial approximation.
Tile-Compressed FITS Kernel for IRAF
Seaman, R.
2011-07-01
The Flexible Image Transport System (FITS) is a ubiquitously supported standard of the astronomical community. Similarly, the Image Reduction and Analysis Facility (IRAF), developed by the National Optical Astronomy Observatory, is a widely used astronomical data reduction package. IRAF supplies compatibility with FITS format data through numerous tools and interfaces. The most integrated of these is IRAF's FITS image kernel that provides access to FITS from any IRAF task that uses the basic IMIO interface. The original FITS kernel is a complex interface of purpose-built procedures that presents growing maintenance issues and lacks recent FITS innovations. A new FITS kernel is being developed at NOAO that is layered on the CFITSIO library from the NASA Goddard Space Flight Center. The simplified interface will minimize maintenance headaches as well as add important new features such as support for the FITS tile-compressed (fpack) format.
A kernel-based approach for biomedical named entity recognition.
Patra, Rakesh; Saha, Sujan Kumar
2013-01-01
Support vector machine (SVM) is one of the popular machine learning techniques used in various text processing tasks including named entity recognition (NER). The performance of the SVM classifier largely depends on the appropriateness of the kernel function. In the last few years a number of task-specific kernel functions have been proposed and used in various text processing tasks, for example, string kernel, graph kernel, tree kernel and so on. So far very few efforts have been devoted to the development of NER task specific kernel. In the literature we found that the tree kernel has been used in NER task only for entity boundary detection or reannotation. The conventional tree kernel is unable to execute the complete NER task on its own. In this paper we have proposed a kernel function, motivated by the tree kernel, which is able to perform the complete NER task. To examine the effectiveness of the proposed kernel, we have applied the kernel function on the openly available JNLPBA 2004 data. Our kernel executes the complete NER task and achieves reasonable accuracy.
Full Waveform Inversion Using Waveform Sensitivity Kernels
Schumacher, Florian; Friederich, Wolfgang
2013-04-01
We present a full waveform inversion concept for applications ranging from seismological to enineering contexts, in which the steps of forward simulation, computation of sensitivity kernels, and the actual inversion are kept separate of each other. We derive waveform sensitivity kernels from Born scattering theory, which for unit material perturbations are identical to the Born integrand for the considered path between source and receiver. The evaluation of such a kernel requires the calculation of Green functions and their strains for single forces at the receiver position, as well as displacement fields and strains originating at the seismic source. We compute these quantities in the frequency domain using the 3D spectral element code SPECFEM3D (Tromp, Komatitsch and Liu, 2008) and the 1D semi-analytical code GEMINI (Friederich and Dalkolmo, 1995) in both, Cartesian and spherical framework. We developed and implemented the modularized software package ASKI (Analysis of Sensitivity and Kernel Inversion) to compute waveform sensitivity kernels from wavefields generated by any of the above methods (support for more methods is planned), where some examples will be shown. As the kernels can be computed independently from any data values, this approach allows to do a sensitivity and resolution analysis first without inverting any data. In the context of active seismic experiments, this property may be used to investigate optimal acquisition geometry and expectable resolution before actually collecting any data, assuming the background model is known sufficiently well. The actual inversion step then, can be repeated at relatively low costs with different (sub)sets of data, adding different smoothing conditions. Using the sensitivity kernels, we expect the waveform inversion to have better convergence properties compared with strategies that use gradients of a misfit function. Also the propagation of the forward wavefield and the backward propagation from the receiver
Inverse of the String Theory KLT Kernel
Mizera, Sebastian
2016-01-01
The field theory Kawai-Lewellen-Tye (KLT) kernel, which relates scattering amplitudes of gravitons and gluons, turns out to be the inverse of a matrix whose components are bi-adjoint scalar partial amplitudes. In this note we propose an analogous construction for the string theory KLT kernel. We present simple diagrammatic rules for the computation of the $\\alpha'$-corrected bi-adjoint scalar amplitudes that are exact in $\\alpha'$. We find compact expressions in terms of graphs, where the standard Feynman propagators $1/p^2$ are replaced by either $1/\\sin (\\pi \\alpha' p^2)$ or $1/\\tan (\\pi \\alpha' p^2)$, which is determined by a recursive procedure.
Reduced multiple empirical kernel learning machine.
Wang, Zhe; Lu, MingZhe; Gao, Daqi
2015-02-01
Multiple kernel learning (MKL) is demonstrated to be flexible and effective in depicting heterogeneous data sources since MKL can introduce multiple kernels rather than a single fixed kernel into applications. However, MKL would get a high time and space complexity in contrast to single kernel learning, which is not expected in real-world applications. Meanwhile, it is known that the kernel mapping ways of MKL generally have two forms including implicit kernel mapping and empirical kernel mapping (EKM), where the latter is less attracted. In this paper, we focus on the MKL with the EKM, and propose a reduced multiple empirical kernel learning machine named RMEKLM for short. To the best of our knowledge, it is the first to reduce both time and space complexity of the MKL with EKM. Different from the existing MKL, the proposed RMEKLM adopts the Gauss Elimination technique to extract a set of feature vectors, which is validated that doing so does not lose much information of the original feature space. Then RMEKLM adopts the extracted feature vectors to span a reduced orthonormal subspace of the feature space, which is visualized in terms of the geometry structure. It can be demonstrated that the spanned subspace is isomorphic to the original feature space, which means that the dot product of two vectors in the original feature space is equal to that of the two corresponding vectors in the generated orthonormal subspace. More importantly, the proposed RMEKLM brings a simpler computation and meanwhile needs a less storage space, especially in the processing of testing. Finally, the experimental results show that RMEKLM owns a much efficient and effective performance in terms of both complexity and classification. The contributions of this paper can be given as follows: (1) by mapping the input space into an orthonormal subspace, the geometry of the generated subspace is visualized; (2) this paper first reduces both the time and space complexity of the EKM-based MKL; (3
Volatile compound formation during argan kernel roasting.
El Monfalouti, Hanae; Charrouf, Zoubida; Giordano, Manuela; Guillaume, Dominique; Kartah, Badreddine; Harhar, Hicham; Gharby, Saïd; Denhez, Clément; Zeppa, Giuseppe
2013-01-01
Virgin edible argan oil is prepared by cold-pressing argan kernels previously roasted at 110 degrees C for up to 25 minutes. The concentration of 40 volatile compounds in virgin edible argan oil was determined as a function of argan kernel roasting time. Most of the volatile compounds begin to be formed after 15 to 25 minutes of roasting. This suggests that a strictly controlled roasting time should allow the modulation of argan oil taste and thus satisfy different types of consumers. This could be of major importance considering the present booming use of edible argan oil.
Learning Rates for -Regularized Kernel Classifiers
Hongzhi Tong
2013-01-01
Full Text Available We consider a family of classification algorithms generated from a regularization kernel scheme associated with -regularizer and convex loss function. Our main purpose is to provide an explicit convergence rate for the excess misclassification error of the produced classifiers. The error decomposition includes approximation error, hypothesis error, and sample error. We apply some novel techniques to estimate the hypothesis error and sample error. Learning rates are eventually derived under some assumptions on the kernel, the input space, the marginal distribution, and the approximation error.
Face Recognition Using Kernel Discriminant Analysis
无
2002-01-01
Linear Discrimiant Analysis (LDA) has demonstrated their success in face recognition. But LDA is difficult to handle the high nonlinear problems, such as changes of large viewpoint and illumination in face recognition. In order to overcome these problems, we investigate Kernel Discriminant Analysis (KDA) for face recognition. This approach adopts the kernel functions to replace the dot products of nonlinear mapping in the high dimensional feature space, and then the nonlinear problem can be solved in the input space conveniently without explicit mapping. Two face databases are used to test KDA approach. The results show that our approach outperforms the conventional PCA(Eigenface) and LDA(Fisherface) approaches.
Regularization techniques for PSF-matching kernels - I. Choice of kernel basis
Becker, A. C.; Homrighausen, D.; Connolly, A. J.; Genovese, C. R.; Owen, R.; Bickerton, S. J.; Lupton, R. H.
2012-09-01
We review current methods for building point spread function (PSF)-matching kernels for the purposes of image subtraction or co-addition. Such methods use a linear decomposition of the kernel on a series of basis functions. The correct choice of these basis functions is fundamental to the efficiency and effectiveness of the matching - the chosen bases should represent the underlying signal using a reasonably small number of shapes, and/or have a minimum number of user-adjustable tuning parameters. We examine methods whose bases comprise multiple Gauss-Hermite polynomials, as well as a form-free basis composed of delta-functions. Kernels derived from delta-functions are unsurprisingly shown to be more expressive; they are able to take more general shapes and perform better in situations where sum-of-Gaussian methods are known to fail. However, due to its many degrees of freedom (the maximum number allowed by the kernel size) this basis tends to overfit the problem and yields noisy kernels having large variance. We introduce a new technique to regularize these delta-function kernel solutions, which bridges the gap between the generality of delta-function kernels and the compactness of sum-of-Gaussian kernels. Through this regularization we are able to create general kernel solutions that represent the intrinsic shape of the PSF-matching kernel with only one degree of freedom, the strength of the regularization λ. The role of λ is effectively to exchange variance in the resulting difference image with variance in the kernel itself. We examine considerations in choosing the value of λ, including statistical risk estimators and the ability of the solution to predict solutions for adjacent areas. Both of these suggest moderate strengths of λ between 0.1 and 1.0, although this optimization is likely data set dependent. This model allows for flexible representations of the convolution kernel that have significant predictive ability and will prove useful in implementing
Jin, Zhonghai; Wielicki, Bruce A.; Loukachine, Constantin; Charlock, Thomas P.; Young, David; Noeel, Stefan
2011-01-01
The radiative kernel approach provides a simple way to separate the radiative response to different climate parameters and to decompose the feedback into radiative and climate response components. Using CERES/MODIS/Geostationary data, we calculated and analyzed the solar spectral reflectance kernels for various climate parameters on zonal, regional, and global spatial scales. The kernel linearity is tested. Errors in the kernel due to nonlinearity can vary strongly depending on climate parameter, wavelength, surface, and solar elevation; they are large in some absorption bands for some parameters but are negligible in most conditions. The spectral kernels are used to calculate the radiative responses to different climate parameter changes in different latitudes. The results show that the radiative response in high latitudes is sensitive to the coverage of snow and sea ice. The radiative response in low latitudes is contributed mainly by cloud property changes, especially cloud fraction and optical depth. The large cloud height effect is confined to absorption bands, while the cloud particle size effect is found mainly in the near infrared. The kernel approach, which is based on calculations using CERES retrievals, is then tested by direct comparison with spectral measurements from Scanning Imaging Absorption Spectrometer for Atmospheric Cartography (SCIAMACHY) (a different instrument on a different spacecraft). The monthly mean interannual variability of spectral reflectance based on the kernel technique is consistent with satellite observations over the ocean, but not over land, where both model and data have large uncertainty. RMS errors in kernel ]derived monthly global mean reflectance over the ocean compared to observations are about 0.001, and the sampling error is likely a major component.
Kernel methods and minimum contrast estimators for empirical deconvolution
Delaigle, Aurore
2010-01-01
We survey classical kernel methods for providing nonparametric solutions to problems involving measurement error. In particular we outline kernel-based methodology in this setting, and discuss its basic properties. Then we point to close connections that exist between kernel methods and much newer approaches based on minimum contrast techniques. The connections are through use of the sinc kernel for kernel-based inference. This `infinite order' kernel is not often used explicitly for kernel-based deconvolution, although it has received attention in more conventional problems where measurement error is not an issue. We show that in a comparison between kernel methods for density deconvolution, and their counterparts based on minimum contrast, the two approaches give identical results on a grid which becomes increasingly fine as the bandwidth decreases. In consequence, the main numerical differences between these two techniques are arguably the result of different approaches to choosing smoothing parameters.
Kernel methods in orthogonalization of multi- and hypervariate data
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...
Variable kernel density estimation in high-dimensional feature spaces
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 AND HARDY'S THEOREM FOR JACOBI TRANSFORM
T. KAWAZOE; LIU JIANMING(刘建明)
2003-01-01
In this paper, the authors obtain sharp upper and lower bounds for the heat kernel associatedwith Jacobi transform, and get some analogues of Hardy's Theorem for Jacobi transform byusing the sharp estimate of the heat kernel.
Gustin, Jeffery L; Jackson, Sean; Williams, Chekeria; Patel, Anokhee; Armstrong, Paul; Peter, Gary F; Settles, A Mark
2013-11-20
Maize kernel density affects milling quality of the grain. Kernel density of bulk samples can be predicted by near-infrared reflectance (NIR) spectroscopy, but no accurate method to measure individual kernel density has been reported. This study demonstrates that individual kernel density and volume are accurately measured using X-ray microcomputed tomography (μCT). Kernel density was significantly correlated with kernel volume, air space within the kernel, and protein content. Embryo density and volume did not influence overall kernel density. Partial least-squares (PLS) regression of μCT traits with single-kernel NIR spectra gave stable predictive models for kernel density (R(2) = 0.78, SEP = 0.034 g/cm(3)) and volume (R(2) = 0.86, SEP = 2.88 cm(3)). Density and volume predictions were accurate for data collected over 10 months based on kernel weights calculated from predicted density and volume (R(2) = 0.83, SEP = 24.78 mg). Kernel density was significantly correlated with bulk test weight (r = 0.80), suggesting that selection of dense kernels can translate to improved agronomic performance.
Kernel Partial Least Squares for Nonlinear Regression and Discrimination
Rosipal, Roman; Clancy, Daniel (Technical Monitor)
2002-01-01
This paper summarizes recent results on applying the method of partial least squares (PLS) in a reproducing kernel Hilbert space (RKHS). A previously proposed kernel PLS regression model was proven to be competitive with other regularized regression methods in RKHS. The family of nonlinear kernel-based PLS models is extended by considering the kernel PLS method for discrimination. Theoretical and experimental results on a two-class discrimination problem indicate usefulness of the method.
Mitigation of artifacts in rtm with migration kernel decomposition
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.
Sparse Event Modeling with Hierarchical Bayesian Kernel Methods
2016-01-05
the kernel function which depends on the application and the model user. This research uses the most popular kernel function, the radial basis...an important role in the nation’s economy. Unfortunately, the system’s reliability is declining due to the aging components of the network [Grier...kernel function. Gaussian Bayesian kernel models became very popular recently and were extended and applied to a number of classification problems. An
Sugar Profile of Kernels as a Marker of Origin and Ripening Time of Peach (Prunus persicae L.).
Stanojević, Marija; Trifković, Jelena; Akšić, Milica Fotirić; Rakonjac, Vera; Nikolić, Dragan; Šegan, Sandra; Milojković-Opsenica, Dušanka
2015-12-01
Large amounts of fruit seeds, especially peach, are discarded annually in juice or conserve producing industries which is a potential waste of valuable resource and serious disposal problem. Regarding the fact that peach seeds can be obtained as a byproduct from processing companies their exploitation should be greater and, consequently more information of cultivars' kernels and their composition is required. A total of 25 samples of kernels from various peach germplasm (including commercial cultivars, perspective hybrids and vineyard peach accessions) differing in origin and ripening time were characterized by evaluation of their sugar composition. Twenty characteristic carbohydrates and sugar alcohols were determined and quantified using high-performance anion-exchange chromatography with pulsed amperometric detection (HPAEC/PAD). Sucrose, glucose and fructose are the most important sugars in peach kernels similar to other representatives of the Rosaceae family. Also, high amounts of sugars in seeds of promising hybrids implies that through conventional breeding programs peach kernels with high sugar content can be obtained. In addition, by the means of several pattern recognition methods the variables that discriminate peach kernels arising from diverse germplasm and different stage of maturity were identified and successful models for further prediction were developed. Sugars such as ribose, trehalose, arabinose, galactitol, fructose, maltose, sorbitol, sucrose, iso-maltotriose were marked as most important for such discrimination.
A process was developed to produce a germ-enriched fraction from hull-less barley using a Fitzpatrick Comminuting Mill followed by sieving. Hulled and hull-less barleys contain 1.5-2.5% oil and, like wheat kernels which contain wheat germ oil, much of the oil in barley kernels is in the germ fracti...
An Extended Ockham Algebra with Endomorphism Kernel Property
Jie FANG
2007-01-01
An algebraic structure (∮) is said to have the endomorphism kernel property if every congruence on (∮) , other than the universal congruence, is the kernel of an endomorphism on (∮) .Inthis paper, we consider the EKP (that is, endomorphism kernel property) for an extended Ockham algebra (∮) . In particular, we describe the structure of the finite symmetric extended de Morgan algebras having EKP.
End-use quality of soft kernel durum wheat
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...
7 CFR 981.61 - Redetermination of kernel weight.
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...
Multiple spectral kernel learning and a gaussian complexity computation.
Reyhani, Nima
2013-07-01
Multiple kernel learning (MKL) partially solves the kernel selection problem in support vector machines and similar classifiers by minimizing the empirical risk over a subset of the linear combination of given kernel matrices. For large sample sets, the size of the kernel matrices becomes a numerical issue. In many cases, the kernel matrix is of low-efficient rank. However, the low-rank property is not efficiently utilized in MKL algorithms. Here, we suggest multiple spectral kernel learning that efficiently uses the low-rank property by finding a kernel matrix from a set of Gram matrices of a few eigenvectors from all given kernel matrices, called a spectral kernel set. We provide a new bound for the gaussian complexity of the proposed kernel set, which depends on both the geometry of the kernel set and the number of Gram matrices. This characterization of the complexity implies that in an MKL setting, adding more kernels may not monotonically increase the complexity, while previous bounds show otherwise.
A Fast and Simple Graph Kernel for RDF
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
7 CFR 981.60 - Determination of kernel weight.
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...
21 CFR 176.350 - Tamarind seed kernel powder.
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...
Heat kernel analysis for Bessel operators on symmetric cones
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...
Stable Kernel Representations as Nonlinear Left Coprime Factorizations
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
Kernel Temporal Differences for Neural Decoding
Jihye Bae
2015-01-01
Full Text Available 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.
Bergman kernel and complex singularity exponent
LEE; HanJin
2009-01-01
We give a precise estimate of the Bergman kernel for the model domain defined by Ω F={(z,w) ∈ C n+1:Im w |F (z)| 2 > 0},where F=(f 1,...,f m) is a holomorphic map from C n to C m,in terms of the complex singularity exponent of F.
Kernel based subspace projection of hyperspectral images
Larsen, Rasmus; Nielsen, Allan Aasbjerg; Arngren, Morten
In hyperspectral image analysis an exploratory approach to analyse the image data is to conduct subspace projections. As linear projections often fail to capture the underlying structure of the data, we present kernel based subspace projections of PCA and Maximum Autocorrelation Factors (MAF...
Analytic properties of the Virasoro modular kernel
Nemkov, Nikita
2016-01-01
On the space of generic conformal blocks the modular transformation of the underlying surface is realized as a linear integral transformation. We show that the analytic properties of conformal block implied by Zamolodchikov's formula are shared by the kernel of the modular transformation and illustrate this by explicit computation in the case of the one-point toric conformal block.
A Cubic Kernel for Feedback Vertex Set
Bodlaender, H.L.
2006-01-01
The FEEDBACK VERTEX SET problem on unweighted, undirected graphs is considered. Improving upon a result by Burrage et al. [7], we show that this problem has a kernel with O(κ3) vertices, i.e., there is a polynomial time algorithm, that given a graph G and an integer κ, finds a graph G' and integer
Analytic properties of the Virasoro modular kernel
Nemkov, Nikita [Moscow Institute of Physics and Technology (MIPT), Dolgoprudny (Russian Federation); Institute for Theoretical and Experimental Physics (ITEP), Moscow (Russian Federation); National University of Science and Technology MISIS, The Laboratory of Superconducting metamaterials, Moscow (Russian Federation)
2017-06-15
On the space of generic conformal blocks the modular transformation of the underlying surface is realized as a linear integral transformation. We show that the analytic properties of conformal block implied by Zamolodchikov's formula are shared by the kernel of the modular transformation and illustrate this by explicit computation in the case of the one-point toric conformal block. (orig.)
Hyperbolic L2-modules with Reproducing Kernels
David EELPODE; Frank SOMMEN
2006-01-01
Abstract In this paper, the Dirac operator on the Klein model for the hyperbolic space is considered. A function space containing L2-functions on the sphere Sm-1 in (R)m, which are boundary values of solutions for this operator, is defined, and it is proved that this gives rise to a Hilbert module with a reproducing kernel.
Protein Structure Prediction Using String Kernels
2006-03-03
Prediction using String Kernels 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) 5d. PROJECT NUMBER 5e. TASK NUMBER...consists of 4352 sequences from SCOP version 1.53 extracted from the Astral database, grouped into families and superfamilies. The dataset is processed
Bergman kernel and complex singularity exponent
CHEN BoYong; LEE HanJin
2009-01-01
We give a precise estimate of the Bergman kernel for the model domain defined by Ω_F = {(z,w) ∈ C~(n+1) : Imw - |F(z)|~2 > 0},where F = (f_1,... ,f_m) is a holomorphic map from C~n to C~m,in terms of the complex singularity exponent of F.
Symbol recognition with kernel density matching.
Zhang, Wan; Wenyin, Liu; Zhang, Kun
2006-12-01
We propose a novel approach to similarity assessment for graphic symbols. Symbols are represented as 2D kernel densities and their similarity is measured by the Kullback-Leibler divergence. Symbol orientation is found by gradient-based angle searching or independent component analysis. Experimental results show the outstanding performance of this approach in various situations.
Developing Linux kernel space device driver
Zheng Wei; Wang Qinruo; Wu Naiyou
2003-01-01
This thesis introduces how to develop kernel level device drivers on Linux platform in detail. On the basis of comparing proc file system with dev file system, we choose PCI devices and USB devices as instances to introduce the method of writing device drivers for character devices by using these two file systems.
Heat Kernel Renormalization on Manifolds with Boundary
Albert, Benjamin I.
2016-01-01
In the monograph Renormalization and Effective Field Theory, Costello gave an inductive position space renormalization procedure for constructing an effective field theory that is based on heat kernel regularization of the propagator. In this paper, we extend Costello's renormalization procedure to a class of manifolds with boundary. In addition, we reorganize the presentation of the preexisting material, filling in details and strengthening the results.
Convolution kernels for multi-wavelength imaging
Boucaud, A.; Bocchio, M.; Abergel, A.; Orieux, F.; Dole, H.; Hadj-Youcef, M. A.
2016-12-01
Astrophysical images issued from different instruments and/or spectral bands often require to be processed together, either for fitting or comparison purposes. However each image is affected by an instrumental response, also known as point-spread function (PSF), that depends on the characteristics of the instrument as well as the wavelength and the observing strategy. Given the knowledge of the PSF in each band, a straightforward way of processing images is to homogenise them all to a target PSF using convolution kernels, so that they appear as if they had been acquired by the same instrument. We propose an algorithm that generates such PSF-matching kernels, based on Wiener filtering with a tunable regularisation parameter. This method ensures all anisotropic features in the PSFs to be taken into account. We compare our method to existing procedures using measured Herschel/PACS and SPIRE PSFs and simulated JWST/MIRI PSFs. Significant gains up to two orders of magnitude are obtained with respect to the use of kernels computed assuming Gaussian or circularised PSFs. A software to compute these kernels is available at https://github.com/aboucaud/pypher
Covariant derivative expansion of the heat kernel
Salcedo, L.L. [Universidad de Granada, Departamento de Fisica Moderna, Granada (Spain)
2004-11-01
Using the technique of labeled operators, compact explicit expressions are given for all traced heat kernel coefficients containing zero, two, four and six covariant derivatives, and for diagonal coefficients with zero, two and four derivatives. The results apply to boundaryless flat space-times and arbitrary non-Abelian scalar and gauge background fields. (orig.)
