Optimization method of superpixel analysis for multi-contrast Jones matrix tomography (Conference Presentation)

NASA Astrophysics Data System (ADS)

Miyazawa, Arata; Hong, Young-Joo; Makita, Shuichi; Kasaragod, Deepa K.; Miura, Masahiro; Yasuno, Yoshiaki

2017-02-01

Local statistics are widely utilized for quantification and image processing of OCT. For example, local mean is used to reduce speckle, local variation of polarization state (degree-of-polarization-uniformity (DOPU)) is used to visualize melanin. Conventionally, these statistics are calculated in a rectangle kernel whose size is uniform over the image. However, the fixed size and shape of the kernel result in a tradeoff between image sharpness and statistical accuracy. Superpixel is a cluster of pixels which is generated by grouping image pixels based on the spatial proximity and similarity of signal values. Superpixels have variant size and flexible shapes which preserve the tissue structure. Here we demonstrate a new superpixel method which is tailored for multifunctional Jones matrix OCT (JM-OCT). This new method forms the superpixels by clustering image pixels in a 6-dimensional (6-D) feature space (spatial two dimensions and four dimensions of optical features). All image pixels were clustered based on their spatial proximity and optical feature similarity. The optical features are scattering, OCT-A, birefringence and DOPU. The method is applied to retinal OCT. Generated superpixels preserve the tissue structures such as retinal layers, sclera, vessels, and retinal pigment epithelium. Hence, superpixel can be utilized as a local statistics kernel which would be more suitable than a uniform rectangle kernel. Superpixelized image also can be used for further image processing and analysis. Since it reduces the number of pixels to be analyzed, it reduce the computational cost of such image processing.

Discrete element method as an approach to model the wheat milling process

USDA-ARS?s Scientific Manuscript database

It is a well-known phenomenon that break-release, particle size, and size distribution of wheat milling are functions of machine operational parameters and grain properties. Due to the non-uniformity of characteristics and properties of wheat kernels, the kernel physical and mechanical properties af...

IR-Improved DGLAP-CS Theory

DOE PAGES

Ward, B. F. L.

2008-01-01

We show that it is possible to improve the infrared aspects of the standard treatment of the DGLAP-CS evolution theory to take into account a large class of higher-order corrections that significantly improve the precision of the theory for any given level of fixed-order calculation of its respective kernels. We illustrate the size of the effects we resum using the moments of the parton distributions.

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

PubMed

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

2018-02-13

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

The Casimir effect in rugby-ball type flux compactifications

NASA Astrophysics Data System (ADS)

Minamitsuji, M.

2008-04-01

We discuss volume stabilization in a 6D braneworld model based on 6D supergravity theory. The internal space is compactified by magnetic flux and contains codimension two 3-branes (conical singularities) as its boundaries. In general the external 4D spacetime is warped and in the unwrapped limit the shape of the internal space looks like a 'rugby ball'. The size of the internal space is not fixed due to the scale invariance of the supergravity theory. We discuss the possibility of volume stabilization by the Casimir effect for a massless, minimally coupled bulk scalar field. The main obstacle in studying this case is that the brane (conical) part of the relevant heat kernel coefficient (a6) has not been formulated. Thus as a first step, we consider the 4D analog model with boundary codimension two 1-branes. The spacetime structure of the 4D model is very similar to that of the original 6D model, where now the relevant heat kernel coefficient is well known. We derive the one-loop effective potential induced by a scalar field in the bulk by employing zeta function regularization with heat kernel analysis. As a result, the volume is stabilized for most possible choices of the parameters. Especially, for a larger degree of warping, our results imply that a large hierarchy between the mass scales and a tiny amount of effective cosmological constant can be realized on the brane. In the non-warped limit the ratio tends to converge to the same value, independently of the bulk gauge coupling constant. Finally, we will analyze volume stabilization in the original model 6D by employing the same mode-sum technique.

Fixed and Data Adaptive Kernels in Cohen’s Class of Time-Frequency Distributions

DTIC Science & Technology

1992-09-01

translated into its associated analytic signal by using the techniques discussed in Chapter Four. 1. Wigner - Ville Distribution function PS = wvd (data,winlen...step,begin,theend) % PS = wvd (data,winlen,step,begin,theend) % ’wvd.ml returns the Wigner - Ville time-frequency distribution % for the input data...12 IV. FIXED KERNEL DISTRIBUTIONS .................................................................. 19 A. WIGNER - VILLE DISTRIBUTION

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

NASA Astrophysics Data System (ADS)

Karaagac, Berat

2018-02-01

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

Adsorption performance of fixed-bed column for the removal of Fe (II) in groundwater using activated carbon made from palm kernel shells

NASA Astrophysics Data System (ADS)

Sylvia, N.; Hakim, L.; Fardian, N.; Yunardi

2018-03-01

When the manganese is under the acceptable limit, then the removal of Fe (II) ion, the common metallic compound contained in groundwater, is one of the most important stages in the processing of groundwater to become potable water. This study was aimed at investigating the performance of a fixed-bed adsorption column filled, with activated carbon prepared from palm kernel shells, in the removal of Fe (II) ion from groundwater. The influence of important parameters such as bed depth and the flow rate was investigated. The bed depth adsorbent was varied at 7.5, 10 and 12 cm. At a different flow rate of 6, 10 and 14 L/minute. The Atomic Absorb Spectrophotometer was used to measure the Fe (II) ion concentration, thereafter the results were confirmed using a breakthrough curve showing that flow rate and bed depth affected the curve. The mathematical model that used to predict the result was the Thomas and Adams-Bohart model. This model is used to process design, in which predicting time and bed depth needed to meet the breakthrough. This study reveals that the Thomas model was the most appropriate one, including the use of Palm Kernel Shell for processing groundwater. According to the Thomas Model, the highest capacity of adsorption (66.189 mg/g) of 0.169-mg/L of groundwater was achieved with a flow rate of 6 L/minute, with the bed depth at 14 cm.

Comparing fixed and variable-width Gaussian networks.

PubMed

Kůrková, Věra; Kainen, Paul C

2014-09-01

The role of width of Gaussians in two types of computational models is investigated: Gaussian radial-basis-functions (RBFs) where both widths and centers vary and Gaussian kernel networks which have fixed widths but varying centers. The effect of width on functional equivalence, universal approximation property, and form of norms in reproducing kernel Hilbert spaces (RKHS) is explored. It is proven that if two Gaussian RBF networks have the same input-output functions, then they must have the same numbers of units with the same centers and widths. Further, it is shown that while sets of input-output functions of Gaussian kernel networks with two different widths are disjoint, each such set is large enough to be a universal approximator. Embedding of RKHSs induced by "flatter" Gaussians into RKHSs induced by "sharper" Gaussians is described and growth of the ratios of norms on these spaces with increasing input dimension is estimated. Finally, large sets of argminima of error functionals in sets of input-output functions of Gaussian RBFs are described. Copyright © 2014 Elsevier Ltd. All rights reserved.

KSOS Computer Program Development Specifications (Type B-5). (Kernelized Secure Operating System). I. Security Kernel (CDRL 0002AF). II. UNIX Emulator (CDRL 0002AG). III. Security-Related Software (CDRL 0002AH).

DTIC Science & Technology

1980-12-01

Commun- ications Corporation, Palo Alto, CA (March 1978). g. [Walter at al. 74] Walter, K.G. et al., " Primitive Models for Computer .. Security", ESD-TR...discussion is followed by a presenta- tion of the Kernel primitive operations upon these objects. All Kernel objects shall be referenced by a common...set of sizes. All process segments, regardless of domain, shall be manipulated by the same set of Kernel segment primitives . User domain segments

Modeling end-use quality in U. S. soft wheat germplasm

USDA-ARS?s Scientific Manuscript database

End-use quality in soft wheat (Triticum aestivum L.) can be assessed by a wide array of measurements, generally categorized into grain, milling, and baking characteristics. Samples were obtained from four regional nurseries. Selected parameters included: test weight, kernel hardness, kernel size, ke...

Generation and optimization of superpixels as image processing kernels for Jones matrix optical coherence tomography

PubMed Central

Miyazawa, Arata; Hong, Young-Joo; Makita, Shuichi; Kasaragod, Deepa; Yasuno, Yoshiaki

2017-01-01

Jones matrix-based polarization sensitive optical coherence tomography (JM-OCT) simultaneously measures optical intensity, birefringence, degree of polarization uniformity, and OCT angiography. The statistics of the optical features in a local region, such as the local mean of the OCT intensity, are frequently used for image processing and the quantitative analysis of JM-OCT. Conventionally, local statistics have been computed with fixed-size rectangular kernels. However, this results in a trade-off between image sharpness and statistical accuracy. We introduce a superpixel method to JM-OCT for generating the flexible kernels of local statistics. A superpixel is a cluster of image pixels that is formed by the pixels’ spatial and signal value proximities. An algorithm for superpixel generation specialized for JM-OCT and its optimization methods are presented in this paper. The spatial proximity is in two-dimensional cross-sectional space and the signal values are the four optical features. Hence, the superpixel method is a six-dimensional clustering technique for JM-OCT pixels. The performance of the JM-OCT superpixels and its optimization methods are evaluated in detail using JM-OCT datasets of posterior eyes. The superpixels were found to well preserve tissue structures, such as layer structures, sclera, vessels, and retinal pigment epithelium. And hence, they are more suitable for local statistics kernels than conventional uniform rectangular kernels. PMID:29082073

Adaptive kernel regression for freehand 3D ultrasound reconstruction

NASA Astrophysics Data System (ADS)

Alshalalfah, Abdel-Latif; Daoud, Mohammad I.; Al-Najar, Mahasen

2017-03-01

Freehand three-dimensional (3D) ultrasound imaging enables low-cost and flexible 3D scanning of arbitrary-shaped organs, where the operator can freely move a two-dimensional (2D) ultrasound probe to acquire a sequence of tracked cross-sectional images of the anatomy. Often, the acquired 2D ultrasound images are irregularly and sparsely distributed in the 3D space. Several 3D reconstruction algorithms have been proposed to synthesize 3D ultrasound volumes based on the acquired 2D images. A challenging task during the reconstruction process is to preserve the texture patterns in the synthesized volume and ensure that all gaps in the volume are correctly filled. This paper presents an adaptive kernel regression algorithm that can effectively reconstruct high-quality freehand 3D ultrasound volumes. The algorithm employs a kernel regression model that enables nonparametric interpolation of the voxel gray-level values. The kernel size of the regression model is adaptively adjusted based on the characteristics of the voxel that is being interpolated. In particular, when the algorithm is employed to interpolate a voxel located in a region with dense ultrasound data samples, the size of the kernel is reduced to preserve the texture patterns. On the other hand, the size of the kernel is increased in areas that include large gaps to enable effective gap filling. The performance of the proposed algorithm was compared with seven previous interpolation approaches by synthesizing freehand 3D ultrasound volumes of a benign breast tumor. The experimental results show that the proposed algorithm outperforms the other interpolation approaches.

Convolving optically addressed VLSI liquid crystal SLM

NASA Astrophysics Data System (ADS)

Jared, David A.; Stirk, Charles W.

1994-03-01

We designed, fabricated, and tested an optically addressed spatial light modulator (SLM) that performs a 3 X 3 kernel image convolution using ferroelectric liquid crystal on VLSI technology. The chip contains a 16 X 16 array of current-mirror-based convolvers with a fixed kernel for finding edges. The pixels are located on 75 micron centers, and the modulators are 20 microns on a side. The array successfully enhanced edges in illumination patterns. We developed a high-level simulation tool (CON) for analyzing the performance of convolving SLM designs. CON has a graphical interface and simulates SLM functions using SPICE-like device models. The user specifies the pixel function along with the device parameters and nonuniformities. We discovered through analysis, simulation and experiment that the operation of current-mirror-based convolver pixels is degraded at low light levels by the variation of transistor threshold voltages inherent to CMOS chips. To function acceptable, the test SLM required the input image to have an minimum irradiance of 10 (mu) W/cm2. The minimum required irradiance can be further reduced by adding a photodarlington near the photodetector or by increasing the size of the transistors used to calculate the convolution.

On Making a Distinguished Vertex Minimum Degree by Vertex Deletion

NASA Astrophysics Data System (ADS)

Betzler, Nadja; Bredereck, Robert; Niedermeier, Rolf; Uhlmann, Johannes

For directed and undirected graphs, we study the problem to make a distinguished vertex the unique minimum-(in)degree vertex through deletion of a minimum number of vertices. The corresponding NP-hard optimization problems are motivated by applications concerning control in elections and social network analysis. Continuing previous work for the directed case, we show that the problem is W[2]-hard when parameterized by the graph's feedback arc set number, whereas it becomes fixed-parameter tractable when combining the parameters "feedback vertex set number" and "number of vertices to delete". For the so far unstudied undirected case, we show that the problem is NP-hard and W[1]-hard when parameterized by the "number of vertices to delete". On the positive side, we show fixed-parameter tractability for several parameterizations measuring tree-likeness, including a vertex-linear problem kernel with respect to the parameter "feedback edge set number". On the contrary, we show a non-existence result concerning polynomial-size problem kernels for the combined parameter "vertex cover number and number of vertices to delete", implying corresponding nonexistence results when replacing vertex cover number by treewidth or feedback vertex set number.

Transient and asymptotic behaviour of the binary breakage problem

NASA Astrophysics Data System (ADS)

Mantzaris, Nikos V.

2005-06-01

The general binary breakage problem with power-law breakage functions and two families of symmetric and asymmetric breakage kernels is studied in this work. A useful transformation leads to an equation that predicts self-similar solutions in its asymptotic limit and offers explicit knowledge of the mean size and particle density at each point in dimensionless time. A novel moving boundary algorithm in the transformed coordinate system is developed, allowing the accurate prediction of the full transient behaviour of the system from the initial condition up to the point where self-similarity is achieved, and beyond if necessary. The numerical algorithm is very rapid and its results are in excellent agreement with known analytical solutions. In the case of the symmetric breakage kernels only unimodal, self-similar number density functions are obtained asymptotically for all parameter values and independent of the initial conditions, while in the case of asymmetric breakage kernels, bimodality appears for high degrees of asymmetry and sharp breakage functions. For symmetric and discrete breakage kernels, self-similarity is not achieved. The solution exhibits sustained oscillations with amplitude that depends on the initial condition and the sharpness of the breakage mechanism, while the period is always fixed and equal to ln 2 with respect to dimensionless time.

Strong consistency of nonparametric Bayes density estimation on compact metric spaces with applications to specific manifolds

PubMed Central

Bhattacharya, Abhishek; Dunson, David B.

2012-01-01

This article considers a broad class of kernel mixture density models on compact metric spaces and manifolds. Following a Bayesian approach with a nonparametric prior on the location mixing distribution, sufficient conditions are obtained on the kernel, prior and the underlying space for strong posterior consistency at any continuous density. The prior is also allowed to depend on the sample size n and sufficient conditions are obtained for weak and strong consistency. These conditions are verified on compact Euclidean spaces using multivariate Gaussian kernels, on the hypersphere using a von Mises-Fisher kernel and on the planar shape space using complex Watson kernels. PMID:22984295

Functional linear models for association analysis of quantitative traits.

PubMed

Fan, Ruzong; Wang, Yifan; Mills, James L; Wilson, Alexander F; Bailey-Wilson, Joan E; Xiong, Momiao

2013-11-01

Functional linear models are developed in this paper for testing associations between quantitative traits and genetic variants, which can be rare variants or common variants or the combination of the two. By treating multiple genetic variants of an individual in a human population as a realization of a stochastic process, the genome of an individual in a chromosome region is a continuum of sequence data rather than discrete observations. The genome of an individual is viewed as a stochastic function that contains both linkage and linkage disequilibrium (LD) information of the genetic markers. By using techniques of functional data analysis, both fixed and mixed effect functional linear models are built to test the association between quantitative traits and genetic variants adjusting for covariates. After extensive simulation analysis, it is shown that the F-distributed tests of the proposed fixed effect functional linear models have higher power than that of sequence kernel association test (SKAT) and its optimal unified test (SKAT-O) for three scenarios in most cases: (1) the causal variants are all rare, (2) the causal variants are both rare and common, and (3) the causal variants are common. The superior performance of the fixed effect functional linear models is most likely due to its optimal utilization of both genetic linkage and LD information of multiple genetic variants in a genome and similarity among different individuals, while SKAT and SKAT-O only model the similarities and pairwise LD but do not model linkage and higher order LD information sufficiently. In addition, the proposed fixed effect models generate accurate type I error rates in simulation studies. We also show that the functional kernel score tests of the proposed mixed effect functional linear models are preferable in candidate gene analysis and small sample problems. The methods are applied to analyze three biochemical traits in data from the Trinity Students Study. © 2013 WILEY PERIODICALS, INC.

Fine-mapping of qGW4.05, a major QTL for kernel weight and size in maize.

PubMed

Chen, Lin; Li, Yong-xiang; Li, Chunhui; Wu, Xun; Qin, Weiwei; Li, Xin; Jiao, Fuchao; Zhang, Xiaojing; Zhang, Dengfeng; Shi, Yunsu; Song, Yanchun; Li, Yu; Wang, Tianyu

2016-04-12

Kernel weight and size are important components of grain yield in cereals. Although some information is available concerning the map positions of quantitative trait loci (QTL) for kernel weight and size in maize, little is known about the molecular mechanisms of these QTLs. qGW4.05 is a major QTL that is associated with kernel weight and size in maize. We combined linkage analysis and association mapping to fine-map and identify candidate gene(s) at qGW4.05. QTL qGW4.05 was fine-mapped to a 279.6-kb interval in a segregating population derived from a cross of Huangzaosi with LV28. By combining the results of regional association mapping and linkage analysis, we identified GRMZM2G039934 as a candidate gene responsible for qGW4.05. Candidate gene-based association mapping was conducted using a panel of 184 inbred lines with variable kernel weights and kernel sizes. Six polymorphic sites in the gene GRMZM2G039934 were significantly associated with kernel weight and kernel size. The results of linkage analysis and association mapping revealed that GRMZM2G039934 is the most likely candidate gene for qGW4.05. These results will improve our understanding of the genetic architecture and molecular mechanisms underlying kernel development in maize.

The effect of relatedness and pack size on territory overlap in African wild dogs.

PubMed

Jackson, Craig R; Groom, Rosemary J; Jordan, Neil R; McNutt, J Weldon

2017-01-01

Spacing patterns mediate competitive interactions between conspecifics, ultimately increasing fitness. The degree of territorial overlap between neighbouring African wild dog ( Lycaon pictus ) packs varies greatly, yet the role of factors potentially affecting the degree of overlap, such as relatedness and pack size, remain unclear. We used movement data from 21 wild dog packs to calculate the extent of territory overlap (20 dyads). On average, unrelated neighbouring packs had low levels of overlap restricted to the peripheral regions of their 95% utilisation kernels. Related neighbours had significantly greater levels of peripheral overlap. Only one unrelated dyad included overlap between 75%-75% kernels, but no 50%-50% kernels overlapped. However, eight of 12 related dyads overlapped between their respective 75% kernels and six between the frequented 50% kernels. Overlap between these more frequented kernels confers a heightened likelihood of encounter, as the mean utilisation intensity per unit area within the 50% kernels was 4.93 times greater than in the 95% kernels, and 2.34 times greater than in the 75% kernels. Related packs spent significantly more time in their 95% kernel overlap zones than did unrelated packs. Pack size appeared to have little effect on overlap between related dyads, yet among unrelated neighbours larger packs tended to overlap more onto smaller packs' territories. However, the true effect is unclear given that the model's confidence intervals overlapped zero. Evidence suggests that costly intraspecific aggression is greatly reduced between related packs. Consequently, the tendency for dispersing individuals to establish territories alongside relatives, where intensively utilised portions of ranges regularly overlap, may extend kin selection and inclusive fitness benefits from the intra-pack to inter-pack level. This natural spacing system can affect survival parameters and the carrying capacity of protected areas, having important management implications for intensively managed populations of this endangered species.

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

PubMed Central

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

2017-01-01

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

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

PubMed

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

2017-01-01

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

Optimal number of features as a function of sample size for various classification rules.

PubMed

Hua, Jianping; Xiong, Zixiang; Lowey, James; Suh, Edward; Dougherty, Edward R

2005-04-15

Given the joint feature-label distribution, increasing the number of features always results in decreased classification error; however, this is not the case when a classifier is designed via a classification rule from sample data. Typically (but not always), for fixed sample size, the error of a designed classifier decreases and then increases as the number of features grows. The potential downside of using too many features is most critical for small samples, which are commonplace for gene-expression-based classifiers for phenotype discrimination. For fixed sample size and feature-label distribution, the issue is to find an optimal number of features. Since only in rare cases is there a known distribution of the error as a function of the number of features and sample size, this study employs simulation for various feature-label distributions and classification rules, and across a wide range of sample and feature-set sizes. To achieve the desired end, finding the optimal number of features as a function of sample size, it employs massively parallel computation. Seven classifiers are treated: 3-nearest-neighbor, Gaussian kernel, linear support vector machine, polynomial support vector machine, perceptron, regular histogram and linear discriminant analysis. Three Gaussian-based models are considered: linear, nonlinear and bimodal. In addition, real patient data from a large breast-cancer study is considered. To mitigate the combinatorial search for finding optimal feature sets, and to model the situation in which subsets of genes are co-regulated and correlation is internal to these subsets, we assume that the covariance matrix of the features is blocked, with each block corresponding to a group of correlated features. Altogether there are a large number of error surfaces for the many cases. These are provided in full on a companion website, which is meant to serve as resource for those working with small-sample classification. For the companion website, please visit http://public.tgen.org/tamu/ofs/ e-dougherty@ee.tamu.edu.

Online Distributed Learning Over Networks in RKH Spaces Using Random Fourier Features

NASA Astrophysics Data System (ADS)

Bouboulis, Pantelis; Chouvardas, Symeon; Theodoridis, Sergios

2018-04-01

We present a novel diffusion scheme for online kernel-based learning over networks. So far, a major drawback of any online learning algorithm, operating in a reproducing kernel Hilbert space (RKHS), is the need for updating a growing number of parameters as time iterations evolve. Besides complexity, this leads to an increased need of communication resources, in a distributed setting. In contrast, the proposed method approximates the solution as a fixed-size vector (of larger dimension than the input space) using Random Fourier Features. This paves the way to use standard linear combine-then-adapt techniques. To the best of our knowledge, this is the first time that a complete protocol for distributed online learning in RKHS is presented. Conditions for asymptotic convergence and boundness of the networkwise regret are also provided. The simulated tests illustrate the performance of the proposed scheme.

Social structure and space use of Amur tigers (Panthera tigris altaica) in Southern Russian Far East based on GPS telemetry data.

PubMed

Hernandez-Blanco, Jose A; Naidenko, Sergei V; Chistopolova, Maria D; Lukarevskiy, Victor S; Kostyrya, Alexey; Rybin, Alexandr; Sorokin, Pavel A; Litvinov, Mikhail N; Kotlyar, Andrey K; Miquelle, Dale G; Rozhnov, Viatcheslav V

2015-07-01

To better understand the spatial structure of Amur tigers (Panthera tigris altaica) at the southern edge of their range we fitted 14 tigers (6♀♀ and 8♂♂) with 15 GPS-Argos collars between 2008 and 2011 in 2 study sites: the Ussuriskii Reserve of southern Sikhote-Alin and the Land of the Leopard National Park in southwest Primorye, Russian Far East. Fixed kernel estimates of male home ranges were larger than those of female home ranges (P < 0.05 [mean 95% fixed kernel(♀) = 401 ± 205 km(2) ; mean 95% fixed kernel(♂) = 778 ± 267 km(2)]). The home range size of females varied greatly, but on average was similar to estimates derived from earlier work further north. Low overlap of adjacent home ranges suggested that females retained exclusive territories. Real core areas of females overlapped only slightly, and remained stable over multiple years. The home ranges of adult males were smaller than those of males to the north, and in contrast to previous studies, high overlap among males indicated the absence of territoriality. Nonetheless, real core areas of males did not overlap, suggesting some spatial separation. In comparison to other tiger populations and other areas of the Russian Far East, the sex ratio in our 2 study areas was highly skewed towards males. We believe this skewed sex ratio resulted in the dissolution of territoriality of males due to an inability to defend individual females, with males resorting to scramble competition for mates. Continued monitoring of these sites to determine whether shifts in the sex ratio might result in a return to male territoriality would provide confirmation of our tentative hypothesis. © 2015 International Society of Zoological Sciences, Institute of Zoology/Chinese Academy of Sciences and Wiley Publishing Asia Pty Ltd.

Estimation of biological parameters of marine organisms using linear and nonlinear acoustic scattering model-based inversion methods.

PubMed

Chu, Dezhang; Lawson, Gareth L; Wiebe, Peter H

2016-05-01

The linear inversion commonly used in fisheries and zooplankton acoustics assumes a constant inversion kernel and ignores the uncertainties associated with the shape and behavior of the scattering targets, as well as other relevant animal parameters. Here, errors of the linear inversion due to uncertainty associated with the inversion kernel are quantified. A scattering model-based nonlinear inversion method is presented that takes into account the nonlinearity of the inverse problem and is able to estimate simultaneously animal abundance and the parameters associated with the scattering model inherent to the kernel. It uses sophisticated scattering models to estimate first, the abundance, and second, the relevant shape and behavioral parameters of the target organisms. Numerical simulations demonstrate that the abundance, size, and behavior (tilt angle) parameters of marine animals (fish or zooplankton) can be accurately inferred from the inversion by using multi-frequency acoustic data. The influence of the singularity and uncertainty in the inversion kernel on the inversion results can be mitigated by examining the singular values for linear inverse problems and employing a non-linear inversion involving a scattering model-based kernel.

SOME ENGINEERING PROPERTIES OF SHELLED AND KERNEL TEA (Camellia sinensis) SEEDS.

PubMed

Altuntas, Ebubekir; Yildiz, Merve

2017-01-01

Camellia sinensis is the source of tea leaves and it is an economic crop now grown around the World. Tea seed oil has been used for cooking in China and other Asian countries for more than a thousand years. Tea is the most widely consumed beverages after water in the world. It is mainly produced in Asia, central Africa, and exported throughout the World. Some engineering properties (size dimensions, sphericity, volume, bulk and true densities, friction coefficient, colour characteristics and mechanical behaviour as rupture force of shelled and kernel tea ( Camellia sinensis ) seeds were determined in this study. This research was carried out for shelled and kernel tea seeds. The shelled tea seeds used in this study were obtained from East-Black Sea Tea Cooperative Institution in Rize city of Turkey. Shelled and kernel tea seeds were characterized as large and small sizes. The average geometric mean diameter and seed mass of the shelled tea seeds were 15.8 mm, 10.7 mm (large size); 1.47 g, 0.49 g (small size); while the average geometric mean diameter and seed mass of the kernel tea seeds were 11.8 mm, 8 mm for large size; 0.97 g, 0.31 g for small size, respectively. The sphericity, surface area and volume values were found to be higher in a larger size than small size for the shelled and kernel tea samples. The shelled tea seed's colour intensity (Chroma) were found between 59.31 and 64.22 for large size, while the kernel tea seed's chroma values were found between 56.04 68.34 for large size, respectively. The rupture force values of kernel tea seeds were higher than shelled tea seeds for the large size along X axis; whereas, the rupture force values of along X axis were higher than Y axis for large size of shelled tea seeds. 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. Some engineering properties, such as geometric mean diameter, sphericity, volume, bulk and true densities, the coefficient of friction, L*, a*, b* colour characteristics and rupture force of shelled and kernel tea ( Camellia sinensis ) seeds will serve to design the equipment used in postharvest treatments.

7 CFR 51.2284 - Size classification.

Code of Federal Regulations, 2010 CFR

2010-01-01

...: “Halves”, “Pieces and Halves”, “Pieces” or “Small Pieces”. The size of portions of kernels in the lot... consists of 85 percent or more, by weight, half kernels, and the remainder three-fourths half kernels. (See § 51.2285.) (b) Pieces and halves. Lot consists of 20 percent or more, by weight, half kernels, and the...

Comparisons of geoid models over Alaska computed with different Stokes' kernel modifications

NASA Astrophysics Data System (ADS)

Li, X.; Wang, Y.

2011-01-01

Various Stokes kernel modification methods have been developed over the years. The goal of this paper is to test the most commonly used Stokes kernel modifications numerically by using Alaska as a test area and EGM08 as a reference model. The tests show that some methods are more sensitive than others to the integration cap sizes. For instance, using the methods of Vaníček and Kleusberg or Featherstone et al. with kernel modification at degree 60, the geoid decreases by 30 cm (on average) when the cap size increases from 1° to 25°. The corresponding changes in the methods of Wong and Gore and Heck and Grüninger are only at the 1 cm level. At high modification degrees, above 360, the methods of Vaníček and Kleusberg and Featherstone et al become unstable because of numerical problems in the modification coefficients; similar conclusions have been reported by Featherstone (2003). In contrast, the methods of Wong and Gore, Heck and Grüninger and the least-squares spectral combination are stable at any modification degree, though they do not provide as good fit as the best case of the Molodenskii-type methods at the GPS/Leveling benchmarks. However, certain tests for choosing the cap size and modification degree have to be performed in advance to avoid abrupt mean geoid changes if the latter methods are applied.

QTL detection for wheat kernel size and quality and the responses of these traits to low nitrogen stress.

PubMed

Cui, Fa; Fan, Xiaoli; Chen, Mei; Zhang, Na; Zhao, Chunhua; Zhang, Wei; Han, Jie; Ji, Jun; Zhao, Xueqiang; Yang, Lijuan; Zhao, Zongwu; Tong, Yiping; Wang, Tao; Li, Junming

2016-03-01

QTLs for kernel characteristics and tolerance to N stress were identified, and the functions of ten known genes with regard to these traits were specified. Kernel size and quality characteristics in wheat (Triticum aestivum L.) ultimately determine the end use of the grain and affect its commodity price, both of which are influenced by the application of nitrogen (N) fertilizer. This study characterized quantitative trait loci (QTLs) for kernel size and quality and examined the responses of these traits to low-N stress using a recombinant inbred line population derived from Kenong 9204 × Jing 411. Phenotypic analyses were conducted in five trials that each included low- and high-N treatments. We identified 109 putative additive QTLs for 11 kernel size and quality characteristics and 49 QTLs for tolerance to N stress, 27 and 14 of which were stable across the tested environments, respectively. These QTLs were distributed across all wheat chromosomes except for chromosomes 3A, 4D, 6D, and 7B. Eleven QTL clusters that simultaneously affected kernel size- and quality-related traits were identified. At nine locations, 25 of the 49 QTLs for N deficiency tolerance coincided with the QTLs for kernel characteristics, indicating their genetic independence. The feasibility of indirect selection of a superior genotype for kernel size and quality under high-N conditions in breeding programs designed for a lower input management system are discussed. In addition, we specified the functions of Glu-A1, Glu-B1, Glu-A3, Glu-B3, TaCwi-A1, TaSus2, TaGS2-D1, PPO-D1, Rht-B1, and Ha with regard to kernel characteristics and the sensitivities of these characteristics to N stress. This study provides useful information for the genetic improvement of wheat kernel size, quality, and resistance to N stress.

The site, size, spatial stability, and energetics of an X-ray flare kernel

NASA Technical Reports Server (NTRS)

Petrasso, R.; Gerassimenko, M.; Nolte, J.

1979-01-01

The site, size evolution, and energetics of an X-ray kernel that dominated a solar flare during its rise and somewhat during its peak are investigated. The position of the kernel remained stationary to within about 3 arc sec over the 30-min interval of observations, despite pulsations in the kernel X-ray brightness in excess of a factor of 10. This suggests a tightly bound, deeply rooted magnetic structure, more plausibly associated with the near chromosphere or low corona rather than with the high corona. The H-alpha flare onset coincided with the appearance of the kernel, again suggesting a close spatial and temporal coupling between the chromospheric H-alpha event and the X-ray kernel. At the first kernel brightness peak its size was no larger than about 2 arc sec, when it accounted for about 40% of the total flare flux. In the second rise phase of the kernel, a source power input of order 2 times 10 to the 24th ergs/sec is minimally required.

Height system unification based on the Fixed Geodetic Boundary Value Problem with limited availability of gravity data

NASA Astrophysics Data System (ADS)

Porz, Lucas; Grombein, Thomas; Seitz, Kurt; Heck, Bernhard; Wenzel, Friedemann

2017-04-01

Regional height reference systems are generally related to individual vertical datums defined by specific tide gauges. The discrepancies of these vertical datums with respect to a unified global datum cause height system biases that range in an order of 1-2 m at a global scale. One approach for unification of height systems relates to the solution of a Geodetic Boundary Value Problem (GBVP). In particular, the fixed GBVP, using gravity disturbances as boundary values, is solved at GNSS/leveling benchmarks, whereupon height datum offsets can be estimated by least squares adjustment. In spherical approximation, the solution of the fixed GBVP is obtained by Hotine's spherical integral formula. However, this method relies on the global availability of gravity data. In practice, gravity data of the necessary resolution and accuracy is not accessible globally. Thus, the integration is restricted to an area within the vicinity of the computation points. The resulting truncation error can reach several meters in height, making height system unification without further consideration of this effect unfeasible. This study analyzes methods for reducing the truncation error by combining terrestrial gravity data with satellite-based global geopotential models and by modifying the integral kernel in order to accelerate the convergence of the resulting potential. For this purpose, EGM2008-derived gravity functionals are used as pseudo-observations to be integrated numerically. Geopotential models of different spectral degrees are implemented using a remove-restore-scheme. Three types of modification are applied to the Hotine-kernel and the convergence of the resulting potential is analyzed. In a further step, the impact of these operations on the estimation of height datum offsets is investigated within a closed loop simulation. A minimum integration radius in combination with a specific modification of the Hotine-kernel is suggested in order to achieve sub-cm accuracy for the estimation of height datum offsets.

Effect of kernel size and mill type on protein, milling yield, and baking quality of hard red spring wheat

USDA-ARS?s Scientific Manuscript database

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, ...

Movements and home ranges of mountain plovers raising broods in three Colorado landscapes

USGS Publications Warehouse

Dreitz, V.J.; Wunder, Michael B.; Knopf, F.L.

2005-01-01

We report movements and home-range sizes of adult Mountain Plovers (Charadrius montanus) with broods on rangeland, agricultural fields, and prairie dog habitats in eastern Colorado. Estimates of home range size (95% fixed kernel) were similar across the three habitats: rangeland (146.1 ha ± 101.5), agricultural fields (131.6 ha ± 74.4), and prairie dog towns (243.3 ha ± 366.3). Our minimum convex polygon estimates of home-range size were comparable to those on rangeland reported by Knopf and Rupert (1996). In addition, movements—defined as the distance between consecutive locations of adults with broods—were equivalent across habitats. However, our findings on prairie dog habitat suggest that home-range size for brood rearing may be related to whether the prairie dog habitat is in a complex of towns or in an isolated town.

Reply to Comments to X. Li and Y. M. Wang (2011) Comparisons of geoid models over Alaska computed with different Stokes' kernel modifications, JGS 1(2): 136-142 by L. E. Sjöberg

NASA Astrophysics Data System (ADS)

Wang, Y.

2012-01-01

The authors thank professor Sjöberg for having interest in our paper. The main goal of the paper is to test kernel modification methods used in geoid computations. Our tests found that Vanicek/Kleusberg's and Featherstone's methods fit the GPS/leveling data the best in the relative sense at various cap sizes. At the same time, we also pointed out that their methods are unstable and the mean values change from dm to meters by just changing the cap size. By contrast, the modification of the Wong and Gore type (including the spectral combination, method of Heck and Grüninger) is stable and insensitive to the truncation degree and cap size. This feature is especially useful when we know the accuracy of the gravity field at different frequency bands. For instance, it is advisable to truncate Stokes' kernel at a degree to which the satellite model is believed to be more accurate than surface data. The method of the Wong and Goretype does this job quite well. In contrast, the low degrees of Stokes' kernel are modified by Molodensky's coefficients tn in Vanicek/Kleusberg's and Featherstone's methods (cf. Eq. (6) in Li and Wang (2011)). It implies that the low degree gravity field of the reference model will be altered by less accurate surface data in the final geoid. This is also the cause of the larger variation in mean values of the geoid.

Investigation of various energy deposition kernel refinements for the convolution/superposition method

PubMed Central

Huang, Jessie Y.; Eklund, David; Childress, Nathan L.; Howell, Rebecca M.; Mirkovic, Dragan; Followill, David S.; Kry, Stephen F.

2013-01-01

Purpose: Several simplifications used in clinical implementations of the convolution/superposition (C/S) method, specifically, density scaling of water kernels for heterogeneous media and use of a single polyenergetic kernel, lead to dose calculation inaccuracies. Although these weaknesses of the C/S method are known, it is not well known which of these simplifications has the largest effect on dose calculation accuracy in clinical situations. The purpose of this study was to generate and characterize high-resolution, polyenergetic, and material-specific energy deposition kernels (EDKs), as well as to investigate the dosimetric impact of implementing spatially variant polyenergetic and material-specific kernels in a collapsed cone C/S algorithm. Methods: High-resolution, monoenergetic water EDKs and various material-specific EDKs were simulated using the EGSnrc Monte Carlo code. Polyenergetic kernels, reflecting the primary spectrum of a clinical 6 MV photon beam at different locations in a water phantom, were calculated for different depths, field sizes, and off-axis distances. To investigate the dosimetric impact of implementing spatially variant polyenergetic kernels, depth dose curves in water were calculated using two different implementations of the collapsed cone C/S method. The first method uses a single polyenergetic kernel, while the second method fully takes into account spectral changes in the convolution calculation. To investigate the dosimetric impact of implementing material-specific kernels, depth dose curves were calculated for a simplified titanium implant geometry using both a traditional C/S implementation that performs density scaling of water kernels and a novel implementation using material-specific kernels. Results: For our high-resolution kernels, we found good agreement with the Mackie et al. kernels, with some differences near the interaction site for low photon energies (<500 keV). For our spatially variant polyenergetic kernels, we found that depth was the most dominant factor affecting the pattern of energy deposition; however, the effects of field size and off-axis distance were not negligible. For the material-specific kernels, we found that as the density of the material increased, more energy was deposited laterally by charged particles, as opposed to in the forward direction. Thus, density scaling of water kernels becomes a worse approximation as the density and the effective atomic number of the material differ more from water. Implementation of spatially variant, polyenergetic kernels increased the percent depth dose value at 25 cm depth by 2.1%–5.8% depending on the field size, while implementation of titanium kernels gave 4.9% higher dose upstream of the metal cavity (i.e., higher backscatter dose) and 8.2% lower dose downstream of the cavity. Conclusions: Of the various kernel refinements investigated, inclusion of depth-dependent and metal-specific kernels into the C/S method has the greatest potential to improve dose calculation accuracy. Implementation of spatially variant polyenergetic kernels resulted in a harder depth dose curve and thus has the potential to affect beam modeling parameters obtained in the commissioning process. For metal implants, the C/S algorithms generally underestimate the dose upstream and overestimate the dose downstream of the implant. Implementation of a metal-specific kernel mitigated both of these errors. PMID:24320507

Quantum kernel applications in medicinal chemistry.

PubMed

Huang, Lulu; Massa, Lou

2012-07-01

Progress in the quantum mechanics of biological molecules is being driven by computational advances. The notion of quantum kernels can be introduced to simplify the formalism of quantum mechanics, making it especially suitable for parallel computation of very large biological molecules. The essential idea is to mathematically break large biological molecules into smaller kernels that are calculationally tractable, and then to represent the full molecule by a summation over the kernels. The accuracy of the kernel energy method (KEM) is shown by systematic application to a great variety of molecular types found in biology. These include peptides, proteins, DNA and RNA. Examples are given that explore the KEM across a variety of chemical models, and to the outer limits of energy accuracy and molecular size. KEM represents an advance in quantum biology applicable to problems in medicine and drug design.

Performance analysis and kernel size study of the Lynx real-time operating system

NASA Technical Reports Server (NTRS)

Liu, Yuan-Kwei; Gibson, James S.; Fernquist, Alan R.

1993-01-01

This paper analyzes the Lynx real-time operating system (LynxOS), which has been selected as the operating system for the Space Station Freedom Data Management System (DMS). The features of LynxOS are compared to other Unix-based operating system (OS). The tools for measuring the performance of LynxOS, which include a high-speed digital timer/counter board, a device driver program, and an application program, are analyzed. The timings for interrupt response, process creation and deletion, threads, semaphores, shared memory, and signals are measured. The memory size of the DMS Embedded Data Processor (EDP) is limited. Besides, virtual memory is not suitable for real-time applications because page swap timing may not be deterministic. Therefore, the DMS software, including LynxOS, has to fit in the main memory of an EDP. To reduce the LynxOS kernel size, the following steps are taken: analyzing the factors that influence the kernel size; identifying the modules of LynxOS that may not be needed in an EDP; adjusting the system parameters of LynxOS; reconfiguring the device drivers used in the LynxOS; and analyzing the symbol table. The reductions in kernel disk size, kernel memory size and total kernel size reduction from each step mentioned above are listed and analyzed.

Local Competition-Based Superpixel Segmentation Algorithm in Remote Sensing

PubMed Central

Liu, Jiayin; Tang, Zhenmin; Cui, Ying; Wu, Guoxing

2017-01-01

Remote sensing technologies have been widely applied in urban environments’ monitoring, synthesis and modeling. Incorporating spatial information in perceptually coherent regions, superpixel-based approaches can effectively eliminate the “salt and pepper” phenomenon which is common in pixel-wise approaches. Compared with fixed-size windows, superpixels have adaptive sizes and shapes for different spatial structures. Moreover, superpixel-based algorithms can significantly improve computational efficiency owing to the greatly reduced number of image primitives. Hence, the superpixel algorithm, as a preprocessing technique, is more and more popularly used in remote sensing and many other fields. In this paper, we propose a superpixel segmentation algorithm called Superpixel Segmentation with Local Competition (SSLC), which utilizes a local competition mechanism to construct energy terms and label pixels. The local competition mechanism leads to energy terms locality and relativity, and thus, the proposed algorithm is less sensitive to the diversity of image content and scene layout. Consequently, SSLC could achieve consistent performance in different image regions. In addition, the Probability Density Function (PDF), which is estimated by Kernel Density Estimation (KDE) with the Gaussian kernel, is introduced to describe the color distribution of superpixels as a more sophisticated and accurate measure. To reduce computational complexity, a boundary optimization framework is introduced to only handle boundary pixels instead of the whole image. We conduct experiments to benchmark the proposed algorithm with the other state-of-the-art ones on the Berkeley Segmentation Dataset (BSD) and remote sensing images. Results demonstrate that the SSLC algorithm yields the best overall performance, while the computation time-efficiency is still competitive. PMID:28604641

Local Competition-Based Superpixel Segmentation Algorithm in Remote Sensing.

PubMed

Liu, Jiayin; Tang, Zhenmin; Cui, Ying; Wu, Guoxing

2017-06-12

Remote sensing technologies have been widely applied in urban environments' monitoring, synthesis and modeling. Incorporating spatial information in perceptually coherent regions, superpixel-based approaches can effectively eliminate the "salt and pepper" phenomenon which is common in pixel-wise approaches. Compared with fixed-size windows, superpixels have adaptive sizes and shapes for different spatial structures. Moreover, superpixel-based algorithms can significantly improve computational efficiency owing to the greatly reduced number of image primitives. Hence, the superpixel algorithm, as a preprocessing technique, is more and more popularly used in remote sensing and many other fields. In this paper, we propose a superpixel segmentation algorithm called Superpixel Segmentation with Local Competition (SSLC), which utilizes a local competition mechanism to construct energy terms and label pixels. The local competition mechanism leads to energy terms locality and relativity, and thus, the proposed algorithm is less sensitive to the diversity of image content and scene layout. Consequently, SSLC could achieve consistent performance in different image regions. In addition, the Probability Density Function (PDF), which is estimated by Kernel Density Estimation (KDE) with the Gaussian kernel, is introduced to describe the color distribution of superpixels as a more sophisticated and accurate measure. To reduce computational complexity, a boundary optimization framework is introduced to only handle boundary pixels instead of the whole image. We conduct experiments to benchmark the proposed algorithm with the other state-of-the-art ones on the Berkeley Segmentation Dataset (BSD) and remote sensing images. Results demonstrate that the SSLC algorithm yields the best overall performance, while the computation time-efficiency is still competitive.

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

PubMed

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

2016-04-01

Kernel processing increases starch digestibility in whole-plant corn silage (WPCS). Corn silage processing score (CSPS), the percentage of starch passing through a 4.75-mm sieve, is widely used to assess degree of kernel breakage in WPCS. However, the geometric mean particle size (GMPS) of the kernel-fraction that passes through the 4.75-mm sieve has not been well described. Therefore, the objectives of this study were (1) to evaluate particle size distribution and digestibility of kernels cut in varied particle sizes; (2) to propose a method to measure GMPS in WPCS kernels; and (3) to evaluate the relationship between CSPS and GMPS of the kernel fraction in WPCS. Composite samples of unfermented, dried kernels from 110 corn hybrids commonly used for silage production were kept whole (WH) or manually cut in 2, 4, 8, 16, 32 or 64 pieces (2P, 4P, 8P, 16P, 32P, and 64P, respectively). Dry sieving to determine GMPS, surface area, and particle size distribution using 9 sieves with nominal square apertures of 9.50, 6.70, 4.75, 3.35, 2.36, 1.70, 1.18, and 0.59 mm and pan, as well as ruminal in situ dry matter (DM) digestibilities were performed for each kernel particle number treatment. Incubation times were 0, 3, 6, 12, and 24 h. The ruminal in situ DM disappearance of unfermented kernels increased with the reduction in particle size of corn kernels. Kernels kept whole had the lowest ruminal DM disappearance for all time points with maximum DM disappearance of 6.9% at 24 h and the greatest disappearance was observed for 64P, followed by 32P and 16P. Samples of WPCS (n=80) from 3 studies representing varied theoretical length of cut settings and processor types and settings were also evaluated. Each WPCS sample was divided in 2 and then dried at 60 °C for 48 h. The CSPS was determined in duplicate on 1 of the split samples, whereas on the other split sample the kernel and stover fractions were separated using a hydrodynamic separation procedure. After separation, the kernel fraction was redried at 60°C for 48 h in a forced-air oven and dry sieved to determine GMPS and surface area. Linear relationships between CSPS from WPCS (n=80) and kernel fraction GMPS, surface area, and proportion passing through the 4.75-mm screen were poor. Strong quadratic relationships between proportion of kernel fraction passing through the 4.75-mm screen and kernel fraction GMPS and surface area were observed. These findings suggest that hydrodynamic separation and dry sieving of the kernel fraction may provide a better assessment of kernel breakage in WPCS than CSPS. Copyright © 2016 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Dissection of Genetic Factors underlying Wheat Kernel Shape and Size in an Elite × Nonadapted Cross using a High Density SNP Linkage Map.

PubMed

Kumar, Ajay; Mantovani, E E; Seetan, R; Soltani, A; Echeverry-Solarte, M; Jain, S; Simsek, S; Doehlert, D; Alamri, M S; Elias, E M; Kianian, S F; Mergoum, M

2016-03-01

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. Copyright © 2016 Crop Science Society of America.

Combining kernel matrix optimization and regularization to improve particle size distribution retrieval

NASA Astrophysics Data System (ADS)

Ma, Qian; Xia, Houping; Xu, Qiang; Zhao, Lei

2018-05-01

A new method combining Tikhonov regularization and kernel matrix optimization by multi-wavelength incidence is proposed for retrieving particle size distribution (PSD) in an independent model with improved accuracy and stability. In comparison to individual regularization or multi-wavelength least squares, the proposed method exhibited better anti-noise capability, higher accuracy and stability. While standard regularization typically makes use of the unit matrix, it is not universal for different PSDs, particularly for Junge distributions. Thus, a suitable regularization matrix was chosen by numerical simulation, with the second-order differential matrix found to be appropriate for most PSD types.

7 CFR 51.2559 - Size classifications.

Code of Federal Regulations, 2014 CFR

2014-01-01

... 7 Agriculture 2 2014-01-01 2014-01-01 false Size classifications. 51.2559 Section 51.2559....2559 Size classifications. (a) The size of pistachio kernels may be specified in connection with the grade in accordance with one of the following size classifications. (1) Jumbo Whole Kernels: 80 percent...

7 CFR 51.2559 - Size classifications.

Code of Federal Regulations, 2013 CFR

2013-01-01

... 7 Agriculture 2 2013-01-01 2013-01-01 false Size classifications. 51.2559 Section 51.2559....2559 Size classifications. (a) The size of pistachio kernels may be specified in connection with the grade in accordance with one of the following size classifications. (1) Jumbo Whole Kernels: 80 percent...

Agile convolutional neural network for pulmonary nodule classification using CT images.

PubMed

Zhao, Xinzhuo; Liu, Liyao; Qi, Shouliang; Teng, Yueyang; Li, Jianhua; Qian, Wei

2018-04-01

To distinguish benign from malignant pulmonary nodules using CT images is critical for their precise diagnosis and treatment. A new Agile convolutional neural network (CNN) framework is proposed to conquer the challenges of a small-scale medical image database and the small size of the nodules, and it improves the performance of pulmonary nodule classification using CT images. A hybrid CNN of LeNet and AlexNet is constructed through combining the layer settings of LeNet and the parameter settings of AlexNet. A dataset with 743 CT image nodule samples is built up based on the 1018 CT scans of LIDC to train and evaluate the Agile CNN model. Through adjusting the parameters of the kernel size, learning rate, and other factors, the effect of these parameters on the performance of the CNN model is investigated, and an optimized setting of the CNN is obtained finally. After finely optimizing the settings of the CNN, the estimation accuracy and the area under the curve can reach 0.822 and 0.877, respectively. The accuracy of the CNN is significantly dependent on the kernel size, learning rate, training batch size, dropout, and weight initializations. The best performance is achieved when the kernel size is set to [Formula: see text], the learning rate is 0.005, the batch size is 32, and dropout and Gaussian initialization are used. This competitive performance demonstrates that our proposed CNN framework and the optimization strategy of the CNN parameters are suitable for pulmonary nodule classification characterized by small medical datasets and small targets. The classification model might help diagnose and treat pulmonary nodules effectively.

7 CFR 51.2113 - Size requirements.

Code of Federal Regulations, 2010 CFR

2010-01-01

... of range in count of whole almond kernels per ounce or in terms of minimum, or minimum and maximum diameter. When a range in count is specified, the whole kernels shall be fairly uniform in size, and the average count per ounce shall be within the range specified. Doubles and broken kernels shall not be used...

High-throughput method for ear phenotyping and kernel weight estimation in maize using ear digital imaging.

PubMed

Makanza, R; Zaman-Allah, M; Cairns, J E; Eyre, J; Burgueño, J; Pacheco, Ángela; Diepenbrock, C; Magorokosho, C; Tarekegne, A; Olsen, M; Prasanna, B M

2018-01-01

Grain yield, ear and kernel attributes can assist to understand the performance of maize plant under different environmental conditions and can be used in the variety development process to address farmer's preferences. These parameters are however still laborious and expensive to measure. A low-cost ear digital imaging method was developed that provides estimates of ear and kernel attributes i.e., ear number and size, kernel number and size as well as kernel weight from photos of ears harvested from field trial plots. The image processing method uses a script that runs in a batch mode on ImageJ; an open source software. Kernel weight was estimated using the total kernel number derived from the number of kernels visible on the image and the average kernel size. Data showed a good agreement in terms of accuracy and precision between ground truth measurements and data generated through image processing. Broad-sense heritability of the estimated parameters was in the range or higher than that for measured grain weight. Limitation of the method for kernel weight estimation is discussed. The method developed in this work provides an opportunity to significantly reduce the cost of selection in the breeding process, especially for resource constrained crop improvement programs and can be used to learn more about the genetic bases of grain yield determinants.

Evaluation of the influence of double and triple Gaussian proton kernel models on accuracy of dose calculations for spot scanning technique.

PubMed

Hirayama, Shusuke; Takayanagi, Taisuke; Fujii, Yusuke; Fujimoto, Rintaro; Fujitaka, Shinichiro; Umezawa, Masumi; Nagamine, Yoshihiko; Hosaka, Masahiro; Yasui, Keisuke; Omachi, Chihiro; Toshito, Toshiyuki

2016-03-01

The main purpose in this study was to present the results of beam modeling and how the authors systematically investigated the influence of double and triple Gaussian proton kernel models on the accuracy of dose calculations for spot scanning technique. The accuracy of calculations was important for treatment planning software (TPS) because the energy, spot position, and absolute dose had to be determined by TPS for the spot scanning technique. The dose distribution was calculated by convolving in-air fluence with the dose kernel. The dose kernel was the in-water 3D dose distribution of an infinitesimal pencil beam and consisted of an integral depth dose (IDD) and a lateral distribution. Accurate modeling of the low-dose region was important for spot scanning technique because the dose distribution was formed by cumulating hundreds or thousands of delivered beams. The authors employed a double Gaussian function as the in-air fluence model of an individual beam. Double and triple Gaussian kernel models were also prepared for comparison. The parameters of the kernel lateral model were derived by fitting a simulated in-water lateral dose profile induced by an infinitesimal proton beam, whose emittance was zero, at various depths using Monte Carlo (MC) simulation. The fitted parameters were interpolated as a function of depth in water and stored as a separate look-up table. These stored parameters for each energy and depth in water were acquired from the look-up table when incorporating them into the TPS. The modeling process for the in-air fluence and IDD was based on the method proposed in the literature. These were derived using MC simulation and measured data. The authors compared the measured and calculated absolute doses at the center of the spread-out Bragg peak (SOBP) under various volumetric irradiation conditions to systematically investigate the influence of the two types of kernel models on the dose calculations. The authors investigated the difference between double and triple Gaussian kernel models. The authors found that the difference between the two studied kernel models appeared at mid-depths and the accuracy of predicting the double Gaussian model deteriorated at the low-dose bump that appeared at mid-depths. When the authors employed the double Gaussian kernel model, the accuracy of calculations for the absolute dose at the center of the SOBP varied with irradiation conditions and the maximum difference was 3.4%. In contrast, the results obtained from calculations with the triple Gaussian kernel model indicated good agreement with the measurements within ±1.1%, regardless of the irradiation conditions. The difference between the results obtained with the two types of studied kernel models was distinct in the high energy region. The accuracy of calculations with the double Gaussian kernel model varied with the field size and SOBP width because the accuracy of prediction with the double Gaussian model was insufficient at the low-dose bump. The evaluation was only qualitative under limited volumetric irradiation conditions. Further accumulation of measured data would be needed to quantitatively comprehend what influence the double and triple Gaussian kernel models had on the accuracy of dose calculations.

SU-E-T-104: An Examination of Dose in the Buildup and Build-Down Regions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tome, W; Kuo, H; Phillips, J

2015-06-15

Purpose: To examine dose in the buildup and build-down regions and compare measurements made with various models and dosimeters Methods: Dose was examined in a 30×30cm {sup 2} phantom of water-equivalent plastic with 10cm of backscatter for various field sizes. Examination was performed with radiochromic film and optically-stimulated-luminescent-dosimeter (OSLD) chips, and compared against a plane-parallel chamber with a correction factor applied to approximate the response of an extrapolation chamber. For the build-down region, a correction factor to account for table absorption and chamber orientation in the posterior-anterior direction was applied. The measurement depths used for the film were halfway throughmore » their sensitive volumes, and a polynomial best fit curve was used to determine the dose to their surfaces. This chamber was also compared with the dose expected in a clinical kernel-based computer model, and a clinical Boltzmann-transport-equation-based (BTE) computer model. The two models were also compared against each other for cases with air gaps in the buildup region. Results: Within 3mm, all dosimeters and models agreed with the chamber within 10% for all field sizes. At the entrance surface, film differed in comparison with the chamber from +90% to +15%, the BTE-model by +140 to +3%, and the kernel-based model by +20% to −25%, decreasing with increasing field size. At the exit surface, film differed in comparison with the chamber from −10% to −15%, the BTE-model by −53% to −50%, the kernel-based model by −55% to −57%, mostly independent of field size. Conclusion: The largest differences compared with the chamber were found at the surface for all field sizes. Differences decreased with increasing field size and increasing depth in phantom. Air gaps in the buildup region cause dose buildup to occur again post-gap, but the effect decreases with increasing phantom thickness prior to the gap.« less

Roaming behaviour and home range estimation of domestic dogs in Aboriginal and Torres Strait Islander communities in northern Australia using four different methods.

PubMed

Dürr, Salome; Ward, Michael P

2014-11-15

Disease transmission parameters are the core of epidemic models, but are difficult to estimate, especially in the absence of outbreak data. Investigation of the roaming behaviour, home range (HR) and utilization distribution (UD) can provide the foundation for such parameter estimation in free-ranging animals. The objectives of this study were to estimate HR and UD of 69 domestic dogs in six Aboriginal and Torres Strait Islander communities in northern Australia and to compare four different methods (the minimum convex polygon, MCP; the location-based kernel density estimation, LKDE; the biased random bridge, BRB; and Time Local Convex Hull, T-LoCoH) for investigation of UD and estimating HR sizes. Global positioning system (GPS) collars were attached to community dogs for a period of 1-3 days and positions (fixes) were recorded every minute. Median core HRs (50% isopleth) of the 69 dogs were estimated to range from 0.2 to 0.4 ha and the more extended HR (95% isopleth) to range from 2.5 to 5.3 ha, depending on the method used. The HR and UD shapes were found to be generally circular around the dog owner's house. However, some individuals were found to roam much more with a HR size of 40-104 ha and cover large areas of their community or occasionally beyond. These far roaming dogs are of particular interest for infectious disease transmission. Occasionally, dogs were taken between communities and out of communities for hunting, which enables the contact of dogs between communities and with wildlife (such as dingoes). The BRB and T-LoCoH are the only two methods applied here which integrate the consecutiveness of GPS locations into the analysis, a substantial advantage. The recently developed BRB method produced significantly larger HR estimates than the other two methods; however, the variability of HR sizes was lower compared to the other methods. Advantages of the BRB method include a more realistic analytical approach (kernel density estimation based on movements rather than on locations), possibilities to deal with irregular time periods between consecutive GPS fixes and parameter specification which respects the characteristics of the GPS unit used to collect the data. The BRB method was therefore the most suitable method for UD estimation in this dataset. The results of this study can further be used to contact rates between the dogs within and between communities, a foundation for estimating transmission parameters for canine infectious disease models, such as a rabies spread model in Australia. Crown Copyright © 2014. Published by Elsevier B.V. All rights reserved.

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

PubMed

Cid, Jaime A; von Davier, Alina A

2015-05-01

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

Flexible Kernel Memory

PubMed Central

Nowicki, Dimitri; Siegelmann, Hava

2010-01-01

This paper introduces a new model of associative memory, capable of both binary and continuous-valued inputs. Based on kernel theory, the memory model is on one hand a generalization of Radial Basis Function networks and, on the other, is in feature space, analogous to a Hopfield network. Attractors can be added, deleted, and updated on-line simply, without harming existing memories, and the number of attractors is independent of input dimension. Input vectors do not have to adhere to a fixed or bounded dimensionality; they can increase and decrease it without relearning previous memories. A memory consolidation process enables the network to generalize concepts and form clusters of input data, which outperforms many unsupervised clustering techniques; this process is demonstrated on handwritten digits from MNIST. Another process, reminiscent of memory reconsolidation is introduced, in which existing memories are refreshed and tuned with new inputs; this process is demonstrated on series of morphed faces. PMID:20552013

The effect of CT technical factors on quantification of lung fissure integrity

NASA Astrophysics Data System (ADS)

Chong, D.; Brown, M. S.; Ochs, R.; Abtin, F.; Brown, M.; Ordookhani, A.; Shaw, G.; Kim, H. J.; Gjertson, D.; Goldin, J. G.

2009-02-01

A new emphysema treatment uses endobronchial valves to perform lobar volume reduction. The degree of fissure completeness may predict treatment efficacy. This study investigated the behavior of a semiautomated algorithm for quantifying lung fissure integrity in CT with respect to reconstruction kernel and dose. Raw CT data was obtained for six asymptomatic patients from a high-risk population for lung cancer. The patients were scanned on either a Siemens Sensation 16 or 64, using a low-dose protocol of 120 kVp, 25 mAs. Images were reconstructed using kernels ranging from smooth to sharp (B10f, B30f, B50f, B70f). Research software was used to simulate an even lower-dose acquisition of 15 mAs, and images were generated at the same kernels resulting in 8 series per patient. The left major fissure was manually contoured axially at regular intervals, yielding 37 contours across all patients. These contours were read into an image analysis and pattern classification system which computed a Fissure Integrity Score (FIS) for each kernel and dose. FIS values were analyzed using a mixed-effects model with kernel and dose as fixed effects and patient as random effect to test for difference due to kernel and dose. Analysis revealed no difference in FIS between the smooth kernels (B10f, B30f) nor between sharp kernels (B50f, B70f), but there was a significant difference between the sharp and smooth groups (p = 0.020). There was no significant difference in FIS between the two low-dose reconstructions (p = 0.882). Using a cutoff of 90%, the number of incomplete fissures increased from 5 to 10 when the imaging protocol changed from B50f to B30f. Reconstruction kernel has a significant effect on quantification of fissure integrity in CT. This has potential implications when selecting patients for endobronchial valve therapy.

Habitat use and home range of the Laysan Teal on Laysan Island, Hawaii

USGS Publications Warehouse

Reynolds, M.H.

2004-01-01

The 24-hour habitat use and home range of the Laysan Teal (Anas laysanensis), an endemic dabbling duck in Hawaii, was studied using radio telemetry during 1998-2000. Radios were retained for a mean of 40 days (0-123 d; 73 adult birds radio-tagged). Comparisons of daily habitat use were made for birds in the morning, day, evening, and night. Most birds showed strong evidence of selective habitat use. Adults preferred the terrestrial vegetation (88%), and avoided the lake and wetlands during the day. At night, 63% of the birds selected the lake and wetlands. Nocturnal habitat use differed significantly between the non-breeding and breeding seasons, while the lake and wetland habitats were used more frequently during the non-breeding season. Most individuals showed strong site fidelity during the study, but habitat selection varied between individuals. Mean home range size was 9.78 ha (SE ?? 2.6) using the fixed kernel estimator (95% kernel; 15 birds, each with >25 locations). The average minimum convex polygon size was 24 ha (SE ?? 5.6). The mean distance traveled between tracking locations was 178 m (SE ?? 30-5), with travel distances between points ranging up to 1,649 m. Tracking duration varied from 31-121 days per bird (mean tracking duration 75 days).

Dissection of genetic factors underlying wheat kernel shape and size in an elite x nonadapted cross using a high density SNP linkage map

USDA-ARS?s Scientific Manuscript database

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 414...

Increasing the Size of a Piece of Popcorn

NASA Astrophysics Data System (ADS)

Quinn, Paul; Hong, Daniel C.; Both, Joseph

2003-03-01

Popcorn is an extremely popular snack food in the world today. Thermodynamics can be used to analyze how popcorn is produced. By treating the popping mechanism of the corn as a thermodynamic expansion, a method of increasing the volume or size of a kernel of popcorn can be studied. By lowering the pressure surrounding the unpopped kernel, one can use a thermodynamic argument to show that the expanded volume of the kernel when it pops must increase. In this project, a variety of experiments are run to test the validity of this theory. The results show that there is a significant increase in the average kernel size when the pressure of the surroundings is reduced.

Increasing the size of a piece of popcorn

NASA Astrophysics Data System (ADS)

Quinn, Paul V.; Hong, Daniel C.; Both, J. A.

2005-08-01

Popcorn is an extremely popular snack food in the world today. Thermodynamics can be used to analyze how popcorn is produced. By treating the popping mechanism of the corn as a thermodynamic expansion, a method of increasing the volume or size of a kernel of popcorn can be studied. By lowering the pressure surrounding the unpopped kernel, one can use a thermodynamic argument to show that the expanded volume of the kernel when it pops must increase. In this project, a variety of experiments are run to test the qualitative validity of this theory. The results show that there is a significant increase in the average kernel size when the pressure of the surroundings is reduced.

Oecophylla longinoda (Hymenoptera: Formicidae) Lead to Increased Cashew Kernel Size and Kernel Quality.

PubMed

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.

Efficient nonparametric n -body force fields from machine learning

NASA Astrophysics Data System (ADS)

Glielmo, Aldo; Zeni, Claudio; De Vita, Alessandro

2018-05-01

We provide a definition and explicit expressions for n -body Gaussian process (GP) kernels, which can learn any interatomic interaction occurring in a physical system, up to n -body contributions, for any value of n . The series is complete, as it can be shown that the "universal approximator" squared exponential kernel can be written as a sum of n -body kernels. These recipes enable the choice of optimally efficient force models for each target system, as confirmed by extensive testing on various materials. We furthermore describe how the n -body kernels can be "mapped" on equivalent representations that provide database-size-independent predictions and are thus crucially more efficient. We explicitly carry out this mapping procedure for the first nontrivial (three-body) kernel of the series, and we show that this reproduces the GP-predicted forces with meV /Å accuracy while being orders of magnitude faster. These results pave the way to using novel force models (here named "M-FFs") that are computationally as fast as their corresponding standard parametrized n -body force fields, while retaining the nonparametric character, the ease of training and validation, and the accuracy of the best recently proposed machine-learning potentials.

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

USDA-ARS?s Scientific Manuscript database

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

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hornung, Richard D.; Hones, Holger E.

