Brooks, S P; Suelter, C H
1986-09-01
An IBM computer program, WILMAN4, is described which calculates the estimates, Km, V and Km/V from initial velocity measurements according to one of four statistical methods. Three of these methods involve linear regression analysis using weights given by assuming: (i) constant absolute error (G.N. Wilkinson, 1961, Biochem J., 80, 324-332), (ii) constant relative error (G. Johansen and R. Lumry, 1961, C.R. Trav. Lab. Carlsberg, 32, 185-214) and (iii) an error function in between the above two cases. (A. Cornish-Bowden, 1976, Principles of Enzyme Kinetics, Butterworths Inc, Boston, Mass., pp. 168-193). The fourth method is a non-parametric procedure derived by Eisenthal and Cornish-Bowden (Biochim. Biophys. Acta, 532 (1974) 268-272). Residuals are obtained by subtracting the experimental and the calculated velocities. Outliers, or residuals which are greater than two experimental standard deviations, can be identified and removed from the data set. If the sequence of positive and negative signs of the residuals is random as determined by a statistical probability calculation, the data set is assumed to obey the Michaelis-Menten equation.
Non-parametric Estimation of a Survival Function with Two-stage Design Studies.
Li, Gang; Tseng, Chi-Hong
2008-06-01
The two-stage design is popular in epidemiology studies and clinical trials due to its cost effectiveness. Typically, the first stage sample contains cheaper and possibly biased information, while the second stage validation sample consists of a subset of subjects with accurate and complete information. In this paper, we study estimation of a survival function with right-censored survival data from a two-stage design. A non-parametric estimator is derived by combining data from both stages. We also study its large sample properties and derive pointwise and simultaneous confidence intervals for the survival function. The proposed estimator effectively reduces the variance and finite-sample bias of the Kaplan-Meier estimator solely based on the second stage validation sample. Finally, we apply our method to a real data set from a medical device post-marketing surveillance study.
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
Structuring feature space: a non-parametric method for volumetric transfer function generation.
Maciejewski, Ross; Woo, Insoo; Chen, Wei; Ebert, David S
2009-01-01
The use of multi-dimensional transfer functions for direct volume rendering has been shown to be an effective means of extracting materials and their boundaries for both scalar and multivariate data. The most common multi-dimensional transfer function consists of a two-dimensional (2D) histogram with axes representing a subset of the feature space (e.g., value vs. value gradient magnitude), with each entry in the 2D histogram being the number of voxels at a given feature space pair. Users then assign color and opacity to the voxel distributions within the given feature space through the use of interactive widgets (e.g., box, circular, triangular selection). Unfortunately, such tools lead users through a trial-and-error approach as they assess which data values within the feature space map to a given area of interest within the volumetric space. In this work, we propose the addition of non-parametric clustering within the transfer function feature space in order to extract patterns and guide transfer function generation. We apply a non-parametric kernel density estimation to group voxels of similar features within the 2D histogram. These groups are then binned and colored based on their estimated density, and the user may interactively grow and shrink the binned regions to explore feature boundaries and extract regions of interest. We also extend this scheme to temporal volumetric data in which time steps of 2D histograms are composited into a histogram volume. A three-dimensional (3D) density estimation is then applied, and users can explore regions within the feature space across time without adjusting the transfer function at each time step. Our work enables users to effectively explore the structures found within a feature space of the volume and provide a context in which the user can understand how these structures relate to their volumetric data. We provide tools for enhanced exploration and manipulation of the transfer function, and we show that the initial
A Non-parametric Approach to Constrain the Transfer Function in Reverberation Mapping
NASA Astrophysics Data System (ADS)
Li, Yan-Rong; Wang, Jian-Min; Bai, Jin-Ming
2016-11-01
Broad emission lines of active galactic nuclei stem from a spatially extended region (broad-line region, BLR) that is composed of discrete clouds and photoionized by the central ionizing continuum. The temporal behaviors of these emission lines are blurred echoes of continuum variations (i.e., reverberation mapping, RM) and directly reflect the structures and kinematic information of BLRs through the so-called transfer function (also known as the velocity-delay map). Based on the previous works of Rybicki and Press and Zu et al., we develop an extended, non-parametric approach to determine the transfer function for RM data, in which the transfer function is expressed as a sum of a family of relatively displaced Gaussian response functions. Therefore, arbitrary shapes of transfer functions associated with complicated BLR geometry can be seamlessly included, enabling us to relax the presumption of a specified transfer function frequently adopted in previous studies and to let it be determined by observation data. We formulate our approach in a previously well-established framework that incorporates the statistical modeling of continuum variations as a damped random walk process and takes into account long-term secular variations which are irrelevant to RM signals. The application to RM data shows the fidelity of our approach.
Chaibub Neto, Elias
2015-01-01
In this paper we propose a vectorized implementation of the non-parametric bootstrap for statistics based on sample moments. Basically, we adopt the multinomial sampling formulation of the non-parametric bootstrap, and compute bootstrap replications of sample moment statistics by simply weighting the observed data according to multinomial counts instead of evaluating the statistic on a resampled version of the observed data. Using this formulation we can generate a matrix of bootstrap weights and compute the entire vector of bootstrap replications with a few matrix multiplications. Vectorization is particularly important for matrix-oriented programming languages such as R, where matrix/vector calculations tend to be faster than scalar operations implemented in a loop. We illustrate the application of the vectorized implementation in real and simulated data sets, when bootstrapping Pearson’s sample correlation coefficient, and compared its performance against two state-of-the-art R implementations of the non-parametric bootstrap, as well as a straightforward one based on a for loop. Our investigations spanned varying sample sizes and number of bootstrap replications. The vectorized bootstrap compared favorably against the state-of-the-art implementations in all cases tested, and was remarkably/considerably faster for small/moderate sample sizes. The same results were observed in the comparison with the straightforward implementation, except for large sample sizes, where the vectorized bootstrap was slightly slower than the straightforward implementation due to increased time expenditures in the generation of weight matrices via multinomial sampling. PMID:26125965
Chaibub Neto, Elias
2015-01-01
In this paper we propose a vectorized implementation of the non-parametric bootstrap for statistics based on sample moments. Basically, we adopt the multinomial sampling formulation of the non-parametric bootstrap, and compute bootstrap replications of sample moment statistics by simply weighting the observed data according to multinomial counts instead of evaluating the statistic on a resampled version of the observed data. Using this formulation we can generate a matrix of bootstrap weights and compute the entire vector of bootstrap replications with a few matrix multiplications. Vectorization is particularly important for matrix-oriented programming languages such as R, where matrix/vector calculations tend to be faster than scalar operations implemented in a loop. We illustrate the application of the vectorized implementation in real and simulated data sets, when bootstrapping Pearson's sample correlation coefficient, and compared its performance against two state-of-the-art R implementations of the non-parametric bootstrap, as well as a straightforward one based on a for loop. Our investigations spanned varying sample sizes and number of bootstrap replications. The vectorized bootstrap compared favorably against the state-of-the-art implementations in all cases tested, and was remarkably/considerably faster for small/moderate sample sizes. The same results were observed in the comparison with the straightforward implementation, except for large sample sizes, where the vectorized bootstrap was slightly slower than the straightforward implementation due to increased time expenditures in the generation of weight matrices via multinomial sampling.
Non-parametric estimation of gap time survival functions for ordered multivariate failure time data.
Schaubel, Douglas E; Cai, Jianwen
2004-06-30
Times between sequentially ordered events (gap times) are often of interest in biomedical studies. For example, in a cancer study, the gap times from incidence-to-remission and remission-to-recurrence may be examined. Such data are usually subject to right censoring, and within-subject failure times are generally not independent. Statistical challenges in the analysis of the second and subsequent gap times include induced dependent censoring and non-identifiability of the marginal distributions. We propose a non-parametric method for constructing one-sample estimators of conditional gap-time specific survival functions. The estimators are uniformly consistent and, upon standardization, converge weakly to a zero-mean Gaussian process, with a covariance function which can be consistently estimated. Simulation studies reveal that the asymptotic approximations are appropriate for finite samples. Methods for confidence bands are provided. The proposed methods are illustrated on a renal failure data set, where the probabilities of transplant wait-listing and kidney transplantation are of interest.
Cadarso-Suárez, Carmen; Roca-Pardiñas, Javier; Figueiras, Adolfo; González-Manteiga, Wenceslao
2005-04-30
The generalized additive, model (GAM) is a powerful and widely used tool that allows researchers to fit, non-parametrically, the effect of continuous predictors on a transformation of the mean response variable. Such a transformation is given by a so-called link function, and in GAMs this link function is assumed to be known. Nevertheless, if an incorrect choice is made for the link, the resulting GAM is misspecified and the results obtained may be misleading. In this paper, we propose a modified version of the local scoring algorithm that allows for the non-parametric estimation of the link function, by using local linear kernel smoothers. To better understand the effect that each covariate produces on the outcome, results are expressed in terms of the non-parametric odds ratio (OR) curves. Bootstrap techniques were used to correct the bias in the OR estimation and to construct point-wise confidence intervals. A simulation study was carried out to assess the behaviour of the resulting estimates. The proposed methodology was illustrated using data from the AIDS Register of Galicia (NW Spain), with a view to assessing the effect of the CD4 lymphocyte count on the probability of being AIDS-diagnosed via Tuberculosis (TB). This application shows how the link's flexibility makes it possible to obtain OR curve estimates that are less sensitive to the presence of outliers and unusual values that are often present in the extremes of the covariate distributions.
Scaling of preferential flow in biopores by parametric or non parametric transfer functions
NASA Astrophysics Data System (ADS)
Zehe, E.; Hartmann, N.; Klaus, J.; Palm, J.; Schroeder, B.
2009-04-01
finally assign the measured hydraulic capacities to these pores. By combining this population of macropores with observed data on soil hydraulic properties we obtain a virtual reality. Flow and transport is simulated for different rainfall forcings comparing two models, Hydrus 3d and Catflow. The simulated cumulative travel depths distributions for different forcings will be linked to the cumulative depth distribution of connected flow paths. The latter describes the fraction of connected paths - where flow resistance is always below a selected threshold that links the surface to a certain critical depth. Systematic variation of the average number of macropores and their depth distributions will show whether a clear link between the simulated travel depths distributions and the depth distribution of connected paths may be identified. The third essential step is to derive a non parametric transfer function that predicts travel depth distributions of tracers and on the long term pesticides based on easy-to-assess subsurface characteristics (mainly density and depth distribution of worm burrows, soil matrix properties), initial conditions and rainfall forcing. Such a transfer function is independent of scale ? as long as we stay in the same ensemble i.e. worm population and soil properties stay the same. Shipitalo, M.J. and Butt, K.R. (1999): Occupancy and geometrical properties of Lumbricus terrestris L. burrows affecting infiltration. Pedobiologia 43:782-794 Zehe E, and Fluehler H. (2001b): Slope scale distribution of flow patterns in soil profiles. J. Hydrol. 247: 116-132.
Abadi, Alireza; Ahmadi, Farzaneh; Alavi Majd, Hamid; Akbari, Mohammad Esmaeil; Abolfazli Khonbi, Zainab; Davoudi Monfared, Esmat
2013-01-01
Background Colon cancer is the third cause of cancer deaths. Although colon cancer survival time has increased in recent years, the mortality rate is still high. The Cox model is the most common regression model often used in medical research in survival analysis, but most of the time the effect of at least one of the independent factors changes over time, so the model cannot be used. In the current study, the survival function for colon cancer patients in Tehran is estimated using non-parametric Bayesian model. Methods In this survival study, 580 patients with colon cancer who were recorded in the Cancer Research Center of Shahid Beheshti University of Medical Sciences since April 2005 to November 2006 were studied and followed up for a period of 5 years. Survival function was plotted with non-parametric Bayesian model and was compared with the Kaplan-Meier curve. Results Of the total of 580 patients, 69.9% of patients were alive. 45.9% of patients were male and the mean age of cancer diagnosis was 65.12 (SD= 12.26) and 87.7 of the patients underwent surgery. There was a significant relationship between age at diagnosis and sex and the survival time while there was a non-significant relationship between the type of treatment and the survival time. The survival functions corresponding to the two treatment groups cross, in comparison with the patients who had no surgery in the first 30 months, showed a higher level of risk in the patients who underwent a surgery. After that, the survival probability for the patients undergoing a surgery has increased. Conclusion The study showed that survival rate has been higher in women and in the patients who were below 60 years at the time of diagnosis. PMID:25250124
Gao, Feng; Manatunga, Amita K; Chen, Shande
2007-02-20
Often in many biomedical and epidemiologic studies, estimating hazards function is of interest. The Breslow's estimator is commonly used for estimating the integrated baseline hazard, but this estimator requires the functional form of covariate effects to be correctly specified. It is generally difficult to identify the true functional form of covariate effects in the presence of time-dependent covariates. To provide a complementary method to the traditional proportional hazard model, we propose a tree-type method which enables simultaneously estimating both baseline hazards function and the effects of time-dependent covariates. Our interest will be focused on exploring the potential data structures rather than formal hypothesis testing. The proposed method approximates the baseline hazards and covariate effects with step-functions. The jump points in time and in covariate space are searched via an algorithm based on the improvement of the full log-likelihood function. In contrast to most other estimating methods, the proposed method estimates the hazards function rather than integrated hazards. The method is applied to model the risk of withdrawal in a clinical trial that evaluates the anti-depression treatment in preventing the development of clinical depression. Finally, the performance of the method is evaluated by several simulation studies.
Kulmala, A; Tenhunen, M
2012-11-07
The signal of the dosimetric detector is generally dependent on the shape and size of the sensitive volume of the detector. In order to optimize the performance of the detector and reliability of the output signal the effect of the detector size should be corrected or, at least, taken into account. The response of the detector can be modelled using the convolution theorem that connects the system input (actual dose), output (measured result) and the effect of the detector (response function) by a linear convolution operator. We have developed the super-resolution and non-parametric deconvolution method for determination of the cylinder symmetric ionization chamber radial response function. We have demonstrated that the presented deconvolution method is able to determine the radial response for the Roos parallel plate ionization chamber with a better than 0.5 mm correspondence with the physical measures of the chamber. In addition, the performance of the method was proved by the excellent agreement between the output factors of the stereotactic conical collimators (4-20 mm diameter) measured by the Roos chamber, where the detector size is larger than the measured field, and the reference detector (diode). The presented deconvolution method has a potential in providing reference data for more accurate physical models of the ionization chamber as well as for improving and enhancing the performance of the detectors in specific dosimetric problems.
[Non-parametric estimation of survival function for recurrent events data].
González, Juan R; Peña, Edsel A
2004-01-01
Recurrent events when we deal with survival studies demand a different methodology from what is used in standard survival analysis. The main problem that we found when we make inference in these kind of studies is that the observations may not be independent. Thus, biased and inefficient estimators can be obtained if we do not take into account this fact. In the independent case, the interocurrence survival function can be estimated by the generalization of the limit product estimator (Peña et al. (2001)). However, if data are correlated, other models should be used such as frailty models or an estimator proposed by Wang and Chang (1999), that take into account the fact that interocurrence times were or not correlated. The aim of this paper has been the illustration of these approaches by using two real data sets.
Fujita, André; Takahashi, Daniel Y; Patriota, Alexandre G; Sato, João R
2014-12-10
Statistical inference of functional magnetic resonance imaging (fMRI) data is an important tool in neuroscience investigation. One major hypothesis in neuroscience is that the presence or not of a psychiatric disorder can be explained by the differences in how neurons cluster in the brain. Therefore, it is of interest to verify whether the properties of the clusters change between groups of patients and controls. The usual method to show group differences in brain imaging is to carry out a voxel-wise univariate analysis for a difference between the mean group responses using an appropriate test and to assemble the resulting 'significantly different voxels' into clusters, testing again at cluster level. In this approach, of course, the primary voxel-level test is blind to any cluster structure. Direct assessments of differences between groups at the cluster level seem to be missing in brain imaging. For this reason, we introduce a novel non-parametric statistical test called analysis of cluster structure variability (ANOCVA), which statistically tests whether two or more populations are equally clustered. The proposed method allows us to compare the clustering structure of multiple groups simultaneously and also to identify features that contribute to the differential clustering. We illustrate the performance of ANOCVA through simulations and an application to an fMRI dataset composed of children with attention deficit hyperactivity disorder (ADHD) and controls. Results show that there are several differences in the clustering structure of the brain between them. Furthermore, we identify some brain regions previously not described to be involved in the ADHD pathophysiology, generating new hypotheses to be tested. The proposed method is general enough to be applied to other types of datasets, not limited to fMRI, where comparison of clustering structures is of interest.
NON-PARAMETRIC ESTIMATION UNDER STRONG DEPENDENCE
Zhao, Zhibiao; Zhang, Yiyun; Li, Runze
2014-01-01
We study non-parametric regression function estimation for models with strong dependence. Compared with short-range dependent models, long-range dependent models often result in slower convergence rates. We propose a simple differencing-sequence based non-parametric estimator that achieves the same convergence rate as if the data were independent. Simulation studies show that the proposed method has good finite sample performance. PMID:25018572
NON-PARAMETRIC ESTIMATION UNDER STRONG DEPENDENCE.
Zhao, Zhibiao; Zhang, Yiyun; Li, Runze
2014-01-01
We study non-parametric regression function estimation for models with strong dependence. Compared with short-range dependent models, long-range dependent models often result in slower convergence rates. We propose a simple differencing-sequence based non-parametric estimator that achieves the same convergence rate as if the data were independent. Simulation studies show that the proposed method has good finite sample performance.
Ragab, Marwa A A; EL-Kimary, Eman I
2014-11-01
A spectrofluorimetric method was used for the estimation of closely related fluorescent reaction products, Fluoxetine and Olanzapine, in their mixture after derivatization of both drugs using 4-chloro-7- nitrobenzo - 2 -oxa-1,3 - diazole (NBD-Cl) in borate buffered medium (pH 9.5) to form highly fluorescent products. The method based on the use of first and second derivative ratio of the emission data along with their convolution using 8-points sin x i or cos x i polynomials (discrete Fourier functions). The proposed method facilitates their simultaneous determination despite the presence of a minor component (Olanzapine) and strong overlapped spectra of the two NBD-Cl fluorescent products of fluoxetine and olanzapine. The accurate and precise estimation of the minor component was achieved after the convolution of the derivative ratio curves. Moreover, the obtained data were subjected to non-parametric linear regression analysis (Theil's method). The work combines the advantages of convolution of derivative ratio curves using discrete Fourier functions together with the reliability and efficacy of the non-parametric analysis of data.
Effects of mergers on non-parametric morphologies
NASA Astrophysics Data System (ADS)
Bignone, Lucas A.; Tissera, Patricia B.; Sillero, Emanuel; Pedrosa, Susana E.; Pellizza, Leonardo J.; Lambas, Diego G.
2017-06-01
We study the effects of mergers on non-parametric morphologies of galaxies. We compute the Gini index, M20, asymmetry and concentration statistics for z = 0 galaxies in the Illustris simulation and compare non-parametric morphologies of major mergers, minor merges, close pairs, distant pairs and unperturbed galaxies. We determine the effectiveness of observational methods based on these statistics to select merging galaxies.
Non-parametric approach to the study of phenotypic stability.
Ferreira, D F; Fernandes, S B; Bruzi, A T; Ramalho, M A P
2016-02-19
The aim of this study was to undertake the theoretical derivations of non-parametric methods, which use linear regressions based on rank order, for stability analyses. These methods were extension different parametric methods used for stability analyses and the result was compared with a standard non-parametric method. Intensive computational methods (e.g., bootstrap and permutation) were applied, and data from the plant-breeding program of the Biology Department of UFLA (Minas Gerais, Brazil) were used to illustrate and compare the tests. The non-parametric stability methods were effective for the evaluation of phenotypic stability. In the presence of variance heterogeneity, the non-parametric methods exhibited greater power of discrimination when determining the phenotypic stability of genotypes.
Non-parametric morphologies of mergers in the Illustris simulation
NASA Astrophysics Data System (ADS)
Bignone, L. A.; Tissera, P. B.; Sillero, E.; Pedrosa, S. E.; Pellizza, L. J.; Lambas, D. G.
2017-02-01
We study non-parametric morphologies of mergers events in a cosmological context, using the Illustris project. We produce mock g-band images comparable to observational surveys from the publicly available Illustris simulation idealized mock images at z = 0. We then measure non-parametric indicators: asymmetry, Gini, M20, clumpiness, and concentration for a set of galaxies with M* > 1010 M⊙. We correlate these automatic statistics with the recent merger history of galaxies and with the presence of close companions. Our main contribution is to assess in a cosmological framework, the empirically derived non-parametric demarcation line and average time-scales used to determine the merger rate observationally. We found that 98 per cent of galaxies above the demarcation line have a close companion or have experienced a recent merger event. On average, merger signatures obtained from the G-M20 criterion anti-correlate clearly with the elapsing time to the last merger event. We also find that the asymmetry correlates with galaxy pair separation and relative velocity, exhibiting the larger enhancements for those systems with pair separations d < 50 h-1 kpc and relative velocities V < 350 km s-1. We find that the G-M20 is most sensitive to recent mergers (∼0.14 Gyr) and to ongoing mergers with stellar mass ratios greater than 0.1. For this indicator, we compute a merger average observability time-scale of ∼0.2 Gyr, in agreement with previous results and demonstrate that the morphologically derived merger rate recovers the intrinsic total merger rate of the simulation and the merger rate as a function of stellar mass.
Halliday, David M; Senik, Mohd Harizal; Stevenson, Carl W; Mason, Rob
2016-08-01
The ability to infer network structure from multivariate neuronal signals is central to computational neuroscience. Directed network analyses typically use parametric approaches based on auto-regressive (AR) models, where networks are constructed from estimates of AR model parameters. However, the validity of using low order AR models for neurophysiological signals has been questioned. A recent article introduced a non-parametric approach to estimate directionality in bivariate data, non-parametric approaches are free from concerns over model validity. We extend the non-parametric framework to include measures of directed conditional independence, using scalar measures that decompose the overall partial correlation coefficient summatively by direction, and a set of functions that decompose the partial coherence summatively by direction. A time domain partial correlation function allows both time and frequency views of the data to be constructed. The conditional independence estimates are conditioned on a single predictor. The framework is applied to simulated cortical neuron networks and mixtures of Gaussian time series data with known interactions. It is applied to experimental data consisting of local field potential recordings from bilateral hippocampus in anaesthetised rats. The framework offers a non-parametric approach to estimation of directed interactions in multivariate neuronal recordings, and increased flexibility in dealing with both spike train and time series data. The framework offers a novel alternative non-parametric approach to estimate directed interactions in multivariate neuronal recordings, and is applicable to spike train and time series data. Copyright © 2016 Elsevier B.V. All rights reserved.