Tedgren, Åsa Carlsson; Plamondon, Mathieu; Beaulieu, Luc
2015-07-07
The aim of this work was to investigate how dose distributions calculated with the collapsed cone (CC) algorithm depend on the size of the water phantom used in deriving the point kernel for multiple scatter. A research version of the CC algorithm equipped with a set of selectable point kernels for multiple-scatter dose that had initially been derived in water phantoms of various dimensions was used. The new point kernels were generated using EGSnrc in spherical water phantoms of radii 5 cm, 7.5 cm, 10 cm, 15 cm, 20 cm, 30 cm and 50 cm. Dose distributions derived with CC in water phantoms of different dimensions and in a CT-based clinical breast geometry were compared to Monte Carlo (MC) simulations using the Geant4-based brachytherapy specific MC code Algebra. Agreement with MC within 1% was obtained when the dimensions of the phantom used to derive the multiple-scatter kernel were similar to those of the calculation phantom. Doses are overestimated at phantom edges when kernels are derived in larger phantoms and underestimated when derived in smaller phantoms (by around 2% to 7% depending on distance from source and phantom dimensions). CC agrees well with MC in the high dose region of a breast implant and is superior to TG43 in determining skin doses for all multiple-scatter point kernel sizes. Increased agreement between CC and MC is achieved when the point kernel is comparable to breast dimensions. The investigated approximation in multiple scatter dose depends on the choice of point kernel in relation to phantom size and yields a significant fraction of the total dose only at distances of several centimeters from a source/implant which correspond to volumes of low doses. The current implementation of the CC algorithm utilizes a point kernel derived in a comparatively large (radius 20 cm) water phantom. A fixed point kernel leads to predictable behaviour of the algorithm with the worst case being a source/implant located well within a patient
Carlsson Tedgren, Åsa; Plamondon, Mathieu; Beaulieu, Luc
2015-07-01
The aim of this work was to investigate how dose distributions calculated with the collapsed cone (CC) algorithm depend on the size of the water phantom used in deriving the point kernel for multiple scatter. A research version of the CC algorithm equipped with a set of selectable point kernels for multiple-scatter dose that had initially been derived in water phantoms of various dimensions was used. The new point kernels were generated using EGSnrc in spherical water phantoms of radii 5 cm, 7.5 cm, 10 cm, 15 cm, 20 cm, 30 cm and 50 cm. Dose distributions derived with CC in water phantoms of different dimensions and in a CT-based clinical breast geometry were compared to Monte Carlo (MC) simulations using the Geant4-based brachytherapy specific MC code Algebra. Agreement with MC within 1% was obtained when the dimensions of the phantom used to derive the multiple-scatter kernel were similar to those of the calculation phantom. Doses are overestimated at phantom edges when kernels are derived in larger phantoms and underestimated when derived in smaller phantoms (by around 2% to 7% depending on distance from source and phantom dimensions). CC agrees well with MC in the high dose region of a breast implant and is superior to TG43 in determining skin doses for all multiple-scatter point kernel sizes. Increased agreement between CC and MC is achieved when the point kernel is comparable to breast dimensions. The investigated approximation in multiple scatter dose depends on the choice of point kernel in relation to phantom size and yields a significant fraction of the total dose only at distances of several centimeters from a source/implant which correspond to volumes of low doses. The current implementation of the CC algorithm utilizes a point kernel derived in a comparatively large (radius 20 cm) water phantom. A fixed point kernel leads to predictable behaviour of the algorithm with the worst case being a source/implant located well within a patient
A Kernel Approach to Multi-Task Learning with Task-Specific Kernels
Wei Wu; Hang Li; Yun-Hua Hu; Rong Jin
2012-01-01
Several kernel-based methods for multi-task learning have been proposed,which leverage relations among tasks as regularization to enhance the overall learning accuracies.These methods assume that the tasks share the same kernel,which could limit their applications because in practice different tasks may need different kernels.The main challenge of introducing multiple kernels into multiple tasks is that models from different reproducing kernel Hilbert spaces (RKHSs) are not comparable,making it difficult to exploit relations among tasks.This paper addresses the challenge by formalizing the problem in the square integrable space (SIS).Specially,it proposes a kernel-based method which makes use of a regularization term defined in SIS to represent task relations.We prove a new representer theorem for the proposed approach in SIS.We further derive a practical method for solving the learning problem and conduct consistency analysis of the method.We discuss the relationship between our method and an existing method.We also give an SVM (support vector machine)-based implementation of our method for multi-label classification.Experiments on an artificial example and two real-world datasets show that the proposed method performs better than the existing method.
Eliazar, Iddo I., E-mail: eliazar@post.tau.ac.il [Holon Institute of Technology, P.O. Box 305, Holon 58102 (Israel); Shlesinger, Michael F., E-mail: mike.shlesinger@navy.mil [Office of Naval Research, Code 30, 875 N. Randolph St., Arlington, VA 22203 (United States)
2013-06-10
Brownian motion is the archetypal model for random transport processes in science and engineering. Brownian motion displays neither wild fluctuations (the “Noah effect”), nor long-range correlations (the “Joseph effect”). The quintessential model for processes displaying the Noah effect is Lévy motion, the quintessential model for processes displaying the Joseph effect is fractional Brownian motion, and the prototypical model for processes displaying both the Noah and Joseph effects is fractional Lévy motion. In this paper we review these four random-motion models–henceforth termed “fractional motions” –via a unified physical setting that is based on Langevin’s equation, the Einstein–Smoluchowski paradigm, and stochastic scaling limits. The unified setting explains the universal macroscopic emergence of fractional motions, and predicts–according to microscopic-level details–which of the four fractional motions will emerge on the macroscopic level. The statistical properties of fractional motions are classified and parametrized by two exponents—a “Noah exponent” governing their fluctuations, and a “Joseph exponent” governing their dispersions and correlations. This self-contained review provides a concise and cohesive introduction to fractional motions.
Bhattacharya Uttam
2014-01-01
Full Text Available Today it is crucial for organizations to pay even greater attention on quality management as the importance of this function in achieving ultimate business objectives is increasingly becoming clearer. Importance of the Quality Management (QM Function in achieving basic need by ensuring compliance with Capability Maturity Model Integrated (CMMI / International Organization for Standardization (ISO is a basic demand from business nowadays. However, QM Function and its processes need to be made much more mature to prevent delivery outages and to achieve business excellence through their review and auditing capability. Many organizations now face challenges in determining the maturity of the QM group along with the service offered by them and the right way to elevate the maturity of the same. The objective of this whitepaper is to propose a new model –the Audit Maturity Model (AMM which will provide organizations with a measure of their maturity in quality management in the perspective of auditing, along with recommendations for preventing delivery outage, and identifying risk to achieve business excellence. This will enable organizations to assess QM maturity higher than basic hygiene and will also help them to identify gaps and to take corrective actions for achieving higher maturity levels. Hence the objective is to envisage a new auditing model as a part of organisation quality management function which can be a guide for them to achieve higher level of maturity and ultimately help to achieve delivery and business excellence.
Geodesic exponential kernels: When Curvature and Linearity Conflict
Feragen, Aase; Lauze, François; Hauberg, Søren
2015-01-01
We consider kernel methods on general geodesic metric spaces and provide both negative and positive results. First we show that the common Gaussian kernel can only be generalized to a positive definite kernel on a geodesic metric space if the space is flat. As a result, for data on a Riemannian...... Laplacian kernel can be generalized while retaining positive definiteness. This implies that geodesic Laplacian kernels can be generalized to some curved spaces, including spheres and hyperbolic spaces. Our theoretical results are verified empirically....
The pre-image problem in kernel methods.
Kwok, James Tin-yau; Tsang, Ivor Wai-hung
2004-11-01
In this paper, we address the problem of finding the pre-image of a feature vector in the feature space induced by a kernel. This is of central importance in some kernel applications, such as on using kernel principal component analysis (PCA) for image denoising. Unlike the traditional method which relies on nonlinear optimization, our proposed method directly finds the location of the pre-image based on distance constraints in the feature space. It is noniterative, involves only linear algebra and does not suffer from numerical instability or local minimum problems. Evaluations on performing kernel PCA and kernel clustering on the USPS data set show much improved performance.
Generalization Performance of Regularized Ranking With Multiscale Kernels.
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.
Kernel Methods for Machine Learning with Life Science Applications
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...... models to kernel learning, and means for restoring the generalizability in both kernel Principal Component Analysis and the Support Vector Machine are proposed. Viability is proved on a wide range of benchmark machine learning data sets....... as innerproducts in the model formulation. This dissertation presents research on improving the performance of standard kernel methods like kernel Principal Component Analysis and the Support Vector Machine. Moreover, the goal of the thesis has been two-fold. The first part focuses on the use of kernel Principal...
Efficient $\\chi ^{2}$ Kernel Linearization via Random Feature Maps.
Yuan, Xiao-Tong; Wang, Zhenzhen; Deng, Jiankang; Liu, Qingshan
2016-11-01
Explicit feature mapping is an appealing way to linearize additive kernels, such as χ(2) kernel for training large-scale support vector machines (SVMs). Although accurate in approximation, feature mapping could pose computational challenges in high-dimensional settings as it expands the original features to a higher dimensional space. To handle this issue in the context of χ(2) kernel SVMs learning, we introduce a simple yet efficient method to approximately linearize χ(2) kernel through random feature maps. The main idea is to use sparse random projection to reduce the dimensionality of feature maps while preserving their approximation capability to the original kernel. We provide approximation error bound for the proposed method. Furthermore, we extend our method to χ(2) multiple kernel SVMs learning. Extensive experiments on large-scale image classification tasks confirm that the proposed approach is able to significantly speed up the training process of the χ(2) kernel SVMs at almost no cost of testing accuracy.
Multiple Kernel Learning in Fisher Discriminant Analysis for Face Recognition
Xiao-Zhang Liu
2013-02-01
Full Text Available Recent applications and developments based on support vector machines (SVMs have shown that using multiple kernels instead of a single one can enhance classifier performance. However, there are few reports on performance of the kernel‐based Fisher discriminant analysis (kernel‐based FDA method with multiple kernels. This paper proposes a multiple kernel construction method for kernel‐based FDA. The constructed kernel is a linear combination of several base kernels with a constraint on their weights. By maximizing the margin maximization criterion (MMC, we present an iterative scheme for weight optimization. The experiments on the FERET and CMU PIE face databases show that, our multiple kernel Fisher discriminant analysis (MKFD achieves high recognition performance, compared with single‐kernel‐based FDA. The experiments also show that the constructed kernel relaxes parameter selection for kernel‐based FDA to some extent.
Hadermann, A. F.
1985-04-09
Soluble polymers are fractionated according to molecular weight by cryogenically comminuting the polymer and introducing the polymer particles, while still in the active state induced by cryogenic grinding, into a liquid having a solvent power selected to produce a coacervate fraction containing high molecular weight polymer species and a dilute polymer solution containing lower molecular weight polymer species. The coacervate may be physically separated from the solution and finds use in the production of antimisting jet fuels and the like.
A Novel Framework for Learning Geometry-Aware Kernels.
Pan, Binbin; Chen, Wen-Sheng; Xu, Chen; Chen, Bo
2016-05-01
The data from real world usually have nonlinear geometric structure, which are often assumed to lie on or close to a low-dimensional manifold in a high-dimensional space. How to detect this nonlinear geometric structure of the data is important for the learning algorithms. Recently, there has been a surge of interest in utilizing kernels to exploit the manifold structure of the data. Such kernels are called geometry-aware kernels and are widely used in the machine learning algorithms. The performance of these algorithms critically relies on the choice of the geometry-aware kernels. Intuitively, a good geometry-aware kernel should utilize additional information other than the geometric information. In many applications, it is required to compute the out-of-sample data directly. However, most of the geometry-aware kernel methods are restricted to the available data given beforehand, with no straightforward extension for out-of-sample data. In this paper, we propose a framework for more general geometry-aware kernel learning. The proposed framework integrates multiple sources of information and enables us to develop flexible and effective kernel matrices. Then, we theoretically show how the learned kernel matrices are extended to the corresponding kernel functions, in which the out-of-sample data can be computed directly. Under our framework, a novel family of geometry-aware kernels is developed. Especially, some existing geometry-aware kernels can be viewed as instances of our framework. The performance of the kernels is evaluated on dimensionality reduction, classification, and clustering tasks. The empirical results show that our kernels significantly improve the performance.
Kernel Density Estimation, Kernel Methods, and Fast Learning in Large Data Sets.
Wang, Shitong; Wang, Jun; Chung, Fu-lai
2014-01-01
Kernel methods such as the standard support vector machine and support vector regression trainings take O(N(3)) time and O(N(2)) space complexities in their naïve implementations, where N is the training set size. It is thus computationally infeasible in applying them to large data sets, and a replacement of the naive method for finding the quadratic programming (QP) solutions is highly desirable. By observing that many kernel methods can be linked up with kernel density estimate (KDE) which can be efficiently implemented by some approximation techniques, a new learning method called fast KDE (FastKDE) is proposed to scale up kernel methods. It is based on establishing a connection between KDE and the QP problems formulated for kernel methods using an entropy-based integrated-squared-error criterion. As a result, FastKDE approximation methods can be applied to solve these QP problems. In this paper, the latest advance in fast data reduction via KDE is exploited. With just a simple sampling strategy, the resulted FastKDE method can be used to scale up various kernel methods with a theoretical guarantee that their performance does not degrade a lot. It has a time complexity of O(m(3)) where m is the number of the data points sampled from the training set. Experiments on different benchmarking data sets demonstrate that the proposed method has comparable performance with the state-of-art method and it is effective for a wide range of kernel methods to achieve fast learning in large data sets.
Hua WANG
2016-01-01
In this paper, we first introduce Lσ 1-(log L)σ 2 conditions satisfied by the variable kernelsΩ (x, z) for 0 ≤ σ 1 ≤ 1 and σ 2 ≥ 0. Under these new smoothness conditions, we will prove the boundedness properties of singular integral operators TΩ , fractional integrals TΩ ,α and parametric Marcinkiewicz integralsμρΩ with variable kernels on the Hardy spaces Hp(Rn) and weak Hardy spaces WHp(Rn). Moreover, by using the interpolation arguments, we can get some corresponding results for the above integral operators with variable kernels on Hardy–Lorentz spaces Hp,q(Rn) for all p
Understanding Multiplication of Fractions.
Sweetland, Robert D.
1984-01-01
Discussed the use of Cuisenaire rods in teaching the multiplication of fractions. Considers whole number times proper fraction, proper fraction multiplied by proper fraction, mixed number times proper fraction, and mixed fraction multiplied by mixed fractions. (JN)
Johnson, L M; Harrison, J H; Davidson, D; Robutti, J L; Swift, M; Mahanna, W C; Shinners, K
2002-04-01
Two experiments were conducted to evaluate the effects of hybrid, maturity, and mechanical processing of whole plant corn on chemical and physical characteristics, particle size, pack density, and dry matter recovery. In the first experiment, hybrid 3845 whole plant corn was harvested at hard dough, one-third milkline, and two-thirds milkline with a theoretical length-of-cut of 6.4 mm. In the second experiment, hybrids 3845 and Quanta were harvested at one-third milkline, two-thirds milkline, and blackline stages of maturity with a theoretical length-of-cut of 12.7 mm. At each stage of maturity, corn was harvested with and without mechanical processing by using a John Deere 5830 harvester with an onboard kernel processor. The percentage of intact corn kernels present in unprocessed corn silage explained 62% of variation in total tract starch digestibility. As the amount of intact kernels increased, total tract starch digestibility decreased. Post-ensiled vitreousness of corn kernels within the corn silage explained 31 and 48% of the variation of total tract starch digestibility for processed and unprocessed treatments, respectively. For a given amount of vitreous starch in corn kernels, total tract starch digestibility was lower for cows fed unprocessed corn silage compared with processed corn silage. This suggests that processing corn silage disrupts the dense protein matrix within the corn kernel where starch is embedded, therefore making the starch more available for digestion. Particle size of corn silage and orts that contained corn silage was reduced when it was processed. Wet pack density was greater for processed compared with unprocessed corn silage.
Wilson Dslash Kernel From Lattice QCD Optimization
Joo, Balint [Jefferson Lab, Newport News, VA; Smelyanskiy, Mikhail [Parallel Computing Lab, Intel Corporation, California, USA; Kalamkar, Dhiraj D. [Parallel Computing Lab, Intel Corporation, India; Vaidyanathan, Karthikeyan [Parallel Computing Lab, Intel Corporation, India
2015-07-01
Lattice Quantum Chromodynamics (LQCD) is a numerical technique used for calculations in Theoretical Nuclear and High Energy Physics. LQCD is traditionally one of the first applications ported to many new high performance computing architectures and indeed LQCD practitioners have been known to design and build custom LQCD computers. Lattice QCD kernels are frequently used as benchmarks (e.g. 168.wupwise in the SPEC suite) and are generally well understood, and as such are ideal to illustrate several optimization techniques. In this chapter we will detail our work in optimizing the Wilson-Dslash kernels for Intel Xeon Phi, however, as we will show the technique gives excellent performance on regular Xeon Architecture as well.
Learning Potential Energy Landscapes using Graph Kernels
Ferré, G; Barros, K
2016-01-01
Recent machine learning methods make it possible to model potential energy of atomic configurations with chemical-level accuracy (as calculated from ab-initio calculations) and at speeds suitable for molecular dynamics simulation. Best performance is achieved when the known physical constraints are encoded in the machine learning models. For example, the atomic energy is invariant under global translations and rotations; it is also invariant to permutations of same-species atoms. Although simple to state, these symmetries are complicated to encode into machine learning algorithms. In this paper, we present a machine learning approach based on graph theory that naturally incorporates translation, rotation, and permutation symmetries. Specifically, we use a random walk graph kernel to measure the similarity of two adjacency matrices, each of which represents a local atomic environment. We show on a standard benchmark that our Graph Approximated Energy (GRAPE) method is competitive with state of the art kernel m...
Viability Kernel for Ecosystem Management Models
Anaya, Eladio Ocana; Oliveros--Ramos, Ricardo; Tam, Jorge
2009-01-01
We consider sustainable management issues formulated within the framework of control theory. The problem is one of controlling a discrete--time dynamical system (e.g. population model) in the presence of state and control constraints, representing conflicting economic and ecological issues for instance. The viability kernel is known to play a basic role for the analysis of such problems and the design of viable control feedbacks, but its computation is not an easy task in general. We study the viability of nonlinear generic ecosystem models under preservation and production constraints. Under simple conditions on the growth rates at the boundary constraints, we provide an explicit description of the viability kernel. A numerical illustration is given for the hake--anchovy couple in the Peruvian upwelling ecosystem.
A kernel version of spatial factor analysis
Nielsen, Allan Aasbjerg
2009-01-01
of PCA and related techniques. An interesting dilemma in reduction of dimensionality of data is the desire to obtain simplicity for better understanding, visualization and interpretation of the data on the one hand, and the desire to retain sufficient detail for adequate representation on the other hand......Based on work by Pearson in 1901, Hotelling in 1933 introduced principal component analysis (PCA). PCA is often used for general feature generation and linear orthogonalization or compression by dimensionality reduction of correlated multivariate data, see Jolliffe for a comprehensive description...... 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...
Quark-hadron duality: pinched kernel approch
Dominguez, C A; Schilcher, K; Spiesberger, H
2016-01-01
Hadronic spectral functions measured by the ALEPH collaboration in the vector and axial-vector channels are used to study potential quark-hadron duality violations (DV). This is done entirely in the framework of pinched kernel finite energy sum rules (FESR), i.e. in a model independent fashion. The kinematical range of the ALEPH data is effectively extended up to $s = 10\\; {\\mbox{GeV}^2}$ by using an appropriate kernel, and assuming that in this region the spectral functions are given by perturbative QCD. Support for this assumption is obtained by using $e^+ e^-$ annihilation data in the vector channel. Results in both channels show a good saturation of the pinched FESR, without further need of explicit models of DV.
Analog Forecasting with Dynamics-Adapted Kernels
Zhao, Zhizhen
2014-01-01
Analog forecasting is a non-parametric 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 state-space reconstruction for dynamical systems and kernel methods developed in harmonic analysis and machine learning. The first improvement is to augment the dimension of the initial data using Takens' delay-coordinate maps to recover information in the initial data lost through partial observations. Then, instead of using Euclidean distances between the states, weighted ensembles of analogs are constructed according to similarity kernels in delay-coordinate space, featuring an explicit dependence on the dynamical vector field generating the data. The eigenvalues and eigenfunctions ...
Asjad, Muhammad Imran; Shah, Nehad Ali; Aleem, Maryam; Khan, Ilyas
2017-08-01
The present study is a comparative analysis of unsteady flows of a second-grade fluid with Newtonian heating and time-fractional derivatives, namely, the Caputo fractional derivative (singular kernel) and the Caputo-Fabrizio fractional derivative (non-singular kernel). A physical model for second-grade fluids is developed with fractional derivatives. The expressions for temperature and velocity fields in dimensionless form as well as rates of heat transfer are determined by means of the Laplace transform technique. Solutions for ordinary cases corresponding to integer order derivatives are also obtained. Numerical computations for a comparison between the solutions of the problem with the Caputo time-fractional derivative, problem with Caputo-Fabrizio time-fractional derivative and of the ordinary fluid problem were made. The influence of some flow parameters and fractional parameter α on temperature field as well as velocity field was presented graphically and in tabular forms.
Searching and Indexing Genomic Databases via Kernelization
Travis eGagie
2015-02-01
Full Text Available The rapid advance of DNA sequencing technologies has yielded databases of thousands of genomes. To search and index these databases effectively, it is important that we take advantage of the similarity between those genomes. Several authors have recently suggested searching or indexing only one reference genome and the parts of the other genomes where they differ. In this paper we survey the twenty-year history of this idea and discuss its relation to kernelization in parameterized complexity.
Kernel based subspace projection of hyperspectral images
Larsen, Rasmus; Nielsen, Allan Aasbjerg; Arngren, Morten
In hyperspectral image analysis an exploratory approach to analyse the image data is to conduct subspace projections. As linear projections often fail to capture the underlying structure of the data, we present kernel based subspace projections of PCA and Maximum Autocorrelation Factors (MAF). Th......). The MAF projection exploits the fact that interesting phenomena in images typically exhibit spatial autocorrelation. The analysis is based on nearinfrared hyperspectral images of maize grains demonstrating the superiority of the kernelbased MAF method....
Biochemical and molecular characterization of Avena indolines and their role in kernel texture.
Gazza, Laura; Taddei, Federica; Conti, Salvatore; Gazzelloni, Gloria; Muccilli, Vera; Janni, Michela; D'Ovidio, Renato; Alfieri, Michela; Redaelli, Rita; Pogna, Norberto E
2015-02-01
Among cereals, Avena sativa is characterized by an extremely soft endosperm texture, which leads to some negative agronomic and technological traits. On the basis of the well-known softening effect of puroindolines in wheat kernel texture, in this study, indolines and their encoding genes are investigated in Avena species at different ploidy levels. Three novel 14 kDa proteins, showing a central hydrophobic domain with four tryptophan residues and here named vromindoline (VIN)-1,2 and 3, were identified. Each VIN protein in diploid oat species was found to be synthesized by a single Vin gene whereas, in hexaploid A. sativa, three Vin-1, three Vin-2 and two Vin-3 genes coding for VIN-1, VIN-2 and VIN-3, respectively, were described and assigned to the A, C or D genomes based on similarity to their counterparts in diploid species. Expression of oat vromindoline transgenes in the extra-hard durum wheat led to accumulation of vromindolines in the endosperm and caused an approximate 50 % reduction of grain hardness, suggesting a central role for vromindolines in causing the extra-soft texture of oat grain. Further, hexaploid oats showed three orthologous genes coding for avenoindolines A and B, with five or three tryptophan residues, respectively, but very low amounts of avenoindolines were found in mature kernels. The present results identify a novel protein family affecting cereal kernel texture and would further elucidate the phylogenetic evolution of Avena genus.