The RAJA Performance Suite is designed to evaluate performance of the RAJA performance portability library on a wide variety of important high performance computing (HPC) algorithmic lulmels. These kernels assess compiler optimizations and various parallel programming model backends accessible through RAJA, such as OpenMP, CUDA, etc. The Initial version of the suite contains 25 computational kernels, each of which appears in 6 variants: Baseline SequcntiaJ, RAJA SequentiaJ, Baseline OpenMP, RAJA OpenMP, Baseline CUDA, RAJA CUDA. All variants of each kernel perform essentially the same mathematical operations and the loop body code for each kernel is identical across all variants. Theremore » are a few kernels, such as those that contain reduction operations, that require CUDA-specific coding for their CUDA variants. ActuaJ computer instructions executed and how they run in parallel differs depending on the parallel programming model backend used and which optimizations are perfonned by the compiler used to build the Perfonnance Suite executable. The Suite will be used primarily by RAJA developers to perform regular assessments of RAJA performance across a range of hardware platforms and compilers as RAJA features are being developed. It will also be used by LLNL hardware and software vendor panners for new defining requirements for future computing platform procurements and acceptance testing. In particular, the RAJA Performance Suite will be used for compiler acceptance testing of the upcoming CORAUSierra machine {initial LLNL delivery expected in late-2017/early 2018) and the CORAL-2 procurement. The Suite will aJso be used to generate concise source code reproducers of compiler and runtime issues we uncover so that we may provide them to relevant vendors to be fixed.« less

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

PubMed

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

2009-08-01

Previous studies have reported effects of pollen source on the oil concentration of maize (Zea mays) kernels through modifications to both the embryo/kernel ratio and embryo oil concentration. The present study expands upon previous analyses by addressing pollen source effects on the growth of kernel structures (i.e. pericarp, endosperm and embryo), allocation of embryo chemical constituents (i.e. oil, protein, starch and soluble sugars), and the anatomy and histology of the embryos. Maize kernels with different oil concentration were obtained from pollinations with two parental genotypes of contrasting oil concentration. The dynamics of the growth of kernel structures and allocation of embryo chemical constituents were analysed during the post-flowering period. Mature kernels were dissected to study the anatomy (embryonic axis and scutellum) and histology [cell number and cell size of the scutellums, presence of sub-cellular structures in scutellum tissue (starch granules, oil and protein bodies)] of the embryos. Plants of all crosses exhibited a similar kernel number and kernel weight. Pollen source modified neither the growth period of kernel structures, nor pericarp growth rate. By contrast, pollen source determined a trade-off between embryo and endosperm growth rates, which impacted on the embryo/kernel ratio of mature kernels. Modifications to the embryo size were mediated by scutellum cell number. Pollen source also affected (P < 0.01) allocation of embryo chemical compounds. Negative correlations among embryo oil concentration and those of starch (r = 0.98, P < 0.01) and soluble sugars (r = 0.95, P < 0.05) were found. Coincidently, embryos with low oil concentration had an increased (P < 0.05-0.10) scutellum cell area occupied by starch granules and fewer oil bodies. The effects of pollen source on both embryo/kernel ratio and allocation of embryo chemicals seems to be related to the early established sink strength (i.e. sink size and sink activity) of the embryos.

Security Tagged Architecture Co-Design (STACD)

DTIC Science & Technology

2015-09-01

components have access to all other system components whether they need it or not. Microkernels [8, 9, 10] seek to reduce the kernel size to improve...does not provide the fine-grained control to allow for formal verification. Microkernels reduce the size of the kernel enough to allow for a formal...verification of the kernel. Tanenbaum [14] documents many of the security virtues of microkernels and argues that the Ring 3 Ring 2 Ring 1

Nonlocal kinetic energy functional from the jellium-with-gap model: Applications to orbital-free density functional theory

NASA Astrophysics Data System (ADS)

Constantin, Lucian A.; Fabiano, Eduardo; Della Sala, Fabio

2018-05-01

Orbital-free density functional theory (OF-DFT) promises to describe the electronic structure of very large quantum systems, being its computational cost linear with the system size. However, the OF-DFT accuracy strongly depends on the approximation made for the kinetic energy (KE) functional. To date, the most accurate KE functionals are nonlocal functionals based on the linear-response kernel of the homogeneous electron gas, i.e., the jellium model. Here, we use the linear-response kernel of the jellium-with-gap model to construct a simple nonlocal KE functional (named KGAP) which depends on the band-gap energy. In the limit of vanishing energy gap (i.e., in the case of metals), the KGAP is equivalent to the Smargiassi-Madden (SM) functional, which is accurate for metals. For a series of semiconductors (with different energy gaps), the KGAP performs much better than SM, and results are close to the state-of-the-art functionals with sophisticated density-dependent kernels.

Exact solutions for mass-dependent irreversible aggregations.

PubMed

Son, Seung-Woo; Christensen, Claire; Bizhani, Golnoosh; Grassberger, Peter; Paczuski, Maya

2011-10-01

We consider the mass-dependent aggregation process (k+1)X→X, given a fixed number of unit mass particles in the initial state. One cluster is chosen proportional to its mass and is merged into one, either with k neighbors in one dimension, or--in the well-mixed case--with k other clusters picked randomly. We find the same combinatorial exact solutions for the probability to find any given configuration of particles on a ring or line, and in the well-mixed case. The mass distribution of a single cluster exhibits scaling laws and the finite-size scaling form is given. The relation to the classical sum kernel of irreversible aggregation is discussed.

Data-Driven Hierarchical Structure Kernel for Multiscale Part-Based Object Recognition

PubMed Central

Wang, Botao; Xiong, Hongkai; Jiang, Xiaoqian; Zheng, Yuan F.

2017-01-01

Detecting generic object categories in images and videos are a fundamental issue in computer vision. However, it faces the challenges from inter and intraclass diversity, as well as distortions caused by viewpoints, poses, deformations, and so on. To solve object variations, this paper constructs a structure kernel and proposes a multiscale part-based model incorporating the discriminative power of kernels. The structure kernel would measure the resemblance of part-based objects in three aspects: 1) the global similarity term to measure the resemblance of the global visual appearance of relevant objects; 2) the part similarity term to measure the resemblance of the visual appearance of distinctive parts; and 3) the spatial similarity term to measure the resemblance of the spatial layout of parts. In essence, the deformation of parts in the structure kernel is penalized in a multiscale space with respect to horizontal displacement, vertical displacement, and scale difference. Part similarities are combined with different weights, which are optimized efficiently to maximize the intraclass similarities and minimize the interclass similarities by the normalized stochastic gradient ascent algorithm. In addition, the parameters of the structure kernel are learned during the training process with regard to the distribution of the data in a more discriminative way. With flexible part sizes on scale and displacement, it can be more robust to the intraclass variations, poses, and viewpoints. Theoretical analysis and experimental evaluations demonstrate that the proposed multiscale part-based representation model with structure kernel exhibits accurate and robust performance, and outperforms state-of-the-art object classification approaches. PMID:24808345

Stochastic Models of Macroscale Quantities for the Prediction of the REV Scale for Multiphase Flow through Porous Media

NASA Astrophysics Data System (ADS)

Talbot, C.; McClure, J. E.; Armstrong, R. T.; Mostaghimi, P.; Hu, Y.; Miller, C. T.

2017-12-01

Microscale experimental and computational methods can be used to evaluate fundamental microscale mechanisms and deduce macroscale constitutive relationships and parameter values. The link between the microscale and the macroscale is especially demanding, because technical issues arise regarding the necessary scale of the system needed for a meaningful set of macroscale measures to be insensitive to the size of the system, which is known as a representative elementary volume (REV). While the REV scale is routinely determined for single-phase flow in porous media, no systematic study of the scale of a REV for the comprehensive set of macroscale measures considered here has been reported in the literature. A comprehensive set of measures of the macroscale state is developed. We further develop and apply methods to predict the REV scale and quantify the uncertainty of the estimate for this set of macroscale quantities. We model the system state in terms of standard errors of macroscale quantities as a multivariate Gaussian process dependent on the size of the domain simulated. We determine predictive distributions of function values and a posterior distributions of weights using standard kernels, as well as a kernel constructed using relationships between physical quantities. For each kernel, we discuss the decay of the mean and covariance with increasing domain size, and use cross-validation to facilitate model selection. The procedure yields a model of the domain size needed to achieve a REV with quantifiable uncertainty. We present results in the context of multiphase fluid flow through a highly resolved realization of sandstone imaged using micro-CT. A 1440x1440x4320 section of the full 2520x2520x5280 imaged medium is simulated using the lattice-Boltzmann method. We compare the fidelity of the predictive model with results obtained by an analogous approach using polynomial regression.

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

PubMed

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

2015-12-22

Built environment studies provide broad evidence that urban characteristics influence physical activity (PA). However, findings are still difficult to compare, due to inconsistent measures assessing urban point characteristics and varying definitions of spatial scale. Both were found to influence the strength of the association between the built environment and PA. We simultaneously evaluated the effect of kernel approaches and network-distances to investigate the association between urban characteristics and physical activity depending on spatial scale and intensity measure. We assessed urban measures of point characteristics such as intersections, public transit stations, and public open spaces in ego-centered network-dependent neighborhoods based on geographical data of one German study region of the IDEFICS study. We calculated point intensities using the simple intensity and kernel approaches based on fixed bandwidths, cross-validated bandwidths including isotropic and anisotropic kernel functions and considering adaptive bandwidths that adjust for residential density. We distinguished six network-distances from 500 m up to 2 km to calculate each intensity measure. A log-gamma regression model was used to investigate the effect of each urban measure on moderate-to-vigorous physical activity (MVPA) of 400 2- to 9.9-year old children who participated in the IDEFICS study. Models were stratified by sex and age groups, i.e. pre-school children (2 to <6 years) and school children (6-9.9 years), and were adjusted for age, body mass index (BMI), education and safety concerns of parents, season and valid weartime of accelerometers. Association between intensity measures and MVPA strongly differed by network-distance, with stronger effects found for larger network-distances. Simple intensity revealed smaller effect estimates and smaller goodness-of-fit compared to kernel approaches. Smallest variation in effect estimates over network-distances was found for kernel intensity measures based on isotropic and anisotropic cross-validated bandwidth selection. We found a strong variation in the association between the built environment and PA of children based on the choice of intensity measure and network-distance. Kernel intensity measures provided stable results over various scales and improved the assessment compared to the simple intensity measure. Considering different spatial scales and kernel intensity methods might reduce methodological limitations in assessing opportunities for PA in the built environment.

Comparison of statistical models to estimate parasite growth rate in the induced blood stage malaria model.

PubMed

Wockner, Leesa F; Hoffmann, Isabell; O'Rourke, Peter; McCarthy, James S; Marquart, Louise

2017-08-25

The efficacy of vaccines aimed at inhibiting the growth of malaria parasites in the blood can be assessed by comparing the growth rate of parasitaemia in the blood of subjects treated with a test vaccine compared to controls. In studies using induced blood stage malaria (IBSM), a type of controlled human malaria infection, parasite growth rate has been measured using models with the intercept on the y-axis fixed to the inoculum size. A set of statistical models was evaluated to determine an optimal methodology to estimate parasite growth rate in IBSM studies. Parasite growth rates were estimated using data from 40 subjects published in three IBSM studies. Data was fitted using 12 statistical models: log-linear, sine-wave with the period either fixed to 48 h or not fixed; these models were fitted with the intercept either fixed to the inoculum size or not fixed. All models were fitted by individual, and overall by study using a mixed effects model with a random effect for the individual. Log-linear models and sine-wave models, with the period fixed or not fixed, resulted in similar parasite growth rate estimates (within 0.05 log 10 parasites per mL/day). Average parasite growth rate estimates for models fitted by individual with the intercept fixed to the inoculum size were substantially lower by an average of 0.17 log 10 parasites per mL/day (range 0.06-0.24) compared with non-fixed intercept models. Variability of parasite growth rate estimates across the three studies analysed was substantially higher (3.5 times) for fixed-intercept models compared with non-fixed intercept models. The same tendency was observed in models fitted overall by study. Modelling data by individual or overall by study had minimal effect on parasite growth estimates. The analyses presented in this report confirm that fixing the intercept to the inoculum size influences parasite growth estimates. The most appropriate statistical model to estimate the growth rate of blood-stage parasites in IBSM studies appears to be a log-linear model fitted by individual and with the intercept estimated in the log-linear regression. Future studies should use this model to estimate parasite growth rates.

Voronoi Cell Patterns: theoretical model and application to submonolayer growth

NASA Astrophysics Data System (ADS)

González, Diego Luis; Einstein, T. L.

2012-02-01

We use a simple fragmentation model to describe the statistical behavior of the Voronoi cell patterns generated by a homogeneous and isotropic set of points in 1D and in 2D. In particular, we are interested in the distribution of sizes of these Voronoi cells. Our model is completely defined by two probability distributions in 1D and again in 2D, the probability to add a new point inside an existing cell and the probability that this new point is at a particular position relative to the preexisting point inside this cell. In 1D the first distribution depends on a single parameter while the second distribution is defined through a fragmentation kernel; in 2D both distributions depend on a single parameter. The fragmentation kernel and the control parameters are closely related to the physical properties of the specific system under study. We apply our model to describe the Voronoi cell patterns of island nucleation for critical island sizes i=0,1,2,3. Experimental results for the Voronoi cells of InAs/GaAs quantum dots are also described by our model.

Rapid simulation of spatial epidemics: a spectral method.

PubMed

Brand, Samuel P C; Tildesley, Michael J; Keeling, Matthew J

2015-04-07

Spatial structure and hence the spatial position of host populations plays a vital role in the spread of infection. In the majority of situations, it is only possible to predict the spatial spread of infection using simulation models, which can be computationally demanding especially for large population sizes. Here we develop an approximation method that vastly reduces this computational burden. We assume that the transmission rates between individuals or sub-populations are determined by a spatial transmission kernel. This kernel is assumed to be isotropic, such that the transmission rate is simply a function of the distance between susceptible and infectious individuals; as such this provides the ideal mechanism for modelling localised transmission in a spatial environment. We show that the spatial force of infection acting on all susceptibles can be represented as a spatial convolution between the transmission kernel and a spatially extended 'image' of the infection state. This representation allows the rapid calculation of stochastic rates of infection using fast-Fourier transform (FFT) routines, which greatly improves the computational efficiency of spatial simulations. We demonstrate the efficiency and accuracy of this fast spectral rate recalculation (FSR) method with two examples: an idealised scenario simulating an SIR-type epidemic outbreak amongst N habitats distributed across a two-dimensional plane; the spread of infection between US cattle farms, illustrating that the FSR method makes continental-scale outbreak forecasting feasible with desktop processing power. The latter model demonstrates which areas of the US are at consistently high risk for cattle-infections, although predictions of epidemic size are highly dependent on assumptions about the tail of the transmission kernel. Copyright © 2015 Elsevier Ltd. All rights reserved.

Benchmarking NWP Kernels on Multi- and Many-core Processors

NASA Astrophysics Data System (ADS)

Michalakes, J.; Vachharajani, M.

2008-12-01

Increased computing power for weather, climate, and atmospheric science has provided direct benefits for defense, agriculture, the economy, the environment, and public welfare and convenience. Today, very large clusters with many thousands of processors are allowing scientists to move forward with simulations of unprecedented size. But time-critical applications such as real-time forecasting or climate prediction need strong scaling: faster nodes and processors, not more of them. Moreover, the need for good cost- performance has never been greater, both in terms of performance per watt and per dollar. For these reasons, the new generations of multi- and many-core processors being mass produced for commercial IT and "graphical computing" (video games) are being scrutinized for their ability to exploit the abundant fine- grain parallelism in atmospheric models. We present results of our work to date identifying key computational kernels within the dynamics and physics of a large community NWP model, the Weather Research and Forecast (WRF) model. We benchmark and optimize these kernels on several different multi- and many-core processors. The goals are to (1) characterize and model performance of the kernels in terms of computational intensity, data parallelism, memory bandwidth pressure, memory footprint, etc. (2) enumerate and classify effective strategies for coding and optimizing for these new processors, (3) assess difficulties and opportunities for tool or higher-level language support, and (4) establish a continuing set of kernel benchmarks that can be used to measure and compare effectiveness of current and future designs of multi- and many-core processors for weather and climate applications.

Intraear Compensation of Field Corn, Zea mays, from Simulated and Naturally Occurring Injury by Ear-Feeding Larvae.

PubMed

Steckel, S; Stewart, S D

2015-06-01

Ear-feeding larvae, such as corn earworm, Helicoverpa zea Boddie (Lepidoptera: Noctuidae), can be important insect pests of field corn, Zea mays L., by feeding on kernels. Recently introduced, stacked Bacillus thuringiensis (Bt) traits provide improved protection from ear-feeding larvae. Thus, our objective was to evaluate how injury to kernels in the ear tip might affect yield when this injury was inflicted at the blister and milk stages. In 2010, simulated corn earworm injury reduced total kernel weight (i.e., yield) at both the blister and milk stage. In 2011, injury to ear tips at the milk stage affected total kernel weight. No differences in total kernel weight were found in 2013, regardless of when or how much injury was inflicted. Our data suggested that kernels within the same ear could compensate for injury to ear tips by increasing in size, but this increase was not always statistically significant or sufficient to overcome high levels of kernel injury. For naturally occurring injury observed on multiple corn hybrids during 2011 and 2012, our analyses showed either no or a minimal relationship between number of kernels injured by ear-feeding larvae and the total number of kernels per ear, total kernel weight, or the size of individual kernels. The results indicate that intraear compensation for kernel injury to ear tips can occur under at least some conditions. © The Authors 2015. Published by Oxford University Press on behalf of Entomological Society of America. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Ontogenetic loss of phenotypic plasticity of age at metamorphosis in tadpoles

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hensley, F.R.

1993-12-01

Amphibian larvae exhibit phenotypic plasticity in size at metamorphosis and duration of the larval period. I used Pseudacris crucifer tadpoles to test two models for predicting tadpole age and size at metamorphosis under changing environmental conditions. The Wilbur-Collins model states that metamorphosis is initiated as a function of a tadpole's size and relative growth rate, and predicts that changes in growth rate throughout the larval period affect age and size at metamorphosis. An alternative model, the fixed-rate model, states that age at metamorphosis is fixed early in larval life, and subsequent changes in growth rate will have no effect onmore » the length of the larval period. My results confirm that food supplies affect both age and size at metamorphosis, but developmental rates became fixed at approximately Gosner (1960) stages 35-37. Neither model completely predicted these results. I suggest that the generally accepted Wilbur-Collins model is improved by incorporating a point of fixed developmental timing. Growth trajectories predicted from this modified model fit the results of this study better than trajectories based on either of the original models. The results of this study suggests a constraint that limits the simultaneous optimization of age and size at metamorphosis. 32 refs., 5 figs., 1 tab.« less

An alternative covariance estimator to investigate genetic heterogeneity in populations.

PubMed

Heslot, Nicolas; Jannink, Jean-Luc

2015-11-26

For genomic prediction and genome-wide association studies (GWAS) using mixed models, covariance between individuals is estimated using molecular markers. Based on the properties of mixed models, using available molecular data for prediction is optimal if this covariance is known. Under this assumption, adding individuals to the analysis should never be detrimental. However, some empirical studies showed that increasing training population size decreased prediction accuracy. Recently, results from theoretical models indicated that even if marker density is high and the genetic architecture of traits is controlled by many loci with small additive effects, the covariance between individuals, which depends on relationships at causal loci, is not always well estimated by the whole-genome kinship. We propose an alternative covariance estimator named K-kernel, to account for potential genetic heterogeneity between populations that is characterized by a lack of genetic correlation, and to limit the information flow between a priori unknown populations in a trait-specific manner. This is similar to a multi-trait model and parameters are estimated by REML and, in extreme cases, it can allow for an independent genetic architecture between populations. As such, K-kernel is useful to study the problem of the design of training populations. K-kernel was compared to other covariance estimators or kernels to examine its fit to the data, cross-validated accuracy and suitability for GWAS on several datasets. It provides a significantly better fit to the data than the genomic best linear unbiased prediction model and, in some cases it performs better than other kernels such as the Gaussian kernel, as shown by an empirical null distribution. In GWAS simulations, alternative kernels control type I errors as well as or better than the classical whole-genome kinship and increase statistical power. No or small gains were observed in cross-validated prediction accuracy. This alternative covariance estimator can be used to gain insight into trait-specific genetic heterogeneity by identifying relevant sub-populations that lack genetic correlation between them. Genetic correlation can be 0 between identified sub-populations by performing automatic selection of relevant sets of individuals to be included in the training population. It may also increase statistical power in GWAS.

Home range and residency status of Northern Goshawks breeding in Minnesota

USGS Publications Warehouse

Boal, C.W.; Andersen, D.E.; Kennedy, P.L.

2003-01-01

We used radio-telemetry to estimate breeding season home-range size of 17 male and 11 female Northern Goshawks (Accipiter gentilis) and combined home ranges of 10 pairs of breeding goshawks in Minnesota. Home-range sizes for male and female goshawks were 2593 and 2494 ha, respectively, using the minimum convex polygon, and 3927 and 5344 ha, respectively, using the 95% fixed kernel. Home ranges of male and female members of 10 goshawk pairs were smaller than combined home-range size of those pairs (mean difference = 3527 ha; 95% CI = 891 to 6164 ha). Throughout the nonbreeding season, the maximum distance from the nest recorded for all but one goshawk was 12.4 km. Goshawks breeding in Minnesota have home ranges similar to or larger than those reported in most other areas. Home-range overlap between members of breeding pairs was typically ???50%, and both members of breeding pairs were associated with breeding home ranges year round. Goshawk management plans based on estimated home-range size of individual hawks may substantially underestimate the area actually used by a nesting pair.

Deep Recurrent Neural Networks for Human Activity Recognition

PubMed Central

Murad, Abdulmajid

2017-01-01

Adopting deep learning methods for human activity recognition has been effective in extracting discriminative features from raw input sequences acquired from body-worn sensors. Although human movements are encoded in a sequence of successive samples in time, typical machine learning methods perform recognition tasks without exploiting the temporal correlations between input data samples. Convolutional neural networks (CNNs) address this issue by using convolutions across a one-dimensional temporal sequence to capture dependencies among input data. However, the size of convolutional kernels restricts the captured range of dependencies between data samples. As a result, typical models are unadaptable to a wide range of activity-recognition configurations and require fixed-length input windows. In this paper, we propose the use of deep recurrent neural networks (DRNNs) for building recognition models that are capable of capturing long-range dependencies in variable-length input sequences. We present unidirectional, bidirectional, and cascaded architectures based on long short-term memory (LSTM) DRNNs and evaluate their effectiveness on miscellaneous benchmark datasets. Experimental results show that our proposed models outperform methods employing conventional machine learning, such as support vector machine (SVM) and k-nearest neighbors (KNN). Additionally, the proposed models yield better performance than other deep learning techniques, such as deep believe networks (DBNs) and CNNs. PMID:29113103

Deep Recurrent Neural Networks for Human Activity Recognition.

PubMed

Murad, Abdulmajid; Pyun, Jae-Young

2017-11-06

Adopting deep learning methods for human activity recognition has been effective in extracting discriminative features from raw input sequences acquired from body-worn sensors. Although human movements are encoded in a sequence of successive samples in time, typical machine learning methods perform recognition tasks without exploiting the temporal correlations between input data samples. Convolutional neural networks (CNNs) address this issue by using convolutions across a one-dimensional temporal sequence to capture dependencies among input data. However, the size of convolutional kernels restricts the captured range of dependencies between data samples. As a result, typical models are unadaptable to a wide range of activity-recognition configurations and require fixed-length input windows. In this paper, we propose the use of deep recurrent neural networks (DRNNs) for building recognition models that are capable of capturing long-range dependencies in variable-length input sequences. We present unidirectional, bidirectional, and cascaded architectures based on long short-term memory (LSTM) DRNNs and evaluate their effectiveness on miscellaneous benchmark datasets. Experimental results show that our proposed models outperform methods employing conventional machine learning, such as support vector machine (SVM) and k-nearest neighbors (KNN). Additionally, the proposed models yield better performance than other deep learning techniques, such as deep believe networks (DBNs) and CNNs.

Initial Simulations of RF Waves in Hot Plasmas Using the FullWave Code

NASA Astrophysics Data System (ADS)

Zhao, Liangji; Svidzinski, Vladimir; Spencer, Andrew; Kim, Jin-Soo

2017-10-01

FullWave is a simulation tool that models RF fields in hot inhomogeneous magnetized plasmas. The wave equations with linearized hot plasma dielectric response are solved in configuration space on adaptive cloud of computational points. The nonlocal hot plasma dielectric response is formulated by calculating the plasma conductivity kernel based on the solution of the linearized Vlasov equation in inhomogeneous magnetic field. In an rf field, the hot plasma dielectric response is limited to the distance of a few particles' Larmor radii, near the magnetic field line passing through the test point. The localization of the hot plasma dielectric response results in a sparse matrix of the problem thus significantly reduces the size of the problem and makes the simulations faster. We will present the initial results of modeling of rf waves using the Fullwave code, including calculation of nonlocal conductivity kernel in 2D Tokamak geometry; the interpolation of conductivity kernel from test points to adaptive cloud of computational points; and the results of self-consistent simulations of 2D rf fields using calculated hot plasma conductivity kernel in a tokamak plasma with reduced parameters. Work supported by the US DOE ``SBIR program.

Background field removal technique using regularization enabled sophisticated harmonic artifact reduction for phase data with varying kernel sizes.

PubMed

Kan, Hirohito; Kasai, Harumasa; Arai, Nobuyuki; Kunitomo, Hiroshi; Hirose, Yasujiro; Shibamoto, Yuta

2016-09-01

An effective background field removal technique is desired for more accurate quantitative susceptibility mapping (QSM) prior to dipole inversion. The aim of this study was to evaluate the accuracy of regularization enabled sophisticated harmonic artifact reduction for phase data with varying spherical kernel sizes (REV-SHARP) method using a three-dimensional head phantom and human brain data. The proposed REV-SHARP method used the spherical mean value operation and Tikhonov regularization in the deconvolution process, with varying 2-14mm kernel sizes. The kernel sizes were gradually reduced, similar to the SHARP with varying spherical kernel (VSHARP) method. We determined the relative errors and relationships between the true local field and estimated local field in REV-SHARP, VSHARP, projection onto dipole fields (PDF), and regularization enabled SHARP (RESHARP). Human experiment was also conducted using REV-SHARP, VSHARP, PDF, and RESHARP. The relative errors in the numerical phantom study were 0.386, 0.448, 0.838, and 0.452 for REV-SHARP, VSHARP, PDF, and RESHARP. REV-SHARP result exhibited the highest correlation between the true local field and estimated local field. The linear regression slopes were 1.005, 1.124, 0.988, and 0.536 for REV-SHARP, VSHARP, PDF, and RESHARP in regions of interest on the three-dimensional head phantom. In human experiments, no obvious errors due to artifacts were present in REV-SHARP. The proposed REV-SHARP is a new method combined with variable spherical kernel size and Tikhonov regularization. This technique might make it possible to be more accurate backgroud field removal and help to achive better accuracy of QSM. Copyright © 2016 Elsevier Inc. All rights reserved.

Genetic Analysis of Kernel Traits in Maize-Teosinte Introgression Populations.

PubMed

Liu, Zhengbin; Garcia, Arturo; McMullen, Michael D; Flint-Garcia, Sherry A

2016-08-09

Seed traits have been targeted by human selection during the domestication of crop species as a way to increase the caloric and nutritional content of food during the transition from hunter-gather to early farming societies. The primary seed trait under selection was likely seed size/weight as it is most directly related to overall grain yield. Additional seed traits involved in seed shape may have also contributed to larger grain. Maize (Zea mays ssp. mays) kernel weight has increased more than 10-fold in the 9000 years since domestication from its wild ancestor, teosinte (Z. mays ssp. parviglumis). In order to study how size and shape affect kernel weight, we analyzed kernel morphometric traits in a set of 10 maize-teosinte introgression populations using digital imaging software. We identified quantitative trait loci (QTL) for kernel area and length with moderate allelic effects that colocalize with kernel weight QTL. Several genomic regions with strong effects during maize domestication were detected, and a genetic framework for kernel traits was characterized by complex pleiotropic interactions. Our results both confirm prior reports of kernel domestication loci and identify previously uncharacterized QTL with a range of allelic effects, enabling future research into the genetic basis of these traits. Copyright © 2016 Liu et al.

Genetic Analysis of Kernel Traits in Maize-Teosinte Introgression Populations

PubMed Central

Liu, Zhengbin; Garcia, Arturo; McMullen, Michael D.; Flint-Garcia, Sherry A.

2016-01-01

Seed traits have been targeted by human selection during the domestication of crop species as a way to increase the caloric and nutritional content of food during the transition from hunter-gather to early farming societies. The primary seed trait under selection was likely seed size/weight as it is most directly related to overall grain yield. Additional seed traits involved in seed shape may have also contributed to larger grain. Maize (Zea mays ssp. mays) kernel weight has increased more than 10-fold in the 9000 years since domestication from its wild ancestor, teosinte (Z. mays ssp. parviglumis). In order to study how size and shape affect kernel weight, we analyzed kernel morphometric traits in a set of 10 maize-teosinte introgression populations using digital imaging software. We identified quantitative trait loci (QTL) for kernel area and length with moderate allelic effects that colocalize with kernel weight QTL. Several genomic regions with strong effects during maize domestication were detected, and a genetic framework for kernel traits was characterized by complex pleiotropic interactions. Our results both confirm prior reports of kernel domestication loci and identify previously uncharacterized QTL with a range of allelic effects, enabling future research into the genetic basis of these traits. PMID:27317774

Increasing the Size of Microwave Popcorn

NASA Astrophysics Data System (ADS)

Smoyer, Justin

2005-03-01

Each year Americans consume approximately 17 billion quarts of popcorn. Since the 1940s, microwaves have been the heating source of choice for most. By treating the popcorn mechanism as a thermodynamic system, it has been shown mathematically and experimentally that reducing the surrounding pressure of the unpopped kernels, results in an increased volume of the kernels [Quinn et al, http://xxx.lanl.gov/abs/cond-mat/0409434 v1 2004]. In this project an alternate method of popping with the microwave was used to further test and confirm this hypothesis. Numerous experimental trials where run to test the validity of the theory. The results show that there is a significant increase in the average kernel size as well as a reduction in the number of unpopped kernels.

Improved scatter correction using adaptive scatter kernel superposition

NASA Astrophysics Data System (ADS)

Sun, M.; Star-Lack, J. M.

2010-11-01

Accurate scatter correction is required to produce high-quality reconstructions of x-ray cone-beam computed tomography (CBCT) scans. This paper describes new scatter kernel superposition (SKS) algorithms for deconvolving scatter from projection data. The algorithms are designed to improve upon the conventional approach whose accuracy is limited by the use of symmetric kernels that characterize the scatter properties of uniform slabs. To model scatter transport in more realistic objects, nonstationary kernels, whose shapes adapt to local thickness variations in the projection data, are proposed. Two methods are introduced: (1) adaptive scatter kernel superposition (ASKS) requiring spatial domain convolutions and (2) fast adaptive scatter kernel superposition (fASKS) where, through a linearity approximation, convolution is efficiently performed in Fourier space. The conventional SKS algorithm, ASKS, and fASKS, were tested with Monte Carlo simulations and with phantom data acquired on a table-top CBCT system matching the Varian On-Board Imager (OBI). All three models accounted for scatter point-spread broadening due to object thickening, object edge effects, detector scatter properties and an anti-scatter grid. Hounsfield unit (HU) errors in reconstructions of a large pelvis phantom with a measured maximum scatter-to-primary ratio over 200% were reduced from -90 ± 58 HU (mean ± standard deviation) with no scatter correction to 53 ± 82 HU with SKS, to 19 ± 25 HU with fASKS and to 13 ± 21 HU with ASKS. HU accuracies and measured contrast were similarly improved in reconstructions of a body-sized elliptical Catphan phantom. The results show that the adaptive SKS methods offer significant advantages over the conventional scatter deconvolution technique.

Spectral Kernel Approach to Study Radiative Response of Climate Variables and Interannual Variability of Reflected Solar Spectrum

NASA Technical Reports Server (NTRS)

Jin, Zhonghai; Wielicki, Bruce A.; Loukachine, Constantin; Charlock, Thomas P.; Young, David; Noeel, Stefan

2011-01-01

The radiative kernel approach provides a simple way to separate the radiative response to different climate parameters and to decompose the feedback into radiative and climate response components. Using CERES/MODIS/Geostationary data, we calculated and analyzed the solar spectral reflectance kernels for various climate parameters on zonal, regional, and global spatial scales. The kernel linearity is tested. Errors in the kernel due to nonlinearity can vary strongly depending on climate parameter, wavelength, surface, and solar elevation; they are large in some absorption bands for some parameters but are negligible in most conditions. The spectral kernels are used to calculate the radiative responses to different climate parameter changes in different latitudes. The results show that the radiative response in high latitudes is sensitive to the coverage of snow and sea ice. The radiative response in low latitudes is contributed mainly by cloud property changes, especially cloud fraction and optical depth. The large cloud height effect is confined to absorption bands, while the cloud particle size effect is found mainly in the near infrared. The kernel approach, which is based on calculations using CERES retrievals, is then tested by direct comparison with spectral measurements from Scanning Imaging Absorption Spectrometer for Atmospheric Cartography (SCIAMACHY) (a different instrument on a different spacecraft). The monthly mean interannual variability of spectral reflectance based on the kernel technique is consistent with satellite observations over the ocean, but not over land, where both model and data have large uncertainty. RMS errors in kernel ]derived monthly global mean reflectance over the ocean compared to observations are about 0.001, and the sampling error is likely a major component.

Resource Efficient Hardware Architecture for Fast Computation of Running Max/Min Filters

PubMed Central

Torres-Huitzil, Cesar

2013-01-01

Running max/min filters on rectangular kernels are widely used in many digital signal and image processing applications. Filtering with a k × k kernel requires of k 2 − 1 comparisons per sample for a direct implementation; thus, performance scales expensively with the kernel size k. Faster computations can be achieved by kernel decomposition and using constant time one-dimensional algorithms on custom hardware. This paper presents a hardware architecture for real-time computation of running max/min filters based on the van Herk/Gil-Werman (HGW) algorithm. The proposed architecture design uses less computation and memory resources than previously reported architectures when targeted to Field Programmable Gate Array (FPGA) devices. Implementation results show that the architecture is able to compute max/min filters, on 1024 × 1024 images with up to 255 × 255 kernels, in around 8.4 milliseconds, 120 frames per second, at a clock frequency of 250 MHz. The implementation is highly scalable for the kernel size with good performance/area tradeoff suitable for embedded applications. The applicability of the architecture is shown for local adaptive image thresholding. PMID:24288456

A new discriminative kernel from probabilistic models.

PubMed

Tsuda, Koji; Kawanabe, Motoaki; Rätsch, Gunnar; Sonnenburg, Sören; Müller, Klaus-Robert

2002-10-01

Recently, Jaakkola and Haussler (1999) proposed a method for constructing kernel functions from probabilistic models. Their so-called Fisher kernel has been combined with discriminative classifiers such as support vector machines and applied successfully in, for example, DNA and protein analysis. Whereas the Fisher kernel is calculated from the marginal log-likelihood, we propose the TOP kernel derived; from tangent vectors of posterior log-odds. Furthermore, we develop a theoretical framework on feature extractors from probabilistic models and use it for analyzing the TOP kernel. In experiments, our new discriminative TOP kernel compares favorably to the Fisher kernel.

Individualism in plant populations: using stochastic differential equations to model individual neighbourhood-dependent plant growth.

PubMed

Lv, Qiming; Schneider, Manuel K; Pitchford, Jonathan W

2008-08-01

We study individual plant growth and size hierarchy formation in an experimental population of Arabidopsis thaliana, within an integrated analysis that explicitly accounts for size-dependent growth, size- and space-dependent competition, and environmental stochasticity. It is shown that a Gompertz-type stochastic differential equation (SDE) model, involving asymmetric competition kernels and a stochastic term which decreases with the logarithm of plant weight, efficiently describes individual plant growth, competition, and variability in the studied population. The model is evaluated within a Bayesian framework and compared to its deterministic counterpart, and to several simplified stochastic models, using distributional validation. We show that stochasticity is an important determinant of size hierarchy and that SDE models outperform the deterministic model if and only if structural components of competition (asymmetry; size- and space-dependence) are accounted for. Implications of these results are discussed in the context of plant ecology and in more general modelling situations.

Kernelized Elastic Net Regularization: Generalization Bounds, and Sparse Recovery.

PubMed

Feng, Yunlong; Lv, Shao-Gao; Hang, Hanyuan; Suykens, Johan A K

2016-03-01

Kernelized elastic net regularization (KENReg) is a kernelization of the well-known elastic net regularization (Zou & Hastie, 2005). The kernel in KENReg is not required to be a Mercer kernel since it learns from a kernelized dictionary in the coefficient space. Feng, Yang, Zhao, Lv, and Suykens (2014) showed that KENReg has some nice properties including stability, sparseness, and generalization. In this letter, we continue our study on KENReg by conducting a refined learning theory analysis. This letter makes the following three main contributions. First, we present refined error analysis on the generalization performance of KENReg. The main difficulty of analyzing the generalization error of KENReg lies in characterizing the population version of its empirical target function. We overcome this by introducing a weighted Banach space associated with the elastic net regularization. We are then able to conduct elaborated learning theory analysis and obtain fast convergence rates under proper complexity and regularity assumptions. Second, we study the sparse recovery problem in KENReg with fixed design and show that the kernelization may improve the sparse recovery ability compared to the classical elastic net regularization. Finally, we discuss the interplay among different properties of KENReg that include sparseness, stability, and generalization. We show that the stability of KENReg leads to generalization, and its sparseness confidence can be derived from generalization. Moreover, KENReg is stable and can be simultaneously sparse, which makes it attractive theoretically and practically.

Gabor-based kernel PCA with fractional power polynomial models for face recognition.

PubMed

Liu, Chengjun

2004-05-01

This paper presents a novel Gabor-based kernel Principal Component Analysis (PCA) method by integrating the Gabor wavelet representation of face images and the kernel PCA method for face recognition. Gabor wavelets first derive desirable facial features characterized by spatial frequency, spatial locality, and orientation selectivity to cope with the variations due to illumination and facial expression changes. The kernel PCA method is then extended to include fractional power polynomial models for enhanced face recognition performance. A fractional power polynomial, however, does not necessarily define a kernel function, as it might not define a positive semidefinite Gram matrix. Note that the sigmoid kernels, one of the three classes of widely used kernel functions (polynomial kernels, Gaussian kernels, and sigmoid kernels), do not actually define a positive semidefinite Gram matrix either. Nevertheless, the sigmoid kernels have been successfully used in practice, such as in building support vector machines. In order to derive real kernel PCA features, we apply only those kernel PCA eigenvectors that are associated with positive eigenvalues. The feasibility of the Gabor-based kernel PCA method with fractional power polynomial models has been successfully tested on both frontal and pose-angled face recognition, using two data sets from the FERET database and the CMU PIE database, respectively. The FERET data set contains 600 frontal face images of 200 subjects, while the PIE data set consists of 680 images across five poses (left and right profiles, left and right half profiles, and frontal view) with two different facial expressions (neutral and smiling) of 68 subjects. The effectiveness of the Gabor-based kernel PCA method with fractional power polynomial models is shown in terms of both absolute performance indices and comparative performance against the PCA method, the kernel PCA method with polynomial kernels, the kernel PCA method with fractional power polynomial models, the Gabor wavelet-based PCA method, and the Gabor wavelet-based kernel PCA method with polynomial kernels.

Preliminary data used to assess the accuracy of estimating female white-tailed deer diel birthing-season home ranges using only daytime locations

USGS Publications Warehouse

Barber-Meyer, Shannon M.; Mech, L. David

2014-01-01

Because many white-tailed deer (Odocoileus virginianus) home-range and habitat-use studies rely only on daytime radio-tracking data, we were interested in whether diurnal data sufficiently represented diel home ranges. We analyzed home-range and core-use size and overlap of 8 adult-female Global-Positioning-System-collared deer during May and June 2001 and 2002 in the Superior National Forest, Minnesota, USA. We used 2 traditional means of analysis: minimum-convex polygons (MCP) and fixed kernels (95% FK, home range and 50% FK, core use) and two methods to partition day and night location data: (1) daytime = 0800-2000 h versus nighttime = 2000-0800 h and (2) sunup versus sundown. We found no statistical difference in size of home-range and core-use areas across day and night comparisons; however, in terms of spatial overlap, approximately 30% of night-range areas on average were not accounted for using daytime locations, with even greater differences between core-use areas (on average approximately 50%). We conclude that diurnal data do not adequately describe diel adult-female-deer, May-June home-ranges due to differences in spatial overlap (location). We suggest research to determine (1) if our findings hold in other circumstances (e.g., exclusive of the parturition period, other age classes, etc.), (2) if our conclusions generalize under other conditions (e.g., across deer range, varying seasons, etc.), (3) if habitat-use conclusions are affected by the incomplete overlap between diurnal and diel data, (4) how many nocturnal locations must be included to generate sufficient overlap, and (5) the influence of using other kernel sizes (e.g., 75%, 90%).

Partial Deconvolution with Inaccurate Blur Kernel.

PubMed

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

2017-10-17

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

Kernel machine methods for integrative analysis of genome-wide methylation and genotyping studies.

PubMed

Zhao, Ni; Zhan, Xiang; Huang, Yen-Tsung; Almli, Lynn M; Smith, Alicia; Epstein, Michael P; Conneely, Karen; Wu, Michael C

2018-03-01

Many large GWAS consortia are expanding to simultaneously examine the joint role of DNA methylation in addition to genotype in the same subjects. However, integrating information from both data types is challenging. In this paper, we propose a composite kernel machine regression model to test the joint epigenetic and genetic effect. Our approach works at the gene level, which allows for a common unit of analysis across different data types. The model compares the pairwise similarities in the phenotype to the pairwise similarities in the genotype and methylation values; and high correspondence is suggestive of association. A composite kernel is constructed to measure the similarities in the genotype and methylation values between pairs of samples. We demonstrate through simulations and real data applications that the proposed approach can correctly control type I error, and is more robust and powerful than using only the genotype or methylation data in detecting trait-associated genes. We applied our method to investigate the genetic and epigenetic regulation of gene expression in response to stressful life events using data that are collected from the Grady Trauma Project. Within the kernel machine testing framework, our methods allow for heterogeneity in effect sizes, nonlinear, and interactive effects, as well as rapid P-value computation. © 2017 WILEY PERIODICALS, INC.

Home range and survival of breeding painted buntings on Sapelo Island, Georgia

USGS Publications Warehouse

Springborn, E.G.; Meyers, J.M.

2005-01-01

The southeastern United States population of the painted bunting (Passerina ciris) has decreased approximately 75% from 1966-1996 based on Breeding Bird Survey trends. Partners in Flight guidelines recommend painted bunting conservation as a high priority with a need for management by state and federal agencies. Basic information on home range and survival of breeding painted buntings will provide managers with required habitat types and estimates of land areas necessary to maintain minimum population sizes for this species. We radiotracked after-second-year male and after-hatching-year female buntings on Sapelo Island, Georgia, during the breeding seasons (late April-early August) of 1997 and 1998. We used the animal movement extension in ArcView to determine fixed-kernel home range in an unmanaged maritime shrub and managed 60-80-year-old pine (Pinus spp.)-oak Quercus spp.) forest. Using the Kaplan-Meier method, we estimated an adult breeding season survival of 1.00 for males (n = 36) and 0.94 (SE = 0.18) for females(n=27). Painted bunting home ranges were smaller in unmanaged maritime shrub (female: kernel (x) over bar = 3.5 ha [95% CI: 2.5-4.51; male: kernel (x) over bar = 3.1 ha [95% CI: 2.3-3.9]) compared to those in managed pine-oak forests (female: kernel (x) over bar = 4.7 ha [95% CI: 2.8-6.6]; male: kernel (x) over bar = 7.0 ha [95% CI: 4.9-9.1]). Buntings nesting in the managed pine-oak forest flew long distances (>= 300 m) to forage in salt marshes, freshwater wetlands, and moist forest clearings. In maritime shrub buntings occupied a compact area and rarely moved long distances. The painted bunting population of Sapelo Island requires conservation of maritime shrub as potential optimum nesting habitat and management of nesting habitat in open-canopy pine-oak sawtimber forests by periodic prescribed fire (every 4-6 years) and timber thinning within a landscape that contains salt marsh or freshwater wetland openings within 700 m of those forests.

Modeling adaptive kernels from probabilistic phylogenetic trees.

PubMed

Nicotra, Luca; Micheli, Alessio

2009-01-01

Modeling phylogenetic interactions is an open issue in many computational biology problems. In the context of gene function prediction we introduce a class of kernels for structured data leveraging on a hierarchical probabilistic modeling of phylogeny among species. We derive three kernels belonging to this setting: a sufficient statistics kernel, a Fisher kernel, and a probability product kernel. The new kernels are used in the context of support vector machine learning. The kernels adaptivity is obtained through the estimation of the parameters of a tree structured model of evolution using as observed data phylogenetic profiles encoding the presence or absence of specific genes in a set of fully sequenced genomes. We report results obtained in the prediction of the functional class of the proteins of the budding yeast Saccharomyces cerevisae which favorably compare to a standard vector based kernel and to a non-adaptive tree kernel function. A further comparative analysis is performed in order to assess the impact of the different components of the proposed approach. We show that the key features of the proposed kernels are the adaptivity to the input domain and the ability to deal with structured data interpreted through a graphical model representation.

Gene-Based Association Analysis for Censored Traits Via Fixed Effect Functional Regressions.

PubMed

Fan, Ruzong; Wang, Yifan; Yan, Qi; Ding, Ying; Weeks, Daniel E; Lu, Zhaohui; Ren, Haobo; Cook, Richard J; Xiong, Momiao; Swaroop, Anand; Chew, Emily Y; Chen, Wei

2016-02-01

Genetic studies of survival outcomes have been proposed and conducted recently, but statistical methods for identifying genetic variants that affect disease progression are rarely developed. Motivated by our ongoing real studies, here we develop Cox proportional hazard models using functional regression (FR) to perform gene-based association analysis of survival traits while adjusting for covariates. The proposed Cox models are fixed effect models where the genetic effects of multiple genetic variants are assumed to be fixed. We introduce likelihood ratio test (LRT) statistics to test for associations between the survival traits and multiple genetic variants in a genetic region. Extensive simulation studies demonstrate that the proposed Cox RF LRT statistics have well-controlled type I error rates. To evaluate power, we compare the Cox FR LRT with the previously developed burden test (BT) in a Cox model and sequence kernel association test (SKAT), which is based on mixed effect Cox models. The Cox FR LRT statistics have higher power than or similar power as Cox SKAT LRT except when 50%/50% causal variants had negative/positive effects and all causal variants are rare. In addition, the Cox FR LRT statistics have higher power than Cox BT LRT. The models and related test statistics can be useful in the whole genome and whole exome association studies. An age-related macular degeneration dataset was analyzed as an example. © 2016 WILEY PERIODICALS, INC.

Gene-based Association Analysis for Censored Traits Via Fixed Effect Functional Regressions

PubMed Central

Fan, Ruzong; Wang, Yifan; Yan, Qi; Ding, Ying; Weeks, Daniel E.; Lu, Zhaohui; Ren, Haobo; Cook, Richard J; Xiong, Momiao; Swaroop, Anand; Chew, Emily Y.; Chen, Wei

2015-01-01

Summary Genetic studies of survival outcomes have been proposed and conducted recently, but statistical methods for identifying genetic variants that affect disease progression are rarely developed. Motivated by our ongoing real studies, we develop here Cox proportional hazard models using functional regression (FR) to perform gene-based association analysis of survival traits while adjusting for covariates. The proposed Cox models are fixed effect models where the genetic effects of multiple genetic variants are assumed to be fixed. We introduce likelihood ratio test (LRT) statistics to test for associations between the survival traits and multiple genetic variants in a genetic region. Extensive simulation studies demonstrate that the proposed Cox RF LRT statistics have well-controlled type I error rates. To evaluate power, we compare the Cox FR LRT with the previously developed burden test (BT) in a Cox model and sequence kernel association test (SKAT) which is based on mixed effect Cox models. The Cox FR LRT statistics have higher power than or similar power as Cox SKAT LRT except when 50%/50% causal variants had negative/positive effects and all causal variants are rare. In addition, the Cox FR LRT statistics have higher power than Cox BT LRT. The models and related test statistics can be useful in the whole genome and whole exome association studies. An age-related macular degeneration dataset was analyzed as an example. PMID:26782979

Para-hydrogen and helium cluster size distributions in free jet expansions based on Smoluchowski theory with kernel scaling.

PubMed

Kornilov, Oleg; Toennies, J Peter

2015-02-21

The size distribution of para-H2 (pH2) clusters produced in free jet expansions at a source temperature of T0 = 29.5 K and pressures of P0 = 0.9-1.96 bars is reported and analyzed according to a cluster growth model based on the Smoluchowski theory with kernel scaling. Good overall agreement is found between the measured and predicted, Nk = A k(a) e(-bk), shape of the distribution. The fit yields values for A and b for values of a derived from simple collision models. The small remaining deviations between measured abundances and theory imply a (pH2)k magic number cluster of k = 13 as has been observed previously by Raman spectroscopy. The predicted linear dependence of b(-(a+1)) on source gas pressure was verified and used to determine the value of the basic effective agglomeration reaction rate constant. A comparison of the corresponding effective growth cross sections σ11 with results from a similar analysis of He cluster size distributions indicates that the latter are much larger by a factor 6-10. An analysis of the three body recombination rates, the geometric sizes and the fact that the He clusters are liquid independent of their size can explain the larger cross sections found for He.

Normalization of CT scans reconstructed with different kernels to reduce variability in emphysema measurements

NASA Astrophysics Data System (ADS)

Gallardo Estrella, L.; van Ginneken, B.; van Rikxoort, E. M.

2013-03-01

Chronic Obstructive Pulmonary Disease (COPD) is a lung disease characterized by progressive air flow limitation caused by emphysema and chronic bronchitis. Emphysema is quantified from chest computed tomography (CT) scans as the percentage of attentuation values below a fixed threshold. The emphysema quantification varies substantially between scans reconstructed with different kernels, limiting the possibilities to compare emphysema quantifications obtained from scans with different reconstruction parameters. In this paper we propose a method to normalize scans reconstructed with different kernels to have the same characteristics as scans reconstructed with a reference kernel and investigate if this normalization reduces the variability in emphysema quantification. The proposed normalization splits a CT scan into different frequency bands based on hierarchical unsharp masking. Normalization is performed by changing the energy in each frequency band to the average energy in each band in the reference kernel. A database of 15 subjects with COPD was constructed for this study. All subjects were scanned at total lung capacity and the scans were reconstructed with four different reconstruction kernels. The normalization was applied to all scans. Emphysema quantification was performed before and after normalization. It is shown that the emphysema score varies substantially before normalization but the variation diminishes after normalization.

A Novel Extreme Learning Machine Classification Model for e-Nose Application Based on the Multiple Kernel Approach.

PubMed

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.

CompareSVM: supervised, Support Vector Machine (SVM) inference of gene regularity networks.

PubMed

Gillani, Zeeshan; Akash, Muhammad Sajid Hamid; Rahaman, M D Matiur; Chen, Ming

2014-11-30

Predication of gene regularity network (GRN) from expression data is a challenging task. There are many methods that have been developed to address this challenge ranging from supervised to unsupervised methods. Most promising methods are based on support vector machine (SVM). There is a need for comprehensive analysis on prediction accuracy of supervised method SVM using different kernels on different biological experimental conditions and network size. We developed a tool (CompareSVM) based on SVM to compare different kernel methods for inference of GRN. Using CompareSVM, we investigated and evaluated different SVM kernel methods on simulated datasets of microarray of different sizes in detail. The results obtained from CompareSVM showed that accuracy of inference method depends upon the nature of experimental condition and size of the network. For network with nodes (<200) and average (over all sizes of networks), SVM Gaussian kernel outperform on knockout, knockdown, and multifactorial datasets compared to all the other inference methods. For network with large number of nodes (~500), choice of inference method depend upon nature of experimental condition. CompareSVM is available at http://bis.zju.edu.cn/CompareSVM/ .

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

PubMed

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.

Considering causal genes in the genetic dissection of kernel traits in common wheat.

PubMed

Mohler, Volker; Albrecht, Theresa; Castell, Adelheid; Diethelm, Manuela; Schweizer, Günther; Hartl, Lorenz

2016-11-01

Genetic factors controlling thousand-kernel weight (TKW) were characterized for their association with other seed traits, including kernel width, kernel length, ratio of kernel width to kernel length (KW/KL), kernel area, and spike number per m 2 (SN). For this purpose, a genetic map was established utilizing a doubled haploid population derived from a cross between German winter wheat cultivars Pamier and Format. Association studies in a diversity panel of elite cultivars supplemented genetic analysis of kernel traits. In both populations, genomic signatures of 13 candidate genes for TKW and kernel size were analyzed. Major quantitative trait loci (QTL) for TKW were identified on chromosomes 1B, 2A, 2D, and 4D, and their locations coincided with major QTL for kernel size traits, supporting the common belief that TKW is a function of other kernel traits. The QTL on chromosome 2A was associated with TKW candidate gene TaCwi-A1 and the QTL on chromosome 4D was associated with dwarfing gene Rht-D1. A minor QTL for TKW on chromosome 6B coincided with TaGW2-6B. The QTL for kernel dimensions that did not affect TKW were detected on eight chromosomes. A major QTL for KW/KL located at the distal tip of chromosome arm 5AS is being reported for the first time. TaSus1-7A and TaSAP-A1, closely linked to each other on chromosome 7A, could be related to a minor QTL for KW/KL. Genetic analysis of SN confirmed its negative correlation with TKW in this cross. In the diversity panel, TaSus1-7A was associated with TKW. Compared to the Pamier/Format bi-parental population where TaCwi-A1a was associated with higher TKW, the same allele reduced grain yield in the diversity panel, suggesting opposite effects of TaCwi-A1 on these two traits.

Predicting the spread of all invasive forest pests in the United States

Treesearch

Emma J. Hudgins; Andrew M. Liebhold; Brian Leung; Regan Early

2017-01-01

We tested whether a general spread model could capture macroecological patterns across all damaging invasive forest pests in the United States. We showed that a common constant dispersal kernel model, simulated from the discovery date, explained 67.94% of the variation in range size across all pests, and had 68.00% locational accuracy between predicted and observed...

Kernel Recursive Least-Squares Temporal Difference Algorithms with Sparsification and Regularization

PubMed Central

Zhu, Qingxin; Niu, Xinzheng

2016-01-01

By combining with sparse kernel methods, least-squares temporal difference (LSTD) algorithms can construct the feature dictionary automatically and obtain a better generalization ability. However, the previous kernel-based LSTD algorithms do not consider regularization and their sparsification processes are batch or offline, which hinder their widespread applications in online learning problems. In this paper, we combine the following five techniques and propose two novel kernel recursive LSTD algorithms: (i) online sparsification, which can cope with unknown state regions and be used for online learning, (ii) L 2 and L 1 regularization, which can avoid overfitting and eliminate the influence of noise, (iii) recursive least squares, which can eliminate matrix-inversion operations and reduce computational complexity, (iv) a sliding-window approach, which can avoid caching all history samples and reduce the computational cost, and (v) the fixed-point subiteration and online pruning, which can make L 1 regularization easy to implement. Finally, simulation results on two 50-state chain problems demonstrate the effectiveness of our algorithms. PMID:27436996

Kernel Recursive Least-Squares Temporal Difference Algorithms with Sparsification and Regularization.

PubMed

Zhang, Chunyuan; Zhu, Qingxin; Niu, Xinzheng

2016-01-01

By combining with sparse kernel methods, least-squares temporal difference (LSTD) algorithms can construct the feature dictionary automatically and obtain a better generalization ability. However, the previous kernel-based LSTD algorithms do not consider regularization and their sparsification processes are batch or offline, which hinder their widespread applications in online learning problems. In this paper, we combine the following five techniques and propose two novel kernel recursive LSTD algorithms: (i) online sparsification, which can cope with unknown state regions and be used for online learning, (ii) L 2 and L 1 regularization, which can avoid overfitting and eliminate the influence of noise, (iii) recursive least squares, which can eliminate matrix-inversion operations and reduce computational complexity, (iv) a sliding-window approach, which can avoid caching all history samples and reduce the computational cost, and (v) the fixed-point subiteration and online pruning, which can make L 1 regularization easy to implement. Finally, simulation results on two 50-state chain problems demonstrate the effectiveness of our algorithms.

Modelling Nonlinear Dynamic Textures using Hybrid DWT-DCT and Kernel PCA with GPU

NASA Astrophysics Data System (ADS)

Ghadekar, Premanand Pralhad; Chopade, Nilkanth Bhikaji

2016-12-01

Most of the real-world dynamic textures are nonlinear, non-stationary, and irregular. Nonlinear motion also has some repetition of motion, but it exhibits high variation, stochasticity, and randomness. Hybrid DWT-DCT and Kernel Principal Component Analysis (KPCA) with YCbCr/YIQ colour coding using the Dynamic Texture Unit (DTU) approach is proposed to model a nonlinear dynamic texture, which provides better results than state-of-art methods in terms of PSNR, compression ratio, model coefficients, and model size. Dynamic texture is decomposed into DTUs as they help to extract temporal self-similarity. Hybrid DWT-DCT is used to extract spatial redundancy. YCbCr/YIQ colour encoding is performed to capture chromatic correlation. KPCA is applied to capture nonlinear motion. Further, the proposed algorithm is implemented on Graphics Processing Unit (GPU), which comprise of hundreds of small processors to decrease time complexity and to achieve parallelism.

A linear-RBF multikernel SVM to classify big text corpora.

PubMed

Romero, R; Iglesias, E L; Borrajo, L

2015-01-01

Support vector machine (SVM) is a powerful technique for classification. However, SVM is not suitable for classification of large datasets or text corpora, because the training complexity of SVMs is highly dependent on the input size. Recent developments in the literature on the SVM and other kernel methods emphasize the need to consider multiple kernels or parameterizations of kernels because they provide greater flexibility. This paper shows a multikernel SVM to manage highly dimensional data, providing an automatic parameterization with low computational cost and improving results against SVMs parameterized under a brute-force search. The model consists in spreading the dataset into cohesive term slices (clusters) to construct a defined structure (multikernel). The new approach is tested on different text corpora. Experimental results show that the new classifier has good accuracy compared with the classic SVM, while the training is significantly faster than several other SVM classifiers.

MO-G-17A-05: PET Image Deblurring Using Adaptive Dictionary Learning

DOE Office of Scientific and Technical Information (OSTI.GOV)

Valiollahzadeh, S; Clark, J; Mawlawi, O

2014-06-15

Purpose: The aim of this work is to deblur PET images while suppressing Poisson noise effects using adaptive dictionary learning (DL) techniques. Methods: The model that relates a blurred and noisy PET image to the desired image is described as a linear transform y=Hm+n where m is the desired image, H is a blur kernel, n is Poisson noise and y is the blurred image. The approach we follow to recover m involves the sparse representation of y over a learned dictionary, since the image has lots of repeated patterns, edges, textures and smooth regions. The recovery is based onmore » an optimization of a cost function having four major terms: adaptive dictionary learning term, sparsity term, regularization term, and MLEM Poisson noise estimation term. The optimization is solved by a variable splitting method that introduces additional variables. We simulated a 128×128 Hoffman brain PET image (baseline) with varying kernel types and sizes (Gaussian 9×9, σ=5.4mm; Uniform 5×5, σ=2.9mm) with additive Poisson noise (Blurred). Image recovery was performed once when the kernel type was included in the model optimization and once with the model blinded to kernel type. The recovered image was compared to the baseline as well as another recovery algorithm PIDSPLIT+ (Setzer et. al.) by calculating PSNR (Peak SNR) and normalized average differences in pixel intensities (NADPI) of line profiles across the images. Results: For known kernel types, the PSNR of the Gaussian (Uniform) was 28.73 (25.1) and 25.18 (23.4) for DL and PIDSPLIT+ respectively. For blinded deblurring the PSNRs were 25.32 and 22.86 for DL and PIDSPLIT+ respectively. NADPI between baseline and DL, and baseline and blurred for the Gaussian kernel was 2.5 and 10.8 respectively. Conclusion: PET image deblurring using dictionary learning seems to be a good approach to restore image resolution in presence of Poisson noise. GE Health Care.« less

Predicting complex traits using a diffusion kernel on genetic markers with an application to dairy cattle and wheat data

PubMed Central

2013-01-01

Background Arguably, genotypes and phenotypes may be linked in functional forms that are not well addressed by the linear additive models that are standard in quantitative genetics. Therefore, developing statistical learning models for predicting phenotypic values from all available molecular information that are capable of capturing complex genetic network architectures is of great importance. Bayesian kernel ridge regression is a non-parametric prediction model proposed for this purpose. Its essence is to create a spatial distance-based relationship matrix called a kernel. Although the set of all single nucleotide polymorphism genotype configurations on which a model is built is finite, past research has mainly used a Gaussian kernel. Results We sought to investigate the performance of a diffusion kernel, which was specifically developed to model discrete marker inputs, using Holstein cattle and wheat data. This kernel can be viewed as a discretization of the Gaussian kernel. The predictive ability of the diffusion kernel was similar to that of non-spatial distance-based additive genomic relationship kernels in the Holstein data, but outperformed the latter in the wheat data. However, the difference in performance between the diffusion and Gaussian kernels was negligible. Conclusions It is concluded that the ability of a diffusion kernel to capture the total genetic variance is not better than that of a Gaussian kernel, at least for these data. Although the diffusion kernel as a choice of basis function may have potential for use in whole-genome prediction, our results imply that embedding genetic markers into a non-Euclidean metric space has very small impact on prediction. Our results suggest that use of the black box Gaussian kernel is justified, given its connection to the diffusion kernel and its similar predictive performance. PMID:23763755

A Novel Extreme Learning Machine Classification Model for e-Nose Application Based on the Multiple Kernel Approach

PubMed Central

Jian, Yulin; Huang, Daoyu; Yan, Jia; Lu, Kun; Huang, Ying; Wen, Tailai; Zeng, Tanyue; Zhong, Shijie; Xie, Qilong

2017-01-01

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. PMID:28629202

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

PubMed

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

2014-09-01

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

Credit scoring analysis using kernel discriminant

NASA Astrophysics Data System (ADS)

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

2018-05-01

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

On The Cloud Processing of Aerosol Particles: An Entraining Air Parcel Model With Two-dimensional Spectral Cloud Microphysics and A New Formulation of The Collection Kernel

NASA Astrophysics Data System (ADS)

Bott, Andreas; Kerkweg, Astrid; Wurzler, Sabine

A study has been made of the modification of aerosol spectra due to cloud pro- cesses and the impact of the modified aerosols on the microphysical structure of future clouds. For this purpose an entraining air parcel model with two-dimensional spectral cloud microphysics has been used. In order to treat collision/coalescence processes in the two-dimensional microphysical module, a new realistic and continuous formu- lation of the collection kernel has been developed. Based on experimental data, the kernel covers the entire investigated size range of aerosols, cloud and rain drops, that is the kernel combines all important coalescence processes such as the collision of cloud drops as well as the impaction scavenging of small aerosols by big raindrops. Since chemical reactions in the gas phase and in cloud drops have an important impact on the physico-chemical properties of aerosol particles, the parcel model has been extended by a chemical module describing gas phase and aqueous phase chemical reactions. However, it will be shown that in the numerical case studies presented in this paper the modification of aerosols by chemical reactions has a minor influence on the microphysical structure of future clouds. The major process yielding in a second cloud event an enhanced formation of rain is the production of large aerosol particles by collision/coalescence processes in the first cloud.

Gradient-based adaptation of general gaussian kernels.

PubMed

Glasmachers, Tobias; Igel, Christian

2005-10-01

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

Kinetic behaviours of aggregate growth driven by time-dependent migration, birth and death

NASA Astrophysics Data System (ADS)

Zhu, Sheng-Qing; Yang, Shun-You; Ke, Jianhong; Lin, Zhenquan

2008-12-01

We propose a dynamic growth model to mimic some social phenomena, such as the evolution of cities' population, in which monomer migrations occur between any two aggregates and monomer birth/death can simultaneously occur in each aggregate. Considering the fact that the rate kernels of migration, birth and death processes may change with time, we assume that the migration rate kernel is ijf(t), and the self-birth and death rate kernels are ig1(t) and ig2(t), respectively. Based on the mean-field rate equation, we obtain the exact solution of this model and then discuss semi-quantitatively the scaling behaviour of the aggregate size distribution at large times. The results show that in the long-time limit, (i) if ∫t0g1(t') dt'/∫t0g2(t') dt' >= 1 or exp{∫t0[g2(t') - g1(t')] dt'}/∫t0f(t') dt' → 0, the aggregate size distribution ak(t) can obey a generalized scaling form; (ii) if ∫t0g1(t') dt'/∫t0g2(t') dt' → 0 and exp ∫t0[g2(t') - g1(t') dt'/∫t0f(t') dt' → ∞, ak(t) can take a scale-free form and decay exponentially in size k; (iii) ak(t) will satisfy a modified scaling law in the remaining cases. Moreover, the total mass of aggregates depends strongly on the net birth rate g1(t) - g2(t) and evolves exponentially as exp{∫t0[g1(t') - g2(t')] dt'}, which is in qualitative agreement with the evolution of the total population of a country in real world.

Data Assimilation on a Quantum Annealing Computer: Feasibility and Scalability

NASA Astrophysics Data System (ADS)

Nearing, G. S.; Halem, M.; Chapman, D. R.; Pelissier, C. S.

