Revisiting the definition of local hardness and hardness kernel.
Polanco-Ramírez, Carlos A; Franco-Pérez, Marco; Carmona-Espíndola, Javier; Gázquez, José L; Ayers, Paul W
2017-05-17
An analysis of the hardness kernel and local hardness is performed to propose new definitions for these quantities that follow a similar pattern to the one that characterizes the quantities associated with softness, that is, we have derived new definitions for which the integral of the hardness kernel over the whole space of one of the variables leads to local hardness, and the integral of local hardness over the whole space leads to global hardness. A basic aspect of the present approach is that global hardness keeps its identity as the second derivative of energy with respect to the number of electrons. Local hardness thus obtained depends on the first and second derivatives of energy and electron density with respect to the number of electrons. When these derivatives are approximated by a smooth quadratic interpolation of energy, the expression for local hardness reduces to the one intuitively proposed by Meneses, Tiznado, Contreras and Fuentealba. However, when one combines the first directional derivatives with smooth second derivatives one finds additional terms that allow one to differentiate local hardness for electrophilic attack from the one for nucleophilic attack. Numerical results related to electrophilic attacks on substituted pyridines, substituted benzenes and substituted ethenes are presented to show the overall performance of the new definition.
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.
Development of nondestructive screening methods for single kernel characterization of wheat
DEFF Research Database (Denmark)
Nielsen, J.P.; Pedersen, D.K.; Munck, L.
2003-01-01
The development of nondestructive screening methods for single seed protein, vitreousness, density, and hardness index has been studied for single kernels of European wheat. A single kernel procedure was applied involving, image analysis, near-infrared transmittance (NIT) spectroscopy, laboratory...... predictability. However, by applying an averaging approach, in which single seed replicate measurements are mathematically simulated, a very good NIT prediction model was achieved. This suggests that the single seed NIT spectra contain hardness information, but that a single seed hardness method with higher...
Grain hardness is a very important trait in determining wheat market class and also influences milling and baking traits. At the grain Hardness (Ha) locus on chromosome 5DS, there are two primary mutations responsible for conveying a harder kernel texture among U.S. hard red spring wheats: (1) the P...
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/
Systematic hardness measurements on single crystals and ...
Indian Academy of Sciences (India)
Vickers and knoop hardness measurements were carried out on CsBr and CsI single crystals. Polycrystalline blanks of CsCl, CsBr and CsI were prepared by melting and characterized by X-ray diffraction. Vickers hardness measurements were carried out on these blanks. The hardness values were correlated with the lattice ...
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
Optimization of flour yield and quality is important in the milling industry. The objective of this study was to determine the effect of kernel size and mill type on flour yield and end-use quality. A hard red spring wheat composite sample was segregated, based on kernel size, into large, medium, ...
Single aflatoxin contaminated corn kernel analysis with fluorescence hyperspectral image
Yao, Haibo; Hruska, Zuzana; Kincaid, Russell; Ononye, Ambrose; Brown, Robert L.; Cleveland, Thomas E.
2010-04-01
Aflatoxins are toxic secondary metabolites of the fungi Aspergillus flavus and Aspergillus parasiticus, among others. Aflatoxin contaminated corn is toxic to domestic animals when ingested in feed and is a known carcinogen associated with liver and lung cancer in humans. Consequently, aflatoxin levels in food and feed are regulated by the Food and Drug Administration (FDA) in the US, allowing 20 ppb (parts per billion) limits in food and 100 ppb in feed for interstate commerce. Currently, aflatoxin detection and quantification methods are based on analytical tests including thin-layer chromatography (TCL) and high performance liquid chromatography (HPLC). These analytical tests require the destruction of samples, and are costly and time consuming. Thus, the ability to detect aflatoxin in a rapid, nondestructive way is crucial to the grain industry, particularly to corn industry. Hyperspectral imaging technology offers a non-invasive approach toward screening for food safety inspection and quality control based on its spectral signature. The focus of this paper is to classify aflatoxin contaminated single corn kernels using fluorescence hyperspectral imagery. Field inoculated corn kernels were used in the study. Contaminated and control kernels under long wavelength ultraviolet excitation were imaged using a visible near-infrared (VNIR) hyperspectral camera. The imaged kernels were chemically analyzed to provide reference information for image analysis. This paper describes a procedure to process corn kernels located in different images for statistical training and classification. Two classification algorithms, Maximum Likelihood and Binary Encoding, were used to classify each corn kernel into "control" or "contaminated" through pixel classification. The Binary Encoding approach had a slightly better performance with accuracy equals to 87% or 88% when 20 ppb or 100 ppb was used as classification threshold, respectively.
Hardness and softness reactivity kernels within the spin-polarized density-functional theory
International Nuclear Information System (INIS)
Chamorro, Eduardo; De Proft, Frank; Geerlings, Paul
2005-01-01
Generalized hardness and softness reactivity kernels are defined within a spin-polarized density-functional theory (SP-DFT) conceptual framework. These quantities constitute the basis for the global, local (i.e., r-position dependent), and nonlocal (i.e., r and r ' -position dependents) indices devoted to the treatment of both charge-transfer and spin-polarization processes in such a reactivity framework. The exact relationships between these descriptors within a SP-DFT framework are derived and the implications for chemical reactivity in such context are outlined
Wheat kernel texture dictates U.S. wheat market class. Durum wheat has limited demand and culinary end-uses compared to bread wheat because of its extremely hard kernel texture which precludes conventional milling. ‘Soft Svevo’, a new durum cultivar with soft kernel texture comparable to a soft whit...
Wheat kernel texture dictates U.S. wheat market class. Durum wheat has limited demand and culinary end-uses compared to bread wheat because of its extremely hard kernel texture which preclude conventional milling. ‘Soft Svevo’, a new durum cultivar with soft kernel texture comparable to a soft white...
Feasibility of detecting Aflatoxin B1 in single maize kernels using hyperspectral imaging
The feasibility of detecting Aflatoxin B1 (AFB1) in single maize kernel inoculated with Aspergillus flavus conidia in the field, as well as its spatial distribution in the kernels, was assessed using near-infrared hyperspectral imaging (HSI) technique. Firstly, an image mask was applied to a pixel-b...
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 ...
Single pass kernel k-means clustering method
Indian Academy of Sciences (India)
easily implemented and is suitable for large data sets, like those in data mining appli- cations. Experimental results show that, with a small loss of quality, the proposed method can significantly reduce the time taken than the conventional kernel k-means cluster- ing method. The proposed method is also compared with other ...
Single pass kernel k-means clustering method
Indian Academy of Sciences (India)
This approach has reduced both time complexity and memory requirements. However, the clustering result of this method will be very much deviated form that obtained using the conventional kernel k-means method. This is because of the fact that pseudo cluster centers in the input space may not represent the exact cluster ...
A HARDWARE SUPPORTED OPERATING SYSTEM KERNEL FOR EMBEDDED HARD REAL-TIME APPLICATIONS
COLNARIC, M; HALANG, WA; TOL, RM
1994-01-01
The concept of the kernel, i.e. the time critical part of a real-time operating system, and its dedicated co-processor, especially tailored for embedded applications, are presented. The co-processor acts as a system controller and operates in conjunction with one or more conventional processors in
Training a Single Sigmoidal Neuron is Hard
Czech Academy of Sciences Publication Activity Database
Šíma, Jiří
2002-01-01
Roč. 14, č. 11 (2002), s. 2709-2729 ISSN 0899-7667 R&D Projects: GA MŠk LN00A056 Keywords : sigmoidal neuron * loading problem * NP-hardness Subject RIV: BA - General Mathematics Impact factor: 2.313, year: 2002
Directory of Open Access Journals (Sweden)
Manzar Ashtari
2012-02-01
Full Text Available Las imágenes por resonancia magnética pesadas en difusión son ampliamente utilizadaspara el estudio de las estructuras cerebrales dentro de la materia blanca del cerebro. Sinembargo, recuperar las orientaciones de los axones puede ser susceptible a errores por elruido dentro de la señal. Una regularización espacial puede mejorar la estimación, perodebe ser realizada cuidadosamente dado que puede remover información espacial ó introducirfalsas orientaciones. En este trabajo se investigaron las ventajas de aplicar un filtroanisotrópico basado en simples y múltiples kerneles de orientación de manojos de axones.Para esto, hemos calculado kerneles locales de difusión basados en modelos de tensoresde difusión y multi tensores de difusión. Mostraremos los beneficios de nuestra propuestaen 3 tipos diferentes de imágenes obtenidas por resonancia magnética pesada en difusión:Datos sintéticos, imágenes humanas tomadas en vivo, y datos obtenidos de un fantasmasimulador de difusión.Diffusion Weighted Magnetic Resonance Imaging is widely used to study the structure ofthe fiber pathways of white matter in the brain. However, the recovered axon orientationscan be prone to error because of the low signal to noise ratio. Spatial regularization canreduce the error, but it must be done carefully so that real spatial information is not removedand false orientations are not introduced. In this paper we investigate the advantagesof applying an anisotropic filter based on single and multiple axon bundle orientation kernels.To this end, we compute local diffusion kernels based on Diffusion Tensor and multiDiffusion Tensor models. We show the benefits of our approach to three different types ofDW-MRI data: synthetic, in vivo human, and acquired from a diffusion phantom.
Single determinant N-representability and the kernel energy method applied to water clusters.
Polkosnik, Walter; Massa, Lou
2017-10-24
The Kernel energy method (KEM) is a quantum chemical calculation method that has been shown to provide accurate energies for large molecules. KEM performs calculations on subsets of a molecule (called kernels) and so the computational difficulty of KEM calculations scales more softly than full molecule methods. Although KEM provides accurate energies those energies are not required to satisfy the variational theorem. In this article, KEM is extended to provide a full molecule single-determinant N-representable one-body density matrix. A kernel expansion for the one-body density matrix analogous to the kernel expansion for energy is defined. This matrix is converted to a normalized projector by an algorithm due to Clinton. The resulting single-determinant N-representable density matrix maps to a quantum mechanically valid wavefunction which satisfies the variational theorem. The process is demonstrated on clusters of three to twenty water molecules. The resulting energies are more accurate than the straightforward KEM energy results and all violations of the variational theorem are resolved. The N-representability studied in this article is applicable to the study of quantum crystallography. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.
Chen, Lili; Zhang, Xi; Wang, Hui
2015-05-01
Obstructive sleep apnea (OSA) is a common sleep disorder that often remains undiagnosed, leading to an increased risk of developing cardiovascular diseases. Polysomnogram (PSG) is currently used as a golden standard for screening OSA. However, because it is time consuming, expensive and causes discomfort, alternative techniques based on a reduced set of physiological signals are proposed to solve this problem. This study proposes a convenient non-parametric kernel density-based approach for detection of OSA using single-lead electrocardiogram (ECG) recordings. Selected physiologically interpretable features are extracted from segmented RR intervals, which are obtained from ECG signals. These features are fed into the kernel density classifier to detect apnea event and bandwidths for density of each class (normal or apnea) are automatically chosen through an iterative bandwidth selection algorithm. To validate the proposed approach, RR intervals are extracted from ECG signals of 35 subjects obtained from a sleep apnea database ( http://physionet.org/cgi-bin/atm/ATM ). The results indicate that the kernel density classifier, with two features for apnea event detection, achieves a mean accuracy of 82.07 %, with mean sensitivity of 83.23 % and mean specificity of 80.24 %. Compared with other existing methods, the proposed kernel density approach achieves a comparably good performance but by using fewer features without significantly losing discriminant power, which indicates that it could be widely used for home-based screening or diagnosis of OSA.
Analysis of ergosterol in single kernel and ground grain by gas chromatography-mass spectrometry.
Dong, Yanhong; Steffenson, Brian J; Mirocha, Chester J
2006-06-14
A method for analyzing ergosterol in a single kernel and ground barley and wheat was developed using gas chromatography-mass spectrometry (GC-MS). Samples were saponified in methanolic KOH. Ergosterol was extracted by "one step" hexane extraction and subsequently silylated by N-trimethylsilylimidazole/trimethylchlorosilane (TMSI/TMCS) reagent at room temperature. The recoveries of ergosterol from ground barley were 96.6, 97.1, 97.1, 88.5, and 90.3% at the levels of 0.2, 1, 5, 10, and 20 microg/g (ppm), respectively. The recoveries from a single kernel were between 93.0 and 95.9%. The precision (coefficient of variance) of the method was in the range 0.8-12.3%. The method detection limit (MDL) and the method quantification limit (MQL) were 18.5 and 55.6 ng/g (ppb), respectively. The ergosterol analysis method developed can be used to handle 80 samples daily by one person, making it suitable for screening cereal cultivars for resistance to fungal infection. The ability for detecting low levels of ergosterol in a single kernel provides a tool to investigate early fungal invasion and to study mechanisms of resistance to fungal diseases.
Hard single diffractive jet production at D0
International Nuclear Information System (INIS)
Abachi, S.; Abbott, B.; Abolins, M.
1996-08-01
Preliminary results from the D null experiment on jet production with forward rapidity gaps in p anti p collisions are presented. A class of dijet events with a forward rapidity gap is observed at center-of-mass energies √s = 1800 GeV and 630 GeV. The number of events with rapidity gaps at both center-of-mass energies is significantly greater than the expectation from multiplicity fluctuations and is consistent with a hard single diffractive process. A small class of events with two forward gaps and central dijets is also observed at 1800 GeV. This topology is consistent with hard double pomeron exchange
Directory of Open Access Journals (Sweden)
T Mohammadi Moghaddam
2015-09-01
Full Text Available Introduction: Pistachio nut is one of the most delicious and nutritious nuts in the world and it is being used as a salted and roasted product or as an ingredient in snacks, ice cream, desserts, etc. (Maghsudi, 2010; Kashaninejad et al. 2006. Roasting is one of the most important food processes which provides useful attributes to the product. One of the objectives of nut roasting is to alter and significantly enhance the flavor, texture, color and appearance of the product (Ozdemir, 2001. In recent years, spectral imaging techniques (i.e. hyperspectral and multispectral imaging have emerged as powerful tools for safequality inspection of various agricultural commodities (Gowen et al., 2007. The objectives of this study were to apply reflectance hyperspectral imaging for non-destructive determination of moisture content and hardness of pistachio kernels roasted in different conditions. Materials and methods: Dried O’hadi pistachio nuts were supplied from a local market in Mashhad. Pistachio nuts were soaked in 5L of 20% salt solution for 20min (Goktas Seyhan, 2003. For roasting process, three temperatures (90, 120 and 150°C, three times (20, 35 and 50 min and three air velocities (0.5, 1.5 and 2.5 m s-1 were applied. The moisture content of pistachio kernels was measured in triplicate using oven drying (3 gr samples at 105 °C for 12 hours. Uniaxial compression test by a 35mm diameter plastic cylinder, was made on the pistachio kernels, which were mounted on a platform. Samples were compressed at a depth of 2mm and speed of 30 mm min-1. A hyperspectral imaging system in the Vis-NIR range (400-1000 nm was employed. The spectral pre-processing techniques: first derivative and second derivative, median filter, Savitzkye-Golay, wavelet, multiplicative scatter correction (MSC and standard normal variate transformation (SNV were used. To make models at PLSR and ANN methods, ParLeS software and Matlab R2009a were used, respectively. The coefficient
Afshar, Saeed; George, Libin; Tapson, Jonathan; van Schaik, André; Hamilton, Tara J
2014-01-01
This paper describes the Synapto-dendritic Kernel Adapting Neuron (SKAN), a simple spiking neuron model that performs statistical inference and unsupervised learning of spatiotemporal spike patterns. SKAN is the first proposed neuron model to investigate the effects of dynamic synapto-dendritic kernels and demonstrate their computational power even at the single neuron scale. The rule-set defining the neuron is simple: there are no complex mathematical operations such as normalization, exponentiation or even multiplication. The functionalities of SKAN emerge from the real-time interaction of simple additive and binary processes. Like a biological neuron, SKAN is robust to signal and parameter noise, and can utilize both in its operations. At the network scale neurons are locked in a race with each other with the fastest neuron to spike effectively "hiding" its learnt pattern from its neighbors. The robustness to noise, high speed, and simple building blocks not only make SKAN an interesting neuron model in computational neuroscience, but also make it ideal for implementation in digital and analog neuromorphic systems which is demonstrated through an implementation in a Field Programmable Gate Array (FPGA). Matlab, Python, and Verilog implementations of SKAN are available at: http://www.uws.edu.au/bioelectronics_neuroscience/bens/reproducible_research.
Franco-Pérez, Marco; Polanco-Ramírez, Carlos A; Gázquez, José L; Ayers, Paul W
2018-03-28
This reply complements the comment of Guégan et al. about our recent work on the revision of the local hardness and the hardness kernel concepts. Guegan et al. analyze our work using a Taylor series expansion of the energy as a functional of the electron density, to show that our procedure opens a new way to define local descriptors. In this contribution we show that the strategy we followed for the local hardness and the hardness kernel is even more general, and that it can be used to derive from a global response function its corresponding local and non-local counterparts by: (1) requiring that the integral over one of the two variables that characterizes the non-local function leads to the local function, and that the integral over the local function leads to the global response index, and (2) assuming that the global and local functions are related through the electronic density, by making use of the chain rule for functional derivatives.
Directory of Open Access Journals (Sweden)
Yi-Hung Liu
2014-07-01
Full Text Available Electroencephalogram-based emotion recognition (EEG-ER has received increasing attention in the fields of health care, affective computing, and brain-computer interface (BCI. However, satisfactory ER performance within a bi-dimensional and non-discrete emotional space using single-trial EEG data remains a challenging task. To address this issue, we propose a three-layer scheme for single-trial EEG-ER. In the first layer, a set of spectral powers of different EEG frequency bands are extracted from multi-channel single-trial EEG signals. In the second layer, the kernel Fisher’s discriminant analysis method is applied to further extract features with better discrimination ability from the EEG spectral powers. The feature vector produced by layer 2 is called a kernel Fisher’s emotion pattern (KFEP, and is sent into layer 3 for further classification where the proposed imbalanced quasiconformal kernel support vector machine (IQK-SVM serves as the emotion classifier. The outputs of the three layer EEG-ER system include labels of emotional valence and arousal. Furthermore, to collect effective training and testing datasets for the current EEG-ER system, we also use an emotion-induction paradigm in which a set of pictures selected from the International Affective Picture System (IAPS are employed as emotion induction stimuli. The performance of the proposed three-layer solution is compared with that of other EEG spectral power-based features and emotion classifiers. Results on 10 healthy participants indicate that the proposed KFEP feature performs better than other spectral power features, and IQK-SVM outperforms traditional SVM in terms of the EEG-ER accuracy. Our findings also show that the proposed EEG-ER scheme achieves the highest classification accuracies of valence (82.68% and arousal (84.79% among all testing methods.
Single image super-resolution via an iterative reproducing kernel Hilbert space method.
Deng, Liang-Jian; Guo, Weihong; Huang, Ting-Zhu
2016-11-01
Image super-resolution, a process to enhance image resolution, has important applications in satellite imaging, high definition television, medical imaging, etc. Many existing approaches use multiple low-resolution images to recover one high-resolution image. In this paper, we present an iterative scheme to solve single image super-resolution problems. It recovers a high quality high-resolution image from solely one low-resolution image without using a training data set. We solve the problem from image intensity function estimation perspective and assume the image contains smooth and edge components. We model the smooth components of an image using a thin-plate reproducing kernel Hilbert space (RKHS) and the edges using approximated Heaviside functions. The proposed method is applied to image patches, aiming to reduce computation and storage. Visual and quantitative comparisons with some competitive approaches show the effectiveness of the proposed method.
Jiang, Fei; Ma, Yanyuan; Wang, Yuanjia
We propose a generalized partially linear functional single index risk score model for repeatedly measured outcomes where the index itself is a function of time. We fuse the nonparametric kernel method and regression spline method, and modify the generalized estimating equation to facilitate estimation and inference. We use local smoothing kernel to estimate the unspecified coefficient functions of time, and use B-splines to estimate the unspecified function of the single index component. The covariance structure is taken into account via a working model, which provides valid estimation and inference procedure whether or not it captures the true covariance. The estimation method is applicable to both continuous and discrete outcomes. We derive large sample properties of the estimation procedure and show different convergence rate of each component of the model. The asymptotic properties when the kernel and regression spline methods are combined in a nested fashion has not been studied prior to this work even in the independent data case.
Kernel PLS Estimation of Single-trial Event-related Potentials
Rosipal, Roman; Trejo, Leonard J.
2004-01-01
Nonlinear kernel partial least squaes (KPLS) regressior, is a novel smoothing approach to nonparametric regression curve fitting. We have developed a KPLS approach to the estimation of single-trial event related potentials (ERPs). For improved accuracy of estimation, we also developed a local KPLS method for situations in which there exists prior knowledge about the approximate latency of individual ERP components. To assess the utility of the KPLS approach, we compared non-local KPLS and local KPLS smoothing with other nonparametric signal processing and smoothing methods. In particular, we examined wavelet denoising, smoothing splines, and localized smoothing splines. We applied these methods to the estimation of simulated mixtures of human ERPs and ongoing electroencephalogram (EEG) activity using a dipole simulator (BESA). In this scenario we considered ongoing EEG to represent spatially and temporally correlated noise added to the ERPs. This simulation provided a reasonable but simplified model of real-world ERP measurements. For estimation of the simulated single-trial ERPs, local KPLS provided a level of accuracy that was comparable with or better than the other methods. We also applied the local KPLS method to the estimation of human ERPs recorded in an experiment on co,onitive fatigue. For these data, the local KPLS method provided a clear improvement in visualization of single-trial ERPs as well as their averages. The local KPLS method may serve as a new alternative to the estimation of single-trial ERPs and improvement of ERP averages.
SCAP-82, Single Scattering, Albedo Scattering, Point-Kernel Analysis in Complex Geometry
International Nuclear Information System (INIS)
Disney, R.K.; Vogtman, S.E.
1987-01-01
1 - Description of problem or function: SCAP solves for radiation transport in complex geometries using the single or albedo scatter point kernel method. The program is designed to calculate the neutron or gamma ray radiation level at detector points located within or outside a complex radiation scatter source geometry or a user specified discrete scattering volume. Geometry is describable by zones bounded by intersecting quadratic surfaces within an arbitrary maximum number of boundary surfaces per zone. Anisotropic point sources are describable as pointwise energy dependent distributions of polar angles on a meridian; isotropic point sources may also be specified. The attenuation function for gamma rays is an exponential function on the primary source leg and the scatter leg with a build- up factor approximation to account for multiple scatter on the scat- ter leg. The neutron attenuation function is an exponential function using neutron removal cross sections on the primary source leg and scatter leg. Line or volumetric sources can be represented as a distribution of isotropic point sources, with un-collided line-of-sight attenuation and buildup calculated between each source point and the detector point. 2 - Method of solution: A point kernel method using an anisotropic or isotropic point source representation is used, line-of-sight material attenuation and inverse square spatial attenuation between the source point and scatter points and the scatter points and detector point is employed. A direct summation of individual point source results is obtained. 3 - Restrictions on the complexity of the problem: - The SCAP program is written in complete flexible dimensioning so that no restrictions are imposed on the number of energy groups or geometric zones. The geometric zone description is restricted to zones defined by boundary surfaces defined by the general quadratic equation or one of its degenerate forms. The only restriction in the program is that the total
Directory of Open Access Journals (Sweden)
Yotam Luz
Full Text Available Spike-Timing Dependent Plasticity (STDP is characterized by a wide range of temporal kernels. However, much of the theoretical work has focused on a specific kernel - the "temporally asymmetric Hebbian" learning rules. Previous studies linked excitatory STDP to positive feedback that can account for the emergence of response selectivity. Inhibitory plasticity was associated with negative feedback that can balance the excitatory and inhibitory inputs. Here we study the possible computational role of the temporal structure of the STDP. We represent the STDP as a superposition of two processes: potentiation and depression. This allows us to model a wide range of experimentally observed STDP kernels, from Hebbian to anti-Hebbian, by varying a single parameter. We investigate STDP dynamics of a single excitatory or inhibitory synapse in purely feed-forward architecture. We derive a mean-field-Fokker-Planck dynamics for the synaptic weight and analyze the effect of STDP structure on the fixed points of the mean field dynamics. We find a phase transition along the Hebbian to anti-Hebbian parameter from a phase that is characterized by a unimodal distribution of the synaptic weight, in which the STDP dynamics is governed by negative feedback, to a phase with positive feedback characterized by a bimodal distribution. The critical point of this transition depends on general properties of the STDP dynamics and not on the fine details. Namely, the dynamics is affected by the pre-post correlations only via a single number that quantifies its overlap with the STDP kernel. We find that by manipulating the STDP temporal kernel, negative feedback can be induced in excitatory synapses and positive feedback in inhibitory. Moreover, there is an exact symmetry between inhibitory and excitatory plasticity, i.e., for every STDP rule of inhibitory synapse there exists an STDP rule for excitatory synapse, such that their dynamics is identical.
Energy Technology Data Exchange (ETDEWEB)
Jin, Zheming [Argonne National Lab. (ANL), Argonne, IL (United States); Yoshii, Kazutomo [Argonne National Lab. (ANL), Argonne, IL (United States); Finkel, Hal [Argonne National Lab. (ANL), Argonne, IL (United States); Cappello, Franck [Argonne National Lab. (ANL), Argonne, IL (United States)
2017-04-20
Open Computing Language (OpenCL) is a high-level language that enables software programmers to explore Field Programmable Gate Arrays (FPGAs) for application acceleration. The Intel FPGA software development kit (SDK) for OpenCL allows a user to specify applications at a high level and explore the performance of low-level hardware acceleration. In this report, we present the FPGA performance and power consumption results of the single-precision floating-point vector add OpenCL kernel using the Intel FPGA SDK for OpenCL on the Nallatech 385A FPGA board. The board features an Arria 10 FPGA. We evaluate the FPGA implementations using the compute unit duplication and kernel vectorization optimization techniques. On the Nallatech 385A FPGA board, the maximum compute kernel bandwidth we achieve is 25.8 GB/s, approximately 76% of the peak memory bandwidth. The power consumption of the FPGA device when running the kernels ranges from 29W to 42W.
DEFF Research Database (Denmark)
Winning, H.; Viereck, N.; Wollenweber, B.
2009-01-01
at terminal spikelet, during grain-filling or at both stages. Principal component trajectories of the total protein content and the protein fractions of flour as well as the H-1 NMR spectra of single wheat kernels, wheat flour, and wheat methanol extracts were analysed to elucidate the metabolic development...... indicating that protein metabolism is influenced by multiple drought events, the H-1 NMR spectra of the methanol extracts of flour from mature grains revealed that the amount of fumaric acid is particularly sensitive to water deficits....
Tang, Y.-H.; Lin, C.-J.; Chiang, K.-R.
2017-06-01
We proposed a single-molecule magnetic junction (SMMJ), composed of a dissociated amine-ended benzene sandwiched between two Co tip-like nanowires. To better simulate the break junction technique for real SMMJs, the first-principles calculation associated with the hard-hard coupling between a amine-linker and Co tip-atom is carried out for SMMJs with mechanical strain and under an external bias. We predict an anomalous magnetoresistance (MR) effect, including strain-induced sign reversal and bias-induced enhancement of the MR value, which is in sharp contrast to the normal MR effect in conventional magnetic tunnel junctions. The underlying mechanism is the interplay between four spin-polarized currents in parallel and anti-parallel magnetic configurations, originated from the pronounced spin-up transmission feature in the parallel case and spiky transmission peaks in other three spin-polarized channels. These intriguing findings may open a new arena in which magnetotransport and hard-hard coupling are closely coupled in SMMJs and can be dually controlled either via mechanical strain or by an external bias.
Uncooled Radiation Hard SiC Schottky VUV Detectors Capable of Single Photon Sensing, Phase I
National Aeronautics and Space Administration — This project seeks to design, fabricate, characterize and commercialize very large area, uncooled and radiative hard 4H-SiC VUV detectors capable of near single...
Growth of ultra radiation hard NaBi(WO4)2 single crystal
International Nuclear Information System (INIS)
Govind Singh, S.; Tyagi, Mohit; Singh, Awadh K.; Sangeeta
2009-01-01
Single crystals of undoped NaBi(WO 4 ) 2 were grown under different condition by Czochralski technique. Radiation hardness of the crystals was studied by irradiating them up to 10''5 and 10''6 Gy dose at a fast rate (2 Gy/sec) using 60 Co as a gamma source. Transmission spectra of the crystal samples were recorded and analyzed. It is found that crystal grown from recrystalized charge shows very good optical quality and excellent radiation hardness. (author)
Single-image hard copy display of musculoskeletal digital radiographs
Legendre, Kevin; Steller Artz, Dorothy E.; Freedman, Matthew T.; Mun, Seong K.
1995-04-01
Screen film radiography often fails to optimally display all regions of anatomy on muskuloskeletal exams due to the wide latitude of tissue densities present. Various techniques of image enhancement have been applied to such exams using computerized radiography but with limited success in improving visualization of structures whose final optical density lies at the extremes of the interpretable range of the film. An existing algorithm for compressing optical density extremes known as dynamic range compression has been used to increase the radiodensity of the retrocardiac region of the chest or to decrease the radiodensity of the edge of the breast in digital mammography. In the skeletal system, there are regions where a single image may contain both areas of decreased exposure that result in light images and areas of higher exposure that result in dark regions of the image. Faced with this problem, the senior author asked Fuji to formulate a modification of the DRC process that incorporates a combination of the curves used for chest and breast images. The newly designed algorithm can thus simultaneously lower the optical density of dark regions of the image and increase the optical density of the less exposed regions. The results of this modification of the DRC algorithm are presented in this paper.
Temperature dependence of hardness in yttria-stabilized zirconia single crystals
Morscher, Gregory N.; Pirouz, Pirouz; Heuer, Arthur H.
1991-01-01
The temperature dependence of hardness and microcracking in single-crystal 9.5-mol pct-Y2O3-fully-stabilized cubic-ZrO2 was studied as a function of orientation. Crack lengths increased with increased temperature up to 500 C; above 800 C, no cracks were found, indicating an indentation brittle-to-ductile transition of about 800 C. The temperature dependence of hardness was reduced around 500 C. Etching studies to delineate the plastic zone around and below indents identified the operative slip systems. The role of dislocations and their interactions within the plastic zone on the hardness and indentation fracture behavior of cubic-ZrO2 are discussed.
A Heterogeneous Multi-core Architecture with a Hardware Kernel for Control Systems
DEFF Research Database (Denmark)
Li, Gang; Guan, Wei; Sierszecki, Krzysztof
2012-01-01
Rapid industrialisation has resulted in a demand for improved embedded control systems with features such as predictability, high processing performance and low power consumption. Software kernel implementation on a single processor is becoming more difficult to satisfy those constraints. This pa......Rapid industrialisation has resulted in a demand for improved embedded control systems with features such as predictability, high processing performance and low power consumption. Software kernel implementation on a single processor is becoming more difficult to satisfy those constraints......). Second, a heterogeneous multi-core architecture is investigated, focusing on its performance in relation to hard real-time constraints and predictable behavior. Third, the hardware implementation of HARTEX is designated to support the heterogeneous multi-core architecture. This hardware kernel has...... several advantages over a similar kernel implemented in software: higher-speed processing capability, parallel computation, and separation between the kernel itself and the applications being run. A microbenchmark has been used to compare the hardware kernel with the software kernel, and compare...
Directory of Open Access Journals (Sweden)
Guangjun Qiu
2018-03-01
Full Text Available The viability and vigor of crop seeds are crucial indicators for evaluating seed quality, and high-quality seeds can increase agricultural yield. The conventional methods for assessing seed viability are time consuming, destructive, and labor intensive. Therefore, a rapid and nondestructive technique for testing seed viability has great potential benefits for agriculture. In this study, single-kernel Fourier transform near-infrared (FT-NIR spectroscopy with a wavelength range of 1000–2500 nm was used to distinguish viable and nonviable supersweet corn seeds. Various preprocessing algorithms coupled with partial least squares discriminant analysis (PLS-DA were implemented to test the performance of classification models. The FT-NIR spectroscopy technique successfully differentiated viable seeds from seeds that were nonviable due to overheating or artificial aging. Correct classification rates for both heat-damaged kernels and artificially aged kernels reached 98.0%. The comprehensive model could also attain an accuracy of 98.7% when combining heat-damaged samples and artificially aged samples into one category. Overall, the FT-NIR technique with multivariate data analysis methods showed great potential capacity in rapidly and nondestructively detecting seed viability in supersweet corn.
Qiu, Guangjun; Lü, Enli; Lu, Huazhong; Xu, Sai; Zeng, Fanguo; Shui, Qin
2018-03-28
The viability and vigor of crop seeds are crucial indicators for evaluating seed quality, and high-quality seeds can increase agricultural yield. The conventional methods for assessing seed viability are time consuming, destructive, and labor intensive. Therefore, a rapid and nondestructive technique for testing seed viability has great potential benefits for agriculture. In this study, single-kernel Fourier transform near-infrared (FT-NIR) spectroscopy with a wavelength range of 1000-2500 nm was used to distinguish viable and nonviable supersweet corn seeds. Various preprocessing algorithms coupled with partial least squares discriminant analysis (PLS-DA) were implemented to test the performance of classification models. The FT-NIR spectroscopy technique successfully differentiated viable seeds from seeds that were nonviable due to overheating or artificial aging. Correct classification rates for both heat-damaged kernels and artificially aged kernels reached 98.0%. The comprehensive model could also attain an accuracy of 98.7% when combining heat-damaged samples and artificially aged samples into one category. Overall, the FT-NIR technique with multivariate data analysis methods showed great potential capacity in rapidly and nondestructively detecting seed viability in supersweet corn.
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...... kernels and did not give acceptable results because of high misclassification. However by using a predefined threshold and classifying entire kernels based on the number of correctly predicted pixels, improved results were achieved (sensitivity and specificity of 0.75 and 0.97). Object-wise classification...... was performed using two methods for feature extraction — score histograms and mean spectra. The model based on score histograms performed better for hard kernel classification (sensitivity and specificity of 0.93 and 0.97), while that of mean spectra gave better results for medium kernels (sensitivity...
McMorrow, Julian J; Cress, Cory D; Gaviria Rojas, William A; Geier, Michael L; Marks, Tobin J; Hersam, Mark C
2017-03-28
Increasingly complex demonstrations of integrated circuit elements based on semiconducting single-walled carbon nanotubes (SWCNTs) mark the maturation of this technology for use in next-generation electronics. In particular, organic materials have recently been leveraged as dopant and encapsulation layers to enable stable SWCNT-based rail-to-rail, low-power complementary metal-oxide-semiconductor (CMOS) logic circuits. To explore the limits of this technology in extreme environments, here we study total ionizing dose (TID) effects in enhancement-mode SWCNT-CMOS inverters that employ organic doping and encapsulation layers. Details of the evolution of the device transport properties are revealed by in situ and in operando measurements, identifying n-type transistors as the more TID-sensitive component of the CMOS system with over an order of magnitude larger degradation of the static power dissipation. To further improve device stability, radiation-hardening approaches are explored, resulting in the observation that SWNCT-CMOS circuits are TID-hard under dynamic bias operation. Overall, this work reveals conditions under which SWCNTs can be employed for radiation-hard integrated circuits, thus presenting significant potential for next-generation satellite and space applications.
Energy Technology Data Exchange (ETDEWEB)
Ramos, Kyle J [Los Alamos National Laboratory; Hooks, David E [Los Alamos National Laboratory; Bahr, David F [WSU
2008-01-01
Investigation of deformation beginning with elasticity and continuing through the elastic-plastic transition to incipient cracking has been conducted for (210), (021), and (001) oriented single crystals of the explosive cyclotrimethylene trinitramine, commonly known as 'RDX' Instrumented indentation was performed with a conical tip over a range of loads. The resulting load-depth data exhibited distinct, reproducible, orientation dependent load excursions demonstrating elastic-plastic transitions. Indent impressions were imaged by scanning probe microscopy. Impressions on the (210) and (001) planes showed deformation pileup features associated with zone axes of slip planes. Clearly discernable slip traces were evident on the (210) plane. The (021) indentations produced significant material pile-up surrounding the impression, but did not contain discrete features associable with specific zone axes. All of the orientations exhibited cracking thresholds at very low loads. The reduced moduli were anisotropic and the hardness's were isotropic indicating limited plasticity. Maximum shear stresses estimated from a Hertzian model, at load excursions, were within a factor of 10 of published shear moduli indicating deformation initiated near the theoretical yield strength presumably by homogeneous nucleation of dislocations. The material strength parameters and apparent deformation pathways inferred from this work are compared to historical microhardness testing and interpretation of anisotropic hardness in which ambiguity of results can be attributed to the effects of cracking and simultaneous slip on multiple systems.