Marginally specified priors for non-parametric Bayesian estimation
Kessler, David C.; Hoff, Peter D.; Dunson, David B.
2014-01-01
Summary Prior specification for non-parametric Bayesian inference involves the difficult task of quantifying prior knowledge about a parameter of high, often infinite, dimension. A statistician is unlikely to have informed opinions about all aspects of such a parameter but will have real information about functionals of the parameter, such as the population mean or variance. The paper proposes a new framework for non-parametric Bayes inference in which the prior distribution for a possibly infinite dimensional parameter is decomposed into two parts: an informative prior on a finite set of functionals, and a non-parametric conditional prior for the parameter given the functionals. Such priors can be easily constructed from standard non-parametric prior distributions in common use and inherit the large support of the standard priors on which they are based. Additionally, posterior approximations under these informative priors can generally be made via minor adjustments to existing Markov chain approximation algorithms for standard non-parametric prior distributions. We illustrate the use of such priors in the context of multivariate density estimation using Dirichlet process mixture models, and in the modelling of high dimensional sparse contingency tables. PMID:25663813
Marginally specified priors for non-parametric Bayesian estimation.
Kessler, David C; Hoff, Peter D; Dunson, David B
2015-01-01
Prior specification for non-parametric Bayesian inference involves the difficult task of quantifying prior knowledge about a parameter of high, often infinite, dimension. A statistician is unlikely to have informed opinions about all aspects of such a parameter but will have real information about functionals of the parameter, such as the population mean or variance. The paper proposes a new framework for non-parametric Bayes inference in which the prior distribution for a possibly infinite dimensional parameter is decomposed into two parts: an informative prior on a finite set of functionals, and a non-parametric conditional prior for the parameter given the functionals. Such priors can be easily constructed from standard non-parametric prior distributions in common use and inherit the large support of the standard priors on which they are based. Additionally, posterior approximations under these informative priors can generally be made via minor adjustments to existing Markov chain approximation algorithms for standard non-parametric prior distributions. We illustrate the use of such priors in the context of multivariate density estimation using Dirichlet process mixture models, and in the modelling of high dimensional sparse contingency tables.
NASA Astrophysics Data System (ADS)
Conroy, Charlie; van Dokkum, Pieter G.; Villaume, Alexa
2017-03-01
It is now well-established that the stellar initial mass function (IMF) can be determined from the absorption line spectra of old stellar systems, and this has been used to measure the IMF and its variation across the early-type galaxy population. Previous work focused on measuring the slope of the IMF over one or more stellar mass intervals, implicitly assuming that this is a good description of the IMF and that the IMF has a universal low-mass cutoff. In this work we consider more flexible IMFs, including two-component power laws with a variable low-mass cutoff and a general non-parametric model. We demonstrate with mock spectra that the detailed shape of the IMF can be accurately recovered as long as the data quality is high (S/N ≳ 300 Å‑1) and cover a wide wavelength range (0.4–1.0 μm). We apply these flexible IMF models to a high S/N spectrum of the center of the massive elliptical galaxy NGC 1407. Fitting the spectrum with non-parametric IMFs, we find that the IMF in the center shows a continuous rise extending toward the hydrogen-burning limit, with a behavior that is well-approximated by a power law with an index of ‑2.7. These results provide strong evidence for the existence of extreme (super-Salpeter) IMFs in the cores of massive galaxies.
Bayesian non-parametrics and the probabilistic approach to modelling
Ghahramani, Zoubin
2013-01-01
Modelling is fundamental to many fields of science and engineering. A model can be thought of as a representation of possible data one could predict from a system. The probabilistic approach to modelling uses probability theory to express all aspects of uncertainty in the model. The probabilistic approach is synonymous with Bayesian modelling, which simply uses the rules of probability theory in order to make predictions, compare alternative models, and learn model parameters and structure from data. This simple and elegant framework is most powerful when coupled with flexible probabilistic models. Flexibility is achieved through the use of Bayesian non-parametrics. This article provides an overview of probabilistic modelling and an accessible survey of some of the main tools in Bayesian non-parametrics. The survey covers the use of Bayesian non-parametrics for modelling unknown functions, density estimation, clustering, time-series modelling, and representing sparsity, hierarchies, and covariance structure. More specifically, it gives brief non-technical overviews of Gaussian processes, Dirichlet processes, infinite hidden Markov models, Indian buffet processes, Kingman’s coalescent, Dirichlet diffusion trees and Wishart processes. PMID:23277609
Jung, S H; Su, J Q
1995-02-15
We propose a non-parametric method to calculate a confidence interval for the difference or ratio of two median failure times for paired observations with censoring. The new method is simple to calculate, does not involve non-parametric density estimates, and is valid asymptotically even when the two underlying distribution functions differ in shape. The method also allows missing observations. We report numerical studies to examine the performance of the new method for practical sample sizes.
Lottery spending: a non-parametric analysis.
Garibaldi, Skip; Frisoli, Kayla; Ke, Li; Lim, Melody
2015-01-01
We analyze the spending of individuals in the United States on lottery tickets in an average month, as reported in surveys. We view these surveys as sampling from an unknown distribution, and we use non-parametric methods to compare properties of this distribution for various demographic groups, as well as claims that some properties of this distribution are constant across surveys. We find that the observed higher spending by Hispanic lottery players can be attributed to differences in education levels, and we dispute previous claims that the top 10% of lottery players consistently account for 50% of lottery sales.
Lottery Spending: A Non-Parametric Analysis
Garibaldi, Skip; Frisoli, Kayla; Ke, Li; Lim, Melody
2015-01-01
We analyze the spending of individuals in the United States on lottery tickets in an average month, as reported in surveys. We view these surveys as sampling from an unknown distribution, and we use non-parametric methods to compare properties of this distribution for various demographic groups, as well as claims that some properties of this distribution are constant across surveys. We find that the observed higher spending by Hispanic lottery players can be attributed to differences in education levels, and we dispute previous claims that the top 10% of lottery players consistently account for 50% of lottery sales. PMID:25642699
Non-parametric transformation for data correlation and integration: From theory to practice
Datta-Gupta, A.; Xue, Guoping; Lee, Sang Heon
1997-08-01
The purpose of this paper is two-fold. First, we introduce the use of non-parametric transformations for correlating petrophysical data during reservoir characterization. Such transformations are completely data driven and do not require a priori functional relationship between response and predictor variables which is the case with traditional multiple regression. The transformations are very general, computationally efficient and can easily handle mixed data types for example, continuous variables such as porosity, permeability and categorical variables such as rock type, lithofacies. The power of the non-parametric transformation techniques for data correlation has been illustrated through synthetic and field examples. Second, we utilize these transformations to propose a two-stage approach for data integration during heterogeneity characterization. The principal advantages of our approach over traditional cokriging or cosimulation methods are: (1) it does not require a linear relationship between primary and secondary data, (2) it exploits the secondary information to its fullest potential by maximizing the correlation between the primary and secondary data, (3) it can be easily applied to cases where several types of secondary or soft data are involved, and (4) it significantly reduces variance function calculations and thus, greatly facilitates non-Gaussian cosimulation. We demonstrate the data integration procedure using synthetic and field examples. The field example involves estimation of pore-footage distribution using well data and multiple seismic attributes.
Non-parametric iterative model constraint graph min-cut for automatic kidney segmentation.
Freiman, M; Kronman, A; Esses, S J; Joskowicz, L; Sosna, J
2010-01-01
We present a new non-parametric model constraint graph min-cut algorithm for automatic kidney segmentation in CT images. The segmentation is formulated as a maximum a-posteriori estimation of a model-driven Markov random field. A non-parametric hybrid shape and intensity model is treated as a latent variable in the energy functional. The latent model and labeling map that minimize the energy functional are then simultaneously computed with an expectation maximization approach. The main advantages of our method are that it does not assume a fixed parametric prior model, which is subjective to inter-patient variability and registration errors, and that it combines both the model and the image information into a unified graph min-cut based segmentation framework. We evaluated our method on 20 kidneys from 10 CT datasets with and without contrast agent for which ground-truth segmentations were generated by averaging three manual segmentations. Our method yields an average volumetric overlap error of 10.95%, and average symmetric surface distance of 0.79 mm. These results indicate that our method is accurate and robust for kidney segmentation.
Integration of Rain Gauge and Doppler Radar Data Using Bayesian Non-Parametric Approach
NASA Astrophysics Data System (ADS)
Mok, C. M.; Heimann, M.; Betz, W.; Straub, D.
2012-12-01
Precipitation is an essential hydrologic process. Accurate representation of the spatial and temporal distribution of rainfall intensity is critical to development of robust calibrated hydrologic models. Rainfall data are commonly collected using rain gauges and Doppler radar. Rain gauge data are more accurate but they only yield information at point locations. Radar data provide continuous spatial information pixel-by-pixel but they are less accurate. This paper presents a Bayesian non-parametric approach for integrating gauge and radar data to develop more accurate and continuous rainfall interpretation. In a Bayesian framework, the resulting probability distribution of rainfall intensity (posterior distribution) at a location at a time step is computed from a prior distribution and a likelihood function. In this paper, the prior distribution is estimated by applying geostatistical methods to rain gauge data. The likelihood function is calculated based on the mismatch errors between the rainfall radar and rainfall gauge data where they overlap. A non-parametric approach allows rainfall spatial structures to be intensity dependent. At each time step, a range of rainfall threshold levels is considered. For each threshold level, rain gauge and radar data are encoded into indicator values with 1 denoting rainfall intensity greater than the threshold level. Radar data are used to characterize the correlation structure of the indicator field. Indicator Kriging using the resulting correlation model is applied to gauge indicator data to compute the prior estimate of the probability of exceeding the rainfall threshold. Fault table based on comparison of gauge and radar indicator values is used to compute the likelihood at a location. The resulting posterior estimate of the probability of exceeding the rainfall threshold represents the cumulative probability density function value corresponding to the rainfall threshold at the location. Available Rain gauge and radar data
Approximately Integrable Linear Statistical Models in Non-Parametric Estimation
1990-08-01
OTIC I EL COPY Lfl 0n Cf) NAPPROXIMATELY INTEGRABLE LINEAR STATISTICAL MODELS IN NON- PARAMETRIC ESTIMATION by B. Ya. Levit University of Maryland...Integrable Linear Statistical Models in Non- Parametric Estimation B. Ya. Levit Sumnmary / The notion of approximately integrable linear statistical models...models related to the study of the "next" order optimality in non- parametric estimation . It appears consistent to keep the exposition at present at the
Non-parametric diffeomorphic image registration with the demons algorithm.
Vercauteren, Tom; Pennec, Xavier; Perchant, Aymeric; Ayache, Nicholas
2007-01-01
We propose a non-parametric diffeomorphic image registration algorithm based on Thirion's demons algorithm. The demons algorithm can be seen as an optimization procedure on the entire space of displacement fields. The main idea of our algorithm is to adapt this procedure to a space of diffeomorphic transformations. In contrast to many diffeomorphic registration algorithms, our solution is computationally efficient since in practice it only replaces an addition of free form deformations by a few compositions. Our experiments show that in addition to being diffeomorphic, our algorithm provides results that are similar to the ones from the demons algorithm but with transformations that are much smoother and closer to the true ones in terms of Jacobians.
Non-parametric transient classification using adaptive wavelets
NASA Astrophysics Data System (ADS)
Varughese, Melvin M.; von Sachs, Rainer; Stephanou, Michael; Bassett, Bruce A.
2015-11-01
Classifying transients based on multiband light curves is a challenging but crucial problem in the era of GAIA and Large Synoptic Sky Telescope since the sheer volume of transients will make spectroscopic classification unfeasible. We present a non-parametric classifier that predicts the transient's class given training data. It implements two novel components: the use of the BAGIDIS wavelet methodology - a characterization of functional data using hierarchical wavelet coefficients - as well as the introduction of a ranked probability classifier on the wavelet coefficients that handles both the heteroscedasticity of the data in addition to the potential non-representativity of the training set. The classifier is simple to implement while a major advantage of the BAGIDIS wavelets is that they are translation invariant. Hence, BAGIDIS does not need the light curves to be aligned to extract features. Further, BAGIDIS is non-parametric so it can be used effectively in blind searches for new objects. We demonstrate the effectiveness of our classifier against the Supernova Photometric Classification Challenge to correctly classify supernova light curves as Type Ia or non-Ia. We train our classifier on the spectroscopically confirmed subsample (which is not representative) and show that it works well for supernova with observed light-curve time spans greater than 100 d (roughly 55 per cent of the data set). For such data, we obtain a Ia efficiency of 80.5 per cent and a purity of 82.4 per cent, yielding a highly competitive challenge score of 0.49. This indicates that our `model-blind' approach may be particularly suitable for the general classification of astronomical transients in the era of large synoptic sky surveys.
Wey, Andrew; Connett, John; Rudser, Kyle
2015-07-01
For estimating conditional survival functions, non-parametric estimators can be preferred to parametric and semi-parametric estimators due to relaxed assumptions that enable robust estimation. Yet, even when misspecified, parametric and semi-parametric estimators can possess better operating characteristics in small sample sizes due to smaller variance than non-parametric estimators. Fundamentally, this is a bias-variance trade-off situation in that the sample size is not large enough to take advantage of the low bias of non-parametric estimation. Stacked survival models estimate an optimally weighted combination of models that can span parametric, semi-parametric, and non-parametric models by minimizing prediction error. An extensive simulation study demonstrates that stacked survival models consistently perform well across a wide range of scenarios by adaptively balancing the strengths and weaknesses of individual candidate survival models. In addition, stacked survival models perform as well as or better than the model selected through cross-validation. Finally, stacked survival models are applied to a well-known German breast cancer study.
Biological Parametric Mapping WITH Robust AND Non-Parametric Statistics
Yang, Xue; Beason-Held, Lori; Resnick, Susan M.; Landman, Bennett A.
2011-01-01
Mapping the quantitative relationship between structure and function in the human brain is an important and challenging problem. Numerous volumetric, surface, regions of interest and voxelwise image processing techniques have been developed to statistically assess potential correlations between imaging and non-imaging metrices. Recently, biological parametric mapping has extended the widely popular statistical parametric mapping approach to enable application of the general linear model to multiple image modalities (both for regressors and regressands) along with scalar valued observations. This approach offers great promise for direct, voxelwise assessment of structural and functional relationships with multiple imaging modalities. However, as presented, the biological parametric mapping approach is not robust to outliers and may lead to invalid inferences (e.g., artifactual low p-values) due to slight mis-registration or variation in anatomy between subjects. To enable widespread application of this approach, we introduce robust regression and non-parametric regression in the neuroimaging context of application of the general linear model. Through simulation and empirical studies, we demonstrate that our robust approach reduces sensitivity to outliers without substantial degradation in power. The robust approach and associated software package provide a reliable way to quantitatively assess voxelwise correlations between structural and functional neuroimaging modalities. PMID:21569856
Biological parametric mapping with robust and non-parametric statistics.
Yang, Xue; Beason-Held, Lori; Resnick, Susan M; Landman, Bennett A
2011-07-15
Mapping the quantitative relationship between structure and function in the human brain is an important and challenging problem. Numerous volumetric, surface, regions of interest and voxelwise image processing techniques have been developed to statistically assess potential correlations between imaging and non-imaging metrices. Recently, biological parametric mapping has extended the widely popular statistical parametric mapping approach to enable application of the general linear model to multiple image modalities (both for regressors and regressands) along with scalar valued observations. This approach offers great promise for direct, voxelwise assessment of structural and functional relationships with multiple imaging modalities. However, as presented, the biological parametric mapping approach is not robust to outliers and may lead to invalid inferences (e.g., artifactual low p-values) due to slight mis-registration or variation in anatomy between subjects. To enable widespread application of this approach, we introduce robust regression and non-parametric regression in the neuroimaging context of application of the general linear model. Through simulation and empirical studies, we demonstrate that our robust approach reduces sensitivity to outliers without substantial degradation in power. The robust approach and associated software package provide a reliable way to quantitatively assess voxelwise correlations between structural and functional neuroimaging modalities. Copyright © 2011 Elsevier Inc. All rights reserved.
Diffeomorphic demons: efficient non-parametric image registration.
Vercauteren, Tom; Pennec, Xavier; Perchant, Aymeric; Ayache, Nicholas
2009-03-01
We propose an efficient non-parametric diffeomorphic image registration algorithm based on Thirion's demons algorithm. In the first part of this paper, we show that Thirion's demons algorithm can be seen as an optimization procedure on the entire space of displacement fields. We provide strong theoretical roots to the different variants of Thirion's demons algorithm. This analysis predicts a theoretical advantage for the symmetric forces variant of the demons algorithm. We show on controlled experiments that this advantage is confirmed in practice and yields a faster convergence. In the second part of this paper, we adapt the optimization procedure underlying the demons algorithm to a space of diffeomorphic transformations. In contrast to many diffeomorphic registration algorithms, our solution is computationally efficient since in practice it only replaces an addition of displacement fields by a few compositions. Our experiments show that in addition to being diffeomorphic, our algorithm provides results that are similar to the ones from the demons algorithm but with transformations that are much smoother and closer to the gold standard, available in controlled experiments, in terms of Jacobians.
A Non-parametric Bayesian Approach for Predicting RNA Secondary Structures
NASA Astrophysics Data System (ADS)
Sato, Kengo; Hamada, Michiaki; Mituyama, Toutai; Asai, Kiyoshi; Sakakibara, Yasubumi
Since many functional RNAs form stable secondary structures which are related to their functions, RNA secondary structure prediction is a crucial problem in bioinformatics. We propose a novel model for generating RNA secondary structures based on a non-parametric Bayesian approach, called hierarchical Dirichlet processes for stochastic context-free grammars (HDP-SCFGs). Here non-parametric means that some meta-parameters, such as the number of non-terminal symbols and production rules, do not have to be fixed. Instead their distributions are inferred in order to be adapted (in the Bayesian sense) to the training sequences provided. The results of our RNA secondary structure predictions show that HDP-SCFGs are more accurate than the MFE-based and other generative models.
kdetrees: non-parametric estimation of phylogenetic tree distributions
Weyenberg, Grady; Huggins, Peter M.; Schardl, Christopher L.; Howe, Daniel K.; Yoshida, Ruriko
2014-01-01
Motivation: Although the majority of gene histories found in a clade of organisms are expected to be generated by a common process (e.g. the coalescent process), it is well known that numerous other coexisting processes (e.g. horizontal gene transfers, gene duplication and subsequent neofunctionalization) will cause some genes to exhibit a history distinct from those of the majority of genes. Such ‘outlying’ gene trees are considered to be biologically interesting, and identifying these genes has become an important problem in phylogenetics. Results: We propose and implement kdetrees, a non-parametric method for estimating distributions of phylogenetic trees, with the goal of identifying trees that are significantly different from the rest of the trees in the sample. Our method compares favorably with a similar recently published method, featuring an improvement of one polynomial order of computational complexity (to quadratic in the number of trees analyzed), with simulation studies suggesting only a small penalty to classification accuracy. Application of kdetrees to a set of Apicomplexa genes identified several unreliable sequence alignments that had escaped previous detection, as well as a gene independently reported as a possible case of horizontal gene transfer. We also analyze a set of Epichloë genes, fungi symbiotic with grasses, successfully identifying a contrived instance of paralogy. Availability and implementation: Our method for estimating tree distributions and identifying outlying trees is implemented as the R package kdetrees and is available for download from CRAN. Contact: ruriko.yoshida@uky.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:24764459
Castellani, Umberto; Cristani, Marco; Combi, Carlo; Murino, Vittorio; Sbarbati, Andrea; Marzola, Pasquina
2008-11-01
This paper presents Visual MRI, an innovative tool for the magnetic resonance imaging (MRI) analysis of tumoral tissues. The main goal of the analysis is to separate each magnetic resonance image in meaningful clusters, highlighting zones which are more probably related with the cancer evolution. Such non-invasive analysis serves to address novel cancer treatments, resulting in a less destabilizing and more effective type of therapy than the chemotherapy-based ones. The advancements brought by Visual MRI are two: first, it is an integration of effective information visualization (IV) techniques into a clustering framework, which separates each MRI image in a set of informative clusters; the second improvement relies in the clustering framework itself, which is derived from a recently re-discovered non-parametric grouping strategy, i.e., the mean shift. The proposed methodology merges visualization methods and data mining techniques, providing a computational framework that allows the physician to move effectively from the MRI image to the images displaying the derived parameter space. An unsupervised non-parametric clustering algorithm, derived from the mean shift paradigm, and called MRI-mean shift, is the novel data mining technique proposed here. The main underlying idea of such approach is that the parameter space is regarded as an empirical probability density function to estimate: the possible separate modes and their attraction basins represent separated clusters. The mean shift algorithm needs sensibility threshold values to be set, which could lead to highly different segmentation results. Usually, these values are set by hands. Here, with the MRI-mean shift algorithm, we propose a strategy based on a structured optimality criterion which faces effectively this issue, resulting in a completely unsupervised clustering framework. A linked brushing visualization technique is then used for representing clusters on the parameter space and on the MRI image
Unifying framework for decomposition models of parametric and non-parametric image registration
NASA Astrophysics Data System (ADS)
Ibrahim, Mazlinda; Chen, Ke
2017-08-01
Image registration aims to find spatial transformations such that the so-called given template image becomes similar in some sense to the reference image. Methods in image registration can be divided into two classes (parametric or non-parametric) based on the degree of freedom of the given method. In parametric image registration, the transformation is governed by a finite set of image features or by expanding the transformation in terms of basis functions. Meanwhile, in non-parametric image registration, the problem is modelled as a functional minimisation problem via the calculus of variations. In this paper, we provide a unifying framework for decomposition models for image registration which combine parametric and non-parametric models. Several variants of the models are presented with focus on the affine, diffusion and linear curvature models. An effective numerical solver is provided for the models as well as experimental results to show the effectiveness, robustness and accuracy of the models. The decomposition model of affine and linear curvature outperforms the competing models based on tested images.