2014-12-01

Data assimilation is one of the ubiquitous and computationally hard problems in the Earth Sciences. In particular, ensemble-based methods require a large number of model evaluations to estimate the prior probability density over system states, and variational methods require adjoint calculations and iteration to locate the maximum a posteriori solution in the presence of nonlinear models and observation operators. Quantum annealing computers (QAC) like the new D-Wave housed at the NASA Ames Research Center can be used for optimization and sampling, and therefore offers a new possibility for efficiently solving hard data assimilation problems. Coding on the QAC is not straightforward: a problem must be posed as a Quadratic Unconstrained Binary Optimization (QUBO) and mapped to a spherical Chimera graph. We have developed a method for compiling nonlinear 4D-Var problems on the D-Wave that consists of five steps: Emulating the nonlinear model and/or observation function using radial basis functions (RBF) or Chebyshev polynomials. Truncating a Taylor series around each RBF kernel. Reducing the Taylor polynomial to a quadratic using ancilla gadgets. Mapping the real-valued quadratic to a fixed-precision binary quadratic. Mapping the fully coupled binary quadratic to a partially coupled spherical Chimera graph using ancilla gadgets. At present the D-Wave contains 512 qbits (with 1024 and 2048 qbit machines due in the next two years); this machine size allows us to estimate only 3 state variables at each satellite overpass. However, QAC's solve optimization problems using a physical (quantum) system, and therefore do not require iterations or calculation of model adjoints. This has the potential to revolutionize our ability to efficiently perform variational data assimilation, as the size of these computers grows in the coming years.

KERNELHR: A program for estimating animal home ranges

USGS Publications Warehouse

Seaman, D.E.; Griffith, B.; Powell, R.A.

1998-01-01

Kernel methods are state of the art for estimating animal home-range area and utilization distribution (UD). The KERNELHR program was developed to provide researchers and managers a tool to implement this extremely flexible set of methods with many variants. KERNELHR runs interactively or from the command line on any personal computer (PC) running DOS. KERNELHR provides output of fixed and adaptive kernel home-range estimates, as well as density values in a format suitable for in-depth statistical and spatial analyses. An additional package of programs creates contour files for plotting in geographic information systems (GIS) and estimates core areas of ranges.

An SVM model with hybrid kernels for hydrological time series

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

Contour detection improved by context-adaptive surround suppression.

PubMed

Sang, Qiang; Cai, Biao; Chen, Hao

2017-01-01

Recently, many image processing applications have taken advantage of a psychophysical and neurophysiological mechanism, called "surround suppression" to extract object contour from a natural scene. However, these traditional methods often adopt a single suppression model and a fixed input parameter called "inhibition level", which needs to be manually specified. To overcome these drawbacks, we propose a novel model, called "context-adaptive surround suppression", which can automatically control the effect of surround suppression according to image local contextual features measured by a surface estimator based on a local linear kernel. Moreover, a dynamic suppression method and its stopping mechanism are introduced to avoid manual intervention. The proposed algorithm is demonstrated and validated by a broad range of experimental results.

Modeling of information flows in natural gas storage facility

NASA Astrophysics Data System (ADS)

Ranjbari, Leyla; Bahar, Arifah; Aziz, Zainal Abdul

2013-09-01

The paper considers the natural-gas storage valuation based on the information-based pricing framework of Brody-Hughston-Macrina (BHM). As opposed to many studies which the associated filtration is considered pre-specified, this work tries to construct the filtration in terms of the information provided to the market. The value of the storage is given by the sum of the discounted expectations of the cash flows under risk-neutral measure, conditional to the constructed filtration with the Brownian bridge noise term. In order to model the flow of information about the cash flows, we assume the existence of a fixed pricing kernel with liquid, homogenous and incomplete market without arbitrage.

Discrete bivariate population balance modelling of heteroaggregation processes.

PubMed

Rollié, Sascha; Briesen, Heiko; Sundmacher, Kai

2009-08-15

Heteroaggregation in binary particle mixtures was simulated with a discrete population balance model in terms of two internal coordinates describing the particle properties. The considered particle species are of different size and zeta-potential. Property space is reduced with a semi-heuristic approach to enable an efficient solution. Aggregation rates are based on deterministic models for Brownian motion and stability, under consideration of DLVO interaction potentials. A charge-balance kernel is presented, relating the electrostatic surface potential to the property space by a simple charge balance. Parameter sensitivity with respect to the fractal dimension, aggregate size, hydrodynamic correction, ionic strength and absolute particle concentration was assessed. Results were compared to simulations with the literature kernel based on geometric coverage effects for clusters with heterogeneous surface properties. In both cases electrostatic phenomena, which dominate the aggregation process, show identical trends: impeded cluster-cluster aggregation at low particle mixing ratio (1:1), restabilisation at high mixing ratios (100:1) and formation of complex clusters for intermediate ratios (10:1). The particle mixing ratio controls the surface coverage extent of the larger particle species. Simulation results are compared to experimental flow cytometric data and show very satisfactory agreement.

Manycore Performance-Portability: Kokkos Multidimensional Array Library

DOE PAGES

Edwards, H. Carter; Sunderland, Daniel; Porter, Vicki; ...

2012-01-01

Large, complex scientific and engineering application code have a significant investment in computational kernels to implement their mathematical models. Porting these computational kernels to the collection of modern manycore accelerator devices is a major challenge in that these devices have diverse programming models, application programming interfaces (APIs), and performance requirements. The Kokkos Array programming model provides library-based approach to implement computational kernels that are performance-portable to CPU-multicore and GPGPU accelerator devices. This programming model is based upon three fundamental concepts: (1) manycore compute devices each with its own memory space, (2) data parallel kernels and (3) multidimensional arrays. Kernel executionmore » performance is, especially for NVIDIA® devices, extremely dependent on data access patterns. Optimal data access pattern can be different for different manycore devices – potentially leading to different implementations of computational kernels specialized for different devices. The Kokkos Array programming model supports performance-portable kernels by (1) separating data access patterns from computational kernels through a multidimensional array API and (2) introduce device-specific data access mappings when a kernel is compiled. An implementation of Kokkos Array is available through Trilinos [Trilinos website, http://trilinos.sandia.gov/, August 2011].« less

Kernel learning at the first level of inference.

PubMed

Cawley, Gavin C; Talbot, Nicola L C

2014-05-01

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

Para-hydrogen and helium cluster size distributions in free jet expansions based on Smoluchowski theory with kernel scaling

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kornilov, Oleg; Toennies, J. Peter

The size distribution of para-H{sub 2} (pH{sub 2}) clusters produced in free jet expansions at a source temperature of T{sub 0} = 29.5 K and pressures of P{sub 0} = 0.9–1.96 bars is reported and analyzed according to a cluster growth model based on the Smoluchowski theory with kernel scaling. Good overall agreement is found between the measured and predicted, N{sub k} = A k{sup a} e{sup −bk}, shape of the distribution. The fit yields values for A and b for values of a derived from simple collision models. The small remaining deviations between measured abundances and theory imply a (pH{submore » 2}){sub k} magic number cluster of k = 13 as has been observed previously by Raman spectroscopy. The predicted linear dependence of b{sup −(a+1)} on source gas pressure was verified and used to determine the value of the basic effective agglomeration reaction rate constant. A comparison of the corresponding effective growth cross sections σ{sub 11} with results from a similar analysis of He cluster size distributions indicates that the latter are much larger by a factor 6-10. An analysis of the three body recombination rates, the geometric sizes and the fact that the He clusters are liquid independent of their size can explain the larger cross sections found for He.« less

Genomic Prediction of Genotype × Environment Interaction Kernel Regression Models.

PubMed

Cuevas, Jaime; Crossa, José; Soberanis, Víctor; Pérez-Elizalde, Sergio; Pérez-Rodríguez, Paulino; Campos, Gustavo de Los; Montesinos-López, O A; Burgueño, Juan

2016-11-01

In genomic selection (GS), genotype × environment interaction (G × E) can be modeled by a marker × environment interaction (M × E). The G × E may be modeled through a linear kernel or a nonlinear (Gaussian) kernel. In this study, we propose using two nonlinear Gaussian kernels: the reproducing kernel Hilbert space with kernel averaging (RKHS KA) and the Gaussian kernel with the bandwidth estimated through an empirical Bayesian method (RKHS EB). We performed single-environment analyses and extended to account for G × E interaction (GBLUP-G × E, RKHS KA-G × E and RKHS EB-G × E) in wheat ( L.) and maize ( L.) data sets. For single-environment analyses of wheat and maize data sets, RKHS EB and RKHS KA had higher prediction accuracy than GBLUP for all environments. For the wheat data, the RKHS KA-G × E and RKHS EB-G × E models did show up to 60 to 68% superiority over the corresponding single environment for pairs of environments with positive correlations. For the wheat data set, the models with Gaussian kernels had accuracies up to 17% higher than that of GBLUP-G × E. For the maize data set, the prediction accuracy of RKHS EB-G × E and RKHS KA-G × E was, on average, 5 to 6% higher than that of GBLUP-G × E. The superiority of the Gaussian kernel models over the linear kernel is due to more flexible kernels that accounts for small, more complex marker main effects and marker-specific interaction effects. Copyright © 2016 Crop Science Society of America.

Food Environment and Weight Change: Does Residential Mobility Matter?

PubMed Central

Laraia, Barbara A.; Downing, Janelle M.; Zhang, Y. Tara; Dow, William H.; Kelly, Maggi; Blanchard, Samuel D.; Adler, Nancy; Schillinger, Dean; Moffet, Howard; Warton, E. Margaret; Karter, Andrew J.

2017-01-01

Abstract Associations between neighborhood food environment and adult body mass index (BMI; weight (kg)/height (m)2) derived using cross-sectional or longitudinal random-effects models may be biased due to unmeasured confounding and measurement and methodological limitations. In this study, we assessed the within-individual association between change in food environment from 2006 to 2011 and change in BMI among adults with type 2 diabetes using clinical data from the Kaiser Permanente Diabetes Registry collected from 2007 to 2011. Healthy food environment was measured using the kernel density of healthful food venues. Fixed-effects models with a 1-year-lagged BMI were estimated. Separate models were fitted for persons who moved and those who did not. Sensitivity analysis using different lag times and kernel density bandwidths were tested to establish the consistency of findings. On average, patients lost 1 pound (0.45 kg) for each standard-deviation improvement in their food environment. This relationship held for persons who remained in the same location throughout the 5-year study period but not among persons who moved. Proximity to food venues that promote nutritious foods alone may not translate into clinically meaningful diet-related health changes. Community-level policies for improving the food environment need multifaceted strategies to invoke clinically meaningful change in BMI among adult patients with diabetes. PMID:28387785

Locally adaptive methods for KDE-based random walk models of reactive transport in porous media

NASA Astrophysics Data System (ADS)

Sole-Mari, G.; Fernandez-Garcia, D.

2017-12-01

Random Walk Particle Tracking (RWPT) coupled with Kernel Density Estimation (KDE) has been recently proposed to simulate reactive transport in porous media. KDE provides an optimal estimation of the area of influence of particles which is a key element to simulate nonlinear chemical reactions. However, several important drawbacks can be identified: (1) the optimal KDE method is computationally intensive and thereby cannot be used at each time step of the simulation; (2) it does not take advantage of the prior information about the physical system and the previous history of the solute plume; (3) even if the kernel is optimal, the relative error in RWPT simulations typically increases over time as the particle density diminishes by dilution. To overcome these problems, we propose an adaptive branching random walk methodology that incorporates the physics, the particle history and maintains accuracy with time. The method allows particles to efficiently split and merge when necessary as well as to optimally adapt their local kernel shape without having to recalculate the kernel size. We illustrate the advantage of the method by simulating complex reactive transport problems in randomly heterogeneous porous media.

The Unified Floating Point Vector Coprocessor for Reconfigurable Hardware

NASA Astrophysics Data System (ADS)

Kathiara, Jainik

There has been an increased interest recently in using embedded cores on FPGAs. Many of the applications that make use of these cores have floating point operations. Due to the complexity and expense of floating point hardware, these algorithms are usually converted to fixed point operations or implemented using floating-point emulation in software. As the technology advances, more and more homogeneous computational resources and fixed function embedded blocks are added to FPGAs and hence implementation of floating point hardware becomes a feasible option. In this research we have implemented a high performance, autonomous floating point vector Coprocessor (FPVC) that works independently within an embedded processor system. We have presented a unified approach to vector and scalar computation, using a single register file for both scalar operands and vector elements. The Hybrid vector/SIMD computational model of FPVC results in greater overall performance for most applications along with improved peak performance compared to other approaches. By parameterizing vector length and the number of vector lanes, we can design an application specific FPVC and take optimal advantage of the FPGA fabric. For this research we have also initiated designing a software library for various computational kernels, each of which adapts FPVC's configuration and provide maximal performance. The kernels implemented are from the area of linear algebra and include matrix multiplication and QR and Cholesky decomposition. We have demonstrated the operation of FPVC on a Xilinx Virtex 5 using the embedded PowerPC.

Nonbreeding home‐range size and survival of lesser prairie‐chickens

USGS Publications Warehouse

Robinson, Samantha G.; Haukos, David A.; Plumb, Reid T.; Lautenbach, Joseph M.; Sullins, Daniel S.; Kraft, John D.; Lautenbach, Jonathan D.; Hagen, Christian A.; Pitman, James C.

2018-01-01

The lesser prairie‐chicken (Tympanuchus pallidicinctus), a species of conservation concern with uncertain regulatory status, has experienced population declines over the past century. Most research on lesser prairie‐chickens has focused on the breeding season, with little research conducted during the nonbreeding season, a period that exerts a strong influence on demography in other upland game birds. We trapped lesser prairie‐chickens on leks and marked them with either global positioning system (GPS) satellite or very high frequency (VHF) transmitters to estimate survival and home‐range size during the nonbreeding season. We monitored 119 marked lesser prairie‐chickens in 3 study areas in Kansas, USA, from 16 September to 14 March in 2013, 2014, and 2015. We estimated home‐range size using Brownian Bridge movement models (GPS transmitters) and fixed kernel density estimators (VHF transmitters), and female survival using Kaplan–Meier known‐fate models. Average home‐range size did not differ between sexes. Estimated home‐range size was 3 times greater for individuals fitted with GPS satellite transmitters ( = 997 ha) than those with VHF transmitters ( = 286 ha), likely a result of the temporal resolution of the different transmitters. Home‐range size of GPS‐marked birds increased 2.8 times relative to the breeding season and varied by study area and year. Home‐range size was smaller in the 2013–2014 nonbreeding season ( = 495 ha) than the following 2 nonbreeding seasons ( = 1,290 ha and  = 1,158 ha), corresponding with drought conditions of 2013, which were alleviated in following years. Female survival () was high relative to breeding season estimates, and did not differ by study area or year ( = 0.73 ± 0.04 [SE]). Future management could remain focused on the breeding season because nonbreeding survival was 39–44% greater than the previous breeding season; however, considerations of total space needs would benefit lesser prairie‐chickens by accounting for the greater spatial requirements during the nonbreeding season. 

Probability of identity by descent in metapopulations.

PubMed Central

Kaj, I; Lascoux, M

1999-01-01

Equilibrium probabilities of identity by descent (IBD), for pairs of genes within individuals, for genes between individuals within subpopulations, and for genes between subpopulations are calculated in metapopulation models with fixed or varying colony sizes. A continuous-time analog to the Moran model was used in either case. For fixed-colony size both propagule and migrant pool models were considered. The varying population size model is based on a birth-death-immigration (BDI) process, to which migration between colonies is added. Wright's F statistics are calculated and compared to previous results. Adding between-island migration to the BDI model can have an important effect on the equilibrium probabilities of IBD and on Wright's index. PMID:10388835

Kernel Partial Least Squares for Nonlinear Regression and Discrimination

NASA Technical Reports Server (NTRS)

Rosipal, Roman; Clancy, Daniel (Technical Monitor)

2002-01-01

This paper summarizes recent results on applying the method of partial least squares (PLS) in a reproducing kernel Hilbert space (RKHS). A previously proposed kernel PLS regression model was proven to be competitive with other regularized regression methods in RKHS. The family of nonlinear kernel-based PLS models is extended by considering the kernel PLS method for discrimination. Theoretical and experimental results on a two-class discrimination problem indicate usefulness of the method.

Hi-Res scan mode in clinical MDCT systems: Experimental assessment of spatial resolution performance

PubMed Central

Cruz-Bastida, Juan P.; Gomez-Cardona, Daniel; Li, Ke; Sun, Heyi; Hsieh, Jiang; Szczykutowicz, Timothy P.; Chen, Guang-Hong

2016-01-01

Purpose: The introduction of a High-Resolution (Hi-Res) scan mode and another associated option that combines Hi-Res mode with the so-called High Definition (HD) reconstruction kernels (referred to as a Hi-Res/HD mode in this paper) in some multi-detector CT (MDCT) systems offers new opportunities to increase spatial resolution for some clinical applications that demand high spatial resolution. The purpose of this work was to quantify the in-plane spatial resolution along both the radial direction and tangential direction for the Hi-Res and Hi-Res/HD scan modes at different off-center positions. Methods: A technique was introduced and validated to address the signal saturation problem encountered in the attempt to quantify spatial resolution for the Hi-Res and Hi-Res/HD scan modes. Using the proposed method, the modulation transfer functions (MTFs) of a 64-slice MDCT system (Discovery CT750 HD, GE Healthcare) equipped with both Hi-Res and Hi-Res/HD modes were measured using a metal bead at nine different off-centered positions (0–16 cm with a step size of 2 cm); at each position, both conventional scans and Hi-Res scans were performed. For each type of scan and position, 80 repeated acquisitions were performed to reduce noise induced uncertainties in the MTF measurements. A total of 15 reconstruction kernels, including eight conventional kernels and seven HD kernels, were used to reconstruct CT images of the bead. An ex vivo animal study consisting of a bone fracture model was performed to corroborate the MTF results, as the detection of this high-contrast and high frequency task is predominantly determined by spatial resolution. Images of this animal model generated by different scan modes and reconstruction kernels were qualitatively compared with the MTF results. Results: At the centered position, the use of Hi-Res mode resulted in a slight improvement in the MTF; each HD kernel generated higher spatial resolution than its counterpart conventional kernel. However, the MTF along the tangential direction of the scan field of view (SFOV) was significantly degraded at off-centered positions, yet the combined Hi-Res/HD mode reduced this azimuthal MTF degradation. Images of the animal bone fracture model confirmed the improved spatial resolution at the off-centered positions through the use of the Hi-Res mode and HD kernels. Conclusions: The Hi-Res/HD scan improve spatial resolution of MDCT systems at both centered and off-centered positions. PMID:27147351

Hi-Res scan mode in clinical MDCT systems: Experimental assessment of spatial resolution performance.

PubMed

Cruz-Bastida, Juan P; Gomez-Cardona, Daniel; Li, Ke; Sun, Heyi; Hsieh, Jiang; Szczykutowicz, Timothy P; Chen, Guang-Hong

2016-05-01

The introduction of a High-Resolution (Hi-Res) scan mode and another associated option that combines Hi-Res mode with the so-called High Definition (HD) reconstruction kernels (referred to as a Hi-Res/HD mode in this paper) in some multi-detector CT (MDCT) systems offers new opportunities to increase spatial resolution for some clinical applications that demand high spatial resolution. The purpose of this work was to quantify the in-plane spatial resolution along both the radial direction and tangential direction for the Hi-Res and Hi-Res/HD scan modes at different off-center positions. A technique was introduced and validated to address the signal saturation problem encountered in the attempt to quantify spatial resolution for the Hi-Res and Hi-Res/HD scan modes. Using the proposed method, the modulation transfer functions (MTFs) of a 64-slice MDCT system (Discovery CT750 HD, GE Healthcare) equipped with both Hi-Res and Hi-Res/HD modes were measured using a metal bead at nine different off-centered positions (0-16 cm with a step size of 2 cm); at each position, both conventional scans and Hi-Res scans were performed. For each type of scan and position, 80 repeated acquisitions were performed to reduce noise induced uncertainties in the MTF measurements. A total of 15 reconstruction kernels, including eight conventional kernels and seven HD kernels, were used to reconstruct CT images of the bead. An ex vivo animal study consisting of a bone fracture model was performed to corroborate the MTF results, as the detection of this high-contrast and high frequency task is predominantly determined by spatial resolution. Images of this animal model generated by different scan modes and reconstruction kernels were qualitatively compared with the MTF results. At the centered position, the use of Hi-Res mode resulted in a slight improvement in the MTF; each HD kernel generated higher spatial resolution than its counterpart conventional kernel. However, the MTF along the tangential direction of the scan field of view (SFOV) was significantly degraded at off-centered positions, yet the combined Hi-Res/HD mode reduced this azimuthal MTF degradation. Images of the animal bone fracture model confirmed the improved spatial resolution at the off-centered positions through the use of the Hi-Res mode and HD kernels. The Hi-Res/HD scan improve spatial resolution of MDCT systems at both centered and off-centered positions.

Parameterized Micro-benchmarking: An Auto-tuning Approach for Complex Applications

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ma, Wenjing; Krishnamoorthy, Sriram; Agrawal, Gagan

2012-05-15

Auto-tuning has emerged as an important practical method for creating highly optimized implementations of key computational kernels and applications. However, the growing complexity of architectures and applications is creating new challenges for auto-tuning. Complex applications can involve a prohibitively large search space that precludes empirical auto-tuning. Similarly, architectures are becoming increasingly complicated, making it hard to model performance. In this paper, we focus on the challenge to auto-tuning presented by applications with a large number of kernels and kernel instantiations. While these kernels may share a somewhat similar pattern, they differ considerably in problem sizes and the exact computation performed.more » We propose and evaluate a new approach to auto-tuning which we refer to as parameterized micro-benchmarking. It is an alternative to the two existing classes of approaches to auto-tuning: analytical model-based and empirical search-based. Particularly, we argue that the former may not be able to capture all the architectural features that impact performance, whereas the latter might be too expensive for an application that has several different kernels. In our approach, different expressions in the application, different possible implementations of each expression, and the key architectural features, are used to derive a simple micro-benchmark and a small parameter space. This allows us to learn the most significant features of the architecture that can impact the choice of implementation for each kernel. We have evaluated our approach in the context of GPU implementations of tensor contraction expressions encountered in excited state calculations in quantum chemistry. We have focused on two aspects of GPUs that affect tensor contraction execution: memory access patterns and kernel consolidation. Using our parameterized micro-benchmarking approach, we obtain a speedup of up to 2 over the version that used default optimizations, but no auto-tuning. We demonstrate that observations made from microbenchmarks match the behavior seen from real expressions. In the process, we make important observations about the memory hierarchy of two of the most recent NVIDIA GPUs, which can be used in other optimization frameworks as well.« less

The Effects of Popping Popcorn Under Reduced Pressure

NASA Astrophysics Data System (ADS)

Quinn, Paul; Cooper, Amanda

2008-03-01

In our experiments, we model the popping of popcorn as an adiabatic process and develop a process for improving the efficiency of popcorn production. By lowering the pressure of the popcorn during the popping process, we induce an increase in popcorn size, while decreasing the number of remaining unpopped kernels. In this project we run numerous experiments using three of the most common popping devices, a movie popcorn maker, a stove pot, and a microwave. We specifically examine the effects of varying the pressure on total sample size, flake size and waste. An empirical relationship is found between these variables and the pressure.

Influence of wheat kernel physical properties on the pulverizing process.

PubMed

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

2014-10-01

The physical properties of wheat kernel were determined and related to pulverizing performance by correlation analysis. Nineteen samples of wheat cultivars about similar level of protein content (11.2-12.8 % w.b.) and obtained from organic farming system were used for analysis. The kernel (moisture content 10 % w.b.) was pulverized by using the laboratory hammer mill equipped with round holes 1.0 mm screen. The specific grinding energy ranged from 120 kJkg(-1) to 159 kJkg(-1). On the basis of data obtained many of significant correlations (p < 0.05) were found between wheat kernel physical properties and pulverizing process of wheat kernel, especially wheat kernel hardness index (obtained on the basis of Single Kernel Characterization System) and vitreousness significantly and positively correlated with the grinding energy indices and the mass fraction of coarse particles (> 0.5 mm). Among the kernel mechanical properties determined on the basis of uniaxial compression test only the rapture force was correlated with the impact grinding results. The results showed also positive and significant relationships between kernel ash content and grinding energy requirements. On the basis of wheat physical properties the multiple linear regression was proposed for predicting the average particle size of pulverized kernel.

Multiple Kernel Learning with Data Augmentation

DTIC Science & Technology

2016-11-22

model, we further show how to make inference and learn parameters in the following section. Note that we still need hyper -parameters (i.e., κ0, θ0, µ0...parameter α and sparsity tuning parameter β, these hyper -parameters are not sensitive to data. As in the BEMKL method, we fix the values of these... hyper -parameters for all datasets. αxn β yn wmf λmf µ0 σ0κ0 θ0 N M × F α ∼ G (κ0, θ0) β ∼ N ( µ0, σ 2 0 ) wmf , λmf | α, β ∼ Equation (8) yn | xn,wmf

Graph wavelet alignment kernels for drug virtual screening.

PubMed

Smalter, Aaron; Huan, Jun; Lushington, Gerald

2009-06-01

In this paper, we introduce a novel statistical modeling technique for target property prediction, with applications to virtual screening and drug design. In our method, we use graphs to model chemical structures and apply a wavelet analysis of graphs to summarize features capturing graph local topology. We design a novel graph kernel function to utilize the topology features to build predictive models for chemicals via Support Vector Machine classifier. We call the new graph kernel a graph wavelet-alignment kernel. We have evaluated the efficacy of the wavelet-alignment kernel using a set of chemical structure-activity prediction benchmarks. Our results indicate that the use of the kernel function yields performance profiles comparable to, and sometimes exceeding that of the existing state-of-the-art chemical classification approaches. In addition, our results also show that the use of wavelet functions significantly decreases the computational costs for graph kernel computation with more than ten fold speedup.

Is there a single best estimator? selection of home range estimators using area- under- the-curve

USGS Publications Warehouse

Walter, W. David; Onorato, Dave P.; Fischer, Justin W.

2015-01-01

Comparisons of fit of home range contours with locations collected would suggest that use of VHF technology is not as accurate as GPS technology to estimate size of home range for large mammals. Estimators of home range collected with GPS technology performed better than those estimated with VHF technology regardless of estimator used. Furthermore, estimators that incorporate a temporal component (third-generation estimators) appeared to be the most reliable regardless of whether kernel-based or Brownian bridge-based algorithms were used and in comparison to first- and second-generation estimators. We defined third-generation estimators of home range as any estimator that incorporates time, space, animal-specific parameters, and habitat. Such estimators would include movement-based kernel density, Brownian bridge movement models, and dynamic Brownian bridge movement models among others that have yet to be evaluated.

Mutually catalyzed birth of population and assets in exchange-driven growth

NASA Astrophysics Data System (ADS)

Lin, Zhenquan; Ke, Jianhong; Ye, Gaoxiang

2006-10-01

We propose an exchange-driven aggregation growth model of population and assets with mutually catalyzed birth to study the interaction between the population and assets in their exchange-driven processes. In this model, monomer (or equivalently, individual) exchange occurs between any pair of aggregates of the same species (population or assets). The rate kernels of the exchanges of population and assets are K(k,l)=Kkl and L(k,l)=Lkl , respectively, at which one monomer migrates from an aggregate of size k to another of size l . Meanwhile, an aggregate of one species can yield a new monomer by the catalysis of an arbitrary aggregate of the other species. The rate kernel of asset-catalyzed population birth is I(k,l)=Iklμ [and that of population-catalyzed asset birth is J(k,l)=Jklν ], at which an aggregate of size k gains a monomer birth when it meets a catalyst aggregate of size l . The kinetic behaviors of the population and asset aggregates are solved based on the rate equations. The evolution of the aggregate size distributions of population and assets is found to fall into one of three categories for different parameters μ and ν : (i) population (asset) aggregates evolve according to the conventional scaling form in the case of μ⩽0 (ν⩽0) , (ii) population (asset) aggregates evolve according to a modified scaling form in the case of ν=0 and μ>0 ( μ=0 and ν>0 ), and (iii) both population and asset aggregates undergo gelation transitions at a finite time in the case of μ=ν>0 .

SU-E-T-329: Dosimetric Impact of Implementing Metal Artifact Reduction Methods and Metal Energy Deposition Kernels for Photon Dose Calculations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Huang, J; Followill, D; Howell, R

2015-06-15

Purpose: To investigate two strategies for reducing dose calculation errors near metal implants: use of CT metal artifact reduction methods and implementation of metal-based energy deposition kernels in the convolution/superposition (C/S) method. Methods: Radiochromic film was used to measure the dose upstream and downstream of titanium and Cerrobend implants. To assess the dosimetric impact of metal artifact reduction methods, dose calculations were performed using baseline, uncorrected images and metal artifact reduction Methods: Philips O-MAR, GE’s monochromatic gemstone spectral imaging (GSI) using dual-energy CT, and GSI imaging with metal artifact reduction software applied (MARs).To assess the impact of metal kernels, titaniummore » and silver kernels were implemented into a commercial collapsed cone C/S algorithm. Results: The CT artifact reduction methods were more successful for titanium than Cerrobend. Interestingly, for beams traversing the metal implant, we found that errors in the dimensions of the metal in the CT images were more important for dose calculation accuracy than reduction of imaging artifacts. The MARs algorithm caused a distortion in the shape of the titanium implant that substantially worsened the calculation accuracy. In comparison to water kernel dose calculations, metal kernels resulted in better modeling of the increased backscatter dose at the upstream interface but decreased accuracy directly downstream of the metal. We also found that the success of metal kernels was dependent on dose grid size, with smaller calculation voxels giving better accuracy. Conclusion: Our study yielded mixed results, with neither the metal artifact reduction methods nor the metal kernels being globally effective at improving dose calculation accuracy. However, some successes were observed. The MARs algorithm decreased errors downstream of Cerrobend by a factor of two, and metal kernels resulted in more accurate backscatter dose upstream of metals. Thus, these two strategies do have the potential to improve accuracy for patients with metal implants in certain scenarios. This work was supported by Public Health Service grants CA 180803 and CA 10953 awarded by the National Cancer Institute, United States of Health and Human Services, and in part by Mobius Medical Systems.« less

Maize early endosperm growth and development: from fertilization through cell type differentiation.

PubMed

Leroux, Brian M; Goodyke, Austin J; Schumacher, Katelyn I; Abbott, Chelsi P; Clore, Amy M; Yadegari, Ramin; Larkins, Brian A; Dannenhoffer, Joanne M

2014-08-01

• Given the worldwide economic importance of maize endosperm, it is surprising that its development is not the most comprehensively studied of the cereals. We present detailed morphometric and cytological descriptions of endosperm development in the maize inbred line B73, for which the genome has been sequenced, and compare its growth with four diverse Nested Association Mapping (NAM) founder lines.• The first 12 d of B73 endosperm development were described using semithin sections of plastic-embedded kernels and confocal microscopy. Longitudinal sections were used to compare endosperm length, thickness, and area.• Morphometric comparison between Arizona- and Michigan-grown B73 showed a common pattern. Early endosperm development was divided into four stages: coenocytic, cellularization through alveolation, cellularization through partitioning, and differentiation. We observed tightly synchronous nuclear divisions in the coenocyte, elucidated that the onset of cellularization was coincident with endosperm size, and identified a previously undefined cell type (basal intermediate zone, BIZ). NAM founders with small mature kernels had larger endosperms (0-6 d after pollination) than lines with large mature kernels.• Our B73-specific model of early endosperm growth links developmental events to relative endosperm size, while accounting for diverse growing conditions. Maize endosperm cellularizes through alveolation, then random partitioning of the central vacuole. This unique cellularization feature of maize contrasts with the smaller endosperms of Arabidopsis, barley, and rice that strictly cellularize through repeated alveolation. NAM analysis revealed differences in endosperm size during early development, which potentially relates to differences in timing of cellularization across diverse lines of maize. © 2014 Botanical Society of America, Inc.

A pressurized cylindrical shell with a fixed end which contains an axial part-through or through crack

NASA Technical Reports Server (NTRS)

Yahsi, O. S.; Erdogan, F.

1983-01-01

A cylindrical shell having a very stiff and plate or a flange is considered. It is assumed that near the end the cylinder contains an axial flaw which may be modeled as a part through surface crack or a through crack. The effect of the end constraining on the stress intensity factor which is the main fracture mechanics parameter is studied. The applied loads acting on the cylinder are assumed to be axisymmetric. Thus the crack problem under consideration is symmetric with respect to the plane of the crack and consequently only the Mode 1 stress intensity factors are nonzero. With this limitation, the general perturbation problem for a cylinder with a built in end containing an axial crack is considered. Reissner's shell theory is used to formulate the problem. The part through crack problem is treated by using a line spring model. In the case of a crack tip terminating at the fixed end it is shown that the integral equations of the shell problem has the same generalized Cauchy kernel as the corresponding plane stress elasticity problem.

Pyramid image codes

NASA Technical Reports Server (NTRS)

Watson, Andrew B.

1990-01-01

All vision systems, both human and machine, transform the spatial image into a coded representation. Particular codes may be optimized for efficiency or to extract useful image features. Researchers explored image codes based on primary visual cortex in man and other primates. Understanding these codes will advance the art in image coding, autonomous vision, and computational human factors. In cortex, imagery is coded by features that vary in size, orientation, and position. Researchers have devised a mathematical model of this transformation, called the Hexagonal oriented Orthogonal quadrature Pyramid (HOP). In a pyramid code, features are segregated by size into layers, with fewer features in the layers devoted to large features. Pyramid schemes provide scale invariance, and are useful for coarse-to-fine searching and for progressive transmission of images. The HOP Pyramid is novel in three respects: (1) it uses a hexagonal pixel lattice, (2) it uses oriented features, and (3) it accurately models most of the prominent aspects of primary visual cortex. The transform uses seven basic features (kernels), which may be regarded as three oriented edges, three oriented bars, and one non-oriented blob. Application of these kernels to non-overlapping seven-pixel neighborhoods yields six oriented, high-pass pyramid layers, and one low-pass (blob) layer.

Detection of ochratoxin A contamination in stored wheat using near-infrared hyperspectral imaging

NASA Astrophysics Data System (ADS)

Senthilkumar, T.; Jayas, D. S.; White, N. D. G.; Fields, P. G.; Gräfenhan, T.

2017-03-01

Near-infrared (NIR) hyperspectral imaging system was used to detect five concentration levels of ochratoxin A (OTA) in contaminated wheat kernels. The wheat kernels artificially inoculated with two different OTA producing Penicillium verrucosum strains, two different non-toxigenic P. verrucosum strains, and sterile control wheat kernels were subjected to NIR hyperspectral imaging. The acquired three-dimensional data were reshaped into readable two-dimensional data. Principal Component Analysis (PCA) was applied to the two dimensional data to identify the key wavelengths which had greater significance in detecting OTA contamination in wheat. Statistical and histogram features extracted at the key wavelengths were used in the linear, quadratic and Mahalanobis statistical discriminant models to differentiate between sterile control, five concentration levels of OTA contamination in wheat kernels, and five infection levels of non-OTA producing P. verrucosum inoculated wheat kernels. The classification models differentiated sterile control samples from OTA contaminated wheat kernels and non-OTA producing P. verrucosum inoculated wheat kernels with a 100% accuracy. The classification models also differentiated between five concentration levels of OTA contaminated wheat kernels and between five infection levels of non-OTA producing P. verrucosum inoculated wheat kernels with a correct classification of more than 98%. The non-OTA producing P. verrucosum inoculated wheat kernels and OTA contaminated wheat kernels subjected to hyperspectral imaging provided different spectral patterns.

The Latent Structure of Dictionaries.

PubMed

Vincent-Lamarre, Philippe; Massé, Alexandre Blondin; Lopes, Marcos; Lord, Mélanie; Marcotte, Odile; Harnad, Stevan

2016-07-01

How many words-and which ones-are sufficient to define all other words? When dictionaries are analyzed as directed graphs with links from defining words to defined words, they reveal a latent structure. Recursively removing all words that are reachable by definition but that do not define any further words reduces the dictionary to a Kernel of about 10% of its size. This is still not the smallest number of words that can define all the rest. About 75% of the Kernel turns out to be its Core, a "Strongly Connected Subset" of words with a definitional path to and from any pair of its words and no word's definition depending on a word outside the set. But the Core cannot define all the rest of the dictionary. The 25% of the Kernel surrounding the Core consists of small strongly connected subsets of words: the Satellites. The size of the smallest set of words that can define all the rest-the graph's "minimum feedback vertex set" or MinSet-is about 1% of the dictionary, about 15% of the Kernel, and part-Core/part-Satellite. But every dictionary has a huge number of MinSets. The Core words are learned earlier, more frequent, and less concrete than the Satellites, which are in turn learned earlier, more frequent, but more concrete than the rest of the Dictionary. In principle, only one MinSet's words would need to be grounded through the sensorimotor capacity to recognize and categorize their referents. In a dual-code sensorimotor/symbolic model of the mental lexicon, the symbolic code could do all the rest through recombinatory definition. Copyright © 2016 Cognitive Science Society, Inc.

Three-Dimensional Sensitivity Kernels of Z/H Amplitude Ratios of Surface and Body Waves

NASA Astrophysics Data System (ADS)

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.

ASIC-based architecture for the real-time computation of 2D convolution with large kernel size

NASA Astrophysics Data System (ADS)

Shao, Rui; Zhong, Sheng; Yan, Luxin

2015-12-01

Bidimensional convolution is a low-level processing algorithm of interest in many areas, but its high computational cost constrains the size of the kernels, especially in real-time embedded systems. This paper presents a hardware architecture for the ASIC-based implementation of 2-D convolution with medium-large kernels. Aiming to improve the efficiency of storage resources on-chip, reducing off-chip bandwidth of these two issues, proposed construction of a data cache reuse. Multi-block SPRAM to cross cached images and the on-chip ping-pong operation takes full advantage of the data convolution calculation reuse, design a new ASIC data scheduling scheme and overall architecture. Experimental results show that the structure can achieve 40× 32 size of template real-time convolution operations, and improve the utilization of on-chip memory bandwidth and on-chip memory resources, the experimental results show that the structure satisfies the conditions to maximize data throughput output , reducing the need for off-chip memory bandwidth.

7 CFR 51.2559 - Size classifications.

Code of Federal Regulations, 2010 CFR

2010-01-01

... STANDARDS) United States Standards for Grades of Shelled Pistachio Nuts § 51.2559 Size classifications. (a) The size of pistachio kernels may be specified in connection with the grade in accordance with one of...

Baker-Akhiezer Spinor Kernel and Tau-functions on Moduli Spaces of Meromorphic Differentials

NASA Astrophysics Data System (ADS)

Kalla, C.; Korotkin, D.

2014-11-01

In this paper we study the Baker-Akhiezer spinor kernel on moduli spaces of meromorphic differentials on Riemann surfaces. We introduce the Baker-Akhiezer tau-function which is related to both the Bergman tau-function (which was studied before in the context of Hurwitz spaces and spaces of holomorphic Abelian and quadratic differentials) and the KP tau-function on such spaces. In particular, we derive variational formulas of Rauch-Ahlfors type on moduli spaces of meromorphic differentials with prescribed singularities: we use the system of homological coordinates, consisting of absolute and relative periods of the meromorphic differential, and show how to vary the fundamental objects associated to a Riemann surface (the matrix of b-periods, normalized Abelian differentials, the Bergman bidifferential, the Szegö kernel and the Baker-Akhiezer spinor kernel) with respect to these coordinates. The variational formulas encode dependence both on the moduli of the Riemann surface and on the choice of meromorphic differential (variation of the meromorphic differential while keeping the Riemann surface fixed corresponds to flows of KP type). Analyzing the global properties of the Bergman and Baker-Akhiezer tau-functions, we establish relationships between various divisor classes on the moduli spaces.

Scalable problems and memory bounded speedup

NASA Technical Reports Server (NTRS)

Sun, Xian-He; Ni, Lionel M.

1992-01-01

In this paper three models of parallel speedup are studied. They are fixed-size speedup, fixed-time speedup and memory-bounded speedup. The latter two consider the relationship between speedup and problem scalability. Two sets of speedup formulations are derived for these three models. One set considers uneven workload allocation and communication overhead and gives more accurate estimation. Another set considers a simplified case and provides a clear picture on the impact of the sequential portion of an application on the possible performance gain from parallel processing. The simplified fixed-size speedup is Amdahl's law. The simplified fixed-time speedup is Gustafson's scaled speedup. The simplified memory-bounded speedup contains both Amdahl's law and Gustafson's scaled speedup as special cases. This study leads to a better understanding of parallel processing.

GPU-accelerated Kernel Regression Reconstruction for Freehand 3D Ultrasound Imaging.

PubMed

Wen, Tiexiang; Li, Ling; Zhu, Qingsong; Qin, Wenjian; Gu, Jia; Yang, Feng; Xie, Yaoqin

2017-07-01

Volume reconstruction method plays an important role in improving reconstructed volumetric image quality for freehand three-dimensional (3D) ultrasound imaging. By utilizing the capability of programmable graphics processing unit (GPU), we can achieve a real-time incremental volume reconstruction at a speed of 25-50 frames per second (fps). After incremental reconstruction and visualization, hole-filling is performed on GPU to fill remaining empty voxels. However, traditional pixel nearest neighbor-based hole-filling fails to reconstruct volume with high image quality. On the contrary, the kernel regression provides an accurate volume reconstruction method for 3D ultrasound imaging but with the cost of heavy computational complexity. In this paper, a GPU-based fast kernel regression method is proposed for high-quality volume after the incremental reconstruction of freehand ultrasound. The experimental results show that improved image quality for speckle reduction and details preservation can be obtained with the parameter setting of kernel window size of [Formula: see text] and kernel bandwidth of 1.0. The computational performance of the proposed GPU-based method can be over 200 times faster than that on central processing unit (CPU), and the volume with size of 50 million voxels in our experiment can be reconstructed within 10 seconds.

Kernel analysis of partial least squares (PLS) regression models.

PubMed

Shinzawa, Hideyuki; Ritthiruangdej, Pitiporn; Ozaki, Yukihiro

2011-05-01

An analytical technique based on kernel matrix representation is demonstrated to provide further chemically meaningful insight into partial least squares (PLS) regression models. The kernel matrix condenses essential information about scores derived from PLS or principal component analysis (PCA). Thus, it becomes possible to establish the proper interpretation of the scores. A PLS model for the total nitrogen (TN) content in multiple Thai fish sauces is built with a set of near-infrared (NIR) transmittance spectra of the fish sauce samples. The kernel analysis of the scores effectively reveals that the variation of the spectral feature induced by the change in protein content is substantially associated with the total water content and the protein hydration. Kernel analysis is also carried out on a set of time-dependent infrared (IR) spectra representing transient evaporation of ethanol from a binary mixture solution of ethanol and oleic acid. A PLS model to predict the elapsed time is built with the IR spectra and the kernel matrix is derived from the scores. The detailed analysis of the kernel matrix provides penetrating insight into the interaction between the ethanol and the oleic acid.

Application of kernel functions for accurate similarity search in large chemical databases.

PubMed

Wang, Xiaohong; Huan, Jun; Smalter, Aaron; Lushington, Gerald H

2010-04-29

Similarity search in chemical structure databases is an important problem with many applications in chemical genomics, drug design, and efficient chemical probe screening among others. It is widely believed that structure based methods provide an efficient way to do the query. Recently various graph kernel functions have been designed to capture the intrinsic similarity of graphs. Though successful in constructing accurate predictive and classification models, graph kernel functions can not be applied to large chemical compound database due to the high computational complexity and the difficulties in indexing similarity search for large databases. To bridge graph kernel function and similarity search in chemical databases, we applied a novel kernel-based similarity measurement, developed in our team, to measure similarity of graph represented chemicals. In our method, we utilize a hash table to support new graph kernel function definition, efficient storage and fast search. We have applied our method, named G-hash, to large chemical databases. Our results show that the G-hash method achieves state-of-the-art performance for k-nearest neighbor (k-NN) classification. Moreover, the similarity measurement and the index structure is scalable to large chemical databases with smaller indexing size, and faster query processing time as compared to state-of-the-art indexing methods such as Daylight fingerprints, C-tree and GraphGrep. Efficient similarity query processing method for large chemical databases is challenging since we need to balance running time efficiency and similarity search accuracy. Our previous similarity search method, G-hash, provides a new way to perform similarity search in chemical databases. Experimental study validates the utility of G-hash in chemical databases.

A Fast Multiple-Kernel Method With Applications to Detect Gene-Environment Interaction.

PubMed

Marceau, Rachel; Lu, Wenbin; Holloway, Shannon; Sale, Michèle M; Worrall, Bradford B; Williams, Stephen R; Hsu, Fang-Chi; Tzeng, Jung-Ying

2015-09-01

Kernel machine (KM) models are a powerful tool for exploring associations between sets of genetic variants and complex traits. Although most KM methods use a single kernel function to assess the marginal effect of a variable set, KM analyses involving multiple kernels have become increasingly popular. Multikernel analysis allows researchers to study more complex problems, such as assessing gene-gene or gene-environment interactions, incorporating variance-component based methods for population substructure into rare-variant association testing, and assessing the conditional effects of a variable set adjusting for other variable sets. The KM framework is robust, powerful, and provides efficient dimension reduction for multifactor analyses, but requires the estimation of high dimensional nuisance parameters. Traditional estimation techniques, including regularization and the "expectation-maximization (EM)" algorithm, have a large computational cost and are not scalable to large sample sizes needed for rare variant analysis. Therefore, under the context of gene-environment interaction, we propose a computationally efficient and statistically rigorous "fastKM" algorithm for multikernel analysis that is based on a low-rank approximation to the nuisance effect kernel matrices. Our algorithm is applicable to various trait types (e.g., continuous, binary, and survival traits) and can be implemented using any existing single-kernel analysis software. Through extensive simulation studies, we show that our algorithm has similar performance to an EM-based KM approach for quantitative traits while running much faster. We also apply our method to the Vitamin Intervention for Stroke Prevention (VISP) clinical trial, examining gene-by-vitamin effects on recurrent stroke risk and gene-by-age effects on change in homocysteine level. © 2015 WILEY PERIODICALS, INC.

Enhancement of encapsulation efficiency of nanoemulsion-containing aripiprazole for the treatment of schizophrenia using mixture experimental design.

PubMed

Masoumi, Hamid Reza Fard; Basri, Mahiran; Samiun, Wan Sarah; Izadiyan, Zahra; Lim, Chaw Jiang

2015-01-01

Aripiprazole is considered as a third-generation antipsychotic drug with excellent therapeutic efficacy in controlling schizophrenia symptoms and was the first atypical anti-psychotic agent to be approved by the US Food and Drug Administration. Formulation of nanoemulsion-containing aripiprazole was carried out using high shear and high pressure homogenizers. Mixture experimental design was selected to optimize the composition of nanoemulsion. A very small droplet size of emulsion can provide an effective encapsulation for delivery system in the body. The effects of palm kernel oil ester (3-6 wt%), lecithin (2-3 wt%), Tween 80 (0.5-1 wt%), glycerol (1.5-3 wt%), and water (87-93 wt%) on the droplet size of aripiprazole nanoemulsions were investigated. The mathematical model showed that the optimum formulation for preparation of aripiprazole nanoemulsion having the desirable criteria was 3.00% of palm kernel oil ester, 2.00% of lecithin, 1.00% of Tween 80, 2.25% of glycerol, and 91.75% of water. Under optimum formulation, the corresponding predicted response value for droplet size was 64.24 nm, which showed an excellent agreement with the actual value (62.23 nm) with residual standard error <3.2%.

Finite-frequency sensitivity kernels for global seismic wave propagation based upon adjoint methods

NASA Astrophysics Data System (ADS)

Liu, Qinya; Tromp, Jeroen

2008-07-01

We determine adjoint equations and Fréchet kernels for global seismic wave propagation based upon a Lagrange multiplier method. We start from the equations of motion for a rotating, self-gravitating earth model initially in hydrostatic equilibrium, and derive the corresponding adjoint equations that involve motions on an earth model that rotates in the opposite direction. Variations in the misfit function χ then may be expressed as , where δlnm = δm/m denotes relative model perturbations in the volume V, δlnd denotes relative topographic variations on solid-solid or fluid-solid boundaries Σ, and ∇Σδlnd denotes surface gradients in relative topographic variations on fluid-solid boundaries ΣFS. The 3-D Fréchet kernel Km determines the sensitivity to model perturbations δlnm, and the 2-D kernels Kd and Kd determine the sensitivity to topographic variations δlnd. We demonstrate also how anelasticity may be incorporated within the framework of adjoint methods. Finite-frequency sensitivity kernels are calculated by simultaneously computing the adjoint wavefield forward in time and reconstructing the regular wavefield backward in time. Both the forward and adjoint simulations are based upon a spectral-element method. We apply the adjoint technique to generate finite-frequency traveltime kernels for global seismic phases (P, Pdiff, PKP, S, SKS, depth phases, surface-reflected phases, surface waves, etc.) in both 1-D and 3-D earth models. For 1-D models these adjoint-generated kernels generally agree well with results obtained from ray-based methods. However, adjoint methods do not have the same theoretical limitations as ray-based methods, and can produce sensitivity kernels for any given phase in any 3-D earth model. The Fréchet kernels presented in this paper illustrate the sensitivity of seismic observations to structural parameters and topography on internal discontinuities. These kernels form the basis of future 3-D tomographic inversions.

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

PubMed

Guo, Qi; Shen, Shu-Ting

2016-04-29

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

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

PubMed

Zhang, Xueying; Song, Qinbao

2015-01-01

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

A Multi-Label Learning Based Kernel Automatic Recommendation Method for Support Vector Machine

PubMed Central

Zhang, Xueying; Song, Qinbao

2015-01-01

Choosing an appropriate kernel is very important and critical when classifying a new problem with Support Vector Machine. So far, more attention has been paid on constructing new kernels and choosing suitable parameter values for a specific kernel function, but less on kernel selection. Furthermore, most of current kernel selection methods focus on seeking a best kernel with the highest classification accuracy via cross-validation, they are time consuming and ignore the differences among the number of support vectors and the CPU time of SVM with different kernels. Considering the tradeoff between classification success ratio and CPU time, there may be multiple kernel functions performing equally well on the same classification problem. Aiming to automatically select those appropriate kernel functions for a given data set, we propose a multi-label learning based kernel recommendation method built on the data characteristics. For each data set, the meta-knowledge data base is first created by extracting the feature vector of data characteristics and identifying the corresponding applicable kernel set. Then the kernel recommendation model is constructed on the generated meta-knowledge data base with the multi-label classification method. Finally, the appropriate kernel functions are recommended to a new data set by the recommendation model according to the characteristics of the new data set. Extensive experiments over 132 UCI benchmark data sets, with five different types of data set characteristics, eleven typical kernels (Linear, Polynomial, Radial Basis Function, Sigmoidal function, Laplace, Multiquadric, Rational Quadratic, Spherical, Spline, Wave and Circular), and five multi-label classification methods demonstrate that, compared with the existing kernel selection methods and the most widely used RBF kernel function, SVM with the kernel function recommended by our proposed method achieved the highest classification performance. PMID:25893896

Sorption Modeling and Verification for Off-Gas Treatment

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tavlarides, Lawrence L.; Lin, Ronghong; Nan, Yue

2015-04-29

The project has made progress toward developing a comprehensive modeling capability for the capture of target species in off gas evolved during the reprocessing of nuclear fuel. The effort has integrated experimentation, model development, and computer code development for adsorption and absorption processes. For adsorption, a modeling library has been initiated to include (a) equilibrium models for uptake of off-gas components by adsorbents, (b) mass transfer models to describe mass transfer to a particle, diffusion through the pores of the particle and adsorption on the active sites of the particle, and (c) interconnection of these models to fixed bed adsorptionmore » modeling which includes advection through the bed. For single-component equilibria, a Generalized Statistical Thermodynamic Adsorption (GSTA) code was developed to represent experimental data from a broad range of isotherm types; this is equivalent to a Langmuir isotherm in the two-parameter case, and was demonstrated for Kr on INL-engineered sorbent HZ PAN, water sorption on molecular sieve A sorbent material (MS3A), and Kr and Xe capture on metal-organic framework (MOF) materials. The GSTA isotherm was extended to multicomponent systems through application of a modified spreading pressure surface activity model and generalized predictive adsorbed solution theory; the result is the capability to estimate multicomponent adsorption equilibria from single-component isotherms. This advance, which enhances the capability to simulate systems related to off-gas treatment, has been demonstrated for a range of real-gas systems in the literature and is ready for testing with data currently being collected for multicomponent systems of interest, including iodine and water on MS3A. A diffusion kinetic model for sorbent pellets involving pore and surface diffusion as well as external mass transfer has been established, and a methodology was developed for determining unknown diffusivity parameters from transient uptake data. Two parallel approaches have been explored for integrating the kernels described above into a mass-transport model for adsorption in fixed beds. In one, the GSTA isotherm kernel has been incorporated into the MOOSE framework; in the other approach, a focused finite-difference framework and PDE kernels have been developed. Issues, including oscillatory behavior in MOOSE solutions to advection-diffusion problems, and opportunities have been identified for each approach, and a path forward has been identified toward developing a stronger modeling platform. Experimental systems were established for collection of microscopic kinetics and equilibria data for single and multicomponent uptake of gaseous species on solid sorbents. The systems, which can operate at ambient temperature to 250°C and dew points from -69 to 17°C, are useful for collecting data needed for modeling performance of sorbents of interest. Experiments were conducted to determine applicable models and parameters for isotherms and mass transfer for water and/or iodine adsorption on MS3A. Validation experiments were also conducted for water adsorption on fixed beds of MS3A. For absorption, work involved modeling with supportive experimentation. A dynamic model was developed to simulate CO 2 absorption with chemical reaction using high alkaline content water solutions. A computer code was developed to implement the model based upon transient mass and energy balances. Experiments were conducted in a laboratory-scale column to determine model parameters. The influence of geometric parameters and operating variables on CO 2 absorption was studied over a wide range of conditions. This project has resulted in 7 publications, with 3 manuscripts in preparation. Also, 15 presentations were given at national meetings of ANS and AIChE and at Material Recovery and Waste Forms Campaign Working Group meetings.« less

Aggressiveness of loose kernel smut isolate from Johnson grass on sorghum line BTx643

USDA-ARS?s Scientific Manuscript database

An isolate of loose kernel smut obtained from Johnson grass was inoculated unto six BTx643 sorghum plants in the greenhouse to determine its aggressiveness. All the BTx643 sorghum plants inoculated with the Johnson grass isolate were infected. Mean size of the teliospores from the Johnson grass, i...

Pollen flow in the wildservice tree, Sorbus torminalis (L.) Crantz. II. Pollen dispersal and heterogeneity in mating success inferred from parent-offspring analysis.

PubMed

Oddou-Muratorio, Sylvie; Klein, Etienne K; Austerlitz, Frédéric

2005-12-01

Knowing the extent of gene movements from parents to offspring is essential to understand the potential of a species to adapt rapidly to a changing environment, and to design appropriate conservation strategies. In this study, we develop a nonlinear statistical model to jointly estimate the pollen dispersal kernel and the heterogeneity in fecundity among phenotypically or environmentally defined groups of males. This model uses genotype data from a sample of fruiting plants, a sample of seeds harvested on each of these plants, and all males within a circumscribed area. We apply this model to a scattered, entomophilous woody species, Sorbus torminalis (L.) Crantz, within a natural population covering more than 470 ha. We estimate a high heterogeneity in male fecundity among ecological groups, both due to phenotype (size of the trees and flowering intensity) and landscape factors (stand density within the neighbourhood). We also show that fat-tailed kernels are the most appropriate to depict the important abilities of long-distance pollen dispersal for this species. Finally, our results reveal that the spatial position of a male with respect to females affects as much its mating success as ecological determinants of male fecundity. Our study thus stresses the interest to account for the dispersal kernel when estimating heterogeneity in male fecundity, and reciprocally.

A Fast Reduced Kernel Extreme Learning Machine.

PubMed

Deng, Wan-Yu; Ong, Yew-Soon; Zheng, Qing-Hua

2016-04-01

In this paper, we present a fast and accurate kernel-based supervised algorithm referred to as the Reduced Kernel Extreme Learning Machine (RKELM). In contrast to the work on Support Vector Machine (SVM) or Least Square SVM (LS-SVM), which identifies the support vectors or weight vectors iteratively, the proposed RKELM randomly selects a subset of the available data samples as support vectors (or mapping samples). By avoiding the iterative steps of SVM, significant cost savings in the training process can be readily attained, especially on Big datasets. RKELM is established based on the rigorous proof of universal learning involving reduced kernel-based SLFN. In particular, we prove that RKELM can approximate any nonlinear functions accurately under the condition of support vectors sufficiency. Experimental results on a wide variety of real world small instance size and large instance size applications in the context of binary classification, multi-class problem and regression are then reported to show that RKELM can perform at competitive level of generalized performance as the SVM/LS-SVM at only a fraction of the computational effort incurred. Copyright © 2015 Elsevier Ltd. All rights reserved.

A Kernel-Based Low-Rank (KLR) Model for Low-Dimensional Manifold Recovery in Highly Accelerated Dynamic MRI.

PubMed

Nakarmi, Ukash; Wang, Yanhua; Lyu, Jingyuan; Liang, Dong; Ying, Leslie

2017-11-01

While many low rank and sparsity-based approaches have been developed for accelerated dynamic magnetic resonance imaging (dMRI), they all use low rankness or sparsity in input space, overlooking the intrinsic nonlinear correlation in most dMRI data. In this paper, we propose a kernel-based framework to allow nonlinear manifold models in reconstruction from sub-Nyquist data. Within this framework, many existing algorithms can be extended to kernel framework with nonlinear models. In particular, we have developed a novel algorithm with a kernel-based low-rank model generalizing the conventional low rank formulation. The algorithm consists of manifold learning using kernel, low rank enforcement in feature space, and preimaging with data consistency. Extensive simulation and experiment results show that the proposed method surpasses the conventional low-rank-modeled approaches for dMRI.

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

NASA Astrophysics Data System (ADS)

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

2018-02-01

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

Generalized functional linear models for gene-based case-control association studies.

PubMed

Fan, Ruzong; Wang, Yifan; Mills, James L; Carter, Tonia C; Lobach, Iryna; Wilson, Alexander F; Bailey-Wilson, Joan E; Weeks, Daniel E; Xiong, Momiao

2014-11-01

By using functional data analysis techniques, we developed generalized functional linear models for testing association between a dichotomous trait and multiple genetic variants in a genetic region while adjusting for covariates. Both fixed and mixed effect models are developed and compared. Extensive simulations show that Rao's efficient score tests of the fixed effect models are very conservative since they generate lower type I errors than nominal levels, and global tests of the mixed effect models generate accurate type I errors. Furthermore, we found that the Rao's efficient score test statistics of the fixed effect models have higher power than the sequence kernel association test (SKAT) and its optimal unified version (SKAT-O) in most cases when the causal variants are both rare and common. When the causal variants are all rare (i.e., minor allele frequencies less than 0.03), the Rao's efficient score test statistics and the global tests have similar or slightly lower power than SKAT and SKAT-O. In practice, it is not known whether rare variants or common variants in a gene region are disease related. All we can assume is that a combination of rare and common variants influences disease susceptibility. Thus, the improved performance of our models when the causal variants are both rare and common shows that the proposed models can be very useful in dissecting complex traits. We compare the performance of our methods with SKAT and SKAT-O on real neural tube defects and Hirschsprung's disease datasets. The Rao's efficient score test statistics and the global tests are more sensitive than SKAT and SKAT-O in the real data analysis. Our methods can be used in either gene-disease genome-wide/exome-wide association studies or candidate gene analyses. © 2014 WILEY PERIODICALS, INC.

Generalized Functional Linear Models for Gene-based Case-Control Association Studies

PubMed Central

Mills, James L.; Carter, Tonia C.; Lobach, Iryna; Wilson, Alexander F.; Bailey-Wilson, Joan E.; Weeks, Daniel E.; Xiong, Momiao

2014-01-01

By using functional data analysis techniques, we developed generalized functional linear models for testing association between a dichotomous trait and multiple genetic variants in a genetic region while adjusting for covariates. Both fixed and mixed effect models are developed and compared. Extensive simulations show that Rao's efficient score tests of the fixed effect models are very conservative since they generate lower type I errors than nominal levels, and global tests of the mixed effect models generate accurate type I errors. Furthermore, we found that the Rao's efficient score test statistics of the fixed effect models have higher power than the sequence kernel association test (SKAT) and its optimal unified version (SKAT-O) in most cases when the causal variants are both rare and common. When the causal variants are all rare (i.e., minor allele frequencies less than 0.03), the Rao's efficient score test statistics and the global tests have similar or slightly lower power than SKAT and SKAT-O. In practice, it is not known whether rare variants or common variants in a gene are disease-related. All we can assume is that a combination of rare and common variants influences disease susceptibility. Thus, the improved performance of our models when the causal variants are both rare and common shows that the proposed models can be very useful in dissecting complex traits. We compare the performance of our methods with SKAT and SKAT-O on real neural tube defects and Hirschsprung's disease data sets. The Rao's efficient score test statistics and the global tests are more sensitive than SKAT and SKAT-O in the real data analysis. Our methods can be used in either gene-disease genome-wide/exome-wide association studies or candidate gene analyses. PMID:25203683

Performance Prediction Toolkit

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chennupati, Gopinath; Santhi, Nanadakishore; Eidenbenz, Stephen

The Performance Prediction Toolkit (PPT), is a scalable co-design tool that contains the hardware and middle-ware models, which accept proxy applications as input in runtime prediction. PPT relies on Simian, a parallel discrete event simulation engine in Python or Lua, that uses the process concept, where each computing unit (host, node, core) is a Simian entity. Processes perform their task through message exchanges to remain active, sleep, wake-up, begin and end. The PPT hardware model of a compute core (such as a Haswell core) consists of a set of parameters, such as clock speed, memory hierarchy levels, their respective sizes,more » cache-lines, access times for different cache levels, average cycle counts of ALU operations, etc. These parameters are ideally read off a spec sheet or are learned using regression models learned from hardware counters (PAPI) data. The compute core model offers an API to the software model, a function called time_compute(), which takes as input a tasklist. A tasklist is an unordered set of ALU, and other CPU-type operations (in particular virtual memory loads and stores). The PPT application model mimics the loop structure of the application and replaces the computational kernels with a call to the hardware model's time_compute() function giving tasklists as input that model the compute kernel. A PPT application model thus consists of tasklists representing kernels and the high-er level loop structure that we like to think of as pseudo code. The key challenge for the hardware model's time_compute-function is to translate virtual memory accesses into actual cache hierarchy level hits and misses.PPT also contains another CPU core level hardware model, Analytical Memory Model (AMM). The AMM solves this challenge soundly, where our previous alternatives explicitly include the L1,L2,L3 hit-rates as inputs to the tasklists. Explicit hit-rates inevitably only reflect the application modeler's best guess, perhaps informed by a few small test problems using hardware counters; also, hard-coded hit-rates make the hardware model insensitive to changes in cache sizes. Alternatively, we use reuse distance distributions in the tasklists. In general, reuse profiles require the application modeler to run a very expensive trace analysis on the real code that realistically can be done at best for small examples.« less

Multiscale asymmetric orthogonal wavelet kernel for linear programming support vector learning and nonlinear dynamic systems identification.

PubMed

Lu, Zhao; Sun, Jing; Butts, Kenneth

2014-05-01

Support vector regression for approximating nonlinear dynamic systems is more delicate than the approximation of indicator functions in support vector classification, particularly for systems that involve multitudes of time scales in their sampled data. The kernel used for support vector learning determines the class of functions from which a support vector machine can draw its solution, and the choice of kernel significantly influences the performance of a support vector machine. In this paper, to bridge the gap between wavelet multiresolution analysis and kernel learning, the closed-form orthogonal wavelet is exploited to construct new multiscale asymmetric orthogonal wavelet kernels for linear programming support vector learning. The closed-form multiscale orthogonal wavelet kernel provides a systematic framework to implement multiscale kernel learning via dyadic dilations and also enables us to represent complex nonlinear dynamics effectively. To demonstrate the superiority of the proposed multiscale wavelet kernel in identifying complex nonlinear dynamic systems, two case studies are presented that aim at building parallel models on benchmark datasets. The development of parallel models that address the long-term/mid-term prediction issue is more intricate and challenging than the identification of series-parallel models where only one-step ahead prediction is required. Simulation results illustrate the effectiveness of the proposed multiscale kernel learning.

Seismic hazard in the Intermountain West

USGS Publications Warehouse

Haller, Kathleen; Moschetti, Morgan P.; Mueller, Charles; Rezaeian, Sanaz; Petersen, Mark D.; Zeng, Yuehua

2015-01-01

The 2014 national seismic-hazard model for the conterminous United States incorporates new scientific results and important model adjustments. The current model includes updates to the historical catalog, which is spatially smoothed using both fixed-length and adaptive-length smoothing kernels. Fault-source characterization improved by adding faults, revising rates of activity, and incorporating new results from combined inversions of geologic and geodetic data. The update also includes a new suite of published ground motion models. Changes in probabilistic ground motion are generally less than 10% in most of the Intermountain West compared to the prior assessment, and ground-motion hazard in four Intermountain West cities illustrates the range and magnitude of change in the region. Seismic hazard at reference sites in Boise and Reno increased as much as 10%, whereas hazard in Salt Lake City decreased 5–6%. The largest change was in Las Vegas, where hazard increased 32–35%.

Detection of Splice Sites Using Support Vector Machine

NASA Astrophysics Data System (ADS)

Varadwaj, Pritish; Purohit, Neetesh; Arora, Bhumika

Automatic identification and annotation of exon and intron region of gene, from DNA sequences has been an important research area in field of computational biology. Several approaches viz. Hidden Markov Model (HMM), Artificial Intelligence (AI) based machine learning and Digital Signal Processing (DSP) techniques have extensively and independently been used by various researchers to cater this challenging task. In this work, we propose a Support Vector Machine based kernel learning approach for detection of splice sites (the exon-intron boundary) in a gene. Electron-Ion Interaction Potential (EIIP) values of nucleotides have been used for mapping character sequences to corresponding numeric sequences. Radial Basis Function (RBF) SVM kernel is trained using EIIP numeric sequences. Furthermore this was tested on test gene dataset for detection of splice site by window (of 12 residues) shifting. Optimum values of window size, various important parameters of SVM kernel have been optimized for a better accuracy. Receiver Operating Characteristic (ROC) curves have been utilized for displaying the sensitivity rate of the classifier and results showed 94.82% accuracy for splice site detection on test dataset.