Characterization of factors underlying the metabolic shifts in developing kernels of colored maize
Hu, Chaoyang; Li, Quanlin; Shen, Xuefang; Quan, Sheng; Lin, Hong; Duan, Lei; Wang, Yifa; Luo, Qian; Qu, Guorun; Han, Qing; Lu, Yuan; Zhang, Dabing; Yuan, Zheng; Shi, Jianxin
2016-01-01
Elucidation of the metabolic pathways determining pigmentation and their underlying regulatory mechanisms in maize kernels is of high importance in attempts to improve the nutritional composition of our food. In this study, we compared dynamics in the transcriptome and metabolome between colored SW93 and white SW48 by integrating RNA-Seq and non-targeted metabolomics. Our data revealed that expression of enzyme coding genes and levels of primary metabolites decreased gradually from 11 to 21 DAP, corresponding well with the physiological change of developing maize kernels from differentiation through reserve accumulation to maturation, which was cultivar independent. A remarkable up-regulation of anthocyanin and phlobaphene pathway distinguished SW93 from SW48, in which anthocyanin regulating transcriptional factors (R1 and C1), enzyme encoding genes involved in both pathways and corresponding metabolic intermediates were up-regulated concurrently in SW93 but not in SW48. The shift from the shikimate pathway of primary metabolism to the flavonoid pathway of secondary metabolism, however, appears to be under posttranscriptional regulation. This study revealed the link between primary metabolism and kernel coloration, which facilitate further study to explore fundamental questions regarding the evolution of seed metabolic capabilities as well as their potential applications in maize improvement regarding both staple and functional foods. PMID:27739524
Wheat kernel dimensions: how do they contribute to kernel weight at an individual QTL level?
Fa Cui; Anming Ding; Jun Li; Chunhua Zhao; Xingfeng Li; Deshun Feng; Xiuqin Wang; Lin Wang; Jurong Gao; Honggang Wang
2011-12-01
Kernel dimensions (KD) contribute greatly to thousand-kernel weight (TKW) in wheat. In the present study, quantitative trait loci (QTL) for TKW, kernel length (KL), kernel width (KW) and kernel diameter ratio (KDR) were detected by both conditional and unconditional QTL mapping methods. Two related F8:9 recombinant inbred line (RIL) populations, comprising 485 and 229 lines, respectively, were used in this study, and the trait phenotypes were evaluated in four environments. Unconditional QTL mapping analysis detected 77 additive QTL for four traits in two populations. Of these, 24 QTL were verified in at least three trials, and five of them were major QTL, thus being of great value for marker assisted selection in breeding programmes. Conditional QTL mapping analysis, compared with unconditional QTL mapping analysis, resulted in reduction in the number of QTL for TKW due to the elimination of TKW variations caused by its conditional traits; based on which we first dissected genetic control system involved in the synthetic process between TKW and KD at an individual QTL level. Results indicated that, at the QTL level, KW had the strongest influence on TKW, followed by KL, and KDR had the lowest level contribution to TKW. In addition, the present study proved that it is not all-inclusive to determine genetic relationships of a pairwise QTL for two related/causal traits based on whether they were co-located. Thus, conditional QTL mapping method should be used to evaluate possible genetic relationships of two related/causal traits.
Absolute Orientation Based on Distance Kernel Functions
Yanbiao Sun
2016-03-01
Full Text Available The classical absolute orientation method is capable of transforming tie points (TPs from a local coordinate system to a global (geodetic coordinate system. The method is based only on a unique set of similarity transformation parameters estimated by minimizing the total difference between all ground control points (GCPs and the fitted points. Nevertheless, it often yields a transformation with poor accuracy, especially in large-scale study cases. To address this problem, this study proposes a novel absolute orientation method based on distance kernel functions, in which various sets of similarity transformation parameters instead of only one set are calculated. When estimating the similarity transformation parameters for TPs using the iterative solution of a non-linear least squares problem, we assigned larger weighting matrices for the GCPs for which the distances from the point are short. The weighting matrices can be evaluated using the distance kernel function as a function of the distances between the GCPs and the TPs. Furthermore, we used the exponential function and the Gaussian function to describe distance kernel functions in this study. To validate and verify the proposed method, six synthetic and two real datasets were tested. The accuracy was significantly improved by the proposed method when compared to the classical method, although a higher computational complexity is experienced.
Physicochemical Properties of Palm Kernel Oil
Amira P. Olaniyi
2014-09-01
Full Text Available Physicochemical analyses were carried out on palm kernel oil (Adin and the following results were obtained: Saponification value; 280.5±56.1 mgKOH/g, acid value; 2.7±0.3 mg KOH/g, Free Fatty Acid (FFA; 1.35±0.15 KOH/g, ester value; 277.8±56.4 mgKOH/g, peroxide value; 14.3±0.8 mEq/kg; iodine value; 15.86±4.02 mgKOH/g, Specific Gravity (S.G value; 0.904, refractive index; 1.412 and inorganic materials; 1.05%. Its odour and colour were heavy burnt smell and burnt brown, respectively. These values were compared with those obtained for groundnut and coconut oils. It was found that the physico-chemical properties of palm kernel oil are comparable to those of groundnut and coconut oils except for the peroxide value (i.e., 14.3±0.8 mEq which was not detectable in groundnut and coconut oils. Also the odour of both groundnut and coconut oils were pleasant while that of the palm kernel oil was not as pleasant (i.e., heavy burnt smell.
Convolution kernels for multi-wavelength imaging
Boucaud, Alexandre; Abergel, Alain; Orieux, François; Dole, Hervé; Hadj-Youcef, Mohamed Amine
2016-01-01
Astrophysical images issued from different instruments and/or spectral bands often require to be processed together, either for fitting or comparison purposes. However each image is affected by an instrumental response, also known as PSF, that depends on the characteristics of the instrument as well as the wavelength and the observing strategy. Given the knowledge of the PSF in each band, a straightforward way of processing images is to homogenise them all to a target PSF using convolution kernels, so that they appear as if they had been acquired by the same instrument. We propose an algorithm that generates such PSF-matching kernels, based on Wiener filtering with a tunable regularisation parameter. This method ensures all anisotropic features in the PSFs to be taken into account. We compare our method to existing procedures using measured Herschel/PACS and SPIRE PSFs and simulated JWST/MIRI PSFs. Significant gains up to two orders of magnitude are obtained with respect to the use of kernels computed assumin...
Kernel methods for phenotyping complex plant architecture.
Kawamura, Koji; Hibrand-Saint Oyant, Laurence; Foucher, Fabrice; Thouroude, Tatiana; Loustau, Sébastien
2014-02-07
The Quantitative Trait Loci (QTL) mapping of plant architecture is a critical step for understanding the genetic determinism of plant architecture. Previous studies adopted simple measurements, such as plant-height, stem-diameter and branching-intensity for QTL mapping of plant architecture. Many of these quantitative traits were generally correlated to each other, which give rise to statistical problem in the detection of QTL. We aim to test the applicability of kernel methods to phenotyping inflorescence architecture and its QTL mapping. We first test Kernel Principal Component Analysis (KPCA) and Support Vector Machines (SVM) over an artificial dataset of simulated inflorescences with different types of flower distribution, which is coded as a sequence of flower-number per node along a shoot. The ability of discriminating the different inflorescence types by SVM and KPCA is illustrated. We then apply the KPCA representation to the real dataset of rose inflorescence shoots (n=1460) obtained from a 98 F1 hybrid mapping population. We find kernel principal components with high heritability (>0.7), and the QTL analysis identifies a new QTL, which was not detected by a trait-by-trait analysis of simple architectural measurements. The main tools developed in this paper could be use to tackle the general problem of QTL mapping of complex (sequences, 3D structure, graphs) phenotypic traits.
A new derivative with normal distribution kernel: Theory, methods and applications
Atangana, Abdon; Gómez-Aguilar, J. F.
2017-06-01
New approach of fractional derivative with a new local kernel is suggested in this paper. The kernel introduced in this work is the well-known normal distribution that is a very common continuous probability distribution. This distribution is very important in statistics and also highly used in natural science and social sciences to portray real-valued random variables whose distributions are not known. Two definitions are suggested namely Atangana-Gómez Averaging in Liouville-Caputo and Riemann-Liouville sense. We presented some relationship with existing integrals transform operators. Numerical approximations for first and second order approximation are derived in detail. Some Applications of the new mathematical tools to describe some real world problems are presented in detail. This is a new door opened the field of statistics, natural and socials sciences.
Laguerre Kernels –Based SVM for Image Classification
Ashraf Afifi
2014-01-01
Full Text Available Support vector machines (SVMs have been promising methods for classification and regression analysis because of their solid mathematical foundations which convey several salient properties that other methods hardly provide. However the performance of SVMs is very sensitive to how the kernel function is selected, the challenge is to choose the kernel function for accurate data classification. In this paper, we introduce a set of new kernel functions derived from the generalized Laguerre polynomials. The proposed kernels could improve the classification accuracy of SVMs for both linear and nonlinear data sets. The proposed kernel functions satisfy Mercer’s condition and orthogonally properties which are important and useful in some applications when the support vector number is needed as in feature selection. The performance of the generalized Laguerre kernels is evaluated in comparison with the existing kernels. It was found that the choice of the kernel function, and the values of the parameters for that kernel are critical for a given amount of data. The proposed kernels give good classification accuracy in nearly all the data sets, especially those of high dimensions.
Identification of Fusarium damaged wheat kernels using image analysis
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.
Implementing Kernel Methods Incrementally by Incremental Nonlinear Projection Trick.
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.
Image quality of mixed convolution kernel in thoracic computed tomography.
Neubauer, Jakob; Spira, Eva Maria; Strube, Juliane; Langer, Mathias; Voss, Christian; Kotter, Elmar
2016-11-01
The mixed convolution kernel alters his properties geographically according to the depicted organ structure, especially for the lung. Therefore, we compared the image quality of the mixed convolution kernel to standard soft and hard kernel reconstructions for different organ structures in thoracic computed tomography (CT) images.Our Ethics Committee approved this prospective study. In total, 31 patients who underwent contrast-enhanced thoracic CT studies were included after informed consent. Axial reconstructions were performed with hard, soft, and mixed convolution kernel. Three independent and blinded observers rated the image quality according to the European Guidelines for Quality Criteria of Thoracic CT for 13 organ structures. The observers rated the depiction of the structures in all reconstructions on a 5-point Likert scale. Statistical analysis was performed with the Friedman Test and post hoc analysis with the Wilcoxon rank-sum test.Compared to the soft convolution kernel, the mixed convolution kernel was rated with a higher image quality for lung parenchyma, segmental bronchi, and the border between the pleura and the thoracic wall (P kernel, the mixed convolution kernel was rated with a higher image quality for aorta, anterior mediastinal structures, paratracheal soft tissue, hilar lymph nodes, esophagus, pleuromediastinal border, large and medium sized pulmonary vessels and abdomen (P kernel cannot fully substitute the standard CT reconstructions. Hard and soft convolution kernel reconstructions still seem to be mandatory for thoracic CT.
Fractional Calculus in Wave Propagation Problems
Mainardi, Francesco
2012-01-01
Fractional calculus, in allowing integrals and derivatives of any positive order (the term "fractional" kept only for historical reasons), can be considered a branch of mathematical physics which mainly deals with integro-differential equations, where integrals are of convolution form with weakly singular kernels of power law type. In recent decades fractional calculus has won more and more interest in applications in several fields of applied sciences. In this lecture we devote our attention to wave propagation problems in linear viscoelastic media. Our purpose is to outline the role of fractional calculus in providing simplest evolution processes which are intermediate between diffusion and wave propagation. The present treatment mainly reflects the research activity and style of the author in the related scientific areas during the last decades.
Muzhir Shaban Al-Ani
2011-05-01
Full Text Available Detecting faces across multiple views is more challenging than in a frontal view. To address this problem,an efficient approach is presented in this paper using a kernel machine based approach for learning suchnonlinear mappings to provide effective view-based representation for multi-view face detection. In thispaper Kernel Principal Component Analysis (KPCA is used to project data into the view-subspaces thencomputed as view-based features. Multi-view face detection is performed by classifying each input imageinto face or non-face class, by using a two class Kernel Support Vector Classifier (KSVC. Experimentalresults demonstrate successful face detection over a wide range of facial variation in color, illuminationconditions, position, scale, orientation, 3D pose, and expression in images from several photo collections.
Lee, Yi-Hsuan; von Davier, Alina A.
2008-01-01
The kernel equating method (von Davier, Holland, & Thayer, 2004) is based on a flexible family of equipercentile-like equating functions that use a Gaussian kernel to continuize the discrete score distributions. While the classical equipercentile, or percentile-rank, equating method carries out the continuization step by linear interpolation,…
Bhattacharyya, Sonalee; Namakshi, Nama; Zunker, Christina; Warshauer, Hiroko K.; Warshauer, Max
2016-01-01
Making math more engaging for students is a challenge that every teacher faces on a daily basis. These authors write that they are constantly searching for rich problem-solving tasks that cover the necessary content, develop critical-thinking skills, and engage student interest. The Mystery Fraction activity provided here focuses on a key number…
Bhattacharyya, Sonalee; Namakshi, Nama; Zunker, Christina; Warshauer, Hiroko K.; Warshauer, Max
2016-01-01
Making math more engaging for students is a challenge that every teacher faces on a daily basis. These authors write that they are constantly searching for rich problem-solving tasks that cover the necessary content, develop critical-thinking skills, and engage student interest. The Mystery Fraction activity provided here focuses on a key number…
Kernels for Vector-Valued Functions: a Review
Alvarez, Mauricio A; Lawrence, Neil D
2011-01-01
Kernel methods are among the most popular techniques in machine learning. From a frequentist/discriminative perspective they play a central role in regularization theory as they provide a natural choice for the hypotheses space and the regularization functional through the notion of reproducing kernel Hilbert spaces. From a Bayesian/generative perspective they are the key in the context of Gaussian processes, where the kernel function is also known as the covariance function. Traditionally, kernel methods have been used in supervised learning problem with scalar outputs and indeed there has been a considerable amount of work devoted to designing and learning kernels. More recently there has been an increasing interest in methods that deal with multiple outputs, motivated partly by frameworks like multitask learning. In this paper, we review different methods to design or learn valid kernel functions for multiple outputs, paying particular attention to the connection between probabilistic and functional method...
Approximating and learning by Lipschitz kernel on the sphere
CAO Fei-long; WANG Chang-miao
2014-01-01
This paper investigates some approximation properties and learning rates of Lips-chitz kernel on the sphere. A perfect convergence rate on the shifts of Lipschitz kernel on the sphere, which is faster than O(n-1/2), is obtained, where n is the number of parameters needed in the approximation. By means of the approximation, a learning rate of regularized least square algorithm with the Lipschitz kernel on the sphere is also deduced.
Kernel based visual tracking with scale invariant features
Risheng Han; Zhongliang Jing; Yuanxiang Li
2008-01-01
The kernel based tracking has two disadvantages:the tracking window size cannot be adjusted efficiently,and the kernel based color distribution may not have enough ability to discriminate object from clutter background.FDr boosting up the feature's discriminating ability,both scale invariant features and kernel based color distribution features are used as descriptors of tracked object.The proposed algorithm can keep tracking object of varying scales even when the surrounding background is similar to the object's appearance.
Classification of maize kernels using NIR hyperspectral imaging
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....
Reproducing wavelet kernel method in nonlinear system identification
WEN Xiang-jun; XU Xiao-ming; CAI Yun-ze
2008-01-01
By combining the wavelet decomposition with kernel method, a practical approach of universal multi-scale wavelet kernels constructed in reproducing kernel Hilbert space (RKHS) is discussed, and an identifica-tion scheme using wavelet support vector machines ( WSVM ) estimator is proposed for nonlinear dynamic sys-tems. The good approximating properties of wavelet kernel function enhance the generalization ability of the pro-posed method, and the comparison of some numerical experimental results between the novel approach and some existing methods is encouraging.
A Kernel-based Account of Bibliometric Measures
Ito, Takahiko; Shimbo, Masashi; Kudo, Taku; Matsumoto, Yuji
The application of kernel methods to citation analysis is explored. We show that a family of kernels on graphs provides a unified perspective on the three bibliometric measures that have been discussed independently: relatedness between documents, global importance of individual documents, and importance of documents relative to one or more (root) documents (relative importance). The framework provided by the kernels establishes relative importance as an intermediate between relatedness and global importance, in which the degree of `relativity,' or the bias between relatedness and importance, is naturally controlled by a parameter characterizing individual kernels in the family.
Isolation of bacterial endophytes from germinated maize kernels.
Rijavec, Tomaz; Lapanje, Ales; Dermastia, Marina; Rupnik, Maja
2007-06-01
The germination of surface-sterilized maize kernels under aseptic conditions proved to be a suitable method for isolation of kernel-associated bacterial endophytes. Bacterial strains identified by partial 16S rRNA gene sequencing as Pantoea sp., Microbacterium sp., Frigoribacterium sp., Bacillus sp., Paenibacillus sp., and Sphingomonas sp. were isolated from kernels of 4 different maize cultivars. Genus Pantoea was associated with a specific maize cultivar. The kernels of this cultivar were often overgrown with the fungus Lecanicillium aphanocladii; however, those exhibiting Pantoea growth were never colonized with it. Furthermore, the isolated bacterium strain inhibited fungal growth in vitro.
WAVELET KERNEL SUPPORT VECTOR MACHINES FOR SPARSE APPROXIMATION
Tong Yubing; Yang Dongkai; Zhang Qishan
2006-01-01
Wavelet, a powerful tool for signal processing, can be used to approximate the target function. For enhancing the sparse property of wavelet approximation, a new algorithm was proposed by using wavelet kernel Support Vector Machines (SVM), which can converge to minimum error with better sparsity. Here, wavelet functions would be firstly used to construct the admitted kernel for SVM according to Mercy theory; then new SVM with this kernel can be used to approximate the target funciton with better sparsity than wavelet approxiamtion itself. The results obtained by our simulation experiment show the feasibility and validity of wavelet kernel support vector machines.
Convolution kernel design and efficient algorithm for sampling density correction.
Johnson, Kenneth O; Pipe, James G
2009-02-01
Sampling density compensation is an important step in non-cartesian image reconstruction. One of the common techniques to determine weights that compensate for differences in sampling density involves a convolution. A new convolution kernel is designed for sampling density attempting to minimize the error in a fully reconstructed image. The resulting weights obtained using this new kernel are compared with various previous methods, showing a reduction in reconstruction error. A computationally efficient algorithm is also presented that facilitates the calculation of the convolution of finite kernels. Both the kernel and the algorithm are extended to 3D. Copyright 2009 Wiley-Liss, Inc.
Fraction Reduction through Continued Fractions
Carley, Holly
2011-01-01
This article presents a method of reducing fractions without factoring. The ideas presented may be useful as a project for motivated students in an undergraduate number theory course. The discussion is related to the Euclidean Algorithm and its variations may lead to projects or early examples involving efficiency of an algorithm.
Using a commercial DNA extraction kit to obtain RNA from mature rice kernels
Few RNA extraction protocols or commercial kits work well with the starchy endosperm of cereal grains. Standard RNA extraction protocols are time consuming, use large amounts of expensive chemicals, and leave behind hazardous wastes. However, there are numerous commercial DNA extraction kits that ...
42 Variability Bugs in the Linux Kernel
Abal, Iago; Brabrand, Claus; Wasowski, Andrzej
2014-01-01
Feature-sensitive verification pursues effective analysis of the exponentially many variants of a program family. However, researchers lack examples of concrete bugs induced by variability, occurring in real large-scale systems. Such a collection of bugs is a requirement for goal-oriented research......, serving to evaluate tool implementations of feature-sensitive analyses by testing them on real bugs. We present a qualitative study of 42 variability bugs collected from bug-fixing commits to the Linux kernel repository. We analyze each of the bugs, and record the results in a database. In addition, we...
40 Variability Bugs in the Linux Kernel
Abal Rivas, Iago; Brabrand, Claus; Wasowski, Andrzej
2014-01-01
is a requirement for goal-oriented research, serving to evaluate tool implementations of feature-sensitive analyses by testing them on real bugs. We present a qualitative study of 40 variability bugs collected from bug-fixing commits to the Linux kernel repository. We investigate each of the 40 bugs, recording......Feature-sensitive verification is a recent field that pursues the effective analysis of the exponential number of variants of a program family. Today researchers lack examples of concrete bugs induced by variability, and occurring in real large-scale software. Such a collection of bugs...
Application of RBAC Model in System Kernel
Guan Keqing
2012-11-01
Full Text Available In the process of development of some technologies about Ubiquitous computing, the application of embedded intelligent devices is booming. Meanwhile, information security will face more serious threats than before. To improve the security of information terminal’s operation system, this paper analyzed the threats to system’s information security which comes from the abnormal operation by processes, and applied RBAC model into the safety management mechanism of operation system’s kernel. We built an access control model of system’s process, and proposed an implement framework. And the methods of implementation of the model for operation systems were illustrated.
SVM multiuser detection based on heuristic kernel
Yang Tao; Hu Bo
2007-01-01
A support vector machine (SVM) based multiuser detection (MUD) scheme in code-division multiple-access (CDMA) system is proposed. In this scheme, the equivalent support vector (SV) is obtained through a kernel sparsity approximation algorithm, which avoids the conventional costly quadratic programming (QP) procedure in SVM. Besides, the coefficient of the SV is attained through the solution to a generalized eigenproblem. Simulation results show that the proposed scheme has almost the same bit error rate (BER) as the standard SVM and is better than minimum mean square error (MMSE) scheme. Meanwhile, it has a low computation complexity.
Characterization of Flour from Avocado Seed Kernel
Macey A. Mahawan; Ma. Francia N. Tenorio; Jaycel A. Gomez; Rosenda A. Bronce
2015-01-01
The study focused on the Characterization of Flour from Avocado Seed Kernel. Based on the findings of the study the percentages of crude protein, crude fiber, crude fat, total carbohydrates, ash and moisture were 7.75, 4.91, 0.71, 74.65, 2.83 and 14.05 respectively. On the other hand the falling number was 495 seconds while gluten was below the detection limit of the method used. Moreover, the sensory evaluation in terms of color, texture and aroma in 0% proportion of Avocado seed flour was m...
Fixed kernel regression for voltammogram feature extraction
Acevedo Rodriguez, F. J.; López-Sastre, R. J.; Gil-Jiménez, P.; Ruiz-Reyes, N.; Maldonado Bascón, S.
2009-12-01
Cyclic voltammetry is an electroanalytical technique for obtaining information about substances under analysis without the need for complex flow systems. However, classifying the information in voltammograms obtained using this technique is difficult. In this paper, we propose the use of fixed kernel regression as a method for extracting features from these voltammograms, reducing the information to a few coefficients. The proposed approach has been applied to a wine classification problem with accuracy rates of over 98%. Although the method is described here for extracting voltammogram information, it can be used for other types of signals.
Kernel based subspace projection of near infrared hyperspectral images of maize kernels
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...
Kernel based subspace projection of near infrared hyperspectral images of maize kernels
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...
Kernel learning at the first level of inference.
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.
Sensitivity Kernels for Flows in Time-Distance Helioseismology: Extension to Spherical Geometry
Böning, Vincent G A; Zima, Wolfgang; Birch, Aaron C; Gizon, Laurent
2016-01-01
We extend an existing Born approximation method for calculating the linear sensitivity of helioseismic travel times to flows from Cartesian to spherical geometry. This development is necessary for using the Born approximation for inferring large-scale flows in the deep solar interior. In a first sanity check, we compare two $f-$mode kernels from our spherical method and from an existing Cartesian method. The horizontal and total integrals agree to within 0.3 %. As a second consistency test, we consider a uniformly rotating Sun and a travel distance of 42 degrees. The analytical travel-time difference agrees with the forward-modelled travel-time difference to within 2 %. In addition, we evaluate the impact of different choices of filter functions on the kernels for a meridional travel distance of 42 degrees. For all filters, the sensitivity is found to be distributed over a large fraction of the convection zone. We show that the kernels depend on the filter function employed in the data analysis process. If mo...
STUDIES ON CONTINUOUS GRINDING PROCESS FOR DRIED WATER CHESTNUT KERNEL
S.K. GARG
2010-06-01
Full Text Available Grinding is a unit operation to break big solid material into smaller pieces. As far as process of grinding is concerned, power consumption, specific energy consumption and particle size distribution and mill capacity are main considerations from engineering point of view. The experiments were conducted to study the effect of speed of mill, sieve size, feed rate and time of grinding on power consumption and average particle diameter of water chestnut in continuous grinding process. Power consumption was measured for a constant feed rate of 1 and 2 kg/h at different speed of the mill varied from 800 to 1200 rpm for the sieve openings of 0.5 mm, 1.0 mm and 2.0 mm. For all the sieve sizes and feed rates, it was observed that as the speed of the mill increases, there is an increase in power consumption and found significantly low for higher sieve size and lower feed rate. The size distribution of the water chestnut kernel for different speeds and sieve sizes at constant feed rate were obtained by sieve analysis. The milling speed has no significant effect on particle size distribution of ground product and mass fraction was minimum at lower feed rate and higher sieve size. Harris model was found best suitable to describe the size distribution in continuous grinding process. Fineness modulus decreases with increase of milling speed for experimental sieve size and feed rate.