Bayesian non parametric modelling of Higgs pair production
NASA Astrophysics Data System (ADS)
Scarpa, Bruno; Dorigo, Tommaso
2017-03-01
Statistical classification models are commonly used to separate a signal from a background. In this talk we face the problem of isolating the signal of Higgs pair production using the decay channel in which each boson decays into a pair of b-quarks. Typically in this context non parametric methods are used, such as Random Forests or different types of boosting tools. We remain in the same non-parametric framework, but we propose to face the problem following a Bayesian approach. A Dirichlet process is used as prior for the random effects in a logit model which is fitted by leveraging the Polya-Gamma data augmentation. Refinements of the model include the insertion in the simple model of P-splines to relate explanatory variables with the response and the use of Bayesian trees (BART) to describe the atoms in the Dirichlet process.
Savitsky, Terrance D; Paddock, Susan M
2014-03-01
We introduce growcurves for R that performs analysis of repeated measures multiple membership (MM) data. This data structure arises in studies under which an intervention is delivered to each subject through the subject's participation in a set of multiple elements that characterize the intervention. In our motivating study design under which subjects receive a group cognitive behavioral therapy (CBT) treatment, an element is a group CBT session and each subject attends multiple sessions that, together, comprise the treatment. The sets of elements, or group CBT sessions, attended by subjects will partly overlap with some of those from other subjects to induce a dependence in their responses. The growcurves package offers two alternative sets of hierarchical models: 1. Separate terms are specified for multivariate subject and MM element random effects, where the subject effects are modeled under a Dirichlet process prior to produce a semi-parametric construction; 2. A single term is employed to model joint subject-by-MM effects. A fully non-parametric dependent Dirichlet process formulation allows exploration of differences in subject responses across different MM elements. This model allows for borrowing information among subjects who express similar longitudinal trajectories for flexible estimation. growcurves deploys "estimation" functions to perform posterior sampling under a suite of prior options. An accompanying set of "plot" functions allow the user to readily extract by-subject growth curves. The design approach intends to anticipate inferential goals with tools that fully extract information from repeated measures data. Computational efficiency is achieved by performing the sampling for estimation functions using compiled C++.
Parametric vs. non-parametric statistics of low resolution electromagnetic tomography (LORETA).
Thatcher, R W; North, D; Biver, C
2005-01-01
This study compared the relative statistical sensitivity of non-parametric and parametric statistics of 3-dimensional current sources as estimated by the EEG inverse solution Low Resolution Electromagnetic Tomography (LORETA). One would expect approximately 5% false positives (classification of a normal as abnormal) at the P < .025 level of probability (two tailed test) and approximately 1% false positives at the P < .005 level. EEG digital samples (2 second intervals sampled 128 Hz, 1 to 2 minutes eyes closed) from 43 normal adult subjects were imported into the Key Institute's LORETA program. We then used the Key Institute's cross-spectrum and the Key Institute's LORETA output files (*.lor) as the 2,394 gray matter pixel representation of 3-dimensional currents at different frequencies. The mean and standard deviation *.lor files were computed for each of the 2,394 gray matter pixels for each of the 43 subjects. Tests of Gaussianity and different transforms were computed in order to best approximate a normal distribution for each frequency and gray matter pixel. The relative sensitivity of parametric vs. non-parametric statistics were compared using a "leave-one-out" cross validation method in which individual normal subjects were withdrawn and then statistically classified as being either normal or abnormal based on the remaining subjects. Log10 transforms approximated Gaussian distribution in the range of 95% to 99% accuracy. Parametric Z score tests at P < .05 cross-validation demonstrated an average misclassification rate of approximately 4.25%, and range over the 2,394 gray matter pixels was 27.66% to 0.11%. At P < .01 parametric Z score cross-validation false positives were 0.26% and ranged from 6.65% to 0% false positives. The non-parametric Key Institute's t-max statistic at P < .05 had an average misclassification error rate of 7.64% and ranged from 43.37% to 0.04% false positives. The nonparametric t-max at P < .01 had an average misclassification rate
Martinez Manzanera, Octavio; Elting, Jan Willem; van der Hoeven, Johannes H.; Maurits, Natasha M.
2016-01-01
In the clinic, tremor is diagnosed during a time-limited process in which patients are observed and the characteristics of tremor are visually assessed. For some tremor disorders, a more detailed analysis of these characteristics is needed. Accelerometry and electromyography can be used to obtain a better insight into tremor. Typically, routine clinical assessment of accelerometry and electromyography data involves visual inspection by clinicians and occasionally computational analysis to obtain objective characteristics of tremor. However, for some tremor disorders these characteristics may be different during daily activity. This variability in presentation between the clinic and daily life makes a differential diagnosis more difficult. A long-term recording of tremor by accelerometry and/or electromyography in the home environment could help to give a better insight into the tremor disorder. However, an evaluation of such recordings using routine clinical standards would take too much time. We evaluated a range of techniques that automatically detect tremor segments in accelerometer data, as accelerometer data is more easily obtained in the home environment than electromyography data. Time can be saved if clinicians only have to evaluate the tremor characteristics of segments that have been automatically detected in longer daily activity recordings. We tested four non-parametric methods and five parametric methods on clinical accelerometer data from 14 patients with different tremor disorders. The consensus between two clinicians regarding the presence or absence of tremor on 3943 segments of accelerometer data was employed as reference. The nine methods were tested against this reference to identify their optimal parameters. Non-parametric methods generally performed better than parametric methods on our dataset when optimal parameters were used. However, one parametric method, employing the high frequency content of the tremor bandwidth under consideration
Bahrami, Sheyda; Shamsi, Mousa
2017-01-01
Functional magnetic resonance imaging (fMRI) is a popular method to probe the functional organization of the brain using hemodynamic responses. In this method, volume images of the entire brain are obtained with a very good spatial resolution and low temporal resolution. However, they always suffer from high dimensionality in the face of classification algorithms. In this work, we combine a support vector machine (SVM) with a self-organizing map (SOM) for having a feature-based classification by using SVM. Then, a linear kernel SVM is used for detecting the active areas. Here, we use SOM for feature extracting and labeling the datasets. SOM has two major advances: (i) it reduces dimension of data sets for having less computational complexity and (ii) it is useful for identifying brain regions with small onset differences in hemodynamic responses. Our non-parametric model is compared with parametric and non-parametric methods. We use simulated fMRI data sets and block design inputs in this paper and consider the contrast to noise ratio (CNR) value equal to 0.6 for simulated datasets. fMRI simulated dataset has contrast 1–4% in active areas. The accuracy of our proposed method is 93.63% and the error rate is 6.37%. PMID:28840116
Bahrami, Sheyda; Shamsi, Mousa
2017-01-01
Functional magnetic resonance imaging (fMRI) is a popular method to probe the functional organization of the brain using hemodynamic responses. In this method, volume images of the entire brain are obtained with a very good spatial resolution and low temporal resolution. However, they always suffer from high dimensionality in the face of classification algorithms. In this work, we combine a support vector machine (SVM) with a self-organizing map (SOM) for having a feature-based classification by using SVM. Then, a linear kernel SVM is used for detecting the active areas. Here, we use SOM for feature extracting and labeling the datasets. SOM has two major advances: (i) it reduces dimension of data sets for having less computational complexity and (ii) it is useful for identifying brain regions with small onset differences in hemodynamic responses. Our non-parametric model is compared with parametric and non-parametric methods. We use simulated fMRI data sets and block design inputs in this paper and consider the contrast to noise ratio (CNR) value equal to 0.6 for simulated datasets. fMRI simulated dataset has contrast 1-4% in active areas. The accuracy of our proposed method is 93.63% and the error rate is 6.37%.
Locally-Based Kernal PLS Smoothing to Non-Parametric Regression Curve Fitting
NASA Technical Reports Server (NTRS)
Rosipal, Roman; Trejo, Leonard J.; Wheeler, Kevin; Korsmeyer, David (Technical Monitor)
2002-01-01
We present a novel smoothing approach to non-parametric regression curve fitting. This is based on kernel partial least squares (PLS) regression in reproducing kernel Hilbert space. It is our concern to apply the methodology for smoothing experimental data where some level of knowledge about the approximate shape, local inhomogeneities or points where the desired function changes its curvature is known a priori or can be derived based on the observed noisy data. We propose locally-based kernel PLS regression that extends the previous kernel PLS methodology by incorporating this knowledge. We compare our approach with existing smoothing splines, hybrid adaptive splines and wavelet shrinkage techniques on two generated data sets.
Non-Parametric Collision Probability for Low-Velocity Encounters
NASA Technical Reports Server (NTRS)
Carpenter, J. Russell
2007-01-01
An implicit, but not necessarily obvious, assumption in all of the current techniques for assessing satellite collision probability is that the relative position uncertainty is perfectly correlated in time. If there is any mis-modeling of the dynamics in the propagation of the relative position error covariance matrix, time-wise de-correlation of the uncertainty will increase the probability of collision over a given time interval. The paper gives some examples that illustrate this point. This paper argues that, for the present, Monte Carlo analysis is the best available tool for handling low-velocity encounters, and suggests some techniques for addressing the issues just described. One proposal is for the use of a non-parametric technique that is widely used in actuarial and medical studies. The other suggestion is that accurate process noise models be used in the Monte Carlo trials to which the non-parametric estimate is applied. A further contribution of this paper is a description of how the time-wise decorrelation of uncertainty increases the probability of collision.
Non-Parametric Bayesian Registration (NParBR) of Body Tumors in DCE-MRI Data.
Pilutti, David; Strumia, Maddalena; Buchert, Martin; Hadjidemetriou, Stathis
2016-04-01
The identification of tumors in the internal organs of chest, abdomen, and pelvis anatomic regions can be performed with the analysis of Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) data. The contrast agent is accumulated differently by pathologic and healthy tissues and that results in a temporally varying contrast in an image series. The internal organs are also subject to potentially extensive movements mainly due to breathing, heart beat, and peristalsis. This contributes to making the analysis of DCE-MRI datasets challenging as well as time consuming. To address this problem we propose a novel pairwise non-rigid registration method with a Non-Parametric Bayesian Registration (NParBR) formulation. The NParBR method uses a Bayesian formulation that assumes a model for the effect of the distortion on the joint intensity statistics, a non-parametric prior for the restored statistics, and also applies a spatial regularization for the estimated registration with Gaussian filtering. A minimally biased intra-dataset atlas is computed for each dataset and used as reference for the registration of the time series. The time series registration method has been tested with 20 datasets of liver, lungs, intestines, and prostate. It has been compared to the B-Splines and to the SyN methods with results that demonstrate that the proposed method improves both accuracy and efficiency.
NASA Astrophysics Data System (ADS)
Yasuda, Muneki; Kataoka, Shun
2017-08-01
In this paper, we address the inverse problem, or the statistical machine learning problem, in Markov random fields with a non-parametric pair-wise energy function with continuous variables. The inverse problem is formulated by maximum likelihood estimation. The exact treatment of maximum likelihood estimation is intractable because of two problems: (1) it includes the evaluation of the partition function and (2) it is formulated in the form of functional optimization. We avoid Problem (1) by using Bethe approximation. Bethe approximation is an approximation technique equivalent to the loopy belief propagation. Problem (2) can be solved by using orthonormal function expansion. Orthonormal function expansion can reduce a functional optimization problem to a function optimization problem. Our method can provide an analytic form of the solution of the inverse problem within the framework of Bethe approximation as a result of variational optimization.
Landmark Constrained Non-parametric Image Registration with Isotropic Tolerances
NASA Astrophysics Data System (ADS)
Papenberg, Nils; Olesch, Janine; Lange, Thomas; Schlag, Peter M.; Fischer, Bernd
The incorporation of additional user knowledge into a nonrigid registration process is a promising topic in modern registration schemes. The combination of intensity based registration and some interactively chosen landmark pairs is a major approach in this direction. There exist different possibilities to incorporate landmark pairs into a variational non-parametric registration framework. As the interactive localization of point landmarks is always prone to errors, a demand for precise landmark matching is bound to fail. Here, the treatment of the distances of corresponding landmarks as penalties within a constrained optimization problem offers the possibility to control the quality of the matching of each landmark pair individually. More precisely, we introduce inequality constraints, which allow for a sphere-like tolerance around each landmark. We illustrate the performance of this new approach for artificial 2D images as well as for the challenging registration of preoperative CT data to intra-operative 3D ultrasound data of the liver.
Non-parametric estimation of spatial variation in relative risk.
Kelsall, J E; Diggle, P J
We consider the problem of estimating the spatial variation in relative risks of two diseases, say, over a geographical region. Using an underlying Poisson point process model, we approach the problem as one of density ratio estimation implemented with a non-parametric kernel smoothing method. In order to assess the significance of any local peaks or troughs in the estimated risk surface, we introduce pointwise tolerance contours which can enhance a greyscale image plot of the estimate. We also propose a Monte Carlo test of the null hypothesis of constant risk over the whole region, to avoid possible over-interpretation of the estimated risk surface. We illustrate the capabilities of the methodology with two epidemiological examples.
Binary Classifier Calibration Using a Bayesian Non-Parametric Approach.
Naeini, Mahdi Pakdaman; Cooper, Gregory F; Hauskrecht, Milos
Learning probabilistic predictive models that are well calibrated is critical for many prediction and decision-making tasks in Data mining. This paper presents two new non-parametric methods for calibrating outputs of binary classification models: a method based on the Bayes optimal selection and a method based on the Bayesian model averaging. The advantage of these methods is that they are independent of the algorithm used to learn a predictive model, and they can be applied in a post-processing step, after the model is learned. This makes them applicable to a wide variety of machine learning models and methods. These calibration methods, as well as other methods, are tested on a variety of datasets in terms of both discrimination and calibration performance. The results show the methods either outperform or are comparable in performance to the state-of-the-art calibration methods.
A Bayesian non-parametric Potts model with application to pre-surgical FMRI data.
Johnson, Timothy D; Liu, Zhuqing; Bartsch, Andreas J; Nichols, Thomas E
2013-08-01
The Potts model has enjoyed much success as a prior model for image segmentation. Given the individual classes in the model, the data are typically modeled as Gaussian random variates or as random variates from some other parametric distribution. In this article, we present a non-parametric Potts model and apply it to a functional magnetic resonance imaging study for the pre-surgical assessment of peritumoral brain activation. In our model, we assume that the Z-score image from a patient can be segmented into activated, deactivated, and null classes, or states. Conditional on the class, or state, the Z-scores are assumed to come from some generic distribution which we model non-parametrically using a mixture of Dirichlet process priors within the Bayesian framework. The posterior distribution of the model parameters is estimated with a Markov chain Monte Carlo algorithm, and Bayesian decision theory is used to make the final classifications. Our Potts prior model includes two parameters, the standard spatial regularization parameter and a parameter that can be interpreted as the a priori probability that each voxel belongs to the null, or background state, conditional on the lack of spatial regularization. We assume that both of these parameters are unknown, and jointly estimate them along with other model parameters. We show through simulation studies that our model performs on par, in terms of posterior expected loss, with parametric Potts models when the parametric model is correctly specified and outperforms parametric models when the parametric model in misspecified.
Kalicka, Renata; Pietrenko-Dabrowska, Anna
2007-03-01
In the paper MRI measurements are used for assessment of brain tissue perfusion and other features and functions of the brain (cerebral blood flow - CBF, cerebral blood volume - CBV, mean transit time - MTT). Perfusion is an important indicator of tissue viability and functioning as in pathological tissue blood flow, vascular and tissue structure are altered with respect to normal tissue. MRI enables diagnosing diseases at an early stage of their course. The parametric and non-parametric approaches to the identification of MRI models are presented and compared. The non-parametric modeling adopts gamma variate functions. The parametric three-compartmental catenary model, based on the general kinetic model, is also proposed. The parameters of the models are estimated on the basis of experimental data. The goodness of fit of the gamma variate and the three-compartmental models to the data and the accuracy of the parameter estimates are compared. Kalman filtering, smoothing the measurements, was adopted to improve the estimate accuracy of the parametric model. Parametric modeling gives a better fit and better parameter estimates than non-parametric and allows an insight into the functioning of the system. To improve the accuracy optimal experiment design related to the input signal was performed.
Bootstrap Prediction Intervals in Non-Parametric Regression with Applications to Anomaly Detection
NASA Technical Reports Server (NTRS)
Kumar, Sricharan; Srivistava, Ashok N.
2012-01-01
Prediction intervals provide a measure of the probable interval in which the outputs of a regression model can be expected to occur. Subsequently, these prediction intervals can be used to determine if the observed output is anomalous or not, conditioned on the input. In this paper, a procedure for determining prediction intervals for outputs of nonparametric regression models using bootstrap methods is proposed. Bootstrap methods allow for a non-parametric approach to computing prediction intervals with no specific assumptions about the sampling distribution of the noise or the data. The asymptotic fidelity of the proposed prediction intervals is theoretically proved. Subsequently, the validity of the bootstrap based prediction intervals is illustrated via simulations. Finally, the bootstrap prediction intervals are applied to the problem of anomaly detection on aviation data.
Non-parametric Algorithm to Isolate Chunks in Response Sequences
Alamia, Andrea; Solopchuk, Oleg; Olivier, Etienne; Zenon, Alexandre
2016-01-01
Chunking consists in grouping items of a sequence into small clusters, named chunks, with the assumed goal of lessening working memory load. Despite extensive research, the current methods used to detect chunks, and to identify different chunking strategies, remain discordant and difficult to implement. Here, we propose a simple and reliable method to identify chunks in a sequence and to determine their stability across blocks. This algorithm is based on a ranking method and its major novelty is that it provides concomitantly both the features of individual chunk in a given sequence, and an overall index that quantifies the chunking pattern consistency across sequences. The analysis of simulated data confirmed the validity of our method in different conditions of noise, chunk lengths and chunk numbers; moreover, we found that this algorithm was particularly efficient in the noise range observed in real data, provided that at least 4 sequence repetitions were included in each experimental block. Furthermore, we applied this algorithm to actual reaction time series gathered from 3 published experiments and were able to confirm the findings obtained in the original reports. In conclusion, this novel algorithm is easy to implement, is robust to outliers and provides concurrent and reliable estimation of chunk position and chunking dynamics, making it useful to study both sequence-specific and general chunking effects. The algorithm is available at: https://github.com/artipago/Non-parametric-algorithm-to-isolate-chunks-in-response-sequences. PMID:27708565
Non-parametric and least squares Langley plot methods
NASA Astrophysics Data System (ADS)
Kiedron, P. W.; Michalsky, J. J.
2015-04-01
Langley plots are used to calibrate sun radiometers primarily for the measurement of the aerosol component of the atmosphere that attenuates (scatters and absorbs) incoming direct solar radiation. In principle, the calibration of a sun radiometer is a straightforward application of the Bouguer-Lambert-Beer law V=V>/i>0e-τ ·m, where a plot of ln (V) voltage vs. m air mass yields a straight line with intercept ln (V0). This ln (V0) subsequently can be used to solve for τ for any measurement of V and calculation of m. This calibration works well on some high mountain sites, but the application of the Langley plot calibration technique is more complicated at other, more interesting, locales. This paper is concerned with ferreting out calibrations at difficult sites and examining and comparing a number of conventional and non-conventional methods for obtaining successful Langley plots. The eleven techniques discussed indicate that both least squares and various non-parametric techniques produce satisfactory calibrations with no significant differences among them when the time series of ln (V0)'s are smoothed and interpolated with median and mean moving window filters.
Non-parametric and least squares Langley plot methods
NASA Astrophysics Data System (ADS)
Kiedron, P. W.; Michalsky, J. J.
2016-01-01
Langley plots are used to calibrate sun radiometers primarily for the measurement of the aerosol component of the atmosphere that attenuates (scatters and absorbs) incoming direct solar radiation. In principle, the calibration of a sun radiometer is a straightforward application of the Bouguer-Lambert-Beer law V = V0e-τ ṡ m, where a plot of ln(V) voltage vs. m air mass yields a straight line with intercept ln(V0). This ln(V0) subsequently can be used to solve for τ for any measurement of V and calculation of m. This calibration works well on some high mountain sites, but the application of the Langley plot calibration technique is more complicated at other, more interesting, locales. This paper is concerned with ferreting out calibrations at difficult sites and examining and comparing a number of conventional and non-conventional methods for obtaining successful Langley plots. The 11 techniques discussed indicate that both least squares and various non-parametric techniques produce satisfactory calibrations with no significant differences among them when the time series of ln(V0)'s are smoothed and interpolated with median and mean moving window filters.
Non-parametric reconstruction of cosmological matter perturbations
González, J.E.; Alcaniz, J.S.; Carvalho, J.C. E-mail: alcaniz@on.br
2016-04-01
Perturbative quantities, such as the growth rate (f) and index (γ), are powerful tools to distinguish different dark energy models or modified gravity theories even if they produce the same cosmic expansion history. In this work, without any assumption about the dynamics of the Universe, we apply a non-parametric method to current measurements of the expansion rate H(z) from cosmic chronometers and high-z quasar data and reconstruct the growth factor and rate of linearised density perturbations in the non-relativistic matter component. Assuming realistic values for the matter density parameter Ω{sub m0}, as provided by current CMB experiments, we also reconstruct the evolution of the growth index γ with redshift. We show that the reconstruction of current H(z) data constrains the growth index to γ=0.56 ± 0.12 (2σ) at z = 0.09, which is in full agreement with the prediction of the ΛCDM model and some of its extensions.
Song, Dong; Wang, Zhuo; Marmarelis, Vasilis Z; Berger, Theodore W
2009-02-01
This paper presents a synergistic parametric and non-parametric modeling study of short-term plasticity (STP) in the Schaffer collateral to hippocampal CA1 pyramidal neuron (SC) synapse. Parametric models in the form of sets of differential and algebraic equations have been proposed on the basis of the current understanding of biological mechanisms active within the system. Non-parametric Poisson-Volterra models are obtained herein from broadband experimental input-output data. The non-parametric model is shown to provide better prediction of the experimental output than a parametric model with a single set of facilitation/depression (FD) process. The parametric model is then validated in terms of its input-output transformational properties using the non-parametric model since the latter constitutes a canonical and more complete representation of the synaptic nonlinear dynamics. Furthermore, discrepancies between the experimentally-derived non-parametric model and the equivalent non-parametric model of the parametric model suggest the presence of multiple FD processes in the SC synapses. Inclusion of an additional set of FD process in the parametric model makes it replicate better the characteristics of the experimentally-derived non-parametric model. This improved parametric model in turn provides the requisite biological interpretability that the non-parametric model lacks.
NASA Astrophysics Data System (ADS)
Takara, K. T.
