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
Research on offense and defense technology for iOS kernel security mechanism
Chu, Sijun; Wu, Hao
2018-04-01
iOS is a strong and widely used mobile device system. It's annual profits make up about 90% of the total profits of all mobile phone brands. Though it is famous for its security, there have been many attacks on the iOS operating system, such as the Trident apt attack in 2016. So it is important to research the iOS security mechanism and understand its weaknesses and put forward targeted protection and security check framework. By studying these attacks and previous jailbreak tools, we can see that an attacker could only run a ROP code and gain kernel read and write permissions based on the ROP after exploiting kernel and user layer vulnerabilities. However, the iOS operating system is still protected by the code signing mechanism, the sandbox mechanism, and the not-writable mechanism of the system's disk area. This is far from the steady, long-lasting control that attackers expect. Before iOS 9, breaking these security mechanisms was usually done by modifying the kernel's important data structures and security mechanism code logic. However, after iOS 9, the kernel integrity protection mechanism was added to the 64-bit operating system and none of the previous methods were adapted to the new versions of iOS [1]. But this does not mean that attackers can not break through. Therefore, based on the analysis of the vulnerability of KPP security mechanism, this paper implements two possible breakthrough methods for kernel security mechanism for iOS9 and iOS10. Meanwhile, we propose a defense method based on kernel integrity detection and sensitive API call detection to defense breakthrough method mentioned above. And we make experiments to prove that this method can prevent and detect attack attempts or invaders effectively and timely.
MARMER, a flexible point-kernel shielding code
International Nuclear Information System (INIS)
Kloosterman, J.L.; Hoogenboom, J.E.
1990-01-01
A point-kernel shielding code entitled MARMER is described. It has several options with respect to geometry input, source description and detector point description which extend the flexibility and usefulness of the code, and which are especially useful in spent fuel shielding. MARMER has been validated using the TN12 spent fuel shipping cask benchmark. (author)
MARMER, a flexible point-kernel shielding code
Energy Technology Data Exchange (ETDEWEB)
Kloosterman, J.L.; Hoogenboom, J.E. (Interuniversitair Reactor Inst., Delft (Netherlands))
1990-01-01
A point-kernel shielding code entitled MARMER is described. It has several options with respect to geometry input, source description and detector point description which extend the flexibility and usefulness of the code, and which are especially useful in spent fuel shielding. MARMER has been validated using the TN12 spent fuel shipping cask benchmark. (author).
Ideal Gas Resonance Scattering Kernel Routine for the NJOY Code
International Nuclear Information System (INIS)
Rothenstein, W.
1999-01-01
In a recent publication an expression for the temperature-dependent double-differential ideal gas scattering kernel is derived for the case of scattering cross sections that are energy dependent. Some tabulations and graphical representations of the characteristics of these kernels are presented in Ref. 2. They demonstrate the increased probability that neutron scattering by a heavy nuclide near one of its pronounced resonances will bring the neutron energy nearer to the resonance peak. This enhances upscattering, when a neutron with energy just below that of the resonance peak collides with such a nuclide. A routine for using the new kernel has now been introduced into the NJOY code. Here, its principal features are described, followed by comparisons between scattering data obtained by the new kernel, and the standard ideal gas kernel, when such comparisons are meaningful (i.e., for constant values of the scattering cross section a 0 K). The new ideal gas kernel for variable σ s 0 (E) at 0 K leads to the correct Doppler-broadened σ s T (E) at temperature T
Local coding based matching kernel method for image classification.
Directory of Open Access Journals (Sweden)
Yan Song
Full Text Available This paper mainly focuses on how to effectively and efficiently measure visual similarity for local feature based representation. Among existing methods, metrics based on Bag of Visual Word (BoV techniques are efficient and conceptually simple, at the expense of effectiveness. By contrast, kernel based metrics are more effective, but at the cost of greater computational complexity and increased storage requirements. We show that a unified visual matching framework can be developed to encompass both BoV and kernel based metrics, in which local kernel plays an important role between feature pairs or between features and their reconstruction. Generally, local kernels are defined using Euclidean distance or its derivatives, based either explicitly or implicitly on an assumption of Gaussian noise. However, local features such as SIFT and HoG often follow a heavy-tailed distribution which tends to undermine the motivation behind Euclidean metrics. Motivated by recent advances in feature coding techniques, a novel efficient local coding based matching kernel (LCMK method is proposed. This exploits the manifold structures in Hilbert space derived from local kernels. The proposed method combines advantages of both BoV and kernel based metrics, and achieves a linear computational complexity. This enables efficient and scalable visual matching to be performed on large scale image sets. To evaluate the effectiveness of the proposed LCMK method, we conduct extensive experiments with widely used benchmark datasets, including 15-Scenes, Caltech101/256, PASCAL VOC 2007 and 2011 datasets. Experimental results confirm the effectiveness of the relatively efficient LCMK method.
Filatov, Gleb; Bauwens, Bruno; Kertész-Farkas, Attila
2018-05-07
Bioinformatics studies often rely on similarity measures between sequence pairs, which often pose a bottleneck in large-scale sequence analysis. Here, we present a new convolutional kernel function for protein sequences called the LZW-Kernel. It is based on code words identified with the Lempel-Ziv-Welch (LZW) universal text compressor. The LZW-Kernel is an alignment-free method, it is always symmetric, is positive, always provides 1.0 for self-similarity and it can directly be used with Support Vector Machines (SVMs) in classification problems, contrary to normalized compression distance (NCD), which often violates the distance metric properties in practice and requires further techniques to be used with SVMs. The LZW-Kernel is a one-pass algorithm, which makes it particularly plausible for big data applications. Our experimental studies on remote protein homology detection and protein classification tasks reveal that the LZW-Kernel closely approaches the performance of the Local Alignment Kernel (LAK) and the SVM-pairwise method combined with Smith-Waterman (SW) scoring at a fraction of the time. Moreover, the LZW-Kernel outperforms the SVM-pairwise method when combined with BLAST scores, which indicates that the LZW code words might be a better basis for similarity measures than local alignment approximations found with BLAST. In addition, the LZW-Kernel outperforms n-gram based mismatch kernels, hidden Markov model based SAM and Fisher kernel, and protein family based PSI-BLAST, among others. Further advantages include the LZW-Kernel's reliance on a simple idea, its ease of implementation, and its high speed, three times faster than BLAST and several magnitudes faster than SW or LAK in our tests. LZW-Kernel is implemented as a standalone C code and is a free open-source program distributed under GPLv3 license and can be downloaded from https://github.com/kfattila/LZW-Kernel. akerteszfarkas@hse.ru. Supplementary data are available at Bioinformatics Online.
An approach to improving the structure of error-handling code in the linux kernel
DEFF Research Database (Denmark)
Saha, Suman; Lawall, Julia; Muller, Gilles
2011-01-01
The C language does not provide any abstractions for exception handling or other forms of error handling, leaving programmers to devise their own conventions for detecting and handling errors. The Linux coding style guidelines suggest placing error handling code at the end of each function, where...... an automatic program transformation that transforms error-handling code into this style. We have applied our transformation to the Linux 2.6.34 kernel source code, on which it reorganizes the error handling code of over 1800 functions, in about 25 minutes....
WASTK: A Weighted Abstract Syntax Tree Kernel Method for Source Code Plagiarism Detection
Directory of Open Access Journals (Sweden)
Deqiang Fu
2017-01-01
Full Text Available In this paper, we introduce a source code plagiarism detection method, named WASTK (Weighted Abstract Syntax Tree Kernel, for computer science education. Different from other plagiarism detection methods, WASTK takes some aspects other than the similarity between programs into account. WASTK firstly transfers the source code of a program to an abstract syntax tree and then gets the similarity by calculating the tree kernel of two abstract syntax trees. To avoid misjudgment caused by trivial code snippets or frameworks given by instructors, an idea similar to TF-IDF (Term Frequency-Inverse Document Frequency in the field of information retrieval is applied. Each node in an abstract syntax tree is assigned a weight by TF-IDF. WASTK is evaluated on different datasets and, as a result, performs much better than other popular methods like Sim and JPlag.
A point kernel shielding code, PKN-HP, for high energy proton incident
Energy Technology Data Exchange (ETDEWEB)
Kotegawa, Hiroshi [Japan Atomic Energy Research Inst., Tokai, Ibaraki (Japan). Tokai Research Establishment
1996-06-01
A point kernel integral technique code PKN-HP, and the related thick target neutron yield data have been developed to calculate neutron and secondary gamma-ray dose equivalents in ordinary concrete and iron shields for fully stopping length C, Cu and U-238 target neutrons produced by 100 MeV-10 GeV proton incident in a 3-dimensional geometry. The comparisons among calculation results of the present code and other calculation techniques, and measured values showed the usefulness of the code. (author)
International Nuclear Information System (INIS)
Kotegawa, Hiroshi; Tanaka, Shun-ichi
1991-09-01
A point-kernel integral technique code, PKN, and the related data library have been developed to calculate neutron and secondary gamma-ray dose equivalents in water, concrete and iron shields for neutron sources in 3-dimensional geometry. The comparison between calculational results of the present code and those of the 1-dimensional transport code ANISN = JR, and the 2-dimensional transport code DOT4.2 showed a sufficient accuracy, and the availability of the PKN code has been confirmed. (author)
International Nuclear Information System (INIS)
Thilagam, L.; Subbaiah, K.V.
2008-01-01
Brachytherapy treatment planning systems (TPS) are always recommended to account for the effect of tissue, applicator and shielding material heterogeneities exist in Intracavitary brachytherapy (ICBT) applicators. Most of the commercially available brachytherapy TPS softwares estimate the absorbed dose at a point, only taking care of the contributions of individual sources and the source distribution, neglecting the dose perturbations arising from the applicator design and construction. So the doses estimated by them are not much accurate under realistic clinical conditions. In this regard, interactive point kernel rode (BrachyTPS) has been developed to perform independent dose calculations by taking into account the effect of these heterogeneities, using two regions build up factors, proposed by Kalos. As primary input data, the code takes patients' planning data including the source specifications, dwell positions, dwell times and it computes the doses at reference points by dose point kernel formalisms, with multi-layer shield build-up factors accounting for the contributions from scattered radiation. In addition to performing dose distribution calculations, this code package is capable of displaying an isodose distribution curve into the patient anatomy images. The primary aim of this study is to validate the developed point kernel code integrated with treatment planning systems against the other tools which are available in the market. In the present work, three brachytherapy applicators commonly used in the treatment of uterine cervical carcinoma, Board of Radiation Isotope and Technology (BRIT) made low dose rate (LDR) applicator, Fletcher Green type LDR applicator and Fletcher Williamson high dose rate (HDR) applicator were studied to test the accuracy of the software
Two-stage sparse coding of region covariance via Log-Euclidean kernels to detect saliency.
Zhang, Ying-Ying; Yang, Cai; Zhang, Ping
2017-05-01
In this paper, we present a novel bottom-up saliency detection algorithm from the perspective of covariance matrices on a Riemannian manifold. Each superpixel is described by a region covariance matrix on Riemannian Manifolds. We carry out a two-stage sparse coding scheme via Log-Euclidean kernels to extract salient objects efficiently. In the first stage, given background dictionary on image borders, sparse coding of each region covariance via Log-Euclidean kernels is performed. The reconstruction error on the background dictionary is regarded as the initial saliency of each superpixel. In the second stage, an improvement of the initial result is achieved by calculating reconstruction errors of the superpixels on foreground dictionary, which is extracted from the first stage saliency map. The sparse coding in the second stage is similar to the first stage, but is able to effectively highlight the salient objects uniformly from the background. Finally, three post-processing methods-highlight-inhibition function, context-based saliency weighting, and the graph cut-are adopted to further refine the saliency map. Experiments on four public benchmark datasets show that the proposed algorithm outperforms the state-of-the-art methods in terms of precision, recall and mean absolute error, and demonstrate the robustness and efficiency of the proposed method. Copyright © 2017 Elsevier Ltd. All rights reserved.
Directory of Open Access Journals (Sweden)
Alessio Gaspar
2007-06-01
Full Text Available This paper discusses how Linux clustering and virtual machine technologies can improve undergraduate students' hands-on experience in operating systems laboratories. Like similar projects, SOFTICE relies on User Mode Linux (UML to provide students with privileged access to a Linux system without creating security breaches on the hosting network. We extend such approaches in two aspects. First, we propose to facilitate adoption of Linux-based laboratories by using a load-balancing cluster made of recycled classroom PCs to remotely serve access to virtual machines. Secondly, we propose a new approach for students to interact with the kernel code.
NARMER-1: a photon point-kernel code with build-up factors
Visonneau, Thierry; Pangault, Laurence; Malouch, Fadhel; Malvagi, Fausto; Dolci, Florence
2017-09-01
This paper presents an overview of NARMER-1, the new generation of photon point-kernel code developed by the Reactor Studies and Applied Mathematics Unit (SERMA) at CEA Saclay Center. After a short introduction giving some history points and the current context of development of the code, the paper exposes the principles implemented in the calculation, the physical quantities computed and surveys the generic features: programming language, computer platforms, geometry package, sources description, etc. Moreover, specific and recent features are also detailed: exclusion sphere, tetrahedral meshes, parallel operations. Then some points about verification and validation are presented. Finally we present some tools that can help the user for operations like visualization and pre-treatment.
Error quantification of the axial nodal diffusion kernel of the DeCART code
International Nuclear Information System (INIS)
Cho, J. Y.; Kim, K. S.; Lee, C. C.
2006-01-01
This paper is to quantify the transport effects involved in the axial nodal diffusion kernel of the DeCART code. The transport effects are itemized into three effects, the homogenization, the diffusion, and the nodal effects. A five pin model consisting of four fuel pins and one non-fuel pin is demonstrated to quantify the transport effects. The transport effects are analyzed for three problems, the single pin (SP), guide tube (GT) and control rod (CR) problems by replacing the non-fuel pin with the fuel pin, a guide-tube and a control rod pins, respectively. The homogenization and diffusion effects are estimated to be about -4 and -50 pcm for the eigenvalue, and less than 2 % for the node power. The nodal effect on the eigenvalue is evaluated to be about -50 pcm in the SP and GT problems, and +350 pcm in the CR problem. Regarding the node power, this effect induces about a 3 % error in the SP and GT problems, and about a 20 % error in the CR problem. The large power error in the CR problem is due to the plane thickness, and it can be decreased by using the adaptive plane size. From the error quantification, it is concluded that the homogenization and the diffusion effects are not controllable if DeCART maintains the diffusion kernel for the axial solution, but the nodal effect is controllable by introducing the adaptive plane size scheme. (authors)
A Realization of Temperature Monitoring System Based on Real-Time Kernel μC/OS and 1-wire Bus
Directory of Open Access Journals (Sweden)
Yanmei Qi
2013-06-01
Full Text Available The traditional temperature monitoring system generally adopt some analog sensors for collecting data and a microcontroller for processing data for the purpose of temperature monitoring. However, this back-fore ground system has the disadvantages that the system has poor real-time property and single function, the amount of sensors is not easy to expand, and the software system has a difficulty in upgrading. Aiming at these disadvantages, the system designed in this paper adopts brand-new hardware and software structures: a digitaltemperature sensor array is connected to 1-wire bus and communicated with a control core through 1-wire bus protocol, thus a great convenience is provided for the expansion of the sensor; a real-time operating system is introduced into the software, an application program capable of realizing various functions runs on the real-time kernel μC/OS-II platform. The application of the real-time kernel also provides a good lower layer interface for the late-stage software upgrading.
Schumacher, F.; Friederich, W.
2015-12-01
We present the modularized software package ASKI which is a flexible and extendable toolbox for seismic full waveform inversion (FWI) as well as sensitivity or resolution analysis operating on the sensitivity matrix. It utilizes established wave propagation codes for solving the forward problem and offers an alternative to the monolithic, unflexible and hard-to-modify codes that have typically been written for solving inverse problems. It is available under the GPL at www.rub.de/aski. The Gauss-Newton FWI method for 3D-heterogeneous elastic earth models is based on waveform sensitivity kernels and can be applied to inverse problems at various spatial scales in both Cartesian and spherical geometries. The kernels are derived in the frequency domain from Born scattering theory as the Fréchet derivatives of linearized full waveform data functionals, quantifying the influence of elastic earth model parameters on the particular waveform data values. As an important innovation, we keep two independent spatial descriptions of the earth model - one for solving the forward problem and one representing the inverted model updates. Thereby we account for the independent needs of spatial model resolution of forward and inverse problem, respectively. Due to pre-integration of the kernels over the (in general much coarser) inversion grid, storage requirements for the sensitivity kernels are dramatically reduced.ASKI can be flexibly extended to other forward codes by providing it with specific interface routines that contain knowledge about forward code-specific file formats and auxiliary information provided by the new forward code. In order to sustain flexibility, the ASKI tools must communicate via file output/input, thus large storage capacities need to be accessible in a convenient way. Storing the complete sensitivity matrix to file, however, permits the scientist full manual control over each step in a customized procedure of sensitivity/resolution analysis and full
International Nuclear Information System (INIS)
Fanaro, L.C.C.B.
1984-01-01
It was developed the BLINDAGE computer code for the radiation transport (neutrons and gammas) calculation. The code uses the removal - diffusion method for neutron transport and point-kernel technique with buil-up factors for gamma-rays. The results obtained through BLINDAGE code are compared with those obtained with the ANISN and SABINE computer codes. (Author) [pt
Zhang, Ying-Ying; Yang, Cai; Zhang, Ping
2017-08-01
In this paper, we present a novel bottom-up saliency detection algorithm from the perspective of covariance matrices on a Riemannian manifold. Each superpixel is described by a region covariance matrix on Riemannian Manifolds. We carry out a two-stage sparse coding scheme via Log-Euclidean kernels to extract salient objects efficiently. In the first stage, given background dictionary on image borders, sparse coding of each region covariance via Log-Euclidean kernels is performed. The reconstruction error on the background dictionary is regarded as the initial saliency of each superpixel. In the second stage, an improvement of the initial result is achieved by calculating reconstruction errors of the superpixels on foreground dictionary, which is extracted from the first stage saliency map. The sparse coding in the second stage is similar to the first stage, but is able to effectively highlight the salient objects uniformly from the background. Finally, three post-processing methods-highlight-inhibition function, context-based saliency weighting, and the graph cut-are adopted to further refine the saliency map. Experiments on four public benchmark datasets show that the proposed algorithm outperforms the state-of-the-art methods in terms of precision, recall and mean absolute error, and demonstrate the robustness and efficiency of the proposed method. Copyright © 2017 Elsevier Ltd. All rights reserved.
International Nuclear Information System (INIS)
1986-03-01
A study on radiation dose control in packages of radioactive waste from nuclear facilities, hospitals and industries, such as sources of Ra-226, Co-60, Ir-192 and Cs-137, is presented. The MAPA and MAPAM computer codes, based on point Kernel theory for calculating doses of several source-shielding type configurations, aiming to assure the safe transport conditions for these sources, was developed. The validation of the code for point sources, using the values provided by NCRP, for the thickness of lead and concrete shieldings, limiting the dose at 100 Mrem/hr for several distances from the source to the detector, was carried out. The validation for non point sources was carried out, measuring experimentally radiation dose from packages developed by Brazilian CNEN/S.P. for removing the sources. (M.C.K.) [pt
Energy Technology Data Exchange (ETDEWEB)
Bindel, Laurent; Clouet, Laurent; Castanier, Eric; Bonnet, Jerome; Fleury, Guillaume; Vermuse, Manuel; Gamess, Andre; Lejeune, Eric [Societe Generale pour les techniques Nouvelles, Saint Quentin en Yvelines (France)
2000-03-01
The present paper presents the capabilities of a new code named PERCEVAL v4.0 and based on the new point kernel described in an attached issue. Two linked codes named SPECTRE{sub G} and GRAPH{sub 3}D are part of the code package in order to establish the energetic source term and visualize the tri-dimensional scene respectively. (author)
Towards Easing the Diagnosis of Bugs in OS Code
DEFF Research Database (Denmark)
Stuart, Henrik; Hansen, René Rydhof; Lawall, Julia Laetitia
2007-01-01
The rapid detection and treatment of bugs in operating systems code is essential to maintain the overall security and dependability of a computing system. A number of techniques have been proposed for detecting bugs, but little has been done to help developers analyze and treat them. In this pa......The rapid detection and treatment of bugs in operating systems code is essential to maintain the overall security and dependability of a computing system. A number of techniques have been proposed for detecting bugs, but little has been done to help developers analyze and treat them....... In this paper we propose to combine bug-finding rules with transformations that automatically introduce bug-fixes or workarounds when a possible bug is detected. This work builds on our previous work on the Coccinelle tool, which targets device driver evolution....
Mairani, A; Valente, M; Battistoni, G; Botta, F; Pedroli, G; Ferrari, A; Cremonesi, M; Di Dia, A; Ferrari, M; Fasso, A
2011-01-01
Purpose: The calculation of patient-specific dose distribution can be achieved by Monte Carlo simulations or by analytical methods. In this study, FLUKA Monte Carlo code has been considered for use in nuclear medicine dosimetry. Up to now, FLUKA has mainly been dedicated to other fields, namely high energy physics, radiation protection, and hadrontherapy. When first employing a Monte Carlo code for nuclear medicine dosimetry, its results concerning electron transport at energies typical of nuclear medicine applications need to be verified. This is commonly achieved by means of calculation of a representative parameter and comparison with reference data. Dose point kernel (DPK), quantifying the energy deposition all around a point isotropic source, is often the one. Methods: FLUKA DPKS have been calculated in both water and compact bone for monoenergetic electrons (10-3 MeV) and for beta emitting isotopes commonly used for therapy ((89)Sr, (90)Y, (131)I, (153)Sm, (177)Lu, (186)Re, and (188)Re). Point isotropic...
Uusijärvi, Helena; Chouin, Nicolas; Bernhardt, Peter; Ferrer, Ludovic; Bardiès, Manuel; Forssell-Aronsson, Eva
2009-08-01
Point kernels describe the energy deposited at a certain distance from an isotropic point source and are useful for nuclear medicine dosimetry. They can be used for absorbed-dose calculations for sources of various shapes and are also a useful tool when comparing different Monte Carlo (MC) codes. The aim of this study was to compare point kernels calculated by using the mixed MC code, PENELOPE (v. 2006), with point kernels calculated by using the condensed-history MC codes, ETRAN, GEANT4 (v. 8.2), and MCNPX (v. 2.5.0). Point kernels for electrons with initial energies of 10, 100, 500, and 1 MeV were simulated with PENELOPE. Spherical shells were placed around an isotropic point source at distances from 0 to 1.2 times the continuous-slowing-down-approximation range (R(CSDA)). Detailed (event-by-event) simulations were performed for electrons with initial energies of less than 1 MeV. For 1-MeV electrons, multiple scattering was included for energy losses less than 10 keV. Energy losses greater than 10 keV were simulated in a detailed way. The point kernels generated were used to calculate cellular S-values for monoenergetic electron sources. The point kernels obtained by using PENELOPE and ETRAN were also used to calculate cellular S-values for the high-energy beta-emitter, 90Y, the medium-energy beta-emitter, 177Lu, and the low-energy electron emitter, 103mRh. These S-values were also compared with the Medical Internal Radiation Dose (MIRD) cellular S-values. The greatest differences between the point kernels (mean difference calculated for distances, electrons was 1.4%, 2.5%, and 6.9% for ETRAN, GEANT4, and MCNPX, respectively, compared to PENELOPE, if omitting the S-values when the activity was distributed on the cell surface for 10-keV electrons. The largest difference between the cellular S-values for the radionuclides, between PENELOPE and ETRAN, was seen for 177Lu (1.2%). There were large differences between the MIRD cellular S-values and those obtained from
Neutron shielding point kernel integral calculation code for personal computer: PKN-pc
International Nuclear Information System (INIS)
Kotegawa, Hiroshi; Sakamoto, Yukio; Nakane, Yoshihiro; Tomita, Ken-ichi; Kurosawa, Naohiro.
1994-07-01
A personal computer version of PKN code, PKN-pc, has been developed to calculate neutron and secondary gamma-ray 1cm depth dose equivalents in water, ordinary concrete and iron for neutron source. Characteristics of PKN code are, to able to calculate dose equivalents in multi-layer three-dimensional system, which are described with two-dimensional surface, for monoenergetic neutron source from 0.01 to 14.9 MeV, 252 Cf fission and 241 Am-Be neutron source quick and easily. In addition to these features, the PKN-pc is possible to process interactive input and to get graphical system configuration and graphical results easily. (author)
International Nuclear Information System (INIS)
Raisali, G.R.
1992-01-01
A series of computer codes based on point kernel technique and also Monte Carlo method have been developed. These codes perform radiation transport calculations for irradiator systems having cartesian, cylindrical and mixed geometries. The monte Carlo calculations, the computer code 'EGS4' has been applied to a radiation processing type problem. This code has been acompanied by a specific user code. The set of codes developed include: GCELLS, DOSMAPM, DOSMAPC2 which simulate the radiation transport in gamma irradiator systems having cylinderical, cartesian, and mixed geometries, respectively. The program 'DOSMAP3' based on point kernel technique, has been also developed for dose rate mapping calculations in carrier type gamma irradiators. Another computer program 'CYLDETM' as a user code for EGS4 has been also developed to simulate dose variations near the interface of heterogeneous media in gamma irradiator systems. In addition a system of computer codes 'PRODMIX' has been developed which calculates the absorbed dose in the products with different densities. validation studies of the calculated results versus experimental dosimetry has been performed and good agreement has been obtained
Botta, F; Mairani, A; Battistoni, G; Cremonesi, M; Di Dia, A; Fassò, A; Ferrari, A; Ferrari, M; Paganelli, G; Pedroli, G; Valente, M
2011-07-01
The calculation of patient-specific dose distribution can be achieved by Monte Carlo simulations or by analytical methods. In this study, FLUKA Monte Carlo code has been considered for use in nuclear medicine dosimetry. Up to now, FLUKA has mainly been dedicated to other fields, namely high energy physics, radiation protection, and hadrontherapy. When first employing a Monte Carlo code for nuclear medicine dosimetry, its results concerning electron transport at energies typical of nuclear medicine applications need to be verified. This is commonly achieved by means of calculation of a representative parameter and comparison with reference data. Dose point kernel (DPK), quantifying the energy deposition all around a point isotropic source, is often the one. FLUKA DPKS have been calculated in both water and compact bone for monoenergetic electrons (10-3 MeV) and for beta emitting isotopes commonly used for therapy (89Sr, 90Y, 131I 153Sm, 177Lu, 186Re, and 188Re). Point isotropic sources have been simulated at the center of a water (bone) sphere, and deposed energy has been tallied in concentric shells. FLUKA outcomes have been compared to PENELOPE v.2008 results, calculated in this study as well. Moreover, in case of monoenergetic electrons in water, comparison with the data from the literature (ETRAN, GEANT4, MCNPX) has been done. Maximum percentage differences within 0.8.RCSDA and 0.9.RCSDA for monoenergetic electrons (RCSDA being the continuous slowing down approximation range) and within 0.8.X90 and 0.9.X90 for isotopes (X90 being the radius of the sphere in which 90% of the emitted energy is absorbed) have been computed, together with the average percentage difference within 0.9.RCSDA and 0.9.X90 for electrons and isotopes, respectively. Concerning monoenergetic electrons, within 0.8.RCSDA (where 90%-97% of the particle energy is deposed), FLUKA and PENELOPE agree mostly within 7%, except for 10 and 20 keV electrons (12% in water, 8.3% in bone). The
Coffey, Stephen; Connell, Joseph
2005-06-01
This paper presents a development platform for real-time image processing based on the ADSP-BF533 Blackfin processor and the MicroC/OS-II real-time operating system (RTOS). MicroC/OS-II is a completely portable, ROMable, pre-emptive, real-time kernel. The Blackfin Digital Signal Processors (DSPs), incorporating the Analog Devices/Intel Micro Signal Architecture (MSA), are a broad family of 16-bit fixed-point products with a dual Multiply Accumulate (MAC) core. In addition, they have a rich instruction set with variable instruction length and both DSP and MCU functionality thus making them ideal for media based applications. Using the MicroC/OS-II for task scheduling and management, the proposed system can capture and process raw RGB data from any standard 8-bit greyscale image sensor in soft real-time and then display the processed result using a simple PC graphical user interface (GUI). Additionally, the GUI allows configuration of the image capture rate and the system and core DSP clock rates thereby allowing connectivity to a selection of image sensors and memory devices. The GUI also allows selection from a set of image processing algorithms based in the embedded operating system.
International Nuclear Information System (INIS)
Boehlke, S.; Niegoth, H.
2012-01-01
In the nuclear power plant Leibstadt (KKL) during the next year large components will be dismantled and stored for final disposal within the interim storage facility ZENT at the NPP site. Before construction of ZENT appropriate estimations of the local dose rate inside and outside the building and the collective dose for the normal operation have to be performed. The shielding calculations are based on the properties of the stored components and radiation sources and on the concepts for working place requirements. The installation of control and monitoring areas will depend on these calculations. For the determination of the shielding potential of concrete walls and steel doors with the defined boundary conditions point-kernel codes like MICROSHIELd registered are used. Complex problems cannot be modeled with this code. Therefore the point-kernel code VISIPLAN registered was developed for the determination of the local dose distribution functions in 3D models. The possibility of motion sequence inputs allows an optimization of collective dose estimations for the operational phases of a nuclear facility.
RKRD: Runtime Kernel Rootkit Detection
Grover, Satyajit; Khosravi, Hormuzd; Kolar, Divya; Moffat, Samuel; Kounavis, Michael E.
In this paper we address the problem of protecting computer systems against stealth malware. The problem is important because the number of known types of stealth malware increases exponentially. Existing approaches have some advantages for ensuring system integrity but sophisticated techniques utilized by stealthy malware can thwart them. We propose Runtime Kernel Rootkit Detection (RKRD), a hardware-based, event-driven, secure and inclusionary approach to kernel integrity that addresses some of the limitations of the state of the art. Our solution is based on the principles of using virtualization hardware for isolation, verifying signatures coming from trusted code as opposed to malware for scalability and performing system checks driven by events. Our RKRD implementation is guided by our goals of strong isolation, no modifications to target guest OS kernels, easy deployment, minimal infra-structure impact, and minimal performance overhead. We developed a system prototype and conducted a number of experiments which show that the per-formance impact of our solution is negligible.
Energy Technology Data Exchange (ETDEWEB)
Boehlke, S.; Niegoth, H. [STEAG Energy Services GmbH, Essen (Germany). Nuclear Technologies; Stalder, I. [Kernkraftwerk Leibstadt AG, Leibstadt (Switzerland)
2012-11-01
In the nuclear power plant Leibstadt (KKL) during the next year large components will be dismantled and stored for final disposal within the interim storage facility ZENT at the NPP site. Before construction of ZENT appropriate estimations of the local dose rate inside and outside the building and the collective dose for the normal operation have to be performed. The shielding calculations are based on the properties of the stored components and radiation sources and on the concepts for working place requirements. The installation of control and monitoring areas will depend on these calculations. For the determination of the shielding potential of concrete walls and steel doors with the defined boundary conditions point-kernel codes like MICROSHIELd {sup registered} are used. Complex problems cannot be modeled with this code. Therefore the point-kernel code VISIPLAN {sup registered} was developed for the determination of the local dose distribution functions in 3D models. The possibility of motion sequence inputs allows an optimization of collective dose estimations for the operational phases of a nuclear facility.
International Nuclear Information System (INIS)
Radhakrishnan, G.
2003-01-01
Full text: Around the PFBR (Prototype Fast Breeder Reactor) reactor assembly, in the peripheral shields special concretes of density 2.4 g/cm 3 and 3.6 g/cm 3 are to be used in complex geometrical shapes. Point-kernel computer code like QAD-CGGP, written for complex shield geometry comes in handy for the shield design optimization of peripheral shields. QAD-CGGP requires data base for the buildup factor data and it contains only ordinary concrete of density 2.3 g/cm 3 . In order to extend the data base for the PFBR special concretes, point isotropic source dose buildup factors have been generated by Monte Carlo method using the computer code MCNP-4A. For the above mentioned special concretes, buildup factor data have been generated in the energy range 0.5 MeV to 10.0 MeV with the thickness ranging from 1 mean free paths (mfp) to 40 mfp. Capo's formula fit of the buildup factor data compatible with QAD-CGGP has been attempted
The rice OsSAG12-2 gene codes for a functional protease that ...
Indian Academy of Sciences (India)
2016-07-11
Jul 11, 2016 ... Senescence is the final stage of plant development. Although ... Down-regulation of OsSAG12-1 in transgenic rice .... Plants were germinated on soil and grown for 15 days under normal conditions before taking photograph.
Investigation of Polymorphisms in Coding Region of OsHKT1 in Relation to Salinity in Rice
Directory of Open Access Journals (Sweden)
Pham Quynh-Hoa
2016-11-01
Full Text Available Rice (Oryza sativa is sensitive to salinity, but the salt tolerance level differs among cultivars, which might result from natural variations in the genes that are responsible for salt tolerance. High-affinity potassium transporter (HKTs has been proven to be involved in salt tolerance in plants. Therefore, we screened for natural nucleotide polymorphism in the coding sequence of OsHKT1, which encodes the HKT protein in eight Vietnamese rice cultivars differing in salt tolerance level. In total, seven nucleotide substitutions in coding sequence of OsHKT1 were found, including two non-synonymous and five synonymous substitutions. Further analysis revealed that these two non-synonymous nucleotide substitutions (G50T and T1209A caused changes in amino acids (Gly17Val and Asp403Glu at signal peptide and the loop of the sixth transmembrane domain, respectively. To assess the potential effect of these substitutions on the protein function, the 3D structure of HKT protein variants was modelled by using PHYRE2 webserver. The results showed that no difference was observed when compared those predicted 3D structure of HKT protein variants with each other. In addition, the codon bias of synonymous substitutions cannot clearly show correlation with salt tolerance level. It might be interesting to further investigate the functional roles of detected non-synonymous substitutions as it might correlate to salt tolerance in rice.
Putting Priors in Mixture Density Mercer Kernels
Srivastava, Ashok N.; Schumann, Johann; Fischer, Bernd
2004-01-01
This paper presents a new methodology for automatic knowledge driven data mining based on the theory of Mercer Kernels, which are highly nonlinear symmetric positive definite mappings from the original image space to a very high, possibly infinite dimensional feature space. We describe a new method called Mixture Density Mercer Kernels to learn kernel function directly from data, rather than using predefined kernels. These data adaptive kernels can en- code prior knowledge in the kernel using a Bayesian formulation, thus allowing for physical information to be encoded in the model. We compare the results with existing algorithms on data from the Sloan Digital Sky Survey (SDSS). The code for these experiments has been generated with the AUTOBAYES tool, which automatically generates efficient and documented C/C++ code from abstract statistical model specifications. The core of the system is a schema library which contains template for learning and knowledge discovery algorithms like different versions of EM, or numeric optimization methods like conjugate gradient methods. The template instantiation is supported by symbolic- algebraic computations, which allows AUTOBAYES to find closed-form solutions and, where possible, to integrate them into the code. The results show that the Mixture Density Mercer-Kernel described here outperforms tree-based classification in distinguishing high-redshift galaxies from low- redshift galaxies by approximately 16% on test data, bagged trees by approximately 7%, and bagged trees built on a much larger sample of data by approximately 2%.
RTOS kernel in portable electrocardiograph
Centeno, C. A.; Voos, J. A.; Riva, G. G.; Zerbini, C.; Gonzalez, E. A.
2011-12-01
This paper presents the use of a Real Time Operating System (RTOS) on a portable electrocardiograph based on a microcontroller platform. All medical device digital functions are performed by the microcontroller. The electrocardiograph CPU is based on the 18F4550 microcontroller, in which an uCOS-II RTOS can be embedded. The decision associated with the kernel use is based on its benefits, the license for educational use and its intrinsic time control and peripherals management. The feasibility of its use on the electrocardiograph is evaluated based on the minimum memory requirements due to the kernel structure. The kernel's own tools were used for time estimation and evaluation of resources used by each process. After this feasibility analysis, the migration from cyclic code to a structure based on separate processes or tasks able to synchronize events is used; resulting in an electrocardiograph running on one Central Processing Unit (CPU) based on RTOS.
RTOS kernel in portable electrocardiograph
International Nuclear Information System (INIS)
Centeno, C A; Voos, J A; Riva, G G; Zerbini, C; Gonzalez, E A
2011-01-01
This paper presents the use of a Real Time Operating System (RTOS) on a portable electrocardiograph based on a microcontroller platform. All medical device digital functions are performed by the microcontroller. The electrocardiograph CPU is based on the 18F4550 microcontroller, in which an uCOS-II RTOS can be embedded. The decision associated with the kernel use is based on its benefits, the license for educational use and its intrinsic time control and peripherals management. The feasibility of its use on the electrocardiograph is evaluated based on the minimum memory requirements due to the kernel structure. The kernel's own tools were used for time estimation and evaluation of resources used by each process. After this feasibility analysis, the migration from cyclic code to a structure based on separate processes or tasks able to synchronize events is used; resulting in an electrocardiograph running on one Central Processing Unit (CPU) based on RTOS.
Directory of Open Access Journals (Sweden)
Hammam Oktajianto
2014-12-01
Full Text Available Gas-cooled nuclear reactor is a Generation IV reactor which has been receiving significant attention due to many desired characteristics such as inherent safety, modularity, relatively low cost, short construction period, and easy financing. High temperature reactor (HTR pebble-bed as one of type of gas-cooled reactor concept is getting attention. In HTR pebble-bed design, radius and enrichment of the fuel kernel are the key parameter that can be chosen freely to determine the desired value of criticality. This paper models HTR pebble-bed 10 MW and determines an effective of enrichment and radius of the fuel (Kernel to get criticality value of reactor. The TRISO particle coated fuel particle which was modelled explicitly and distributed in the fuelled region of the fuel pebbles using a Simple-Cubic (SC lattice. The pebble-bed balls and moderator balls distributed in the core zone using a Body-Centred Cubic lattice with assumption of a fresh fuel by the fuel enrichment was 7-17% at 1% range and the size of the fuel radius was 175-300 µm at 25 µm ranges. The geometrical model of the full reactor is obtained by using lattice and universe facilities provided by MCNP4C. The details of model are discussed with necessary simplifications. Criticality calculations were conducted by Monte Carlo transport code MCNP4C and continuous energy nuclear data library ENDF/B-VI. From calculation results can be concluded that an effective of enrichment and radius of fuel (Kernel to achieve a critical condition was the enrichment of 15-17% at a radius of 200 µm, the enrichment of 13-17% at a radius of 225 µm, the enrichments of 12-15% at radius of 250 µm, the enrichments of 11-14% at a radius of 275 µm and the enrichment of 10-13% at a radius of 300 µm, so that the effective of enrichments and radii of fuel (Kernel can be considered in the HTR 10 MW. Keywords—MCNP4C, HTR, enrichment, radius, criticality
Control Transfer in Operating System Kernels
1994-05-13
microkernel system that runs less code in the kernel address space. To realize the performance benefit of allocating stacks in unmapped kseg0 memory, the...review how I modified the Mach 3.0 kernel to use continuations. Because of Mach’s message-passing microkernel structure, interprocess communication was...critical control transfer paths, deeply- nested call chains are undesirable in any case because of the function call overhead. 4.1.3 Microkernel Operating
Advanced Development of Certified OS Kernels
2015-06-01
Mobile Processes,” ACM Trans. Program. Lang. Syst., vol. 32, no. 5, 2010. 220 [36] S. D. Brookes, “A Semantics for Concurrent Separation Logic,” in...the weights of traces. The logic can be used for interactive stack-bound development or as a backend for verified static analysis tools. For clarity...logic as a backend for static stack-bound analysis tools since they would be required to also prove memory safety. To meet Challenge 3, we
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...
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. Copyright © 2014 Elsevier Ltd. All rights reserved.
Directory of Open Access Journals (Sweden)
Claudia Antonia Vieira Rossetto
2005-04-01
Full Text Available O objetivo deste trabalho foi avaliar a contaminação e o potencial para síntese de aflatoxinas pelos isolados do grupo Aspergillus flavus em grãos armazenados de amendoim (Arachis hypogaea L., que foram produzidos com distintos procedimentos de calagem, de colheita e de secagem. Para isto, foram avaliadas doze amostras de grãos de amendoim, cv. Botutatu, provenientes de plantas cultivadas em área que recebeu ou não a aplicação de calcário, colhidas aos 104, 114 e 124 dias após a semeadura e secas em condições ambientais e em estufa. Aos 12 e 18 meses de armazenamento, os grãos foram tratados com hipoclorito de sódio e incubados em BDA, a 20°C, por cinco dias. As espécies do grupo Aspergillus flavus foram identificadas após incubação em meio ADM. Posteriormente, o potencial toxígeno foi avaliado pelo método da cromatografia de camada delgada. A análise da freqüência de fungos revelou que os grãos de amendoim armazenados estavam contaminados por Aspergillus spp., Penicillium spp. e Fusarium spp. Os grãos de amendoim, provenientes da colheita antecipada, apresentaram maior contaminação pelo grupo Aspergillus flavus, sendo menor a proporção destes com potencial toxígeno.The objective of this work was to evaluate the effect of the storage on the potential of aflatoxin production by isolates from Aspergillus flavus group in peanut (Arachis hypogaea L.. These kernels were obtained from a field experiment with two areas (with or without lime, three times of harvest (104, 114 and 124 days after planting and two types of dryer conditions (ambient and chamber with forced air. After 12 and 18 months of storage, the kernels were treated with sodium hypochloride and incubated in a PDA at 20°C during five days. The isolates from Aspergillus flavus group were identified after incubation in ADM culture medium. The toxigenic potential was analyzed by thin layer chromatography. The genera detected were Aspergillus, Penicillium and
Digital signal processing with kernel methods
Rojo-Alvarez, José Luis; Muñoz-Marí, Jordi; Camps-Valls, Gustavo
2018-01-01
A realistic and comprehensive review of joint approaches to machine learning and signal processing algorithms, with application to communications, multimedia, and biomedical engineering systems Digital Signal Processing with Kernel Methods reviews the milestones in the mixing of classical digital signal processing models and advanced kernel machines statistical learning tools. It explains the fundamental concepts from both fields of machine learning and signal processing so that readers can quickly get up to speed in order to begin developing the concepts and application software in their own research. Digital Signal Processing with Kernel Methods provides a comprehensive overview of kernel methods in signal processing, without restriction to any application field. It also offers example applications and detailed benchmarking experiments with real and synthetic datasets throughout. Readers can find further worked examples with Matlab source code on a website developed by the authors. * Presents the necess...
The Flux OSKit: A Substrate for Kernel and Language Research
1997-10-01
unclassified Standard Form 298 (Rev. 8-98) Prescribed by ANSI Std Z39-18 tions. Our own microkernel -based OS, Fluke [17], puts almost all of the OSKit to use...kernels distance the language from the hardware; even microkernels and other extensible kernels enforce some default policy which often conflicts with a...be particu- larly useful in these research projects. 6.1.1 The Fluke OS In 1996 we developed an entirely new microkernel - based system called Fluke
Optimized Kernel Entropy Components.