Pareto-path multitask multiple kernel learning.
Li, Cong; Georgiopoulos, Michael; Anagnostopoulos, Georgios C
2015-01-01
A traditional and intuitively appealing Multitask Multiple Kernel Learning (MT-MKL) method is to optimize the sum (thus, the average) of objective functions with (partially) shared kernel function, which allows information sharing among the tasks. We point out that the obtained solution corresponds to a single point on the Pareto Front (PF) of a multiobjective optimization problem, which considers the concurrent optimization of all task objectives involved in the Multitask Learning (MTL) problem. Motivated by this last observation and arguing that the former approach is heuristic, we propose a novel support vector machine MT-MKL framework that considers an implicitly defined set of conic combinations of task objectives. We show that solving our framework produces solutions along a path on the aforementioned PF and that it subsumes the optimization of the average of objective functions as a special case. Using the algorithms we derived, we demonstrate through a series of experimental results that the framework is capable of achieving a better classification performance, when compared with other similar MTL approaches.
The Kernel Estimation in Biosystems Engineering
Esperanza Ayuga Téllez
2008-04-01
Full Text Available In many fields of biosystems engineering, it is common to find works in which statistical information is analysed that violates the basic hypotheses necessary for the conventional forecasting methods. For those situations, it is necessary to find alternative methods that allow the statistical analysis considering those infringements. Non-parametric function estimation includes methods that fit a target function locally, using data from a small neighbourhood of the point. Weak assumptions, such as continuity and differentiability of the target function, are rather used than "a priori" assumption of the global target function shape (e.g., linear or quadratic. In this paper a few basic rules of decision are enunciated, for the application of the non-parametric estimation method. These statistical rules set up the first step to build an interface usermethod for the consistent application of kernel estimation for not expert users. To reach this aim, univariate and multivariate estimation methods and density function were analysed, as well as regression estimators. In some cases the models to be applied in different situations, based on simulations, were defined. Different biosystems engineering applications of the kernel estimation are also analysed in this review.
Scientific Computing Kernels on the Cell Processor
Williams, Samuel W.; Shalf, John; Oliker, Leonid; Kamil, Shoaib; Husbands, Parry; Yelick, Katherine
2007-04-04
The slowing pace of commodity microprocessor performance improvements combined with ever-increasing chip power demands has become of utmost concern to computational scientists. As a result, the high performance computing community is examining alternative architectures that address the limitations of modern cache-based designs. In this work, we examine the potential of using the recently-released STI Cell processor as a building block for future high-end computing systems. Our work contains several novel contributions. First, we introduce a performance model for Cell and apply it to several key scientific computing kernels: dense matrix multiply, sparse matrix vector multiply, stencil computations, and 1D/2D FFTs. The difficulty of programming Cell, which requires assembly level intrinsics for the best performance, makes this model useful as an initial step in algorithm design and evaluation. Next, we validate the accuracy of our model by comparing results against published hardware results, as well as our own implementations on a 3.2GHz Cell blade. Additionally, we compare Cell performance to benchmarks run on leading superscalar (AMD Opteron), VLIW (Intel Itanium2), and vector (Cray X1E) architectures. Our work also explores several different mappings of the kernels and demonstrates a simple and effective programming model for Cell's unique architecture. Finally, we propose modest microarchitectural modifications that could significantly increase the efficiency of double-precision calculations. Overall results demonstrate the tremendous potential of the Cell architecture for scientific computations in terms of both raw performance and power efficiency.
Christiansen, Charlotte
2001-01-01
The paper aims to improve the knowledge of the empirical properties of the long maturity region of the forward rate curve. Firstly, the theoretical negative correlation between the slope at the long end of the forward rate curve and the term structure variance is recovered empirically and found...... to be statistically significant. Secondly, the expectations hypothesis is analyzed for the long maturity region of the forward rate curve using "forward rate" regressions. The expectations hypothesis is numerically close to being accepted but is statistically rejected. The findings provide mixed support...
PŠENIČKOVÁ, Nikola
2014-01-01
The bachelor thesis deals with school maturity of children and is aimed at pre-school children at the age of 6 years or, if necessary, older. The aim of this thesis is to capture the differences between children who start a year later than they were supposed to and children who went to enrolment for the first time and present the reasons for postponing the start of the school attendance. The theoretical part focuses on the issue of maturity of pre-school children and also deals with their rea...
Scheduler Activations on BSD: Sharing Thread Management Between Kernel and Application
Small, Christopher A.; Seltzer, Margo I.
1995-01-01
There are two commonly used thread models: kernel level threads and user level threads. Kernel level threads suffer from the cost of frequent user-kernel domain crossings and fixed kernel scheduling priorities. User level threads are not integrated with the kernel, blocking all threads whenever one thread is blocked. The Scheduler Activations model, proposed by Anderson et al. [ANDE91], combines kernel CPU al location decisions with application control over thread scheduling. This paper discu...
A multi-scale kernel bundle for LDDMM
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...
THE COLLOCATION METHODS FOR SINGULAR INTEGRAL EQUATIONS WITH CAUCHY KERNELS
无
2000-01-01
This paper applies the singular integral operators,singular quadrature operators and discretization matrices associated withsingular integral equations with Cauchy kernels, which are established in [1],to give a unified framework for various collocation methods of numericalsolutions of singular integral equations with Cauchy kernels. Under theframework, the coincidence of the direct quadrature method and the indirectquadrature method is very simple and obvious.
Extracting Feature Model Changes from the Linux Kernel Using FMDiff
Dintzner, N.J.R.; Van Deursen, A.; Pinzger, M.
2014-01-01
The Linux kernel feature model has been studied as an example of large scale evolving feature model and yet details of its evolution are not known. We present here a classification of feature changes occurring on the Linux kernel feature model, as well as a tool, FMDiff, designed to automatically ex
KERNEL IDEALS AND CONGRUENCES ON MS-ALGEBRAS
Luo Congwen; Zeng Yanlu
2006-01-01
In this article, the authors describe the largest congruence induced by a kernel ideal of an MS-algebra and characterize those MS-algebras on which all the congruences are in a one-to-one correspondence with the kernel ideals.
Integral Transform Methods: A Critical Review of Various Kernels
Orlandini, Giuseppina; Turro, Francesco
2017-03-01
Some general remarks about integral transform approaches to response functions are made. Their advantage for calculating cross sections at energies in the continuum is stressed. In particular we discuss the class of kernels that allow calculations of the transform by matrix diagonalization. A particular set of such kernels, namely the wavelets, is tested in a model study.
Integrating the Gradient of the Thin Wire Kernel
Champagne, Nathan J.; Wilton, Donald R.
2008-01-01
A formulation for integrating the gradient of the thin wire kernel is presented. This approach employs a new expression for the gradient of the thin wire kernel derived from a recent technique for numerically evaluating the exact thin wire kernel. This approach should provide essentially arbitrary accuracy and may be used with higher-order elements and basis functions using the procedure described in [4].When the source and observation points are close, the potential integrals over wire segments involving the wire kernel are split into parts to handle the singular behavior of the integrand [1]. The singularity characteristics of the gradient of the wire kernel are different than those of the wire kernel, and the axial and radial components have different singularities. The characteristics of the gradient of the wire kernel are discussed in [2]. To evaluate the near electric and magnetic fields of a wire, the integration of the gradient of the wire kernel needs to be calculated over the source wire. Since the vector bases for current have constant direction on linear wire segments, these integrals reduce to integrals of the form
ON APPROXIMATION BY SPHERICAL REPRODUCING KERNEL HILBERT SPACES
无
2007-01-01
The spherical approximation between two nested reproducing kernels Hilbert spaces generated from different smooth kernels is investigated. It is shown that the functions of a space can be approximated by that of the subspace with better smoothness. Furthermore, the upper bound of approximation error is given.
End-use quality of soft kernel durum wheat
Kernel texture is a major determinant of end-use quality of wheat. Durum wheat is known for its very hard texture, which influences how it is milled and for what products it is well suited. We developed soft kernel durum wheat lines via Ph1b-mediated homoeologous recombination with Dr. Leonard Joppa...
Optimal Bandwidth Selection in Observed-Score Kernel Equating
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…
Comparison of Kernel Equating and Item Response Theory Equating Methods
Meng, Yu
2012-01-01
The kernel method of test equating is a unified approach to test equating with some advantages over traditional equating methods. Therefore, it is important to evaluate in a comprehensive way the usefulness and appropriateness of the Kernel equating (KE) method, as well as its advantages and disadvantages compared with several popular item…
Integral transform methods: a critical review of various kernels
Orlandini, Giuseppina
2016-01-01
Some general remarks about integral transform approaches to response functions are made. Their advantage for calculating cross sections at energies in the continuum is stressed. In particular we discuss the class of kernels that allow calculations of the transform by matrix diagonalization. A particular set of such kernels, namely the wavelets, is tested in a model study.
Indigenous Methods in Preserving Bush Mango Kernels in Cameroon
Zac Tchoundjeu
2005-01-01
Full Text Available Traditional practices for preserving Irvingia wombolu and Irvingia gabonensis (bush mango kernels were assessed in a survey covering twelve villages (Dongo, Bouno, Gribi [East], Elig-Nkouma, Nkom I, Ngoumou [Centre], Bidjap, Nko’ovos, Ondodo [South], Besong-Abang, Ossing and Kembong [Southwest], in the humid lowland forest zone of Cameroon. All the interviewed households that own trees of species were found to preserve kernels in periods of abundance, excluding Elig-Nkouma (87.5%. Eighty nine and 85% did so in periods of scarcity for I. wombolu and I. gabonensis respectively. Seventeen and twenty-nine kernel preservation practices were recorded for I. wombolu and I. gabonensis respectively. Most were based on continuous heating of the kernels or kernel by-products (cakes. The most commonly involved keeping the sun-dried kernels in a plastic bag on a bamboo rack hung above the fireplace in the kitchen. A 78% of interviews households reported preserving I. wombolu kernels for less than one year while 22% preserved it for more than one year with 1.9% for two years, the normal length of the off-season period for trees in the wild. Cakes wrapped with leaves and kept on a bamboo rack hung over the fireplace were reported by households in the East and South provinces to store Irvingia gabonensis longer (more than one year. Further studies on the utilization of heat for preserving and canning bush mango kernels are recommended.
THE HEAT KERNEL ON THE CAYLEY HEISENBERG GROUP
Luan Jingwen; Zhu Fuliu
2005-01-01
The authors obtain an explicit expression of the heat kernel for the Cayley Heisenberg group of order n by using the stochastic integral method of Gaveau. Apart from the standard Heisenberg group and the quaternionic Heisenberg group, this is the only nilpotent Lie group on which an explicit formula for the heat kernel has been obtained.
Oven-drying reduces ruminal starch degradation in maize kernels
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 (consist
Open Problem: Kernel methods on manifolds and metric spaces
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...
Efficient Kernel-based 2DPCA for Smile Stages Recognition
Fitri Damayanti
2012-03-01
Full Text Available Recently, an approach called two-dimensional principal component analysis (2DPCA has been proposed for smile stages representation and recognition. The essence of 2DPCA is that it computes the eigenvectors of the so-called image covariance matrix without matrix-to-vector conversion so the size of the image covariance matrix are much smaller, easier to evaluate covariance matrix, computation cost is reduced and the performance is also improved than traditional PCA. In an effort to improve and perfect the performance of smile stages recognition, in this paper, we propose efficient Kernel based 2DPCA concepts. The Kernelization of 2DPCA can be benefit to develop the nonlinear structures in the input data. This paper discusses comparison of standard Kernel based 2DPCA and efficient Kernel based 2DPCA for smile stages recognition. The results of experiments show that Kernel based 2DPCA achieve better performance in comparison with the other approaches. While the use of efficient Kernel based 2DPCA can speed up the training procedure of standard Kernel based 2DPCA thus the algorithm can achieve much more computational efficiency and remarkably save the memory consuming compared to the standard Kernel based 2DPCA.
Oven-drying reduces ruminal starch degradation in maize kernels
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
Lp-boundedness of flag kernels on homogeneous groups
Glowacki, Pawel
2010-01-01
We prove that the flag kernel singular integral operators of Nagel-Ricci-Stein on a homogeneous group are bounded on the Lp spaces. The gradation associated with the kernels is the natural gradation of the underlying Lie algebra. Our main tools are the Littlewood-Paley theory and a symbolic calculus combined in the spirit of Duoandikoetxea and Rubio de Francia.
Extracting Feature Model Changes from the Linux Kernel Using FMDiff
Dintzner, N.J.R.; Van Deursen, A.; Pinzger, M.
2014-01-01
The Linux kernel feature model has been studied as an example of large scale evolving feature model and yet details of its evolution are not known. We present here a classification of feature changes occurring on the Linux kernel feature model, as well as a tool, FMDiff, designed to automatically
Gaussian kernel operators on white noise functional spaces
骆顺龙; 严加安
2000-01-01
The Gaussian kernel operators on white noise functional spaces, including second quantization, Fourier-Mehler transform, scaling, renormalization, etc. are studied by means of symbol calculus, and characterized by the intertwining relations with annihilation and creation operators. The infinitesimal generators of the Gaussian kernel operators are second order white noise operators of which the number operator and the Gross Laplacian are particular examples.
Evolutionary optimization of kernel weights improves protein complex comembership prediction.
Hulsman, Marc; Reinders, Marcel J T; de Ridder, Dick
2009-01-01
In recent years, more and more high-throughput data sources useful for protein complex prediction have become available (e.g., gene sequence, mRNA expression, and interactions). The integration of these different data sources can be challenging. Recently, it has been recognized that kernel-based classifiers are well suited for this task. However, the different kernels (data sources) are often combined using equal weights. Although several methods have been developed to optimize kernel weights, no large-scale example of an improvement in classifier performance has been shown yet. In this work, we employ an evolutionary algorithm to determine weights for a larger set of kernels by optimizing a criterion based on the area under the ROC curve. We show that setting the right kernel weights can indeed improve performance. We compare this to the existing kernel weight optimization methods (i.e., (regularized) optimization of the SVM criterion or aligning the kernel with an ideal kernel) and find that these do not result in a significant performance improvement and can even cause a decrease in performance. Results also show that an expert approach of assigning high weights to features with high individual performance is not necessarily the best strategy.
Visualization of nonlinear kernel models in neuroimaging by sensitivity maps
Rasmussen, Peter Mondrup; Madsen, Kristoffer Hougaard; Lund, Torben Ellegaard
2011-01-01
There is significant current interest in decoding mental states from neuroimages. In this context kernel methods, e.g., support vector machines (SVM) are frequently adopted to learn statistical relations between patterns of brain activation and experimental conditions. In this paper we focus......, and conclude that the sensitivity map is a versatile and computationally efficient tool for visualization of nonlinear kernel models in neuroimaging....
Rao, Guodong; Sui, Jinkai; Zhang, Jianguo
2016-01-01
The content of walnut metabolites is related to its nutritive value and physiological characteristics, however, comprehensive information concerning the metabolome of walnut kernels is limited. In this study we analyzed the metabolites of walnut kernels at five developmental stages from filling to ripening using GC-MS-based untargeted metabolomics; of a total 252 peaks identified, 85 metabolites were positively identified. Further statistical analysis revealed that these 85 metabolites covered different types of metabolism pathways. PCA scores revealed that the metabolic compositions of the embryo are different at each stage, while the metabolic composition of the endotesta could not be significantly separated into distinct groups. Additionally, 7225 metabolite-metabolite correlations were detected in walnut kernel by a Pearson correlation coefficient approach; during screening of the calculated correlations, 463 and 1047 were determined to be significant with r(2)≥0.49 and had a false discovery rate (FDR) ≤0.05 in endotesta and embryo, respectively. This work provides the first comprehensive metabolomic study of walnut kernels and reveals that most of the carbohydrate and protein-derived carbon was transferred into other compounds, such as fatty acids, during the maturation of walnuts, which may potentially provide the basis for further studies on walnut kernel metabolism.
Guodong Rao
2016-06-01
Full Text Available The content of walnut metabolites is related to its nutritive value and physiological characteristics, however, comprehensive information concerning the metabolome of walnut kernels is limited. In this study we analyzed the metabolites of walnut kernels at five developmental stages from filling to ripening using GC-MS-based untargeted metabolomics; of a total 252 peaks identified, 85 metabolites were positively identified. Further statistical analysis revealed that these 85 metabolites covered different types of metabolism pathways. PCA scores revealed that the metabolic compositions of the embryo are different at each stage, while the metabolic composition of the endotesta could not be significantly separated into distinct groups. Additionally, 7225 metabolite-metabolite correlations were detected in walnut kernel by a Pearson correlation coefficient approach; during screening of the calculated correlations, 463 and 1047 were determined to be significant with r2≥0.49 and had a false discovery rate (FDR ≤0.05 in endotesta and embryo, respectively. This work provides the first comprehensive metabolomic study of walnut kernels and reveals that most of the carbohydrate and protein-derived carbon was transferred into other compounds, such as fatty acids, during the maturation of walnuts, which may potentially provide the basis for further studies on walnut kernel metabolism.
Technology Maturity is Technology Superiority
2008-09-09
Dominant Air Power: Design For Tomorrow…Deliver Today 2 TECHNOLOGY MATURITY CONFERENCE • ONE DEFINITION OF MATURITY – GOOD JUDGEMENT COMES FROM...EXPERIENCE—EXPERIENCE COMES FROM BAD JUDGEMENT Dominant Air Power: Design For Tomorrow…Deliver Today 3 TECHNOLOGY MATURITY CONFERENCE • THIS WILL BE A...2008 TECHNOLOGY MATURITY CONFERENCE “ TECHNOLOGY MATURITY IS TECHNOLOGY SUPERIORITY” Aeronautical Systems Center Dr. Tom Christian ASC/EN, WPAFB OH
Higher Order Kernels and Locally Affine LDDMM Registration
Sommer, Stefan; Darkner, Sune; Pennec, Xavier
2011-01-01
To achieve sparse description that allows intuitive analysis, we aim to represent deformation with a basis containing interpretable elements, and we wish to use elements that have the description capacity to represent the deformation compactly. We accomplish this by introducing higher order kernels in the LDDMM registration framework. The kernels allow local description of affine transformations and subsequent compact description of non-translational movement and of the entire non-rigid deformation. This is obtained with a representation that contains directly interpretable information from both mathematical and modeling perspectives. We develop the mathematical construction behind the higher order kernels, we show the implications for sparse image registration and deformation description, and we provide examples of how the capacity of the kernels enables registration with a very low number of parameters. The capacity and interpretability of the kernels lead to natural modeling of articulated movement, and th...
Hypothesis testing using pairwise distances and associated kernels
Sejdinovic, Dino; Sriperumbudur, Bharath; Fukumizu, Kenji
2012-01-01
We provide a unifying framework linking two classes of statistics used in two-sample and independence testing: on the one hand, the energy distances and distance covariances from the statistics literature; on the other, distances between embeddings of distributions to reproducing kernel Hilbert spaces (RKHS), as established in machine learning. The equivalence holds when energy distances are computed with semimetrics of negative type, in which case a kernel may be defined such that the RKHS distance between distributions corresponds exactly to the energy distance. We determine the class of probability distributions for which kernels induced by semimetrics are characteristic (that is, for which embeddings of the distributions to an RKHS are injective). Finally, we investigate the performance of this family of kernels in two-sample and independence tests: we show in particular that the energy distance most commonly employed in statistics is just one member of a parametric family of kernels, and that other choic...
An iterative modified kernel based on training data
Zhi-xiang ZHOU; Feng-qing HAN
2009-01-01
To improve performance of a support vector regression, a new method for a modified kernel function is proposed. In this method, information of all samples is included in the kernel function with conformal mapping. Thus the kernel function is data-dependent. With a random initial parameter, the kernel function is modified re-peatedly until a satisfactory result is achieved. Compared with the conventional model, the improved approach does not need to select parameters of the kernel function. Sim-ulation is carried out for the one-dimension continuous function and a case of strong earthquakes. The results show that the improved approach has better learning ability and forecasting precision than the traditional model. With the increase of the iteration number, the figure of merit decreases and converges. The speed of convergence depends on the parameters used in the algorithm.
Nonlinear Statistical Process Monitoring and Fault Detection Using Kernel ICA
ZHANG Xi; YAN Wei-wu; ZHAO Xu; SHAO Hui-he
2007-01-01
A novel nonlinear process monitoring and fault detection method based on kernel independent component analysis (ICA) is proposed. The kernel ICA method is a two-phase algorithm: whitened kernel principal component (KPCA) plus ICA. KPCA spheres data and makes the data structure become as linearly separable as possible by virtue of an implicit nonlinear mapping determined by kernel. ICA seeks the projection directions in the KPCA whitened space, making the distribution of the projected data as non-gaussian as possible. The application to the fluid catalytic cracking unit (FCCU) simulated process indicates that the proposed process monitoring method based on kernel ICA can effectively capture the nonlinear relationship in process variables. Its performance significantly outperforms monitoring method based on ICA or KPCA.
Anatomically-aided PET reconstruction using the kernel method
Hutchcroft, Will; Wang, Guobao; Chen, Kevin T.; Catana, Ciprian; Qi, Jinyi
2016-09-01
This paper extends the kernel method that was proposed previously for dynamic PET reconstruction, to incorporate anatomical side information into the PET reconstruction model. In contrast to existing methods that incorporate anatomical information using a penalized likelihood framework, the proposed method incorporates this information in the simpler maximum likelihood (ML) formulation and is amenable to ordered subsets. The new method also does not require any segmentation of the anatomical image to obtain edge information. We compare the kernel method with the Bowsher method for anatomically-aided PET image reconstruction through a simulated data set. Computer simulations demonstrate that the kernel method offers advantages over the Bowsher method in region of interest quantification. Additionally the kernel method is applied to a 3D patient data set. The kernel method results in reduced noise at a matched contrast level compared with the conventional ML expectation maximization algorithm.
OSKI: A Library of Automatically Tuned Sparse Matrix Kernels
Vuduc, R; Demmel, J W; Yelick, K A
2005-07-19
The Optimized Sparse Kernel Interface (OSKI) is a collection of low-level primitives that provide automatically tuned computational kernels on sparse matrices, for use by solver libraries and applications. These kernels include sparse matrix-vector multiply and sparse triangular solve, among others. The primary aim of this interface is to hide the complex decision-making process needed to tune the performance of a kernel implementation for a particular user's sparse matrix and machine, while also exposing the steps and potentially non-trivial costs of tuning at run-time. This paper provides an overview of OSKI, which is based on our research on automatically tuned sparse kernels for modern cache-based superscalar machines.
OSKI: A library of automatically tuned sparse matrix kernels
Vuduc, Richard; Demmel, James W.; Yelick, Katherine A.
2005-01-01
The Optimized Sparse Kernel Interface (OSKI) is a collection of low-level primitives that provide automatically tuned computational kernels on sparse matrices, for use by solver libraries and applications. These kernels include sparse matrix-vector multiply and sparse triangular solve, among others. The primary aim of this interface is to hide the complex decisionmaking process needed to tune the performance of a kernel implementation for a particular user's sparse matrix and machine, while also exposing the steps and potentially non-trivial costs of tuning at run-time. This paper provides an overview of OSKI, which is based on our research on automatically tuned sparse kernels for modern cache-based superscalar machines.
CRKSPH - A Conservative Reproducing Kernel Smoothed Particle Hydrodynamics Scheme
Frontiere, Nicholas; Owen, J Michael
2016-01-01
We present a formulation of smoothed particle hydrodynamics (SPH) that employs a first-order consistent reproducing kernel function, exactly interpolating linear fields with particle tracers. Previous formulations using reproducing kernel (RK) interpolation have had difficulties maintaining conservation of momentum due to the fact the RK kernels are not, in general, spatially symmetric. Here, we utilize a reformulation of the fluid equations such that mass, momentum, and energy are all manifestly conserved without any assumption about kernel symmetries. Additionally, by exploiting the increased accuracy of the RK method's gradient, we formulate a simple limiter for the artificial viscosity that reduces the excess diffusion normally incurred by the ordinary SPH artificial viscosity. Collectively, we call our suite of modifications to the traditional SPH scheme Conservative Reproducing Kernel SPH, or CRKSPH. CRKSPH retains the benefits of traditional SPH methods (such as preserving Galilean invariance and manif...