2015-12-01
This paper describes a non-parametric frequency analysis method for hydrological extreme-value samples with a size larger than 100, verifying the estimation accuracy with a computer intensive statistics (CIS) resampling such as the bootstrap. Probable maximum values are also incorporated into the analysis for extreme events larger than a design level of flood control. Traditional parametric frequency analysis methods of extreme values include the following steps: Step 1: Collecting and checking extreme-value data; Step 2: Enumerating probability distributions that would be fitted well to the data; Step 3: Parameter estimation; Step 4: Testing goodness of fit; Step 5: Checking the variability of quantile (T-year event) estimates by the jackknife resampling method; and Step_6: Selection of the best distribution (final model). The non-parametric method (NPM) proposed here can skip Steps 2, 3, 4 and 6. Comparing traditional parameter methods (PM) with the NPM, this paper shows that PM often underestimates 100-year quantiles for annual maximum rainfall samples with records of more than 100 years. Overestimation examples are also demonstrated. The bootstrap resampling can do bias correction for the NPM and can also give the estimation accuracy as the bootstrap standard error. This NPM has advantages to avoid various difficulties in above-mentioned steps in the traditional PM. Probable maximum events are also incorporated into the NPM as an upper bound of the hydrological variable. Probable maximum precipitation (PMP) and probable maximum flood (PMF) can be a new parameter value combined with the NPM. An idea how to incorporate these values into frequency analysis is proposed for better management of disasters that exceed the design level. The idea stimulates more integrated approach by geoscientists and statisticians as well as encourages practitioners to consider the worst cases of disasters in their disaster management planning and practices.
NASA Astrophysics Data System (ADS)
Matos, José P.; Schaefli, Bettina; Schleiss, Anton J.
2017-04-01
Uncertainty affects hydrological modelling efforts from the very measurements (or forecasts) that serve as inputs to the more or less inaccurate predictions that are produced. Uncertainty is truly inescapable in hydrology and yet, due to the theoretical and technical hurdles associated with its quantification, it is at times still neglected or estimated only qualitatively. In recent years the scientific community has made a significant effort towards quantifying this hydrologic prediction uncertainty. Despite this, most of the developed methodologies can be computationally demanding, are complex from a theoretical point of view, require substantial expertise to be employed, and are constrained by a number of assumptions about the model error distribution. These assumptions limit the reliability of many methods in case of errors that show particular cases of non-normality, heteroscedasticity, or autocorrelation. The present contribution builds on a non-parametric data-driven approach that was developed for uncertainty quantification in operational (real-time) forecasting settings. The approach is based on the concept of Pareto optimality and can be used as a standalone forecasting tool or as a postprocessor. By virtue of its non-parametric nature and a general operating principle, it can be applied directly and with ease to predictions of streamflow, water stage, or even accumulated runoff. Also, it is a methodology capable of coping with high heteroscedasticity and seasonal hydrological regimes (e.g. snowmelt and rainfall driven events in the same catchment). Finally, the training and operation of the model are very fast, making it a tool particularly adapted to operational use. To illustrate its practical use, the uncertainty quantification method is coupled with a process-based hydrological model to produce statistically reliable forecasts for an Alpine catchment located in Switzerland. Results are presented and discussed in terms of their reliability and
A non-parametric consistency test of the ΛCDM model with Planck CMB data
NASA Astrophysics Data System (ADS)
Aghamousa, Amir; Hamann, Jan; Shafieloo, Arman
2017-09-01
Non-parametric reconstruction methods, such as Gaussian process (GP) regression, provide a model-independent way of estimating an underlying function and its uncertainty from noisy data. We demonstrate how GP-reconstruction can be used as a consistency test between a given data set and a specific model by looking for structures in the residuals of the data with respect to the model's best-fit. Applying this formalism to the Planck temperature and polarisation power spectrum measurements, we test their global consistency with the predictions of the base ΛCDM model. Our results do not show any serious inconsistencies, lending further support to the interpretation of the base ΛCDM model as cosmology's gold standard.
Parametric and non-parametric estimation of speech formants: application to infant cry.
Fort, A; Ismaelli, A; Manfredi, C; Bruscaglioni, P
1996-12-01
The present paper addresses the issue of correctly estimating the peaks in the speech envelope (formants) occurring in newborn infant cry. Clinical studies have shown that the analysis of such spectral characteristics is a helpful noninvasive diagnostic tool. In fact it can be applied to explore brain function at very early stage of child development, for a timely diagnosis of neonatal disease and malformation. The paper focuses on the performance comparison between some classical parametric and non-parametric estimation techniques particularly well suited for the present application, specifically the LP, ARX and cepstrum approaches. It is shown that, if the model order is correctly chosen, parametric methods are in general more reliable and robust against noise, but exhibit a less uniform behaviour than cepstrum. The methods are compared also in terms of tracking capability, since the signals under study are nonstationary. Both simulated and real signals are used in order to outline the relevant features of the proposed approaches.
A non-parametric model for the cosmic velocity field
NASA Astrophysics Data System (ADS)
Branchini, E.; Teodoro, L.; Frenk, C. S.; Schmoldt, I.; Efstathiou, G.; White, S. D. M.; Saunders, W.; Sutherland, W.; Rowan-Robinson, M.; Keeble, O.; Tadros, H.; Maddox, S.; Oliver, S.
1999-09-01
We present a self-consistent non-parametric model of the local cosmic velocity field derived from the distribution of IRAS galaxies in the PSCz redshift survey. The survey has been analysed using two independent methods, both based on the assumptions of gravitational instability and linear biasing. The two methods, which give very similar results, have been tested and calibrated on mock PSCz catalogues constructed from cosmological N-body simulations. The denser sampling provided by the PSCz survey compared with previous IRAS galaxy surveys allows an improved reconstruction of the density and velocity fields out to large distances. The most striking feature of the model velocity field is a coherent large-scale streaming motion along the baseline connecting Perseus-Pisces, the Local Supercluster, the Great Attractor and the Shapley Concentration. We find no evidence for back-infall on to the Great Attractor. Instead, material behind and around the Great Attractor is inferred to be streaming towards the Shapley Concentration, aided by the compressional push of two large nearby underdensities. The PSCz model velocities compare well with those predicted from the 1.2-Jy redshift survey of IRAS galaxies and, perhaps surprisingly, with those predicted from the distribution of Abell/ACO clusters, out to 140h^-1Mpc. Comparison of the real-space density fields (or, alternatively, the peculiar velocity fields) inferred from the PSCz and cluster catalogues gives a relative (linear) bias parameter between clusters and IRAS galaxies of b_c=4.4+/-0.6. Finally, we implement a likelihood analysis that uses all the available information on peculiar velocities in our local Universe to estimate beta_Omega 0 0.6 b_0.6 -0.15 +0.22 (1sigma), where b is the bias parameter for IRAS galaxies.
Non-parametric PSF estimation from celestial transit solar images using blind deconvolution
NASA Astrophysics Data System (ADS)
González, Adriana; Delouille, Véronique; Jacques, Laurent
2016-01-01
Context: Characterization of instrumental effects in astronomical imaging is important in order to extract accurate physical information from the observations. The measured image in a real optical instrument is usually represented by the convolution of an ideal image with a Point Spread Function (PSF). Additionally, the image acquisition process is also contaminated by other sources of noise (read-out, photon-counting). The problem of estimating both the PSF and a denoised image is called blind deconvolution and is ill-posed. Aims: We propose a blind deconvolution scheme that relies on image regularization. Contrarily to most methods presented in the literature, our method does not assume a parametric model of the PSF and can thus be applied to any telescope. Methods: Our scheme uses a wavelet analysis prior model on the image and weak assumptions on the PSF. We use observations from a celestial transit, where the occulting body can be assumed to be a black disk. These constraints allow us to retain meaningful solutions for the filter and the image, eliminating trivial, translated, and interchanged solutions. Under an additive Gaussian noise assumption, they also enforce noise canceling and avoid reconstruction artifacts by promoting the whiteness of the residual between the blurred observations and the cleaned data. Results: Our method is applied to synthetic and experimental data. The PSF is estimated for the SECCHI/EUVI instrument using the 2007 Lunar transit, and for SDO/AIA using the 2012 Venus transit. Results show that the proposed non-parametric blind deconvolution method is able to estimate the core of the PSF with a similar quality to parametric methods proposed in the literature. We also show that, if these parametric estimations are incorporated in the acquisition model, the resulting PSF outperforms both the parametric and non-parametric methods.
Chan, Kwun Chuen Gary; Yam, Sheung Chi Phillip; Zhang, Zheng
2015-01-01
Summary The estimation of average treatment effects based on observational data is extremely important in practice and has been studied by generations of statisticians under different frameworks. Existing globally efficient estimators require non-parametric estimation of a propensity score function, an outcome regression function or both, but their performance can be poor in practical sample sizes. Without explicitly estimating either functions, we consider a wide class calibration weights constructed to attain an exact three-way balance of the moments of observed covariates among the treated, the control, and the combined group. The wide class includes exponential tilting, empirical likelihood and generalized regression as important special cases, and extends survey calibration estimators to different statistical problems and with important distinctions. Global semiparametric efficiency for the estimation of average treatment effects is established for this general class of calibration estimators. The results show that efficiency can be achieved by solely balancing the covariate distributions without resorting to direct estimation of propensity score or outcome regression function. We also propose a consistent estimator for the efficient asymptotic variance, which does not involve additional functional estimation of either the propensity score or the outcome regression functions. The proposed variance estimator outperforms existing estimators that require a direct approximation of the efficient influence function. PMID:27346982
Chan, Kwun Chuen Gary; Yam, Sheung Chi Phillip; Zhang, Zheng
2016-06-01
The estimation of average treatment effects based on observational data is extremely important in practice and has been studied by generations of statisticians under different frameworks. Existing globally efficient estimators require non-parametric estimation of a propensity score function, an outcome regression function or both, but their performance can be poor in practical sample sizes. Without explicitly estimating either functions, we consider a wide class calibration weights constructed to attain an exact three-way balance of the moments of observed covariates among the treated, the control, and the combined group. The wide class includes exponential tilting, empirical likelihood and generalized regression as important special cases, and extends survey calibration estimators to different statistical problems and with important distinctions. Global semiparametric efficiency for the estimation of average treatment effects is established for this general class of calibration estimators. The results show that efficiency can be achieved by solely balancing the covariate distributions without resorting to direct estimation of propensity score or outcome regression function. We also propose a consistent estimator for the efficient asymptotic variance, which does not involve additional functional estimation of either the propensity score or the outcome regression functions. The proposed variance estimator outperforms existing estimators that require a direct approximation of the efficient influence function.
NASA Astrophysics Data System (ADS)
Verrelst, Jochem; Rivera, Juan Pablo; Veroustraete, Frank; Muñoz-Marí, Jordi; Clevers, Jan G. P. W.; Camps-Valls, Gustau; Moreno, José
2015-10-01
Given the forthcoming availability of Sentinel-2 (S2) images, this paper provides a systematic comparison of retrieval accuracy and processing speed of a multitude of parametric, non-parametric and physically-based retrieval methods using simulated S2 data. An experimental field dataset (SPARC), collected at the agricultural site of Barrax (Spain), was used to evaluate different retrieval methods on their ability to estimate leaf area index (LAI). With regard to parametric methods, all possible band combinations for several two-band and three-band index formulations and a linear regression fitting function have been evaluated. From a set of over ten thousand indices evaluated, the best performing one was an optimized three-band combination according to (ρ560 -ρ1610 -ρ2190) / (ρ560 +ρ1610 +ρ2190) with a 10-fold cross-validation RCV2 of 0.82 (RMSECV : 0.62). This family of methods excel for their fast processing speed, e.g., 0.05 s to calibrate and validate the regression function, and 3.8 s to map a simulated S2 image. With regard to non-parametric methods, 11 machine learning regression algorithms (MLRAs) have been evaluated. This methodological family has the advantage of making use of the full optical spectrum as well as flexible, nonlinear fitting. Particularly kernel-based MLRAs lead to excellent results, with variational heteroscedastic (VH) Gaussian Processes regression (GPR) as the best performing method, with a RCV2 of 0.90 (RMSECV : 0.44). Additionally, the model is trained and validated relatively fast (1.70 s) and the processed image (taking 73.88 s) includes associated uncertainty estimates. More challenging is the inversion of a PROSAIL based radiative transfer model (RTM). After the generation of a look-up table (LUT), a multitude of cost functions and regularization options were evaluated. The best performing cost function is Pearson's χ -square. It led to a R2 of 0.74 (RMSE: 0.80) against the validation dataset. While its validation went fast
NASA Astrophysics Data System (ADS)
Goto, Y.; Kasahara, Y.; Sato, T.
2005-12-01
The Earth's plasmasphere is investigated not only for scientific interests but also for engineering applications since the plasmaspheric plasma cannot be ignored for high-precision navigation and positioning from artificial satellites. The electron density in the plasmasphere is generally observed from spacecraft because the ionosphere disturbs us from direct observations from the ground. In the present study, we introduce an estimation method of the plasmaspheric electron density profile using whistler waves which are one of the most familiar VLF waves observed from satellites in the plasmasphere. While the propagation characteristics of ducted whistlers were used to acquire the signature of the plasmasphere, those of non-ducted ones were rarely used because of their complexity. The propagation characteristics of non-ducted whistlers cannot be calculated analytically but numerically. Recent advancement of computer technology made it possible to trace a few million of ray paths in a short time, and the initial ray parameters at wave sources are easily translated into those at observation points, which are a simple mapping. The estimation method is based on a model fitting in which an non-parametric model is used to represent the electron density profile like computer tomography in order not to deform the information of observed wave data. The wave normal directions and the spectrums of whistlers can be theoretically calculated for a given electron density profile by ray tracing. Comparing these theoretical values with observed ones, an electron density profile which is consistent to a given wave parameter set is obtained.
A non-parametric method for building predictive genetic tests on high-dimensional data.
Ye, Chengyin; Cui, Yuehua; Wei, Changshuai; Elston, Robert C; Zhu, Jun; Lu, Qing
2011-01-01
Predictive tests that capitalize on emerging genetic findings hold great promise for enhanced personalized healthcare. With the emergence of a large amount of data from genome-wide association studies (GWAS), interest has shifted towards high-dimensional risk prediction. To form predictive genetic tests on high-dimensional data, we propose a non-parametric method, called the 'forward ROC method'. The method adopts a computationally efficient algorithm to search for environment risk factors, genetic predictors on the entire genome, and their possible interactions for an optimal risk prediction model, without relying on prior knowledge of known risk factors. An efficient yet powerful procedure is also incorporated into the method to handle missing data. Through simulations and real data applications, we found our proposed method outperformed the existing approaches. We applied the new method to the Wellcome Trust rheumatoid arthritis GWAS dataset with a total of 460,547 markers. The results from the risk prediction analysis suggested important roles of HLA-DRB1 and PTPN22 in predicting rheumatoid arthritis. We proposed a powerful and robust approach for high-dimensional risk prediction. The new method will facilitate future risk prediction that considers a large number of predictors and their interaction for improved performance. Copyright © 2011 S. Karger AG, Basel.
Non-parametric 3D map of the intergalactic medium using the Lyman-alpha forest
NASA Astrophysics Data System (ADS)
Cisewski, Jessi; Croft, Rupert A. C.; Freeman, Peter E.; Genovese, Christopher R.; Khandai, Nishikanta; Ozbek, Melih; Wasserman, Larry
2014-05-01
Visualizing the high-redshift Universe is difficult due to the dearth of available data; however, the Lyman-alpha forest provides a means to map the intergalactic medium at redshifts not accessible to large galaxy surveys. Large-scale structure surveys, such as the Baryon Oscillation Spectroscopic Survey (BOSS), have collected quasar (QSO) spectra that enable the reconstruction of H I density fluctuations. The data fall on a collection of lines defined by the lines of sight (LOS) of the QSO, and a major issue with producing a 3D reconstruction is determining how to model the regions between the LOS. We present a method that produces a 3D map of this relatively uncharted portion of the Universe by employing local polynomial smoothing, a non-parametric methodology. The performance of the method is analysed on simulated data that mimics the varying number of LOS expected in real data, and then is applied to a sample region selected from BOSS. Evaluation of the reconstruction is assessed by considering various features of the predicted 3D maps including visual comparison of slices, probability density functions (PDFs), counts of local minima and maxima, and standardized correlation functions. This 3D reconstruction allows for an initial investigation of the topology of this portion of the Universe using persistent homology.
Spectral decompositions of multiple time series: a Bayesian non-parametric approach.
Macaro, Christian; Prado, Raquel
2014-01-01
We consider spectral decompositions of multiple time series that arise in studies where the interest lies in assessing the influence of two or more factors. We write the spectral density of each time series as a sum of the spectral densities associated to the different levels of the factors. We then use Whittle's approximation to the likelihood function and follow a Bayesian non-parametric approach to obtain posterior inference on the spectral densities based on Bernstein-Dirichlet prior distributions. The prior is strategically important as it carries identifiability conditions for the models and allows us to quantify our degree of confidence in such conditions. A Markov chain Monte Carlo (MCMC) algorithm for posterior inference within this class of frequency-domain models is presented.We illustrate the approach by analyzing simulated and real data via spectral one-way and two-way models. In particular, we present an analysis of functional magnetic resonance imaging (fMRI) brain responses measured in individuals who participated in a designed experiment to study pain perception in humans.
Pérez-Rodríguez, Paulino; Gianola, Daniel; González-Camacho, Juan Manuel; Crossa, José; Manès, Yann; Dreisigacker, Susanne
2012-01-01
In genome-enabled prediction, parametric, semi-parametric, and non-parametric regression models have been used. This study assessed the predictive ability of linear and non-linear models using dense molecular markers. The linear models were linear on marker effects and included the Bayesian LASSO, Bayesian ridge regression, Bayes A, and Bayes B. The non-linear models (this refers to non-linearity on markers) were reproducing kernel Hilbert space (RKHS) regression, Bayesian regularized neural networks (BRNN), and radial basis function neural networks (RBFNN). These statistical models were compared using 306 elite wheat lines from CIMMYT genotyped with 1717 diversity array technology (DArT) markers and two traits, days to heading (DTH) and grain yield (GY), measured in each of 12 environments. It was found that the three non-linear models had better overall prediction accuracy than the linear regression specification. Results showed a consistent superiority of RKHS and RBFNN over the Bayesian LASSO, Bayesian ridge regression, Bayes A, and Bayes B models. PMID:23275882
A non-parametric approach to anomaly detection in hyperspectral images
NASA Astrophysics Data System (ADS)
Veracini, Tiziana; Matteoli, Stefania; Diani, Marco; Corsini, Giovanni; de Ceglie, Sergio U.
2010-10-01
In the past few years, spectral analysis of data collected by hyperspectral sensors aimed at automatic anomaly detection has become an interesting area of research. In this paper, we are interested in an Anomaly Detection (AD) scheme for hyperspectral images in which spectral anomalies are defined with respect to a statistical model of the background Probability Density Function (PDF).The characterization of the PDF of hyperspectral imagery is not trivial. We approach the background PDF estimation through the Parzen Windowing PDF estimator (PW). PW is a flexible and valuable tool for accurately modeling unknown PDFs in a non-parametric fashion. Although such an approach is well known and has been widely employed, its use within an AD scheme has been not investigated yet. For practical purposes, the PW ability to estimate PDFs is strongly influenced by the choice of the bandwidth matrix, which controls the degree of smoothing of the resulting PDF approximation. Here, a Bayesian approach is employed to carry out the bandwidth selection. The resulting estimated background PDF is then used to detect spectral anomalies within a detection scheme based on the Neyman-Pearson approach. Real hyperspectral imagery is used for an experimental evaluation of the proposed strategy.
Galindo-Garre, Francisca; Hidalgo, María Dolores; Guilera, Georgina; Pino, Oscar; Rojo, J Emilio; Gómez-Benito, Juana
2015-03-01
The World Health Organization Disability Assessment Schedule II (WHO-DAS II) is a multidimensional instrument developed for measuring disability. It comprises six domains (getting around, self-care, getting along with others, life activities and participation in society). The main purpose of this paper is the evaluation of the psychometric properties for each domain of the WHO-DAS II with parametric and non-parametric Item Response Theory (IRT) models. A secondary objective is to assess whether the WHO-DAS II items within each domain form a hierarchy of invariantly ordered severity indicators of disability. A sample of 352 patients with a schizophrenia spectrum disorder is used in this study. The 36 items WHO-DAS II was administered during the consultation. Partial Credit and Mokken scale models are used to study the psychometric properties of the questionnaire. The psychometric properties of the WHO-DAS II scale are satisfactory for all the domains. However, we identify a few items that do not discriminate satisfactorily between different levels of disability and cannot be invariantly ordered in the scale. In conclusion the WHO-DAS II can be used to assess overall disability in patients with schizophrenia, but some domains are too general to assess functionality in these patients because they contain items that are not applicable to this pathology. Copyright © 2014 John Wiley & Sons, Ltd.
The binned bispectrum estimator: template-based and non-parametric CMB non-Gaussianity searches
NASA Astrophysics Data System (ADS)
Bucher, Martin; Racine, Benjamin; van Tent, Bartjan
2016-05-01
We describe the details of the binned bispectrum estimator as used for the official 2013 and 2015 analyses of the temperature and polarization CMB maps from the ESA Planck satellite. The defining aspect of this estimator is the determination of a map bispectrum (3-point correlation function) that has been binned in harmonic space. For a parametric determination of the non-Gaussianity in the map (the so-called fNL parameters), one takes the inner product of this binned bispectrum with theoretically motivated templates. However, as a complementary approach one can also smooth the binned bispectrum using a variable smoothing scale in order to suppress noise and make coherent features stand out above the noise. This allows one to look in a model-independent way for any statistically significant bispectral signal. This approach is useful for characterizing the bispectral shape of the galactic foreground emission, for which a theoretical prediction of the bispectral anisotropy is lacking, and for detecting a serendipitous primordial signal, for which a theoretical template has not yet been put forth. Both the template-based and the non-parametric approaches are described in this paper.
THE DARK MATTER PROFILE OF THE MILKY WAY: A NON-PARAMETRIC RECONSTRUCTION
Pato, Miguel; Iocco, Fabio
2015-04-10
We present the results of a new, non-parametric method to reconstruct the Galactic dark matter profile directly from observations. Using the latest kinematic data to track the total gravitational potential and the observed distribution of stars and gas to set the baryonic component, we infer the dark matter contribution to the circular velocity across the Galaxy. The radial derivative of this dynamical contribution is then estimated to extract the dark matter profile. The innovative feature of our approach is that it makes no assumption on the functional form or shape of the profile, thus allowing for a clean determination with no theoretical bias. We illustrate the power of the method by constraining the spherical dark matter profile between 2.5 and 25 kpc away from the Galactic center. The results show that the proposed method, free of widely used assumptions, can already be applied to pinpoint the dark matter distribution in the Milky Way with competitive accuracy, and paves the way for future developments.