SEMI-SUPERVISED OBJECT RECOGNITION USING STRUCTURE KERNEL

PubMed Central

Wang, Botao; Xiong, Hongkai; Jiang, Xiaoqian; Ling, Fan

2013-01-01

Object recognition is a fundamental problem in computer vision. Part-based models offer a sparse, flexible representation of objects, but suffer from difficulties in training and often use standard kernels. In this paper, we propose a positive definite kernel called “structure kernel”, which measures the similarity of two part-based represented objects. The structure kernel has three terms: 1) the global term that measures the global visual similarity of two objects; 2) the part term that measures the visual similarity of corresponding parts; 3) the spatial term that measures the spatial similarity of geometric configuration of parts. The contribution of this paper is to generalize the discriminant capability of local kernels to complex part-based object models. Experimental results show that the proposed kernel exhibit higher accuracy than state-of-art approaches using standard kernels. PMID:23666108

Lesion contrast and detection using sonoelastographic shear velocity imaging: preliminary results

NASA Astrophysics Data System (ADS)

Hoyt, Kenneth; Parker, Kevin J.

2007-03-01

This paper assesses lesion contrast and detection using sonoelastographic shear velocity imaging. Shear wave interference patterns, termed crawling waves, for a two phase medium were simulated assuming plane wave conditions. Shear velocity estimates were computed using a spatial autocorrelation algorithm that operates in the direction of shear wave propagation for a given kernel size. Contrast was determined by analyzing shear velocity estimate transition between mediums. Experimental results were obtained using heterogeneous phantoms with spherical inclusions (5 or 10 mm in diameter) characterized by elevated shear velocities. Two vibration sources were applied to opposing phantom edges and scanned (orthogonal to shear wave propagation) with an ultrasound scanner equipped for sonoelastography. Demodulated data was saved and transferred to an external computer for processing shear velocity images. Simulation results demonstrate shear velocity transition between contrasting mediums is governed by both estimator kernel size and source vibration frequency. Experimental results from phantoms further indicates that decreasing estimator kernel size produces corresponding decrease in shear velocity estimate transition between background and inclusion material albeit with an increase in estimator noise. Overall, results demonstrate the ability to generate high contrast shear velocity images using sonoelastographic techniques and detect millimeter-sized lesions.

Voronoi cell patterns: Theoretical model and applications

NASA Astrophysics Data System (ADS)

González, Diego Luis; Einstein, T. L.

2011-11-01

We use a simple fragmentation model to describe the statistical behavior of the Voronoi cell patterns generated by a homogeneous and isotropic set of points in 1D and in 2D. In particular, we are interested in the distribution of sizes of these Voronoi cells. Our model is completely defined by two probability distributions in 1D and again in 2D, the probability to add a new point inside an existing cell and the probability that this new point is at a particular position relative to the preexisting point inside this cell. In 1D the first distribution depends on a single parameter while the second distribution is defined through a fragmentation kernel; in 2D both distributions depend on a single parameter. The fragmentation kernel and the control parameters are closely related to the physical properties of the specific system under study. We use our model to describe the Voronoi cell patterns of several systems. Specifically, we study the island nucleation with irreversible attachment, the 1D car-parking problem, the formation of second-level administrative divisions, and the pattern formed by the Paris Métro stations.

Cut and join operator ring in tensor models

NASA Astrophysics Data System (ADS)

Itoyama, H.; Mironov, A.; Morozov, A.

2018-07-01

Recent advancement of rainbow tensor models based on their superintegrability (manifesting itself as the existence of an explicit expression for a generic Gaussian correlator) has allowed us to bypass the long-standing problem seen as the lack of eigenvalue/determinant representation needed to establish the KP/Toda integrability. As the mandatory next step, we discuss in this paper how to provide an adequate designation to each of the connected gauge-invariant operators that form a double coset, which is required to cleverly formulate a tree-algebra generalization of the Virasoro constraints. This problem goes beyond the enumeration problem per se tied to the permutation group, forcing us to introduce a few gauge fixing procedures to the coset. We point out that the permutation-based labeling, which has proven to be relevant for the Gaussian averages is, via interesting complexity, related to the one based on the keystone trees, whose algebra will provide the tensor counterpart of the Virasoro algebra for matrix models. Moreover, our simple analysis reveals the existence of nontrivial kernels and co-kernels for the cut operation and for the join operation respectively that prevent a straightforward construction of the non-perturbative RG-complete partition function and the identification of truly independent time variables. We demonstrate these problems by the simplest non-trivial Aristotelian RGB model with one complex rank-3 tensor, studying its ring of gauge-invariant operators, generated by the keystone triple with the help of four operations: addition, multiplication, cut and join.

Resonant frequency analysis of Timoshenko nanowires with surface stress for different boundary conditions

NASA Astrophysics Data System (ADS)

He, Qilu; Lilley, Carmen M.

2012-10-01

The influence of both surface and shear effects on the resonant frequency of nanowires (NWs) was studied by incorporating the Young-Laplace equation with the Timoshenko beam theory. Face-centered-cubic metal NWs were studied. A dimensional analysis of the resonant frequencies for fixed-fixed gold (100) NWs were compared to molecular dynamic simulations. Silver NWs with diameters from 10 nm-500 nm were modeled as a cantilever, simply supported and fixed-fixed system for aspect ratios from 2.5-20 to identify the shear, surface, and size effects on the resonant frequencies. The shear effect was found to have a larger significance than surface effects when the aspect ratios were small (i.e., <5) regardless of size for the diameters modeled. Finally, as the aspect ratio grows, the surface effect becomes significant for the smaller diameter NWs.

Reduced multiple empirical kernel learning machine.

PubMed

Wang, Zhe; Lu, MingZhe; Gao, Daqi

2015-02-01

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

Precipitation growth in convective clouds. [hail

NASA Technical Reports Server (NTRS)

Srivastava, R. C.

1981-01-01

Analytical solutions to the equations of both the growth and motion of hailstones in updrafts and of cloud water contents which vary linearly with height were used to investigate hail growth in a model cloud. A strong correlation was found between the hail embyro starting position and its trajectory and final size. A simple model of the evolution of particle size distribution by coalescence and spontaneous and binary disintegrations was formulated. Solutions for the mean mass of the distribution and the equilibrium size distribution were obtained for the case of constant collection kernel and disintegration parameters. Azimuthal scans of Doppler velocity at a number of elevation angles were used to calculate high resolution vertical profiles of particle speed and horizontal divergence (the vertical air velocity) in a region of widespread precipitation trailing a mid-latitude squall line.

Sparse Event Modeling with Hierarchical Bayesian Kernel Methods

DTIC Science & Technology

2016-01-05

SECURITY CLASSIFICATION OF: The research objective of this proposal was to develop a predictive Bayesian kernel approach to model count data based on...several predictive variables. Such an approach, which we refer to as the Poisson Bayesian kernel model , is able to model the rate of occurrence of...which adds specificity to the model and can make nonlinear data more manageable. Early results show that the 1. REPORT DATE (DD-MM-YYYY) 4. TITLE

How bandwidth selection algorithms impact exploratory data analysis using kernel density estimation.

PubMed

Harpole, Jared K; Woods, Carol M; Rodebaugh, Thomas L; Levinson, Cheri A; Lenze, Eric J

2014-09-01

Exploratory data analysis (EDA) can reveal important features of underlying distributions, and these features often have an impact on inferences and conclusions drawn from data. Graphical analysis is central to EDA, and graphical representations of distributions often benefit from smoothing. A viable method of estimating and graphing the underlying density in EDA is kernel density estimation (KDE). This article provides an introduction to KDE and examines alternative methods for specifying the smoothing bandwidth in terms of their ability to recover the true density. We also illustrate the comparison and use of KDE methods with 2 empirical examples. Simulations were carried out in which we compared 8 bandwidth selection methods (Sheather-Jones plug-in [SJDP], normal rule of thumb, Silverman's rule of thumb, least squares cross-validation, biased cross-validation, and 3 adaptive kernel estimators) using 5 true density shapes (standard normal, positively skewed, bimodal, skewed bimodal, and standard lognormal) and 9 sample sizes (15, 25, 50, 75, 100, 250, 500, 1,000, 2,000). Results indicate that, overall, SJDP outperformed all methods. However, for smaller sample sizes (25 to 100) either biased cross-validation or Silverman's rule of thumb was recommended, and for larger sample sizes the adaptive kernel estimator with SJDP was recommended. Information is provided about implementing the recommendations in the R computing language. PsycINFO Database Record (c) 2014 APA, all rights reserved.

Abiotic stress growth conditions induce different responses in kernel iron concentration across genotypically distinct maize inbred varieties

PubMed Central

Kandianis, Catherine B.; Michenfelder, Abigail S.; Simmons, Susan J.; Grusak, Michael A.; Stapleton, Ann E.

2013-01-01

The improvement of grain nutrient profiles for essential minerals and vitamins through breeding strategies is a target important for agricultural regions where nutrient poor crops like maize contribute a large proportion of the daily caloric intake. Kernel iron concentration in maize exhibits a broad range. However, the magnitude of genotype by environment (GxE) effects on this trait reduces the efficacy and predictability of selection programs, particularly when challenged with abiotic stress such as water and nitrogen limitations. Selection has also been limited by an inverse correlation between kernel iron concentration and the yield component of kernel size in target environments. Using 25 maize inbred lines for which extensive genome sequence data is publicly available, we evaluated the response of kernel iron density and kernel mass to water and nitrogen limitation in a managed field stress experiment using a factorial design. To further understand GxE interactions we used partition analysis to characterize response of kernel iron and weight to abiotic stressors among all genotypes, and observed two patterns: one characterized by higher kernel iron concentrations in control over stress conditions, and another with higher kernel iron concentration under drought and combined stress conditions. Breeding efforts for this nutritional trait could exploit these complementary responses through combinations of favorable allelic variation from these already well-characterized genetic stocks. PMID:24363659

Ranking Support Vector Machine with Kernel Approximation

PubMed Central

Dou, Yong

2017-01-01

Learning to rank algorithm has become important in recent years due to its successful application in information retrieval, recommender system, and computational biology, and so forth. Ranking support vector machine (RankSVM) is one of the state-of-art ranking models and has been favorably used. Nonlinear RankSVM (RankSVM with nonlinear kernels) can give higher accuracy than linear RankSVM (RankSVM with a linear kernel) for complex nonlinear ranking problem. However, the learning methods for nonlinear RankSVM are still time-consuming because of the calculation of kernel matrix. In this paper, we propose a fast ranking algorithm based on kernel approximation to avoid computing the kernel matrix. We explore two types of kernel approximation methods, namely, the Nyström method and random Fourier features. Primal truncated Newton method is used to optimize the pairwise L2-loss (squared Hinge-loss) objective function of the ranking model after the nonlinear kernel approximation. Experimental results demonstrate that our proposed method gets a much faster training speed than kernel RankSVM and achieves comparable or better performance over state-of-the-art ranking algorithms. PMID:28293256

Ranking Support Vector Machine with Kernel Approximation.

PubMed

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

2017-01-01

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

MIAG12: A Triticum timopheevii-derived powdery mildew resistance gene in common wheat on chromosome 7AL

USDA-ARS?s Scientific Manuscript database

Wheat powdery mildew is an economically important disease in cool and humid 2 environments. Powdery mildew causes yield losses as high as 48 percent through a reduction in 3 tiller survival, kernels per head and kernel size. Race-specific host resistance is the most 4 consistent, environmentally fri...

Kernel and Traditional Equipercentile Equating with Degrees of Presmoothing. Research Report. ETS RR-07-15

ERIC Educational Resources Information Center

Moses, Tim; Holland, Paul

2007-01-01

The purpose of this study was to empirically evaluate the impact of loglinear presmoothing accuracy on equating bias and variability across chained and post-stratification equating methods, kernel and percentile-rank continuization methods, and sample sizes. The results of evaluating presmoothing on equating accuracy generally agreed with those of…

Image quality of mixed convolution kernel in thoracic computed tomography.

PubMed

Neubauer, Jakob; Spira, Eva Maria; Strube, Juliane; Langer, Mathias; Voss, Christian; Kotter, Elmar

2016-11-01

The mixed convolution kernel alters his properties geographically according to the depicted organ structure, especially for the lung. Therefore, we compared the image quality of the mixed convolution kernel to standard soft and hard kernel reconstructions for different organ structures in thoracic computed tomography (CT) images.Our Ethics Committee approved this prospective study. In total, 31 patients who underwent contrast-enhanced thoracic CT studies were included after informed consent. Axial reconstructions were performed with hard, soft, and mixed convolution kernel. Three independent and blinded observers rated the image quality according to the European Guidelines for Quality Criteria of Thoracic CT for 13 organ structures. The observers rated the depiction of the structures in all reconstructions on a 5-point Likert scale. Statistical analysis was performed with the Friedman Test and post hoc analysis with the Wilcoxon rank-sum test.Compared to the soft convolution kernel, the mixed convolution kernel was rated with a higher image quality for lung parenchyma, segmental bronchi, and the border between the pleura and the thoracic wall (P < 0.03). Compared to the hard convolution kernel, the mixed convolution kernel was rated with a higher image quality for aorta, anterior mediastinal structures, paratracheal soft tissue, hilar lymph nodes, esophagus, pleuromediastinal border, large and medium sized pulmonary vessels and abdomen (P < 0.004) but a lower image quality for trachea, segmental bronchi, lung parenchyma, and skeleton (P < 0.001).The mixed convolution kernel cannot fully substitute the standard CT reconstructions. Hard and soft convolution kernel reconstructions still seem to be mandatory for thoracic CT.

Introducing etch kernels for efficient pattern sampling and etch bias prediction

NASA Astrophysics Data System (ADS)

Weisbuch, François; Lutich, Andrey; Schatz, Jirka

2018-01-01

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

Bayesian Optimization for Neuroimaging Pre-processing in Brain Age Classification and Prediction

PubMed Central

Lancaster, Jenessa; Lorenz, Romy; Leech, Rob; Cole, James H.

2018-01-01

Neuroimaging-based age prediction using machine learning is proposed as a biomarker of brain aging, relating to cognitive performance, health outcomes and progression of neurodegenerative disease. However, even leading age-prediction algorithms contain measurement error, motivating efforts to improve experimental pipelines. T1-weighted MRI is commonly used for age prediction, and the pre-processing of these scans involves normalization to a common template and resampling to a common voxel size, followed by spatial smoothing. Resampling parameters are often selected arbitrarily. Here, we sought to improve brain-age prediction accuracy by optimizing resampling parameters using Bayesian optimization. Using data on N = 2003 healthy individuals (aged 16–90 years) we trained support vector machines to (i) distinguish between young (<22 years) and old (>50 years) brains (classification) and (ii) predict chronological age (regression). We also evaluated generalisability of the age-regression model to an independent dataset (CamCAN, N = 648, aged 18–88 years). Bayesian optimization was used to identify optimal voxel size and smoothing kernel size for each task. This procedure adaptively samples the parameter space to evaluate accuracy across a range of possible parameters, using independent sub-samples to iteratively assess different parameter combinations to arrive at optimal values. When distinguishing between young and old brains a classification accuracy of 88.1% was achieved, (optimal voxel size = 11.5 mm3, smoothing kernel = 2.3 mm). For predicting chronological age, a mean absolute error (MAE) of 5.08 years was achieved, (optimal voxel size = 3.73 mm3, smoothing kernel = 3.68 mm). This was compared to performance using default values of 1.5 mm3 and 4mm respectively, resulting in MAE = 5.48 years, though this 7.3% improvement was not statistically significant. When assessing generalisability, best performance was achieved when applying the entire Bayesian optimization framework to the new dataset, out-performing the parameters optimized for the initial training dataset. Our study outlines the proof-of-principle that neuroimaging models for brain-age prediction can use Bayesian optimization to derive case-specific pre-processing parameters. Our results suggest that different pre-processing parameters are selected when optimization is conducted in specific contexts. This potentially motivates use of optimization techniques at many different points during the experimental process, which may improve statistical sensitivity and reduce opportunities for experimenter-led bias. PMID:29483870

Hyperspectral Image Classification via Kernel Sparse Representation

DTIC Science & Technology

2013-01-01

classification algorithms. Moreover, the spatial coherency across neighboring pixels is also incorporated through a kernelized joint sparsity model , where...joint sparsity model , where all of the pixels within a small neighborhood are jointly represented in the feature space by selecting a few common training...hyperspectral imagery, joint spar- sity model , kernel methods, sparse representation. I. INTRODUCTION HYPERSPECTRAL imaging sensors capture images

7 CFR 51.2284 - Size classification.

Code of Federal Regulations, 2013 CFR

2013-01-01

... 7 Agriculture 2 2013-01-01 2013-01-01 false Size classification. 51.2284 Section 51.2284...) Size Requirements § 51.2284 Size classification. The following classifications are provided to describe... of kernels in the lot shall conform to the requirements of the specified classification as defined...

7 CFR 51.2284 - Size classification.

Code of Federal Regulations, 2014 CFR

2014-01-01

... 7 Agriculture 2 2014-01-01 2014-01-01 false Size classification. 51.2284 Section 51.2284...) Size Requirements § 51.2284 Size classification. The following classifications are provided to describe... of kernels in the lot shall conform to the requirements of the specified classification as defined...

Modelling ranging behaviour of female orang-utans: a case study in Tuanan, Central Kalimantan, Indonesia.

PubMed

Wartmann, Flurina M; Purves, Ross S; van Schaik, Carel P

2010-04-01

Quantification of the spatial needs of individuals and populations is vitally important for management and conservation. Geographic information systems (GIS) have recently become important analytical tools in wildlife biology, improving our ability to understand animal movement patterns, especially when very large data sets are collected. This study aims at combining the field of GIS with primatology to model and analyse space-use patterns of wild orang-utans. Home ranges of female orang-utans in the Tuanan Mawas forest reserve in Central Kalimantan, Indonesia were modelled with kernel density estimation methods. Kernel results were compared with minimum convex polygon estimates, and were found to perform better, because they were less sensitive to sample size and produced more reliable estimates. Furthermore, daily travel paths were calculated from 970 complete follow days. Annual ranges for the resident females were approximately 200 ha and remained stable over several years; total home range size was estimated to be 275 ha. On average, each female shared a third of her home range with each neighbouring female. Orang-utan females in Tuanan built their night nest on average 414 m away from the morning nest, whereas average daily travel path length was 777 m. A significant effect of fruit availability on day path length was found. Sexually active females covered longer distances per day and may also temporarily expand their ranges.

An efficient method to determine double Gaussian fluence parameters in the eclipse™ proton pencil beam model.

PubMed

Shen, Jiajian; Liu, Wei; Stoker, Joshua; Ding, Xiaoning; Anand, Aman; Hu, Yanle; Herman, Michael G; Bues, Martin

2016-12-01

To find an efficient method to configure the proton fluence for a commercial proton pencil beam scanning (PBS) treatment planning system (TPS). An in-water dose kernel was developed to mimic the dose kernel of the pencil beam convolution superposition algorithm, which is part of the commercial proton beam therapy planning software, eclipse™ (Varian Medical Systems, Palo Alto, CA). The field size factor (FSF) was calculated based on the spot profile reconstructed by the in-house dose kernel. The workflow of using FSFs to find the desirable proton fluence is presented. The in-house derived spot profile and FSF were validated by a direct comparison with those calculated by the eclipse TPS. The validation included 420 comparisons of the FSFs from 14 proton energies, various field sizes from 2 to 20 cm and various depths from 20% to 80% of proton range. The relative in-water lateral profiles between the in-house calculation and the eclipse TPS agree very well even at the level of 10 -4 . The FSFs between the in-house calculation and the eclipse TPS also agree well. The maximum deviation is within 0.5%, and the standard deviation is less than 0.1%. The authors' method significantly reduced the time to find the desirable proton fluences of the clinical energies. The method is extensively validated and can be applied to any proton centers using PBS and the eclipse TPS.

Pattern sampling for etch model calibration

NASA Astrophysics Data System (ADS)

Weisbuch, François; Lutich, Andrey; Schatz, Jirka

2017-06-01

Successful patterning requires good control of the photolithography and etch processes. While compact litho models, mainly based on rigorous physics, can predict very well the contours printed in photoresist, pure empirical etch models are less accurate and more unstable. Compact etch models are based on geometrical kernels to compute the litho-etch biases that measure the distance between litho and etch contours. The definition of the kernels as well as the choice of calibration patterns is critical to get a robust etch model. This work proposes to define a set of independent and anisotropic etch kernels -"internal, external, curvature, Gaussian, z_profile" - designed to capture the finest details of the resist contours and represent precisely any etch bias. By evaluating the etch kernels on various structures it is possible to map their etch signatures in a multi-dimensional space and analyze them to find an optimal sampling of structures to train an etch model. The method was specifically applied to a contact layer containing many different geometries and was used to successfully select appropriate calibration structures. The proposed kernels evaluated on these structures were combined to train an etch model significantly better than the standard one. We also illustrate the usage of the specific kernel "z_profile" which adds a third dimension to the description of the resist profile.

Indetermination of particle sizing by laser diffraction in the anomalous size ranges

NASA Astrophysics Data System (ADS)

Pan, Linchao; Ge, Baozhen; Zhang, Fugen

2017-09-01

The laser diffraction method is widely used to measure particle size distributions. It is generally accepted that the scattering angle becomes smaller and the angles to the location of the main peak of scattered energy distributions in laser diffraction instruments shift to smaller values with increasing particle size. This specific principle forms the foundation of the laser diffraction method. However, this principle is not entirely correct for non-absorbing particles in certain size ranges and these particle size ranges are called anomalous size ranges. Here, we derive the analytical formulae for the bounds of the anomalous size ranges and discuss the influence of the width of the size segments on the signature of the Mie scattering kernel. This anomalous signature of the Mie scattering kernel will result in an indetermination of the particle size distribution when measured by laser diffraction instruments in the anomalous size ranges. By using the singular-value decomposition method we interpret the mechanism of occurrence of this indetermination in detail and then validate its existence by using inversion simulations.

Higher-order finite-difference formulation of periodic Orbital-free Density Functional Theory

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ghosh, Swarnava; Suryanarayana, Phanish, E-mail: phanish.suryanarayana@ce.gatech.edu

2016-02-15

We present a real-space formulation and higher-order finite-difference implementation of periodic Orbital-free Density Functional Theory (OF-DFT). Specifically, utilizing a local reformulation of the electrostatic and kernel terms, we develop a generalized framework for performing OF-DFT simulations with different variants of the electronic kinetic energy. In particular, we propose a self-consistent field (SCF) type fixed-point method for calculations involving linear-response kinetic energy functionals. In this framework, evaluation of both the electronic ground-state and forces on the nuclei are amenable to computations that scale linearly with the number of atoms. We develop a parallel implementation of this formulation using the finite-difference discretization.more » We demonstrate that higher-order finite-differences can achieve relatively large convergence rates with respect to mesh-size in both the energies and forces. Additionally, we establish that the fixed-point iteration converges rapidly, and that it can be further accelerated using extrapolation techniques like Anderson's mixing. We validate the accuracy of the results by comparing the energies and forces with plane-wave methods for selected examples, including the vacancy formation energy in Aluminum. Overall, the suitability of the proposed formulation for scalable high performance computing makes it an attractive choice for large-scale OF-DFT calculations consisting of thousands of atoms.« less

7 CFR 51.2559 - Size classifications.

Code of Federal Regulations, 2012 CFR

2012-01-01

... 7 Agriculture 2 2012-01-01 2012-01-01 false Size classifications. 51.2559 Section 51.2559... STANDARDS) United States Standards for Grades of Shelled Pistachio Nuts § 51.2559 Size classifications. (a... the following size classifications. (1) Jumbo Whole Kernels: 80 percent or more by weight shall be...

7 CFR 51.2559 - Size classifications.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 7 Agriculture 2 2011-01-01 2011-01-01 false Size classifications. 51.2559 Section 51.2559... STANDARDS) United States Standards for Grades of Shelled Pistachio Nuts § 51.2559 Size classifications. (a... the following size classifications. (1) Jumbo Whole Kernels: 80 percent or more by weight shall be...

Vis-NIR spectrometric determination of Brix and sucrose in sugar production samples using kernel partial least squares with interval selection based on the successive projections algorithm.

PubMed

de Almeida, Valber Elias; de Araújo Gomes, Adriano; de Sousa Fernandes, David Douglas; Goicoechea, Héctor Casimiro; Galvão, Roberto Kawakami Harrop; Araújo, Mario Cesar Ugulino

2018-05-01

This paper proposes a new variable selection method for nonlinear multivariate calibration, combining the Successive Projections Algorithm for interval selection (iSPA) with the Kernel Partial Least Squares (Kernel-PLS) modelling technique. The proposed iSPA-Kernel-PLS algorithm is employed in a case study involving a Vis-NIR spectrometric dataset with complex nonlinear features. The analytical problem consists of determining Brix and sucrose content in samples from a sugar production system, on the basis of transflectance spectra. As compared to full-spectrum Kernel-PLS, the iSPA-Kernel-PLS models involve a smaller number of variables and display statistically significant superiority in terms of accuracy and/or bias in the predictions. Published by Elsevier B.V.

Nonlocal Poisson-Fermi model for ionic solvent.

PubMed

Xie, Dexuan; Liu, Jinn-Liang; Eisenberg, Bob

2016-07-01

We propose a nonlocal Poisson-Fermi model for ionic solvent that includes ion size effects and polarization correlations among water molecules in the calculation of electrostatic potential. It includes the previous Poisson-Fermi models as special cases, and its solution is the convolution of a solution of the corresponding nonlocal Poisson dielectric model with a Yukawa-like kernel function. The Fermi distribution is shown to be a set of optimal ionic concentration functions in the sense of minimizing an electrostatic potential free energy. Numerical results are reported to show the difference between a Poisson-Fermi solution and a corresponding Poisson solution.

Apollo

DOE Office of Scientific and Technical Information (OSTI.GOV)

Beckingsal, David; Gamblin, Todd

Modern performance portability frameworks provide application developers with a flexible way to determine how to run application kernels, however, they provide no guidance as to the best configuration for a given kernel. Apollo provides a model-generation framework that, when integrated with the RAJA library, uses lightweight decision tree models to select the fastest execution configuration on a per-kernel basis

Margin-maximizing feature elimination methods for linear and nonlinear kernel-based discriminant functions.

PubMed

Aksu, Yaman; Miller, David J; Kesidis, George; Yang, Qing X

2010-05-01

Feature selection for classification in high-dimensional spaces can improve generalization, reduce classifier complexity, and identify important, discriminating feature "markers." For support vector machine (SVM) classification, a widely used technique is recursive feature elimination (RFE). We demonstrate that RFE is not consistent with margin maximization, central to the SVM learning approach. We thus propose explicit margin-based feature elimination (MFE) for SVMs and demonstrate both improved margin and improved generalization, compared with RFE. Moreover, for the case of a nonlinear kernel, we show that RFE assumes that the squared weight vector 2-norm is strictly decreasing as features are eliminated. We demonstrate this is not true for the Gaussian kernel and, consequently, RFE may give poor results in this case. MFE for nonlinear kernels gives better margin and generalization. We also present an extension which achieves further margin gains, by optimizing only two degrees of freedom--the hyperplane's intercept and its squared 2-norm--with the weight vector orientation fixed. We finally introduce an extension that allows margin slackness. We compare against several alternatives, including RFE and a linear programming method that embeds feature selection within the classifier design. On high-dimensional gene microarray data sets, University of California at Irvine (UCI) repository data sets, and Alzheimer's disease brain image data, MFE methods give promising results.

Application of kernel method in fluorescence molecular tomography

NASA Astrophysics Data System (ADS)

Zhao, Yue; Baikejiang, Reheman; Li, Changqing

2017-02-01

Reconstruction of fluorescence molecular tomography (FMT) is an ill-posed inverse problem. Anatomical guidance in the FMT reconstruction can improve FMT reconstruction efficiently. We have developed a kernel method to introduce the anatomical guidance into FMT robustly and easily. The kernel method is from machine learning for pattern analysis and is an efficient way to represent anatomical features. For the finite element method based FMT reconstruction, we calculate a kernel function for each finite element node from an anatomical image, such as a micro-CT image. Then the fluorophore concentration at each node is represented by a kernel coefficient vector and the corresponding kernel function. In the FMT forward model, we have a new system matrix by multiplying the sensitivity matrix with the kernel matrix. Thus, the kernel coefficient vector is the unknown to be reconstructed following a standard iterative reconstruction process. We convert the FMT reconstruction problem into the kernel coefficient reconstruction problem. The desired fluorophore concentration at each node can be calculated accordingly. Numerical simulation studies have demonstrated that the proposed kernel-based algorithm can improve the spatial resolution of the reconstructed FMT images. In the proposed kernel method, the anatomical guidance can be obtained directly from the anatomical image and is included in the forward modeling. One of the advantages is that we do not need to segment the anatomical image for the targets and background.

Home range size of Tengmalm's owl during breeding in Central Europe is determined by prey abundance.

PubMed

Kouba, Marek; Bartoš, Luděk; Tomášek, Václav; Popelková, Alena; Šťastný, Karel; Zárybnická, Markéta

2017-01-01

Animal home ranges typically characterized by their size, shape and a given time interval can be affected by many different biotic and abiotic factors. However, despite the fact that many studies have addressed home ranges, our knowledge of the factors influencing the size of area occupied by different animals is, in many cases, still quite poor, especially among raptors. Using radio-telemetry (VHF; 2.1 g tail-mounted tags) we studied movements of 20 Tengmalm's owl (Aegolius funereus) males during the breeding season in a mountain area of Central Europe (the Czech Republic, the Ore Mountains: 50° 40' N, 13° 35' E) between years 2006-2010, determined their average hunting home range size and explored what factors affected the size of home range utilised. The mean breeding home range size calculated according to 95% fixed kernel density estimator was 190.7 ± 65.7 ha (± SD) with a median value of 187.1 ha. Home range size was affected by prey abundance, presence or absence of polygyny, the number of fledglings, and weather conditions. Home range size increased with decreasing prey abundance. Polygynously mated males had overall larger home range than those mated monogamously, and individuals with more fledged young possessed larger home range compared to those with fewer raised fledglings. Finally, we found that home ranges recorded during harsh weather (nights with strong wind speed and/or heavy rain) were smaller in size than those registered during better weather. Overall, the results provide novel insights into what factors may influence home range size and emphasize the prey abundance as a key factor for breeding dynamics in Tengmalm's owl.

Comparative microstructure study of oil palm fruit bunch fibre, mesocarp and kernels after microwave pre-treatment

NASA Astrophysics Data System (ADS)

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

2017-07-01

The implementation of microwave technology in palm oil processing offers numerous advantages; besides elimination of polluted palm oil mill effluent, it also reduces energy consumption, processing time and space. However, microwave exposure could damage a material’s microstructure which affected the quality of fruit that can be related to its physical structure including the texture and appearance. In this work, empty fruit bunches, mesocarp and kernel was microwave dried and their respective microstructures were examined. The microwave pretreatments were conducted at 100W and 200W and the microstructure investigation of both treated and untreated samples were evaluated using scanning electron microscope. The micrographs demonstrated that microwave does not significantly influence kernel and mesocarp but noticeable change was found on the empty fruit bunches where the sizes of the granular starch were reduced and a small portion of the silica bodies were disrupted. From the experimental data, the microwave irradiation was shown to be efficiently applied on empty fruit bunches followed by mesocarp and kernel as significant weight loss and size reduction was observed after the microwave treatments. The current work showed that microwave treatment did not change the physical surfaces of samples but sample shrinkage is observed.

Multivariate temporal dictionary learning for EEG.

PubMed

Barthélemy, Q; Gouy-Pailler, C; Isaac, Y; Souloumiac, A; Larue, A; Mars, J I

2013-04-30

This article addresses the issue of representing electroencephalographic (EEG) signals in an efficient way. While classical approaches use a fixed Gabor dictionary to analyze EEG signals, this article proposes a data-driven method to obtain an adapted dictionary. To reach an efficient dictionary learning, appropriate spatial and temporal modeling is required. Inter-channels links are taken into account in the spatial multivariate model, and shift-invariance is used for the temporal model. Multivariate learned kernels are informative (a few atoms code plentiful energy) and interpretable (the atoms can have a physiological meaning). Using real EEG data, the proposed method is shown to outperform the classical multichannel matching pursuit used with a Gabor dictionary, as measured by the representative power of the learned dictionary and its spatial flexibility. Moreover, dictionary learning can capture interpretable patterns: this ability is illustrated on real data, learning a P300 evoked potential. Copyright © 2013 Elsevier B.V. All rights reserved.

Omnibus Risk Assessment via Accelerated Failure Time Kernel Machine Modeling

PubMed Central

Sinnott, Jennifer A.; Cai, Tianxi

2013-01-01

Summary Integrating genomic information with traditional clinical risk factors to improve the prediction of disease outcomes could profoundly change the practice of medicine. However, the large number of potential markers and possible complexity of the relationship between markers and disease make it difficult to construct accurate risk prediction models. Standard approaches for identifying important markers often rely on marginal associations or linearity assumptions and may not capture non-linear or interactive effects. In recent years, much work has been done to group genes into pathways and networks. Integrating such biological knowledge into statistical learning could potentially improve model interpretability and reliability. One effective approach is to employ a kernel machine (KM) framework, which can capture nonlinear effects if nonlinear kernels are used (Scholkopf and Smola, 2002; Liu et al., 2007, 2008). For survival outcomes, KM regression modeling and testing procedures have been derived under a proportional hazards (PH) assumption (Li and Luan, 2003; Cai et al., 2011). In this paper, we derive testing and prediction methods for KM regression under the accelerated failure time model, a useful alternative to the PH model. We approximate the null distribution of our test statistic using resampling procedures. When multiple kernels are of potential interest, it may be unclear in advance which kernel to use for testing and estimation. We propose a robust Omnibus Test that combines information across kernels, and an approach for selecting the best kernel for estimation. The methods are illustrated with an application in breast cancer. PMID:24328713

Forecasting landslide activations by means of GA-SAKe. An example of application to three case studies in Calabria (Southern Italy)

NASA Astrophysics Data System (ADS)

Iovine, Giulio G. R.; De Rango, Alessio; Gariano, Stefano L.; Terranova, Oreste G.

2016-04-01

GA-SAKe - the Genetic-Algorithm based release of the hydrological model SAKe (Self Adaptive Kernel) - allows to forecast the timing of activation of landslides [1, 2], based on dates of landslide activations and rainfall series. The model can be applied to either single or set of similar landslides in a homogeneous context. Calibration of the model is performed through Genetic-Algorithm, and provides families of optimal, discretized solutions (kernels) that maximize the fitness function. The mobility functions are obtained through convolution of the optimal kernels with rain series. The shape of the kernel, including its base time, is related to magnitude of the landslide and hydro-geological complexity of the slope. Once validated, the model can be applied to estimate the timing of future landslide activations in the same study area, by employing measured or forecasted rainfall. GA-SAKe is here employed to analyse the historical activations of three rock slides in Calabria (Southern Italy), threatening villages and main infrastructures. In particular: 1) the Acri-Serra di Buda case, developed within a Sackung, involving weathered crystalline and metamorphic rocks; for this case study, 6 dates of activation are available; 2) the San Fili-Uncino case, developed in clay and conglomerate overlaying gneiss and biotitic schist; for this case study, 7 dates of activation are available [2]; 3) the San Benedetto Ullano-San Rocco case, developed in weathered metamorphic rocks; for this case study, 3 dates of activation are available [1, 3, 4, 5]. The obtained results are quite promising, given the high performance of the model against slope movements characterized by numerous historical activations. Obtained results, in terms of shape and base time of the kernels, are compared by taking into account types and sizes of the considered case studies, and involved rock types. References [1] Terranova O.G., Iaquinta P., Gariano S.L., Greco R. & Iovine G. (2013) In: Landslide Science and Practice, Margottini, Canuti, Sassa (Eds.), Vol. 3, pp.73-79. [2] Terranova O.G., Gariano S.L., Iaquinta P. & Iovine G.G.R. (2015). Geosci. Model Dev., 8, 1955-1978. [3] Iovine G., Iaquinta P. & Terranova O. (2009). In Anderssen, Braddock & Newham (Eds.), Proc. 18th World IMACS Congr. and MODSIM09 Int. Congr. on Modelling and Simulation, pp. 2686-2693. [4] Iovine G., Lollino P., Gariano S.L. & Terranova O.G. (2010). NHESS, 10, 2341-2354. [5] Capparelli G., Iaquinta P., Iovine G., Terranova O.G. & Versace P. (2012). Natural Hazards, 61(1), pp.247-256.

Enhancement of encapsulation efficiency of nanoemulsion-containing aripiprazole for the treatment of schizophrenia using mixture experimental design

PubMed Central

Fard Masoumi, Hamid Reza; Basri, Mahiran; Sarah Samiun, Wan; Izadiyan, Zahra; Lim, Chaw Jiang

2015-01-01

Aripiprazole is considered as a third-generation antipsychotic drug with excellent therapeutic efficacy in controlling schizophrenia symptoms and was the first atypical anti-psychotic agent to be approved by the US Food and Drug Administration. Formulation of nanoemulsion-containing aripiprazole was carried out using high shear and high pressure homogenizers. Mixture experimental design was selected to optimize the composition of nanoemulsion. A very small droplet size of emulsion can provide an effective encapsulation for delivery system in the body. The effects of palm kernel oil ester (3–6 wt%), lecithin (2–3 wt%), Tween 80 (0.5–1 wt%), glycerol (1.5–3 wt%), and water (87–93 wt%) on the droplet size of aripiprazole nanoemulsions were investigated. The mathematical model showed that the optimum formulation for preparation of aripiprazole nanoemulsion having the desirable criteria was 3.00% of palm kernel oil ester, 2.00% of lecithin, 1.00% of Tween 80, 2.25% of glycerol, and 91.75% of water. Under optimum formulation, the corresponding predicted response value for droplet size was 64.24 nm, which showed an excellent agreement with the actual value (62.23 nm) with residual standard error <3.2%. PMID:26508853

Multiscale Support Vector Learning With Projection Operator Wavelet Kernel for Nonlinear Dynamical System Identification.

PubMed

Lu, Zhao; Sun, Jing; Butts, Kenneth

2016-02-03

A giant leap has been made in the past couple of decades with the introduction of kernel-based learning as a mainstay for designing effective nonlinear computational learning algorithms. In view of the geometric interpretation of conditional expectation and the ubiquity of multiscale characteristics in highly complex nonlinear dynamic systems [1]-[3], this paper presents a new orthogonal projection operator wavelet kernel, aiming at developing an efficient computational learning approach for nonlinear dynamical system identification. In the framework of multiresolution analysis, the proposed projection operator wavelet kernel can fulfill the multiscale, multidimensional learning to estimate complex dependencies. The special advantage of the projection operator wavelet kernel developed in this paper lies in the fact that it has a closed-form expression, which greatly facilitates its application in kernel learning. To the best of our knowledge, it is the first closed-form orthogonal projection wavelet kernel reported in the literature. It provides a link between grid-based wavelets and mesh-free kernel-based methods. Simulation studies for identifying the parallel models of two benchmark nonlinear dynamical systems confirm its superiority in model accuracy and sparsity.

Testing models of parental investment strategy and offspring size in ants.

PubMed

Gilboa, Smadar; Nonacs, Peter

2006-01-01

Parental investment strategies can be fixed or flexible. A fixed strategy predicts making all offspring a single 'optimal' size. Dynamic models predict flexible strategies with more than one optimal size of offspring. Patterns in the distribution of offspring sizes may thus reveal the investment strategy. Static strategies should produce normal distributions. Dynamic strategies should often result in non-normal distributions. Furthermore, variance in morphological traits should be positively correlated with the length of developmental time the traits are exposed to environmental influences. Finally, the type of deviation from normality (i.e., skewed left or right, or platykurtic) should be correlated with the average offspring size. To test the latter prediction, we used simulations to detect significant departures from normality and categorize distribution types. Data from three species of ants strongly support the predicted patterns for dynamic parental investment. Offspring size distributions are often significantly non-normal. Traits fixed earlier in development, such as head width, are less variable than final body weight. The type of distribution observed correlates with mean female dry weight. The overall support for a dynamic parental investment model has implications for life history theory. Predicted conflicts over parental effort, sex investment ratios, and reproductive skew in cooperative breeders follow from assumptions of static parental investment strategies and omnipresent resource limitations. By contrast, with flexible investment strategies such conflicts can be either absent or maladaptive.

Pressure Sensitivity Kernels Applied to Time-reversal Acoustics

DTIC Science & Technology

2009-06-29

experimental data, along with an internal wave model, using various metrics. The linear limitations of the kernels are explored in the context of time...Acknowledgments . . . . . . . . . . . . . . . . . . . . . . 82 3.A Internal wave modeling . . . . . . . . . . . . . . . . . . . 82 Bibliography...multipaths corresponding to direct path, single surface/bottom bounce, double bounce off the surface and bot- tom, Bottom: Time-domain sensitivity kernel for

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

NASA Astrophysics Data System (ADS)

Sumarjaya, I. W.

2017-10-01

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

A Bayesian Nonparametric Meta-Analysis Model

ERIC Educational Resources Information Center

Karabatsos, George; Talbott, Elizabeth; Walker, Stephen G.

2015-01-01

In a meta-analysis, it is important to specify a model that adequately describes the effect-size distribution of the underlying population of studies. The conventional normal fixed-effect and normal random-effects models assume a normal effect-size population distribution, conditionally on parameters and covariates. For estimating the mean overall…

Multiple kernel learning using single stage function approximation for binary classification problems

NASA Astrophysics Data System (ADS)

Shiju, S.; Sumitra, S.

2017-12-01

In this paper, the multiple kernel learning (MKL) is formulated as a supervised classification problem. We dealt with binary classification data and hence the data modelling problem involves the computation of two decision boundaries of which one related with that of kernel learning and the other with that of input data. In our approach, they are found with the aid of a single cost function by constructing a global reproducing kernel Hilbert space (RKHS) as the direct sum of the RKHSs corresponding to the decision boundaries of kernel learning and input data and searching that function from the global RKHS, which can be represented as the direct sum of the decision boundaries under consideration. In our experimental analysis, the proposed model had shown superior performance in comparison with that of existing two stage function approximation formulation of MKL, where the decision functions of kernel learning and input data are found separately using two different cost functions. This is due to the fact that single stage representation helps the knowledge transfer between the computation procedures for finding the decision boundaries of kernel learning and input data, which inturn boosts the generalisation capacity of the model.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liang, Bin; Li, Yongbao; Liu, Bo

Purpose: CyberKnife system is initially equipped with fixed circular cones for stereotactic radiosurgery. Two dose calculation algorithms, Ray-Tracing and Monte Carlo, are available in the supplied treatment planning system. A multileaf collimator system was recently introduced in the latest generation of system, capable of arbitrarily shaped treatment field. The purpose of this study is to develop a model based dose calculation algorithm to better handle the lateral scatter in an irregularly shaped small field for the CyberKnife system. Methods: A pencil beam dose calculation algorithm widely used in linac based treatment planning system was modified. The kernel parameters and intensitymore » profile were systematically determined by fitting to the commissioning data. The model was tuned using only a subset of measured data (4 out of 12 cones) and applied to all fixed circular cones for evaluation. The root mean square (RMS) of the difference between the measured and calculated tissue-phantom-ratios (TPRs) and off-center-ratio (OCR) was compared. Three cone size correction techniques were developed to better fit the OCRs at the penumbra region, which are further evaluated by the output factors (OFs). The pencil beam model was further validated against measurement data on the variable dodecagon-shaped Iris collimators and a half-beam blocked field. Comparison with Ray-Tracing and Monte Carlo methods was also performed on a lung SBRT case. Results: The RMS between the measured and calculated TPRs is 0.7% averaged for all cones, with the descending region at 0.5%. The RMSs of OCR at infield and outfield regions are both at 0.5%. The distance to agreement (DTA) at the OCR penumbra region is 0.2 mm. All three cone size correction models achieve the same improvement in OCR agreement, with the effective source shift model (SSM) preferred, due to their ability to predict more accurately the OF variations with the source to axis distance (SAD). In noncircular field validation, the pencil beam calculated results agreed well with the film measurement of both Iris collimators and the half-beam blocked field, fared much better than the Ray-Tracing calculation. Conclusions: The authors have developed a pencil beam dose calculation model for the CyberKnife system. The dose calculation accuracy is better than the standard linac based system because the model parameters were specifically tuned to the CyberKnife system and geometry correction factors. The model handles better the lateral scatter and has the potential to be used for the irregularly shaped fields. Comprehensive validations on MLC equipped system are necessary for its clinical implementation. It is reasonably fast enough to be used during plan optimization.« less

Optimisation of quantitative lung SPECT applied to mild COPD: a software phantom simulation study.

PubMed

Norberg, Pernilla; Olsson, Anna; Alm Carlsson, Gudrun; Sandborg, Michael; Gustafsson, Agnetha

2015-01-01

The amount of inhomogeneities in a (99m)Tc Technegas single-photon emission computed tomography (SPECT) lung image, caused by reduced ventilation in lung regions affected by chronic obstructive pulmonary disease (COPD), is correlated to disease advancement. A quantitative analysis method, the CVT method, measuring these inhomogeneities was proposed in earlier work. To detect mild COPD, which is a difficult task, optimised parameter values are needed. In this work, the CVT method was optimised with respect to the parameter values of acquisition, reconstruction and analysis. The ordered subset expectation maximisation (OSEM) algorithm was used for reconstructing the lung SPECT images. As a first step towards clinical application of the CVT method in detecting mild COPD, this study was based on simulated SPECT images of an advanced anthropomorphic lung software phantom including respiratory and cardiac motion, where the mild COPD lung had an overall ventilation reduction of 5%. The best separation between healthy and mild COPD lung images as determined using the CVT measure of ventilation inhomogeneity and 125 MBq (99m)Tc was obtained using a low-energy high-resolution collimator (LEHR) and a power 6 Butterworth post-filter with a cutoff frequency of 0.6 to 0.7 cm(-1). Sixty-four reconstruction updates and a small kernel size should be used when the whole lung is analysed, and for the reduced lung a greater number of updates and a larger kernel size are needed. A LEHR collimator and 125 (99m)Tc MBq together with an optimal combination of cutoff frequency, number of updates and kernel size, gave the best result. Suboptimal selections of either cutoff frequency, number of updates and kernel size will reduce the imaging system's ability to detect mild COPD in the lung phantom.

Theoretical size distribution of fossil taxa: analysis of a null model

PubMed Central

Reed, William J; Hughes, Barry D

2007-01-01

Background This article deals with the theoretical size distribution (of number of sub-taxa) of a fossil taxon arising from a simple null model of macroevolution. Model New species arise through speciations occurring independently and at random at a fixed probability rate, while extinctions either occur independently and at random (background extinctions) or cataclysmically. In addition new genera are assumed to arise through speciations of a very radical nature, again assumed to occur independently and at random at a fixed probability rate. Conclusion The size distributions of the pioneering genus (following a cataclysm) and of derived genera are determined. Also the distribution of the number of genera is considered along with a comparison of the probability of a monospecific genus with that of a monogeneric family. PMID:17376249

Bias and Precision of Measures of Association for a Fixed-Effect Multivariate Analysis of Variance Model

ERIC Educational Resources Information Center

Kim, Soyoung; Olejnik, Stephen

2005-01-01

The sampling distributions of five popular measures of association with and without two bias adjusting methods were examined for the single factor fixed-effects multivariate analysis of variance model. The number of groups, sample sizes, number of outcomes, and the strength of association were manipulated. The results indicate that all five…

Modeling the spread and control of foot-and-mouth disease in Pennsylvania following its discovery and options for control

PubMed Central

Tildesley, Michael J.; Smith, Gary; Keeling, Matt J.

2013-01-01

In this paper, we simulate outbreaks of foot-and-mouth disease in the Commonwealth of Pennsylvania, USA – after the introduction of a state-wide movement ban – as they might unfold in the presence of mitigation strategies. We have adapted a model previously used to investigate FMD control policies in the UK to examine the potential for disease spread given an infection seeded in each county in Pennsylvania. The results are highly dependent upon the county of introduction and the spatial scale of transmission. Should the transmission kernel be identical to that for the UK, the epidemic impact is limited to fewer than 20 premises, regardless of the county of introduction. However, for wider kernels where infection can spread further, outbreaks seeded in or near the county with highest density of premises and animals result in large epidemics (>150 premises). Ring culling and vaccination reduce epidemic size, with the optimal radius of the rings being dependent upon the county of introduction. Should the kernel width exceed a given county-dependent threshold, ring culling is unable to control the epidemic. We find that a vaccinate-to-live policy is generally preferred to ring culling (in terms of reducing the overall number of premises culled), indicating that well-targeted control can dramatically reduce the risk of large scale outbreaks of foot-and-mouth disease occurring in Pennsylvania. PMID:22169708

Reduced kernel recursive least squares algorithm for aero-engine degradation prediction

NASA Astrophysics Data System (ADS)

Zhou, Haowen; Huang, Jinquan; Lu, Feng

2017-10-01

Kernel adaptive filters (KAFs) generate a linear growing radial basis function (RBF) network with the number of training samples, thereby lacking sparseness. To deal with this drawback, traditional sparsification techniques select a subset of original training data based on a certain criterion to train the network and discard the redundant data directly. Although these methods curb the growth of the network effectively, it should be noted that information conveyed by these redundant samples is omitted, which may lead to accuracy degradation. In this paper, we present a novel online sparsification method which requires much less training time without sacrificing the accuracy performance. Specifically, a reduced kernel recursive least squares (RKRLS) algorithm is developed based on the reduced technique and the linear independency. Unlike conventional methods, our novel methodology employs these redundant data to update the coefficients of the existing network. Due to the effective utilization of the redundant data, the novel algorithm achieves a better accuracy performance, although the network size is significantly reduced. Experiments on time series prediction and online regression demonstrate that RKRLS algorithm requires much less computational consumption and maintains the satisfactory accuracy performance. Finally, we propose an enhanced multi-sensor prognostic model based on RKRLS and Hidden Markov Model (HMM) for remaining useful life (RUL) estimation. A case study in a turbofan degradation dataset is performed to evaluate the performance of the novel prognostic approach.

Metabolic network prediction through pairwise rational kernels.

PubMed

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

2014-09-26

Metabolic networks are represented by the set of metabolic pathways. Metabolic pathways are a series of biochemical reactions, in which the product (output) from one reaction serves as the substrate (input) to another reaction. Many pathways remain incompletely characterized. One of the major challenges of computational biology is to obtain better models of metabolic pathways. Existing models are dependent on the annotation of the genes. This propagates error accumulation when the pathways are predicted by incorrectly annotated genes. Pairwise classification methods are supervised learning methods used to classify new pair of entities. Some of these classification methods, e.g., Pairwise Support Vector Machines (SVMs), use pairwise kernels. Pairwise kernels describe similarity measures between two pairs of entities. Using pairwise kernels to handle sequence data requires long processing times and large storage. Rational kernels are kernels based on weighted finite-state transducers that represent similarity measures between sequences or automata. They have been effectively used in problems that handle large amount of sequence information such as protein essentiality, natural language processing and machine translations. We create a new family of pairwise kernels using weighted finite-state transducers (called Pairwise Rational Kernel (PRK)) to predict metabolic pathways from a variety of biological data. PRKs take advantage of the simpler representations and faster algorithms of transducers. Because raw sequence data can be used, the predictor model avoids the errors introduced by incorrect gene annotations. We then developed several experiments with PRKs and Pairwise SVM to validate our methods using the metabolic network of Saccharomyces cerevisiae. As a result, when PRKs are used, our method executes faster in comparison with other pairwise kernels. Also, when we use PRKs combined with other simple kernels that include evolutionary information, the accuracy values have been improved, while maintaining lower construction and execution times. The power of using kernels is that almost any sort of data can be represented using kernels. Therefore, completely disparate types of data can be combined to add power to kernel-based machine learning methods. When we compared our proposal using PRKs with other similar kernel, the execution times were decreased, with no compromise of accuracy. We also proved that by combining PRKs with other kernels that include evolutionary information, the accuracy can also also be improved. As our proposal can use any type of sequence data, genes do not need to be properly annotated, avoiding accumulation errors because of incorrect previous annotations.

Quantum entanglement in photoactive prebiotic systems.

PubMed

Tamulis, Arvydas; Grigalavicius, Mantas

2014-06-01

This paper contains the review of quantum entanglement investigations in living systems, and in the quantum mechanically modelled photoactive prebiotic kernel systems. We define our modelled self-assembled supramolecular photoactive centres, composed of one or more sensitizer molecules, precursors of fatty acids and a number of water molecules, as a photoactive prebiotic kernel systems. We propose that life first emerged in the form of such minimal photoactive prebiotic kernel systems and later in the process of evolution these photoactive prebiotic kernel systems would have produced fatty acids and covered themselves with fatty acid envelopes to become the minimal cells of the Fatty Acid World. Specifically, we model self-assembling of photoactive prebiotic systems with observed quantum entanglement phenomena. We address the idea that quantum entanglement was important in the first stages of origins of life and evolution of the biospheres because simultaneously excite two prebiotic kernels in the system by appearance of two additional quantum entangled excited states, leading to faster growth and self-replication of minimal living cells. The quantum mechanically modelled possibility of synthesizing artificial self-reproducing quantum entangled prebiotic kernel systems and minimal cells also impacts the possibility of the most probable path of emergence of protocells on the Earth or elsewhere. We also examine the quantum entangled logic gates discovered in the modelled systems composed of two prebiotic kernels. Such logic gates may have application in the destruction of cancer cells or becoming building blocks of new forms of artificial cells including magnetically active ones.

Sensitivities Kernels of Seismic Traveltimes and Amplitudes for Quality Factor and Boundary Topography

NASA Astrophysics Data System (ADS)

Hsieh, M.; Zhao, L.; Ma, K.

2010-12-01

Finite-frequency approach enables seismic tomography to fully utilize the spatial and temporal distributions of the seismic wavefield to improve resolution. In achieving this goal, one of the most important tasks is to compute efficiently and accurately the (Fréchet) sensitivity kernels of finite-frequency seismic observables such as traveltime and amplitude to the perturbations of model parameters. In scattering-integral approach, the Fréchet kernels are expressed in terms of the strain Green tensors (SGTs), and a pre-established SGT database is necessary to achieve practical efficiency for a three-dimensional reference model in which the SGTs must be calculated numerically. Methods for computing Fréchet kernels for seismic velocities have long been established. In this study, we develop algorithms based on the finite-difference method for calculating Fréchet kernels for the quality factor Qμ and seismic boundary topography. Kernels for the quality factor can be obtained in a way similar to those for seismic velocities with the help of the Hilbert transform. The effects of seismic velocities and quality factor on either traveltime or amplitude are coupled. Kernels for boundary topography involve spatial gradient of the SGTs and they also exhibit interesting finite-frequency characteristics. Examples of quality factor and boundary topography kernels will be shown for a realistic model for the Taiwan region with three-dimensional velocity variation as well as surface and Moho discontinuity topography.

GPU Acceleration of Mean Free Path Based Kernel Density Estimators for Monte Carlo Neutronics Simulations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Burke, TImothy P.; Kiedrowski, Brian C.; Martin, William R.

Kernel Density Estimators (KDEs) are a non-parametric density estimation technique that has recently been applied to Monte Carlo radiation transport simulations. Kernel density estimators are an alternative to histogram tallies for obtaining global solutions in Monte Carlo tallies. With KDEs, a single event, either a collision or particle track, can contribute to the score at multiple tally points with the uncertainty at those points being independent of the desired resolution of the solution. Thus, KDEs show potential for obtaining estimates of a global solution with reduced variance when compared to a histogram. Previously, KDEs have been applied to neutronics formore » one-group reactor physics problems and fixed source shielding applications. However, little work was done to obtain reaction rates using KDEs. This paper introduces a new form of the MFP KDE that is capable of handling general geometries. Furthermore, extending the MFP KDE to 2-D problems in continuous energy introduces inaccuracies to the solution. An ad-hoc solution to these inaccuracies is introduced that produces errors smaller than 4% at material interfaces.« less

The effect of smelting time and composition of palm kernel shell charcoal reductant toward extractive Pomalaa nickel laterite ore in mini electric arc furnace

NASA Astrophysics Data System (ADS)

Sihotang, Iqbal Huda; Supriyatna, Yayat Iman; Ismail, Ika; Sulistijono

2018-04-01

Indonesia is a country that is rich in natural resources. Being a third country which has a nickel laterite ore in the world after New Caledonia and Philippines. However, the processing of nickel laterite ore to increase its levels in Indonesia is still lacking. In the processing of nickel laterite ore into metal, it can be processed by pyrometallurgy method that typically use coal as a reductant. However, coal is a non-renewable energy and have high enough levels of pollution. One potentially replace is the biomass, that is a renewable energy. Palm kernel shell are biomass that can be used as a reductant because it has a fairly high fix carbon content. This research aims to make nickel laterite ores become metal using palm kernel shell charcoal as reductant in mini electric arc furnace. The result show that the best smelting time of this research is 60 minutes with the best composition of the reductant is 2,000 gram.

TH-C-BRD-04: Beam Modeling and Validation with Triple and Double Gaussian Dose Kernel for Spot Scanning Proton Beams

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hirayama, S; Takayanagi, T; Fujii, Y

2014-06-15

Purpose: To present the validity of our beam modeling with double and triple Gaussian dose kernels for spot scanning proton beams in Nagoya Proton Therapy Center. This study investigates the conformance between the measurements and calculation results in absolute dose with two types of beam kernel. Methods: A dose kernel is one of the important input data required for the treatment planning software. The dose kernel is the 3D dose distribution of an infinitesimal pencil beam of protons in water and consists of integral depth doses and lateral distributions. We have adopted double and triple Gaussian model as lateral distributionmore » in order to take account of the large angle scattering due to nuclear reaction by fitting simulated inwater lateral dose profile for needle proton beam at various depths. The fitted parameters were interpolated as a function of depth in water and were stored as a separate look-up table for the each beam energy. The process of beam modeling is based on the method of MDACC [X.R.Zhu 2013]. Results: From the comparison results between the absolute doses calculated by double Gaussian model and those measured at the center of SOBP, the difference is increased up to 3.5% in the high-energy region because the large angle scattering due to nuclear reaction is not sufficiently considered at intermediate depths in the double Gaussian model. In case of employing triple Gaussian dose kernels, the measured absolute dose at the center of SOBP agrees with calculation within ±1% regardless of the SOBP width and maximum range. Conclusion: We have demonstrated the beam modeling results of dose distribution employing double and triple Gaussian dose kernel. Treatment planning system with the triple Gaussian dose kernel has been successfully verified and applied to the patient treatment with a spot scanning technique in Nagoya Proton Therapy Center.« less

Analysis Monthly Import of Palm Oil Products Using Box-Jenkins Model

NASA Astrophysics Data System (ADS)

Ahmad, Nurul F. Y.; Khalid, Kamil; Saifullah Rusiman, Mohd; Ghazali Kamardan, M.; Roslan, Rozaini; Che-Him, Norziha

2018-04-01

The palm oil industry has been an important component of the national economy especially the agriculture sector. The aim of this study is to identify the pattern of import of palm oil products, to model the time series using Box-Jenkins model and to forecast the monthly import of palm oil products. The method approach is included in the statistical test for verifying the equivalence model and statistical measurement of three models, namely Autoregressive (AR) model, Moving Average (MA) model and Autoregressive Moving Average (ARMA) model. The model identification of all product import palm oil is different in which the AR(1) was found to be the best model for product import palm oil while MA(3) was found to be the best model for products import palm kernel oil. For the palm kernel, MA(4) was found to be the best model. The results forecast for the next four months for products import palm oil, palm kernel oil and palm kernel showed the most significant decrease compared to the actual data.

Genomic similarity and kernel methods I: advancements by building on mathematical and statistical foundations.

PubMed

Schaid, Daniel J

2010-01-01

Measures of genomic similarity are the basis of many statistical analytic methods. We review the mathematical and statistical basis of similarity methods, particularly based on kernel methods. A kernel function converts information for a pair of subjects to a quantitative value representing either similarity (larger values meaning more similar) or distance (smaller values meaning more similar), with the requirement that it must create a positive semidefinite matrix when applied to all pairs of subjects. This review emphasizes the wide range of statistical methods and software that can be used when similarity is based on kernel methods, such as nonparametric regression, linear mixed models and generalized linear mixed models, hierarchical models, score statistics, and support vector machines. The mathematical rigor for these methods is summarized, as is the mathematical framework for making kernels. This review provides a framework to move from intuitive and heuristic approaches to define genomic similarities to more rigorous methods that can take advantage of powerful statistical modeling and existing software. A companion paper reviews novel approaches to creating kernels that might be useful for genomic analyses, providing insights with examples [1]. Copyright © 2010 S. Karger AG, Basel.

Omnibus risk assessment via accelerated failure time kernel machine modeling.

PubMed

Sinnott, Jennifer A; Cai, Tianxi

2013-12-01

Integrating genomic information with traditional clinical risk factors to improve the prediction of disease outcomes could profoundly change the practice of medicine. However, the large number of potential markers and possible complexity of the relationship between markers and disease make it difficult to construct accurate risk prediction models. Standard approaches for identifying important markers often rely on marginal associations or linearity assumptions and may not capture non-linear or interactive effects. In recent years, much work has been done to group genes into pathways and networks. Integrating such biological knowledge into statistical learning could potentially improve model interpretability and reliability. One effective approach is to employ a kernel machine (KM) framework, which can capture nonlinear effects if nonlinear kernels are used (Scholkopf and Smola, 2002; Liu et al., 2007, 2008). For survival outcomes, KM regression modeling and testing procedures have been derived under a proportional hazards (PH) assumption (Li and Luan, 2003; Cai, Tonini, and Lin, 2011). In this article, we derive testing and prediction methods for KM regression under the accelerated failure time (AFT) model, a useful alternative to the PH model. We approximate the null distribution of our test statistic using resampling procedures. When multiple kernels are of potential interest, it may be unclear in advance which kernel to use for testing and estimation. We propose a robust Omnibus Test that combines information across kernels, and an approach for selecting the best kernel for estimation. The methods are illustrated with an application in breast cancer. © 2013, The International Biometric Society.

Revisiting the Cramér Rao Lower Bound for Elastography: Predicting the Performance of Axial, Lateral and Polar Strain Elastograms.

PubMed

Verma, Prashant; Doyley, Marvin M

2017-09-01

We derived the Cramér Rao lower bound for 2-D estimators employed in quasi-static elastography. To illustrate the theory, we modeled the 2-D point spread function as a sinc-modulated sine pulse in the axial direction and as a sinc function in the lateral direction. We compared theoretical predictions of the variance incurred in displacements and strains when quasi-static elastography was performed under varying conditions (different scanning methods, different configuration of conventional linear array imaging and different-size kernels) with those measured from simulated or experimentally acquired data. We performed studies to illustrate the application of the derived expressions when performing vascular elastography with plane wave and compounded plane wave imaging. Standard deviations in lateral displacements were an order higher than those in axial. Additionally, the derived expressions predicted that peak performance should occur when 2% strain is applied, the same order of magnitude as observed in simulations (1%) and experiments (1%-2%). We assessed how different configurations of conventional linear array imaging (number of active reception and transmission elements) influenced the quality of axial and lateral strain elastograms. The theoretical expressions predicted that 2-D echo tracking should be performed with wide kernels, but the length of the kernels should be selected using knowledge of the magnitude of the applied strain: specifically, longer kernels for small strains (<5%) and shorter kernels for larger strains. Although the general trends of theoretical predictions and experimental observations were similar, biases incurred during beamforming and subsample displacement estimation produced noticeable differences. Copyright © 2017 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.

Image re-sampling detection through a novel interpolation kernel.

PubMed

Hilal, Alaa

2018-06-01

Image re-sampling involved in re-size and rotation transformations is an essential element block in a typical digital image alteration. Fortunately, traces left from such processes are detectable, proving that the image has gone a re-sampling transformation. Within this context, we present in this paper two original contributions. First, we propose a new re-sampling interpolation kernel. It depends on five independent parameters that controls its amplitude, angular frequency, standard deviation, and duration. Then, we demonstrate its capacity to imitate the same behavior of the most frequent interpolation kernels used in digital image re-sampling applications. Secondly, the proposed model is used to characterize and detect the correlation coefficients involved in re-sampling transformations. The involved process includes a minimization of an error function using the gradient method. The proposed method is assessed over a large database of 11,000 re-sampled images. Additionally, it is implemented within an algorithm in order to assess images that had undergone complex transformations. Obtained results demonstrate better performance and reduced processing time when compared to a reference method validating the suitability of the proposed approaches. Copyright © 2018 Elsevier B.V. All rights reserved.

Richardson-Lucy deblurring for the star scene under a thinning motion path

NASA Astrophysics Data System (ADS)

Su, Laili; Shao, Xiaopeng; Wang, Lin; Wang, Haixin; Huang, Yining

2015-05-01

This paper puts emphasis on how to model and correct image blur that arises from a camera's ego motion while observing a distant star scene. Concerning the significance of accurate estimation of point spread function (PSF), a new method is employed to obtain blur kernel by thinning star motion path. In particular, how the blurred star image can be corrected to reconstruct the clear scene with a thinning motion blur model which describes the camera's path is presented. This thinning motion path to build blur kernel model is more effective at modeling the spatially motion blur introduced by camera's ego motion than conventional blind estimation of kernel-based PSF parameterization. To gain the reconstructed image, firstly, an improved thinning algorithm is used to obtain the star point trajectory, so as to extract the blur kernel of the motion-blurred star image. Then how motion blur model can be incorporated into the Richardson-Lucy (RL) deblurring algorithm, which reveals its overall effectiveness, is detailed. In addition, compared with the conventional estimated blur kernel, experimental results show that the proposed method of using thinning algorithm to get the motion blur kernel is of less complexity, higher efficiency and better accuracy, which contributes to better restoration of the motion-blurred star images.