Communication: The electronic structure of matter probed with a single femtosecond hard x-ray pulse
Directory of Open Access Journals (Sweden)
J. Szlachetko
2014-03-01
Full Text Available Physical, biological, and chemical transformations are initiated by changes in the electronic configuration of the species involved. These electronic changes occur on the timescales of attoseconds (10−18 s to femtoseconds (10−15 s and drive all subsequent electronic reorganization as the system moves to a new equilibrium or quasi-equilibrium state. The ability to detect the dynamics of these electronic changes is crucial for understanding the potential energy surfaces upon which chemical and biological reactions take place. Here, we report on the determination of the electronic structure of matter using a single self-seeded femtosecond x-ray pulse from the Linac Coherent Light Source hard x-ray free electron laser. By measuring the high energy resolution off-resonant spectrum (HEROS, we were able to obtain information about the electronic density of states with a single femtosecond x-ray pulse. We show that the unoccupied electronic states of the scattering atom may be determined on a shot-to-shot basis and that the measured spectral shape is independent of the large intensity fluctuations of the incoming x-ray beam. Moreover, we demonstrate the chemical sensitivity and single-shot capability and limitations of HEROS, which enables the technique to track the electronic structural dynamics in matter on femtosecond time scales, making it an ideal probe technique for time-resolved X-ray experiments.
International Nuclear Information System (INIS)
Hristova, I.
2007-12-01
We present the analysis of data taken in the years 2002-2004 with the 27.56 GeV positron beam of the HERA storage ring at DESY and the internal transversely polarised hydrogen fixed target of the HERMES experiment. Events with a scattered positron and a produced pion are selected. Exclusive production of single pions, e + p→e +' nπ + , is ensured by requiring the missing mass in the event to be equal to the mass of the neutron, which is not detected. The cross section for this process depends on the Bjorken scaling variable, the four-momentum transfer, and the transverse four-momentum transfer, whose average values for our sample are left angle x right angle =0.12, left angle Q 2 right angle =2.3 GeV 2 , left angle t' right angle =-0.18 GeV 2 , respectively, and two azimuthal angles: the angle φ between the scattering and production planes (their common line contains the virtual photon), and the angle φ S between the scattering plane and the target polarisation vector. The hard scattering is selected by requiring Q 2 >1 GeV 2 . The asymmetry, also called transverse-target single-spin azimuthal asymmetry, is defined as the ratio of the difference to the sum of the cross sections for positive and negative target polarisation. It is characterised by six azimuthal sine modulations, whose amplitudes can vary from -1 to 1. We measure the asymmetry from a sample of 2093 events with a signal-to-background ratio of 1: 1. At average kinematics, the values of the amplitudes are found to be small or consistent with zero, except for the amplitude A sinφ S UT,meas =0.38±0.06(stat) +0.12 -0.06 (syst). The amplitude of main interest for comparison with theory, A sin(φ-φ S ) UT,meas =0.09±0.05(stat) +0.10 -0.03 (syst), after correction for the background contribution becomes A sin(φ-φ S ) UT,bg.cor =0.22 ±0.13(stat) +0.10 -0.04 (syst). As a function of t', the measured values of this amplitude increase as √(-t') and at larger vertical stroke t' vertical stroke the
Energy Technology Data Exchange (ETDEWEB)
Hristova, I.
2007-12-15
We present the analysis of data taken in the years 2002-2004 with the 27.56 GeV positron beam of the HERA storage ring at DESY and the internal transversely polarised hydrogen fixed target of the HERMES experiment. Events with a scattered positron and a produced pion are selected. Exclusive production of single pions, e{sup +}p{yields}e{sup +'}n{pi}{sup +}, is ensured by requiring the missing mass in the event to be equal to the mass of the neutron, which is not detected. The cross section for this process depends on the Bjorken scaling variable, the four-momentum transfer, and the transverse four-momentum transfer, whose average values for our sample are left angle x right angle =0.12, left angle Q{sup 2} right angle =2.3 GeV{sup 2}, left angle t' right angle =-0.18 GeV{sup 2}, respectively, and two azimuthal angles: the angle {phi} between the scattering and production planes (their common line contains the virtual photon), and the angle {phi}{sub S} between the scattering plane and the target polarisation vector. The hard scattering is selected by requiring Q{sup 2}>1 GeV{sup 2}. The asymmetry, also called transverse-target single-spin azimuthal asymmetry, is defined as the ratio of the difference to the sum of the cross sections for positive and negative target polarisation. It is characterised by six azimuthal sine modulations, whose amplitudes can vary from -1 to 1. We measure the asymmetry from a sample of 2093 events with a signal-to-background ratio of 1: 1. At average kinematics, the values of the amplitudes are found to be small or consistent with zero, except for the amplitude A{sup sin{phi}{sub SUT,meas}}=0.38{+-}0.06(stat){sup +0.12}{sub -0.06}(syst). The amplitude of main interest for comparison with theory, A{sup sin({phi}-{phi}{sub S})}{sub UT,meas}=0.09{+-}0.05(stat){sup +0.10}{sub -0.03}(syst), after correction for the background contribution becomes A{sup sin({phi}-{phi}{sub S})}{sub UT,bg.cor}=0.22 {+-}0.13(stat){sup +0.10}{sub -0
Single-Event Gate Rupture in Power MOSFETs: A New Radiation Hardness Assurance Approach
Lauenstein, Jean-Marie
2011-01-01
Almost every space mission uses vertical power metal-semiconductor-oxide field-effect transistors (MOSFETs) in its power-supply circuitry. These devices can fail catastrophically due to single-event gate rupture (SEGR) when exposed to energetic heavy ions. To reduce SEGR failure risk, the off-state operating voltages of the devices are derated based upon radiation tests at heavy-ion accelerator facilities. Testing is very expensive. Even so, data from these tests provide only a limited guide to on-orbit performance. In this work, a device simulation-based method is developed to measure the response to strikes from heavy ions unavailable at accelerator facilities but posing potential risk on orbit. This work is the first to show that the present derating factor, which was established from non-radiation reliability concerns, is appropriate to reduce on-orbit SEGR failure risk when applied to data acquired from ions with appropriate penetration range. A second important outcome of this study is the demonstration of the capability and usefulness of this simulation technique for augmenting SEGR data from accelerator beam facilities. The mechanisms of SEGR are two-fold: the gate oxide is weakened by the passage of the ion through it, and the charge ionized along the ion track in the silicon transiently increases the oxide electric field. Most hardness assurance methodologies consider the latter mechanism only. This work demonstrates through experiment and simulation that the gate oxide response should not be neglected. In addition, the premise that the temporary weakening of the oxide due to the ion interaction with it, as opposed to due to the transient oxide field generated from within the silicon, is validated. Based upon these findings, a new approach to radiation hardness assurance for SEGR in power MOSFETs is defined to reduce SEGR risk in space flight projects. Finally, the potential impact of accumulated dose over the course of a space mission on SEGR
Single-image hard-copy display of the spine utilizing digital radiography
Artz, Dorothy S.; Janchar, Timothy; Milzman, David; Freedman, Matthew T.; Mun, Seong K.
1997-04-01
Regions of the entire spine contain a wide latitude of tissue densities within the imaged field of view presenting a problem for adequate radiological evaluation. With screen/film technology, the optimal technique for one area of the radiograph is sub-optimal for another area. Computed radiography (CR) with its inherent wide dynamic range, has been shown to be better than screen/film for lateral cervical spine imaging, but limitations are still present with standard image processing. By utilizing a dynamic range control (DRC) algorithm based on unsharp masking and signal transformation prior to gradation and frequency processing within the CR system, more vertebral bodies can be seen on a single hard copy display of the lateral cervical, thoracic, and thoracolumbar examinations. Examinations of the trauma cross-table lateral cervical spine, lateral thoracic spine, and lateral thoracolumbar spine were collected on live patient using photostimulable storage phosphor plates, the Fuji FCR 9000 reader, and the Fuji AC-3 computed radiography reader. Two images were produced from a single exposure; one with standard image processing and the second image with the standard process and the additional DRC algorithm. Both sets were printed from a Fuji LP 414 laser printer. Two different DRC algorithms were applied depending on which portion of the spine was not well visualized. One algorithm increased optical density and the second algorithm decreased optical density. The resultant image pairs were then reviewed by a panel of radiologists. Images produced with the additional DRC algorithm demonstrated improved visualization of previously 'under exposed' and 'over exposed' regions within the same image. Where lung field had previously obscured bony detail of the lateral thoracolumbar spine due to 'over exposure,' the image with the DRC applied to decrease the optical density allowed for easy visualization of the entire area of interest. For areas of the lateral cervical spine
Morris, Craig F; Beecher, Brian S
2012-07-01
Kernel vitreosity is an important trait of wheat grain, but its developmental control is not completely known. We developed back-cross seven (BC(7)) near-isogenic lines in the soft white spring wheat cultivar Alpowa that lack the distal portion of chromosome 5D short arm. From the final back-cross, 46 BC(7)F(2) plants were isolated. These plants exhibited a complete and perfect association between kernel vitreosity (i.e. vitreous, non-vitreous or mixed) and Single Kernel Characterization System (SKCS) hardness. Observed segregation of 10:28:7 fit a 1:2:1 Chi-square. BC(7)F(2) plants classified as heterozygous for both SKCS hardness and kernel vitreosity (n = 29) were selected and a single vitreous and non-vitreous kernel were selected, and grown to maturity and subjected to SKCS analysis. The resultant phenotypic ratios were, from non-vitreous kernels, 23:6:0, and from vitreous kernels, 0:1:28, soft:heterozygous:hard, respectively. Three of these BC(7)F(2) heterozygous plants were selected and 40 kernels each drawn at random, grown to maturity and subjected to SKCS analysis. Phenotypic segregation ratios were 7:27:6, 11:20:9, and 3:28:9, soft:heterozygous:hard. Chi-square analysis supported a 1:2:1 segregation for one plant but not the other two, in which cases the two homozygous classes were under-represented. Twenty-two paired BC(7)F(2):F(3) full sibs were compared for kernel hardness, weight, size, density and protein content. SKCS hardness index differed markedly, 29.4 for the lines with a complete 5DS, and 88.6 for the lines possessing the deletion. The soft non-vitreous kernels were on average significantly heavier, by nearly 20%, and were slightly larger. Density and protein contents were similar, however. The results provide strong genetic evidence that gene(s) on distal 5DS control not only kernel hardness but also the manner in which the endosperm develops, viz. whether it is vitreous or non-vitreous.
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
Directory of Open Access Journals (Sweden)
Taito Osaka
2017-11-01
Full Text Available Temporal coherence is one of the most fundamental characteristics of light, connecting to spectral information through the Fourier transform relationship between time and frequency. Interferometers with a variable path-length difference (PLD between the two branches have widely been employed to characterize temporal coherence properties for broad spectral regimes. Hard X-ray interferometers reported previously, however, have strict limitations in their operational photon energies, due to the specific optical layouts utilized to satisfy the stringent requirement for extreme stability of the PLD at sub-ångström scales. The work presented here characterizes the temporal coherence of hard X-ray free-electron laser (XFEL pulses by capturing single-shot interferograms. Since the stability requirement is drastically relieved with this approach, it was possible to build a versatile hard X-ray interferometer composed of six separate optical elements to cover a wide photon energy range from 6.5 to 11.5 keV while providing a large variable delay time of up to 47 ps at 10 keV. A high visibility of up to 0.55 was observed at a photon energy of 10 keV. The visibility measurement as a function of time delay reveals a mean coherence time of 5.9 ± 0.7 fs, which agrees with that expected from the single-shot spectral information. This is the first result of characterizing the temporal coherence of XFEL pulses in the hard X-ray regime and is an important milestone towards ultra-high energy resolutions at micro-electronvolt levels in time-domain X-ray spectroscopy, which will open up new opportunities for revealing dynamic properties in diverse systems on timescales from femtoseconds to nanoseconds, associated with fluctuations from ångström to nanometre spatial scales.
Oliva-Pascual-Vaca, Ángel; Heredia-Rizo, Alberto Marcos; Barbosa-Romero, Alejandro; Oliva-Pascual-Vaca, Jesús; Rodríguez-Blanco, Cleofás; Tejero-García, Sergio
2014-06-01
The purpose of this study was to evaluate the hardness of the paraspinal muscles in the convexity and concavity of patients with scoliosis curvatures and in the upper trapezius (UT) muscle in subjects with mild idiopathic scoliosis (IS) and to observe the correlation between the myotonometer (MYO) measurements and the value of body mass index (BMI) and the Cobb angle. The sample included 13 patients with a single-curve mild IS (Risser sign ≤ 4) at thoracic, lumbar, or thoracolumbar level (mean Cobb angle of 11.53º). Seven females and 6 males were recruited, with a mean age of 12.84 ± 3.06 (9-18) years. A MYO was used to examine the differences in muscle hardness on both sides of the scoliosis curvature at several points: (a) apex of the curve, (b) upper and lower limits of the curve, and (c) the midpoint between the apex and the upper limit and between the apex and the lower limit. The UT was also explored. Although the MYO recorded lower values in all points on the concave side of the scoliosis, there were no significant differences in the comparison between sides (P > .05). No association was observed between BMI and MYO values, whereas the Cobb angle negatively correlated with muscle hardness only at 2 points on the convex side. The preliminary findings show that, in subjects with a single-curve mild IS, muscular hardness in the UT and paraspinal muscles, as assessed using a MYO, was not found to differ between the concave and the convex sides at different reference levels. Copyright © 2014 National University of Health Sciences. Published by Mosby, Inc. All rights reserved.
Anard, D; Kirsch-Volders, M; Elhajouji, A; Belpaeme, K; Lison, D
1997-01-01
Hard metals (WC-Co) are made of a mixture of cobalt metal (Co, 5-10%) and tungsten carbide particles (WC, >80%). Excessive inhalation of WC-Co is associated with the occurrence of different lung diseases including an excess of lung cancers. The elective toxicity of hard metal is based on a physico-chemical interaction between cobalt metal and tungsten carbide particles to produce activated oxygen species. The aim of the present study was to assess the genotoxic activity of hard metal particles as compared with Co and WC alone. In human peripheral lymphocytes incubated with Co or WC-Co, a dose- and time-dependent increased production of DNA single strand breaks (ssb) was evidenced by alkaline single cell gel electrophoresis (SCGE) and modified alkaline elution (AE) assays. Addition of 1 M formate, a hydroxyl radical scavenger, had a protective effect against the production of ssb by both WC-Co or Co alone. On the basis of an equivalent cobalt-content, WC-Co produced significantly more ssb than Co. WC alone did not produce DNA ssb detectable by the AE assay, but results obtained with the SCGE assay may suggest that it either allows some uncoiling of the chromatin loops or induces the formation of slowly migrating fragments. Overall, this in vitro study is the first demonstration of the clastogenic property of cobalt metal-containing dusts. The results are consistent with the implication of an increased production of hydroxyl radicals when Co is mixed with WC particles. The SCGE results also suggest that WC may modify the structure of the chromatin, leading to an increased DNA sensitivity to clastogenic effects. Both mechanisms are not mutually exclusive and may concurrently contribute to the greater clastogenic activity of WC-Co dust. This property of WC-Co particles may account for the excess of lung cancers observed in hard metal workers.
Experimental demonstration of a single-spike hard-X-ray free-electron laser starting from noise
International Nuclear Information System (INIS)
Marinelli, A.; MacArthur, J.; Emma, P.; Guetg, M.; Field, C.
2017-01-01
In this letter, we report the experimental demonstration of single-spike hard-X-ray free-electron laser pulses starting from noise with multi-eV bandwidth. Here, this is accomplished by shaping a low-charge electron beam with a slotted emittance spoiler and by adjusting the transport optics to optimize the beam-shaping accuracy. Based on elementary free-electron laser scaling laws, we estimate the pulse duration to be less than 1 fs full-width at half-maximum.
Energy Technology Data Exchange (ETDEWEB)
Ahmad, H.A. [Department of Physics, Faculty of Science, Universiti Putra Malaysia, UPM, 43400 Serdang, Selangor (Malaysia); Saiden, N.M., E-mail: nlaily@upm.edu.my [Department of Physics, Faculty of Science, Universiti Putra Malaysia, UPM, 43400 Serdang, Selangor (Malaysia); Saion, E.; Azis, R.S.; Mamat, M.S. [Department of Physics, Faculty of Science, Universiti Putra Malaysia, UPM, 43400 Serdang, Selangor (Malaysia); Hashim, M. [Advanced Material and Nanotechnology Laboratory, Institute of Advanced Technology, Universiti Putra Malaysia, UPM, 43400 Serdang, Selangor (Malaysia)
2017-04-15
Nanocomposite magnets consist of soft and hard ferrite phases are known as an exchange spring magnet when they are sufficiently spin exchange coupled. Hard and soft ferrites offer high value of coercivity, H{sub c} and saturation magnetization, M{sub s} respectively. In order to obtain a better permanent magnet, both soft and hard ferrite phases need to be “exchange coupled”. The nanoparticles were prepared by a simple one-pot technique of 80% soft phase and 20% hard phase. This technique involves a single reaction mixture of metal nitrates and aqueous solution of varied amounts of polyvinylpyrrolidone (PVP). The heat treatment applied was at 800 °C for 3 h. The synthesized composites were characterized by Transmission Electron Microscope (TEM), Fourier Transform Infra-red (FT-IR), Energy Dispersive X-Ray (EDX), X-ray diffraction (XRD) and Vibrating sample magnetometer (VSM). The coexistence of two phases, Ni{sub 0.5}Zn{sub 0.5}Fe{sub 2}O{sub 4} and SrFe{sub 12}O{sub 19} were observed by XRD patterns. It also verified by the EDX that no impurities detected. The magnetic properties of nanocomposite ferrites for 0.06 g/ml PVP gives a better properties of H{sub c} 932 G and M{sub s} 39.0 emu/g with average particle size obtained from FESEM was 49.2 nm. The concentration of PVP used gives effect on the magnetic properties of the samples. - Highlights: • Amount of PVP play important roles in controlling the particle size distribution and magnetic properties. • This is a novel technique to produce nanocomposite ferrites effectively. • This study contributes better understanding on magnetic properties in nanoparticle composite magnets.
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)
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`.
Oscillations of a single Abrikosov vortex in hard type-II superconductors
Rusakov, V. F.; Chabanenko, V. V.; Nabiałek, A.; Chumak, O. M.
2017-06-01
During the last decade, detection and manipulation of single vortex lines in bulk superconductors have been achieved experimentally. Electrodynamic response of pinned vortices in the high-frequency range is instrumental in studying specific aspects of their behavior. The present paper reviews the state of the art in studies of the oscillations of a single Abrikosov vortex in type II superconductors. The equations for free and forced oscillations of a single elastic vortex line are analyzed taking into account different forces affecting its motion: pinning, elasticity, viscosity and the Lorenz force. The equations also account for the inertial properties of a vortex due to various mechanisms of massiveness. The nature and magnitude of the vortex effective mass caused by some of the mechanisms are discussed in the paper. The roles of each force and inertia in the free oscillation spectrum are thoroughly analyzed. For the De Gennes and Matricon mode (at about a megahertz) with parabolic dispersion and the pinning force taken into account, there is an activation threshold. Taking into account the effective vortex mass in the equation of motion leads to the occurrence of a high-frequency mode (at about a terahertz) in the oscillation spectrum which is also of the activation nature. Estimations of the characteristic frequencies for these modes are given for two common superconductors, NbTi and anisotropic YBaCuO. The paper also presents the features of the resonant behavior of an elastic massive vortex line arising under an external uniform harmonic driving force that decays into the bulk of the sample, taking into account all the above forces. The frequency and temperature dependences of the energy absorption by a vortex line are analyzed. Maximum absorption in the low-frequency mode corresponds to the threshold frequency, while that in the high-frequency mode corresponds to the vortex cyclotron frequency. Vortex manipulation experiments and vortex dynamics simulation
Biochemical and molecular characterization of Avena indolines and their role in kernel texture.
Gazza, Laura; Taddei, Federica; Conti, Salvatore; Gazzelloni, Gloria; Muccilli, Vera; Janni, Michela; D'Ovidio, Renato; Alfieri, Michela; Redaelli, Rita; Pogna, Norberto E
2015-02-01
Among cereals, Avena sativa is characterized by an extremely soft endosperm texture, which leads to some negative agronomic and technological traits. On the basis of the well-known softening effect of puroindolines in wheat kernel texture, in this study, indolines and their encoding genes are investigated in Avena species at different ploidy levels. Three novel 14 kDa proteins, showing a central hydrophobic domain with four tryptophan residues and here named vromindoline (VIN)-1,2 and 3, were identified. Each VIN protein in diploid oat species was found to be synthesized by a single Vin gene whereas, in hexaploid A. sativa, three Vin-1, three Vin-2 and two Vin-3 genes coding for VIN-1, VIN-2 and VIN-3, respectively, were described and assigned to the A, C or D genomes based on similarity to their counterparts in diploid species. Expression of oat vromindoline transgenes in the extra-hard durum wheat led to accumulation of vromindolines in the endosperm and caused an approximate 50 % reduction of grain hardness, suggesting a central role for vromindolines in causing the extra-soft texture of oat grain. Further, hexaploid oats showed three orthologous genes coding for avenoindolines A and B, with five or three tryptophan residues, respectively, but very low amounts of avenoindolines were found in mature kernels. The present results identify a novel protein family affecting cereal kernel texture and would further elucidate the phylogenetic evolution of Avena genus.
Novel QCD Aspects of Hard Diffraction,Antishadowing, and Single-Spin Asymmetries
Energy Technology Data Exchange (ETDEWEB)
Brodsky, S.
2004-10-15
It is usually assumed--following the parton model--that the leading-twist structure functions measured in deep inelastic lepton-proton scattering are simply the probability distributions for finding quarks and gluons in the target nucleon. In fact, gluon exchange between the outgoing quarks and the target spectators effects the leading-twist structure functions in a profound way, leading to diffractive leptoproduction processes, shadowing and antishadowing of nuclear structure functions, and target spin asymmetries, physics not incorporated in the light-front wavefunctions of the target computed in isolation. In particular, final-state interactions from gluon exchange lead to single-spin asymmetries in semi-inclusive deep inelastic lepton-proton scattering which are not power-law suppressed in the Bjorken limit. The shadowing and antishadowing of nuclear structure functions in the Gribov-Glauber picture is due respectively to the destructive and constructive interference of amplitudes arising from the multiple-scattering of quarks in the nucleus. The effective quark-nucleon scattering amplitude includes Pomeron and Odderon contributions from multi-gluon exchange as well as Reggeon quark-exchange contributions. Part of the anomalous NuTeV result for sin{sup 2} {theta}{sub W} could be due to the non-universality of nuclear antishadowing for charged and neutral currents. Detailed measurements of the nuclear dependence of individual quark structure functions are thus needed to establish the distinctive phenomenology of shadowing and antishadowing and to make the NuTeV results definitive. I also discuss diffraction dissociation as a tool for resolving hadron substructure Fock state by Fock state and for producing leading heavy quark systems.
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
Kroah-Hartman, Greg
2009-01-01
Linux Kernel in a Nutshell covers the entire range of kernel tasks, starting with downloading the source and making sure that the kernel is in sync with the versions of the tools you need. In addition to configuration and installation steps, the book offers reference material and discussions of related topics such as control of kernel options at runtime.
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
[Study of genetic models of maize kernel traits].
Zhang, H W; Kong, F L
2000-01-01
Two sets of NCII mating design including 21 different maize inbreds were used to study the genetic models of five maize kernel traits--kernel length, width, ratio of kernel length and width, kernel thickness and weight per 100 kernels. Ten generations including P1, P2, F1, F2, B1, B2 and their reciprocal crosses RF1, RF2, RB1, RB2 were obtained. Three years' data were obtained and analyzed using mainly two methods: (1) precision identification for single cross and (2) mixed liner model MINQUE approach for diallel design. Method 1 showed that kernel traits were primarily controlled by maternal dominance, endosperm additive and dominance effect (maternal dominance > endosperm additive > endosperm dominance). Cytoplasmic effect was detected in one of the two crosses studied. Method 2 revealed that in the total variance of kernel traits, maternal genotypic effect contributed more than 60%, endosperm genotypic effect contributed less than 40%. Cytoplasmic effect only existed in kernel length and 100 kernel weight, with the range of 10% to 30%. The results indicated that kernel genetic performance was quite largely controlled by maternal genotypic effect.
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.
DEFF Research Database (Denmark)
Schleger, P.; Casalta, H.; Hadfield, R.
1995-01-01
We present measurements of Ortho-III phase correlations in an untwinned single crystal of YBa2Cu3O6.77 by neutron scattering and the novel method of hard (95 keV) X-ray scattering. The Ortho-III ordering is essentially two-dimensional, exhibiting Lorentzian peak shapes in the a-b plane. At room...
International Nuclear Information System (INIS)
Park, J. W.; Lee, J. H.; Lee, J. S.; Kil, J. G.; Choi, B. H.; Han, Z. H.
2001-01-01
Single or mixed ions of N, He, C were implanted onto the transparent PET(Polyethylen Terephtalate) with the ion energies of less than 100 keV and the surface hardness, light transmittance and electrical conductivity were examined. As measured with nanoindentation, mixed ion implantations such as N + +He + or N + + C + exhibited more increase in the surface hardness than the single ion implantation. Especially, implantation of C+N ions increased the surface hardness by about three times as compared to the implantation of N ion alone, which means more than 10 times increase than the untreated PET. Surface electrical conductivity was increased along with the hardness increase. The conductivity increase was more proportional to the hardness when used the higher ion energy and ion dose, while it did not show any relationship at as low as 50 keV of ion energy. The light at the 550 nm wavelength (visual range) transmitted more than 85%, which is close to that of as-received PET, and at the wavelength below 300 nm(UV range) the rays were absorbed more than 95% as traveling through the sheet, implying that there are processing parameters which the ion implanted PET maintains the transparency and absorbs the UV rays
Hard electronics; Hard electronics
Energy Technology Data Exchange (ETDEWEB)
NONE
1997-03-01
Hard material technologies were surveyed to establish the hard electronic technology which offers superior characteristics under hard operational or environmental conditions as compared with conventional Si devices. The following technologies were separately surveyed: (1) The device and integration technologies of wide gap hard semiconductors such as SiC, diamond and nitride, (2) The technology of hard semiconductor devices for vacuum micro- electronics technology, and (3) The technology of hard new material devices for oxides. The formation technology of oxide thin films made remarkable progress after discovery of oxide superconductor materials, resulting in development of an atomic layer growth method and mist deposition method. This leading research is expected to solve such issues difficult to be easily realized by current Si technology as high-power, high-frequency and low-loss devices in power electronics, high temperature-proof and radiation-proof devices in ultimate electronics, and high-speed and dense- integrated devices in information electronics. 432 refs., 136 figs., 15 tabs.
Lasfargues, G; Lardot, C; Delos, M; Lauwerys, R; Lison, D
1995-05-01
Epidemiological and clinical studies suggest that inhalation of cobalt metal dust (Co) mixed with tungsten carbide particles (WC), but not of cobalt dust alone, may cause interstitial pulmonary lesions (hard metal disease). In previous experimental studies in the rat, we have demonstrated the greater acute pulmonary toxicity of a WC-Co mixture compared to Co or WC alone. The present study was undertaken to compare in the same animal model the delayed lung response after intratracheal administration of Co or WC-Co particles (cobalt particle 6.3 wt%). The responses were also compared with those obtained after treatment with arsenic trioxide and crystalline silica used a reference materials producing an acute toxic insult and a progressive fibrogenic response, respectively. Cellular (total and differential counts) and biochemical parameters (LDH, N-acetyl-beta-D-glucosaminidase, total protein, albumin, fibronectin, and hyaluronic acid) were measured in bronchoalveolar lavage fluid following single and repeated intratracheal instillations. The results indicate that the delayed lung response observed after WC-Co is different from that after cobalt metal alone. A single intratracheal dose of WC-Co (1, 5, or 10 mg/100 g body wt) induced an acute alveolitis which persisted for at least 1 month. Four months after a single instillation of WC-Co, no clear histological lung fibrosis could however be evidenced, indicating a reversibility of the lesions. The effects of cobalt (0.06, 0.3, or 0.6 mg/100 g body wt) or tungsten carbide alone (1, 5, 10 mg/ 100 g body wt) were very modest, if any. Following repeated intratracheal instillations (four administrations at 1-month interval), increased lung hydroxyproline content and histopathological evidence of interstitial fibrosis were observed after WC-Co (4 x 1 mg/100 g body wt), but not after administration of each component separately, i.e., Co (4 x 0.06 mg/100 g body wt) or WC (4 x 1 mg/100 g body wt). The mechanism of the fibrotic
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
Robust visual tracking via speedup multiple kernel ridge regression
Qian, Cheng; Breckon, Toby P.; Li, Hui
2015-09-01
Most of the tracking methods attempt to build up feature spaces to represent the appearance of a target. However, limited by the complex structure of the distribution of features, the feature spaces constructed in a linear manner cannot characterize the nonlinear structure well. We propose an appearance model based on kernel ridge regression for visual tracking. Dense sampling is fulfilled around the target image patches to collect the training samples. In order to obtain a kernel space in favor of describing the target appearance, multiple kernel learning is introduced into the selection of kernels. Under the framework, instead of a single kernel, a linear combination of kernels is learned from the training samples to create a kernel space. Resorting to the circulant property of a kernel matrix, a fast interpolate iterative algorithm is developed to seek coefficients that are assigned to these kernels so as to give an optimal combination. After the regression function is learned, all candidate image patches gathered are taken as the input of the function, and the candidate with the maximal response is regarded as the object image patch. Extensive experimental results demonstrate that the proposed method outperforms other state-of-the-art tracking methods.
Multidimensional kernel estimation
Milosevic, Vukasin
2015-01-01
Kernel estimation is one of the non-parametric methods used for estimation of probability density function. Its first ROOT implementation, as part of RooFit package, has one major issue, its evaluation time is extremely slow making in almost unusable. The goal of this project was to create a new class (TKNDTree) which will follow the original idea of kernel estimation, greatly improve the evaluation time (using the TKTree class for storing the data and creating different user-controlled modes of evaluation) and add the interpolation option, for 2D case, with the help of the new Delaunnay2D class.
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...
DEFF Research Database (Denmark)
Barndorff-Nielsen, Ole Eiler; Hansen, P. Reinhard; Lunde, Asger
2009-01-01
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...
Waqas Khaliq, M.; Butt, M. Z.; Saleem, Murtaza
2017-07-01
Cylindrical specimens of (1 0 4) oriented zinc single crystal (diameter = 6 mm and length = 5 mm) were irradiated with 500 keV C+1 ions with the help of a Pelletron accelerator. Six specimens were irradiated in an ultra-high vacuum (~10‒8 Torr) with different ion doses, namely 3.94 × 1014, 3.24 × 1015, 5.33 × 1015, 7.52 × 1015, 1.06 × 1016, and 1.30 × 1016 ions cm-2. A field emission scanning electron microscope (FESEM) was utilized for the morphological study of the irradiated specimens. Formation of nano- and sub-micron size rods, clusters, flower- and fork-like structures, etc, was observed. Surface roughness of the irradiated specimens showed an increasing trend with the ions dose. Energy dispersive x-ray spectroscopy (EDX) helped to determine chemical modifications in the specimens. It was found that carbon content varied in the range 22.86-31.20 wt.% and that oxygen content was almost constant, with an average value of 10.16 wt.%. The balance content was zinc. Structural parameters, i.e. crystallite size and lattice strain, were determined by Williamson-Hall analysis using x-ray diffraction (XRD) patterns of the irradiated specimens. Both crystallite size and lattice strain showed a decreasing trend with the increasing ions dose. A good linear relationship between crystallite size and lattice strain was observed. Surface hardness depicted a decreasing trend with the ions dose and followed an inverse Hall-Petch relation. FTIR spectra of the specimens revealed that absorption bands gradually diminish as the dose of singly-charged carbon ions is increased from 3.94 × 1014 ions cm-1 to 1.30 × 1016 ions cm-1. This indicates progressive deterioration of chemical bonds with the increase in ion dose.
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.
Comparison of soft and hard-switching effiency in a three-level single phase 60kW dc-ac converter
DEFF Research Database (Denmark)
Munk-Nielsen, Stig; Teodorescu, Remus; Bech, Michael Møller
2003-01-01
Efficiency measurements on a three-level single-phase soft-switched converter are presented and show a slightly improved efficiency compared with the hard-switched converter for output powers higher than 25 % of rated power. The resonant converter switches are Zero Voltage Switched (ZVS......) and a simple resonant circuit is used. Increased resonant converter efficiency enables a reduction in the semiconductor size pr. watt output power or an increase the switching frequency....
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...... returns measured over 5 or 10 minutes intervals. We show the new estimator is substantially more precise....
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...... returns measured over 5 or 10 min intervals. We show that the new estimator is substantially more precise....
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
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.
Directory of Open Access Journals (Sweden)
Yulin Jian
2017-06-01
Full Text Available A novel classification model, named the quantum-behaved particle swarm optimization (QPSO-based weighted multiple kernel extreme learning machine (QWMK-ELM, is proposed in this paper. Experimental validation is carried out with two different electronic nose (e-nose datasets. Being different from the existing multiple kernel extreme learning machine (MK-ELM algorithms, the combination coefficients of base kernels are regarded as external parameters of single-hidden layer feedforward neural networks (SLFNs. The combination coefficients of base kernels, the model parameters of each base kernel, and the regularization parameter are optimized by QPSO simultaneously before implementing the kernel extreme learning machine (KELM with the composite kernel function. Four types of common single kernel functions (Gaussian kernel, polynomial kernel, sigmoid kernel, and wavelet kernel are utilized to constitute different composite kernel functions. Moreover, the method is also compared with other existing classification methods: extreme learning machine (ELM, kernel extreme learning machine (KELM, k-nearest neighbors (KNN, support vector machine (SVM, multi-layer perceptron (MLP, radical basis function neural network (RBFNN, and probabilistic neural network (PNN. The results have demonstrated that the proposed QWMK-ELM outperforms the aforementioned methods, not only in precision, but also in efficiency for gas classification.
Jian, Yulin; Huang, Daoyu; Yan, Jia; Lu, Kun; Huang, Ying; Wen, Tailai; Zeng, Tanyue; Zhong, Shijie; Xie, Qilong
2017-06-19
A novel classification model, named the quantum-behaved particle swarm optimization (QPSO)-based weighted multiple kernel extreme learning machine (QWMK-ELM), is proposed in this paper. Experimental validation is carried out with two different electronic nose (e-nose) datasets. Being different from the existing multiple kernel extreme learning machine (MK-ELM) algorithms, the combination coefficients of base kernels are regarded as external parameters of single-hidden layer feedforward neural networks (SLFNs). The combination coefficients of base kernels, the model parameters of each base kernel, and the regularization parameter are optimized by QPSO simultaneously before implementing the kernel extreme learning machine (KELM) with the composite kernel function. Four types of common single kernel functions (Gaussian kernel, polynomial kernel, sigmoid kernel, and wavelet kernel) are utilized to constitute different composite kernel functions. Moreover, the method is also compared with other existing classification methods: extreme learning machine (ELM), kernel extreme learning machine (KELM), k-nearest neighbors (KNN), support vector machine (SVM), multi-layer perceptron (MLP), radical basis function neural network (RBFNN), and probabilistic neural network (PNN). The results have demonstrated that the proposed QWMK-ELM outperforms the aforementioned methods, not only in precision, but also in efficiency for gas classification.
International Nuclear Information System (INIS)
TANNENBAUM, M.J.
2005-01-01
Hard scattering in p-p collisions, discovered at the CERN ISR in 1972 by the method of leading particles, proved that the partons of Deeply Inelastic Scattering strongly interacted with each other. Further ISR measurements utilizing inclusive single or pairs of hadrons established that high p T particles are produced from states with two roughly back-to-back jets which are the result of scattering of constituents of the nucleons as described by Quantum Chromodynamics (QCD), which was developed during the course of these measurements. These techniques, which are the only practical method to study hard-scattering and jet phenomena in Au+Au central collisions at RHIC energies, are reviewed, as an introduction to present RHIC measurements
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 ...