Bayesian non-parametric approaches to reconstructing oscillatory systems and the Nyquist limit
NASA Astrophysics Data System (ADS)
Žurauskienė, Justina; Kirk, Paul; Thorne, Thomas; Stumpf, Michael P. H.
Reconstructing continuous signals from discrete time-points is a challenging inverse problem encountered in many scientific and engineering applications. For oscillatory signals classical results due to Nyquist set the limit below which it becomes impossible to reliably reconstruct the oscillation dynamics. Here we revisit this problem for vector-valued outputs and apply Bayesian non-parametric approaches in order to solve the function estimation problem. The main aim of the current paper is to map how we can use of correlations among different outputs to reconstruct signals at a sampling rate that lies below the Nyquist rate. We show that it is possible to use multiple-output Gaussian processes to capture dependences between outputs which facilitate reconstruction of signals in situation where conventional Gaussian processes (i.e. this aimed at describing scalar signals) fail, and we delineate the phase and frequency dependence of the reliability of this type of approach. In addition to simple toy-models we also consider the dynamics of the tumour suppressor gene p53, which exhibits oscillations under physiological conditions, and which can be reconstructed more reliably in our new framework.
Non-parametric estimators of a monotonic dose-response curve and bootstrap confidence intervals.
Dilleen, Maria; Heimann, Günter; Hirsch, Ian
2003-03-30
In this paper we consider study designs which include a placebo and an active control group as well as several dose groups of a new drug. A monotonically increasing dose-response function is assumed, and the objective is to estimate a dose with equivalent response to the active control group, including a confidence interval for this dose. We present different non-parametric methods to estimate the monotonic dose-response curve. These are derived from the isotonic regression estimator, a non-negative least squares estimator, and a bias adjusted non-negative least squares estimator using linear interpolation. The different confidence intervals are based upon an approach described by Korn, and upon two different bootstrap approaches. One of these bootstrap approaches is standard, and the second ensures that resampling is done from empiric distributions which comply with the order restrictions imposed. In our simulations we did not find any differences between the two bootstrap methods, and both clearly outperform Korn's confidence intervals. The non-negative least squares estimator yields biased results for moderate sample sizes. The bias adjustment for this estimator works well, even for small and moderate sample sizes, and surprisingly outperforms the isotonic regression method in certain situations.
Non-parametric early seizure detection in an animal model of temporal lobe epilepsy
NASA Astrophysics Data System (ADS)
Talathi, Sachin S.; Hwang, Dong-Uk; Spano, Mark L.; Simonotto, Jennifer; Furman, Michael D.; Myers, Stephen M.; Winters, Jason T.; Ditto, William L.; Carney, Paul R.
2008-03-01
The performance of five non-parametric, univariate seizure detection schemes (embedding delay, Hurst scale, wavelet scale, nonlinear autocorrelation and variance energy) were evaluated as a function of the sampling rate of EEG recordings, the electrode types used for EEG acquisition, and the spatial location of the EEG electrodes in order to determine the applicability of the measures in real-time closed-loop seizure intervention. The criteria chosen for evaluating the performance were high statistical robustness (as determined through the sensitivity and the specificity of a given measure in detecting a seizure) and the lag in seizure detection with respect to the seizure onset time (as determined by visual inspection of the EEG signal by a trained epileptologist). An optimality index was designed to evaluate the overall performance of each measure. For the EEG data recorded with microwire electrode array at a sampling rate of 12 kHz, the wavelet scale measure exhibited better overall performance in terms of its ability to detect a seizure with high optimality index value and high statistics in terms of sensitivity and specificity.
Scene Parsing With Integration of Parametric and Non-Parametric Models
NASA Astrophysics Data System (ADS)
Shuai, Bing; Zuo, Zhen; Wang, Gang; Wang, Bing
2016-05-01
We adopt Convolutional Neural Networks (CNNs) to be our parametric model to learn discriminative features and classifiers for local patch classification. Based on the occurrence frequency distribution of classes, an ensemble of CNNs (CNN-Ensemble) are learned, in which each CNN component focuses on learning different and complementary visual patterns. The local beliefs of pixels are output by CNN-Ensemble. Considering that visually similar pixels are indistinguishable under local context, we leverage the global scene semantics to alleviate the local ambiguity. The global scene constraint is mathematically achieved by adding a global energy term to the labeling energy function, and it is practically estimated in a non-parametric framework. A large margin based CNN metric learning method is also proposed for better global belief estimation. In the end, the integration of local and global beliefs gives rise to the class likelihood of pixels, based on which maximum marginal inference is performed to generate the label prediction maps. Even without any post-processing, we achieve state-of-the-art results on the challenging SiftFlow and Barcelona benchmarks.
Scene Parsing With Integration of Parametric and Non-Parametric Models.
Shuai, Bing; Zuo, Zhen; Wang, Gang; Wang, Bing
2016-05-01
We adopt convolutional neural networks (CNNs) to be our parametric model to learn discriminative features and classifiers for local patch classification. Based on the occurrence frequency distribution of classes, an ensemble of CNNs (CNN-Ensemble) are learned, in which each CNN component focuses on learning different and complementary visual patterns. The local beliefs of pixels are output by CNN-Ensemble. Considering that visually similar pixels are indistinguishable under local context, we leverage the global scene semantics to alleviate the local ambiguity. The global scene constraint is mathematically achieved by adding a global energy term to the labeling energy function, and it is practically estimated in a non-parametric framework. A large margin-based CNN metric learning method is also proposed for better global belief estimation. In the end, the integration of local and global beliefs gives rise to the class likelihood of pixels, based on which maximum marginal inference is performed to generate the label prediction maps. Even without any post-processing, we achieve the state-of-the-art results on the challenging SiftFlow and Barcelona benchmarks.
Pérez-Rodríguez, Paulino; Gianola, Daniel; González-Camacho, Juan Manuel; Crossa, José; Manès, Yann; Dreisigacker, Susanne
2012-12-01
In genome-enabled prediction, parametric, semi-parametric, and non-parametric regression models have been used. This study assessed the predictive ability of linear and non-linear models using dense molecular markers. The linear models were linear on marker effects and included the Bayesian LASSO, Bayesian ridge regression, Bayes A, and Bayes B. The non-linear models (this refers to non-linearity on markers) were reproducing kernel Hilbert space (RKHS) regression, Bayesian regularized neural networks (BRNN), and radial basis function neural networks (RBFNN). These statistical models were compared using 306 elite wheat lines from CIMMYT genotyped with 1717 diversity array technology (DArT) markers and two traits, days to heading (DTH) and grain yield (GY), measured in each of 12 environments. It was found that the three non-linear models had better overall prediction accuracy than the linear regression specification. Results showed a consistent superiority of RKHS and RBFNN over the Bayesian LASSO, Bayesian ridge regression, Bayes A, and Bayes B models.
The Dark Matter Profile of the Milky Way: A Non-parametric Reconstruction
NASA Astrophysics Data System (ADS)
Pato, Miguel; Iocco, Fabio
2015-04-01
We present the results of a new, non-parametric method to reconstruct the Galactic dark matter profile directly from observations. Using the latest kinematic data to track the total gravitational potential and the observed distribution of stars and gas to set the baryonic component, we infer the dark matter contribution to the circular velocity across the Galaxy. The radial derivative of this dynamical contribution is then estimated to extract the dark matter profile. The innovative feature of our approach is that it makes no assumption on the functional form or shape of the profile, thus allowing for a clean determination with no theoretical bias. We illustrate the power of the method by constraining the spherical dark matter profile between 2.5 and 25 kpc away from the Galactic center. The results show that the proposed method, free of widely used assumptions, can already be applied to pinpoint the dark matter distribution in the Milky Way with competitive accuracy, and paves the way for future developments.
Ragab, Marwa A A; Youssef, Rasha M
2013-11-01
New hybrid chemometric method has been applied to the emission response data. It deals with convolution of emission data using 8-points sin xi polynomials (discrete Fourier functions) after the derivative treatment of these emission data. This new application was used for the simultaneous determination of Fexofenadine and Montelukast in bulk and pharmaceutical preparation. It was found beneficial in the resolution of partially overlapping emission spectra of this mixture. The application of this chemometric method was found beneficial in eliminating different types of interferences common in spectrofluorimetry such as overlapping emission spectra and self- quenching. Not only this chemometric approache was applied to the emission data but also the obtained data were subjected to non-parametric linear regression analysis (Theil's method). The presented work compares the application of Theil's method in handling the response data, with the least-squares parametric regression method, which is considered the de facto standard method used for regression. So this work combines the advantages of derivative and convolution using discrete Fourier function together with the reliability and efficacy of the non-parametric analysis of data. Theil's method was found to be superior to the method of least squares as it could effectively circumvent any outlier data points.
Likelihood approaches to the non-parametric two-sample problem for right-censored data.
Troendle, James F; Yu, Kai F
2006-07-15
The classical two-sample problem with random right-censoring is considered. We show that non- parametric likelihood techniques can be used to obtain tests for either the identity hypothesis or the non-parametric Behrens-Fisher hypothesis (NBFH). In the case of the identity hypothesis, a special imputed permutation distribution is used to estimate the distribution under the null hypothesis. In the case of the NBFH, simulation from the constrained non-parametric maximum likelihood estimate is used. Simulation shows that the tests using either approximation have excellent control of the type I error rate, even with quite small sample sizes. Further, for Lehmann-type alternatives the likelihood-based methods have similar power to the logrank test, while for the non-Lehmann-type alternatives tried here the likelihood-based methods have superior power.
Non-parametric seismic hazard analysis in the presence of incomplete data
NASA Astrophysics Data System (ADS)
Yazdani, Azad; Mirzaei, Sajjad; Dadkhah, Koroush
2017-01-01
The distribution of earthquake magnitudes plays a crucial role in the estimation of seismic hazard parameters. Due to the complexity of earthquake magnitude distribution, non-parametric approaches are recommended over classical parametric methods. The main deficiency of the non-parametric approach is the lack of complete magnitude data in almost all cases. This study aims to introduce an imputation procedure for completing earthquake catalog data that will allow the catalog to be used for non-parametric density estimation. Using a Monte Carlo simulation, the efficiency of introduced approach is investigated. This study indicates that when a magnitude catalog is incomplete, the imputation procedure can provide an appropriate tool for seismic hazard assessment. As an illustration, the imputation procedure was applied to estimate earthquake magnitude distribution in Tehran, the capital city of Iran.
A Non-Parametric Probability Density Estimator and Some Applications.
1984-05-01
ESTIMATOR AND SOME APPLICATIONS Ronald P. Fuchs, B.S., M.S. Major, USAF Approved: oe Jt / 6 ’.°, Accep ted: Dean, School of Engineering .-7% Preface...4. Sensitivity to Support Estimation 35 5. Estimate of Density Function With No Subsampling 45 6 . Density Estimate Generated from Subsample One 46 7...Comparison of Distribution Function Average Square Errors (n-100) 61 6 . ASE for Basic and Parameterized Estimates 84 7. Distribution Function Method
Bayesian non-parametric inference for stochastic epidemic models using Gaussian Processes
Xu, Xiaoguang; Kypraios, Theodore; O'Neill, Philip D.
2016-01-01
This paper considers novel Bayesian non-parametric methods for stochastic epidemic models. Many standard modeling and data analysis methods use underlying assumptions (e.g. concerning the rate at which new cases of disease will occur) which are rarely challenged or tested in practice. To relax these assumptions, we develop a Bayesian non-parametric approach using Gaussian Processes, specifically to estimate the infection process. The methods are illustrated with both simulated and real data sets, the former illustrating that the methods can recover the true infection process quite well in practice, and the latter illustrating that the methods can be successfully applied in different settings. PMID:26993062
A general non-parametric classifier applied to discriminating surface water from terrain shadows
NASA Technical Reports Server (NTRS)
Eppler, W. G.
1975-01-01
A general non-parametric classifier is described in the context of discriminating surface water from terrain shadows. In addition to using non-parametric statistics, this classifier permits the use of a cost matrix to assign different penalties to various types of misclassifications. The approach also differs from conventional classifiers in that it applies the maximum-likelihood criterion to overall class probabilities as opposed to the standard practice of choosing the most likely individual subclass. The classifier performance is evaluated using two different effectiveness measures for a specific set of ERTS data.
Software For Computing Selected Functions
NASA Technical Reports Server (NTRS)
Grant, David C.
1992-01-01
Technical memorandum presents collection of software packages in Ada implementing mathematical functions used in science and engineering. Provides programmer with function support in Pascal and FORTRAN, plus support for extended-precision arithmetic and complex arithmetic. Valuable for testing new computers, writing computer code, or developing new computer integrated circuits.
Xu, J L; Prorok, P C
1995-12-30
The goal of screening programmes for cancer is early detection and treatment with a consequent reduction in mortality from the disease. Screening programmes need to assess the true benefit of screening, that is, the length of time of extension of survival beyond the time of advancement of diagnosis (lead-time). This paper presents a non-parametric method to estimate the survival function of the post-lead-time survival (or extra survival time) of screen-detected cancer cases based on the observed total life time, namely, the sum of the lead-time and the extra survival time. We apply the method to the well-known data set of the HIP (Health Insurance Plan of Greater New York) breast cancer screening study. We make comparisons with the survival of other groups of cancer cases not detected by screening such as interval cases, cases among individuals who refused screening, and randomized control cases. As compared with Walter and Stitt's model, in which they made parametric assumptions for the extra survival time, our non-parametric method provides a better fit to HIP data in the sense that our estimator for the total survival time has a smaller sum of squares of residuals.
Simulation Validation Using a Non-Parametric Statistical Method
2006-12-01
information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and...paradox of sorts: the simulation is supposed to alleviate the need to conduct costly live tests, but the live tests are the best indication that the...event population is statistically similar to the hypothetical (or computed) simulation population. Like the simulation output populations, the live
A non-parametric peak calling algorithm for DamID-Seq.
Li, Renhua; Hempel, Leonie U; Jiang, Tingbo
2015-01-01
Protein-DNA interactions play a significant role in gene regulation and expression. In order to identify transcription factor binding sites (TFBS) of double sex (DSX)-an important transcription factor in sex determination, we applied the DNA adenine methylation identification (DamID) technology to the fat body tissue of Drosophila, followed by deep sequencing (DamID-Seq). One feature of DamID-Seq data is that induced adenine methylation signals are not assured to be symmetrically distributed at TFBS, which renders the existing peak calling algorithms for ChIP-Seq, including SPP and MACS, inappropriate for DamID-Seq data. This challenged us to develop a new algorithm for peak calling. A challenge in peaking calling based on sequence data is estimating the averaged behavior of background signals. We applied a bootstrap resampling method to short sequence reads in the control (Dam only). After data quality check and mapping reads to a reference genome, the peaking calling procedure compromises the following steps: 1) reads resampling; 2) reads scaling (normalization) and computing signal-to-noise fold changes; 3) filtering; 4) Calling peaks based on a statistically significant threshold. This is a non-parametric method for peak calling (NPPC). We also used irreproducible discovery rate (IDR) analysis, as well as ChIP-Seq data to compare the peaks called by the NPPC. We identified approximately 6,000 peaks for DSX, which point to 1,225 genes related to the fat body tissue difference between female and male Drosophila. Statistical evidence from IDR analysis indicated that these peaks are reproducible across biological replicates. In addition, these peaks are comparable to those identified by use of ChIP-Seq on S2 cells, in terms of peak number, location, and peaks width.
Martínez-Camblor, Pablo
2017-02-01
Meta-analyses, broadly defined as the quantitative review and synthesis of the results of related but independent comparable studies, allow to know the state of the art of one considered topic. Since the amount of available bibliography has enhanced in almost all fields and, specifically, in biomedical research, its popularity has drastically increased during the last decades. In particular, different methodologies have been developed in order to perform meta-analytic studies of diagnostic tests for both fixed- and random-effects models. From a parametric point of view, these techniques often compute a bivariate estimation for the sensitivity and the specificity by using only one threshold per included study. Frequently, an overall receiver operating characteristic curve based on a bivariate normal distribution is also provided. In this work, the author deals with the problem of estimating an overall receiver operating characteristic curve from a fully non-parametric approach when the data come from a meta-analysis study i.e. only certain information about the diagnostic capacity is available. Both fixed- and random-effects models are considered. In addition, the proposed methodology lets to use the information of all cut-off points available (not only one of them) in the selected original studies. The performance of the method is explored through Monte Carlo simulations. The observed results suggest that the proposed estimator is better than the reference one when the reported information is related to a threshold based on the Youden index and when information for two or more points are provided. Real data illustrations are included.
ERIC Educational Resources Information Center
Agasisti, Tommaso
2011-01-01
The objective of this paper is an efficiency analysis concerning higher education systems in European countries. Data have been extracted from OECD data-sets (Education at a Glance, several years), using a non-parametric technique--data envelopment analysis--to calculate efficiency scores. This paper represents the first attempt to conduct such an…
Non-parametric deprojection of surface brightness profiles of galaxies in generalised geometries
NASA Astrophysics Data System (ADS)
Chakrabarty, D.
2010-02-01
Aims: We present a new Bayesian non-parametric deprojection algorithm DOPING (Deprojection of Observed Photometry using an INverse Gambit), that is designed to extract 3-D luminosity density distributions ρ from observed surface brightness maps I, in generalised geometries, while taking into account changes in intrinsic shape with radius, using a penalised likelihood approach and an Markov Chain Monte Carlo optimiser. Methods: We provide the most likely solution to the integral equation that represents deprojection of the measured I to ρ. In order to keep the solution modular, we choose to express ρ as a function of the line-of-sight (LOS) coordinate z. We calculate the extent of the system along the z-axis, for a given point on the image that lies within an identified isophotal annulus. The extent along the LOS is binned and density is held a constant over each such z-bin. The code begins with a seed density and at the beginning of an iterative step, the trial ρ is updated. Comparison of the projection of the current choice of ρ and the observed I defines the likelihood function (which is supplemented by Laplacian regularisation), the maximal region of which is sought by the optimiser (Metropolis Hastings). Results: The algorithm is successfully tested on a set of test galaxies, the morphology of which ranges from an elliptical galaxy with varying eccentricity to an infinitesimally thin disk galaxy marked by an abruptly varying eccentricity profile. Applications are made to faint dwarf elliptical galaxy Ic 3019 and another dwarf elliptical that is characterised by a central spheroidal nuclear component superimposed upon a more extended flattened component. The result of deprojection of the X-ray image of cluster A1413 - assumed triaxial - the axial ratios and inclination of which are taken from the literature, is also presented.
NASA Astrophysics Data System (ADS)
Delgado, Manuel J.; Verstraeten, Gert; Hanssen, Ramon F.
2013-12-01
The rapid urban expansion of the Greater Cairo region from 2004 to 2010 is computed by performing a linear analysis of four land use maps obtained after classifying SAR data using a non-parametric supervised classifier based on artificial neural network with simple design. The availability of SAR data made it possible to produce land use maps for 5 out of the 7 years, obtaining information about the evolution of the morphology of the city with a temporal resolution of nearly 1 year. Urban area has grown with 150% during the period under study. During that period, part of the new urban development took place within the Nile floodplain, despite the effort of the Egyptian government to restrict new developments to the Western and Eastern desert plateaus.
Density Estimation Trees as fast non-parametric modelling tools
NASA Astrophysics Data System (ADS)
Anderlini, Lucio
2016-10-01
A Density Estimation Tree (DET) is a decision trees trained on a multivariate dataset to estimate the underlying probability density function. While not competitive with kernel techniques in terms of accuracy, DETs are incredibly fast, embarrassingly parallel and relatively small when stored to disk. These properties make DETs appealing in the resource- expensive horizon of the LHC data analysis. Possible applications may include selection optimization, fast simulation and fast detector calibration. In this contribution I describe the algorithm and its implementation made available to the HEP community as a RooFit object. A set of applications under discussion within the LHCb Collaboration are also briefly illustrated.
Network Coding for Function Computation
ERIC Educational Resources Information Center
Appuswamy, Rathinakumar
2011-01-01
In this dissertation, the following "network computing problem" is considered. Source nodes in a directed acyclic network generate independent messages and a single receiver node computes a target function f of the messages. The objective is to maximize the average number of times f can be computed per network usage, i.e., the "computing…
Parametric and Non-Parametric Vibration-Based Structural Identification Under Earthquake Excitation
NASA Astrophysics Data System (ADS)
Pentaris, Fragkiskos P.; Fouskitakis, George N.
2014-05-01
The problem of modal identification in civil structures is of crucial importance, and thus has been receiving increasing attention in recent years. Vibration-based methods are quite promising as they are capable of identifying the structure's global characteristics, they are relatively easy to implement and they tend to be time effective and less expensive than most alternatives [1]. This paper focuses on the off-line structural/modal identification of civil (concrete) structures subjected to low-level earthquake excitations, under which, they remain within their linear operating regime. Earthquakes and their details are recorded and provided by the seismological network of Crete [2], which 'monitors' the broad region of south Hellenic arc, an active seismic region which functions as a natural laboratory for earthquake engineering of this kind. A sufficient number of seismic events are analyzed in order to reveal the modal characteristics of the structures under study, that consist of the two concrete buildings of the School of Applied Sciences, Technological Education Institute of Crete, located in Chania, Crete, Hellas. Both buildings are equipped with high-sensitivity and accuracy seismographs - providing acceleration measurements - established at the basement (structure's foundation) presently considered as the ground's acceleration (excitation) and at all levels (ground floor, 1st floor, 2nd floor and terrace). Further details regarding the instrumentation setup and data acquisition may be found in [3]. The present study invokes stochastic, both non-parametric (frequency-based) and parametric methods for structural/modal identification (natural frequencies and/or damping ratios). Non-parametric methods include Welch-based spectrum and Frequency response Function (FrF) estimation, while parametric methods, include AutoRegressive (AR), AutoRegressive with eXogeneous input (ARX) and Autoregressive Moving-Average with eXogeneous input (ARMAX) models[4, 5
A Non-Parametric Item Response Theory Evaluation of the CAGE Instrument Among Older Adults.