Joint and collaborative representation with local Volterra kernels convolution feature for face recognition

NASA Astrophysics Data System (ADS)

Feng, Guang; Li, Hengjian; Dong, Jiwen; Chen, Xi; Yang, Huiru

2018-04-01

In this paper, we proposed a joint and collaborative representation with Volterra kernel convolution feature (JCRVK) for face recognition. Firstly, the candidate face images are divided into sub-blocks in the equal size. The blocks are extracted feature using the two-dimensional Voltera kernels discriminant analysis, which can better capture the discrimination information from the different faces. Next, the proposed joint and collaborative representation is employed to optimize and classify the local Volterra kernels features (JCR-VK) individually. JCR-VK is very efficiently for its implementation only depending on matrix multiplication. Finally, recognition is completed by using the majority voting principle. Extensive experiments on the Extended Yale B and AR face databases are conducted, and the results show that the proposed approach can outperform other recently presented similar dictionary algorithms on recognition accuracy.

Optimal focal-plane restoration

NASA Technical Reports Server (NTRS)

Reichenbach, Stephen E.; Park, Stephen K.

1989-01-01

Image restoration can be implemented efficiently by calculating the convolution of the digital image and a small kernel during image acquisition. Processing the image in the focal-plane in this way requires less computation than traditional Fourier-transform-based techniques such as the Wiener filter and constrained least-squares filter. Here, the values of the convolution kernel that yield the restoration with minimum expected mean-square error are determined using a frequency analysis of the end-to-end imaging system. This development accounts for constraints on the size and shape of the spatial kernel and all the components of the imaging system. Simulation results indicate the technique is effective and efficient.

Flood susceptibility mapping using a novel ensemble weights-of-evidence and support vector machine models in GIS

NASA Astrophysics Data System (ADS)

Tehrany, Mahyat Shafapour; Pradhan, Biswajeet; Jebur, Mustafa Neamah

2014-05-01

Flood is one of the most devastating natural disasters that occur frequently in Terengganu, Malaysia. Recently, ensemble based techniques are getting extremely popular in flood modeling. In this paper, weights-of-evidence (WoE) model was utilized first, to assess the impact of classes of each conditioning factor on flooding through bivariate statistical analysis (BSA). Then, these factors were reclassified using the acquired weights and entered into the support vector machine (SVM) model to evaluate the correlation between flood occurrence and each conditioning factor. Through this integration, the weak point of WoE can be solved and the performance of the SVM will be enhanced. The spatial database included flood inventory, slope, stream power index (SPI), topographic wetness index (TWI), altitude, curvature, distance from the river, geology, rainfall, land use/cover (LULC), and soil type. Four kernel types of SVM (linear kernel (LN), polynomial kernel (PL), radial basis function kernel (RBF), and sigmoid kernel (SIG)) were used to investigate the performance of each kernel type. The efficiency of the new ensemble WoE and SVM method was tested using area under curve (AUC) which measured the prediction and success rates. The validation results proved the strength and efficiency of the ensemble method over the individual methods. The best results were obtained from RBF kernel when compared with the other kernel types. Success rate and prediction rate for ensemble WoE and RBF-SVM method were 96.48% and 95.67% respectively. The proposed ensemble flood susceptibility mapping method could assist researchers and local governments in flood mitigation strategies.

Single Image Super-Resolution Based on Multi-Scale Competitive Convolutional Neural Network

PubMed Central

Qu, Xiaobo; He, Yifan

2018-01-01

Deep convolutional neural networks (CNNs) are successful in single-image super-resolution. Traditional CNNs are limited to exploit multi-scale contextual information for image reconstruction due to the fixed convolutional kernel in their building modules. To restore various scales of image details, we enhance the multi-scale inference capability of CNNs by introducing competition among multi-scale convolutional filters, and build up a shallow network under limited computational resources. The proposed network has the following two advantages: (1) the multi-scale convolutional kernel provides the multi-context for image super-resolution, and (2) the maximum competitive strategy adaptively chooses the optimal scale of information for image reconstruction. Our experimental results on image super-resolution show that the performance of the proposed network outperforms the state-of-the-art methods. PMID:29509666

Single Image Super-Resolution Based on Multi-Scale Competitive Convolutional Neural Network.

PubMed

Du, Xiaofeng; Qu, Xiaobo; He, Yifan; Guo, Di

2018-03-06

Deep convolutional neural networks (CNNs) are successful in single-image super-resolution. Traditional CNNs are limited to exploit multi-scale contextual information for image reconstruction due to the fixed convolutional kernel in their building modules. To restore various scales of image details, we enhance the multi-scale inference capability of CNNs by introducing competition among multi-scale convolutional filters, and build up a shallow network under limited computational resources. The proposed network has the following two advantages: (1) the multi-scale convolutional kernel provides the multi-context for image super-resolution, and (2) the maximum competitive strategy adaptively chooses the optimal scale of information for image reconstruction. Our experimental results on image super-resolution show that the performance of the proposed network outperforms the state-of-the-art methods.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pavlou, A. T.; Betzler, B. R.; Burke, T. P.

Uncertainties in the composition and fabrication of fuel compacts for the Fort St. Vrain (FSV) high temperature gas reactor have been studied by performing eigenvalue sensitivity studies that represent the key uncertainties for the FSV neutronic analysis. The uncertainties for the TRISO fuel kernels were addressed by developing a suite of models for an 'average' FSV fuel compact that models the fuel as (1) a mixture of two different TRISO fuel particles representing fissile and fertile kernels, (2) a mixture of four different TRISO fuel particles representing small and large fissile kernels and small and large fertile kernels and (3)more » a stochastic mixture of the four types of fuel particles where every kernel has its diameter sampled from a continuous probability density function. All of the discrete diameter and continuous diameter fuel models were constrained to have the same fuel loadings and packing fractions. For the non-stochastic discrete diameter cases, the MCNP compact model arranged the TRISO fuel particles on a hexagonal honeycomb lattice. This lattice-based fuel compact was compared to a stochastic compact where the locations (and kernel diameters for the continuous diameter cases) of the fuel particles were randomly sampled. Partial core configurations were modeled by stacking compacts into fuel columns containing graphite. The differences in eigenvalues between the lattice-based and stochastic models were small but the runtime of the lattice-based fuel model was roughly 20 times shorter than with the stochastic-based fuel model. (authors)« less

Using Adjoint Methods to Improve 3-D Velocity Models of Southern California

NASA Astrophysics Data System (ADS)

Liu, Q.; Tape, C.; Maggi, A.; Tromp, J.

2006-12-01

We use adjoint methods popular in climate and ocean dynamics to calculate Fréchet derivatives for tomographic inversions in southern California. The Fréchet derivative of an objective function χ(m), where m denotes the Earth model, may be written in the generic form δχ=int Km(x) δln m(x) d3x, where δln m=δ m/m denotes the relative model perturbation. For illustrative purposes, we construct the 3-D finite-frequency banana-doughnut kernel Km, corresponding to the misfit of a single traveltime measurement, by simultaneously computing the 'adjoint' wave field s† forward in time and reconstructing the regular wave field s backward in time. The adjoint wave field is produced by using the time-reversed velocity at the receiver as a fictitious source, while the regular wave field is reconstructed on the fly by propagating the last frame of the wave field saved by a previous forward simulation backward in time. The approach is based upon the spectral-element method, and only two simulations are needed to produce density, shear-wave, and compressional-wave sensitivity kernels. This method is applied to the SCEC southern California velocity model. Various density, shear-wave, and compressional-wave sensitivity kernels are presented for different phases in the seismograms. We also generate 'event' kernels for Pnl, S and surface waves, which are the Fréchet kernels of misfit functions that measure the P, S or surface wave traveltime residuals at all the receivers simultaneously for one particular event. Effectively, an event kernel is a sum of weighted Fréchet kernels, with weights determined by the associated traveltime anomalies. By the nature of the 3-D simulation, every event kernel is also computed based upon just two simulations, i.e., its construction costs the same amount of computation time as an individual banana-doughnut kernel. One can think of the sum of the event kernels for all available earthquakes, called the 'misfit' kernel, as a graphical representation of the gradient of the misfit function. With the capability of computing both the value of the misfit function and its gradient, which assimilates the traveltime anomalies, we are ready to use a non-linear conjugate gradient algorithm to iteratively improve velocity models of southern California.

Performance of fly ash based geopolymer incorporating palm kernel shell for lightweight concrete

NASA Astrophysics Data System (ADS)

Razak, Rafiza Abd; Abdullah, Mohd Mustafa Al Bakri; Yahya, Zarina; Jian, Ang Zhi; Nasri, Armia

2017-09-01

A concrete which cement is totally replaced by source material such as fly ash and activated by highly alkaline solutions is known as geopolymer concrete. Fly ash is the most common source material for geopolymer because it is a by-product material, so it can get easily from all around the world. An investigation has been carried out to select the most suitable ingredients of geopolymer concrete so that the geopolymer concrete can achieve the desire compressive strength. The samples were prepared to determine the suitable percentage of palm kernel shell used in geopolymer concrete and cured for 7 days in oven. After that, other samples were prepared by using the suitable percentage of palm kernel shell and cured for 3, 14, 21 and 28 days in oven. The control sample consisting of ordinary Portland cement and palm kernel shell and cured for 28 days were prepared too. The NaOH concentration of 12M, ratio Na2SiO3 to NaOH of 2.5, ratio fly ash to alkaline activator solution of 2.0 and ratio water to geopolymer of 0.35 were fixed throughout the research. The density obtained for the samples were 1.78 kg/m3, water absorption of 20.41% and the compressive strength of 14.20 MPa. The compressive strength of geopolymer concrete is still acceptable as lightweight concrete although the compressive strength is lower than OPC concrete. Therefore, the proposed method by using fly ash mixed with 10% of palm kernel shell can be used to design geopolymer concrete.

Mixed kernel function support vector regression for global sensitivity analysis

NASA Astrophysics Data System (ADS)

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.

Accelerating population balance-Monte Carlo simulation for coagulation dynamics from the Markov jump model, stochastic algorithm and GPU parallel computing

DOE Office of Scientific and Technical Information (OSTI.GOV)

Xu, Zuwei; Zhao, Haibo, E-mail: klinsmannzhb@163.com; Zheng, Chuguang

2015-01-15

This paper proposes a comprehensive framework for accelerating population balance-Monte Carlo (PBMC) simulation of particle coagulation dynamics. By combining Markov jump model, weighted majorant kernel and GPU (graphics processing unit) parallel computing, a significant gain in computational efficiency is achieved. The Markov jump model constructs a coagulation-rule matrix of differentially-weighted simulation particles, so as to capture the time evolution of particle size distribution with low statistical noise over the full size range and as far as possible to reduce the number of time loopings. Here three coagulation rules are highlighted and it is found that constructing appropriate coagulation rule providesmore » a route to attain the compromise between accuracy and cost of PBMC methods. Further, in order to avoid double looping over all simulation particles when considering the two-particle events (typically, particle coagulation), the weighted majorant kernel is introduced to estimate the maximum coagulation rates being used for acceptance–rejection processes by single-looping over all particles, and meanwhile the mean time-step of coagulation event is estimated by summing the coagulation kernels of rejected and accepted particle pairs. The computational load of these fast differentially-weighted PBMC simulations (based on the Markov jump model) is reduced greatly to be proportional to the number of simulation particles in a zero-dimensional system (single cell). Finally, for a spatially inhomogeneous multi-dimensional (multi-cell) simulation, the proposed fast PBMC is performed in each cell, and multiple cells are parallel processed by multi-cores on a GPU that can implement the massively threaded data-parallel tasks to obtain remarkable speedup ratio (comparing with CPU computation, the speedup ratio of GPU parallel computing is as high as 200 in a case of 100 cells with 10 000 simulation particles per cell). These accelerating approaches of PBMC are demonstrated in a physically realistic Brownian coagulation case. The computational accuracy is validated with benchmark solution of discrete-sectional method. The simulation results show that the comprehensive approach can attain very favorable improvement in cost without sacrificing computational accuracy.« less

Geographically weighted regression model on poverty indicator

NASA Astrophysics Data System (ADS)

Slamet, I.; Nugroho, N. F. T. A.; Muslich

2017-12-01

In this research, we applied geographically weighted regression (GWR) for analyzing the poverty in Central Java. We consider Gaussian Kernel as weighted function. The GWR uses the diagonal matrix resulted from calculating kernel Gaussian function as a weighted function in the regression model. The kernel weights is used to handle spatial effects on the data so that a model can be obtained for each location. The purpose of this paper is to model of poverty percentage data in Central Java province using GWR with Gaussian kernel weighted function and to determine the influencing factors in each regency/city in Central Java province. Based on the research, we obtained geographically weighted regression model with Gaussian kernel weighted function on poverty percentage data in Central Java province. We found that percentage of population working as farmers, population growth rate, percentage of households with regular sanitation, and BPJS beneficiaries are the variables that affect the percentage of poverty in Central Java province. In this research, we found the determination coefficient R2 are 68.64%. There are two categories of district which are influenced by different of significance factors.

System characterization of neuronal excitability in the hippocampus and its relevance to observed dynamics of spontaneous seizure-like transitions.

PubMed

Zalay, Osbert C; Serletis, Demitre; Carlen, Peter L; Bardakjian, Berj L

2010-06-01

Most forms of epilepsy are marked by seizure episodes that arise spontaneously. The low-magnesium/high-potassium (low-Mg(2+)/high-K(+)) experimental model of epilepsy is an acute model that produces spontaneous, recurring seizure-like events (SLEs). To elucidate the nature of spontaneous seizure transitions and their relationship to neuronal excitability, whole-cell recordings from the intact hippocampus were undertaken in vitro, and the response of hippocampal CA3 neurons to Gaussian white noise injection was obtained before and after treatment with various concentrations of low-Mg(2+)/high-K(+) solution. A second-order Volterra kernel model was estimated for each of the input-output response pairs. The spectral energy of the responses was also computed, providing a quantitative measure of neuronal excitability. Changes in duration and amplitude of the first-order kernel correlated positively with the spectral energy increase following treatment with low-Mg(2+)/high-K(+) solution, suggesting that variations in neuronal excitability are coded by the system kernels, in part by differences to the profile of the first-order kernel. In particular, kernel duration was more sensitive than amplitude to changes in spectral energy, and correlated more strongly with kernel area. An oscillator network model of the hippocampal CA3 was constructed to investigate the relationship of kernel duration to network excitability, and the model was able to generate spontaneous, recurrent SLEs by increasing the duration of a mode function analogous to the first-order kernel. Results from the model indicated that disruption to the dynamic balance of feedback was responsible for seizure-like transitions and the observed intermittency of SLEs. A physiological candidate for feedback imbalance consistent with the network model is the destabilizing interaction of extracellular potassium and paroxysmal neuronal activation. Altogether, these results (1) validate a mathematical model for epileptiform activity in the hippocampus by quantifying and subsequently correlating its behavior with an experimental, in vitro model of epilepsy; (2) elucidate a possible mechanism for epileptogenesis; and (3) pave the way for control studies in epilepsy utilizing the herein proposed experimental and mathematical setup.

System characterization of neuronal excitability in the hippocampus and its relevance to observed dynamics of spontaneous seizure-like transitions

NASA Astrophysics Data System (ADS)

Zalay, Osbert C.; Serletis, Demitre; Carlen, Peter L.; Bardakjian, Berj L.

2010-06-01

Most forms of epilepsy are marked by seizure episodes that arise spontaneously. The low-magnesium/high-potassium (low-Mg2+/high-K+) experimental model of epilepsy is an acute model that produces spontaneous, recurring seizure-like events (SLEs). To elucidate the nature of spontaneous seizure transitions and their relationship to neuronal excitability, whole-cell recordings from the intact hippocampus were undertaken in vitro, and the response of hippocampal CA3 neurons to Gaussian white noise injection was obtained before and after treatment with various concentrations of low-Mg2+/high-K+ solution. A second-order Volterra kernel model was estimated for each of the input-output response pairs. The spectral energy of the responses was also computed, providing a quantitative measure of neuronal excitability. Changes in duration and amplitude of the first-order kernel correlated positively with the spectral energy increase following treatment with low-Mg2+/high-K+ solution, suggesting that variations in neuronal excitability are coded by the system kernels, in part by differences to the profile of the first-order kernel. In particular, kernel duration was more sensitive than amplitude to changes in spectral energy, and correlated more strongly with kernel area. An oscillator network model of the hippocampal CA3 was constructed to investigate the relationship of kernel duration to network excitability, and the model was able to generate spontaneous, recurrent SLEs by increasing the duration of a mode function analogous to the first-order kernel. Results from the model indicated that disruption to the dynamic balance of feedback was responsible for seizure-like transitions and the observed intermittency of SLEs. A physiological candidate for feedback imbalance consistent with the network model is the destabilizing interaction of extracellular potassium and paroxysmal neuronal activation. Altogether, these results (1) validate a mathematical model for epileptiform activity in the hippocampus by quantifying and subsequently correlating its behavior with an experimental, in vitro model of epilepsy; (2) elucidate a possible mechanism for epileptogenesis; and (3) pave the way for control studies in epilepsy utilizing the herein proposed experimental and mathematical setup.

An automatic optimum kernel-size selection technique for edge enhancement

USGS Publications Warehouse

Chavez, Pat S.; Bauer, Brian P.

1982-01-01

Edge enhancement is a technique that can be considered, to a first order, a correction for the modulation transfer function of an imaging system. Digital imaging systems sample a continuous function at discrete intervals so that high-frequency information cannot be recorded at the same precision as lower frequency data. Because of this, fine detail or edge information in digital images is lost. Spatial filtering techniques can be used to enhance the fine detail information that does exist in the digital image, but the filter size is dependent on the type of area being processed. A technique has been developed by the authors that uses the horizontal first difference to automatically select the optimum kernel-size that should be used to enhance the edges that are contained in the image. 

Accuracy of estimating wolf summer territories by daytime locations

USGS Publications Warehouse

Demma, D.J.; Mech, L.D.

2011-01-01

We used locations of 6 wolves (Canis lupus) in Minnesota from Global Positioning System (GPS) collars to compare day-versus-night locations to estimate territory size and location during summer. We employed both minimum convex polygon (MCP) and fixed kernel (FK) methods. We used two methods to partition GPS locations for day-versus-night home-range comparisons: (1) daytime = 0800-2000 Ah; nighttime = 2000-0800 Ah; and (2) sunup versus sundown. Regardless of location-partitioning method, mean area of daytime MCPs did not differ significantly from nighttime MCPs. Similarly, mean area of daytime FKs (95% probability contour) were not significantly different from nightime FKs. FK core use areas (50% probability contour) did not differ between daytime and nighttime nor between sunup and sundown locations. We conclude that in areas similar to our study area day-only locations are adequate for describing the location, extent and core use areas of summer wolf territories by both MCP and FK methods. ?? 2011 American Midland Naturalist.

Accuracy of estimating wolf summer territories by daytime locations

USGS Publications Warehouse

Demma, Dominic J.; Mech, L. David

2011-01-01

We used locations of 6 wolves (Canis lupus) in Minnesota from Global Positioning System (GPS) collars to compare day-versus-night locations to estimate territory size and location during summer. We employed both minimum convex polygon (MCP) and fixed kernel (FK) methods. We used two methods to partition GPS locations for day-versus-night home-range comparisons: (1) daytime = 0800–2000 h; nighttime = 2000–0800 h; and (2) sunup versus sundown. Regardless of location-partitioning method, mean area of daytime MCPs did not differ significantly from nighttime MCPs. Similarly, mean area of daytime FKs (95% probability contour) were not significantly different from nightime FKs. FK core use areas (50% probability contour) did not differ between daytime and nighttime nor between sunup and sundown locations. We conclude that in areas similar to our study area day-only locations are adequate for describing the location, extent and core use areas of summer wolf territories by both MCP and FK methods.

Genomic-Enabled Prediction in Maize Using Kernel Models with Genotype × Environment Interaction

PubMed Central

Bandeira e Sousa, Massaine; Cuevas, Jaime; de Oliveira Couto, Evellyn Giselly; Pérez-Rodríguez, Paulino; Jarquín, Diego; Fritsche-Neto, Roberto; Burgueño, Juan; Crossa, Jose

2017-01-01

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. PMID:28455415

Genomic-Enabled Prediction in Maize Using Kernel Models with Genotype × Environment Interaction.

PubMed

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.

Stochastic oscillations in models of epidemics on a network of cities

NASA Astrophysics Data System (ADS)

Rozhnova, G.; Nunes, A.; McKane, A. J.

2011-11-01

We carry out an analytic investigation of stochastic oscillations in a susceptible-infected-recovered model of disease spread on a network of n cities. In the model a fraction fjk of individuals from city k commute to city j, where they may infect, or be infected by, others. Starting from a continuous-time Markov description of the model the deterministic equations, which are valid in the limit when the population of each city is infinite, are recovered. The stochastic fluctuations about the fixed point of these equations are derived by use of the van Kampen system-size expansion. The fixed point structure of the deterministic equations is remarkably simple: A unique nontrivial fixed point always exists and has the feature that the fraction of susceptible, infected, and recovered individuals is the same for each city irrespective of its size. We find that the stochastic fluctuations have an analogously simple dynamics: All oscillations have a single frequency, equal to that found in the one-city case. We interpret this phenomenon in terms of the properties of the spectrum of the matrix of the linear approximation of the deterministic equations at the fixed point.

Speeding Up the Bilateral Filter: A Joint Acceleration Way.

PubMed

Dai, Longquan; Yuan, Mengke; Zhang, Xiaopeng

2016-06-01

Computational complexity of the brute-force implementation of the bilateral filter (BF) depends on its filter kernel size. To achieve the constant-time BF whose complexity is irrelevant to the kernel size, many techniques have been proposed, such as 2D box filtering, dimension promotion, and shiftability property. Although each of the above techniques suffers from accuracy and efficiency problems, previous algorithm designers were used to take only one of them to assemble fast implementations due to the hardness of combining them together. Hence, no joint exploitation of these techniques has been proposed to construct a new cutting edge implementation that solves these problems. Jointly employing five techniques: kernel truncation, best N-term approximation as well as previous 2D box filtering, dimension promotion, and shiftability property, we propose a unified framework to transform BF with arbitrary spatial and range kernels into a set of 3D box filters that can be computed in linear time. To the best of our knowledge, our algorithm is the first method that can integrate all these acceleration techniques and, therefore, can draw upon one another's strong point to overcome deficiencies. The strength of our method has been corroborated by several carefully designed experiments. In particular, the filtering accuracy is significantly improved without sacrificing the efficiency at running time.

Optimizing Support Vector Machine Parameters with Genetic Algorithm for Credit Risk Assessment

NASA Astrophysics Data System (ADS)

Manurung, Jonson; Mawengkang, Herman; Zamzami, Elviawaty

2017-12-01

Support vector machine (SVM) is a popular classification method known to have strong generalization capabilities. SVM can solve the problem of classification and linear regression or nonlinear kernel which can be a learning algorithm for the ability of classification and regression. However, SVM also has a weakness that is difficult to determine the optimal parameter value. SVM calculates the best linear separator on the input feature space according to the training data. To classify data which are non-linearly separable, SVM uses kernel tricks to transform the data into a linearly separable data on a higher dimension feature space. The kernel trick using various kinds of kernel functions, such as : linear kernel, polynomial, radial base function (RBF) and sigmoid. Each function has parameters which affect the accuracy of SVM classification. To solve the problem genetic algorithms are proposed to be applied as the optimal parameter value search algorithm thus increasing the best classification accuracy on SVM. Data taken from UCI repository of machine learning database: Australian Credit Approval. The results show that the combination of SVM and genetic algorithms is effective in improving classification accuracy. Genetic algorithms has been shown to be effective in systematically finding optimal kernel parameters for SVM, instead of randomly selected kernel parameters. The best accuracy for data has been upgraded from kernel Linear: 85.12%, polynomial: 81.76%, RBF: 77.22% Sigmoid: 78.70%. However, for bigger data sizes, this method is not practical because it takes a lot of time.

Theoretical size distribution of fossil taxa: analysis of a null model.

PubMed

Reed, William J; Hughes, Barry D

2007-03-22

This article deals with the theoretical size distribution (of number of sub-taxa) of a fossil taxon arising from a simple null model of macroevolution. New species arise through speciations occurring independently and at random at a fixed probability rate, while extinctions either occur independently and at random (background extinctions) or cataclysmically. In addition new genera are assumed to arise through speciations of a very radical nature, again assumed to occur independently and at random at a fixed probability rate. The size distributions of the pioneering genus (following a cataclysm) and of derived genera are determined. Also the distribution of the number of genera is considered along with a comparison of the probability of a monospecific genus with that of a monogeneric family.

CLAss-Specific Subspace Kernel Representations and Adaptive Margin Slack Minimization for Large Scale Classification.

PubMed

Yu, Yinan; Diamantaras, Konstantinos I; McKelvey, Tomas; Kung, Sun-Yuan

2018-02-01

In kernel-based classification models, given limited computational power and storage capacity, operations over the full kernel matrix becomes prohibitive. In this paper, we propose a new supervised learning framework using kernel models for sequential data processing. The framework is based on two components that both aim at enhancing the classification capability with a subset selection scheme. The first part is a subspace projection technique in the reproducing kernel Hilbert space using a CLAss-specific Subspace Kernel representation for kernel approximation. In the second part, we propose a novel structural risk minimization algorithm called the adaptive margin slack minimization to iteratively improve the classification accuracy by an adaptive data selection. We motivate each part separately, and then integrate them into learning frameworks for large scale data. We propose two such frameworks: the memory efficient sequential processing for sequential data processing and the parallelized sequential processing for distributed computing with sequential data acquisition. We test our methods on several benchmark data sets and compared with the state-of-the-art techniques to verify the validity of the proposed techniques.

Scaling in the vicinity of the four-state Potts fixed point

NASA Astrophysics Data System (ADS)

Blöte, H. W. J.; Guo, Wenan; Nightingale, M. P.

2017-08-01

We study a self-dual generalization of the Baxter-Wu model, employing results obtained by transfer matrix calculations of the magnetic scaling dimension and the free energy. While the pure critical Baxter-Wu model displays the critical behavior of the four-state Potts fixed point in two dimensions, in the sense that logarithmic corrections are absent, the introduction of different couplings in the up- and down triangles moves the model away from this fixed point, so that logarithmic corrections appear. Real couplings move the model into the first-order range, away from the behavior displayed by the nearest-neighbor, four-state Potts model. We also use complex couplings, which bring the model in the opposite direction characterized by the same type of logarithmic corrections as present in the four-state Potts model. Our finite-size analysis confirms in detail the existing renormalization theory describing the immediate vicinity of the four-state Potts fixed point.

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

PubMed

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

2015-05-01

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

Dynamic characteristics of oxygen consumption.

PubMed

Ye, Lin; Argha, Ahmadreza; Yu, Hairong; Celler, Branko G; Nguyen, Hung T; Su, Steven

2018-04-23

Previous studies have indicated that oxygen uptake ([Formula: see text]) is one of the most accurate indices for assessing the cardiorespiratory response to exercise. In most existing studies, the response of [Formula: see text] is often roughly modelled as a first-order system due to the inadequate stimulation and low signal to noise ratio. To overcome this difficulty, this paper proposes a novel nonparametric kernel-based method for the dynamic modelling of [Formula: see text] response to provide a more robust estimation. Twenty healthy non-athlete participants conducted treadmill exercises with monotonous stimulation (e.g., single step function as input). During the exercise, [Formula: see text] was measured and recorded by a popular portable gas analyser ([Formula: see text], COSMED). Based on the recorded data, a kernel-based estimation method was proposed to perform the nonparametric modelling of [Formula: see text]. For the proposed method, a properly selected kernel can represent the prior modelling information to reduce the dependence of comprehensive stimulations. Furthermore, due to the special elastic net formed by [Formula: see text] norm and kernelised [Formula: see text] norm, the estimations are smooth and concise. Additionally, the finite impulse response based nonparametric model which estimated by the proposed method can optimally select the order and fit better in terms of goodness-of-fit comparing to classical methods. Several kernels were introduced for the kernel-based [Formula: see text] modelling method. The results clearly indicated that the stable spline (SS) kernel has the best performance for [Formula: see text] modelling. Particularly, based on the experimental data from 20 participants, the estimated response from the proposed method with SS kernel was significantly better than the results from the benchmark method [i.e., prediction error method (PEM)] ([Formula: see text] vs [Formula: see text]). The proposed nonparametric modelling method is an effective method for the estimation of the impulse response of VO 2 -Speed system. Furthermore, the identified average nonparametric model method can dynamically predict [Formula: see text] response with acceptable accuracy during treadmill exercise.

Development of a kernel function for clinical data.

PubMed

Daemen, Anneleen; De Moor, Bart

2009-01-01

For most diseases and examinations, clinical data such as age, gender and medical history guides clinical management, despite the rise of high-throughput technologies. To fully exploit such clinical information, appropriate modeling of relevant parameters is required. As the widely used linear kernel function has several disadvantages when applied to clinical data, we propose a new kernel function specifically developed for this data. This "clinical kernel function" more accurately represents similarities between patients. Evidently, three data sets were studied and significantly better performances were obtained with a Least Squares Support Vector Machine when based on the clinical kernel function compared to the linear kernel function.

Discrimination of raw and processed Dipsacus asperoides by near infrared spectroscopy combined with least squares-support vector machine and random forests

NASA Astrophysics Data System (ADS)

Xin, Ni; Gu, Xiao-Feng; Wu, Hao; Hu, Yu-Zhu; Yang, Zhong-Lin

2012-04-01

Most herbal medicines could be processed to fulfill the different requirements of therapy. The purpose of this study was to discriminate between raw and processed Dipsacus asperoides, a common traditional Chinese medicine, based on their near infrared (NIR) spectra. Least squares-support vector machine (LS-SVM) and random forests (RF) were employed for full-spectrum classification. Three types of kernels, including linear kernel, polynomial kernel and radial basis function kernel (RBF), were checked for optimization of LS-SVM model. For comparison, a linear discriminant analysis (LDA) model was performed for classification, and the successive projections algorithm (SPA) was executed prior to building an LDA model to choose an appropriate subset of wavelengths. The three methods were applied to a dataset containing 40 raw herbs and 40 corresponding processed herbs. We ran 50 runs of 10-fold cross validation to evaluate the model's efficiency. The performance of the LS-SVM with RBF kernel (RBF LS-SVM) was better than the other two kernels. The RF, RBF LS-SVM and SPA-LDA successfully classified all test samples. The mean error rates for the 50 runs of 10-fold cross validation were 1.35% for RBF LS-SVM, 2.87% for RF, and 2.50% for SPA-LDA. The best classification results were obtained by using LS-SVM with RBF kernel, while RF was fast in the training and making predictions.

Improving Genomic Prediction in Cassava Field Experiments Using Spatial Analysis.

PubMed

Elias, Ani A; Rabbi, Ismail; Kulakow, Peter; Jannink, Jean-Luc

2018-01-04

Cassava ( Manihot esculenta Crantz) is an important staple food in sub-Saharan Africa. Breeding experiments were conducted at the International Institute of Tropical Agriculture in cassava to select elite parents. Taking into account the heterogeneity in the field while evaluating these trials can increase the accuracy in estimation of breeding values. We used an exploratory approach using the parametric spatial kernels Power, Spherical, and Gaussian to determine the best kernel for a given scenario. The spatial kernel was fit simultaneously with a genomic kernel in a genomic selection model. Predictability of these models was tested through a 10-fold cross-validation method repeated five times. The best model was chosen as the one with the lowest prediction root mean squared error compared to that of the base model having no spatial kernel. Results from our real and simulated data studies indicated that predictability can be increased by accounting for spatial variation irrespective of the heritability of the trait. In real data scenarios we observed that the accuracy can be increased by a median value of 3.4%. Through simulations, we showed that a 21% increase in accuracy can be achieved. We also found that Range (row) directional spatial kernels, mostly Gaussian, explained the spatial variance in 71% of the scenarios when spatial correlation was significant. Copyright © 2018 Elias et al.

Modelling and estimating pollen movement in oilseed rape (Brassica napus) at the landscape scale using genetic markers.

PubMed

Devaux, C; Lavigne, C; Austerlitz, F; Klein, E K

2007-02-01

Understanding patterns of pollen movement at the landscape scale is important for establishing management rules following the release of genetically modified (GM) crops. We use here a mating model adapted to cultivated species to estimate dispersal kernels from the genotypes of the progenies of male-sterile plants positioned at different sampling sites within a 10 x 10-km oilseed rape production area. Half of the pollen clouds sampled by the male-sterile plants originated from uncharacterized pollen sources that could consist of both large volunteer and feral populations, and fields within and outside the study area. The geometric dispersal kernel was the most appropriate to predict pollen movement in the study area. It predicted a much larger proportion of long-distance pollination than previously fitted dispersal kernels. This best-fitting mating model underestimated the level of differentiation among pollen clouds but could predict its spatial structure. The estimation method was validated on simulated genotypic data, and proved to provide good estimates of both the shape of the dispersal kernel and the rate and composition of pollen issued from uncharacterized pollen sources. The best dispersal kernel fitted here, the geometric kernel, should now be integrated into models that aim at predicting gene flow at the landscape level, in particular between GM and non-GM crops.

Explaining Support Vector Machines: A Color Based Nomogram

PubMed Central

Van Belle, Vanya; Van Calster, Ben; Van Huffel, Sabine; Suykens, Johan A. K.; Lisboa, Paulo

2016-01-01

Problem setting Support vector machines (SVMs) are very popular tools for classification, regression and other problems. Due to the large choice of kernels they can be applied with, a large variety of data can be analysed using these tools. Machine learning thanks its popularity to the good performance of the resulting models. However, interpreting the models is far from obvious, especially when non-linear kernels are used. Hence, the methods are used as black boxes. As a consequence, the use of SVMs is less supported in areas where interpretability is important and where people are held responsible for the decisions made by models. Objective In this work, we investigate whether SVMs using linear, polynomial and RBF kernels can be explained such that interpretations for model-based decisions can be provided. We further indicate when SVMs can be explained and in which situations interpretation of SVMs is (hitherto) not possible. Here, explainability is defined as the ability to produce the final decision based on a sum of contributions which depend on one single or at most two input variables. Results Our experiments on simulated and real-life data show that explainability of an SVM depends on the chosen parameter values (degree of polynomial kernel, width of RBF kernel and regularization constant). When several combinations of parameter values yield the same cross-validation performance, combinations with a lower polynomial degree or a larger kernel width have a higher chance of being explainable. Conclusions This work summarizes SVM classifiers obtained with linear, polynomial and RBF kernels in a single plot. Linear and polynomial kernels up to the second degree are represented exactly. For other kernels an indication of the reliability of the approximation is presented. The complete methodology is available as an R package and two apps and a movie are provided to illustrate the possibilities offered by the method. PMID:27723811

Model fitting for small skin permeability data sets: hyperparameter optimisation in Gaussian Process Regression.

PubMed

Ashrafi, Parivash; Sun, Yi; Davey, Neil; Adams, Roderick G; Wilkinson, Simon C; Moss, Gary Patrick

2018-03-01

The aim of this study was to investigate how to improve predictions from Gaussian Process models by optimising the model hyperparameters. Optimisation methods, including Grid Search, Conjugate Gradient, Random Search, Evolutionary Algorithm and Hyper-prior, were evaluated and applied to previously published data. Data sets were also altered in a structured manner to reduce their size, which retained the range, or 'chemical space' of the key descriptors to assess the effect of the data range on model quality. The Hyper-prior Smoothbox kernel results in the best models for the majority of data sets, and they exhibited significantly better performance than benchmark quantitative structure-permeability relationship (QSPR) models. When the data sets were systematically reduced in size, the different optimisation methods generally retained their statistical quality, whereas benchmark QSPR models performed poorly. The design of the data set, and possibly also the approach to validation of the model, is critical in the development of improved models. The size of the data set, if carefully controlled, was not generally a significant factor for these models and that models of excellent statistical quality could be produced from substantially smaller data sets. © 2018 Royal Pharmaceutical Society.

Effect of finite sample size on feature selection and classification: a simulation study.

PubMed

Way, Ted W; Sahiner, Berkman; Hadjiiski, Lubomir M; Chan, Heang-Ping

2010-02-01

The small number of samples available for training and testing is often the limiting factor in finding the most effective features and designing an optimal computer-aided diagnosis (CAD) system. Training on a limited set of samples introduces bias and variance in the performance of a CAD system relative to that trained with an infinite sample size. In this work, the authors conducted a simulation study to evaluate the performances of various combinations of classifiers and feature selection techniques and their dependence on the class distribution, dimensionality, and the training sample size. The understanding of these relationships will facilitate development of effective CAD systems under the constraint of limited available samples. Three feature selection techniques, the stepwise feature selection (SFS), sequential floating forward search (SFFS), and principal component analysis (PCA), and two commonly used classifiers, Fisher's linear discriminant analysis (LDA) and support vector machine (SVM), were investigated. Samples were drawn from multidimensional feature spaces of multivariate Gaussian distributions with equal or unequal covariance matrices and unequal means, and with equal covariance matrices and unequal means estimated from a clinical data set. Classifier performance was quantified by the area under the receiver operating characteristic curve Az. The mean Az values obtained by resubstitution and hold-out methods were evaluated for training sample sizes ranging from 15 to 100 per class. The number of simulated features available for selection was chosen to be 50, 100, and 200. It was found that the relative performance of the different combinations of classifier and feature selection method depends on the feature space distributions, the dimensionality, and the available training sample sizes. The LDA and SVM with radial kernel performed similarly for most of the conditions evaluated in this study, although the SVM classifier showed a slightly higher hold-out performance than LDA for some conditions and vice versa for other conditions. PCA was comparable to or better than SFS and SFFS for LDA at small samples sizes, but inferior for SVM with polynomial kernel. For the class distributions simulated from clinical data, PCA did not show advantages over the other two feature selection methods. Under this condition, the SVM with radial kernel performed better than the LDA when few training samples were available, while LDA performed better when a large number of training samples were available. None of the investigated feature selection-classifier combinations provided consistently superior performance under the studied conditions for different sample sizes and feature space distributions. In general, the SFFS method was comparable to the SFS method while PCA may have an advantage for Gaussian feature spaces with unequal covariance matrices. The performance of the SVM with radial kernel was better than, or comparable to, that of the SVM with polynomial kernel under most conditions studied.

Non-parametric wall model and methods of identifying boundary conditions for moments in gas flow equations

NASA Astrophysics Data System (ADS)

Liao, Meng; To, Quy-Dong; Léonard, Céline; Monchiet, Vincent

2018-03-01

In this paper, we use the molecular dynamics simulation method to study gas-wall boundary conditions. Discrete scattering information of gas molecules at the wall surface is obtained from collision simulations. The collision data can be used to identify the accommodation coefficients for parametric wall models such as Maxwell and Cercignani-Lampis scattering kernels. Since these scattering kernels are based on a limited number of accommodation coefficients, we adopt non-parametric statistical methods to construct the kernel to overcome these issues. Different from parametric kernels, the non-parametric kernels require no parameter (i.e. accommodation coefficients) and no predefined distribution. We also propose approaches to derive directly the Navier friction and Kapitza thermal resistance coefficients as well as other interface coefficients associated with moment equations from the non-parametric kernels. The methods are applied successfully to systems composed of CH4 or CO2 and graphite, which are of interest to the petroleum industry.

A robust, high-throughput method for computing maize ear, cob, and kernel attributes automatically from images.

PubMed

Miller, Nathan D; Haase, Nicholas J; Lee, Jonghyun; Kaeppler, Shawn M; de Leon, Natalia; Spalding, Edgar P

2017-01-01

Grain yield of the maize plant depends on the sizes, shapes, and numbers of ears and the kernels they bear. An automated pipeline that can measure these components of yield from easily-obtained digital images is needed to advance our understanding of this globally important crop. Here we present three custom algorithms designed to compute such yield components automatically from digital images acquired by a low-cost platform. One algorithm determines the average space each kernel occupies along the cob axis using a sliding-window Fourier transform analysis of image intensity features. A second counts individual kernels removed from ears, including those in clusters. A third measures each kernel's major and minor axis after a Bayesian analysis of contour points identifies the kernel tip. Dimensionless ear and kernel shape traits that may interrelate yield components are measured by principal components analysis of contour point sets. Increased objectivity and speed compared to typical manual methods are achieved without loss of accuracy as evidenced by high correlations with ground truth measurements and simulated data. Millimeter-scale differences among ear, cob, and kernel traits that ranged more than 2.5-fold across a diverse group of inbred maize lines were resolved. This system for measuring maize ear, cob, and kernel attributes is being used by multiple research groups as an automated Web service running on community high-throughput computing and distributed data storage infrastructure. Users may create their own workflow using the source code that is staged for download on a public repository. © 2016 The Authors. The Plant Journal published by Society for Experimental Biology and John Wiley & Sons Ltd.

Half-blind remote sensing image restoration with partly unknown degradation

NASA Astrophysics Data System (ADS)

Xie, Meihua; Yan, Fengxia

2017-01-01

The problem of image restoration has been extensively studied for its practical importance and theoretical interest. This paper mainly discusses the problem of image restoration with partly unknown kernel. In this model, the degraded kernel function is known but its parameters are unknown. With this model, we should estimate the parameters in Gaussian kernel and the real image simultaneity. For this new problem, a total variation restoration model is put out and an intersect direction iteration algorithm is designed. Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index Measurement (SSIM) are used to measure the performance of the method. Numerical results show that we can estimate the parameters in kernel accurately, and the new method has both much higher PSNR and much higher SSIM than the expectation maximization (EM) method in many cases. In addition, the accuracy of estimation is not sensitive to noise. Furthermore, even though the support of the kernel is unknown, we can also use this method to get accurate estimation.

Unified Heat Kernel Regression for Diffusion, Kernel Smoothing and Wavelets on Manifolds and Its Application to Mandible Growth Modeling in CT Images

PubMed Central

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

2014-01-01

We present a novel kernel regression framework for smoothing scalar surface data using the Laplace-Beltrami eigenfunctions. Starting with the heat kernel constructed from the eigenfunctions, we formulate a new bivariate kernel regression framework as a weighted eigenfunction expansion with the heat kernel as the weights. The new kernel regression is mathematically equivalent to isotropic heat diffusion, kernel smoothing and recently popular diffusion wavelets. Unlike many previous partial differential equation based approaches involving diffusion, our approach represents the solution of diffusion analytically, reducing numerical inaccuracy and slow convergence. The numerical implementation is validated on a unit sphere using spherical harmonics. As an illustration, we have applied the method in characterizing the localized growth pattern of mandible surfaces obtained in CT images from subjects between ages 0 and 20 years by regressing the length of displacement vectors with respect to the template surface. PMID:25791435

Experiences modeling ocean circulation problems on a 30 node commodity cluster with 3840 GPU processor cores.

NASA Astrophysics Data System (ADS)

Hill, C.

2008-12-01

Low cost graphic cards today use many, relatively simple, compute cores to deliver support for memory bandwidth of more than 100GB/s and theoretical floating point performance of more than 500 GFlop/s. Right now this performance is, however, only accessible to highly parallel algorithm implementations that, (i) can use a hundred or more, 32-bit floating point, concurrently executing cores, (ii) can work with graphics memory that resides on the graphics card side of the graphics bus and (iii) can be partially expressed in a language that can be compiled by a graphics programming tool. In this talk we describe our experiences implementing a complete, but relatively simple, time dependent shallow-water equations simulation targeting a cluster of 30 computers each hosting one graphics card. The implementation takes into account the considerations (i), (ii) and (iii) listed previously. We code our algorithm as a series of numerical kernels. Each kernel is designed to be executed by multiple threads of a single process. Kernels are passed memory blocks to compute over which can be persistent blocks of memory on a graphics card. Each kernel is individually implemented using the NVidia CUDA language but driven from a higher level supervisory code that is almost identical to a standard model driver. The supervisory code controls the overall simulation timestepping, but is written to minimize data transfer between main memory and graphics memory (a massive performance bottle-neck on current systems). Using the recipe outlined we can boost the performance of our cluster by nearly an order of magnitude, relative to the same algorithm executing only on the cluster CPU's. Achieving this performance boost requires that many threads are available to each graphics processor for execution within each numerical kernel and that the simulations working set of data can fit into the graphics card memory. As we describe, this puts interesting upper and lower bounds on the problem sizes for which this technology is currently most useful. However, many interesting problems fit within this envelope. Looking forward, we extrapolate our experience to estimate full-scale ocean model performance and applicability. Finally we describe preliminary hybrid mixed 32-bit and 64-bit experiments with graphics cards that support 64-bit arithmetic, albeit at a lower performance.

Design of a multiple kernel learning algorithm for LS-SVM by convex programming.

PubMed

Jian, Ling; Xia, Zhonghang; Liang, Xijun; Gao, Chuanhou

2011-06-01

As a kernel based method, the performance of least squares support vector machine (LS-SVM) depends on the selection of the kernel as well as the regularization parameter (Duan, Keerthi, & Poo, 2003). Cross-validation is efficient in selecting a single kernel and the regularization parameter; however, it suffers from heavy computational cost and is not flexible to deal with multiple kernels. In this paper, we address the issue of multiple kernel learning for LS-SVM by formulating it as semidefinite programming (SDP). Furthermore, we show that the regularization parameter can be optimized in a unified framework with the kernel, which leads to an automatic process for model selection. Extensive experimental validations are performed and analyzed. Copyright © 2011 Elsevier Ltd. All rights reserved.

Novel near-infrared sampling apparatus for single kernel analysis of oil content in maize.

PubMed

Janni, James; Weinstock, B André; Hagen, Lisa; Wright, Steve

2008-04-01

A method of rapid, nondestructive chemical and physical analysis of individual maize (Zea mays L.) kernels is needed for the development of high value food, feed, and fuel traits. Near-infrared (NIR) spectroscopy offers a robust nondestructive method of trait determination. However, traditional NIR bulk sampling techniques cannot be applied successfully to individual kernels. Obtaining optimized single kernel NIR spectra for applied chemometric predictive analysis requires a novel sampling technique that can account for the heterogeneous forms, morphologies, and opacities exhibited in individual maize kernels. In this study such a novel technique is described and compared to less effective means of single kernel NIR analysis. Results of the application of a partial least squares (PLS) derived model for predictive determination of percent oil content per individual kernel are shown.

High-Performance Reactive Particle Tracking with Adaptive Representation

NASA Astrophysics Data System (ADS)

Schmidt, M.; Benson, D. A.; Pankavich, S.

2017-12-01

Lagrangian particle tracking algorithms have been shown to be effective tools for modeling chemical reactions in imperfectly-mixed media. One disadvantage of these algorithms is the possible need to employ large numbers of particles in simulations, depending on the concentration covariance structure, and these large particle numbers can lead to long computation times. Two distinct approaches have recently arisen to overcome this. One method employs spatial kernels that are related to a specified, reduced particle number; however, over-wide kernels, dictated by a very low particle number, lead to an excess of reaction calculations and cause a reduction in performance. Another formulation involves hybrid particles that carry multiple species of reactant, wherein each particle is treated as its own well-mixed volume, obviating the need for large numbers of particles for each species but still requiring a fixed number of hybrid particles. Here, we combine these two approaches and demonstrate an improved method for simulating a given system in a computationally efficient manner. Additionally, the independent nature of transport and reaction calculations in this approach allows for significant gains via parallelization in an MPI or OpenMP context. For benchmarking, we choose a CO2 injection simulation with dissolution and precipitation of calcite and dolomite, allowing us to derive the proper treatment of interaction between solid and aqueous phases.

Single image super-resolution based on convolutional neural networks

NASA Astrophysics Data System (ADS)

Zou, Lamei; Luo, Ming; Yang, Weidong; Li, Peng; Jin, Liujia

2018-03-01

We present a deep learning method for single image super-resolution (SISR). The proposed approach learns end-to-end mapping between low-resolution (LR) images and high-resolution (HR) images. The mapping is represented as a deep convolutional neural network which inputs the LR image and outputs the HR image. Our network uses 5 convolution layers, which kernels size include 5×5, 3×3 and 1×1. In our proposed network, we use residual-learning and combine different sizes of convolution kernels at the same layer. The experiment results show that our proposed method performs better than the existing methods in reconstructing quality index and human visual effects on benchmarked images.

Free Fermions and the Classical Compact Groups

NASA Astrophysics Data System (ADS)

Cunden, Fabio Deelan; Mezzadri, Francesco; O'Connell, Neil

2018-06-01

There is a close connection between the ground state of non-interacting fermions in a box with classical (absorbing, reflecting, and periodic) boundary conditions and the eigenvalue statistics of the classical compact groups. The associated determinantal point processes can be extended in two natural directions: (i) we consider the full family of admissible quantum boundary conditions (i.e., self-adjoint extensions) for the Laplacian on a bounded interval, and the corresponding projection correlation kernels; (ii) we construct the grand canonical extensions at finite temperature of the projection kernels, interpolating from Poisson to random matrix eigenvalue statistics. The scaling limits in the bulk and at the edges are studied in a unified framework, and the question of universality is addressed. Whether the finite temperature determinantal processes correspond to the eigenvalue statistics of some matrix models is, a priori, not obvious. We complete the picture by constructing a finite temperature extension of the Haar measure on the classical compact groups. The eigenvalue statistics of the resulting grand canonical matrix models (of random size) corresponds exactly to the grand canonical measure of free fermions with classical boundary conditions.

On randomized algorithms for numerical solution of applied Fredholm integral equations of the second kind

NASA Astrophysics Data System (ADS)

Voytishek, Anton V.; Shipilov, Nikolay M.

2017-11-01

In this paper, the systematization of numerical (implemented on a computer) randomized functional algorithms for approximation of a solution of Fredholm integral equation of the second kind is carried out. Wherein, three types of such algorithms are distinguished: the projection, the mesh and the projection-mesh methods. The possibilities for usage of these algorithms for solution of practically important problems is investigated in detail. The disadvantages of the mesh algorithms, related to the necessity of calculation values of the kernels of integral equations in fixed points, are identified. On practice, these kernels have integrated singularities, and calculation of their values is impossible. Thus, for applied problems, related to solving Fredholm integral equation of the second kind, it is expedient to use not mesh, but the projection and the projection-mesh randomized algorithms.

Distributed smoothed tree kernel for protein-protein interaction extraction from the biomedical literature

PubMed Central

Murugesan, Gurusamy; Abdulkadhar, Sabenabanu; Natarajan, Jeyakumar

2017-01-01

Automatic extraction of protein-protein interaction (PPI) pairs from biomedical literature is a widely examined task in biological information extraction. Currently, many kernel based approaches such as linear kernel, tree kernel, graph kernel and combination of multiple kernels has achieved promising results in PPI task. However, most of these kernel methods fail to capture the semantic relation information between two entities. In this paper, we present a special type of tree kernel for PPI extraction which exploits both syntactic (structural) and semantic vectors information known as Distributed Smoothed Tree kernel (DSTK). DSTK comprises of distributed trees with syntactic information along with distributional semantic vectors representing semantic information of the sentences or phrases. To generate robust machine learning model composition of feature based kernel and DSTK were combined using ensemble support vector machine (SVM). Five different corpora (AIMed, BioInfer, HPRD50, IEPA, and LLL) were used for evaluating the performance of our system. Experimental results show that our system achieves better f-score with five different corpora compared to other state-of-the-art systems. PMID:29099838

Distributed smoothed tree kernel for protein-protein interaction extraction from the biomedical literature.

PubMed

Murugesan, Gurusamy; Abdulkadhar, Sabenabanu; Natarajan, Jeyakumar

2017-01-01

Automatic extraction of protein-protein interaction (PPI) pairs from biomedical literature is a widely examined task in biological information extraction. Currently, many kernel based approaches such as linear kernel, tree kernel, graph kernel and combination of multiple kernels has achieved promising results in PPI task. However, most of these kernel methods fail to capture the semantic relation information between two entities. In this paper, we present a special type of tree kernel for PPI extraction which exploits both syntactic (structural) and semantic vectors information known as Distributed Smoothed Tree kernel (DSTK). DSTK comprises of distributed trees with syntactic information along with distributional semantic vectors representing semantic information of the sentences or phrases. To generate robust machine learning model composition of feature based kernel and DSTK were combined using ensemble support vector machine (SVM). Five different corpora (AIMed, BioInfer, HPRD50, IEPA, and LLL) were used for evaluating the performance of our system. Experimental results show that our system achieves better f-score with five different corpora compared to other state-of-the-art systems.

Food Environment and Weight Change: Does Residential Mobility Matter?: The Diabetes Study of Northern California (DISTANCE).

PubMed

Laraia, Barbara A; Downing, Janelle M; Zhang, Y Tara; Dow, William H; Kelly, Maggi; Blanchard, Samuel D; Adler, Nancy; Schillinger, Dean; Moffet, Howard; Warton, E Margaret; Karter, Andrew J

2017-05-01

Associations between neighborhood food environment and adult body mass index (BMI; weight (kg)/height (m)2) derived using cross-sectional or longitudinal random-effects models may be biased due to unmeasured confounding and measurement and methodological limitations. In this study, we assessed the within-individual association between change in food environment from 2006 to 2011 and change in BMI among adults with type 2 diabetes using clinical data from the Kaiser Permanente Diabetes Registry collected from 2007 to 2011. Healthy food environment was measured using the kernel density of healthful food venues. Fixed-effects models with a 1-year-lagged BMI were estimated. Separate models were fitted for persons who moved and those who did not. Sensitivity analysis using different lag times and kernel density bandwidths were tested to establish the consistency of findings. On average, patients lost 1 pound (0.45 kg) for each standard-deviation improvement in their food environment. This relationship held for persons who remained in the same location throughout the 5-year study period but not among persons who moved. Proximity to food venues that promote nutritious foods alone may not translate into clinically meaningful diet-related health changes. Community-level policies for improving the food environment need multifaceted strategies to invoke clinically meaningful change in BMI among adult patients with diabetes. © The Author 2017. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

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

PubMed

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

2017-10-01

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

Fast generation of sparse random kernel graphs

DOE PAGES

Hagberg, Aric; Lemons, Nathan; Du, Wen -Bo

2015-09-10

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 timemore » at most ο(n(logn)²). As an example, we show how to generate samples of power-law degree distribution graphs with tunable assortativity.« less

Trade off between variable and fixed size normalization in orthogonal polynomials based iris recognition system.

PubMed

Krishnamoorthi, R; Anna Poorani, G

2016-01-01

Iris normalization is an important stage in any iris biometric, as it has a propensity to trim down the consequences of iris distortion. To indemnify the variation in size of the iris owing to the action of stretching or enlarging the pupil in iris acquisition process and camera to eyeball distance, two normalization schemes has been proposed in this work. In the first method, the iris region of interest is normalized by converting the iris into the variable size rectangular model in order to avoid the under samples near the limbus border. In the second method, the iris region of interest is normalized by converting the iris region into a fixed size rectangular model in order to avoid the dimensional discrepancies between the eye images. The performance of the proposed normalization methods is evaluated with orthogonal polynomials based iris recognition in terms of FAR, FRR, GAR, CRR and EER.

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

PubMed

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

2018-04-28

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.

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

NASA Astrophysics Data System (ADS)

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.

On supervised graph Laplacian embedding CA model & kernel construction and its application

NASA Astrophysics Data System (ADS)

Zeng, Junwei; Qian, Yongsheng; Wang, Min; Yang, Yongzhong

2017-01-01

There are many methods to construct kernel with given data attribute information. Gaussian radial basis function (RBF) kernel is one of the most popular ways to construct a kernel. The key observation is that in real-world data, besides the data attribute information, data label information also exists, which indicates the data class. In order to make use of both data attribute information and data label information, in this work, we propose a supervised kernel construction method. Supervised information from training data is integrated into standard kernel construction process to improve the discriminative property of resulting kernel. A supervised Laplacian embedding cellular automaton model is another key application developed for two-lane heterogeneous traffic flow with the safe distance and large-scale truck. Based on the properties of traffic flow in China, we re-calibrate the cell length, velocity, random slowing mechanism and lane-change conditions and use simulation tests to study the relationships among the speed, density and flux. The numerical results show that the large-scale trucks will have great effects on the traffic flow, which are relevant to the proportion of the large-scale trucks, random slowing rate and the times of the lane space change.

Approximate kernel competitive learning.

PubMed

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

2015-03-01

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

Genuine quark state versus dynamically generated structure for the Roper resonance

NASA Astrophysics Data System (ADS)

Golli, B.; Osmanović, H.; Širca, S.; Švarc, A.

2018-03-01

In view of the recent results of lattice QCD simulation in the P 11 partial wave that has found no clear signal for the three-quark Roper state we investigate a different mechanism for the formation of the Roper resonance in a coupled channel approach including the π N , π Δ , and σ N channels. We fix the pion-baryon vertices in the underlying quark model while the s -wave sigma-baryon interaction is introduced phenomenologically with the coupling strength, the mass, and the width of the σ meson as free parameters. The Laurent-Pietarinen expansion is used to extract the information about the S -matrix pole. The Lippmann-Schwinger equation for the K matrix with a separable kernel is solved to all orders. For sufficiently strong σ N N coupling the kernel becomes singular and a quasibound state emerges at around 1.4 GeV, dominated by the σ N component and reflecting itself in a pole of the S matrix. The alternative mechanism involving a (1s ) 22 s quark resonant state is added to the model and the interplay of the dynamically generated state and the three-quark resonant state is studied. It turns out that for the mass of the three-quark resonant state above 1.6 GeV the mass of the resonance is determined solely by the dynamically generated state, nonetheless, the inclusion of the three-quark resonant state is imperative to reproduce the experimental width and the modulus of the resonance pole.

Phylodynamic Inference with Kernel ABC and Its Application to HIV Epidemiology.

PubMed

Poon, Art F Y

2015-09-01

The shapes of phylogenetic trees relating virus populations are determined by the adaptation of viruses within each host, and by the transmission of viruses among hosts. Phylodynamic inference attempts to reverse this flow of information, estimating parameters of these processes from the shape of a virus phylogeny reconstructed from a sample of genetic sequences from the epidemic. A key challenge to phylodynamic inference is quantifying the similarity between two trees in an efficient and comprehensive way. In this study, I demonstrate that a new distance measure, based on a subset tree kernel function from computational linguistics, confers a significant improvement over previous measures of tree shape for classifying trees generated under different epidemiological scenarios. Next, I incorporate this kernel-based distance measure into an approximate Bayesian computation (ABC) framework for phylodynamic inference. ABC bypasses the need for an analytical solution of model likelihood, as it only requires the ability to simulate data from the model. I validate this "kernel-ABC" method for phylodynamic inference by estimating parameters from data simulated under a simple epidemiological model. Results indicate that kernel-ABC attained greater accuracy for parameters associated with virus transmission than leading software on the same data sets. Finally, I apply the kernel-ABC framework to study a recent outbreak of a recombinant HIV subtype in China. Kernel-ABC provides a versatile framework for phylodynamic inference because it can fit a broader range of models than methods that rely on the computation of exact likelihoods. © The Author 2015. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.

ℓ(p)-Norm multikernel learning approach for stock market price forecasting.

PubMed

Shao, Xigao; Wu, Kun; Liao, Bifeng

2012-01-01

Linear multiple kernel learning model has been used for predicting financial time series. However, ℓ(1)-norm multiple support vector regression is rarely observed to outperform trivial baselines in practical applications. To allow for robust kernel mixtures that generalize well, we adopt ℓ(p)-norm multiple kernel support vector regression (1 ≤ p < ∞) as a stock price prediction model. The optimization problem is decomposed into smaller subproblems, and the interleaved optimization strategy is employed to solve the regression model. The model is evaluated on forecasting the daily stock closing prices of Shanghai Stock Index in China. Experimental results show that our proposed model performs better than ℓ(1)-norm multiple support vector regression model.

Technique for fixing a temporalis muscle using a titanium plate to the implanted hydroxyapatite ceramics for bone defects.

PubMed

Ono, I; Tateshita, T; Sasaki, T; Matsumoto, M; Kodama, N

2001-05-01

We devised a technique to fix the temporalis muscle to the transplanted hydroxyapatite implant by using a titanium plate, which is fixed to the hydroxyapatite ceramic implant by screws and achieves good clinical results. The size, shape, and curvature of the hydroxyapatite ceramic implants were determined according to full-scale models fabricated using the laser lithographic modeling method from computed tomography data. A titanium plate was then fixed with screws on the implant before implantation, and then the temporalis muscle was refixed to the holes at both ends of the plate. The application of this technique reduced the hospitalization time and achieved good results esthetically.

Optimization of fixture layouts of glass laser optics using multiple kernel regression.

PubMed

Su, Jianhua; Cao, Enhua; Qiao, Hong

2014-05-10

We aim to build an integrated fixturing model to describe the structural properties and thermal properties of the support frame of glass laser optics. Therefore, (a) a near global optimal set of clamps can be computed to minimize the surface shape error of the glass laser optic based on the proposed model, and (b) a desired surface shape error can be obtained by adjusting the clamping forces under various environmental temperatures based on the model. To construct the model, we develop a new multiple kernel learning method and call it multiple kernel support vector functional regression. The proposed method uses two layer regressions to group and order the data sources by the weights of the kernels and the factors of the layers. Because of that, the influences of the clamps and the temperature can be evaluated by grouping them into different layers.

Application of stochastic weighted algorithms to a multidimensional silica particle model

DOE Office of Scientific and Technical Information (OSTI.GOV)

Menz, William J.; Patterson, Robert I.A.; Wagner, Wolfgang

2013-09-01

Highlights: •Stochastic weighted algorithms (SWAs) are developed for a detailed silica model. •An implementation of SWAs with the transition kernel is presented. •The SWAs’ solutions converge to the direct simulation algorithm’s (DSA) solution. •The efficiency of SWAs is evaluated for this multidimensional particle model. •It is shown that SWAs can be used for coagulation problems in industrial systems. -- Abstract: This paper presents a detailed study of the numerical behaviour of stochastic weighted algorithms (SWAs) using the transition regime coagulation kernel and a multidimensional silica particle model. The implementation in the SWAs of the transition regime coagulation kernel and associatedmore » majorant rates is described. The silica particle model of Shekar et al. [S. Shekar, A.J. Smith, W.J. Menz, M. Sander, M. Kraft, A multidimensional population balance model to describe the aerosol synthesis of silica nanoparticles, Journal of Aerosol Science 44 (2012) 83–98] was used in conjunction with this coagulation kernel to study the convergence properties of SWAs with a multidimensional particle model. High precision solutions were calculated with two SWAs and also with the established direct simulation algorithm. These solutions, which were generated using large number of computational particles, showed close agreement. It was thus demonstrated that SWAs can be successfully used with complex coagulation kernels and high dimensional particle models to simulate real-world systems.« less

Extraction process of palm kernel cake as a source of mannan for feed additive on poultry diet

NASA Astrophysics Data System (ADS)

Tafsin, M.; Hanafi, N. D.; Yusraini, E.

2017-05-01

Palm Kernel Cake (PKC) is a by-product of palm kernel oil extraction and found in large quantity in Indonesia. The inclusion of PKC on poultry diet are limited due to some nutritional problems such as anti-nutritional properties (mannan). On the other hand, mannan containing polysaccharides play in various biological functions particularly in enhancing the immune response and to control pathogen in poultry. The research objective to find out the extraction process of PKC and conducted at animal nutrition and Feed Science Laboratory, Agricultural Faculty, University of Sumatera Utara. Various extraction methode were used in this experiment, including fraction analysis used 7 number sieves, and followed by water and acetic acid extraction. The result indicated that PKC had different particle size according to sieve size and dominated by particle size 850 um. The analysis of sugar content indicated that each particle size had different characteristic on treatment by hot water extraction. The particle size 180—850 um had higher sugar content than coarse PKC (2000—3000 um). The total sugar content were recovered vary between 0.9—3,2% from PKC were extracted. Treatment grinding method followed by hot water extraction (100—120 °C, 1 h) increased total sugar content than previous treatments and reach 8% from PKC were extracted. Utilisation acetic acid decreased the total amount of total sugar from PKC were extracted. It is concluded that treatment by hot temperature (110—120 °C) for 1 h show highest yield to extract sugar from PKC.

Modelling groundwater fractal flow with fractional differentiation via Mittag-Leffler law

NASA Astrophysics Data System (ADS)

Ahokposi, D. P.; Atangana, Abdon; Vermeulen, D. P.

2017-04-01

Modelling the flow of groundwater within a network of fractures is perhaps one of the most difficult exercises within the field of geohydrology. This physical problem has attracted the attention of several scientists across the globe. Already two different types of differentiations have been used to attempt modelling this problem including the classical and the fractional differentiation. In this paper, we employed the most recent concept of differentiation based on the non-local and non-singular kernel called the generalized Mittag-Leffler function, to reshape the model of groundwater fractal flow. We presented the existence of positive solution of the new model. Using the fixed-point approach, we established the uniqueness of the positive solution. We solve the new model with three different numerical schemes including implicit, explicit and Crank-Nicholson numerical methods. Experimental data collected from four constant discharge tests conducted in a typical fractured crystalline rock aquifer of the Northern Limb (Bushveld Complex) in the Limpopo Province (South Africa) are compared with the numerical solutions. It is worth noting that the four boreholes (BPAC1, BPAC2, BPAC3, and BPAC4) are located on Faults.

Putting Priors in Mixture Density Mercer Kernels

NASA Technical Reports Server (NTRS)

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

2004-01-01

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

Effects of Japanese beetle (Coleoptera: Scarabaeidae) and silk clipping in field corn.