Composition Kernel: A Software Solution for Constructing a Multi-OS Embedded System
Directory of Open Access Journals (Sweden)
Kinebuchi Yuki
2010-01-01
Full Text Available Abstract Modern high-end embedded systems require both predictable real-time scheduling and high-level abstraction interface to their OS kernels. Since these features are difficult to be balanced by a single OS, some methods that accommodate multiple different versions of OS kernels, typically real-time OS and general purpose OS, into a single device have been proposed. The hybrid kernel, one of those methods, executes a general purpose OS kernel as a task of real-time OS which can support those features with reasonable engineering effort. However when adapting the approach to various combinations of OS kernels, which is required in the real-world embedded system design, the engineering effort of modifying the kernel becomes not negligible. This article introduce a method called a composition kernel which uses a thin abstraction layer for accommodating kernels without making direct dependencies between them. The authors developed the abstraction layer on an SH-4A processor and executed kernels on top of it. The amount of modifications to the kernels was significantly smaller than that in related work, while introducing only negligible verhead to the performance of the kernels.
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...... information to be automatically incorporated in registrations and promises to improve the standard framework in several aspects. We present the mathematical foundations of LDDKBM and derive the KB-EPDiff evolution equations, which provide optimal warps in this new framework. To illustrate the resulting...
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....
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.
Buton, C.; Dawiec, A.; Graber-Bolis, J.; Arnaud, K.; Bérar, J. F.; Blanc, N.; Boudet, N.; Clémens, J. C.; Debarbieux, F.; Delpierre, P.; Dinkespiler, B.; Gastaldi, T.; Hustache, S.; Morel, C.; Pangaud, P.; Perez-Ponce, H.; Vigeolas, E.
2014-09-01
The CHIPSPECT consortium aims at building a large multi-modules CdTe based photon counting detector for hard X-ray applications. For this purpose, we tested nine XPAD3.2 single chip hybrids in various configurations (i.e. Ohmic vs. Schottky contacts or electrons vs. holes collection mode) in order to select the most performing and best suited configuration for our experimental requirements. Measurements have been done using both X-ray synchrotron beams and 241Am source. Preliminary results on the image quality, calibration, stability, homogeneity and linearity of the different types of detectors are presented.
Kojima, Sadaoki; Ikenouchi, Takahito; Arikawa, Yasunobu; Sakata, Shohei; Zhang, Zhe; Abe, Yuki; Nakai, Mitsuo; Nishimura, Hiroaki; Shiraga, Hiroyuki; Ozaki, Tetsuo; Miyamoto, Shuji; Yamaguchi, Masashi; Takemoto, Akinori; Fujioka, Shinsuke; Azechi, Hiroshi
2016-04-01
Hard X-ray spectroscopy is an essential diagnostics used to understand physical processes that take place in high energy density plasmas produced by intense laser-plasma interactions. A bundle of hard X-ray detectors, of which the responses have different energy thresholds, is used as a conventional single-shot spectrometer for high-flux (>10(13) photons/shot) hard X-rays. However, high energy resolution (Δhv/hv spectrometer because its energy resolution is limited by energy differences between the response thresholds. Experimental demonstration of a Compton X-ray spectrometer has already been performed for obtaining higher energy resolution than that of DET spectrometers. In this paper, we describe design details of the Compton X-ray spectrometer, especially dependence of energy resolution and absolute response on photon-electron converter design and its background reduction scheme, and also its application to the laser-plasma interaction experiment. The developed spectrometer was used for spectroscopy of bremsstrahlung X-rays generated by intense laser-plasma interactions using a 200 μm thickness SiO2 converter. The X-ray spectrum obtained with the Compton X-ray spectrometer is consistent with that obtained with a DET X-ray spectrometer, furthermore higher certainly of a spectral intensity is obtained with the Compton X-ray spectrometer than that with the DET X-ray spectrometer in the photon energy range above 5 MeV.
Guégan, F; Lamine, W; Chermette, H; Morell, C
2018-03-28
In a recent article Polanco-Ramirez et al. proposed new definitions of local chemical potential and local hardness starting from the first derivative of the energy with respect to the number of electrons and a smart use of the chain rule. In this comment we show that this derivation appears naturally in the Taylor expansion of the energy, showing that the construction of Polanco-Ramirez et al. is not artificially built.
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...
Classification Using the Zipfian Kernel
Czech Academy of Sciences Publication Activity Database
Jiřina, Marcel; Jiřina jr., M.
2015-01-01
Roč. 32, č. 2 (2015), s. 305-326 ISSN 0176-4268 R&D Projects: GA TA ČR TA01010490 Institutional support: RVO:67985807 Keywords : kernel machine * Zipfian kernel * multivariate data * correlation dimension * harmonic series * classification Subject RIV: JC - Computer Hardware ; Software Impact factor: 1.147, year: 2015
Model Selection in Kernel Ridge Regression
DEFF Research Database (Denmark)
Exterkate, Peter
, 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...... applicable, and we recommend their use instead of the popular polynomial kernels in general settings, in which no information on the data-generating process is available....
Viscosity kernel of molecular fluids
DEFF Research Database (Denmark)
Puscasu, Ruslan; Todd, Billy; Daivis, Peter
2010-01-01
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......The wave-vector dependent shear viscosities for butane and freely jointed chains have been determined. The transverse momentum density and stress autocorrelation functions have been determined by equilibrium molecular dynamics in both atomic and molecular hydrodynamic formalisms. The density......, 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...
Model selection in kernel ridge regression
DEFF Research Database (Denmark)
Exterkate, Peter
2013-01-01
. 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...... 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 applicable. Therefore, their use is recommended instead of the popular polynomial kernels in general settings, where no information...
Implementation of kernels on the Maestro processor
Suh, Jinwoo; Kang, D. I. D.; Crago, S. P.
Currently, most microprocessors use multiple cores to increase performance while limiting power usage. Some processors use not just a few cores, but tens of cores or even 100 cores. One such many-core microprocessor is the Maestro processor, which is based on Tilera's TILE64 processor. The Maestro chip is a 49-core, general-purpose, radiation-hardened processor designed for space applications. The Maestro processor, unlike the TILE64, has a floating point unit (FPU) in each core for improved floating point performance. The Maestro processor runs at 342 MHz clock frequency. On the Maestro processor, we implemented several widely used kernels: matrix multiplication, vector add, FIR filter, and FFT. We measured and analyzed the performance of these kernels. The achieved performance was up to 5.7 GFLOPS, and the speedup compared to single tile was up to 49 using 49 tiles.
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.
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...
Wheat Quality Council, Hard Spring Wheat Technical Committee, 2017 Crop
Nine experimental lines of hard spring wheat were grown at up to six locations in 2017 and evaluated for kernel, milling, and bread baking quality against the check variety Glenn. Wheat samples were submitted through the Wheat Quality Council and processed and milled at the USDA-ARS Hard Red Spring...
International Nuclear Information System (INIS)
Kojima, Sadaoki; Ikenouchi, Takahito; Arikawa, Yasunobu; Sakata, Shohei; Zhang, Zhe; Abe, Yuki; Nakai, Mitsuo; Nishimura, Hiroaki; Shiraga, Hiroyuki; Fujioka, Shinsuke; Azechi, Hiroshi; Ozaki, Tetsuo; Miyamoto, Shuji; Yamaguchi, Masashi; Takemoto, Akinori
2016-01-01
Hard X-ray spectroscopy is an essential diagnostics used to understand physical processes that take place in high energy density plasmas produced by intense laser-plasma interactions. A bundle of hard X-ray detectors, of which the responses have different energy thresholds, is used as a conventional single-shot spectrometer for high-flux (>10 13 photons/shot) hard X-rays. However, high energy resolution (Δhv/hv < 0.1) is not achievable with a differential energy threshold (DET) X-ray spectrometer because its energy resolution is limited by energy differences between the response thresholds. Experimental demonstration of a Compton X-ray spectrometer has already been performed for obtaining higher energy resolution than that of DET spectrometers. In this paper, we describe design details of the Compton X-ray spectrometer, especially dependence of energy resolution and absolute response on photon-electron converter design and its background reduction scheme, and also its application to the laser-plasma interaction experiment. The developed spectrometer was used for spectroscopy of bremsstrahlung X-rays generated by intense laser-plasma interactions using a 200 μm thickness SiO 2 converter. The X-ray spectrum obtained with the Compton X-ray spectrometer is consistent with that obtained with a DET X-ray spectrometer, furthermore higher certainly of a spectral intensity is obtained with the Compton X-ray spectrometer than that with the DET X-ray spectrometer in the photon energy range above 5 MeV.
Dispersion representations for hard exclusive processes. Beyond the born approximation
Energy Technology Data Exchange (ETDEWEB)
Diehl, M. [Deutsches Elektronen-Synchrotron (DESY), Hamburg (Germany); Ivanov, D.Yu. [Sobolev Institute of Mathematics, Novosibirsk (Russian Federation)
2007-07-15
Several hard exclusive scattering processes admit a description in terms of generalized parton distributions and perturbative hard-scattering kernels. Both the physical amplitude and the hard-scattering kernels fulfill dispersion relations. We give a detailed investigation of their consistency at all orders in perturbation theory. The results shed light on the information about generalized parton distributions that can be extracted from the real and imaginary parts of exclusive amplitudes. They also provide a practical consistency check for models of these distributions in which Lorentz invariance is not exactly satisfied. (orig.)
Yan, Qi; Xiao, Li-Qun; Su, Mei-Ying; Mei, Yan; Shi, Bin
This systematic review aimed to compare immediate protocols with conventional protocols of single-tooth implants in terms of changes in the surrounding hard and soft tissue in the esthetic area. Electronic and manual searches were performed in PubMed, EMBASE, Cochrane, and other data systems for research articles published between January 2001 and December 2014. Only randomized controlled trials (RCTs) reporting on hard and or soft tissue characteristics following a single-tooth implant were included. Based on the protocol used in each study, the included studies were categorized into three groups to assess the relationships between the factors and related esthetic indexes. Variables such as marginal bone level changes (mesial, distal, and mean bone level), peri-implant soft tissue changes (papilla level, midbuccal mucosa, and probing depth), and other esthetic indices were taken into consideration. The data were analyzed using RevMan version 5.3, Stata 12, and GRADEpro 3.6.1 software. A total of 13 RCTs met the inclusion criteria. Four studies examined immediate implant placement, five studies examined immediate implant restoration, and four studies examined immediate loading. Comparing the bone level changes following immediate and conventional restoration, no significant differences were found in the bone level of the mesial site (standard mean difference [SMD] = -0.04 mm; 95% confidence interval [CI]: -0.25 to 0.17 mm), the distal site (SMD = -0.15 mm; 95% CI: -0.38 to 0.09 mm), and the mean bone level changes (SMD = 0.05 mm; 95% CI: -0.18 to 0.27 mm). The difference in the marginal bone level changes between immediate and conventional loading was also not statistically significant (SMD = -0.05 mm; 95% CI: -0.15 to 0.06 mm for the mesial site and SMD = -0.02 mm; 95% CI: -0.09 to 0.05 mm for the distal site). Soft tissue changes following immediate and conventional restoration reported no significant differences in the papillae level of the mesial site (SMD = 0
Signaling in Early Maize Kernel Development.
Doll, Nicolas M; Depège-Fargeix, Nathalie; Rogowsky, Peter M; Widiez, Thomas
2017-03-06
Developing the next plant generation within the seed requires the coordination of complex programs driving pattern formation, growth, and differentiation of the three main seed compartments: the embryo (future plant), the endosperm (storage compartment), representing the two filial tissues, and the surrounding maternal tissues. This review focuses on the signaling pathways and molecular players involved in early maize kernel development. In the 2 weeks following pollination, functional tissues are shaped from single cells, readying the kernel for filling with storage compounds. Although the overall picture of the signaling pathways regulating embryo and endosperm development remains fragmentary, several types of molecular actors, such as hormones, sugars, or peptides, have been shown to be involved in particular aspects of these developmental processes. These molecular actors are likely to be components of signaling pathways that lead to transcriptional programming mediated by transcriptional factors. Through the integrated action of these components, multiple types of information received by cells or tissues lead to the correct differentiation and patterning of kernel compartments. In this review, recent advances regarding the four types of molecular actors (hormones, sugars, peptides/receptors, and transcription factors) involved in early maize development are presented. Copyright © 2017 The Author. Published by Elsevier Inc. All rights reserved.
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.
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...
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.
Heat kernels and critical limits
Pickrell, Doug
2007-01-01
This paper is an exposition of several questions linking heat kernel measures on infinite dimensional Lie groups, limits associated with critical Sobolev exponents, and Feynmann-Kac measures for sigma models.
Wendel, Erica; Cawthon, Stephanie W; Ge, Jin Jin; Beretvas, S Natasha
2015-04-01
The authors assessed the quality of single-case design (SCD) studies that assess the impact of interventions on outcomes for individuals who are deaf or hard-of-hearing (DHH). More specifically, the What Works Clearinghouse (WWC) standards for SCD research were used to assess design quality and the strength of evidence of peer-reviewed studies available in the peer-reviewed, published literature. The analysis yielded four studies that met the WWC standards for design quality, of which two demonstrated moderate to strong evidence for efficacy of the studied intervention. Results of this review are discussed in light of the benefits and the challenges to applying the WWC design standards to research with DHH individuals and other diverse, low-incidence populations. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.
Cannon, Joanna E; Guardino, Caroline; Antia, Shirin D; Luckner, John L
2016-01-01
The field of education of deaf and hard of hearing (DHH) students has a paucity of evidence-based practices (EBPs) to guide instruction. The authors discussed how the research methodology of single-case design (SCD) can be used to build EBPs through direct and systematic replication of studies. An overview of SCD research methods is presented, including an explanation of how internal and external validity issues are addressed, and why SCD is appropriate for intervention research with DHH children. The authors then examine the SCD research in the field according to quality indicators (QIs; at the individual level and as a body of evidence) to determine the existing evidence base. Finally, future replication areas are recommended to fill the gaps in SCD research with students who are DHH in order to add to the evidence base in the field.
Multineuron spike train analysis with R-convolution linear combination kernel.
Tezuka, Taro
2018-06-01
A spike train kernel provides an effective way of decoding information represented by a spike train. Some spike train kernels have been extended to multineuron spike trains, which are simultaneously recorded spike trains obtained from multiple neurons. However, most of these multineuron extensions were carried out in a kernel-specific manner. In this paper, a general framework is proposed for extending any single-neuron spike train kernel to multineuron spike trains, based on the R-convolution kernel. Special subclasses of the proposed R-convolution linear combination kernel are explored. These subclasses have a smaller number of parameters and make optimization tractable when the size of data is limited. The proposed kernel was evaluated using Gaussian process regression for multineuron spike trains recorded from an animal brain. It was compared with the sum kernel and the population Spikernel, which are existing ways of decoding multineuron spike trains using kernels. The results showed that the proposed approach performs better than these kernels and also other commonly used neural decoding methods. Copyright © 2018 Elsevier Ltd. All rights reserved.
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.
Searching remote homology with spectral clustering with symmetry in neighborhood cluster kernels.
Maulik, Ujjwal; Sarkar, Anasua
2013-01-01
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.
Option Valuation with Volatility Components, Fat Tails, and Non-Monotonic Pricing Kernels
DEFF Research Database (Denmark)
Babaoglu, Kadir; Christoffersen, Peter; Heston, Steven L.
We nest multiple volatility components, fat tails and a U-shaped pricing kernel in a single option model and compare their contribution to describing returns and option data. All three features lead to statistically significant model improvements. A U-shaped pricing kernel is economically most im...
Yang, Yan-Zhuo; Ding, Shuo; Wang, Yong; Li, Cui-Ling; Shen, Yun; Meeley, Robert; McCarty, Donald R; Tan, Bao-Cai
2017-06-01
Vitamin B 6 , an essential cofactor for a range of biochemical reactions and a potent antioxidant, plays important roles in plant growth, development, and stress tolerance. Vitamin B 6 deficiency causes embryo lethality in Arabidopsis ( Arabidopsis thaliana ), but the specific role of vitamin B 6 biosynthesis in endosperm development has not been fully addressed, especially in monocot crops, where endosperm constitutes the major portion of the grain. Through molecular characterization of a small kernel2 ( smk2 ) mutant in maize, we reveal that vitamin B 6 has differential effects on embryogenesis and endosperm development in maize. The B 6 vitamer pyridoxal 5'-phosphate (PLP) is drastically reduced in both the smk2 embryo and the endosperm. However, whereas embryogenesis of the smk2 mutant is arrested at the transition stage, endosperm formation is nearly normal. Cloning reveals that Smk2 encodes the glutaminase subunit of the PLP synthase complex involved in vitamin B 6 biosynthesis de novo. Smk2 partially complements the Arabidopsis vitamin B 6 -deficient mutant pdx2.1 and Saccharomyces cerevisiae pyridoxine auxotrophic mutant MML21. Smk2 is constitutively expressed in the maize plant, including developing embryos. Analysis of B 6 vitamers indicates that the endosperm accumulates a large amount of pyridoxamine 5'-phosphate (PMP). These results indicate that vitamin B 6 is essential to embryogenesis but has a reduced role in endosperm development in maize. The vitamin B 6 required for seed development is synthesized in the seed, and the endosperm accumulates PMP probably as a storage form of vitamin B 6 . © 2017 American Society of Plant Biologists. All Rights Reserved.
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...
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.
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...
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
Batched Triangular Dense Linear Algebra Kernels for Very Small Matrix Sizes on GPUs
Charara, Ali
2017-03-06
Batched dense linear algebra kernels are becoming ubiquitous in scientific applications, ranging from tensor contractions in deep learning to data compression in hierarchical low-rank matrix approximation. Within a single API call, these kernels are capable of simultaneously launching up to thousands of similar matrix computations, removing the expensive overhead of multiple API calls while increasing the occupancy of the underlying hardware. A challenge is that for the existing hardware landscape (x86, GPUs, etc.), only a subset of the required batched operations is implemented by the vendors, with limited support for very small problem sizes. We describe the design and performance of a new class of batched triangular dense linear algebra kernels on very small data sizes using single and multiple GPUs. By deploying two-sided recursive formulations, stressing the register usage, maintaining data locality, reducing threads synchronization and fusing successive kernel calls, the new batched kernels outperform existing state-of-the-art implementations.
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.
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 squ...... 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....
Ponce-García, Néstor; Ramírez-Wong, Benjamín; Torres-Chávez, Patricia I; Figueroa-Cárdenas, Juan de Dios; Serna-Saldívar, Sergio O; Cortez-Rocha, Mario O; Escalante-Aburto, Anayansi
2017-03-01
The aim of this research was to evaluate the visco-elastic properties of conditioned wheat kernels and their doughs by applying the compression test under a small strain. Conditioned wheat kernels and their doughs, from soft and hard wheat classes were evaluated for total work (W t ), elastic work (W e ) and plastic work (W p ). Soft wheat kernels showed lower W e than W p , while the hard wheat kernels had a W e that was higher than W p . Regarding dough visco-elasticity, cultivars from soft and hard wheat showed higher W p than W e . The degree of elasticity (DE%) of the conditioned wheat kernel related to its dough decreased ∼46% in both wheat classes. The W t , W e and W p from the soft wheat kernel and dough correlated with physico-chemical and farinographic flour tests. The W t , W p and the maximum compression force (F max ) of the dough from hard wheat class presented highly significant negative correlations with wet gluten. The visco-elasticity parameters from compression test presented significant differences among conditioned wheat classes and their doughs. © 2016 Society of Chemical Industry. © 2016 Society of Chemical Industry.
Metabolisable energy values of whole palm kernel and palm kernel ...
African Journals Online (AJOL)
4.12 Kcal/kg DM. 4.36 and 4.13 Kcal/kg DM, respectively were the corresponding values for broiler chickens. No interaction between ingredients and birds was found but there were interactions among the bioavailable energy systems and the bird types. Keywords: Metabolisable energy, palm kernel layers, broilers.
Spine labeling in axial magnetic resonance imaging via integral kernels.
Miles, Brandon; Ben Ayed, Ismail; Hojjat, Seyed-Parsa; Wang, Michael H; Li, Shuo; Fenster, Aaron; Garvin, Gregory J
2016-12-01
This study investigates a fast integral-kernel algorithm for classifying (labeling) the vertebra and disc structures in axial magnetic resonance images (MRI). The method is based on a hierarchy of feature levels, where pixel classifications via non-linear probability product kernels (PPKs) are followed by classifications of 2D slices, individual 3D structures and groups of 3D structures. The algorithm further embeds geometric priors based on anatomical measurements of the spine. Our classifier requires evaluations of computationally expensive integrals at each pixel, and direct evaluations of such integrals would be prohibitively time consuming. We propose an efficient computation of kernel density estimates and PPK evaluations for large images and arbitrary local window sizes via integral kernels. Our method requires a single user click for a whole 3D MRI volume, runs nearly in real-time, and does not require an intensive external training. Comprehensive evaluations over T1-weighted axial lumbar spine data sets from 32 patients demonstrate a competitive structure classification accuracy of 99%, along with a 2D slice classification accuracy of 88%. To the best of our knowledge, such a structure classification accuracy has not been reached by the existing spine labeling algorithms. Furthermore, we believe our work is the first to use integral kernels in the context of medical images. Copyright Â© 2016 Elsevier Ltd. All rights reserved.
Energy Technology Data Exchange (ETDEWEB)
Hahn, C., E-mail: christoph.hahn@uni-jena.de; Höfer, S.; Kämpfer, T. [Helmholtz Institute Jena, 07743 Jena (Germany); Institute of Optics and Quantum Electronics, University of Jena, 07743 Jena (Germany); Weber, G.; Märtin, R. [Helmholtz Institute Jena, 07743 Jena (Germany); GSI Helmholtzzentrum für Schwerionenforschung GmbH, 64291 Darmstadt (Germany); Stöhlker, Th. [Helmholtz Institute Jena, 07743 Jena (Germany); Institute of Optics and Quantum Electronics, University of Jena, 07743 Jena (Germany); GSI Helmholtzzentrum für Schwerionenforschung GmbH, 64291 Darmstadt (Germany)
2016-04-15
Single-photon spectroscopy of pulsed, high-intensity sources of hard X-rays — such as laser-generated plasmas — is often hampered by the pileup of several photons absorbed by the unsegmented, large-volume sensors routinely used for the detection of high-energy radiation. Detectors based on the Timepix chip, with a segmentation pitch of 55 μm and the possibility to be equipped with high-Z sensor chips, constitute an attractive alternative to commonly used passive solutions such as image plates. In this report, we present energy calibration and characterization measurements of such devices. The achievable energy resolution is comparable to that of scintillators for γ spectroscopy. Moreover, we also introduce a simple two-detector Compton polarimeter setup with a polarimeter quality of (98 ± 1)%. Finally, a proof-of-principle polarimetry experiment is discussed, where we studied the linear polarization of bremsstrahlung emitted by a laser-driven plasma and found an indication of the X-ray polarization direction depending on the polarization state of the incident laser pulse.
Hahn, C; Weber, G; Märtin, R; Höfer, S; Kämpfer, T; Stöhlker, Th
2016-04-01
Single-photon spectroscopy of pulsed, high-intensity sources of hard X-rays - such as laser-generated plasmas - is often hampered by the pileup of several photons absorbed by the unsegmented, large-volume sensors routinely used for the detection of high-energy radiation. Detectors based on the Timepix chip, with a segmentation pitch of 55 μm and the possibility to be equipped with high-Z sensor chips, constitute an attractive alternative to commonly used passive solutions such as image plates. In this report, we present energy calibration and characterization measurements of such devices. The achievable energy resolution is comparable to that of scintillators for γ spectroscopy. Moreover, we also introduce a simple two-detector Compton polarimeter setup with a polarimeter quality of (98 ± 1)%. Finally, a proof-of-principle polarimetry experiment is discussed, where we studied the linear polarization of bremsstrahlung emitted by a laser-driven plasma and found an indication of the X-ray polarization direction depending on the polarization state of the incident laser pulse.
Pei, Bo; Sun, Chao; Xue, Ruoyan; Xue, Yuan; Zhao, Ying; Zong, Ya-qi; Lin, Wei; Wang, Pei
2016-02-01
To describe a novel surgical strategy for circumferentially decompressing the T10 -L1 spinal canal when impinged upon by single level hard thoracic herniated disc (HTHD) via a modified costotransversectomy approach. This is a retrospective review of 26 patients (17 men, 9 women; mean age at surgery 48.5 years, range 20-77 years) who had undergone single level HTHD between T10 -L1 by circumferential decompression via a modified costotransversectomy approach. The characteristics of the approach are using a posterior midline covered incision, which keeps the paraspinal muscle intact and ensures direct visualization of circumferential spinal cord decompression of single level HTHD between T10 -L1 . The average operative time was 208 ± 36 min (range, 154-300 min), mean blood loss 789 ± 361 mL (range, 300-2000 mL), mean preoperative and postoperative mJOA scores 5.2 ± 1.5 and 9.0 ± 1.3, respectively (t = 19.7, P < 0.05). The rate of recovery of neurological function ranged from 33.3% to 100%. The ASIA grade improved in 24 patients (92.3%) and stabilized (no grade change) in two (7.7%). MRI indicated that the cross-sectional area of the dural sac at the level of maximum compression increased from 45.0 ± 5.8 mm(2) preoperatively to 113.5 ± 6.1 mm(2) postoperatively (t = 68.2, P < 0.05). Anterior tibialis muscle strength of the 15 patients with foot drop had a mean recovery rate of 95% at final follow-up. One patient who resumed work early after the surgery showed a significantly augmented Cobb angle. One patient had transient postoperative cerebrospinal fluid leakage. No patients showed neurological deterioration. This procedure achieves sufficient direct visualization for circumferential decompression of the spinal cord via a posterior midline covered costotransversectomy approach with friendly bleeding control and without muscle sacrifice. It is a reasonable alternative treatment option for thoracic myelopathy caused by single level HTHD between T10 -L1 . © 2016
Srinivasan, R.; Daw, M. S.; Noebe, R. D.; Mills, M. J.
2003-01-01
Ni-44at.% Al and Ni-50at.% single crystals were tested in compression in the hard (001) orientations. The dislocation processes and deformation behavior were studied as a function of temperature, strain and strain rate. A slip transition in NiAl occurs from alpha(111) slip to non-alphaaaaaaaaaaa9111) slip at intermediate temperatures. In Ni-50at.% Al single crystal, only alpha(010) dislocations are observed above the slip transition temperature. In contrast, alpha(101)(101) glide has been observed to control deformation beyond the slip transition temperature in Ni-44at.%Al. alpha(101) dislocations are observed primarily along both (111) directions in the glide plane. High-resolution transmission electron microscopy observations show that the core of the alpha(101) dislocations along these directions is decomposed into two alpha(010) dislocations, separated by a distance of approximately 2nm. The temperature window of stability for these alpha(101) dislocations depends upon the strain rate. At a strain rate of 1.4 x 10(exp -4)/s, lpha(101) dislocations are observed between 800 and 1000K. Complete decomposition of a alpha(101) dislocations into alpha(010) dislocations occurs beyond 1000K, leading to alpha(010) climb as the deformation mode at higher temperature. At lower strain rates, decomposition of a alpha(101) dislocations has been observed to occur along the edge orientation at temperatures below 1000K. Embedded-atom method calculations and experimental results indicate that alpha(101) dislocation have a large Peieris stress at low temperature. Based on the present microstructural observations and a survey of the literature with respect to vacancy content and diffusion in NiAl, a model is proposed for alpha(101)(101) glide in Ni-44at.%Al, and for the observed yield strength versus temperature behavior of Ni-Al alloys at intermediate and high temperatures.
Reducing Kernel Development Complexity in Distributed Environments
Lèbre , Adrien; Lottiaux , Renaud; Focht , Erich; Morin , Christine
2008-01-01
Setting up generic and fully transparent distributed services for clusters implies complex and tedious kernel developments. More flexible approaches such as user-space libraries are usually preferred with the drawback of requiring application recompilation. A second approach consists in using specific kernel modules (such as FUSE in Gnu/Linux system) to transfer kernel complexity into user space. In this paper, we present a new way to design and implement kernel distributed services for clust...
Oops! What about a Million Kernel Oopses?
Guo , Lisong; Senna Tschudin , Peter; Kono , Kenji; Muller , Gilles; Lawall , Julia
2013-01-01
When a failure occurs in the Linux kernel, the kernel emits an "oops", summarizing the execution context of the failure. Kernel oopses describe real Linux errors, and thus can help prioritize debugging efforts and motivate the design of tools to improve the reliability of Linux code. Nevertheless, the information is only meaningful if it is representative and can be interpreted correctly. In this paper, we study a repository of kernel oopses collected over 8 months by Red Hat. We consider the...
Derivative Kernels: Numerics and Applications.
Hosseini, Mahdi S; Plataniotis, Konstantinos N
2017-10-01
A generalized framework for numerical differentiation (ND) is proposed for constructing a finite impulse response (FIR) filter in closed form. The framework regulates the frequency response of ND filters for arbitrary derivative-order and cutoff frequency selected parameters relying on interpolating power polynomials and maximally flat design techniques. Compared with the state-of-the-art solutions, such as Gaussian kernels, the proposed ND filter is sharply localized in the Fourier domain with ripple-free artifacts. Here, we construct 2D MaxFlat kernels for image directional differentiation to calculate image differentials for arbitrary derivative order, cutoff level and steering angle. The resulted kernel library renders a new solution capable of delivering discrete approximation of gradients, Hessian, and higher-order tensors in numerous applications. We tested the utility of this library on three different imaging applications with main focus on the unsharp masking. The reported results highlight the high efficiency of the 2D MaxFlat kernel and its versatility with respect to robustness and parameter control accuracy.
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
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
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
Influence of soft kernel texture on the flour and baking quality of durum wheat
Durum wheat is predominantly grown in semi-arid to arid environments where common wheat does not flourish, especially in the Middle East, North Africa, Mediterranean Basin, and portions of North America. Durum kernels are extraordinarily hard when compared to their common wheat counterparts. Due to ...
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.
Directory of Open Access Journals (Sweden)
Violeta Andjelković
2011-06-01
Full Text Available Molecular and metabolic response of plants to a combination of two abiotic stresses is unique and cannot be directly extrapolated from the response of plants to each of the stresses individually. cDNA macroarray has become a useful tool to analyze expression profiles and compare the similarities and differences of various expression patterns. A macroarray of approximately 2,500 maize (Zea mays L. cDNAs was used for transcriptome profiling in response to single and simultaneous application of water and high temperature stress of maize developing kernels at 15 days after pollination. All stress treatments (water stress-WS, heat stress-HS and their combined application-CS induced changes in expression of 106 transcripts with 54 up-regulated and 52 down-regulated. There were 11 up-regulated and 15 down-regulated transcripts in common for all three stresses. Although these common transcripts showed existence of a mutual mechanism in stress response, the 23 transcripts induced only in CS indicate that plants responded in a different manner when exposed to simultaneous effects of both stresses. A glimpse of functions regulated under WS, HS and CS is provided, and also the common and different responses between individual and simultaneous stresses.A resposta molecular e metabólica de plantas a uma combinação de dois estresses abióticos é singular, e não pode ser diretamente extrapolada da resposta das plantas a cada um dos estresses individualmente. O macroarranjo do cDNA, tornou-se uma ferramenta útil para analisar os perfís de expressão e comparar as similaridades e diferenças de vários padrões de expressão. Um macroarranjo de 2.500 cDNAs de milho (Zea mays L. foi usado para traçar um perfil de transcriptoma em resposta ao stress ocasionado por uma única e simultânea aplicação de água e alta temperatura em espigas em desenvolvimento, 15 dias após a polinização. Todos os tratamentos de stress (stress de água - SA, stress de calor
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.
El Hamed, Ahmed Mamdouh Abd; Elmoghazy, Hazem; Aldahshoury, Mohamed; Riad, Ahmed; Mostafa, Mohammed; Farag, Fawzy; Gamal, Wael
2017-01-01
Objective To evaluate the stone hardness in predicting the need for single or two sessions of retrograde intrarenal surgery (RIRS) for renal pelvis stones of 2–3 cm in size. Material and methods Ninety-six patients (64 male and 32 female) with only renal stones (2.5±0.3 cm) underwent RIRS using flexible 7.5 Fr ureteroscope (FURS). The stone hardness was evaluated by preoperative non-contrast computed tomography (NCCT). The patients were divided into two groups based on stone hardness: Group I (n=54) (hard stones - Hounsfield Unit (HU) >1000) and group II (n=42) (not hard stone - HU <1000). The stone-free rate, the operative time, any intra or postoperative complications and the need for second sessions of RIRS were evaluated. Results All stones were successfully accessed. Intraoperative complications were not reported. The initial stone-free rate was 40% in Group I and 95% in Group II after a single session (p=0.01). A second session FURS was needed in 32 cases of Group I (40%) where postoperative CT showed significant residual stone fragments of 6±2 mm, and stone-free rate up to 100 percent. On the contrary only 2 cases from Group II underwent second session FURS (p=0.01). The operative times were 75±15 minutes in Group I and 55±13 minutes in Group II (p<0.01). Six patients (4 in group I and 2 in group II) had postoperative high-grade fever (Clavien Grade II). Conclusion Stone hardness had a significant impact on the decision of performing single versus two sessions of FURS for renal pelvic stones of 2–3 cm rather than the stone size alone. PMID:28717539
El Hamed, Ahmed Mamdouh Abd; Elmoghazy, Hazem; Aldahshoury, Mohamed; Riad, Ahmed; Mostafa, Mohammed; Farag, Fawzy; Gamal, Wael
2017-06-01
To evaluate the stone hardness in predicting the need for single or two sessions of retrograde intrarenal surgery (RIRS) for renal pelvis stones of 2-3 cm in size. Ninety-six patients (64 male and 32 female) with only renal stones (2.5±0.3 cm) underwent RIRS using flexible 7.5 Fr ureteroscope (FURS). The stone hardness was evaluated by preoperative non-contrast computed tomography (NCCT). The patients were divided into two groups based on stone hardness: Group I (n=54) (hard stones - Hounsfield Unit (HU) >1000) and group II (n=42) (not hard stone - HU <1000). The stone-free rate, the operative time, any intra or postoperative complications and the need for second sessions of RIRS were evaluated. All stones were successfully accessed. Intraoperative complications were not reported. The initial stone-free rate was 40% in Group I and 95% in Group II after a single session (p=0.01). A second session FURS was needed in 32 cases of Group I (40%) where postoperative CT showed significant residual stone fragments of 6±2 mm, and stone-free rate up to 100 percent. On the contrary only 2 cases from Group II underwent second session FURS (p=0.01). The operative times were 75±15 minutes in Group I and 55±13 minutes in Group II (p<0.01). Six patients (4 in group I and 2 in group II) had postoperative high-grade fever (Clavien Grade II). Stone hardness had a significant impact on the decision of performing single versus two sessions of FURS for renal pelvic stones of 2-3 cm rather than the stone size alone.
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.
Three-Dimensional Sensitivity Kernels of Z/H Amplitude Ratios of Surface and Body Waves
Bao, X.; Shen, Y.
2017-12-01
The ellipticity of Rayleigh wave particle motion, or Z/H amplitude ratio, has received increasing attention in inversion for shallow Earth structures. Previous studies of the Z/H ratio assumed one-dimensional (1D) velocity structures beneath the receiver, ignoring the effects of three-dimensional (3D) heterogeneities on wave amplitudes. This simplification may introduce bias in the resulting models. Here we present 3D sensitivity kernels of the Z/H ratio to Vs, Vp, and density perturbations, based on finite-difference modeling of wave propagation in 3D structures and the scattering-integral method. Our full-wave approach overcomes two main issues in previous studies of Rayleigh wave ellipticity: (1) the finite-frequency effects of wave propagation in 3D Earth structures, and (2) isolation of the fundamental mode Rayleigh waves from Rayleigh wave overtones and converted Love waves. In contrast to the 1D depth sensitivity kernels in previous studies, our 3D sensitivity kernels exhibit patterns that vary with azimuths and distances to the receiver. The laterally-summed 3D sensitivity kernels and 1D depth sensitivity kernels, based on the same homogeneous reference model, are nearly identical with small differences that are attributable to the single period of the 1D kernels and a finite period range of the 3D kernels. We further verify the 3D sensitivity kernels by comparing the predictions from the kernels with the measurements from numerical simulations of wave propagation for models with various small-scale perturbations. We also calculate and verify the amplitude kernels for P waves. This study shows that both Rayleigh and body wave Z/H ratios provide vertical and lateral constraints on the structure near the receiver. With seismic arrays, the 3D kernels afford a powerful tool to use the Z/H ratios to obtain accurate and high-resolution Earth models.
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....