A novel extended kernel recursive least squares algorithm.
Zhu, Pingping; Chen, Badong; Príncipe, José C
2012-08-01
In this paper, a novel extended kernel recursive least squares algorithm is proposed combining the kernel recursive least squares algorithm and the Kalman filter or its extensions to estimate or predict signals. Unlike the extended kernel recursive least squares (Ex-KRLS) algorithm proposed by Liu, the state model of our algorithm is still constructed in the original state space and the hidden state is estimated using the Kalman filter. The measurement model used in hidden state estimation is learned by the kernel recursive least squares algorithm (KRLS) in reproducing kernel Hilbert space (RKHS). The novel algorithm has more flexible state and noise models. We apply this algorithm to vehicle tracking and the nonlinear Rayleigh fading channel tracking, and compare the tracking performances with other existing algorithms.
Virtual screening with support vector machines and structure kernels
Mahé, Pierre
2007-01-01
Support vector machines and kernel methods have recently gained considerable attention in chemoinformatics. They offer generally good performance for problems of supervised classification or regression, and provide a flexible and computationally efficient framework to include relevant information and prior knowledge about the data and problems to be handled. In particular, with kernel methods molecules do not need to be represented and stored explicitly as vectors or fingerprints, but only to be compared to each other through a comparison function technically called a kernel. While classical kernels can be used to compare vector or fingerprint representations of molecules, completely new kernels were developed in the recent years to directly compare the 2D or 3D structures of molecules, without the need for an explicit vectorization step through the extraction of molecular descriptors. While still in their infancy, these approaches have already demonstrated their relevance on several toxicity prediction and s...
3-D waveform tomography sensitivity kernels for anisotropic media
Djebbi, R.
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.
Sarah, De Laethauwer; Jan, De Riek; Geert, Haesaert
2014-01-01
Pre-harvest sprouting (PHS) during wet and cool harvest periods remains a serious problem in the production of cereals like barley, wheat and triticale. Being involved in dormancy induction and maintenance during seed development, abscisic acid (ABA) may play a key role to improve dormancy level and hence PHS-tolerance in these grains. In this study, we investigated the ABA levels and expression profiles of ABA biosynthesis and degradation genes during kernel development to explore the potential of these genes for improving PHS-tolerance in wheat and triticale. Plants of a PHS-tolerant and a PHS-susceptible variety of both wheat and triticale were grown under controlled conditions from flowering to harvest. At regular time points, kernels were harvested for ABA analysis and RNA extraction. RNA extracts were used in an RT-qPCR assay to obtain expression profiles of the ABA synthesis genes ZEP, NCED1 and NCED2 and the ABA degradation genes CYP707A1 and CYP707A2. In contrast to reports in Arabidopsis, the ZEP gene was predominantly expressed towards harvest maturity in both wheat and triticale. NCED1 expression coincided well with the observed ABA levels during kernel development, while NCED2 expression was mainly detected in early development, indicating a potential role for dormancy induction. ABA degradation towards harvest maturity was mainly associated with increased CYP707A1 expression, whereas CYP707A2 expression appeared to correlate with the regulation of ABA levels during kernel development. However, no differential expression of the investigated genes was detected between PHS-tolerant and PHS-susceptible varieties.
Maturing interorganisational information systems
Plomp, M.G.A.
2012-01-01
This thesis consists of nine chapters, divided over five parts. PART I is an introduction and the last part contains the conclusions. The remaining, intermediate parts are: PART II: Developing a maturity model for chain digitisation. This part contains two related studies concerning the development
Maturing interorganisational information systems
Plomp, M.G.A.
2012-01-01
This thesis consists of nine chapters, divided over five parts. PART I is an introduction and the last part contains the conclusions. The remaining, intermediate parts are: PART II: Developing a maturity model for chain digitisation. This part contains two related studies concerning the development
Mechanics of bacteriophage maturation
Roos, Wouter H.; Gertsman, Ilya; May, Eric R.; Brooks III, Charles L.; Johnson, John E.; Wuite, Gijs J. L.
2012-01-01
Capsid maturation with large-scale subunit reorganization occurs in virtually all viruses that use a motor to package nucleic acid into preformed particles. A variety of ensemble studies indicate that the particles gain greater stability during this process, however, it is unknown which material
Mathes, Eugene W.; Deuger, Donna J.
Jealousy may be perceived as either good or bad depending upon the moral maturity of the individual. To investigate this conclusion, a study was conducted testing two hypothesis: a positive relationship exists between conventional moral reasoning (reference to norms and laws) and the endorsement and level of jealousy; and a negative relationship…
Genomic Prediction of Genotype × Environment Interaction Kernel Regression Models.
Cuevas, Jaime; Crossa, José; Soberanis, Víctor; Pérez-Elizalde, Sergio; Pérez-Rodríguez, Paulino; Campos, Gustavo de Los; Montesinos-López, O A; Burgueño, Juan
2016-11-01
In genomic selection (GS), genotype × environment interaction (G × E) can be modeled by a marker × environment interaction (M × E). The G × E may be modeled through a linear kernel or a nonlinear (Gaussian) kernel. In this study, we propose using two nonlinear Gaussian kernels: the reproducing kernel Hilbert space with kernel averaging (RKHS KA) and the Gaussian kernel with the bandwidth estimated through an empirical Bayesian method (RKHS EB). We performed single-environment analyses and extended to account for G × E interaction (GBLUP-G × E, RKHS KA-G × E and RKHS EB-G × E) in wheat ( L.) and maize ( L.) data sets. For single-environment analyses of wheat and maize data sets, RKHS EB and RKHS KA had higher prediction accuracy than GBLUP for all environments. For the wheat data, the RKHS KA-G × E and RKHS EB-G × E models did show up to 60 to 68% superiority over the corresponding single environment for pairs of environments with positive correlations. For the wheat data set, the models with Gaussian kernels had accuracies up to 17% higher than that of GBLUP-G × E. For the maize data set, the prediction accuracy of RKHS EB-G × E and RKHS KA-G × E was, on average, 5 to 6% higher than that of GBLUP-G × E. The superiority of the Gaussian kernel models over the linear kernel is due to more flexible kernels that accounts for small, more complex marker main effects and marker-specific interaction effects.
Pope, Benjamin; Hinkley, Sasha; Ireland, Michael J; Greenbaum, Alexandra; Latyshev, Alexey; Monnier, John D; Martinache, Frantz
2015-01-01
At present, the principal limitation on the resolution and contrast of astronomical imaging instruments comes from aberrations in the optical path, which may be imposed by the Earth's turbulent atmosphere or by variations in the alignment and shape of the telescope optics. These errors can be corrected physically, with active and adaptive optics, and in post-processing of the resulting image. A recently-developed adaptive optics post-processing technique, called kernel phase interferometry, uses linear combinations of phases that are self-calibrating with respect to small errors, with the goal of constructing observables that are robust against the residual optical aberrations in otherwise well-corrected imaging systems. Here we present a direct comparison between kernel phase and the more established competing techniques, aperture masking interferometry, point spread function (PSF) fitting and bispectral analysis. We resolve the alpha Ophiuchi binary system near periastron, using the Palomar 200-Inch Telesco...
Heat kernel method and its applications
Avramidi, Ivan G
2015-01-01
The heart of the book is the development of a short-time asymptotic expansion for the heat kernel. This is explained in detail and explicit examples of some advanced calculations are given. In addition some advanced methods and extensions, including path integrals, jump diffusion and others are presented. The book consists of four parts: Analysis, Geometry, Perturbations and Applications. The first part shortly reviews of some background material and gives an introduction to PDEs. The second part is devoted to a short introduction to various aspects of differential geometry that will be needed later. The third part and heart of the book presents a systematic development of effective methods for various approximation schemes for parabolic differential equations. The last part is devoted to applications in financial mathematics, in particular, stochastic differential equations. Although this book is intended for advanced undergraduate or beginning graduate students in, it should also provide a useful reference ...
Image Processing Variations with Analytic Kernels
Garnett, John B; Vese, Luminita A
2012-01-01
Let $f\\in L^1(\\R^d)$ be real. The Rudin-Osher-Fatemi model is to minimize $\\|u\\|_{\\dot{BV}}+\\lambda\\|f-u\\|_{L^2}^2$, in which one thinks of $f$ as a given image, $\\lambda > 0$ as a "tuning parameter", $u$ as an optimal "cartoon" approximation to $f$, and $f-u$ as "noise" or "texture". Here we study variations of the R-O-F model having the form $\\inf_u\\{\\|u\\|_{\\dot{BV}}+\\lambda \\|K*(f-u)\\|_{L^p}^q\\}$ where $K$ is a real analytic kernel such as a Gaussian. For these functionals we characterize the minimizers $u$ and establish several of their properties, including especially their smoothness properties. In particular we prove that on any open set on which $u \\in W^{1,1}$ and $\
The Dynamical Kernel Scheduler - Part 1
Adelmann, Andreas; Suter, Andreas
2015-01-01
Emerging processor architectures such as GPUs and Intel MICs provide a huge performance potential for high performance computing. However developing software using these hardware accelerators introduces additional challenges for the developer such as exposing additional parallelism, dealing with different hardware designs and using multiple development frameworks in order to use devices from different vendors. The Dynamic Kernel Scheduler (DKS) is being developed in order to provide a software layer between host application and different hardware accelerators. DKS handles the communication between the host and device, schedules task execution, and provides a library of built-in algorithms. Algorithms available in the DKS library will be written in CUDA, OpenCL and OpenMP. Depending on the available hardware, the DKS can select the appropriate implementation of the algorithm. The first DKS version was created using CUDA for the Nvidia GPUs and OpenMP for Intel MIC. DKS was further integrated in OPAL (Object-or...
Online Learning of Noisy Data with Kernels
Cesa-Bianchi, Nicolò; Shamir, Ohad
2010-01-01
We study online learning when individual instances are corrupted by random noise. We assume the noise distribution is unknown, and may change over time with no restriction other than having zero mean and bounded variance. Our technique relies on a family of unbiased estimators for non-linear functions, which may be of independent interest. We show that a variant of online gradient descent can learn functions in any dot-product (e.g., polynomial) or Gaussian kernel space with any analytic convex loss function. Our variant uses randomized estimates that need to query a random number of noisy copies of each instance, where with high probability this number is upper bounded by a constant. Allowing such multiple queries cannot be avoided: Indeed, we show that online learning is in general impossible when only one noisy copy of each instance can be accessed.
Index-free Heat Kernel Coefficients
De van Ven, A E M
1998-01-01
Using index-free notation, we present the diagonal values of the first five heat kernel coefficients associated with a general Laplace-type operator on a compact Riemannian space without boundary. The fifth coefficient appears here for the first time. For a flat space with a gauge connection, the sixth coefficient is given too. Also provided are the leading terms for any coefficient, both in ascending and descending powers of the Yang-Mills and Riemann curvatures, to the same order as required for the fourth coefficient. These results are obtained by directly solving the relevant recursion relations, working in Fock-Schwinger gauge and Riemann normal coordinates. Our procedure is thus noncovariant, but we show that for any coefficient the `gauged' respectively `curved' version is found from the corresponding `non-gauged' respectively `flat' coefficient by making some simple covariant substitutions. These substitutions being understood, the coefficients retain their `flat' form and size. In this sense the fift...
Kernel density estimation using graphical processing unit
Sunarko, Su'ud, Zaki
2015-09-01
Kernel density estimation for particles distributed over a 2-dimensional space is calculated using a single graphical processing unit (GTX 660Ti GPU) and CUDA-C language. Parallel calculations are done for particles having bivariate normal distribution and by assigning calculations for equally-spaced node points to each scalar processor in the GPU. The number of particles, blocks and threads are varied to identify favorable configuration. Comparisons are obtained by performing the same calculation using 1, 2 and 4 processors on a 3.0 GHz CPU using MPICH 2.0 routines. Speedups attained with the GPU are in the range of 88 to 349 times compared the multiprocessor CPU. Blocks of 128 threads are found to be the optimum configuration for this case.
Learning RoboCup-Keepaway with Kernels
Jung, Tobias
2012-01-01
We apply kernel-based methods to solve the difficult reinforcement learning problem of 3vs2 keepaway in RoboCup simulated soccer. Key challenges in keepaway are the high-dimensionality of the state space (rendering conventional discretization-based function approximation like tilecoding infeasible), the stochasticity due to noise and multiple learning agents needing to cooperate (meaning that the exact dynamics of the environment are unknown) and real-time learning (meaning that an efficient online implementation is required). We employ the general framework of approximate policy iteration with least-squares-based policy evaluation. As underlying function approximator we consider the family of regularization networks with subset of regressors approximation. The core of our proposed solution is an efficient recursive implementation with automatic supervised selection of relevant basis functions. Simulation results indicate that the behavior learned through our approach clearly outperforms the best results obta...
Heat kernel measures on random surfaces
Klevtsov, Semyon
2015-01-01
The heat kernel on the symmetric space of positive definite Hermitian matrices is used to endow the spaces of Bergman metrics of degree k on a Riemann surface M with a family of probability measures depending on a choice of the background metric. Under a certain matrix-metric correspondence, each positive definite Hermitian matrix corresponds to a Kahler metric on M. The one and two point functions of the random metric are calculated in a variety of limits as k and t tend to infinity. In the limit when the time t goes to infinity the fluctuations of the random metric around the background metric are the same as the fluctuations of random zeros of holomorphic sections. This is due to the fact that the random zeros form the boundary of the space of Bergman metrics.
Pattern Programmable Kernel Filter for Bot Detection
Kritika Govind
2012-05-01
Full Text Available Bots earn their unique name as they perform a wide variety of automated task. These tasks include stealing sensitive\tuser\tinformation. Detection of bots using solutions such as behavioral\tcorrelation of\tflow\trecords,\tgroup activity\tin DNS traffic, observing\tthe periodic repeatability in\tcommunication, etc., lead to monitoring\tthe network traffic\tand\tthen\tclassifying them as Bot or normal traffic.\tOther solutions for\tBot detection\tinclude kernel level key stroke verification, system call initialization,\tIP black listing, etc. In the first\ttwo solutions\tthere is no assurance\tthat\tthe\tpacket carrying user information is prevented from being sent to the attacker and the latter suffers from the problem of\tIP spoofing. This motivated\tus to think\tof a solution that would\tfilter\tout\tthe malicious\tpackets\tbefore being\tput onto\tthe network. To come out with such a\tsolution,\ta real time\tbot\tattack\twas\tgenerated with SpyEye Exploit kit and traffic\tcharacteristics were analyzed. The analysis revealed the existence\tof a unique repeated communication\tbetween\tthe Zombie machine\tand\tthe botmaster. This motivated us to propose, a Pattern\tProgrammable Kernel\tFilter (PPKF\tfor filtering out the malicious\tpackets generated by bots.\tPPKF was developed\tusing the\twindows\tfiltering platform (WFP filter engine.\tPPKF was programmed to\tfilter\tout\tthe\tpackets\twith\tunique pattern which were observed\tfrom\tthe\tbot\tattack experiments. Further\tPPKF was found\tto completely suppress the\tflow\tof packets having the programmed uniqueness in them thus preventing the functioning of bots in terms of user information being sent to the Botmaster.
Fractional complex transforms for fractional differential equations
Ibrahim, Rabha W
2012-01-01
The fractional complex transform is employed to convert fractional differential equations analytically in the sense of the Srivastava-Owa fractional operator and its generalization in the unit disk...
Associative morphological memories based on variations of the kernel and dual kernel methods.
Sussner, Peter
2003-01-01
Morphological associative memories (MAMs) belong to the class of morphological neural networks. The recording scheme used in the original MAM models is similar to the correlation recording recipe. Recording is achieved by means of a maximum (MXY model) or minimum (WXY model) of outer products. Notable features of autoassociative morphological memories (AMMs) include optimal absolute storage capacity and one-step convergence. Heteroassociative morphological memories (HMMs) do not have these properties and are not very well understood. The fixed points of AMMs can be characterized exactly in terms of the original patterns. Unfortunately, AMM fixed points include a large number of spurious memories. In this paper, we combine the MXX model and variations of the kernel method to produce new autoassociative and heteroassociative memories. We also introduce a dual kernel method. A new, dual model is given by a combination of the WXX model and a variation of the dual kernel method. The new MAM models exhibit better error correction capabilities than MXX and WXX and a reduced number of spurious memories which can be easily described in terms of the fundamental memories.
Stochastic Model of Maturation and Vesicular Exchange in Cellular Organelles
Vagne, Quentin
2016-01-01
The dynamical organization of membrane-bound organelles along intracellular transport pathways relies on vesicular exchange between organelles and on biochemical maturation of the organelle content by specific enzymes. The relative importance of each mechanism in controlling organelle dynamics remains controversial, in particular for transport through the Golgi apparatus. Using a stochastic model, we show that full maturation of membrane-bound compartments can be seen as the stochastic escape from a steady-state in which export is dominated by vesicular exchange. We show that full maturation can contribute a significant fraction of the total out-flux for small organelles such as endosomes and Golgi cisternae.
L.R. Molina
2002-04-01
digestibility of fibrous fractions of silages of six sorghum genotypes in different stages of maturation, four fistulated male crossbred Holstein-Zebu bovines fed with grass hay ad libitum were used. Silages samples were dried and milled and then placed in nylon bags and introduced into the rumen through the fistulas. After different times of incubation (6, 12, 24, 48, 72 and 96 hours later, the nylon bags were removed, the samples from the same genotype, animal and time of incubation were transformed into a homogenous pool and milled, and then stored for further analysis of fibrous fractions. It was used the split-plot design, in which the parcels were the silages of different genotypes, the sub-parcels were the periods of harvesting and the sub-sub-parcels were the periods of incubation. In the period 1, except at 6, 24 and 96 hours of incubation, no differences among hybrids related to the NDF mean disappearance were found. In the period 2, differences among hybrids just after 12 and 96 hours of incubation, and in the period 3, just after 12 hours of incubation were found. There was stabilization of the fermentative processes from the 72 hours period ahead, except in the BR 304 hybrid at the periods 1 and 3 and BRS 701 at the period 3. No differences among hybrids related to the ADF mean disappearance at any time, except for the 96 hours period in period 1 and the 72 hours period in period 3 were observed. The degradation graphics demonstrate a stabilization tendency of the fermentative process at the 72 hours period, except for the BR 304 hybrid in the period 1 and the BRS 701 hybrid in the period 3. The percentage of grains in silages did not influence the disappearance of the fibrous fractions. However, the stage of maturation of the plant influenced the effective degradability of fibrous fractions, benefiting early stages.
A fractional generalization of the classical lattice dynamics approach
Michelitsch, T M; Riascos, A P; Nowakowski, A F; Nicolleau, F C G A
2016-01-01
We develop physically admissible lattice models in the harmonic approximation which define by Hamilton's variational principle fractional Laplacian matrices of the forms of power law matrix functions on the n -dimensional periodic and infinite lattice in n=1,2,3,..n=1,2,3,.. dimensions. The present model which is based on Hamilton's variational principle is confined to conservative non-dissipative isolated systems. The present approach yields the discrete analogue of the continuous space fractional Laplacian kernel. As continuous fractional calculus generalizes differential operators such as the Laplacian to non-integer powers of Laplacian operators, the fractional lattice approach developed in this paper generalized difference operators such as second difference operators to their fractional (non-integer) powers. Whereas differential operators and difference operators constitute local operations, their fractional generalizations introduce nonlocal long-range features. This is true for discrete and continuous...
Fractional complex transform for fractional differential equations
Lİ, Zheng Biao; HE, Ji Huan
2010-01-01
Fractional complex transform is proposed to convert fractional differential equations into ordinary differential equations, so that all analytical methods devoted to advanced calculus can be easily...
Classification of maize kernels using NIR hyperspectral imaging.
Williams, Paul J; Kucheryavskiy, Sergey
2016-10-15
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 kernels and did not give acceptable results because of high misclassification. However by using a predefined threshold and classifying entire kernels based on the number of correctly predicted pixels, improved results were achieved (sensitivity and specificity of 0.75 and 0.97). Object-wise classification was performed using two methods for feature extraction - score histograms and mean spectra. The model based on score histograms performed better for hard kernel classification (sensitivity and specificity of 0.93 and 0.97), while that of mean spectra gave better results for medium kernels (sensitivity and specificity of 0.95 and 0.93). Both feature extraction methods can be recommended for classification of maize kernels on production scale.
Gaussian kernel width optimization for sparse Bayesian learning.
Mohsenzadeh, Yalda; Sheikhzadeh, Hamid
2015-04-01
Sparse kernel methods have been widely used in regression and classification applications. The performance and the sparsity of these methods are dependent on the appropriate choice of the corresponding kernel functions and their parameters. Typically, the kernel parameters are selected using a cross-validation approach. In this paper, a learning method that is an extension of the relevance vector machine (RVM) is presented. The proposed method can find the optimal values of the kernel parameters during the training procedure. This algorithm uses an expectation-maximization approach for updating kernel parameters as well as other model parameters; therefore, the speed of convergence and computational complexity of the proposed method are the same as the standard RVM. To control the convergence of this fully parameterized model, the optimization with respect to the kernel parameters is performed using a constraint on these parameters. The proposed method is compared with the typical RVM and other competing methods to analyze the performance. The experimental results on the commonly used synthetic data, as well as benchmark data sets, demonstrate the effectiveness of the proposed method in reducing the performance dependency on the initial choice of the kernel parameters.
Training Lp norm multiple kernel learning in the primal.
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.
Spectrum-based kernel length estimation for Gaussian process classification.
Wang, Liang; Li, Chuan
2014-06-01
Recent studies have shown that Gaussian process (GP) classification, a discriminative supervised learning approach, has achieved competitive performance in real applications compared with most state-of-the-art supervised learning methods. However, the problem of automatic model selection in GP classification, involving the kernel function form and the corresponding parameter values (which are unknown in advance), remains a challenge. To make GP classification a more practical tool, this paper presents a novel spectrum analysis-based approach for model selection by refining the GP kernel function to match the given input data. Specifically, we target the problem of GP kernel length scale estimation. Spectrums are first calculated analytically from the kernel function itself using the autocorrelation theorem as well as being estimated numerically from the training data themselves. Then, the kernel length scale is automatically estimated by equating the two spectrum values, i.e., the kernel function spectrum equals to the estimated training data spectrum. Compared with the classical Bayesian method for kernel length scale estimation via maximizing the marginal likelihood (which is time consuming and could suffer from multiple local optima), extensive experimental results on various data sets show that our proposed method is both efficient and accurate.
Per-Sample Multiple Kernel Approach for Visual Concept Learning
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.
Widely Linear Complex-Valued Kernel Methods for Regression
Boloix-Tortosa, Rafael; Murillo-Fuentes, Juan Jose; Santos, Irene; Perez-Cruz, Fernando
2017-10-01
Usually, complex-valued RKHS are presented as an straightforward application of the real-valued case. In this paper we prove that this procedure yields a limited solution for regression. We show that another kernel, here denoted as pseudo kernel, is needed to learn any function in complex-valued fields. Accordingly, we derive a novel RKHS to include it, the widely RKHS (WRKHS). When the pseudo-kernel cancels, WRKHS reduces to complex-valued RKHS of previous approaches. We address the kernel and pseudo-kernel design, paying attention to the kernel and the pseudo-kernel being complex-valued. In the experiments included we report remarkable improvements in simple scenarios where real a imaginary parts have different similitude relations for given inputs or cases where real and imaginary parts are correlated. In the context of these novel results we revisit the problem of non-linear channel equalization, to show that the WRKHS helps to design more efficient solutions.
Localized Multiple Kernel Learning Via Sample-Wise Alternating Optimization.
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.
Per-Sample Multiple Kernel Approach for Visual Concept Learning
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.
L. Alberts
1988-03-01
Full Text Available The concept is developed that modern technology, because of its relationship with pure science, can never really become mature, but will always grow as the pool of scientific knowledge grows. Parameters indicating to some extent the degree of technological prowess in a society are compared for a spectrum of countries. It is clear that in spite of some internationally outstanding successes. South Africa must be regarded on average as a developing society.