Izquierdo-Verdiguier, Emma; Laparra, Valero; Jenssen, Robert; Gomez-Chova, Luis; Camps-Valls, Gustau
2017-06-01
This brief addresses two main issues of the standard kernel entropy component analysis (KECA) algorithm: the optimization of the kernel decomposition and the optimization of the Gaussian kernel parameter. KECA roughly reduces to a sorting of the importance of kernel eigenvectors by entropy instead of variance, as in the kernel principal components analysis. In this brief, we propose an extension of the KECA method, named optimized KECA (OKECA), that directly extracts the optimal features retaining most of the data entropy by means of compacting the information in very few features (often in just one or two). The proposed method produces features which have higher expressive power. In particular, it is based on the independent component analysis framework, and introduces an extra rotation to the eigen decomposition, which is optimized via gradient-ascent search. This maximum entropy preservation suggests that OKECA features are more efficient than KECA features for density estimation. In addition, a critical issue in both the methods is the selection of the kernel parameter, since it critically affects the resulting performance. Here, we analyze the most common kernel length-scale selection criteria. The results of both the methods are illustrated in different synthetic and real problems. Results show that OKECA returns projections with more expressive power than KECA, the most successful rule for estimating the kernel parameter is based on maximum likelihood, and OKECA is more robust to the selection of the length-scale parameter in kernel density estimation.
DEFF Research Database (Denmark)
Barndorff-Nielsen, Ole Eiler; Hansen, Peter Reinhard; Lunde, Asger
2011-01-01
In a recent paper we have introduced the class of realised kernel estimators of the increments of quadratic variation in the presence of noise. We showed that this estimator is consistent and derived its limit distribution under various assumptions on the kernel weights. In this paper we extend our...... that subsampling is impotent, in the sense that subsampling has no effect on the asymptotic distribution. Perhaps surprisingly, for the efficient smooth kernels, such as the Parzen kernel, we show that subsampling is harmful as it increases the asymptotic variance. We also study the performance of subsampled...
Energy Technology Data Exchange (ETDEWEB)
Duff, I.
1994-12-31
This workshop focuses on kernels for iterative software packages. Specifically, the three speakers discuss various aspects of sparse BLAS kernels. Their topics are: `Current status of user lever sparse BLAS`; Current status of the sparse BLAS toolkit`; and `Adding matrix-matrix and matrix-matrix-matrix multiply to the sparse BLAS toolkit`.
Validation of Born Traveltime Kernels
Baig, A. M.; Dahlen, F. A.; Hung, S.
2001-12-01
Most inversions for Earth structure using seismic traveltimes rely on linear ray theory to translate observed traveltime anomalies into seismic velocity anomalies distributed throughout the mantle. However, ray theory is not an appropriate tool to use when velocity anomalies have scale lengths less than the width of the Fresnel zone. In the presence of these structures, we need to turn to a scattering theory in order to adequately describe all of the features observed in the waveform. By coupling the Born approximation to ray theory, the first order dependence of heterogeneity on the cross-correlated traveltimes (described by the Fréchet derivative or, more colourfully, the banana-doughnut kernel) may be determined. To determine for what range of parameters these banana-doughnut kernels outperform linear ray theory, we generate several random media specified by their statistical properties, namely the RMS slowness perturbation and the scale length of the heterogeneity. Acoustic waves are numerically generated from a point source using a 3-D pseudo-spectral wave propagation code. These waves are then recorded at a variety of propagation distances from the source introducing a third parameter to the problem: the number of wavelengths traversed by the wave. When all of the heterogeneity has scale lengths larger than the width of the Fresnel zone, ray theory does as good a job at predicting the cross-correlated traveltime as the banana-doughnut kernels do. Below this limit, wavefront healing becomes a significant effect and ray theory ceases to be effective even though the kernels remain relatively accurate provided the heterogeneity is weak. The study of wave propagation in random media is of a more general interest and we will also show our measurements of the velocity shift and the variance of traveltime compare to various theoretical predictions in a given regime.
Classification With Truncated Distance Kernel.
Huang, Xiaolin; Suykens, Johan A K; Wang, Shuning; Hornegger, Joachim; Maier, Andreas
2018-05-01
This brief proposes a truncated distance (TL1) kernel, which results in a classifier that is nonlinear in the global region but is linear in each subregion. With this kernel, the subregion structure can be trained using all the training data and local linear classifiers can be established simultaneously. The TL1 kernel has good adaptiveness to nonlinearity and is suitable for problems which require different nonlinearities in different areas. Though the TL1 kernel is not positive semidefinite, some classical kernel learning methods are still applicable which means that the TL1 kernel can be directly used in standard toolboxes by replacing the kernel evaluation. In numerical experiments, the TL1 kernel with a pregiven parameter achieves similar or better performance than the radial basis function kernel with the parameter tuned by cross validation, implying the TL1 kernel a promising nonlinear kernel for classification tasks.
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
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 capab...
Directory of Open Access Journals (Sweden)
Eliana Maria Guarienti
2005-09-01
Full Text Available Cerca de 90% da produção de trigo no Brasil está localizada nos estados do Paraná, do Rio Grande do Sul e de Santa Catarina. Nesses estados, a variabilidade climática é muito expressiva, tornando a produção tritícola uma atividade de risco e fazendo com que o decréscimo da produção e da produtividade de trigo seja objeto de questionamento de grande número de investigadores. Este trabalho teve por objetivo verificar a influência da precipitação pluvial, da umidade relativa do ar e de excesso e déficit hídrico do solo no peso do hectolitro, peso de mil grãos e rendimento de grãos. Foram usados dados de experimentos com a cultivar de trigo Embrapa 16, conduzidos durante os anos de 1990 a 1998, em sete locais do Rio Grande do Sul e em quatro locais de Santa Catarina. A análise estatística realizada foi correlação múltipla. Verificou-se que: a a precipitação pluvial e o excesso hídrico do solo afetaram negativamente o peso do hectolitro, peso de mil grãos e rendimento de grãos, e a umidade relativa do ar influenciou tanto positiva quanto negativamente essas variáveis; b o déficit hídrico do solo afetou positivamente o peso do hectolitro, peso de mil grãos e rendimento de grãos após a maturação fisiológica, isto é, nos dez primeiros dias anteriores à colheita, e negativamente nos demais períodos.About 90% of the wheat production in Brazil is located in Paraná, Rio Grande do Sul and Santa Catarina states. In these states there is a considerable climatic variability and consequently wheat production becomes a risky activity. Therefore, the decrease of wheat production and grain yield has been analyzed by a great number of investigators. This work aimed to verify the influence of rainfall, relative humidity, and water excess and deficit on test weight, thousand kernel weight, and grain yield of wheat. Data of Embrapa 16 wheat cultivar, obtained during the 1990-98 period, in seven Rio Grande do Sul state
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
Doranti Tiritan, Carolina; Hackspacher, Peter C.; Glasmacher, Ulrich A.
2014-05-01
The Poços de Caldas Plateau in the southeastern Brazil, and it is characterized by a high relief topography supported by the pre-Cambrian crystalline rocks and by the Poços de Caldas Alkaline Massif (PCAM). Ulbrich et al (2002) determine that the ages for the predominant PCAM intermediate rocks were constrained ~83Ma. In addition, geologic observations indicates the phonolites, tinguaites and nepheline syenites were emplaced in a continuous and rapid sequence lasting between 1 to 2 Ma. The topography is characterized by dissected plateau with irregular topographic ridges and peaks with elevations between 900 and 1300m (a.s.l.) on the metamorphic basement and from 1300 to 1700m (a.s.l) on the PCAM region. Therefore, the aim of the work was quantify the main processes that were responsible for the evolution of the landscape by using methods as the low temperature thermochronology and the 3D thermokinematic modeling, for obtaining data of uplift and erosion rates and to correlate them with the thermal gradients of the region. The 3D thermokinematic modeling was obtained using the software code PECUBE (Braun 2003).
DEFF Research Database (Denmark)
Barndorff-Nielsen, Ole Eiler; Hansen, P. Reinhard; Lunde, Asger
2009-01-01
and find a remarkable level of agreement. We identify some features of the high-frequency data, which are challenging for realized kernels. They are when there are local trends in the data, over periods of around 10 minutes, where the prices and quotes are driven up or down. These can be associated......Realized kernels use high-frequency data to estimate daily volatility of individual stock prices. They can be applied to either trade or quote data. Here we provide the details of how we suggest implementing them in practice. We compare the estimates based on trade and quote data for the same stock...
Adaptive metric kernel regression
DEFF Research Database (Denmark)
Goutte, Cyril; Larsen, Jan
2000-01-01
Kernel smoothing is a widely used non-parametric pattern recognition technique. By nature, it suffers from the curse of dimensionality and is usually difficult to apply to high input dimensions. In this contribution, we propose an algorithm that adapts the input metric used in multivariate...... regression by minimising a cross-validation estimate of the generalisation error. This allows to automatically adjust the importance of different dimensions. The improvement in terms of modelling performance is illustrated on a variable selection task where the adaptive metric kernel clearly outperforms...
Adaptive Metric Kernel Regression
DEFF Research Database (Denmark)
Goutte, Cyril; Larsen, Jan
1998-01-01
Kernel smoothing is a widely used nonparametric pattern recognition technique. By nature, it suffers from the curse of dimensionality and is usually difficult to apply to high input dimensions. In this paper, we propose an algorithm that adapts the input metric used in multivariate regression...... by minimising a cross-validation estimate of the generalisation error. This allows one to automatically adjust the importance of different dimensions. The improvement in terms of modelling performance is illustrated on a variable selection task where the adaptive metric kernel clearly outperforms the standard...
Kernel methods for deep learning
Cho, Youngmin
2012-01-01
We introduce a new family of positive-definite kernels that mimic the computation in large neural networks. We derive the different members of this family by considering neural networks with different activation functions. Using these kernels as building blocks, we also show how to construct other positive-definite kernels by operations such as composition, multiplication, and averaging. We explore the use of these kernels in standard models of supervised learning, such as support vector mach...
DEFF Research Database (Denmark)
Barndorff-Nielsen, Ole; Hansen, Peter Reinhard; Lunde, Asger
We propose a multivariate realised kernel to estimate the ex-post covariation of log-prices. We show this new consistent estimator is guaranteed to be positive semi-definite and is robust to measurement noise of certain types and can also handle non-synchronous trading. It is the first estimator...
DEFF Research Database (Denmark)
Sommer, Stefan Horst; Lauze, Francois Bernard; Nielsen, Mads
2011-01-01
In the LDDMM framework, optimal warps for image registration are found as end-points of critical paths for an energy functional, and the EPDiff equations describe the evolution along such paths. The Large Deformation Diffeomorphic Kernel Bundle Mapping (LDDKBM) extension of LDDMM allows scale space...
Spafford, Eugene H.; Mckendry, Martin S.
1986-01-01
An overview of the internal structure of the Clouds kernel was presented. An indication of how these structures will interact in the prototype Clouds implementation is given. Many specific details have yet to be determined and await experimentation with an actual working system.
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.
Viscosity kernel of molecular fluids
DEFF Research Database (Denmark)
Puscasu, Ruslan; Todd, Billy; Daivis, Peter
2010-01-01
, temperature, and chain length dependencies of the reciprocal and real-space viscosity kernels are presented. We find that the density has a major effect on the shape of the kernel. The temperature range and chain lengths considered here have by contrast less impact on the overall normalized shape. Functional...... forms that fit the wave-vector-dependent kernel data over a large density and wave-vector range have also been tested. Finally, a structural normalization of the kernels in physical space is considered. Overall, the real-space viscosity kernel has a width of roughly 3–6 atomic diameters, which means...
Directory of Open Access Journals (Sweden)
Fernanda Fátima Caniato
2007-12-01
Full Text Available O objetivo deste trabalho foi quantificar os teores de açúcares solúveis totais (AST, açúcares redutores (AR e amido em nove cultivares de milho colhidos no estádio verde, uma vez que estes influenciam diretamente o sabor, o aroma e a aceitação do consumidor. O experimento foi conduzido em Ponte Nova, MG, no ano agrícola 2002/2003. Para a determinação das características, foi retirada uma amostra de três espigas de cada unidade experimental. O ponto de colheita foi identificado por método empírico, quando o estilo-estigma se desprendia da espiga com facilidade. As cultivares apresentaram umidades diferentes no momento da colheita, variando de 56 a 64%. Não foi detectada diferença entre as cultivares quanto aos teores de AR, porém, P3232 e AG4051 apresentaram boa relação entre AR e AST, destacando-se para esta finalidade.Total soluble sugars (TSS, reducing sugars (RS, moisture and starch directly influence flavor and consumption. In this work, TSS, RS and starch were determined at harvest in young kernels of nine corn cultivars. The experiment was carried out in Ponte Nova, MG, Brazil. To determine chemical characters of young kernels a sample was obtained of three cobs from each experimental plot. Corn ears were harvested using an empirical method, when the silk was loose from the ear. Moisture varied among samples of different cultivars at harvest. Moisture content ranged from 56 to 64%. There were no differences among cultivars regarding RS. However, cultivars P3232 and AG4051 showed good RS to TSS ratio, being adequate for fresh consumption.
Verified OS Interface Code Synthesis
2016-12-01
results into the larger proof framework of the seL4 microkernel to be directly usable in practice. Beyond the stated project goals, the solution...CakeML, can now also be used in the Isabelle/HOL system that was used for the verified seL4 microkernel. This combination increases proof productivity...were used for the verified ML compiler CakeML, can now also be used in the Isabelle/HOL system that was used for the verified seL4 microkernel. This
Variable Kernel Density Estimation
Terrell, George R.; Scott, David W.
1992-01-01
We investigate some of the possibilities for improvement of univariate and multivariate kernel density estimates by varying the window over the domain of estimation, pointwise and globally. Two general approaches are to vary the window width by the point of estimation and by point of the sample observation. The first possibility is shown to be of little efficacy in one variable. In particular, nearest-neighbor estimators in all versions perform poorly in one and two dimensions, but begin to b...
Steerability of Hermite Kernel
Czech Academy of Sciences Publication Activity Database
Yang, Bo; Flusser, Jan; Suk, Tomáš
2013-01-01
Roč. 27, č. 4 (2013), 1354006-1-1354006-25 ISSN 0218-0014 R&D Projects: GA ČR GAP103/11/1552 Institutional support: RVO:67985556 Keywords : Hermite polynomials * Hermite kernel * steerability * adaptive filtering Subject RIV: JD - Computer Applications, Robotics Impact factor: 0.558, year: 2013 http://library.utia.cas.cz/separaty/2013/ZOI/yang-0394387. pdf
Scuba: scalable kernel-based gene prioritization.
Zampieri, Guido; Tran, Dinh Van; Donini, Michele; Navarin, Nicolò; Aiolli, Fabio; Sperduti, Alessandro; Valle, Giorgio
2018-01-25
The uncovering of genes linked to human diseases is a pressing challenge in molecular biology and precision medicine. This task is often hindered by the large number of candidate genes and by the heterogeneity of the available information. Computational methods for the prioritization of candidate genes can help to cope with these problems. In particular, kernel-based methods are a powerful resource for the integration of heterogeneous biological knowledge, however, their practical implementation is often precluded by their limited scalability. We propose Scuba, a scalable kernel-based method for gene prioritization. It implements a novel multiple kernel learning approach, based on a semi-supervised perspective and on the optimization of the margin distribution. Scuba is optimized to cope with strongly unbalanced settings where known disease genes are few and large scale predictions are required. Importantly, it is able to efficiently deal both with a large amount of candidate genes and with an arbitrary number of data sources. As a direct consequence of scalability, Scuba integrates also a new efficient strategy to select optimal kernel parameters for each data source. We performed cross-validation experiments and simulated a realistic usage setting, showing that Scuba outperforms a wide range of state-of-the-art methods. Scuba achieves state-of-the-art performance and has enhanced scalability compared to existing kernel-based approaches for genomic data. This method can be useful to prioritize candidate genes, particularly when their number is large or when input data is highly heterogeneous. The code is freely available at https://github.com/gzampieri/Scuba .
Linear and kernel methods for multivariate change detection
DEFF Research Database (Denmark)
Canty, Morton J.; Nielsen, Allan Aasbjerg
2012-01-01
), as well as maximum autocorrelation factor (MAF) and minimum noise fraction (MNF) analyses of IR-MAD images, both linear and kernel-based (nonlinear), may further enhance change signals relative to no-change background. IDL (Interactive Data Language) implementations of IR-MAD, automatic radiometric...... normalization, and kernel PCA/MAF/MNF transformations are presented that function as transparent and fully integrated extensions of the ENVI remote sensing image analysis environment. The train/test approach to kernel PCA is evaluated against a Hebbian learning procedure. Matlab code is also available...... that allows fast data exploration and experimentation with smaller datasets. New, multiresolution versions of IR-MAD that accelerate convergence and that further reduce no-change background noise are introduced. Computationally expensive matrix diagonalization and kernel image projections are programmed...
Performance analysis and kernel size study of the Lynx real-time operating system
Liu, Yuan-Kwei; Gibson, James S.; Fernquist, Alan R.
1993-01-01
This paper analyzes the Lynx real-time operating system (LynxOS), which has been selected as the operating system for the Space Station Freedom Data Management System (DMS). The features of LynxOS are compared to other Unix-based operating system (OS). The tools for measuring the performance of LynxOS, which include a high-speed digital timer/counter board, a device driver program, and an application program, are analyzed. The timings for interrupt response, process creation and deletion, threads, semaphores, shared memory, and signals are measured. The memory size of the DMS Embedded Data Processor (EDP) is limited. Besides, virtual memory is not suitable for real-time applications because page swap timing may not be deterministic. Therefore, the DMS software, including LynxOS, has to fit in the main memory of an EDP. To reduce the LynxOS kernel size, the following steps are taken: analyzing the factors that influence the kernel size; identifying the modules of LynxOS that may not be needed in an EDP; adjusting the system parameters of LynxOS; reconfiguring the device drivers used in the LynxOS; and analyzing the symbol table. The reductions in kernel disk size, kernel memory size and total kernel size reduction from each step mentioned above are listed and analyzed.
Kernel Machine SNP-set Testing under Multiple Candidate Kernels
Wu, Michael C.; Maity, Arnab; Lee, Seunggeun; Simmons, Elizabeth M.; Harmon, Quaker E.; Lin, Xinyi; Engel, Stephanie M.; Molldrem, Jeffrey J.; Armistead, Paul M.
2013-01-01
Joint testing for the cumulative effect of multiple single nucleotide polymorphisms grouped on the basis of prior biological knowledge has become a popular and powerful strategy for the analysis of large scale genetic association studies. The kernel machine (KM) testing framework is a useful approach that has been proposed for testing associations between multiple genetic variants and many different types of complex traits by comparing pairwise similarity in phenotype between subjects to pairwise similarity in genotype, with similarity in genotype defined via a kernel function. An advantage of the KM framework is its flexibility: choosing different kernel functions allows for different assumptions concerning the underlying model and can allow for improved power. In practice, it is difficult to know which kernel to use a priori since this depends on the unknown underlying trait architecture and selecting the kernel which gives the lowest p-value can lead to inflated type I error. Therefore, we propose practical strategies for KM testing when multiple candidate kernels are present based on constructing composite kernels and based on efficient perturbation procedures. We demonstrate through simulations and real data applications that the procedures protect the type I error rate and can lead to substantially improved power over poor choices of kernels and only modest differences in power versus using the best candidate kernel. PMID:23471868
MULTITASKER, Multitasking Kernel for C and FORTRAN Under UNIX
International Nuclear Information System (INIS)
Brooks, E.D. III
1988-01-01
1 - Description of program or function: MULTITASKER implements a multitasking kernel for the C and FORTRAN programming languages that runs under UNIX. The kernel provides a multitasking environment which serves two purposes. The first is to provide an efficient portable environment for the development, debugging, and execution of production multiprocessor programs. The second is to provide a means of evaluating the performance of a multitasking program on model multiprocessor hardware. The performance evaluation features require no changes in the application program source and are implemented as a set of compile- and run-time options in the kernel. 2 - Method of solution: The FORTRAN interface to the kernel is identical in function to the CRI multitasking package provided for the Cray XMP. This provides a migration path to high speed (but small N) multiprocessors once the application has been coded and debugged. With use of the UNIX m4 macro preprocessor, source compatibility can be achieved between the UNIX code development system and the target Cray multiprocessor. The kernel also provides a means of evaluating a program's performance on model multiprocessors. Execution traces may be obtained which allow the user to determine kernel overhead, memory conflicts between various tasks, and the average concurrency being exploited. The kernel may also be made to switch tasks every cpu instruction with a random execution ordering. This allows the user to look for unprotected critical regions in the program. These features, implemented as a set of compile- and run-time options, cause extra execution overhead which is not present in the standard production version of the kernel
Smolka, Gert
1994-01-01
Oz is a concurrent language providing for functional, object-oriented, and constraint programming. This paper defines Kernel Oz, a semantically complete sublanguage of Oz. It was an important design requirement that Oz be definable by reduction to a lean kernel language. The definition of Kernel Oz introduces three essential abstractions: the Oz universe, the Oz calculus, and the actor model. The Oz universe is a first-order structure defining the values and constraints Oz computes with. The ...
2010-01-01
... 7 Agriculture 8 2010-01-01 2010-01-01 false Edible kernel. 981.7 Section 981.7 Agriculture... Regulating Handling Definitions § 981.7 Edible kernel. Edible kernel means a kernel, piece, or particle of almond kernel that is not inedible. [41 FR 26852, June 30, 1976] ...
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 as...
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, or...
DEFF Research Database (Denmark)
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...... which has these three properties which are all essential for empirical work in this area. We derive the large sample asymptotics of this estimator and assess its accuracy using a Monte Carlo study. We implement the estimator on some US equity data, comparing our results to previous work which has used...
Clustering via Kernel Decomposition
DEFF Research Database (Denmark)
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....
Global Polynomial Kernel Hazard Estimation
DEFF Research Database (Denmark)
Hiabu, Munir; Miranda, Maria Dolores Martínez; Nielsen, Jens Perch
2015-01-01
This paper introduces a new bias reducing method for kernel hazard estimation. The method is called global polynomial adjustment (GPA). It is a global correction which is applicable to any kernel hazard estimator. The estimator works well from a theoretical point of view as it asymptotically redu...
A compact kernel for the calculus of inductive constructions
Indian Academy of Sciences (India)
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 ...
Bruemmer, David J [Idaho Falls, ID
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.
Wilson and Domainwall Kernels on Oakforest-PACS
Kanamori, Issaku; Matsufuru, Hideo
2018-03-01
We report the performance of Wilson and Domainwall Kernels on a new Intel Xeon Phi Knights Landing based machine named Oakforest-PACS, which is co-hosted by University of Tokyo and Tsukuba University and is currently fastest in Japan. This machine uses Intel Omni-Path for the internode network. We compare performance with several types of implementation including that makes use of the Grid library. The code is incorporated with the code set Bridge++.
Design and Implementation of Malicious Code Detection Platform for iOS System%iOS系统恶意代码检测平台设计与实现
Institute of Scientific and Technical Information of China (English)
田庆宜
2013-01-01
随着苹果手机日益普及，苹果终端已成为黑客重要的攻击目标。黑客利用恶意代码窃取个人信息及窃财犯罪层出不穷。但目前执法部门缺乏对苹果iOS移动平台恶意代码的检测平台。文章以iOS平台安全模型为基础，在分析国内外攻击方法的基础上，设计了苹果恶意代码检测框架，在此基础上构筑了对iOS的app应用恶意代码检测平台。经实际测试，本平台具备对iOS系统恶意代码通讯流量及文件改变的检测能力。本平台总体成本较低，有利于装备基层执法部门。%With the increasing popularity of Apple mobile phone, apple terminal has become the important target of hackers. The malicious hackers steal personal information and crime emerge in an endless stream. But the law enforcement departments lack of detection platform for Apple iOS mobile platform of malicious code. This paper is based on the iOS platform security model, designed the apple of malicious code detection framework, on the basis of iOS app application platform to build a malicious code detection. The actual test, the platform has the ability of detecting malicious code iOS system communication trafifc and ifle change. The overall cost of the platform is relatively low, in favor of equipment in the basic law enforcement.
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, including...
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
DEFF Research Database (Denmark)
Nielsen, Allan Aasbjerg
2009-01-01
. Schölkopf et al. introduce kernel PCA. Shawe-Taylor and Cristianini is an excellent reference for kernel methods in general. Bishop and Press et al. describe kernel methods among many other subjects. Nielsen and Canty use kernel PCA to detect change in univariate airborne digital camera images. The kernel...... version of PCA handles nonlinearities by implicitly transforming data into high (even infinite) dimensional feature space via the kernel function and then performing a linear analysis in that space. In this paper we shall apply kernel versions of PCA, maximum autocorrelation factor (MAF) analysis...
kernel oil by lipolytic organisms
African Journals Online (AJOL)
USER
2010-08-02
Aug 2, 2010 ... Rancidity of extracted cashew oil was observed with cashew kernel stored at 70, 80 and 90% .... method of American Oil Chemist Society AOCS (1978) using glacial ..... changes occur and volatile products are formed that are.
Energy Technology Data Exchange (ETDEWEB)
Sheu, R.-D.; Chui, C.-S.; Jiang, S.-H. E-mail: shjiang@mx.nthu.edu.tw
2003-12-01
A simplified method, based on the integral of the first collision kernel, is presented for performing gamma-ray skyshine calculations for the collimated sources. The first collision kernels were calculated in air for a reference air density by use of the EGS4 Monte Carlo code. These kernels can be applied to other air densities by applying density corrections. The integral first collision kernel (IFCK) method has been used to calculate two of the ANSI/ANS skyshine benchmark problems and the results were compared with a number of other commonly used codes. Our results were generally in good agreement with others but only spend a small fraction of the computation time required by the Monte Carlo calculations. The scheme of the IFCK method for dealing with lots of source collimation geometry is also presented in this study.
Multivariate and semiparametric kernel regression
Härdle, Wolfgang; Müller, Marlene
1997-01-01
The paper gives an introduction to theory and application of multivariate and semiparametric kernel smoothing. Multivariate nonparametric density estimation is an often used pilot tool for examining the structure of data. Regression smoothing helps in investigating the association between covariates and responses. We concentrate on kernel smoothing using local polynomial fitting which includes the Nadaraya-Watson estimator. Some theory on the asymptotic behavior and bandwidth selection is pro...
DEFF Research Database (Denmark)
Barndorff-Nielsen, Ole E.
The density function of the gamma distribution is used as shift kernel in Brownian semistationary processes modelling the timewise behaviour of the velocity in turbulent regimes. This report presents exact and asymptotic properties of the second order structure function under such a model......, and relates these to results of von Karmann and Horwath. But first it is shown that the gamma kernel is interpretable as a Green’s function....
Point kernels and superposition methods for scatter dose calculations in brachytherapy
International Nuclear Information System (INIS)
Carlsson, A.K.
2000-01-01
Point kernels have been generated and applied for calculation of scatter dose distributions around monoenergetic point sources for photon energies ranging from 28 to 662 keV. Three different approaches for dose calculations have been compared: a single-kernel superposition method, a single-kernel superposition method where the point kernels are approximated as isotropic and a novel 'successive-scattering' superposition method for improved modelling of the dose from multiply scattered photons. An extended version of the EGS4 Monte Carlo code was used for generating the kernels and for benchmarking the absorbed dose distributions calculated with the superposition methods. It is shown that dose calculation by superposition at and below 100 keV can be simplified by using isotropic point kernels. Compared to the assumption of full in-scattering made by algorithms currently in clinical use, the single-kernel superposition method improves dose calculations in a half-phantom consisting of air and water. Further improvements are obtained using the successive-scattering superposition method, which reduces the overestimates of dose close to the phantom surface usually associated with kernel superposition methods at brachytherapy photon energies. It is also shown that scatter dose point kernels can be parametrized to biexponential functions, making them suitable for use with an effective implementation of the collapsed cone superposition algorithm. (author)
Protein fold recognition using geometric kernel data fusion.
Zakeri, Pooya; Jeuris, Ben; Vandebril, Raf; Moreau, Yves
2014-07-01
Various approaches based on features extracted from protein sequences and often machine learning methods have been used in the prediction of protein folds. Finding an efficient technique for integrating these different protein features has received increasing attention. In particular, kernel methods are an interesting class of techniques for integrating heterogeneous data. Various methods have been proposed to fuse multiple kernels. Most techniques for multiple kernel learning focus on learning a convex linear combination of base kernels. In addition to the limitation of linear combinations, working with such approaches could cause a loss of potentially useful information. We design several techniques to combine kernel matrices by taking more involved, geometry inspired means of these matrices instead of convex linear combinations. We consider various sequence-based protein features including information extracted directly from position-specific scoring matrices and local sequence alignment. We evaluate our methods for classification on the SCOP PDB-40D benchmark dataset for protein fold recognition. The best overall accuracy on the protein fold recognition test set obtained by our methods is ∼ 86.7%. This is an improvement over the results of the best existing approach. Moreover, our computational model has been developed by incorporating the functional domain composition of proteins through a hybridization model. It is observed that by using our proposed hybridization model, the protein fold recognition accuracy is further improved to 89.30%. Furthermore, we investigate the performance of our approach on the protein remote homology detection problem by fusing multiple string kernels. The MATLAB code used for our proposed geometric kernel fusion frameworks are publicly available at http://people.cs.kuleuven.be/∼raf.vandebril/homepage/software/geomean.php?menu=5/. © The Author 2014. Published by Oxford University Press.
Analysis of Linux kernel as a complex network
International Nuclear Information System (INIS)
Gao, Yichao; Zheng, Zheng; Qin, Fangyun
2014-01-01
Operating system (OS) acts as an intermediary between software and hardware in computer-based systems. In this paper, we analyze the core of the typical Linux OS, Linux kernel, as a complex network to investigate its underlying design principles. It is found that the Linux Kernel Network (LKN) is a directed network and its out-degree follows an exponential distribution while the in-degree follows a power-law distribution. The correlation between topology and functions is also explored, by which we find that LKN is a highly modularized network with 12 key communities. Moreover, we investigate the robustness of LKN under random failures and intentional attacks. The result shows that the failure of the large in-degree nodes providing basic services will do more damage on the whole system. Our work may shed some light on the design of complex software systems
Structured Kernel Dictionary Learning with Correlation Constraint for Object Recognition.
Wang, Zhengjue; Wang, Yinghua; Liu, Hongwei; Zhang, Hao
2017-06-21
In this paper, we propose a new discriminative non-linear dictionary learning approach, called correlation constrained structured kernel KSVD, for object recognition. The objective function for dictionary learning contains a reconstructive term and a discriminative term. In the reconstructive term, signals are implicitly non-linearly mapped into a space, where a structured kernel dictionary, each sub-dictionary of which lies in the span of the mapped signals from the corresponding class, is established. In the discriminative term, by analyzing the classification mechanism, the correlation constraint is proposed in kernel form, constraining the correlations between different discriminative codes, and restricting the coefficient vectors to be transformed into a feature space, where the features are highly correlated inner-class and nearly independent between-classes. The objective function is optimized by the proposed structured kernel KSVD. During the classification stage, the specific form of the discriminative feature is needless to be known, while the inner product of the discriminative feature with kernel matrix embedded is available, and is suitable for a linear SVM classifier. Experimental results demonstrate that the proposed approach outperforms many state-of-the-art dictionary learning approaches for face, scene and synthetic aperture radar (SAR) vehicle target recognition.
Influence Function and Robust Variant of Kernel Canonical Correlation Analysis
Alam, Md. Ashad; Fukumizu, Kenji; Wang, Yu-Ping
2017-01-01
Many unsupervised kernel methods rely on the estimation of the kernel covariance operator (kernel CO) or kernel cross-covariance operator (kernel CCO). Both kernel CO and kernel CCO are sensitive to contaminated data, even when bounded positive definite kernels are used. To the best of our knowledge, there are few well-founded robust kernel methods for statistical unsupervised learning. In addition, while the influence function (IF) of an estimator can characterize its robustness, asymptotic ...
PERI - auto-tuning memory-intensive kernels for multicore
International Nuclear Information System (INIS)
Williams, S; Carter, J; Oliker, L; Shalf, J; Yelick, K; Bailey, D; Datta, K
2008-01-01
We present an auto-tuning approach to optimize application performance on emerging multicore architectures. The methodology extends the idea of search-based performance optimizations, popular in linear algebra and FFT libraries, to application-specific computational kernels. Our work applies this strategy to sparse matrix vector multiplication (SpMV), the explicit heat equation PDE on a regular grid (Stencil), and a lattice Boltzmann application (LBMHD). We explore one of the broadest sets of multicore architectures in the high-performance computing literature, including the Intel Xeon Clovertown, AMD Opteron Barcelona, Sun Victoria Falls, and the Sony-Toshiba-IBM (STI) Cell. Rather than hand-tuning each kernel for each system, we develop a code generator for each kernel that allows us identify a highly optimized version for each platform, while amortizing the human programming effort. Results show that our auto-tuned kernel applications often achieve a better than 4x improvement compared with the original code. Additionally, we analyze a Roofline performance model for each platform to reveal hardware bottlenecks and software challenges for future multicore systems and applications
PERI - Auto-tuning Memory Intensive Kernels for Multicore
Energy Technology Data Exchange (ETDEWEB)
Bailey, David H; Williams, Samuel; Datta, Kaushik; Carter, Jonathan; Oliker, Leonid; Shalf, John; Yelick, Katherine; Bailey, David H
2008-06-24
We present an auto-tuning approach to optimize application performance on emerging multicore architectures. The methodology extends the idea of search-based performance optimizations, popular in linear algebra and FFT libraries, to application-specific computational kernels. Our work applies this strategy to Sparse Matrix Vector Multiplication (SpMV), the explicit heat equation PDE on a regular grid (Stencil), and a lattice Boltzmann application (LBMHD). We explore one of the broadest sets of multicore architectures in the HPC literature, including the Intel Xeon Clovertown, AMD Opteron Barcelona, Sun Victoria Falls, and the Sony-Toshiba-IBM (STI) Cell. Rather than hand-tuning each kernel for each system, we develop a code generator for each kernel that allows us to identify a highly optimized version for each platform, while amortizing the human programming effort. Results show that our auto-tuned kernel applications often achieve a better than 4X improvement compared with the original code. Additionally, we analyze a Roofline performance model for each platform to reveal hardware bottlenecks and software challenges for future multicore systems and applications.
Kernel versions of some orthogonal transformations
DEFF Research Database (Denmark)
Nielsen, Allan Aasbjerg
Kernel versions of orthogonal transformations such as principal components are based on a dual formulation also termed Q-mode analysis in which the data enter into the analysis via inner products in the Gram matrix only. In the kernel version the inner products of the original data are replaced...... by inner products between nonlinear mappings into higher dimensional feature space. Via kernel substitution also known as the kernel trick these inner products between the mappings are in turn replaced by a kernel function and all quantities needed in the analysis are expressed in terms of this kernel...... function. This means that we need not know the nonlinear mappings explicitly. Kernel principal component analysis (PCA) and kernel minimum noise fraction (MNF) analyses handle nonlinearities by implicitly transforming data into high (even infinite) dimensional feature space via the kernel function...
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
DEFF Research Database (Denmark)
Exterkate, Peter
Kernel ridge regression is gaining popularity as a data-rich nonlinear forecasting tool, which is applicable in many different contexts. This paper investigates the influence of the choice of kernel and the setting of tuning parameters on forecast accuracy. We review several popular kernels......, including polynomial kernels, the Gaussian kernel, and the Sinc kernel. We interpret the latter two kernels in terms of their smoothing properties, and we relate the tuning parameters associated to all these kernels to smoothness measures of the prediction function and to the signal-to-noise ratio. Based...... on these interpretations, we provide guidelines for selecting the tuning parameters from small grids using cross-validation. A Monte Carlo study confirms the practical usefulness of these rules of thumb. Finally, the flexible and smooth functional forms provided by the Gaussian and Sinc kernels makes them widely...
Integral equations with contrasting kernels
Directory of Open Access Journals (Sweden)
Theodore Burton
2008-01-01
Full Text Available In this paper we study integral equations of the form $x(t=a(t-\\int^t_0 C(t,sx(sds$ with sharply contrasting kernels typified by $C^*(t,s=\\ln (e+(t-s$ and $D^*(t,s=[1+(t-s]^{-1}$. The kernel assigns a weight to $x(s$ and these kernels have exactly opposite effects of weighting. Each type is well represented in the literature. Our first project is to show that for $a\\in L^2[0,\\infty$, then solutions are largely indistinguishable regardless of which kernel is used. This is a surprise and it leads us to study the essential differences. In fact, those differences become large as the magnitude of $a(t$ increases. The form of the kernel alone projects necessary conditions concerning the magnitude of $a(t$ which could result in bounded solutions. Thus, the next project is to determine how close we can come to proving that the necessary conditions are also sufficient. The third project is to show that solutions will be bounded for given conditions on $C$ regardless of whether $a$ is chosen large or small; this is important in real-world problems since we would like to have $a(t$ as the sum of a bounded, but badly behaved function, and a large well behaved function.
Kernel 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
Model selection for Gaussian kernel PCA denoising
DEFF Research Database (Denmark)
Jørgensen, Kasper Winther; Hansen, Lars Kai
2012-01-01
We propose kernel Parallel Analysis (kPA) for automatic kernel scale and model order selection in Gaussian kernel PCA. Parallel Analysis [1] is based on a permutation test for covariance and has previously been applied for model order selection in linear PCA, we here augment the procedure to also...... tune the Gaussian kernel scale of radial basis function based kernel PCA.We evaluate kPA for denoising of simulated data and the US Postal data set of handwritten digits. We find that kPA outperforms other heuristics to choose the model order and kernel scale in terms of signal-to-noise ratio (SNR...
Galiano Molina, Sebastián
2007-01-01
The main project’s objective is to design and build an OS deployment system taking advantage of the Linux OS and the Open Source community developments. This means to use existing technologies that modularize the system. With this philosophy in mind, the number of developed code lines within the project is keeping as small as possible. As REMBO, the OS deployment system to develop has to be transparent to the user. This means a system with a friendly user interface and no te...
Searching remote homology with spectral clustering with symmetry in neighborhood cluster kernels.
Directory of Open Access Journals (Sweden)
Ujjwal Maulik
Full Text Available Remote homology detection among proteins utilizing only the unlabelled sequences is a central problem in comparative genomics. The existing cluster kernel methods based on neighborhoods and profiles and the Markov clustering algorithms are currently the most popular methods for protein family recognition. The deviation from random walks with inflation or dependency on hard threshold in similarity measure in those methods requires an enhancement for homology detection among multi-domain proteins. We propose to combine spectral clustering with neighborhood kernels in Markov similarity for enhancing sensitivity in detecting homology independent of "recent" paralogs. The spectral clustering approach with new combined local alignment kernels more effectively exploits the unsupervised protein sequences globally reducing inter-cluster walks. When combined with the corrections based on modified symmetry based proximity norm deemphasizing outliers, the technique proposed in this article outperforms other state-of-the-art cluster kernels among all twelve implemented kernels. The comparison with the state-of-the-art string and mismatch kernels also show the superior performance scores provided by the proposed kernels. Similar performance improvement also is found over an existing large dataset. Therefore the proposed spectral clustering framework over combined local alignment kernels with modified symmetry based correction achieves superior performance for unsupervised remote homolog detection even in multi-domain and promiscuous domain proteins from Genolevures database families with better biological relevance. Source code available upon request.sarkar@labri.fr.
DEFF Research Database (Denmark)
Walder, Christian; Henao, Ricardo; Mørup, Morten
We present three generalisations of Kernel Principal Components Analysis (KPCA) which incorporate knowledge of the class labels of a subset of the data points. The first, MV-KPCA, penalises within class variances similar to Fisher discriminant analysis. The second, LSKPCA is a hybrid of least...... squares regression and kernel PCA. The final LR-KPCA is an iteratively reweighted version of the previous which achieves a sigmoid loss function on the labeled points. We provide a theoretical risk bound as well as illustrative experiments on real and toy data sets....
Calculation of dose point kernels for five radionuclides used in radio-immunotherapy
International Nuclear Information System (INIS)
Okigaki, S.; Ito, A.; Uchida, I.; Tomaru, T.
1994-01-01
With the recent interest in radioimmunotherapy, attention has been given to calculation of dose distribution from beta rays and monoenergetic electrons in tissue. Dose distribution around a point source of a beta ray emitting radioisotope is referred to as a beta dose point kernel. Beta dose point kernels for five radionuclides such as 131 I, 186 Re, 32 P, 188 Re, and 90 Y appropriate for radioimmunotherapy are calculated by Monte Carlo method using the EGS4 code system. Present results were compared with the published data of experiments and other calculations. Accuracy and precisions of beta dose point kernels are discussed. (author)
Model selection in kernel ridge regression
DEFF Research Database (Denmark)
Exterkate, Peter
2013-01-01
Kernel ridge regression is a technique to perform ridge regression with a potentially infinite number of nonlinear transformations of the independent variables as regressors. This method is gaining popularity as a data-rich nonlinear forecasting tool, which is applicable in many different contexts....... The influence of the choice of kernel and the setting of tuning parameters on forecast accuracy is investigated. Several popular kernels are reviewed, including polynomial kernels, the Gaussian kernel, and the Sinc kernel. The latter two kernels are interpreted in terms of their smoothing properties......, and the tuning parameters associated to all these kernels are related to smoothness measures of the prediction function and to the signal-to-noise ratio. Based on these interpretations, guidelines are provided for selecting the tuning parameters from small grids using cross-validation. A Monte Carlo study...
Multiple Kernel Learning with Data Augmentation
2016-11-22
JMLR: Workshop and Conference Proceedings 63:49–64, 2016 ACML 2016 Multiple Kernel Learning with Data Augmentation Khanh Nguyen nkhanh@deakin.edu.au...University, Australia Editors: Robert J. Durrant and Kee-Eung Kim Abstract The motivations of multiple kernel learning (MKL) approach are to increase... kernel expres- siveness capacity and to avoid the expensive grid search over a wide spectrum of kernels . A large amount of work has been proposed to
A kernel version of multivariate alteration detection
DEFF Research Database (Denmark)
Nielsen, Allan Aasbjerg; Vestergaard, Jacob Schack
2013-01-01
Based on the established methods kernel canonical correlation analysis and multivariate alteration detection we introduce a kernel version of multivariate alteration detection. A case study with SPOT HRV data shows that the kMAD variates focus on extreme change observations.......Based on the established methods kernel canonical correlation analysis and multivariate alteration detection we introduce a kernel version of multivariate alteration detection. A case study with SPOT HRV data shows that the kMAD variates focus on extreme change observations....
Sun, L.G.; De Visser, C.C.; Chu, Q.P.; Mulder, J.A.
2012-01-01
The optimality of the kernel number and kernel centers plays a significant role in determining the approximation power of nearly all kernel methods. However, the process of choosing optimal kernels is always formulated as a global optimization task, which is hard to accomplish. Recently, an
Cold moderator scattering kernels
International Nuclear Information System (INIS)
MacFarlane, R.E.
1989-01-01
New thermal-scattering-law files in ENDF format have been developed for solid methane, liquid methane liquid ortho- and para-hydrogen, and liquid ortho- and para-deuterium using up-to-date models that include such effects as incoherent elastic scattering in the solid, diffusion and hindered vibration and rotations in the liquids, and spin correlations for the hydrogen and deuterium. These files were generated with the new LEAPR module of the NJOY Nuclear Data Processing System. Other modules of this system were used to produce cross sections for these moderators in the correct format for the continuous-energy Monte Carlo code (MCNP) being used for cold-moderator-design calculations at the Los Alamos Neutron Scattering Center (LANSCE). 20 refs., 14 figs
Complex use of cottonseed kernels
Energy Technology Data Exchange (ETDEWEB)
Glushenkova, A I
1977-01-01
A review with 41 references is made on the manufacture of oil, protein, and other products from cottonseed, the effects of gossypol on protein yield and quality and technology of gossypol removal. A process eliminating thermal treatment of the kernels and permitting the production of oil, proteins, phytin, gossypol, sugar, sterols, phosphatides, tocopherols, and residual shells and baggase is described.