Berthelsen, Connie Bøttcher; Hølge-Hazelton, Bibi
2018-04-01
To explore how nurse researchers in clinical positions experience the presence of a nursing research culture in clinical practice. Higher demands in the hospitals for increasing the quality of patient care engender a higher demand for the skills of health professionals and evidence-based practice. However, the utilisation of nursing research in clinical practice is still limited. Intrinsic single case study design underlined by a constructivist perspective. Data were produced through a focus group interview with seven nurse researchers employed in clinical practice in two university hospitals in Zealand, Denmark, to capture the intrinsic aspects of the concept of nursing research culture in the context of clinical practice. A thematic analysis was conducted based on Braun and Clarke's theoretical guideline. "Caught between a rock and a hard place" was constructed as the main theme describing how nurse researchers in clinical positions experience the presence of a nursing research culture in clinical practice. The main theme was supported by three subthemes: Minimal academic tradition affects nursing research; Minimal recognition from physicians affects nursing research; and Moving towards a research culture. The nurse researchers in this study did not experience the presence of a nursing research culture in clinical practice, however; they called for more attention on removing barriers against research utilisation, promotion of applied research and interdisciplinary research collaboration, and passionate management support. The results of this case study show the pressure which nurse researchers employed in clinical practice are exposed to, and give examples on how to accommodate the further development of a nursing research culture in clinical practice. © 2017 John Wiley & Sons Ltd.
American Society for Testing and Materials. Philadelphia
2007-01-01
1.1 Conversion Table 1 presents data in the Rockwell C hardness range on the relationship among Brinell hardness, Vickers hardness, Rockwell hardness, Rockwell superficial hardness, Knoop hardness, and Scleroscope hardness of non-austenitic steels including carbon, alloy, and tool steels in the as-forged, annealed, normalized, and quenched and tempered conditions provided that they are homogeneous. 1.2 Conversion Table 2 presents data in the Rockwell B hardness range on the relationship among Brinell hardness, Vickers hardness, Rockwell hardness, Rockwell superficial hardness, Knoop hardness, and Scleroscope hardness of non-austenitic steels including carbon, alloy, and tool steels in the as-forged, annealed, normalized, and quenched and tempered conditions provided that they are homogeneous. 1.3 Conversion Table 3 presents data on the relationship among Brinell hardness, Vickers hardness, Rockwell hardness, Rockwell superficial hardness, and Knoop hardness of nickel and high-nickel alloys (nickel content o...
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.
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.
International Nuclear Information System (INIS)
Dan, J.P.; Boving, H.J.; Hintermann, H.E.
1993-01-01
Hard, wear resistant and low friction coatings are presently produced on a world-wide basis, by different processes such as electrochemical or electroless methods, spray technologies, thermochemical, CVD and PVD. Some of the most advanced processes, especially those dedicated to thin film depositions, basically belong to CVD or PVD technologies, and will be looked at in more detail. The hard coatings mainly consist of oxides, nitrides, carbides, borides or carbon. Over the years, many processes have been developed which are variations and/or combinations of the basic CVD and PVD methods. The main difference between these two families of deposition techniques is that the CVD is an elevated temperature process (≥ 700 C), while the PVD on the contrary, is rather a low temperature process (≤ 500 C); this of course influences the choice of substrates and properties of the coating/substrate systems. Fundamental aspects of the vapor phase deposition techniques and some of their influences on coating properties will be discussed, as well as the very important interactions between deposit and substrate: diffusions, internal stress, etc. Advantages and limitations of CVD and PVD respectively will briefly be reviewed and examples of applications of the layers will be given. Parallel to the development and permanent updating of surface modification technologies, an effort was made to create novel characterisation methods. A close look will be given to the coating adherence control by means of the scratch test, at the coating hardness measurement by means of nanoindentation, at the coating wear resistance by means of a pin-on-disc tribometer, and at the surface quality evaluation by Atomic Force Microscopy (AFM). Finally, main important trends will be highlighted. (orig.)
Nonlinear Deep Kernel Learning for Image Annotation.
Jiu, Mingyuan; Sahbi, Hichem
2017-02-08
Multiple kernel learning (MKL) is a widely used technique for kernel design. Its principle consists in learning, for a given support vector classifier, the most suitable convex (or sparse) linear combination of standard elementary kernels. However, these combinations are shallow and often powerless to capture the actual similarity between highly semantic data, especially for challenging classification tasks such as image annotation. In this paper, we redefine multiple kernels using deep multi-layer networks. In this new contribution, a deep multiple kernel is recursively defined as a multi-layered combination of nonlinear activation functions, each one involves a combination of several elementary or intermediate kernels, and results into a positive semi-definite deep kernel. We propose four different frameworks in order to learn the weights of these networks: supervised, unsupervised, kernel-based semisupervised and Laplacian-based semi-supervised. When plugged into support vector machines (SVMs), the resulting deep kernel networks show clear gain, compared to several shallow kernels for the task of image annotation. Extensive experiments and analysis on the challenging ImageCLEF photo annotation benchmark, the COREL5k database and the Banana dataset validate the effectiveness of the proposed method.
A framework for dense triangular matrix kernels on various manycore architectures
Charara, Ali
2017-06-06
We present a new high-performance framework for dense triangular Basic Linear Algebra Subroutines (BLAS) kernels, ie, triangular matrix-matrix multiplication (TRMM) and triangular solve (TRSM), on various manycore architectures. This is an extension of a previous work on a single GPU by the same authors, presented at the EuroPar\\'16 conference, in which we demonstrated the effectiveness of recursive formulations in enhancing the performance of these kernels. In this paper, the performance of triangular BLAS kernels on a single GPU is further enhanced by implementing customized in-place CUDA kernels for TRMM and TRSM, which are called at the bottom of the recursion. In addition, a multi-GPU implementation of TRMM and TRSM is proposed and we show an almost linear performance scaling, as the number of GPUs increases. Finally, the algorithmic recursive formulation of these triangular BLAS kernels is in fact oblivious to the targeted hardware architecture. We, therefore, port these recursive kernels to homogeneous x86 hardware architectures by relying on the vendor optimized BLAS implementations. Results reported on various hardware architectures highlight a significant performance improvement against state-of-the-art implementations. These new kernels are freely available in the KAUST BLAS (KBLAS) open-source library at https://github.com/ecrc/kblas.
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.
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...
kFOIL: Learning simple relational kernels
Landwehr, Niels; Passerini, Andrea; De Raedt, Luc; Frasconi, Paolo
2006-01-01
A novel and simple combination of inductive logic programming with kernel methods is presented. The kFOIL algorithm integrates the well-known inductive logic programming system FOIL with kernel methods. The feature space is constructed by leveraging FOIL search for a set of relevant clauses. The search is driven by the performance obtained by a support vector machine based on the resulting kernel. In this way, kFOIL implements a dynamic propositionalization approach. Both classification an...
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.
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.
7 CFR 51.1403 - Kernel color classification.
2010-01-01
... models of pecan kernels, illustrate the color intensities implied by the terms “golden,” “light brown... Kernel color classification. (a) The skin color of pecan kernels may be described in terms of the color...
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
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.
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)
Modelling Issues in Kernel Ridge Regression
P. Exterkate (Peter)
2011-01-01
textabstractKernel 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
Kernel method for corrections to scaling.
Harada, Kenji
2015-07-01
Scaling analysis, in which one infers scaling exponents and a scaling function in a scaling law from given data, is a powerful tool for determining universal properties of critical phenomena in many fields of science. However, there are corrections to scaling in many cases, and then the inference problem becomes ill-posed by an uncontrollable irrelevant scaling variable. We propose a new kernel method based on Gaussian process regression to fix this problem generally. We test the performance of the new kernel method for some example cases. In all cases, when the precision of the example data increases, inference results of the new kernel method correctly converge. Because there is no limitation in the new kernel method for the scaling function even with corrections to scaling, unlike in the conventional method, the new kernel method can be widely applied to real data in critical phenomena.
Multiple kernel SVR based on the MRE for remote sensing water depth fusion detection
Wang, Jinjin; Ma, Yi; Zhang, Jingyu
2018-03-01
Remote sensing has an important means of water depth detection in coastal shallow waters and reefs. Support vector regression (SVR) is a machine learning method which is widely used in data regression. In this paper, SVR is used to remote sensing multispectral bathymetry. Aiming at the problem that the single-kernel SVR method has a large error in shallow water depth inversion, the mean relative error (MRE) of different water depth is retrieved as a decision fusion factor with single kernel SVR method, a multi kernel SVR fusion method based on the MRE is put forward. And taking the North Island of the Xisha Islands in China as an experimentation area, the comparison experiments with the single kernel SVR method and the traditional multi-bands bathymetric method are carried out. The results show that: 1) In range of 0 to 25 meters, the mean absolute error(MAE)of the multi kernel SVR fusion method is 1.5m,the MRE is 13.2%; 2) Compared to the 4 single kernel SVR method, the MRE of the fusion method reduced 1.2% (1.9%) 3.4% (1.8%), and compared to traditional multi-bands method, the MRE reduced 1.9%; 3) In 0-5m depth section, compared to the single kernel method and the multi-bands method, the MRE of fusion method reduced 13.5% to 44.4%, and the distribution of points is more concentrated relative to y=x.
Adaptive Shape Kernel-Based Mean Shift Tracker in Robot Vision System
Directory of Open Access Journals (Sweden)
Chunmei Liu
2016-01-01
Full Text Available This paper proposes an adaptive shape kernel-based mean shift tracker using a single static camera for the robot vision system. The question that we address in this paper is how to construct such a kernel shape that is adaptive to the object shape. We perform nonlinear manifold learning technique to obtain the low-dimensional shape space which is trained by training data with the same view as the tracking video. The proposed kernel searches the shape in the low-dimensional shape space obtained by nonlinear manifold learning technique and constructs the adaptive kernel shape in the high-dimensional shape space. It can improve mean shift tracker performance to track object position and object contour and avoid the background clutter. In the experimental part, we take the walking human as example to validate that our method is accurate and robust to track human position and describe human contour.
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.
Karmeshu; Gupta, Varun; Kadambari, K V
2011-06-01
A single neuronal model incorporating distributed delay (memory)is proposed. The stochastic model has been formulated as a Stochastic Integro-Differential Equation (SIDE) which results in the underlying process being non-Markovian. A detailed analysis of the model when the distributed delay kernel has exponential form (weak delay) has been carried out. The selection of exponential kernel has enabled the transformation of the non-Markovian model to a Markovian model in an extended state space. For the study of First Passage Time (FPT) with exponential delay kernel, the model has been transformed to a system of coupled Stochastic Differential Equations (SDEs) in two-dimensional state space. Simulation studies of the SDEs provide insight into the effect of weak delay kernel on the Inter-Spike Interval(ISI) distribution. A measure based on Jensen-Shannon divergence is proposed which can be used to make a choice between two competing models viz. distributed delay model vis-á-vis LIF model. An interesting feature of the model is that the behavior of (CV(t))((ISI)) (Coefficient of Variation) of the ISI distribution with respect to memory kernel time constant parameter η reveals that neuron can switch from a bursting state to non-bursting state as the noise intensity parameter changes. The membrane potential exhibits decaying auto-correlation structure with or without damped oscillatory behavior depending on the choice of parameters. This behavior is in agreement with empirically observed pattern of spike count in a fixed time window. The power spectral density derived from the auto-correlation function is found to exhibit single and double peaks. The model is also examined for the case of strong delay with memory kernel having the form of Gamma distribution. In contrast to fast decay of damped oscillations of the ISI distribution for the model with weak delay kernel, the decay of damped oscillations is found to be slower for the model with strong delay kernel.
Durum (T. turgidum subsp. durum) wheat production worldwide is substantially less than that of common wheat (Triticum aestivum). Durum kernels are extremely hard; leading to most durum wheat being milled into semolina. Durum wheat production is limited in part due to the relatively limited end-user ...
Approximate N3LO Higgs-boson production cross section using physical-kernel constraints
International Nuclear Information System (INIS)
Florian, D. de; Moch, S.; Hamburg Univ.; Vogt, A.
2014-08-01
The single-logarithmic enhancement of the physical kernel for Higgs production by gluon-gluon fusion in the heavy top-quark limit is employed to derive the leading so far unknown contributions, ln 5,4,3 (1-z), to the N 3 LO coefficient function in the threshold expansion. Also using knowledge from Higgs-exchange DIS to estimate the remaining terms not vanishing for z=m 2 H /s→1, these results are combined with the recently completed soft+virtual contributions to provide an uncertainty band for the complete N 3 LO correction. For the 2008 MSTW parton distributions these N 3 LO contributions increase the cross section at 14 TeV by (10±2)% and (3±2.5)% for the standard choices μ R =m H and μ R =m H /2 of the renormalization scale. The remaining uncertainty arising from the hard-scattering cross sections can be quantified as no more than 5%, which is smaller than that due to the strong coupling and the parton distributions.
Santini, Ario; Naaman, Reem Khalil; Aldossary, Mohammed Saeed
2017-04-01
To quantify light energy transmission through two bulk-fill resin-based composites and to measure the top to bottom surface Vickers hardness ratio (VHratio) of samples of various incremental thicknesses, using either a single-wave or dual-wave light curing unit (LCU). Tetric EvoCeram Bulk Fill (TECBF) and SonicFill (SF) were studied. Using MARC-RC, the irradiance delivered to the top surface of the samples 2, 3, 4 and 5 mm thick (n= 5 for each thickness) was adjusted to 800 mW/cm2 for 20 seconds (16 J/cm2) using either a single-wave, Bluephase or a dual-wave, Bluephase G2 LCUs. Light energy transmission through to the bottom surface of the specimens was measured at real time using MARC-RC. The Vickers hardness (VH) was determined using Vickers micro hardness tester and the VHratio was calculated. Data were analyzed using a general linear model in Minitab 16; α= 0.05. TECBF was more translucent than SF (Pcured with the dual-wave Bluephase G2). SF showed significantly higher VH ratio than TECBF at all different thickness levels (P 0.05). TECBF showed significantly greater VH ratio when cured with the single-wave Bluephase than when using the dual-wave Bluephase G2 (Plight energy through to the bottom surface and the VHratio are material dependent. Although TECBF is more translucent than SF, it showed lower VHratio compared to SF when cured with dual-wave Bluephase G2.
Hardness variability in commercial technologies
International Nuclear Information System (INIS)
Shaneyfelt, M.R.; Winokur, P.S.; Meisenheimer, T.L.; Sexton, F.W.; Roeske, S.B.; Knoll, M.G.
1994-01-01
The radiation hardness of commercial Floating Gate 256K E 2 PROMs from a single diffusion lot was observed to vary between 5 to 25 krad(Si) when irradiated at a low dose rate of 64 mrad(Si)/s. Additional variations in E 2 PROM hardness were found to depend on bias condition and failure mode (i.e., inability to read or write the memory), as well as the foundry at which the part was manufactured. This variability is related to system requirements, and it is shown that hardness level and variability affect the allowable mode of operation for E 2 PROMs in space applications. The radiation hardness of commercial 1-Mbit CMOS SRAMs from Micron, Hitachi, and Sony irradiated at 147 rad(Si)/s was approximately 12, 13, and 19 krad(Si), respectively. These failure levels appear to be related to increases in leakage current during irradiation. Hardness of SRAMs from each manufacturer varied by less than 20%, but differences between manufacturers are significant. The Qualified Manufacturer's List approach to radiation hardness assurance is suggested as a way to reduce variability and to improve the hardness level of commercial technologies
DEFF Research Database (Denmark)
Bøttcher Berthelsen, Connie; Hølge-Hazelton, Bibi
2017-01-01
for the skills of health professionals and evidence-based practice. However, the utilization of nursing research in clinical practice is still limited. DESIGN: Intrinsic single-case study design underlined by a constructivist perspective. METHODS: Data were produced through a focus group interview with seven......: 'Caught between a rock and a hard place' was constructed as the main theme describing how nurse researchers in clinical positions experience the presence of a nursing research culture in clinical practice. The main theme was supported by three sub-themes: Minimal academic tradition affects nursing...
A Visual Approach to Investigating Shared and Global Memory Behavior of CUDA Kernels
Rosen, Paul
2013-06-01
We present an approach to investigate the memory behavior of a parallel kernel executing on thousands of threads simultaneously within the CUDA architecture. Our top-down approach allows for quickly identifying any significant differences between the execution of the many blocks and warps. As interesting warps are identified, we allow further investigation of memory behavior by visualizing the shared memory bank conflicts and global memory coalescence, first with an overview of a single warp with many operations and, subsequently, with a detailed view of a single warp and a single operation. We demonstrate the strength of our approach in the context of a parallel matrix transpose kernel and a parallel 1D Haar Wavelet transform kernel. © 2013 The Author(s) Computer Graphics Forum © 2013 The Eurographics Association and Blackwell Publishing Ltd.
Kernel modifications and tryptophan content in QPM segregating generations
Directory of Open Access Journals (Sweden)
-Ignjatović-Micić Dragana
2010-01-01
Full Text Available Maize has poor nutritional value due to deficiency of two essential amino acids - tryptophan and lysine. Although recessive opaque2 (o2 mutation significantly increases their content in the endosperm, incorporation of opaque2 into high yielding cultivars was not commercially successful, because of its numerous agronomic and processing problems due to soft endosperm. Quality protein maize - QPM has lately been introduced as opaque2 maize with improved endosperm hardness and improved agronomic traits, but mostly within tropical and subtropical germplasm. The ongoing breeding project at MRI includes improvement of MRI opaque2 lines and conversion of standard lines to QPM germplasm. The main selection steps in QPM breeding involve assessing kernel modifications and tryptophan level in each generation. Herein, we present the results of the analysis for these traits on F3 and BC1F1 generations of QPM x opaque2, opaque2 x QPM and standard lines x QPM crosses. The results showed that the majority the genotypes had kernel types 2 and 3 (good modifications. The whole grain tryptophan content in F3 and BC1F1 genotypes of crosses between QPM and opaque2 germplasm was at the quality protein level, with a few exceptions. All BC1F1 genotypes of standard lines x QPM had tryptophan content in the range of normal maize, while majority of F3 genotypes had tryptophan content at level of QPM. The progeny (with increased tryptophan levels of QPM and opaque2 crosses had significantly higher tryptophan content compared to the progeny of crosses between standard and QPM lines - 0.098 to 0.114 and 0.080, respectively. All genotypes that had poorly modified kernels and/or low tryptophan content will be discarded from further breeding.
Optimizing memory-bound SYMV kernel on GPU hardware accelerators
Abdelfattah, Ahmad
2013-01-01
Hardware accelerators are becoming ubiquitous high performance scientific computing. They are capable of delivering an unprecedented level of concurrent execution contexts. High-level programming language extensions (e.g., CUDA), profiling tools (e.g., PAPI-CUDA, CUDA Profiler) are paramount to improve productivity, while effectively exploiting the underlying hardware. We present an optimized numerical kernel for computing the symmetric matrix-vector product on nVidia Fermi GPUs. Due to its inherent memory-bound nature, this kernel is very critical in the tridiagonalization of a symmetric dense matrix, which is a preprocessing step to calculate the eigenpairs. Using a novel design to address the irregular memory accesses by hiding latency 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 precision arithmetics, respectively. © 2013 Springer-Verlag.
Distance Based Multiple Kernel ELM: A Fast Multiple Kernel Learning Approach
Directory of Open Access Journals (Sweden)
Chengzhang Zhu
2015-01-01
Full Text Available We propose a distance based multiple kernel extreme learning machine (DBMK-ELM, which provides a two-stage multiple kernel learning approach with high efficiency. Specifically, DBMK-ELM first projects multiple kernels into a new space, in which new instances are reconstructed based on the distance of different sample labels. Subsequently, an l2-norm regularization least square, in which the normal vector corresponds to the kernel weights of a new kernel, is trained based on these new instances. After that, the new kernel is utilized to train and test extreme learning machine (ELM. Extensive experimental results demonstrate the superior performance of the proposed DBMK-ELM in terms of the accuracy and the computational cost.
NLO corrections to the Kernel of the BKP-equations
International Nuclear Information System (INIS)
Bartels, J.; Lipatov, L.N.; Vacca, G.P.
2012-01-01
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→3 kernel, computed in the tree approximation.
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... the provisions of this section. (a) Tamarind seed kernel powder is the ground kernel of tamarind seed...
Higher-order Gaussian kernel in bootstrap boosting algorithm ...
African Journals Online (AJOL)
The bootstrap boosting algorithm is a bias reduction scheme. The adoption of higher-order Gaussian kernel in a bootstrap boosting algorithm in kernel density estimation was investigated. The algorithm used the higher-order. Gaussian kernel instead of the regular fixed kernels. A comparison of the scheme with existing ...
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...
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 ...
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.
Classification of EEG Signals Using a Multiple Kernel Learning Support Vector Machine
Directory of Open Access Journals (Sweden)
Xiaoou Li
2014-07-01
Full Text Available In this study, a multiple kernel learning support vector machine algorithm is proposed for the identification of EEG signals including mental and cognitive tasks, which is a key component in EEG-based brain computer interface (BCI systems. The presented BCI approach included three stages: (1 a pre-processing step was performed to improve the general signal quality of the EEG; (2 the features were chosen, including wavelet packet entropy and Granger causality, respectively; (3 a multiple kernel learning support vector machine (MKL-SVM based on a gradient descent optimization algorithm was investigated to classify EEG signals, in which the kernel was defined as a linear combination of polynomial kernels and radial basis function kernels. Experimental results showed that the proposed method provided better classification performance compared with the SVM based on a single kernel. For mental tasks, the average accuracies for 2-class, 3-class, 4-class, and 5-class classifications were 99.20%, 81.25%, 76.76%, and 75.25% respectively. Comparing stroke patients with healthy controls using the proposed algorithm, we achieved the average classification accuracies of 89.24% and 80.33% for 0-back and 1-back tasks respectively. Our results indicate that the proposed approach is promising for implementing human-computer interaction (HCI, especially for mental task classification and identifying suitable brain impairment candidates.
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...
National Aeronautics and Space Administration — This data set includes the complete set of Hayabusa SPICE data files (kernel files'') for the surveying and collection phases of the mission. The SPICE data files,...
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...
Multiple Kernel Spectral Regression for Dimensionality Reduction
Directory of Open Access Journals (Sweden)
Bing Liu
2013-01-01
Full Text Available Traditional manifold learning algorithms, such as locally linear embedding, Isomap, and Laplacian eigenmap, only provide the embedding results of the training samples. To solve the out-of-sample extension problem, spectral regression (SR solves the problem of learning an embedding function by establishing a regression framework, which can avoid eigen-decomposition of dense matrices. Motivated by the effectiveness of SR, we incorporate multiple kernel learning (MKL into SR for dimensionality reduction. The proposed approach (termed MKL-SR seeks an embedding function in the Reproducing Kernel Hilbert Space (RKHS induced by the multiple base kernels. An MKL-SR algorithm is proposed to improve the performance of kernel-based SR (KSR further. Furthermore, the proposed MKL-SR algorithm can be performed in the supervised, unsupervised, and semi-supervised situation. Experimental results on supervised classification and semi-supervised classification demonstrate the effectiveness and efficiency of our algorithm.
National Aeronautics and Space Administration — This data set includes the complete set of MESSENGER SPICE data files (''kernel files''), which can be accessed using SPICE software. The SPICE data contains...
National Aeronautics and Space Administration — This data set includes the complete set of Cassini SPICE data files (kernel files''), which can be accessed using SPICE software. The SPICE data contains geometric...
National Aeronautics and Space Administration — This data set includes the complete set of NEAR SPICE data files (kernel files'), which can be accessed using SPICE software. The SPICE data contain geometric and...
National Aeronautics and Space Administration — This data set includes the complete set of Stardust SPICE data files (kernel files'), which can be accessed using SPICE software. The SPICE data contains geometric...
National Aeronautics and Space Administration — This data set includes the MSL SPICE data files (kernel files''), which can be accessed using SPICE software. The SPICE data contain geometric and other ancillary...
Bandwidth Selection for Weighted Kernel Density Estimation
Wang, Bin; Wang, Xiaofeng
2007-01-01
In the this paper, the authors propose to estimate the density of a targeted population with a weighted kernel density estimator (wKDE) based on a weighted sample. Bandwidth selection for wKDE is discussed. Three mean integrated squared error based bandwidth estimators are introduced and their performance is illustrated via Monte Carlo simulation. The least-squares cross-validation method and the adaptive weight kernel density estimator are also studied. The authors also consider the boundary...
Some Remarks on the Symmetry Kernel Test
Baszczyńska, Aleksandra
2013-01-01
The paper presents chosen statistical tests used to verify the hypothesis of the symmetry of random variable’s distribution. Detailed analysis of the symmetry kernel test is made. The properties of the regarded symmetry kernel test are compared with the other symmetry tests using Monte Carlo methods. The symmetry tests are used, as an example, in analysis of the distribution of the Human Development Index (HDI). W pracy przedstawiono wybrane statystyczne testy wykorzystywane w ...
On the Inclusion Relation of Reproducing Kernel Hilbert Spaces
Zhang, Haizhang; Zhao, Liang
2011-01-01
To help understand various reproducing kernels used in applied sciences, we investigate the inclusion relation of two reproducing kernel Hilbert spaces. Characterizations in terms of feature maps of the corresponding reproducing kernels are established. A full table of inclusion relations among widely-used translation invariant kernels is given. Concrete examples for Hilbert-Schmidt kernels are presented as well. We also discuss the preservation of such a relation under various operations of ...
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 ...
Theory of hard diffraction and rapidity gaps
International Nuclear Information System (INIS)
Del Duca, V.
1995-06-01
In this talk we review the models describing the hard diffractive production of jets or more generally high-mass states in presence of rapidity gaps in hadron-hadron and lepton-hadron collisions. By rapidity gaps we mean regions on the lego plot in (pseudo)-rapidity and azimuthal angle where no hadrons are produced, between the jet(s) and an elastically scattered hadron (single hard diffraction) or between two jets (double hard diffraction). (orig.)
Flexibly imposing periodicity in kernel independent FMM: A multipole-to-local operator approach
Yan, Wen; Shelley, Michael
2018-02-01
An important but missing component in the application of the kernel independent fast multipole method (KIFMM) is the capability for flexibly and efficiently imposing singly, doubly, and triply periodic boundary conditions. In most popular packages such periodicities are imposed with the hierarchical repetition of periodic boxes, which may give an incorrect answer due to the conditional convergence of some kernel sums. Here we present an efficient method to properly impose periodic boundary conditions using a near-far splitting scheme. The near-field contribution is directly calculated with the KIFMM method, while the far-field contribution is calculated with a multipole-to-local (M2L) operator which is independent of the source and target point distribution. The M2L operator is constructed with the far-field portion of the kernel function to generate the far-field contribution with the downward equivalent source points in KIFMM. This method guarantees the sum of the near-field & far-field converge pointwise to results satisfying periodicity and compatibility conditions. The computational cost of the far-field calculation observes the same O (N) complexity as FMM and is designed to be small by reusing the data computed by KIFMM for the near-field. The far-field calculations require no additional control parameters, and observes the same theoretical error bound as KIFMM. We present accuracy and timing test results for the Laplace kernel in singly periodic domains and the Stokes velocity kernel in doubly and triply periodic domains.
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.
Energy Technology Data Exchange (ETDEWEB)
Beaux, M.F., E-mail: mbeaux@lanl.gov [MPA Division, Los Alamos National Laboratory, Los Alamos, NM 87545 (United States); Durakiewicz, T. [MPA Division, Los Alamos National Laboratory, Los Alamos, NM 87545 (United States); Moreschini, L.; Grioni, M. [IPN, Ecole Polytechnique Federale (EPFL), CH-1015 Lausanne (Switzerland); Offi, F. [CNISM and Dipartimento de Fisica, Universita Roma Tre, Via della Vasca Navale 84, 1-00146 Rome (Italy); Monaco, G. [European Synchrotron Radiation Facility, B.P. 220, F-38042 Grenoble (France); Panaccione, G. [Istituto Officina dei Materiali CNR, Laboratorio TASC, Area Science Park, Basovizza S.S. 14 Km 163.5, I-34012 Trieste, 9 (Italy); Joyce, J.J.; Bauer, E.D.; Sarrao, J.L. [MPA Division, Los Alamos National Laboratory, Los Alamos, NM 87545 (United States); Butterfield, M.T. [KLA-Tencor, 1 Technology Drive, Milpitas, CA (United States); Guziewicz, E. [Institute of Physics, Polish Academy of Sciences, Warsaw (Poland)
2011-11-15
Highlights: {yields} Electronic structure of single crystal UPd{sub 3}, UGe{sub 2}, and USb{sub 2} was measured by hard X-ray and angle-resolved photoemission spectroscopy. {yields} Angle resolved photoemission results demonstrate hybridization between U 5f and Pd 4d electrons within UPd{sub 3}. {yields} HAXPES probing of bulk features within of UPd{sub 3}, UGe{sub 2}, and USb{sub 2} samples with native oxide contamination demonstrated. {yields} Two distinct spectral features identified for Sb I and Sb II sites within USb{sub 2} HAXPES spectrum. {yields} Line shape analysis reveals correlations between Doniach-Sunjic asymmetry coefficients and 5f localization. - Abstract: Electronic structure of single crystal UPd{sub 3}, UGe{sub 2}, and USb{sub 2} has been measured from hard X-ray photoelectron spectroscopy (HAXPES) with 7.6 keV photons at the European Synchrotron Radiation Facility (ESRF). Lower photon energy angle-resolved photoelectron spectroscopy (ARPES) was also performed at the Synchrotron Radiation Center (SRC). Herein the following results are presented: (i) ARPES results demonstrate hybridization between the U 5f and Pd 4d electrons within UPd{sub 3}. (ii) The greatly reduced surface sensitivity of HAXPES enabled observation of the bulk core levels in spite of surface oxidation. Photoelectron mean-free-path versus oxide layer thickness considerations were used to model the effectiveness of HAXPES for probing bulk features of in-air cleaved samples. (iii) Two distinct features separated by 800 meV were observed for the Sb 3d core level. These two features are attributed to manifestations of two distinct Sb sites within the USb{sub 2} single crystal as supported by consideration of interatomic distances and enthalpy-of-formation. (iv) Doniach-Sunjic line shape analysis of core level spectral features revealed correlations between asymmetry coefficients and 5f localization.
Anato, F M; Sinzogan, A A C; Offenberg, J; Adandonon, A; Wargui, R B; Deguenon, J M; Ayelo, P M; Vayssières, J-F; Kossou, D K
2017-06-01
Weaver ants, Oecophylla spp., are known to positively affect cashew, Anacardium occidentale L., raw nut yield, but their effects on the kernels have not been reported. We compared nut size and the proportion of marketable kernels between raw nuts collected from trees with and without ants. Raw nuts collected from trees with weaver ants were 2.9% larger than nuts from control trees (i.e., without weaver ants), leading to 14% higher proportion of marketable kernels. On trees with ants, the kernel: raw nut ratio from nuts damaged by formic acid was 4.8% lower compared with nondamaged nuts from the same trees. Weaver ants provided three benefits to cashew production by increasing yields, yielding larger nuts, and by producing greater proportions of marketable kernel mass. © The Authors 2017. Published by Oxford University Press on behalf of Entomological Society of America. All rights reserved. For Permissions, please email: journals.permissions@oup.com.
Chanpiwat, Penradee; Hanh, Hoang Thi; Bang, Sunbaek; Kim, Kyoung-Woong
2017-06-01
In order to assess the effects of phosphate, silicate and natural organic matter (NOM) on arsenic removal by ferric chloride, batch coprecipitation experiments were conducted over a wide pH range using synthetic hard and soft groundwaters, similar to those found in northern Vietnam. The efficiency of arsenic removal from synthetic groundwater by coprecipitation with FeCl3 was remarkably decreased by the effects of PO4 3-, SiO4 4- and NOM. The negative effects of SiO4 4- and NOM on arsenic removal were not as strong as that of PO4 3-. Combining PO4 3- and SiO4 4- increased the negative effects on both arsenite (As3+) and arsenate (As5+) removal. The introduction of NOM into the synthetic groundwater containing both PO4 3- and SiO4 4- markedly magnified the negative effects on arsenic removal. In contrast, both Ca2+ and Mg2+ substantially increased the removal of As3+ at pH 8-12 and the removal of As5+ over the entire pH range. In the presence of Ca2+ and Mg2+, the interaction of NOM with Fe was either removed or the arsenic binding to Fe-NOM colloidal associations and/or dissolved complexes were flocculated. Removal of arsenic using coprecipitation by FeCl3 could not sufficiently reduce arsenic contents in the groundwater (350 μg/L) to meet the WHO guideline for drinking water (10 μg/L), especially when the arsenic-rich groundwater also contains co-occurring solutes such as PO4 3-, SiO4 4- and NOM; therefore, other remediation processes, such as membrane technology, should be introduced or additionally applied after this coprecipitation process, to ensure the safety of drinking water.
Kernel based orthogonalization for change detection in hyperspectral images
DEFF Research Database (Denmark)
Nielsen, Allan Aasbjerg
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 MNF analyses handle 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. An example shows the successful application of (kernel PCA and) kernel MNF analysis to change detection in HyMap data covering a small agricultural area near Lake Waging-Taching, Bavaria, in Southern Germany. In the change detection...
Bayesian Genomic Prediction with Genotype × Environment Interaction Kernel Models
Cuevas, Jaime; Crossa, José; Montesinos-López, Osval A.; Burgueño, Juan; Pérez-Rodríguez, Paulino; de los Campos, Gustavo
2016-01-01
The phenomenon of genotype × environment (G × E) interaction in plant breeding decreases selection accuracy, thereby negatively affecting genetic gains. Several genomic prediction models incorporating G × E have been recently developed and used in genomic selection of plant breeding programs. Genomic prediction models for assessing multi-environment G × E interaction are extensions of a single-environment model, and have advantages and limitations. In this study, we propose two multi-environment Bayesian genomic models: the first model considers genetic effects (u) that can be assessed by the Kronecker product of variance–covariance matrices of genetic correlations between environments and genomic kernels through markers under two linear kernel methods, linear (genomic best linear unbiased predictors, GBLUP) and Gaussian (Gaussian kernel, GK). The other model has the same genetic component as the first model (u) plus an extra component, f, that captures random effects between environments that were not captured by the random effects u. We used five CIMMYT data sets (one maize and four wheat) that were previously used in different studies. Results show that models with G × E always have superior prediction ability than single-environment models, and the higher prediction ability of multi-environment models with u and f over the multi-environment model with only u occurred 85% of the time with GBLUP and 45% of the time with GK across the five data sets. The latter result indicated that including the random effect f is still beneficial for increasing prediction ability after adjusting by the random effect u. PMID:27793970
Bayesian Genomic Prediction with Genotype × Environment Interaction Kernel Models
Directory of Open Access Journals (Sweden)
Jaime Cuevas
2017-01-01
Full Text Available The phenomenon of genotype × environment (G × E interaction in plant breeding decreases selection accuracy, thereby negatively affecting genetic gains. Several genomic prediction models incorporating G × E have been recently developed and used in genomic selection of plant breeding programs. Genomic prediction models for assessing multi-environment G × E interaction are extensions of a single-environment model, and have advantages and limitations. In this study, we propose two multi-environment Bayesian genomic models: the first model considers genetic effects ( u that can be assessed by the Kronecker product of variance–covariance matrices of genetic correlations between environments and genomic kernels through markers under two linear kernel methods, linear (genomic best linear unbiased predictors, GBLUP and Gaussian (Gaussian kernel, GK. The other model has the same genetic component as the first model ( u plus an extra component, f, that captures random effects between environments that were not captured by the random effects u . We used five CIMMYT data sets (one maize and four wheat that were previously used in different studies. Results show that models with G × E always have superior prediction ability than single-environment models, and the higher prediction ability of multi-environment models with u and f over the multi-environment model with only u occurred 85% of the time with GBLUP and 45% of the time with GK across the five data sets. The latter result indicated that including the random effect f is still beneficial for increasing prediction ability after adjusting by the random effect u .
Bayesian Genomic Prediction with Genotype × Environment Interaction Kernel Models.
Cuevas, Jaime; Crossa, José; Montesinos-López, Osval A; Burgueño, Juan; Pérez-Rodríguez, Paulino; de Los Campos, Gustavo
2017-01-05
The phenomenon of genotype × environment (G × E) interaction in plant breeding decreases selection accuracy, thereby negatively affecting genetic gains. Several genomic prediction models incorporating G × E have been recently developed and used in genomic selection of plant breeding programs. Genomic prediction models for assessing multi-environment G × E interaction are extensions of a single-environment model, and have advantages and limitations. In this study, we propose two multi-environment Bayesian genomic models: the first model considers genetic effects [Formula: see text] that can be assessed by the Kronecker product of variance-covariance matrices of genetic correlations between environments and genomic kernels through markers under two linear kernel methods, linear (genomic best linear unbiased predictors, GBLUP) and Gaussian (Gaussian kernel, GK). The other model has the same genetic component as the first model [Formula: see text] plus an extra component, F: , that captures random effects between environments that were not captured by the random effects [Formula: see text] We used five CIMMYT data sets (one maize and four wheat) that were previously used in different studies. Results show that models with G × E always have superior prediction ability than single-environment models, and the higher prediction ability of multi-environment models with [Formula: see text] over the multi-environment model with only u occurred 85% of the time with GBLUP and 45% of the time with GK across the five data sets. The latter result indicated that including the random effect f is still beneficial for increasing prediction ability after adjusting by the random effect [Formula: see text]. Copyright © 2017 Cuevas et al.