Inverse Integral Kernel for Diffusion in a Harmonic Potential
Kosugi, Taichi
2014-05-01
The inverse integral kernel for diffusion in a harmonic potential of an overdamped Brownian particle is derived in the present study. It is numerically demonstrated that a sufficiently large number of polynomials for the calculation of the inverse integral kernel are needed for the accurate reproduction of a probability distribution function at past. The inverse integral kernel derived can be used around each of the minima of a generic potential, provided that the lifetimes of the population in the neighboring higher wells are much longer than the negative time lapse.
Non-Rigid Object Tracking by Anisotropic Kernel Mean Shift
无
2007-01-01
Mean shift, an iterative procedure that shifts each data point to the average of data points in its neighborhood, has been applied to object tracker. However, the traditional mean shift tracker by isotropic kernel often loses the object with the changing object structure in video sequences, especially when the object structure varies fast. This paper proposes a non-rigid object tracker by anisotropic kernel mean shift in which the shape, scale, and orientation of the kernels adapt to the changing object structure. The experimental results show that the new tracker is self-adaptive and approximately twice faster than the traditional tracker, which ensures the robustness and real time of tracking.
Bergman kernel function on Hua construction of the fourth type
无
2007-01-01
This paper introduces the Hua construction and presents the holomorphic automorphism group of the Hua construction of the fourth type. Utilizing the Bergman kernel function, under the condition of holomorphic automorphism and the standard complete orthonormal system of the semi-Reinhardt domain, the infinite series form of the Bergman kernel function is derived. By applying the properties of polynomial and Γ functions, various identification relations of the aforementioned form are developed and the explicit formula of the Bergman kernel function for the Hua construction of the fourth type is obtained, which suggest that many of the previously-reported results are only the special cases of our findings.
The Bergman kernel function of some Reinhardt domains (Ⅱ)
龚昇; 郑学安
2000-01-01
The boundary behavior of the Bergman kernel function of a kind of Reinhardt domain is studied. Upper and lower bounds for the Bergman kernel function are found at the diagonal points ( Z, Z) Let Q be the Reinhardt domainwhere is the Standard Euclidean norm in and let K( Z, W) be the Bergman kernel function of Ω. Then there exist two positive constants m and M, and a function F such thatholds for every Z∈Ω . Hereand is the defining function of Ω The constants m and M depend only on Ω = This result extends some previous known results.
The Bergman Kernels on Generalized Exceptional Hua Domains
殷慰萍; 赵振刚
2001-01-01
@@Yin Weiping introduce four types of Hua domain which are built on four types of Cartan domain and the Bergman kernels on these four types of Hua domain can be computed in explicit formulas[1]. In this paper, two types of domains defined by (10), (11) (see below) are introduced which are built on two exceptional Cartan domains. And We compute Bergman Kernels explicitly for these two domains. We also study the asymptotic behavior of the Bergman kernel function near boundary points, drawing on Appell's multivariable hypergeometric function.
Flour quality and kernel hardness connection in winter wheat
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.
Linear and kernel methods for multi- and hypervariate change detection
Nielsen, Allan Aasbjerg; Canty, Morton J.
2010-01-01
code exists which allows for fast data exploration and experimentation with smaller datasets. Computationally demanding kernelization of test data with training data and kernel image projections have been programmed to run on massively parallel CUDA-enabled graphics processors, when available, giving...... the primal eigenvectors. IDL (Interactive Data Language) implementations of IR-MAD, automatic radiometric normalization and kernel PCA/MAF/MNF transformations have been written which function as transparent and fully integrated extensions of the ENVI remote sensing image analysis environment. Also, Matlab...
FUZZY PRINCIPAL COMPONENT ANALYSIS AND ITS KERNEL BASED MODEL
无
2007-01-01
Principal Component Analysis (PCA) is one of the most important feature extraction methods, and Kernel Principal Component Analysis (KPCA) is a nonlinear extension of PCA based on kernel methods. In real world, each input data may not be fully assigned to one class and it may partially belong to other classes. Based on the theory of fuzzy sets, this paper presents Fuzzy Principal Component Analysis (FPCA) and its nonlinear extension model, i.e., Kernel-based Fuzzy Principal Component Analysis (KFPCA). The experimental results indicate that the proposed algorithms have good performances.
Mercer Kernel Based Fuzzy Clustering Self-Adaptive Algorithm
李侃; 刘玉树
2004-01-01
A novel mercer kernel based fuzzy clustering self-adaptive algorithm is presented. The mercer kernel method is introduced to the fuzzy c-means clustering. It may map implicitly the input data into the high-dimensional feature space through the nonlinear transformation. Among other fuzzy c-means and its variants, the number of clusters is first determined. A self-adaptive algorithm is proposed. The number of clusters, which is not given in advance, can be gotten automatically by a validity measure function. Finally, experiments are given to show better performance with the method of kernel based fuzzy c-means self-adaptive algorithm.
Capturing Option Anomalies with a Variance-Dependent Pricing Kernel
Christoffersen, Peter; Heston, Steven; Jacobs, Kris
2013-01-01
We develop a GARCH option model with a new pricing kernel allowing for a variance premium. While the pricing kernel is monotonic in the stock return and in variance, its projection onto the stock return is nonmonotonic. A negative variance premium makes it U shaped. We present new semiparametric...... evidence to confirm this U-shaped relationship between the risk-neutral and physical probability densities. The new pricing kernel substantially improves our ability to reconcile the time-series properties of stock returns with the cross-section of option prices. It provides a unified explanation...
Role of NifS in maturation of glutamine phosphoribosylpyrophosphate amidotransferase.
Chen, S; Zheng, L; Dean, D R; Zalkin, H
1997-12-01
Glutamine phosphoribosylpyrophosphate amidotransferase from Bacillus subtilis is synthesized as an inactive precursor that requires two maturation steps: incorporation of a [4Fe-4S] center and cleavage of an 11-residue NH2-terminal propeptide. Overproduction from a multicopy plasmid in Escherichia coli leads to the formation of soluble proenzyme and mature enzyme forms as well as a small fraction of insoluble proenzyme. Heterologous expression of Azotobacter vinelandii nifS from a compatible plasmid increased the maturation of the soluble proenzyme three- to fourfold without influencing the content of the insoluble fraction. These results support a role for NifS in heterologous Fe-S cluster assembly and enzyme maturation.
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.
Liu, Yingyi
2017-09-08
Prior studies on fraction magnitude understanding focused mainly on students with relatively sufficient formal instruction on fractions whose fraction magnitude understanding is relatively mature. This study fills a research gap by investigating fraction magnitude understanding in the early stages of fraction instruction. It extends previous findings to children with limited and primary formal fraction instruction. Thirty-five fourth graders with limited fraction instruction and forty fourth graders with primary fraction instruction were recruited from a Chinese primary school. Children's fraction magnitude understanding was assessed with a fraction number line estimation task. Approximate number system (ANS) acuity was assessed with a dot discrimination task. Whole number knowledge was assessed with a whole number line estimation task. General reading and mathematics achievements were collected concurrently and 1 year later. In children with limited fraction instruction, fraction representation was linear and fraction magnitude understanding was concurrently related to both ANS and whole number knowledge. In children with primary fraction instruction, fraction magnitude understanding appeared to (marginally) significantly predict general mathematics achievement 1 year later. Fraction magnitude understanding emerged early during formal instruction of fractions. ANS and whole number knowledge were related to fraction magnitude understanding when children first began to learn about fractions in school. The predictive value of fraction magnitude understanding is likely constrained by its sophistication level. © 2017 The British Psychological Society.
Herrmann, Richard
2014-01-01
A family of generalized Erdelyi-Kober type fractional integrals is interpreted geometrically as a distortion of the rotationally invariant integral kernel of the Riesz fractional integral in terms of generalized Cassini ovaloids on $R^N$. Based on this geometric view, several extensions are discussed.
Rajan S; Thirunalasundari T; Jeeva S
2011-01-01
Objective:To evaluate the phytochemical and anti-bacterial efficacy of the seed kernel extract ofMangifera indica (M. indica) against the enteropathogen,Shigella dysenteriae (S. dysenteriae), isolated from the diarrhoeal stool specimens.Methods: The preliminary phytochemical screening was performed by the standard methods as described by Harborne. Cold extraction method was employed to extract the bioactive compounds from mango seed kernel. Disc diffusion method was adopted to screen antibacterial activity. Minimum inhibitory concentration (MIC) was evaluated by agar dilution method. The crude extracts were partially purified by thin layer chromatography(TLC) and the fractions were analyzed by high performance thin layer chromatography(HPTLC)to identify the bioactive compounds.Results:Phytochemical scrutiny ofM. indica indicated the presence of phytochemical constituents such as alkaloids, gums, flavanoids, phenols, saponins, steroids, tannins and xanthoproteins. Antibacterial activity was observed in two crude extracts and various fractions viz. hexane, benzene, chloroform, methanol and water.MIC of methanol fraction was found to be (95±11.8) μg/mL. MIC of other fractions ranged from130-380 μg/mL.Conclusions: The present study confirmed that each crude extracts and fractions ofM. indica have significant antimicrobial activity against the isolated pathogenS. dysenteriae. The antibacterial activity may be due to the phytochemical constituents of the mango seed kernel. The phytochemical tannin could be the reason for its antibacterial activity.
Sugiarto, Bunga; Rizal, Arra'di Nur; Galinium, Maulahikmah; Atmadiputra, Pradana; Rubianto, Melvin; Fahmi, Husni; Sampurno, Tri; Kisworo, Marsudi
2012-01-01
In this paper, we present an implementation of CLNP ground-to-ground packet processing for ATN in Linux kernel version 2.6. We present the big picture of CLNP packet processing, the details of input, routing, and output processing functions, and the implementation of each function based on ISO 8473-1. The functions implemented in this work are PDU header decomposition, header format analysis, header error detection, error reporting, reassembly, source routing, congestion notification, forwarding, composition, segmentation, and transmit to device functions. Each function is initially implemented and tested as a separated loadable kernel module. These modules are successfully loaded into Linux kernel 2.6.
Riauka, Terence A.; Hooper, H. Richard; Gortel, Zbigniew W.
1996-07-01
Experimental tests for non-uniform attenuating media are performed to validate theoretical expressions for the photon detection kernel, obtained from a recently proposed analytical theory of photon propagation and detection for SPECT. The theoretical multi-dimensional integral expressions for the photon detection kernel, which are computed numerically, describe the probability that a photon emitted from a given source voxel will trigger detection of a photon at a particular projection pixel. The experiments were performed using a cylindrical water-filled phantom with large cylindrical air-filled inserts to simulate inhomogeneity of the medium. A point-like, a short thin cylindrical and a large cylindrical radiation source of were placed at various positions within the phantom. The values numerically calculated from the theoretical kernel expressions are in very good agreement with the experimentally measured data. The significance of Compton-scattered photons in planar image formation is discussed and highlighted by these results. Using both experimental measurements and the calculated values obtained from the theory, the kernel's size is investigated. This is done by determining the square pixel neighbourhood of the gamma camera that must be connected to a particular radiation source voxel to account for a specific fraction of all counts recorded at all camera pixels. It is shown that the kernel's size is primarily dependent upon the source position and the properties of the attenuating medium through Compton scattering events, with 3D depth-dependent collimator resolution playing an important but secondary role, at least for imaging situations involving parallel hole collimation. By considering small point-like sources within a non-uniform elliptical phantom, approximating the human thorax, it is demonstrated that on average a area of the camera plane is required to collect 85% of the total count recorded. This is a significantly larger connectivity than the area
Bioconversion of palm kernel meal for aquaculture: Experiences ...
SERVER
2008-04-17
Apr 17, 2008 ... countries where so much agro-industry by-products exist such as palm kernel meal, .... as basic ingredients for margarine production, confectionery, animal ..... Sciences, Universiti Sains Malaysia, Penang 11800, Malaysia.
OSCILLATORY SINGULAR INTEGRALS WITH VARIABLE ROUGH KERNEL, Ⅱ
Tang Lin; Yang Dachun
2003-01-01
Let n≥2. In this paper, the author establishes the L2(Rn)-boundedness of some oscillatory singular inte-grals with variable rough kernels by means of some estimates on hypergeometric functions and confluent hy-pergeometric funtions.
A Security Kernel Architecture Based Trusted Computing Platform
CHEN You-lei; SHEN Chang-xiang
2005-01-01
A security kernel architecture built on trusted computing platform in the light of thinking about trusted computing is presented. According to this architecture, a new security module TCB (Trusted Computing Base) is added to the operation system kernel and two operation interface modes are provided for the sake of self-protection. The security kernel is divided into two parts and trusted mechanism is separated from security functionality. The TCB module implements the trusted mechanism such as measurement and attestation,while the other components of security kernel provide security functionality based on these mechanisms. This architecture takes full advantage of functions provided by trusted platform and clearly defines the security perimeter of TCB so as to assure self-security from architectural vision. We also present function description of TCB and discuss the strengths and limitations comparing with other related researches.
A Thermodynamic Model for Argon Plasma Kernel Formation
James Keck
2010-11-01
Full Text Available Plasma kernel formation of argon is studied experimentally and theoretically. The experiments have been performed in a constant volume cylindrical vessel located in a shadowgraph system. The experiments have been done in constant pressure. The energy of plasma is supplied by an ignition system through two electrodes located in the vessel. The experiments have been done with two different spark energies to study the effect of input energy on kernel growth and its properties. A thermodynamic model employing mass and energy balance was developed to predict the experimental data. The agreement between experiments and model prediction is very good. The effect of various parameters such as initial temperature, initial radius of the kernel, and the radiation energy loss have been investigated and it has been concluded that initial condition is very important on formation and expansion of the kernel.
Kernel based eigenvalue-decomposition methods for analysing ham
Christiansen, Asger Nyman; Nielsen, Allan Aasbjerg; Møller, Flemming
2010-01-01
conditions and finding useful additives to hinder the color to change rapidly. To be able to prove which methods of storing and additives work, Danisco wants to monitor the development of the color of meat in a slice of ham as a function of time, environment and ingredients. We have chosen to use multi...... 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...
Palmprint Recognition by Applying Wavelet-Based Kernel PCA
Murat Ekinci; Murat Aykut
2008-01-01
This paper presents a wavelet-based kernel Principal Component Analysis (PCA) method by integrating the Daubechies wavelet representation of palm images and the kernel PCA method for palmprint recognition. Kernel PCA is a technique for nonlinear dimension reduction of data with an underlying nonlinear spatial structure. The intensity values of the palmprint image are first normalized by using mean and standard deviation. The palmprint is then transformed into the wavelet domain to decompose palm images and the lowest resolution subband coefficients are chosen for palm representation.The kernel PCA method is then applied to extract non-linear features from the subband coefficients. Finally, similarity measurement is accomplished by using weighted Euclidean linear distance-based nearest neighbor classifier. Experimental results on PolyU Palmprint Databases demonstrate that the proposed approach achieves highly competitive performance with respect to the published palmprint recognition approaches.
Screening of the kernels of Pentadesma butyracea from various ...
Gwla10
The plant producing type 1 kernels (with medium length, low width and high thickness) and ... Recent works on the biological activities of P. butyracea showed that the ... collection sites belong to various agroecological zones of Benin. Thus ...
438 Adaptive Kernel in Meshsize Boosting Algorithm in KDE (Pp ...
FIRST LADY
2011-01-18
Jan 18, 2011 ... classifier is boosted by suitably re-weighting the data. This weight ... Methods. Algorithm on Boosting Kernel Density Estimates and Bias Reduction ... Gaussian (since all distributions tend to be normal as n, the sample size,.
Removal of Lead from Aqueous Solution by Palm Kernel Fibre
NJD
E. Augustine Ofomaja,a* I. Emmanuel Unuabonaha and N. Abiola Oladojab ... The sorption of lead on palm kernel fibre, an agricultural waste product, has been studied. ... present a cheap and cost-effective alternative for lead removal.
Estimates of Oseen kernels in weighted $L^{p}$ spaces
KRAČMAR, Stanislav; Novotný, Antonín; Pokorný, Milan
2001-01-01
We study convolutions with Oseen kernels (weakly singular and singular) in both two- and three-dimensional space. We give a detailed weighted $L^{p}$ theory for $ p\\in(1;\\infty$ ] for anisotropic weights.
INTEGRAL COLLISION KERNEL FOR THE GROWTH OF AEROSOL PARTICLES
Hongyong Xie
2005-01-01
Integral collision kernel is elucidated using experimental results for titania, silica and alumina nanoparticles synthesized by FCVD process, and titania submicron particles synthesized in a tube furnace reactor. The integral collision kernel was obtained from a particle number balance equation by the integration of collision rates from the kinetic theory of dilute gases for the free-molecule regime, from the Smoluchowski theory for the continuum regime, and by a semi-empirical interpolation for the transition regime between the two limiting regimes. Comparisons have been made on particle size and the integral collision kernel, showing that the predicted integral collision kernel agreed well with the experimental results in Knudsen number range from about 1.5 to 20.
Analysis of Kernel Approach in Fuzzy-Based Image Classifications
Mragank Singhal
2013-03-01
Full Text Available This paper presents a framework of kernel approach in the field of fuzzy based image classification in remote sensing. The goal of image classification is to separate images according to their visual content into two or more disjoint classes. Fuzzy logic is relatively young theory. Major advantage of this theory is that it allows the natural description, in linguistic terms, of problems that should be solved rather than in terms of relationships between precise numerical values. This paper describes how remote sensing data with uncertainty are handled with fuzzy based classification using Kernel approach for land use/land cover maps generation. The introduction to fuzzification using Kernel approach provides the basis for the development of more robust approaches to the remote sensing classification problem. The kernel explicitly defines a similarity measure between two samples and implicitly represents the mapping of the input space to the feature space.
Bergstra, Jan A.
2015-01-01
In the context of an involutive meadow a precise definition of fractions is formulated and on that basis formal definitions of various classes of fractions are given. The definitions follow the fractions as terms paradigm. That paradigm is compared with two competing paradigms for storytelling on fractions: fractions as values and fractions as pairs.
Digital Communications Channel Equalisation Using the Kernel Adaline.
Mitchinson, B.; Harrison, R F
2000-01-01
For transmission of digital signals over a linear channel with additive white gaussian noise, it has been shown that the optimal symbol decision equaliser is non-linear. The Kernel Adaline algorithm, a non-linear generalisation of Widrow's and Hoff's Adaline, has been shown to be capable of learning arbitrary non-linear decision boundaries, whilst retaining the desirable convergence properties of the linear Adaline. This work investigates the use of the Kernel Adaline as equaliser for such ch...
Music Emotion Detection Using Hierarchical Sparse Kernel Machines
Yu-Hao Chin; Chang-Hong Lin; Ernestasia Siahaan; Jia-Ching Wang
2014-01-01
For music emotion detection, this paper presents a music emotion verification system based on hierarchical sparse kernel machines. With the proposed system, we intend to verify if a music clip possesses happiness emotion or not. There are two levels in the hierarchical sparse kernel machines. In the first level, a set of acoustical features are extracted, and principle component analysis (PCA) is implemented to reduce the dimension. The acoustical features are utilized to generate the first-l...
Reconstructing Concept Lattices Usingnth-Order Context Kernels
SHEN Xiajiong; XU Bin; LIU Zongtian
2006-01-01
To be different from traditional algorithms for concept lattice constructing, a method based on nth-order context kernel is suggested in this paper.The context kernels support generating small lattices for sub-contexts split by a given context.The final concept lattice is reconstructed by combining these small lattices.All relevant algorithms are implemented in a system IsoFCA.Test shows that the method yields concept lattices in lower time complexity than Godin algorithm in practical case.
Nonlinear stochastic system identification of skin using volterra kernels.
Chen, Yi; Hunter, Ian W
2013-04-01
Volterra kernel stochastic system identification is a technique that can be used to capture and model nonlinear dynamics in biological systems, including the nonlinear properties of skin during indentation. A high bandwidth and high stroke Lorentz force linear actuator system was developed and used to test the mechanical properties of bulk skin and underlying tissue in vivo using a non-white input force and measuring an output position. These short tests (5 s) were conducted in an indentation configuration normal to the skin surface and in an extension configuration tangent to the skin surface. Volterra kernel solution methods were used including a fast least squares procedure and an orthogonalization solution method. The practical modifications, such as frequency domain filtering, necessary for working with low-pass filtered inputs are also described. A simple linear stochastic system identification technique had a variance accounted for (VAF) of less than 75%. Representations using the first and second Volterra kernels had a much higher VAF (90-97%) as well as a lower Akaike information criteria (AICc) indicating that the Volterra kernel models were more efficient. The experimental second Volterra kernel matches well with results from a dynamic-parameter nonlinearity model with fixed mass as a function of depth as well as stiffness and damping that increase with depth into the skin. A study with 16 subjects showed that the kernel peak values have mean coefficients of variation (CV) that ranged from 3 to 8% and showed that the kernel principal components were correlated with location on the body, subject mass, body mass index (BMI), and gender. These fast and robust methods for Volterra kernel stochastic system identification can be applied to the characterization of biological tissues, diagnosis of skin diseases, and determination of consumer product efficacy.
Bergman kernel and metric on non-smooth pseudoconvex domains
陈伯勇; 张锦豪
1999-01-01
A stability theorem of the Bergman kernel and completeness of the Bergman metric have been proved on a type of non-smooth pseudoconvex domains defined in the following way: D={z∈U|r(z)<0} where U is a neighbourhood of (?) and r is a continuous plurisubharmonic function on U. A continuity principle of the Bergman Kernel for pseudoconvex domains with Lipschitz boundary is also given, which answers a problem of Boas.
A compact kernel for the calculus of inductive constructions
A Asperti; W Ricciotti; C Sacerdoti Coen; E Tassi
2009-02-01
The paper describes the new kernel for the Calculus of Inductive Constructions (CIC) implemented inside the Matita Interactive Theorem Prover. The design of the new kernel has been completely revisited since the ﬁrst release, resulting in a remarkably compact implementation of about 2300 lines of OCaml code. The work is meant for people interested in implementation aspects of Interactive Provers, and is not self contained. In particular, it requires good acquaintance with Type Theory and functional programming languages.
Kernel approximation for solving few-body integral equations
Christie, I.; Eyre, D.
1986-06-01
This paper investigates an approximate method for solving integral equations that arise in few-body problems. The method is to replace the kernel by a degenerate kernel defined on a finite dimensional subspace of piecewise Lagrange polynomials. Numerical accuracy of the method is tested by solving the two-body Lippmann-Schwinger equation with non-separable potentials, and the three-body Amado-Lovelace equation with separable two-body potentials.
GPU Acceleration of Image Convolution using Spatially-varying Kernel
Hartung, Steven; Shukla, Hemant; Miller, J. Patrick; Pennypacker, Carlton
2012-01-01
Image subtraction in astronomy is a tool for transient object discovery such as asteroids, extra-solar planets and supernovae. To match point spread functions (PSFs) between images of the same field taken at different times a convolution technique is used. Particularly suitable for large-scale images is a computationally intensive spatially-varying kernel. The underlying algorithm is inherently massively parallel due to unique kernel generation at every pixel location. The spatially-varying k...
X-Y separable pyramid steerable scalable kernels
Shy, Douglas; Perona, Pietro
1994-01-01
A new method for generating X-Y separable, steerable, scalable approximations of filter kernels is proposed which is based on a generalization of the singular value decomposition (SVD) to three dimensions. This “pseudo-SVD” improves upon a previous scheme due to Perona (1992) in that it reduces convolution time and storage requirements. An adaptation of the pseudo-SVD is proposed to generate steerable and scalable kernels which are suitable for use with a Laplacian pyramid. The properties of ...
ON APPROXIMATION BY REPRODUCING KERNEL SPACES IN WEIGHTED Lp SPACES
Baohuai SHENG
2007-01-01
In this paper, we investigate the order of approximation by reproducing kernel spaces on (-1, 1) in weighted Lp spaces. We first restate the translation network from the view of reproducing kernel spaces and then construct a sequence of approximating operators with the help of Jacobi orthogonal polynomials, with which we establish a kind of Jackson inequality to describe the error estimate.Finally, The results are used to discuss an approximation problem arising from learning theory.