Kernel regression with functional response
Ferraty, Frédéric; Laksaci, Ali; Tadj, Amel; Vieu, Philippe
2011-01-01
We consider kernel regression estimate when both the response variable and the explanatory one are functional. The rates of uniform almost complete convergence are stated as function of the small ball probability of the predictor and as function of the entropy of the set on which uniformity is obtained.
GRIM : Leveraging GPUs for Kernel integrity monitoring
Koromilas, Lazaros; Vasiliadis, Giorgos; Athanasopoulos, Ilias; Ioannidis, Sotiris
2016-01-01
Kernel rootkits can exploit an operating system and enable future accessibility and control, despite all recent advances in software protection. A promising defense mechanism against rootkits is Kernel Integrity Monitor (KIM) systems, which inspect the kernel text and data to discover any malicious
Paramecium: An Extensible Object-Based Kernel
van Doorn, L.; Homburg, P.; Tanenbaum, A.S.
1995-01-01
In this paper we describe the design of an extensible kernel, called Paramecium. This kernel uses an object-based software architecture which together with instance naming, late binding and explicit overrides enables easy reconfiguration. Determining which components reside in the kernel protection
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…
Veto-Consensus Multiple Kernel Learning
Zhou, Y.; Hu, N.; Spanos, C.J.
2016-01-01
We propose Veto-Consensus Multiple Kernel Learning (VCMKL), a novel way of combining multiple kernels such that one class of samples is described by the logical intersection (consensus) of base kernelized decision rules, whereas the other classes by the union (veto) of their complements. The
Directory of Open Access Journals (Sweden)
Senyue Zhang
2016-01-01
Full Text Available According to the characteristics that the kernel function of extreme learning machine (ELM and its performance have a strong correlation, a novel extreme learning machine based on a generalized triangle Hermitian kernel function was proposed in this paper. First, the generalized triangle Hermitian kernel function was constructed by using the product of triangular kernel and generalized Hermite Dirichlet kernel, and the proposed kernel function was proved as a valid kernel function of extreme learning machine. Then, the learning methodology of the extreme learning machine based on the proposed kernel function was presented. The biggest advantage of the proposed kernel is its kernel parameter values only chosen in the natural numbers, which thus can greatly shorten the computational time of parameter optimization and retain more of its sample data structure information. Experiments were performed on a number of binary classification, multiclassification, and regression datasets from the UCI benchmark repository. The experiment results demonstrated that the robustness and generalization performance of the proposed method are outperformed compared to other extreme learning machines with different kernels. Furthermore, the learning speed of proposed method is faster than support vector machine (SVM methods.
Zhang, Wencan; Leong, Siew Mun; Zhao, Feifei; Zhao, Fangju; Yang, Tiankui; Liu, Shaoquan
2018-05-01
With an interest to enhance the aroma of palm kernel oil (PKO), Viscozyme L, an enzyme complex containing a wide range of carbohydrases, was applied to alter the carbohydrates in palm kernels (PK) to modulate the formation of volatiles upon kernel roasting. After Viscozyme treatment, the content of simple sugars and free amino acids in PK increased by 4.4-fold and 4.5-fold, respectively. After kernel roasting and oil extraction, significantly more 2,5-dimethylfuran, 2-[(methylthio)methyl]-furan, 1-(2-furanyl)-ethanone, 1-(2-furyl)-2-propanone, 5-methyl-2-furancarboxaldehyde and 2-acetyl-5-methylfuran but less 2-furanmethanol and 2-furanmethanol acetate were found in treated PKO; the correlation between their formation and simple sugar profile was estimated by using partial least square regression (PLS1). Obvious differences in pyrroles and Strecker aldehydes were also found between the control and treated PKOs. Principal component analysis (PCA) clearly discriminated the treated PKOs from that of control PKOs on the basis of all volatile compounds. Such changes in volatiles translated into distinct sensory attributes, whereby treated PKO was more caramelic and burnt after aqueous extraction and more nutty, roasty, caramelic and smoky after solvent extraction. Copyright © 2018 Elsevier Ltd. All rights reserved.
Wigner functions defined with Laplace transform kernels.
Oh, Se Baek; Petruccelli, Jonathan C; Tian, Lei; Barbastathis, George
2011-10-24
We propose a new Wigner-type phase-space function using Laplace transform kernels--Laplace kernel Wigner function. Whereas momentum variables are real in the traditional Wigner function, the Laplace kernel Wigner function may have complex momentum variables. Due to the property of the Laplace transform, a broader range of signals can be represented in complex phase-space. We show that the Laplace kernel Wigner function exhibits similar properties in the marginals as the traditional Wigner function. As an example, we use the Laplace kernel Wigner function to analyze evanescent waves supported by surface plasmon polariton. © 2011 Optical Society of America
Credit scoring analysis using kernel discriminant
Widiharih, T.; Mukid, M. A.; Mustafid
2018-05-01
Credit scoring model is an important tool for reducing the risk of wrong decisions when granting credit facilities to applicants. This paper investigate the performance of kernel discriminant model in assessing customer credit risk. Kernel discriminant analysis is a non- parametric method which means that it does not require any assumptions about the probability distribution of the input. The main ingredient is a kernel that allows an efficient computation of Fisher discriminant. We use several kernel such as normal, epanechnikov, biweight, and triweight. The models accuracy was compared each other using data from a financial institution in Indonesia. The results show that kernel discriminant can be an alternative method that can be used to determine who is eligible for a credit loan. In the data we use, it shows that a normal kernel is relevant to be selected for credit scoring using kernel discriminant model. Sensitivity and specificity reach to 0.5556 and 0.5488 respectively.
Testing Infrastructure for Operating System Kernel Development
DEFF Research Database (Denmark)
Walter, Maxwell; Karlsson, Sven
2014-01-01
Testing is an important part of system development, and to test effectively we require knowledge of the internal state of the system under test. Testing an operating system kernel is a challenge as it is the operating system that typically provides access to this internal state information. Multi......-core kernels pose an even greater challenge due to concurrency and their shared kernel state. In this paper, we present a testing framework that addresses these challenges by running the operating system in a virtual machine, and using virtual machine introspection to both communicate with the kernel...... and obtain information about the system. We have also developed an in-kernel testing API that we can use to develop a suite of unit tests in the kernel. We are using our framework for for the development of our own multi-core research kernel....
Kernel parameter dependence in spatial factor analysis
DEFF Research Database (Denmark)
Nielsen, Allan Aasbjerg
2010-01-01
kernel PCA. Shawe-Taylor and Cristianini [4] is an excellent reference for kernel methods in general. Bishop [5] and Press et al. [6] describe kernel methods among many other subjects. The kernel version of PCA handles nonlinearities by implicitly transforming data into high (even infinite) dimensional...... feature space via the kernel function and then performing a linear analysis in that space. In this paper we shall apply a kernel version of maximum autocorrelation factor (MAF) [7, 8] analysis to irregularly sampled stream sediment geochemistry data from South Greenland and illustrate the dependence...... of the kernel width. The 2,097 samples each covering on average 5 km2 are analyzed chemically for the content of 41 elements....
Kernel Bayesian ART and ARTMAP.
Masuyama, Naoki; Loo, Chu Kiong; Dawood, Farhan
2018-02-01
Adaptive Resonance Theory (ART) is one of the successful approaches to resolving "the plasticity-stability dilemma" in neural networks, and its supervised learning model called ARTMAP is a powerful tool for classification. Among several improvements, such as Fuzzy or Gaussian based models, the state of art model is Bayesian based one, while solving the drawbacks of others. However, it is known that the Bayesian approach for the high dimensional and a large number of data requires high computational cost, and the covariance matrix in likelihood becomes unstable. This paper introduces Kernel Bayesian ART (KBA) and ARTMAP (KBAM) by integrating Kernel Bayes' Rule (KBR) and Correntropy Induced Metric (CIM) to Bayesian ART (BA) and ARTMAP (BAM), respectively, while maintaining the properties of BA and BAM. The kernel frameworks in KBA and KBAM are able to avoid the curse of dimensionality. In addition, the covariance-free Bayesian computation by KBR provides the efficient and stable computational capability to KBA and KBAM. Furthermore, Correntropy-based similarity measurement allows improving the noise reduction ability even in the high dimensional space. The simulation experiments show that KBA performs an outstanding self-organizing capability than BA, and KBAM provides the superior classification ability than BAM, respectively. Copyright © 2017 Elsevier Ltd. All rights reserved.
Calculation of the Kernel scattering for thermal neutrons in H2O e D2O
International Nuclear Information System (INIS)
Leal, L.C.; Assis, J.T. de
1981-01-01
A computer code, using the Nelkin-and Butler models for the calculations of the Kernel scattering, was developed. Calculations of the thermal neutron flux in an homogeneous-and infinite medium with a 1 /v absorber in 30 energy groups were done and compared with experimental data. The reactors parameters calculated by the Hammer code (in the original version and with the new library generated by the authors' code) are presented. (E.G) [pt
Schnack, D. D.; Glasser, A. H.
1996-11-01
NIMROD is a new code system that is being developed for the analysis of modern fusion experiments. It is being designed from the beginning to make the maximum use of massively parallel computer architectures and computer graphics. The NIMROD physics kernel solves the three-dimensional, time-dependent two-fluid equations with neo-classical effects in toroidal geometry of arbitrary poloidal cross section. The NIMROD system also includes a pre-processor, a grid generator, and a post processor. User interaction with NIMROD is facilitated by a modern graphical user interface (GUI). The NIMROD project is using Quality Function Deployment (QFD) team management techniques to minimize re-engineering and reduce code development time. This paper gives an overview of the NIMROD project. Operation of the GUI is demonstrated, and the first results from the physics kernel are given.
A Framework for Simplifying the Development of Kernel Schedulers: Design and Performance Evaluation
DEFF Research Database (Denmark)
Muller, Gilles; Lawall, Julia Laetitia; Duschene, Hervé
2005-01-01
Writing a new scheduler and integrating it into an existing OS is a daunting task, requiring the understanding of multiple low-level kernel mechanisms. Indeed, implementing a new scheduler is outside the expertise of application programmers, even though they are the ones who understand best the s...
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...
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.
Convergence of barycentric coordinates to barycentric kernels
Kosinka, Jiří
2016-01-01
We investigate the close correspondence between barycentric coordinates and barycentric kernels from the point of view of the limit process when finer and finer polygons converge to a smooth convex domain. We show that any barycentric kernel is the limit of a set of barycentric coordinates and prove that the convergence rate is quadratic. Our convergence analysis extends naturally to barycentric interpolants and mappings induced by barycentric coordinates and kernels. We verify our theoretical convergence results numerically on several examples.
Kernel principal component analysis for change detection
DEFF Research Database (Denmark)
Nielsen, Allan Aasbjerg; Morton, J.C.
2008-01-01
region acquired at two different time points. If change over time does not dominate the scene, the projection of the original two bands onto the second eigenvector will show change over time. In this paper a kernel version of PCA is used to carry out the analysis. Unlike ordinary PCA, kernel PCA...... with a Gaussian kernel successfully finds the change observations in a case where nonlinearities are introduced artificially....
Davis, T Gene
2010-01-01
Learn the guidelines of integrating Java with native Mac OS X applications with this Devloper Reference book. Java is used to create nearly every type of application that exists and is one of the most required skills of employers seeking computer programmers. Java code and its libraries can be integrated with Mac OS X features, and this book shows you how to do just that. You'll learn to write Java programs on OS X and you'll even discover how to integrate them with the Cocoa APIs.: Shows how Java programs can be integrated with any Mac OS X feature, such as NSView widgets or screen savers; Re
Partial Deconvolution with Inaccurate Blur Kernel.
Ren, Dongwei; Zuo, Wangmeng; Zhang, David; Xu, Jun; Zhang, Lei
2017-10-17
Most non-blind deconvolution methods are developed under the error-free kernel assumption, and are not robust to inaccurate blur kernel. Unfortunately, despite the great progress in blind deconvolution, estimation error remains inevitable during blur kernel estimation. Consequently, severe artifacts such as ringing effects and distortions are likely to be introduced in the non-blind deconvolution stage. In this paper, we tackle this issue by suggesting: (i) a partial map in the Fourier domain for modeling kernel estimation error, and (ii) a partial deconvolution model for robust deblurring with inaccurate blur kernel. The partial map is constructed by detecting the reliable Fourier entries of estimated blur kernel. And partial deconvolution is applied to wavelet-based and learning-based models to suppress the adverse effect of kernel estimation error. Furthermore, an E-M algorithm is developed for estimating the partial map and recovering the latent sharp image alternatively. Experimental results show that our partial deconvolution model is effective in relieving artifacts caused by inaccurate blur kernel, and can achieve favorable deblurring quality on synthetic and real blurry images.Most non-blind deconvolution methods are developed under the error-free kernel assumption, and are not robust to inaccurate blur kernel. Unfortunately, despite the great progress in blind deconvolution, estimation error remains inevitable during blur kernel estimation. Consequently, severe artifacts such as ringing effects and distortions are likely to be introduced in the non-blind deconvolution stage. In this paper, we tackle this issue by suggesting: (i) a partial map in the Fourier domain for modeling kernel estimation error, and (ii) a partial deconvolution model for robust deblurring with inaccurate blur kernel. The partial map is constructed by detecting the reliable Fourier entries of estimated blur kernel. And partial deconvolution is applied to wavelet-based and learning
Process for producing metal oxide kernels and kernels so obtained
International Nuclear Information System (INIS)
Lelievre, Bernard; Feugier, Andre.
1974-01-01
The process desbribed is for producing fissile or fertile metal oxide kernels used in the fabrication of fuels for high temperature nuclear reactors. This process consists in adding to an aqueous solution of at least one metallic salt, particularly actinide nitrates, at least one chemical compound capable of releasing ammonia, in dispersing drop by drop the solution thus obtained into a hot organic phase to gel the drops and transform them into solid particles. These particles are then washed, dried and treated to turn them into oxide kernels. The organic phase used for the gel reaction is formed of a mixture composed of two organic liquids, one acting as solvent and the other being a product capable of extracting the anions from the metallic salt of the drop at the time of gelling. Preferably an amine is used as product capable of extracting the anions. Additionally, an alcohol that causes a part dehydration of the drops can be employed as solvent, thus helping to increase the resistance of the particles [fr
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.
Dense Medium Machine Processing Method for Palm Kernel/ Shell ...
African Journals Online (AJOL)
ADOWIE PERE
Cracked palm kernel is a mixture of kernels, broken shells, dusts and other impurities. In ... machine processing method using dense medium, a separator, a shell collector and a kernel .... efficiency, ease of maintenance and uniformity of.
Mitigation of artifacts in rtm with migration kernel decomposition
Zhan, Ge; Schuster, Gerard T.
2012-01-01
The migration kernel for reverse-time migration (RTM) can be decomposed into four component kernels using Born scattering and migration theory. Each component kernel has a unique physical interpretation and can be interpreted differently
Optimization of Finite-Differencing Kernels for Numerical Relativity Applications
Directory of Open Access Journals (Sweden)
Roberto Alfieri
2018-05-01
Full Text Available A simple optimization strategy for the computation of 3D finite-differencing kernels on many-cores architectures is proposed. The 3D finite-differencing computation is split direction-by-direction and exploits two level of parallelism: in-core vectorization and multi-threads shared-memory parallelization. The main application of this method is to accelerate the high-order stencil computations in numerical relativity codes. Our proposed method provides substantial speedup in computations involving tensor contractions and 3D stencil calculations on different processor microarchitectures, including Intel Knight Landing.
Ranking Support Vector Machine with Kernel Approximation
Directory of Open Access Journals (Sweden)
Kai Chen
2017-01-01
Full Text Available Learning to rank algorithm has become important in recent years due to its successful application in information retrieval, recommender system, and computational biology, and so forth. Ranking support vector machine (RankSVM is one of the state-of-art ranking models and has been favorably used. Nonlinear RankSVM (RankSVM with nonlinear kernels can give higher accuracy than linear RankSVM (RankSVM with a linear kernel for complex nonlinear ranking problem. However, the learning methods for nonlinear RankSVM are still time-consuming because of the calculation of kernel matrix. In this paper, we propose a fast ranking algorithm based on kernel approximation to avoid computing the kernel matrix. We explore two types of kernel approximation methods, namely, the Nyström method and random Fourier features. Primal truncated Newton method is used to optimize the pairwise L2-loss (squared Hinge-loss objective function of the ranking model after the nonlinear kernel approximation. Experimental results demonstrate that our proposed method gets a much faster training speed than kernel RankSVM and achieves comparable or better performance over state-of-the-art ranking algorithms.
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.
Sentiment classification with interpolated information diffusion kernels
Raaijmakers, S.
2007-01-01
Information diffusion kernels - similarity metrics in non-Euclidean information spaces - have been found to produce state of the art results for document classification. In this paper, we present a novel approach to global sentiment classification using these kernels. We carry out a large array of
Evolution kernel for the Dirac field
International Nuclear Information System (INIS)
Baaquie, B.E.
1982-06-01
The evolution kernel for the free Dirac field is calculated using the Wilson lattice fermions. We discuss the difficulties due to which this calculation has not been previously performed in the continuum theory. The continuum limit is taken, and the complete energy eigenfunctions as well as the propagator are then evaluated in a new manner using the kernel. (author)
Panel data specifications in nonparametric kernel regression
DEFF Research Database (Denmark)
Czekaj, Tomasz Gerard; Henningsen, Arne
parametric panel data estimators to analyse the production technology of Polish crop farms. The results of our nonparametric kernel regressions generally differ from the estimates of the parametric models but they only slightly depend on the choice of the kernel functions. Based on economic reasoning, we...
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…
Kernel Korner : The Linux keyboard driver
Brouwer, A.E.
1995-01-01
Our Kernel Korner series continues with an article describing the Linux keyboard driver. This article is not for "Kernel Hackers" only--in fact, it will be most useful to those who wish to use their own keyboard to its fullest potential, and those who want to write programs to take advantage of the
Metabolic network prediction through pairwise rational kernels.
Roche-Lima, Abiel; Domaratzki, Michael; Fristensky, Brian
2014-09-26
Metabolic networks are represented by the set of metabolic pathways. Metabolic pathways are a series of biochemical reactions, in which the product (output) from one reaction serves as the substrate (input) to another reaction. Many pathways remain incompletely characterized. One of the major challenges of computational biology is to obtain better models of metabolic pathways. Existing models are dependent on the annotation of the genes. This propagates error accumulation when the pathways are predicted by incorrectly annotated genes. Pairwise classification methods are supervised learning methods used to classify new pair of entities. Some of these classification methods, e.g., Pairwise Support Vector Machines (SVMs), use pairwise kernels. Pairwise kernels describe similarity measures between two pairs of entities. Using pairwise kernels to handle sequence data requires long processing times and large storage. Rational kernels are kernels based on weighted finite-state transducers that represent similarity measures between sequences or automata. They have been effectively used in problems that handle large amount of sequence information such as protein essentiality, natural language processing and machine translations. We create a new family of pairwise kernels using weighted finite-state transducers (called Pairwise Rational Kernel (PRK)) to predict metabolic pathways from a variety of biological data. PRKs take advantage of the simpler representations and faster algorithms of transducers. Because raw sequence data can be used, the predictor model avoids the errors introduced by incorrect gene annotations. We then developed several experiments with PRKs and Pairwise SVM to validate our methods using the metabolic network of Saccharomyces cerevisiae. As a result, when PRKs are used, our method executes faster in comparison with other pairwise kernels. Also, when we use PRKs combined with other simple kernels that include evolutionary information, the accuracy
PKI, Gamma Radiation Reactor Shielding Calculation by Point-Kernel Method
International Nuclear Information System (INIS)
Li Chunhuai; Zhang Liwu; Zhang Yuqin; Zhang Chuanxu; Niu Xihua
1990-01-01
1 - Description of program or function: This code calculates radiation shielding problem of gamma-ray in geometric space. 2 - Method of solution: PKI uses a point kernel integration technique, describes radiation shielding geometric space by using geometric space configuration method and coordinate conversion, and makes use of calculation result of reactor primary shielding and flow regularity in loop system for coolant
Isolation of a kernel oleoyl-ACP thioesterase gene from the oil palm ...
African Journals Online (AJOL)
We have isolated a cDNA clone from the developing kernel of the oil palm Elaeis guineensis which encodes a thioesterase enzyme. Its highest homology was to the Brassica napus oleoyl-ACP thioesterase with which it had 72% homology at the nucleotide level, over the coding region examined, and 83% identity (90% ...
2012-06-14
modifies the same kernel memory as the first. Race conditions can be prevented using synchronization primitives (e.g., locks, semaphores ...exception and provides generic data structures and primitives to encourage code reuse by developers [Bov05]. These structures, that all programmers are
Benchmarking NWP Kernels on Multi- and Many-core Processors
Michalakes, J.; Vachharajani, M.
2008-12-01
Increased computing power for weather, climate, and atmospheric science has provided direct benefits for defense, agriculture, the economy, the environment, and public welfare and convenience. Today, very large clusters with many thousands of processors are allowing scientists to move forward with simulations of unprecedented size. But time-critical applications such as real-time forecasting or climate prediction need strong scaling: faster nodes and processors, not more of them. Moreover, the need for good cost- performance has never been greater, both in terms of performance per watt and per dollar. For these reasons, the new generations of multi- and many-core processors being mass produced for commercial IT and "graphical computing" (video games) are being scrutinized for their ability to exploit the abundant fine- grain parallelism in atmospheric models. We present results of our work to date identifying key computational kernels within the dynamics and physics of a large community NWP model, the Weather Research and Forecast (WRF) model. We benchmark and optimize these kernels on several different multi- and many-core processors. The goals are to (1) characterize and model performance of the kernels in terms of computational intensity, data parallelism, memory bandwidth pressure, memory footprint, etc. (2) enumerate and classify effective strategies for coding and optimizing for these new processors, (3) assess difficulties and opportunities for tool or higher-level language support, and (4) establish a continuing set of kernel benchmarks that can be used to measure and compare effectiveness of current and future designs of multi- and many-core processors for weather and climate applications.
A Stochastic Proof of the Resonant Scattering Kernel and its Applications for Gen IV Reactors Type
International Nuclear Information System (INIS)
Becker, B.; Dagan, R.; Broeders, C.H.M.; Lohnert, G.
2008-01-01
Monte Carlo codes such as MCNP are widely accepted as almost-reference for reactor analysis. The Monte Carlo Code should therefore use as few as possible approximations in order to produce 'experimental-level' calculations. In this study we deal with one of the most problematic approximations done in MCNP in which the resonances are ignored for the secondary neutron energy distribution, namely the change of the energy and angular direction of the neutron after interaction with a heavy isotope with pronounced resonances. The endeavour of exploiting the influence of the resonances on the scattering kernel goes back to 1944 where E. Wigner and J. Wilkins developed the first temperature dependent scattering kernel. However only in 1998, the full analytical solution for the double differential resonant dependent scattering kernel was suggested by W. Rothenstein and R. Dagan. An independent stochastic approach is presented for the first time to confirm the above analytical kernel with a complete different methodology. Moreover, by manipulating in a subtle manner the scattering subroutine COLIDN of MCNP, it is proven that this very subroutine is, to some extent, inappropriate as well as the relevant explanation in the MCNP manual. The impact of this improved resonance dependent scattering kernel on diverse types of reactors, in particular for the Generation IV innovative core design HTR, is shown to be significant. (authors)
The OKE Corral : Code organisation and reconfiguration at runtime using active linking
Bos, Herbert; Samwel, Bart
2002-01-01
The OKE Corral is an active network environment which allows third-party active code to configure an active node’s code organisation at any level, including the kernel. Using the safety properties of an open kernel environment and a simple ‘Click-like’ software model, third parties are able to load
An Internal Data Non-hiding Type Real-time Kernel and its Application to the Mechatronics Controller
Yoshida, Toshio
For the mechatronics equipment controller that controls robots and machine tools, high-speed motion control processing is essential. The software system of the controller like other embedded systems is composed of three layers software such as real-time kernel layer, middleware layer, and application software layer on the dedicated hardware. The application layer in the top layer is composed of many numbers of tasks, and application function of the system is realized by the cooperation between these tasks. In this paper we propose an internal data non-hiding type real-time kernel in which customizing the task control is possible only by change in the program code of the task side without any changes in the program code of real-time kernel. It is necessary to reduce the overhead caused by the real-time kernel task control for the speed-up of the motion control of the mechatronics equipment. For this, customizing the task control function is needed. We developed internal data non-cryptic type real-time kernel ZRK to evaluate this method, and applied to the control of the multi system automatic lathe. The effect of the speed-up of the task cooperation processing was able to be confirmed by combined task control processing on the task side program code using an internal data non-hiding type real-time kernel ZRK.
iOS and OS X network programming cookbook
Hoffman, Jon
2014-01-01
This book follows a recipe-based approach that will heavily focus on the code and how to integrate the samples with the reader's projects.Each recipe consists of one or more methods that you can put directly into your app and use.This book is ideal for developers that want to create network applications for the Apple OS X or iOS platforms. All examples are written in Objective-C using XCode as the IDE. Knowledge of Objective-C and X-Code is essential.
Bayesian Kernel Mixtures for Counts.
Canale, Antonio; Dunson, David B
2011-12-01
Although Bayesian nonparametric mixture models for continuous data are well developed, there is a limited literature on related approaches for count data. A common strategy is to use a mixture of Poissons, which unfortunately is quite restrictive in not accounting for distributions having variance less than the mean. Other approaches include mixing multinomials, which requires finite support, and using a Dirichlet process prior with a Poisson base measure, which does not allow smooth deviations from the Poisson. As a broad class of alternative models, we propose to use nonparametric mixtures of rounded continuous kernels. An efficient Gibbs sampler is developed for posterior computation, and a simulation study is performed to assess performance. Focusing on the rounded Gaussian case, we generalize the modeling framework to account for multivariate count data, joint modeling with continuous and categorical variables, and other complications. The methods are illustrated through applications to a developmental toxicity study and marketing data. This article has supplementary material online.
Mercure IV code application to the external dose computation from low and medium level wastes
International Nuclear Information System (INIS)
Tomassini, T.
1985-01-01
In the present work the external dose from low and medium level wastes is calculated using MERCURE IV code. The code utilizes MONTECARLO method for integrating multigroup line of sight attenuation Kernels
Anisotropic hydrodynamics with a scalar collisional kernel
Almaalol, Dekrayat; Strickland, Michael
2018-04-01
Prior studies of nonequilibrium dynamics using anisotropic hydrodynamics have used the relativistic Anderson-Witting scattering kernel or some variant thereof. In this paper, we make the first study of the impact of using a more realistic scattering kernel. For this purpose, we consider a conformal system undergoing transversally homogenous and boost-invariant Bjorken expansion and take the collisional kernel to be given by the leading order 2 ↔2 scattering kernel in scalar λ ϕ4 . We consider both classical and quantum statistics to assess the impact of Bose enhancement on the dynamics. We also determine the anisotropic nonequilibrium attractor of a system subject to this collisional kernel. We find that, when the near-equilibrium relaxation-times in the Anderson-Witting and scalar collisional kernels are matched, the scalar kernel results in a higher degree of momentum-space anisotropy during the system's evolution, given the same initial conditions. Additionally, we find that taking into account Bose enhancement further increases the dynamically generated momentum-space anisotropy.
Study on the scattering law and scattering kernel of hydrogen in zirconium hydride
International Nuclear Information System (INIS)
Jiang Xinbiao; Chen Wei; Chen Da; Yin Banghua; Xie Zhongsheng
1999-01-01
The nuclear analytical model of calculating scattering law and scattering kernel for the uranium zirconium hybrid reactor is described. In the light of the acoustic and optic model of zirconium hydride, its frequency distribution function f(ω) is given and the scattering law of hydrogen in zirconium hydride is obtained by GASKET. The scattering kernel σ l (E 0 →E) of hydrogen bound in zirconium hydride is provided by the SMP code in the standard WIMS cross section library. Along with this library, WIMS is used to calculate the thermal neutron energy spectrum of fuel cell. The results are satisfied
Jdpd: an open java simulation kernel for molecular fragment dissipative particle dynamics.
van den Broek, Karina; Kuhn, Hubert; Zielesny, Achim
2018-05-21
Jdpd is an open Java simulation kernel for Molecular Fragment Dissipative Particle Dynamics with parallelizable force calculation, efficient caching options and fast property calculations. It is characterized by an interface and factory-pattern driven design for simple code changes and may help to avoid problems of polyglot programming. Detailed input/output communication, parallelization and process control as well as internal logging capabilities for debugging purposes are supported. The new kernel may be utilized in different simulation environments ranging from flexible scripting solutions up to fully integrated "all-in-one" simulation systems.
Automatic performance tuning of parallel and accelerated seismic imaging kernels
Haberdar, Hakan
2014-01-01
With the increased complexity and diversity of mainstream high performance computing systems, significant effort is required to tune parallel applications in order to achieve the best possible performance for each particular platform. This task becomes more and more challenging and requiring a larger set of skills. Automatic performance tuning is becoming a must for optimizing applications such as Reverse Time Migration (RTM) widely used in seismic imaging for oil and gas exploration. An empirical search based auto-tuning approach is applied to the MPI communication operations of the parallel isotropic and tilted transverse isotropic kernels. The application of auto-tuning using the Abstract Data and Communication Library improved the performance of the MPI communications as well as developer productivity by providing a higher level of abstraction. Keeping productivity in mind, we opted toward pragma based programming for accelerated computation on latest accelerated architectures such as GPUs using the fairly new OpenACC standard. The same auto-tuning approach is also applied to the OpenACC accelerated seismic code for optimizing the compute intensive kernel of the Reverse Time Migration application. The application of such technique resulted in an improved performance of the original code and its ability to adapt to different execution environments.
Support for NUMA hardware in HelenOS
Horký, Vojtěch
2011-01-01
The goal of this master thesis is to extend HelenOS operating system with the support for ccNUMA hardware. The text of the thesis contains a brief introduction to ccNUMA hardware, an overview of NUMA features and relevant features of HelenOS (memory management, scheduling, etc.). The thesis analyses various design decisions of the implementation of NUMA support -- introducing the hardware topology into the kernel data structures, propagating this information to user space, thread affinity to ...
Higher-Order Hybrid Gaussian Kernel in Meshsize Boosting Algorithm
African Journals Online (AJOL)
In this paper, we shall use higher-order hybrid Gaussian kernel in a meshsize boosting algorithm in kernel density estimation. Bias reduction is guaranteed in this scheme like other existing schemes but uses the higher-order hybrid Gaussian kernel instead of the regular fixed kernels. A numerical verification of this scheme ...
NLO corrections to the Kernel of the BKP-equations
Energy Technology Data Exchange (ETDEWEB)
Bartels, J. [Hamburg Univ. (Germany). 2. Inst. fuer Theoretische Physik; Fadin, V.S. [Budker Institute of Nuclear Physics, Novosibirsk (Russian Federation); Novosibirskij Gosudarstvennyj Univ., Novosibirsk (Russian Federation); Lipatov, L.N. [Hamburg Univ. (Germany). 2. Inst. fuer Theoretische Physik; Petersburg Nuclear Physics Institute, Gatchina, St. Petersburg (Russian Federation); Vacca, G.P. [INFN, Sezione di Bologna (Italy)
2012-10-02
We present results for the NLO kernel of the BKP equations for composite states of three reggeized gluons in the Odderon channel, both in QCD and in N=4 SYM. The NLO kernel consists of the NLO BFKL kernel in the color octet representation and the connected 3{yields}3 kernel, computed in the tree approximation.
Adaptive Kernel in Meshsize Boosting Algorithm in KDE ...
African Journals Online (AJOL)
This paper proposes the use of adaptive kernel in a meshsize boosting algorithm in kernel density estimation. The algorithm is a bias reduction scheme like other existing schemes but uses adaptive kernel instead of the regular fixed kernels. An empirical study for this scheme is conducted and the findings are comparatively ...
Adaptive Kernel In The Bootstrap Boosting Algorithm In KDE ...
African Journals Online (AJOL)
This paper proposes the use of adaptive kernel in a bootstrap boosting algorithm in kernel density estimation. The algorithm is a bias reduction scheme like other existing schemes but uses adaptive kernel instead of the regular fixed kernels. An empirical study for this scheme is conducted and the findings are comparatively ...
Kernel maximum autocorrelation factor and minimum noise fraction transformations
DEFF Research Database (Denmark)
Nielsen, Allan Aasbjerg
2010-01-01
in hyperspectral HyMap scanner data covering a small agricultural area, and 3) maize kernel inspection. In the cases shown, the kernel MAF/MNF transformation performs better than its linear counterpart as well as linear and kernel PCA. The leading kernel MAF/MNF variates seem to possess the ability to adapt...
2010-01-01
... 7 Agriculture 2 2010-01-01 2010-01-01 false Half-kernel. 51.1441 Section 51.1441 Agriculture... Standards for Grades of Shelled Pecans Definitions § 51.1441 Half-kernel. Half-kernel means one of the separated halves of an entire pecan kernel with not more than one-eighth of its original volume missing...
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 than...
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 for...
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 color...
The Linux kernel as flexible product-line architecture
M. de Jonge (Merijn)
2002-01-01
textabstractThe Linux kernel source tree is huge ($>$ 125 MB) and inflexible (because it is difficult to add new kernel components). We propose to make this architecture more flexible by assembling kernel source trees dynamically from individual kernel components. Users then, can select what
Parsimonious Wavelet Kernel Extreme Learning Machine
Directory of Open Access Journals (Sweden)
Wang Qin
2015-11-01
Full Text Available In this study, a parsimonious scheme for wavelet kernel extreme learning machine (named PWKELM was introduced by combining wavelet theory and a parsimonious algorithm into kernel extreme learning machine (KELM. In the wavelet analysis, bases that were localized in time and frequency to represent various signals effectively were used. Wavelet kernel extreme learning machine (WELM maximized its capability to capture the essential features in “frequency-rich” signals. The proposed parsimonious algorithm also incorporated significant wavelet kernel functions via iteration in virtue of Householder matrix, thus producing a sparse solution that eased the computational burden and improved numerical stability. The experimental results achieved from the synthetic dataset and a gas furnace instance demonstrated that the proposed PWKELM is efficient and feasible in terms of improving generalization accuracy and real time performance.
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...
Kernel Learning of Histogram of Local Gabor Phase Patterns for Face Recognition
Directory of Open Access Journals (Sweden)
Bineng Zhong
2008-06-01
Full Text Available This paper proposes a new face recognition method, named kernel learning of histogram of local Gabor phase pattern (K-HLGPP, which is based on DaugmanÃ¢Â€Â™s method for iris recognition and the local XOR pattern (LXP operator. Unlike traditional Gabor usage exploiting the magnitude part in face recognition, we encode the Gabor phase information for face classification by the quadrant bit coding (QBC method. Two schemes are proposed for face recognition. One is based on the nearest-neighbor classifier with chi-square as the similarity measurement, and the other makes kernel discriminant analysis for HLGPP (K-HLGPP using histogram intersection and Gaussian-weighted chi-square kernels. The comparative experiments show that K-HLGPP achieves a higher recognition rate than other well-known face recognition systems on the large-scale standard FERET, FERET200, and CAS-PEAL-R1 databases.
Feature selection and multi-kernel learning for sparse representation on a manifold
Wang, Jim Jing-Yan
2014-03-01
Sparse representation has been widely studied as a part-based data representation method and applied in many scientific and engineering fields, such as bioinformatics and medical imaging. It seeks to represent a data sample as a sparse linear combination of some basic items in a dictionary. Gao etal. (2013) recently proposed Laplacian sparse coding by regularizing the sparse codes with an affinity graph. However, due to the noisy features and nonlinear distribution of the data samples, the affinity graph constructed directly from the original feature space is not necessarily a reliable reflection of the intrinsic manifold of the data samples. To overcome this problem, we integrate feature selection and multiple kernel learning into the sparse coding on the manifold. To this end, unified objectives are defined for feature selection, multiple kernel learning, sparse coding, and graph regularization. By optimizing the objective functions iteratively, we develop novel data representation algorithms with feature selection and multiple kernel learning respectively. Experimental results on two challenging tasks, N-linked glycosylation prediction and mammogram retrieval, demonstrate that the proposed algorithms outperform the traditional sparse coding methods. © 2013 Elsevier Ltd.
Feature selection and multi-kernel learning for sparse representation on a manifold.
Wang, Jim Jing-Yan; Bensmail, Halima; Gao, Xin
2014-03-01
Sparse representation has been widely studied as a part-based data representation method and applied in many scientific and engineering fields, such as bioinformatics and medical imaging. It seeks to represent a data sample as a sparse linear combination of some basic items in a dictionary. Gao et al. (2013) recently proposed Laplacian sparse coding by regularizing the sparse codes with an affinity graph. However, due to the noisy features and nonlinear distribution of the data samples, the affinity graph constructed directly from the original feature space is not necessarily a reliable reflection of the intrinsic manifold of the data samples. To overcome this problem, we integrate feature selection and multiple kernel learning into the sparse coding on the manifold. To this end, unified objectives are defined for feature selection, multiple kernel learning, sparse coding, and graph regularization. By optimizing the objective functions iteratively, we develop novel data representation algorithms with feature selection and multiple kernel learning respectively. Experimental results on two challenging tasks, N-linked glycosylation prediction and mammogram retrieval, demonstrate that the proposed algorithms outperform the traditional sparse coding methods. Copyright © 2013 Elsevier Ltd. All rights reserved.
Uranium kernel formation via internal gelation
International Nuclear Information System (INIS)
Hunt, R.D.; Collins, J.L.
2004-01-01
In the 1970s and 1980s, U.S. Department of Energy (DOE) conducted numerous studies on the fabrication of nuclear fuel particles using the internal gelation process. These amorphous kernels were prone to flaking or breaking when gases tried to escape from the kernels during calcination and sintering. These earlier kernels would not meet today's proposed specifications for reactor fuel. In the interim, the internal gelation process has been used to create hydrous metal oxide microspheres for the treatment of nuclear waste. With the renewed interest in advanced nuclear fuel by the DOE, the lessons learned from the nuclear waste studies were recently applied to the fabrication of uranium kernels, which will become tri-isotropic (TRISO) fuel particles. These process improvements included equipment modifications, small changes to the feed formulations, and a new temperature profile for the calcination and sintering. The modifications to the laboratory-scale equipment and its operation as well as small changes to the feed composition increased the product yield from 60% to 80%-99%. The new kernels were substantially less glassy, and no evidence of flaking was found. Finally, key process parameters were identified, and their effects on the uranium microspheres and kernels are discussed. (orig.)
Quantum tomography, phase-space observables and generalized Markov kernels
International Nuclear Information System (INIS)
Pellonpaeae, Juha-Pekka
2009-01-01
We construct a generalized Markov kernel which transforms the observable associated with the homodyne tomography into a covariant phase-space observable with a regular kernel state. Illustrative examples are given in the cases of a 'Schroedinger cat' kernel state and the Cahill-Glauber s-parametrized distributions. Also we consider an example of a kernel state when the generalized Markov kernel cannot be constructed.
Sitompul, Monica Angelina
2015-01-01
Have been conducted Determination of Iodin Value by method titration to some Hydrogenated Palm Kernel Oil (HPKO) and Refined Bleached Deodorized Palm Kernel Oil (RBDPKO). The result of analysis obtained the Iodin Value in Hydrogenated Palm Kernel Oil (A) = 0,16 gr I2/100gr, Hydrogenated Palm Kernel Oil (B) = 0,20 gr I2/100gr, Hydrogenated Palm Kernel Oil (C) = 0,24 gr I2/100gr. And in Refined Bleached Deodorized Palm Kernel Oil (A) = 17,51 gr I2/100gr, Refined Bleached Deodorized Palm Kernel ...
Epileptic Seizure Detection with Log-Euclidean Gaussian Kernel-Based Sparse Representation.
Yuan, Shasha; Zhou, Weidong; Wu, Qi; Zhang, Yanli
2016-05-01
Epileptic seizure detection plays an important role in the diagnosis of epilepsy and reducing the massive workload of reviewing electroencephalography (EEG) recordings. In this work, a novel algorithm is developed to detect seizures employing log-Euclidean Gaussian kernel-based sparse representation (SR) in long-term EEG recordings. Unlike the traditional SR for vector data in Euclidean space, the log-Euclidean Gaussian kernel-based SR framework is proposed for seizure detection in the space of the symmetric positive definite (SPD) matrices, which form a Riemannian manifold. Since the Riemannian manifold is nonlinear, the log-Euclidean Gaussian kernel function is applied to embed it into a reproducing kernel Hilbert space (RKHS) for performing SR. The EEG signals of all channels are divided into epochs and the SPD matrices representing EEG epochs are generated by covariance descriptors. Then, the testing samples are sparsely coded over the dictionary composed by training samples utilizing log-Euclidean Gaussian kernel-based SR. The classification of testing samples is achieved by computing the minimal reconstructed residuals. The proposed method is evaluated on the Freiburg EEG dataset of 21 patients and shows its notable performance on both epoch-based and event-based assessments. Moreover, this method handles multiple channels of EEG recordings synchronously which is more speedy and efficient than traditional seizure detection methods.
Exact Heat Kernel on a Hypersphere and Its Applications in Kernel SVM
Directory of Open Access Journals (Sweden)
Chenchao Zhao
2018-01-01
Full Text Available Many contemporary statistical learning methods assume a Euclidean feature space. This paper presents a method for defining similarity based on hyperspherical geometry and shows that it often improves the performance of support vector machine compared to other competing similarity measures. Specifically, the idea of using heat diffusion on a hypersphere to measure similarity has been previously proposed and tested by Lafferty and Lebanon [1], demonstrating promising results based on a heuristic heat kernel obtained from the zeroth order parametrix expansion; however, how well this heuristic kernel agrees with the exact hyperspherical heat kernel remains unknown. This paper presents a higher order parametrix expansion of the heat kernel on a unit hypersphere and discusses several problems associated with this expansion method. We then compare the heuristic kernel with an exact form of the heat kernel expressed in terms of a uniformly and absolutely convergent series in high-dimensional angular momentum eigenmodes. Being a natural measure of similarity between sample points dwelling on a hypersphere, the exact kernel often shows superior performance in kernel SVM classifications applied to text mining, tumor somatic mutation imputation, and stock market analysis.
Kernel smoothing dos dados de chuva no Nordeste
Directory of Open Access Journals (Sweden)
Nyedja F. M. Barbosa
2014-07-01
Full Text Available O regime de chuvas sobre o Nordeste do Brasil é bastante complexo, sendo considerado sazonal, além de sofrer fortes influências dos fenômenos El Niño, La Niña e outros sistemas meteorológicos, como o dipolo, atuantes sobre as bacias do oceano Atlântico Tropical. Neste trabalho foi aplicada a técnica matemática-computacional de interpolação do Kernel Smoothing nos dados de precipitação pluvial sobre o Nordeste, coletados no período de 1904 a 1998, provenientes de 2.283 estações meteorológicas. Os cálculos foram desenvolvidos por meio do software "Kernel", escrito em linguagem C e Cuda o que possibilitou fazer a interpolação de mais de 26 milhões de medidas de precipitação pluvial, permitindo gerar mapas de intensidade de chuva sobre toda a região e calcular estatísticas para a precipitação do Nordeste em escalas mensais e anuais. De acordo com as interpolações realizadas foi possível detectar, dentre o período estudado, os anos mais secos e mais chuvosos, a distribuição espacial das chuvas em cada mês, bem como a característica da precipitação pluviométrica em épocas de El Niño e La Niña.
Aflatoxin contamination of developing corn kernels.