Pattern Classification of Signals Using Fisher Kernels
Directory of Open Access Journals (Sweden)
Yashodhan Athavale
2012-01-01
Full Text Available The intention of this study is to gauge the performance of Fisher kernels for dimension simplification and classification of time-series signals. Our research work has indicated that Fisher kernels have shown substantial improvement in signal classification by enabling clearer pattern visualization in three-dimensional space. In this paper, we will exhibit the performance of Fisher kernels for two domains: financial and biomedical. The financial domain study involves identifying the possibility of collapse or survival of a company trading in the stock market. For assessing the fate of each company, we have collected financial time-series composed of weekly closing stock prices in a common time frame, using Thomson Datastream software. The biomedical domain study involves knee signals collected using the vibration arthrometry technique. This study uses the severity of cartilage degeneration for classifying normal and abnormal knee joints. In both studies, we apply Fisher Kernels incorporated with a Gaussian mixture model (GMM for dimension transformation into feature space, which is created as a three-dimensional plot for visualization and for further classification using support vector machines. From our experiments we observe that Fisher Kernel usage fits really well for both kinds of signals, with low classification error rates.
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.
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
Evaluation of palm kernel fibers (PKFs for production of asbestos-free automotive brake pads
Directory of Open Access Journals (Sweden)
K.K. Ikpambese
2016-01-01
Full Text Available In this study, asbestos-free automotive brake pads produced from palm kernel fibers with epoxy-resin binder was evaluated. Resins varied in formulations and properties such as friction coefficient, wear rate, hardness test, porosity, noise level, temperature, specific gravity, stopping time, moisture effects, surface roughness, oil and water absorptions rates, and microstructure examination were investigated. Other basic engineering properties of mechanical overload, thermal deformation fading behaviour shear strength, cracking resistance, over-heat recovery, and effect on rotor disc, caliper pressure, pad grip effect and pad dusting effect were also investigated. The results obtained indicated that the wear rate, coefficient of friction, noise level, temperature, and stopping time of the produced brake pads increased as the speed increases. The results also show that porosity, hardness, moisture content, specific gravity, surface roughness, and oil and water absorption rates remained constant with increase in speed. The result of microstructure examination revealed that worm surfaces were characterized by abrasion wear where the asperities were ploughed thereby exposing the white region of palm kernel fibers, thus increasing the smoothness of the friction materials. Sample S6 with composition of 40% epoxy-resin, 10% palm wastes, 6% Al2O3, 29% graphite, and 15% calcium carbonate gave better properties. The result indicated that palm kernel fibers can be effectively used as a replacement for asbestos in brake pad production.
Discriminating oat and groat kernels from other grains using near infrared spectroscopy
Oat and groats can be discriminated from other grains such as barley, wheat, rye, and triticale (non-oats) using near infrared spectroscopy. The two instruments tested were the manual version of the ARS-USDA Single Kernel Near Infrared (SKNIR) and the automated QualySense QSorter Explorer high-speed...
Option Valuation with Volatility Components, Fat Tails, and Nonlinear Pricing Kernels
DEFF Research Database (Denmark)
Babaoglu, Kadir Gokhan; Christoffersen, Peter; Heston, Steven
We nest multiple volatility components, fat tails and a U-shaped pricing kernel in a single option model and compare their contribution to describing returns and option data. All three features lead to statistically significant model improvements. A second volatility factor is economically most i...
Diversity of maize kernels from a breeding program for protein quality III: Ionome profiling
Densities of single and multiple macro- and micronutrients have been estimated in mature kernels of 1,348 accessions in 13 maize genotypes. The germplasm belonged to stiff stalk (SS) and non-stiff stalk (NS) heterotic groups (HG) with one (S1) to four (S4) years of inbreeding (IB), or open pollinati...
Two axiomatizations of the kernel of TU games: bilateral and converse reduced game properties
Driessen, Theo; Hu, C.-C.
We provide two axiomatic characterizations of the kernel of TU games by means of both bilateral consistency and converse consistency with respect to two types of two-person reduced games. According to the first type, the worth of any single player in the two-person reduced game is derived from the
Eucalyptus-Palm Kernel Oil Blends: A Complete Elimination of Diesel in a 4-Stroke VCR Diesel Engine
Directory of Open Access Journals (Sweden)
Srinivas Kommana
2015-01-01
Full Text Available Fuels derived from biomass are mostly preferred as alternative fuels for IC engines as they are abundantly available and renewable in nature. The objective of the study is to identify the parameters that influence gross indicated fuel conversion efficiency and how they are affected by the use of biodiesel relative to petroleum diesel. Important physicochemical properties of palm kernel oil and eucalyptus blend were experimentally evaluated and found within acceptable limits of relevant standards. As most of vegetable oils are edible, growing concern for trying nonedible and waste fats as alternative to petrodiesel has emerged. In present study diesel fuel is completely replaced by biofuels, namely, methyl ester of palm kernel oil and eucalyptus oil in various blends. Different blends of palm kernel oil and eucalyptus oil are prepared on volume basis and used as operating fuel in single cylinder 4-stroke variable compression ratio diesel engine. Performance and emission characteristics of these blends are studied by varying the compression ratio. In the present experiment methyl ester extracted from palm kernel oil is considered as ignition improver and eucalyptus oil is considered as the fuel. The blends taken are PKE05 (palm kernel oil 95 + eucalyptus 05, PKE10 (palm kernel oil 90 + eucalyptus 10, and PKE15 (palm kernel 85 + eucalyptus 15. The results obtained by operating with these fuels are compared with results of pure diesel; finally the most preferable combination and the preferred compression ratio are identified.
Phenolic constituents of shea (Vitellaria paradoxa) kernels.
Maranz, Steven; Wiesman, Zeev; Garti, Nissim
2003-10-08
Analysis of the phenolic constituents of shea (Vitellaria paradoxa) kernels by LC-MS revealed eight catechin compounds-gallic acid, catechin, epicatechin, epicatechin gallate, gallocatechin, epigallocatechin, gallocatechin gallate, and epigallocatechin gallate-as well as quercetin and trans-cinnamic acid. The mean kernel content of the eight catechin compounds was 4000 ppm (0.4% of kernel dry weight), with a 2100-9500 ppm range. Comparison of the profiles of the six major catechins from 40 Vitellaria provenances from 10 African countries showed that the relative proportions of these compounds varied from region to region. Gallic acid was the major phenolic compound, comprising an average of 27% of the measured total phenols and exceeding 70% in some populations. Colorimetric analysis (101 samples) of total polyphenols extracted from shea butter into hexane gave an average of 97 ppm, with the values for different provenances varying between 62 and 135 ppm of total polyphenols.
Kernel Method for Nonlinear Granger Causality
Marinazzo, Daniele; Pellicoro, Mario; Stramaglia, Sebastiano
2008-04-01
Important information on the structure of complex systems can be obtained by measuring to what extent the individual components exchange information among each other. The linear Granger approach, to detect cause-effect relationships between time series, has emerged in recent years as a leading statistical technique to accomplish this task. Here we generalize Granger causality to the nonlinear case using the theory of reproducing kernel Hilbert spaces. Our method performs linear Granger causality in the feature space of suitable kernel functions, assuming arbitrary degree of nonlinearity. We develop a new strategy to cope with the problem of overfitting, based on the geometry of reproducing kernel Hilbert spaces. Applications to coupled chaotic maps and physiological data sets are presented.
The scalar field kernel in cosmological spaces
Energy Technology Data Exchange (ETDEWEB)
Koksma, Jurjen F; Prokopec, Tomislav [Institute for Theoretical Physics (ITP) and Spinoza Institute, Utrecht University, Postbus 80195, 3508 TD Utrecht (Netherlands); Rigopoulos, Gerasimos I [Helsinki Institute of Physics, University of Helsinki, PO Box 64, FIN-00014 (Finland)], E-mail: J.F.Koksma@phys.uu.nl, E-mail: T.Prokopec@phys.uu.nl, E-mail: gerasimos.rigopoulos@helsinki.fi
2008-06-21
We construct the quantum-mechanical evolution operator in the functional Schroedinger picture-the kernel-for a scalar field in spatially homogeneous FLRW spacetimes when the field is (a) free and (b) coupled to a spacetime-dependent source term. The essential element in the construction is the causal propagator, linked to the commutator of two Heisenberg picture scalar fields. We show that the kernels can be expressed solely in terms of the causal propagator and derivatives of the causal propagator. Furthermore, we show that our kernel reveals the standard light cone structure in FLRW spacetimes. We finally apply the result to Minkowski spacetime, to de Sitter spacetime and calculate the forward time evolution of the vacuum in a general FLRW spacetime.
Fast Generation of Sparse Random Kernel Graphs.
Hagberg, Aric; Lemons, Nathan
2015-01-01
The development of kernel-based inhomogeneous random graphs has provided models that are flexible enough to capture many observed characteristics of real networks, and that are also mathematically tractable. We specify a class of inhomogeneous random graph models, called random kernel graphs, that produces sparse graphs with tunable graph properties, and we develop an efficient generation algorithm to sample random instances from this model. As real-world networks are usually large, it is essential that the run-time of generation algorithms scales better than quadratically in the number of vertices n. We show that for many practical kernels our algorithm runs in time at most (n(logn)2). As a practical example we show how to generate samples of power-law degree distribution graphs with tunable assortativity.
Robust C-Loss Kernel Classifiers.
Xu, Guibiao; Hu, Bao-Gang; Principe, Jose C
2018-03-01
The correntropy-induced loss (C-loss) function has the nice property of being robust to outliers. In this paper, we study the C-loss kernel classifier with the Tikhonov regularization term, which is used to avoid overfitting. After using the half-quadratic optimization algorithm, which converges much faster than the gradient optimization algorithm, we find out that the resulting C-loss kernel classifier is equivalent to an iterative weighted least square support vector machine (LS-SVM). This relationship helps explain the robustness of iterative weighted LS-SVM from the correntropy and density estimation perspectives. On the large-scale data sets which have low-rank Gram matrices, we suggest to use incomplete Cholesky decomposition to speed up the training process. Moreover, we use the representer theorem to improve the sparseness of the resulting C-loss kernel classifier. Experimental results confirm that our methods are more robust to outliers than the existing common classifiers.
Thermodynamic hardness and the maximum hardness principle.
Franco-Pérez, Marco; Gázquez, José L; Ayers, Paul W; Vela, Alberto
2017-08-21
An alternative definition of hardness (called the thermodynamic hardness) within the grand canonical ensemble formalism is proposed in terms of the partial derivative of the electronic chemical potential with respect to the thermodynamic chemical potential of the reservoir, keeping the temperature and the external potential constant. This temperature dependent definition may be interpreted as a measure of the propensity of a system to go through a charge transfer process when it interacts with other species, and thus it keeps the philosophy of the original definition. When the derivative is expressed in terms of the three-state ensemble model, in the regime of low temperatures and up to temperatures of chemical interest, one finds that for zero fractional charge, the thermodynamic hardness is proportional to T -1 (I-A), where I is the first ionization potential, A is the electron affinity, and T is the temperature. However, the thermodynamic hardness is nearly zero when the fractional charge is different from zero. Thus, through the present definition, one avoids the presence of the Dirac delta function. We show that the chemical hardness defined in this way provides meaningful and discernible information about the hardness properties of a chemical species exhibiting integer or a fractional average number of electrons, and this analysis allowed us to establish a link between the maximum possible value of the hardness here defined, with the minimum softness principle, showing that both principles are related to minimum fractional charge and maximum stability conditions.
Ensemble-based forecasting at Horns Rev: Ensemble conversion and kernel dressing
DEFF Research Database (Denmark)
Pinson, Pierre; Madsen, Henrik
For management and trading purposes, information on short-term wind generation (from few hours to few days ahead) is even more crucial at large offshore wind farms, since they concentrate a large capacity at a single location. The most complete information that can be provided today consists....... The obtained ensemble forecasts of wind power are then converted into predictive distributions with an original adaptive kernel dressing method. The shape of the kernels is driven by a mean-variance model, the parameters of which are recursively estimated in order to maximize the overall skill of obtained...
Reproducing kernel method with Taylor expansion for linear Volterra integro-differential equations
Directory of Open Access Journals (Sweden)
Azizallah Alvandi
2017-06-01
Full Text Available This research aims of the present a new and single algorithm for linear integro-differential equations (LIDE. To apply the reproducing Hilbert kernel method, there is made an equivalent transformation by using Taylor series for solving LIDEs. Shown in series form is the analytical solution in the reproducing kernel space and the approximate solution $ u_{N} $ is constructed by truncating the series to $ N $ terms. It is easy to prove the convergence of $ u_{N} $ to the analytical solution. The numerical solutions from the proposed method indicate that this approach can be implemented easily which shows attractive features.
Kernel principal component analysis for change detection
DEFF Research Database (Denmark)
Nielsen, Allan Aasbjerg; Morton, J.C.
2008-01-01
Principal component analysis (PCA) is often used to detect change over time in remotely sensed images. A commonly used technique consists of finding the projections along the two eigenvectors for data consisting of two variables which represent the same spectral band covering the same geographical...... 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...
Panel data specifications in nonparametric kernel regression
DEFF Research Database (Denmark)
Czekaj, Tomasz Gerard; Henningsen, Arne
We discuss nonparametric regression models for panel data. A fully nonparametric panel data specification that uses the time variable and the individual identifier as additional (categorical) explanatory variables is considered to be the most suitable. We use this estimator and conventional...... 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...... found the estimates of the fully nonparametric panel data model to be more reliable....
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
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
Directory of Open Access Journals (Sweden)
Haorui Liu
2016-01-01
Full Text Available In the car control systems, it is hard to measure some key vehicle states directly and accurately when running on the road and the cost of the measurement is high as well. To address these problems, a vehicle state estimation method based on the kernel principal component analysis and the improved Elman neural network is proposed. Combining with nonlinear vehicle model of three degrees of freedom (3 DOF, longitudinal, lateral, and yaw motion, this paper applies the method to the soft sensor of the vehicle states. The simulation results of the double lane change tested by Matlab/SIMULINK cosimulation prove the KPCA-IENN algorithm (kernel principal component algorithm and improved Elman neural network to be quick and precise when tracking the vehicle states within the nonlinear area. This algorithm method can meet the software performance requirements of the vehicle states estimation in precision, tracking speed, noise suppression, and other aspects.
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
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...
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.
Double hard scattering without double counting
Diehl, Markus; Gaunt, Jonathan R.; Schönwald, Kay
2017-06-01
Double parton scattering in proton-proton collisions includes kinematic regions in which two partons inside a proton originate from the perturbative splitting of a single parton. This leads to a double counting problem between single and double hard scattering. We present a solution to this problem, which allows for the definition of double parton distributions as operator matrix elements in a proton, and which can be used at higher orders in perturbation theory. We show how the evaluation of double hard scattering in this framework can provide a rough estimate for the size of the higher-order contributions to single hard scattering that are affected by double counting. In a numeric study, we identify situations in which these higher-order contributions must be explicitly calculated and included if one wants to attain an accuracy at which double hard scattering becomes relevant, and other situations where such contributions may be neglected.
Double hard scattering without double counting
Energy Technology Data Exchange (ETDEWEB)
Diehl, Markus [Deutsches Elektronen-Synchrotron (DESY), Hamburg (Germany); Gaunt, Jonathan R. [VU Univ. Amsterdam (Netherlands). NIKHEF Theory Group; Schoenwald, Kay [Deutsches Elektronen-Synchrotron (DESY), Zeuthen (Germany)
2017-02-15
Double parton scattering in proton-proton collisions includes kinematic regions in which two partons inside a proton originate from the perturbative splitting of a single parton. This leads to a double counting problem between single and double hard scattering. We present a solution to this problem, which allows for the definition of double parton distributions as operator matrix elements in a proton, and which can be used at higher orders in perturbation theory. We show how the evaluation of double hard scattering in this framework can provide a rough estimate for the size of the higher-order contributions to single hard scattering that are affected by double counting. In a numeric study, we identify situations in which these higher-order contributions must be explicitly calculated and included if one wants to attain an accuracy at which double hard scattering becomes relevant, and other situations where such contributions may be neglected.
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.
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.)
42 Variability Bugs in the Linux Kernel
DEFF Research Database (Denmark)
Abal, Iago; Brabrand, Claus; Wasowski, Andrzej
2014-01-01
, serving to evaluate tool implementations of feature-sensitive analyses by testing them on real bugs. We present a qualitative study of 42 variability bugs collected from bug-fixing commits to the Linux kernel repository. We analyze each of the bugs, and record the results in a database. In addition, we...
40 Variability Bugs in the Linux Kernel
DEFF Research Database (Denmark)
Abal Rivas, Iago; Brabrand, Claus; Wasowski, Andrzej
2014-01-01
is a requirement for goal-oriented research, serving to evaluate tool implementations of feature-sensitive analyses by testing them on real bugs. We present a qualitative study of 40 variability bugs collected from bug-fixing commits to the Linux kernel repository. We investigate each of the 40 bugs, recording...
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
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
Graph Bundling by Kernel Density Estimation
Hurter, C.; Ersoy, O.; Telea, A.
We present a fast and simple method to compute bundled layouts of general graphs. For this, we first transform a given graph drawing into a density map using kernel density estimation. Next, we apply an image sharpening technique which progressively merges local height maxima by moving the convolved
Evaluation of different combinations of palm kernel cake - and cotton ...
African Journals Online (AJOL)
... sole palm kernel cake based diets than those fed combinations of palm kernel cake and cottonseed cake. It is concluded that palm kernel cake alone (without any combination with cottonseed cake) is adequate as protein source in compounding protein supplements for West African Dwarf goats for profitable performance.
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...
determination of bio-energy potential of palm kernel shell
African Journals Online (AJOL)
88888888
2012-11-03
Nov 3, 2012 ... Palm Kernel Shell (PKS) is an economically and environmentally sustainable raw material for ... oil and palm kernel oil production, palm oil fibre, effluent, kernel shell and empty fruit bunch are re- garded as wastes. According to Luangkiattikhun et ... use as concrete reinforcement in construction indus-.
Dense Medium Machine Processing Method for Palm Kernel/ Shell ...
African Journals Online (AJOL)
ADOWIE PERE
ABSTRACT: A machine processing method for the separation of cracked palm kernel from the shells using ... Cracked palm kernel is a mixture of kernels, broken shells, dusts and other impurities. In order to produce ... Received 31 September 2017, received in revised form 18 October 2017, accepted 29 November 2017.
An Investigation of Kernel Data Attacks and Countermeasures
2017-02-14
demonstrate that attackers can stealthily subvert various kernel security mechanism s and develop a new keylogger , which is more stealthy than existing... keyloggers .. By classifying kernel data into different categories and handling them separately, we propose a defense mechanism and evaluate its...a computer system. 15. SUBJECT TERMS Kernel Data, Rootkit, Keylogger , Countermeasure 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF a. REPORT b
Learning a peptide-protein binding affinity predictor with kernel ridge regression.
Giguère, Sébastien; Marchand, Mario; Laviolette, François; Drouin, Alexandre; Corbeil, Jacques
2013-03-05
The cellular function of a vast majority of proteins is performed through physical interactions with other biomolecules, which, most of the time, are other proteins. Peptides represent templates of choice for mimicking a secondary structure in order to modulate protein-protein interaction. They are thus an interesting class of therapeutics since they also display strong activity, high selectivity, low toxicity and few drug-drug interactions. Furthermore, predicting peptides that would bind to a specific MHC alleles would be of tremendous benefit to improve vaccine based therapy and possibly generate antibodies with greater affinity. Modern computational methods have the potential to accelerate and lower the cost of drug and vaccine discovery by selecting potential compounds for testing in silico prior to biological validation. We propose a specialized string kernel for small bio-molecules, peptides and pseudo-sequences of binding interfaces. The kernel incorporates physico-chemical properties of amino acids and elegantly generalizes eight kernels, comprised of the Oligo, the Weighted Degree, the Blended Spectrum, and the Radial Basis Function. We provide a low complexity dynamic programming algorithm for the exact computation of the kernel and a linear time algorithm for it's approximation. Combined with kernel ridge regression and SupCK, a novel binding pocket kernel, the proposed kernel yields biologically relevant and good prediction accuracy on the PepX database. For the first time, a machine learning predictor is capable of predicting the binding affinity of any peptide to any protein with reasonable accuracy. The method was also applied to both single-target and pan-specific Major Histocompatibility Complex class II benchmark datasets and three Quantitative Structure Affinity Model benchmark datasets. On all benchmarks, our method significantly (p-value ≤ 0.057) outperforms the current state-of-the-art methods at predicting peptide-protein binding
Wheat kernel dimensions: how do they contribute to kernel weight at ...
Indian Academy of Sciences (India)
2011-12-02
Dec 2, 2011 ... Wheat kernel dimensions: how do they contribute to kernel weight at an individual QTL level? FA CUI1, 2†, ANMING DING1†, JUN LI1, 3†, CHUNHUA ZHAO1†, XINGFENG LI1, DESHUN FENG1,. XIUQIN WANG4, LIN WANG1, 5, JURONG GAO1 and HONGGANG WANG1∗. 1State Key Laboratory of Crop ...
Dou, Yao; Liu, Xiangguo; Yin, Yuejia; Han, Siping; Lu, Yang; Liu, Yang; Hao, Dongyun
2015-01-30
The 14-3-3 proteins are a group of regulatory proteins of divergent functions in plants. However, little is known about their roles in maize kernel development. Using publically available gene expression profiling data, we found that two 14-3-3 species genes, zmgf14-4 and zmgf14-6, exhibited prominent expression profiles over other 14-3-3 protein genes during maize kernel development. More than 5000 transcripts of these two genes were identified accounting for about 1/10 of the total transcripts of genes correlating to maize kernel development. We constructed a proteomics pipeline based on the affinity chromatography, in combination with 2-DE and LC-MS/MS technologies to identify the specific client proteins of the two proteins for their functional characterization. Consequently, we identified 77 specific client proteins from the developing kernels of the inbred maize B73. More than 60% of the client proteins were commonly affinity-identified by the two 14-3-3 species and are predicted to be implicated in the fundamental functions of metabolism, protein destination and storage. In addition, we found ZmGF14-4 specifically bound to the disease- or defense-relating proteins, whilst ZmGF14-6 tended to interact with the proteins involving metabolism and cell structure. Our findings provide primary insights into the functional roles of 14-3-3 proteins in maize kernel development. Maize kernel development is a complicated physiological process for its importance in both genetics and cereal breeding. 14-3-3 proteins form a multi-gene family participating in regulations of developmental processes in plants. However, the correlation between this protein family and maize kernel development has hardly been studied. We have for the first time found 12 14-3-3 protein genes from maize genome and studied in silico the gene transcription profiling of these genes. Comparative studies revealed that maize kernel development aroused a great number of gene expression, among which 14
Online Regularized and Kernelized Extreme Learning Machines with Forgetting Mechanism
Directory of Open Access Journals (Sweden)
Xinran Zhou
2014-01-01
Full Text Available To apply the single hidden-layer feedforward neural networks (SLFN to identify time-varying system, online regularized extreme learning machine (ELM with forgetting mechanism (FORELM and online kernelized ELM with forgetting mechanism (FOKELM are presented in this paper. The FORELM updates the output weights of SLFN recursively by using Sherman-Morrison formula, and it combines advantages of online sequential ELM with forgetting mechanism (FOS-ELM and regularized online sequential ELM (ReOS-ELM; that is, it can capture the latest properties of identified system by studying a certain number of the newest samples and also can avoid issue of ill-conditioned matrix inversion by regularization. The FOKELM tackles the problem of matrix expansion of kernel based incremental ELM (KB-IELM by deleting the oldest sample according to the block matrix inverse formula when samples occur continually. The experimental results show that the proposed FORELM and FOKELM have better stability than FOS-ELM and have higher accuracy than ReOS-ELM in nonstationary environments; moreover, FORELM and FOKELM have time efficiencies superiority over dynamic regression extreme learning machine (DR-ELM under certain conditions.
Directory of Open Access Journals (Sweden)
Yongliang Lin
2016-10-01
Full Text Available In this paper, we propose a multiple kernel relevance vector machine (RVM method based on the adaptive cloud particle swarm optimization (PSO algorithm to map landslide susceptibility in the low hill area of Sichuan Province, China. In the multi-kernel structure, the kernel selection problem can be solved by adjusting the kernel weight, which determines the single kernel contribution of the final kernel mapping. The weights and parameters of the multi-kernel function were optimized using the PSO algorithm. In addition, the convergence speed of the PSO algorithm was increased using cloud theory. To ensure the stability of the prediction model, the result of a five-fold cross-validation method was used as the fitness of the PSO algorithm. To verify the results, receiver operating characteristic curves (ROC and landslide dot density (LDD were used. The results show that the model that used a heterogeneous kernel (a combination of two different kernel functions had a larger area under the ROC curve (0.7616 and a lower prediction error ratio (0.28% than did the other types of kernel models employed in this study. In addition, both the sum of two high susceptibility zone LDDs (6.71/100 km2 and the sum of two low susceptibility zone LDDs (0.82/100 km2 demonstrated that the landslide susceptibility map based on the heterogeneous kernel model was closest to the historical landslide distribution. In conclusion, the results obtained in this study can provide very useful information for disaster prevention and land-use planning in the study area.
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.
Waingarten, Erik; Bosboom, Jeffrey William; Demaine, Erik D; Hesterberg, Adam Classen; Lynch, Jayson R.
2016-01-01
Nintendo’s Mario Kart is perhaps the most popular racing video game franchise. Players race alone or against opponents to finish in the fastest time possible. Players can also use items to attack and defend from other racers. We prove two hardness results for generalized Mario Kart: deciding whether a driver can finish a course alone in some given time is NP-hard, and deciding whether a player can beat an opponent in a race is PSPACE-hard.
Utilizing Temporal Information in fMRI Decoding: Classifier Using Kernel Regression Methods
Chu, Carlton; Mourão-Miranda, Janaina; Chiu, Yu-Chin; Kriegeskorte, Nikolaus; Tan, Geoffrey; Ashburner, John
2011-01-01
This paper describes a general kernel regression approach to predict experimental conditions from activity patterns acquired with functional magnetic resonance image (fMRI). The standard approach is to use classifiers that predict conditions from activity patterns. Our approach involves training different regression machines for each experimental condition, so that a predicted temporal profile is computed for each condition. A decision function is then used to classify the responses from the testing volumes into the corresponding category, by comparing the predicted temporal profile elicited by each event, against a canonical haemodynamic response function. This approach utilizes the temporal information in the fMRI signal and maintains more training samples in order to improve the classification accuracy over an existing strategy. This paper also introduces efficient techniques of temporal compaction, which operate directly on kernel matrices for kernel classification algorithms such as the support vector machine (SVM). Temporal compacting can convert the kernel computed from each fMRI volume directly into the kernel computed from beta-maps, average of volumes or spatial-temporal kernel. The proposed method was applied to three different datasets. The first one is a block-design experiment with three conditions of image stimuli. The method outperformed the SVM classifiers of three different types of temporal compaction in single-subject leave-one-block-out cross-validation. Our method achieved 100% classification accuracy for six of the subjects and an average of 94% accuracy across all 16 subjects, exceeding the best SVM classification result, which was 83% accuracy (p=0.008). The second dataset is also a block-design experiment with two conditions of visual attention (left or right). Our method yielded 96% accuracy and SVM yielded 92% (p=0.005). The third dataset is from a fast event-related experiment with two categories of visual objects. Our method achieved
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...... models to kernel learning, and means for restoring the generalizability in both kernel Principal Component Analysis and the Support Vector Machine are proposed. Viability is proved on a wide range of benchmark machine learning data sets....... as innerproducts in the model formulation. This dissertation presents research on improving the performance of standard kernel methods like kernel Principal Component Analysis and the Support Vector Machine. Moreover, the goal of the thesis has been two-fold. The first part focuses on the use of kernel Principal...
Single pass kernel k-means clustering method
Indian Academy of Sciences (India)
2016-08-26
Aug 26, 2016 ... Department of Computer Science and Engineering, Srinivasa Ramanujan Institute of Technology, Anantapur 515701, India; Department of Computer Science and Engineering, Rajeev Gandhi Memorial College of Engineering and Technology, Nandyal 518501, India; Department of Computer Science and ...
Detection of Fusarium in single wheat kernels using spectral Imaging
Polder, G.; Heijden, van der G.W.A.M.; Waalwijk, C.; Young, I.T.
2005-01-01
Fusarium head blight (FHB) is a harmful fungal disease that occurs in small grains. Non-destructive detection of this disease is traditionally done using spectroscopy or image processing. In this paper the combination of these two in the form of spectral imaging is evaluated. Transmission spectral
Systematic hardness measurements on single crystals and ...
Indian Academy of Sciences (India)
Unknown
nuclear fuel container technology (Fullam 1972). While there is an enormous amount of work on the crystal growth of alkali halides with NaCl structure, work ..... Grateful thanks are due to D E Schuele, Michelson. Professor, Case Western Reserve University, for keeping the expensive CsBr and CsI crystals at our disposal.
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.
Directory of Open Access Journals (Sweden)
Davi de Holanda Cavalcante
2012-03-01
Full Text Available The present work aimed at evaluating the effects of single or paired increase of water’s total alkalinity (TA and total hardness (TH on the performance of Nile tilapia juveniles’ growth and culture water quality. Twenty five 25-L outdoor polyethylene aquaria were used to hold experimental fish (0.82 ± 0.06 g; 10 fish per aquarium for 6 weeks. There were two conditions of TA (low or high and of TH (moderate or high in the culture water, obtained by the application of different salts (CaCO3, Na2CO3 and CaSO4 upon a previously acidified water, all at the same rate. Water quality and growth performance variables were observed in each replicate. The acidification of the supply water with HCl has resulted in significantly lower final body weight (p < 0.05. Except for the Na2CO3, growth performance of tilapia has improved significantly after CaCO3 liming or CaSO4 application (p < 0.05 and no significant difference was detected between these last two fish groups (p > 0.05. It was concluded that beyond a minimum level of TA (≥ 20 mg L-1 CaCO3 and TH (≥ 20 mg L-1 CaCO3, it is also important that fish culture waters have a TH/TA ratio higher than 1.
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...
Searching and Indexing Genomic Databases via Kernelization
Directory of Open Access Journals (Sweden)
Travis eGagie
2015-02-01
Full Text Available The rapid advance of DNA sequencing technologies has yielded databases of thousands of genomes. To search and index these databases effectively, it is important that we take advantage of the similarity between those genomes. Several authors have recently suggested searching or indexing only one reference genome and the parts of the other genomes where they differ. In this paper we survey the twenty-year history of this idea and discuss its relation to kernelization in parameterized complexity.
Kernel based subspace projection of hyperspectral images
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......). The MAF projection exploits the fact that interesting phenomena in images typically exhibit spatial autocorrelation. The analysis is based on nearinfrared hyperspectral images of maize grains demonstrating the superiority of the kernelbased MAF method....
Multiple Kernel Learning with Data Augmentation
2016-11-22
et al., 2010; Sun et al., 2010). Particularly, Sun et al. (2010) developed an efficient method based on sequential minimal optimization (SMO). The...http://www.robots.ox.ac.uk/~vgg/data/ flowers /17/ 58 Multiple Kernel Learning with Data Augmentation Algorithm 2 MKL with Data Augmentation approach for...Maria-Elena Nilsback and Andrew Zisserman. A visual vocabulary for flower classification. In Com- puter Vision and Pattern Recognition, 2006 IEEE Computer
Multiple Kernel Spectral Regression for Dimensionality Reduction
Liu, Bing; Xia, Shixiong; Zhou, Yong
2013-01-01
Traditional manifold learning algorithms, such as locally linear embedding, Isomap, and Laplacian eigenmap, only provide the embedding results of the training samples. To solve the out-of-sample extension problem, spectral regression (SR) solves the problem of learning an embedding function by establishing a regression framework, which can avoid eigen-decomposition of dense matrices. Motivated by the effectiveness of SR, we incorporate multiple kernel learning (MKL) into SR for dimensionality...
Searching and Indexing Genomic Databases via Kernelization.
Gagie, Travis; Puglisi, Simon J
2015-01-01
The rapid advance of DNA sequencing technologies has yielded databases of thousands of genomes. To search and index these databases effectively, it is important that we take advantage of the similarity between those genomes. Several authors have recently suggested searching or indexing only one reference genome and the parts of the other genomes where they differ. In this paper, we survey the 20-year history of this idea and discuss its relation to kernelization in parameterized complexity.
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)
Multiple kernel learning for dimensionality reduction.
Lin, Yen-Yu; Liu, Tyng-Luh; Fuh, Chiou-Shann
2011-06-01
In solving complex visual learning tasks, adopting multiple descriptors to more precisely characterize the data has been a feasible way for improving performance. The resulting data representations are typically high-dimensional and assume diverse forms. Hence, finding a way of transforming them into a unified space of lower dimension generally facilitates the underlying tasks such as object recognition or clustering. To this end, the proposed approach (termed MKL-DR) generalizes the framework of multiple kernel learning for dimensionality reduction, and distinguishes itself with the following three main contributions: first, our method provides the convenience of using diverse image descriptors to describe useful characteristics of various aspects about the underlying data. Second, it extends a broad set of existing dimensionality reduction techniques to consider multiple kernel learning, and consequently improves their effectiveness. Third, by focusing on the techniques pertaining to dimensionality reduction, the formulation introduces a new class of applications with the multiple kernel learning framework to address not only the supervised learning problems but also the unsupervised and semi-supervised ones.
Hauser, D. L.; Buras, D. F.; Corbin, J. M.
1987-01-01
Rubber-hardness tester modified for use on rigid polyurethane foam. Provides objective basis for evaluation of improvements in foam manufacturing and inspection. Typical acceptance criterion requires minimum hardness reading of 80 on modified tester. With adequate correlation tests, modified tester used to measure indirectly tensile and compressive strengths of foam.
Hardness and excitation energy
Indian Academy of Sciences (India)
Unknown
In the density functional theory the total energy E[n] is a unique ... The hardness η of an electronic system is defined7 as . 2. 1. 2. 1. 2. 2 ... η ε ε. = −. (6). Electronegativity, hardness and softness have proved to be very useful quantities in the chemical reactivity theory. Nevertheless, the definitions above cannot be applied with ...
Indian Academy of Sciences (India)
This paper presents a new formula for calculating the hardness of metallic crystals, resulted from the research on the critical grain size with stable dislocations. The formula is = 6 /[(1 – )], where is the hardness, the coefficient, the shear modulus, the Poisson's ratio, a function of the radius of an atom () ...
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
2014-01-01
Comprehensive Hard Materials deals with the production, uses and properties of the carbides, nitrides and borides of these metals and those of titanium, as well as tools of ceramics, the superhard boron nitrides and diamond and related compounds. Articles include the technologies of powder production (including their precursor materials), milling, granulation, cold and hot compaction, sintering, hot isostatic pressing, hot-pressing, injection moulding, as well as on the coating technologies for refractory metals, hard metals and hard materials. The characterization, testing, quality assurance and applications are also covered. Comprehensive Hard Materials provides meaningful insights on materials at the leading edge of technology. It aids continued research and development of these materials and as such it is a critical information resource to academics and industry professionals facing the technological challenges of the future. Hard materials operate at the leading edge of technology, and continued res...
Kernel methods and flexible inference for complex stochastic dynamics
Capobianco, Enrico
2008-07-01
Approximation theory suggests that series expansions and projections represent standard tools for random process applications from both numerical and statistical standpoints. Such instruments emphasize the role of both sparsity and smoothness for compression purposes, the decorrelation power achieved in the expansion coefficients space compared to the signal space, and the reproducing kernel property when some special conditions are met. We consider these three aspects central to the discussion in this paper, and attempt to analyze the characteristics of some known approximation instruments employed in a complex application domain such as financial market time series. Volatility models are often built ad hoc, parametrically and through very sophisticated methodologies. But they can hardly deal with stochastic processes with regard to non-Gaussianity, covariance non-stationarity or complex dependence without paying a big price in terms of either model mis-specification or computational efficiency. It is thus a good idea to look at other more flexible inference tools; hence the strategy of combining greedy approximation and space dimensionality reduction techniques, which are less dependent on distributional assumptions and more targeted to achieve computationally efficient performances. Advantages and limitations of their use will be evaluated by looking at algorithmic and model building strategies, and by reporting statistical diagnostics.