Abdin, Edimansyah; Sagayadevan, Vathsala; Vaingankar, Janhavi Ajit; Picco, Louisa; Chong, Siow Ann; Subramaniam, Mythily
2017-08-04
The validity of the CAGE using item response theory (IRT) has not yet been examined in older adult population. This study aims to investigate the psychometric properties of the CAGE using both non-parametric and parametric IRT models, assess whether there is any differential item functioning (DIF) by age, gender and ethnicity and examine the measurement precision at the cut-off scores. We used data from the Well-being of the Singapore Elderly study to conduct Mokken scaling analysis (MSA), dichotomous Rasch and 2-parameter logistic IRT models. The measurement precision at the cut-off scores were evaluated using classification accuracy (CA) and classification consistency (CC). The MSA showed the overall scalability H index was 0.459, indicating a medium performing instrument. All items were found to be homogenous, measuring the same construct and able to discriminate well between respondents with high levels of the construct and the ones with lower levels. The item discrimination ranged from 1.07 to 6.73 while the item difficulty ranged from 0.33 to 2.80. Significant DIF was found for 2-item across ethnic group. More than 90% (CC and CA ranged from 92.5% to 94.3%) of the respondents were consistently and accurately classified by the CAGE cut-off scores of 2 and 3. The current study provides new evidence on the validity of the CAGE from the IRT perspective. This study provides valuable information of each item in the assessment of the overall severity of alcohol problem and the precision of the cut-off scores in older adult population.
Program Computes Thermodynamic Functions
NASA Technical Reports Server (NTRS)
Mcbride, Bonnie J.; Gordon, Sanford
1994-01-01
PAC91 is latest in PAC (Properties and Coefficients) series. Two principal features are to provide means of (1) generating theoretical thermodynamic functions from molecular constants and (2) least-squares fitting of these functions to empirical equations. PAC91 written in FORTRAN 77 to be machine-independent.
Symbolic functions from neural computation.
Smolensky, Paul
2012-07-28
Is thought computation over ideas? Turing, and many cognitive scientists since, have assumed so, and formulated computational systems in which meaningful concepts are encoded by symbols which are the objects of computation. Cognition has been carved into parts, each a function defined over such symbols. This paper reports on a research program aimed at computing these symbolic functions without computing over the symbols. Symbols are encoded as patterns of numerical activation over multiple abstract neurons, each neuron simultaneously contributing to the encoding of multiple symbols. Computation is carried out over the numerical activation values of such neurons, which individually have no conceptual meaning. This is massively parallel numerical computation operating within a continuous computational medium. The paper presents an axiomatic framework for such a computational account of cognition, including a number of formal results. Within the framework, a class of recursive symbolic functions can be computed. Formal languages defined by symbolic rewrite rules can also be specified, the subsymbolic computations producing symbolic outputs that simultaneously display central properties of both facets of human language: universal symbolic grammatical competence and statistical, imperfect performance.
Kong, Xiangrong; Mas, Valeria; Archer, Kellie J
2008-02-26
With the popularity of DNA microarray technology, multiple groups of researchers have studied the gene expression of similar biological conditions. Different methods have been developed to integrate the results from various microarray studies, though most of them rely on distributional assumptions, such as the t-statistic based, mixed-effects model, or Bayesian model methods. However, often the sample size for each individual microarray experiment is small. Therefore, in this paper we present a non-parametric meta-analysis approach for combining data from independent microarray studies, and illustrate its application on two independent Affymetrix GeneChip studies that compared the gene expression of biopsies from kidney transplant recipients with chronic allograft nephropathy (CAN) to those with normal functioning allograft. The simulation study comparing the non-parametric meta-analysis approach to a commonly used t-statistic based approach shows that the non-parametric approach has better sensitivity and specificity. For the application on the two CAN studies, we identified 309 distinct genes that expressed differently in CAN. By applying Fisher's exact test to identify enriched KEGG pathways among those genes called differentially expressed, we found 6 KEGG pathways to be over-represented among the identified genes. We used the expression measurements of the identified genes as predictors to predict the class labels for 6 additional biopsy samples, and the predicted results all conformed to their pathologist diagnosed class labels. We present a new approach for combining data from multiple independent microarray studies. This approach is non-parametric and does not rely on any distributional assumptions. The rationale behind the approach is logically intuitive and can be easily understood by researchers not having advanced training in statistics. Some of the identified genes and pathways have been reported to be relevant to renal diseases. Further study on the
Phang, T.L.; Neville, M.C.; Rudolph, M.; Hunter, L.
2008-01-01
Trajectory clustering is a novel and statistically well-founded method for clustering time series data from gene expression arrays. Trajectory clustering uses non-parametric statistics and is hence not sensitive to the particular distributions underlying gene expression data. Each cluster is clearly defined in terms of direction of change of expression for successive time points (its ‘trajectory’), and therefore has easily appreciated biological meaning. Applying the method to a dataset from mouse mammary gland development, we demonstrate that it produces different clusters than Hierarchical, K-means, and Jackknife clustering methods, even when those methods are applied to differences between successive time points. Compared to all of the other methods, trajectory clustering was better able to match a manual clustering by a domain expert, and was better able to cluster groups of genes with known related functions. PMID:12603041
Computational Models for Neuromuscular Function
Valero-Cuevas, Francisco J.; Hoffmann, Heiko; Kurse, Manish U.; Kutch, Jason J.; Theodorou, Evangelos A.
2011-01-01
Computational models of the neuromuscular system hold the potential to allow us to reach a deeper understanding of neuromuscular function and clinical rehabilitation by complementing experimentation. By serving as a means to distill and explore specific hypotheses, computational models emerge from prior experimental data and motivate future experimental work. Here we review computational tools used to understand neuromuscular function including musculoskeletal modeling, machine learning, control theory, and statistical model analysis. We conclude that these tools, when used in combination, have the potential to further our understanding of neuromuscular function by serving as a rigorous means to test scientific hypotheses in ways that complement and leverage experimental data. PMID:21687779
Bayesian inference for longitudinal data with non-parametric treatment effects.
Müller, Peter; Quintana, Fernando A; Rosner, Gary L; Maitland, Michael L
2014-04-01
We consider inference for longitudinal data based on mixed-effects models with a non-parametric Bayesian prior on the treatment effect. The proposed non-parametric Bayesian prior is a random partition model with a regression on patient-specific covariates. The main feature and motivation for the proposed model is the use of covariates with a mix of different data formats and possibly high-order interactions in the regression. The regression is not explicitly parameterized. It is implied by the random clustering of subjects. The motivating application is a study of the effect of an anticancer drug on a patient's blood pressure. The study involves blood pressure measurements taken periodically over several 24-h periods for 54 patients. The 24-h periods for each patient include a pretreatment period and several occasions after the start of therapy.
Non-parametric Bayesian human motion recognition using a single MEMS tri-axial accelerometer.
Ahmed, M Ejaz; Song, Ju Bin
2012-09-27
In this paper, we propose a non-parametric clustering method to recognize the number of human motions using features which are obtained from a single microelectromechanical system (MEMS) accelerometer. Since the number of human motions under consideration is not known a priori and because of the unsupervised nature of the proposed technique, there is no need to collect training data for the human motions. The infinite Gaussian mixture model (IGMM) and collapsed Gibbs sampler are adopted to cluster the human motions using extracted features. From the experimental results, we show that the unanticipated human motions are detected and recognized with significant accuracy, as compared with the parametric Fuzzy C-Mean (FCM) technique, the unsupervised K-means algorithm, and the non-parametric mean-shift method.
Non-Parametric Bayesian Human Motion Recognition Using a Single MEMS Tri-Axial Accelerometer
Ahmed, M. Ejaz; Song, Ju Bin
2012-01-01
In this paper, we propose a non-parametric clustering method to recognize the number of human motions using features which are obtained from a single microelectromechanical system (MEMS) accelerometer. Since the number of human motions under consideration is not known a priori and because of the unsupervised nature of the proposed technique, there is no need to collect training data for the human motions. The infinite Gaussian mixture model (IGMM) and collapsed Gibbs sampler are adopted to cluster the human motions using extracted features. From the experimental results, we show that the unanticipated human motions are detected and recognized with significant accuracy, as compared with the parametric Fuzzy C-Mean (FCM) technique, the unsupervised K-means algorithm, and the non-parametric mean-shift method. PMID:23201992
Bayesian non-parametric inference for stochastic epidemic models using Gaussian Processes.
Xu, Xiaoguang; Kypraios, Theodore; O'Neill, Philip D
2016-10-01
This paper considers novel Bayesian non-parametric methods for stochastic epidemic models. Many standard modeling and data analysis methods use underlying assumptions (e.g. concerning the rate at which new cases of disease will occur) which are rarely challenged or tested in practice. To relax these assumptions, we develop a Bayesian non-parametric approach using Gaussian Processes, specifically to estimate the infection process. The methods are illustrated with both simulated and real data sets, the former illustrating that the methods can recover the true infection process quite well in practice, and the latter illustrating that the methods can be successfully applied in different settings. © The Author 2016. Published by Oxford University Press.
Lan, Ling; Datta, Somnath
2010-04-01
As a type of multivariate survival data, multistate models have a wide range of applications, notably in cancer and infectious disease progression studies. In this article, we revisit the problem of estimation of state occupation, entry and exit times in a multistate model where various estimators have been proposed in the past under a variety of parametric and non-parametric assumptions. We focus on two non-parametric approaches, one using a product limit formula as recently proposed in Datta and Sundaram(1) and a novel approach using a fractional risk set calculation followed by a subtraction formula to calculate the state occupation probability of a transient state. A numerical comparison between the two methods is presented using detailed simulation studies. We show that the new estimators have lower statistical errors of estimation of state occupation probabilities for the distant states. We illustrate the two methods using a pubertal development data set obtained from the NHANES III.(2).
A non-parametric approach to estimate the total deviation index for non-normal data.
Perez-Jaume, Sara; Carrasco, Josep L
2015-11-10
Concordance indices are used to assess the degree of agreement between different methods that measure the same characteristic. In this context, the total deviation index (TDI) is an unscaled concordance measure that quantifies to which extent the readings from the same subject obtained by different methods may differ with a certain probability. Common approaches to estimate the TDI assume data are normally distributed and linearity between response and effects (subjects, methods and random error). Here, we introduce a new non-parametric methodology for estimation and inference of the TDI that can deal with any kind of quantitative data. The present study introduces this non-parametric approach and compares it with the already established methods in two real case examples that represent situations of non-normal data (more specifically, skewed data and count data). The performance of the already established methodologies and our approach in these contexts is assessed by means of a simulation study.
System and Method of Use for Non-parametric Circular Autocorrelation for Signal Processing
2012-07-30
0012] Wald , A. and J. Wolfowitz , An exact test for randomness in the non–Parametric case based on serial correlation, Annals of Mathematical...Statistics Vol. 14, No. 4, pages 378–388, 1943, (hereinafter “ Wald and Wolfowitz ”) provides a non-parametric permutations method such that if n is...present disclosure models accurately and efficiently. 8 [0015] Wald and Wolfowitz generally describe the properties of hxxR , in the context
System Availability: Time Dependence and Statistical Inference by (Semi) Non-Parametric Methods
1988-08-01
Technical FROM -TO 1988 August T 42 16. SUPPLEMENTARY NOTATION 17. COSATI CODES 18 SUBJECT TERMS (Continue on reverse if necessary and identify by block...availability in finite time (not steady-state or long -run), and to non-parametric estimates. 20 DISTRIBUTION, AVAILABILITY OF ABSTRACT 21 ABSTRACT...productivity of commercial nuclear power plants; in that arena it is quantified by probabilistic risk assessment (PRA). Relaued finite state
Automatic computation of transfer functions
Atcitty, Stanley; Watson, Luke Dale
2015-04-14
Technologies pertaining to the automatic computation of transfer functions for a physical system are described herein. The physical system is one of an electrical system, a mechanical system, an electromechanical system, an electrochemical system, or an electromagnetic system. A netlist in the form of a matrix comprises data that is indicative of elements in the physical system, values for the elements in the physical system, and structure of the physical system. Transfer functions for the physical system are computed based upon the netlist.
A Comparison of Parametric and Non-Parametric Methods Applied to a Likert Scale.
Mircioiu, Constantin; Atkinson, Jeffrey
2017-05-10
A trenchant and passionate dispute over the use of parametric versus non-parametric methods for the analysis of Likert scale ordinal data has raged for the past eight decades. The answer is not a simple "yes" or "no" but is related to hypotheses, objectives, risks, and paradigms. In this paper, we took a pragmatic approach. We applied both types of methods to the analysis of actual Likert data on responses from different professional subgroups of European pharmacists regarding competencies for practice. Results obtained show that with "large" (>15) numbers of responses and similar (but clearly not normal) distributions from different subgroups, parametric and non-parametric analyses give in almost all cases the same significant or non-significant results for inter-subgroup comparisons. Parametric methods were more discriminant in the cases of non-similar conclusions. Considering that the largest differences in opinions occurred in the upper part of the 4-point Likert scale (ranks 3 "very important" and 4 "essential"), a "score analysis" based on this part of the data was undertaken. This transformation of the ordinal Likert data into binary scores produced a graphical representation that was visually easier to understand as differences were accentuated. In conclusion, in this case of Likert ordinal data with high response rates, restraining the analysis to non-parametric methods leads to a loss of information. The addition of parametric methods, graphical analysis, analysis of subsets, and transformation of data leads to more in-depth analyses.
Functional Programming in Computer Science
Anderson, Loren James; Davis, Marion Kei
2016-01-19
We explore functional programming through a 16-week internship at Los Alamos National Laboratory. Functional programming is a branch of computer science that has exploded in popularity over the past decade due to its high-level syntax, ease of parallelization, and abundant applications. First, we summarize functional programming by listing the advantages of functional programming languages over the usual imperative languages, and we introduce the concept of parsing. Second, we discuss the importance of lambda calculus in the theory of functional programming. Lambda calculus was invented by Alonzo Church in the 1930s to formalize the concept of effective computability, and every functional language is essentially some implementation of lambda calculus. Finally, we display the lasting products of the internship: additions to a compiler and runtime system for the pure functional language STG, including both a set of tests that indicate the validity of updates to the compiler and a compiler pass that checks for illegal instances of duplicate names.
Non-parametric determination of H and He interstellar fluxes from cosmic-ray data
NASA Astrophysics Data System (ADS)
Ghelfi, A.; Barao, F.; Derome, L.; Maurin, D.
2016-06-01
Context. Top-of-atmosphere (TOA) cosmic-ray (CR) fluxes from satellites and balloon-borne experiments are snapshots of the solar activity imprinted on the interstellar (IS) fluxes. Given a series of snapshots, the unknown IS flux shape and the level of modulation (for each snapshot) can be recovered. Aims: We wish (i) to provide the most accurate determination of the IS H and He fluxes from TOA data alone; (ii) to obtain the associated modulation levels (and uncertainties) while fully accounting for the correlations with the IS flux uncertainties; and (iii) to inspect whether the minimal force-field approximation is sufficient to explain all the data at hand. Methods: Using H and He TOA measurements, including the recent high-precision AMS, BESS-Polar, and PAMELA data, we performed a non-parametric fit of the IS fluxes JISH,~He and modulation level φi for each data-taking period. We relied on a Markov chain Monte Carlo (MCMC) engine to extract the probability density function and correlations (hence the credible intervals) of the sought parameters. Results: Although H and He are the most abundant and best measured CR species, several datasets had to be excluded from the analysis because of inconsistencies with other measurements. From the subset of data passing our consistency cut, we provide ready-to-use best-fit and credible intervals for the H and He IS fluxes from MeV/n to PeV/n energy (with a relative precision in the range [ 2-10% ] at 1σ). Given the strong correlation between JIS and φi parameters, the uncertainties on JIS translate into Δφ ≈ ± 30 MV (at 1σ) for all experiments. We also find that the presence of 3He in He data biases φ towards higher φ values by ~30 MV. The force-field approximation, despite its limitation, gives an excellent (χ2/d.o.f. = 1.02) description of the recent high-precision TOA H and He fluxes. Conclusions: The analysis must be extended to different charge species and more realistic modulation models. It would benefit
Computer Experiments for Function Approximations
Chang, A; Izmailov, I; Rizzo, S; Wynter, S; Alexandrov, O; Tong, C
2007-10-15
This research project falls in the domain of response surface methodology, which seeks cost-effective ways to accurately fit an approximate function to experimental data. Modeling and computer simulation are essential tools in modern science and engineering. A computer simulation can be viewed as a function that receives input from a given parameter space and produces an output. Running the simulation repeatedly amounts to an equivalent number of function evaluations, and for complex models, such function evaluations can be very time-consuming. It is then of paramount importance to intelligently choose a relatively small set of sample points in the parameter space at which to evaluate the given function, and then use this information to construct a surrogate function that is close to the original function and takes little time to evaluate. This study was divided into two parts. The first part consisted of comparing four sampling methods and two function approximation methods in terms of efficiency and accuracy for simple test functions. The sampling methods used were Monte Carlo, Quasi-Random LP{sub {tau}}, Maximin Latin Hypercubes, and Orthogonal-Array-Based Latin Hypercubes. The function approximation methods utilized were Multivariate Adaptive Regression Splines (MARS) and Support Vector Machines (SVM). The second part of the study concerned adaptive sampling methods with a focus on creating useful sets of sample points specifically for monotonic functions, functions with a single minimum and functions with a bounded first derivative.
Neural computation of arithmetic functions
NASA Technical Reports Server (NTRS)
Siu, Kai-Yeung; Bruck, Jehoshua
1990-01-01
An area of application of neural networks is considered. A neuron is modeled as a linear threshold gate, and the network architecture considered is the layered feedforward network. It is shown how common arithmetic functions such as multiplication and sorting can be efficiently computed in a shallow neural network. Some known results are improved by showing that the product of two n-bit numbers and sorting of n n-bit numbers can be computed by a polynomial-size neural network using only four and five unit delays, respectively. Moreover, the weights of each threshold element in the neural networks require O(log n)-bit (instead of n-bit) accuracy. These results can be extended to more complicated functions such as multiple products, division, rational functions, and approximation of analytic functions.
A Comparison of Parametric and Non-Parametric Methods Applied to a Likert Scale
Mircioiu, Constantin; Atkinson, Jeffrey
2017-01-01
A trenchant and passionate dispute over the use of parametric versus non-parametric methods for the analysis of Likert scale ordinal data has raged for the past eight decades. The answer is not a simple “yes” or “no” but is related to hypotheses, objectives, risks, and paradigms. In this paper, we took a pragmatic approach. We applied both types of methods to the analysis of actual Likert data on responses from different professional subgroups of European pharmacists regarding competencies for practice. Results obtained show that with “large” (>15) numbers of responses and similar (but clearly not normal) distributions from different subgroups, parametric and non-parametric analyses give in almost all cases the same significant or non-significant results for inter-subgroup comparisons. Parametric methods were more discriminant in the cases of non-similar conclusions. Considering that the largest differences in opinions occurred in the upper part of the 4-point Likert scale (ranks 3 “very important” and 4 “essential”), a “score analysis” based on this part of the data was undertaken. This transformation of the ordinal Likert data into binary scores produced a graphical representation that was visually easier to understand as differences were accentuated. In conclusion, in this case of Likert ordinal data with high response rates, restraining the analysis to non-parametric methods leads to a loss of information. The addition of parametric methods, graphical analysis, analysis of subsets, and transformation of data leads to more in-depth analyses. PMID:28970438
Non-parametric trend analysis of water quality data of rivers in Kansas
Yu, Y.-S.; Zou, S.; Whittemore, D.
1993-01-01
Surface water quality data for 15 sampling stations in the Arkansas, Verdigris, Neosho, and Walnut river basins inside the state of Kansas were analyzed to detect trends (or lack of trends) in 17 major constituents by using four different non-parametric methods. The results show that concentrations of specific conductance, total dissolved solids, calcium, total hardness, sodium, potassium, alkalinity, sulfate, chloride, total phosphorus, ammonia plus organic nitrogen, and suspended sediment generally have downward trends. Some of the downward trends are related to increases in discharge, while others could be caused by decreases in pollution sources. Homogeneity tests show that both station-wide trends and basinwide trends are non-homogeneous. ?? 1993.
Generalized Correlation Coefficient for Non-Parametric Analysis of Microarray Time-Course Data.
Tan, Qihua; Thomassen, Mads; Burton, Mark; Mose, Kristian Fredløv; Andersen, Klaus Ejner; Hjelmborg, Jacob; Kruse, Torben
2017-06-06
Modeling complex time-course patterns is a challenging issue in microarray study due to complex gene expression patterns in response to the time-course experiment. We introduce the generalized correlation coefficient and propose a combinatory approach for detecting, testing and clustering the heterogeneous time-course gene expression patterns. Application of the method identified nonlinear time-course patterns in high agreement with parametric analysis. We conclude that the non-parametric nature in the generalized correlation analysis could be an useful and efficient tool for analyzing microarray time-course data and for exploring the complex relationships in the omics data for studying their association with disease and health.
Kerschbamer, Rudolf
2015-01-01
This paper proposes a geometric delineation of distributional preference types and a non-parametric approach for their identification in a two-person context. It starts with a small set of assumptions on preferences and shows that this set (i) naturally results in a taxonomy of distributional archetypes that nests all empirically relevant types considered in previous work; and (ii) gives rise to a clean experimental identification procedure – the Equality Equivalence Test – that discriminates between archetypes according to core features of preferences rather than properties of specific modeling variants. As a by-product the test yields a two-dimensional index of preference intensity. PMID:26089571
Modeling a MEMS deformable mirror using non-parametric estimation techniques.
Guzmán, Dani; Juez, Francisco Javier de Cos; Myers, Richard; Guesalaga, Andrés; Lasheras, Fernando Sánchez
2010-09-27
Using non-parametric estimation techniques, we have modeled an area of 126 actuators of a micro-electro-mechanical deformable mirror with 1024 actuators. These techniques produce models applicable to open-loop adaptive optics, where the turbulent wavefront is measured before it hits the deformable mirror. The model's input is the wavefront correction to apply to the mirror and its output is the set of voltages to shape the mirror. Our experiments have achieved positioning errors of 3.1% rms of the peak-to-peak wavefront excursion.
Kerschbamer, Rudolf
2015-05-01
This paper proposes a geometric delineation of distributional preference types and a non-parametric approach for their identification in a two-person context. It starts with a small set of assumptions on preferences and shows that this set (i) naturally results in a taxonomy of distributional archetypes that nests all empirically relevant types considered in previous work; and (ii) gives rise to a clean experimental identification procedure - the Equality Equivalence Test - that discriminates between archetypes according to core features of preferences rather than properties of specific modeling variants. As a by-product the test yields a two-dimensional index of preference intensity.