Amer, M A
2005-01-01
Preharvest of corn and its contamination with aflatoxin is a serious problem. Some environmental and cultural factors responsible for infection and subsequent aflatoxin production were investigated in this study. Stage of growth and location of kernels on corn ears were found to be one of the important factors in the process of kernel infection with A. flavus & A. parasiticus. The results showed positive correlation between the stage of growth and kernel infection. Treatment of corn with aflatoxin reduced germination, protein and total nitrogen contents. Total and reducing soluble sugar was increase in corn kernels as response to infection. Sucrose and protein content were reduced in case of both pathogens. Shoot system length, seeding fresh weigh and seedling dry weigh was also affected. Both pathogens induced reduction of starch content. Healthy corn seedlings treated with aflatoxin solution were badly affected. Their leaves became yellow then, turned brown with further incubation. Moreover, their total chlorophyll and protein contents showed pronounced decrease. On the other hand, total phenolic compounds were increased. Histopathological studies indicated that A. flavus & A. parasiticus could colonize corn silks and invade developing kernels. Germination of A. flavus spores was occurred and hyphae spread rapidly across the silk, producing extensive growth and lateral branching. Conidiophores and conidia had formed in and on the corn silk. Temperature and relative humidity greatly influenced the growth of A. flavus & A. parasiticus and aflatoxin production.
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.
Classification of Hyperspectral Images Using Kernel Fully Constrained Least Squares
Directory of Open Access Journals (Sweden)
Jianjun Liu
2017-11-01
Full Text Available As a widely used classifier, sparse representation classification (SRC has shown its good performance for hyperspectral image classification. Recent works have highlighted that it is the collaborative representation mechanism under SRC that makes SRC a highly effective technique for classification purposes. If the dimensionality and the discrimination capacity of a test pixel is high, other norms (e.g., ℓ 2 -norm can be used to regularize the coding coefficients, except for the sparsity ℓ 1 -norm. In this paper, we show that in the kernel space the nonnegative constraint can also play the same role, and thus suggest the investigation of kernel fully constrained least squares (KFCLS for hyperspectral image classification. Furthermore, in order to improve the classification performance of KFCLS by incorporating spatial-spectral information, we investigate two kinds of spatial-spectral methods using two regularization strategies: (1 the coefficient-level regularization strategy, and (2 the class-level regularization strategy. Experimental results conducted on four real hyperspectral images demonstrate the effectiveness of the proposed KFCLS, and show which way to incorporate spatial-spectral information efficiently in the regularization framework.
Directory of Open Access Journals (Sweden)
Al Mehedi Hasan
2017-07-01
subcellular localization prediction to find out which kernel is the best for SVM. We have evaluated our system on a combined dataset containing 5447 single-localized proteins (originally published as part of the Höglund dataset and 3056 multi-localized proteins (originally published as part of the DBMLoc set. This dataset was used by Briesemeister et al. in their extensive comparison of multilocalization prediction system. The experimental results indicate that the system based on SVM with the Laplace kernel, termed LKLoc, not only achieves a higher accuracy than the system using other kernels but also shows significantly better results than those obtained from other top systems (MDLoc, BNCs, YLoc+. The source code of this prediction system is available upon request.
The Classification of Diabetes Mellitus Using Kernel k-means
Alamsyah, M.; Nafisah, Z.; Prayitno, E.; Afida, A. M.; Imah, E. M.
2018-01-01
Diabetes Mellitus is a metabolic disorder which is characterized by chronicle hypertensive glucose. Automatics detection of diabetes mellitus is still challenging. This study detected diabetes mellitus by using kernel k-Means algorithm. Kernel k-means is an algorithm which was developed from k-means algorithm. Kernel k-means used kernel learning that is able to handle non linear separable data; where it differs with a common k-means. The performance of kernel k-means in detecting diabetes mellitus is also compared with SOM algorithms. The experiment result shows that kernel k-means has good performance and a way much better than SOM.
Object classification and detection with context kernel descriptors
DEFF Research Database (Denmark)
Pan, Hong; Olsen, Søren Ingvor; Zhu, Yaping
2014-01-01
Context information is important in object representation. By embedding context cue of image attributes into kernel descriptors, we propose a set of novel kernel descriptors called Context Kernel Descriptors (CKD) for object classification and detection. The motivation of CKD is to use spatial...... consistency of image attributes or features defined within a neighboring region to improve the robustness of descriptor matching in kernel space. For feature selection, Kernel Entropy Component Analysis (KECA) is exploited to learn a subset of discriminative CKD. Different from Kernel Principal Component...
Wang, Shunfang; Nie, Bing; Yue, Kun; Fei, Yu; Li, Wenjia; Xu, Dongshu
2017-12-15
Kernel discriminant analysis (KDA) is a dimension reduction and classification algorithm based on nonlinear kernel trick, which can be novelly used to treat high-dimensional and complex biological data before undergoing classification processes such as protein subcellular localization. Kernel parameters make a great impact on the performance of the KDA model. Specifically, for KDA with the popular Gaussian kernel, to select the scale parameter is still a challenging problem. Thus, this paper introduces the KDA method and proposes a new method for Gaussian kernel parameter selection depending on the fact that the differences between reconstruction errors of edge normal samples and those of interior normal samples should be maximized for certain suitable kernel parameters. Experiments with various standard data sets of protein subcellular localization show that the overall accuracy of protein classification prediction with KDA is much higher than that without KDA. Meanwhile, the kernel parameter of KDA has a great impact on the efficiency, and the proposed method can produce an optimum parameter, which makes the new algorithm not only perform as effectively as the traditional ones, but also reduce the computational time and thus improve efficiency.
The computer code system for reactor radiation shielding in design of nuclear power plant
International Nuclear Information System (INIS)
Li Chunhuai; Fu Shouxin; Liu Guilian
1995-01-01
The computer code system used in reactor radiation shielding design of nuclear power plant includes the source term codes, discrete ordinate transport codes, Monte Carlo and Albedo Monte Carlo codes, kernel integration codes, optimization code, temperature field code, skyshine code, coupling calculation codes and some processing codes for data libraries. This computer code system has more satisfactory variety of codes and complete sets of data library. It is widely used in reactor radiation shielding design and safety analysis of nuclear power plant and other nuclear facilities
Kernel abortion in maize. II. Distribution of 14C among kernel carboydrates
International Nuclear Information System (INIS)
Hanft, J.M.; Jones, R.J.
1986-01-01
This study was designed to compare the uptake and distribution of 14 C among fructose, glucose, sucrose, and starch in the cob, pedicel, and endosperm tissues of maize (Zea mays L.) kernels induced to abort by high temperature with those that develop normally. Kernels cultured in vitro at 309 and 35 0 C were transferred to [ 14 C]sucrose media 10 days after pollination. Kernels cultured at 35 0 C aborted prior to the onset of linear dry matter accumulation. Significant uptake into the cob, pedicel, and endosperm of radioactivity associated with the soluble and starch fractions of the tissues was detected after 24 hours in culture on atlageled media. After 8 days in culture on [ 14 C]sucrose media, 48 and 40% of the radioactivity associated with the cob carbohydrates was found in the reducing sugars at 30 and 35 0 C, respectively. Of the total carbohydrates, a higher percentage of label was associated with sucrose and lower percentage with fructose and glucose in pedicel tissue of kernels cultured at 35 0 C compared to kernels cultured at 30 0 C. These results indicate that sucrose was not cleaved to fructose and glucose as rapidly during the unloading process in the pedicel of kernels induced to abort by high temperature. Kernels cultured at 35 0 C had a much lower proportion of label associated with endosperm starch (29%) than did kernels cultured at 30 0 C (89%). Kernels cultured at 35 0 C had a correspondingly higher proportion of 14 C in endosperm fructose, glucose, and sucrose
Fluidization calculation on nuclear fuel kernel coating
International Nuclear Information System (INIS)
Sukarsono; Wardaya; Indra-Suryawan
1996-01-01
The fluidization of nuclear fuel kernel coating was calculated. The bottom of the reactor was in the from of cone on top of the cone there was a cylinder, the diameter of the cylinder for fluidization was 2 cm and at the upper part of the cylinder was 3 cm. Fluidization took place in the cone and the first cylinder. The maximum and the minimum velocity of the gas of varied kernel diameter, the porosity and bed height of varied stream gas velocity were calculated. The calculation was done by basic program
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
Directory of Open Access Journals (Sweden)
Bérenger Bramas
2018-04-01
Full Text Available The sparse matrix-vector product (SpMV is a fundamental operation in many scientific applications from various fields. The High Performance Computing (HPC community has therefore continuously invested a lot of effort to provide an efficient SpMV kernel on modern CPU architectures. Although it has been shown that block-based kernels help to achieve high performance, they are difficult to use in practice because of the zero padding they require. In the current paper, we propose new kernels using the AVX-512 instruction set, which makes it possible to use a blocking scheme without any zero padding in the matrix memory storage. We describe mask-based sparse matrix formats and their corresponding SpMV kernels highly optimized in assembly language. Considering that the optimal blocking size depends on the matrix, we also provide a method to predict the best kernel to be used utilizing a simple interpolation of results from previous executions. We compare the performance of our approach to that of the Intel MKL CSR kernel and the CSR5 open-source package on a set of standard benchmark matrices. We show that we can achieve significant improvements in many cases, both for sequential and for parallel executions. Finally, we provide the corresponding code in an open source library, called SPC5.
Energy Technology Data Exchange (ETDEWEB)
Fajeau, M; Nguyen, L T; Saunier, J [Commissariat a l' Energie Atomique, Centre d' Etudes Nucleaires de Saclay, 91 - Gif-sur-Yvette (France)
1966-09-01
This code handles the following problems: -1) Analysis of thermal experiments on a water loop at high or low pressure; steady state or transient behavior; -2) Analysis of thermal and hydrodynamic behavior of water-cooled and moderated reactors, at either high or low pressure, with boiling permitted; fuel elements are assumed to be flat plates: - Flowrate in parallel channels coupled or not by conduction across plates, with conditions of pressure drops or flowrate, variable or not with respect to time is given; the power can be coupled to reactor kinetics calculation or supplied by the code user. The code, containing a schematic representation of safety rod behavior, is a one dimensional, multi-channel code, and has as its complement (FLID), a one-channel, two-dimensional code. (authors) [French] Ce code permet de traiter les problemes ci-dessous: 1. Depouillement d'essais thermiques sur boucle a eau, haute ou basse pression, en regime permanent ou transitoire; 2. Etudes thermiques et hydrauliques de reacteurs a eau, a plaques, a haute ou basse pression, ebullition permise: - repartition entre canaux paralleles, couples on non par conduction a travers plaques, pour des conditions de debit ou de pertes de charge imposees, variables ou non dans le temps; - la puissance peut etre couplee a la neutronique et une representation schematique des actions de securite est prevue. Ce code (Cactus) a une dimension d'espace et plusieurs canaux, a pour complement Flid qui traite l'etude d'un seul canal a deux dimensions. (auteurs)
Comparative Analysis of Kernel Methods for Statistical Shape Learning
National Research Council Canada - National Science Library
Rathi, Yogesh; Dambreville, Samuel; Tannenbaum, Allen
2006-01-01
.... In this work, we perform a comparative analysis of shape learning techniques such as linear PCA, kernel PCA, locally linear embedding and propose a new method, kernelized locally linear embedding...
Variable kernel density estimation in high-dimensional feature spaces
CSIR Research Space (South Africa)
Van der Walt, Christiaan M
2017-02-01
Full Text Available Estimating the joint probability density function of a dataset is a central task in many machine learning applications. In this work we address the fundamental problem of kernel bandwidth estimation for variable kernel density estimation in high...
Influence of differently processed mango seed kernel meal on ...
African Journals Online (AJOL)
Influence of differently processed mango seed kernel meal on performance response of west African ... and TD( consisted spear grass and parboiled mango seed kernel meal with concentrate diet in a ratio of 35:30:35). ... HOW TO USE AJOL.
On methods to increase the security of the Linux kernel
International Nuclear Information System (INIS)
Matvejchikov, I.V.
2014-01-01
Methods to increase the security of the Linux kernel for the implementation of imposed protection tools have been examined. The methods of incorporation into various subsystems of the kernel on the x86 architecture have been described [ru
Linear and kernel methods for multi- and hypervariate change detection
DEFF Research Database (Denmark)
Nielsen, Allan Aasbjerg; Canty, Morton J.
2010-01-01
. Principal component analysis (PCA) as well as maximum autocorrelation factor (MAF) and minimum noise fraction (MNF) analyses of IR-MAD images, both linear and kernel-based (which are nonlinear), may further enhance change signals relative to no-change background. The kernel versions are based on a dual...... formulation, also termed Q-mode analysis, in which the data enter into the analysis via inner products in the Gram matrix only. In the kernel version the inner products of the original data are replaced by inner products between nonlinear mappings into higher dimensional feature space. Via kernel substitution......, also known as the kernel trick, these inner products between the mappings are in turn replaced by a kernel function and all quantities needed in the analysis are expressed in terms of the kernel function. This means that we need not know the nonlinear mappings explicitly. Kernel principal component...
Kernel methods in orthogonalization of multi- and hypervariate data
DEFF Research Database (Denmark)
Nielsen, Allan Aasbjerg
2009-01-01
A kernel version of maximum autocorrelation factor (MAF) analysis is described very briefly and applied to change detection in remotely sensed hyperspectral image (HyMap) data. The kernel version is based on a dual formulation also termed Q-mode analysis in which the data enter into the analysis...... via inner products in the Gram matrix only. In the kernel version the inner products are replaced by inner products between nonlinear mappings into higher dimensional feature space of the original data. Via kernel substitution also known as the kernel trick these inner products between the mappings...... are in turn replaced by a kernel function and all quantities needed in the analysis are expressed in terms of this kernel function. This means that we need not know the nonlinear mappings explicitly. Kernel PCA and MAF analysis handle nonlinearities by implicitly transforming data into high (even infinite...
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
SECURITY CLASSIFICATION OF: The research objective of this proposal was to develop a predictive Bayesian kernel approach to model count data based on...several predictive variables. Such an approach, which we refer to as the Poisson Bayesian kernel model, is able to model the rate of occurrence of... kernel methods made use of: (i) the Bayesian property of improving predictive accuracy as data are dynamically obtained, and (ii) the kernel function
Relationship between attenuation coefficients and dose-spread kernels
International Nuclear Information System (INIS)
Boyer, A.L.
1988-01-01
Dose-spread kernels can be used to calculate the dose distribution in a photon beam by convolving the kernel with the primary fluence distribution. The theoretical relationships between various types and components of dose-spread kernels relative to photon attenuation coefficients are explored. These relations can be valuable as checks on the conservation of energy by dose-spread kernels calculated by analytic or Monte Carlo methods
Fabrication of Uranium Oxycarbide Kernels for HTR Fuel
International Nuclear Information System (INIS)
Barnes, Charles; Richardson, Clay; Nagley, Scott; Hunn, John; Shaber, Eric
2010-01-01
Babcock and Wilcox (B and W) has been producing high quality uranium oxycarbide (UCO) kernels for Advanced Gas Reactor (AGR) fuel tests at the Idaho National Laboratory. In 2005, 350-(micro)m, 19.7% 235U-enriched UCO kernels were produced for the AGR-1 test fuel. Following coating of these kernels and forming the coated-particles into compacts, this fuel was irradiated in the Advanced Test Reactor (ATR) from December 2006 until November 2009. B and W produced 425-(micro)m, 14% enriched UCO kernels in 2008, and these kernels were used to produce fuel for the AGR-2 experiment that was inserted in ATR in 2010. B and W also produced 500-(micro)m, 9.6% enriched UO2 kernels for the AGR-2 experiments. Kernels of the same size and enrichment as AGR-1 were also produced for the AGR-3/4 experiment. In addition to fabricating enriched UCO and UO2 kernels, B and W has produced more than 100 kg of natural uranium UCO kernels which are being used in coating development tests. Successive lots of kernels have demonstrated consistent high quality and also allowed for fabrication process improvements. Improvements in kernel forming were made subsequent to AGR-1 kernel production. Following fabrication of AGR-2 kernels, incremental increases in sintering furnace charge size have been demonstrated. Recently small scale sintering tests using a small development furnace equipped with a residual gas analyzer (RGA) has increased understanding of how kernel sintering parameters affect sintered kernel properties. The steps taken to increase throughput and process knowledge have reduced kernel production costs. Studies have been performed of additional modifications toward the goal of increasing capacity of the current fabrication line to use for production of first core fuel for the Next Generation Nuclear Plant (NGNP) and providing a basis for the design of a full scale fuel fabrication facility.
Consistent Estimation of Pricing Kernels from Noisy Price Data
Vladislav Kargin
2003-01-01
If pricing kernels are assumed non-negative then the inverse problem of finding the pricing kernel is well-posed. The constrained least squares method provides a consistent estimate of the pricing kernel. When the data are limited, a new method is suggested: relaxed maximization of the relative entropy. This estimator is also consistent. Keywords: $\\epsilon$-entropy, non-parametric estimation, pricing kernel, inverse problems.
Evaluation of the OpenCL AES Kernel using the Intel FPGA SDK for OpenCL
Energy Technology Data Exchange (ETDEWEB)
Jin, Zheming [Argonne National Lab. (ANL), Argonne, IL (United States); Yoshii, Kazutomo [Argonne National Lab. (ANL), Argonne, IL (United States); Finkel, Hal [Argonne National Lab. (ANL), Argonne, IL (United States); Cappello, Franck [Argonne National Lab. (ANL), Argonne, IL (United States)
2017-04-20
The OpenCL standard is an open programming model for accelerating algorithms on heterogeneous computing system. OpenCL extends the C-based programming language for developing portable codes on different platforms such as CPU, Graphics processing units (GPUs), Digital Signal Processors (DSPs) and Field Programmable Gate Arrays (FPGAs). The Intel FPGA SDK for OpenCL is a suite of tools that allows developers to abstract away the complex FPGA-based development flow for a high-level software development flow. Users can focus on the design of hardware-accelerated kernel functions in OpenCL and then direct the tools to generate the low-level FPGA implementations. The approach makes the FPGA-based development more accessible to software users as the needs for hybrid computing using CPUs and FPGAs are increasing. It can also significantly reduce the hardware development time as users can evaluate different ideas with high-level language without deep FPGA domain knowledge. In this report, we evaluate the performance of the kernel using the Intel FPGA SDK for OpenCL and Nallatech 385A FPGA board. Compared to the M506 module, the board provides more hardware resources for a larger design exploration space. The kernel performance is measured with the compute kernel throughput, an upper bound to the FPGA throughput. The report presents the experimental results in details. The Appendix lists the kernel source code.
Quantum logic in dagger kernel categories
Heunen, C.; Jacobs, B.P.F.
2009-01-01
This paper investigates quantum logic from the perspective of categorical logic, and starts from minimal assumptions, namely the existence of involutions/daggers and kernels. The resulting structures turn out to (1) encompass many examples of interest, such as categories of relations, partial
Quantum logic in dagger kernel categories
Heunen, C.; Jacobs, B.P.F.; Coecke, B.; Panangaden, P.; Selinger, P.
2011-01-01
This paper investigates quantum logic from the perspective of categorical logic, and starts from minimal assumptions, namely the existence of involutions/daggers and kernels. The resulting structures turn out to (1) encompass many examples of interest, such as categories of relations, partial
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.
Flexible Scheduling in Multimedia Kernels: An Overview
Jansen, P.G.; Scholten, Johan; Laan, Rene; Chow, W.S.
1999-01-01
Current Hard Real-Time (HRT) kernels have their timely behaviour guaranteed on the cost of a rather restrictive use of the available resources. This makes current HRT scheduling techniques inadequate for use in a multimedia environment where we can make a considerable profit by a better and more
Reproducing kernel Hilbert spaces of Gaussian priors
Vaart, van der A.W.; Zanten, van J.H.; Clarke, B.; Ghosal, S.
2008-01-01
We review definitions and properties of reproducing kernel Hilbert spaces attached to Gaussian variables and processes, with a view to applications in nonparametric Bayesian statistics using Gaussian priors. The rate of contraction of posterior distributions based on Gaussian priors can be described
A synthesis of empirical plant dispersal kernels
Czech Academy of Sciences Publication Activity Database
Bullock, J. M.; González, L. M.; Tamme, R.; Götzenberger, Lars; White, S. M.; Pärtel, M.; Hooftman, D. A. P.
2017-01-01
Roč. 105, č. 1 (2017), s. 6-19 ISSN 0022-0477 Institutional support: RVO:67985939 Keywords : dispersal kernel * dispersal mode * probability density function Subject RIV: EH - Ecology, Behaviour OBOR OECD: Ecology Impact factor: 5.813, year: 2016
Analytic continuation of weighted Bergman kernels
Czech Academy of Sciences Publication Activity Database
Engliš, Miroslav
2010-01-01
Roč. 94, č. 6 (2010), s. 622-650 ISSN 0021-7824 R&D Projects: GA AV ČR IAA100190802 Keywords : Bergman kernel * analytic continuation * Toeplitz operator Subject RIV: BA - General Mathematics Impact factor: 1.450, year: 2010 http://www.sciencedirect.com/science/article/pii/S0021782410000942
On convergence of kernel learning estimators
Norkin, V.I.; Keyzer, M.A.
2009-01-01
The paper studies convex stochastic optimization problems in a reproducing kernel Hilbert space (RKHS). The objective (risk) functional depends on functions from this RKHS and takes the form of a mathematical expectation (integral) of a nonnegative integrand (loss function) over a probability
Analytic properties of the Virasoro modular kernel
Energy Technology Data Exchange (ETDEWEB)
Nemkov, Nikita [Moscow Institute of Physics and Technology (MIPT), Dolgoprudny (Russian Federation); Institute for Theoretical and Experimental Physics (ITEP), Moscow (Russian Federation); National University of Science and Technology MISIS, The Laboratory of Superconducting metamaterials, Moscow (Russian Federation)
2017-06-15
On the space of generic conformal blocks the modular transformation of the underlying surface is realized as a linear integral transformation. We show that the analytic properties of conformal block implied by Zamolodchikov's formula are shared by the kernel of the modular transformation and illustrate this by explicit computation in the case of the one-point toric conformal block. (orig.)
Kernel based subspace projection of hyperspectral images
DEFF Research Database (Denmark)
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...
Kernel Temporal Differences for Neural Decoding
Bae, Jihye; Sanchez Giraldo, Luis G.; Pohlmeyer, Eric A.; Francis, Joseph T.; Sanchez, Justin C.; Príncipe, José C.
2015-01-01
We study the feasibility and capability of the kernel temporal difference (KTD)(λ) algorithm for neural decoding. KTD(λ) is an online, kernel-based learning algorithm, which has been introduced to estimate value functions in reinforcement learning. This algorithm combines kernel-based representations with the temporal difference approach to learning. One of our key observations is that by using strictly positive definite kernels, algorithm's convergence can be guaranteed for policy evaluation. The algorithm's nonlinear functional approximation capabilities are shown in both simulations of policy evaluation and neural decoding problems (policy improvement). KTD can handle high-dimensional neural states containing spatial-temporal information at a reasonable computational complexity allowing real-time applications. When the algorithm seeks a proper mapping between a monkey's neural states and desired positions of a computer cursor or a robot arm, in both open-loop and closed-loop experiments, it can effectively learn the neural state to action mapping. Finally, a visualization of the coadaptation process between the decoder and the subject shows the algorithm's capabilities in reinforcement learning brain machine interfaces. PMID:25866504
Scattering kernels and cross sections working group
International Nuclear Information System (INIS)
Russell, G.; MacFarlane, B.; Brun, T.
1998-01-01
Topics addressed by this working group are: (1) immediate needs of the cold-moderator community and how to fill them; (2) synthetic scattering kernels; (3) very simple synthetic scattering functions; (4) measurements of interest; and (5) general issues. Brief summaries are given for each of these topics
Enhanced gluten properties in soft kernel durum wheat
Soft kernel durum wheat is a relatively recent development (Morris et al. 2011 Crop Sci. 51:114). The soft kernel trait exerts profound effects on kernel texture, flour milling including break flour yield, milling energy, and starch damage, and dough water absorption (DWA). With the caveat of reduce...
Predictive Model Equations for Palm Kernel (Elaeis guneensis J ...
African Journals Online (AJOL)
Estimated error of ± 0.18 and ± 0.2 are envisaged while applying the models for predicting palm kernel and sesame oil colours respectively. Keywords: Palm kernel, Sesame, Palm kernel, Oil Colour, Process Parameters, Model. Journal of Applied Science, Engineering and Technology Vol. 6 (1) 2006 pp. 34-38 ...
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
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 settlement...
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 producing...
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...
Heat kernel analysis for Bessel operators on symmetric cones
DEFF Research Database (Denmark)
Möllers, Jan
2014-01-01
. The heat kernel is explicitly given in terms of a multivariable $I$-Bessel function on $Ω$. Its corresponding heat kernel transform defines a continuous linear operator between $L^p$-spaces. The unitary image of the $L^2$-space under the heat kernel transform is characterized as a weighted Bergmann space...
A 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 981.61 - 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 almonds...
Single pass kernel k-means clustering method
Indian Academy of Sciences (India)
paper proposes a simple and faster version of the kernel k-means clustering ... It has been considered as an important tool ... On the other hand, kernel-based clustering methods, like kernel k-means clus- ..... able at the UCI machine learning repository (Murphy 1994). ... All the data sets have only numeric valued features.
International Nuclear Information System (INIS)
Uchida, Isao; Yamada, Yasuhiko; Yamashita, Takashi; Okigaki, Shigeyasu; Oyamada, Hiyoshimaru; Ito, Akira.
1995-01-01
In radiotherapy with radiopharmaceuticals, more accurate estimates of the three-dimensional (3-D) distribution of absorbed dose is important in specifying the activity to be administered to patients to deliver a prescribed absorbed dose to target volumes without exceeding the toxicity limit of normal tissues in the body. A calculation algorithm for the purpose has already been developed by the authors. An accurate 3-D distribution of absorbed dose based on the algorithm is given by convolution of the 3-D dose matrix for a unit cubic voxel containing unit cumulated activity, which is obtained by transforming a dose point kernel into a 3-D cubic dose matrix, with the 3-D cumulated activity distribution given by the same voxel size. However, beta-dose point kernels affecting accurate estimates of the 3-D absorbed dose distribution have been different among the investigators. The purpose of this study is to elucidate how different beta-dose point kernels in water influence on the estimates of the absorbed dose distribution due to the dose point kernel convolution method by the authors. Computer simulations were performed using the MIRD thyroid and lung phantoms under assumption of uniform activity distribution of 32 P. Using beta-dose point kernels derived from Monte Carlo simulations (EGS-4 or ACCEPT computer code), the differences among their point kernels gave little differences for the mean and maximum absorbed dose estimates for the MIRD phantoms used. In the estimates of mean and maximum absorbed doses calculated using different cubic voxel sizes (4x4x4 mm and 8x8x8 mm) for the MIRD thyroid phantom, the maximum absorbed doses for the 4x4x4 mm-voxel were estimated approximately 7% greater than the cases of the 8x8x8 mm-voxel. They were found in every beta-dose point kernel used in this study. On the other hand, the percentage difference of the mean absorbed doses in the both voxel sizes for each beta-dose point kernel was less than approximately 0.6%. (author)
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
Mac OS X for Unix Geeks (Leopard)
Rothman, Ernest E; Rosen, Rich
2009-01-01
If you've been lured to Mac OS X because of its Unix roots, this invaluable book serves as a bridge between Apple's Darwin OS and the more traditional Unix systems. The new edition offers a complete tour of Mac OS X's Unix shell for Leopard and Tiger, and helps you find the facilities that replace or correspond to standard Unix utilities. Learn how to compile code, link to libraries, and port Unix software to Mac OS X and much more with this concise guide.
Code system BCG for gamma-ray skyshine calculation
International Nuclear Information System (INIS)
Ryufuku, Hiroshi; Numakunai, Takao; Miyasaka, Shun-ichi; Minami, Kazuyoshi.
1979-03-01
A code system BCG has been developed for calculating conveniently and efficiently gamma-ray skyshine doses using the transport calculation codes ANISN and DOT and the point-kernel calculation codes G-33 and SPAN. To simplify the input forms to the system, the forms for these codes are unified, twelve geometric patterns are introduced to give material regions, and standard data are available as a library. To treat complex arrangements of source and shield, it is further possible to use successively the code such that the results from one code may be used as input data to the same or other code. (author)
Kernel based orthogonalization for change detection in hyperspectral images
DEFF Research Database (Denmark)
Nielsen, Allan Aasbjerg
function and all quantities needed in the analysis are expressed in terms of this kernel function. This means that we need not know the nonlinear mappings explicitly. Kernel PCA and MNF analyses handle nonlinearities by implicitly transforming data into high (even infinite) dimensional feature space via...... analysis all 126 spectral bands of the HyMap are included. Changes on the ground are most likely due to harvest having taken place between the two acquisitions and solar effects (both solar elevation and azimuth have changed). Both types of kernel analysis emphasize change and unlike kernel PCA, kernel MNF...
A laser optical method for detecting corn kernel defects
Energy Technology Data Exchange (ETDEWEB)
Gunasekaran, S.; Paulsen, M. R.; Shove, G. C.
1984-01-01
An opto-electronic instrument was developed to examine individual corn kernels and detect various kernel defects according to reflectance differences. A low power helium-neon (He-Ne) laser (632.8 nm, red light) was used as the light source in the instrument. Reflectance from good and defective parts of corn kernel surfaces differed by approximately 40%. Broken, chipped, and starch-cracked kernels were detected with nearly 100% accuracy; while surface-split kernels were detected with about 80% accuracy. (author)
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.
Windows Vista Kernel-Mode: Functions, Security Enhancements and Flaws
Directory of Open Access Journals (Sweden)
Mohammed D. ABDULMALIK
2008-06-01
Full Text Available Microsoft has made substantial enhancements to the kernel of the Microsoft Windows Vista operating system. Kernel improvements are significant because the kernel provides low-level operating system functions, including thread scheduling, interrupt and exception dispatching, multiprocessor synchronization, and a set of routines and basic objects.This paper describes some of the kernel security enhancements for 64-bit edition of Windows Vista. We also point out some weakness areas (flaws that can be attacked by malicious leading to compromising the kernel.
Difference between standard and quasi-conformal BFKL kernels
International Nuclear Information System (INIS)
Fadin, V.S.; Fiore, R.; Papa, A.
2012-01-01
As it was recently shown, the colour singlet BFKL kernel, taken in Möbius representation in the space of impact parameters, can be written in quasi-conformal shape, which is unbelievably simple compared with the conventional form of the BFKL kernel in momentum space. It was also proved that the total kernel is completely defined by its Möbius representation. In this paper we calculated the difference between standard and quasi-conformal BFKL kernels in momentum space and discovered that it is rather simple. Therefore we come to the conclusion that the simplicity of the quasi-conformal kernel is caused mainly by using the impact parameter space.
Analytic scattering kernels for neutron thermalization studies
International Nuclear Information System (INIS)
Sears, V.F.
1990-01-01
Current plans call for the inclusion of a liquid hydrogen or deuterium cold source in the NRU replacement vessel. This report is part of an ongoing study of neutron thermalization in such a cold source. Here, we develop a simple analytical model for the scattering kernel of monatomic and diatomic liquids. We also present the results of extensive numerical calculations based on this model for liquid hydrogen, liquid deuterium, and mixtures of the two. These calculations demonstrate the dependence of the scattering kernel on the incident and scattered-neutron energies, the behavior near rotational thresholds, the dependence on the centre-of-mass pair correlations, the dependence on the ortho concentration, and the dependence on the deuterium concentration in H 2 /D 2 mixtures. The total scattering cross sections are also calculated and compared with available experimental results
Quantized kernel least mean square algorithm.
Chen, Badong; Zhao, Songlin; Zhu, Pingping; Príncipe, José C
2012-01-01
In this paper, we propose a quantization approach, as an alternative of sparsification, to curb the growth of the radial basis function structure in kernel adaptive filtering. The basic idea behind this method is to quantize and hence compress the input (or feature) space. Different from sparsification, the new approach uses the "redundant" data to update the coefficient of the closest center. In particular, a quantized kernel least mean square (QKLMS) algorithm is developed, which is based on a simple online vector quantization method. The analytical study of the mean square convergence has been carried out. The energy conservation relation for QKLMS is established, and on this basis we arrive at a sufficient condition for mean square convergence, and a lower and upper bound on the theoretical value of the steady-state excess mean square error. Static function estimation and short-term chaotic time-series prediction examples are presented to demonstrate the excellent performance.
Kernel-based tests for joint independence
DEFF Research Database (Denmark)
Pfister, Niklas; Bühlmann, Peter; Schölkopf, Bernhard
2018-01-01
if the $d$ variables are jointly independent, as long as the kernel is characteristic. Based on an empirical estimate of dHSIC, we define three different non-parametric hypothesis tests: a permutation test, a bootstrap test and a test based on a Gamma approximation. We prove that the permutation test......We investigate the problem of testing whether $d$ random variables, which may or may not be continuous, are jointly (or mutually) independent. Our method builds on ideas of the two variable Hilbert-Schmidt independence criterion (HSIC) but allows for an arbitrary number of variables. We embed...... the $d$-dimensional joint distribution and the product of the marginals into a reproducing kernel Hilbert space and define the $d$-variable Hilbert-Schmidt independence criterion (dHSIC) as the squared distance between the embeddings. In the population case, the value of dHSIC is zero if and only...
Wilson Dslash Kernel From Lattice QCD Optimization
Energy Technology Data Exchange (ETDEWEB)
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.
International Nuclear Information System (INIS)
Zuhair; Suwoto
2009-01-01
Main characteristics of PBR comes from utilization of coated particle fuels dispersed in pebble fuels . Because of vibration, fuel kernel can be grouped into cluster and in these cases, neutronic characteristics of pebble fuel significantly changes . In this study, cluster is modeled structural form consisting of uniform cubic cells with eight neighborhood TRISO particles . Neutronic characteristics was investigated by calculating pebble-bed reactor multiplication factor as a function of fuel kernel radius at various enrichments . The calculation results using MCNP5 code with ENDF/BVI neutron library show that k eff value depends on the average fuel radius and reaches its minimum when all kernels have the same radius, i.e. 0.0280 cm . With this radius, the total kernel surface area achieves maximum value . The dependence of k eff on fuel kernel radius decreases in relation to the increase in uranium enrichment . However, k eff value is not affected by fuel kernel radius when the uranium is 100% enriched . From these result, it can be concluded that, exception of uranium enrichment, the selection of fuel kernel radius should be considered thoroughly in designing a PBR, since this parameter provides significant influences on neutronic characteristics of the reactor. (author)
A Kernel for Protein Secondary Structure Prediction
Guermeur , Yann; Lifchitz , Alain; Vert , Régis
2004-01-01
http://mitpress.mit.edu/catalog/item/default.asp?ttype=2&tid=10338&mode=toc; International audience; Multi-class support vector machines have already proved efficient in protein secondary structure prediction as ensemble methods, to combine the outputs of sets of classifiers based on different principles. In this chapter, their implementation as basic prediction methods, processing the primary structure or the profile of multiple alignments, is investigated. A kernel devoted to the task is in...
Scalar contribution to the BFKL kernel
International Nuclear Information System (INIS)
Gerasimov, R. E.; Fadin, V. S.
2010-01-01
The contribution of scalar particles to the kernel of the Balitsky-Fadin-Kuraev-Lipatov (BFKL) equation is calculated. A great cancellation between the virtual and real parts of this contribution, analogous to the cancellation in the quark contribution in QCD, is observed. The reason of this cancellation is discovered. This reason has a common nature for particles with any spin. Understanding of this reason permits to obtain the total contribution without the complicated calculations, which are necessary for finding separate pieces.
Weighted Bergman Kernels for Logarithmic Weights
Czech Academy of Sciences Publication Activity Database
Engliš, Miroslav
2010-01-01
Roč. 6, č. 3 (2010), s. 781-813 ISSN 1558-8599 R&D Projects: GA AV ČR IAA100190802 Keywords : Bergman kernel * Toeplitz operator * logarithmic weight * pseudodifferential operator Subject RIV: BA - General Mathematics Impact factor: 0.462, year: 2010 http://www.intlpress.com/site/pub/pages/journals/items/pamq/content/vols/0006/0003/a008/
Heat kernels and zeta functions on fractals
International Nuclear Information System (INIS)
Dunne, Gerald V
2012-01-01
On fractals, spectral functions such as heat kernels and zeta functions exhibit novel features, very different from their behaviour on regular smooth manifolds, and these can have important physical consequences for both classical and quantum physics in systems having fractal properties. This article is part of a special issue of Journal of Physics A: Mathematical and Theoretical in honour of Stuart Dowker's 75th birthday devoted to ‘Applications of zeta functions and other spectral functions in mathematics and physics’. (paper)
Exploiting graph kernels for high performance biomedical relation extraction.
Panyam, Nagesh C; Verspoor, Karin; Cohn, Trevor; Ramamohanarao, Kotagiri
2018-01-30
Relation extraction from biomedical publications is an important task in the area of semantic mining of text. Kernel methods for supervised relation extraction are often preferred over manual feature engineering methods, when classifying highly ordered structures such as trees and graphs obtained from syntactic parsing of a sentence. Tree kernels such as the Subset Tree Kernel and Partial Tree Kernel have been shown to be effective for classifying constituency parse trees and basic dependency parse graphs of a sentence. Graph kernels such as the All Path Graph kernel (APG) and Approximate Subgraph Matching (ASM) kernel have been shown to be suitable for classifying general graphs with cycles, such as the enhanced dependency parse graph of a sentence. In this work, we present a high performance Chemical-Induced Disease (CID) relation extraction system. We present a comparative study of kernel methods for the CID task and also extend our study to the Protein-Protein Interaction (PPI) extraction task, an important biomedical relation extraction task. We discuss novel modifications to the ASM kernel to boost its performance and a method to apply graph kernels for extracting relations expressed in multiple sentences. Our system for CID relation extraction attains an F-score of 60%, without using external knowledge sources or task specific heuristic or rules. In comparison, the state of the art Chemical-Disease Relation Extraction system achieves an F-score of 56% using an ensemble of multiple machine learning methods, which is then boosted to 61% with a rule based system employing task specific post processing rules. For the CID task, graph kernels outperform tree kernels substantially, and the best performance is obtained with APG kernel that attains an F-score of 60%, followed by the ASM kernel at 57%. The performance difference between the ASM and APG kernels for CID sentence level relation extraction is not significant. In our evaluation of ASM for the PPI task, ASM
Identification of Fusarium damaged wheat kernels using image analysis
Directory of Open Access Journals (Sweden)
Ondřej Jirsa
2011-01-01
Full Text Available Visual evaluation of kernels damaged by Fusarium spp. pathogens is labour intensive and due to a subjective approach, it can lead to inconsistencies. Digital imaging technology combined with appropriate statistical methods can provide much faster and more accurate evaluation of the visually scabby kernels proportion. The aim of the present study was to develop a discrimination model to identify wheat kernels infected by Fusarium spp. using digital image analysis and statistical methods. Winter wheat kernels from field experiments were evaluated visually as healthy or damaged. Deoxynivalenol (DON content was determined in individual kernels using an ELISA method. Images of individual kernels were produced using a digital camera on dark background. Colour and shape descriptors were obtained by image analysis from the area representing the kernel. Healthy and damaged kernels differed significantly in DON content and kernel weight. Various combinations of individual shape and colour descriptors were examined during the development of the model using linear discriminant analysis. In addition to basic descriptors of the RGB colour model (red, green, blue, very good classification was also obtained using hue from the HSL colour model (hue, saturation, luminance. The accuracy of classification using the developed discrimination model based on RGBH descriptors was 85 %. The shape descriptors themselves were not specific enough to distinguish individual kernels.
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.
Abdelfattah, Ahmad
2015-01-15
performance experiments show improvements ranging from 10% and up to more than fourfold speedup against competitive GPU MVM approaches. Performance impacts on high-level numerical libraries and a computational astronomy application are highlighted, since such memory-bound kernels are often located in innermost levels of the software chain. The excellent performance obtained in this work has led to the adoption of code in NVIDIAs widely distributed cuBLAS library.
Green, David L.; Berry, Lee A.; Simpson, Adam B.; Younkin, Timothy R.
2018-04-01
We present the KINETIC-J code, a computational kernel for evaluating the linearized Vlasov equation with application to calculating the kinetic plasma response (current) to an applied time harmonic wave electric field. This code addresses the need for a configuration space evaluation of the plasma current to enable kinetic full-wave solvers for waves in hot plasmas to move beyond the limitations of the traditional Fourier spectral methods. We benchmark the kernel via comparison with the standard k →-space forms of the hot plasma conductivity tensor.
Kernel based subspace projection of near infrared hyperspectral images of maize kernels
DEFF Research Database (Denmark)
Larsen, Rasmus; Arngren, Morten; Hansen, Per Waaben
2009-01-01
In this paper we present an exploratory analysis of hyper- spectral 900-1700 nm images of maize kernels. The imaging device is a line scanning hyper spectral camera using a broadband NIR illumi- nation. In order to explore the hyperspectral data we compare a series of subspace projection methods ......- tor transform outperform the linear methods as well as kernel principal components in producing interesting projections of the data.......In this paper we present an exploratory analysis of hyper- spectral 900-1700 nm images of maize kernels. The imaging device is a line scanning hyper spectral camera using a broadband NIR illumi- nation. In order to explore the hyperspectral data we compare a series of subspace projection methods...... including principal component analysis and maximum autocorrelation factor analysis. The latter utilizes the fact that interesting phenomena in images exhibit spatial autocorrelation. However, linear projections often fail to grasp the underlying variability on the data. Therefore we propose to use so...
Proof and implementation of the stochastic formula for ideal gas, energy dependent scattering kernel
International Nuclear Information System (INIS)
Becker, B.; Dagan, R.; Lohnert, G.
2009-01-01
The ideal gas, scattering kernel for heavy nuclei with pronounced resonances was developed [Rothenstein, W., Dagan, R., 1998. Ann. Nucl. Energy 25, 209-222], proved and implemented [Rothenstein, W., 2004 Ann. Nucl. Energy 31, 9-23] in the data processing code NJOY [Macfarlane, R.E., Muir, D.W., 1994. The NJOY Nuclear Data Processing System Version 91, LA-12740-M] from which the scattering probability tables were prepared [Dagan, R., 2005. Ann. Nucl. Energy 32, 367-377]. Those tables were introduced to the well known MCNP code [X-5 Monte Carlo Team. MCNP - A General Monte Carlo N-Particle Transport Code version 5 LA-UR-03-1987 code] via the 'mt' input cards in the same manner as it is done for light nuclei in the thermal energy range. In this study we present an alternative methodology for solving the double differential energy dependent scattering kernel which is based solely on stochastic consideration as far as the scattering probabilities are concerned. The solution scheme is based on an alternative rejection scheme suggested by Rothenstein [Rothenstein, W. ENS conference 1994 Tel Aviv]. Based on comparison with the above mentioned analytical (probability S(α,β)-tables) approach it is confirmed that the suggested rejection scheme provides accurate results. The uncertainty concerning the magnitude of the bias due to the enhanced multiple rejections during the sampling procedure are proved to lie within 1-2 standard deviations for all practical cases that were analysed.