PubMed

Steckel, Sandy; Stewart, S D; Tindall, K V

2013-10-01

Japanese beetle (Popillia japonica Newman) is an emerging silk-feeding insect found in fields in the lower Corn Belt and Midsouthern United States. Studies were conducted in 2010 and 2011 to evaluate how silk clipping in corn affects pollination and yield parameters. Manually clipping silks once daily had modest effects on yield parameters. Sustained clipping by either manually clipping silks three times per day or by caging Japanese beetles onto ears affected total kernel weight if it occurred during early silking (R1 growth stage). Manually clipping silks three times per day for the first 5 d of silking affected the number of kernels per ear, total kernel weight, and the weight of individual kernels. Caged beetles fed on silks and, depending on the number of beetles caged per ear, reduced the number of kernels per ear. Caging eight beetles per ear significantly reduced total kernel weight compared with noninfested ears. Drought stress before anthesis appeared to magnify the impact of silk clipping by Japanese beetles. There was evidence of some compensation for reduced pollination by increasing the size of pollinated kernels within the ear. Our results showed that it requires sustained silk clipping during the first week of silking to have substantial impacts on pollination and yield parameters, at least under good growing conditions. Some states recommend treating for Japanese beetle when three Japanese beetles per ear are found, silks are clipped to < 13 mm, and pollination is < 50% complete, and that recommendation appears to be adequate.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Schreiner, S.; Paschal, C.B.; Galloway, R.L.

Four methods of producing maximum intensity projection (MIP) images were studied and compared. Three of the projection methods differ in the interpolation kernel used for ray tracing. The interpolation kernels include nearest neighbor interpolation, linear interpolation, and cubic convolution interpolation. The fourth projection method is a voxel projection method that is not explicitly a ray-tracing technique. The four algorithms` performance was evaluated using a computer-generated model of a vessel and using real MR angiography data. The evaluation centered around how well an algorithm transferred an object`s width to the projection plane. The voxel projection algorithm does not suffer from artifactsmore » associated with the nearest neighbor algorithm. Also, a speed-up in the calculation of the projection is seen with the voxel projection method. Linear interpolation dramatically improves the transfer of width information from the 3D MRA data set over both nearest neighbor and voxel projection methods. Even though the cubic convolution interpolation kernel is theoretically superior to the linear kernel, it did not project widths more accurately than linear interpolation. A possible advantage to the nearest neighbor interpolation is that the size of small vessels tends to be exaggerated in the projection plane, thereby increasing their visibility. The results confirm that the way in which an MIP image is constructed has a dramatic effect on information contained in the projection. The construction method must be chosen with the knowledge that the clinical information in the 2D projections in general will be different from that contained in the original 3D data volume. 27 refs., 16 figs., 2 tabs.« less

Laser induced spark ignition of methane-oxygen mixtures

NASA Technical Reports Server (NTRS)

Santavicca, D. A.; Ho, C.; Reilly, B. J.; Lee, T.-W.

1991-01-01

Results from an experimental study of laser induced spark ignition of methane-oxygen mixtures are presented. The experiments were conducted at atmospheric pressure and 296 K under laminar pre-mixed and turbulent-incompletely mixed conditions. A pulsed, frequency doubled Nd:YAG laser was used as the ignition source. Laser sparks with energies of 10 mJ and 40 mJ were used, as well as a conventional electrode spark with an effective energy of 6 mJ. Measurements were made of the flame kernel radius as a function of time using pulsed laser shadowgraphy. The initial size of the spark ignited flame kernel was found to correlate reasonably well with breakdown energy as predicted by the Taylor spherical blast wave model. The subsequent growth rate of the flame kernel was found to increase with time from a value less than to a value greater than the adiabatic, unstretched laminar growth rate. This behavior was attributed to the combined effects of flame stretch and an apparent wrinkling of the flame surface due to the extremely rapid acceleration of the flame. The very large laminar flame speed of methane-oxygen mixtures appears to be the dominant factor affecting the growth rate of spark ignited flame kernels, with the mode of ignition having a small effect. The effect of incomplete fuel-oxidizer mixing was found to have a significant effect on the growth rate, one which was greater than could simply be accounted for by the effect of local variations in the equivalence ratio on the local flame speed.

Observation of a 3D Magnetic Null Point

DOE Office of Scientific and Technical Information (OSTI.GOV)

Romano, P.; Falco, M.; Guglielmino, S. L.

2017-03-10

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

Impact of deep learning on the normalization of reconstruction kernel effects in imaging biomarker quantification: a pilot study in CT emphysema

NASA Astrophysics Data System (ADS)

Jin, Hyeongmin; Heo, Changyong; Kim, Jong Hyo

2018-02-01

Differing reconstruction kernels are known to strongly affect the variability of imaging biomarkers and thus remain as a barrier in translating the computer aided quantification techniques into clinical practice. This study presents a deep learning application to CT kernel conversion which converts a CT image of sharp kernel to that of standard kernel and evaluates its impact on variability reduction of a pulmonary imaging biomarker, the emphysema index (EI). Forty cases of low-dose chest CT exams obtained with 120kVp, 40mAs, 1mm thickness, of 2 reconstruction kernels (B30f, B50f) were selected from the low dose lung cancer screening database of our institution. A Fully convolutional network was implemented with Keras deep learning library. The model consisted of symmetric layers to capture the context and fine structure characteristics of CT images from the standard and sharp reconstruction kernels. Pairs of the full-resolution CT data set were fed to input and output nodes to train the convolutional network to learn the appropriate filter kernels for converting the CT images of sharp kernel to standard kernel with a criterion of measuring the mean squared error between the input and target images. EIs (RA950 and Perc15) were measured with a software package (ImagePrism Pulmo, Seoul, South Korea) and compared for the data sets of B50f, B30f, and the converted B50f. The effect of kernel conversion was evaluated with the mean and standard deviation of pair-wise differences in EI. The population mean of RA950 was 27.65 +/- 7.28% for B50f data set, 10.82 +/- 6.71% for the B30f data set, and 8.87 +/- 6.20% for the converted B50f data set. The mean of pair-wise absolute differences in RA950 between B30f and B50f is reduced from 16.83% to 1.95% using kernel conversion. Our study demonstrates the feasibility of applying the deep learning technique for CT kernel conversion and reducing the kernel-induced variability of EI quantification. The deep learning model has a potential to improve the reliability of imaging biomarker, especially in evaluating the longitudinal changes of EI even when the patient CT scans were performed with different kernels.

Visco-elastic controlled-source full waveform inversion without surface waves

NASA Astrophysics Data System (ADS)

Paschke, Marco; Krause, Martin; Bleibinhaus, Florian

2016-04-01

We developed a frequency-domain visco-elastic full waveform inversion for onshore seismic experiments with topography. The forward modeling is based on a finite-difference time-domain algorithm by Robertsson that uses the image-method to ensure a stress-free condition at the surface. The time-domain data is Fourier-transformed at every point in the model space during the forward modeling for a given set of frequencies. The motivation for this approach is the reduced amount of memory when computing kernels, and the straightforward implementation of the multiscale approach. For the inversion, we calculate the Frechet derivative matrix explicitly, and we implement a Levenberg-Marquardt scheme that allows for computing the resolution matrix. To reduce the size of the Frechet derivative matrix, and to stabilize the inversion, an adapted inverse mesh is used. The node spacing is controlled by the velocity distribution and the chosen frequencies. To focus the inversion on body waves (P, P-coda, and S) we mute the surface waves from the data. Consistent spatiotemporal weighting factors are applied to the wavefields during the Fourier transform to obtain the corresponding kernels. We test our code with a synthetic study using the Marmousi model with arbitrary topography. This study also demonstrates the importance of topography and muting surface waves in controlled-source full waveform inversion.

Size and moisture distribution characteristics of walnuts and their components

USDA-ARS?s Scientific Manuscript database

The objective of this study was to determine the size characteristics and moisture content (MC) distributions of individual walnuts and their components, including hulls, shells and kernels under different harvest conditions. Measurements were carried out for three walnut varieties, Tulare, Howard a...

A scalable kernel-based semisupervised metric learning algorithm with out-of-sample generalization ability.

PubMed

Yeung, Dit-Yan; Chang, Hong; Dai, Guang

2008-11-01

In recent years, metric learning in the semisupervised setting has aroused a lot of research interest. One type of semisupervised metric learning utilizes supervisory information in the form of pairwise similarity or dissimilarity constraints. However, most methods proposed so far are either limited to linear metric learning or unable to scale well with the data set size. In this letter, we propose a nonlinear metric learning method based on the kernel approach. By applying low-rank approximation to the kernel matrix, our method can handle significantly larger data sets. Moreover, our low-rank approximation scheme can naturally lead to out-of-sample generalization. Experiments performed on both artificial and real-world data show very promising results.

Home range and space use patterns of flathead catfish during the summer-fall period in two Missouri streams

USGS Publications Warehouse

Vokoun, Jason C.; Rabeni, Charles F.

2005-01-01

Flathead catfish Pylodictis olivaris were radio-tracked in the Grand River and Cuivre River, Missouri, from late July until they moved to overwintering habitats in late October. Fish moved within a definable area, and although occasional long-distance movements occurred, the fish typically returned to the previously occupied area. Seasonal home range was calculated with the use of kernel density estimation, which can be interpreted as a probabilistic utilization distribution that documents the internal structure of the estimate by delineating portions of the range that was used a specified percentage of the time. A traditional linear range also was reported. Most flathead catfish (89%) had one 50% kernel-estimated core area, whereas 11% of the fish split their time between two core areas. Core areas were typically in the middle of the 90% kernel-estimated home range (58%), although several had core areas in upstream (26%) and downstream (16%) portions of the home range. Home-range size did not differ based on river, sex, or size and was highly variable among individuals. The median 95% kernel estimate was 1,085 m (range, 70– 69,090 m) for all fish. The median 50% kernel-estimated core area was 135 m (10–2,260 m). The median linear range was 3,510 m (150–50,400 m). Fish pairs with core areas in the same and neighboring pools had static joint space use values of up to 49% (area of intersection index), indicating substantial overlap and use of the same area. However, all fish pairs had low dynamic joint space use values (<0.07; coefficient of association), indicating that fish pairs were temporally segregated, rarely occurring in the same location at the same time.

MPACT Subgroup Self-Shielding Efficiency Improvements

DOE Office of Scientific and Technical Information (OSTI.GOV)

Stimpson, Shane; Liu, Yuxuan; Collins, Benjamin S.

Recent developments to improve the efficiency of the MOC solvers in MPACT have yielded effective kernels that loop over several energy groups at once, rather that looping over one group at a time. These kernels have produced roughly a 2x speedup on the MOC sweeping time during eigenvalue calculation. However, the self-shielding subgroup calculation had not been reevaluated to take advantage of these new kernels, which typically requires substantial solve time. The improvements covered in this report start by integrating the multigroup kernel concepts into the subgroup calculation, which are then used as the basis for further extensions. The nextmore » improvement that is covered is what is currently being termed as “Lumped Parameter MOC”. Because the subgroup calculation is a purely fixed source problem and multiple sweeps are performed only to update the boundary angular fluxes, the sweep procedure can be condensed to allow for the instantaneous propagation of the flux across a spatial domain, without the need to sweep along all segments in a ray. Once the boundary angular fluxes are considered to be converged, an additional sweep that will tally the scalar flux is completed. The last improvement that is investigated is the possible reduction of the number of azimuthal angles per octant in the shielding sweep. Typically 16 azimuthal angles per octant are used for self-shielding and eigenvalue calculations, but it is possible that the self-shielding sweeps are less sensitive to the number of angles than the full eigenvalue calculation.« less

Analysis of Drude model using fractional derivatives without singular kernels

NASA Astrophysics Data System (ADS)

Jiménez, Leonardo Martínez; García, J. Juan Rosales; Contreras, Abraham Ortega; Baleanu, Dumitru

2017-11-01

We report study exploring the fractional Drude model in the time domain, using fractional derivatives without singular kernels, Caputo-Fabrizio (CF), and fractional derivatives with a stretched Mittag-Leffler function. It is shown that the velocity and current density of electrons moving through a metal depend on both the time and the fractional order 0 < γ ≤ 1. Due to non-singular fractional kernels, it is possible to consider complete memory effects in the model, which appear neither in the ordinary model, nor in the fractional Drude model with Caputo fractional derivative. A comparison is also made between these two representations of the fractional derivatives, resulting a considered difference when γ < 0.8.

Performance Modeling in CUDA Streams - A Means for High-Throughput Data Processing.

PubMed

Li, Hao; Yu, Di; Kumar, Anand; Tu, Yi-Cheng

2014-10-01

Push-based database management system (DBMS) is a new type of data processing software that streams large volume of data to concurrent query operators. The high data rate of such systems requires large computing power provided by the query engine. In our previous work, we built a push-based DBMS named G-SDMS to harness the unrivaled computational capabilities of modern GPUs. A major design goal of G-SDMS is to support concurrent processing of heterogenous query processing operations and enable resource allocation among such operations. Understanding the performance of operations as a result of resource consumption is thus a premise in the design of G-SDMS. With NVIDIA's CUDA framework as the system implementation platform, we present our recent work on performance modeling of CUDA kernels running concurrently under a runtime mechanism named CUDA stream . Specifically, we explore the connection between performance and resource occupancy of compute-bound kernels and develop a model that can predict the performance of such kernels. Furthermore, we provide an in-depth anatomy of the CUDA stream mechanism and summarize the main kernel scheduling disciplines in it. Our models and derived scheduling disciplines are verified by extensive experiments using synthetic and real-world CUDA kernels.

G-Hash: Towards Fast Kernel-based Similarity Search in Large Graph Databases.

PubMed

Wang, Xiaohong; Smalter, Aaron; Huan, Jun; Lushington, Gerald H

2009-01-01

Structured data including sets, sequences, trees and graphs, pose significant challenges to fundamental aspects of data management such as efficient storage, indexing, and similarity search. With the fast accumulation of graph databases, similarity search in graph databases has emerged as an important research topic. Graph similarity search has applications in a wide range of domains including cheminformatics, bioinformatics, sensor network management, social network management, and XML documents, among others.Most of the current graph indexing methods focus on subgraph query processing, i.e. determining the set of database graphs that contains the query graph and hence do not directly support similarity search. In data mining and machine learning, various graph kernel functions have been designed to capture the intrinsic similarity of graphs. Though successful in constructing accurate predictive and classification models for supervised learning, graph kernel functions have (i) high computational complexity and (ii) non-trivial difficulty to be indexed in a graph database.Our objective is to bridge graph kernel function and similarity search in graph databases by proposing (i) a novel kernel-based similarity measurement and (ii) an efficient indexing structure for graph data management. Our method of similarity measurement builds upon local features extracted from each node and their neighboring nodes in graphs. A hash table is utilized to support efficient storage and fast search of the extracted local features. Using the hash table, a graph kernel function is defined to capture the intrinsic similarity of graphs and for fast similarity query processing. We have implemented our method, which we have named G-hash, and have demonstrated its utility on large chemical graph databases. Our results show that the G-hash method achieves state-of-the-art performance for k-nearest neighbor (k-NN) classification. Most importantly, the new similarity measurement and the index structure is scalable to large database with smaller indexing size, faster indexing construction time, and faster query processing time as compared to state-of-the-art indexing methods such as C-tree, gIndex, and GraphGrep.

Kernel Machine SNP-set Testing under Multiple Candidate Kernels

PubMed Central

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

2013-01-01

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

Protein fold recognition using geometric kernel data fusion.

PubMed

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.

Evaluation of aerial survey methods for Dall's sheep

USGS Publications Warehouse

Udevitz, Mark S.; Shults, Brad S.; Adams, Layne G.; Kleckner, Christopher

2006-01-01

Most Dall's sheep (Ovis dalli dalli) population-monitoring efforts use intensive aerial surveys with no attempt to estimate variance or adjust for potential sightability bias. We used radiocollared sheep to assess factors that could affect sightability of Dall's sheep in standard fixed-wing and helicopter surveys and to evaluate feasibility of methods that might account for sightability bias. Work was conducted in conjunction with annual aerial surveys of Dall's sheep in the western Baird Mountains, Alaska, USA, in 2000–2003. Overall sightability was relatively high compared with other aerial wildlife surveys, with 88% of the available, marked sheep detected in our fixed-wing surveys. Total counts from helicopter surveys were not consistently larger than counts from fixed-wing surveys of the same units, and detection probabilities did not differ for the 2 aircraft types. Our results suggest that total counts from helicopter surveys cannot be used to obtain reliable estimates of detection probabilities for fixed-wing surveys. Groups containing radiocollared sheep often changed in size and composition before they could be observed by a second crew in units that were double-surveyed. Double-observer methods that require determination of which groups were detected by each observer will be infeasible unless survey procedures can be modified so that groups remain more stable between observations. Mean group sizes increased during our study period, and our logistic regression sightability model indicated that detection probabilities increased with group size. Mark–resight estimates of annual population sizes were similar to sightability-model estimates, and confidence intervals overlapped broadly. We recommend the sightability-model approach as the most effective and feasible of the alternatives we considered for monitoring Dall's sheep populations.

Small convolution kernels for high-fidelity image restoration

NASA Technical Reports Server (NTRS)

Reichenbach, Stephen E.; Park, Stephen K.

1991-01-01

An algorithm is developed for computing the mean-square-optimal values for small, image-restoration kernels. The algorithm is based on a comprehensive, end-to-end imaging system model that accounts for the important components of the imaging process: the statistics of the scene, the point-spread function of the image-gathering device, sampling effects, noise, and display reconstruction. Subject to constraints on the spatial support of the kernel, the algorithm generates the kernel values that restore the image with maximum fidelity, that is, the kernel minimizes the expected mean-square restoration error. The algorithm is consistent with the derivation of the spatially unconstrained Wiener filter, but leads to a small, spatially constrained kernel that, unlike the unconstrained filter, can be efficiently implemented by convolution. Simulation experiments demonstrate that for a wide range of imaging systems these small kernels can restore images with fidelity comparable to images restored with the unconstrained Wiener filter.

The quantitative properties of three soft X-ray flare kernels observed with the AS&E X-ray telescope on Skylab

NASA Technical Reports Server (NTRS)

Kahler, S. W.; Petrasso, R. D.; Kane, S. R.

1976-01-01

The physical parameters for the kernels of three solar X-ray flare events have been deduced using photographic data from the S-054 X-ray telescope on Skylab as the primary data source and 1-8 and 8-20 A fluxes from Solrad 9 as the secondary data source. The kernels had diameters of about 5-7 seconds of arc and in two cases electron densities at least as high as 0.3 trillion per cu cm. The lifetimes of the kernels were 5-10 min. The presence of thermal conduction during the decay phases is used to argue: (1) that kernels are entire, not small portions of, coronal loop structures, and (2) that flare heating must continue during the decay phase. We suggest a simple geometric model to explain the role of kernels in flares in which kernels are identified with emerging flux regions.

The distal portion of the short arm of wheat (Triticum aestivum L.) chromosome 5D controls endosperm vitreosity and grain hardness.

PubMed

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.

ℓ p-Norm Multikernel Learning Approach for Stock Market Price Forecasting

PubMed Central

Shao, Xigao; Wu, Kun; Liao, Bifeng

2012-01-01

Linear multiple kernel learning model has been used for predicting financial time series. However, ℓ 1-norm multiple support vector regression is rarely observed to outperform trivial baselines in practical applications. To allow for robust kernel mixtures that generalize well, we adopt ℓ p-norm multiple kernel support vector regression (1 ≤ p < ∞) as a stock price prediction model. The optimization problem is decomposed into smaller subproblems, and the interleaved optimization strategy is employed to solve the regression model. The model is evaluated on forecasting the daily stock closing prices of Shanghai Stock Index in China. Experimental results show that our proposed model performs better than ℓ 1-norm multiple support vector regression model. PMID:23365561

A simple shear limited, single size, time dependent flocculation model

NASA Astrophysics Data System (ADS)

Kuprenas, R.; Tran, D. A.; Strom, K.

2017-12-01

This research focuses on the modeling of flocculation of cohesive sediment due to turbulent shear, specifically, investigating the dependency of flocculation on the concentration of cohesive sediment. Flocculation is important in larger sediment transport models as cohesive particles can create aggregates which are orders of magnitude larger than their unflocculated state. As the settling velocity of each particle is determined by the sediment size, density, and shape, accounting for this aggregation is important in determining where the sediment is deposited. This study provides a new formulation for flocculation of cohesive sediment by modifying the Winterwerp (1998) flocculation model (W98) so that it limits floc size to that of the Kolmogorov micro length scale. The W98 model is a simple approach that calculates the average floc size as a function of time. Because of its simplicity, the W98 model is ideal for implementing into larger sediment transport models; however, the model tends to over predict the dependency of the floc size on concentration. It was found that the modification of the coefficients within the original model did not allow for the model to capture the dependency on concentration. Therefore, a new term within the breakup kernel of the W98 formulation was added. The new formulation results is a single size, shear limited, and time dependent flocculation model that is able to effectively capture the dependency of the equilibrium size of flocs on both suspended sediment concentration and the time to equilibrium. The overall behavior of the new model is explored and showed align well with other studies on flocculation. Winterwerp, J. C. (1998). A simple model for turbulence induced flocculation of cohesive sediment. .Journal of Hydraulic Research, 36(3):309-326.

Parametric and non-parametric modeling of short-term synaptic plasticity. Part I: computational study

PubMed Central

Marmarelis, Vasilis Z.; Berger, Theodore W.

2009-01-01

Parametric and non-parametric modeling methods are combined to study the short-term plasticity (STP) of synapses in the central nervous system (CNS). The nonlinear dynamics of STP are modeled by means: (1) previously proposed parametric models based on mechanistic hypotheses and/or specific dynamical processes, and (2) non-parametric models (in the form of Volterra kernels) that transforms the presynaptic signals into postsynaptic signals. In order to synergistically use the two approaches, we estimate the Volterra kernels of the parametric models of STP for four types of synapses using synthetic broadband input–output data. Results show that the non-parametric models accurately and efficiently replicate the input–output transformations of the parametric models. Volterra kernels provide a general and quantitative representation of the STP. PMID:18506609

Multi-environment QTL analysis of grain morphology traits and fine mapping of a kernel-width QTL in Zheng58 × SK maize population.

PubMed

Raihan, Mohammad Sharif; Liu, Jie; Huang, Juan; Guo, Huan; Pan, Qingchun; Yan, Jianbing

2016-08-01

Sixteen major QTLs regulating maize kernel traits were mapped in multiple environments and one of them, qKW - 9.2 , was restricted to 630 Kb, harboring 28 putative gene models. To elucidate the genetic basis of kernel traits, a quantitative trait locus (QTL) analysis was conducted in a maize recombinant inbred line population derived from a cross between two diverse parents Zheng58 and SK, evaluated across eight environments. Construction of a high-density linkage map was based on 13,703 single-nucleotide polymorphism markers, covering 1860.9 cM of the whole genome. In total, 18, 26, 23, and 19 QTLs for kernel length, width, thickness, and 100-kernel weight, respectively, were detected on the basis of a single-environment analysis, and each QTL explained 3.2-23.7 % of the phenotypic variance. Sixteen major QTLs, which could explain greater than 10 % of the phenotypic variation, were mapped in multiple environments, implying that kernel traits might be controlled by many minor and multiple major QTLs. The major QTL qKW-9.2 with physical confidence interval of 1.68 Mbp, affecting kernel width, was then selected for fine mapping using heterogeneous inbred families. At final, the location of the underlying gene was narrowed down to 630 Kb, harboring 28 putative candidate-gene models. This information will enhance molecular breeding for kernel traits and simultaneously assist the gene cloning underlying this QTL, helping to reveal the genetic basis of kernel development in maize.

Application of SWIR hyperspectral imaging and chemometrics for identification of aflatoxin B1 contaminated maize kernels

NASA Astrophysics Data System (ADS)

Kimuli, Daniel; Wang, Wei; Wang, Wei; Jiang, Hongzhe; Zhao, Xin; Chu, Xuan

2018-03-01

A short-wave infrared (SWIR) hyperspectral imaging system (1000-2500 nm) combined with chemometric data analysis was used to detect aflatoxin B1 (AFB1) on surfaces of 600 kernels of four yellow maize varieties from different States of the USA (Georgia, Illinois, Indiana and Nebraska). For each variety, four AFB1 solutions (10, 20, 100 and 500 ppb) were artificially deposited on kernels and a control group was generated from kernels treated with methanol solution. Principal component analysis (PCA), partial least squares discriminant analysis (PLSDA) and factorial discriminant analysis (FDA) were applied to explore and classify maize kernels according to AFB1 contamination. PCA results revealed partial separation of control kernels from AFB1 contaminated kernels for each variety while no pattern of separation was observed among pooled samples. A combination of standard normal variate and first derivative pre-treatments produced the best PLSDA classification model with accuracy of 100% and 96% in calibration and validation, respectively, from Illinois variety. The best AFB1 classification results came from FDA on raw spectra with accuracy of 100% in calibration and validation for Illinois and Nebraska varieties. However, for both PLSDA and FDA models, poor AFB1 classification results were obtained for pooled samples relative to individual varieties. SWIR spectra combined with chemometrics and spectra pre-treatments showed the possibility of detecting maize kernels of different varieties coated with AFB1. The study further suggests that increase of maize kernel constituents like water, protein, starch and lipid in a pooled sample may have influence on detection accuracy of AFB1 contamination.

On one solution of Volterra integral equations of second kind

NASA Astrophysics Data System (ADS)

Myrhorod, V.; Hvozdeva, I.

2016-10-01

A solution of Volterra integral equations of the second kind with separable and difference kernels based on solutions of corresponding equations linking the kernel and resolvent is suggested. On the basis of a discrete functions class, the equations linking the kernel and resolvent are obtained and the methods of their analytical solutions are proposed. A mathematical model of the gas-turbine engine state modification processes in the form of Volterra integral equation of the second kind with separable kernel is offered.

Resolvability of regional density structure

NASA Astrophysics Data System (ADS)

Plonka, A.; Fichtner, A.

2016-12-01

Lateral density variations are the source of mass transport in the Earth at all scales, acting as drivers of convectivemotion. However, the density structure of the Earth remains largely unknown since classic seismic observables and gravityprovide only weak constraints with strong trade-offs. Current density models are therefore often based on velocity scaling,making strong assumptions on the origin of structural heterogeneities, which may not necessarily be correct. Our goal is to assessif 3D density structure may be resolvable with emerging full-waveform inversion techniques. We have previously quantified the impact of regional-scale crustal density structure on seismic waveforms with the conclusion that reasonably sized density variations within thecrust can leave a strong imprint on both travel times and amplitudes, and, while this can produce significant biases in velocity and Q estimates, the seismic waveform inversion for density may become feasible. In this study we performprincipal component analyses of sensitivity kernels for P velocity, S velocity, and density. This is intended to establish theextent to which these kernels are linearly independent, i.e. the extent to which the different parameters may be constrainedindependently. Since the density imprint we observe is not exclusively linked to travel times and amplitudes of specific phases,we consider waveform differences between complete seismograms. We test the method using a known smooth model of the crust and seismograms with clear Love and Rayleigh waves, showing that - as expected - the first principal kernel maximizes sensitivity to SH and SV velocity structure, respectively, and that the leakage between S velocity, P velocity and density parameter spaces is minimal in the chosen setup. Next, we apply the method to data from 81 events around the Iberian Penninsula, registered in total by 492 stations. The objective is to find a principal kernel which would maximize the sensitivity to density, potentially allowing for independent density resolution, and, as the final goal, for direct density inversion.

Finite-frequency tomography using adjoint methods-Methodology and examples using membrane surface waves

NASA Astrophysics Data System (ADS)

Tape, Carl; Liu, Qinya; Tromp, Jeroen

2007-03-01

We employ adjoint methods in a series of synthetic seismic tomography experiments to recover surface wave phase-speed models of southern California. Our approach involves computing the Fréchet derivative for tomographic inversions via the interaction between a forward wavefield, propagating from the source to the receivers, and an `adjoint' wavefield, propagating from the receivers back to the source. The forward wavefield is computed using a 2-D spectral-element method (SEM) and a phase-speed model for southern California. A `target' phase-speed model is used to generate the `data' at the receivers. We specify an objective or misfit function that defines a measure of misfit between data and synthetics. For a given receiver, the remaining differences between data and synthetics are time-reversed and used as the source of the adjoint wavefield. For each earthquake, the interaction between the regular and adjoint wavefields is used to construct finite-frequency sensitivity kernels, which we call event kernels. An event kernel may be thought of as a weighted sum of phase-specific (e.g. P) banana-doughnut kernels, with weights determined by the measurements. The overall sensitivity is simply the sum of event kernels, which defines the misfit kernel. The misfit kernel is multiplied by convenient orthonormal basis functions that are embedded in the SEM code, resulting in the gradient of the misfit function, that is, the Fréchet derivative. A non-linear conjugate gradient algorithm is used to iteratively improve the model while reducing the misfit function. We illustrate the construction of the gradient and the minimization algorithm, and consider various tomographic experiments, including source inversions, structural inversions and joint source-structure inversions. Finally, we draw connections between classical Hessian-based tomography and gradient-based adjoint tomography.

INTERDISCIPLINARY PHYSICS AND RELATED AREAS OF SCIENCE AND TECHNOLOGY: Competition Between Self-birth and Catalyzed Death in Aggregation Growth with Catalysis Injection

NASA Astrophysics Data System (ADS)

Chen, Dan; Lin, Zhen-Quan; Sun, Yun-Fei; Ke, Jian-Hong

2009-12-01

We propose two irreversible aggregation growth models of aggregates of two distinct species (A and B) to study the interactions between virus aggregates and medicine efficacy aggregates in the virus-medicine cooperative evolution system. The A-species aggregates evolve driven by self monomer birth and B-species aggregate-catalyzed monomer death in model I and by self birth, catalyzed death, and self monomer exchange reactions in model II, while the catalyst B-species aggregates are assumed to be injected into the system sustainedly or at a periodic time-dependent rate. The kinetic behaviors of the A-species aggregates are investigated by the rate equation approach based on the mean-field theory with the self birth rate kernel IA(k) = Ik, catalyzed death rate kernel JAB(k) = Jk and self exchange rate kernel KA (k, l) = Kkl. The kinetic behaviors of the A-species aggregates are mainly dominated by the competition between the two effects of the self birth (with the effective rate I) and the catalyzed death (with the effective rate JB0), while the effects of the self exchanges of the A-species aggregates which appear in an effective rate KA0 play important roles in the cases of I > JB0 and I = JB0. The evolution behaviors of the total mass MA1(t) and the total aggregate number MA0 (t) are obtained, and the aggregate size distribution αk (t) of species A is found to approach a generalized scaling form in the case of I >= JB0 and a special modified scaling form in the case of I < JB0. The periodical evolution of the B-monomers concentration plays an exponential form of the periodic modulation.

A new discrete dipole kernel for quantitative susceptibility mapping.

PubMed

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

2018-09-01

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

An Ensemble Approach to Building Mercer Kernels with Prior Information

NASA Technical Reports Server (NTRS)

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

2005-01-01

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

Super-resolution fusion of complementary panoramic images based on cross-selection kernel regression interpolation.

PubMed

Chen, Lidong; Basu, Anup; Zhang, Maojun; Wang, Wei; Liu, Yu

2014-03-20

A complementary catadioptric imaging technique was proposed to solve the problem of low and nonuniform resolution in omnidirectional imaging. To enhance this research, our paper focuses on how to generate a high-resolution panoramic image from the captured omnidirectional image. To avoid the interference between the inner and outer images while fusing the two complementary views, a cross-selection kernel regression method is proposed. First, in view of the complementarity of sampling resolution in the tangential and radial directions between the inner and the outer images, respectively, the horizontal gradients in the expected panoramic image are estimated based on the scattered neighboring pixels mapped from the outer, while the vertical gradients are estimated using the inner image. Then, the size and shape of the regression kernel are adaptively steered based on the local gradients. Furthermore, the neighboring pixels in the next interpolation step of kernel regression are also selected based on the comparison between the horizontal and vertical gradients. In simulation and real-image experiments, the proposed method outperforms existing kernel regression methods and our previous wavelet-based fusion method in terms of both visual quality and objective evaluation.

Improved modeling of clinical data with kernel methods.

PubMed

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

2012-02-01

Despite the rise of high-throughput technologies, clinical data such as age, gender and medical history guide clinical management for most diseases and examinations. To improve clinical management, available patient information should be fully exploited. This requires appropriate modeling of relevant parameters. When kernel methods are used, traditional kernel functions such as the linear kernel are often applied to the set of clinical parameters. These kernel functions, however, have their disadvantages due to the specific characteristics of clinical data, being a mix of variable types with each variable its own range. We propose a new kernel function specifically adapted to the characteristics of clinical data. The clinical kernel function provides a better representation of patients' similarity by equalizing the influence of all variables and taking into account the range r of the variables. Moreover, it is robust with respect to changes in r. Incorporated in a least squares support vector machine, the new kernel function results in significantly improved diagnosis, prognosis and prediction of therapy response. This is illustrated on four clinical data sets within gynecology, with an average increase in test area under the ROC curve (AUC) of 0.023, 0.021, 0.122 and 0.019, respectively. Moreover, when combining clinical parameters and expression data in three case studies on breast cancer, results improved overall with use of the new kernel function and when considering both data types in a weighted fashion, with a larger weight assigned to the clinical parameters. The increase in AUC with respect to a standard kernel function and/or unweighted data combination was maximum 0.127, 0.042 and 0.118 for the three case studies. For clinical data consisting of variables of different types, the proposed kernel function--which takes into account the type and range of each variable--has shown to be a better alternative for linear and non-linear classification problems. Copyright © 2011 Elsevier B.V. All rights reserved.

[Spectral scatter correction of coal samples based on quasi-linear local weighted method].

PubMed

Lei, Meng; Li, Ming; Ma, Xiao-Ping; Miao, Yan-Zi; Wang, Jian-Sheng

2014-07-01

The present paper puts forth a new spectral correction method based on quasi-linear expression and local weighted function. The first stage of the method is to search 3 quasi-linear expressions to replace the original linear expression in MSC method, such as quadratic, cubic and growth curve expression. Then the local weighted function is constructed by introducing 4 kernel functions, such as Gaussian, Epanechnikov, Biweight and Triweight kernel function. After adding the function in the basic estimation equation, the dependency between the original and ideal spectra is described more accurately and meticulously at each wavelength point. Furthermore, two analytical models were established respectively based on PLS and PCA-BP neural network method, which can be used for estimating the accuracy of corrected spectra. At last, the optimal correction mode was determined by the analytical results with different combination of quasi-linear expression and local weighted function. The spectra of the same coal sample have different noise ratios while the coal sample was prepared under different particle sizes. To validate the effectiveness of this method, the experiment analyzed the correction results of 3 spectral data sets with the particle sizes of 0.2, 1 and 3 mm. The results show that the proposed method can eliminate the scattering influence, and also can enhance the information of spectral peaks. This paper proves a more efficient way to enhance the correlation between corrected spectra and coal qualities significantly, and improve the accuracy and stability of the analytical model substantially.

A Robustness Testing Campaign for IMA-SP Partitioning Kernels

NASA Astrophysics Data System (ADS)

Grixti, Stephen; Lopez Trecastro, Jorge; Sammut, Nicholas; Zammit-Mangion, David

2015-09-01

With time and space partitioned architectures becoming increasingly appealing to the European space sector, the dependability of partitioning kernel technology is a key factor to its applicability in European Space Agency projects. This paper explores the potential of the data type fault model, which injects faults through the Application Program Interface, in partitioning kernel robustness testing. This fault injection methodology has been tailored to investigate its relevance in uncovering vulnerabilities within partitioning kernels and potentially contributing towards fault removal campaigns within this domain. This is demonstrated through a robustness testing case study of the XtratuM partitioning kernel for SPARC LEON3 processors. The robustness campaign exposed a number of vulnerabilities in XtratuM, exhibiting the potential benefits of using such a methodology for the robustness assessment of partitioning kernels.

Modelling the angular effects on satellite retrieved LST at global scale using a land surface classification

NASA Astrophysics Data System (ADS)

Ermida, Sofia; DaCamara, Carlos C.; Trigo, Isabel F.; Pires, Ana C.; Ghent, Darren

2017-04-01

Land Surface Temperature (LST) is a key climatological variable and a diagnostic parameter of land surface conditions. Remote sensing constitutes the most effective method to observe LST over large areas and on a regular basis. Although LST estimation from remote sensing instruments operating in the Infrared (IR) is widely used and has been performed for nearly 3 decades, there is still a list of open issues. One of these is the LST dependence on viewing and illumination geometry. This effect introduces significant discrepancies among LST estimations from different sensors, overlapping in space and time, that are not related to uncertainties in the methodologies or input data used. Furthermore, these directional effects deviate LST products from an ideally defined LST, which should represent to the ensemble of directional radiometric temperature of all surface elements within the FOV. Angular effects on LST are here conveniently estimated by means of a kernel model of the surface thermal emission, which describes the angular dependence of LST as a function of viewing and illumination geometry. The model is calibrated using LST data as provided by a wide range of sensors to optimize spatial coverage, namely: 1) a LEO sensor - the Moderate Resolution Imaging Spectroradiometer (MODIS) on-board NASA's TERRA and AQUA; and 2) 3 GEO sensors - the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on-board EUMETSAT's Meteosat Second Generation (MSG), the Japanese Meteorological Imager (JAMI) on-board the Japanese Meteorological Association (JMA) Multifunction Transport SATellite (MTSAT-2), and NASA's Geostationary Operational Environmental Satellites (GOES). As shown in our previous feasibility studies the sampling of illumination and view angles has a high impact on the obtained model parameters. This impact may be mitigated when the sampling size is increased by aggregating pixels with similar surface conditions. Here we propose a methodology where land surface is stratified by means of a cluster analysis using information on land cover type, fraction of vegetation cover and topography. The kernel model is then adjusted to LST data corresponding to each cluster. It is shown that the quality of the cluster based kernel model is very close to the pixel based one. Furthermore, the reduced number of parameters (limited to the number of identified clusters, instead of a pixel-by-pixel model calibration) allows improving the kernel model trough the incorporation of a seasonal component. The application of the here discussed procedure towards the harmonization of LST products from multi-sensors is on the framework of the ESA DUE GlobTemperature project.

Robotic Intelligence Kernel: Driver

DOE Office of Scientific and Technical Information (OSTI.GOV)

The INL Robotic Intelligence Kernel-Driver is built on top of the RIK-A and implements a dynamic autonomy structure. The RIK-D is used to orchestrate hardware for sensing and action as well as software components for perception, communication, behavior and world modeling into a single cognitive behavior kernel that provides intrinsic intelligence for a wide variety of unmanned ground vehicle systems.

TaGW2, a Good Reflection of Wheat Polyploidization and Evolution.

PubMed

Qin, Lin; Zhao, Junjie; Li, Tian; Hou, Jian; Zhang, Xueyong; Hao, Chenyang

2017-01-01

Hexaploid wheat consists of three subgenomes, namely, A, B, and D. These well-characterized ancestral genomes also exist at the diploid and tetraploid levels, thereby rendering wheat as a good model species for studying polyploidization. Here, we performed intra- and inter-species comparative analyses of wheat and its relatives to dissect polymorphism and differentiation of the TaGW2 genes. Our results showed that genetic diversity of TaGW2 decreased with progression from the diploids to tetraploids and hexaploids. The strongest selection occurred in the promoter regions of TaGW2-6A and TaGW2-6B . Phylogenetic trees clearly indicated that Triticum urartu and Ae. speltoides were the donors of the A and B genomes in tetraploid and hexaploid wheats. Haplotypes detected among hexaploid genotypes traced back to the tetraploid level. Fst and π values revealed that the strongest selection on TaGW2 occurred at the tetraploid level rather than in hexaploid wheat. This infers that grain size enlargement, especially increased kernel width, mainly occurred in tetraploid genotypes. In addition, relative expression levels of TaGW2s significantly declined from the diploid level to tetraploids and hexaploids, further indicating that these genes negatively regulate kernel size. Our results also revealed that the polyploidization events possibly caused much stronger differentiation than domestication and breeding.

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

PubMed

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

2017-12-15

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

Home ranges and habitat use of sloth bears Melursus ursinus inornatus in Wasgomuwa National Park, Sri Lanka

USGS Publications Warehouse

Ratnayeke, S.; Van Manen, F.T.; Padmalal, U.K.G.K.

2007-01-01

We studied home ranges and habitat selection of 10 adult sloth bears Melursus ursinus inornatus at Wasgomuwa National Park, Sri Lanka during 2002-2003. Very little is known about the ecology and behaviour of M. u. inornatus, which is a subspecies found in Sri Lanka. Our study was undertaken to assess space and habitat requirements typical of a viable population of M. u. inornatus to facilitate future conservation efforts. We captured and radio-collared 10 adult sloth bears and used the telemetry data to assess home-range size and habitat use. Mean 95% fixed kernel home ranges were 2.2 km2 (SE = 0.61) and 3.8 km2 (SE = 1.01) for adult females and males, respectively. Although areas outside the national park were accessible to bears, home ranges were almost exclusively situated within the national park boundaries. Within the home ranges, high forests were used more and abandoned agricultural fields (chenas) were used less than expected based on availability. Our estimates of home-range size are among the smallest reported for any species of bear. Thus, despite its relatively small size, Wasgomuwa National Park may support a sizeable population of sloth bears. The restriction of human activity within protected areas may be necessary for long-term viability of sloth bear populations in Sri Lanka as is maintenance of forest or scrub cover in areas with existing sloth bear populations and along potential travel corridors. ?? Wildlife Biology 2007.

Surface composition of alloys

NASA Astrophysics Data System (ADS)

Sachtler, W. M. H.

1984-11-01

In equilibrium, the composition of the surface of an alloy will, in general, differ from that of the bulk. The broken-bond model is applicable to alloys with atoms of virtually equal size. If the heat of alloy formation is zero, the component of lower heat of atomization is found enriched in the surface. If both partners have equal heats of sublimination, the surface of a diluted alloy is enriched with the minority component. Size effects can enhance or weaken the electronic effects. In general, lattice strain can be relaxed by precipitating atoms of deviating size on the surface. Two-phase alloys are described by the "cherry model", i.e. one alloy phase, the "kernel" is surrounded by another alloy, the "flesh", and the surface of the outer phase, the "skin" displays a deviating surface composition as in monophasic alloys. In the presence of molecules capable of forming chemical bonds with individual metal atoms, "chemisorption induced surface segregation" can be observed at low temperatures, i.e. the surface becomes enriched with the metal forming the stronger chemisorption bonds.

Effect of solid fat content on structure in ice creams containing palm kernel oil and high-oleic sunflower oil.

PubMed

Sung, Kristine K; Goff, H Douglas

2010-04-01

The development of a structural fat network in ice cream as influenced by the solid:liquid fat ratio at the time of freezing/whipping was investigated. The solid fat content was varied with blends of a hard fraction of palm kernel oil (PKO) and high-oleic sunflower oil ranging from 40% to 100% PKO. Fat globule size and adsorbed protein levels in mix and overrun, fat destabilization, meltdown resistance, and air bubble size in ice cream were measured. It was found that blends comprising 60% to 80% solid fat produced the highest rates of fat destabilization that could be described as partial coalescence (as opposed to coalescence), lowest rates of meltdown, and smallest air bubble sizes. Lower levels of solid fat produced fat destabilization that was better characterized as coalescence, leading to loss of structural integrity, whereas higher levels of solid fat led to lower levels of fat network formation and thus also to reduced structural integrity. Blends of highly saturated palm kernel oil and monounsaturated high-oleic sunflower oil were used to modify the solid:liquid ratio of fat blends used for ice cream manufacture. Blends that contained 60% to 80% solid fat at freezing/whipping temperatures produced optimal structures leading to low rates of meltdown. This provides a useful reference for manufacturers to help in the selection of appropriate fat blends for nondairy-fat ice cream.

Wintering ecology of sympatric subspecies of Sandhill Crane: Correlations between body size, site fidelity, and movement patterns

USGS Publications Warehouse

Ivey, Gary L.; Dugger, Bruce D.; Herziger, Caroline P.; Casazza, Michael L.; Fleskes, Joseph P.

2015-01-01

Body size is known to correlate with many aspects of life history in birds, and this knowledge can be used to manage and conserve bird species. However, few studies have compared the wintering ecology of sympatric subspecies that vary significantly in body size. We used radiotelemetry to examine the relationship between body size and site fidelity, movements, and home range in 2 subspecies of Sandhill Crane (Grus canadensis) wintering in the Sacramento–San Joaquin Delta of California, USA. Both subspecies showed high interannual return rates to the Delta study area, but Greater Sandhill Cranes (G. c. tabida) showed stronger within-winter fidelity to landscapes in our study region and to roost complexes within landscapes than did Lesser Sandhill Cranes (G. c. canadensis). Foraging flights from roost sites were shorter for G. c. tabida than for G. c. canadensis (1.9 ± 0.01 km vs. 4.5 ± 0.01 km, respectively) and, consequently, the mean size of 95% fixed-kernel winter home ranges was an order of magnitude smaller for G. c. tabida than for G. c. canadensis (1.9 ± 0.4 km2 vs. 21.9 ± 1.9 km2, respectively). Strong site fidelity indicates that conservation planning to manage for adequate food resources around traditional roost sites can be effective for meeting the habitat needs of these cranes, but the scale of conservation efforts should differ by subspecies. Analysis of movement patterns suggests that conservation planners and managers should consider all habitats within 5 km of a known G. c. tabida roost and within 10 km of a G. c. canadensis roost when planning for habitat management, mitigation, acquisition, and easements.

The use of kernel local Fisher discriminant analysis for the channelization of the Hotelling model observer

NASA Astrophysics Data System (ADS)

Wen, Gezheng; Markey, Mia K.

2015-03-01

It is resource-intensive to conduct human studies for task-based assessment of medical image quality and system optimization. Thus, numerical model observers have been developed as a surrogate for human observers. The Hotelling observer (HO) is the optimal linear observer for signal-detection tasks, but the high dimensionality of imaging data results in a heavy computational burden. Channelization is often used to approximate the HO through a dimensionality reduction step, but how to produce channelized images without losing significant image information remains a key challenge. Kernel local Fisher discriminant analysis (KLFDA) uses kernel techniques to perform supervised dimensionality reduction, which finds an embedding transformation that maximizes betweenclass separability and preserves within-class local structure in the low-dimensional manifold. It is powerful for classification tasks, especially when the distribution of a class is multimodal. Such multimodality could be observed in many practical clinical tasks. For example, primary and metastatic lesions may both appear in medical imaging studies, but the distributions of their typical characteristics (e.g., size) may be very different. In this study, we propose to use KLFDA as a novel channelization method. The dimension of the embedded manifold (i.e., the result of KLFDA) is a counterpart to the number of channels in the state-of-art linear channelization. We present a simulation study to demonstrate the potential usefulness of KLFDA for building the channelized HOs (CHOs) and generating reliable decision statistics for clinical tasks. We show that the performance of the CHO with KLFDA channels is comparable to that of the benchmark CHOs.

An energy efficient and high speed architecture for convolution computing based on binary resistive random access memory

NASA Astrophysics Data System (ADS)

Liu, Chen; Han, Runze; Zhou, Zheng; Huang, Peng; Liu, Lifeng; Liu, Xiaoyan; Kang, Jinfeng

2018-04-01

In this work we present a novel convolution computing architecture based on metal oxide resistive random access memory (RRAM) to process the image data stored in the RRAM arrays. The proposed image storage architecture shows performances of better speed-device consumption efficiency compared with the previous kernel storage architecture. Further we improve the architecture for a high accuracy and low power computing by utilizing the binary storage and the series resistor. For a 28 × 28 image and 10 kernels with a size of 3 × 3, compared with the previous kernel storage approach, the newly proposed architecture shows excellent performances including: 1) almost 100% accuracy within 20% LRS variation and 90% HRS variation; 2) more than 67 times speed boost; 3) 71.4% energy saving.

A continued fraction resummation form of bath relaxation effect in the spin-boson model

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gong, Zhihao; Tang, Zhoufei; Wu, Jianlan, E-mail: jianlanwu@zju.edu.cn

2015-02-28

In the spin-boson model, a continued fraction form is proposed to systematically resum high-order quantum kinetic expansion (QKE) rate kernels, accounting for the bath relaxation effect beyond the second-order perturbation. In particular, the analytical expression of the sixth-order QKE rate kernel is derived for resummation. With higher-order correction terms systematically extracted from higher-order rate kernels, the resummed quantum kinetic expansion approach in the continued fraction form extends the Pade approximation and can fully recover the exact quantum dynamics as the expansion order increases.

On the Asymptotic Behavior of the Kernel Function in the Generalized Langevin Equation: A One-Dimensional Lattice Model

NASA Astrophysics Data System (ADS)

Chu, Weiqi; Li, Xiantao

2018-01-01

We present some estimates for the memory kernel function in the generalized Langevin equation, derived using the Mori-Zwanzig formalism from a one-dimensional lattice model, in which the particles interactions are through nearest and second nearest neighbors. The kernel function can be explicitly expressed in a matrix form. The analysis focuses on the decay properties, both spatially and temporally, revealing a power-law behavior in both cases. The dependence on the level of coarse-graining is also studied.

A Projection and Density Estimation Method for Knowledge Discovery

PubMed Central

Stanski, Adam; Hellwich, Olaf

2012-01-01

A key ingredient to modern data analysis is probability density estimation. However, it is well known that the curse of dimensionality prevents a proper estimation of densities in high dimensions. The problem is typically circumvented by using a fixed set of assumptions about the data, e.g., by assuming partial independence of features, data on a manifold or a customized kernel. These fixed assumptions limit the applicability of a method. In this paper we propose a framework that uses a flexible set of assumptions instead. It allows to tailor a model to various problems by means of 1d-decompositions. The approach achieves a fast runtime and is not limited by the curse of dimensionality as all estimations are performed in 1d-space. The wide range of applications is demonstrated at two very different real world examples. The first is a data mining software that allows the fully automatic discovery of patterns. The software is publicly available for evaluation. As a second example an image segmentation method is realized. It achieves state of the art performance on a benchmark dataset although it uses only a fraction of the training data and very simple features. PMID:23049675

Eigensolutions of nonviscously damped systems based on the fixed-point iteration

NASA Astrophysics Data System (ADS)

Lázaro, Mario

2018-03-01

In this paper, nonviscous, nonproportional, symmetric vibrating structures are considered. Nonviscously damped systems present dissipative forces depending on the time history of the response via kernel hereditary functions. Solutions of the free motion equation leads to a nonlinear eigenvalue problem involving mass, stiffness and damping matrices, this latter as dependent on frequency. Viscous damping can be considered as a particular case, involving damping forces as function of the instantaneous velocity of the degrees of freedom. In this work, a new numerical procedure to compute eigensolutions is proposed. The method is based on the construction of certain recursive functions which, under a iterative scheme, allow to reach eigenvalues and eigenvectors simultaneously and avoiding computation of eigensensitivities. Eigenvalues can be read then as fixed-points of those functions. A deep analysis of the convergence is carried out, focusing specially on relating the convergence conditions and error-decay rate to the damping model features, such as the nonproportionality and the viscoelasticity. The method is validated using two 6 degrees of freedom numerical examples involving both nonviscous and viscous damping and a continuous system with a local nonviscous damper. The convergence and the sequences behavior are in agreement with the results foreseen by the theory.

Embryo and endosperm development in wheat (Triticum aestivum L.) kernels subjected to drought stress.

PubMed

Fábián, Attila; Jäger, Katalin; Rakszegi, Mariann; Barnabás, Beáta

2011-04-01

The aim of the present work was to reveal the histological alterations triggered in developing wheat kernels by soil drought stress during early seed development resulting in yield losses at harvest. For this purpose, observations were made on the effect of drought stress, applied in a controlled environment from the 5th to the 9th day after pollination, on the kernel morphology, starch content and grain yield of the drought-sensitive Cappelle Desprez and drought-tolerant Plainsman V winter wheat (Triticum aestivum L.) varieties. As a consequence of water withdrawal, there was a decrease in the size of the embryos and the number of A-type starch granules deposited in the endosperm, while the development of aleurone cells and the degradation of the cell layers surrounding the ovule were significantly accelerated in both genotypes. In addition, the number of B-type starch granules per cell was significantly reduced. Drought stress affected the rate of grain filling shortened the grain-filling and ripening period and severely reduced the yield. With respect to the recovery of vegetative tissues, seed set and yield, the drought-tolerant Plainsman V responded significantly better to drought stress than Cappelle Desprez. The reduction in the size of the mature embryos was significantly greater in the sensitive genotype. Compared to Plainsman V, the endosperm cells of Cappelle Desprez accumulated significantly fewer B-type starch granules. In stressed kernels of the tolerant genotype, the accumulation of protein bodies occurred significantly earlier than in the sensitive variety.

Learn the Lagrangian: A Vector-Valued RKHS Approach to Identifying Lagrangian Systems.

PubMed

Cheng, Ching-An; Huang, Han-Pang

2016-12-01

We study the modeling of Lagrangian systems with multiple degrees of freedom. Based on system dynamics, canonical parametric models require ad hoc derivations and sometimes simplification for a computable solution; on the other hand, due to the lack of prior knowledge in the system's structure, modern nonparametric models in machine learning face the curse of dimensionality, especially in learning large systems. In this paper, we bridge this gap by unifying the theories of Lagrangian systems and vector-valued reproducing kernel Hilbert space. We reformulate Lagrangian systems with kernels that embed the governing Euler-Lagrange equation-the Lagrangian kernels-and show that these kernels span a subspace capturing the Lagrangian's projection as inverse dynamics. By such property, our model uses only inputs and outputs as in machine learning and inherits the structured form as in system dynamics, thereby removing the need for the mundane derivations for new systems as well as the generalization problem in learning from scratches. In effect, it learns the system's Lagrangian, a simpler task than directly learning the dynamics. To demonstrate, we applied the proposed kernel to identify the robot inverse dynamics in simulations and experiments. Our results present a competitive novel approach to identifying Lagrangian systems, despite using only inputs and outputs.

Performance Modeling in CUDA Streams - A Means for High-Throughput Data Processing

PubMed Central

Li, Hao; Yu, Di; Kumar, Anand; Tu, Yi-Cheng

2015-01-01

Push-based database management system (DBMS) is a new type of data processing software that streams large volume of data to concurrent query operators. The high data rate of such systems requires large computing power provided by the query engine. In our previous work, we built a push-based DBMS named G-SDMS to harness the unrivaled computational capabilities of modern GPUs. A major design goal of G-SDMS is to support concurrent processing of heterogenous query processing operations and enable resource allocation among such operations. Understanding the performance of operations as a result of resource consumption is thus a premise in the design of G-SDMS. With NVIDIA’s CUDA framework as the system implementation platform, we present our recent work on performance modeling of CUDA kernels running concurrently under a runtime mechanism named CUDA stream. Specifically, we explore the connection between performance and resource occupancy of compute-bound kernels and develop a model that can predict the performance of such kernels. Furthermore, we provide an in-depth anatomy of the CUDA stream mechanism and summarize the main kernel scheduling disciplines in it. Our models and derived scheduling disciplines are verified by extensive experiments using synthetic and real-world CUDA kernels. PMID:26566545

Applications of discrete element method in modeling of grain postharvest operations

USDA-ARS?s Scientific Manuscript database

Grain kernels are finite and discrete materials. Although flowing grain can behave like a continuum fluid at times, the discontinuous behavior exhibited by grain kernels cannot be simulated solely with conventional continuum-based computer modeling such as finite-element or finite-difference methods...

Assessing Predictive Properties of Genome-Wide Selection in Soybeans

PubMed Central

Xavier, Alencar; Muir, William M.; Rainey, Katy Martin

2016-01-01

Many economically important traits in plant breeding have low heritability or are difficult to measure. For these traits, genomic selection has attractive features and may boost genetic gains. Our goal was to evaluate alternative scenarios to implement genomic selection for yield components in soybean (Glycine max L. merr). We used a nested association panel with cross validation to evaluate the impacts of training population size, genotyping density, and prediction model on the accuracy of genomic prediction. Our results indicate that training population size was the factor most relevant to improvement in genome-wide prediction, with greatest improvement observed in training sets up to 2000 individuals. We discuss assumptions that influence the choice of the prediction model. Although alternative models had minor impacts on prediction accuracy, the most robust prediction model was the combination of reproducing kernel Hilbert space regression and BayesB. Higher genotyping density marginally improved accuracy. Our study finds that breeding programs seeking efficient genomic selection in soybeans would best allocate resources by investing in a representative training set. PMID:27317786

Direct Measurement of Wave Kernels in Time-Distance Helioseismology

NASA Technical Reports Server (NTRS)

Duvall, T. L., Jr.

2006-01-01

Solar f-mode waves are surface-gravity waves which propagate horizontally in a thin layer near the photosphere with a dispersion relation approximately that of deep water waves. At the power maximum near 3 mHz, the wavelength of 5 Mm is large enough for various wave scattering properties to be observable. Gizon and Birch (2002,ApJ,571,966)h ave calculated kernels, in the Born approximation, for the sensitivity of wave travel times to local changes in damping rate and source strength. In this work, using isolated small magnetic features as approximate point-sourc'e scatterers, such a kernel has been measured. The observed kernel contains similar features to a theoretical damping kernel but not for a source kernel. A full understanding of the effect of small magnetic features on the waves will require more detailed modeling.

Boundary conditions for gas flow problems from anisotropic scattering kernels

NASA Astrophysics Data System (ADS)

To, Quy-Dong; Vu, Van-Huyen; Lauriat, Guy; Léonard, Céline

2015-10-01

The paper presents an interface model for gas flowing through a channel constituted of anisotropic wall surfaces. Using anisotropic scattering kernels and Chapman Enskog phase density, the boundary conditions (BCs) for velocity, temperature, and discontinuities including velocity slip and temperature jump at the wall are obtained. Two scattering kernels, Dadzie and Méolans (DM) kernel, and generalized anisotropic Cercignani-Lampis (ACL) are examined in the present paper, yielding simple BCs at the wall fluid interface. With these two kernels, we rigorously recover the analytical expression for orientation dependent slip shown in our previous works [Pham et al., Phys. Rev. E 86, 051201 (2012) and To et al., J. Heat Transfer 137, 091002 (2015)] which is in good agreement with molecular dynamics simulation results. More important, our models include both thermal transpiration effect and new equations for the temperature jump. While the same expression depending on the two tangential accommodation coefficients is obtained for slip velocity, the DM and ACL temperature equations are significantly different. The derived BC equations associated with these two kernels are of interest for the gas simulations since they are able to capture the direction dependent slip behavior of anisotropic interfaces.

Understanding and optimizing the dual excipient functionality of sodium lauryl sulfate in tablet formulation of poorly water soluble drug: wetting and lubrication.

PubMed

Aljaberi, Ahmad; Chatterji, Ashish; Dong, Zedong; Shah, Navnit H; Malick, Waseem; Singhal, Dharmendra; Sandhu, Harpreet K

2013-01-01

To evaluate and optimize sodium lauryl sulfate (SLS) and magnesium stearate (Mg.St) levels, with respect to dissolution and compaction, in a high dose, poorly soluble drug tablet formulation. A model poorly soluble drug was formulated using high shear aqueous granulation. A D-optimal design was used to evaluate and model the effect of granulation conditions, size of milling screen, SLS and Mg.St levels on tablet compaction and ejection. The compaction profiles were generated using a Presster(©) compaction simulator. Dissolution of the kernels was performed using a USP dissolution apparatus II and intrinsic dissolution was determined using a stationary disk system. Unlike kernels dissolution which failed to discriminate between tablets prepared with various SLS contents, the intrinsic dissolution rate showed that a SLS level of 0.57% was sufficient to achieve the required release profile while having minimal effect on compaction. The formulation factors that affect tablet compaction and ejection were identified and satisfactorily modeled. The design space of best factor setting to achieve optimal compaction and ejection properties was successfully constructed by RSM analysis. A systematic study design helped identify the critical factors and provided means to optimize the functionality of key excipient to design robust drug product.

Regularized Embedded Multiple Kernel Dimensionality Reduction for Mine Signal Processing.

PubMed

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.

QTL Analysis of Kernel-Related Traits in Maize Using an Immortalized F2 Population

PubMed Central

Hu, Yanmin; Li, Weihua; Fu, Zhiyuan; Ding, Dong; Li, Haochuan; Qiao, Mengmeng; Tang, Jihua

2014-01-01

Kernel size and weight are important determinants of grain yield in maize. In this study, multivariate conditional and unconditional quantitative trait loci (QTL), and digenic epistatic analyses were utilized in order to elucidate the genetic basis for these kernel-related traits. Five kernel-related traits, including kernel weight (KW), volume (KV), length (KL), thickness (KT), and width (KWI), were collected from an immortalized F2 (IF2) maize population comprising of 243 crosses performed at two separate locations over a span of two years. A total of 54 unconditional main QTL for these five kernel-related traits were identified, many of which were clustered in chromosomal bins 6.04–6.06, 7.02–7.03, and 10.06–10.07. In addition, qKL3, qKWI6, qKV10a, qKV10b, qKW10a, and qKW7a were detected across multiple environments. Sixteen main QTL were identified for KW conditioned on the other four kernel traits (KL, KWI, KT, and KV). Thirteen main QTL were identified for KV conditioned on three kernel-shape traits. Conditional mapping analysis revealed that KWI and KV had the strongest influence on KW at the individual QTL level, followed by KT, and then KL; KV was mostly strongly influenced by KT, followed by KWI, and was least impacted by KL. Digenic epistatic analysis identified 18 digenic interactions involving 34 loci over the entire genome. However, only a small proportion of them were identical to the main QTL we detected. Additionally, conditional digenic epistatic analysis revealed that the digenic epistasis for KW and KV were entirely determined by their constituent traits. The main QTL identified in this study for determining kernel-related traits with high broad-sense heritability may play important roles during kernel development. Furthermore, digenic interactions were shown to exert relatively large effects on KL (the highest AA and DD effects were 4.6% and 6.7%, respectively) and KT (the highest AA effects were 4.3%). PMID:24586932

DUAL STATE-PARAMETER UPDATING SCHEME ON A CONCEPTUAL HYDROLOGIC MODEL USING SEQUENTIAL MONTE CARLO FILTERS

NASA Astrophysics Data System (ADS)

Noh, Seong Jin; Tachikawa, Yasuto; Shiiba, Michiharu; Kim, Sunmin

Applications of data assimilation techniques have been widely used to improve upon the predictability of hydrologic modeling. Among various data assimilation techniques, sequential Monte Carlo (SMC) filters, known as "particle filters" provide the capability to handle non-linear and non-Gaussian state-space models. This paper proposes a dual state-parameter updating scheme (DUS) based on SMC methods to estimate both state and parameter variables of a hydrologic model. We introduce a kernel smoothing method for the robust estimation of uncertain model parameters in the DUS. The applicability of the dual updating scheme is illustrated using the implementation of the storage function model on a middle-sized Japanese catchment. We also compare performance results of DUS combined with various SMC methods, such as SIR, ASIR and RPF.

Curve fitting of the corporate recovery rates: the comparison of Beta distribution estimation and kernel density estimation.

PubMed

Chen, Rongda; Wang, Ze

2013-01-01

Recovery rate is essential to the estimation of the portfolio's loss and economic capital. Neglecting the randomness of the distribution of recovery rate may underestimate the risk. The study introduces two kinds of models of distribution, Beta distribution estimation and kernel density distribution estimation, to simulate the distribution of recovery rates of corporate loans and bonds. As is known, models based on Beta distribution are common in daily usage, such as CreditMetrics by J.P. Morgan, Portfolio Manager by KMV and Losscalc by Moody's. However, it has a fatal defect that it can't fit the bimodal or multimodal distributions such as recovery rates of corporate loans and bonds as Moody's new data show. In order to overcome this flaw, the kernel density estimation is introduced and we compare the simulation results by histogram, Beta distribution estimation and kernel density estimation to reach the conclusion that the Gaussian kernel density distribution really better imitates the distribution of the bimodal or multimodal data samples of corporate loans and bonds. Finally, a Chi-square test of the Gaussian kernel density estimation proves that it can fit the curve of recovery rates of loans and bonds. So using the kernel density distribution to precisely delineate the bimodal recovery rates of bonds is optimal in credit risk management.

Curve Fitting of the Corporate Recovery Rates: The Comparison of Beta Distribution Estimation and Kernel Density Estimation

PubMed Central

Chen, Rongda; Wang, Ze

2013-01-01

Recovery rate is essential to the estimation of the portfolio’s loss and economic capital. Neglecting the randomness of the distribution of recovery rate may underestimate the risk. The study introduces two kinds of models of distribution, Beta distribution estimation and kernel density distribution estimation, to simulate the distribution of recovery rates of corporate loans and bonds. As is known, models based on Beta distribution are common in daily usage, such as CreditMetrics by J.P. Morgan, Portfolio Manager by KMV and Losscalc by Moody’s. However, it has a fatal defect that it can’t fit the bimodal or multimodal distributions such as recovery rates of corporate loans and bonds as Moody’s new data show. In order to overcome this flaw, the kernel density estimation is introduced and we compare the simulation results by histogram, Beta distribution estimation and kernel density estimation to reach the conclusion that the Gaussian kernel density distribution really better imitates the distribution of the bimodal or multimodal data samples of corporate loans and bonds. Finally, a Chi-square test of the Gaussian kernel density estimation proves that it can fit the curve of recovery rates of loans and bonds. So using the kernel density distribution to precisely delineate the bimodal recovery rates of bonds is optimal in credit risk management. PMID:23874558

An iterative kernel based method for fourth order nonlinear equation with nonlinear boundary condition

NASA Astrophysics Data System (ADS)

Azarnavid, Babak; Parand, Kourosh; Abbasbandy, Saeid

2018-06-01

This article discusses an iterative reproducing kernel method with respect to its effectiveness and capability of solving a fourth-order boundary value problem with nonlinear boundary conditions modeling beams on elastic foundations. Since there is no method of obtaining reproducing kernel which satisfies nonlinear boundary conditions, the standard reproducing kernel methods cannot be used directly to solve boundary value problems with nonlinear boundary conditions as there is no knowledge about the existence and uniqueness of the solution. The aim of this paper is, therefore, to construct an iterative method by the use of a combination of reproducing kernel Hilbert space method and a shooting-like technique to solve the mentioned problems. Error estimation for reproducing kernel Hilbert space methods for nonlinear boundary value problems have yet to be discussed in the literature. In this paper, we present error estimation for the reproducing kernel method to solve nonlinear boundary value problems probably for the first time. Some numerical results are given out to demonstrate the applicability of the method.