Lasrado, Lester Allan; Vatrapu, Ravi; Mukkamala, Raghava Rao
2017-01-01
of understanding of the potential impact of (a) choice of the quantitative approach, and (b) scale of measurement on the design and assessment of the maturity model. To address these two methodological issues, we analysed a social media maturity data set and computed maturity scores using different quantitative......This paper presents results from an ongoing empirical study that seeks to understand the influence of different quantitative methods on the design and assessment of maturity models. Although there have been many academic publications on maturity models, there exists a significant lack...
The Dynamic Kernel Scheduler-Part 1
Adelmann, Andreas; Locans, Uldis; Suter, Andreas
2016-10-01
Emerging processor architectures such as GPUs and Intel MICs provide a huge performance potential for high performance computing. However developing software that uses these hardware accelerators introduces additional challenges for the developer. These challenges may include exposing increased parallelism, handling different hardware designs, and using multiple development frameworks in order to utilise devices from different vendors. The Dynamic Kernel Scheduler (DKS) is being developed in order to provide a software layer between the host application and different hardware accelerators. DKS handles the communication between the host and the device, schedules task execution, and provides a library of built-in algorithms. Algorithms available in the DKS library will be written in CUDA, OpenCL, and OpenMP. Depending on the available hardware, the DKS can select the appropriate implementation of the algorithm. The first DKS version was created using CUDA for the Nvidia GPUs and OpenMP for Intel MIC. DKS was further integrated into OPAL (Object-oriented Parallel Accelerator Library) in order to speed up a parallel FFT based Poisson solver and Monte Carlo simulations for particle-matter interaction used for proton therapy degrader modelling. DKS was also used together with Minuit2 for parameter fitting, where χ2 and max-log-likelihood functions were offloaded to the hardware accelerator. The concepts of the DKS, first results, and plans for the future will be shown in this paper.
Characterization of Flour from Avocado Seed Kernel
Macey A. Mahawan
2015-11-01
Full Text Available The study focused on the Characterization of Flour from Avocado Seed Kernel. Based on the findings of the study the percentages of crude protein, crude fiber, crude fat, total carbohydrates, ash and moisture were 7.75, 4.91, 0.71, 74.65, 2.83 and 14.05 respectively. On the other hand the falling number was 495 seconds while gluten was below the detection limit of the method used. Moreover, the sensory evaluation in terms of color, texture and aroma in 0% proportion of Avocado seed flour was moderate like and slight like for 25% and 50% proportions of Avocado seed flour. On the otherhand, the taste of the biscuits prepared with 0% Avocado seed flour was moderate like, in 25% proportion of Avocado seed flour were slight like and in 50% proportion was neither liked nor disliked. The overall acceptability results for 0% proportion of Avocado seed flour was moderate like and slight like for 25% and 50% proportions of Avocado seed flour. Furthermore, the computed p values for the comparison of the level of acceptability in terms of color, texture, aroma, taste and overall acceptability of biscuits using 0%, 25%, and 50% avocado seed flour were lower than 0.05. Thus the null hypothesis is rejected.
The spectrum of kernel random matrices
Karoui, Noureddine El
2010-01-01
We place ourselves in the setting of high-dimensional statistical inference where the number of variables $p$ in a dataset of interest is of the same order of magnitude as the number of observations $n$. We consider the spectrum of certain kernel random matrices, in particular $n\\times n$ matrices whose $(i,j)$th entry is $f(X_i'X_j/p)$ or $f(\\Vert X_i-X_j\\Vert^2/p)$ where $p$ is the dimension of the data, and $X_i$ are independent data vectors. Here $f$ is assumed to be a locally smooth function. The study is motivated by questions arising in statistics and computer science where these matrices are used to perform, among other things, nonlinear versions of principal component analysis. Surprisingly, we show that in high-dimensions, and for the models we analyze, the problem becomes essentially linear--which is at odds with heuristics sometimes used to justify the usage of these methods. The analysis also highlights certain peculiarities of models widely studied in random matrix theory and raises some questio...
Local Kernel for Brains Classification in Schizophrenia
Castellani, U.; Rossato, E.; Murino, V.; Bellani, M.; Rambaldelli, G.; Tansella, M.; Brambilla, P.
In this paper a novel framework for brain classification is proposed in the context of mental health research. A learning by example method is introduced by combining local measurements with non linear Support Vector Machine. Instead of considering a voxel-by-voxel comparison between patients and controls, we focus on landmark points which are characterized by local region descriptors, namely Scale Invariance Feature Transform (SIFT). Then, matching is obtained by introducing the local kernel for which the samples are represented by unordered set of features. Moreover, a new weighting approach is proposed to take into account the discriminative relevance of the detected groups of features. Experiments have been performed including a set of 54 patients with schizophrenia and 54 normal controls on which region of interest (ROI) have been manually traced by experts. Preliminary results on Dorso-lateral PreFrontal Cortex (DLPFC) region are promising since up to 75% of successful classification rate has been obtained with this technique and the performance has improved up to 85% when the subjects have been stratified by sex.
An Ensemble Approach to Building Mercer Kernels with Prior Information
Srivastava, Ashok N.; Schumann, Johann; Fischer, Bernd
2005-01-01
This paper presents a new methodology for automatic knowledge driven data mining based on the theory of Mercer Kernels, which are highly nonlinear symmetric positive definite mappings from the original image space to a very high, possibly dimensional feature space. we describe a new method called Mixture Density Mercer Kernels to learn kernel function directly from data, rather than using pre-defined kernels. These data adaptive kernels can encode prior knowledge in the kernel using a Bayesian formulation, thus allowing for physical information to be encoded in the model. Specifically, we demonstrate the use of the algorithm in situations with extremely small samples of data. We compare the results with existing algorithms on data from the Sloan Digital Sky Survey (SDSS) and demonstrate the method's superior performance against standard methods. The code for these experiments has been generated with the AUTOBAYES tool, which automatically generates efficient and documented C/C++ code from abstract statistical model specifications. The core of the system is a schema library which contains templates for learning and knowledge discovery algorithms like different versions of EM, or numeric optimization methods like conjugate gradient methods. The template instantiation is supported by symbolic-algebraic computations, which allows AUTOBAYES to find closed-form solutions and, where possible, to integrate them into the code.
Kernel-Based Nonlinear Discriminant Analysis for Face Recognition
LIU QingShan (刘青山); HUANG Rui (黄锐); LU HanQing (卢汉清); MA SongDe (马颂德)
2003-01-01
Linear subspace analysis methods have been successfully applied to extract features for face recognition. But they are inadequate to represent the complex and nonlinear variations of real face images, such as illumination, facial expression and pose variations, because of their linear properties. In this paper, a nonlinear subspace analysis method, Kernel-based Nonlinear Discriminant Analysis (KNDA), is presented for face recognition, which combines the nonlinear kernel trick with the linear subspace analysis method - Fisher Linear Discriminant Analysis (FLDA).First, the kernel trick is used to project the input data into an implicit feature space, then FLDA is performed in this feature space. Thus nonlinear discriminant features of the input data are yielded. In addition, in order to reduce the computational complexity, a geometry-based feature vectors selection scheme is adopted. Another similar nonlinear subspace analysis is Kernel-based Principal Component Analysis (KPCA), which combines the kernel trick with linear Principal Component Analysis (PCA). Experiments are performed with the polynomial kernel, and KNDA is compared with KPCA and FLDA. Extensive experimental results show that KNDA can give a higher recognition rate than KPCA and FLDA.
Nonlinear Forecasting With Many Predictors Using Kernel Ridge Regression
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......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 predictive regression model is based on a shrinkage estimator to avoid overfitting. We extend the kernel ridge regression methodology to enable its use for economic time-series forecasting, by including lags of the dependent variable or other individual variables as predictors, as typically desired...... in macroeconomic and financial applications. Monte Carlo simulations as well as an empirical application to various key measures of real economic activity confirm that kernel ridge regression can produce more accurate forecasts than traditional linear and nonlinear methods for dealing with many predictors based...
Searching for efficient Markov chain Monte Carlo proposal kernels.
Yang, Ziheng; Rodríguez, Carlos E
2013-11-26
Markov chain Monte Carlo (MCMC) or the Metropolis-Hastings algorithm is a simulation algorithm that has made modern Bayesian statistical inference possible. Nevertheless, the efficiency of different Metropolis-Hastings proposal kernels has rarely been studied except for the Gaussian proposal. Here we propose a unique class of Bactrian kernels, which avoid proposing values that are very close to the current value, and compare their efficiency with a number of proposals for simulating different target distributions, with efficiency measured by the asymptotic variance of a parameter estimate. The uniform kernel is found to be more efficient than the Gaussian kernel, whereas the Bactrian kernel is even better. When optimal scales are used for both, the Bactrian kernel is at least 50% more efficient than the Gaussian. Implementation in a Bayesian program for molecular clock dating confirms the general applicability of our results to generic MCMC algorithms. Our results refute a previous claim that all proposals had nearly identical performance and will prompt further research into efficient MCMC proposals.
Multiple Crop Classification Using Various Support Vector Machine Kernel Functions
Rupali R. Surase
2015-01-01
Full Text Available This study was carried out with techniques of Remote Sensing (RS based crop discrimination and area estimation with single date approach. Several kernel functions are employed and compared in this study for mapping the input space with including linear, sigmoid, and polynomial and Radial Basis Function (RBF. The present study highlights the advantages of Remote Sensing (RS and Geographic Information System (GIS techniques for analyzing the land use/land cover mapping for Aurangabad region of Maharashtra, India. Single date, cloud free IRS-Resourcesat-1 LISS-III data was used for further classification on training set for supervised classification. ENVI 4.4 is used for image analysis and interpretation. The experimental tests show that system is achieved 94.82% using SVM with kernel functions including Polynomial kernel function compared with Radial Basis Function, Sigmoid and linear kernel. The Overall Accuracy (OA to up to 5.17% in comparison to using sigmoid kernel function, and up to 3.45% in comparison to a 3rd degree polynomial kernel function and RBF with 200 as a penalty parameter.
Sparse kernel learning with LASSO and Bayesian inference algorithm.
Gao, Junbin; Kwan, Paul W; Shi, Daming
2010-03-01
Kernelized LASSO (Least Absolute Selection and Shrinkage Operator) has been investigated in two separate recent papers [Gao, J., Antolovich, M., & Kwan, P. H. (2008). L1 LASSO and its Bayesian inference. In W. Wobcke, & M. Zhang (Eds.), Lecture notes in computer science: Vol. 5360 (pp. 318-324); Wang, G., Yeung, D. Y., & Lochovsky, F. (2007). The kernel path in kernelized LASSO. In International conference on artificial intelligence and statistics (pp. 580-587). San Juan, Puerto Rico: MIT Press]. This paper is concerned with learning kernels under the LASSO formulation via adopting a generative Bayesian learning and inference approach. A new robust learning algorithm is proposed which produces a sparse kernel model with the capability of learning regularized parameters and kernel hyperparameters. A comparison with state-of-the-art methods for constructing sparse regression models such as the relevance vector machine (RVM) and the local regularization assisted orthogonal least squares regression (LROLS) is given. The new algorithm is also demonstrated to possess considerable computational advantages. Copyright 2009 Elsevier Ltd. All rights reserved.
Kernels by Monochromatic Paths and Color-Perfect Digraphs
Galeana-Śanchez Hortensia
2016-05-01
Full Text Available For a digraph D, V (D and A(D will denote the sets of vertices and arcs of D respectively. In an arc-colored digraph, a subset K of V(D is said to be kernel by monochromatic paths (mp-kernel if (1 for any two different vertices x, y in N there is no monochromatic directed path between them (N is mp-independent and (2 for each vertex u in V (D \\ N there exists v ∈ N such that there is a monochromatic directed path from u to v in D (N is mp-absorbent. If every arc in D has a different color, then a kernel by monochromatic paths is said to be a kernel. Two associated digraphs to an arc-colored digraph are the closure and the color-class digraph CC(D. In this paper we will approach an mp-kernel via the closure of induced subdigraphs of D which have the property of having few colors in their arcs with respect to D. We will introduce the concept of color-perfect digraph and we are going to prove that if D is an arc-colored digraph such that D is a quasi color-perfect digraph and CC(D is not strong, then D has an mp-kernel. Previous interesting results are generalized, as for example Richardson′s Theorem.
Representative sets and irrelevant vertices: New tools for kernelization
Kratsch, Stefan
2011-01-01
Recent work of the present authors provided a polynomial kernel for Odd Cycle Transversal by introducing matroid-based tools into kernelization. In the current work we further establish the usefulness of matroid theory to kernelization by showing applications of a result on representative sets due to Lov\\'asz (Combinatorial Surveys 1977) and Marx (TCS 2009). We give two types of applications: 1. Direct applications of the representative objects idea. In this direction, we give a polynomial kernel for Almost 2-SAT by reducing the problem to a cut problem with pairs of vertices as sinks, and subsequently reducing the set of pairs to a representative subset of bounded size. This implies polynomial kernels for several other problems, including Vertex Cover parameterized by the size of the LP gap, and the RHorn-Backdoor Deletion Set problem from practical SAT solving. We also get a polynomial kernel for Multiway Cut with deletable terminals, by producing a representative set of vertices, of bounded size, which is ...
Kernel CMAC: an Efficient Neural Network for Classification and Regression
Gábor Horváth
2006-01-01
Full Text Available Kernel methods in learning machines have been developed in the last decade asnew techniques for solving classification and regression problems. Kernel methods havemany advantageous properties regarding their learning and generalization capabilities,but for getting the solution usually the computationally complex quadratic programming isrequired. To reduce computational complexity a lot of different versions have beendeveloped. These versions apply different kernel functions, utilize the training data indifferent ways or apply different criterion functions. This paper deals with a special kernelnetwork, which is based on the CMAC neural network. Cerebellar Model ArticulationController (CMAC has some attractive features: fast learning capability and thepossibility of efficient digital hardware implementation. Besides these attractive featuresthe modelling and generalization capabilities of a CMAC may be rather limited. The papershows that kernel CMAC – an extended version of the classical CMAC networkimplemented in a kernel form – improves that properties of the classical versionsignificantly. Both the modelling and the generalization capabilities are improved while thelimited computational complexity is maintained. The paper shows the architecture of thisnetwork and presents the relation between the classical CMAC and the kernel networks.The operation of the proposed architecture is illustrated using some common benchmarkproblems.
Linear fractional diffusion-wave equation for scientists and engineers
Povstenko, Yuriy
2015-01-01
This book systematically presents solutions to the linear time-fractional diffusion-wave equation. It introduces the integral transform technique and discusses the properties of the Mittag-Leffler, Wright, and Mainardi functions that appear in the solutions. The time-nonlocal dependence between the flux and the gradient of the transported quantity with the “long-tail” power kernel results in the time-fractional diffusion-wave equation with the Caputo fractional derivative. Time-nonlocal generalizations of classical Fourier’s, Fick’s and Darcy’s laws are considered and different kinds of boundary conditions for this equation are discussed (Dirichlet, Neumann, Robin, perfect contact). The book provides solutions to the fractional diffusion-wave equation with one, two and three space variables in Cartesian, cylindrical and spherical coordinates. The respective sections of the book can be used for university courses on fractional calculus, heat and mass transfer, transport processes in porous media and ...
Out-of-Sample Extensions for Non-Parametric Kernel Methods.
Pan, Binbin; Chen, Wen-Sheng; Chen, Bo; Xu, Chen; Lai, Jianhuang
2017-02-01
Choosing suitable kernels plays an important role in the performance of kernel methods. Recently, a number of studies were devoted to developing nonparametric kernels. Without assuming any parametric form of the target kernel, nonparametric kernel learning offers a flexible scheme to utilize the information of the data, which may potentially characterize the data similarity better. The kernel methods using nonparametric kernels are referred to as nonparametric kernel methods. However, many nonparametric kernel methods are restricted to transductive learning, where the prediction function is defined only over the data points given beforehand. They have no straightforward extension for the out-of-sample data points, and thus cannot be applied to inductive learning. In this paper, we show how to make the nonparametric kernel methods applicable to inductive learning. The key problem of out-of-sample extension is how to extend the nonparametric kernel matrix to the corresponding kernel function. A regression approach in the hyper reproducing kernel Hilbert space is proposed to solve this problem. Empirical results indicate that the out-of-sample performance is comparable to the in-sample performance in most cases. Experiments on face recognition demonstrate the superiority of our nonparametric kernel method over the state-of-the-art parametric kernel methods.
Variable Order and Distributed Order Fractional Operators
Lorenzo, Carl F.; Hartley, Tom T.
2002-01-01
Many physical processes appear to exhibit fractional order behavior that may vary with time or space. The continuum of order in the fractional calculus allows the order of the fractional operator to be considered as a variable. This paper develops the concept of variable and distributed order fractional operators. Definitions based on the Riemann-Liouville definitions are introduced and behavior of the operators is studied. Several time domain definitions that assign different arguments to the order q in the Riemann-Liouville definition are introduced. For each of these definitions various characteristics are determined. These include: time invariance of the operator, operator initialization, physical realization, linearity, operational transforms. and memory characteristics of the defining kernels. A measure (m2) for memory retentiveness of the order history is introduced. A generalized linear argument for the order q allows the concept of "tailored" variable order fractional operators whose a, memory may be chosen for a particular application. Memory retentiveness (m2) and order dynamic behavior are investigated and applications are shown. The concept of distributed order operators where the order of the time based operator depends on an additional independent (spatial) variable is also forwarded. Several definitions and their Laplace transforms are developed, analysis methods with these operators are demonstrated, and examples shown. Finally operators of multivariable and distributed order are defined in their various applications are outlined.
The influence of fractional diffusion in Fisher-KPP equations
Cabre, Xavier
2012-01-01
We study the Fisher-KPP equation where the Laplacian is replaced by the generator of a Feller semigroup with power decaying kernel, an important example being the fractional Laplacian. In contrast with the case of the stan- dard Laplacian where the stable state invades the unstable one at constant speed, we prove that with fractional diffusion, generated for instance by a stable L\\'evy process, the front position is exponential in time. Our results provide a mathe- matically rigorous justification of numerous heuristics about this model.
Local coding based matching kernel method for image classification.
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.
Modeling non-maturing liabilities
von Feilitzen, Helena
2011-01-01
Non‐maturing liabilities, such as savings accounts, lack both predetermined maturity and reset dates due to the fact that the depositor is free to withdraw funds at any time and that the depository institution is free to change the rate. These attributes complicate the risk management of such products and no standardized solution exists. The problem is important however since non‐maturing liabilities typically make up a considerable part of the funding of a bank. In this report different mode...
Antioxidant phytochemicals in hazelnut kernel (Corylus avellana L.) and hazelnut byproducts.
Shahidi, Fereidoon; Alasalvar, Cesarettin; Liyana-Pathirana, Chandrika M
2007-02-21
Antioxidant efficacies of ethanol extracts of defatted raw hazelnut kernel and hazelnut byproducts (skin, hard shell, green leafy cover, and tree leaf) were evaluated by monitoring total antioxidant activity (TAA) and free-radical scavenging activity tests [hydrogen peroxide, superoxide radical, and 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical], together with antioxidant activity in a beta-carotene-linoleate model system, inhibition of oxidation of human low-density lipoprotein (LDL) cholesterol, and inhibition of strand breaking of supercoiled deoxyribonucleic acid (DNA). In addition, yield, content of phenolics, and phenolic acid profiles (free and esterified fractions) were also examined. Generally, extracts of hazelnut byproducts (skin, hard shell, green leafy cover, and tree leaf) exhibited stronger activities than hazelnut kernel at all concentrations tested. Hazelnut extracts examined showed different antioxidative efficacies, expected to be related to the presence of phenolic compounds. Among samples, extracts of hazelnut skin, in general, showed superior antioxidative efficacy and higher phenolic content as compared to other extracts. Five phenolic acids (gallic acid, caffeic acid, p-coumaric acid, ferulic acid, and sinapic acid) were tentatively identified and quantified (both free and esterified forms). Extracts contained different levels of phenolic acids. These results suggest that hazelnut byproducts could potentially be considered as an excellent and readily available source of natural antioxidants.
Using hybrid GPU/CPU kernel splitting to accelerate spherical convolutions
Sutter, P. M.; Wandelt, B. D.; Elsner, F.
2015-06-01
We present a general method for accelerating by more than an order of magnitude the convolution of pixelated functions on the sphere with a radially-symmetric kernel. Our method splits the kernel into a compact real-space component and a compact spherical harmonic space component. These components can then be convolved in parallel using an inexpensive commodity GPU and a CPU. We provide models for the computational cost of both real-space and Fourier space convolutions and an estimate for the approximation error. Using these models we can determine the optimum split that minimizes the wall clock time for the convolution while satisfying the desired error bounds. We apply this technique to the problem of simulating a cosmic microwave background (CMB) anisotropy sky map at the resolution typical of the high resolution maps produced by the Planck mission. For the main Planck CMB science channels we achieve a speedup of over a factor of ten, assuming an acceptable fractional rms error of order 10-5 in the power spectrum of the output map.
Mean kernels to improve gravimetric geoid determination based on modified Stokes's integration
Hirt, C.
2011-11-01
Gravimetric geoid computation is often based on modified Stokes's integration, where Stokes's integral is evaluated with some stochastic or deterministic kernel modification. Accurate numerical evaluation of Stokes's integral requires the modified kernel to be integrated across the area of each discretised grid cell (mean kernel). Evaluating the modified kernel at the center of the cell (point kernel) is an approximation, which may result in larger numerical integration errors near the computation point, where the modified kernel exhibits a strongly nonlinear behavior. The present study deals with the computation of whole-of-the-cell mean values of modified kernels, exemplified here with the Featherstone-Evans-Olliver (1998) kernel modification [Featherstone, W.E., Evans, J.D., Olliver, J.G., 1998. A Meissl-modified Vaníček and Kleusberg kernel to reduce the truncation error in gravimetric geoid computations. Journal of Geodesy 72(3), 154-160]. We investigate two approaches (analytical and numerical integration), which are capable of providing accurate mean kernels. The analytical integration approach is based on kernel weighting factors which are used for the conversion of point to mean kernels. For the efficient numerical integration, Gauss-Legendre quadrature is applied. The comparison of mean kernels from both approaches shows a satisfactory mutual agreement at the level of 10 -4 and better, which is considered to be sufficient for practical geoid computation requirements. Closed-loop tests based on the EGM2008 geopotential model demonstrate that using mean instead of point kernels reduces numerical integration errors by ˜65%. The use of mean kernels is recommended in remove-compute-restore geoid determination with the Featherstone-Evans-Olliver (1998) kernel or any other kernel modification under the condition that the kernel changes rapidly across the cells in the neighborhood of the computation point.
Maturational and Non-Maturational Factors in Heritage Language Acquisition
Moon, Ji Hye
2012-01-01
This dissertation aims to understand the maturational and non-maturational aspects of early bilingualism and language attrition in heritage speakers who have acquired their L1 incompletely in childhood. The study highlights the influential role of age and input dynamics in early L1 development, where the timing of reduction in L1 input and the…
Capturing Option Anomalies with a Variance-Dependent Pricing Kernel
Christoffersen, Peter; Heston, Steven; Jacobs, Kris
2013-01-01
We develop a GARCH option model with a new pricing kernel allowing for a variance premium. While the pricing kernel is monotonic in the stock return and in variance, its projection onto the stock return is nonmonotonic. A negative variance premium makes it U shaped. We present new semiparametric ...... for the implied volatility puzzle, the overreaction of long-term options to changes in short-term variance, and the fat tails of the risk-neutral return distribution relative to the physical distribution....... evidence to confirm this U-shaped relationship between the risk-neutral and physical probability densities. The new pricing kernel substantially improves our ability to reconcile the time-series properties of stock returns with the cross-section of option prices. It provides a unified explanation......We develop a GARCH option model with a new pricing kernel allowing for a variance premium. While the pricing kernel is monotonic in the stock return and in variance, its projection onto the stock return is nonmonotonic. A negative variance premium makes it U shaped. We present new semiparametric...