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.
Genomic-Enabled Prediction in Maize Using Kernel Models with Genotype × Environment Interaction.
Bandeira E Sousa, Massaine; Cuevas, Jaime; de Oliveira Couto, Evellyn Giselly; Pérez-Rodríguez, Paulino; Jarquín, Diego; Fritsche-Neto, Roberto; Burgueño, Juan; Crossa, Jose
2017-06-07
Multi-environment trials are routinely conducted in plant breeding to select candidates for the next selection cycle. In this study, we compare the prediction accuracy of four developed genomic-enabled prediction models: (1) single-environment, main genotypic effect model (SM); (2) multi-environment, main genotypic effects model (MM); (3) multi-environment, single variance G×E deviation model (MDs); and (4) multi-environment, environment-specific variance G×E deviation model (MDe). Each of these four models were fitted using two kernel methods: a linear kernel Genomic Best Linear Unbiased Predictor, GBLUP (GB), and a nonlinear kernel Gaussian kernel (GK). The eight model-method combinations were applied to two extensive Brazilian maize data sets (HEL and USP data sets), having different numbers of maize hybrids evaluated in different environments for grain yield (GY), plant height (PH), and ear height (EH). Results show that the MDe and the MDs models fitted with the Gaussian kernel (MDe-GK, and MDs-GK) had the highest prediction accuracy. For GY in the HEL data set, the increase in prediction accuracy of SM-GK over SM-GB ranged from 9 to 32%. For the MM, MDs, and MDe models, the increase in prediction accuracy of GK over GB ranged from 9 to 49%. For GY in the USP data set, the increase in prediction accuracy of SM-GK over SM-GB ranged from 0 to 7%. For the MM, MDs, and MDe models, the increase in prediction accuracy of GK over GB ranged from 34 to 70%. For traits PH and EH, gains in prediction accuracy of models with GK compared to models with GB were smaller than those achieved in GY. Also, these gains in prediction accuracy decreased when a more difficult prediction problem was studied. Copyright © 2017 Bandeira e Sousa et al.
Genomic-Enabled Prediction in Maize Using Kernel Models with Genotype × Environment Interaction
Directory of Open Access Journals (Sweden)
Massaine Bandeira e Sousa
2017-06-01
Full Text Available Multi-environment trials are routinely conducted in plant breeding to select candidates for the next selection cycle. In this study, we compare the prediction accuracy of four developed genomic-enabled prediction models: (1 single-environment, main genotypic effect model (SM; (2 multi-environment, main genotypic effects model (MM; (3 multi-environment, single variance G×E deviation model (MDs; and (4 multi-environment, environment-specific variance G×E deviation model (MDe. Each of these four models were fitted using two kernel methods: a linear kernel Genomic Best Linear Unbiased Predictor, GBLUP (GB, and a nonlinear kernel Gaussian kernel (GK. The eight model-method combinations were applied to two extensive Brazilian maize data sets (HEL and USP data sets, having different numbers of maize hybrids evaluated in different environments for grain yield (GY, plant height (PH, and ear height (EH. Results show that the MDe and the MDs models fitted with the Gaussian kernel (MDe-GK, and MDs-GK had the highest prediction accuracy. For GY in the HEL data set, the increase in prediction accuracy of SM-GK over SM-GB ranged from 9 to 32%. For the MM, MDs, and MDe models, the increase in prediction accuracy of GK over GB ranged from 9 to 49%. For GY in the USP data set, the increase in prediction accuracy of SM-GK over SM-GB ranged from 0 to 7%. For the MM, MDs, and MDe models, the increase in prediction accuracy of GK over GB ranged from 34 to 70%. For traits PH and EH, gains in prediction accuracy of models with GK compared to models with GB were smaller than those achieved in GY. Also, these gains in prediction accuracy decreased when a more difficult prediction problem was studied.
Xu, Meng; Yan, Yaming; Liu, Yanying; Shi, Qiang
2018-04-01
The Nakajima-Zwanzig generalized master equation provides a formally exact framework to simulate quantum dynamics in condensed phases. Yet, the exact memory kernel is hard to obtain and calculations based on perturbative expansions are often employed. By using the spin-boson model as an example, we assess the convergence of high order memory kernels in the Nakajima-Zwanzig generalized master equation. The exact memory kernels are calculated by combining the hierarchical equation of motion approach and the Dyson expansion of the exact memory kernel. High order expansions of the memory kernels are obtained by extending our previous work to calculate perturbative expansions of open system quantum dynamics [M. Xu et al., J. Chem. Phys. 146, 064102 (2017)]. It is found that the high order expansions do not necessarily converge in certain parameter regimes where the exact kernel show a long memory time, especially in cases of slow bath, weak system-bath coupling, and low temperature. Effectiveness of the Padé and Landau-Zener resummation approaches is tested, and the convergence of higher order rate constants beyond Fermi's golden rule is investigated.
Kumar, Ashish; Kumar, Pawan; Koundal, Rajkesh; Agnihotri, Vijai K
2016-09-01
A rapid and selective analytical method was developed to simultaneously quantify seven polyphenolic compounds (gallic acid, catechin, epicatechin, quercetin, kaempferol, syringic acid and p-coumaric acid). 15 phenolics of diverse groups in 80 % ethanolic extracts of jacquemont's hazelnut ( Corylus jacquemontii ) kernels and its byproducts from western Himalaya using ultra-performance liquid chromatography coupled with tandem mass spectrometry (UPLC-MS/MS) were identified. The developed analytical method showed excellent linearity, repeatability and accuracy. Total phenols concentrations were found to be 4446, 1199 and 105 mg gallic acid equivalent (GAE)/Kg of dried extract for jacquemont's hazelnut skin, hard shell and kernels respectively. Antioxidant potential of defatted, raw jacquemont's hazelnut skin, hard shell and kernel extracts assessed by 2,2'-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid) diammonium salt (ABTS), 2,2'-diphenyl-1-picrylhydrazyl (DPPH) methods were increased in a dose-dependent manner. The IC 50 values were observed as 23.12, 51.32, 136.46 and 45.73, 63.65, 169.30 μg/ml for jacquemont's hazelnut skin, hard shell, kernels by DPPH and ABTS assays, respectively. The high phenolic contents in jacquemont's hazelnut skin contributed towards their free radical scavenging capacities.
A Tensor-Product-Kernel Framework for Multiscale Neural Activity Decoding and Control
Li, Lin; Brockmeier, Austin J.; Choi, John S.; Francis, Joseph T.; Sanchez, Justin C.; Príncipe, José C.
2014-01-01
Brain machine interfaces (BMIs) have attracted intense attention as a promising technology for directly interfacing computers or prostheses with the brain's motor and sensory areas, thereby bypassing the body. The availability of multiscale neural recordings including spike trains and local field potentials (LFPs) brings potential opportunities to enhance computational modeling by enriching the characterization of the neural system state. However, heterogeneity on data type (spike timing versus continuous amplitude signals) and spatiotemporal scale complicates the model integration of multiscale neural activity. In this paper, we propose a tensor-product-kernel-based framework to integrate the multiscale activity and exploit the complementary information available in multiscale neural activity. This provides a common mathematical framework for incorporating signals from different domains. The approach is applied to the problem of neural decoding and control. For neural decoding, the framework is able to identify the nonlinear functional relationship between the multiscale neural responses and the stimuli using general purpose kernel adaptive filtering. In a sensory stimulation experiment, the tensor-product-kernel decoder outperforms decoders that use only a single neural data type. In addition, an adaptive inverse controller for delivering electrical microstimulation patterns that utilizes the tensor-product kernel achieves promising results in emulating the responses to natural stimulation. PMID:24829569
Kernel-Based Sensor Fusion With Application to Audio-Visual Voice Activity Detection
Dov, David; Talmon, Ronen; Cohen, Israel
2016-12-01
In this paper, we address the problem of multiple view data fusion in the presence of noise and interferences. Recent studies have approached this problem using kernel methods, by relying particularly on a product of kernels constructed separately for each view. From a graph theory point of view, we analyze this fusion approach in a discrete setting. More specifically, based on a statistical model for the connectivity between data points, we propose an algorithm for the selection of the kernel bandwidth, a parameter, which, as we show, has important implications on the robustness of this fusion approach to interferences. Then, we consider the fusion of audio-visual speech signals measured by a single microphone and by a video camera pointed to the face of the speaker. Specifically, we address the task of voice activity detection, i.e., the detection of speech and non-speech segments, in the presence of structured interferences such as keyboard taps and office noise. We propose an algorithm for voice activity detection based on the audio-visual signal. Simulation results show that the proposed algorithm outperforms competing fusion and voice activity detection approaches. In addition, we demonstrate that a proper selection of the kernel bandwidth indeed leads to improved performance.
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...... 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......- tor transform outperform the linear methods as well as kernel principal components in producing interesting projections of the data....
Directory of Open Access Journals (Sweden)
Marleni Limonu1
2008-12-01
Full Text Available The objective of this research was to study the effects of pre-gelatinization and freezing processes on physico-chemical characteristics of young corn kernel instant. The results showed that pre-gelatinization and slow freezing processes significantly affected bulk density, rehidration capacity, hardness and cooking time of young corn kernel instant. The study of water sorption isothermic showed that the product had a sigmoid curve. Based on this curve, shelf life of the product had been calculated. The YCKI waxy, YCKI Flint, and YCKI Sweet products packed in alufo had shelf life of 7.2, 12.1 and 13.8 months respectively.
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...... Analysis (KPCA) that only keeps features contributing mostly to image reconstruction, KECA selects the CKD that contribute mostly to the Rényi entropy of the image. These CKD are discriminative as they relate to the density distribution of the histogram of image attributes. We report superior performance...
Visualization of nonlinear kernel models in neuroimaging by sensitivity maps
DEFF Research Database (Denmark)
Rasmussen, Peter Mondrup; Hansen, Lars Kai; Madsen, Kristoffer Hougaard
show that the performance of linear models is reduced for certain scan labelings/categorizations in this data set, while the nonlinear models provide more flexibility. We show that the sensitivity map can be used to visualize nonlinear versions of kernel logistic regression, the kernel Fisher...... discriminant, and the SVM, and conclude that the sensitivity map is a versatile and computationally efficient tool for visualization of nonlinear kernel models in neuroimaging...
Nonlinear Forecasting with Many Predictors using Kernel Ridge Regression
Peter Exterkate; Patrick J.F. Groenen; Christiaan Heij; Dick van Dijk
2011-01-01
textabstractThis paper puts forward kernel ridge regression as an approach for forecasting with many predictors that are related nonlinearly to the target variable. In kernel ridge regression, the observed predictor variables are mapped nonlinearly into a high-dimensional space, where estimation of the predictive regression model is based on a shrinkage estimator to avoid overfitting. We extend the kernel ridge regression methodology to enable its use for economic time-series forecasting, by ...
Indian Academy of Sciences (India)
Administrator
-known Hall–Petch relationship predicts that the strength or hardness of conventional metal alloys increases with decreasing grain sizes. However, the rela- tionship fails when the grain size is down to nanometers as many experimental results ...
2013-01-01
Background Secaloindoline a (Sina) and secaloindoline b (Sinb) genes of hexaploid triticale (x Triticosecale Wittmack) are orthologs of puroindoline a (Pina) and puroindoline b (Pinb) in hexaploid wheat (Triticum aestivum L.). It has already been proven that RNA interference (RNAi)-based silencing of Pina and Pinb genes significantly decreased the puroindoline a and puroindoline b proteins in wheat and essentially increased grain hardness (J Exp Bot 62:4025-4036, 2011). The function of Sina and Sinb in triticale was tested by means of RNAi silencing and compared to wheat. Results Novel Sina and Sinb alleles in wild-type plants of cv. Wanad were identified and their expression profiles characterized. Alignment with wheat Pina-D1a and Pinb-D1a alleles showed 95% and 93.3% homology with Sina and Sinb coding sequences. Twenty transgenic lines transformed with two hpRNA silencing cassettes directed to silence Sina or Sinb were obtained by the Agrobacterium-mediated method. A significant decrease of expression of both Sin genes in segregating progeny of tested T1 lines was observed independent of the silencing cassette used. The silencing was transmitted to the T4 kernel generation. The relative transcript level was reduced by up to 99% in T3 progeny with the mean for the sublines being around 90%. Silencing of the Sin genes resulted in a substantial decrease of secaloindoline a and secaloindoline b content. The identity of SIN peptides was confirmed by mass spectrometry. The hardness index, measured by the SKCS (Single Kernel Characterization System) method, ranged from 22 to 56 in silent lines and from 37 to 49 in the control, and the mean values were insignificantly lower in the silent ones, proving increased softness. Additionally, the mean total seed protein content of silenced lines was about 6% lower compared with control lines. Correlation coefficients between hardness and transcript level were weakly positive. Conclusions We documented that RNAi-based silencing
Energy Technology Data Exchange (ETDEWEB)
Balamurugan, S. [Centre for Crystal Growth, SSN College of Engineering, Kalavakkam, Chennai 603 110 (India); Ramasamy, P., E-mail: ramasamyp@ssn.edu.in [Centre for Crystal Growth, SSN College of Engineering, Kalavakkam, Chennai 603 110 (India); Sharma, S.K. [LMDDD, RRCAT, Indore (India); Inkong, Yutthapong; Manyum, Prapun [School of Physics, Institute of Science, Suranaree University of Technology (Thailand)
2009-10-15
<0 0 1> directed potassium dihydrogen orthophosphate (KDP) single crystal was grown by Sankaranarayanan-Ramasamy (SR) method. The <0 0 1> oriented seed crystals were mounted at the bottom of the platform and the size of the crystals were 10 mm diameter, 110 mm height. Two different growths were tried, in one the crystal diameter was the ampoule's inner diameter and in the other the crystal thickness was less than the ampoule diameter. In the first case only the top four pyramidal faces were existing whereas in the second case the top four pyramidal faces and four prismatic faces were existing through out the growth. The crystals were grown using same stoichiometric solution. The results of the two growths are discussed in this paper. The grown crystals were characterized by high-resolution X-ray diffractometry (HRXRD), laser damage threshold, dielectric, thermal analysis, UV-vis spectroscopy and microhardness studies. The HRXRD analysis indicates that the crystalline perfection is excellent without having any very low angle internal structural grain boundaries. Laser damage threshold value has been determined using Nd:glass laser operating at 1054 nm. The damage threshold for the KDP crystal is greater than 4.55 GW cm{sup -2}. The dielectric constant was higher and the dielectric loss was less in SR method grown crystal as against conventional method grown crystal. In thermal analysis, the starting of decomposition nature is similar in SR method grown KDP crystal and conventional method grown crystal. The SR method grown KDP has higher transmittance and higher hardness value compared to conventional method grown crystals.
Magnetization behavior of hard/soft-magnetic composite pillar
International Nuclear Information System (INIS)
Tanaka, T.; Matsuzaki, J.; Kurisu, H.; Yamamoto, S.
2008-01-01
Hard/soft-magnetic composite pillar array medium is proposed for ultra-high-density recording media. Magnetization reversal process for a single hard/soft-magnetic composite pillar in the medium is calculated using the Landau-Lifshitz-Gilbert equation. Magnetization reversal of the soft-magnetic unit helps the magnetization reversal for the hard-magnetic unit, and the effective coercivity for the hard-magnetic unit is greatly reduced. Thereby saturation recording to the high-K u -hard-magnetic material used for perpendicular magnetic recording will be realizable
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....
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......) dimensional feature space via the kernel function and then performing a linear analysis in that space. An example shows the successful application of kernel MAF analysis to change detection in HyMap data covering a small agricultural area near Lake Waging-Taching, Bavaria, Germany....
Visualization of nonlinear kernel models in neuroimaging by sensitivity maps
DEFF Research Database (Denmark)
Rasmussen, P.M.; Madsen, Kristoffer H; Lund, T.E.
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...... show that the performance of linear models is reduced for certain scan labelings/categorizations in this data set, while the nonlinear models provide more flexibility. We show that the sensitivity map can be used to visualize nonlinear versions of kernel logistic regression, the kernel Fisher...
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
Index-free Heat Kernel Coefficients
van de Ven, Anton E. M.
1997-01-01
Using index-free notation, we present the diagonal values of the first five heat kernel coefficients associated with a general Laplace-type operator on a compact Riemannian space without boundary. The fifth coefficient appears here for the first time. For a flat space with a gauge connection, the sixth coefficient is given too. Also provided are the leading terms for any coefficient, both in ascending and descending powers of the Yang-Mills and Riemann curvatures, to the same order as require...
Localized Multiple Kernel Learning A Convex Approach
2016-11-22
1/2 . Theorem 9 (CLMKL Generalization Error Bounds) Assume that km(x, x) ≤ B, ∀m ∈ NM , x ∈ X . Suppose the loss function ℓ is L- Lipschitz and...mathematical foundation (e.g., Schölkopf and Smola, 2002). The performance of such algorithms, however, crucially depends on the involved kernel function ...approaches to localized MKL (reviewed in Section 1.1) optimize non-convex objective functions . This puts their generalization ability into doubt. Indeed
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.
Kernel-based machine learning techniques for infrasound signal classification
Tuma, Matthias; Igel, Christian; Mialle, Pierrick
2014-05-01
Infrasound monitoring is one of four remote sensing technologies continuously employed by the CTBTO Preparatory Commission. The CTBTO's infrasound network is designed to monitor the Earth for potential evidence of atmospheric or shallow underground nuclear explosions. Upon completion, it will comprise 60 infrasound array stations distributed around the globe, of which 47 were certified in January 2014. Three stages can be identified in CTBTO infrasound data processing: automated processing at the level of single array stations, automated processing at the level of the overall global network, and interactive review by human analysts. At station level, the cross correlation-based PMCC algorithm is used for initial detection of coherent wavefronts. It produces estimates for trace velocity and azimuth of incoming wavefronts, as well as other descriptive features characterizing a signal. Detected arrivals are then categorized into potentially treaty-relevant versus noise-type signals by a rule-based expert system. This corresponds to a binary classification task at the level of station processing. In addition, incoming signals may be grouped according to their travel path in the atmosphere. The present work investigates automatic classification of infrasound arrivals by kernel-based pattern recognition methods. It aims to explore the potential of state-of-the-art machine learning methods vis-a-vis the current rule-based and task-tailored expert system. To this purpose, we first address the compilation of a representative, labeled reference benchmark dataset as a prerequisite for both classifier training and evaluation. Data representation is based on features extracted by the CTBTO's PMCC algorithm. As classifiers, we employ support vector machines (SVMs) in a supervised learning setting. Different SVM kernel functions are used and adapted through different hyperparameter optimization routines. The resulting performance is compared to several baseline classifiers. All
Hardness amplification in nondeterministic logspace
Gupta, Sushmita
2007-01-01
A hard problem is one which cannot be easily computed by efficient algorithms. Hardness amplification is a procedure which takes as input a problem of mild hardness and returns a problem of higher hardness. This is closely related to the task of decoding certain error-correcting codes. We show amplification from mild average case hardness to higher average case hardness for nondeterministic logspace and worst-to-average amplification for nondeterministic linspace. Finally we explore possible ...
vanDijk, P; Wit, HP; Segenhout, JM
1997-01-01
Wiener kernel analysis was used to characterize the auditory pathway from tympanic membrane to single primary auditory nerve fibers in the European edible frog, Rana esculenta. Nerve fiber signals were recorded in response to white Gaussian noise. By cross-correlating the noise stimulus and the
Index-free heat kernel coefficients
van de Ven, Anton E. M.
1998-08-01
Using index-free notation, we present the diagonal values 0264-9381/15/8/014/img1 of the first five heat kernel coefficients 0264-9381/15/8/014/img2 associated with a general Laplace-type operator on a compact Riemannian space without boundary. The fifth coefficient 0264-9381/15/8/014/img3 appears here for the first time. For the special case of a flat space, but with a gauge connection, the sixth coefficient is given too. Also provided are the leading terms for any coefficient, both in ascending and descending powers of the Yang-Mills and Riemann curvatures, to the same order as required for the fourth coefficient. These results are obtained by directly solving the relevant recursion relations, working in the Fock-Schwinger gauge and Riemann normal coordinates. Our procedure is thus non-covariant, but we show that for any coefficient the `gauged', respectively `curved', version is found from the corresponding `non-gauged', respectively `flat', coefficient by making some simple covariant substitutions. These substitutions being understood, the coefficients retain their `flat' form and size. In this sense the fifth and sixth coefficient have only 26 and 75 terms, respectively, allowing us to write them down. Using index-free notation also clarifies the general structure of the heat kernel coefficients. In particular, in flat space we find that from the fifth coefficient onward, certain scalars are absent. This may be relevant for the anomalies of quantum field theories in ten or more dimensions.
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.
Kernelized rank learning for personalized drug recommendation.
He, Xiao; Folkman, Lukas; Borgwardt, Karsten
2018-03-08
Large-scale screenings of cancer cell lines with detailed molecular profiles against libraries of pharmacological compounds are currently being performed in order to gain a better understanding of the genetic component of drug response and to enhance our ability to recommend therapies given a patient's molecular profile. These comprehensive screens differ from the clinical setting in which (1) medical records only contain the response of a patient to very few drugs, (2) drugs are recommended by doctors based on their expert judgment, and (3) selecting the most promising therapy is often more important than accurately predicting the sensitivity to all potential drugs. Current regression models for drug sensitivity prediction fail to account for these three properties. We present a machine learning approach, named Kernelized Rank Learning (KRL), that ranks drugs based on their predicted effect per cell line (patient), circumventing the difficult problem of precisely predicting the sensitivity to the given drug. Our approach outperforms several state-of-the-art predictors in drug recommendation, particularly if the training dataset is sparse, and generalizes to patient data. Our work phrases personalized drug recommendation as a new type of machine learning problem with translational potential to the clinic. The Python implementation of KRL and scripts for running our experiments are available at https://github.com/BorgwardtLab/Kernelized-Rank-Learning. xiao.he@bsse.ethz.ch, lukas.folkman@bsse.ethz.ch. Supplementary data are available at Bioinformatics online.
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.
Yekkehkhany, B.; Safari, A.; Homayouni, S.; Hasanlou, M.
2014-10-01
In this paper, a framework is developed based on Support Vector Machines (SVM) for crop classification using polarimetric features extracted from multi-temporal Synthetic Aperture Radar (SAR) imageries. The multi-temporal integration of data not only improves the overall retrieval accuracy but also provides more reliable estimates with respect to single-date data. Several kernel functions are employed and compared in this study for mapping the input space to higher Hilbert dimension space. These kernel functions include linear, polynomials and Radial Based Function (RBF). The method is applied to several UAVSAR L-band SAR images acquired over an agricultural area near Winnipeg, Manitoba, Canada. In this research, the temporal alpha features of H/A/α decomposition method are used in classification. The experimental tests show an SVM classifier with RBF kernel for three dates of data increases the Overall Accuracy (OA) to up to 3% in comparison to using linear kernel function, and up to 1% in comparison to a 3rd degree polynomial kernel function.
Predictive Model Equations for Palm Kernel (Elaeis guneensis J ...
African Journals Online (AJOL)
A 3-factor experimental design was used to determine the influence of moisture content, roasting duration and temperature on palm kernel and sesame oil colours. Four levels each of these parameters were used. The data obtained were used to develop prediction models for palm kernel and sesame oil colours. Coefficient ...
Evaluation of enzyme supplementation of palm kernel meal-based ...
African Journals Online (AJOL)
Journal of Agriculture, Forestry and the Social Sciences ... The results of this study showed that broilers can tolerate 20% inclusion rate of palm kernel meal in their rations without enzyme supplementation and partially replacing maize with palm kernel meal at that level of inclusion can reduce the cost of production of ...
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...
Efficient methods for robust classification under uncertainty in kernel matrices
Ben-Tal, A.; Bhadra, S.; Bhattacharyya, C.; Nemirovski, A.
2012-01-01
In this paper we study the problem of designing SVM classifiers when the kernel matrix, K , is affected by uncertainty. Specifically K is modeled as a positive affine combination of given positive semi definite kernels, with the coefficients ranging in a norm-bounded uncertainty set. We treat the
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 ...
Homotopy deform method for reproducing kernel space for ...
Indian Academy of Sciences (India)
In this paper, the combination of homotopy deform method (HDM) and simplified reproducing kernel method (SRKM) is introduced for solving the boundary value problems (BVPs) of nonlinear differential equations. The solution methodology is based on Adomian decomposition and reproducing kernel method (RKM).
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 ...
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
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...
High-power asymptotics of some weighted harmonic Bergman kernels
Czech Academy of Sciences Publication Activity Database
Engliš, Miroslav
2016-01-01
Roč. 271, č. 5 (2016), s. 1243-1261 ISSN 0022-1236 Institutional support: RVO:67985840 Keywords : Bergman kernel * harmonic Bergman kernel * asymptotic expansion Subject RIV: BA - General Mathematics Impact factor: 1.254, year: 2016 http://www.sciencedirect.com/science/article/pii/S0022123616301513
Efficient Kernel-based 2DPCA for Smile Stages Recognition
Directory of Open Access Journals (Sweden)
Fitri Damayanti
2012-03-01
Full Text Available Recently, an approach called two-dimensional principal component analysis (2DPCA has been proposed for smile stages representation and recognition. The essence of 2DPCA is that it computes the eigenvectors of the so-called image covariance matrix without matrix-to-vector conversion so the size of the image covariance matrix are much smaller, easier to evaluate covariance matrix, computation cost is reduced and the performance is also improved than traditional PCA. In an effort to improve and perfect the performance of smile stages recognition, in this paper, we propose efficient Kernel based 2DPCA concepts. The Kernelization of 2DPCA can be benefit to develop the nonlinear structures in the input data. This paper discusses comparison of standard Kernel based 2DPCA and efficient Kernel based 2DPCA for smile stages recognition. The results of experiments show that Kernel based 2DPCA achieve better performance in comparison with the other approaches. While the use of efficient Kernel based 2DPCA can speed up the training procedure of standard Kernel based 2DPCA thus the algorithm can achieve much more computational efficiency and remarkably save the memory consuming compared to the standard Kernel based 2DPCA.
Nutritional evaluation of palm kernel meal types: 1. Proximate ...
African Journals Online (AJOL)
Studies were conducted to determine the proximate composition and metabolizable energy values of palm kernel meal (PKM) types. The PKM types studied were obtained from Okomu, Presco and Envoy Oil Mills and were either mechanically or solvent extracted using different varieties of palm kernels. Samples of PKM ...
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...
Screening of the kernels of Pentadesma butyracea from various ...
African Journals Online (AJOL)
Gwla10
Pentadesma butyracea Sabine (Clusiaceae) is a ligneous forest species of multipurpose uses. It is widely distributed in Africa from Guinea-Bissau to the West of the Democratic Republic of Congo. This study screened the kernel of P. butyracea on the basis of their physico-chemical properties. Six types of kernels were ...
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
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.
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.
Nonlinear Forecasting with Many Predictors using Kernel Ridge Regression
P. Exterkate (Peter); P.J.F. Groenen (Patrick); C. Heij (Christiaan); D.J.C. van Dijk (Dick)
2011-01-01
textabstractThis 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
On sensitivity kernels for 'wave-equation' transmission tomography
Hoop, Maarten V. de; Hilst, R.D. van der
2004-01-01
We combine seismological scattering theory with the theory of distributions to study some properties of sensitivity kernels for finite frequency seismic delay times. The theory to be used for calculating the kernels depends on the way the measurements are made. For example, the sensitivity to the
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.
Hard scattering in γp interactions
International Nuclear Information System (INIS)
Ahmed, T.; Andreev, V.; Andrieu, B.
1992-10-01
We report on the investigation of the final state in interactions of quasi-real photons with protons. The data were taken with the H1 detector at the HERA ep collider. Evidence for hard interactions is seen in both single particle spectra and jet formation. The data can best be described by inclusion of resolved photon processes as predicted by QCD. (orig.)
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.
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.
Triso coating development progress for uranium nitride kernels
Energy Technology Data Exchange (ETDEWEB)
Jolly, Brian C. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Lindemer, Terrence [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Terrani, Kurt A. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
2015-08-01
In support of fully ceramic matrix (FCM) fuel development [1-2], coating development work is ongoing at the Oak Ridge National Laboratory (ORNL) to produce tri-structural isotropic (TRISO) coated fuel particles with UN kernels [3]. The nitride kernels are used to increase fissile density in these SiC-matrix fuel pellets with details described elsewhere [4]. The advanced gas reactor (AGR) program at ORNL used fluidized bed chemical vapor deposition (FBCVD) techniques for TRISO coating of UCO (two phase mixture of UO2 and UCx) kernels [5]. Similar techniques were employed for coating of the UN kernels, however significant changes in processing conditions were required to maintain acceptable coating properties due to physical property and dimensional differences between the UCO and UN kernels (Table 1).
Energy Technology Data Exchange (ETDEWEB)
Kugler, W.
2007-01-15
Hard exclusive processes in high energy electron proton scattering offer the opportunity to get access to a new generation of parton distributions, the so-called generalized parton distributions (GPDs). This functions provide more detailed informations about the structure of the nucleon than the usual PDFs obtained from DIS. In this work we present a detailed analysis of exclusive processes, especially of hard exclusive meson production. We investigated the influence of exclusive produced mesons on the semi-inclusive production of mesons at fixed target experiments like HERMES. Further we give a detailed analysis of higher order corrections (NLO) for the exclusive production of mesons in a very broad range of kinematics. (orig.)
International Nuclear Information System (INIS)
Maor, Uri; Tel Aviv Univ.
1995-09-01
The role of s-channel unitarity screening corrections, calculated in the eikonal approximation, is investigated for soft Pomeron exchange responsible for elastic and diffractive hadron scattering in the high energy limit. We examine the differences between our results and those obtained from the supercritical Pomeron-Regge model with no such corrections. It is shown that screening saturation is attained at different scales for different channels. We then proceed to discuss the new HERA data on hard (PQCD) Pomeron diffractive channels and discuss the relationship between the soft and hard Pomerons and the relevance of our analysis to this problem. (author). 18 refs, 9 figs, 1 tab
2003-01-01
CERN will be organizing a special information day on Friday, 27th June, designed to promote the wearing of hard hats and ensure that they are worn correctly. A new prevention campaign will also be launched.The event will take place in the hall of the Main Building from 11.30 a.m. to 2.00 p.m., when you will be able to come and try on various models of hard hat, including some of the very latest innovative designs, ask questions and pass on any comments and suggestions.
Session: Hard Rock Penetration
Energy Technology Data Exchange (ETDEWEB)
Tennyson, George P. Jr.; Dunn, James C.; Drumheller, Douglas S.; Glowka, David A.; Lysne, Peter
1992-01-01
This session at the Geothermal Energy Program Review X: Geothermal Energy and the Utility Market consisted of five presentations: ''Hard Rock Penetration - Summary'' by George P. Tennyson, Jr.; ''Overview - Hard Rock Penetration'' by James C. Dunn; ''An Overview of Acoustic Telemetry'' by Douglas S. Drumheller; ''Lost Circulation Technology Development Status'' by David A. Glowka; ''Downhole Memory-Logging Tools'' by Peter Lysne.
Performance and Emission of VCR-CI Engine with palm kernel and eucalyptus blends
Directory of Open Access Journals (Sweden)
Srinivas kommana
2016-09-01
Full Text Available This study aims at complete replacement of conventional diesel fuel by biodiesel. In order to achieve that, palm kernel oil and eucalyptus oil blend has been chosen. Eucalyptus oil was blended with methyl ester of palm kernel oil in 5%, 10% and 15% by volume. Tests were conducted with diesel fuel and blends on a 4 stroke VCR diesel engine for comparative analysis with 220 bar injection pressure and 19:1 compression ratio. All the test fuels were used in computerized 4 stroke single cylinder variable compression ratio engine at five different loads (0, 6, 12, 18 and 24 N m. Present investigation depicts the improved combustion and reduced emissions for the PKO85% + EuO15% blend when compared to diesel at full load conditions.
A weighted string kernel for protein fold recognition.
Nojoomi, Saghi; Koehl, Patrice
2017-08-25
Alignment-free methods for comparing protein sequences have proved to be viable alternatives to approaches that first rely on an alignment of the sequences to be compared. Much work however need to be done before those methods provide reliable fold recognition for proteins whose sequences share little similarity. We have recently proposed an alignment-free method based on the concept of string kernels, SeqKernel (Nojoomi and Koehl, BMC Bioinformatics, 2017, 18:137). In this previous study, we have shown that while Seqkernel performs better than standard alignment-based methods, its applications are potentially limited, because of biases due mostly to sequence length effects. In this study, we propose improvements to SeqKernel that follows two directions. First, we developed a weighted version of the kernel, WSeqKernel. Second, we expand the concept of string kernels into a novel framework for deriving information on amino acids from protein sequences. Using a dataset that only contains remote homologs, we have shown that WSeqKernel performs remarkably well in fold recognition experiments. We have shown that with the appropriate weighting scheme, we can remove the length effects on the kernel values. WSeqKernel, just like any alignment-based sequence comparison method, depends on a substitution matrix. We have shown that this matrix can be optimized so that sequence similarity scores correlate well with structure similarity scores. Starting from no information on amino acid similarity, we have shown that we can derive a scoring matrix that echoes the physico-chemical properties of amino acids. We have made progress in characterizing and parametrizing string kernels as alignment-based methods for comparing protein sequences, and we have shown that they provide a framework for extracting sequence information from structure.
3-D sensitivity kernels of the Rayleigh wave ellipticity
Maupin, Valérie
2017-10-01
The ellipticity of the Rayleigh wave at the surface depends on the seismic structure beneath and in the vicinity of the seismological station where it is measured. We derive here the expression and compute the 3-D kernels that describe this dependence with respect to S-wave velocity, P-wave velocity and density. Near-field terms as well as coupling to Love waves are included in the expressions. We show that the ellipticity kernels are the difference between the amplitude kernels of the radial and vertical components of motion. They show maximum values close to the station, but with a complex pattern, even when smoothing in a finite-frequency range is used to remove the oscillatory pattern present in mono-frequency kernels. In order to follow the usual data processing flow, we also compute and analyse the kernels of the ellipticity averaged over incoming wave backazimuth. The kernel with respect to P-wave velocity has the simplest lateral variation and is in good agreement with commonly used 1-D kernels. The kernels with respect to S-wave velocity and density are more complex and we have not been able to find a good correlation between the 3-D and 1-D kernels. Although it is clear that the ellipticity is mostly sensitive to the structure within half-a-wavelength of the station, the complexity of the kernels within this zone prevents simple approximations like a depth dependence times a lateral variation to be useful in the inversion of the ellipticity.
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
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.
Heat kernel methods for Lifshitz theories
Barvinsky, Andrei O.; Blas, Diego; Herrero-Valea, Mario; Nesterov, Dmitry V.; Pérez-Nadal, Guillem; Steinwachs, Christian F.
2017-06-01
We study the one-loop covariant effective action of Lifshitz theories using the heat kernel technique. The characteristic feature of Lifshitz theories is an anisotropic scaling between space and time. This is enforced by the existence of a preferred foliation of space-time, which breaks Lorentz invariance. In contrast to the relativistic case, covariant Lifshitz theories are only invariant under diffeomorphisms preserving the foliation structure. We develop a systematic method to reduce the calculation of the effective action for a generic Lifshitz operator to an algorithm acting on known results for relativistic operators. In addition, we present techniques that drastically simplify the calculation for operators with special properties. We demonstrate the efficiency of these methods by explicit applications.
Modified kernel-based nonlinear feature extraction.
Energy Technology Data Exchange (ETDEWEB)
Ma, J. (Junshui); Perkins, S. J. (Simon J.); Theiler, J. P. (James P.); Ahalt, S. (Stanley)
2002-01-01
Feature Extraction (FE) techniques are widely used in many applications to pre-process data in order to reduce the complexity of subsequent processes. A group of Kernel-based nonlinear FE ( H E ) algorithms has attracted much attention due to their high performance. However, a serious limitation that is inherent in these algorithms -- the maximal number of features extracted by them is limited by the number of classes involved -- dramatically degrades their flexibility. Here we propose a modified version of those KFE algorithms (MKFE), This algorithm is developed from a special form of scatter-matrix, whose rank is not determined by the number of classes involved, and thus breaks the inherent limitation in those KFE algorithms. Experimental results suggest that MKFE algorithm is .especially useful when the training set is small.