TURBULENCE-INDUCED RELATIVE VELOCITY OF DUST PARTICLES. IV. THE COLLISION KERNEL

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pan, Liubin; Padoan, Paolo, E-mail: lpan@cfa.harvard.edu, E-mail: ppadoan@icc.ub.edu

Motivated by its importance for modeling dust particle growth in protoplanetary disks, we study turbulence-induced collision statistics of inertial particles as a function of the particle friction time, τ{sub p}. We show that turbulent clustering significantly enhances the collision rate for particles of similar sizes with τ{sub p} corresponding to the inertial range of the flow. If the friction time, τ{sub p,} {sub h}, of the larger particle is in the inertial range, the collision kernel per unit cross section increases with increasing friction time, τ{sub p,} {sub l}, of the smaller particle and reaches the maximum at τ{sub p,}more » {sub l} = τ{sub p,} {sub h}, where the clustering effect peaks. This feature is not captured by the commonly used kernel formula, which neglects the effect of clustering. We argue that turbulent clustering helps alleviate the bouncing barrier problem for planetesimal formation. We also investigate the collision velocity statistics using a collision-rate weighting factor to account for higher collision frequency for particle pairs with larger relative velocity. For τ{sub p,} {sub h} in the inertial range, the rms relative velocity with collision-rate weighting is found to be invariant with τ{sub p,} {sub l} and scales with τ{sub p,} {sub h} roughly as ∝ τ{sub p,h}{sup 1/2}. The weighting factor favors collisions with larger relative velocity, and including it leads to more destructive and less sticking collisions. We compare two collision kernel formulations based on spherical and cylindrical geometries. The two formulations give consistent results for the collision rate and the collision-rate weighted statistics, except that the spherical formulation predicts more head-on collisions than the cylindrical formulation.« less

Deep neural mapping support vector machines.

PubMed

Li, Yujian; Zhang, Ting

2017-09-01

The choice of kernel has an important effect on the performance of a support vector machine (SVM). The effect could be reduced by NEUROSVM, an architecture using multilayer perceptron for feature extraction and SVM for classification. In binary classification, a general linear kernel NEUROSVM can be theoretically simplified as an input layer, many hidden layers, and an SVM output layer. As a feature extractor, the sub-network composed of the input and hidden layers is first trained together with a virtual ordinary output layer by backpropagation, then with the output of its last hidden layer taken as input of the SVM classifier for further training separately. By taking the sub-network as a kernel mapping from the original input space into a feature space, we present a novel model, called deep neural mapping support vector machine (DNMSVM), from the viewpoint of deep learning. This model is also a new and general kernel learning method, where the kernel mapping is indeed an explicit function expressed as a sub-network, different from an implicit function induced by a kernel function traditionally. Moreover, we exploit a two-stage procedure of contrastive divergence learning and gradient descent for DNMSVM to jointly training an adaptive kernel mapping instead of a kernel function, without requirement of kernel tricks. As a whole of the sub-network and the SVM classifier, the joint training of DNMSVM is done by using gradient descent to optimize the objective function with the sub-network layer-wise pre-trained via contrastive divergence learning of restricted Boltzmann machines. Compared to the separate training of NEUROSVM, the joint training is a new algorithm for DNMSVM to have advantages over NEUROSVM. Experimental results show that DNMSVM can outperform NEUROSVM and RBFSVM (i.e., SVM with the kernel of radial basis function), demonstrating its effectiveness. Copyright © 2017 Elsevier Ltd. All rights reserved.

Movement ecology: size-specific behavioral response of an invasive snail to food availability.

PubMed

Snider, Sunny B; Gilliam, James F

2008-07-01

Immigration, emigration, migration, and redistribution describe processes that involve movement of individuals. These movements are an essential part of contemporary ecological models, and understanding how movement is affected by biotic and abiotic factors is important for effectively modeling ecological processes that depend on movement. We asked how phenotypic heterogeneity (body size) and environmental heterogeneity (food resource level) affect the movement behavior of an aquatic snail (Tarebia granifera), and whether including these phenotypic and environmental effects improves advection-diffusion models of movement. We postulated various elaborations of the basic advection diffusion model as a priori working hypotheses. To test our hypotheses we measured individual snail movements in experimental streams at high- and low-food resource treatments. Using these experimental movement data, we examined the dependency of model selection on resource level and body size using Akaike's Information Criterion (AIC). At low resources, large individuals moved faster than small individuals, producing a platykurtic movement distribution; including size dependency in the model improved model performance. In stark contrast, at high resources, individuals moved upstream together as a wave, and body size differences largely disappeared. The model selection exercise indicated that population heterogeneity is best described by the advection component of movement for this species, because the top-ranked model included size dependency in advection, but not diffusion. Also, all probable models included resource dependency. Thus population and environmental heterogeneities both influence individual movement behaviors and the population-level distribution kernels, and their interaction may drive variation in movement behaviors in terms of both advection rates and diffusion rates. A behaviorally informed modeling framework will integrate the sentient response of individuals in terms of movement and enhance our ability to accurately model ecological processes that depend on animal movement.

Young Women’s Dynamic Family Size Preferences in the Context of Transitioning Fertility

PubMed Central

Yeatman, Sara; Sennott, Christie; Culpepper, Steven

2013-01-01

Dynamic theories of family size preferences posit that they are not a fixed and stable goal but rather are akin to a moving target that changes within individuals over time. Nonetheless, in high-fertility contexts, changes in family size preferences tend to be attributed to low construct validity and measurement error instead of genuine revisions in preferences. To address the appropriateness of this incongruity, the present study examines evidence for the sequential model of fertility among a sample of young Malawian women living in a context of transitioning fertility. Using eight waves of closely spaced data and fixed-effects models, we find that these women frequently change their reported family size preferences and that these changes are often associated with changes in their relationship and reproductive circumstances. The predictability of change gives credence to the argument that ideal family size is a meaningful construct, even in this higher-fertility setting. Changes are not equally predictable across all women, however, and gamma regression results demonstrate that women for whom reproduction is a more distant goal change their fertility preferences in less-predictable ways. PMID:23619999

Young women's dynamic family size preferences in the context of transitioning fertility.

PubMed

Yeatman, Sara; Sennott, Christie; Culpepper, Steven

2013-10-01

Dynamic theories of family size preferences posit that they are not a fixed and stable goal but rather are akin to a moving target that changes within individuals over time. Nonetheless, in high-fertility contexts, changes in family size preferences tend to be attributed to low construct validity and measurement error instead of genuine revisions in preferences. To address the appropriateness of this incongruity, the present study examines evidence for the sequential model of fertility among a sample of young Malawian women living in a context of transitioning fertility. Using eight waves of closely spaced data and fixed-effects models, we find that these women frequently change their reported family size preferences and that these changes are often associated with changes in their relationship and reproductive circumstances. The predictability of change gives credence to the argument that ideal family size is a meaningful construct, even in this higher-fertility setting. Changes are not equally predictable across all women, however, and gamma regression results demonstrate that women for whom reproduction is a more distant goal change their fertility preferences in less-predictable ways.

Classification and quantification analysis of peach kernel from different origins with near-infrared diffuse reflection spectroscopy

PubMed Central

Liu, Wei; Wang, Zhen-Zhong; Qing, Jian-Ping; Li, Hong-Juan; Xiao, Wei

2014-01-01

Background: Peach kernels which contain kinds of fatty acids play an important role in the regulation of a variety of physiological and biological functions. Objective: To establish an innovative and rapid diffuse reflectance near-infrared spectroscopy (DR-NIR) analysis method along with chemometric techniques for the qualitative and quantitative determination of a peach kernel. Materials and Methods: Peach kernel samples from nine different origins were analyzed with high-performance liquid chromatography (HPLC) as a reference method. DR-NIR is in the spectral range 1100-2300 nm. Principal component analysis (PCA) and partial least squares regression (PLSR) algorithm were applied to obtain prediction models, The Savitzky-Golay derivative and first derivative were adopted for the spectral pre-processing, PCA was applied to classify the varieties of those samples. For the quantitative calibration, the models of linoleic and oleinic acids were established with the PLSR algorithm and the optimal principal component (PC) numbers were selected with leave-one-out (LOO) cross-validation. The established models were evaluated with the root mean square error of deviation (RMSED) and corresponding correlation coefficients (R2). Results: The PCA results of DR-NIR spectra yield clear classification of the two varieties of peach kernel. PLSR had a better predictive ability. The correlation coefficients of the two calibration models were above 0.99, and the RMSED of linoleic and oleinic acids were 1.266% and 1.412%, respectively. Conclusion: The DR-NIR combined with PCA and PLSR algorithm could be used efficiently to identify and quantify peach kernels and also help to solve variety problem. PMID:25422544

A stepwise, 'test-all-positives' methodology to assess gluten-kernel contamination at the serving-size level in gluten-free (GF) oat production.

PubMed

Chen, Yumin; Fritz, Ronald D; Kock, Lindsay; Garg, Dinesh; Davis, R Mark; Kasturi, Prabhakar

2018-02-01

A step-wise, 'test-all-positive-gluten' analytical methodology has been developed and verified to assess kernel-based gluten contamination (i.e., wheat, barley and rye kernels) during gluten-free (GF) oat production. It targets GF claim compliance at the serving-size level (of a pouch or approximately 40-50g). Oat groats are collected from GF oat production following a robust attribute-based sampling plan then split into 75-g subsamples, and ground. R-Biopharm R5 sandwich ELISA R7001 is used for analysis of all the first15-g portions of the ground sample. A >20-ppm result disqualifies the production lot, while a >5 to <20-ppm result triggers complete analysis of the remaining 60-g of ground sample, analyzed in 15-g portions. If all five 15-g test results are <20ppm, and their average is <10.67ppm (since a 20-ppm contaminant in 40g of oats would dilute to 10.67ppm in 75-g), the lot is passed. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

An Efficient Implementation of Deep Convolutional Neural Networks for MRI Segmentation.

PubMed

Hoseini, Farnaz; Shahbahrami, Asadollah; Bayat, Peyman

2018-02-27

Image segmentation is one of the most common steps in digital image processing, classifying a digital image into different segments. The main goal of this paper is to segment brain tumors in magnetic resonance images (MRI) using deep learning. Tumors having different shapes, sizes, brightness and textures can appear anywhere in the brain. These complexities are the reasons to choose a high-capacity Deep Convolutional Neural Network (DCNN) containing more than one layer. The proposed DCNN contains two parts: architecture and learning algorithms. The architecture and the learning algorithms are used to design a network model and to optimize parameters for the network training phase, respectively. The architecture contains five convolutional layers, all using 3 × 3 kernels, and one fully connected layer. Due to the advantage of using small kernels with fold, it allows making the effect of larger kernels with smaller number of parameters and fewer computations. Using the Dice Similarity Coefficient metric, we report accuracy results on the BRATS 2016, brain tumor segmentation challenge dataset, for the complete, core, and enhancing regions as 0.90, 0.85, and 0.84 respectively. The learning algorithm includes the task-level parallelism. All the pixels of an MR image are classified using a patch-based approach for segmentation. We attain a good performance and the experimental results show that the proposed DCNN increases the segmentation accuracy compared to previous techniques.

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

PubMed

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

2018-05-07

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

Genetic, Genomic, and Breeding Approaches to Further Explore Kernel Composition Traits and Grain Yield in Maize

ERIC Educational Resources Information Center

Da Silva, Helena Sofia Pereira

2009-01-01

Maize ("Zea mays L.") is a model species well suited for the dissection of complex traits which are often of commercial value. The purpose of this research was to gain a deeper understanding of the genetic control of maize kernel composition traits starch, protein, and oil concentration, and also kernel weight and grain yield. Germplasm with…

7 CFR 51.1437 - Size classifications for halves.

Code of Federal Regulations, 2010 CFR

2010-01-01

... weight of half-kernels after all pieces, particles and dust, shell, center wall, and foreign material..., particles, and dust. In order to allow for variations incident to proper sizing and handling, not more than 15 percent, by weight, of any lot may consist of pieces, particles, and dust: Provided, That not more...

7 CFR 51.1437 - Size classifications for halves.

Code of Federal Regulations, 2012 CFR

2012-01-01

... weight of half-kernels after all pieces, particles and dust, shell, center wall, and foreign material..., particles, and dust. In order to allow for variations incident to proper sizing and handling, not more than 15 percent, by weight, of any lot may consist of pieces, particles, and dust: Provided, That not more...

7 CFR 51.1437 - Size classifications for halves.

Code of Federal Regulations, 2011 CFR

2011-01-01

... weight of half-kernels after all pieces, particles and dust, shell, center wall, and foreign material..., particles, and dust. In order to allow for variations incident to proper sizing and handling, not more than 15 percent, by weight, of any lot may consist of pieces, particles, and dust: Provided, That not more...

Optimization of light source parameters in the photodynamic therapy of heterogeneous prostate

NASA Astrophysics Data System (ADS)

Li, Jun; Altschuler, Martin D.; Hahn, Stephen M.; Zhu, Timothy C.

2008-08-01

The three-dimensional (3D) heterogeneous distributions of optical properties in a patient prostate can now be measured in vivo. Such data can be used to obtain a more accurate light-fluence kernel. (For specified sources and points, the kernel gives the fluence delivered to a point by a source of unit strength.) In turn, the kernel can be used to solve the inverse problem that determines the source strengths needed to deliver a prescribed photodynamic therapy (PDT) dose (or light-fluence) distribution within the prostate (assuming uniform drug concentration). We have developed and tested computational procedures to use the new heterogeneous data to optimize delivered light-fluence. New problems arise, however, in quickly obtaining an accurate kernel following the insertion of interstitial light sources and data acquisition. (1) The light-fluence kernel must be calculated in 3D and separately for each light source, which increases kernel size. (2) An accurate kernel for light scattering in a heterogeneous medium requires ray tracing and volume partitioning, thus significant calculation time. To address these problems, two different kernels were examined and compared for speed of creation and accuracy of dose. Kernels derived more quickly involve simpler algorithms. Our goal is to achieve optimal dose planning with patient-specific heterogeneous optical data applied through accurate kernels, all within clinical times. The optimization process is restricted to accepting the given (interstitially inserted) sources, and determining the best source strengths with which to obtain a prescribed dose. The Cimmino feasibility algorithm is used for this purpose. The dose distribution and source weights obtained for each kernel are analyzed. In clinical use, optimization will also be performed prior to source insertion to obtain initial source positions, source lengths and source weights, but with the assumption of homogeneous optical properties. For this reason, we compare the results from heterogeneous optical data with those obtained from average homogeneous optical properties. The optimized treatment plans are also compared with the reference clinical plan, defined as the plan with sources of equal strength, distributed regularly in space, which delivers a mean value of prescribed fluence at detector locations within the treatment region. The study suggests that comprehensive optimization of source parameters (i.e. strengths, lengths and locations) is feasible, thus allowing acceptable dose coverage in a heterogeneous prostate PDT within the time constraints of the PDT procedure.

Effects of model complexity and priors on estimation using sequential importance sampling/resampling for species conservation

USGS Publications Warehouse

Dunham, Kylee; Grand, James B.

2016-01-01

We examined the effects of complexity and priors on the accuracy of models used to estimate ecological and observational processes, and to make predictions regarding population size and structure. State-space models are useful for estimating complex, unobservable population processes and making predictions about future populations based on limited data. To better understand the utility of state space models in evaluating population dynamics, we used them in a Bayesian framework and compared the accuracy of models with differing complexity, with and without informative priors using sequential importance sampling/resampling (SISR). Count data were simulated for 25 years using known parameters and observation process for each model. We used kernel smoothing to reduce the effect of particle depletion, which is common when estimating both states and parameters with SISR. Models using informative priors estimated parameter values and population size with greater accuracy than their non-informative counterparts. While the estimates of population size and trend did not suffer greatly in models using non-informative priors, the algorithm was unable to accurately estimate demographic parameters. This model framework provides reasonable estimates of population size when little to no information is available; however, when information on some vital rates is available, SISR can be used to obtain more precise estimates of population size and process. Incorporating model complexity such as that required by structured populations with stage-specific vital rates affects precision and accuracy when estimating latent population variables and predicting population dynamics. These results are important to consider when designing monitoring programs and conservation efforts requiring management of specific population segments.

Anatomically-Aided PET Reconstruction Using the Kernel Method

PubMed Central

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

2016-01-01

This paper extends the kernel method that was proposed previously for dynamic PET reconstruction, to incorporate anatomical side information into the PET reconstruction model. In contrast to existing methods that incorporate anatomical information using a penalized likelihood framework, the proposed method incorporates this information in the simpler maximum likelihood (ML) formulation and is amenable to ordered subsets. The new method also does not require any segmentation of the anatomical image to obtain edge information. We compare the kernel method with the Bowsher method for anatomically-aided PET image reconstruction through a simulated data set. Computer simulations demonstrate that the kernel method offers advantages over the Bowsher method in region of interest (ROI) quantification. Additionally the kernel method is applied to a 3D patient data set. The kernel method results in reduced noise at a matched contrast level compared with the conventional ML expectation maximization (EM) algorithm. PMID:27541810

Anatomically-aided PET reconstruction using the kernel method.

PubMed

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

2016-09-21

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

Anatomically-aided PET reconstruction using the kernel method

NASA Astrophysics Data System (ADS)

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

2016-09-01

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

A prototype computer-aided modelling tool for life-support system models

NASA Technical Reports Server (NTRS)

Preisig, H. A.; Lee, Tae-Yeong; Little, Frank

1990-01-01

Based on the canonical decomposition of physical-chemical-biological systems, a prototype kernel has been developed to efficiently model alternative life-support systems. It supports (1) the work in an interdisciplinary group through an easy-to-use mostly graphical interface, (2) modularized object-oriented model representation, (3) reuse of models, (4) inheritance of structures from model object to model object, and (5) model data base. The kernel is implemented in Modula-II and presently operates on an IBM PC.

A Markov model for the temporal dynamics of balanced random networks of finite size

PubMed Central

Lagzi, Fereshteh; Rotter, Stefan

2014-01-01

The balanced state of recurrent networks of excitatory and inhibitory spiking neurons is characterized by fluctuations of population activity about an attractive fixed point. Numerical simulations show that these dynamics are essentially nonlinear, and the intrinsic noise (self-generated fluctuations) in networks of finite size is state-dependent. Therefore, stochastic differential equations with additive noise of fixed amplitude cannot provide an adequate description of the stochastic dynamics. The noise model should, rather, result from a self-consistent description of the network dynamics. Here, we consider a two-state Markovian neuron model, where spikes correspond to transitions from the active state to the refractory state. Excitatory and inhibitory input to this neuron affects the transition rates between the two states. The corresponding nonlinear dependencies can be identified directly from numerical simulations of networks of leaky integrate-and-fire neurons, discretized at a time resolution in the sub-millisecond range. Deterministic mean-field equations, and a noise component that depends on the dynamic state of the network, are obtained from this model. The resulting stochastic model reflects the behavior observed in numerical simulations quite well, irrespective of the size of the network. In particular, a strong temporal correlation between the two populations, a hallmark of the balanced state in random recurrent networks, are well represented by our model. Numerical simulations of such networks show that a log-normal distribution of short-term spike counts is a property of balanced random networks with fixed in-degree that has not been considered before, and our model shares this statistical property. Furthermore, the reconstruction of the flow from simulated time series suggests that the mean-field dynamics of finite-size networks are essentially of Wilson-Cowan type. We expect that this novel nonlinear stochastic model of the interaction between neuronal populations also opens new doors to analyze the joint dynamics of multiple interacting networks. PMID:25520644

Bifurcation analysis of a discrete-time ratio-dependent predator-prey model with Allee Effect

NASA Astrophysics Data System (ADS)

Cheng, Lifang; Cao, Hongjun

2016-09-01

A discrete-time predator-prey model with Allee effect is investigated in this paper. We consider the strong and the weak Allee effect (the population growth rate is negative and positive at low population density, respectively). From the stability analysis and the bifurcation diagrams, we get that the model with Allee effect (strong or weak) growth function and the model with logistic growth function have somewhat similar bifurcation structures. If the predator growth rate is smaller than its death rate, two species cannot coexist due to having no interior fixed points. When the predator growth rate is greater than its death rate and other parameters are fixed, the model can have two interior fixed points. One is always unstable, and the stability of the other is determined by the integral step size, which decides the species coexistence or not in some extent. If we increase the value of the integral step size, then the bifurcated period doubled orbits or invariant circle orbits may arise. So the numbers of the prey and the predator deviate from one stable state and then circulate along the period orbits or quasi-period orbits. When the integral step size is increased to a critical value, chaotic orbits may appear with many uncertain period-windows, which means that the numbers of prey and predator will be chaotic. In terms of bifurcation diagrams and phase portraits, we know that the complexity degree of the model with strong Allee effect decreases, which is related to the fact that the persistence of species can be determined by the initial species densities.

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

PubMed

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

2017-06-21

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

Simulation of parametric model towards the fixed covariate of right censored lung cancer data

NASA Astrophysics Data System (ADS)

Afiqah Muhamad Jamil, Siti; Asrul Affendi Abdullah, M.; Kek, Sie Long; Ridwan Olaniran, Oyebayo; Enera Amran, Syahila

2017-09-01

In this study, simulation procedure was applied to measure the fixed covariate of right censored data by using parametric survival model. The scale and shape parameter were modified to differentiate the analysis of parametric regression survival model. Statistically, the biases, mean biases and the coverage probability were used in this analysis. Consequently, different sample sizes were employed to distinguish the impact of parametric regression model towards right censored data with 50, 100, 150 and 200 number of sample. R-statistical software was utilised to develop the coding simulation with right censored data. Besides, the final model of right censored simulation was compared with the right censored lung cancer data in Malaysia. It was found that different values of shape and scale parameter with different sample size, help to improve the simulation strategy for right censored data and Weibull regression survival model is suitable fit towards the simulation of survival of lung cancer patients data in Malaysia.

A method of smoothed particle hydrodynamics using spheroidal kernels

NASA Technical Reports Server (NTRS)

Fulbright, Michael S.; Benz, Willy; Davies, Melvyn B.

1995-01-01

We present a new method of three-dimensional smoothed particle hydrodynamics (SPH) designed to model systems dominated by deformation along a preferential axis. These systems cause severe problems for SPH codes using spherical kernels, which are best suited for modeling systems which retain rough spherical symmetry. Our method allows the smoothing length in the direction of the deformation to evolve independently of the smoothing length in the perpendicular plane, resulting in a kernel with a spheroidal shape. As a result the spatial resolution in the direction of deformation is significantly improved. As a test case we present the one-dimensional homologous collapse of a zero-temperature, uniform-density cloud, which serves to demonstrate the advantages of spheroidal kernels. We also present new results on the problem of the tidal disruption of a star by a massive black hole.

Compound analysis via graph kernels incorporating chirality.

PubMed

Brown, J B; Urata, Takashi; Tamura, Takeyuki; Arai, Midori A; Kawabata, Takeo; Akutsu, Tatsuya

2010-12-01

High accuracy is paramount when predicting biochemical characteristics using Quantitative Structural-Property Relationships (QSPRs). Although existing graph-theoretic kernel methods combined with machine learning techniques are efficient for QSPR model construction, they cannot distinguish topologically identical chiral compounds which often exhibit different biological characteristics. In this paper, we propose a new method that extends the recently developed tree pattern graph kernel to accommodate stereoisomers. We show that Support Vector Regression (SVR) with a chiral graph kernel is useful for target property prediction by demonstrating its application to a set of human vitamin D receptor ligands currently under consideration for their potential anti-cancer effects.

Multitasking kernel for the C and Fortran programming languages

DOE Office of Scientific and Technical Information (OSTI.GOV)

Brooks, E.D. III

1984-09-01

A multitasking kernel for the C and Fortran programming languages which runs on the Unix operating system is presented. The kernel provides a multitasking environment which serves two purposes. The first is to provide an efficient portable environment for the coding, debugging and execution of production multiprocessor programs. The second is to provide a means of evaluating the performance of a multitasking program on model multiprocessors. The performance evaluation features require no changes in the source code of the application and are implemented as a set of compile and run time options in the kernel.

On the Computation of Optimal Designs for Certain Time Series Models with Applications to Optimal Quantile Selection for Location or Scale Parameter Estimation.

DTIC Science & Technology

1981-07-01

process is observed over all of (0,1], the reproducing kernel Hilbert space (RKHS) techniques developed by Parzen (1961a, 1961b) 2 may be used to construct...covariance kernel,R, for the process (1.1) is the reproducing kernel for a reproducing kernel Hilbert space (RKHS) which will be denoted as H(R) (c.f...2.6), it is known that (c.f. Eubank, Smith and Smith (1981a, 1981b)), i) H(R) is a Hilbert function space consisting of functions which satisfy for fEH

Deep kernel learning method for SAR image target recognition

NASA Astrophysics Data System (ADS)

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

2017-10-01

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

TaGS5-3A, a grain size gene selected during wheat improvement for larger kernel and yield.

PubMed

Ma, Lin; Li, Tian; Hao, Chenyang; Wang, Yuquan; Chen, Xinhong; Zhang, Xueyong

2016-05-01

Grain size is a dominant component of grain weight in cereals. Earlier studies have shown that OsGS5 plays a major role in regulating both grain size and weight in rice via promotion of cell division. In this study, we isolated TaGS5 homoeologues in wheat and mapped them on chromosomes 3A, 3B and 3D. Temporal and spatial expression analysis showed that TaGS5 homoeologues were preferentially expressed in young spikes and developing grains. Two alleles of TaGS5-3A, TaGS5-3A-T and TaGS5-3A-G were identified in wheat accessions, and a functional marker was developed to discriminate them. Association analysis revealed that TaGS5-3A-T was significantly correlated with larger grain size and higher thousand kernel weight. Biochemical assays showed that TaGS5-3A-T possesses a higher enzymatic activity than TaGS5-3A-G. Transgenic rice lines overexpressing TaGS5-3A-T also exhibited larger grain size and higher thousand kernel weight than TaGS5-3A-G lines, and the transcript levels of cell cycle-related genes in TaGS5-3A-T lines were higher than those in TaGS5-3A-G lines. Furthermore, systematic evolution analysis in diploid, tetraploid and hexaploid wheat showed that TaGS5-3A underwent strong artificial selection during wheat polyploidization events and the frequency changes of two alleles demonstrated that TaGS5-3A-T was favoured in global modern wheat cultivars. These results suggest that TaGS5-3A is a positive regulator of grain size and its favoured allele TaGS5-3A-T exhibits a larger potential application in wheat high-yield breeding. © 2015 Society for Experimental Biology, Association of Applied Biologists and John Wiley & Sons Ltd.

Effects of Class Size on Alternative Educational Outcomes across Disciplines

ERIC Educational Resources Information Center

Cheng, Dorothy A.

2011-01-01

This is the first study to use self-reported ratings of student learning, instructor recommendations, and course recommendations as the outcome measure to estimate class size effects, doing so across 24 disciplines. Fixed-effects models controlling for heterogeneous courses and instructors reveal that increasing enrollment has negative and…

Accounting for animal movement in estimation of resource selection functions: sampling and data analysis.

PubMed

Forester, James D; Im, Hae Kyung; Rathouz, Paul J

2009-12-01

Patterns of resource selection by animal populations emerge as a result of the behavior of many individuals. Statistical models that describe these population-level patterns of habitat use can miss important interactions between individual animals and characteristics of their local environment; however, identifying these interactions is difficult. One approach to this problem is to incorporate models of individual movement into resource selection models. To do this, we propose a model for step selection functions (SSF) that is composed of a resource-independent movement kernel and a resource selection function (RSF). We show that standard case-control logistic regression may be used to fit the SSF; however, the sampling scheme used to generate control points (i.e., the definition of availability) must be accommodated. We used three sampling schemes to analyze simulated movement data and found that ignoring sampling and the resource-independent movement kernel yielded biased estimates of selection. The level of bias depended on the method used to generate control locations, the strength of selection, and the spatial scale of the resource map. Using empirical or parametric methods to sample control locations produced biased estimates under stronger selection; however, we show that the addition of a distance function to the analysis substantially reduced that bias. Assuming a uniform availability within a fixed buffer yielded strongly biased selection estimates that could be corrected by including the distance function but remained inefficient relative to the empirical and parametric sampling methods. As a case study, we used location data collected from elk in Yellowstone National Park, USA, to show that selection and bias may be temporally variable. Because under constant selection the amount of bias depends on the scale at which a resource is distributed in the landscape, we suggest that distance always be included as a covariate in SSF analyses. This approach to modeling resource selection is easily implemented using common statistical tools and promises to provide deeper insight into the movement ecology of animals.

7 CFR 51.2128 - Damage.

Code of Federal Regulations, 2011 CFR

2011-01-01

... rubbed off with the fingers; (c) Gum, when a film of shiny, resinous appearing substance affects an area... the kernel is excessively thin for its size, or when materially withered, shrunken, leathery, tough or...

Transforce lingual appliances pre-adjusted invisible appliances simplify treatment.

PubMed

Clark, William John

2011-01-01

Transforce lingual appliances are designed to be used in conjunction with conventional fixed appliances. Lingual arch development is normally followed by bonded fixed appliances to detail the occlusion. Alternatively Transforce appliance treatment is an efficient method of preparing complex malocclusions prior to a finishing stage with invisible appliances. This approach is ideal for adult treatment, using light continuous forces for arch development with appliances that are comfortable to wear. Sagittal and Transverse appliances are designed for arch development in a range of sizes for contracted arches. They can be used to treat all classes of malocclusion and are pre-adjusted fixed/removable devices for non-compliance treatment. Force modules with nickel titanium coil springs enclosed in a tube deliver a gentle, biocompatible continuous force with a long range of action. They are excellent for mixed dentition and ideal for adult arch development. There are multiple sizes for upper and lower arch development and a sizing chart may be placed over a study model for correct selection, eliminating the need for laboratory work.

Adiabatic-connection fluctuation-dissipation DFT for the structural properties of solids—The renormalized ALDA and electron gas kernels

DOE Office of Scientific and Technical Information (OSTI.GOV)

Patrick, Christopher E., E-mail: chripa@fysik.dtu.dk; Thygesen, Kristian S., E-mail: thygesen@fysik.dtu.dk

2015-09-14

We present calculations of the correlation energies of crystalline solids and isolated systems within the adiabatic-connection fluctuation-dissipation formulation of density-functional theory. We perform a quantitative comparison of a set of model exchange-correlation kernels originally derived for the homogeneous electron gas (HEG), including the recently introduced renormalized adiabatic local-density approximation (rALDA) and also kernels which (a) satisfy known exact limits of the HEG, (b) carry a frequency dependence, or (c) display a 1/k{sup 2} divergence for small wavevectors. After generalizing the kernels to inhomogeneous systems through a reciprocal-space averaging procedure, we calculate the lattice constants and bulk moduli of a testmore » set of 10 solids consisting of tetrahedrally bonded semiconductors (C, Si, SiC), ionic compounds (MgO, LiCl, LiF), and metals (Al, Na, Cu, Pd). We also consider the atomization energy of the H{sub 2} molecule. We compare the results calculated with different kernels to those obtained from the random-phase approximation (RPA) and to experimental measurements. We demonstrate that the model kernels correct the RPA’s tendency to overestimate the magnitude of the correlation energy whilst maintaining a high-accuracy description of structural properties.« less

Models of fragmentation with composite power laws

NASA Astrophysics Data System (ADS)

Tavassoli, Z.; Rodgers, G. J.

1999-06-01

Some models for binary fragmentation are introduced in which a time dependent transition size produces two regions of fragment sizes above and below the transition size. In the first model we assume a fixed rate of fragmentation for the largest fragment and two different rates of fragmentation in the two regions of sizes above and below the transition size. The model is solved exactly in the long time limit to reveal stable time-invariant solutions for the fragment size and mass distributions. These solutions exhibit composite power law behaviours; power laws with two different exponents for fragments in smaller and larger regions. A special case of the model with no fragmentation in the smaller size region is also examined. Another model is also introduced which have three regions of fragment sizes with different rates of fragmentation. The similarities between the stable distributions in our models and composite power law distributions from experimental work on shock fragmentation of long thin glass rods and thick clay plates are discussed.

Assessing the blood volume and heart rate responses during haemodialysis in fluid overloaded patients using support vector regression.

PubMed

Javed, Faizan; Savkin, Andrey V; Chan, Gregory S H; Middleton, Paul M; Malouf, Philip; Steel, Elizabeth; Mackie, James; Lovell, Nigel H

2009-11-01

This study aims to assess the blood volume and heart rate (HR) responses during haemodialysis in fluid overloaded patients by a nonparametric nonlinear regression approach based on a support vector machine (SVM). Relative blood volume (RBV) and electrocardiogram (ECG) was recorded from 23 haemodynamically stable renal failure patients during regular haemodialysis. Modelling was performed on 18 fluid overloaded patients (fluid removal of >2 L). SVM-based regression was used to obtain the models of RBV change with time as well as the percentage change in HR with respect to RBV. Mean squared error (MSE) and goodness of fit (R(2)) were used for comparison among different kernel functions. The design parameters were estimated using a grid search approach and the selected models were validated by a k-fold cross-validation technique. For the model of HR versus RBV change, a radial basis function (RBF) kernel (MSE = 17.37 and R(2) = 0.932) gave the least MSE compared to linear (MSE = 25.97 and R(2) = 0.898) and polynomial (MSE = 18.18 and R(2)= 0.929). The MSE was significantly lower for training data set when using RBF kernel compared to other kernels (p < 0.01). The RBF kernel also provided a slightly better fit of RBV change with time (MSE = 1.12 and R(2) = 0.91) compared to a linear kernel (MSE = 1.46 and R(2) = 0.88). The modelled HR response was characterized by an initial drop and a subsequent rise during progressive reduction in RBV, which may be interpreted as the reflex response to a transition from central hypervolaemia to hypovolaemia. These modelled curves can be used as references to a controller that can be designed to regulate the haemodynamic variables to ensure the stability of patients undergoing haemodialysis.

SU-F-T-428: An Optimization-Based Commissioning Tool for Finite Size Pencil Beam Dose Calculations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, Y; Tian, Z; Song, T

Purpose: Finite size pencil beam (FSPB) algorithms are commonly used to pre-calculate the beamlet dose distribution for IMRT treatment planning. FSPB commissioning, which usually requires fine tuning of the FSPB kernel parameters, is crucial to the dose calculation accuracy and hence the plan quality. Yet due to the large number of beamlets, FSPB commissioning could be very tedious. This abstract reports an optimization-based FSPB commissioning tool we have developed in MatLab to facilitate the commissioning. Methods: A FSPB dose kernel generally contains two types of parameters: the profile parameters determining the dose kernel shape, and a 2D scaling factors accountingmore » for the longitudinal and off-axis corrections. The former were fitted using the penumbra of a reference broad beam’s dose profile with Levenberg-Marquardt algorithm. Since the dose distribution of a broad beam is simply a linear superposition of the dose kernel of each beamlet calculated with the fitted profile parameters and scaled using the scaling factors, these factors could be determined by solving an optimization problem which minimizes the discrepancies between the calculated dose of broad beams and the reference dose. Results: We have commissioned a FSPB algorithm for three linac photon beams (6MV, 15MV and 6MVFFF). Dose of four field sizes (6*6cm2, 10*10cm2, 15*15cm2 and 20*20cm2) were calculated and compared with the reference dose exported from Eclipse TPS system. For depth dose curves, the differences are less than 1% of maximum dose after maximum dose depth for most cases. For lateral dose profiles, the differences are less than 2% of central dose at inner-beam regions. The differences of the output factors are within 1% for all the three beams. Conclusion: We have developed an optimization-based commissioning tool for FSPB algorithms to facilitate the commissioning, providing sufficient accuracy of beamlet dose calculation for IMRT optimization.« less

Seismic Imaging of VTI, HTI and TTI based on Adjoint Methods

NASA Astrophysics Data System (ADS)

Rusmanugroho, H.; Tromp, J.

2014-12-01

Recent studies show that isotropic seismic imaging based on adjoint method reduces low-frequency artifact caused by diving waves, which commonly occur in two-wave wave-equation migration, such as Reverse Time Migration (RTM). Here, we derive new expressions of sensitivity kernels for Vertical Transverse Isotropy (VTI) using the Thomsen parameters (ɛ, δ, γ) plus the P-, and S-wave speeds (α, β) as well as via the Chen & Tromp (GJI 2005) parameters (A, C, N, L, F). For Horizontal Transverse Isotropy (HTI), these parameters depend on an azimuthal angle φ, where the tilt angle θ is equivalent to 90°, and for Tilted Transverse Isotropy (TTI), these parameters depend on both the azimuth and tilt angles. We calculate sensitivity kernels for each of these two approaches. Individual kernels ("images") are numerically constructed based on the interaction between the regular and adjoint wavefields in smoothed models which are in practice estimated through Full-Waveform Inversion (FWI). The final image is obtained as a result of summing all shots, which are well distributed to sample the target model properly. The impedance kernel, which is a sum of sensitivity kernels of density and the Thomsen or Chen & Tromp parameters, looks crisp and promising for seismic imaging. The other kernels suffer from low-frequency artifacts, similar to traditional seismic imaging conditions. However, all sensitivity kernels are important for estimating the gradient of the misfit function, which, in combination with a standard gradient-based inversion algorithm, is used to minimize the objective function in FWI.

An improved robust blind motion de-blurring algorithm for remote sensing images

NASA Astrophysics Data System (ADS)

He, Yulong; Liu, Jin; Liang, Yonghui

2016-10-01

Shift-invariant motion blur can be modeled as a convolution of the true latent image and the blur kernel with additive noise. Blind motion de-blurring estimates a sharp image from a motion blurred image without the knowledge of the blur kernel. This paper proposes an improved edge-specific motion de-blurring algorithm which proved to be fit for processing remote sensing images. We find that an inaccurate blur kernel is the main factor to the low-quality restored images. To improve image quality, we do the following contributions. For the robust kernel estimation, first, we adapt the multi-scale scheme to make sure that the edge map could be constructed accurately; second, an effective salient edge selection method based on RTV (Relative Total Variation) is used to extract salient structure from texture; third, an alternative iterative method is introduced to perform kernel optimization, in this step, we adopt l1 and l0 norm as the priors to remove noise and ensure the continuity of blur kernel. For the final latent image reconstruction, an improved adaptive deconvolution algorithm based on TV-l2 model is used to recover the latent image; we control the regularization weight adaptively in different region according to the image local characteristics in order to preserve tiny details and eliminate noise and ringing artifacts. Some synthetic remote sensing images are used to test the proposed algorithm, and results demonstrate that the proposed algorithm obtains accurate blur kernel and achieves better de-blurring results.

Calculation of plasma dielectric response in inhomogeneous magnetic field near electron cyclotron resonance

NASA Astrophysics Data System (ADS)

Evstatiev, Evstati; Svidzinski, Vladimir; Spencer, Andy; Galkin, Sergei

2014-10-01

Full wave 3-D modeling of RF fields in hot magnetized nonuniform plasma requires calculation of nonlocal conductivity kernel describing the dielectric response of such plasma to the RF field. In many cases, the conductivity kernel is a localized function near the test point which significantly simplifies numerical solution of the full wave 3-D problem. Preliminary results of feasibility analysis of numerical calculation of the conductivity kernel in a 3-D hot nonuniform magnetized plasma in the electron cyclotron frequency range will be reported. This case is relevant to modeling of ECRH in ITER. The kernel is calculated by integrating the linearized Vlasov equation along the unperturbed particle's orbits. Particle's orbits in the nonuniform equilibrium magnetic field are calculated numerically by one of the Runge-Kutta methods. RF electric field is interpolated on a specified grid on which the conductivity kernel is discretized. The resulting integrals in the particle's initial velocity and time are then calculated numerically. Different optimization approaches of the integration are tested in this feasibility analysis. Work is supported by the U.S. DOE SBIR program.

Basilar-membrane responses to broadband noise modeled using linear filters with rational transfer functions.

PubMed

Recio-Spinoso, Alberto; Fan, Yun-Hui; Ruggero, Mario A

2011-05-01

Basilar-membrane responses to white Gaussian noise were recorded using laser velocimetry at basal sites of the chinchilla cochlea with characteristic frequencies near 10 kHz and first-order Wiener kernels were computed by cross correlation of the stimuli and the responses. The presence or absence of minimum-phase behavior was explored by fitting the kernels with discrete linear filters with rational transfer functions. Excellent fits to the kernels were obtained with filters with transfer functions including zeroes located outside the unit circle, implying nonminimum-phase behavior. These filters accurately predicted basilar-membrane responses to other noise stimuli presented at the same level as the stimulus for the kernel computation. Fits with all-pole and other minimum-phase discrete filters were inferior to fits with nonminimum-phase filters. Minimum-phase functions predicted from the amplitude functions of the Wiener kernels by Hilbert transforms were different from the measured phase curves. These results, which suggest that basilar-membrane responses do not have the minimum-phase property, challenge the validity of models of cochlear processing, which incorporate minimum-phase behavior. © 2011 IEEE

Rate-Compatible LDPC Codes with Linear Minimum Distance

NASA Technical Reports Server (NTRS)

Divsalar, Dariush; Jones, Christopher; Dolinar, Samuel

2009-01-01

A recently developed method of constructing protograph-based low-density parity-check (LDPC) codes provides for low iterative decoding thresholds and minimum distances proportional to block sizes, and can be used for various code rates. A code constructed by this method can have either fixed input block size or fixed output block size and, in either case, provides rate compatibility. The method comprises two submethods: one for fixed input block size and one for fixed output block size. The first mentioned submethod is useful for applications in which there are requirements for rate-compatible codes that have fixed input block sizes. These are codes in which only the numbers of parity bits are allowed to vary. The fixed-output-blocksize submethod is useful for applications in which framing constraints are imposed on the physical layers of affected communication systems. An example of such a system is one that conforms to one of many new wireless-communication standards that involve the use of orthogonal frequency-division modulation

Post-release monitoring of Antillean manatees: an assessment of the Brazilian rehabilitation and release programme

USGS Publications Warehouse

Normande, Iran C.; Malhado, Ana C. M.; Reid, James P.; Viana Junior, P.C.; Savaget, P. V. S.; Correia, R. A.; Luna, F. O.; R. J. Ladle,

2016-01-01

Mammalian reintroduction programmes frequently aim to reconnect isolated sub-populations and restore population viability. However, these long-term objectives are rarely evaluated due to the inadequacy of post-release monitoring. Here, we report the results of a unique long term telemetry-based monitoring programme for rehabilitated Antillean manatees (Trichechus manatus manatus) reintroduced into selected sites in northeast Brazil with the aim of reconnecting isolated relict populations. Twenty-one satellite-tagged rehabilitated manatees, 13 males and 8 females, were released into the wild from two sites between November 2008 and June 2013. Individual accumulation curves were plotted and home ranges were calculated through the fixed kernel method using 95% of the utilization distribution. The number and size of the Centres of Activity (COAs) were calculated using 50% of the utilization distribution. Manatees displayed a dichotomous pattern of movement, with individuals either characterized by sedentary habits or by much more extensive movements. Moreover, home range size was not significantly influenced by gender, age at release or release site. COAs were strongly associated with sheltered conditions within reefs and estuaries, and also by the presence of freshwater and feeding sites. Our data confirm that manatee reintroductions in Brazil have the potential to reconnect distant sub-populations. However, pre-release identification of potential long-distance migrants is currently unfeasible, and further analysis would be required to confirm genetic mixing of distant sub-populations.

Moisture Adsorption Isotherm and Storability of Hazelnut Inshells and Kernels Produced in Oregon, USA.

PubMed

Jung, Jooyeoun; Wang, Wenjie; McGorrin, Robert J; Zhao, Yanyun

2018-02-01

Moisture adsorption isotherms and storability of dried hazelnut inshells and kernels produced in Oregon were evaluated and compared among cultivars, including Barcelona, Yamhill, and Jefferson. Experimental moisture adsorption data fitted to Guggenheim-Anderson-de Boer (GAB) model, showing less hygroscopic properties in Yamhill than other cultivars of inshells and kernels due to lower content of carbohydrate and protein, but higher content of fat. The safe levels of moisture content (MC, dry basis) of dried inshells and kernels for reaching kernel water activity (a w ) ≤0.65 were estimated using the GAB model as 11.3% and 5.0% for Barcelona, 9.4% and 4.2% for Yamhill, and 10.7% and 4.9% for Jefferson, respectively. Storage conditions (2 °C at 85% to 95% relative humidity [RH], 10 °C at 65% to 75% RH, and 27 °C at 35% to 45% RH), times (0, 4, 8, or 12 mo), and packaging methods (atmosphere vs. vacuum) affected MC, a w , bioactive compounds, lipid oxidation, and enzyme activity of dried hazelnut inshells or kernels. For inshells packaged at woven polypropylene bag, MC and a w of inshells and kernels (inside shells) increased at 2 and 10 °C, but decreased at 27 °C during storage. For kernels, lipid oxidation and polyphenol oxidase activity also increased with extended storage time (P < 0.05), and MC and a w of vacuum packaged samples were more stable during storage than those atmospherically packaged ones. Principal component analysis showed correlation of kernel qualities with storage condition, time, and packaging method. This study demonstrated that the ideal storage condition or packaging method varied among cultivars due to their different moisture adsorption and physicochemical and enzymatic stability during storage. Moisture adsorption isotherm of hazelnut inshells and kernels is useful for predicting the storability of nuts. This study found that water adsorption and storability varied among the different cultivars of nuts, in which Yamhill was less hygroscopic than Barcelona and Jefferson, thus more stable during storage. For ensuring food safety and quality of nuts during storage, each cultivar of kernels should be dried to a certain level of MC. Lipid oxidation and enzyme activity of kernel could be increased with extended storage time. Vacuum packaging was recommended to kernels for reducing moisture adsorption during storage. © 2018 Institute of Food Technologists®.

Prediction of heterotrimeric protein complexes by two-phase learning using neighboring kernels

PubMed Central

2014-01-01

Background Protein complexes play important roles in biological systems such as gene regulatory networks and metabolic pathways. Most methods for predicting protein complexes try to find protein complexes with size more than three. It, however, is known that protein complexes with smaller sizes occupy a large part of whole complexes for several species. In our previous work, we developed a method with several feature space mappings and the domain composition kernel for prediction of heterodimeric protein complexes, which outperforms existing methods. Results We propose methods for prediction of heterotrimeric protein complexes by extending techniques in the previous work on the basis of the idea that most heterotrimeric protein complexes are not likely to share the same protein with each other. We make use of the discriminant function in support vector machines (SVMs), and design novel feature space mappings for the second phase. As the second classifier, we examine SVMs and relevance vector machines (RVMs). We perform 10-fold cross-validation computational experiments. The results suggest that our proposed two-phase methods and SVM with the extended features outperform the existing method NWE, which was reported to outperform other existing methods such as MCL, MCODE, DPClus, CMC, COACH, RRW, and PPSampler for prediction of heterotrimeric protein complexes. Conclusions We propose two-phase prediction methods with the extended features, the domain composition kernel, SVMs and RVMs. The two-phase method with the extended features and the domain composition kernel using SVM as the second classifier is particularly useful for prediction of heterotrimeric protein complexes. PMID:24564744

Dynamic PET Image reconstruction for parametric imaging using the HYPR kernel method

NASA Astrophysics Data System (ADS)

Spencer, Benjamin; Qi, Jinyi; Badawi, Ramsey D.; Wang, Guobao

2017-03-01

Dynamic PET image reconstruction is a challenging problem because of the ill-conditioned nature of PET and the lowcounting statistics resulted from short time-frames in dynamic imaging. The kernel method for image reconstruction has been developed to improve image reconstruction of low-count PET data by incorporating prior information derived from high-count composite data. In contrast to most of the existing regularization-based methods, the kernel method embeds image prior information in the forward projection model and does not require an explicit regularization term in the reconstruction formula. Inspired by the existing highly constrained back-projection (HYPR) algorithm for dynamic PET image denoising, we propose in this work a new type of kernel that is simpler to implement and further improves the kernel-based dynamic PET image reconstruction. Our evaluation study using a physical phantom scan with synthetic FDG tracer kinetics has demonstrated that the new HYPR kernel-based reconstruction can achieve a better region-of-interest (ROI) bias versus standard deviation trade-off for dynamic PET parametric imaging than the post-reconstruction HYPR denoising method and the previously used nonlocal-means kernel.

Classification of Microarray Data Using Kernel Fuzzy Inference System

PubMed Central

Kumar Rath, Santanu

2014-01-01

The DNA microarray classification technique has gained more popularity in both research and practice. In real data analysis, such as microarray data, the dataset contains a huge number of insignificant and irrelevant features that tend to lose useful information. Classes with high relevance and feature sets with high significance are generally referred for the selected features, which determine the samples classification into their respective classes. In this paper, kernel fuzzy inference system (K-FIS) algorithm is applied to classify the microarray data (leukemia) using t-test as a feature selection method. Kernel functions are used to map original data points into a higher-dimensional (possibly infinite-dimensional) feature space defined by a (usually nonlinear) function ϕ through a mathematical process called the kernel trick. This paper also presents a comparative study for classification using K-FIS along with support vector machine (SVM) for different set of features (genes). Performance parameters available in the literature such as precision, recall, specificity, F-measure, ROC curve, and accuracy are considered to analyze the efficiency of the classification model. From the proposed approach, it is apparent that K-FIS model obtains similar results when compared with SVM model. This is an indication that the proposed approach relies on kernel function. PMID:27433543

OPC modeling by genetic algorithm

NASA Astrophysics Data System (ADS)

Huang, W. C.; Lai, C. M.; Luo, B.; Tsai, C. K.; Tsay, C. S.; Lai, C. W.; Kuo, C. C.; Liu, R. G.; Lin, H. T.; Lin, B. J.

2005-05-01

Optical proximity correction (OPC) is usually used to pre-distort mask layouts to make the printed patterns as close to the desired shapes as possible. For model-based OPC, a lithographic model to predict critical dimensions after lithographic processing is needed. The model is usually obtained via a regression of parameters based on experimental data containing optical proximity effects. When the parameters involve a mix of the continuous (optical and resist models) and the discrete (kernel numbers) sets, the traditional numerical optimization method may have difficulty handling model fitting. In this study, an artificial-intelligent optimization method was used to regress the parameters of the lithographic models for OPC. The implemented phenomenological models were constant-threshold models that combine diffused aerial image models with loading effects. Optical kernels decomposed from Hopkin"s equation were used to calculate aerial images on the wafer. Similarly, the numbers of optical kernels were treated as regression parameters. This way, good regression results were obtained with different sets of optical proximity effect data.

A Decision-making Model for a Two-stage Production-delivery System in SCM Environment

NASA Astrophysics Data System (ADS)

Feng, Ding-Zhong; Yamashiro, Mitsuo

A decision-making model is developed for an optimal production policy in a two-stage production-delivery system that incorporates a fixed quantity supply of finished goods to a buyer at a fixed interval of time. First, a general cost model is formulated considering both supplier (of raw materials) and buyer (of finished products) sides. Then an optimal solution to the problem is derived on basis of the cost model. Using the proposed model and its optimal solution, one can determine optimal production lot size for each stage, optimal number of transportation for semi-finished goods, and optimal quantity of semi-finished goods transported each time to meet the lumpy demand of consumers. Also, we examine the sensitivity of raw materials ordering and production lot size to changes in ordering cost, transportation cost and manufacturing setup cost. A pragmatic computation approach for operational situations is proposed to solve integer approximation solution. Finally, we give some numerical examples.

Popping the Kernel Modeling the States of Matter

ERIC Educational Resources Information Center

Hitt, Austin; White, Orvil; Hanson, Debbie

2005-01-01

This article discusses how to use popcorn to engage students in model building and to teach them about the nature of matter. Popping kernels is a simple and effective method to connect the concepts of heat, motion, and volume with the different phases of matter. Before proceeding with the activity the class should discuss the nature of scientific…

Point kernel calculations of skyshine exposure rates

DOE Office of Scientific and Technical Information (OSTI.GOV)

Roseberry, M.L.; Shultis, J.K.

1982-02-01

A simple point kernel model is presented for the calculation of skyshine exposure rates arising from the atmospheric reflection of gamma radiation produced by a vertically collimated or a shielded point source. This model is shown to be in good agreement with benchmark experimental data from a /sup 60/Co source for distances out to 700 m.

Effects of study area size on home range estimates of common bottlenose dolphins Tursiops truncatus

PubMed Central

Nekolny, Samantha R; Denny, Matthew; Biedenbach, George; Howells, Elisabeth M; Mazzoil, Marilyn; Durden, Wendy N; Moreland, Lydia; David Lambert, J

2017-01-01

Abstract Knowledge of an animal’s home range is a crucial component in making informed management decisions. However, many home range studies are limited by study area size, and therefore may underestimate the size of the home range. In many cases, individuals have been shown to travel outside of the study area and utilize a larger area than estimated by the study design. In this study, data collected by multiple research groups studying bottlenose dolphins on the east coast of Florida were combined to determine how home range estimates increased with increasing study area size. Home range analyses utilized photo-identification data collected from 6 study areas throughout the St Johns River (SJR; Jacksonville, FL, USA) and adjacent waterways, extending a total of 253 km to the southern end of Mosquito Lagoon in the Indian River Lagoon Estuarine System. Univariate kernel density estimates (KDEs) were computed for individuals with 10 or more sightings (n = 20). Kernels were calculated for the primary study area (SJR) first, then additional kernels were calculated by combining the SJR and the next adjacent waterway; this continued in an additive fashion until all study areas were included. The 95% and 50% KDEs calculated for the SJR alone ranged from 21 to 35 km and 4 to 19 km, respectively. The 95% and 50% KDEs calculated for all combined study areas ranged from 116 to 217 km and 9 to 70 km, respectively. This study illustrates the degree to which home range may be underestimated by the use of limited study areas and demonstrates the benefits of conducting collaborative science. PMID:29492031

Effects of study area size on home range estimates of common bottlenose dolphins Tursiops truncatus.

PubMed

Nekolny, Samantha R; Denny, Matthew; Biedenbach, George; Howells, Elisabeth M; Mazzoil, Marilyn; Durden, Wendy N; Moreland, Lydia; David Lambert, J; Gibson, Quincy A

2017-12-01

Knowledge of an animal's home range is a crucial component in making informed management decisions. However, many home range studies are limited by study area size, and therefore may underestimate the size of the home range. In many cases, individuals have been shown to travel outside of the study area and utilize a larger area than estimated by the study design. In this study, data collected by multiple research groups studying bottlenose dolphins on the east coast of Florida were combined to determine how home range estimates increased with increasing study area size. Home range analyses utilized photo-identification data collected from 6 study areas throughout the St Johns River (SJR; Jacksonville, FL, USA) and adjacent waterways, extending a total of 253 km to the southern end of Mosquito Lagoon in the Indian River Lagoon Estuarine System. Univariate kernel density estimates (KDEs) were computed for individuals with 10 or more sightings ( n =  20). Kernels were calculated for the primary study area (SJR) first, then additional kernels were calculated by combining the SJR and the next adjacent waterway; this continued in an additive fashion until all study areas were included. The 95% and 50% KDEs calculated for the SJR alone ranged from 21 to 35 km and 4 to 19 km, respectively. The 95% and 50% KDEs calculated for all combined study areas ranged from 116 to 217 km and 9 to 70 km, respectively. This study illustrates the degree to which home range may be underestimated by the use of limited study areas and demonstrates the benefits of conducting collaborative science.

Improving the quality of reconstructed X-ray CT images of polymer gel dosimeters: zero-scan coupled with adaptive mean filtering.

PubMed

Kakakhel, M B; Jirasek, A; Johnston, H; Kairn, T; Trapp, J V

2017-03-01

This study evaluated the feasibility of combining the 'zero-scan' (ZS) X-ray computed tomography (CT) based polymer gel dosimeter (PGD) readout with adaptive mean (AM) filtering for improving the signal to noise ratio (SNR), and to compare these results with available average scan (AS) X-ray CT readout techniques. NIPAM PGD were manufactured, irradiated with 6 MV photons, CT imaged and processed in Matlab. AM filter for two iterations, with 3 × 3 and 5 × 5 pixels (kernel size), was used in two scenarios (a) the CT images were subjected to AM filtering (pre-processing) and these were further employed to generate AS and ZS gel images, and (b) the AS and ZS images were first reconstructed from the CT images and then AM filtering was carried out (post-processing). SNR was computed in an ROI of 30 × 30 for different pre and post processing cases. Results showed that the ZS technique combined with AM filtering resulted in improved SNR. Using the previously-recommended 25 images for reconstruction the ZS pre-processed protocol can give an increase of 44% and 80% in SNR for 3 × 3 and 5 × 5 kernel sizes respectively. However, post processing using both techniques and filter sizes introduced blur and a reduction in the spatial resolution. Based on this work, it is possible to recommend that the ZS method may be combined with pre-processed AM filtering using appropriate kernel size, to produce a large increase in the SNR of the reconstructed PGD images.

Coalescence of repelling colloidal droplets: a route to monodisperse populations.

PubMed

Roger, Kevin; Botet, Robert; Cabane, Bernard

2013-05-14

Populations of droplets or particles dispersed in a liquid may evolve through Brownian collisions, aggregation, and coalescence. We have found a set of conditions under which these populations evolve spontaneously toward a narrow size distribution. The experimental system consists of poly(methyl methacrylate) (PMMA) nanodroplets dispersed in a solvent (acetone) + nonsolvent (water) mixture. These droplets carry electrical charges, located on the ionic end groups of the macromolecules. We used time-resolved small angle X-ray scattering to determine their size distribution. We find that the droplets grow through coalescence events: the average radius (R) increases logarithmically with elapsed time while the relative width σR/(R) of the distribution decreases as the inverse square root of (R). We interpret this evolution as resulting from coalescence events that are hindered by ionic repulsions between droplets. We generalize this evolution through a simulation of the Smoluchowski kinetic equation, with a kernel that takes into account the interactions between droplets. In the case of vanishing or attractive interactions, all droplet encounters lead to coalescence. The corresponding kernel leads to the well-known "self-preserving" particle distribution of the coalescence process, where σR/(R) increases to a plateau value. However, for droplets that interact through long-range ionic repulsions, "large + small" droplet encounters are more successful at coalescence than "large + large" encounters. We show that the corresponding kernel leads to a particular scaling of the droplet-size distribution-known as the "second-scaling law" in the theory of critical phenomena, where σR/(R) decreases as 1/√(R) and becomes independent of the initial distribution. We argue that this scaling explains the narrow size distributions of colloidal dispersions that have been synthesized through aggregation processes.

TaGW2, a Good Reflection of Wheat Polyploidization and Evolution

PubMed Central

Qin, Lin; Zhao, Junjie; Li, Tian; Hou, Jian; Zhang, Xueyong; Hao, Chenyang

2017-01-01

Hexaploid wheat consists of three subgenomes, namely, A, B, and D. These well-characterized ancestral genomes also exist at the diploid and tetraploid levels, thereby rendering wheat as a good model species for studying polyploidization. Here, we performed intra- and inter-species comparative analyses of wheat and its relatives to dissect polymorphism and differentiation of the TaGW2 genes. Our results showed that genetic diversity of TaGW2 decreased with progression from the diploids to tetraploids and hexaploids. The strongest selection occurred in the promoter regions of TaGW2-6A and TaGW2-6B. Phylogenetic trees clearly indicated that Triticum urartu and Ae. speltoides were the donors of the A and B genomes in tetraploid and hexaploid wheats. Haplotypes detected among hexaploid genotypes traced back to the tetraploid level. Fst and π values revealed that the strongest selection on TaGW2 occurred at the tetraploid level rather than in hexaploid wheat. This infers that grain size enlargement, especially increased kernel width, mainly occurred in tetraploid genotypes. In addition, relative expression levels of TaGW2s significantly declined from the diploid level to tetraploids and hexaploids, further indicating that these genes negatively regulate kernel size. Our results also revealed that the polyploidization events possibly caused much stronger differentiation than domestication and breeding. PMID:28326096

Modeling Caribou Movements: Seasonal Ranges and Migration Routes of the Central Arctic Herd

PubMed Central

Nicholson, Kerry L.; Arthur, Stephen M.; Horne, Jon S.; Garton, Edward O.; Del Vecchio, Patricia A.

2016-01-01

Migration is an important component of the life history of many animals, but persistence of large-scale terrestrial migrations is being challenged by environmental changes that fragment habitats and create obstacles to animal movements. In northern Alaska, the Central Arctic herd (CAH) of barren-ground caribou (Rangifer tarandus granti) is known to migrate over large distances, but the herd’s seasonal distributions and migratory movements are not well documented. From 2003–2007, we used GPS radio-collars to determine seasonal ranges and migration routes of 54 female caribou from the CAH. We calculated Brownian bridges to model fall and spring migrations for each year and used the mean of these over all 4 years to identify areas that were used repeatedly. Annual estimates of sizes of seasonal ranges determined by 90% fixed kernel utilization distributions were similar between summer and winter (X̅ = 27,929 SE = 1,064 and X̅ = 26,585 SE = 4912 km2, respectively). Overlap between consecutive summer and winter ranges varied from 3.3–18.3%. Percent overlap between summer ranges used during consecutive years (X̅ = 62.4% SE = 3.7%) was higher than for winter ranges (X̅ = 42.8% SE = 5.9%). Caribou used multiple migration routes each year, but some areas were used by caribou during all years, suggesting that these areas should be managed to allow for continued utilization by caribou. Restoring migration routes after they have been disturbed or fragmented is challenging. However, prior knowledge of movements and threats may facilitate maintenance of migratory paths and seasonal ranges necessary for long-term persistence of migratory species. PMID:27045587

Correlation and classification of single kernel fluorescence hyperspectral data with aflatoxin concentration in corn kernels inoculated with Aspergillus flavus spores.

PubMed

Yao, H; Hruska, Z; Kincaid, R; Brown, R; Cleveland, T; Bhatnagar, D

2010-05-01

The objective of this study was to examine the relationship between fluorescence emissions of corn kernels inoculated with Aspergillus flavus and aflatoxin contamination levels within the kernels. Aflatoxin contamination in corn has been a long-standing problem plaguing the grain industry with potentially devastating consequences to corn growers. In this study, aflatoxin-contaminated corn kernels were produced through artificial inoculation of corn ears in the field with toxigenic A. flavus spores. The kernel fluorescence emission data were taken with a fluorescence hyperspectral imaging system when corn kernels were excited with ultraviolet light. Raw fluorescence image data were preprocessed and regions of interest in each image were created for all kernels. The regions of interest were used to extract spectral signatures and statistical information. The aflatoxin contamination level of single corn kernels was then chemically measured using affinity column chromatography. A fluorescence peak shift phenomenon was noted among different groups of kernels with different aflatoxin contamination levels. The fluorescence peak shift was found to move more toward the longer wavelength in the blue region for the highly contaminated kernels and toward the shorter wavelengths for the clean kernels. Highly contaminated kernels were also found to have a lower fluorescence peak magnitude compared with the less contaminated kernels. It was also noted that a general negative correlation exists between measured aflatoxin and the fluorescence image bands in the blue and green regions. The correlation coefficients of determination, r(2), was 0.72 for the multiple linear regression model. The multivariate analysis of variance found that the fluorescence means of four aflatoxin groups, <1, 1-20, 20-100, and >or=100 ng g(-1) (parts per billion), were significantly different from each other at the 0.01 level of alpha. Classification accuracy under a two-class schema ranged from 0.84 to 0.91 when a threshold of either 20 or 100 ng g(-1) was used. Overall, the results indicate that fluorescence hyperspectral imaging may be applicable in estimating aflatoxin content in individual corn kernels.

Kernel-Smoothing Estimation of Item Characteristic Functions for Continuous Personality Items: An Empirical Comparison with the Linear and the Continuous-Response Models

ERIC Educational Resources Information Center

Ferrando, Pere J.

2004-01-01

This study used kernel-smoothing procedures to estimate the item characteristic functions (ICFs) of a set of continuous personality items. The nonparametric ICFs were compared with the ICFs estimated (a) by the linear model and (b) by Samejima's continuous-response model. The study was based on a conditioned approach and used an error-in-variables…

Simulating ground water-lake interactions: Approaches and insights

USGS Publications Warehouse

Hunt, R.J.; Haitjema, H.M.; Krohelski, J.T.; Feinstein, D.T.

2003-01-01

Approaches for modeling lake-ground water interactions have evolved significantly from early simulations that used fixed lake stages specified as constant head to sophisticated LAK packages for MODFLOW. Although model input can be complex, the LAK package capabilities and output are superior to methods that rely on a fixed lake stage and compare well to other simple methods where lake stage can be calculated. Regardless of the approach, guidelines presented here for model grid size, location of three-dimensional flow, and extent of vertical capture can facilitate the construction of appropriately detailed models that simulate important lake-ground water interactions without adding unnecessary complexity. In addition to MODFLOW approaches, lake simulation has been formulated in terms of analytic elements. The analytic element lake package had acceptable agreement with a published LAK1 problem, even though there were differences in the total lake conductance and number of layers used in the two models. The grid size used in the original LAK1 problem, however, violated a grid size guideline presented in this paper. Grid sensitivity analyses demonstrated that an appreciable discrepancy in the distribution of stream and lake flux was related to the large grid size used in the original LAK1 problem. This artifact is expected regardless of MODFLOW LAK package used. When the grid size was reduced, a finite-difference formulation approached the analytic element results. These insights and guidelines can help ensure that the proper lake simulation tool is being selected and applied.