GUI2QAD, Graphical Interface for QAD-CGPIC, Point Kernel for Shielding Calculations
International Nuclear Information System (INIS)
2001-01-01
1 - Description of program or function: GUI2QAD is an aid in preparation of input for the included QAD-CGPIC program, which is based on CCC-493/QAD-CGGP and PICTURE. QAD-CGPIC, which is included in this distribution, is a Fortran code for neutron and gamma-ray shielding calculations by the point kernel method. Provision is available to interactively view the geometry of the system. QAD-CG calculates fast-neutron and gamma-ray penetration through various shield configurations defined by combinatorial geometry specifications. The code can use the ANS-6.4.3 1990 buildup factor compilation (26 materials). 2 - Methods:The code QAD-CGPIC is based on point kernel method and has a provision to select either GP or Capo's build up factors. 3 - Restrictions on the complexity of the problem: Details on restrictions and limitations are available in the RSICC code manual CCC-493/QAD-CGGP. Because CCC-493 was obsoleted by CCC-645/QAD-CGGP-A, the CCC-493 documentation is not online but is included with this package. This package includes a Graphical User Interface to facilitate use
Kernel based eigenvalue-decomposition methods for analysing ham
DEFF Research Database (Denmark)
Christiansen, Asger Nyman; Nielsen, Allan Aasbjerg; Møller, Flemming
2010-01-01
methods, such as PCA, MAF or MNF. We therefore investigated the applicability of kernel based versions of these transformation. This meant implementing the kernel based methods and developing new theory, since kernel based MAF and MNF is not described in the literature yet. The traditional methods only...... have two factors that are useful for segmentation and none of them can be used to segment the two types of meat. The kernel based methods have a lot of useful factors and they are able to capture the subtle differences in the images. This is illustrated in Figure 1. You can see a comparison of the most...... useful factor of PCA and kernel based PCA respectively in Figure 2. The factor of the kernel based PCA turned out to be able to segment the two types of meat and in general that factor is much more distinct, compared to the traditional factor. After the orthogonal transformation a simple thresholding...
Classification of maize kernels using NIR hyperspectral imaging
DEFF Research Database (Denmark)
Williams, Paul; Kucheryavskiy, Sergey V.
2016-01-01
NIR hyperspectral imaging was evaluated to classify maize kernels of three hardness categories: hard, medium and soft. Two approaches, pixel-wise and object-wise, were investigated to group kernels according to hardness. The pixel-wise classification assigned a class to every pixel from individual...... and specificity of 0.95 and 0.93). Both feature extraction methods can be recommended for classification of maize kernels on production scale....
Ideal gas scattering kernel for energy dependent cross-sections
International Nuclear Information System (INIS)
Rothenstein, W.; Dagan, R.
1998-01-01
A third, and final, paper on the calculation of the joint kernel for neutron scattering by an ideal gas in thermal agitation is presented, when the scattering cross-section is energy dependent. The kernel is a function of the neutron energy after scattering, and of the cosine of the scattering angle, as in the case of the ideal gas kernel for a constant bound atom scattering cross-section. The final expression is suitable for numerical calculations
Embedded real-time operating system micro kernel design
Cheng, Xiao-hui; Li, Ming-qiang; Wang, Xin-zheng
2005-12-01
Embedded systems usually require a real-time character. Base on an 8051 microcontroller, an embedded real-time operating system micro kernel is proposed consisting of six parts, including a critical section process, task scheduling, interruption handle, semaphore and message mailbox communication, clock managent and memory managent. Distributed CPU and other resources are among tasks rationally according to the importance and urgency. The design proposed here provides the position, definition, function and principle of micro kernel. The kernel runs on the platform of an ATMEL AT89C51 microcontroller. Simulation results prove that the designed micro kernel is stable and reliable and has quick response while operating in an application system.
An SVM model with hybrid kernels for hydrological time series
Wang, C.; Wang, H.; Zhao, X.; Xie, Q.
2017-12-01
Support Vector Machine (SVM) models have been widely applied to the forecast of climate/weather and its impact on other environmental variables such as hydrologic response to climate/weather. When using SVM, the choice of the kernel function plays the key role. Conventional SVM models mostly use one single type of kernel function, e.g., radial basis kernel function. Provided that there are several featured kernel functions available, each having its own advantages and drawbacks, a combination of these kernel functions may give more flexibility and robustness to SVM approach, making it suitable for a wide range of application scenarios. This paper presents such a linear combination of radial basis kernel and polynomial kernel for the forecast of monthly flowrate in two gaging stations using SVM approach. The results indicate significant improvement in the accuracy of predicted series compared to the approach with either individual kernel function, thus demonstrating the feasibility and advantages of such hybrid kernel approach for SVM applications.
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.
Dose point kernels for beta-emitting radioisotopes
International Nuclear Information System (INIS)
Prestwich, W.V.; Chan, L.B.; Kwok, C.S.; Wilson, B.
1986-01-01
Knowledge of the dose point kernel corresponding to a specific radionuclide is required to calculate the spatial dose distribution produced in a homogeneous medium by a distributed source. Dose point kernels for commonly used radionuclides have been calculated previously using as a basis monoenergetic dose point kernels derived by numerical integration of a model transport equation. The treatment neglects fluctuations in energy deposition, an effect which has been later incorporated in dose point kernels calculated using Monte Carlo methods. This work describes new calculations of dose point kernels using the Monte Carlo results as a basis. An analytic representation of the monoenergetic dose point kernels has been developed. This provides a convenient method both for calculating the dose point kernel associated with a given beta spectrum and for incorporating the effect of internal conversion. An algebraic expression for allowed beta spectra has been accomplished through an extension of the Bethe-Bacher approximation, and tested against the exact expression. Simplified expression for first-forbidden shape factors have also been developed. A comparison of the calculated dose point kernel for 32 P with experimental data indicates good agreement with a significant improvement over the earlier results in this respect. An analytic representation of the dose point kernel associated with the spectrum of a single beta group has been formulated. 9 references, 16 figures, 3 tables
Hadamard Kernel SVM with applications for breast cancer outcome predictions.
Jiang, Hao; Ching, Wai-Ki; Cheung, Wai-Shun; Hou, Wenpin; Yin, Hong
2017-12-21
Breast cancer is one of the leading causes of deaths for women. It is of great necessity to develop effective methods for breast cancer detection and diagnosis. Recent studies have focused on gene-based signatures for outcome predictions. Kernel SVM for its discriminative power in dealing with small sample pattern recognition problems has attracted a lot attention. But how to select or construct an appropriate kernel for a specified problem still needs further investigation. Here we propose a novel kernel (Hadamard Kernel) in conjunction with Support Vector Machines (SVMs) to address the problem of breast cancer outcome prediction using gene expression data. Hadamard Kernel outperform the classical kernels and correlation kernel in terms of Area under the ROC Curve (AUC) values where a number of real-world data sets are adopted to test the performance of different methods. Hadamard Kernel SVM is effective for breast cancer predictions, either in terms of prognosis or diagnosis. It may benefit patients by guiding therapeutic options. Apart from that, it would be a valuable addition to the current SVM kernel families. We hope it will contribute to the wider biology and related communities.
Parameter optimization in the regularized kernel minimum noise fraction transformation
DEFF Research Database (Denmark)
Nielsen, Allan Aasbjerg; Vestergaard, Jacob Schack
2012-01-01
Based on the original, linear minimum noise fraction (MNF) transformation and kernel principal component analysis, a kernel version of the MNF transformation was recently introduced. Inspired by we here give a simple method for finding optimal parameters in a regularized version of kernel MNF...... analysis. We consider the model signal-to-noise ratio (SNR) as a function of the kernel parameters and the regularization parameter. In 2-4 steps of increasingly refined grid searches we find the parameters that maximize the model SNR. An example based on data from the DLR 3K camera system is given....
Suitability of point kernel dose calculation techniques in brachytherapy treatment planning
Directory of Open Access Journals (Sweden)
Lakshminarayanan Thilagam
2010-01-01
Full Text Available Brachytherapy treatment planning system (TPS is necessary to estimate the dose to target volume and organ at risk (OAR. TPS is always recommended to account for the effect of tissue, applicator and shielding material heterogeneities exist in applicators. However, most brachytherapy TPS software packages estimate the absorbed dose at a point, taking care of only the contributions of individual sources and the source distribution, neglecting the dose perturbations arising from the applicator design and construction. There are some degrees of uncertainties in dose rate estimations under realistic clinical conditions. In this regard, an attempt is made to explore the suitability of point kernels for brachytherapy dose rate calculations and develop new interactive brachytherapy package, named as BrachyTPS, to suit the clinical conditions. BrachyTPS is an interactive point kernel code package developed to perform independent dose rate calculations by taking into account the effect of these heterogeneities, using two regions build up factors, proposed by Kalos. The primary aim of this study is to validate the developed point kernel code package integrated with treatment planning computational systems against the Monte Carlo (MC results. In the present work, three brachytherapy applicators commonly used in the treatment of uterine cervical carcinoma, namely (i Board of Radiation Isotope and Technology (BRIT low dose rate (LDR applicator and (ii Fletcher Green type LDR applicator (iii Fletcher Williamson high dose rate (HDR applicator, are studied to test the accuracy of the software. Dose rates computed using the developed code are compared with the relevant results of the MC simulations. Further, attempts are also made to study the dose rate distribution around the commercially available shielded vaginal applicator set (Nucletron. The percentage deviations of BrachyTPS computed dose rate values from the MC results are observed to be within plus/minus 5
Analysis of Advanced Fuel Kernel Technology
International Nuclear Information System (INIS)
Oh, Seung Chul; Jeong, Kyung Chai; Kim, Yeon Ku; Kim, Young Min; Kim, Woong Ki; Lee, Young Woo; Cho, Moon Sung
2010-03-01
The reference fuel for prismatic reactor concepts is based on use of an LEU UCO TRISO fissile particle. This fuel form was selected in the early 1980s for large high-temperature gas-cooled reactor (HTGR) concepts using LEU, and the selection was reconfirmed for modular designs in the mid-1980s. Limited existing irradiation data on LEU UCO TRISO fuel indicate the need for a substantial improvement in performance with regard to in-pile gaseous fission product release. Existing accident testing data on LEU UCO TRISO fuel are extremely limited, but it is generally expected that performance would be similar to that of LEU UO 2 TRISO fuel if performance under irradiation were successfully improved. Initial HTGR fuel technology was based on carbide fuel forms. In the early 1980s, as HTGR technology was transitioning from high-enriched uranium (HEU) fuel to LEU fuel. An initial effort focused on LEU prismatic design for large HTGRs resulted in the selection of UCO kernels for the fissile particles and thorium oxide (ThO 2 ) for the fertile particles. The primary reason for selection of the UCO kernel over UO 2 was reduced CO pressure, allowing higher burnup for equivalent coating thicknesses and reduced potential for kernel migration, an important failure mechanism in earlier fuels. A subsequent assessment in the mid-1980s considering modular HTGR concepts again reached agreement on UCO for the fissile particle for a prismatic design. In the early 1990s, plant cost-reduction studies led to a decision to change the fertile material from thorium to natural uranium, primarily because of a lower long-term decay heat level for the natural uranium fissile particles. Ongoing economic optimization in combination with anticipated capabilities of the UCO particles resulted in peak fissile particle burnup projection of 26% FIMA in steam cycle and gas turbine concepts
Implementation of the On-the-fly Encryption for the Linux OS Based on Certified CPS
Directory of Open Access Journals (Sweden)
Alexander Mikhailovich Korotin
2013-02-01
Full Text Available The article is devoted to tools for on-the-fly encryption and a method to implement such tool for the Linux OS based on a certified CPS.The idea is to modify the existing tool named eCryptfs. Russian cryptographic algorithms will be used in the user and kernel modes.
Learning Rotation for Kernel Correlation Filter
Hamdi, Abdullah
2017-08-11
Kernel Correlation Filters have shown a very promising scheme for visual tracking in terms of speed and accuracy on several benchmarks. However it suffers from problems that affect its performance like occlusion, rotation and scale change. This paper tries to tackle the problem of rotation by reformulating the optimization problem for learning the correlation filter. This modification (RKCF) includes learning rotation filter that utilizes circulant structure of HOG feature to guesstimate rotation from one frame to another and enhance the detection of KCF. Hence it gains boost in overall accuracy in many of OBT50 detest videos with minimal additional computation.
Research of Performance Linux Kernel File Systems
Directory of Open Access Journals (Sweden)
Andrey Vladimirovich Ostroukh
2015-10-01
Full Text Available The article describes the most common Linux Kernel File Systems. The research was carried out on a personal computer, the characteristics of which are written in the article. The study was performed on a typical workstation running GNU/Linux with below characteristics. On a personal computer for measuring the file performance, has been installed the necessary software. Based on the results, conclusions and proposed recommendations for use of file systems. Identified and recommended by the best ways to store data.
Fixed kernel regression for voltammogram feature extraction
International Nuclear Information System (INIS)
Acevedo Rodriguez, F J; López-Sastre, R J; Gil-Jiménez, P; Maldonado Bascón, S; Ruiz-Reyes, N
2009-01-01
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
Reciprocity relation for multichannel coupling kernels
International Nuclear Information System (INIS)
Cotanch, S.R.; Satchler, G.R.
1981-01-01
Assuming time-reversal invariance of the many-body Hamiltonian, it is proven that the kernels in a general coupled-channels formulation are symmetric, to within a specified spin-dependent phase, under the interchange of channel labels and coordinates. The theorem is valid for both Hermitian and suitably chosen non-Hermitian Hamiltonians which contain complex effective interactions. While of direct practical consequence for nuclear rearrangement reactions, the reciprocity relation is also appropriate for other areas of physics which involve coupled-channels analysis
Wheat kernel dimensions: how do they contribute to kernel weight at ...
Indian Academy of Sciences (India)
2011-12-02
Dec 2, 2011 ... yield components, is greatly influenced by kernel dimensions. (KD), such as ..... six linkage gaps, and it covered 3010.70 cM of the whole genome with an ...... Ersoz E. et al. 2009 The Genetic architecture of maize flowering.
DEFF Research Database (Denmark)
Arenas-Garcia, J.; Petersen, K.; Camps-Valls, G.
2013-01-01
correlation analysis (CCA), and orthonormalized PLS (OPLS), as well as their nonlinear extensions derived by means of the theory of reproducing kernel Hilbert spaces (RKHSs). We also review their connections to other methods for classification and statistical dependence estimation and introduce some recent...
International Nuclear Information System (INIS)
Brandao, S.B.
1987-01-01
The level structure of 189 Os has been studied by Coulomb excitation using 35 Cl, 28 Si, 16 O beams. GOSIA, a code written to analyze multiple Coulomb excitation, was used to obtain the reduced probabilities of transition B(E2). The results for interband and intraband turned out possible the classification of the states following Nilsson levels. Gamma-rays originating from deexcitation of 216.7 and 219.4 keV have been separated and the reduced probability of transition has been measured. (A.C.A.S.) [pt
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. Copyright © 2014 Elsevier Ltd. All rights reserved.
The Kernel Estimation in Biosystems Engineering
Directory of Open Access Journals (Sweden)
Esperanza Ayuga Téllez
2008-04-01
Full Text Available In many fields of biosystems engineering, it is common to find works in which statistical information is analysed that violates the basic hypotheses necessary for the conventional forecasting methods. For those situations, it is necessary to find alternative methods that allow the statistical analysis considering those infringements. Non-parametric function estimation includes methods that fit a target function locally, using data from a small neighbourhood of the point. Weak assumptions, such as continuity and differentiability of the target function, are rather used than "a priori" assumption of the global target function shape (e.g., linear or quadratic. In this paper a few basic rules of decision are enunciated, for the application of the non-parametric estimation method. These statistical rules set up the first step to build an interface usermethod for the consistent application of kernel estimation for not expert users. To reach this aim, univariate and multivariate estimation methods and density function were analysed, as well as regression estimators. In some cases the models to be applied in different situations, based on simulations, were defined. Different biosystems engineering applications of the kernel estimation are also analysed in this review.
Consistent Valuation across Curves Using Pricing Kernels
Directory of Open Access Journals (Sweden)
Andrea Macrina
2018-03-01
Full Text Available The general problem of asset pricing when the discount rate differs from the rate at which an asset’s cash flows accrue is considered. A pricing kernel framework is used to model an economy that is segmented into distinct markets, each identified by a yield curve having its own market, credit and liquidity risk characteristics. The proposed framework precludes arbitrage within each market, while the definition of a curve-conversion factor process links all markets in a consistent arbitrage-free manner. A pricing formula is then derived, referred to as the across-curve pricing formula, which enables consistent valuation and hedging of financial instruments across curves (and markets. As a natural application, a consistent multi-curve framework is formulated for emerging and developed inter-bank swap markets, which highlights an important dual feature of the curve-conversion factor process. Given this multi-curve framework, existing multi-curve approaches based on HJM and rational pricing kernel models are recovered, reviewed and generalised and single-curve models extended. In another application, inflation-linked, currency-based and fixed-income hybrid securities are shown to be consistently valued using the across-curve valuation method.
Aligning Biomolecular Networks Using Modular Graph Kernels
Towfic, Fadi; Greenlee, M. Heather West; Honavar, Vasant
Comparative analysis of biomolecular networks constructed using measurements from different conditions, tissues, and organisms offer a powerful approach to understanding the structure, function, dynamics, and evolution of complex biological systems. We explore a class of algorithms for aligning large biomolecular networks by breaking down such networks into subgraphs and computing the alignment of the networks based on the alignment of their subgraphs. The resulting subnetworks are compared using graph kernels as scoring functions. We provide implementations of the resulting algorithms as part of BiNA, an open source biomolecular network alignment toolkit. Our experiments using Drosophila melanogaster, Saccharomyces cerevisiae, Mus musculus and Homo sapiens protein-protein interaction networks extracted from the DIP repository of protein-protein interaction data demonstrate that the performance of the proposed algorithms (as measured by % GO term enrichment of subnetworks identified by the alignment) is competitive with some of the state-of-the-art algorithms for pair-wise alignment of large protein-protein interaction networks. Our results also show that the inter-species similarity scores computed based on graph kernels can be used to cluster the species into a species tree that is consistent with the known phylogenetic relationships among the species.
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.
Formal truncations of connected kernel equations
International Nuclear Information System (INIS)
Dixon, R.M.
1977-01-01
The Connected Kernel Equations (CKE) of Alt, Grassberger and Sandhas (AGS); Kouri, Levin and Tobocman (KLT); and Bencze, Redish and Sloan (BRS) are compared against reaction theory criteria after formal channel space and/or operator truncations have been introduced. The Channel Coupling Class concept is used to study the structure of these CKE's. The related wave function formalism of Sandhas, of L'Huillier, Redish and Tandy and of Kouri, Krueger and Levin are also presented. New N-body connected kernel equations which are generalizations of the Lovelace three-body equations are derived. A method for systematically constructing fewer body models from the N-body BRS and generalized Lovelace (GL) equations is developed. The formally truncated AGS, BRS, KLT and GL equations are analyzed by employing the criteria of reciprocity and two-cluster unitarity. Reciprocity considerations suggest that formal truncations of BRS, KLT and GL equations can lead to reciprocity-violating results. This study suggests that atomic problems should employ three-cluster connected truncations and that the two-cluster connected truncations should be a useful starting point for nuclear systems
Scientific Computing Kernels on the Cell Processor
Energy Technology Data Exchange (ETDEWEB)
Williams, Samuel W.; Shalf, John; Oliker, Leonid; Kamil, Shoaib; Husbands, Parry; Yelick, Katherine
2007-04-04
The slowing pace of commodity microprocessor performance improvements combined with ever-increasing chip power demands has become of utmost concern to computational scientists. As a result, the high performance computing community is examining alternative architectures that address the limitations of modern cache-based designs. In this work, we examine the potential of using the recently-released STI Cell processor as a building block for future high-end computing systems. Our work contains several novel contributions. First, we introduce a performance model for Cell and apply it to several key scientific computing kernels: dense matrix multiply, sparse matrix vector multiply, stencil computations, and 1D/2D FFTs. The difficulty of programming Cell, which requires assembly level intrinsics for the best performance, makes this model useful as an initial step in algorithm design and evaluation. Next, we validate the accuracy of our model by comparing results against published hardware results, as well as our own implementations on a 3.2GHz Cell blade. Additionally, we compare Cell performance to benchmarks run on leading superscalar (AMD Opteron), VLIW (Intel Itanium2), and vector (Cray X1E) architectures. Our work also explores several different mappings of the kernels and demonstrates a simple and effective programming model for Cell's unique architecture. Finally, we propose modest microarchitectural modifications that could significantly increase the efficiency of double-precision calculations. Overall results demonstrate the tremendous potential of the Cell architecture for scientific computations in terms of both raw performance and power efficiency.
Delimiting areas of endemism through kernel interpolation.
Oliveira, Ubirajara; Brescovit, Antonio D; Santos, Adalberto J
2015-01-01
We propose a new approach for identification of areas of endemism, the Geographical Interpolation of Endemism (GIE), based on kernel spatial interpolation. This method differs from others in being independent of grid cells. This new approach is based on estimating the overlap between the distribution of species through a kernel interpolation of centroids of species distribution and areas of influence defined from the distance between the centroid and the farthest point of occurrence of each species. We used this method to delimit areas of endemism of spiders from Brazil. To assess the effectiveness of GIE, we analyzed the same data using Parsimony Analysis of Endemism and NDM and compared the areas identified through each method. The analyses using GIE identified 101 areas of endemism of spiders in Brazil GIE demonstrated to be effective in identifying areas of endemism in multiple scales, with fuzzy edges and supported by more synendemic species than in the other methods. The areas of endemism identified with GIE were generally congruent with those identified for other taxonomic groups, suggesting that common processes can be responsible for the origin and maintenance of these biogeographic units.
Delimiting areas of endemism through kernel interpolation.
Directory of Open Access Journals (Sweden)
Ubirajara Oliveira
Full Text Available We propose a new approach for identification of areas of endemism, the Geographical Interpolation of Endemism (GIE, based on kernel spatial interpolation. This method differs from others in being independent of grid cells. This new approach is based on estimating the overlap between the distribution of species through a kernel interpolation of centroids of species distribution and areas of influence defined from the distance between the centroid and the farthest point of occurrence of each species. We used this method to delimit areas of endemism of spiders from Brazil. To assess the effectiveness of GIE, we analyzed the same data using Parsimony Analysis of Endemism and NDM and compared the areas identified through each method. The analyses using GIE identified 101 areas of endemism of spiders in Brazil GIE demonstrated to be effective in identifying areas of endemism in multiple scales, with fuzzy edges and supported by more synendemic species than in the other methods. The areas of endemism identified with GIE were generally congruent with those identified for other taxonomic groups, suggesting that common processes can be responsible for the origin and maintenance of these biogeographic units.
Thermal neutron scattering kernels for sapphire and silicon single crystals
International Nuclear Information System (INIS)
Cantargi, F.; Granada, J.R.; Mayer, R.E.
2015-01-01
Highlights: • Thermal cross section libraries for sapphire and silicon single crystals were generated. • Debye model was used to represent the vibrational frequency spectra to feed the NJOY code. • Sapphire total cross section was measured at Centro Atómico Bariloche. • Cross section libraries were validated with experimental data available. - Abstract: Sapphire and silicon are materials usually employed as filters in facilities with thermal neutron beams. Due to the lack of the corresponding thermal cross section libraries for those materials, necessary in calculations performed in order to optimize beams for specific applications, here we present the generation of new thermal neutron scattering kernels for those materials. The Debye model was used in both cases to represent the vibrational frequency spectra required to feed the NJOY nuclear data processing system in order to produce the corresponding libraries in ENDF and ACE format. These libraries were validated with available experimental data, some from the literature and others obtained at the pulsed neutron source at Centro Atómico Bariloche
Enhancement and prediction of modulus of elasticity of palm kernel shell concrete
International Nuclear Information System (INIS)
Alengaram, U. Johnson; Mahmud, Hilmi; Jumaat, Mohd Zamin
2011-01-01
Research highlights: → Micro-pores of size 16-24 μm were found on the outer surface of palm kernel shell. → Infilling of pores by mineral admixtures was evident. → Sand content influenced both modulus of elasticity and compressive strength. → Proposed equation predicts modulus of elasticity within ±1.5 kN/mm 2 of test results. -- Abstract: This paper presents results of an investigation conducted to enhance and predict the modulus of elasticity (MOE) of palm kernel shell concrete (PKSC). Scanning electron microscopic (SEM) analysis on palm kernel shell (PKS) was conducted. Further, the effect of varying sand and PKS contents and mineral admixtures (silica fume and fly ash) on compressive strength and MOE was investigated. The variables include water-to-binder (w/b) and sand-to-cement (s/c) ratios. Nine concrete mixes were prepared, and tests on static and dynamic moduli of elasticity and compressive strength were conducted. The SEM result showed presence of large number of micro-pores on PKS. The mineral admixtures uniformly filled the micro-pores on the outer surface of PKS. Further, the increase in sand content coupled with reduction in PKS content enhanced the compressive strength and static MOE: The highest MOE recorded in this investigation, 11 kN/mm 2 , was twice that previously published. Moreover, the proposed equation based on CEB/FIP code formula appears to predict the MOE close to the experimental values.
Directory of Open Access Journals (Sweden)
Roberto Luiz d'Avila
2010-12-01
Full Text Available O presente artigo objetivou discutir a codificação moral da prática médica, mostrando a necessidade de ampliar a formação tecnicista com elementos humanitários. Neste sentido, abordou primeiramente a Medicina como ciência e arte pautada em princípios morais. Posteriormente, revisou o contexto histórico da codificação moral médica, enfocando a realidade brasileira. Por fim, discutiu-se o código médico vigente, destacando a importância de não considerá-lo como ferramenta estritamente punitiva, mas de orientação para a promoção do bem-estar dos pacientes e melhoria da sociedade. Concluindo, indicou-se a necessidade de as escolas médicas oferecerem, além de formação técnica, uma preparação contínua em temas humanitários, promovendo o desenvolvimento moral dos estudantes e futuros médicos.The aim of this article is to discuss how the moral code for medicine is produced and to demonstrate the need for technical training to be expanded to include humanitarian features. To this end, it first addresses medicine as a science and as an art founded on moral principles. Then, it reviews the historical context of the production of the moral codes for medicine, with a specific focus on Brazil. Finally, it discusses the prevailing medical code, pointing out the importance of not regarding it as a strictly punitive tool, but as a set of guidelines for the promotion of the well-being of patients and the improvement of society in general. It concludes by indicating the need for medical schools to go beyond technical training and provide on-going preparation for dealing with humanitarian issues, thereby developing the sense of morality among the students and future doctors.
Directory of Open Access Journals (Sweden)
Fabio Burderi
2007-05-01
Full Text Available Motivated by the study of decipherability conditions for codes weaker than Unique Decipherability (UD, we introduce the notion of coding partition. Such a notion generalizes that of UD code and, for codes that are not UD, allows to recover the ``unique decipherability" at the level of the classes of the partition. By tacking into account the natural order between the partitions, we define the characteristic partition of a code X as the finest coding partition of X. This leads to introduce the canonical decomposition of a code in at most one unambiguouscomponent and other (if any totally ambiguouscomponents. In the case the code is finite, we give an algorithm for computing its canonical partition. This, in particular, allows to decide whether a given partition of a finite code X is a coding partition. This last problem is then approached in the case the code is a rational set. We prove its decidability under the hypothesis that the partition contains a finite number of classes and each class is a rational set. Moreover we conjecture that the canonical partition satisfies such a hypothesis. Finally we consider also some relationships between coding partitions and varieties of codes.
Efficient 3D movement-based kernel density estimator and application to wildlife ecology
Tracey-PR, Jeff; Sheppard, James K.; Lockwood, Glenn K.; Chourasia, Amit; Tatineni, Mahidhar; Fisher, Robert N.; Sinkovits, Robert S.
2014-01-01
We describe an efficient implementation of a 3D movement-based kernel density estimator for determining animal space use from discrete GPS measurements. This new method provides more accurate results, particularly for species that make large excursions in the vertical dimension. The downside of this approach is that it is much more computationally expensive than simpler, lower-dimensional models. Through a combination of code restructuring, parallelization and performance optimization, we were able to reduce the time to solution by up to a factor of 1000x, thereby greatly improving the applicability of the method.
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
Replacement Value of Palm Kernel Meal for Maize on Carcass ...
African Journals Online (AJOL)
This study was conducted to evaluate the effect of replacing maize with palm kernel meal on nutrient composition, fatty acid profile and sensory qualities of the meat of turkeys fed the dietary treatments. Six dietary treatments were formulated using palm kernel meal to replace maize at 0, 20, 40, 60, 80 and 100 percent.
Effect of Palm Kernel Cake Replacement and Enzyme ...
African Journals Online (AJOL)
A feeding trial which lasted for twelve weeks was conducted to study the performance of finisher pigs fed five different levels of palm kernel cake replacement for maize (0%, 40%, 40%, 60%, 60%) in a maize-palm kernel cake based ration with or without enzyme supplementation. It was a completely randomized design ...
Capturing option anomalies with a variance-dependent pricing kernel
Christoffersen, P.; Heston, S.; Jacobs, K.
2013-01-01
We develop a GARCH option model with a variance premium by combining the Heston-Nandi (2000) dynamic with a new pricing kernel that nests Rubinstein (1976) and Brennan (1979). While the pricing kernel is monotonic in the stock return and in variance, its projection onto the stock return is
Nonlinear Forecasting With Many Predictors Using Kernel Ridge Regression
DEFF Research Database (Denmark)
Exterkate, Peter; Groenen, Patrick J.F.; Heij, Christiaan
This paper puts forward kernel ridge regression as an approach for forecasting with many predictors that are related nonlinearly to the target variable. In kernel ridge regression, the observed predictor variables are mapped nonlinearly into a high-dimensional space, where estimation of the predi...
Commutators of Integral Operators with Variable Kernels on Hardy ...
Indian Academy of Sciences (India)
Home; Journals; Proceedings – Mathematical Sciences; Volume 115; Issue 4. Commutators of Integral Operators with Variable Kernels on Hardy Spaces. Pu Zhang Kai Zhao. Volume 115 Issue 4 November 2005 pp 399-410 ... Keywords. Singular and fractional integrals; variable kernel; commutator; Hardy space.
Discrete non-parametric kernel estimation for global sensitivity analysis
International Nuclear Information System (INIS)
Senga Kiessé, Tristan; Ventura, Anne
2016-01-01
This work investigates the discrete kernel approach for evaluating the contribution of the variance of discrete input variables to the variance of model output, via analysis of variance (ANOVA) decomposition. Until recently only the continuous kernel approach has been applied as a metamodeling approach within sensitivity analysis framework, for both discrete and continuous input variables. Now the discrete kernel estimation is known to be suitable for smoothing discrete functions. We present a discrete non-parametric kernel estimator of ANOVA decomposition of a given model. An estimator of sensitivity indices is also presented with its asymtotic convergence rate. Some simulations on a test function analysis and a real case study from agricultural have shown that the discrete kernel approach outperforms the continuous kernel one for evaluating the contribution of moderate or most influential discrete parameters to the model output. - Highlights: • We study a discrete kernel estimation for sensitivity analysis of a model. • A discrete kernel estimator of ANOVA decomposition of the model is presented. • Sensitivity indices are calculated for discrete input parameters. • An estimator of sensitivity indices is also presented with its convergence rate. • An application is realized for improving the reliability of environmental models.
Kernel Function Tuning for Single-Layer Neural Networks
Czech Academy of Sciences Publication Activity Database
Vidnerová, Petra; Neruda, Roman
-, accepted 28.11. 2017 (2018) ISSN 2278-0149 R&D Projects: GA ČR GA15-18108S Institutional support: RVO:67985807 Keywords : single-layer neural networks * kernel methods * kernel function * optimisation Subject RIV: IN - Informatics, Computer Science http://www.ijmerr.com/
Geodesic exponential kernels: When Curvature and Linearity Conflict
DEFF Research Database (Denmark)
Feragen, Aase; Lauze, François; Hauberg, Søren
2015-01-01
manifold, the geodesic Gaussian kernel is only positive definite if the Riemannian manifold is Euclidean. This implies that any attempt to design geodesic Gaussian kernels on curved Riemannian manifolds is futile. However, we show that for spaces with conditionally negative definite distances the geodesic...
Denoising by semi-supervised kernel PCA preimaging
DEFF Research Database (Denmark)
Hansen, Toke Jansen; Abrahamsen, Trine Julie; Hansen, Lars Kai
2014-01-01
Kernel Principal Component Analysis (PCA) has proven a powerful tool for nonlinear feature extraction, and is often applied as a pre-processing step for classification algorithms. In denoising applications Kernel PCA provides the basis for dimensionality reduction, prior to the so-called pre-imag...
Design and construction of palm kernel cracking and separation ...
African Journals Online (AJOL)
Design and construction of palm kernel cracking and separation machines. ... Username, Password, Remember me, or Register. DOWNLOAD FULL TEXT Open Access DOWNLOAD FULL TEXT Subscription or Fee Access. Design and construction of palm kernel cracking and separation machines. JO Nordiana, K ...
Kernel Methods for Machine Learning with Life Science Applications
DEFF Research Database (Denmark)
Abrahamsen, Trine Julie
Kernel methods refer to a family of widely used nonlinear algorithms for machine learning tasks like classification, regression, and feature extraction. By exploiting the so-called kernel trick straightforward extensions of classical linear algorithms are enabled as long as the data only appear a...
Genetic relationship between plant growth, shoot and kernel sizes in ...
African Journals Online (AJOL)
Maize (Zea mays L.) ear vascular tissue transports nutrients that contribute to grain yield. To assess kernel heritabilities that govern ear development and plant growth, field studies were conducted to determine the combining abilities of parents that differed for kernel-size, grain-filling rates and shoot-size. Thirty two hybrids ...
A relationship between Gel'fand-Levitan and Marchenko kernels
International Nuclear Information System (INIS)
Kirst, T.; Von Geramb, H.V.; Amos, K.A.
1989-01-01
An integral equation which relates the output kernels of the Gel'fand-Levitan and Marchenko inverse scattering equations is specified. Structural details of this integral equation are studied when the S-matrix is a rational function, and the output kernels are separable in terms of Bessel, Hankel and Jost solutions. 4 refs
Boundary singularity of Poisson and harmonic Bergman kernels
Czech Academy of Sciences Publication Activity Database
Engliš, Miroslav
2015-01-01
Roč. 429, č. 1 (2015), s. 233-272 ISSN 0022-247X R&D Projects: GA AV ČR IAA100190802 Institutional support: RVO:67985840 Keywords : harmonic Bergman kernel * Poisson kernel * pseudodifferential boundary operators Subject RIV: BA - General Mathematics Impact factor: 1.014, year: 2015 http://www.sciencedirect.com/science/article/pii/S0022247X15003170
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
Real time kernel performance monitoring with SystemTap
CERN. Geneva
2018-01-01
SystemTap is a dynamic method of monitoring and tracing the operation of a running Linux kernel. In this talk I will present a few practical use cases where SystemTap allowed me to turn otherwise complex userland monitoring tasks in simple kernel probes.
Resolvent kernel for the Kohn Laplacian on Heisenberg groups
Directory of Open Access Journals (Sweden)
Neur Eddine Askour
2002-07-01
Full Text Available We present a formula that relates the Kohn Laplacian on Heisenberg groups and the magnetic Laplacian. Then we obtain the resolvent kernel for the Kohn Laplacian and find its spectral density. We conclude by obtaining the Green kernel for fractional powers of the Kohn Laplacian.
Reproducing Kernels and Coherent States on Julia Sets
Energy Technology Data Exchange (ETDEWEB)
Thirulogasanthar, K., E-mail: santhar@cs.concordia.ca; Krzyzak, A. [Concordia University, Department of Computer Science and Software Engineering (Canada)], E-mail: krzyzak@cs.concordia.ca; Honnouvo, G. [Concordia University, Department of Mathematics and Statistics (Canada)], E-mail: g_honnouvo@yahoo.fr
2007-11-15
We construct classes of coherent states on domains arising from dynamical systems. An orthonormal family of vectors associated to the generating transformation of a Julia set is found as a family of square integrable vectors, and, thereby, reproducing kernels and reproducing kernel Hilbert spaces are associated to Julia sets. We also present analogous results on domains arising from iterated function systems.
Reproducing Kernels and Coherent States on Julia Sets
International Nuclear Information System (INIS)
Thirulogasanthar, K.; Krzyzak, A.; Honnouvo, G.
2007-01-01
We construct classes of coherent states on domains arising from dynamical systems. An orthonormal family of vectors associated to the generating transformation of a Julia set is found as a family of square integrable vectors, and, thereby, reproducing kernels and reproducing kernel Hilbert spaces are associated to Julia sets. We also present analogous results on domains arising from iterated function systems
A multi-scale kernel bundle for LDDMM
DEFF Research Database (Denmark)
Sommer, Stefan Horst; Nielsen, Mads; Lauze, Francois Bernard
2011-01-01
The Large Deformation Diffeomorphic Metric Mapping framework constitutes a widely used and mathematically well-founded setup for registration in medical imaging. At its heart lies the notion of the regularization kernel, and the choice of kernel greatly affects the results of registrations...
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…
An analysis of 1-D smoothed particle hydrodynamics kernels
International Nuclear Information System (INIS)
Fulk, D.A.; Quinn, D.W.
1996-01-01
In this paper, the smoothed particle hydrodynamics (SPH) kernel is analyzed, resulting in measures of merit for one-dimensional SPH. Various methods of obtaining an objective measure of the quality and accuracy of the SPH kernel are addressed. Since the kernel is the key element in the SPH methodology, this should be of primary concern to any user of SPH. The results of this work are two measures of merit, one for smooth data and one near shocks. The measure of merit for smooth data is shown to be quite accurate and a useful delineator of better and poorer kernels. The measure of merit for non-smooth data is not quite as accurate, but results indicate the kernel is much less important for these types of problems. In addition to the theory, 20 kernels are analyzed using the measure of merit demonstrating the general usefulness of the measure of merit and the individual kernels. In general, it was decided that bell-shaped kernels perform better than other shapes. 12 refs., 16 figs., 7 tabs
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…
Computing an element in the lexicographic kernel of a game
Faigle, U.; Kern, Walter; Kuipers, Jeroen
The lexicographic kernel of a game lexicographically maximizes the surplusses $s_{ij}$ (rather than the excesses as would the nucleolus). We show that an element in the lexicographic kernel can be computed efficiently, provided we can efficiently compute the surplusses $s_{ij}(x)$ corresponding to a
Computing an element in the lexicographic kernel of a game
Faigle, U.; Kern, Walter; Kuipers, J.
2002-01-01
The lexicographic kernel of a game lexicographically maximizes the surplusses $s_{ij}$ (rather than the excesses as would the nucleolus). We show that an element in the lexicographic kernel can be computed efficiently, provided we can efficiently compute the surplusses $s_{ij}(x)$ corresponding to a
3-D waveform tomography sensitivity kernels for anisotropic media
Djebbi, Ramzi
2014-01-01
The complications in anisotropic multi-parameter inversion lie in the trade-off between the different anisotropy parameters. We compute the tomographic waveform sensitivity kernels for a VTI acoustic medium perturbation as a tool to investigate this ambiguity between the different parameters. We use dynamic ray tracing to efficiently handle the expensive computational cost for 3-D anisotropic models. Ray tracing provides also the ray direction information necessary for conditioning the sensitivity kernels to handle anisotropy. The NMO velocity and η parameter kernels showed a maximum sensitivity for diving waves which results in a relevant choice of those parameters in wave equation tomography. The δ parameter kernel showed zero sensitivity; therefore it can serve as a secondary parameter to fit the amplitude in the acoustic anisotropic inversion. Considering the limited penetration depth of diving waves, migration velocity analysis based kernels are introduced to fix the depth ambiguity with reflections and compute sensitivity maps in the deeper parts of the model.
Anatomically-aided PET reconstruction using the kernel method.
Hutchcroft, Will; Wang, Guobao; Chen, Kevin T; Catana, Ciprian; Qi, Jinyi
2016-09-21
This paper extends the kernel method that was proposed previously for dynamic PET reconstruction, to incorporate anatomical side information into the PET reconstruction model. In contrast to existing methods that incorporate anatomical information using a penalized likelihood framework, the proposed method incorporates this information in the simpler maximum likelihood (ML) formulation and is amenable to ordered subsets. The new method also does not require any segmentation of the anatomical image to obtain edge information. We compare the kernel method with the Bowsher method for anatomically-aided PET image reconstruction through a simulated data set. Computer simulations demonstrate that the kernel method offers advantages over the Bowsher method in region of interest quantification. Additionally the kernel method is applied to a 3D patient data set. The kernel method results in reduced noise at a matched contrast level compared with the conventional ML expectation maximization algorithm.
Open Problem: Kernel methods on manifolds and metric spaces
DEFF Research Database (Denmark)
Feragen, Aasa; Hauberg, Søren
2016-01-01
Radial kernels are well-suited for machine learning over general geodesic metric spaces, where pairwise distances are often the only computable quantity available. We have recently shown that geodesic exponential kernels are only positive definite for all bandwidths when the input space has strong...... linear properties. This negative result hints that radial kernel are perhaps not suitable over geodesic metric spaces after all. Here, however, we present evidence that large intervals of bandwidths exist where geodesic exponential kernels have high probability of being positive definite over finite...... datasets, while still having significant predictive power. From this we formulate conjectures on the probability of a positive definite kernel matrix for a finite random sample, depending on the geometry of the data space and the spread of the sample....
Compactly Supported Basis Functions as Support Vector Kernels for Classification.
Wittek, Peter; Tan, Chew Lim
2011-10-01
Wavelet kernels have been introduced for both support vector regression and classification. Most of these wavelet kernels do not use the inner product of the embedding space, but use wavelets in a similar fashion to radial basis function kernels. Wavelet analysis is typically carried out on data with a temporal or spatial relation between consecutive data points. We argue that it is possible to order the features of a general data set so that consecutive features are statistically related to each other, thus enabling us to interpret the vector representation of an object as a series of equally or randomly spaced observations of a hypothetical continuous signal. By approximating the signal with compactly supported basis functions and employing the inner product of the embedding L2 space, we gain a new family of wavelet kernels. Empirical results show a clear advantage in favor of these kernels.
On the Automatic Evolution of an OS Kernel Using Temporal Logic and AOP
DEFF Research Database (Denmark)
Åberg, Rickard; Lawall, Julia Laetitia; Sudholt, Mario
2003-01-01
aspect-oriented programing, temporal logic, process scheduling, Linux, domain-specific languages......aspect-oriented programing, temporal logic, process scheduling, Linux, domain-specific languages...
Söderberg, Jonas; Alm Carlsson, Gudrun; Ahnesjö, Anders
2003-10-01
When dedicated software is lacking, treatment planning for fast neutron therapy is sometimes performed using dose calculation algorithms designed for photon beam therapy. In this work Monte Carlo derived neutron pencil kernels in water were parametrized using the photon dose algorithm implemented in the Nucletron TMS (treatment management system) treatment planning system. A rectangular fast-neutron fluence spectrum with energies 0-40 MeV (resembling a polyethylene filtered p(41)+ Be spectrum) was used. Central axis depth doses and lateral dose distributions were calculated and compared with the corresponding dose distributions from Monte Carlo calculations for homogeneous water and heterogeneous slab phantoms. All absorbed doses were normalized to the reference dose at 10 cm depth for a field of radius 5.6 cm in a 30 × 40 × 20 cm3 water test phantom. Agreement to within 7% was found in both the lateral and the depth dose distributions. The deviations could be explained as due to differences in size between the test phantom and that used in deriving the pencil kernel (radius 200 cm, thickness 50 cm). In the heterogeneous phantom, the TMS, with a directly applied neutron pencil kernel, and Monte Carlo calculated absorbed doses agree approximately for muscle but show large deviations for media such as adipose or bone. For the latter media, agreement was substantially improved by correcting the absorbed doses calculated in TMS with the neutron kerma factor ratio and the stopping power ratio between tissue and water. The multipurpose Monte Carlo code FLUKA was used both in calculating the pencil kernel and in direct calculations of absorbed dose in the phantom.
Improved modeling of clinical data with kernel methods.