Heat kernel method and its applications
Avramidi, Ivan G
2015-01-01
The heart of the book is the development of a short-time asymptotic expansion for the heat kernel. This is explained in detail and explicit examples of some advanced calculations are given. In addition some advanced methods and extensions, including path integrals, jump diffusion and others are presented. The book consists of four parts: Analysis, Geometry, Perturbations and Applications. The first part shortly reviews of some background material and gives an introduction to PDEs. The second part is devoted to a short introduction to various aspects of differential geometry that will be needed later. The third part and heart of the book presents a systematic development of effective methods for various approximation schemes for parabolic differential equations. The last part is devoted to applications in financial mathematics, in particular, stochastic differential equations. Although this book is intended for advanced undergraduate or beginning graduate students in, it should also provide a useful reference ...
Muller, M.
2007-01-01
Aircraft jet engines have to be able to withstand infernal conditions. Extreme heat and bitter cold tax coatings to the limit. Materials expert Dr Ir. Wim Sloof fits atoms together to develop rock-hard coatings. The latest invention in this field is known as ceramic matrix composites. Sloof has signed an agreement with a number of parties to investigate this material further.
Indian Academy of Sciences (India)
Administrator
where H is the hardness, k the coefficient, G the shear modulus, ν the Poisson's ratio, η a function of the radius of an atom (r) and the electron density at the atom interface (n). The formula will not only be used to testify the critical grain size with stable dislocations, but also play an important role in the understanding of ...
Indian Academy of Sciences (India)
First page Back Continue Last page Overview Graphics. Hardness of Clustering. Both k-means and k-medians intractable (when n and d are both inputs even for k =2). The best known deterministic algorithms. are based on Voronoi partitioning that. takes about time. Need for approximation – “close” to optimal.
Hardness and excitation energy
Indian Academy of Sciences (India)
It is shown that the first excitation energy can be given by the Kohn-Sham hardness (i.e. the energy difference of the ground-state lowest unoccupied and highest occupied levels) plus an extra term coming from the partial derivative of the ensemble exchange-correlation energy with respect to the weighting factor in the ...
Muller, M.
2007-01-01
Aircraft jet engines have to be able to withstand infernal conditions. Extreme heat and bitter cold tax coatings to the limit. Materials expert Dr Ir. Wim Sloof fits atoms together to develop rock-hard coatings. The latest invention in this field is known as ceramic matrix composites. Sloof has
Hardness and excitation energy
Indian Academy of Sciences (India)
... the ground-state lowest unoccupied and highest occupied levels) plus an extra term coming from the partial derivative of the ensemble exchange-correlation energy with respect to the weighting factor in the limit → 0. It is proposed that the first excitation energy can be used as a reactivity index instead of the hardness.
Celluclast 1.5L pretreatment enhanced aroma of palm kernels and oil after kernel roasting.
Zhang, Wencan; Zhao, Fangju; Yang, Tiankui; Zhao, Feifei; Liu, Shaoquan
2017-12-01
The aroma of palm kernel oil (PKO) affects its applications. Little information is available on how enzymatic modification of palm kernels (PK) affects PK and PKO aroma after kernel roasting. Celluclast (cellulase) pretreatment of PK resulted in a 2.4-fold increment in the concentration of soluble sugars, with glucose being increased by 6.0-fold. Higher levels of 1.7-, 1.8- and 1.9-fold of O-heterocyclic volatile compounds were found in the treated PK after roasting at 180 °C for 8, 14 and 20 min respectively relative to the corresponding control, with furfural, 5-methyl-2-furancarboxaldehyde, 2-furanmethanol and maltol in particularly higher amounts. Volatile differences between PKOs from control and treated PK were also found, though less obvious owing to the aqueous extraction process. Principal component analysis based on aroma-active compounds revealed that upon the proceeding of roasting, the differentiation between control and treated PK was enlarged while that of corresponding PKOs was less clear-cut. Celluclast pretreatment enabled the medium roasted PK to impart more nutty, roasty and caramelic odor and the corresponding PKO to impart more caramelic but less roasty and burnt notes. Celluclast pretreatment of PK followed by roasting may be a promising new way of improving PKO aroma. © 2017 Society of Chemical Industry. © 2017 Society of Chemical Industry.
Failure analysis and shock protection of external hard disk drive ...
African Journals Online (AJOL)
Technology for processing and storage of data in portable external storage hard disks has increasingly improved over the years. Currently, terabytes of data can be stored in one portable external storage hard disk drive. Storing such amount of data on a single disk on itself is a risk. Several instances of data lost by big ...
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.
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.
Localized Multiple Kernel Learning Via Sample-Wise Alternating Optimization.
Han, Yina; Yang, Kunde; Ma, Yuanliang; Liu, Guizhong
2014-01-01
Our objective is to train support vector machines (SVM)-based localized multiple kernel learning (LMKL), using the alternating optimization between the standard SVM solvers with the local combination of base kernels and the sample-specific kernel weights. The advantage of alternating optimization developed from the state-of-the-art MKL is the SVM-tied overall complexity and the simultaneous optimization on both the kernel weights and the classifier. Unfortunately, in LMKL, the sample-specific character makes the updating of kernel weights a difficult quadratic nonconvex problem. In this paper, starting from a new primal-dual equivalence, the canonical objective on which state-of-the-art methods are based is first decomposed into an ensemble of objectives corresponding to each sample, namely, sample-wise objectives. Then, the associated sample-wise alternating optimization method is conducted, in which the localized kernel weights can be independently obtained by solving their exclusive sample-wise objectives, either linear programming (for l1-norm) or with closed-form solutions (for lp-norm). At test time, the learnt kernel weights for the training data are deployed based on the nearest-neighbor rule. Hence, to guarantee their generality among the test part, we introduce the neighborhood information and incorporate it into the empirical loss when deriving the sample-wise objectives. Extensive experiments on four benchmark machine learning datasets and two real-world computer vision datasets demonstrate the effectiveness and efficiency of the proposed algorithm.
Per-Sample Multiple Kernel Approach for Visual Concept Learning
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.
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.
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.
Testan, Peter R.
1987-04-01
A number of Color Hard Copy (CHC) market drivers are currently indicating strong growth in the use of CHC technologies for the business graphics marketplace. These market drivers relate to product, software, color monitors and color copiers. The use of color in business graphics allows more information to be relayed than is normally the case in a monochrome format. The communicative powers of full-color computer generated output in the business graphics application area will continue to induce end users to desire and require color in their future applications. A number of color hard copy technologies will be utilized in the presentation graphics arena. Thermal transfer, ink jet, photographic and electrophotographic technologies are all expected to be utilized in the business graphics presentation application area in the future. Since the end of 1984, the availability of color application software packages has grown significantly. Sales revenue generated by business graphics software is expected to grow at a compound annual growth rate of just over 40 percent to 1990. Increased availability of packages to allow the integration of text and graphics is expected. Currently, the latest versions of page description languages such as Postscript, Interpress and DDL all support color output. The use of color monitors will also drive the demand for color hard copy in the business graphics market place. The availability of higher resolution screens is allowing color monitors to be easily used for both text and graphics applications in the office environment. During 1987, the sales of color monitors are expected to surpass the sales of monochrome monitors. Another major color hard copy market driver will be the color copier. In order to take advantage of the communications power of computer generated color output, multiple copies are required for distribution. Product introductions of a new generation of color copiers is now underway with additional introductions expected
Gilkey-de Witt heat kernel expansion and zero modes
International Nuclear Information System (INIS)
Alonso-Izquierdo, A.; Mateos Guilarte, J.
2013-01-01
In this paper we propose a generalization of the Gilkey-de Witt heat kernel expansion, designed to provide us with a precise estimation of the heat trace of non-negative Schr¨odinger type differential operators with non-trivial kernel over all the domain of its “inverse temperature” variable β. We apply this modified approach to compute effectively the one-loop kink mass shift for some models whose kink fluctuation operator spectrum is unknown and the only alternative to estimate this magnitude is the use of the heat kernel expansion techniques.
Visualization of nonlinear kernel models in neuroimaging by sensitivity maps
DEFF Research Database (Denmark)
Rasmussen, P.M.; Madsen, Kristoffer H; Lund, T.E.
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...
A Truly Jitter-Free Real-Time Kernel
DEFF Research Database (Denmark)
Marian, Nicolae; Jiang, Peng
2008-01-01
Hardware-Software co-design is a powerful method nowadays for the embedded system development. Reducing time to the market, more accuracy and interactivity with the whole system by the co-design developments are available. The paper considers and investigates a co-design solution applied to a real-time...... kernel (RTK) named HARTEX. The objective was to even more improve the timing performances of the kernel in terms of minimized, constant overhead (jitter free), in an application transparent manner. The co-design solution partitions the kernel between a pure software part, with constant overhead...
Robust Learning With Kernel Mean $p$-Power Error Loss.
Chen, Badong; Xing, Lei; Wang, Xin; Qin, Jing; Zheng, Nanning
2017-07-25
Correntropy is a second order statistical measure in kernel space, which has been successfully applied in robust learning and signal processing. In this paper, we define a nonsecond order statistical measure in kernel space, called the kernel mean-p power error (KMPE), including the correntropic loss (C-Loss) as a special case. Some basic properties of KMPE are presented. In particular, we apply the KMPE to extreme learning machine (ELM) and principal component analysis (PCA), and develop two robust learning algorithms, namely ELM-KMPE and PCA-KMPE. Experimental results on synthetic and benchmark data show that the developed algorithms can achieve better performance when compared with some existing methods.
1986-02-28
r The equation for three events becomes: P.A + B + C) P(A) + P(B) + P(C) - P(AB) - P(AC) - P(BC) (4) + ?( ABC ) A This rule can be extended to any... programa - tic matters. 2. A sequence of hardness related events that need to take place for a successful program. These start with receiving the system
Forecasting Crude Oil Price Using EEMD and RVM with Adaptive PSO-Based Kernels
Directory of Open Access Journals (Sweden)
Taiyong Li
2016-12-01
Full Text Available Crude oil, as one of the most important energy sources in the world, plays a crucial role in global economic events. An accurate prediction for crude oil price is an interesting and challenging task for enterprises, governments, investors, and researchers. To cope with this issue, in this paper, we proposed a method integrating ensemble empirical mode decomposition (EEMD, adaptive particle swarm optimization (APSO, and relevance vector machine (RVM—namely, EEMD-APSO-RVM—to predict crude oil price based on the “decomposition and ensemble” framework. Specifically, the raw time series of crude oil price were firstly decomposed into several intrinsic mode functions (IMFs and one residue by EEMD. Then, RVM with combined kernels was applied to predict target value for the residue and each IMF individually. To improve the prediction performance of each component, an extended particle swarm optimization (PSO was utilized to simultaneously optimize the weights and parameters of single kernels for the combined kernel of RVM. Finally, simple addition was used to aggregate all the predicted results of components into an ensemble result as the final result. Extensive experiments were conducted on the crude oil spot price of the West Texas Intermediate (WTI to illustrate and evaluate the proposed method. The experimental results are superior to those by several state-of-the-art benchmark methods in terms of root mean squared error (RMSE, mean absolute percent error (MAPE, and directional statistic (Dstat, showing that the proposed EEMD-APSO-RVM is promising for forecasting crude oil price.
Energy Technology Data Exchange (ETDEWEB)
Dimits, A M; Wang, C; Caflisch, R; Cohen, B I; Huang, Y
2008-08-06
We investigate the accuracy of and assumptions underlying the numerical binary Monte-Carlo collision operator due to Nanbu [K. Nanbu, Phys. Rev. E 55 (1997)]. The numerical experiments that resulted in the parameterization of the collision kernel used in Nanbu's operator are argued to be an approximate realization of the Coulomb-Lorentz pitch-angle scattering process, for which an analytical solution for the collision kernel is available. It is demonstrated empirically that Nanbu's collision operator quite accurately recovers the effects of Coulomb-Lorentz pitch-angle collisions, or processes that approximate these (such interspecies Coulomb collisions with very small mass ratio) even for very large values of the collisional time step. An investigation of the analytical solution shows that Nanbu's parameterized kernel is highly accurate for small values of the normalized collision time step, but loses some of its accuracy for larger values of the time step. Careful numerical and analytical investigations are presented, which show that the time dependence of the relaxation of a temperature anisotropy by Coulomb-Lorentz collisions has a richer structure than previously thought, and is not accurately represented by an exponential decay with a single decay rate. Finally, a practical collision algorithm is proposed that for small-mass-ratio interspecies Coulomb collisions improves on the accuracy of Nanbu's algorithm.
Ridge Regression and Other Kernels for Genomic Selection with R Package rrBLUP
Directory of Open Access Journals (Sweden)
Jeffrey B. Endelman
2011-11-01
Full Text Available Many important traits in plant breeding are polygenic and therefore recalcitrant to traditional marker-assisted selection. Genomic selection addresses this complexity by including all markers in the prediction model. A key method for the genomic prediction of breeding values is ridge regression (RR, which is equivalent to best linear unbiased prediction (BLUP when the genetic covariance between lines is proportional to their similarity in genotype space. This additive model can be broadened to include epistatic effects by using other kernels, such as the Gaussian, which represent inner products in a complex feature space. To facilitate the use of RR and nonadditive kernels in plant breeding, a new software package for R called rrBLUP has been developed. At its core is a fast maximum-likelihood algorithm for mixed models with a single variance component besides the residual error, which allows for efficient prediction with unreplicated training data. Use of the rrBLUP software is demonstrated through several examples, including the identification of optimal crosses based on superior progeny value. In cross-validation tests, the prediction accuracy with nonadditive kernels was significantly higher than RR for wheat ( L. grain yield but equivalent for several maize ( L. traits.
2012-01-01
Background Despite the availability of conventional devices for making single-cell manipulations, determining the hardness of a single cell remains difficult. Here, we consider the cell to be a linear elastic body and apply Young’s modulus (modulus of elasticity), which is defined as the ratio of the repulsive force (stress) in response to the applied strain. In this new method, a scanning probe microscope (SPM) is operated with a cantilever in the “contact-and-push” mode, and the cantilever is applied to the cell surface over a set distance (applied strain). Results We determined the hardness of the following bacterial cells: Escherichia coli, Staphylococcus aureus, Pseudomonas aeruginosa, and five Bacillus spp. In log phase, these strains had a similar Young’s modulus, but Bacillus spp. spores were significantly harder than the corresponding vegetative cells. There was a positive, linear correlation between the hardness of bacterial spores and heat or ultraviolet (UV) resistance. Conclusions Using this technique, the hardness of a single vegetative bacterial cell or spore could be determined based on Young’s modulus. As an application of this technique, we demonstrated that the hardness of individual bacterial spores was directly proportional to heat and UV resistance, which are the conventional measures of physical durability. This technique allows the rapid and direct determination of spore durability and provides a valuable and innovative method for the evaluation of physical properties in the field of microbiology. PMID:22676476
Directory of Open Access Journals (Sweden)
Nakanishi Koichi
2012-06-01
Full Text Available Abstract Background Despite the availability of conventional devices for making single-cell manipulations, determining the hardness of a single cell remains difficult. Here, we consider the cell to be a linear elastic body and apply Young’s modulus (modulus of elasticity, which is defined as the ratio of the repulsive force (stress in response to the applied strain. In this new method, a scanning probe microscope (SPM is operated with a cantilever in the “contact-and-push” mode, and the cantilever is applied to the cell surface over a set distance (applied strain. Results We determined the hardness of the following bacterial cells: Escherichia coli, Staphylococcus aureus, Pseudomonas aeruginosa, and five Bacillus spp. In log phase, these strains had a similar Young’s modulus, but Bacillus spp. spores were significantly harder than the corresponding vegetative cells. There was a positive, linear correlation between the hardness of bacterial spores and heat or ultraviolet (UV resistance. Conclusions Using this technique, the hardness of a single vegetative bacterial cell or spore could be determined based on Young’s modulus. As an application of this technique, we demonstrated that the hardness of individual bacterial spores was directly proportional to heat and UV resistance, which are the conventional measures of physical durability. This technique allows the rapid and direct determination of spore durability and provides a valuable and innovative method for the evaluation of physical properties in the field of microbiology.
Verification of helical tomotherapy delivery using autoassociative kernel regression
International Nuclear Information System (INIS)
Seibert, Rebecca M.; Ramsey, Chester R.; Garvey, Dustin R.; Wesley Hines, J.; Robison, Ben H.; Outten, Samuel S.
2007-01-01
Quality assurance (QA) is a topic of major concern in the field of intensity modulated radiation therapy (IMRT). The standard of practice for IMRT is to perform QA testing for individual patients to verify that the dose distribution will be delivered to the patient. The purpose of this study was to develop a new technique that could eventually be used to automatically evaluate helical tomotherapy treatments during delivery using exit detector data. This technique uses an autoassociative kernel regression (AAKR) model to detect errors in tomotherapy delivery. AAKR is a novel nonparametric model that is known to predict a group of correct sensor values when supplied a group of sensor values that is usually corrupted or contains faults such as machine failure. This modeling scheme is especially suited for the problem of monitoring the fluence values found in the exit detector data because it is able to learn the complex detector data relationships. This scheme still applies when detector data are summed over many frames with a low temporal resolution and a variable beam attenuation resulting from patient movement. Delivery sequences from three archived patients (prostate, lung, and head and neck) were used in this study. Each delivery sequence was modified by reducing the opening time for random individual multileaf collimator (MLC) leaves by random amounts. The error and error-free treatments were delivered with different phantoms in the path of the beam. Multiple autoassociative kernel regression (AAKR) models were developed and tested by the investigators using combinations of the stored exit detector data sets from each delivery. The models proved robust and were able to predict the correct or error-free values for a projection, which had a single MLC leaf decrease its opening time by less than 10 msec. The model also was able to determine machine output errors. The average uncertainty value for the unfaulted projections ranged from 0.4% to 1.8% of the detector
Effects of kernel weight and source-limitation on wheat grain yield ...
African Journals Online (AJOL)
Also, source levels were manipulated through 50% spikelet removal at anthesis to evaluate cultivar source/sink limitations to kernel growth. The results depicted that grain yield, kernel number per spike and 1000 kernel weight were reduced by 24.1%, 9.2% and 23.7% in warmer environment, respectively. Hence, kernel ...
Systematic approach in optimizing numerical memory-bound kernels on GPU
Abdelfattah, Ahmad
2013-01-01
The use of GPUs has been very beneficial in accelerating dense linear algebra computational kernels (DLA). Many high performance numerical libraries like CUBLAS, MAGMA, and CULA provide BLAS and LAPACK implementations on GPUs as well as hybrid computations involving both, CPUs and GPUs. GPUs usually score better performance than CPUs for compute-bound operations, especially those characterized by a regular data access pattern. This paper highlights a systematic approach for efficiently implementing memory-bound DLA kernels on GPUs, by taking advantage of the underlying device\\'s architecture (e.g., high throughput). This methodology proved to outperform existing state-of-the-art GPU implementations for the symmetric matrix-vector multiplication (SYMV), characterized by an irregular data access pattern, in a recent work (Abdelfattah et. al, VECPAR 2012). We propose to extend this methodology to the general matrix-vector multiplication (GEMV) kernel. The performance results show that our GEMV implementation achieves better performance for relatively small to medium matrix sizes, making it very influential in calculating the Hessenberg and bidiagonal reductions of general matrices (radar applications), which are the first step toward computing eigenvalues and singular values, respectively. Considering small and medium size matrices (≤4500), our GEMV kernel achieves an average 60% improvement in single precision (SP) and an average 25% in double precision (DP) over existing open-source and commercial software solutions. These results improve reduction algorithms for both small and large matrices. The improved GEMV performances engender an averge 30% (SP) and 15% (DP) in Hessenberg reduction and up to 25% (SP) and 14% (DP) improvement for the bidiagonal reduction over the implementation provided by CUBLAS 5.0. © 2013 Springer-Verlag.
NEW HORIZONS SPICE KERNELS V1.0
National Aeronautics and Space Administration — This data set includes the complete set of New Horizons (NH) SPICE data files (kernel files'), which can be accessed using SPICE software. The SPICE data contain...
Kernel based collaborative recommender system for e-purchasing
Indian Academy of Sciences (India)
Home; Journals; Sadhana; Volume 35; Issue 5. Kernel based collaborative recommender system for -purchasing ... Recommender system a new marketing strategy plays an important role particularly in an electronic commerce environment. Among the various recommender systems, collaborative recommender system ...
Homotopy deform method for reproducing kernel space for ...
Indian Academy of Sciences (India)
2016-09-23
Sep 23, 2016 ... Nonlinear differential equations; the homotopy deform method; the simplified reproducing kernel ... an equivalent integro differential equation. ... an algorithm for solving nonlinear multipoint BVPs by combining homotopy perturbation and variational iteration methods. Most recently, Duan and Rach [12].
Quantitative trait locus (QTL) mapping for 100-kernel weight of ...
African Journals Online (AJOL)
hope&shola
2010-12-06
Zea mays L.), related to yield. To realize its ... Key words: Maize (Zea mays L.), 100-kernel weight, quantitative trait locus (QTL), recombinant inbred line. (RIL), nitrogen ... cient approach to realize genetic basis of trait, some.
Homotopy deform method for reproducing kernel space for ...
Indian Academy of Sciences (India)
2016-09-23
s12043-016-1269-8. Homotopy deform method for reproducing kernel space for nonlinear boundary value problems. MIN-QIANG XU. ∗ and YING-ZHEN LIN. School of Science, Zhuhai Campus, Beijing Institute of Technology, ...
MARS EXPLORATION ROVER 2 SPICE KERNELS V1.0
National Aeronautics and Space Administration — This data set includes the complete set of Mars Exploration Rover 2 SPICE data files (kernel files'), which can be accessed using SPICE software. The SPICE data...
ROSETTA ORBITER/LANDER SPICE KERNELS V1.0
National Aeronautics and Space Administration — This data set includes the complete set of Rosetta mission SPICE data files (kernel files''), which can be accessed using SPICE software. The SPICE data contains...
National Aeronautics and Space Administration — This data set includes the complete set of Mars Global Surveyor SPICE data files (kernel files''), which can be accessed using SPICE software. The SPICE data...
MARS EXPLORATION ROVER 1 SPICE KERNELS V1.0
National Aeronautics and Space Administration — This data set includes the complete set of Mars Exploration Rover 1 SPICE data files (kernel files''), which can be accessed using SPICE software. The SPICE data...
Linear and kernel methods for multi- and hypervariate change detection
DEFF Research Database (Denmark)
Nielsen, Allan Aasbjerg; Canty, Morton J.
2010-01-01
The iteratively re-weighted multivariate alteration detection (IR-MAD) algorithm may be used both for unsuper- vised change detection in multi- and hyperspectral remote sensing imagery as well as for automatic radiometric normalization of multi- or hypervariate multitemporal image sequences...... code exists which allows for fast data exploration and experimentation with smaller datasets. Computationally demanding kernelization of test data with training data and kernel image projections have been programmed to run on massively parallel CUDA-enabled graphics processors, when available, giving....... 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...
DEEP SPACE 1 SPICE KERNELS V1.0
National Aeronautics and Space Administration — This data set includes the complete set of Deep Space 1 (DS1) SPICE data files (kernel files''), which can be accessed using SPICE software. The SPICE data contains...
CLEMENTINE MOON SPICE KERNELS V1.0
National Aeronautics and Space Administration — This data set includes the complete set of Clementine SPICE data files (kernel files''), which can be accessed using SPICE software. The SPICE data contains...
Identification and validation of genomic regions influencing kernel zinc and iron in maize.
Hindu, Vemuri; Palacios-Rojas, Natalia; Babu, Raman; Suwarno, Willy B; Rashid, Zerka; Usha, Rayalcheruvu; Saykhedkar, Gajanan R; Nair, Sudha K
2018-03-24
Genome-wide association study (GWAS) on 923 maize lines and validation in bi-parental populations identified significant genomic regions for kernel-Zinc and-Iron in maize. Bio-fortification of maize with elevated Zinc (Zn) and Iron (Fe) holds considerable promise for alleviating under-nutrition among the world's poor. Bio-fortification through molecular breeding could be an economical strategy for developing nutritious maize, and hence in this study, we adopted GWAS to identify markers associated with high kernel-Zn and Fe in maize and subsequently validated marker-trait associations in independent bi-parental populations. For GWAS, we evaluated a diverse maize association mapping panel of 923 inbred lines across three environments and detected trait associations using high-density Single nucleotide polymorphism (SNPs) obtained through genotyping-by-sequencing. Phenotyping trials of the GWAS panel showed high heritability and moderate correlation between kernel-Zn and Fe concentrations. GWAS revealed a total of 46 SNPs (Zn-20 and Fe-26) significantly associated (P ≤ 5.03 × 10 -05 ) with kernel-Zn and Fe concentrations with some of these associated SNPs located within previously reported QTL intervals for these traits. Three double-haploid (DH) populations were developed using lines identified from the panel that were contrasting for these micronutrients. The DH populations were phenotyped at two environments and were used for validating significant SNPs (P ≤ 1 × 10 -03 ) based on single marker QTL analysis. Based on this analysis, 11 (Zn) and 11 (Fe) SNPs were found to have significant effect on the trait variance (P ≤ 0.01, R 2 ≥ 0.05) in at least one bi-parental population. These findings are being pursued in the kernel-Zn and Fe breeding program, and could hold great value in functional analysis and possible cloning of high-value genes for these traits in maize.
7 CFR 981.401 - Adjusted kernel weight.
2010-01-01
... weight of delivery 10,000 10,000 2. Percent of edible kernel weight 53.0 84.0 3. Less weight loss in processing 1 1.00 0 4. Less excess moisture of edible kernels (excess moisture×line 2) 1.06 1.68 5. Net... Agriculture Regulations of the Department of Agriculture (Continued) AGRICULTURAL MARKETING SERVICE (Marketing...
Palm kernel shell as aggregate for light weight concrete | Idah ...
African Journals Online (AJOL)
The palm kernel, cement, sand and gravel were mixed and cast in steel or cast iron moulds of 150mm2 cubes. The results show that the PK1 with ratio of 1 :2:3: 1· of cement, sand, gravel. and palm kernel shells respectively gave the highest compressive strength of 8.03N/mm2 after 28 days of curing. Comparing the results ...
Mathematical Modelling of Thin Layer Dried Cashew Kernels | Asiru ...
African Journals Online (AJOL)
In this paper mathematical models describing thin layer drying of cashew kernels in a batch dryer were presented. The range of drying air temperature was 70 – 110°C. The initial moisture content of the cashew kernels was 9.29% (d.b.) and the final moisture content was in the range of 3.5 to 4.6% dry-basis. Seven different ...
Assessing Gamma kernels and BSS/LSS processes
DEFF Research Database (Denmark)
Barndorff-Nielsen, Ole E.
This paper reviews the roles of gamma type kernels in the theory and modelling for Brownian and Lévy semistationary processes. Applications to financial econometrics and the physics of turbulence are pointed out.......This paper reviews the roles of gamma type kernels in the theory and modelling for Brownian and Lévy semistationary processes. Applications to financial econometrics and the physics of turbulence are pointed out....
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
Commutators of integral operators with variable kernels on Hardy ...
Indian Academy of Sciences (India)
(1.2). Then T ,0 is the singular integral with variable kernel, and we simply write it as T . The. Lp-boundedness of the singular integral operator with variable kernel appears in [1] (see also [3,7]). It turns out that such kind of operators are much more closely related to the elliptic partial differential equations of second order with ...
Resummed memory kernels in generalized system-bath master equations
Mavros, Michael G.; Van Voorhis, Troy
2014-08-01
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.
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 spectral clustering with memory effect
Langone, Rocco; Alzate, Carlos; Suykens, Johan A. K.
2013-05-01
Evolving graphs describe many natural phenomena changing over time, such as social relationships, trade markets, metabolic networks etc. In this framework, performing community detection and analyzing the cluster evolution represents a critical task. Here we propose a new model for this purpose, where the smoothness of the clustering results over time can be considered as a valid prior knowledge. It is based on a constrained optimization formulation typical of Least Squares Support Vector Machines (LS-SVM), where the objective function is designed to explicitly incorporate temporal smoothness. The latter allows the model to cluster the current data well and to be consistent with the recent history. We also propose new model selection criteria in order to carefully choose the hyper-parameters of our model, which is a crucial issue to achieve good performances. We successfully test the model on four toy problems and on a real world network. We also compare our model with Evolutionary Spectral Clustering, which is a state-of-the-art algorithm for community detection of evolving networks, illustrating that the kernel spectral clustering with memory effect can achieve better or equal performances.
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.
Calpain cleavage prediction using multiple kernel learning.
Directory of Open Access Journals (Sweden)
David A DuVerle
Full Text Available Calpain, an intracellular Ca²⁺-dependent cysteine protease, is known to play a role in a wide range of metabolic pathways through limited proteolysis of its substrates. However, only a limited number of these substrates are currently known, with the exact mechanism of substrate recognition and cleavage by calpain still largely unknown. While previous research has successfully applied standard machine-learning algorithms to accurately predict substrate cleavage by other similar types of proteases, their approach does not extend well to calpain, possibly due to its particular mode of proteolytic action and limited amount of experimental data. Through the use of Multiple Kernel Learning, a recent extension to the classic Support Vector Machine framework, we were able to train complex models based on rich, heterogeneous feature sets, leading to significantly improved prediction quality (6% over highest AUC score produced by state-of-the-art methods. In addition to producing a stronger machine-learning model for the prediction of calpain cleavage, we were able to highlight the importance and role of each feature of substrate sequences in defining specificity: primary sequence, secondary structure and solvent accessibility. Most notably, we showed there existed significant specificity differences across calpain sub-types, despite previous assumption to the contrary. Prediction accuracy was further successfully validated using, as an unbiased test set, mutated sequences of calpastatin (endogenous inhibitor of calpain modified to no longer block calpain's proteolytic action. An online implementation of our prediction tool is available at http://calpain.org.
Self-organization as an iterative kernel smoothing process.
Mulier, F; Cherkassky, V
1995-11-01
Kohonen's self-organizing map, when described in a batch processing mode, can be interpreted as a statistical kernel smoothing problem. The batch SOM algorithm consists of two steps. First, the training data are partitioned according to the Voronoi regions of the map unit locations. Second, the units are updated by taking weighted centroids of the data falling into the Voronoi regions, with the weighing function given by the neighborhood. Then, the neighborhood width is decreased and steps 1, 2 are repeated. The second step can be interpreted as a statistical kernel smoothing problem where the neighborhood function corresponds to the kernel and neighborhood width corresponds to kernel span. To determine the new unit locations, kernel smoothing is applied to the centroids of the Voronoi regions in the topological space. This interpretation leads to some new insights concerning the role of the neighborhood and dimensionality reduction. It also strengthens the algorithm's connection with the Principal Curve algorithm. A generalized self-organizing algorithm is proposed, where the kernel smoothing step is replaced with an arbitrary nonparametric regression method.
Sun, Yueping; Zhang, Yu; Li, Jiao
2017-01-01
Chemical-induced disease relations (CID) are crucial in various biomedical tasks. In the CID task of Biocreative V, no classifiers with multiple kernels have been developed. In this study, a multiple kernel learning-boosting (MKLB) method is proposed. Different kernel functions according to different types of features were constucted and boosted, each of which were learned with multiple kernels. Our multiple kernel learning-boosting (MKLB) method achieved a F1 score of 0.5068 without incorporating knowledge bases.
Distribution functions in systems of hard dumbbells and linear hard triatomics near a hard wall.
Boublik, Tomás
2008-12-04
An extension of the theoretical approach to determine the distribution function in the inhomogeneous systems of hard spheres near a planar hard wall (based on the evaluation of the background correlation function in terms of the residual chemical potentials of the hard particle, hard wall, and the corresponding combined body) is extended to inhomogeneous systems of hard dumbbells (HD) and hard triatomics (HT). The perpendicular and parallel orientations of both HD and HT with respect to the hard wall are considered, and the way of the evaluation of the residual chemical potentials in terms of the geometric quantities-a volume, surface area, and the mean curvature integral, divided by 4pi-of a particle, hard wall, and the corresponding combined body is outlined. The inhomogeneous systems hard wall + hard dumbbell with the site-site distance L* = 0.6 and reduced density rho* = 0.491 and hard wall + hard triatomics with L* = 1.6 and packing fraction y = 0.409 are studied, and the obtained distribution functions are compared with simulation data. A fair agreement in the most important range of distances was found.
Calculation of the time resolution of the J-PET tomograph using kernel density estimation
Raczyński, L.; Wiślicki, W.; Krzemień, W.; Kowalski, P.; Alfs, D.; Bednarski, T.; Białas, P.; Curceanu, C.; Czerwiński, E.; Dulski, K.; Gajos, A.; Głowacz, B.; Gorgol, M.; Hiesmayr, B.; Jasińska, B.; Kamińska, D.; Korcyl, G.; Kozik, T.; Krawczyk, N.; Kubicz, E.; Mohammed, M.; Pawlik-Niedźwiecka, M.; Niedźwiecki, S.; Pałka, M.; Rudy, Z.; Rundel, O.; Sharma, N. G.; Silarski, M.; Smyrski, J.; Strzelecki, A.; Wieczorek, A.; Zgardzińska, B.; Zieliński, M.; Moskal, P.
2017-06-01
In this paper we estimate the time resolution of the J-PET scanner built from plastic scintillators. We incorporate the method of signal processing using the Tikhonov regularization framework and the kernel density estimation method. We obtain simple, closed-form analytical formulae for time resolution. The proposed method is validated using signals registered by means of the single detection unit of the J-PET tomograph built from a 30 cm long plastic scintillator strip. It is shown that the experimental and theoretical results obtained for the J-PET scanner equipped with vacuum tube photomultipliers are consistent.
Directory of Open Access Journals (Sweden)
Xiong Luo
2017-07-01
Full Text Available Recently, inspired by correntropy, kernel risk-sensitive loss (KRSL has emerged as a novel nonlinear similarity measure defined in kernel space, which achieves a better computing performance. After applying the KRSL to adaptive filtering, the corresponding minimum kernel risk-sensitive loss (MKRSL algorithm has been developed accordingly. However, MKRSL as a traditional kernel adaptive filter (KAF method, generates a growing radial basis functional (RBF network. In response to that limitation, through the use of online vector quantization (VQ technique, this article proposes a novel KAF algorithm, named quantized MKRSL (QMKRSL to curb the growth of the RBF network structure. Compared with other quantized methods, e.g., quantized kernel least mean square (QKLMS and quantized kernel maximum correntropy (QKMC, the efficient performance surface makes QMKRSL converge faster and filter more accurately, while maintaining the robustness to outliers. Moreover, considering that QMKRSL using traditional gradient descent method may fail to make full use of the hidden information between the input and output spaces, we also propose an intensified QMKRSL using a bilateral gradient technique named QMKRSL_BG, in an effort to further improve filtering accuracy. Short-term chaotic time-series prediction experiments are conducted to demonstrate the satisfactory performance of our algorithms.
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
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.
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.
Near infrared hyperspectral imaging of blends of conventional and waxy hard wheats
Directory of Open Access Journals (Sweden)
Stephen R. Delwiche
2018-02-01
Full Text Available Recent development of hard winter waxy (amylose-free wheat adapted to the North American climate has prompted the quest to find a rapid method that will determine mixture levels of conventional wheat in lots of identity preserved waxy wheat. Previous work documented the use of conventional near infrared (NIR reflectance spectroscopy to determine the mixture level of conventional wheat in waxy wheat, with an examined range, through binary sample mixture preparation, of 0–100% (weight conventional / weight total. The current study examines the ability of NIR hyperspectral imaging of intact kernels to determine mixture levels. Twenty-nine mixtures (0, 1, 2, 3, 4, 5, 10, 15, …, 95, 96, 97, 98, 99, 100% were formed from known genotypes of waxy and conventional wheat. Two-class partial least squares discriminant analysis (PLSDA and statistical pattern recognition classifier models were developed for identifying each kernel in the images as conventional or waxy. Along with these approaches, conventional PLS1 regression modelling was performed on means of kernel spectra within each mixture test sample. Results indicated close agreement between all three approaches, with standard errors of prediction for the better preprocess transformations (PLSDA models or better classifiers (pattern recognition models of approximately 9 percentage units. Although such error rates were slightly greater than ones previously published using non-imaging NIR analysis of bulk whole kernel wheat and wheat meal, the HSI technique offers an advantage of its potential use in sorting operations.
Impact of Triticum mosaic virus infection on hard winter wheat milling and bread baking quality.