Home range and movements of Feral cats on Mauna Kea, Hawai'i

USGS Publications Warehouse

Goltz, Dan M.; Hess, S.C.; Brinck, K.W.; Banko, P.C.; Danner, R.M.

2008-01-01

Feral cats Felis catus in dry subalpine woodland of Mauna Kea, Hawai'i, live in low density and exhibit some of the largest reported home ranges in the literature. While 95% fixed kemel home range estimates for three females averaged 772 ha, four males averaged 1 418 ha, and one male maintained a home range of 2 050 ha. Mean daily movement rates between sexes overlapped widely and did not differ significantly (P = 0.083). Log-transformed 95% kernel home ranges for males were significantly larger than those of females (P = 0.024), but 25% kernel home ranges for females were larger than those of males (P = 0.017). Moreover, log-transformed home ranges of males were also significantly larger than those of females in this and seven other studies from the Pacific region (P = 0.044). Feral cats present a major threat to endangered Hawaiian birds, but knowledge of their ecology can be used for management by optimizing trap spacing and creating buffer zones around conservation areas.

Improved belief propagation algorithm finds many Bethe states in the random-field Ising model on random graphs

NASA Astrophysics Data System (ADS)

Perugini, G.; Ricci-Tersenghi, F.

2018-01-01

We first present an empirical study of the Belief Propagation (BP) algorithm, when run on the random field Ising model defined on random regular graphs in the zero temperature limit. We introduce the notion of extremal solutions for the BP equations, and we use them to fix a fraction of spins in their ground state configuration. At the phase transition point the fraction of unconstrained spins percolates and their number diverges with the system size. This in turn makes the associated optimization problem highly non trivial in the critical region. Using the bounds on the BP messages provided by the extremal solutions we design a new and very easy to implement BP scheme which is able to output a large number of stable fixed points. On one hand this new algorithm is able to provide the minimum energy configuration with high probability in a competitive time. On the other hand we found that the number of fixed points of the BP algorithm grows with the system size in the critical region. This unexpected feature poses new relevant questions about the physics of this class of models.

GENERAL: Kinetic Behaviors of Catalysis-Driven Growth of Three-Species Aggregates on Base of Exchange-Driven Aggregations

NASA Astrophysics Data System (ADS)

Sun, Yun-Fei; Chen, Dan; Lin, Zhen-Quan; Ke, Jian-Hong

2009-06-01

We propose a solvable aggregation model to mimic the evolution of population A, asset B, and the quantifiable resource C in a society. In this system, the population and asset aggregates themselves grow through self-exchanges with the rate kernels K1(k, j) = K1kj and K2(k, j) = K2kj, respectively. The actions of the population and asset aggregations on the aggregation evolution of resource aggregates are described by the population-catalyzed monomer death of resource aggregates and asset-catalyzed monomer birth of resource aggregates with the rate kernels J1(k, j) = J1k and J2(k, j) = J2k, respectively. Meanwhile, the asset and resource aggregates conjunctly catalyze the monomer birth of population aggregates with the rate kernel I1(k, i, j) = I1kiμjη, and population and resource aggregates conjunctly catalyze the monomer birth of asset aggregates with the rate kernel I2(k, i, j) = I2kivjη. The kinetic behaviors of species A, B, and C are investigated by means of the mean-field rate equation approach. The effects of the population-catalyzed death and asset-catalyzed birth on the evolution of resource aggregates based on the self-exchanges of population and asset appear in effective forms. The coefficients of the effective population-catalyzed death and the asset-catalyzed birth are expressed as J1e = J1/K1 and J2e = J2/K2, respectively. The aggregate size distribution of C species is found to be crucially dominated by the competition between the effective death and the effective birth. It satisfies the conventional scaling form, generalized scaling form, and modified scaling form in the cases of J1e < J2e, J1e = J2e, and J1e > J2e, respectively. Meanwhile, we also find the aggregate size distributions of populations and assets both fall into two distinct categories for different parameters μ, ν, and η: (i) When μ = ν = η = 0 and μ = ν = 0, η = 1, the population and asset aggregates obey the generalized scaling forms; and (ii) When μ = ν = 1, η = 0, and μ = ν = η = 1, the population and asset aggregates experience gelation transitions at finite times and the scaling forms break down.

Derivation of aerodynamic kernel functions

NASA Technical Reports Server (NTRS)

Dowell, E. H.; Ventres, C. S.

1973-01-01

The method of Fourier transforms is used to determine the kernel function which relates the pressure on a lifting surface to the prescribed downwash within the framework of Dowell's (1971) shear flow model. This model is intended to improve upon the potential flow aerodynamic model by allowing for the aerodynamic boundary layer effects neglected in the potential flow model. For simplicity, incompressible, steady flow is considered. The proposed method is illustrated by deriving known results from potential flow theory.

Representing perturbed dynamics in biological network models

NASA Astrophysics Data System (ADS)

Stoll, Gautier; Rougemont, Jacques; Naef, Felix

2007-07-01

We study the dynamics of gene activities in relatively small size biological networks (up to a few tens of nodes), e.g., the activities of cell-cycle proteins during the mitotic cell-cycle progression. Using the framework of deterministic discrete dynamical models, we characterize the dynamical modifications in response to structural perturbations in the network connectivities. In particular, we focus on how perturbations affect the set of fixed points and sizes of the basins of attraction. Our approach uses two analytical measures: the basin entropy H and the perturbation size Δ , a quantity that reflects the distance between the set of fixed points of the perturbed network and that of the unperturbed network. Applying our approach to the yeast-cell-cycle network introduced by Li [Proc. Natl. Acad. Sci. U.S.A. 101, 4781 (2004)] provides a low-dimensional and informative fingerprint of network behavior under large classes of perturbations. We identify interactions that are crucial for proper network function, and also pinpoint functionally redundant network connections. Selected perturbations exemplify the breadth of dynamical responses in this cell-cycle model.

A locally adaptive kernel regression method for facies delineation

NASA Astrophysics Data System (ADS)

Fernàndez-Garcia, D.; Barahona-Palomo, M.; Henri, C. V.; Sanchez-Vila, X.

2015-12-01

Facies delineation is defined as the separation of geological units with distinct intrinsic characteristics (grain size, hydraulic conductivity, mineralogical composition). A major challenge in this area stems from the fact that only a few scattered pieces of hydrogeological information are available to delineate geological facies. Several methods to delineate facies are available in the literature, ranging from those based only on existing hard data, to those including secondary data or external knowledge about sedimentological patterns. This paper describes a methodology to use kernel regression methods as an effective tool for facies delineation. The method uses both the spatial and the actual sampled values to produce, for each individual hard data point, a locally adaptive steering kernel function, self-adjusting the principal directions of the local anisotropic kernels to the direction of highest local spatial correlation. The method is shown to outperform the nearest neighbor classification method in a number of synthetic aquifers whenever the available number of hard data is small and randomly distributed in space. In the case of exhaustive sampling, the steering kernel regression method converges to the true solution. Simulations ran in a suite of synthetic examples are used to explore the selection of kernel parameters in typical field settings. It is shown that, in practice, a rule of thumb can be used to obtain suboptimal results. The performance of the method is demonstrated to significantly improve when external information regarding facies proportions is incorporated. Remarkably, the method allows for a reasonable reconstruction of the facies connectivity patterns, shown in terms of breakthrough curves performance.

Habitat use, movements and home range of wintering Lesser Scaup in Florida

USGS Publications Warehouse

Herring, G.; Collazo, J.A.

2005-01-01

Radio telemetry and diurnal time activity budgets were used to show that wintering Lesser Scaup (Aythya affinis) used different habitats for comfort and feeding activities at Merritt Island National Wildlife Refuge (Merritt Island), Florida and adjacent estuarine areas. Management should take this spatial consideration into account. The same data were used to determine if habitat use differed between sexes. Data on movements and home range were used to evaluate habitat quality and potential effects of human disturbance. Scaup foraged more in impounded wetlands and rested more in open estuarine regions. Mean distance between diurnal and nocturnal sites was 2.7 km (SE ?? 0.3), and was similar between sexes and from mid to late winter. Male and female fixed kernel home ranges and core use areas did not differ. Mean fixed kernel 95% home range and 50% core use areas were 15.1 km2 (SE ?? 2.0) and 2.7 km2 (SE ?? 0.5) respectively, representing 3% and 0.5% of surveyed habitats. Males and females used habitats similarly and short distances traveled between diurnal and nocturnal sites suggested that habitat conditions were similar across the impounded wetlands and shallow portions of both the Indian River and Banana River. Sedentary or short movements suggested that disturbance was probably negligible at the principal areas used by Lesser Scaup. Habitat management strategies for scaup should not be restricted to Merritt Island. Adjustments should be made to take into account that maintenance activities occur in adjacent estuarine areas as well.

Visualization of Oil Body Distribution in Jatropha curcas L. by Four-Wave Mixing Microscopy

NASA Astrophysics Data System (ADS)

Ishii, Makiko; Uchiyama, Susumu; Ozeki, Yasuyuki; Kajiyama, Sin'ichiro; Itoh, Kazuyoshi; Fukui, Kiichi

2013-06-01

Jatropha curcas L. (jatropha) is a superior oil crop for biofuel production. To improve the oil yield of jatropha by breeding, the development of effective and reliable tools to evaluate the oil production efficiency is essential. The characteristics of the jatropha kernel, which contains a large amount of oil, are not fully understood yet. Here, we demonstrate the application of four-wave mixing (FWM) microscopy to visualize the distribution of oil bodies in a jatropha kernel without staining. FWM microscopy enables us to visualize the size and morphology of oil bodies and to determine the oil content in the kernel to be 33.2%. The signal obtained from FWM microscopy comprises both of stimulated parametric emission (SPE) and coherent anti-Stokes Raman scattering (CARS) signals. In the present situation, where a very short pump pulse is employed, the SPE signal is believed to dominate the FWM signal.

Deep Restricted Kernel Machines Using Conjugate Feature Duality.

PubMed

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.

A novel sample size formula for the weighted log-rank test under the proportional hazards cure model.

PubMed

Xiong, Xiaoping; Wu, Jianrong

2017-01-01

The treatment of cancer has progressed dramatically in recent decades, such that it is no longer uncommon to see a cure or log-term survival in a significant proportion of patients with various types of cancer. To adequately account for the cure fraction when designing clinical trials, the cure models should be used. In this article, a sample size formula for the weighted log-rank test is derived under the fixed alternative hypothesis for the proportional hazards cure models. Simulation showed that the proposed sample size formula provides an accurate estimation of sample size for designing clinical trials under the proportional hazards cure models. Copyright © 2016 John Wiley & Sons, Ltd.

Application of Tools to Measure PCB Microbial Dechlorination and Flux into Water During In-situ Treatment of Sediments

DTIC Science & Technology

2011-08-01

flocs within a radius of 2 flocs’ centerline would be intercepted by the settling particle . The curvilinear kernel assumes only smaller particle hit...Aerobic Sediment Slurry……………………………………………………………...11 Study 4. Modeling the Impact of Flocculation on the Fate of Organic and Inorganic Particles ...suspended particles at the beginning of free settling period………....………46 Figure 4.2: Three fOC distribution trends: small, uniform, and size-variable

Kinetic Behavior of Exchange-Driven Growth with Catalyzed-Birth Processes

NASA Astrophysics Data System (ADS)

Wang, Hai-Feng; Lin, Zhen-Quan; Kong, Xiang-Mu

2006-12-01

Two catalyzed-birth models of n-species (n>=2) aggregates with exchange-driven growth processes are proposed and compared. In the first one, the exchange reaction occurs between any two aggregates Amk and Amj of the same species with the rate kernels Km(k,j) = Kmkj (m = 1,2,...,n, n>=2), and aggregates of An species catalyze a monomer-birth of Al species (l = 1,2,...,n-1) with the catalysis rate kernel Jl(k,j) = Jlkjυ. The kinetic behaviors are investigated by means of the mean-field theory. We find that the evolution behavior of aggregate-size distribution alk(t) of Al species depends crucially on the value of the catalysis rate parameter υ: (i) alk(t) obeys the conventional scaling law in the case of υ<=0, (ii) alk(t) satisfies a modified scaling form in the case of υ>0. In the second model, the mechanism of monomer-birth of An-species catalyzed by Al species is added on the basis of the first model, that is, the aggregates of Al and An species catalyze each other to cause monomer-birth. The kinetic behaviors of Al and An species are found to fall into two categories for the different υ: (i) growth obeying conventional scaling form with υ<=0, (ii) gelling at finite time with υ>0.

General methodology for nonlinear modeling of neural systems with Poisson point-process inputs.

PubMed

Marmarelis, V Z; Berger, T W

2005-07-01

This paper presents a general methodological framework for the practical modeling of neural systems with point-process inputs (sequences of action potentials or, more broadly, identical events) based on the Volterra and Wiener theories of functional expansions and system identification. The paper clarifies the distinctions between Volterra and Wiener kernels obtained from Poisson point-process inputs. It shows that only the Wiener kernels can be estimated via cross-correlation, but must be defined as zero along the diagonals. The Volterra kernels can be estimated far more accurately (and from shorter data-records) by use of the Laguerre expansion technique adapted to point-process inputs, and they are independent of the mean rate of stimulation (unlike their P-W counterparts that depend on it). The Volterra kernels can also be estimated for broadband point-process inputs that are not Poisson. Useful applications of this modeling approach include cases where we seek to determine (model) the transfer characteristics between one neuronal axon (a point-process 'input') and another axon (a point-process 'output') or some other measure of neuronal activity (a continuous 'output', such as population activity) with which a causal link exists.

Towards smart energy systems: application of kernel machine regression for medium term electricity load forecasting.

PubMed

Alamaniotis, Miltiadis; Bargiotas, Dimitrios; Tsoukalas, Lefteri H

2016-01-01

Integration of energy systems with information technologies has facilitated the realization of smart energy systems that utilize information to optimize system operation. To that end, crucial in optimizing energy system operation is the accurate, ahead-of-time forecasting of load demand. In particular, load forecasting allows planning of system expansion, and decision making for enhancing system safety and reliability. In this paper, the application of two types of kernel machines for medium term load forecasting (MTLF) is presented and their performance is recorded based on a set of historical electricity load demand data. The two kernel machine models and more specifically Gaussian process regression (GPR) and relevance vector regression (RVR) are utilized for making predictions over future load demand. Both models, i.e., GPR and RVR, are equipped with a Gaussian kernel and are tested on daily predictions for a 30-day-ahead horizon taken from the New England Area. Furthermore, their performance is compared to the ARMA(2,2) model with respect to mean average percentage error and squared correlation coefficient. Results demonstrate the superiority of RVR over the other forecasting models in performing MTLF.

Sparse kernel methods for high-dimensional survival data.

PubMed

Evers, Ludger; Messow, Claudia-Martina

2008-07-15

Sparse kernel methods like support vector machines (SVM) have been applied with great success to classification and (standard) regression settings. Existing support vector classification and regression techniques however are not suitable for partly censored survival data, which are typically analysed using Cox's proportional hazards model. As the partial likelihood of the proportional hazards model only depends on the covariates through inner products, it can be 'kernelized'. The kernelized proportional hazards model however yields a solution that is dense, i.e. the solution depends on all observations. One of the key features of an SVM is that it yields a sparse solution, depending only on a small fraction of the training data. We propose two methods. One is based on a geometric idea, where-akin to support vector classification-the margin between the failed observation and the observations currently at risk is maximised. The other approach is based on obtaining a sparse model by adding observations one after another akin to the Import Vector Machine (IVM). Data examples studied suggest that both methods can outperform competing approaches. Software is available under the GNU Public License as an R package and can be obtained from the first author's website http://www.maths.bris.ac.uk/~maxle/software.html.

Screening of the aerodynamic and biophysical properties of barley malt

NASA Astrophysics Data System (ADS)

Ghodsvali, Alireza; Farzaneh, Vahid; Bakhshabadi, Hamid; Zare, Zahra; Karami, Zahra; Mokhtarian, Mohsen; Carvalho, Isabel. S.

2016-10-01

An understanding of the aerodynamic and biophysical properties of barley malt is necessary for the appropriate design of equipment for the handling, shipping, dehydration, grading, sorting and warehousing of this strategic crop. Malting is a complex biotechnological process that includes steeping; germination and finally, the dehydration of cereal grains under controlled temperature and humidity conditions. In this investigation, the biophysical properties of barley malt were predicted using two models of artificial neural networks as well as response surface methodology. Stepping time and germination time were selected as the independent variables and 1 000 kernel weight, kernel density and terminal velocity were selected as the dependent variables (responses). The obtained outcomes showed that the artificial neural network model, with a logarithmic sigmoid activation function, presents more precise results than the response surface model in the prediction of the aerodynamic and biophysical properties of produced barley malt. This model presented the best result with 8 nodes in the hidden layer and significant correlation coefficient values of 0.783, 0.767 and 0.991 were obtained for responses one thousand kernel weight, kernel density, and terminal velocity, respectively. The outcomes indicated that this novel technique could be successfully applied in quantitative and qualitative monitoring within the malting process.

The square lattice Ising model on the rectangle II: finite-size scaling limit

NASA Astrophysics Data System (ADS)

Hucht, Alfred

2017-06-01

Based on the results published recently (Hucht 2017 J. Phys. A: Math. Theor. 50 065201), the universal finite-size contributions to the free energy of the square lattice Ising model on the L× M rectangle, with open boundary conditions in both directions, are calculated exactly in the finite-size scaling limit L, M\\to∞ , T\\to Tc , with fixed temperature scaling variable x\\propto(T/Tc-1)M and fixed aspect ratio ρ\\propto L/M . We derive exponentially fast converging series for the related Casimir potential and Casimir force scaling functions. At the critical point T=Tc we confirm predictions from conformal field theory (Cardy and Peschel 1988 Nucl. Phys. B 300 377, Kleban and Vassileva 1991 J. Phys. A: Math. Gen. 24 3407). The presence of corners and the related corner free energy has dramatic impact on the Casimir scaling functions and leads to a logarithmic divergence of the Casimir potential scaling function at criticality.

GASAKe: forecasting landslide activations by a genetic-algorithms-based hydrological model

NASA Astrophysics Data System (ADS)

Terranova, O. G.; Gariano, S. L.; Iaquinta, P.; Iovine, G. G. R.

2015-07-01

GASAKe is a new hydrological model aimed at forecasting the triggering of landslides. The model is based on genetic algorithms and allows one to obtain thresholds for the prediction of slope failures using dates of landslide activations and rainfall series. It can be applied to either single landslides or a set of similar slope movements in a homogeneous environment. Calibration of the model provides families of optimal, discretized solutions (kernels) that maximize the fitness function. Starting from the kernels, the corresponding mobility functions (i.e., the predictive tools) can be obtained through convolution with the rain series. The base time of the kernel is related to the magnitude of the considered slope movement, as well as to the hydro-geological complexity of the site. Generally, shorter base times are expected for shallow slope instabilities compared to larger-scale phenomena. Once validated, the model can be applied to estimate the timing of future landslide activations in the same study area, by employing measured or forecasted rainfall series. Examples of application of GASAKe to a medium-size slope movement (the Uncino landslide at San Fili, in Calabria, southern Italy) and to a set of shallow landslides (in the Sorrento Peninsula, Campania, southern Italy) are discussed. In both cases, a successful calibration of the model has been achieved, despite unavoidable uncertainties concerning the dates of occurrence of the slope movements. In particular, for the Sorrento Peninsula case, a fitness of 0.81 has been obtained by calibrating the model against 10 dates of landslide activation; in the Uncino case, a fitness of 1 (i.e., neither missing nor false alarms) has been achieved using five activations. As for temporal validation, the experiments performed by considering further dates of activation have also proved satisfactory. In view of early-warning applications for civil protection, the capability of the model to simulate the occurrences of the Uncino landslide has been tested by means of a progressive, self-adaptive procedure. Finally, a sensitivity analysis has been performed by taking into account the main parameters of the model. The obtained results are quite promising, given the high performance of the model against different types of slope instabilities characterized by several historical activations. Nevertheless, further refinements are still needed for application to landslide risk mitigation within early-warning and decision-support systems.

Measurement of vascular wall attenuation: comparison of CT angiography using model-based iterative reconstruction with standard filtered back-projection algorithm CT in vitro.

PubMed

Suzuki, Shigeru; Machida, Haruhiko; Tanaka, Isao; Ueno, Eiko

2012-11-01

To compare the performance of model-based iterative reconstruction (MBIR) with that of standard filtered back projection (FBP) for measuring vascular wall attenuation. After subjecting 9 vascular models (actual attenuation value of wall, 89 HU) with wall thickness of 0.5, 1.0, or 1.5 mm that we filled with contrast material of 275, 396, or 542 HU to scanning using 64-detector computed tomography (CT), we reconstructed images using MBIR and FBP (Bone, Detail kernels) and measured wall attenuation at the center of the wall for each model. We performed attenuation measurements for each model and additional supportive measurements by a differentiation curve. We analyzed statistics using analyzes of variance with repeated measures. Using the Bone kernel, standard deviation of the measurement exceeded 30 HU in most conditions. In measurements at the wall center, the attenuation values obtained using MBIR were comparable to or significantly closer to the actual wall attenuation than those acquired using Detail kernel. Using differentiation curves, we could measure attenuation for models with walls of 1.0- or 1.5-mm thickness using MBIR but only those of 1.5-mm thickness using Detail kernel. We detected no significant differences among the attenuation values of the vascular walls of either thickness (MBIR, P=0.1606) or among the 3 densities of intravascular contrast material (MBIR, P=0.8185; Detail kernel, P=0.0802). Compared with FBP, MBIR reduces both reconstruction blur and image noise simultaneously, facilitates recognition of vascular wall boundaries, and can improve accuracy in measuring wall attenuation. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Island size distribution with hindered aggregation

NASA Astrophysics Data System (ADS)

González, Diego Luis; Camargo, Manuel; Sánchez, Julián A.

2018-05-01

We study the effect of hindered aggregation on the island formation processes for a one-dimensional model of epitaxial growth with arbitrary nucleus size i . In the proposed model, the attachment of monomers to islands is hindered by an aggregation barrier, ɛa, which decreases the hopping rate of monomers to the islands. As ɛa increases, the system exhibits a crossover between two different regimes; namely, from diffusion-limited aggregation to attachment-limited aggregation. The island size distribution, P (s ) , is calculated for different values of ɛa by a self-consistent approach involving the nucleation and aggregation capture kernels. The results given by the analytical model are compared with those from kinetic Monte Carlo simulations, finding a close agreement between both sets of data for all considered values of i and ɛa. As the aggregation barrier increases, the spatial effect of fluctuations on the density of monomers can be neglected and P (s ) smoothly approximates to the limit distribution P (s ) =δs ,i +1 . In the crossover regime the system features a complex and rich behavior, which can be explained in terms of the characteristic timescales of different microscopic processes.

In silico toxicity prediction by support vector machine and SMILES representation-based string kernel.

PubMed

Cao, D-S; Zhao, J-C; Yang, Y-N; Zhao, C-X; Yan, J; Liu, S; Hu, Q-N; Xu, Q-S; Liang, Y-Z

2012-01-01

There is a great need to assess the harmful effects or toxicities of chemicals to which man is exposed. In the present paper, the simplified molecular input line entry specification (SMILES) representation-based string kernel, together with the state-of-the-art support vector machine (SVM) algorithm, were used to classify the toxicity of chemicals from the US Environmental Protection Agency Distributed Structure-Searchable Toxicity (DSSTox) database network. In this method, the molecular structure can be directly encoded by a series of SMILES substrings that represent the presence of some chemical elements and different kinds of chemical bonds (double, triple and stereochemistry) in the molecules. Thus, SMILES string kernel can accurately and directly measure the similarities of molecules by a series of local information hidden in the molecules. Two model validation approaches, five-fold cross-validation and independent validation set, were used for assessing the predictive capability of our developed models. The results obtained indicate that SVM based on the SMILES string kernel can be regarded as a very promising and alternative modelling approach for potential toxicity prediction of chemicals.

[Crop geometry identification based on inversion of semiempirical BRDF models].

PubMed

Huang, Wen-jiang; Wang, Jin-di; Mu, Xi-han; Wang, Ji-hua; Liu, Liang-yun; Liu, Qiang; Niu, Zheng

2007-10-01

Investigations have been made on identification of erective and horizontal varieties by bidirectional canopy reflected spectrum and semi-empirical bidirectional reflectance distribution function (BRDF) models. The qualitative effect of leaf area index (LAI) and average leaf angle (ALA) on crop canopy reflected spectrum was studied. The structure parameter sensitive index (SPEI) based on the weight for the volumetric kernel (fvol), the weight for the geometric kernel (fgeo), and the weight for constant corresponding to isotropic reflectance (fiso), was defined in the present study for crop geometry identification. However, the weights associated with the kernels of semi-empirical BRDF model do not have a direct relationship with measurable biophysical parameters. Therefore, efforts have focused on trying to find the relation between these semi-empirical BRDF kernel weights and various vegetation structures. SPEI was proved to be more sensitive to identify crop geometry structures than structural scattering index (SSI) and normalized difference f-index (NDFI), SPEI could be used to distinguish erective and horizontal geometry varieties. So, it is feasible to identify horizontal and erective varieties of wheat by bidirectional canopy reflected spectrum.

A Wavelet Support Vector Machine Combination Model for Singapore Tourist Arrival to Malaysia

NASA Astrophysics Data System (ADS)

Rafidah, A.; Shabri, Ani; Nurulhuda, A.; Suhaila, Y.

2017-08-01

In this study, wavelet support vector machine model (WSVM) is proposed and applied for monthly data Singapore tourist time series prediction. The WSVM model is combination between wavelet analysis and support vector machine (SVM). In this study, we have two parts, first part we compare between the kernel function and second part we compare between the developed models with single model, SVM. The result showed that kernel function linear better than RBF while WSVM outperform with single model SVM to forecast monthly Singapore tourist arrival to Malaysia.

Intrinsic frame transport for a model of nematic liquid crystal

NASA Astrophysics Data System (ADS)

Cozzini, S.; Rull, L. F.; Ciccotti, G.; Paolini, G. V.

1997-02-01

We present a computer simulation study of the dynamical properties of a nematic liquid crystal model. The diffusional motion of the nematic director is taken into account in our calculations in order to give a proper estimate of the transport coefficients. Differently from other groups we do not attempt to stabilize the director through rigid constraints or applied external fields. We instead define an intrinsic frame which moves along with the director at each step of the simulation. The transport coefficients computed in the intrinsic frame are then compared against the ones calculated in the fixed laboratory frame, to show the inadequacy of the latter for systems with less than 500 molecules. Using this general scheme on the Gay-Berne liquid crystal model, we evidence the natural motion of the director and attempt to quantify its intrinsic time scale and size dependence. Through extended simulations of systems of different size we calculate the diffusion and viscosity coefficients of this model and compare our results with values previously obtained with fixed director.

Small-kernel, constrained least-squares restoration of sampled image data

NASA Technical Reports Server (NTRS)

Hazra, Rajeeb; Park, Stephen K.

1992-01-01

Following the work of Park (1989), who extended a derivation of the Wiener filter based on the incomplete discrete/discrete model to a more comprehensive end-to-end continuous/discrete/continuous model, it is shown that a derivation of the constrained least-squares (CLS) filter based on the discrete/discrete model can also be extended to this more comprehensive continuous/discrete/continuous model. This results in an improved CLS restoration filter, which can be efficiently implemented as a small-kernel convolution in the spatial domain.

Active impulsive noise control using maximum correntropy with adaptive kernel size

NASA Astrophysics Data System (ADS)

Lu, Lu; Zhao, Haiquan

2017-03-01

The active noise control (ANC) based on the principle of superposition is an attractive method to attenuate the noise signals. However, the impulsive noise in the ANC systems will degrade the performance of the controller. In this paper, a filtered-x recursive maximum correntropy (FxRMC) algorithm is proposed based on the maximum correntropy criterion (MCC) to reduce the effect of outliers. The proposed FxRMC algorithm does not requires any priori information of the noise characteristics and outperforms the filtered-x least mean square (FxLMS) algorithm for impulsive noise. Meanwhile, in order to adjust the kernel size of FxRMC algorithm online, a recursive approach is proposed through taking into account the past estimates of error signals over a sliding window. Simulation and experimental results in the context of active impulsive noise control demonstrate that the proposed algorithms achieve much better performance than the existing algorithms in various noise environments.

Validation of two-dimensional and three-dimensional measurements of subpleural alveolar size parameters by optical coherence tomography

PubMed Central

Warger, William C.; Hostens, Jeroen; Namati, Eman; Birngruber, Reginald; Bouma, Brett E.; Tearney, Guillermo J.

2012-01-01

Abstract. Optical coherence tomography (OCT) has been increasingly used for imaging pulmonary alveoli. Only a few studies, however, have quantified individual alveolar areas, and the validity of alveolar volumes represented within OCT images has not been shown. To validate quantitative measurements of alveoli from OCT images, we compared the cross-sectional area, perimeter, volume, and surface area of matched subpleural alveoli from microcomputed tomography (micro-CT) and OCT images of fixed air-filled swine samples. The relative change in size between different alveoli was extremely well correlated (r>0.9, P<0.0001), but OCT images underestimated absolute sizes compared to micro-CT by 27% (area), 7% (perimeter), 46% (volume), and 25% (surface area) on average. We hypothesized that the differences resulted from refraction at the tissue–air interfaces and developed a ray-tracing model that approximates the reconstructed alveolar size within OCT images. Using this model and OCT measurements of the refractive index for lung tissue (1.41 for fresh, 1.53 for fixed), we derived equations to obtain absolute size measurements of superellipse and circular alveoli with the use of predictive correction factors. These methods and results should enable the quantification of alveolar sizes from OCT images in vivo. PMID:23235834

Validation of two-dimensional and three-dimensional measurements of subpleural alveolar size parameters by optical coherence tomography

NASA Astrophysics Data System (ADS)

Unglert, Carolin I.; Warger, William C.; Hostens, Jeroen; Namati, Eman; Birngruber, Reginald; Bouma, Brett E.; Tearney, Guillermo J.

2012-12-01

Optical coherence tomography (OCT) has been increasingly used for imaging pulmonary alveoli. Only a few studies, however, have quantified individual alveolar areas, and the validity of alveolar volumes represented within OCT images has not been shown. To validate quantitative measurements of alveoli from OCT images, we compared the cross-sectional area, perimeter, volume, and surface area of matched subpleural alveoli from microcomputed tomography (micro-CT) and OCT images of fixed air-filled swine samples. The relative change in size between different alveoli was extremely well correlated (r>0.9, P<0.0001), but OCT images underestimated absolute sizes compared to micro-CT by 27% (area), 7% (perimeter), 46% (volume), and 25% (surface area) on average. We hypothesized that the differences resulted from refraction at the tissue-air interfaces and developed a ray-tracing model that approximates the reconstructed alveolar size within OCT images. Using this model and OCT measurements of the refractive index for lung tissue (1.41 for fresh, 1.53 for fixed), we derived equations to obtain absolute size measurements of superellipse and circular alveoli with the use of predictive correction factors. These methods and results should enable the quantification of alveolar sizes from OCT images in vivo.

Volterra series truncation and kernel estimation of nonlinear systems in the frequency domain

NASA Astrophysics Data System (ADS)

Zhang, B.; Billings, S. A.

2017-02-01

The Volterra series model is a direct generalisation of the linear convolution integral and is capable of displaying the intrinsic features of a nonlinear system in a simple and easy to apply way. Nonlinear system analysis using Volterra series is normally based on the analysis of its frequency-domain kernels and a truncated description. But the estimation of Volterra kernels and the truncation of Volterra series are coupled with each other. In this paper, a novel complex-valued orthogonal least squares algorithm is developed. The new algorithm provides a powerful tool to determine which terms should be included in the Volterra series expansion and to estimate the kernels and thus solves the two problems all together. The estimated results are compared with those determined using the analytical expressions of the kernels to validate the method. To further evaluate the effectiveness of the method, the physical parameters of the system are also extracted from the measured kernels. Simulation studies demonstrates that the new approach not only can truncate the Volterra series expansion and estimate the kernels of a weakly nonlinear system, but also can indicate the applicability of the Volterra series analysis in a severely nonlinear system case.

Investigating the Impact of Aerosol Deposition on Snow Melt over the Greenland Ice Sheet Using a New Kernel

NASA Astrophysics Data System (ADS)

Li, Y.; Flanner, M.

2017-12-01

Accelerating surface melt on the Greenland Ice Sheet (GrIS) has led to a doubling of Greenland's contribution to global sea level rise during recent decades. The darkening effect due to black carbon (BC), dust, and other light absorbing impurities (LAI) enhances snow melt by boosting its absorption of solar energy. It is therefore important for coupled aerosol-climate and ice sheet models to include snow darkening effects from LAI, and yet most do not. In this study, we develop an aerosol deposition—snow melt kernel based on the Community Earth System Model (CESM) to investigate changes in melt flux due to variations in the amount and timing of aerosol deposition on the GrIS. The Community Land Model (CLM) component of CESM is driven with a large range of aerosol deposition fluxes to determine non-linear relationships between melt perturbation and deposition amount occurring in different months and location (thereby capturing variations in base state associated with elevation and latitude). The kernel product will include climatological-mean effects and standard deviations associated with interannual variability. Finally, the kernel will allow aerosol deposition fluxes from any global or regional aerosol model to be translated into surface melt perturbations of the GrIS, thus extending the utility of state-of-the-art aerosol models.

Experimentally reducing clutch size reveals a fixed upper limit to egg size in snakes, evidence from the king ratsnake, Elaphe carinata.

PubMed

Ji, Xiang; Du, Wei-Guo; Li, Hong; Lin, Long-Hui

2006-08-01

Snakes are free of the pelvic girdle's constraint on maximum offspring size, and therefore present an opportunity to investigate the upper limit to offspring size without the limit imposed by the pelvic girdle dimension. We used the king ratsnake (Elaphe carinata) as a model animal to examine whether follicle ablation may result in enlargement of egg size in snakes and, if so, whether there is a fixed upper limit to egg size. Females with small sized yolking follicles were assigned to three manipulated, one sham-manipulated and one control treatments in mid-May, and two, four or six yolking follicles in the manipulated females were then ablated. Females undergoing follicle ablation produced fewer, but larger as well as more elongated, eggs than control females primarily by increasing egg length. This finding suggests that follicle ablation may result in enlargement of egg size in E. carinata. Mean values for egg width remained almost unchanged across the five treatments, suggesting that egg width is more likely to be shaped by the morphological feature of the oviduct. Clutch mass dropped dramatically in four- and six-follicle ablated females. The function describing the relationship between size and number of eggs reveals that egg size increases with decreasing clutch size at an ever-decreasing rate, with the tangent slope of the function for the six-follicle ablation treatment being -0.04. According to the function describing instantaneous variation in tangent slope, the maximum value of tangent slope should converge towards zero. This result provides evidence that there is a fixed upper limit to egg size in E. carinata.

Prediction of Heterodimeric Protein Complexes from Weighted Protein-Protein Interaction Networks Using Novel Features and Kernel Functions

PubMed Central

Ruan, Peiying; Hayashida, Morihiro; Maruyama, Osamu; Akutsu, Tatsuya

2013-01-01

Since many proteins express their functional activity by interacting with other proteins and forming protein complexes, it is very useful to identify sets of proteins that form complexes. For that purpose, many prediction methods for protein complexes from protein-protein interactions have been developed such as MCL, MCODE, RNSC, PCP, RRW, and NWE. These methods have dealt with only complexes with size of more than three because the methods often are based on some density of subgraphs. However, heterodimeric protein complexes that consist of two distinct proteins occupy a large part according to several comprehensive databases of known complexes. In this paper, we propose several feature space mappings from protein-protein interaction data, in which each interaction is weighted based on reliability. Furthermore, we make use of prior knowledge on protein domains to develop feature space mappings, domain composition kernel and its combination kernel with our proposed features. We perform ten-fold cross-validation computational experiments. These results suggest that our proposed kernel considerably outperforms the naive Bayes-based method, which is the best existing method for predicting heterodimeric protein complexes. PMID:23776458

A fast non-local means algorithm based on integral image and reconstructed similar kernel

NASA Astrophysics Data System (ADS)

Lin, Zheng; Song, Enmin

2018-03-01

Image denoising is one of the essential methods in digital image processing. The non-local means (NLM) denoising approach is a remarkable denoising technique. However, its time complexity of the computation is high. In this paper, we design a fast NLM algorithm based on integral image and reconstructed similar kernel. First, the integral image is introduced in the traditional NLM algorithm. In doing so, it reduces a great deal of repetitive operations in the parallel processing, which will greatly improves the running speed of the algorithm. Secondly, in order to amend the error of the integral image, we construct a similar window resembling the Gaussian kernel in the pyramidal stacking pattern. Finally, in order to eliminate the influence produced by replacing the Gaussian weighted Euclidean distance with Euclidean distance, we propose a scheme to construct a similar kernel with a size of 3 x 3 in a neighborhood window which will reduce the effect of noise on a single pixel. Experimental results demonstrate that the proposed algorithm is about seventeen times faster than the traditional NLM algorithm, yet produce comparable results in terms of Peak Signal-to- Noise Ratio (the PSNR increased 2.9% in average) and perceptual image quality.

Spatial Variability of Organic Carbon in a Fractured Mudstone and Its Effect on the Retention and Release of Trichloroethene (TCE)

NASA Astrophysics Data System (ADS)

Sole-Mari, G.; Fernandez-Garcia, D.

2016-12-01

Random Walk Particle Tracking (RWPT) coupled with Kernel Density Estimation (KDE) has been recently proposed to simulate reactive transport in porous media. KDE provides an optimal estimation of the area of influence of particles which is a key element to simulate nonlinear chemical reactions. However, several important drawbacks can be identified: (1) the optimal KDE method is computationally intensive and thereby cannot be used at each time step of the simulation; (2) it does not take advantage of the prior information about the physical system and the previous history of the solute plume; (3) even if the kernel is optimal, the relative error in RWPT simulations typically increases over time as the particle density diminishes by dilution. To overcome these problems, we propose an adaptive branching random walk methodology that incorporates the physics, the particle history and maintains accuracy with time. The method allows particles to efficiently split and merge when necessary as well as to optimally adapt their local kernel shape without having to recalculate the kernel size. We illustrate the advantage of the method by simulating complex reactive transport problems in randomly heterogeneous porous media.

SU-G-206-15: Effects of Dose Reduction On Emphysema Score

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lo, P; Wahi-Anwar, M; Kim, H

Purpose: The purpose of this study was to investigate the effects of reducing radiation dose levels on emphysema scores from lung cancer screening CT exams. Methods: 52 cases were selected from the National Lung Screening Trial (NLST) patients for which we had both the image series and the raw CT data. All scans were acquired with fixed effective mAs (25 for standard-sized patients, 40 for large patients) on a 64-slice scanner (Sensation 64, Siemens Healthcare) using 120kV, 64×0.6mm collimation and pitch 1.0. All images were reconstructed with 1mm slice thickness, B50 kernel. Based on a previously-published technique, we added noisemore » to the raw data to simulate reduced-dose versions at 50% and 25% of the original dose (approximately 1.0- and 0.5-mGy CTDIvol). Lung segmentations were obtained via region growing from manual seed point at a threshold of 600HU followed by manual removal of trachea and major airways. Lung segmentations were only performed on original dose scans, and mapped to simulated reduced-dose scans. Emphysema scores based on relative area of lung with attenuation values lower than −950HU (RA950) were computed for all cases. Results: Average RA950 of all 50 cases were 31.6 (±5.5), 32.5 (±4.9) and 32.8 (±4.6) for 100%, 50% and 25% dose level respectively. The average absolute difference in RA950 between simulated and original dose scans were 1.0 (±0.7) and 1.4 (±1.1) for 50% and 25% dose level respectively. Conclusion: RA950 is relatively robust to dose level, with a difference of no more than 5 from the original dose scans. The average RA950 of this population was high for a two reasons: This was a high risk population of patients with substantial smoking history; The use of B50 kernel, which may be biased towards high emphysema scores. Further exploration with smoother kernels will be conducted in the future. Institutional research agreement, Siemens Healthcare; Past recipient, research grant support, Siemens Healthcare; Consultant, Toshiba America Medical Systems; Consultant, Samsung Electronics; NIH grant support from U01 CA181156.« less

Anaerobic treatment of winery wastewater in fixed bed reactors.

PubMed

Ganesh, Rangaraj; Rajinikanth, Rajagopal; Thanikal, Joseph V; Ramanujam, Ramamoorty Alwar; Torrijos, Michel

2010-06-01

The treatment of winery wastewater in three upflow anaerobic fixed-bed reactors (S9, S30 and S40) with low density floating supports of varying size and specific surface area was investigated. A maximum OLR of 42 g/l day with 80 +/- 0.5% removal efficiency was attained in S9, which had supports with the highest specific surface area. It was found that the efficiency of the reactors increased with decrease in size and increase in specific surface area of the support media. Total biomass accumulation in the reactors was also found to vary as a function of specific surface area and size of the support medium. The Stover-Kincannon kinetic model predicted satisfactorily the performance of the reactors. The maximum removal rate constant (U(max)) was 161.3, 99.0 and 77.5 g/l day and the saturation value constant (K(B)) was 162.0, 99.5 and 78.0 g/l day for S9, S30 and S40, respectively. Due to their higher biomass retention potential, the supports used in this study offer great promise as media in anaerobic fixed bed reactors. Anaerobic fixed-bed reactors with these supports can be applied as high-rate systems for the treatment of large volumes of wastewaters typically containing readily biodegradable organics, such as the winery wastewater.

Some comparisons of complexity in dictionary-based and linear computational models.

PubMed

Gnecco, Giorgio; Kůrková, Věra; Sanguineti, Marcello

2011-03-01

Neural networks provide a more flexible approximation of functions than traditional linear regression. In the latter, one can only adjust the coefficients in linear combinations of fixed sets of functions, such as orthogonal polynomials or Hermite functions, while for neural networks, one may also adjust the parameters of the functions which are being combined. However, some useful properties of linear approximators (such as uniqueness, homogeneity, and continuity of best approximation operators) are not satisfied by neural networks. Moreover, optimization of parameters in neural networks becomes more difficult than in linear regression. Experimental results suggest that these drawbacks of neural networks are offset by substantially lower model complexity, allowing accuracy of approximation even in high-dimensional cases. We give some theoretical results comparing requirements on model complexity for two types of approximators, the traditional linear ones and so called variable-basis types, which include neural networks, radial, and kernel models. We compare upper bounds on worst-case errors in variable-basis approximation with lower bounds on such errors for any linear approximator. Using methods from nonlinear approximation and integral representations tailored to computational units, we describe some cases where neural networks outperform any linear approximator. Copyright © 2010 Elsevier Ltd. All rights reserved.

Difference in postprandial GLP-1 response despite similar glucose kinetics after consumption of wheat breads with different particle size in healthy men.

PubMed

Eelderink, Coby; Noort, Martijn W J; Sozer, Nesli; Koehorst, Martijn; Holst, Jens J; Deacon, Carolyn F; Rehfeld, Jens F; Poutanen, Kaisa; Vonk, Roel J; Oudhuis, Lizette; Priebe, Marion G

2017-04-01

Underlying mechanisms of the beneficial health effects of low glycemic index starchy foods are not fully elucidated yet. We varied the wheat particle size to obtain fiber-rich breads with a high and low glycemic response and investigated the differences in postprandial glucose kinetics and metabolic response after their consumption. Ten healthy male volunteers participated in a randomized, crossover study, consuming 13 C-enriched breads with different structures; a control bread (CB) made from wheat flour combined with wheat bran, and a kernel bread (KB) where 85 % of flour was substituted with broken wheat kernels. The structure of the breads was characterized extensively. The use of stable isotopes enabled calculation of glucose kinetics: rate of appearance of exogenous glucose, endogenous glucose production, and glucose clearance rate. Additionally, postprandial plasma concentrations of glucose, insulin, glucagon, incretins, cholecystokinin, and bile acids were analyzed. Despite the attempt to obtain a bread with a low glycemic response by replacing flour by broken kernels, the glycemic response and glucose kinetics were quite similar after consumption of CB and KB. Interestingly, the glucagon-like peptide-1 (GLP-1) response was much lower after KB compared to CB (iAUC, P < 0.005). A clear postprandial increase in plasma conjugated bile acids was observed after both meals. Substitution of 85 % wheat flour by broken kernels in bread did not result in a difference in glucose response and kinetics, but in a pronounced difference in GLP-1 response. Thus, changing the processing conditions of wheat for baking bread can influence the metabolic response beyond glycemia and may therefore influence health.

Geographically Weighted Regression Model with Kernel Bisquare and Tricube Weighted Function on Poverty Percentage Data in Central Java Province

NASA Astrophysics Data System (ADS)

Nugroho, N. F. T. A.; Slamet, I.

2018-05-01

Poverty is a socio-economic condition of a person or group of people who can not fulfil their basic need to maintain and develop a dignified life. This problem still cannot be solved completely in Central Java Province. Currently, the percentage of poverty in Central Java is 13.32% which is higher than the national poverty rate which is 11.13%. In this research, data of percentage of poor people in Central Java Province has been analyzed through geographically weighted regression (GWR). The aim of this research is therefore to model poverty percentage data in Central Java Province using GWR with weighted function of kernel bisquare, and tricube. As the results, we obtained GWR model with bisquare and tricube kernel weighted function on poverty percentage data in Central Java province. From the GWR model, there are three categories of region which are influenced by different of significance factors.

Rainbow Fourier Transform

NASA Technical Reports Server (NTRS)

Alexandrov, Mikhail D.; Cairns, Brian; Mishchenko, Michael I.

2012-01-01

We present a novel technique for remote sensing of cloud droplet size distributions. Polarized reflectances in the scattering angle range between 135deg and 165deg exhibit a sharply defined rainbow structure, the shape of which is determined mostly by single scattering properties of cloud particles, and therefore, can be modeled using the Mie theory. Fitting the observed rainbow with such a model (computed for a parameterized family of particle size distributions) has been used for cloud droplet size retrievals. We discovered that the relationship between the rainbow structures and the corresponding particle size distributions is deeper than it had been commonly understood. In fact, the Mie theory-derived polarized reflectance as a function of reduced scattering angle (in the rainbow angular range) and the (monodisperse) particle radius appears to be a proxy to a kernel of an integral transform (similar to the sine Fourier transform on the positive semi-axis). This approach, called the rainbow Fourier transform (RFT), allows us to accurately retrieve the shape of the droplet size distribution by the application of the corresponding inverse transform to the observed polarized rainbow. While the basis functions of the proxy-transform are not exactly orthogonal in the finite angular range, this procedure needs to be complemented by a simple regression technique, which removes the retrieval artifacts. This non-parametric approach does not require any a priori knowledge of the droplet size distribution functional shape and is computationally fast (no look-up tables, no fitting, computations are the same as for the forward modeling).

Detection probability in aerial surveys of feral horses

USGS Publications Warehouse

Ransom, Jason I.

2011-01-01

Observation bias pervades data collected during aerial surveys of large animals, and although some sources can be mitigated with informed planning, others must be addressed using valid sampling techniques that carefully model detection probability. Nonetheless, aerial surveys are frequently employed to count large mammals without applying such methods to account for heterogeneity in visibility of animal groups on the landscape. This often leaves managers and interest groups at odds over decisions that are not adequately informed. I analyzed detection of feral horse (Equus caballus) groups by dual independent observers from 24 fixed-wing and 16 helicopter flights using mixed-effect logistic regression models to investigate potential sources of observation bias. I accounted for observer skill, population location, and aircraft type in the model structure and analyzed the effects of group size, sun effect (position related to observer), vegetation type, topography, cloud cover, percent snow cover, and observer fatigue on detection of horse groups. The most important model-averaged effects for both fixed-wing and helicopter surveys included group size (fixed-wing: odds ratio = 0.891, 95% CI = 0.850–0.935; helicopter: odds ratio = 0.640, 95% CI = 0.587–0.698) and sun effect (fixed-wing: odds ratio = 0.632, 95% CI = 0.350–1.141; helicopter: odds ratio = 0.194, 95% CI = 0.080–0.470). Observer fatigue was also an important effect in the best model for helicopter surveys, with detection probability declining after 3 hr of survey time (odds ratio = 0.278, 95% CI = 0.144–0.537). Biases arising from sun effect and observer fatigue can be mitigated by pre-flight survey design. Other sources of bias, such as those arising from group size, topography, and vegetation can only be addressed by employing valid sampling techniques such as double sampling, mark–resight (batch-marked animals), mark–recapture (uniquely marked and identifiable animals), sightability bias correction models, and line transect distance sampling; however, some of these techniques may still only partially correct for negative observation biases.

Bayesian Genomic Prediction with Genotype × Environment Interaction Kernel Models

PubMed Central

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.

PubMed

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.

Nonparametric Stochastic Model for Uncertainty Quantifi cation of Short-term Wind Speed Forecasts

NASA Astrophysics Data System (ADS)

AL-Shehhi, A. M.; Chaouch, M.; Ouarda, T.

2014-12-01

Wind energy is increasing in importance as a renewable energy source due to its potential role in reducing carbon emissions. It is a safe, clean, and inexhaustible source of energy. The amount of wind energy generated by wind turbines is closely related to the wind speed. Wind speed forecasting plays a vital role in the wind energy sector in terms of wind turbine optimal operation, wind energy dispatch and scheduling, efficient energy harvesting etc. It is also considered during planning, design, and assessment of any proposed wind project. Therefore, accurate prediction of wind speed carries a particular importance and plays significant roles in the wind industry. Many methods have been proposed in the literature for short-term wind speed forecasting. These methods are usually based on modeling historical fixed time intervals of the wind speed data and using it for future prediction. The methods mainly include statistical models such as ARMA, ARIMA model, physical models for instance numerical weather prediction and artificial Intelligence techniques for example support vector machine and neural networks. In this paper, we are interested in estimating hourly wind speed measures in United Arab Emirates (UAE). More precisely, we predict hourly wind speed using a nonparametric kernel estimation of the regression and volatility functions pertaining to nonlinear autoregressive model with ARCH model, which includes unknown nonlinear regression function and volatility function already discussed in the literature. The unknown nonlinear regression function describe the dependence between the value of the wind speed at time t and its historical data at time t -1, t - 2, … , t - d. This function plays a key role to predict hourly wind speed process. The volatility function, i.e., the conditional variance given the past, measures the risk associated to this prediction. Since the regression and the volatility functions are supposed to be unknown, they are estimated using nonparametric kernel methods. In addition, to the pointwise hourly wind speed forecasts, a confidence interval is also provided which allows to quantify the uncertainty around the forecasts.

The spatial sensitivity of Sp converted waves-kernels and their applications

NASA Astrophysics Data System (ADS)

Mancinelli, N. J.; Fischer, K. M.

2017-12-01

We have developed a framework for improved imaging of strong lateral variations in crust and upper mantle seismic discontinuity structure using teleseismic S-to-P (Sp) scattered waves. In our framework, we rapidly compute scattered wave sensitivities to velocity perturbations in a one-dimensional background model using ray-theoretical methods to account for timing, scattering, and geometrical spreading effects. The kernels accurately describe the amplitude and phase information of a scattered waveform, which we confirm by benchmarking against kernels derived from numerical solutions of the wave equation. The kernels demonstrate that the amplitude of an Sp converted wave at a given time is sensitive to structure along a quasi-hyperbolic curve, such that structure far from the direct ray path can influence the measurements. We use synthetic datasets to explore two potential applications of the scattered wave sensitivity kernels. First, we back-project scattered energy back to its origin using the kernel adjoint operator. This approach successfully images mantle interfaces at depths of 120-180 km with up to 20 km of vertical relief over lateral distances of 100 km (i.e., undulations with a maximal 20% grade) when station spacing is 10 km. Adjacent measurements sum coherently at nodes where gradients in seismic properties occur, and destructively interfere at nodes lacking gradients. In cases where the station spacing is greater than 10 km, the destructive interference can be incomplete, and smearing along the isochrons can occur. We demonstrate, however, that model smoothing can dampen these artifacts. This method is relatively fast, and accurately retrieves the positions of the interfaces, but it generally does not retrieve the strength of the velocity perturbations. Therefore, in our second approach, we attempt to invert directly for velocity perturbations from our reference model using an iterative conjugate-directions scheme.

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

PubMed Central

Li, Kan; Príncipe, José C.

2018-01-01

This paper presents a novel real-time dynamic framework for quantifying time-series structure in spoken words using spikes. Audio signals are converted into multi-channel spike trains using a biologically-inspired leaky integrate-and-fire (LIF) spike generator. These spike trains are mapped into a function space of infinite dimension, i.e., a Reproducing Kernel Hilbert Space (RKHS) using point-process kernels, where a state-space model learns the dynamics of the multidimensional spike input using gradient descent learning. This kernelized recurrent system is very parsimonious and achieves the necessary memory depth via feedback of its internal states when trained discriminatively, utilizing the full context of the phoneme sequence. A main advantage of modeling nonlinear dynamics using state-space trajectories in the RKHS is that it imposes no restriction on the relationship between the exogenous input and its internal state. We are free to choose the input representation with an appropriate kernel, and changing the kernel does not impact the system nor the learning algorithm. Moreover, we show that this novel framework can outperform both traditional hidden Markov model (HMM) speech processing as well as neuromorphic implementations based on spiking neural network (SNN), yielding accurate and ultra-low power word spotters. As a proof of concept, we demonstrate its capabilities using the benchmark TI-46 digit corpus for isolated-word automatic speech recognition (ASR) or keyword spotting. Compared to HMM using Mel-frequency cepstral coefficient (MFCC) front-end without time-derivatives, our MFCC-KAARMA offered improved performance. For spike-train front-end, spike-KAARMA also outperformed state-of-the-art SNN solutions. Furthermore, compared to MFCCs, spike trains provided enhanced noise robustness in certain low signal-to-noise ratio (SNR) regime. PMID:29666568

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

PubMed

Li, Kan; Príncipe, José C

2018-01-01

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

A multitasking, multisinked, multiprocessor data acquisition front end

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fox, R.; Au, R.; Molen, A.V.

1989-10-01

The authors have developed a generalized data acquisition front end system which is based on MC68020 processors running a commercial real time kernel (rhoSOS), and implemented primarily in a high level language (C). This system has been attached to the back end on-line computing system at NSCL via our high performance ETHERNET protocol. Data may be simultaneously sent to any number of back end systems. Fixed fraction sampling along links to back end computing is also supported. A nonprocedural program generator simplifies the development of experiment specific code.

The impact of multiple endpoint dependency on Q and I(2) in meta-analysis.

PubMed

Thompson, Christopher Glen; Becker, Betsy Jane

2014-09-01

A common assumption in meta-analysis is that effect sizes are independent. When correlated effect sizes are analyzed using traditional univariate techniques, this assumption is violated. This research assesses the impact of dependence arising from treatment-control studies with multiple endpoints on homogeneity measures Q and I(2) in scenarios using the unbiased standardized-mean-difference effect size. Univariate and multivariate meta-analysis methods are examined. Conditions included different overall outcome effects, study sample sizes, numbers of studies, between-outcomes correlations, dependency structures, and ways of computing the correlation. The univariate approach used typical fixed-effects analyses whereas the multivariate approach used generalized least-squares (GLS) estimates of a fixed-effects model, weighted by the inverse variance-covariance matrix. Increased dependence among effect sizes led to increased Type I error rates from univariate models. When effect sizes were strongly dependent, error rates were drastically higher than nominal levels regardless of study sample size and number of studies. In contrast, using GLS estimation to account for multiple-endpoint dependency maintained error rates within nominal levels. Conversely, mean I(2) values were not greatly affected by increased amounts of dependency. Last, we point out that the between-outcomes correlation should be estimated as a pooled within-groups correlation rather than using a full-sample estimator that does not consider treatment/control group membership. Copyright © 2014 John Wiley & Sons, Ltd.

Dispersion and sampling of adult Dermacentor andersoni in rangeland in Western North America.

PubMed

Rochon, K; Scoles, G A; Lysyk, T J

2012-03-01

A fixed precision sampling plan was developed for off-host populations of adult Rocky Mountain wood tick, Dermacentor andersoni (Stiles) based on data collected by dragging at 13 locations in Alberta, Canada; Washington; and Oregon. In total, 222 site-date combinations were sampled. Each site-date combination was considered a sample, and each sample ranged in size from 86 to 250 10 m2 quadrats. Analysis of simulated quadrats ranging in size from 10 to 50 m2 indicated that the most precise sample unit was the 10 m2 quadrat. Samples taken when abundance < 0.04 ticks per 10 m2 were more likely to not depart significantly from statistical randomness than samples taken when abundance was greater. Data were grouped into ten abundance classes and assessed for fit to the Poisson and negative binomial distributions. The Poisson distribution fit only data in abundance classes < 0.02 ticks per 10 m2, while the negative binomial distribution fit data from all abundance classes. A negative binomial distribution with common k = 0.3742 fit data in eight of the 10 abundance classes. Both the Taylor and Iwao mean-variance relationships were fit and used to predict sample sizes for a fixed level of precision. Sample sizes predicted using the Taylor model tended to underestimate actual sample sizes, while sample sizes estimated using the Iwao model tended to overestimate actual sample sizes. Using a negative binomial with common k provided estimates of required sample sizes closest to empirically calculated sample sizes.

Use of computer code for dose distribution studies in A 60CO industrial irradiator

NASA Astrophysics Data System (ADS)

Piña-Villalpando, G.; Sloan, D. P.

1995-09-01

This paper presents a benchmark comparison between calculated and experimental absorbed dose values tor a typical product, in a 60Co industrial irradiator, located at ININ, México. The irradiator is a two levels, two layers system with overlapping product configuration with activity around 300kCi. Experimental values were obtanied from routine dosimetry, using red acrylic pellets. Typical product was Petri dishes packages, apparent density 0.13 g/cm3; that product was chosen because uniform size, large quantity and low density. Minimum dose was fixed in 15 kGy. Calculated values were obtained from QAD-CGGP code. This code uses a point kernel technique, build-up factors fitting was done by geometrical progression and combinatorial geometry is used for system description. Main modifications for the code were related with source sumilation, using punctual sources instead of pencils and an energy and anisotropic emission spectrums were included. Results were, for maximum dose, calculated value (18.2 kGy) was 8% higher than experimental average value (16.8 kGy); for minimum dose, calculated value (13.8 kGy) was 3% higher than experimental average value (14.3 kGy).

Three-dimensional holoscopic image coding scheme using high-efficiency video coding with kernel-based minimum mean-square-error estimation

NASA Astrophysics Data System (ADS)

Liu, Deyang; An, Ping; Ma, Ran; Yang, Chao; Shen, Liquan; Li, Kai

2016-07-01

Three-dimensional (3-D) holoscopic imaging, also known as integral imaging, light field imaging, or plenoptic imaging, can provide natural and fatigue-free 3-D visualization. However, a large amount of data is required to represent the 3-D holoscopic content. Therefore, efficient coding schemes for this particular type of image are needed. A 3-D holoscopic image coding scheme with kernel-based minimum mean square error (MMSE) estimation is proposed. In the proposed scheme, the coding block is predicted by an MMSE estimator under statistical modeling. In order to obtain the signal statistical behavior, kernel density estimation (KDE) is utilized to estimate the probability density function of the statistical modeling. As bandwidth estimation (BE) is a key issue in the KDE problem, we also propose a BE method based on kernel trick. The experimental results demonstrate that the proposed scheme can achieve a better rate-distortion performance and a better visual rendering quality.

Discontinuous functional for linear-response time-dependent density-functional theory: The exact-exchange kernel and approximate forms

NASA Astrophysics Data System (ADS)

Hellgren, Maria; Gross, E. K. U.

2013-11-01

We present a detailed study of the exact-exchange (EXX) kernel of time-dependent density-functional theory with an emphasis on its discontinuity at integer particle numbers. It was recently found that this exact property leads to sharp peaks and step features in the kernel that diverge in the dissociation limit of diatomic systems [Hellgren and Gross, Phys. Rev. APLRAAN1050-294710.1103/PhysRevA.85.022514 85, 022514 (2012)]. To further analyze the discontinuity of the kernel, we here make use of two different approximations to the EXX kernel: the Petersilka Gossmann Gross (PGG) approximation and a common energy denominator approximation (CEDA). It is demonstrated that whereas the PGG approximation neglects the discontinuity, the CEDA includes it explicitly. By studying model molecular systems it is shown that the so-called field-counteracting effect in the density-functional description of molecular chains can be viewed in terms of the discontinuity of the static kernel. The role of the frequency dependence is also investigated, highlighting its importance for long-range charge-transfer excitations as well as inner-shell excitations.

Carbothermic synthesis of 820 μm uranium nitride kernels: Literature review, thermodynamics, analysis, and related experiments

NASA Astrophysics Data System (ADS)

Lindemer, T. B.; Voit, S. L.; Silva, C. M.; Besmann, T. M.; Hunt, R. D.

2014-05-01

The US Department of Energy is developing a new nuclear fuel that would be less susceptible to ruptures during a loss-of-coolant accident. The fuel would consist of tristructural isotropic coated particles with uranium nitride (UN) kernels with diameters near 825 μm. This effort explores factors involved in the conversion of uranium oxide-carbon microspheres into UN kernels. An analysis of previous studies with sufficient experimental details is provided. Thermodynamic calculations were made to predict pressures of carbon monoxide and other relevant gases for several reactions that can be involved in the conversion of uranium oxides and carbides into UN. Uranium oxide-carbon microspheres were heated in a microbalance with an attached mass spectrometer to determine details of calcining and carbothermic conversion in argon, nitrogen, and vacuum. A model was derived from experiments on the vacuum conversion to uranium oxide-carbide kernels. UN-containing kernels were fabricated using this vacuum conversion as part of the overall process. Carbonitride kernels of ∼89% of theoretical density were produced along with several observations concerning the different stages of the process.

Multi-PSF fusion in image restoration of range-gated systems

NASA Astrophysics Data System (ADS)

Wang, Canjin; Sun, Tao; Wang, Tingfeng; Miao, Xikui; Wang, Rui

2018-07-01

For the task of image restoration, an accurate estimation of degrading PSF/kernel is the premise of recovering a visually superior image. The imaging process of range-gated imaging system in atmosphere associates with lots of factors, such as back scattering, background radiation, diffraction limit and the vibration of the platform. On one hand, due to the difficulty of constructing models for all factors, the kernels from physical-model based methods are not strictly accurate and practical. On the other hand, there are few strong edges in images, which brings significant errors to most of image-feature-based methods. Since different methods focus on different formation factors of the kernel, their results often complement each other. Therefore, we propose an approach which combines physical model with image features. With an fusion strategy using GCRF (Gaussian Conditional Random Fields) framework, we get a final kernel which is closer to the actual one. Aiming at the problem that ground-truth image is difficult to obtain, we then propose a semi data-driven fusion method in which different data sets are used to train fusion parameters. Finally, a semi blind restoration strategy based on EM (Expectation Maximization) and RL (Richardson-Lucy) algorithm is proposed. Our methods not only models how the lasers transfer in the atmosphere and imaging in the ICCD (Intensified CCD) plane, but also quantifies other unknown degraded factors using image-based methods, revealing how multiple kernel elements interact with each other. The experimental results demonstrate that our method achieves better performance than state-of-the-art restoration approaches.

Learning a peptide-protein binding affinity predictor with kernel ridge regression

PubMed Central

2013-01-01

Background 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. Results 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. Conclusion On all benchmarks, our method significantly (p-value ≤ 0.057) outperforms the current state-of-the-art methods at predicting peptide-protein binding affinities. The proposed approach is flexible and can be applied to predict any quantitative biological activity. Moreover, generating reliable peptide-protein binding affinities will also improve system biology modelling of interaction pathways. Lastly, the method should be of value to a large segment of the research community with the potential to accelerate the discovery of peptide-based drugs and facilitate vaccine development. The proposed kernel is freely available at http://graal.ift.ulaval.ca/downloads/gs-kernel/. PMID:23497081

Study of the mechanisms for flame stabilization in gas turbine model combustors using kHz laser diagnostics

NASA Astrophysics Data System (ADS)

Boxx, Isaac; Carter, Campbell D.; Stöhr, Michael; Meier, Wolfgang

2013-05-01

An image-processing routine was developed to autonomously identify and statistically characterize flame-kernel events, wherein OH (from a planar laser-induced fluorescence, PLIF, measurement) appears in the probe region away from the contiguous OH layer. This routine was applied to datasets from two gas turbine model combustors that consist of thousands of joint OH-velocity images from kHz framerate OH-PLIF and particle image velocimetry (PIV). Phase sorting of the kernel centroids with respect to the dominant fluid-dynamic structure of the combustors (a helical precessing vortex core, PVC) indicates through-plane transport of reacting fluid best explains their sudden appearance in the PLIF images. The concentration of flame-kernel events around the periphery of the mean location of the PVC indicates they are likely the result of wrinkling and/or breakup of the primary flame sheet associated with the passage of the PVC as it circumscribes the burner centerline. The prevailing through-plane velocity of the swirling flow-field transports these fragments into the imaging plane of the OH-PLIF system. The lack of flame-kernel events near the center of the PVC (in which there is lower strain and longer fluid-dynamic residence times) indicates that auto-ignition is not a likely explanation for these flame kernels in a majority of cases. The lack of flame-kernel centroid variation in one flame in which there is no PVC further supports this explanation.

Genomic-Enabled Prediction Kernel Models with Random Intercepts for Multi-environment Trials.

PubMed

Cuevas, Jaime; Granato, Italo; Fritsche-Neto, Roberto; Montesinos-Lopez, Osval A; Burgueño, Juan; Bandeira E Sousa, Massaine; Crossa, José

2018-03-28

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 ([Formula: see text]) 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. Copyright © 2018 Cuevas et al.

Genomic-Enabled Prediction Kernel Models with Random Intercepts for Multi-environment Trials

PubMed Central

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