Daemen, Anneleen; Timmerman, Dirk; Van den Bosch, Thierry; Bottomley, Cecilia; Kirk, Emma; Van Holsbeke, Caroline; Valentin, Lil; Bourne, Tom; De Moor, Bart
2012-02-01
Despite the rise of high-throughput technologies, clinical data such as age, gender and medical history guide clinical management for most diseases and examinations. To improve clinical management, available patient information should be fully exploited. This requires appropriate modeling of relevant parameters. When kernel methods are used, traditional kernel functions such as the linear kernel are often applied to the set of clinical parameters. These kernel functions, however, have their disadvantages due to the specific characteristics of clinical data, being a mix of variable types with each variable its own range. We propose a new kernel function specifically adapted to the characteristics of clinical data. The clinical kernel function provides a better representation of patients' similarity by equalizing the influence of all variables and taking into account the range r of the variables. Moreover, it is robust with respect to changes in r. Incorporated in a least squares support vector machine, the new kernel function results in significantly improved diagnosis, prognosis and prediction of therapy response. This is illustrated on four clinical data sets within gynecology, with an average increase in test area under the ROC curve (AUC) of 0.023, 0.021, 0.122 and 0.019, respectively. Moreover, when combining clinical parameters and expression data in three case studies on breast cancer, results improved overall with use of the new kernel function and when considering both data types in a weighted fashion, with a larger weight assigned to the clinical parameters. The increase in AUC with respect to a standard kernel function and/or unweighted data combination was maximum 0.127, 0.042 and 0.118 for the three case studies. For clinical data consisting of variables of different types, the proposed kernel function--which takes into account the type and range of each variable--has shown to be a better alternative for linear and non-linear classification problems
A method for manufacturing kernels of metallic oxides and the thus obtained kernels
International Nuclear Information System (INIS)
Lelievre Bernard; Feugier, Andre.
1973-01-01
A method is described for manufacturing fissile or fertile metal oxide kernels, consisting in adding at least a chemical compound capable of releasing ammonia to an aqueous solution of actinide nitrates dispersing the thus obtained solution dropwise in a hot organic phase so as to gelify the drops and transform them into solid particles, washing drying and treating said particles so as to transform them into oxide kernels. Such a method is characterized in that the organic phase used in the gel-forming reactions comprises a mixture of two organic liquids, one of which acts as a solvent, whereas the other is a product capable of extracting the metal-salt anions from the drops while the gel forming reaction is taking place. This can be applied to the so-called high temperature nuclear reactors [fr
Brunner, Stefan; Delcourt, David
2008-01-01
In the only book that completely covers ScreenOS, six key members of Juniper Network's ScreenOS development team help you troubleshoot secure networks using ScreenOS firewall appliances. Over 200 recipes address a wide range of security issues, provide step-by-step solutions, and include discussions of why the recipes work, so you can easily set up and keep ScreenOS systems on track. The easy-to-follow format enables you to find the topic and specific recipe you need right away.
Learning molecular energies using localized graph kernels
Ferré, Grégoire; Haut, Terry; Barros, Kipton
2017-03-01
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. This Graph Approximated Energy (GRAPE) approach is flexible and admits many possible extensions. We benchmark a simple version of GRAPE by predicting atomization energies on a standard dataset of organic molecules.
Enforcing the use of API functions in Linux code
DEFF Research Database (Denmark)
Lawall, Julia; Muller, Gilles; Palix, Nicolas Jean-Michel
2009-01-01
In the Linux kernel source tree, header files typically define many small functions that have a simple behavior but are critical to ensure readability, correctness, and maintainability. We have observed, however, that some Linux code does not use these functions systematically. In this paper, we...... in the header file include/linux/usb.h....
Comparison of Os-Os and Re-Os dating results of molybdenites
International Nuclear Information System (INIS)
Xie Zhi; Sun Weidong; Chen Jiangfeng
2002-01-01
Two molybdenite samples from the Middle-Low Reaches of the Yangtze River were dated by both Os-Os and Re-Os methods. Os-Os and Re-Os dating give identical age results for the two samples. An experiment of step distillation of Os-Os method suggests that no isotopic fractionation is observed between fractions obtained by the experiment. The results prove that the Os-Os method can avoid the problems in the Re-Os method, simplify the experimental procedure, give creditable age data, and have unique advantage that no quantitative extracting of Os is required
Directory of Open Access Journals (Sweden)
Allan Ulisses Carvalho de Melo
Full Text Available Os códigos de ética profissional são normas jurídicas (resoluções de autarquias federais elaboradas pelos membros das mais diversas categorias de trabalhadores com o intuito de orientar a condutas desses profissionais no que diz respeito à ética na relação com os pacientes, com seus pares e com a sociedade. O objetivo deste estudo foi realizar uma análise comparativa entre os Códigos de Ética Odontológica e Médica. Observou-se que as diferenças entre estes documentos deontológicos surgem muito mais em virtude das particularidades de cada profissão do que por abordagens distintas frente a problemas similares. Concluiu-se que os Códigos de Ética Odontológica e Médica apresentam muito mais pontos em comum do que diferenças, mas seria interessante que os Conselhos de classe ao propor atualizações e modificações dos seus atuais códigos observassem o que as outras profissões da saúde contemplam em suas normas deontológicas, com o intuito de levar em consideração aspectos que também poderiam ser importantes para sua classe profissional de modo a engrandecer os códigos tornando um pouco mais fácil para os médicos e cirurgiões-dentistas a tomada de decisões éticas no seu trabalho diário em benefício da saúde do ser humano e da coletividade.Los códigos de ética profesional son normas (resoluciones de autoridades federales preparadas por los miembros de las diferentes categorías de trabajadores con el fin de orientar la conducta ética de estos profesionales en la relación con pacientes, colegas y la sociedad. El objetivo de este estudio fue realizar un análisis comparativo de los códigos de ética en Odontología y Medicina. Se observó que las diferencias entre estos documentos se deben más a las particularidades de cada profesión que a enfoques distintos frente a problemas similares. Se concluyó que los códigos de ética en Odontología y Medicina presentan más puntos en común que diferencias, pero
Optimizing The Performance of Streaming Numerical Kernels On The IBM Blue Gene/P PowerPC 450
Malas, Tareq
2011-07-01
Several emerging petascale architectures use energy-efficient processors with vectorized computational units and in-order thread processing. On these architectures the sustained performance of streaming numerical kernels, ubiquitous in the solution of partial differential equations, represents a formidable challenge despite the regularity of memory access. Sophisticated optimization techniques beyond the capabilities of modern compilers are required to fully utilize the Central Processing Unit (CPU). The aim of the work presented here is to improve the performance of streaming numerical kernels on high performance architectures by developing efficient algorithms to utilize the vectorized floating point units. The importance of the development time demands the creation of tools to enable simple yet direct development in assembly to utilize the power-efficient cores featuring in-order execution and multiple-issue units. We implement several stencil kernels for a variety of cached memory scenarios using our Python instruction simulation and generation tool. Our technique simplifies the development of efficient assembly code for the IBM Blue Gene/P supercomputer\\'s PowerPC 450. This enables us to perform high-level design, construction, verification, and simulation on a subset of the CPU\\'s instruction set. Our framework has the capability to implement streaming numerical kernels on current and future high performance architectures. Finally, we present several automatically generated implementations, including a 27-point stencil achieving a 1.7x speedup over the best previously published results.
DEFF Research Database (Denmark)
Taasti, Vicki Trier; Knudsen, Helge; Holzscheiter, Michael
2015-01-01
The Monte Carlo particle transport code SHIELD-HIT12A is designed to simulate therapeutic beams for cancer radiotherapy with fast ions. SHIELD-HIT12A allows creation of antiproton beam kernels for the treatment planning system TRiP98, but first it must be benchmarked against experimental data. An...
Stochastic subset selection for learning with kernel machines.
Rhinelander, Jason; Liu, Xiaoping P
2012-06-01
Kernel machines have gained much popularity in applications of machine learning. Support vector machines (SVMs) are a subset of kernel machines and generalize well for classification, regression, and anomaly detection tasks. The training procedure for traditional SVMs involves solving a quadratic programming (QP) problem. The QP problem scales super linearly in computational effort with the number of training samples and is often used for the offline batch processing of data. Kernel machines operate by retaining a subset of observed data during training. The data vectors contained within this subset are referred to as support vectors (SVs). The work presented in this paper introduces a subset selection method for the use of kernel machines in online, changing environments. Our algorithm works by using a stochastic indexing technique when selecting a subset of SVs when computing the kernel expansion. The work described here is novel because it separates the selection of kernel basis functions from the training algorithm used. The subset selection algorithm presented here can be used in conjunction with any online training technique. It is important for online kernel machines to be computationally efficient due to the real-time requirements of online environments. Our algorithm is an important contribution because it scales linearly with the number of training samples and is compatible with current training techniques. Our algorithm outperforms standard techniques in terms of computational efficiency and provides increased recognition accuracy in our experiments. We provide results from experiments using both simulated and real-world data sets to verify our algorithm.
Multiple kernel boosting framework based on information measure for classification
International Nuclear Information System (INIS)
Qi, Chengming; Wang, Yuping; Tian, Wenjie; Wang, Qun
2016-01-01
The performance of kernel-based method, such as support vector machine (SVM), is greatly affected by the choice of kernel function. Multiple kernel learning (MKL) is a promising family of machine learning algorithms and has attracted many attentions in recent years. MKL combines multiple sub-kernels to seek better results compared to single kernel learning. In order to improve the efficiency of SVM and MKL, in this paper, the Kullback–Leibler kernel function is derived to develop SVM. The proposed method employs an improved ensemble learning framework, named KLMKB, which applies Adaboost to learning multiple kernel-based classifier. In the experiment for hyperspectral remote sensing image classification, we employ feature selected through Optional Index Factor (OIF) to classify the satellite image. We extensively examine the performance of our approach in comparison to some relevant and state-of-the-art algorithms on a number of benchmark classification data sets and hyperspectral remote sensing image data set. Experimental results show that our method has a stable behavior and a noticeable accuracy for different data set.
Per-Sample Multiple Kernel Approach for Visual Concept Learning
Directory of Open Access Journals (Sweden)
Ling-Yu Duan
2010-01-01
Full Text Available Learning visual concepts from images is an important yet challenging problem in computer vision and multimedia research areas. Multiple kernel learning (MKL methods have shown great advantages in visual concept learning. As a visual concept often exhibits great appearance variance, a canonical MKL approach may not generate satisfactory results when a uniform kernel combination is applied over the input space. In this paper, we propose a per-sample multiple kernel learning (PS-MKL approach to take into account intraclass diversity for improving discrimination. PS-MKL determines sample-wise kernel weights according to kernel functions and training samples. Kernel weights as well as kernel-based classifiers are jointly learned. For efficient learning, PS-MKL employs a sample selection strategy. Extensive experiments are carried out over three benchmarking datasets of different characteristics including Caltech101, WikipediaMM, and Pascal VOC'07. PS-MKL has achieved encouraging performance, comparable to the state of the art, which has outperformed a canonical MKL.
Per-Sample Multiple Kernel Approach for Visual Concept Learning
Directory of Open Access Journals (Sweden)
Tian Yonghong
2010-01-01
Full Text Available Abstract Learning visual concepts from images is an important yet challenging problem in computer vision and multimedia research areas. Multiple kernel learning (MKL methods have shown great advantages in visual concept learning. As a visual concept often exhibits great appearance variance, a canonical MKL approach may not generate satisfactory results when a uniform kernel combination is applied over the input space. In this paper, we propose a per-sample multiple kernel learning (PS-MKL approach to take into account intraclass diversity for improving discrimination. PS-MKL determines sample-wise kernel weights according to kernel functions and training samples. Kernel weights as well as kernel-based classifiers are jointly learned. For efficient learning, PS-MKL employs a sample selection strategy. Extensive experiments are carried out over three benchmarking datasets of different characteristics including Caltech101, WikipediaMM, and Pascal VOC'07. PS-MKL has achieved encouraging performance, comparable to the state of the art, which has outperformed a canonical MKL.
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.
Deep Restricted Kernel Machines Using Conjugate Feature Duality.
Suykens, Johan A K
2017-08-01
The aim of this letter is to propose a theory of deep restricted kernel machines offering new foundations for deep learning with kernel machines. From the viewpoint of deep learning, it is partially related to restricted Boltzmann machines, which are characterized by visible and hidden units in a bipartite graph without hidden-to-hidden connections and deep learning extensions as deep belief networks and deep Boltzmann machines. From the viewpoint of kernel machines, it includes least squares support vector machines for classification and regression, kernel principal component analysis (PCA), matrix singular value decomposition, and Parzen-type models. A key element is to first characterize these kernel machines in terms of so-called conjugate feature duality, yielding a representation with visible and hidden units. It is shown how this is related to the energy form in restricted Boltzmann machines, with continuous variables in a nonprobabilistic setting. In this new framework of so-called restricted kernel machine (RKM) representations, the dual variables correspond to hidden features. Deep RKM are obtained by coupling the RKMs. The method is illustrated for deep RKM, consisting of three levels with a least squares support vector machine regression level and two kernel PCA levels. In its primal form also deep feedforward neural networks can be trained within this framework.
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. Copyright © 2013 Elsevier Ltd. All rights reserved.
Gradient-based adaptation of general gaussian kernels.
Glasmachers, Tobias; Igel, Christian
2005-10-01
Gradient-based optimizing of gaussian kernel functions is considered. The gradient for the adaptation of scaling and rotation of the input space is computed to achieve invariance against linear transformations. This is done by using the exponential map as a parameterization of the kernel parameter manifold. By restricting the optimization to a constant trace subspace, the kernel size can be controlled. This is, for example, useful to prevent overfitting when minimizing radius-margin generalization performance measures. The concepts are demonstrated by training hard margin support vector machines on toy data.
On weights which admit the reproducing kernel of Bergman type
Directory of Open Access Journals (Sweden)
Zbigniew Pasternak-Winiarski
1992-01-01
Full Text Available In this paper we consider (1 the weights of integration for which the reproducing kernel of the Bergman type can be defined, i.e., the admissible weights, and (2 the kernels defined by such weights. It is verified that the weighted Bergman kernel has the analogous properties as the classical one. We prove several sufficient conditions and necessary and sufficient conditions for a weight to be an admissible weight. We give also an example of a weight which is not of this class. As a positive example we consider the weight μ(z=(Imz2 defined on the unit disk in ℂ.
Visualization of nonlinear kernel models in neuroimaging by sensitivity maps
DEFF Research Database (Denmark)
Rasmussen, Peter Mondrup; Hansen, Lars Kai; Madsen, Kristoffer Hougaard
There is significant current interest in decoding mental states from neuroimages. In this context kernel methods, e.g., support vector machines (SVM) are frequently adopted to learn statistical relations between patterns of brain activation and experimental conditions. In this paper we focus...... on visualization of such nonlinear kernel models. Specifically, we investigate the sensitivity map as a technique for generation of global summary maps of kernel classification methods. We illustrate the performance of the sensitivity map on functional magnetic resonance (fMRI) data based on visual stimuli. We...
Flour quality and kernel hardness connection in winter wheat
Directory of Open Access Journals (Sweden)
Szabó B. P.
2016-12-01
Full Text Available Kernel hardness is controlled by friabilin protein and it depends on the relation between protein matrix and starch granules. Friabilin is present in high concentration in soft grain varieties and in low concentration in hard grain varieties. The high gluten, hard wheat our generally contains about 12.0–13.0% crude protein under Mid-European conditions. The relationship between wheat protein content and kernel texture is usually positive and kernel texture influences the power consumption during milling. Hard-textured wheat grains require more grinding energy than soft-textured grains.
Deep kernel learning method for SAR image target recognition
Chen, Xiuyuan; Peng, Xiyuan; Duan, Ran; Li, Junbao
2017-10-01
With the development of deep learning, research on image target recognition has made great progress in recent years. Remote sensing detection urgently requires target recognition for military, geographic, and other scientific research. This paper aims to solve the synthetic aperture radar image target recognition problem by combining deep and kernel learning. The model, which has a multilayer multiple kernel structure, is optimized layer by layer with the parameters of Support Vector Machine and a gradient descent algorithm. This new deep kernel learning method improves accuracy and achieves competitive recognition results compared with other learning methods.
Explicit signal to noise ratio in reproducing kernel Hilbert spaces
DEFF Research Database (Denmark)
Gomez-Chova, Luis; Nielsen, Allan Aasbjerg; Camps-Valls, Gustavo
2011-01-01
This paper introduces a nonlinear feature extraction method based on kernels for remote sensing data analysis. The proposed approach is based on the minimum noise fraction (MNF) transform, which maximizes the signal variance while also minimizing the estimated noise variance. We here propose...... an alternative kernel MNF (KMNF) in which the noise is explicitly estimated in the reproducing kernel Hilbert space. This enables KMNF dealing with non-linear relations between the noise and the signal features jointly. Results show that the proposed KMNF provides the most noise-free features when confronted...
Cid, Jaime A; von Davier, Alina A
2015-05-01
Test equating is a method of making the test scores from different test forms of the same assessment comparable. In the equating process, an important step involves continuizing the discrete score distributions. In traditional observed-score equating, this step is achieved using linear interpolation (or an unscaled uniform kernel). In the kernel equating (KE) process, this continuization process involves Gaussian kernel smoothing. It has been suggested that the choice of bandwidth in kernel smoothing controls the trade-off between variance and bias. In the literature on estimating density functions using kernels, it has also been suggested that the weight of the kernel depends on the sample size, and therefore, the resulting continuous distribution exhibits bias at the endpoints, where the samples are usually smaller. The purpose of this article is (a) to explore the potential effects of atypical scores (spikes) at the extreme ends (high and low) on the KE method in distributions with different degrees of asymmetry using the randomly equivalent groups equating design (Study I), and (b) to introduce the Epanechnikov and adaptive kernels as potential alternative approaches to reducing boundary bias in smoothing (Study II). The beta-binomial model is used to simulate observed scores reflecting a range of different skewed shapes.
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.
Rezaee, Malahat; Basri, Mahiran; Rahman, Raja Noor Zaliha Raja Abdul; Salleh, Abu Bakar; Chaibakhsh, Naz; Karjiban, Roghayeh Abedi
2014-01-01
Response surface methodology was employed to study the effect of formulation composition variables, water content (60%-80%, w/w) and oil and surfactant (O/S) ratio (0.17-1.33), as well as high-shear emulsification conditions, mixing rate (300-3,000 rpm) and mixing time (5-30 minutes) on the properties of sodium diclofenac-loaded palm kernel oil esters-nanoemulsions. The two response variables were droplet size and viscosity. Optimization of the conditions according to the four variables was performed for preparation of the nanoemulsions with the minimum values of particle size and viscosity. The results showed that the experimental data could be sufficiently fitted into a third-order polynomial model with multiple regression coefficients (R(2) ) of 0.938 and 0.994 for the particle size and viscosity, respectively. Water content, O/S ratio and mixing time, quadrics of all independent variables, interaction between O/S ratio and mixing rate and between mixing time and rate, as well as cubic term of water content had a significant effect (Pdiclofenac nanoemulsions were predicted to be: 71.36% water content; 0.69 O/S ratio; 950 rpm mixing rate, and 5 minute mixing time. The optimized formulation showed good storage stability in different temperatures.
Code system to compute radiation dose in human phantoms
International Nuclear Information System (INIS)
Ryman, J.C.; Cristy, M.; Eckerman, K.F.; Davis, J.L.; Tang, J.S.; Kerr, G.D.
1986-01-01
Monte Carlo photon transport code and a code using Monte Carlo integration of a point kernel have been revised to incorporate human phantom models for an adult female, juveniles of various ages, and a pregnant female at the end of the first trimester of pregnancy, in addition to the adult male used earlier. An analysis code has been developed for deriving recommended values of specific absorbed fractions of photon energy. The computer code system and calculational method are described, emphasizing recent improvements in methods
International Nuclear Information System (INIS)
Kang, Sang Ho; Lee, Seung Gi; Chung, Chan Young; Lee, Choon Sik; Lee, Jai Ki
2001-01-01
In order to comply with revised national regulationson radiological protection and to implement recent nuclear data and dose conversion factors, KOPEC developed a new point kernel gamma and beta ray shielding analysis computer program. This new code, named VisualShield, adopted mass attenuation coefficient and buildup factors from recent ANSI/ANS standards and flux-to-dose conversion factors from the International Commission on Radiological Protection (ICRP) Publication 74 for estimation of effective/equivalent dose recommended in ICRP 60. VisualShield utilizes graphical user interfaces and 3-D visualization of the geometric configuration for preparing input data sets and analyzing results, which leads users to error free processing with visual effects. Code validation and data analysis were performed by comparing the results of various calculations to the data outputs of previous programs such as MCNP 4B, ISOSHLD-II, QAD-CGGP, etc
Free and open source software at CERN: integration of drivers in the Linux kernel
International Nuclear Information System (INIS)
Gonzalez Cobas, J.D.; Iglesias Gonsalvez, S.; Howard Lewis, J.; Serrano, J.; Vanga, M.; Cota, E.G.; Rubini, A.; Vaga, F.
2012-01-01
Most device drivers written for accelerator control systems suffer from a severe lack of portability due to the ad hoc nature of the code, often embodied with intimate knowledge of the particular machine it is deployed in. In this paper we challenge this practice by arguing for the opposite approach: development in the open, which in our case translates into the integration of our code within the Linux kernel. We make our case by describing the upstream merge effort of the tsi148 driver, a critical (and complex) component of the control system. The encouraging results from this effort have then led us to follow the same approach with two more ambitious projects, currently in the works: Linux support for the upcoming FMC boards and a new I/O subsystem. (authors)
Efficient Online Subspace Learning With an Indefinite Kernel for Visual Tracking and Recognition
Liwicki, Stephan; Zafeiriou, Stefanos; Tzimiropoulos, Georgios; Pantic, Maja
2012-01-01
We propose an exact framework for online learning with a family of indefinite (not positive) kernels. As we study the case of nonpositive kernels, we first show how to extend kernel principal component analysis (KPCA) from a reproducing kernel Hilbert space to Krein space. We then formulate an
International Nuclear Information System (INIS)
Drozdowicz, K.
1995-01-01
A comprehensive unified description of the application of Granada's Synthetic Model to the slow-neutron scattering by the molecular systems is continued. Detailed formulae for the zero-order energy transfer kernel are presented basing on the general formalism of the model. An explicit analytical formula for the total scattering cross section as a function of the incident neutron energy is also obtained. Expressions of the free gas model for the zero-order scattering kernel and for total scattering kernel are considered as a sub-case of the Synthetic Model. (author). 10 refs
Professional iOS database application programming
Alessi, Patrick
2013-01-01
Updated and revised coverage that includes the latest versions of iOS and Xcode Whether you're a novice or experienced developer, you will want to dive into this updated resource on database application programming for the iPhone and iPad. Packed with more than 50 percent new and revised material - including completely rebuilt code, screenshots, and full coverage of new features pertaining to database programming and enterprise integration in iOS 6 - this must-have book intends to continue the precedent set by the previous edition by helping thousands of developers master database
JANCZUKOWICZ , Ewa
2013-01-01
Firefox OS is an operating system for mobile devices such as smartphones and tablets. It is developed by Mozilla but it aims to be free from any proprietary technology. It lets users run applications developed entirely using web technologies, like HTML5, JavaScript, CSS. It is not directly competing with iOS. It has some common target markets with Android. For now Firefox OS targets specific type of clients - people that don't have smartphones yet. Available devices are low-end and as a resul...
A kernel adaptive algorithm for quaternion-valued inputs.
Paul, Thomas K; Ogunfunmi, Tokunbo
2015-10-01
The use of quaternion data can provide benefit in applications like robotics and image recognition, and particularly for performing transforms in 3-D space. Here, we describe a kernel adaptive algorithm for quaternions. A least mean square (LMS)-based method was used, resulting in the derivation of the quaternion kernel LMS (Quat-KLMS) algorithm. Deriving this algorithm required describing the idea of a quaternion reproducing kernel Hilbert space (RKHS), as well as kernel functions suitable with quaternions. A modified HR calculus for Hilbert spaces was used to find the gradient of cost functions defined on a quaternion RKHS. In addition, the use of widely linear (or augmented) filtering is proposed to improve performance. The benefit of the Quat-KLMS and widely linear forms in learning nonlinear transformations of quaternion data are illustrated with simulations.
Bioconversion of palm kernel meal for aquaculture: Experiences ...
African Journals Online (AJOL)
SERVER
2008-04-17
Apr 17, 2008 ... es as well as food supplies have existed traditionally with coastal regions of Liberia and ..... Contamination of palm kernel meal with Aspergillus ... Sciences, Universiti Sains Malaysia, Penang 11800, Malaysia. Aquacult. Res.
The effect of apricot kernel flour incorporation on the ...
African Journals Online (AJOL)
STORAGESEVER
2009-01-05
Jan 5, 2009 ... 2Department of Food Engineering, Erciyes University 38039, Kayseri, Turkey. Accepted 27 ... Key words: Noodle; apricot kernel, flour, cooking, sensory properties. ... their simple preparation requirement, desirable sensory.
3-D waveform tomography sensitivity kernels for anisotropic media
Djebbi, Ramzi; Alkhalifah, Tariq Ali
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
Kernel-based noise filtering of neutron detector signals
International Nuclear Information System (INIS)
Park, Moon Ghu; Shin, Ho Cheol; Lee, Eun Ki
2007-01-01
This paper describes recently developed techniques for effective filtering of neutron detector signal noise. In this paper, three kinds of noise filters are proposed and their performance is demonstrated for the estimation of reactivity. The tested filters are based on the unilateral kernel filter, unilateral kernel filter with adaptive bandwidth and bilateral filter to show their effectiveness in edge preservation. Filtering performance is compared with conventional low-pass and wavelet filters. The bilateral filter shows a remarkable improvement compared with unilateral kernel and wavelet filters. The effectiveness and simplicity of the unilateral kernel filter with adaptive bandwidth is also demonstrated by applying it to the reactivity measurement performed during reactor start-up physics tests
DEFF Research Database (Denmark)
Cox, Geoff
Speaking Code begins by invoking the “Hello World” convention used by programmers when learning a new language, helping to establish the interplay of text and code that runs through the book. Interweaving the voice of critical writing from the humanities with the tradition of computing and software...
Resummed memory kernels in generalized system-bath master equations
International Nuclear Information System (INIS)
Mavros, Michael G.; Van Voorhis, Troy
2014-01-01
Generalized master equations provide a concise formalism for studying reduced population dynamics. Usually, these master equations require a perturbative expansion of the memory kernels governing the dynamics; in order to prevent divergences, these expansions must be resummed. Resummation techniques of perturbation series are ubiquitous in physics, but they have not been readily studied for the time-dependent memory kernels used in generalized master equations. In this paper, we present a comparison of different resummation techniques for such memory kernels up to fourth order. We study specifically the spin-boson Hamiltonian as a model system bath Hamiltonian, treating the diabatic coupling between the two states as a perturbation. A novel derivation of the fourth-order memory kernel for the spin-boson problem is presented; then, the second- and fourth-order kernels are evaluated numerically for a variety of spin-boson parameter regimes. We find that resumming the kernels through fourth order using a Padé approximant results in divergent populations in the strong electronic coupling regime due to a singularity introduced by the nature of the resummation, and thus recommend a non-divergent exponential resummation (the “Landau-Zener resummation” of previous work). The inclusion of fourth-order effects in a Landau-Zener-resummed kernel is shown to improve both the dephasing rate and the obedience of detailed balance over simpler prescriptions like the non-interacting blip approximation, showing a relatively quick convergence on the exact answer. The results suggest that including higher-order contributions to the memory kernel of a generalized master equation and performing an appropriate resummation can provide a numerically-exact solution to system-bath dynamics for a general spectral density, opening the way to a new class of methods for treating system-bath dynamics
On Improving Convergence Rates for Nonnegative Kernel Density Estimators
Terrell, George R.; Scott, David W.
1980-01-01
To improve the rate of decrease of integrated mean square error for nonparametric kernel density estimators beyond $0(n^{-\\frac{4}{5}}),$ we must relax the constraint that the density estimate be a bonafide density function, that is, be nonnegative and integrate to one. All current methods for kernel (and orthogonal series) estimators relax the nonnegativity constraint. In this paper we show how to achieve similar improvement by relaxing the integral constraint only. This is important in appl...
Improved Variable Window Kernel Estimates of Probability Densities
Hall, Peter; Hu, Tien Chung; Marron, J. S.
1995-01-01
Variable window width kernel density estimators, with the width varying proportionally to the square root of the density, have been thought to have superior asymptotic properties. The rate of convergence has been claimed to be as good as those typical for higher-order kernels, which makes the variable width estimators more attractive because no adjustment is needed to handle the negativity usually entailed by the latter. However, in a recent paper, Terrell and Scott show that these results ca...
Graphical analyses of connected-kernel scattering equations
International Nuclear Information System (INIS)
Picklesimer, A.
1982-10-01
Simple graphical techniques are employed to obtain a new (simultaneous) derivation of a large class of connected-kernel scattering equations. This class includes the Rosenberg, Bencze-Redish-Sloan, and connected-kernel multiple scattering equations as well as a host of generalizations of these and other equations. The graphical method also leads to a new, simplified form for some members of the class and elucidates the general structural features of the entire class
RubyMotion iOS develoment essentials
Nalwaya, Abhishek
2013-01-01
This is a step-by-step book that builds on your knowledge by adding to an example app over the course of each chapter. Each topic uses example code that can be compiled and tested to show how things work practically instead of just telling you the theory. Complicated tasks are broken down into easy to follow steps with clear explanations of what each line of code is doing.Whether you are a novice to iOS development or looking for a simpler alternative to Objective-C; with RubyMotion iOS Development Essentials, you will become a pro at writing great iOS apps
Salus: Kernel Support for Secure Process Compartments
Directory of Open Access Journals (Sweden)
Raoul Strackx
2015-01-01
Full Text Available Consumer devices are increasingly being used to perform security and privacy critical tasks. The software used to perform these tasks is often vulnerable to attacks, due to bugs in the application itself or in included software libraries. Recent work proposes the isolation of security-sensitive parts of applications into protected modules, each of which can be accessed only through a predefined public interface. But most parts of an application can be considered security-sensitive at some level, and an attacker who is able to gain inapplication level access may be able to abuse services from protected modules. We propose Salus, a Linux kernel modification that provides a novel approach for partitioning processes into isolated compartments sharing the same address space. Salus significantly reduces the impact of insecure interfaces and vulnerable compartments by enabling compartments (1 to restrict the system calls they are allowed to perform, (2 to authenticate their callers and callees and (3 to enforce that they can only be accessed via unforgeable references. We describe the design of Salus, report on a prototype implementation and evaluate it in terms of security and performance. We show that Salus provides a significant security improvement with a low performance overhead, without relying on any non-standard hardware support.
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.
KERNEL MAD ALGORITHM FOR RELATIVE RADIOMETRIC NORMALIZATION
Directory of Open Access Journals (Sweden)
Y. Bai
2016-06-01
Full Text Available The multivariate alteration detection (MAD algorithm is commonly used in relative radiometric normalization. This algorithm is based on linear canonical correlation analysis (CCA which can analyze only linear relationships among bands. Therefore, we first introduce a new version of MAD in this study based on the established method known as kernel canonical correlation analysis (KCCA. The proposed method effectively extracts the non-linear and complex relationships among variables. We then conduct relative radiometric normalization experiments on both the linear CCA and KCCA version of the MAD algorithm with the use of Landsat-8 data of Beijing, China, and Gaofen-1(GF-1 data derived from South China. Finally, we analyze the difference between the two methods. Results show that the KCCA-based MAD can be satisfactorily applied to relative radiometric normalization, this algorithm can well describe the nonlinear relationship between multi-temporal images. This work is the first attempt to apply a KCCA-based MAD algorithm to relative radiometric normalization.
Craiger, Philip; Burke, Paul
This paper describes procedures for conducting forensic examinations of Apple Macs running Mac OS X. The target disk mode is used to create a forensic duplicate of a Mac hard drive and preview it. Procedures are discussed for recovering evidence from allocated space, unallocated space, slack space and virtual memory. Furthermore, procedures are described for recovering trace evidence from Mac OS X default email, web browser and instant messaging applications, as well as evidence pertaining to commands executed from a terminal.
A new discrete dipole kernel for quantitative susceptibility mapping.
Milovic, Carlos; Acosta-Cabronero, Julio; Pinto, José Miguel; Mattern, Hendrik; Andia, Marcelo; Uribe, Sergio; Tejos, Cristian
2018-09-01
Most approaches for quantitative susceptibility mapping (QSM) are based on a forward model approximation that employs a continuous Fourier transform operator to solve a differential equation system. Such formulation, however, is prone to high-frequency aliasing. The aim of this study was to reduce such errors using an alternative dipole kernel formulation based on the discrete Fourier transform and discrete operators. The impact of such an approach on forward model calculation and susceptibility inversion was evaluated in contrast to the continuous formulation both with synthetic phantoms and in vivo MRI data. The discrete kernel demonstrated systematically better fits to analytic field solutions, and showed less over-oscillations and aliasing artifacts while preserving low- and medium-frequency responses relative to those obtained with the continuous kernel. In the context of QSM estimation, the use of the proposed discrete kernel resulted in error reduction and increased sharpness. This proof-of-concept study demonstrated that discretizing the dipole kernel is advantageous for QSM. The impact on small or narrow structures such as the venous vasculature might by particularly relevant to high-resolution QSM applications with ultra-high field MRI - a topic for future investigations. The proposed dipole kernel has a straightforward implementation to existing QSM routines. Copyright © 2018 Elsevier Inc. All rights reserved.
Exploration of Shorea robusta (Sal seeds, kernels and its oil
Directory of Open Access Journals (Sweden)
Shashi Kumar C.
2016-12-01
Full Text Available Physical, mechanical, and chemical properties of Shorea robusta seed with wing, seed without wing, and kernel were investigated in the present work. The physico-chemical composition of sal oil was also analyzed. The physico-mechanical properties and proximate composition of seed with wing, seed without wing, and kernel at three moisture contents of 9.50% (w.b, 9.54% (w.b, and 12.14% (w.b, respectively, were studied. The results show that the moisture content of the kernel was highest as compared to seed with wing and seed without wing. The sphericity of the kernel was closer to that of a sphere as compared to seed with wing and seed without wing. The hardness of the seed with wing (32.32, N/mm and seed without wing (42.49, N/mm was lower than the kernels (72.14, N/mm. The proximate composition such as moisture, protein, carbohydrates, oil, crude fiber, and ash content were also determined. The kernel (30.20%, w/w contains higher oil percentage as compared to seed with wing and seed without wing. The scientific data from this work are important for designing of equipment and processes for post-harvest value addition of sal seeds.
Omnibus risk assessment via accelerated failure time kernel machine modeling.
Sinnott, Jennifer A; Cai, Tianxi
2013-12-01
Integrating genomic information with traditional clinical risk factors to improve the prediction of disease outcomes could profoundly change the practice of medicine. However, the large number of potential markers and possible complexity of the relationship between markers and disease make it difficult to construct accurate risk prediction models. Standard approaches for identifying important markers often rely on marginal associations or linearity assumptions and may not capture non-linear or interactive effects. In recent years, much work has been done to group genes into pathways and networks. Integrating such biological knowledge into statistical learning could potentially improve model interpretability and reliability. One effective approach is to employ a kernel machine (KM) framework, which can capture nonlinear effects if nonlinear kernels are used (Scholkopf and Smola, 2002; Liu et al., 2007, 2008). For survival outcomes, KM regression modeling and testing procedures have been derived under a proportional hazards (PH) assumption (Li and Luan, 2003; Cai, Tonini, and Lin, 2011). In this article, we derive testing and prediction methods for KM regression under the accelerated failure time (AFT) model, a useful alternative to the PH model. We approximate the null distribution of our test statistic using resampling procedures. When multiple kernels are of potential interest, it may be unclear in advance which kernel to use for testing and estimation. We propose a robust Omnibus Test that combines information across kernels, and an approach for selecting the best kernel for estimation. The methods are illustrated with an application in breast cancer. © 2013, The International Biometric Society.
Proteome analysis of the almond kernel (Prunus dulcis).
Li, Shugang; Geng, Fang; Wang, Ping; Lu, Jiankang; Ma, Meihu
2016-08-01
Almond (Prunus dulcis) is a popular tree nut worldwide and offers many benefits to human health. However, the importance of almond kernel proteins in the nutrition and function in human health requires further evaluation. The present study presents a systematic evaluation of the proteins in the almond kernel using proteomic analysis. The nutrient and amino acid content in almond kernels from Xinjiang is similar to that of American varieties; however, Xinjiang varieties have a higher protein content. Two-dimensional electrophoresis analysis demonstrated a wide distribution of molecular weights and isoelectric points of almond kernel proteins. A total of 434 proteins were identified by LC-MS/MS, and most were proteins that were experimentally confirmed for the first time. Gene ontology (GO) analysis of the 434 proteins indicated that proteins involved in primary biological processes including metabolic processes (67.5%), cellular processes (54.1%), and single-organism processes (43.4%), the main molecular function of almond kernel proteins are in catalytic activity (48.0%), binding (45.4%) and structural molecule activity (11.9%), and proteins are primarily distributed in cell (59.9%), organelle (44.9%), and membrane (22.8%). Almond kernel is a source of a wide variety of proteins. This study provides important information contributing to the screening and identification of almond proteins, the understanding of almond protein function, and the development of almond protein products. © 2015 Society of Chemical Industry. © 2015 Society of Chemical Industry.
International Nuclear Information System (INIS)
Matijevic, M.; Grgic, D.; Jecmenica, R.
2016-01-01
This paper presents comparison of the Krsko Power Plant simplified Spent Fuel Pool (SFP) dose rates using different computational shielding methodologies. The analysis was performed to estimate limiting gamma dose rates on wall mounted level instrumentation in case of significant loss of cooling water. The SFP was represented with simple homogenized cylinders (point kernel and Monte Carlo (MC)) or cuboids (MC) using uranium, iron, water, and dry-air as bulk region materials. The pool is divided on the old and new section where the old one has three additional subsections representing fuel assemblies (FAs) with different burnup/cooling time (60 days, 1 year and 5 years). The new section represents the FAs with the cooling time of 10 years. The time dependent fuel assembly isotopic composition was calculated using ORIGEN2 code applied to the depletion of one of the fuel assemblies present in the pool (AC-29). The source used in Microshield calculation is based on imported isotopic activities. The time dependent photon spectra with total source intensity from Microshield multigroup point kernel calculations was then prepared for two hybrid deterministic-stochastic sequences. One is based on SCALE/MAVRIC (Monaco and Denovo) methodology and another uses Monte Carlo code MCNP6.1.1b and ADVANTG3.0.1. code. Even though this model is a fairly simple one, the layers of shielding materials are thick enough to pose a significant shielding problem for MC method without the use of effective variance reduction (VR) technique. For that purpose the ADVANTG code was used to generate VR parameters (SB cards in SDEF and WWINP file) for MCNP fixed-source calculation using continuous energy transport. ADVATNG employs a deterministic forward-adjoint transport solver Denovo which implements CADIS/FW-CADIS methodology. Denovo implements a structured, Cartesian-grid SN solver based on the Koch-Baker-Alcouffe parallel transport sweep algorithm across x-y domain blocks. This was first
Utilizing GPUs to Accelerate Turbomachinery CFD Codes
MacCalla, Weylin; Kulkarni, Sameer
2016-01-01
GPU computing has established itself as a way to accelerate parallel codes in the high performance computing world. This work focuses on speeding up APNASA, a legacy CFD code used at NASA Glenn Research Center, while also drawing conclusions about the nature of GPU computing and the requirements to make GPGPU worthwhile on legacy codes. Rewriting and restructuring of the source code was avoided to limit the introduction of new bugs. The code was profiled and investigated for parallelization potential, then OpenACC directives were used to indicate parallel parts of the code. The use of OpenACC directives was not able to reduce the runtime of APNASA on either the NVIDIA Tesla discrete graphics card, or the AMD accelerated processing unit. Additionally, it was found that in order to justify the use of GPGPU, the amount of parallel work being done within a kernel would have to greatly exceed the work being done by any one portion of the APNASA code. It was determined that in order for an application like APNASA to be accelerated on the GPU, it should not be modular in nature, and the parallel portions of the code must contain a large portion of the code's computation time.
ZZ THERMOS, Multigroup P0 to P5 Thermal Scattering Kernels from ENDF/B Scattering Law Data
International Nuclear Information System (INIS)
McCrosson, F.J.; Finch, D.R.
1975-01-01
1 - Description of problem or function: Number of groups: 30-group THERMOS thermal scattering kernels. Nuclides: Molecular H 2 O, Molecular D 2 O, Graphite, Polyethylene, Benzene, Zr bound in ZrHx, H bound in ZrHx, Beryllium-9, Beryllium Oxide, Uranium Dioxide. Origin: ENDF/B library. Weighting Spectrum: yes. These data are 30-group THERMOS thermal scattering kernels for P0 to P5 Legendre orders for every temperature of every material from s(alpha,beta) data stored in the ENDF/B library. These scattering kernels were generated using the FLANGE2 computer code (NESC Abstract 368). To test the kernels, the integral properties of each set of kernels were determined by a precision integration of the diffusion length equation and compared to experimental measurements of these properties. In general, the agreement was very good. Details of the methods used and results obtained are contained in the reference. The scattering kernels are organized into a two volume magnetic tape library from which they may be retrieved easily for use in any 30-group THERMOS library. The contents of the tapes are as follows - (Material: ZA/Temperatures (degrees K)): Molecular H 2 O: 100.0/296, 350, 400, 450, 500, 600, Molecular D 2 O: 101.0/296, 350, 400, 450, 500, 600, Graphite: 6000.0/296, 400, 500, 600, 700, 800, Polyethylene: 205.0/296, 350 Benzene: 106.0/296, 350, 400, 450, 500, 600, Zr bound in ZrHx: 203.0/296, 400, 500, 600, 700, 800, H bound in ZrHx: 230.0/296, 400, 500, 600, 700, 800, Beryllium-9: 4009.0/296, 400, 500, 600, 700, 800, Beryllium Oxide: 200.0/296, 400, 500, 600, 700, 800, Uranium Dioxide: 207.0/296, 400, 500, 600, 700, 800 2 - Method of solution: Kernel generation is performed by direct integration of the thermal scattering law data to obtain the differential scattering cross sections for each Legendre order. The integral parameter calculation is done by precision integration of the diffusion length equation for several moderator absorption cross sections followed by a
Physical and chemical characteristics of toilet soap made from apricot kernel oil and palm stearin.
Directory of Open Access Journals (Sweden)
Girgis, Adel Y.
1998-12-01
Full Text Available The objective of the present work was to use apricot kernel oil with palm stearin in toilet soap-making. Apricot kernel oil was obtained from apricot kernel seed (Prunus armeniaca through hydraulic pressing (12000lb/in^{2}. Kernel contained 43.3% oil. The fatty acids of apricot kernel oil had high oleic acid (81.73% while, the major of the fatty acid in palm stearin was palmitic acid (55.17%. Eight of the toilet soap samples were prepared from apricot kernel oil, palm kernel oil and palm stearin at different ratios. The structure of soap samples nº1 and 8 were sticky and with bad physical properties. On the other hand, the physical characteristics of blends n^{os} 2, 3, 4, 5 and 6 had firm consistency and creamy lather while, in soap nº 7, its were moderatement; i. e. medium hard makeup with fairly lather. After storage (6 months on a shelf at room temperature, all soaps (nº1-8 were declined in their moisture content. On contrary, the total fatty acids of the same samples were augmented at different ratios during storage. Physical characteristics of soap samples n^{os} 2, 3, 4, 5, 6 and 7 were increased after the storage time (6 months, their consistencies were very firm with creamy lather and reducement in their erosion from handwashing ratios was observed. It can be recommended that apricot kernel oil can be used in the manufacturing of toilet soap until ratio 50% of the fatty blend (the blend was bear palm stearin.