FUNCTION GENERATOR FOR ANALOGUE COMPUTERS
Skramstad, H.K.; Wright, J.H.; Taback, L.
1961-12-12
An improved analogue computer is designed which can be used to determine the final ground position of radioactive fallout particles in an atomic cloud. The computer determines the fallout pattern on the basis of known wind velocity and direction at various altitudes, and intensity of radioactivity in the mushroom cloud as a function of particle size and initial height in the cloud. The output is then displayed on a cathode-ray tube so that the average or total luminance of the tube screen at any point represents the intensity of radioactive fallout at the geographical location represented by that point. (AEC)
Non-parametrically Measuring Dark Matter Profiles in the Milky Way's Dwarf Spheroidals
NASA Astrophysics Data System (ADS)
Jardel, John; Gebhardt, K.
2013-01-01
The Milky Way's population of dwarf spheroidal (dSph) satellites has received much attention as a test site for the Cold Dark Matter (CDM) model for structure formation. Dynamical modeling, using the motions of the stars to trace the unknown mass distribution, is well-suited to test predictions of CDM by measuring the radial density profiles of the dark matter (DM) halos in which the dSphs reside. These studies reveal DM profiles with constant-density cores, in contrast to the cuspy profiles predicted from DM-only simulations. To resolve this discrepancy, many believe that feedback from baryons can alter the DM profiles and turn cusps into cores. Since it is difficult to simulate these complex baryonic processes with high fidelity, there are not many robust predictions for how feedback should affect the dSphs. We therefore do not know the type of DM profile to look for in these systems. This motivates a study to measure the DM profiles of dSphs non-parametrically to detect profiles other than the traditional cored and cuspy profiles most studies explore. I will present early results from a study using orbit-based models to non-parametrically measure the DM profiles of several of the bright Milky Way dSphs. The DM profiles measured will place observational constraints on the effects of feedback in low-mass galaxies.
A web application for evaluating Phase I methods using a non-parametric optimal benchmark.
Wages, Nolan A; Varhegyi, Nikole
2017-10-01
In evaluating the performance of Phase I dose-finding designs, simulation studies are typically conducted to assess how often a method correctly selects the true maximum tolerated dose under a set of assumed dose-toxicity curves. A necessary component of the evaluation process is to have some concept for how well a design can possibly perform. The notion of an upper bound on the accuracy of maximum tolerated dose selection is often omitted from the simulation study, and the aim of this work is to provide researchers with accessible software to quickly evaluate the operating characteristics of Phase I methods using a benchmark. The non-parametric optimal benchmark is a useful theoretical tool for simulations that can serve as an upper limit for the accuracy of maximum tolerated dose identification based on a binary toxicity endpoint. It offers researchers a sense of the plausibility of a Phase I method's operating characteristics in simulation. We have developed an R shiny web application for simulating the benchmark. The web application has the ability to quickly provide simulation results for the benchmark and requires no programming knowledge. The application is free to access and use on any device with an Internet browser. The application provides the percentage of correct selection of the maximum tolerated dose and an accuracy index, operating characteristics typically used in evaluating the accuracy of dose-finding designs. We hope this software will facilitate the use of the non-parametric optimal benchmark as an evaluation tool in dose-finding simulation.
Measuring Dark Matter Profiles Non-Parametrically in Dwarf Spheroidals: An Application to Draco
NASA Astrophysics Data System (ADS)
Jardel, John R.; Gebhardt, Karl; Fabricius, Maximilian H.; Drory, Niv; Williams, Michael J.
2013-02-01
We introduce a novel implementation of orbit-based (or Schwarzschild) modeling that allows dark matter density profiles to be calculated non-parametrically in nearby galaxies. Our models require no assumptions to be made about velocity anisotropy or the dark matter profile. The technique can be applied to any dispersion-supported stellar system, and we demonstrate its use by studying the Local Group dwarf spheroidal galaxy (dSph) Draco. We use existing kinematic data at larger radii and also present 12 new radial velocities within the central 13 pc obtained with the VIRUS-W integral field spectrograph on the 2.7 m telescope at McDonald Observatory. Our non-parametric Schwarzschild models find strong evidence that the dark matter profile in Draco is cuspy for 20 <= r <= 700 pc. The profile for r >= 20 pc is well fit by a power law with slope α = -1.0 ± 0.2, consistent with predictions from cold dark matter simulations. Our models confirm that, despite its low baryon content relative to other dSphs, Draco lives in a massive halo.
A Bayesian Non-Parametric Potts Model with Application to Pre-Surgical FMRI Data
Johnson, Timothy D.; Liu, Zhuqing; Bartsch, Andreas J.; Nichols, Thomas E.
2013-01-01
The Potts model has enjoyed much success as a prior model for image segmentation. Given the individual classes in the model, the data are typically modeled as Gaussian random variates or as random variates from some other parametric distribution. In this manuscript we present a non-parametric Potts model and apply it to an FMRI study for the pre-surgical assessment of peritumoral brain activation. In our model we assume that the Z-score image from a patient can be segmented into activated, deactivated and null classes, or states. Conditional on the class, or state, the Z-scores are assumed to come from some generic distribution which we model non-parametrically using a mixture of Dirichlet process priors within the Bayesian framework. The posterior distribution of the model parameters is estimated with a Markov chain Monte Carlo algorithm and Bayesian decision theory is used to make the final classifications. Our Potts prior model includes two parameters, the standard spatial regularization parameter and a parameter that can be interpreted as the a priori probability that each voxel belong to the null, or background state, conditional on the lack of spatial regularization. We assume that both of these parameters are unknown, and jointly estimate them along with other model parameters. We show through simulation studies that our model performs on par, in terms of posterior expected loss, with parametric Potts models when the parametric model is correctly specified, and outperforms parametric models when the parametric model in misspecified. PMID:22627277
Application of the LSQR algorithm in non-parametric estimation of aerosol size distribution
NASA Astrophysics Data System (ADS)
He, Zhenzong; Qi, Hong; Lew, Zhongyuan; Ruan, Liming; Tan, Heping; Luo, Kun
2016-05-01
Based on the Least Squares QR decomposition (LSQR) algorithm, the aerosol size distribution (ASD) is retrieved in non-parametric approach. The direct problem is solved by the Anomalous Diffraction Approximation (ADA) and the Lambert-Beer Law. An optimal wavelength selection method is developed to improve the retrieval accuracy of the ASD. The proposed optimal wavelength set is selected by the method which can make the measurement signals sensitive to wavelength and decrease the degree of the ill-condition of coefficient matrix of linear systems effectively to enhance the anti-interference ability of retrieval results. Two common kinds of monomodal and bimodal ASDs, log-normal (L-N) and Gamma distributions, are estimated, respectively. Numerical tests show that the LSQR algorithm can be successfully applied to retrieve the ASD with high stability in the presence of random noise and low susceptibility to the shape of distributions. Finally, the experimental measurement ASD over Harbin in China is recovered reasonably. All the results confirm that the LSQR algorithm combined with the optimal wavelength selection method is an effective and reliable technique in non-parametric estimation of ASD.
NASA Astrophysics Data System (ADS)
Kalpathy-Cramer, Jayashree; Ozertem, Umut; Hersh, William; Fuss, Martin; Erdogmus, Deniz
2009-02-01
Radiation therapy is one of the most effective treatments used in the treatment of about half of all people with cancer. A critical goal in radiation therapy is to deliver optimal radiation doses to the perceived tumor while sparing the surrounding healthy tissues. Radiation oncologists often manually delineate normal and diseased structures on 3D-CT scans, a time consuming task. We present a segmentation algorithm using non-parametric snakes and principal curves that can be used in an automatic or semi-supervised fashion. It provides fast segmentation that is robust with respect to noisy edges and does not require the user to optimize a variety of parameters, unlike many segmentation algorithms. It allows multiple cues to be incorporated easily for the purposes of estimating the edge probability density. These cues, including texture, intensity and shape priors, can be used simultaneously to delineate tumors and normal anatomy, thereby increasing the robustness of the algorithm. The notion of principal curves is used to interpolate between data points in sparse areas. We compare the results using a non-parametric snake technique with a gold standard consisting of manually delineated structures for tumors as well as normal organs.
MEASURING DARK MATTER PROFILES NON-PARAMETRICALLY IN DWARF SPHEROIDALS: AN APPLICATION TO DRACO
Jardel, John R.; Gebhardt, Karl; Fabricius, Maximilian H.; Williams, Michael J.; Drory, Niv
2013-02-15
We introduce a novel implementation of orbit-based (or Schwarzschild) modeling that allows dark matter density profiles to be calculated non-parametrically in nearby galaxies. Our models require no assumptions to be made about velocity anisotropy or the dark matter profile. The technique can be applied to any dispersion-supported stellar system, and we demonstrate its use by studying the Local Group dwarf spheroidal galaxy (dSph) Draco. We use existing kinematic data at larger radii and also present 12 new radial velocities within the central 13 pc obtained with the VIRUS-W integral field spectrograph on the 2.7 m telescope at McDonald Observatory. Our non-parametric Schwarzschild models find strong evidence that the dark matter profile in Draco is cuspy for 20 {<=} r {<=} 700 pc. The profile for r {>=} 20 pc is well fit by a power law with slope {alpha} = -1.0 {+-} 0.2, consistent with predictions from cold dark matter simulations. Our models confirm that, despite its low baryon content relative to other dSphs, Draco lives in a massive halo.
Non-parametric genetic prediction of complex traits with latent Dirichlet process regression models.
Zeng, Ping; Zhou, Xiang
2017-09-06
Using genotype data to perform accurate genetic prediction of complex traits can facilitate genomic selection in animal and plant breeding programs, and can aid in the development of personalized medicine in humans. Because most complex traits have a polygenic architecture, accurate genetic prediction often requires modeling all genetic variants together via polygenic methods. Here, we develop such a polygenic method, which we refer to as the latent Dirichlet process regression model. Dirichlet process regression is non-parametric in nature, relies on the Dirichlet process to flexibly and adaptively model the effect size distribution, and thus enjoys robust prediction performance across a broad spectrum of genetic architectures. We compare Dirichlet process regression with several commonly used prediction methods with simulations. We further apply Dirichlet process regression to predict gene expressions, to conduct PrediXcan based gene set test, to perform genomic selection of four traits in two species, and to predict eight complex traits in a human cohort.Genetic prediction of complex traits with polygenic architecture has wide application from animal breeding to disease prevention. Here, Zeng and Zhou develop a non-parametric genetic prediction method based on latent Dirichlet Process regression models.
Non-parametric estimation and doubly-censored data: general ideas and applications to AIDS.
Jewell, N P
In many epidemiologic studies of human immunodeficiency virus (HIV) disease, interest focuses on the distribution of the length of the interval of time between two events. In many such cases, statistical estimation of properties of this distribution is complicated by the fact that observation of the times of both events is subject to intervalcensoring so that the length of time between the events is never observed exactly. Following DeGruttola and Lagakos, we call such data doubly-censored. Jewell, Malani and Vittinghoff showed that, with certain assumptions and for a particular doubly-censored data structure, non-parametric maximum likelihood estimation of the interval length distribution is equivalent to non-parametric estimation of a mixing distribution. Here, we extend these ideas to various other kinds of doubly-censored data. We consider application of the methods to various studies generated by investigations into the natural history of HIV disease with particular attention given to estimation of the distribution of time between infection of an individual (an index case) and transmission of HIV to their sexual partner.
Kvist, Kajsa; Gerster, Mette; Andersen, Per Kragh; Kessing, Lars Vedel
2007-12-30
For recurrent events there is evidence that misspecification of the frailty distribution can cause severe bias in estimated regression coefficients (Am. J. Epidemiol 1998; 149:404-411; Statist. Med. 2006; 25:1672-1684). In this paper we adapt a procedure originally suggested in (Biometrika 1999; 86:381-393) for parallel data for checking the gamma frailty to recurrent events. To apply the model checking procedure, a consistent non-parametric estimator for the marginal gap time distributions is needed. This is in general not possible due to induced dependent censoring in the recurrent events setting, however, in (Biometrika 1999; 86:59-70) a non-parametric estimator for the joint gap time distributions based on the principle of inverse probability of censoring weights is suggested. Here, we attempt to apply this estimator in the model checking procedure and the performance of the method is investigated with simulations and applied to Danish registry data. The method is further investigated using the usual Kaplan-Meier estimator and a marginalized estimator for the marginal gap time distributions. We conclude that the procedure only works when the recurrent event is common and when the intra-individual association between gap times is weak.
A non-parametric Bayesian approach for clustering and tracking non-stationarities of neural spikes.
Shalchyan, Vahid; Farina, Dario
2014-02-15
Neural spikes from multiple neurons recorded in a multi-unit signal are usually separated by clustering. Drifts in the position of the recording electrode relative to the neurons over time cause gradual changes in the position and shapes of the clusters, challenging the clustering task. By dividing the data into short time intervals, Bayesian tracking of the clusters based on Gaussian cluster model has been previously proposed. However, the Gaussian cluster model is often not verified for neural spikes. We present a Bayesian clustering approach that makes no assumptions on the distribution of the clusters and use kernel-based density estimation of the clusters in every time interval as a prior for Bayesian classification of the data in the subsequent time interval. The proposed method was tested and compared to Gaussian model-based approach for cluster tracking by using both simulated and experimental datasets. The results showed that the proposed non-parametric kernel-based density estimation of the clusters outperformed the sequential Gaussian model fitting in both simulated and experimental data tests. Using non-parametric kernel density-based clustering that makes no assumptions on the distribution of the clusters enhances the ability of tracking cluster non-stationarity over time with respect to the Gaussian cluster modeling approach. Copyright © 2013 Elsevier B.V. All rights reserved.
NASA Astrophysics Data System (ADS)
Cotini, Stefano; Ripamonti, Emanuele; Caccianiga, Alessandro; Colpi, Monica; Della Ceca, Roberto; Mapelli, Michela; Severgnini, Paola; Segreto, Alberto
2013-05-01
We investigate the possible link between mergers and the enhanced activity of supermassive black holes (SMBHs) at the centre of galaxies, by comparing the merger fraction of a local sample (0.003 ≤ z < 0.03) of active galaxies - 59 active galactic nuclei host galaxies selected from the All-Sky Swift Burst Alert Telescope (BAT) Survey - with an appropriate control sample (247 sources extracted from the HyperLeda catalogue) that has the same redshift distribution as the BAT sample. We detect the interacting systems in the two samples on the basis of non-parametric structural indexes of concentration (C), asymmetry (A), clumpiness (S), Gini coefficient (G) and second-order momentum of light (M20). In particular, we propose a new morphological criterion, based on a combination of all these indexes, that improves the identification of interacting systems. We also present a new software - PyCASSo (PYTHON CAS software) - for the automatic computation of the structural indexes. After correcting for the completeness and reliability of the method, we find that the fraction of interacting galaxies among the active population (20{^{+ 7}_{- 5}} per cent) exceeds the merger fraction of the control sample (4{^{+ 1.7}_{- 1.2}} per cent). Choosing a mass-matched control sample leads to equivalent results, although with slightly lower statistical significance. Our findings support the scenario in which mergers trigger the nuclear activity of SMBHs.
Computational complexity of Boolean functions
NASA Astrophysics Data System (ADS)
Korshunov, Aleksei D.
2012-02-01
Boolean functions are among the fundamental objects of discrete mathematics, especially in those of its subdisciplines which fall under mathematical logic and mathematical cybernetics. The language of Boolean functions is convenient for describing the operation of many discrete systems such as contact networks, Boolean circuits, branching programs, and some others. An important parameter of discrete systems of this kind is their complexity. This characteristic has been actively investigated starting from Shannon's works. There is a large body of scientific literature presenting many fundamental results. The purpose of this survey is to give an account of the main results over the last sixty years related to the complexity of computation (realization) of Boolean functions by contact networks, Boolean circuits, and Boolean circuits without branching. Bibliography: 165 titles.
NASA Astrophysics Data System (ADS)
Pai, K. Gurudatha
Non-parametric frequency domain system identification methods have been in use for many years now. Many books and publications explain these methods briefly but fail to examine the practical usage of these methods. Much of the literature is full of assumptions relating to the structure and noise in the system. The statistical results derived with these assumptions are seldom validated and almost never used. The implementation details and statistical properties are a few of the factors that influence the applicability, limitation, robustness and hence the practical usage of such algorithms. In this study, some statistical properties such as mean, variance and probability distribution functions are derived independently and are implemented as a Matlab RTM toolbox. This toolbox is used to study a simple known linear time invariant system, large and complex linearized models of power grid and the physical western North American Power System (wNAPS). The importance of input design, particularly long duration, low power, periodic input and its effects on statistical measures and performance of these methods are also investigated.
NASA Astrophysics Data System (ADS)
Desai, Shantanu; Popławski, Nikodem J.
2016-04-01
The coupling between spin and torsion in the Einstein-Cartan-Sciama-Kibble theory of gravity generates gravitational repulsion at very high densities, which prevents a singularity in a black hole and may create there a new universe. We show that quantum particle production in such a universe near the last bounce, which represents the Big Bang, gives the dynamics that solves the horizon, flatness, and homogeneity problems in cosmology. For a particular range of the particle production coefficient, we obtain a nearly constant Hubble parameter that gives an exponential expansion of the universe with more than 60 e-folds, which lasts about ∼10-42 s. This scenario can thus explain cosmic inflation without requiring a fundamental scalar field and reheating. From the obtained time dependence of the scale factor, we follow the prescription of Ellis and Madsen to reconstruct in a non-parametric way a scalar field potential which gives the same dynamics of the early universe. This potential gives the slow-roll parameters of cosmic inflation, from which we calculate the tensor-to-scalar ratio, the scalar spectral index of density perturbations, and its running as functions of the production coefficient. We find that these quantities do not significantly depend on the scale factor at the Big Bounce. Our predictions for these quantities are consistent with the Planck 2015 observations.
Non-parametric analysis of LANDSAT maps using neural nets and parallel computers
NASA Technical Reports Server (NTRS)
Salu, Yehuda; Tilton, James
1991-01-01
Nearest neighbor approaches and a new neural network, the Binary Diamond, are used for the classification of images of ground pixels obtained by LANDSAT satellite. The performances are evaluated by comparing classifications of a scene in the vicinity of Washington DC. The problem of optimal selection of categories is addressed as a step in the classification process.
Titman, Andrew C
2014-07-01
A likelihood based approach to obtaining non-parametric estimates of the failure time distribution is developed for the copula based model of Wang et al. (Lifetime Data Anal 18:434-445, 2012) for current status data under dependent observation. Maximization of the likelihood involves a generalized pool-adjacent violators algorithm. The estimator coincides with the standard non-parametric maximum likelihood estimate under an independence model. Confidence intervals for the estimator are constructed based on a smoothed bootstrap. It is also shown that the non-parametric failure distribution is only identifiable if the copula linking the observation and failure time distributions is fully-specified. The method is illustrated on a previously analyzed tumorigenicity dataset.
Assessing T cell clonal size distribution: a non-parametric approach.
Bolkhovskaya, Olesya V; Zorin, Daniil Yu; Ivanchenko, Mikhail V
2014-01-01
Clonal structure of the human peripheral T-cell repertoire is shaped by a number of homeostatic mechanisms, including antigen presentation, cytokine and cell regulation. Its accurate tuning leads to a remarkable ability to combat pathogens in all their variety, while systemic failures may lead to severe consequences like autoimmune diseases. Here we develop and make use of a non-parametric statistical approach to assess T cell clonal size distributions from recent next generation sequencing data. For 41 healthy individuals and a patient with ankylosing spondylitis, who undergone treatment, we invariably find power law scaling over several decades and for the first time calculate quantitatively meaningful values of decay exponent. It has proved to be much the same among healthy donors, significantly different for an autoimmune patient before the therapy, and converging towards a typical value afterwards. We discuss implications of the findings for theoretical understanding and mathematical modeling of adaptive immunity.
Developing two non-parametric performance models for higher learning institutions
NASA Astrophysics Data System (ADS)
Kasim, Maznah Mat; Kashim, Rosmaini; Rahim, Rahela Abdul; Khan, Sahubar Ali Muhamed Nadhar
2016-08-01
Measuring the performance of higher learning Institutions (HLIs) is a must for these institutions to improve their excellence. This paper focuses on formation of two performance models: efficiency and effectiveness models by utilizing a non-parametric method, Data Envelopment Analysis (DEA). The proposed models are validated by measuring the performance of 16 public universities in Malaysia for year 2008. However, since data for one of the variables is unavailable, an estimate was used as a proxy to represent the real data. The results show that average efficiency and effectiveness scores were 0.817 and 0.900 respectively, while six universities were fully efficient and eight universities were fully effective. A total of six universities were both efficient and effective. It is suggested that the two proposed performance models would work as complementary methods to the existing performance appraisal method or as alternative methods in monitoring the performance of HLIs especially in Malaysia.
Browning, Sharon R; Browning, Brian L
2015-09-03
Existing methods for estimating historical effective population size from genetic data have been unable to accurately estimate effective population size during the most recent past. We present a non-parametric method for accurately estimating recent effective population size by using inferred long segments of identity by descent (IBD). We found that inferred segments of IBD contain information about effective population size from around 4 generations to around 50 generations ago for SNP array data and to over 200 generations ago for sequence data. In human populations that we examined, the estimates of effective size were approximately one-third of the census size. We estimate the effective population size of European-ancestry individuals in the UK four generations ago to be eight million and the effective population size of Finland four generations ago to be 0.7 million. Our method is implemented in the open-source IBDNe software package.
Browning, Sharon R.; Browning, Brian L.
2015-01-01
Existing methods for estimating historical effective population size from genetic data have been unable to accurately estimate effective population size during the most recent past. We present a non-parametric method for accurately estimating recent effective population size by using inferred long segments of identity by descent (IBD). We found that inferred segments of IBD contain information about effective population size from around 4 generations to around 50 generations ago for SNP array data and to over 200 generations ago for sequence data. In human populations that we examined, the estimates of effective size were approximately one-third of the census size. We estimate the effective population size of European-ancestry individuals in the UK four generations ago to be eight million and the effective population size of Finland four generations ago to be 0.7 million. Our method is implemented in the open-source IBDNe software package. PMID:26299365
Zerzucha, Piotr; Boguszewska, Dominika; Zagdańska, Barbara; Walczak, Beata
2012-03-16
Spot detection is a mandatory step in all available software packages dedicated to the analysis of 2D gel images. As the majority of spots do not represent individual proteins, spot detection can obscure the results of data analysis significantly. This problem can be overcome by a pixel-level analysis of 2D images. Differences between the spot and the pixel-level approaches are demonstrated by variance analysis for real data sets (part of a larger research project initiated to investigate the molecular mechanism of the response of the potato to drought stress). As the method of choice for the analysis of data variation, the non-parametric MANOVA was chosen. NP-MANOVA is recommended as a flexible and very fast tool for the evaluation of the statistical significance of the factor(s) studied.