Miller, Rebecca A; Martin, T Joe; Seifers, Dallas L
2012-03-15
Triticum mosaic virus (TriMV) is a newly discovered wheat virus. Information regarding the effect of wheat viruses on milling and baking quality is limited. The objective of this study was to determine the impact of TriMV infection on the kernel characteristics, milling yield and bread baking quality of wheat. Commercial hard winter varieties evaluated included RonL, Danby and Jagalene. The TriMV resistance of RonL is low, while that of Danby and Jagalene is unknown. KS96HW10-3, a germplasm with high TriMV resistance, was included as a control. Plots of each variety were inoculated with TriMV at the two- to three-leaf stage. Trials were conducted at two locations in two crop years. TriMV infection had no effect on the kernel characteristics, flour yield or baking properties of KS96HW10-3. The effect of TriMV on the kernel characteristics of RonL, Danby and Jagalene was not consistent between crop years and presumably an environmental effect. The flour milling and bread baking properties of these three varieties were not significantly affected by TriMV infection. TriMV infection of wheat plants did not affect harvested wheat kernel characteristics, flour milling properties or white pan bread baking quality. Copyright © 2011 Society of Chemical Industry.
DEFF Research Database (Denmark)
Moos, Lejf
2009-01-01
The governance and leadership at transnational, national and school level seem to be converging into a number of isomorphic forms as we see a tendency towards substituting 'hard' forms of governance, that are legally binding, with 'soft' forms based on persuasion and advice. This article analyses...... of Denmark, and finally the third layer: the leadership used in Danish schools. The use of 'soft governance' is shifting the focus of governance and leadership from decisions towards influence and power and thus shifting the focus of the processes from the decision-making itself towards more focus...... the understanding of relations and the coherence of processes of influence/power/governance, the article introduces a communications model of decision-making processes as processes of influence....
Zirconium nitride hard coatings
International Nuclear Information System (INIS)
Roman, Daiane; Amorim, Cintia Lugnani Gomes de; Soares, Gabriel Vieira; Figueroa, Carlos Alejandro; Baumvol, Israel Jacob Rabin; Basso, Rodrigo Leonardo de Oliveira
2010-01-01
Zirconium nitride (ZrN) nanometric films were deposited onto different substrates, in order to study the surface crystalline microstructure and also to investigate the electrochemical behavior to obtain a better composition that minimizes corrosion reactions. The coatings were produced by physical vapor deposition (PVD). The influence of the nitrogen partial pressure, deposition time and temperature over the surface properties was studied. Rutherford backscattering spectrometry (RBS), X-ray photoelectron spectroscopy (XPS), X-ray diffraction (XRD), scanning electron microscopy (SEM) and corrosion experiments were performed to characterize the ZrN hard coatings. The ZrN films properties and microstructure changes according to the deposition parameters. The corrosion resistance increases with temperature used in the films deposition. Corrosion tests show that ZrN coating deposited by PVD onto titanium substrate can improve the corrosion resistance. (author)
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.
Interest Extraction Using Relevance Feedback with Kernel Method
Hidekazu, Yanagimoto; Sigeru, Omatu
In this paper, we propose interest extraction using the relevance feedback with the kernel method. In the field of machine learning, the kernel method has been used. Since the classifier using the kernel method creates a discriminant function in a feature space, the discriminant function is a nonlinear function in a input space. The kernel method is used for the Support Vector Machine (SVM), the Kernel PCA, and so on. The SVM set a discriminant hyperplane between positive data and negative data. Hence, a distance between the hyperplane and a training sample is not important in the SVM. It is difficult to use the SVM to score other samples. Our goal is to create a method which scores the other samples in the feature space. We propose the relevance feedback which is carried out in the feature space. Hence, this relevance feedback can deal with nonlinearity of data. We compare the proposed method with the common relevance feedback using test collection NTCIR2. Finally, we comfirm the proposed method is superior to the common method through simulations.
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.
CELLULOSE EXTRACTION FROM PALM KERNEL CAKE USING LIQUID PHASE OXIDATION
Directory of Open Access Journals (Sweden)
FARM YAN YAN
2009-03-01
Full Text Available Cellulose is widely used in many aspect and industries such as food industry, pharmaceutical, paint, polymers, and many more. Due to the increasing demand in the market, studies and work to produce cellulose are still rapidly developing. In this work, liquid phase oxidation was used to extract cellulose from palm kernel cake to separate hemicellulose, cellulose and lignin. The method is basically a two-step process. Palm kernel cake was pretreated in hot water at 180°C and followed by liquid oxidation process with 30% H2O2 at 60°C at atmospheric pressure. The process parameters are hot water treatment time, ratio of palm kernel cake to H2O2, liquid oxidation reaction temperature and time. Analysis of the process parameters on production cellulose from palm kernel cake was performed by using Response Surface Methodology. The recovered cellulose was further characterized by Fourier Transform Infrared (FTIR. Through the hot water treatment, hemicellulose in the palm kernel cake was successfully recovered as saccharides and thus leaving lignin and cellulose. Lignin was converted to water soluble compounds in liquid oxidation step which contains small molecular weight fatty acid as HCOOH and CH3COOH and almost pure cellulose was recovered.
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.
Multiple kernel sparse representations for supervised and unsupervised learning.
Thiagarajan, Jayaraman J; Ramamurthy, Karthikeyan Natesan; Spanias, Andreas
2014-07-01
In complex visual recognition tasks, it is typical to adopt multiple descriptors, which describe different aspects of the images, for obtaining an improved recognition performance. Descriptors that have diverse forms can be fused into a unified feature space in a principled manner using kernel methods. Sparse models that generalize well to the test data can be learned in the unified kernel space, and appropriate constraints can be incorporated for application in supervised and unsupervised learning. In this paper, we propose to perform sparse coding and dictionary learning in the multiple kernel space, where the weights of the ensemble kernel are tuned based on graph-embedding principles such that class discrimination is maximized. In our proposed algorithm, dictionaries are inferred using multiple levels of 1D subspace clustering in the kernel space, and the sparse codes are obtained using a simple levelwise pursuit scheme. Empirical results for object recognition and image clustering show that our algorithm outperforms existing sparse coding based approaches, and compares favorably to other state-of-the-art methods.
Janka hardness using nonstandard specimens
David W. Green; Marshall Begel; William Nelson
2006-01-01
Janka hardness determined on 1.5- by 3.5-in. specimens (2Ã4s) was found to be equivalent to that determined using the 2- by 2-in. specimen specified in ASTM D 143. Data are presented on the relationship between Janka hardness and the strength of clear wood. Analysis of historical data determined using standard specimens indicated no difference between side hardness...
An Efficient Kernel Optimization Method for Radar High-Resolution Range Profile Recognition
Directory of Open Access Journals (Sweden)
Chen Bo
2007-01-01
Full Text Available A kernel optimization method based on fusion kernel for high-resolution range profile (HRRP is proposed in this paper. Based on the fusion of -norm and -norm Gaussian kernels, our method combines the different characteristics of them so that not only is the kernel function optimized but also the speckle fluctuations of HRRP are restrained. Then the proposed method is employed to optimize the kernel of kernel principle component analysis (KPCA and the classification performance of extracted features is evaluated via support vector machines (SVMs classifier. Finally, experimental results on the benchmark and radar-measured data sets are compared and analyzed to demonstrate the efficiency of our method.
This particular object was used up until 2012 in the Data Centre. It slots into one of the Disk Server trays. Hard disks were invented in the 1950s. They started as large disks up to 20 inches in diameter holding just a few megabytes (link is external). They were originally called "fixed disks" or "Winchesters" (a code name used for a popular IBM product). They later became known as "hard disks" to distinguish them from "floppy disks (link is external)." Hard disks have a hard platter that holds the magnetic medium, as opposed to the flexible plastic film found in tapes and floppies.
Hard machining under dry conditions with hard PVD coatings on cemented carbide endmills
International Nuclear Information System (INIS)
Fleischer, W.; Baranski, N.; Kolk, G.J. van der; Stockmann, Y.; Kunen, H.; Hoppe, S.
2001-01-01
Machining of hardened steel needs cutting tools for extreme conditions. Not only the cemented carbide tool material, but also the hard or ultra hard coating determines the tool life and cutting performance on the work piece. For milling operation in hardened material 1.2379 with a hardness between 60 and 62 HRc, endmills coated with different TiAlN layers in single or multilayer design and also top coatings with friction performance are used. Cutting tests with investigations of the wear on the cutting edge and in situ infrared temperature measurements by video camera showed large differences in tool performance. According to these results the limitation of cutting time or cutting length is, in some cases, not only caused by the wear on the tool, but also by the surface temperature of the work piece material. (author)
Directory of Open Access Journals (Sweden)
Ajay Kumar
2016-03-01
Full Text Available Wheat kernel shape and size has been under selection since early domestication. Kernel morphology is a major consideration in wheat breeding, as it impacts grain yield and quality. A population of 160 recombinant inbred lines (RIL, developed using an elite (ND 705 and a nonadapted genotype (PI 414566, was extensively phenotyped in replicated field trials and genotyped using Infinium iSelect 90K assay to gain insight into the genetic architecture of kernel shape and size. A high density genetic map consisting of 10,172 single nucleotide polymorphism (SNP markers, with an average marker density of 0.39 cM/marker, identified a total of 29 genomic regions associated with six grain shape and size traits; ∼80% of these regions were associated with multiple traits. The analyses showed that kernel length (KL and width (KW are genetically independent, while a large number (∼59% of the quantitative trait loci (QTL for kernel shape traits were in common with genomic regions associated with kernel size traits. The most significant QTL was identified on chromosome 4B, and could be an ortholog of major rice grain size and shape gene or . Major and stable loci also were identified on the homeologous regions of Group 5 chromosomes, and in the regions of (6A and (7A genes. Both parental genotypes contributed equivalent positive QTL alleles, suggesting that the nonadapted germplasm has a great potential for enhancing the gene pool for grain shape and size. This study provides new knowledge on the genetic dissection of kernel morphology, with a much higher resolution, which may aid further improvement in wheat yield and quality using genomic tools.
The spinor heat kernel in maximally symmetric spaces
International Nuclear Information System (INIS)
Camporesi, R.
1992-01-01
The heat kernel K(x, x', t) of the iterated Dirac operator on an N-dimensional simply connected maximally symmetric Riemannian manifold is calculated. On the odd-dimensional hyperbolic spaces K is a Minakshisundaram-DeWitt expansion which terminates to the coefficient a (N-1)/2 and is exact. On the odd spheres the heat kernel may be written as an image sum of WKB kernels, each term corresponding to a classical path (geodesic). In the even dimensional case the WKB approximation is not exact, but a closed form of K is derived both in terms of (spherical) eigenfunctions and of a 'sum over classical paths'. The spinor Plancherel measure μ(λ) and ζ function in the hyperbolic case are also calculated. A simple relation between the analytic structure of μ on H N and the degeneracies of the Dirac operator on S N is found. (orig.)
Purification and characterization of riproximin from Ximenia americana fruit kernels.
Bayer, Helene; Ey, Noreen; Wattenberg, Andreas; Voss, Cristina; Berger, Martin R
2012-03-01
Highly pure riproximin was isolated from the fruit kernels of Ximenia americana, a defined, seasonally available and potentially unlimited herbal source. The newly established purification procedure included an initial aqueous extraction, removal of lipids with chloroform and subsequent chromatographic purification steps on a strong anion exchange resin and lactosyl-Sepharose. Consistent purity and stable biological properties were shown over several purification batches. The purified, kernel-derived riproximin was characterized in comparison to the African plant material riproximin and revealed highly similar biochemical and biological properties but differences in the electrophoresis pattern and mass spectrometry peptide profile. Our results suggest that although the purified fruit kernel riproximin consists of a mixture of closely related isoforms, it provides a reliable basis for further research and development of this type II ribosome inactivating protein (RIP). Copyright Â© 2011 Elsevier Inc. All rights reserved.
Reconstruction of noisy and blurred images using blur kernel
Ellappan, Vijayan; Chopra, Vishal
2017-11-01
Blur is a common in so many digital images. Blur can be caused by motion of the camera and scene object. In this work we proposed a new method for deblurring images. This work uses sparse representation to identify the blur kernel. By analyzing the image coordinates Using coarse and fine, we fetch the kernel based image coordinates and according to that observation we get the motion angle of the shaken or blurred image. Then we calculate the length of the motion kernel using radon transformation and Fourier for the length calculation of the image and we use Lucy Richardson algorithm which is also called NON-Blind(NBID) Algorithm for more clean and less noisy image output. All these operation will be performed in MATLAB IDE.
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.
Optimized data fusion for kernel k-means clustering.
Yu, Shi; Tranchevent, Léon-Charles; Liu, Xinhai; Glänzel, Wolfgang; Suykens, Johan A K; De Moor, Bart; Moreau, Yves
2012-05-01
This paper presents a novel optimized kernel k-means algorithm (OKKC) to combine multiple data sources for clustering analysis. The algorithm uses an alternating minimization framework to optimize the cluster membership and kernel coefficients as a nonconvex problem. In the proposed algorithm, the problem to optimize the cluster membership and the problem to optimize the kernel coefficients are all based on the same Rayleigh quotient objective; therefore the proposed algorithm converges locally. OKKC has a simpler procedure and lower complexity than other algorithms proposed in the literature. Simulated and real-life data fusion applications are experimentally studied, and the results validate that the proposed algorithm has comparable performance, moreover, it is more efficient on large-scale data sets. (The Matlab implementation of OKKC algorithm is downloadable from http://homes.esat.kuleuven.be/~sistawww/bio/syu/okkc.html.).
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.
Regularized Embedded Multiple Kernel Dimensionality Reduction for Mine Signal Processing
Li, Shuang; Liu, Bing; Zhang, Chen
2016-01-01
Traditional multiple kernel dimensionality reduction models are generally based on graph embedding and manifold assumption. But such assumption might be invalid for some high-dimensional or sparse data due to the curse of dimensionality, which has a negative influence on the performance of multiple kernel learning. In addition, some models might be ill-posed if the rank of matrices in their objective functions was not high enough. To address these issues, we extend the traditional graph embedding framework and propose a novel regularized embedded multiple kernel dimensionality reduction method. Different from the conventional convex relaxation technique, the proposed algorithm directly takes advantage of a binary search and an alternative optimization scheme to obtain optimal solutions efficiently. The experimental results demonstrate the effectiveness of the proposed method for supervised, unsupervised, and semisupervised scenarios. PMID:27247562
Regularized Embedded Multiple Kernel Dimensionality Reduction for Mine Signal Processing.
Li, Shuang; Liu, Bing; Zhang, Chen
2016-01-01
Traditional multiple kernel dimensionality reduction models are generally based on graph embedding and manifold assumption. But such assumption might be invalid for some high-dimensional or sparse data due to the curse of dimensionality, which has a negative influence on the performance of multiple kernel learning. In addition, some models might be ill-posed if the rank of matrices in their objective functions was not high enough. To address these issues, we extend the traditional graph embedding framework and propose a novel regularized embedded multiple kernel dimensionality reduction method. Different from the conventional convex relaxation technique, the proposed algorithm directly takes advantage of a binary search and an alternative optimization scheme to obtain optimal solutions efficiently. The experimental results demonstrate the effectiveness of the proposed method for supervised, unsupervised, and semisupervised scenarios.
Regularized Embedded Multiple Kernel Dimensionality Reduction for Mine Signal Processing
Directory of Open Access Journals (Sweden)
Shuang Li
2016-01-01
Full Text Available Traditional multiple kernel dimensionality reduction models are generally based on graph embedding and manifold assumption. But such assumption might be invalid for some high-dimensional or sparse data due to the curse of dimensionality, which has a negative influence on the performance of multiple kernel learning. In addition, some models might be ill-posed if the rank of matrices in their objective functions was not high enough. To address these issues, we extend the traditional graph embedding framework and propose a novel regularized embedded multiple kernel dimensionality reduction method. Different from the conventional convex relaxation technique, the proposed algorithm directly takes advantage of a binary search and an alternative optimization scheme to obtain optimal solutions efficiently. The experimental results demonstrate the effectiveness of the proposed method for supervised, unsupervised, and semisupervised scenarios.
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 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...... on the complexification $G_{\\mathbb C}/K_{\\mathbb C}$ of $Ω$, the weight being expressed explicitly in terms of a multivariable $K$-Bessel function on $Ω$. Even in the special case of the symmetric cone $Ω=\\mathbb{R}_+$ these results seem to be new.......We investigate the heat equation corresponding to the Bessel operators on a symmetric cone $Ω=G/K$. These operators form a one-parameter family of elliptic self-adjoint second order differential operators and occur in the Lie algebra action of certain unitary highest weight representations...
Linear and kernel methods for multivariate change detection
DEFF Research Database (Denmark)
Canty, Morton J.; Nielsen, Allan Aasbjerg
2012-01-01
The iteratively reweighted multivariate alteration detection (IR-MAD) algorithm may be used both for unsupervised change detection in multi- and hyperspectral remote sensing imagery and for automatic radiometric normalization of multitemporal image sequences. Principal components analysis (PCA......), 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...
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.
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.
Weighted Feature Gaussian Kernel SVM for Emotion Recognition
Directory of Open Access Journals (Sweden)
Wei Wei
2016-01-01
Full Text Available 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.
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.
Aflatoxin detection in whole corn kernels using hyperspectral methods
Casasent, David; Chen, Xue-Wen
2004-03-01
Hyperspectral (HS) data for the inspection of whole corn kernels for aflatoxin is considered. The high-dimensionality of HS data requires feature extraction or selection for good classifier generalization. For fast and inexpensive data collection, only several features (λ responses) can be used. These are obtained by feature selection from the full HS response. A new high dimensionality branch and bound (HDBB) feature selection algorithm is used; it is found to be optimum, fast and very efficient. Initial results indicate that HS data is very promising for aflatoxin detection in whole kernel corn.
Source identity and kernel functions for Inozemtsev-type systems
Energy Technology Data Exchange (ETDEWEB)
Langmann, Edwin [Department of Theoretical Physics, Royal Institute of Technology KTH, SE-106 91 Stockholm (Sweden); Takemura, Kouichi [Department of Mathematics, Chuo University, 1-13-27 Kasuga, Bunkyo-ku, Tokyo 112-8551 (Japan)
2012-08-15
The Inozemtsev Hamiltonian is an elliptic generalization of the differential operator defining the BC{sub N} trigonometric quantum Calogero-Sutherland model, and its eigenvalue equation is a natural many-variable generalization of the Heun differential equation. We present kernel functions for Inozemtsev Hamiltonians and Chalykh-Feigin-Veselov-Sergeev-type deformations thereof. Our main result is a solution of a heat-type equation for a generalized Inozemtsev Hamiltonian which is the source of all these kernel functions. Applications are given, including a derivation of simple exact eigenfunctions and eigenvalues of the Inozemtsev Hamiltonian.
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
Laser thermographic technologies for hard copy recording
Bessmel'tsev, Viktor P.; Baev, Sergej G.
1995-04-01
Methods of hard copies recording based on thermal interaction of the beam from CO2 or YAG lasers with various kinds of films on any substrates have been developed. The recording processes are single-step and require no additional development. Among them are: (1) Laser thermodestruction of thin mask layers or of a material surface on any kinds of substrates. (2) Laser thermochemical reactions of thermal decomposition of metal salts in solid state phase on a surface of various hygroscopic substrates. The laser recording devices using the methods, described above have been developed and are manufactured now; they allow one to record hard copies with a size of up to 27 X 31 inches, a resolution of 4000 dpi.
Hard spheres out of equilibrium
Hermes, M.
2010-01-01
In this thesis, experiments and simulations are combined to investigate the nonequilibrium behaviour of hard spheres. In the first chapters we use Molecular Dynamics simulations to investigate the dynamic glass transition of polydisperse hard spheres. We show that this dynamic transition is
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.
Genomic-Enabled Prediction Kernel Models with Random Intercepts for Multi-environment Trials
Cuevas, Jaime; Granato, Italo; Fritsche-Neto, Roberto; Montesinos-Lopez, Osval A.; Burgueño, Juan; Bandeira e Sousa, Massaine; Crossa, José
2018-01-01
In this study, we compared the prediction accuracy of the main genotypic effect model (MM) without G×E interactions, the multi-environment single variance G×E deviation model (MDs), and the multi-environment environment-specific variance G×E deviation model (MDe) where the random genetic effects of the lines are modeled with the markers (or pedigree). With the objective of further modeling the genetic residual of the lines, we incorporated the random intercepts of the lines (l) and generated another three models. Each of these 6 models were fitted with a linear kernel method (Genomic Best Linear Unbiased Predictor, GB) and a Gaussian Kernel (GK) method. We compared these 12 model-method combinations with another two multi-environment G×E interactions models with unstructured variance-covariances (MUC) using GB and GK kernels (4 model-method). Thus, we compared the genomic-enabled prediction accuracy of a total of 16 model-method combinations on two maize data sets with positive phenotypic correlations among environments, and on two wheat data sets with complex G×E that includes some negative and close to zero phenotypic correlations among environments. The two models (MDs and MDE with the random intercept of the lines and the GK method) were computationally efficient and gave high prediction accuracy in the two maize data sets. Regarding the more complex G×E wheat data sets, the prediction accuracy of the model-method combination with G×E, MDs and MDe, including the random intercepts of the lines with GK method had important savings in computing time as compared with the G×E interaction multi-environment models with unstructured variance-covariances but with lower genomic prediction accuracy. PMID:29476023
Generation of gamma-ray streaming kernels through cylindrical ducts via Monte Carlo method
International Nuclear Information System (INIS)
Kim, Dong Su
1992-02-01
Since radiation streaming through penetrations is often the critical consideration in protection against exposure of personnel in a nuclear facility, it has been of great concern in radiation shielding design and analysis. Several methods have been developed and applied to the analysis of the radiation streaming in the past such as ray analysis method, single scattering method, albedo method, and Monte Carlo method. But they may be used for order-of-magnitude calculations and where sufficient margin is available, except for the Monte Carlo method which is accurate but requires a lot of computing time. This study developed a Monte Carlo method and constructed a data library of solutions using the Monte Carlo method for radiation streaming through a straight cylindrical duct in concrete walls of a broad, mono-directional, monoenergetic gamma-ray beam of unit intensity. The solution named as plane streaming kernel is the average dose rate at duct outlet and was evaluated for 20 source energies from 0 to 10 MeV, 36 source incident angles from 0 to 70 degrees, 5 duct radii from 10 to 30 cm, and 16 wall thicknesses from 0 to 100 cm. It was demonstrated that average dose rate due to an isotropic point source at arbitrary positions can be well approximated using the plane streaming kernel with acceptable error. Thus, the library of the plane streaming kernels can be used for the accurate and efficient analysis of radiation streaming through a straight cylindrical duct in concrete walls due to arbitrary distributions of gamma-ray sources
Directory of Open Access Journals (Sweden)
A. Miraliakbari
2016-06-01
Full Text Available With the increasing demand for the digital survey and acquisition of road pavement conditions, there is also the parallel growing need for the development of automated techniques for the analysis and evaluation of the actual road conditions. This is due in part to the resulting large volumes of road pavement data captured through digital surveys, and also to the requirements for rapid data processing and evaluations. In this study, the Canon 5D Mark II RGB camera with a resolution of 21 megapixels is used for the road pavement condition mapping. Even though many imaging and mapping sensors are available, the development of automated pavement distress detection, recognition and extraction systems for pavement condition is still a challenge. In order to detect and extract pavement cracks, a comparative evaluation of kernel-based segmentation methods comprising line filtering (LF, local binary pattern (LBP and high-pass filtering (HPF is carried out. While the LF and LBP methods are based on the principle of rotation-invariance for pattern matching, the HPF applies the same principle for filtering, but with a rotational invariant matrix. With respect to the processing speeds, HPF is fastest due to the fact that it is based on a single kernel, as compared to LF and LBP which are based on several kernels. Experiments with 20 sample images which contain linear, block and alligator cracks are carried out. On an average a completeness of distress extraction with values of 81.2%, 76.2% and 81.1% have been found for LF, HPF and LBP respectively.
A kernel-based multivariate feature selection method for microarray data classification.
Directory of Open Access Journals (Sweden)
Shiquan Sun
Full Text Available High dimensionality and small sample sizes, and their inherent risk of overfitting, pose great challenges for constructing efficient classifiers in microarray data classification. Therefore a feature selection technique should be conducted prior to data classification to enhance prediction performance. In general, filter methods can be considered as principal or auxiliary selection mechanism because of their simplicity, scalability, and low computational complexity. However, a series of trivial examples show that filter methods result in less accurate performance because they ignore the dependencies of features. Although few publications have devoted their attention to reveal the relationship of features by multivariate-based methods, these methods describe relationships among features only by linear methods. While simple linear combination relationship restrict the improvement in performance. In this paper, we used kernel method to discover inherent nonlinear correlations among features as well as between feature and target. Moreover, the number of orthogonal components was determined by kernel Fishers linear discriminant analysis (FLDA in a self-adaptive manner rather than by manual parameter settings. In order to reveal the effectiveness of our method we performed several experiments and compared the results between our method and other competitive multivariate-based features selectors. In our comparison, we used two classifiers (support vector machine, [Formula: see text]-nearest neighbor on two group datasets, namely two-class and multi-class datasets. Experimental results demonstrate that the performance of our method is better than others, especially on three hard-classify datasets, namely Wang's Breast Cancer, Gordon's Lung Adenocarcinoma and Pomeroy's Medulloblastoma.
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
Pest development possibility in storage on the kernels and spikes of spelt wheat (Triticum spelta)
Almaši, Radmila; Poslončec, Danijela
2012-01-01
Storing the spelt wheat (Triticum spelta) depends of the type of storage. If we stored spikes, which contains two kernel of spelt tightly wrapped with tailings, development and reproduction of the most important pests is limited or impossible. Great impacts on the stored pests progeny and percentage of damaged kernels have the number of available kernel and the way of storage (kernels-spikes). Grain moth (Sitotroga cerealella Oliv.) developed more numerous progeny when spelt wheat stores in k...
Effects of substituting groundnut cake with acacia seed kernel meal ...
African Journals Online (AJOL)
The study examined the effects of replacing groundnut cake (GNC) with Acacia nilotica seed kernel meal (ASKM) in the diets of broilers and the effects of such on ... Serum metabolites were not affected by the treatment except alkaline phosphatasc and billirubin that were significantly (P < 0.05) lowered by 20% inclusion of ...
Production of glycerol from palm kernel oil | Antia | Nigerian Journal ...
African Journals Online (AJOL)
Glycerol production using Palm Kernel Oil (PKO) as a potential raw material was investigated. PKO was optimally hydrolyzed at 268 °C and 500psi (34 atm) pressure using only water. A 96.85 percent maximum yield of the extent of hydrolysis at 61.86 percent water and 38.14 percent oil was achieved The percentage Df ...
Acetolactate Synthase Activity in Developing Maize (Zea mays L.) Kernels
Muhitch, Michael J.
1988-01-01
Acetolactate synthase (EC 4.1.3.18) activity was examined in maize (Zea mays L.) endosperm and embryos as a function of kernel development. When assayed using unpurified homogenates, embryo acetolactate synthase activity appeared less sensitive to inhibition by leucine + valine and by the imidazolinone herbicide imazapyr than endosperm acetolactate synthase activity. Evidence is presented to show that pyruvate decarboxylase contributes to apparent acetolactate synthase activity in crude embryo extracts and a modification of the acetolactate synthase assay is proposed to correct for the presence of pyruvate decarboxylase in unpurified plant homogenates. Endosperm acetolactate synthase activity increased rapidly during early kernel development, reaching a maximum of 3 micromoles acetoin per hour per endosperm at 25 days after pollination. In contrast, embryo activity was low in young kernels and steadily increased throughout development to a maximum activity of 0.24 micromole per hour per embryo by 45 days after pollination. The sensitivity of both endosperm and embryo acetolactate synthase activities to feedback inhibition by leucine + valine did not change during kernel development. The results are compared to those found for other enzymes of nitrogen metabolism and discussed with respect to the potential roles of the embryo and endosperm in providing amino acids for storage protein synthesis. PMID:16665871
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.
Music emotion detection using hierarchical sparse kernel machines.
Chin, Yu-Hao; Lin, Chang-Hong; Siahaan, Ernestasia; Wang, Jia-Ching
2014-01-01
For music emotion detection, this paper presents a music emotion verification system based on hierarchical sparse kernel machines. With the proposed system, we intend to verify if a music clip possesses happiness emotion or not. There are two levels in the hierarchical sparse kernel machines. In the first level, a set of acoustical features are extracted, and principle component analysis (PCA) is implemented to reduce the dimension. The acoustical features are utilized to generate the first-level decision vector, which is a vector with each element being a significant value of an emotion. The significant values of eight main emotional classes are utilized in this paper. To calculate the significant value of an emotion, we construct its 2-class SVM with calm emotion as the global (non-target) side of the SVM. The probability distributions of the adopted acoustical features are calculated and the probability product kernel is applied in the first-level SVMs to obtain first-level decision vector feature. In the second level of the hierarchical system, we merely construct a 2-class relevance vector machine (RVM) with happiness as the target side and other emotions as the background side of the RVM. The first-level decision vector is used as the feature with conventional radial basis function kernel. The happiness verification threshold is built on the probability value. In the experimental results, the detection error tradeoff (DET) curve shows that the proposed system has a good performance on verifying if a music clip reveals happiness emotion.
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....
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.
potential use of mangifera indica seed kernel and citrus aurantiifolia ...
African Journals Online (AJOL)
HOD
*Corresponding author tel: + 234 – 803 – 823 – 1628, currently a doctoral student at the Department of Civil. Engineering, Kwame Nkrumah University of Science and Technology, Kumasi, GHANA. POTENTIAL USE OF MANGIFERA INDICA SEED KERNEL AND CITRUS. AURANTIIFOLIA SEED IN WATER DISINFECTION.
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 ...
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.
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 ...
Matrix kernels for MEG and EEG source localization and imaging
Energy Technology Data Exchange (ETDEWEB)
Mosher, J.C.; Lewis, P.S. [Los Alamos National Lab., NM (United States); Leahy, R.M. [University of Southern California, Los Angeles, CA (United States)
1994-12-31
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.
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
Palm kernel agar: An alternative culture medium for rapid detection ...
African Journals Online (AJOL)
The feasibility of using palm kernel agar (PKA) as an alternative culture medium to desiccated coconut agar (DCA), the conventional medium for the recovery of aflatoxigenic fungi from mixed cultures and the detection of aflatoxigenic fungi and direct visual determination of aflatoxins in agricultural commodities was ...
Contribution of granule bound starch synthase in kernel modification
African Journals Online (AJOL)
ACSS
The stability of both GBSS and zein proteins coupled with the use of SDS-PAGE implied that only one was more reliable and required further validation. It was clear from this study, that kernel modification was regulated by complex genetic interactions. Fairly distinct systems such as the starch synthases, zein proteins and ...
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
Disinfection studies of Nahar (Mesua ferrea) seed kernel oil using ...
African Journals Online (AJOL)
GREGORY
2011-12-16
Dec 16, 2011 ... kinetics. Heterotrophic plate count, using CFU/ml, pour plate method, 35°C / 48 h, plate count agar were employed to evaluate the disinfection and its ... Key words: Nahar (Mesua ferrea) seed kernel oil, extraction, gum Arabic, disinfection, kinetics. ..... Modeling of Chemical Kinetics and Reactor Design.
Physicochemical characteristics of kernel during fruit maturation of ...
African Journals Online (AJOL)
Physicochemical characteristics of kernels from four cultivars of coconut were studied with the aim of increasing the value of coconut palm (Cocos nucifera L.), the main income of most equatorial coastal farmers. Studies were undertaken on West African Tall (WAT), Malaysian Yellow Dwarf (MYD), Equatorial Guinea Green ...
General method of boundary correction in kernel regression estimation
African Journals Online (AJOL)
Kernel estimators of both density and regression functions are not consistent near the nite end points of their supports. In other words, boundary eects seriously aect the performance of these estimators. In this paper, we combine the transformation and the reflection methods in order to introduce a new general method of ...
Isolation and identification of bioactive compounds from kernel seed ...
African Journals Online (AJOL)
The ethanol extract and ethyl acetate fraction of Mangifera indica kernel seed cake inhibited the growth of Staphylococcus aureus and Pseudomonas aeruginosa. The bioactive compounds were isolated and identified by NMR, UV and mass spectrometry as methyl gallate, gallic acid and penta-O-galloylglucose. The
determination of bio-energy potential of palm kernel shell
African Journals Online (AJOL)
88888888
2012-11-03
Nov 3, 2012 ... Keywords: palm kernel shell, bioenergy, thermogravimetric analysis, pyrolysis, gasification ... tain higher energy density fuels. Fast Pyrolysis is the thermal decomposition of biomass for bio-char, bio- oil and combustible gas production in the absence of ... Calorific Value of Coal and Coke) was used for the.
Characteristics of traditionally processed shea kernels and butter
Honfo, G.F.; Linnemann, A.R.; Akissoe, N.; Soumanou, M.M.; Boekel, van M.A.J.S.
2013-01-01
The traditional production of shea butter requires a heat treatment of the nuts. This study compared the end products derived by two commonly used heat treatments, namely smoking and boiling followed by sun-drying. Neither treatment influenced the moisture content of the kernels (8–10%), but the
Higher dimensional kernel methods | Osemwenkhae | Journal of the ...
African Journals Online (AJOL)
The multivariate kernel density estimator (MKDE) for the analysis of data in more than one dimension is presented. This removes the cumbersome nature associated with the interpretation of multivariate results when compared with most common multivariate schemes. The effect of varying the window width in MKDE with the ...
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
Genetic relationship between plant growth, shoot and kernel sizes in ...
African Journals Online (AJOL)
Thirty two hybrids were derived from large (LG) and small (SM) kernel plants by inter-crossing parents that differed for shoot-size at silking [Long-Shoot (LS) and Small-Shoot (SS)]. Each hybrid was grown in replicated experiments at Fargo and Casselton stations, N.D., USA in 2002. Juvenile plant height, leaf number and ...
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 ...
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...
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 ...
Cowling–Price Theorem and Characterization of Heat Kernel on ...
Indian Academy of Sciences (India)
We extend the uncertainty principle, the Cowling–Price theorem, on non-compact Riemannian symmetric spaces . We establish a characterization of the heat kernel of the Laplace–Beltrami operator on from integral estimates of the Cowling–Price type.
A compact kernel for the calculus of inductive constructions
Indian Academy of Sciences (India)
design of the new kernel has been completely revisited since the first release, result- ing in a remarkably ... Our first implementation of Matita, taking us about 10 man years work, spread over a period of. 5 years (Asperti et al 2006) ... Our conclusion is that it is due to a myriad of minor design choices not sufficiently meditated ...
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
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...
Wiener kernel analysis of a noise-evoked otoacoustic emission
van Dijk, P; Maat, A; Wit, H P
1997-01-01
In one specimen of the frog species, Rana esculenta, the following were measured: (1) a spontaneous otoacoustic emission; (2) a click-evoked otoacoustic emissions; and (3) a noise evoked otoacoustic emission. From the noise evoked emission response, a first-and a second-order Wiener kernel and the
Capturing Option Anomalies with a Variance-Dependent Pricing Kernel
DEFF Research Database (Denmark)
Christoffersen, Peter; Heston, Steven; Jacobs, Kris
2013-01-01
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...
Cowling–Price theorem and characterization of heat kernel on ...
Indian Academy of Sciences (India)
R. Narasimhan (Krishtel eMaging) 1461 1996 Oct 15 13:05:22
Keywords. Hardy's theorem; spherical harmonics; symmetric space; Jacobi function; heat kernel. 1. Introduction. Our starting point in this paper is the classical Hardy's ... solutions of the heat equation of the Laplace–Beltrami operator. .... similar argument with the role of ˜z and zn reversed and the induction hypothesis for d =.
A compact kernel for the calculus of inductive constructions
Indian Academy of Sciences (India)
For flexibility reasons, it is useful to remove fixed universes (of the form Typei) in favour of universe variables ... We plan to let the user dynamically configure these aspects of the type system in the next release of. Matita. ..... A minor difference between the old and the new kernel is the management of non dependent binders.
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.
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.
FED RAW OR AUTOCLAVED N EEM SEED KERNELS IN DIETS
African Journals Online (AJOL)
Autoclaving improved (P<0.05) erythrocyte (RBC) production and cockerels fed diets with 150 g/kg heat- treated neem kernels had superior (P<0.05) packed cell volume (PCV), RBC number and haemogblobin concentration compared to those of birds on basal diet. Neem diets generally induced (P<0.05) lymphocytosis ...
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.
Nutritional evaluation of fermented palm kernel cake using red tilapia
African Journals Online (AJOL)
ONOS
2010-01-25
Jan 25, 2010 ... The use of palm kernel cake (PKC) and other plant residues in fish feeding especially under extensive aquaculture have been ... significant (P 三 0.05) increase in the levels of phosphorus, calcium and copper in the carcass of fish raised on ... advantageous in the development of supplementary or complete ...
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 ...
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