CELLULOSE EXTRACTION FROM PALM KERNEL CAKE USING LIQUID PHASE OXIDATION
FARM YAN YAN
2009-03-01
Full Text Available Cellulose is widely used in many aspect and industries such as food industry, pharmaceutical, paint, polymers, and many more. Due to the increasing demand in the market, studies and work to produce cellulose are still rapidly developing. In this work, liquid phase oxidation was used to extract cellulose from palm kernel cake to separate hemicellulose, cellulose and lignin. The method is basically a two-step process. Palm kernel cake was pretreated in hot water at 180°C and followed by liquid oxidation process with 30% H2O2 at 60°C at atmospheric pressure. The process parameters are hot water treatment time, ratio of palm kernel cake to H2O2, liquid oxidation reaction temperature and time. Analysis of the process parameters on production cellulose from palm kernel cake was performed by using Response Surface Methodology. The recovered cellulose was further characterized by Fourier Transform Infrared (FTIR. Through the hot water treatment, hemicellulose in the palm kernel cake was successfully recovered as saccharides and thus leaving lignin and cellulose. Lignin was converted to water soluble compounds in liquid oxidation step which contains small molecular weight fatty acid as HCOOH and CH3COOH and almost pure cellulose was recovered.
CRKSPH - A Conservative Reproducing Kernel Smoothed Particle Hydrodynamics Scheme
Frontiere, Nicholas; Raskin, Cody D.; Owen, J. Michael
2017-03-01
We present a formulation of smoothed particle hydrodynamics (SPH) that utilizes a first-order consistent reproducing kernel, a smoothing function that exactly interpolates linear fields with particle tracers. Previous formulations using reproducing kernel (RK) interpolation have had difficulties maintaining conservation of momentum due to the fact the RK kernels are not, in general, spatially symmetric. Here, we utilize a reformulation of the fluid equations such that mass, linear momentum, and energy are all rigorously conserved without any assumption about kernel symmetries, while additionally maintaining approximate angular momentum conservation. Our approach starts from a rigorously consistent interpolation theory, where we derive the evolution equations to enforce the appropriate conservation properties, at the sacrifice of full consistency in the momentum equation. Additionally, by exploiting the increased accuracy of the RK method's gradient, we formulate a simple limiter for the artificial viscosity that reduces the excess diffusion normally incurred by the ordinary SPH artificial viscosity. Collectively, we call our suite of modifications to the traditional SPH scheme Conservative Reproducing Kernel SPH, or CRKSPH. CRKSPH retains many benefits of traditional SPH methods (such as preserving Galilean invariance and manifest conservation of mass, momentum, and energy) while improving on many of the shortcomings of SPH, particularly the overly aggressive artificial viscosity and zeroth-order inaccuracy. We compare CRKSPH to two different modern SPH formulations (pressure based SPH and compatibly differenced SPH), demonstrating the advantages of our new formulation when modeling fluid mixing, strong shock, and adiabatic phenomena.
Semi-supervised learning for ordinal Kernel Discriminant Analysis.
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.
Face detection based on multiple kernel learning algorithm
Sun, Bo; Cao, Siming; He, Jun; Yu, Lejun
2016-09-01
Face detection is important for face localization in face or facial expression recognition, etc. The basic idea is to determine whether there is a face in an image or not, and also its location, size. It can be seen as a binary classification problem, which can be well solved by support vector machine (SVM). Though SVM has strong model generalization ability, it has some limitations, which will be deeply analyzed in the paper. To access them, we study the principle and characteristics of the Multiple Kernel Learning (MKL) and propose a MKL-based face detection algorithm. In the paper, we describe the proposed algorithm in the interdisciplinary research perspective of machine learning and image processing. After analyzing the limitation of describing a face with a single feature, we apply several ones. To fuse them well, we try different kernel functions on different feature. By MKL method, the weight of each single function is determined. Thus, we obtain the face detection model, which is the kernel of the proposed method. Experiments on the public data set and real life face images are performed. We compare the performance of the proposed algorithm with the single kernel-single feature based algorithm and multiple kernels-single feature based algorithm. The effectiveness of the proposed algorithm is illustrated. Keywords: face detection, feature fusion, SVM, MKL
A one-class kernel fisher criterion for outlier detection.
Dufrenois, Franck
2015-05-01
Recently, Dufrenois and Noyer proposed a one class Fisher's linear discriminant to isolate normal data from outliers. In this paper, a kernelized version of their criterion is presented. Originally on the basis of an iterative optimization process, alternating between subspace selection and clustering, I show here that their criterion has an upper bound making these two problems independent. In particular, the estimation of the label vector is formulated as an unconstrained binary linear problem (UBLP) which can be solved using an iterative perturbation method. Once the label vector is estimated, an optimal projection subspace is obtained by solving a generalized eigenvalue problem. Like many other kernel methods, the performance of the proposed approach depends on the choice of the kernel. Constructed with a Gaussian kernel, I show that the proposed contrast measure is an efficient indicator for selecting an optimal kernel width. This property simplifies the model selection problem which is typically solved by costly (generalized) cross-validation procedures. Initialization, convergence analysis, and computational complexity are also discussed. Lastly, the proposed algorithm is compared with recent novelty detectors on synthetic and real data sets.
A Novel Kernel for Least Squares Support Vector Machine
FENG Wei; ZHAO Yong-ping; DU Zhong-hua; LI De-cai; WANG Li-feng
2012-01-01
Extreme learning machine(ELM) has attracted much attention in recent years due to its fast convergence and good performance.Merging both ELM and support vector machine is an important trend,thus yielding an ELM kernel.ELM kernel based methods are able to solve the nonlinear problems by inducing an explicit mapping compared with the commonly-used kernels such as Gaussian kernel.In this paper,the ELM kernel is extended to the least squares support vector regression(LSSVR),so ELM-LSSVR was proposed.ELM-LSSVR can be used to reduce the training and test time simultaneously without extra techniques such as sequential minimal optimization and pruning mechanism.Moreover,the memory space for the training and test was relieved.To confirm the efficacy and feasibility of the proposed ELM-LSSVR,the experiments are reported to demonstrate that ELM-LSSVR takes the advantage of training and test time with comparable accuracy to other algorithms.
Aleurone cell identity is suppressed following connation in maize kernels.
Geisler-Lee, Jane; Gallie, Daniel R
2005-09-01
Expression of the cytokinin-synthesizing isopentenyl transferase enzyme under the control of the Arabidopsis (Arabidopsis thaliana) SAG12 senescence-inducible promoter reverses the normal abortion of the lower floret from a maize (Zea mays) spikelet. Following pollination, the upper and lower floret pistils fuse, producing a connated kernel with two genetically distinct embryos and the endosperms fused along their abgerminal face. Therefore, ectopic synthesis of cytokinin was used to position two independent endosperms within a connated kernel to determine how the fused endosperm would affect the development of the two aleurone layers along the fusion plane. Examination of the connated kernel revealed that aleurone cells were present for only a short distance along the fusion plane whereas starchy endosperm cells were present along most of the remainder of the fusion plane, suggesting that aleurone development is suppressed when positioned between independent starchy endosperms. Sporadic aleurone cells along the fusion plane were observed and may have arisen from late or imperfect fusion of the endosperms of the connated kernel, supporting the observation that a peripheral position at the surface of the endosperm and not proximity to maternal tissues such as the testa and pericarp are important for aleurone development. Aleurone mosaicism was observed in the crown region of nonconnated SAG12-isopentenyl transferase kernels, suggesting that cytokinin can also affect aleurone development.
Kernel Methods for Mining Instance Data in Ontologies
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.
Spine labeling in axial magnetic resonance imaging via integral kernels.
Miles, Brandon; Ben Ayed, Ismail; Hojjat, Seyed-Parsa; Wang, Michael H; Li, Shuo; Fenster, Aaron; Garvin, Gregory J
2016-12-01
This study investigates a fast integral-kernel algorithm for classifying (labeling) the vertebra and disc structures in axial magnetic resonance images (MRI). The method is based on a hierarchy of feature levels, where pixel classifications via non-linear probability product kernels (PPKs) are followed by classifications of 2D slices, individual 3D structures and groups of 3D structures. The algorithm further embeds geometric priors based on anatomical measurements of the spine. Our classifier requires evaluations of computationally expensive integrals at each pixel, and direct evaluations of such integrals would be prohibitively time consuming. We propose an efficient computation of kernel density estimates and PPK evaluations for large images and arbitrary local window sizes via integral kernels. Our method requires a single user click for a whole 3D MRI volume, runs nearly in real-time, and does not require an intensive external training. Comprehensive evaluations over T1-weighted axial lumbar spine data sets from 32 patients demonstrate a competitive structure classification accuracy of 99%, along with a 2D slice classification accuracy of 88%. To the best of our knowledge, such a structure classification accuracy has not been reached by the existing spine labeling algorithms. Furthermore, we believe our work is the first to use integral kernels in the context of medical images. Copyright Â© 2016 Elsevier Ltd. All rights reserved.
Time-fractional particle deposition in porous media
Xu, Jianping
2017-05-01
In the percolation process where fluids carry small solid particles, particle deposition causes a real-time permeability change of the medium as the swarm of particles propagates along the medium. Then the permeability change influences percolation and deposition behaviors as a feedback. This fact triggers memory effect in the deposition dynamics, which means the particulate transport and deposition behaviors become history-dependent. In this paper, we conduct the time-fractional generalization of the classical phenomenological model of particle deposition in porous media to incorporate the memory effect. We tested and compared the effects of employing different types of fractional operators, i.e. the Riemann-Liouville type, the Hadamard type and the Prabhakar type. Numerical simulation results show that the system behaviors vary according to the change of distinct memory kernels in an expected way. We then discuss the physical meaning of the time-fractional generalization. It is shown that different types of fractional operators unanimously ground themselves on the local-Newtonian time transformation in a complex system, which is equivalent to a class of history integrals. By the introduction of various memory kernels, it enables the model to more powerfully fit and approximate observed data. Further, the fundamental meaning of this work is not to show which fractional operator is ‘better’, but to argue collectively the legitimacy and practicality of a non-Markovian particle deposition dynamics in porous media, and in fact it is admissible to a bunch of memory kernels which differ greatly from each other in functional forms. Hopefully the presented generalized mass conservation formalism offers a broader framework to investigate transport problems in porous media.
Tenreiro Machado, J. A.
2015-08-01
This paper addresses the matrix representation of dynamical systems in the perspective of fractional calculus. Fractional elements and fractional systems are interpreted under the light of the classical Cole-Cole, Davidson-Cole, and Havriliak-Negami heuristic models. Numerical simulations for an electrical circuit enlighten the results for matrix based models and high fractional orders. The conclusions clarify the distinction between fractional elements and fractional systems.
Lifting kernel-based sprite codec
Dasu, Aravind R.; Panchanathan, Sethuraman
2000-12-01
The International Standards Organization (ISO) has proposed a family of standards for compression of image and video sequences, including the JPEG, MPEG-1 and MPEG-2. The latest MPEG-4 standard has many new dimensions to coding and manipulation of visual content. A video sequence usually contains a background object and many foreground objects. Portions of this background may not be visible in certain frames due to the occlusion of the foreground objects or camera motion. MPEG-4 introduces the novel concepts of Video Object Planes (VOPs) and Sprites. A VOP is a visual representation of real world objects with shapes that need not be rectangular. Sprite is a large image composed of pixels belonging to a video object visible throughout a video segment. Since a sprite contains all parts of the background that were at least visible once, it can be used for direct reconstruction of the background Video Object Plane (VOP). Sprite reconstruction is dependent on the mode in which it is transmitted. In the Static sprite mode, the entire sprite is decoded as an Intra VOP before decoding the individual VOPs. Since sprites consist of the information needed to display multiple frames of a video sequence, they are typically much larger than a single frame of video. Therefore a static sprite can be considered as a large static image. In this paper, a novel solution to address the problem of spatial scalability has been proposed, where the sprite is encoded in Discrete Wavelet Transform (DWT). A lifting kernel method of DWT implementation has been used for encoding and decoding sprites. Modifying the existing lifting scheme while maintaining it to be shape adaptive results in a reduced complexity. The proposed scheme has the advantages of (1) avoiding the need for any extensions to image or tile border pixels and is hence superior to the DCT based low latency scheme (used in the current MPEG-4 verification model), (2) mapping the in place computed wavelet coefficients into a zero
Butcher, H.C.; Wagner, G.J.; Siegelman, H.W.
1977-06-01
The development of techniques to isolate and purify relatively large quantities of intact vacuoles from mature tissues permits direct biochemical analysis of this ubiquitous mature plant cell organelle. Vacuoles and a fraction enriched in soluble cytoplasmic constituents were quantitatively prepared from Hippeastrum flower petal protoplasts. Vacuolar lysate and soluble cytoplasmic fractions were examined for acid hydrolase activities commonly associated with animal lysosomes, and pH optima were determined. Esterase, protease, carboxypeptidase, ..beta..-galactosidase, ..cap alpha..-glycosidase and ..beta..-glycosidase, not found in the vacuole lysate fraction, were components of the soluble cytoplasmic fraction. Acid phosphatase, RNase and DNase were present in both fractions. Vacuolar enzyme activities were also examined as a function of flower development from bud through senescent stages. The data obtained are not consistent with the concept that the mature plant cell vacuole functions as a generalized lysosome.
Düsterhöft, E M; Bonte, A W; Venekamp, J C; Voragen, A G
1993-09-01
Main fractions from multi-component polysaccharidase preparations (Driselase, Gamanase and an experimental preparation of fungal origin), previously used for the enzymic treatment of cell wall materials from sunflower and palm-kernel meals, were sub-fractionated by different chromatographic techniques to evaluate the contribution of each of their constituent activities in cell wall degradation. Based on activity measurements, 5- to 10-fold purification was achieved for the major enzymes but residual side-activities were still detectable in most sub-fractions. Solubilization of non-starch polysaccharides from the cell wall materials by the resulting pectolytic, xylanolytic, cellulolytic and mannanolytic sub-fractions and by highly purified glucanases, arabinanases and xylanases was, when acting individually, very low (1% to 5%). With few exceptions, the solubilizing effect of the main fractions could only be slightly enhanced by supplementation with pectolytic, cellulolytic or mannanolytic sub-fractions or by highly purified enzymes. The extent of solubilization remained mostly lower than the sum of both individually obtained values. In the degradation of palm-kernel cell wall material, however, synergistic action of mannanases and glucanases was observed. The hydrolysis of pectic compounds in sunflower cell wall material was most effective when polygalacturonases, arabinanases and rhamnogalacturonan-degrading activities were applied together. The resistance of 4-O-methyl-glucuronoxylan, the major hemicellulosic polymer in the cell wall material from sunflower meal, to enzymic hydrolysis was not only caused by its location in the cell wall or interlinkage to other polymers but also by its primary structure. Neither purified endo-xylanase nor the crude parent preparation were able to achieve complete hydrolysis of this polysaccharide after extraction.
Gómez-Aguilar, J. F.; Atangana, Abdon
2017-01-01
Some physical problems found in nature can follow the power law; others can follow the Mittag-Leffler law and others the exponential decay law. On the other hand, one can observe in nature a physical problem that combines the three laws, it is therefore important to provide a new fractional operator that could possibly be used to model such physical problem. In this paper, we suggest a fractional operator power-law-exponential-Mittag-Leffler kernel with three fractional orders. Some very useful properties are obtained. Numerical solutions were obtained for three examples proposed. The results show that the new fractional operators are powerful mathematical tools to model complex problems.
Kernel density estimation of a multidimensional efficiency profile
Poluektov, Anton
2014-01-01
Kernel density estimation is a convenient way to estimate the probability density of a distribution given the sample of data points. However, it has certain drawbacks: proper description of the density using narrow kernels needs large data samples, whereas if the kernel width is large, boundaries and narrow structures tend to be smeared. Here, an approach to correct for such effects, is proposed that uses an approximate density to describe narrow structures and boundaries. The approach is shown to be well suited for the description of the efficiency shape over a multidimensional phase space in a typical particle physics analysis. An example is given for the five-dimensional phase space of the $\\Lambda_b^0\\to D^0p\\pi$ decay.
A method of smoothed particle hydrodynamics using spheroidal kernels
Fulbright, Michael S.; Benz, Willy; Davies, Melvyn B.
1995-01-01
We present a new method of three-dimensional smoothed particle hydrodynamics (SPH) designed to model systems dominated by deformation along a preferential axis. These systems cause severe problems for SPH codes using spherical kernels, which are best suited for modeling systems which retain rough spherical symmetry. Our method allows the smoothing length in the direction of the deformation to evolve independently of the smoothing length in the perpendicular plane, resulting in a kernel with a spheroidal shape. As a result the spatial resolution in the direction of deformation is significantly improved. As a test case we present the one-dimensional homologous collapse of a zero-temperature, uniform-density cloud, which serves to demonstrate the advantages of spheroidal kernels. We also present new results on the problem of the tidal disruption of a star by a massive black hole.
Synthesis And Characterization of Biodiesel From Nigerian Palm Kernel oil.
IGBOKWE, J. O.
2016-07-01
Full Text Available Biodiesel was produced from Nigerian Palm kernel oil through direct base- catalyzed transesterification process using methanol and sodium hydroxide as alcohol and catalyst respectively. The transesterification process involved 1 liter of Palm kernel oil, 200ml of methanol, 1.0% NaOH, reaction temperature of 65 degree Celsius and reaction time of 90mins and an average biodiesel yield of 87.67% was obtained. The produced biodiesel was blended with diesel fuel at a ratio of 20% biodiesel to 80% diesel fuel (by volume. The neat biodiesel and its blend were characterized using the ASTM methods. The results showed that the properties of the neat palm kernel oil biodiesel and its blend fall within the American Society for Testing and Materials (ASTM specifications for Biodiesel fuels hence confirming their suitability as alternative fuels for modern diesel engines.
Regularized Embedded Multiple Kernel Dimensionality Reduction for Mine Signal Processing.
Li, Shuang; Liu, Bing; Zhang, Chen
2016-01-01
Traditional multiple kernel dimensionality reduction models are generally based on graph embedding and manifold assumption. But such assumption might be invalid for some high-dimensional or sparse data due to the curse of dimensionality, which has a negative influence on the performance of multiple kernel learning. In addition, some models might be ill-posed if the rank of matrices in their objective functions was not high enough. To address these issues, we extend the traditional graph embedding framework and propose a novel regularized embedded multiple kernel dimensionality reduction method. Different from the conventional convex relaxation technique, the proposed algorithm directly takes advantage of a binary search and an alternative optimization scheme to obtain optimal solutions efficiently. The experimental results demonstrate the effectiveness of the proposed method for supervised, unsupervised, and semisupervised scenarios.
Regularized Embedded Multiple Kernel Dimensionality Reduction for Mine Signal Processing
Shuang Li
2016-01-01
Full Text Available Traditional multiple kernel dimensionality reduction models are generally based on graph embedding and manifold assumption. But such assumption might be invalid for some high-dimensional or sparse data due to the curse of dimensionality, which has a negative influence on the performance of multiple kernel learning. In addition, some models might be ill-posed if the rank of matrices in their objective functions was not high enough. To address these issues, we extend the traditional graph embedding framework and propose a novel regularized embedded multiple kernel dimensionality reduction method. Different from the conventional convex relaxation technique, the proposed algorithm directly takes advantage of a binary search and an alternative optimization scheme to obtain optimal solutions efficiently. The experimental results demonstrate the effectiveness of the proposed method for supervised, unsupervised, and semisupervised scenarios.
Semisupervised Kernel Marginal Fisher Analysis for Face Recognition
Ziqiang Wang
2013-01-01
Full Text Available 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.
Recurrent kernel machines: computing with infinite echo state networks.
Hermans, Michiel; Schrauwen, Benjamin
2012-01-01
Echo state networks (ESNs) are large, random recurrent neural networks with a single trained linear readout layer. Despite the untrained nature of the recurrent weights, they are capable of performing universal computations on temporal input data, which makes them interesting for both theoretical research and practical applications. The key to their success lies in the fact that the network computes a broad set of nonlinear, spatiotemporal mappings of the input data, on which linear regression or classification can easily be performed. One could consider the reservoir as a spatiotemporal kernel, in which the mapping to a high-dimensional space is computed explicitly. In this letter, we build on this idea and extend the concept of ESNs to infinite-sized recurrent neural networks, which can be considered recursive kernels that subsequently can be used to create recursive support vector machines. We present the theoretical framework, provide several practical examples of recursive kernels, and apply them to typical temporal tasks.
A multi-label learning based kernel automatic recommendation method for support vector machine.
Zhang, Xueying; Song, Qinbao
2015-01-01
Choosing an appropriate kernel is very important and critical when classifying a new problem with Support Vector Machine. So far, more attention has been paid on constructing new kernels and choosing suitable parameter values for a specific kernel function, but less on kernel selection. Furthermore, most of current kernel selection methods focus on seeking a best kernel with the highest classification accuracy via cross-validation, they are time consuming and ignore the differences among the number of support vectors and the CPU time of SVM with different kernels. Considering the tradeoff between classification success ratio and CPU time, there may be multiple kernel functions performing equally well on the same classification problem. Aiming to automatically select those appropriate kernel functions for a given data set, we propose a multi-label learning based kernel recommendation method built on the data characteristics. For each data set, the meta-knowledge data base is first created by extracting the feature vector of data characteristics and identifying the corresponding applicable kernel set. Then the kernel recommendation model is constructed on the generated meta-knowledge data base with the multi-label classification method. Finally, the appropriate kernel functions are recommended to a new data set by the recommendation model according to the characteristics of the new data set. Extensive experiments over 132 UCI benchmark data sets, with five different types of data set characteristics, eleven typical kernels (Linear, Polynomial, Radial Basis Function, Sigmoidal function, Laplace, Multiquadric, Rational Quadratic, Spherical, Spline, Wave and Circular), and five multi-label classification methods demonstrate that, compared with the existing kernel selection methods and the most widely used RBF kernel function, SVM with the kernel function recommended by our proposed method achieved the highest classification performance.
Michael E. Kautzman
2015-03-01
Full Text Available The mycotoxins associated with specific Fusarium fungal infections of grains are a threat to global food and feed security. These fungal infestations are referred to as Fusarium Head Blight (FHB and lead to Fusarium Damaged Kernels (FDK. Incidence of FDK >0.25% will lower the grade, with a tolerance of 5% FDK for export feed grain. During infestation, the fungi can produce a variety of mycotoxins, the most common being deoxynivalenol (DON. Fusarium Damaged Kernels have been associated with reduced crude protein (CP, lowering nutritional, functional and grade value. New technology has been developed using Near Infrared Transmittance (NIT spectra that estimate CP of individual kernels of wheat, barley and durum. Our objective is to evaluate the technology's capability to reduce FDK and DON of downgraded wheat and ability to salvage high quality safe kernels. In five FDK downgraded sources of wheat, the lowest 20% CP kernels had significantly increased FDK and DON with the high CP fractions having decreased FDK and DON, thousand kernel weights (TKW and bushel weight (Bu. Strong positive correlations were observed between FDK and DON (r = 0.90; FDK and grade (r = 0.62 and DON and grade (r = 0.62. Negative correlations were observed between FDK and DON with CP (r = −0.27 and −0.32; TKW (r = −0.45 and −0.54 and Bu (r = −0.79 and −0.74. Results show improved quality and value of Fusarium downgraded grain using this technology.
Maira R. Segura-Campos
2013-01-01
Full Text Available Hypertension is one of the most common worldwide diseases in humans. Angiotensin I-converting enzyme (ACE plays an important role in regulating blood pressure and hypertension. An evaluation was done on the effect of Alcalase hydrolysis of defatted Jatropha curcas kernel meal on ACE inhibitory activity in the resulting hydrolysate and its purified fractions. Alcalase exhibited broad specificity and produced a protein hydrolysate with a 21.35% degree of hydrolysis and 34.87% ACE inhibition. Ultrafiltration of the hydrolysate produced peptide fractions with increased biological activity (24.46–61.41%. Hydrophobic residues contributed substantially to the peptides’ inhibitory potency. The 5–10 and <1 kDa fractions were selected for further fractionation by gel filtration chromatography. ACE inhibitory activity (% ranged from 22.66 to 45.96% with the 5–10 kDa ultrafiltered fraction and from 36.91 to 55.83% with the <1 kDa ultrafiltered fraction. The highest ACE inhibitory activity was observed in F2 ( μg/mL from the 5–10 kDa fraction and F1 ( μg/mL from the <1 kDa fraction. ACE inhibitory fractions from Jatropha kernel have potential applications in alternative hypertension therapies, adding a new application for the Jatropha plant protein fraction and improving the financial viability and sustainability of a Jatropha-based biodiesel industry.