El objetivo del presente trabajo fue el uso del aceite de semilla de albaricoque con estearina de palma en la fabricación de jabón de tocador. El aceite de semilla de albaricoque (Prunus armeniaca se obtuvo por presión hidráulica (12000lb/in^{2}, y la semilla contenía el 43.3% de aceite. Los ácidos grasos del aceite de semilla de albaricoque tenían altos contenidos de ácido oleico (81.73% mientras, el ácido graso mayoritario en la estearina de palma fue el ácido palm
Fusion PIC code performance analysis on the Cori KNL system
Energy Technology Data Exchange (ETDEWEB)
Koskela, Tuomas S. [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC); Deslippe, Jack [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC); Friesen, Brian [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC); Raman, Karthic [INTEL Corp. (United States)
2017-05-25
We study the attainable performance of Particle-In-Cell codes on the Cori KNL system by analyzing a miniature particle push application based on the fusion PIC code XGC1. We start from the most basic building blocks of a PIC code and build up the complexity to identify the kernels that cost the most in performance and focus optimization efforts there. Particle push kernels operate at high AI and are not likely to be memory bandwidth or even cache bandwidth bound on KNL. Therefore, we see only minor benefits from the high bandwidth memory available on KNL, and achieving good vectorization is shown to be the most beneficial optimization path with theoretical yield of up to 8x speedup on KNL. In practice we are able to obtain up to a 4x gain from vectorization due to limitations set by the data layout and memory latency.
Directory of Open Access Journals (Sweden)
Anthony McCosker
2014-03-01
Full Text Available As well as introducing the Coding Labour section, the authors explore the diffusion of code across the material contexts of everyday life, through the objects and tools of mediation, the systems and practices of cultural production and organisational management, and in the material conditions of labour. Taking code beyond computation and software, their specific focus is on the increasingly familiar connections between code and labour with a focus on the codification and modulation of affect through technologies and practices of management within the contemporary work organisation. In the grey literature of spreadsheets, minutes, workload models, email and the like they identify a violence of forms through which workplace affect, in its constant flux of crisis and ‘prodromal’ modes, is regulated and governed.
Moghadam, Maryam Khazaee; Asl, Alireza Kamali; Geramifar, Parham; Zaidi, Habib
2016-01-01
Purpose: The aim of this work is to evaluate the application of tissue-specific dose kernels instead of water dose kernels to improve the accuracy of patient-specific dosimetry by taking tissue heterogeneities into consideration. Materials and Methods: Tissue-specific dose point kernels (DPKs) and
DEFF Research Database (Denmark)
Petersen, Annette
of kernels promoted (10 and 60 kernels/day for the general population and cancer patients, respectively), exposures exceeded the ARfD 17–413 and 3–71 times in toddlers and adults, respectively. The estimated maximum quantity of apricot kernels (or raw apricot material) that can be consumed without exceeding...
Generalized synthetic kernel approximation for elastic moderation of fast neutrons
International Nuclear Information System (INIS)
Yamamoto, Koji; Sekiya, Tamotsu; Yamamura, Yasunori.
1975-01-01
A method of synthetic kernel approximation is examined in some detail with a view to simplifying the treatment of the elastic moderation of fast neutrons. A sequence of unified kernel (fsub(N)) is introduced, which is then divided into two subsequences (Wsub(n)) and (Gsub(n)) according to whether N is odd (Wsub(n)=fsub(2n-1), n=1,2, ...) or even (Gsub(n)=fsub(2n), n=0,1, ...). The W 1 and G 1 kernels correspond to the usual Wigner and GG kernels, respectively, and the Wsub(n) and Gsub(n) kernels for n>=2 represent generalizations thereof. It is shown that the Wsub(n) kernel solution with a relatively small n (>=2) is superior on the whole to the Gsub(n) kernel solution for the same index n, while both converge to the exact values with increasing n. To evaluate the collision density numerically and rapidly, a simple recurrence formula is derived. In the asymptotic region (except near resonances), this recurrence formula allows calculation with a relatively coarse mesh width whenever hsub(a)<=0.05 at least. For calculations in the transient lethargy region, a mesh width of order epsilon/10 is small enough to evaluate the approximate collision density psisub(N) with an accuracy comparable to that obtained analytically. It is shown that, with the present method, an order of approximation of about n=7 should yield a practically correct solution diviating not more than 1% in collision density. (auth.)
Unsupervised multiple kernel learning for heterogeneous data integration.
Mariette, Jérôme; Villa-Vialaneix, Nathalie
2018-03-15
Recent high-throughput sequencing advances have expanded the breadth of available omics datasets and the integrated analysis of multiple datasets obtained on the same samples has allowed to gain important insights in a wide range of applications. However, the integration of various sources of information remains a challenge for systems biology since produced datasets are often of heterogeneous types, with the need of developing generic methods to take their different specificities into account. We propose a multiple kernel framework that allows to integrate multiple datasets of various types into a single exploratory analysis. Several solutions are provided to learn either a consensus meta-kernel or a meta-kernel that preserves the original topology of the datasets. We applied our framework to analyse two public multi-omics datasets. First, the multiple metagenomic datasets, collected during the TARA Oceans expedition, was explored to demonstrate that our method is able to retrieve previous findings in a single kernel PCA as well as to provide a new image of the sample structures when a larger number of datasets are included in the analysis. To perform this analysis, a generic procedure is also proposed to improve the interpretability of the kernel PCA in regards with the original data. Second, the multi-omics breast cancer datasets, provided by The Cancer Genome Atlas, is analysed using a kernel Self-Organizing Maps with both single and multi-omics strategies. The comparison of these two approaches demonstrates the benefit of our integration method to improve the representation of the studied biological system. Proposed methods are available in the R package mixKernel, released on CRAN. It is fully compatible with the mixOmics package and a tutorial describing the approach can be found on mixOmics web site http://mixomics.org/mixkernel/. jerome.mariette@inra.fr or nathalie.villa-vialaneix@inra.fr. Supplementary data are available at Bioinformatics online.
Fumonisins in corn: correlation with Fusarium sp. count, damaged kernels, protein and lipid content
Directory of Open Access Journals (Sweden)
Elisabete Yurie Sataque Ono
2006-01-01
Full Text Available Natural fungal and fumonisin contamination were evaluated in 109 freshly harvested corn samples from Paraná State and correlated to damaged kernels (%. In addition, healthy and damaged kernels of 24 corn samples were selected in order to compare the mycoflora profile and fumonisin levels. The correlation among protein/lipid content and fumonisin levels was also analyzed in the 15 most frequently cultivated corn hybrids. Total fungal colony count in 109 freshly harvested corn samples ranged from 1.9x10(4 to 3.5x10(6 CFU/g, Fusarium sp. count from 1.0x10³ to 2.2x10(6 CFU/g, and fumonisin levels from 0.13 to 20.38 µg/g. Total fungal colony/Fusarium sp. count and fumonisin levels showed positive correlation (p A contaminação natural por fungos e fumonisinas foi avaliada em 109 amostras de milho recém-colhido do Estado do Paraná e correlacionada com grãos ardidos (%. Além disso, grãos sadios e ardidos de 24 amostras de milho foram selecionados a fim de comparar o perfil da microbiota fúngica e níveis de fumonisinas. A correlação entre os teores de proteínas/lipídios e os níveis de fumonisinas também foi analisada nos 15 híbridos de milho mais freqüentemente cultivados no Estado do Paraná. A contagem total de fungos em 109 amostras de milho recém-colhido variou de 1,9x10(4 a 3,5x10(6 UFC/g, Fusarium sp. de 1,0x10³ a 2,2x10(6 UFC/g e, níveis de fumonisinas de 0,13 a 20,38 µg/g. A contagem total de fungos/Fusarium spp. e níveis de fumonisinas apresentaram correlação positiva (p<0,05. Adicionalmente, houve uma correlação positiva entre grãos ardidos (% e a contagem total de fungos/ Fusarium spp. (p < 0,05. Os níveis de fumonisinas nos grãos sadios variaram de 0,57 a 20,38 µg/g, enquanto que nos grãos ardidos variaram de 68,96 a 336,38 µg/g. Não foi observada correlação significativa entre os níveis de fumonisinas e os teores de proteínas/lipídios. Esses resultados ratificam a importância do monitoramento
Collision kernels in the eikonal approximation for Lennard-Jones interaction potential
International Nuclear Information System (INIS)
Zielinska, S.
1985-03-01
The velocity changing collisions are conveniently described by collisional kernels. These kernels depend on an interaction potential and there is a necessity for evaluating them for realistic interatomic potentials. Using the collision kernels, we are able to investigate the redistribution of atomic population's caused by the laser light and velocity changing collisions. In this paper we present the method of evaluating the collision kernels in the eikonal approximation. We discuss the influence of the potential parameters Rsub(o)sup(i), epsilonsub(o)sup(i) on kernel width for a given atomic state. It turns out that unlike the collision kernel for the hard sphere model of scattering the Lennard-Jones kernel is not so sensitive to changes of Rsub(o)sup(i) as the previous one. Contrary to the general tendency of approximating collisional kernels by the Gaussian curve, kernels for the Lennard-Jones potential do not exhibit such a behaviour. (author)
Energy Technology Data Exchange (ETDEWEB)
Ravishankar, C., Hughes Network Systems, Germantown, MD
1998-05-08
Speech is the predominant means of communication between human beings and since the invention of the telephone by Alexander Graham Bell in 1876, speech services have remained to be the core service in almost all telecommunication systems. Original analog methods of telephony had the disadvantage of speech signal getting corrupted by noise, cross-talk and distortion Long haul transmissions which use repeaters to compensate for the loss in signal strength on transmission links also increase the associated noise and distortion. On the other hand digital transmission is relatively immune to noise, cross-talk and distortion primarily because of the capability to faithfully regenerate digital signal at each repeater purely based on a binary decision. Hence end-to-end performance of the digital link essentially becomes independent of the length and operating frequency bands of the link Hence from a transmission point of view digital transmission has been the preferred approach due to its higher immunity to noise. The need to carry digital speech became extremely important from a service provision point of view as well. Modem requirements have introduced the need for robust, flexible and secure services that can carry a multitude of signal types (such as voice, data and video) without a fundamental change in infrastructure. Such a requirement could not have been easily met without the advent of digital transmission systems, thereby requiring speech to be coded digitally. The term Speech Coding is often referred to techniques that represent or code speech signals either directly as a waveform or as a set of parameters by analyzing the speech signal. In either case, the codes are transmitted to the distant end where speech is reconstructed or synthesized using the received set of codes. A more generic term that is applicable to these techniques that is often interchangeably used with speech coding is the term voice coding. This term is more generic in the sense that the
Optimal codes as Tanner codes with cyclic component codes
DEFF Research Database (Denmark)
Høholdt, Tom; Pinero, Fernando; Zeng, Peng
2014-01-01
In this article we study a class of graph codes with cyclic code component codes as affine variety codes. Within this class of Tanner codes we find some optimal binary codes. We use a particular subgraph of the point-line incidence plane of A(2,q) as the Tanner graph, and we are able to describe ...
Bivariate discrete beta Kernel graduation of mortality data.
Mazza, Angelo; Punzo, Antonio
2015-07-01
Various parametric/nonparametric techniques have been proposed in literature to graduate mortality data as a function of age. Nonparametric approaches, as for example kernel smoothing regression, are often preferred because they do not assume any particular mortality law. Among the existing kernel smoothing approaches, the recently proposed (univariate) discrete beta kernel smoother has been shown to provide some benefits. Bivariate graduation, over age and calendar years or durations, is common practice in demography and actuarial sciences. In this paper, we generalize the discrete beta kernel smoother to the bivariate case, and we introduce an adaptive bandwidth variant that may provide additional benefits when data on exposures to the risk of death are available; furthermore, we outline a cross-validation procedure for bandwidths selection. Using simulations studies, we compare the bivariate approach proposed here with its corresponding univariate formulation and with two popular nonparametric bivariate graduation techniques, based on Epanechnikov kernels and on P-splines. To make simulations realistic, a bivariate dataset, based on probabilities of dying recorded for the US males, is used. Simulations have confirmed the gain in performance of the new bivariate approach with respect to both the univariate and the bivariate competitors.
Mixed kernel function support vector regression for global sensitivity analysis
Cheng, Kai; Lu, Zhenzhou; Wei, Yuhao; Shi, Yan; Zhou, Yicheng
2017-11-01
Global sensitivity analysis (GSA) plays an important role in exploring the respective effects of input variables on an assigned output response. Amongst the wide sensitivity analyses in literature, the Sobol indices have attracted much attention since they can provide accurate information for most models. In this paper, a mixed kernel function (MKF) based support vector regression (SVR) model is employed to evaluate the Sobol indices at low computational cost. By the proposed derivation, the estimation of the Sobol indices can be obtained by post-processing the coefficients of the SVR meta-model. The MKF is constituted by the orthogonal polynomials kernel function and Gaussian radial basis kernel function, thus the MKF possesses both the global characteristic advantage of the polynomials kernel function and the local characteristic advantage of the Gaussian radial basis kernel function. The proposed approach is suitable for high-dimensional and non-linear problems. Performance of the proposed approach is validated by various analytical functions and compared with the popular polynomial chaos expansion (PCE). Results demonstrate that the proposed approach is an efficient method for global sensitivity analysis.
On flame kernel formation and propagation in premixed gases
Energy Technology Data Exchange (ETDEWEB)
Eisazadeh-Far, Kian; Metghalchi, Hameed [Northeastern University, Mechanical and Industrial Engineering Department, Boston, MA 02115 (United States); Parsinejad, Farzan [Chevron Oronite Company LLC, Richmond, CA 94801 (United States); Keck, James C. [Massachusetts Institute of Technology, Cambridge, MA 02139 (United States)
2010-12-15
Flame kernel formation and propagation in premixed gases have been studied experimentally and theoretically. The experiments have been carried out at constant pressure and temperature in a constant volume vessel located in a high speed shadowgraph system. The formation and propagation of the hot plasma kernel has been simulated for inert gas mixtures using a thermodynamic model. The effects of various parameters including the discharge energy, radiation losses, initial temperature and initial volume of the plasma have been studied in detail. The experiments have been extended to flame kernel formation and propagation of methane/air mixtures. The effect of energy terms including spark energy, chemical energy and energy losses on flame kernel formation and propagation have been investigated. The inputs for this model are the initial conditions of the mixture and experimental data for flame radii. It is concluded that these are the most important parameters effecting plasma kernel growth. The results of laminar burning speeds have been compared with previously published results and are in good agreement. (author)
Insights from Classifying Visual Concepts with Multiple Kernel Learning
Binder, Alexander; Nakajima, Shinichi; Kloft, Marius; Müller, Christina; Samek, Wojciech; Brefeld, Ulf; Müller, Klaus-Robert; Kawanabe, Motoaki
2012-01-01
Combining information from various image features has become a standard technique in concept recognition tasks. However, the optimal way of fusing the resulting kernel functions is usually unknown in practical applications. Multiple kernel learning (MKL) techniques allow to determine an optimal linear combination of such similarity matrices. Classical approaches to MKL promote sparse mixtures. Unfortunately, 1-norm regularized MKL variants are often observed to be outperformed by an unweighted sum kernel. The main contributions of this paper are the following: we apply a recently developed non-sparse MKL variant to state-of-the-art concept recognition tasks from the application domain of computer vision. We provide insights on benefits and limits of non-sparse MKL and compare it against its direct competitors, the sum-kernel SVM and sparse MKL. We report empirical results for the PASCAL VOC 2009 Classification and ImageCLEF2010 Photo Annotation challenge data sets. Data sets (kernel matrices) as well as further information are available at http://doc.ml.tu-berlin.de/image_mkl/(Accessed 2012 Jun 25). PMID:22936970
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. Copyright © 2016 Elsevier Ltd. All rights reserved.
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.
De como os alunos entendem os textos
Directory of Open Access Journals (Sweden)
Peronard Th, Marianne
1991-01-01
Full Text Available Nosso interesse na compreensão do texto escrito apresenta uma tendência pedagógica. Tal perspectiva guiou a seleção de nossa estrutura teórica, os limites de nosso trabalho experimental e a natureza dos instrumentos que usamos, tanto durante a avaliação das habilidades de compreensão dos alunos como durante as aplicações instrucionais. Nosso foco tem se localizado na compreensão inferencial, a qual medimos pedindo aos estudantes que verbalizassem as inferências necessárias para conbstruir uma interpretação coerente. Para garantir uma sanidade ecológica, temos nos mantido o mais próximo possível de uma situação escolar, um fato que, em resumo, parece ter sido autodestruidor. Nossos resultados mostram fracas habilidades de compreensão por parte dos sujeitos (aproximadamente 2600 alunos. Suas respostas manifestam pouco raciocínio inferencial e suas estratégias, baixa adaptação à estrutura ou conteúdo do texto. De fato a estratégia mais comum parece ser a que nós chamamos de "estratégia lexical restrita", que é mais uma "estratégia de resposta" do que uma estratégia de compreensão
International Nuclear Information System (INIS)
Quezada G, S.; Espinosa P, G.; Centeno P, J.; Sanchez M, H.
2017-09-01
This paper presents the Aztheca code, which is formed by the mathematical models of neutron kinetics, power generation, heat transfer, core thermo-hydraulics, recirculation systems, dynamic pressure and level models and control system. The Aztheca code is validated with plant data, as well as with predictions from the manufacturer when the reactor operates in a stationary state. On the other hand, to demonstrate that the model is applicable during a transient, an event occurred in a nuclear power plant with a BWR reactor is selected. The plant data are compared with the results obtained with RELAP-5 and the Aztheca model. The results show that both RELAP-5 and the Aztheca code have the ability to adequately predict the behavior of the reactor. (Author)
OS Friendly Microprocessor Architecture
2017-04-01
NOTES Patrick La Fratta is now affiliated with Micron Technology, Inc., Boise, Idaho. 14. ABSTRACT We present an introduction to the patented ...Operating System Friendly Microprocessor Architecture (OSFA). The software framework to support the hardware-level security features is currently patent ...Army is assignee. OS Friendly Microprocessor Architecture. United States Patent 9122610. 2015 Sep. 2. Jungwirth P, inventor; US Army is assignee
... other conditions, such as an Achilles tendon injury, ankle sprain or talus fracture. Diagnosis of os trigonum syndrome begins with questions from the doctor about the development of symptoms. After the foot and ankle are examined, x-rays or other imaging tests ...
DEFF Research Database (Denmark)
Soon, Winnie; Cox, Geoff
2018-01-01
a computational and poetic composition for two screens: on one of these, texts and voices are repeated and disrupted by mathematical chaos, together exploring the performativity of code and language; on the other, is a mix of a computer programming syntax and human language. In this sense queer code can...... be understood as both an object and subject of study that intervenes in the world’s ‘becoming' and how material bodies are produced via human and nonhuman practices. Through mixing the natural and computer language, this article presents a script in six parts from a performative lecture for two persons...
International Nuclear Information System (INIS)
Rattan, D.S.
1993-11-01
NSURE stands for Near-Surface Repository code. NSURE is a performance assessment code. developed for the safety assessment of near-surface disposal facilities for low-level radioactive waste (LLRW). Part one of this report documents the NSURE model, governing equations and formulation of the mathematical models, and their implementation under the SYVAC3 executive. The NSURE model simulates the release of nuclides from an engineered vault, their subsequent transport via the groundwater and surface water pathways tot he biosphere, and predicts the resulting dose rate to a critical individual. Part two of this report consists of a User's manual, describing simulation procedures, input data preparation, output and example test cases
Utilization of the Nelkin model in a Hammer computer code for calculation the reactor parameters
International Nuclear Information System (INIS)
Leal, L.C.
1980-07-01
The possibility of modifying the HAMMER code, in the thermal part, by changing the thermal neutron scattering Kernel of its library for another one calculated in a subprogramm which can be incorporated to the code, is studied. This subprogramm uses the original version of the Nelkin model instead of its approximation which is used in the HAMMER. It has the advantage of giving the values of the Kernel for any temperature of the reactor for the approximations P 0 , P 1 , P 2 and P 3 . (Author) [pt
Semisupervised kernel marginal Fisher analysis for face recognition.
Wang, Ziqiang; Sun, Xia; Sun, Lijun; Huang, Yuchun
2013-01-01
Dimensionality reduction is a key problem in face recognition due to the high-dimensionality of face image. To effectively cope with this problem, a novel dimensionality reduction algorithm called semisupervised kernel marginal Fisher analysis (SKMFA) for face recognition is proposed in this paper. SKMFA can make use of both labelled and unlabeled samples to learn the projection matrix for nonlinear dimensionality reduction. Meanwhile, it can successfully avoid the singularity problem by not calculating the matrix inverse. In addition, in order to make the nonlinear structure captured by the data-dependent kernel consistent with the intrinsic manifold structure, a manifold adaptive nonparameter kernel is incorporated into the learning process of SKMFA. Experimental results on three face image databases demonstrate the effectiveness of our proposed algorithm.
Capturing Option Anomalies with a Variance-Dependent Pricing Kernel
DEFF Research Database (Denmark)
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...... 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....
Heat Kernel Asymptotics of Zaremba Boundary Value Problem
Energy Technology Data Exchange (ETDEWEB)
Avramidi, Ivan G. [Department of Mathematics, New Mexico Institute of Mining and Technology (United States)], E-mail: iavramid@nmt.edu
2004-03-15
The Zaremba boundary-value problem is a boundary value problem for Laplace-type second-order partial differential operators acting on smooth sections of a vector bundle over a smooth compact Riemannian manifold with smooth boundary but with discontinuous boundary conditions, which include Dirichlet boundary conditions on one part of the boundary and Neumann boundary conditions on another part of the boundary. We study the heat kernel asymptotics of Zaremba boundary value problem. The construction of the asymptotic solution of the heat equation is described in detail and the heat kernel is computed explicitly in the leading approximation. Some of the first nontrivial coefficients of the heat kernel asymptotic expansion are computed explicitly.
Weighted Feature Gaussian Kernel SVM for Emotion Recognition.
Wei, Wei; Jia, Qingxuan
2016-01-01
Emotion recognition with weighted feature based on facial expression is a challenging research topic and has attracted great attention in the past few years. This paper presents a novel method, utilizing subregion recognition rate to weight kernel function. First, we divide the facial expression image into some uniform subregions and calculate corresponding recognition rate and weight. Then, we get a weighted feature Gaussian kernel function and construct a classifier based on Support Vector Machine (SVM). At last, the experimental results suggest that the approach based on weighted feature Gaussian kernel function has good performance on the correct rate in emotion recognition. The experiments on the extended Cohn-Kanade (CK+) dataset show that our method has achieved encouraging recognition results compared to the state-of-the-art methods.
Rational kernels for Arabic Root Extraction and Text Classification
Directory of Open Access Journals (Sweden)
Attia Nehar
2016-04-01
Full Text Available In this paper, we address the problems of Arabic Text Classification and root extraction using transducers and rational kernels. We introduce a new root extraction approach on the basis of the use of Arabic patterns (Pattern Based Stemmer. Transducers are used to model these patterns and root extraction is done without relying on any dictionary. Using transducers for extracting roots, documents are transformed into finite state transducers. This document representation allows us to use and explore rational kernels as a framework for Arabic Text Classification. Root extraction experiments are conducted on three word collections and yield 75.6% of accuracy. Classification experiments are done on the Saudi Press Agency dataset and N-gram kernels are tested with different values of N. Accuracy and F1 report 90.79% and 62.93% respectively. These results show that our approach, when compared with other approaches, is promising specially in terms of accuracy and F1.
Energy Technology Data Exchange (ETDEWEB)
Delbecq, J.M
1999-07-01
The Aster code is a 2D or 3D finite-element calculation code for structures developed by the R and D direction of Electricite de France (EdF). This dossier presents a complete overview of the characteristics and uses of the Aster code: introduction of version 4; the context of Aster (organisation of the code development, versions, systems and interfaces, development tools, quality assurance, independent validation); static mechanics (linear thermo-elasticity, Euler buckling, cables, Zarka-Casier method); non-linear mechanics (materials behaviour, big deformations, specific loads, unloading and loss of load proportionality indicators, global algorithm, contact and friction); rupture mechanics (G energy restitution level, restitution level in thermo-elasto-plasticity, 3D local energy restitution level, KI and KII stress intensity factors, calculation of limit loads for structures), specific treatments (fatigue, rupture, wear, error estimation); meshes and models (mesh generation, modeling, loads and boundary conditions, links between different modeling processes, resolution of linear systems, display of results etc..); vibration mechanics (modal and harmonic analysis, dynamics with shocks, direct transient dynamics, seismic analysis and aleatory dynamics, non-linear dynamics, dynamical sub-structuring); fluid-structure interactions (internal acoustics, mass, rigidity and damping); linear and non-linear thermal analysis; steels and metal industry (structure transformations); coupled problems (internal chaining, internal thermo-hydro-mechanical coupling, chaining with other codes); products and services. (J.S.)
DEFF Research Database (Denmark)
Ejsing-Duun, Stine; Hansbøl, Mikala
Denne rapport rummer evaluering og dokumentation af Coding Class projektet1. Coding Class projektet blev igangsat i skoleåret 2016/2017 af IT-Branchen i samarbejde med en række medlemsvirksomheder, Københavns kommune, Vejle Kommune, Styrelsen for IT- og Læring (STIL) og den frivillige forening...... Coding Pirates2. Rapporten er forfattet af Docent i digitale læringsressourcer og forskningskoordinator for forsknings- og udviklingsmiljøet Digitalisering i Skolen (DiS), Mikala Hansbøl, fra Institut for Skole og Læring ved Professionshøjskolen Metropol; og Lektor i læringsteknologi, interaktionsdesign......, design tænkning og design-pædagogik, Stine Ejsing-Duun fra Forskningslab: It og Læringsdesign (ILD-LAB) ved Institut for kommunikation og psykologi, Aalborg Universitet i København. Vi har fulgt og gennemført evaluering og dokumentation af Coding Class projektet i perioden november 2016 til maj 2017...
Andrews, Ken; Divsalar, Dariush; Dolinar, Sam; Moision, Bruce; Hamkins, Jon; Pollara, Fabrizio
2007-01-01
This slide presentation reviews the objectives, meeting goals and overall NASA goals for the NASA Data Standards Working Group. The presentation includes information on the technical progress surrounding the objective, short LDPC codes, and the general results on the Pu-Pw tradeoff.
International Nuclear Information System (INIS)
Lindemuth, I.R.
1979-01-01
This report describes ANIMAL, a two-dimensional Eulerian magnetohydrodynamic computer code. ANIMAL's physical model also appears. Formulated are temporal and spatial finite-difference equations in a manner that facilitates implementation of the algorithm. Outlined are the functions of the algorithm's FORTRAN subroutines and variables
Indian Academy of Sciences (India)
Home; Journals; Resonance – Journal of Science Education; Volume 15; Issue 7. Network Coding. K V Rashmi Nihar B Shah P Vijay Kumar. General Article Volume 15 Issue 7 July 2010 pp 604-621. Fulltext. Click here to view fulltext PDF. Permanent link: https://www.ias.ac.in/article/fulltext/reso/015/07/0604-0621 ...
International Nuclear Information System (INIS)
Cramer, S.N.
1984-01-01
The MCNP code is the major Monte Carlo coupled neutron-photon transport research tool at the Los Alamos National Laboratory, and it represents the most extensive Monte Carlo development program in the United States which is available in the public domain. The present code is the direct descendent of the original Monte Carlo work of Fermi, von Neumaum, and Ulam at Los Alamos in the 1940s. Development has continued uninterrupted since that time, and the current version of MCNP (or its predecessors) has always included state-of-the-art methods in the Monte Carlo simulation of radiation transport, basic cross section data, geometry capability, variance reduction, and estimation procedures. The authors of the present code have oriented its development toward general user application. The documentation, though extensive, is presented in a clear and simple manner with many examples, illustrations, and sample problems. In addition to providing the desired results, the output listings give a a wealth of detailed information (some optional) concerning each state of the calculation. The code system is continually updated to take advantage of advances in computer hardware and software, including interactive modes of operation, diagnostic interrupts and restarts, and a variety of graphical and video aids
Indian Academy of Sciences (India)
Home; Journals; Resonance – Journal of Science Education; Volume 10; Issue 1. Expander Codes - The Sipser–Spielman Construction. Priti Shankar. General Article Volume 10 ... Author Affiliations. Priti Shankar1. Department of Computer Science and Automation, Indian Institute of Science Bangalore 560 012, India.
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.
Broken rice kernels and the kinetics of rice hydration and texture during cooking.
Saleh, Mohammed; Meullenet, Jean-Francois
2013-05-01
During rice milling and processing, broken kernels are inevitably present, although to date it has been unclear as to how the presence of broken kernels affects rice hydration and cooked rice texture. Therefore, this work intended to study the effect of broken kernels in a rice sample on rice hydration and texture during cooking. Two medium-grain and two long-grain rice cultivars were harvested, dried and milled, and the broken kernels were separated from unbroken kernels. Broken rice kernels were subsequently combined with unbroken rice kernels forming treatments of 0, 40, 150, 350 or 1000 g kg(-1) broken kernels ratio. Rice samples were then cooked and the moisture content of the cooked rice, the moisture uptake rate, and rice hardness and stickiness were measured. As the amount of broken rice kernels increased, rice sample texture became increasingly softer (P hardness was negatively correlated to the percentage of broken kernels in rice samples. Differences in the proportions of broken rice in a milled rice sample play a major role in determining the texture properties of cooked rice. Variations in the moisture migration kinetics between broken and unbroken kernels caused faster hydration of the cores of broken rice kernels, with greater starch leach-out during cooking affecting the texture of the cooked rice. The texture of cooked rice can be controlled, to some extent, by varying the proportion of broken kernels in milled rice. © 2012 Society of Chemical Industry.
Measurement of Weight of Kernels in a Simulated Cylindrical Fuel Compact for HTGR
International Nuclear Information System (INIS)
Kim, Woong Ki; Lee, Young Woo; Kim, Young Min; Kim, Yeon Ku; Eom, Sung Ho; Jeong, Kyung Chai; Cho, Moon Sung; Cho, Hyo Jin; Kim, Joo Hee
2011-01-01
The TRISO-coated fuel particle for the high temperature gas-cooled reactor (HTGR) is composed of a nuclear fuel kernel and outer coating layers. The coated particles are mixed with graphite matrix to make HTGR fuel element. The weight of fuel kernels in an element is generally measured by the chemical analysis or a gamma-ray spectrometer. Although it is accurate to measure the weight of kernels by the chemical analysis, the samples used in the analysis cannot be put again in the fabrication process. Furthermore, radioactive wastes are generated during the inspection procedure. The gamma-ray spectrometer requires an elaborate reference sample to reduce measurement errors induced from the different geometric shape of test sample from that of reference sample. X-ray computed tomography (CT) is an alternative to measure the weight of kernels in a compact nondestructively. In this study, X-ray CT is applied to measure the weight of kernels in a cylindrical compact containing simulated TRISO-coated particles with ZrO 2 kernels. The volume of kernels as well as the number of kernels in the simulated compact is measured from the 3-D density information. The weight of kernels was calculated from the volume of kernels or the number of kernels. Also, the weight of kernels was measured by extracting the kernels from a compact to review the result of the X-ray CT application
Fernández-Delgado, Manuel; Cernadas, Eva; Barro, Senén; Ribeiro, Jorge; Neves, José
2014-02-01
The Direct Kernel Perceptron (DKP) (Fernández-Delgado et al., 2010) is a very simple and fast kernel-based classifier, related to the Support Vector Machine (SVM) and to the Extreme Learning Machine (ELM) (Huang, Wang, & Lan, 2011), whose α-coefficients are calculated directly, without any iterative training, using an analytical closed-form expression which involves only the training patterns. The DKP, which is inspired by the Direct Parallel Perceptron, (Auer et al., 2008), uses a Gaussian kernel and a linear classifier (perceptron). The weight vector of this classifier in the feature space minimizes an error measure which combines the training error and the hyperplane margin, without any tunable regularization parameter. This weight vector can be translated, using a variable change, to the α-coefficients, and both are determined without iterative calculations. We calculate solutions using several error functions, achieving the best trade-off between accuracy and efficiency with the linear function. These solutions for the α coefficients can be considered alternatives to the ELM with a new physical meaning in terms of error and margin: in fact, the linear and quadratic DKP are special cases of the two-class ELM when the regularization parameter C takes the values C=0 and C=∞. The linear DKP is extremely efficient and much faster (over a vast collection of 42 benchmark and real-life data sets) than 12 very popular and accurate classifiers including SVM, Multi-Layer Perceptron, Adaboost, Random Forest and Bagging of RPART decision trees, Linear Discriminant Analysis, K-Nearest Neighbors, ELM, Probabilistic Neural Networks, Radial Basis Function neural networks and Generalized ART. Besides, despite its simplicity and extreme efficiency, DKP achieves higher accuracies than 7 out of 12 classifiers, exhibiting small differences with respect to the best ones (SVM, ELM, Adaboost and Random Forest), which are much slower. Thus, the DKP provides an easy and fast way
Energy Technology Data Exchange (ETDEWEB)
Cheong, Kwang-Ho; Suh, Tae-Suk; Lee, Hyoung-Koo; Choe, Bo-Young [The Catholic Univ. of Korea, Seoul (Korea, Republic of); Kim, Hoi-Nam; Yoon, Sei-Chul [Kangnam St. Mary' s Hospital, Seoul (Korea, Republic of)
2002-07-01
Accurate dose calculation in radiation treatment planning is most important for successful treatment. Since human body is composed of various materials and not an ideal shape, it is not easy to calculate the accurate effective dose in the patients. Many methods have been proposed to solve inhomogeneity and surface contour problems. Monte Carlo simulations are regarded as the most accurate method, but it is not appropriate for routine planning because it takes so much time. Pencil beam kernel based convolution/superposition methods were also proposed to correct those effects. Nowadays, many commercial treatment planning systems have adopted this algorithm as a dose calculation engine. The purpose of this study is to verify the accuracy of the dose calculated from pencil beam kernel based treatment planning system comparing to Monte Carlo simulations and measurements especially in inhomogeneous region. Home-made inhomogeneous phantom, Helax-TMS ver. 6.0 and Monte Carlo code BEAMnrc and DOSXYZnrc were used in this study. In homogeneous media, the accuracy was acceptable but in inhomogeneous media, the errors were more significant. However in general clinical situation, pencil beam kernel based convolution algorithm is thought to be a valuable tool to calculate the dose.
Theoretical developments for interpreting kernel spectral clustering from alternative viewpoints
Directory of Open Access Journals (Sweden)
Diego Peluffo-Ordóñez
2017-08-01
Full Text Available To perform an exploration process over complex structured data within unsupervised settings, the so-called kernel spectral clustering (KSC is one of the most recommended and appealing approaches, given its versatility and elegant formulation. In this work, we explore the relationship between (KSC and other well-known approaches, namely normalized cut clustering and kernel k-means. To do so, we first deduce a generic KSC model from a primal-dual formulation based on least-squares support-vector machines (LS-SVM. For experiments, KSC as well as other consider methods are assessed on image segmentation tasks to prove their usability.
Modelling microwave heating of discrete samples of oil palm kernels
International Nuclear Information System (INIS)
Law, M.C.; Liew, E.L.; Chang, S.L.; Chan, Y.S.; Leo, C.P.
2016-01-01
Highlights: • Microwave (MW) drying of oil palm kernels is experimentally determined and modelled. • MW heating of discrete samples of oil palm kernels (OPKs) is simulated. • OPK heating is due to contact effect, MW interference and heat transfer mechanisms. • Electric field vectors circulate within OPKs sample. • Loosely-packed arrangement improves temperature uniformity of OPKs. - Abstract: Recently, microwave (MW) pre-treatment of fresh palm fruits has showed to be environmentally friendly compared to the existing oil palm milling process as it eliminates the condensate production of palm oil mill effluent (POME) in the sterilization process. Moreover, MW-treated oil palm fruits (OPF) also possess better oil quality. In this work, the MW drying kinetic of the oil palm kernels (OPK) was determined experimentally. Microwave heating/drying of oil palm kernels was modelled and validated. The simulation results show that temperature of an OPK is not the same over the entire surface due to constructive and destructive interferences of MW irradiance. The volume-averaged temperature of an OPK is higher than its surface temperature by 3–7 °C, depending on the MW input power. This implies that point measurement of temperature reading is inadequate to determine the temperature history of the OPK during the microwave heating process. The simulation results also show that arrangement of OPKs in a MW cavity affects the kernel temperature profile. The heating of OPKs were identified to be affected by factors such as local electric field intensity due to MW absorption, refraction, interference, the contact effect between kernels and also heat transfer mechanisms. The thermal gradient patterns of OPKs change as the heating continues. The cracking of OPKs is expected to occur first in the core of the kernel and then it propagates to the kernel surface. The model indicates that drying of OPKs is a much slower process compared to its MW heating. The model is useful
Graphical analyses of connected-kernel scattering equations
International Nuclear Information System (INIS)
Picklesimer, A.
1983-01-01
Simple graphical techniques are employed to obtain a new (simultaneous) derivation of a large class of connected-kernel scattering equations. This class includes the Rosenberg, Bencze-Redish-Sloan, and connected-kernel multiple scattering equations as well as a host of generalizations of these and other equations. The basic result is the application of graphical methods to the derivation of interaction-set equations. This yields a new, simplified form for some members of the class and elucidates the general structural features of the entire class
Reproducing Kernel Method for Solving Nonlinear Differential-Difference Equations
Directory of Open Access Journals (Sweden)
Reza Mokhtari
2012-01-01
Full Text Available On the basis of reproducing kernel Hilbert spaces theory, an iterative algorithm for solving some nonlinear differential-difference equations (NDDEs is presented. The analytical solution is shown in a series form in a reproducing kernel space, and the approximate solution , is constructed by truncating the series to terms. The convergence of , to the analytical solution is also proved. Results obtained by the proposed method imply that it can be considered as a simple and accurate method for solving such differential-difference problems.
Kernel and divergence techniques in high energy physics separations
Bouř, Petr; Kůs, Václav; Franc, Jiří
2017-10-01
Binary decision trees under the Bayesian decision technique are used for supervised classification of high-dimensional data. We present a great potential of adaptive kernel density estimation as the nested separation method of the supervised binary divergence decision tree. Also, we provide a proof of alternative computing approach for kernel estimates utilizing Fourier transform. Further, we apply our method to Monte Carlo data set from the particle accelerator Tevatron at DØ experiment in Fermilab and provide final top-antitop signal separation results. We have achieved up to 82 % AUC while using the restricted feature selection entering the signal separation procedure.
Rebootless Linux Kernel Patching with Ksplice Uptrack at BNL
International Nuclear Information System (INIS)
Hollowell, Christopher; Pryor, James; Smith, Jason
2012-01-01
Ksplice/Oracle Uptrack is a software tool and update subscription service which allows system administrators to apply security and bug fix patches to the Linux kernel running on servers/workstations without rebooting them. The RHIC/ATLAS Computing Facility (RACF) at Brookhaven National Laboratory (BNL) has deployed Uptrack on nearly 2,000 hosts running Scientific Linux and Red Hat Enterprise Linux. The use of this software has minimized downtime, and increased our security posture. In this paper, we provide an overview of Ksplice's rebootless kernel patch creation/insertion mechanism, and our experiences with Uptrack.
Employment of kernel methods on wind turbine power performance assessment
DEFF Research Database (Denmark)
Skrimpas, Georgios Alexandros; Sweeney, Christian Walsted; Marhadi, Kun S.
2015-01-01
A power performance assessment technique is developed for the detection of power production discrepancies in wind turbines. The method employs a widely used nonparametric pattern recognition technique, the kernel methods. The evaluation is based on the trending of an extracted feature from...... the kernel matrix, called similarity index, which is introduced by the authors for the first time. The operation of the turbine and consequently the computation of the similarity indexes is classified into five power bins offering better resolution and thus more consistent root cause analysis. The accurate...
Sparse kernel orthonormalized PLS for feature extraction in large datasets
DEFF Research Database (Denmark)
Arenas-García, Jerónimo; Petersen, Kaare Brandt; Hansen, Lars Kai
2006-01-01
In this paper we are presenting a novel multivariate analysis method for large scale problems. Our scheme is based on a novel kernel orthonormalized partial least squares (PLS) variant for feature extraction, imposing sparsity constrains in the solution to improve scalability. The algorithm...... is tested on a benchmark of UCI data sets, and on the analysis of integrated short-time music features for genre prediction. The upshot is that the method has strong expressive power even with rather few features, is clearly outperforming the ordinary kernel PLS, and therefore is an appealing method...
Directory of Open Access Journals (Sweden)
Janice R. LIMA
1999-01-01
Full Text Available Amêndoas de castanha-de-caju fritas e salgadas foram acondicionadas em três embalagens flexíveis (PP/PE=polipropileno/polietileno; PETmet/PE= polietileno tereftalato metalizado/polietileno; PET/Al/PEBD= polietileno tereftalato/alumínio/polietileno de baixa densidade com diferentes propriedades de barreira ao vapor de água e ao oxigênio. As amêndoas foram armazenadas durante 1 ano, a 30° C e 80% de umidade relativa. No final do período de 1 ano de armazenamento, realizou-se análise sensorial descritiva quantitativa (ADQ. Os termos descritivos levantados para caracterização sensorial das amêndoas foram, para aparência: cor torrada, uniformidade de cor e rugosidade; para aroma: castanha torrada, doce, ranço e velho; para sabor: castanha torrada, doce, ranço, velho, sal e amargo; para textura: crocância. Observou-se que os fatores mais diretamente responsáveis pela perda de qualidade sensorial das amêndoas de castanha-de-caju foram desenvolvimento de aroma e sabor de velho e de ranço, perda de sabor e aroma de castanha torrada e perda de crocância. Após o período de armazenamento, estes fatores foram observados com maior intensidade nas amêndoas embaladas em PP/PE.Shelled, roasted and salted cashew nut kernels were packaged in three different flexible materials (PP/PE= polypropylene / polyethylene; PETmet/PE= metallized polyethylene terephthalate / polyethylene; PET/Al/LDPE= polyethylene terephthalate / aluminum foil / low density polyethylene , with different barrier properties. Kernels were stored for one year at 30° C and 80% relative humidity. Quantitative descriptive sensory analysis (QDA were performed at the end of storage time. Descriptive terms obtained for kernels characterization were brown color, color uniformity and rugosity for appearance; toasted kernel, sweet, old and rancidity for odor; toasted kernel, sweet, old rancidity, salt and bitter for taste, crispness for texture. QDA showed that factors responsible
Directory of Open Access Journals (Sweden)
Chuang Lin
2015-01-01
Full Text Available Kernel Locality Preserving Projection (KLPP algorithm can effectively preserve the neighborhood structure of the database using the kernel trick. We have known that supervised KLPP (SKLPP can preserve within-class geometric structures by using label information. However, the conventional SKLPP algorithm endures the kernel selection which has significant impact on the performances of SKLPP. In order to overcome this limitation, a method named supervised kernel optimized LPP (SKOLPP is proposed in this paper, which can maximize the class separability in kernel learning. The proposed method maps the data from the original space to a higher dimensional kernel space using a data-dependent kernel. The adaptive parameters of the data-dependent kernel are automatically calculated through optimizing an objective function. Consequently, the nonlinear features extracted by SKOLPP have larger discriminative ability compared with SKLPP and are more adaptive to the input data. Experimental results on ORL, Yale, AR, and Palmprint databases showed the effectiveness of the proposed method.
Aspergillus flavus and Fusarium verticillioides infect maize kernels and contaminate them with the mycotoxins aflatoxin and fumonisin, respectively. Combined histological examination of fungal colonization and transcriptional changes in maize kernels at 4, 12, 24, 48, and 72 hours post inoculation (...
Directory of Open Access Journals (Sweden)
Hailun Wang
2017-01-01
Full Text Available Support vector regression algorithm is widely used in fault diagnosis of rolling bearing. A new model parameter selection method for support vector regression based on adaptive fusion of the mixed kernel function is proposed in this paper. We choose the mixed kernel function as the kernel function of support vector regression. The mixed kernel function of the fusion coefficients, kernel function parameters, and regression parameters are combined together as the parameters of the state vector. Thus, the model selection problem is transformed into a nonlinear system state estimation problem. We use a 5th-degree cubature Kalman filter to estimate the parameters. In this way, we realize the adaptive selection of mixed kernel function weighted coefficients and the kernel parameters, the regression parameters. Compared with a single kernel function, unscented Kalman filter (UKF support vector regression algorithms, and genetic algorithms, the decision regression function obtained by the proposed method has better generalization ability and higher prediction accuracy.
The heating of UO_2 kernels in argon gas medium on the physical properties of sintered UO_2 kernels
International Nuclear Information System (INIS)
Damunir; Sri Rinanti Susilowati; Ariyani Kusuma Dewi
2015-01-01
The heating of UO_2 kernels in argon gas medium on the physical properties of sinter UO_2 kernels was conducted. The heated of the UO_2 kernels was conducted in a sinter reactor of a bed type. The sample used was the UO_2 kernels resulted from the reduction results at 800 °C temperature for 3 hours that had the density of 8.13 g/cm"3; porosity of 0.26; O/U ratio of 2.05; diameter of 1146 μm and sphericity of 1.05. The sample was put into a sinter reactor, then it was vacuumed by flowing the argon gas at 180 mmHg pressure to drain the air from the reactor. After that, the cooling water and argon gas were continuously flowed with the pressure of 5 mPa with 1.5 liter/minutes velocity. The reactor temperature was increased and variated at 1200-1500 °C temperature and for 1-4 hours. The sinters UO_2 kernels resulted from the study were analyzed in term of their physical properties including the density, porosity, diameter, sphericity, and specific surface area. The density was analyzed using pycnometer with CCl_4 solution. The porosity was determined using Haynes equation. The diameters and sphericity were showed using the Dino-lite microscope. The specific surface area was determined using surface area meter Nova-1000. The obtained products showed the the heating of UO_2 kernel in argon gas medium were influenced on the physical properties of sinters UO_2 kernel. The condition of best relatively at 1400 °C temperature and 2 hours time. The product resulted from the study was relatively at its best when heating was conducted at 1400 °C temperature and 2 hours time, produced sinters UO_2 kernel with density of 10.14 gr/ml; porosity of 7 %; diameters of 893 μm; sphericity of 1.07 and specific surface area of 4.68 m"2/g with solidify shrinkage of 22 %. (author)
International Nuclear Information System (INIS)
Altomare, S.; Minton, G.
1975-02-01
PANDA is a new two-group one-dimensional (slab/cylinder) neutron diffusion code designed to replace and extend the FAB series. PANDA allows for the nonlinear effects of xenon, enthalpy and Doppler. Fuel depletion is allowed. PANDA has a completely general search facility which will seek criticality, maximize reactivity, or minimize peaking. Any single parameter may be varied in a search. PANDA is written in FORTRAN IV, and as such is nearly machine independent. However, PANDA has been written with the present limitations of the Westinghouse CDC-6600 system in mind. Most computation loops are very short, and the code is less than half the useful 6600 memory size so that two jobs can reside in the core at once. (auth)
Biasing anisotropic scattering kernels for deep-penetration Monte Carlo calculations
International Nuclear Information System (INIS)
Carter, L.L.; Hendricks, J.S.
1983-01-01
The exponential transform is often used to improve the efficiency of deep-penetration Monte Carlo calculations. This technique is usually implemented by biasing the distance-to-collision kernel of the transport equation, but leaving the scattering kernel unchanged. Dwivedi obtained significant improvements in efficiency by biasing an isotropic scattering kernel as well as the distance-to-collision kernel. This idea is extended to anisotropic scattering, particularly the highly forward Klein-Nishina scattering of gamma rays
The dipole form of the gluon part of the BFKL kernel
International Nuclear Information System (INIS)
Fadin, V.S.; Fiore, R.; Grabovsky, A.V.; Papa, A.
2007-01-01
The dipole form of the gluon part of the color singlet BFKL kernel in the next-to-leading order (NLO) is obtained in the coordinate representation by direct transfer from the momentum representation, where the kernel was calculated before. With this paper the transformation of the NLO BFKL kernel to the dipole form, started a few months ago with the quark part of the kernel, is completed
Directory of Open Access Journals (Sweden)
Hjalmar Rosengren
2006-12-01
Full Text Available We study multivariable Christoffel-Darboux kernels, which may be viewed as reproducing kernels for antisymmetric orthogonal polynomials, and also as correlation functions for products of characteristic polynomials of random Hermitian matrices. Using their interpretation as reproducing kernels, we obtain simple proofs of Pfaffian and determinant formulas, as well as Schur polynomial expansions, for such kernels. In subsequent work, these results are applied in combinatorics (enumeration of marked shifted tableaux and number theory (representation of integers as sums of squares.
Coupling individual kernel-filling processes with source-sink interactions into GREENLAB-Maize.
Ma, Yuntao; Chen, Youjia; Zhu, Jinyu; Meng, Lei; Guo, Yan; Li, Baoguo; Hoogenboom, Gerrit
2018-02-13
Failure to account for the variation of kernel growth in a cereal crop simulation model may cause serious deviations in the estimates of crop yield. The goal of this research was to revise the GREENLAB-Maize model to incorporate source- and sink-limited allocation approaches to simulate the dry matter accumulation of individual kernels of an ear (GREENLAB-Maize-Kernel). The model used potential individual kernel growth rates to characterize the individual potential sink demand. The remobilization of non-structural carbohydrates from reserve organs to kernels was also incorporated. Two years of field experiments were conducted to determine the model parameter values and to evaluate the model using two maize hybrids with different plant densities and pollination treatments. Detailed observations were made on the dimensions and dry weights of individual kernels and other above-ground plant organs throughout the seasons. Three basic traits characterizing an individual kernel were compared on simulated and measured individual kernels: (1) final kernel size; (2) kernel growth rate; and (3) duration of kernel filling. Simulations of individual kernel growth closely corresponded to experimental data. The model was able to reproduce the observed dry weight of plant organs well. Then, the source-sink dynamics and the remobilization of carbohydrates for kernel growth were quantified to show that remobilization processes accompanied source-sink dynamics during the kernel-filling process. We conclude that the model may be used to explore options for optimizing plant kernel yield by matching maize management to the environment, taking into account responses at the level of individual kernels. © The Author(s) 2018. Published by Oxford University Press on behalf of the Annals of Botany Company. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
International Nuclear Information System (INIS)
Gara, P.; Martin, E.
1983-01-01
The CANAL code presented here optimizes a realistic iron free extraction channel which has to provide a given transversal magnetic field law in the median plane: the current bars may be curved, have finite lengths and cooling ducts and move in a restricted transversal area; terminal connectors may be added, images of the bars in pole pieces may be included. A special option optimizes a real set of circular coils [fr
Python for Development of OpenMP and CUDA Kernels for Multidimensional Data
International Nuclear Information System (INIS)
Bell, Zane W.; Davidson, Gregory G.; D'Azevedo, Ed F.; Evans, Thomas M.; Joubert, Wayne; Munro, John K. Jr.; Patlolla, Dilip Reddy; Vacaliuc, Bogdan
2011-01-01
Design of data structures for high performance computing (HPC) is one of the principal challenges facing researchers looking to utilize heterogeneous computing machinery. Heterogeneous systems derive cost, power, and speed efficiency by being composed of the appropriate hardware for the task. Yet, each type of processor requires a specific organization of the application state in order to achieve peak performance. Discovering this and refactoring the code can be a challenging and time-consuming task for the researcher, as the data structures and the computational model must be co-designed. We present a methodology that uses Python as the environment for which to explore tradeoffs in both the data structure design as well as the code executing on the computation accelerator. Our method enables multi-dimensional arrays to be used effectively in any target environment. We have chosen to focus on OpenMP and CUDA environments, thus exploring the development of optimized kernels for the two most common classes of computing hardware available today: multi-core CPU and GPU. Python s large palette of file and network access routines, its associative indexing syntax and support for common HPC environments makes it relevant for diverse hardware ranging from laptops through computing clusters to the highest performance supercomputers. Our work enables researchers to accelerate the development of their codes on the computing hardware of their choice.
Flexible Scheduling by Deadline Inheritance in Soft Real Time Kernels
Jansen, P.G.; Wygerink, Emiel
1996-01-01
Current Hard Real Time (HRT) kernels have their timely behaviour guaranteed on the cost of a rather restrictive use of the available resources. This makes HRT scheduling techniques inadequate for use in Soft Real Time (SRT) environment where we can make a considerable profit by a better and more
Mycological deterioration of stored palm kernels recovered from oil ...
African Journals Online (AJOL)
Palm kernels obtained from Pioneer Oil Mill Ltd. were stored for eight (8) weeks and examined for their microbiological quality and proximate composition. Seven (7) different fungal species were isolated by serial dilution plate technique. The fungal species included Aspergillus flavus Link; A nidulans Eidem; A niger ...
Metabolite identification through multiple kernel learning on fragmentation trees.
Shen, Huibin; Dührkop, Kai; Böcker, Sebastian; Rousu, Juho
2014-06-15
Metabolite identification from tandem mass spectrometric data is a key task in metabolomics. Various computational methods have been proposed for the identification of metabolites from tandem mass spectra. Fragmentation tree methods explore the space of possible ways in which the metabolite can fragment, and base the metabolite identification on scoring of these fragmentation trees. Machine learning methods have been used to map mass spectra to molecular fingerprints; predicted fingerprints, in turn, can be used to score candidate molecular structures. Here, we combine fragmentation tree computations with kernel-based machine learning to predict molecular fingerprints and identify molecular structures. We introduce a family of kernels capturing the similarity of fragmentation trees, and combine these kernels using recently proposed multiple kernel learning approaches. Experiments on two large reference datasets show that the new methods significantly improve molecular fingerprint prediction accuracy. These improvements result in better metabolite identification, doubling the number of metabolites ranked at the top position of the candidates list. © The Author 2014. Published by Oxford University Press.
Notes on a storage manager for the Clouds kernel
Pitts, David V.; Spafford, Eugene H.
1986-01-01
The Clouds project is research directed towards producing a reliable distributed computing system. The initial goal is to produce a kernel which provides a reliable environment with which a distributed operating system can be built. The Clouds kernal consists of a set of replicated subkernels, each of which runs on a machine in the Clouds system. Each subkernel is responsible for the management of resources on its machine; the subkernal components communicate to provide the cooperation necessary to meld the various machines into one kernel. The implementation of a kernel-level storage manager that supports reliability is documented. The storage manager is a part of each subkernel and maintains the secondary storage residing at each machine in the distributed system. In addition to providing the usual data transfer services, the storage manager ensures that data being stored survives machine and system crashes, and that the secondary storage of a failed machine is recovered (made consistent) automatically when the machine is restarted. Since the storage manager is part of the Clouds kernel, efficiency of operation is also a concern.
On Convergence of Kernel Density Estimates in Particle Filtering
Czech Academy of Sciences Publication Activity Database
Coufal, David
2016-01-01
Roč. 52, č. 5 (2016), s. 735-756 ISSN 0023-5954 Grant - others:GA ČR(CZ) GA16-03708S; SVV(CZ) 260334/2016 Institutional support: RVO:67985807 Keywords : Fourier analysis * kernel methods * particle filter Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 0.379, year: 2016
Screening of the kernels of Pentadesma butyracea from various ...
African Journals Online (AJOL)
Gwla10
Joseph D. Hounhouigan. 2. 1Laboratoire de .... laboratory. Kernels were washed and dried at 45°C for 72 h before analysis. ... generated values allow calculating the various shape ... (LLYOD Instruments, USA) fit with a 0.42 cm thick blade with a triangular ... vacuum. Extraction was run in triplicate on germ, albumen and.
Some engineering properties of shelled and kernel tea ( Camellia ...
African Journals Online (AJOL)
Some engineering properties (size dimensions, sphericity, volume, bulk and true densities, friction coefficient, colour characteristics and mechanical behaviour as rupture ... The static coefficients of friction of shelled and kernel tea seeds for the large and small sizes higher values for rubber than the other friction surfaces.
Deep sequencing of RNA from ancient maize kernels
DEFF Research Database (Denmark)
Fordyce, Sarah Louise; Avila Arcos, Maria del Carmen; Rasmussen, Morten
2013-01-01
The characterization of biomolecules from ancient samples can shed otherwise unobtainable insights into the past. Despite the fundamental role of transcriptomal change in evolution, the potential of ancient RNA remains unexploited - perhaps due to dogma associated with the fragility of RNA. We hy...... maize kernels. The results suggest that ancient seed transcriptomics may offer a powerful new tool with which to study plant domestication....
Effect of Coconut ( cocus Nucifera ) and Palm Kernel ( eleasis ...
African Journals Online (AJOL)
Effect of Coconut ( cocus Nucifera ) and Palm Kernel ( eleasis Guinensis ) Oil Supplmented Diets on Serum Lipid Profile of Albino Wistar Rats. ... were fed normal rat pellet. At the end of the feeding period, animals were anaesthetized under chloroform vapor, dissected and blood obtained via cardiac puncture into tubes.
Calculation of Volterra kernels for solutions of nonlinear differential equations
van Hemmen, JL; Kistler, WM; Thomas, EGF
2000-01-01
We consider vector-valued autonomous differential equations of the form x' = f(x) + phi with analytic f and investigate the nonanticipative solution operator phi bar right arrow A(phi) in terms of its Volterra series. We show that Volterra kernels of order > 1 occurring in the series expansion of
Moderate deviations principles for the kernel estimator of ...
African Journals Online (AJOL)
Abstract. The aim of this paper is to provide pointwise and uniform moderate deviations principles for the kernel estimator of a nonrandom regression function. Moreover, we give an application of these moderate deviations principles to the construction of condence regions for the regression function. Resume. L'objectif de ...
Hollow microspheres with a tungsten carbide kernel for PEMFC application.
d'Arbigny, Julien Bernard; Taillades, Gilles; Marrony, Mathieu; Jones, Deborah J; Rozière, Jacques
2011-07-28
Tungsten carbide microspheres comprising an outer shell and a compact kernel prepared by a simple hydrothermal method exhibit very high surface area promoting a high dispersion of platinum nanoparticles, and an exceptionally high electrochemically active surface area (EAS) stability compared to the usual Pt/C electrocatalysts used for PEMFC application.
Fractional quantum integral operator with general kernels and applications
Babakhani, Azizollah; Neamaty, Abdolali; Yadollahzadeh, Milad; Agahi, Hamzeh
In this paper, we first introduce the concept of fractional quantum integral with general kernels, which generalizes several types of fractional integrals known from the literature. Then we give more general versions of some integral inequalities for this operator, thus generalizing some previous results obtained by many researchers.2,8,25,29,30,36
Optimizing Multiple Kernel Learning for the Classification of UAV Data
Directory of Open Access Journals (Sweden)
Caroline M. Gevaert
2016-12-01
Full Text Available Unmanned Aerial Vehicles (UAVs are capable of providing high-quality orthoimagery and 3D information in the form of point clouds at a relatively low cost. Their increasing popularity stresses the necessity of understanding which algorithms are especially suited for processing the data obtained from UAVs. The features that are extracted from the point cloud and imagery have different statistical characteristics and can be considered as heterogeneous, which motivates the use of Multiple Kernel Learning (MKL for classification problems. In this paper, we illustrate the utility of applying MKL for the classification of heterogeneous features obtained from UAV data through a case study of an informal settlement in Kigali, Rwanda. Results indicate that MKL can achieve a classification accuracy of 90.6%, a 5.2% increase over a standard single-kernel Support Vector Machine (SVM. A comparison of seven MKL methods indicates that linearly-weighted kernel combinations based on simple heuristics are competitive with respect to computationally-complex, non-linear kernel combination methods. We further underline the importance of utilizing appropriate feature grouping strategies for MKL, which has not been directly addressed in the literature, and we propose a novel, automated feature grouping method that achieves a high classification accuracy for various MKL methods.
Corruption clubs: empirical evidence from kernel density estimates
Herzfeld, T.; Weiss, Ch.
2007-01-01
A common finding of many analytical models is the existence of multiple equilibria of corruption. Countries characterized by the same economic, social and cultural background do not necessarily experience the same levels of corruption. In this article, we use Kernel Density Estimation techniques to
Finite Gaussian Mixture Approximations to Analytically Intractable Density Kernels
DEFF Research Database (Denmark)
Khorunzhina, Natalia; Richard, Jean-Francois
The objective of the paper is that of constructing finite Gaussian mixture approximations to analytically intractable density kernels. The proposed method is adaptive in that terms are added one at the time and the mixture is fully re-optimized at each step using a distance measure that approxima...
Disinfection studies of Nahar (Mesua ferrea) seed kernel oil using ...
African Journals Online (AJOL)
GREGORY
2011-12-16
Dec 16, 2011 ... with a k value of -0.040. Key words: Nahar (Mesua ferrea) seed kernel oil, extraction, gum Arabic, disinfection, kinetics. INTRODUCTION. Disinfection plays a key role in the reclamation and reuse of wastewater for eliminating infectious diseases, this, in part, augments domestic water supply and decreases ...
Improved Interpolation Kernels for Super-resolution Algorithms
DEFF Research Database (Denmark)
Rasti, Pejman; Orlova, Olga; Tamberg, Gert
2016-01-01
Super resolution (SR) algorithms are widely used in forensics investigations to enhance the resolution of images captured by surveillance cameras. Such algorithms usually use a common interpolation algorithm to generate an initial guess for the desired high resolution (HR) image. This initial guess...... when their original interpolation kernel is replaced by the ones introduced in this work....
Briquetting of Palm Kernel Shell | Ugwu | Journal of Applied ...
African Journals Online (AJOL)
In several developing countries, briquettes from agricultural residues contribute significantly to the energy mix especially for small scale and household requirements. In this work, briquettes were produced from Palm kernel shell. This was achieved by carbonising the shell to get the charcoal followed by the pulverization of ...
Controller synthesis for L2 behaviors using rational kernel representations
Mutsaers, M.E.C.; Weiland, S.
2008-01-01
This paper considers the controller synthesis problem for the class of linear time-invariant L2 behaviors. We introduce classes of LTI L2 systems whose behavior can be represented as the kernel of a rational operator. Given a plant and a controlled system in this class, an algorithm is developed
Recent sea level change analysed with kernel EOF
DEFF Research Database (Denmark)
Nielsen, Allan Aasbjerg; Andersen, Ole Baltazar; Knudsen, Per
2009-01-01
-2008. Preliminary analysis shows some interesting features related to climate change and particularly the pulsing of the El Niño/Southern Oscillation. Large scale ocean events associated with the El Niño/Southern Oscillation related signals are conveniently concentrated in the first SSH kernel EOF modes....
Polynomial kernels for deletion to classes of acyclic digraphs
Mnich, Matthias; van Leeuwen, E.J.
2017-01-01
We consider the problem to find a set X of vertices (or arcs) with |X| ≤ k in a given digraph G such that D = G − X is an acyclic digraph. In its generality, this is Directed Feedback Vertex Set (or Directed Feedback Arc Set); the existence of a polynomial kernel for these problems is a notorious
Nutritional evaluation of fermented palm kernel cake using red tilapia
African Journals Online (AJOL)
The use of palm kernel cake (PKC) and other plant residues in fish feeding especially under extensive aquaculture have been in practice for a long time. On the other hand, the use of microbial-based feedstuff is increasing. In this study, the performance of red tilapia raised on Trichoderma longibrachiatum fermented PKC ...
Preparation and characterization of active carbon using palm kernel ...
African Journals Online (AJOL)
Activated carbons were prepared from Palm kernel shells. Carbonization temperature was 6000C, at a residence time of 5 min for each process. Chemical activation was done by heating a mixture of carbonized material and the activating agents at a temperature of 700C to form a paste, followed by subsequent cooling and ...
Matrix kernels for MEG and EEG source localization and imaging
International Nuclear Information System (INIS)
Mosher, J.C.; Lewis, P.S.; Leahy, R.M.
1994-01-01
The most widely used model for electroencephalography (EEG) and magnetoencephalography (MEG) assumes a quasi-static approximation of Maxwell's equations and a piecewise homogeneous conductor model. Both models contain an incremental field element that linearly relates an incremental source element (current dipole) to the field or voltage at a distant point. The explicit form of the field element is dependent on the head modeling assumptions and sensor configuration. Proper characterization of this incremental element is crucial to the inverse problem. The field element can be partitioned into the product of a vector dependent on sensor characteristics and a matrix kernel dependent only on head modeling assumptions. We present here the matrix kernels for the general boundary element model (BEM) and for MEG spherical models. We show how these kernels are easily interchanged in a linear algebraic framework that includes sensor specifics such as orientation and gradiometer configuration. We then describe how this kernel is easily applied to ''gain'' or ''transfer'' matrices used in multiple dipole and source imaging models
An Adaptive Genetic Association Test Using Double Kernel Machines.
Zhan, Xiang; Epstein, Michael P; Ghosh, Debashis
2015-10-01
Recently, gene set-based approaches have become very popular in gene expression profiling studies for assessing how genetic variants are related to disease outcomes. Since most genes are not differentially expressed, existing pathway tests considering all genes within a pathway suffer from considerable noise and power loss. Moreover, for a differentially expressed pathway, it is of interest to select important genes that drive the effect of the pathway. In this article, we propose an adaptive association test using double kernel machines (DKM), which can both select important genes within the pathway as well as test for the overall genetic pathway effect. This DKM procedure first uses the garrote kernel machines (GKM) test for the purposes of subset selection and then the least squares kernel machine (LSKM) test for testing the effect of the subset of genes. An appealing feature of the kernel machine framework is that it can provide a flexible and unified method for multi-dimensional modeling of the genetic pathway effect allowing for both parametric and nonparametric components. This DKM approach is illustrated with application to simulated data as well as to data from a neuroimaging genetics study.
Evaluating Equating Results: Percent Relative Error for Chained Kernel Equating
Jiang, Yanlin; von Davier, Alina A.; Chen, Haiwen
2012-01-01
This article presents a method for evaluating equating results. Within the kernel equating framework, the percent relative error (PRE) for chained equipercentile equating was computed under the nonequivalent groups with anchor test (NEAT) design. The method was applied to two data sets to obtain the PRE, which can be used to measure equating…
Structured Kernel Subspace Learning for Autonomous Robot Navigation.
Kim, Eunwoo; Choi, Sungjoon; Oh, Songhwai
2018-02-14
This paper considers two important problems for autonomous robot navigation in a dynamic environment, where the goal is to predict pedestrian motion and control a robot with the prediction for safe navigation. While there are several methods for predicting the motion of a pedestrian and controlling a robot to avoid incoming pedestrians, it is still difficult to safely navigate in a dynamic environment due to challenges, such as the varying quality and complexity of training data with unwanted noises. This paper addresses these challenges simultaneously by proposing a robust kernel subspace learning algorithm based on the recent advances in nuclear-norm and l 1 -norm minimization. We model the motion of a pedestrian and the robot controller using Gaussian processes. The proposed method efficiently approximates a kernel matrix used in Gaussian process regression by learning low-rank structured matrix (with symmetric positive semi-definiteness) to find an orthogonal basis, which eliminates the effects of erroneous and inconsistent data. Based on structured kernel subspace learning, we propose a robust motion model and motion controller for safe navigation in dynamic environments. We evaluate the proposed robust kernel learning in various tasks, including regression, motion prediction, and motion control problems, and demonstrate that the proposed learning-based systems are robust against outliers and outperform existing regression and navigation methods.
Bayesian Frequency Domain Identification of LTI Systems with OBFs kernels
Darwish, M.A.H.; Lataire, J.P.G.; Tóth, R.
2017-01-01
Regularised Frequency Response Function (FRF) estimation based on Gaussian process regression formulated directly in the frequency-domain has been introduced recently The underlying approach largely depends on the utilised kernel function, which encodes the relevant prior knowledge on the system
Single pass kernel k-means clustering method
Indian Academy of Sciences (India)
In unsupervised classiﬁcation, kernel -means clustering method has been shown to perform better than conventional -means clustering method in ... 518501, India; Department of Computer Science and Engineering, Jawaharlal Nehru Technological University, Anantapur College of Engineering, Anantapur 515002, India ...
Szegö Kernels and Asymptotic Expansions for Legendre Polynomials
Directory of Open Access Journals (Sweden)
Roberto Paoletti
2017-01-01
Full Text Available We present a geometric approach to the asymptotics of the Legendre polynomials Pk,n+1, based on the Szegö kernel of the Fermat quadric hypersurface, leading to complete asymptotic expansions holding on expanding subintervals of [-1,1].
Magnetic resonance imaging of single rice kernels during cooking
Mohoric, A.; Vergeldt, F.J.; Gerkema, E.; Jager, de P.A.; Duynhoven, van J.P.M.; Dalen, van G.; As, van H.
2004-01-01
The RARE imaging method was used to monitor the cooking of single rice kernels in real time and with high spatial resolution in three dimensions. The imaging sequence is optimized for rapid acquisition of signals with short relaxation times using centered out RARE. Short scan time and high spatial
Optimizing memory-bound SYMV kernel on GPU hardware accelerators
Abdelfattah, Ahmad; Dongarra, Jack; Keyes, David E.; Ltaief, Hatem
2013-01-01
and increasing bandwidth, our preliminary asymptotic results show 3.5x and 2.5x fold speedups over the similar CUBLAS 4.0 kernel, and 7-8% and 30% fold improvement over the Matrix Algebra on GPU and Multicore Architectures (MAGMA) library in single and double
Visualization of nonlinear kernel models in neuroimaging by sensitivity maps
DEFF Research Database (Denmark)
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 on v...
Effects of de-oiled palm kernel cake based fertilizers on sole maize ...
African Journals Online (AJOL)
A study was conducted to determine the effect of de-oiled palm kernel cake based fertilizer formulations on the yield of sole maize and cassava crops. Two de-oiled palm kernel cake based fertilizer formulations A and B were compounded from different proportions of de-oiled palm kernel cake, urea, muriate of potash and ...
DEFF Research Database (Denmark)
Chen, Tianshi; Andersen, Martin Skovgaard; Ljung, Lennart
2014-01-01
Model estimation and structure detection with short data records are two issues that receive increasing interests in System Identification. In this paper, a multiple kernel-based regularization method is proposed to handle those issues. Multiple kernels are conic combinations of fixed kernels...
Differential metabolome analysis of field-grown maize kernels in response to drought stress
Drought stress constrains maize kernel development and can exacerbate aflatoxin contamination. In order to identify drought responsive metabolites and explore pathways involved in kernel responses, a metabolomics analysis was conducted on kernels from a drought tolerant line, Lo964, and a sensitive ...
Using the Intel Math Kernel Library on Peregrine | High-Performance
Computing | NREL the Intel Math Kernel Library on Peregrine Using the Intel Math Kernel Library on Peregrine Learn how to use the Intel Math Kernel Library (MKL) with Peregrine system software. MKL architectures. Core math functions in MKL include BLAS, LAPACK, ScaLAPACK, sparse solvers, fast Fourier
Kernel based pattern analysis methods using eigen-decompositions for reading Icelandic sagas
DEFF Research Database (Denmark)
Christiansen, Asger Nyman; Carstensen, Jens Michael
We want to test the applicability of kernel based eigen-decomposition methods, compared to the traditional eigen-decomposition methods. We have implemented and tested three kernel based methods methods, namely PCA, MAF and MNF, all using a Gaussian kernel. We tested the methods on a multispectral...... image of a page in the book 'hauksbok', which contains Icelandic sagas....
International Nuclear Information System (INIS)
Drago, A.; Klersy, R.; Simoni, O.; Schrader, K.H.
1975-08-01
Experimental observations on unidirectional UO 2 kernel migration in TRISO type coated particle fuels are reported. An analysis of the experimental results on the basis of data and models from the literature is reported. The stoichiometric composition of the kernel is considered the main parameter that, associated with a temperature gradient, controls the unidirectional kernel migration
Occurrence of 'super soft' wheat kernel texture in hexaploid and tetraploid wheats
Wheat kernel texture is a key trait that governs milling performance, flour starch damage, flour particle size, flour hydration properties, and baking quality. Kernel texture is commonly measured using the Perten Single Kernel Characterization System (SKCS). The SKCS returns texture values (Hardness...
Genome-wide Association Analysis of Kernel Weight in Hard Winter Wheat
Wheat kernel weight is an important and heritable component of wheat grain yield and a key predictor of flour extraction. Genome-wide association analysis was conducted to identify genomic regions associated with kernel weight and kernel weight environmental response in 8 trials of 299 hard winter ...
Gaussian interaction profile kernels for predicting drug-target interaction.
van Laarhoven, Twan; Nabuurs, Sander B; Marchiori, Elena
2011-11-01
The in silico prediction of potential interactions between drugs and target proteins is of core importance for the identification of new drugs or novel targets for existing drugs. However, only a tiny portion of all drug-target pairs in current datasets are experimentally validated interactions. This motivates the need for developing computational methods that predict true interaction pairs with high accuracy. We show that a simple machine learning method that uses the drug-target network as the only source of information is capable of predicting true interaction pairs with high accuracy. Specifically, we introduce interaction profiles of drugs (and of targets) in a network, which are binary vectors specifying the presence or absence of interaction with every target (drug) in that network. We define a kernel on these profiles, called the Gaussian Interaction Profile (GIP) kernel, and use a simple classifier, (kernel) Regularized Least Squares (RLS), for prediction drug-target interactions. We test comparatively the effectiveness of RLS with the GIP kernel on four drug-target interaction networks used in previous studies. The proposed algorithm achieves area under the precision-recall curve (AUPR) up to 92.7, significantly improving over results of state-of-the-art methods. Moreover, we show that using also kernels based on chemical and genomic information further increases accuracy, with a neat improvement on small datasets. These results substantiate the relevance of the network topology (in the form of interaction profiles) as source of information for predicting drug-target interactions. Software and Supplementary Material are available at http://cs.ru.nl/~tvanlaarhoven/drugtarget2011/. tvanlaarhoven@cs.ru.nl; elenam@cs.ru.nl. Supplementary data are available at Bioinformatics online.
A kernel for open source drug discovery in tropical diseases.
Ortí, Leticia; Carbajo, Rodrigo J; Pieper, Ursula; Eswar, Narayanan; Maurer, Stephen M; Rai, Arti K; Taylor, Ginger; Todd, Matthew H; Pineda-Lucena, Antonio; Sali, Andrej; Marti-Renom, Marc A
2009-01-01
Conventional patent-based drug development incentives work badly for the developing world, where commercial markets are usually small to non-existent. For this reason, the past decade has seen extensive experimentation with alternative R&D institutions ranging from private-public partnerships to development prizes. Despite extensive discussion, however, one of the most promising avenues-open source drug discovery-has remained elusive. We argue that the stumbling block has been the absence of a critical mass of preexisting work that volunteers can improve through a series of granular contributions. Historically, open source software collaborations have almost never succeeded without such "kernels". HERE, WE USE A COMPUTATIONAL PIPELINE FOR: (i) comparative structure modeling of target proteins, (ii) predicting the localization of ligand binding sites on their surfaces, and (iii) assessing the similarity of the predicted ligands to known drugs. Our kernel currently contains 143 and 297 protein targets from ten pathogen genomes that are predicted to bind a known drug or a molecule similar to a known drug, respectively. The kernel provides a source of potential drug targets and drug candidates around which an online open source community can nucleate. Using NMR spectroscopy, we have experimentally tested our predictions for two of these targets, confirming one and invalidating the other. The TDI kernel, which is being offered under the Creative Commons attribution share-alike license for free and unrestricted use, can be accessed on the World Wide Web at http://www.tropicaldisease.org. We hope that the kernel will facilitate collaborative efforts towards the discovery of new drugs against parasites that cause tropical diseases.
Adaptive kernel regression for freehand 3D ultrasound reconstruction
Alshalalfah, Abdel-Latif; Daoud, Mohammad I.; Al-Najar, Mahasen
2017-03-01
Freehand three-dimensional (3D) ultrasound imaging enables low-cost and flexible 3D scanning of arbitrary-shaped organs, where the operator can freely move a two-dimensional (2D) ultrasound probe to acquire a sequence of tracked cross-sectional images of the anatomy. Often, the acquired 2D ultrasound images are irregularly and sparsely distributed in the 3D space. Several 3D reconstruction algorithms have been proposed to synthesize 3D ultrasound volumes based on the acquired 2D images. A challenging task during the reconstruction process is to preserve the texture patterns in the synthesized volume and ensure that all gaps in the volume are correctly filled. This paper presents an adaptive kernel regression algorithm that can effectively reconstruct high-quality freehand 3D ultrasound volumes. The algorithm employs a kernel regression model that enables nonparametric interpolation of the voxel gray-level values. The kernel size of the regression model is adaptively adjusted based on the characteristics of the voxel that is being interpolated. In particular, when the algorithm is employed to interpolate a voxel located in a region with dense ultrasound data samples, the size of the kernel is reduced to preserve the texture patterns. On the other hand, the size of the kernel is increased in areas that include large gaps to enable effective gap filling. The performance of the proposed algorithm was compared with seven previous interpolation approaches by synthesizing freehand 3D ultrasound volumes of a benign breast tumor. The experimental results show that the proposed algorithm outperforms the other interpolation approaches.
Directory of Open Access Journals (Sweden)
Marco Antonio Maschio Cardozo Chaga
1997-12-01
Full Text Available Num texto anterior - publicado no Boletim Nelic, n. 1 -, destaquei os pressupostos editoriais que visavam a execução do "Projeto Folhetim", apontando também as razões pedagógicas e a veia esclarecedora que a publicação pretendia atingir. Neste sentido, o imperativo deveria ser a formação de um suplemento com a finalidade principal de proporcionar ao leitor uma massa de textos capazes de refletir e questionar a recente história brasileira.
Crespo Cepeda, Rodrigo; El Yamri El Khatibi, Meriem; Carrera García, Juan Manuel
2015-01-01
Las Smart Cities son, indudablemente, el futuro próximo de la tecnología al que nos acercamos cada día, lo que se puede observar en la abundancia de dispositivos móviles entre la población, que informatizan la vida cotidiana mediante el uso de la geolocalización y la información. Pretendemos unir estos dos ámbitos con CtOS Enabler para crear un estándar de uso que englobe todos los sistemas de Smart Cities y facilite a los desarrolladores de dicho software la creación de nuevas herramientas. ...
Borges Abel, António
2008-01-01
A porta, na sua qualidade de fronteira entre espaços e como elemento fundamental na definição do "dentro" e do "fora", do caos e do Cosmos, na definição do "círculo fronteiriço" e, consequentemente, do "ser fronteiriço". Morfologias urbanas: Évora como estudo de caso. Propostas genéricas de desenvolvimento/crescimento urbano, tendo como princípio orientador a relação dicotómica com o território.
Chung, Moo K; Qiu, Anqi; Seo, Seongho; Vorperian, Houri K
2015-05-01
We present a novel kernel regression framework for smoothing scalar surface data using the Laplace-Beltrami eigenfunctions. Starting with the heat kernel constructed from the eigenfunctions, we formulate a new bivariate kernel regression framework as a weighted eigenfunction expansion with the heat kernel as the weights. The new kernel method is mathematically equivalent to isotropic heat diffusion, kernel smoothing and recently popular diffusion wavelets. The numerical implementation is validated on a unit sphere using spherical harmonics. As an illustration, the method is applied to characterize the localized growth pattern of mandible surfaces obtained in CT images between ages 0 and 20 by regressing the length of displacement vectors with respect to a surface template. Copyright © 2015 Elsevier B.V. All rights reserved.
DEFF Research Database (Denmark)
Bonnet, Philippe; Culler, David; Estrin, Deborah
2006-01-01
This memo describes the goals and organization structure of the TinyOS Alliance. It covers membership, the working group forums for contribution, intellectual property, source licensing, and the TinyOS Steering Committee (TSC)....
From concatenated codes to graph codes
DEFF Research Database (Denmark)
Justesen, Jørn; Høholdt, Tom
2004-01-01
We consider codes based on simple bipartite expander graphs. These codes may be seen as the first step leading from product type concatenated codes to more complex graph codes. We emphasize constructions of specific codes of realistic lengths, and study the details of decoding by message passing...
The MC21 Monte Carlo Transport Code
International Nuclear Information System (INIS)
Sutton TM; Donovan TJ; Trumbull TH; Dobreff PS; Caro E; Griesheimer DP; Tyburski LJ; Carpenter DC; Joo H
2007-01-01
MC21 is a new Monte Carlo neutron and photon transport code currently under joint development at the Knolls Atomic Power Laboratory and the Bettis Atomic Power Laboratory. MC21 is the Monte Carlo transport kernel of the broader Common Monte Carlo Design Tool (CMCDT), which is also currently under development. The vision for CMCDT is to provide an automated, computer-aided modeling and post-processing environment integrated with a Monte Carlo solver that is optimized for reactor analysis. CMCDT represents a strategy to push the Monte Carlo method beyond its traditional role as a benchmarking tool or ''tool of last resort'' and into a dominant design role. This paper describes various aspects of the code, including the neutron physics and nuclear data treatments, the geometry representation, and the tally and depletion capabilities
Memory bottlenecks and memory contention in multi-core Monte Carlo transport codes
International Nuclear Information System (INIS)
Tramm, J.R.; Siegel, A.R.
2013-01-01
The simulation of whole nuclear cores through the use of Monte Carlo codes requires an impracticably long time-to-solution. We have extracted a kernel that executes only the most computationally expensive steps of the Monte Carlo particle transport algorithm - the calculation of macroscopic cross sections - in an effort to expose bottlenecks within multi-core, shared memory architectures. (authors)
Allen, Mitch
2009-01-01
A Note from the Author and from O'Reilly Media about what this bookdoes--and doesn't--do: Palm webOS is a brand new platform and represents a very different type ofoperating system where the web runtime is used as the basis for the UI andApplication model. Palm and O'Reilly felt that it was important to have abook available to help developers get a basic understanding of the new Palmplatform at the time that the SDK was released; this timing played a majorrole in the content and structure of the book. Ideally this book would have been a complete reference of the new platformbut that wasn't
Guimarães, Maria Helena
2008-01-01
Os interesses comuns da União Europeia e da América Latina em matéria de integração bi-regional emanam, em grande medida, de factores exógenos, e em particular da crescente concorrência económica a nível global, do impasse negocial do Ciclo de Doha e da política de bilateralismo dos Estados Unidos da América. Com base numa abordagem de cariz sistémico argumenta-se que uma parceria entre as duas regiões é instrumental para enfrentar a concorrência global, sustenta-se que tal ...
Analysis of Drude model using fractional derivatives without singular kernels
Directory of Open Access Journals (Sweden)
Jiménez Leonardo Martínez
2017-11-01
Full Text Available We report study exploring the fractional Drude model in the time domain, using fractional derivatives without singular kernels, Caputo-Fabrizio (CF, and fractional derivatives with a stretched Mittag-Leffler function. It is shown that the velocity and current density of electrons moving through a metal depend on both the time and the fractional order 0 < γ ≤ 1. Due to non-singular fractional kernels, it is possible to consider complete memory effects in the model, which appear neither in the ordinary model, nor in the fractional Drude model with Caputo fractional derivative. A comparison is also made between these two representations of the fractional derivatives, resulting a considered difference when γ < 0.8.
Development of Cold Neutron Scattering Kernels for Advanced Moderators
International Nuclear Information System (INIS)
Granada, J. R.; Cantargi, F.
2010-01-01
The development of scattering kernels for a number of molecular systems was performed, including a set of hydrogeneous methylated aromatics such as toluene, mesitylene, and mixtures of those. In order to partially validate those new libraries, we compared predicted total cross sections with experimental data obtained in our laboratory. In addition, we have introduced a new model to describe the interaction of slow neutrons with solid methane in phase II (stable phase below T = 20.4 K, atmospheric pressure). Very recently, a new scattering kernel to describe the interaction of slow neutrons with solid Deuterium was also developed. The main dynamical characteristics of that system are contained in the formalism, the elastic processes involving coherent and incoherent contributions are fully described, as well as the spin-correlation effects.
Integral equations with difference kernels on finite intervals
Sakhnovich, Lev A
2015-01-01
This book focuses on solving integral equations with difference kernels on finite intervals. The corresponding problem on the semiaxis was previously solved by N. Wiener–E. Hopf and by M.G. Krein. The problem on finite intervals, though significantly more difficult, may be solved using our method of operator identities. This method is also actively employed in inverse spectral problems, operator factorization and nonlinear integral equations. Applications of the obtained results to optimal synthesis, light scattering, diffraction, and hydrodynamics problems are discussed in this book, which also describes how the theory of operators with difference kernels is applied to stable processes and used to solve the famous M. Kac problems on stable processes. In this second edition these results are extensively generalized and include the case of all Levy processes. We present the convolution expression for the well-known Ito formula of the generator operator, a convolution expression that has proven to be fruitful...
Fault Localization for Synchrophasor Data using Kernel Principal Component Analysis
Directory of Open Access Journals (Sweden)
CHEN, R.
2017-11-01
Full Text Available In this paper, based on Kernel Principal Component Analysis (KPCA of Phasor Measurement Units (PMU data, a nonlinear method is proposed for fault location in complex power systems. Resorting to the scaling factor, the derivative for a polynomial kernel is obtained. Then, the contribution of each variable to the T2 statistic is derived to determine whether a bus is the fault component. Compared to the previous Principal Component Analysis (PCA based methods, the novel version can combat the characteristic of strong nonlinearity, and provide the precise identification of fault location. Computer simulations are conducted to demonstrate the improved performance in recognizing the fault component and evaluating its propagation across the system based on the proposed method.
Some comments on cold hydrogenous moderators, simple synthetic kernels and benchmark calculations
International Nuclear Information System (INIS)
Dorning, J.
1997-09-01
The author comments on three general subjects which are not directly related, but which in his opinion are very relevant to the objectives of the workshop. The first of these is parahydrogen moderators, about which recurring questions have been raised during the Workshop. The second topic is related to the use of simple synthetic scattering kernels in conjunction with the neutron transport equation to carry out elementary mathematical analyses and simple computational analyses in order to understand the gross physics of time-dependent neutron transport initiated by pulsed sources in cold moderators. The third subject is that of 'simple' benchmark calculations by which is meant calculations that are simple compared to the very large scale combined spallation, slowing-down, thermalization calculations using MCNP and other large Monte Carlo codes. Such benchmark problems can be created so that they are closely related to both the geometric configuration and material composition of cold moderators of interest and still can be solved using steady-state deterministic transport codes to calculate the asymptotic time-decay constant, and the time-asymptotic energy spectrum of neutrons in the cold moderator and the spectrum of the cold neutrons leaking from it (neither of which should be expected to be Maxwellian in these small leakage-dominated systems). These would provide rather precise benchmark solutions against which the results of the large scale calculations carried out for the whole spallation, slowing-down, thermalization system -- for the same decoupled cold moderator -- could be compared.
Directory of Open Access Journals (Sweden)
Xianglin ZHU
2014-06-01
Full Text Available To resolve the online detection difficulty of some important state variables in fermentation process with traditional instruments, a soft sensing modeling method based on relevance vector machine (RVM with a hybrid kernel function is presented. Based on the characteristic analysis of two commonly-used kernel functions, that is, local Gaussian kernel function and global polynomial kernel function, a hybrid kernel function combing merits of Gaussian kernel function and polynomial kernel function is constructed. To design optimal parameters of this kernel function, the particle swarm optimization (PSO algorithm is applied. The proposed modeling method is used to predict the value of cell concentration in the Lysine fermentation process. Simulation results show that the presented hybrid-kernel RVM model has a better accuracy and performance than the single kernel RVM model.