Depth Transfer: Depth Extraction from Video Using Non-Parametric Sampling.
Karsch, Kevin; Liu, Ce; Kang, Sing Bing
2014-11-01
We describe a technique that automatically generates plausible depth maps from videos using non-parametric depth sampling. We demonstrate our technique in cases where past methods fail (non-translating cameras and dynamic scenes). Our technique is applicable to single images as well as videos. For videos, we use local motion cues to improve the inferred depth maps, while optical flow is used to ensure temporal depth consistency. For training and evaluation, we use a Kinect-based system to collect a large data set containing stereoscopic videos with known depths. We show that our depth estimation technique outperforms the state-of-the-art on benchmark databases. Our technique can be used to automatically convert a monoscopic video into stereo for 3D visualization, and we demonstrate this through a variety of visually pleasing results for indoor and outdoor scenes, including results from the feature film Charade.
Factors associated with malnutrition among tribal children in India: a non-parametric approach.
Debnath, Avijit; Bhattacharjee, Nairita
2014-06-01
The purpose of this study is to identify the determinants of malnutrition among the tribal children in India. The investigation is based on secondary data compiled from the National Family Health Survey-3. We used a classification and regression tree model, a non-parametric approach, to address the objective. Our analysis shows that breastfeeding practice, economic status, antenatal care of mother and women's decision-making autonomy are negatively associated with malnutrition among tribal children. We identify maternal malnutrition and urban concentration of household as the two risk factors for child malnutrition. The identified associated factors may be used for designing and targeting preventive programmes for malnourished tribal children. © The Author [2014]. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.
Yaghotipoor, Anita; Farshadfar, E
2007-08-15
In order to determine phenotypic stability and contribution of yield components in the phenotypic stability of grain yield 21 genotypes of chickpea were evaluated in a randomized complete block design with three replications under rainfed and irrigated conditions in college of Agriculture, Razi University of Kermanshah, Iran, across 4 years. Non-parametric combined analysis of variance showed high significant differences for genotypes and genotype-environment interaction indicating the presence of genetic variation and possibility of selection for stable genotypes. The genotype number 8 (Filip92-9c) with minimum Si(2) and Si(2) of yield stability and grain yield in one parameter also revealed that genotype Filip92-9c was the most desirable variety for both yield and yield stability. Component analysis using Ci-value displayed that number of shrub per unit area has the most contribution on the grain yield phenotypic stability.
Bandeen-Roche, Karen; Ning, Jing
2008-03-01
Most research on the study of associations among paired failure times has either assumed time invariance or been based on complex measures or estimators. Little has accommodated competing risks. This paper targets the conditional cause-specific hazard ratio, henceforth called the cause-specific cross ratio, a recent modification of the conditional hazard ratio designed to accommodate competing risks data. Estimation is accomplished by an intuitive, non-parametric method that localizes Kendall's tau. Time variance is accommodated through a partitioning of space into 'bins' between which the strength of association may differ. Inferential procedures are developed, small-sample performance is evaluated and the methods are applied to the investigation of familial association in dementia onset.
The application of non-parametric statistical techniques to an ALARA programme.
Moon, J H; Cho, Y H; Kang, C S
2001-01-01
For the cost-effective reduction of occupational radiation dose (ORD) at nuclear power plants, it is necessary to identify what are the processes of repetitive high ORD during maintenance and repair operations. To identify the processes, the point values such as mean and median are generally used, but they sometimes lead to misjudgment since they cannot show other important characteristics such as dose distributions and frequencies of radiation jobs. As an alternative, the non-parametric analysis method is proposed, which effectively identifies the processes of repetitive high ORD. As a case study, the method is applied to ORD data of maintenance and repair processes at Kori Units 3 and 4 that are pressurised water reactors with 950 MWe capacity and have been operating since 1986 and 1987 respectively, in Korea and the method is demonstrated to be an efficient way of analysing the data.
Robust non-parametric tests for complex-repeated measures problems in ophthalmology.
Brombin, Chiara; Midena, Edoardo; Salmaso, Luigi
2013-12-01
The NonParametric Combination methodology (NPC) of dependent permutation tests allows the experimenter to face many complex multivariate testing problems and represents a convincing and powerful alternative to standard parametric methods. The main advantage of this approach lies in its flexibility in handling any type of variable (categorical and quantitative, with or without missing values) while at the same time taking dependencies among those variables into account without the need of modelling them. NPC methodology enables to deal with repeated measures, paired data, restricted alternative hypotheses, missing data (completely at random or not), high-dimensional and small sample size data. Hence, NPC methodology can offer a significant contribution to successful research in biomedical studies with several endpoints, since it provides reasonably efficient solutions and clear interpretations of inferential results. Pesarin F. Multivariate permutation tests: with application in biostatistics. Chichester-New York: John Wiley &Sons, 2001; Pesarin F, Salmaso L. Permutation tests for complex data: theory, applications and software. Chichester, UK: John Wiley &Sons, 2010. We focus on non-parametric permutation solutions to two real-case studies in ophthalmology, concerning complex-repeated measures problems. For each data set, different analyses are presented, thus highlighting characteristic aspects of the data structure itself. Our goal is to present different solutions to multivariate complex case studies, guiding researchers/readers to choose, from various possible interpretations of a problem, the one that has the highest flexibility and statistical power under a set of less stringent assumptions. MATLAB code has been implemented to carry out the analyses.
Rights, Jason D; Sterba, Sonya K
2016-11-01
Multilevel data structures are common in the social sciences. Often, such nested data are analysed with multilevel models (MLMs) in which heterogeneity between clusters is modelled by continuously distributed random intercepts and/or slopes. Alternatively, the non-parametric multilevel regression mixture model (NPMM) can accommodate the same nested data structures through discrete latent class variation. The purpose of this article is to delineate analytic relationships between NPMM and MLM parameters that are useful for understanding the indirect interpretation of the NPMM as a non-parametric approximation of the MLM, with relaxed distributional assumptions. We define how seven standard and non-standard MLM specifications can be indirectly approximated by particular NPMM specifications. We provide formulas showing how the NPMM can serve as an approximation of the MLM in terms of intraclass correlation, random coefficient means and (co)variances, heteroscedasticity of residuals at level 1, and heteroscedasticity of residuals at level 2. Further, we discuss how these relationships can be useful in practice. The specific relationships are illustrated with simulated graphical demonstrations, and direct and indirect interpretations of NPMM classes are contrasted. We provide an R function to aid in implementing and visualizing an indirect interpretation of NPMM classes. An empirical example is presented and future directions are discussed. © 2016 The British Psychological Society.
NASA Astrophysics Data System (ADS)
Gallego, A.; Benavent-Climent, A.; Romo-Melo, L.
2015-08-01
The paper proposes a new application of non-parametric statistical processing of signals recorded from vibration tests for damage detection and evaluation on I-section steel segments. The steel segments investigated constitute the energy dissipating part of a new type of hysteretic damper that is used for passive control of buildings and civil engineering structures subjected to earthquake-type dynamic loadings. Two I-section steel segments with different levels of damage were instrumented with piezoceramic sensors and subjected to controlled white noise random vibrations. The signals recorded during the tests were processed using two non-parametric methods (the power spectral density method and the frequency response function method) that had never previously been applied to hysteretic dampers. The appropriateness of these methods for quantifying the level of damage on the I-shape steel segments is validated experimentally. Based on the results of the random vibrations, the paper proposes a new index that predicts the level of damage and the proximity of failure of the hysteretic damper.
Pair distribution function computed tomography.
Jacques, Simon D M; Di Michiel, Marco; Kimber, Simon A J; Yang, Xiaohao; Cernik, Robert J; Beale, Andrew M; Billinge, Simon J L
2013-01-01
An emerging theme of modern composites and devices is the coupling of nanostructural properties of materials with their targeted arrangement at the microscale. Of the imaging techniques developed that provide insight into such designer materials and devices, those based on diffraction are particularly useful. However, to date, these have been heavily restrictive, providing information only on materials that exhibit high crystallographic ordering. Here we describe a method that uses a combination of X-ray atomic pair distribution function analysis and computed tomography to overcome this limitation. It allows the structure of nanocrystalline and amorphous materials to be identified, quantified and mapped. We demonstrate the method with a phantom object and subsequently apply it to resolving, in situ, the physicochemical states of a heterogeneous catalyst system. The method may have potential impact across a range of disciplines from materials science, biomaterials, geology, environmental science, palaeontology and cultural heritage to health.
Metacognition: computation, biology and function.
Fleming, Stephen M; Dolan, Raymond J; Frith, Christopher D
2012-05-19
Many complex systems maintain a self-referential check and balance. In animals, such reflective monitoring and control processes have been grouped under the rubric of metacognition. In this introductory article to a Theme Issue on metacognition, we review recent and rapidly progressing developments from neuroscience, cognitive psychology, computer science and philosophy of mind. While each of these areas is represented in detail by individual contributions to the volume, we take this opportunity to draw links between disciplines, and highlight areas where further integration is needed. Specifically, we cover the definition, measurement, neurobiology and possible functions of metacognition, and assess the relationship between metacognition and consciousness. We propose a framework in which level of representation, order of behaviour and access consciousness are orthogonal dimensions of the conceptual landscape.
Metacognition: computation, biology and function
Fleming, Stephen M.; Dolan, Raymond J.; Frith, Christopher D.
2012-01-01
Many complex systems maintain a self-referential check and balance. In animals, such reflective monitoring and control processes have been grouped under the rubric of metacognition. In this introductory article to a Theme Issue on metacognition, we review recent and rapidly progressing developments from neuroscience, cognitive psychology, computer science and philosophy of mind. While each of these areas is represented in detail by individual contributions to the volume, we take this opportunity to draw links between disciplines, and highlight areas where further integration is needed. Specifically, we cover the definition, measurement, neurobiology and possible functions of metacognition, and assess the relationship between metacognition and consciousness. We propose a framework in which level of representation, order of behaviour and access consciousness are orthogonal dimensions of the conceptual landscape. PMID:22492746
NASA Astrophysics Data System (ADS)
Löw, Fabian; Conrad, Christopher; Michel, Ulrich
2015-10-01
This study addressed the classification of multi-temporal satellite data from RapidEye by considering different classifier algorithms and decision fusion. Four non-parametric classifier algorithms, decision tree (DT), random forest (RF), support vector machine (SVM), and multilayer perceptron (MLP), were applied to map crop types in various irrigated landscapes in Central Asia. A novel decision fusion strategy to combine the outputs of the classifiers was proposed. This approach is based on randomly selecting subsets of the input dataset and aggregating the probabilistic outputs of the base classifiers with another meta-classifier. During the decision fusion, the reliability of each base classifier algorithm was considered to exclude less reliable inputs at the class-basis. The spatial and temporal transferability of the classifiers was evaluated using data sets from four different agricultural landscapes with different spatial extents and from different years. A detailed accuracy assessment showed that none of the stand-alone classifiers was the single best performing. Despite the very good performance of the base classifiers, there was still up to 50% disagreement in the maps produced by the two single best classifiers, RF and SVM. The proposed fusion strategy, however, increased overall accuracies up to 6%. In addition, it was less sensitive to reduced training set sizes and produced more realistic land use maps with less speckle. The proposed fusion approach was better transferable to data sets from other years, i.e. resulted in higher accuracies for the investigated classes. The fusion approach is computationally efficient and appears well suited for mapping diverse crop categories based on sensors with a similar high repetition rate and spatial resolution like RapidEye, for instance the upcoming Sentinel-2 mission.
ERIC Educational Resources Information Center
Maydeu-Olivares, Albert
2005-01-01
Chernyshenko, Stark, Chan, Drasgow, and Williams (2001) investigated the fit of Samejima's logistic graded model and Levine's non-parametric MFS model to the scales of two personality questionnaires and found that the graded model did not fit well. We attribute the poor fit of the graded model to small amounts of multidimensionality present in…
ERIC Educational Resources Information Center
Maydeu-Olivares, Albert
2005-01-01
Chernyshenko, Stark, Chan, Drasgow, and Williams (2001) investigated the fit of Samejima's logistic graded model and Levine's non-parametric MFS model to the scales of two personality questionnaires and found that the graded model did not fit well. We attribute the poor fit of the graded model to small amounts of multidimensionality present in…
Non-parametric photic entrainment of Djungarian hamsters with different rhythmic phenotypes.
Schöttner, Konrad; Hauer, Jane; Weinert, Dietmar
To investigate the role of non-parametric light effects in entrainment, Djungarian hamsters of two different circadian phenotypes were exposed to skeleton photoperiods, or to light pulses at different circadian times, to compile phase response curves (PRCs). Wild-type (WT) hamsters show daily rhythms of locomotor activity in accord with the ambient light/dark conditions, with activity onset and offset strongly coupled to light-off and light-on, respectively. Hamsters of the delayed activity onset (DAO) phenotype, in contrast, progressively delay their activity onset, whereas activity offset remains coupled to light-on. The present study was performed to better understand the underlying mechanisms of this phenomenon. Hamsters of DAO and WT phenotypes were kept first under standard housing conditions with a 14:10 h light-dark cycle, and then exposed to skeleton photoperiods (one or two 15-min light pulses of 100 lx at the times of the former light-dark and/or dark-light transitions). In a second experiment, hamsters of both phenotypes were transferred to constant darkness and allowed to free-run until the lengths of the active (α) and resting (ρ) periods were equal (α:ρ = 1). At this point, animals were then exposed to light pulses (100 lx, 15 min) at different circadian times (CTs). Phase and period changes were estimated separately for activity onset and offset. When exposed to skeleton-photoperiods with one or two light pulses, the daily activity patterns of DAO and WT hamsters were similar to those obtained under conditions of a complete 14:10 h light-dark cycle. However, in the case of giving only one light pulse at the time of the former light-dark transition, animals temporarily free-ran until activity offset coincided with the light pulse. These results show that photic entrainment of the circadian activity rhythm is attained primarily via non-parametric mechanisms, with the "morning" light pulse being the essential cue. In the second experiment, typical
Karathanasis, Nestoras; Tsamardinos, Ioannis
2016-01-01
Background The advance of omics technologies has made possible to measure several data modalities on a system of interest. In this work, we illustrate how the Non-Parametric Combination methodology, namely NPC, can be used for simultaneously assessing the association of different molecular quantities with an outcome of interest. We argue that NPC methods have several potential applications in integrating heterogeneous omics technologies, as for example identifying genes whose methylation and transcriptional levels are jointly deregulated, or finding proteins whose abundance shows the same trends of the expression of their encoding genes. Results We implemented the NPC methodology within “omicsNPC”, an R function specifically tailored for the characteristics of omics data. We compare omicsNPC against a range of alternative methods on simulated as well as on real data. Comparisons on simulated data point out that omicsNPC produces unbiased / calibrated p-values and performs equally or significantly better than the other methods included in the study; furthermore, the analysis of real data show that omicsNPC (a) exhibits higher statistical power than other methods, (b) it is easily applicable in a number of different scenarios, and (c) its results have improved biological interpretability. Conclusions The omicsNPC function competitively behaves in all comparisons conducted in this study. Taking into account that the method (i) requires minimal assumptions, (ii) it can be used on different studies designs and (iii) it captures the dependences among heterogeneous data modalities, omicsNPC provides a flexible and statistically powerful solution for the integrative analysis of different omics data. PMID:27812137
Computing Functions by Approximating the Input
ERIC Educational Resources Information Center
Goldberg, Mayer
2012-01-01
In computing real-valued functions, it is ordinarily assumed that the input to the function is known, and it is the output that we need to approximate. In this work, we take the opposite approach: we show how to compute the values of some transcendental functions by approximating the input to these functions, and obtaining exact answers for their…
Li, Ming; Gardiner, Joseph C; Breslau, Naomi; Anthony, James C; Lu, Qing
2014-07-01
Cox-regression-based methods have been commonly used for the analyses of survival outcomes, such as age-at-disease-onset. These methods generally assume the hazard functions are proportional among various risk groups. However, such an assumption may not be valid in genetic association studies, especially when complex interactions are involved. In addition, genetic association studies commonly adopt case-control designs. Direct use of Cox regression to case-control data may yield biased estimators and incorrect statistical inference. We propose a non-parametric approach, the weighted Nelson-Aalen (WNA) approach, for detecting genetic variants that are associated with age-dependent outcomes. The proposed approach can be directly applied to prospective cohort studies, and can be easily extended for population-based case-control studies. Moreover, it does not rely on any assumptions of the disease inheritance models, and is able to capture high-order gene-gene interactions. Through simulations, we show the proposed approach outperforms Cox-regression-based methods in various scenarios. We also conduct an empirical study of progression of nicotine dependence by applying the WNA approach to three independent datasets from the Study of Addiction: Genetics and Environment. In the initial dataset, two SNPs, rs6570989 and rs2930357, located in genes GRIK2 and CSMD1, are found to be significantly associated with the progression of nicotine dependence (ND). The joint association is further replicated in two independent datasets. Further analysis suggests that these two genes may interact and be associated with the progression of ND. As demonstrated by the simulation studies and real data analysis, the proposed approach provides an efficient tool for detecting genetic interactions associated with age-at-onset outcomes.
Brain reserve and cognitive decline: a non-parametric systematic review.
Valenzuela, Michael J; Sachdev, Perminder
2006-08-01
A previous companion paper to this report (Valenzuela and Sachdev, Psychological Medicine 2006, 36, 441-454) suggests a link between behavioural brain reserve and incident dementia; however, the issues of covariate control and ascertainment bias were not directly addressed. Our aim was to quantitatively review an independent set of longitudinal studies of cognitive change in order to clarify these factors. Cohort studies of the effects of education, occupation, and mental activities on cognitive decline were of interest. Abstracts were identified in MEDLINE (1966-September 2004), CURRENT CONTENTS (to September 2004), PsychINFO (1984-September 2004), Cochrane Library Databases and reference lists from relevant articles. Eighteen studies met inclusion criteria. Key information was extracted by both reviewers onto a standard template with a high level of agreement. Cognitive decline studies were integrated using a non-parametric method after converting outcome data onto a common effect size metric. Higher behavioural brain reserve was related to decreased longitudinal cognitive decline after control for covariates in source studies (phi=1.70, p<0.001). This effect was robust to correction for both multiple predictors and multiple outcome measures and was the result of integrating data derived from more than 47000 individuals. This study affirms that the link between behavioural brain reserve and incident dementia is most likely due to fundamentally different cognitive trajectories rather than confound factors.
Non-parametric three-way mixed ANOVA with aligned rank tests.
Oliver-Rodríguez, Juan C; Wang, X T
2015-02-01
Research problems that require a non-parametric analysis of multifactor designs with repeated measures arise in the behavioural sciences. There is, however, a lack of available procedures in commonly used statistical packages. In the present study, a generalization of the aligned rank test for the two-way interaction is proposed for the analysis of the typical sources of variation in a three-way analysis of variance (ANOVA) with repeated measures. It can be implemented in the usual statistical packages. Its statistical properties are tested by using simulation methods with two sample sizes (n = 30 and n = 10) and three distributions (normal, exponential and double exponential). Results indicate substantial increases in power for non-normal distributions in comparison with the usual parametric tests. Similar levels of Type I error for both parametric and aligned rank ANOVA were obtained with non-normal distributions and large sample sizes. Degrees-of-freedom adjustments for Type I error control in small samples are proposed. The procedure is applied to a case study with 30 participants per group where it detects gender differences in linguistic abilities in blind children not shown previously by other methods.
Trend Analysis of Golestan's Rivers Discharges Using Parametric and Non-parametric Methods
NASA Astrophysics Data System (ADS)
Mosaedi, Abolfazl; Kouhestani, Nasrin
2010-05-01
One of the major problems in human life is climate changes and its problems. Climate changes will cause changes in rivers discharges. The aim of this research is to investigate the trend analysis of seasonal and yearly rivers discharges of Golestan province (Iran). In this research four trend analysis method including, conjunction point, linear regression, Wald-Wolfowitz and Mann-Kendall, for analyzing of river discharges in seasonal and annual periods in significant level of 95% and 99% were applied. First, daily discharge data of 12 hydrometrics stations with a length of 42 years (1965-2007) were selected, after some common statistical tests such as, homogeneity test (by applying G-B and M-W tests), the four mentioned trends analysis tests were applied. Results show that in all stations, for summer data time series, there are decreasing trends with a significant level of 99% according to Mann-Kendall (M-K) test. For autumn time series data, all four methods have similar results. For other periods, the results of these four tests were more or less similar together. While, for some stations the results of tests were different. Keywords: Trend Analysis, Discharge, Non-parametric methods, Wald-Wolfowitz, The Mann-Kendall test, Golestan Province.
Non-Parametric Approach for Trend Delineation in the Canadian Prairie
NASA Astrophysics Data System (ADS)
Agboma, C. O.; Snelgrove, K. R.
2006-12-01
The recurrent issue of drought in the Canadian Prairie has been a subject of great interest in the academia as well as in the national policy making circle owing to the obvious multifarious negative impacts of drought on people and the environment within this region. Mann-Kendall test in conjunction with LOWESS (LOcally WEighted Scatterplot Smoothing) method were employed in the investigation of two hydrologic variables (streamflow and precipitation) to ascertain the trend pattern in ten different stations in the Upper Assiniboine River Basin located on the Prairie for a 30-year period. The results obtained revealed p-values which were greater than the prescribed alpha value of 0.05 in some of the stations signifying a strong common trend direction in these variables over the years. Non-parametric techniques are robust and resourceful for trend detection and analysis in that no prior normality assumptions are made about the data used. A clearer understanding of any anomaly in the regional trend of these variables is pertinent for a sound water resources management including drought monitoring and forecasting.
Zoffoli, Luca; Ditroilo, Massimiliano; Federici, Ario; Lucertini, Francesco
2017-09-09
This study used surface electromyography (EMG) to investigate the regions and patterns of activity of the external oblique (EO), erector spinae longissimus (ES), multifidus (MU) and rectus abdominis (RA) muscles during walking (W) and pole walking (PW) performed at different speeds and grades. Eighteen healthy adults undertook W and PW on a motorized treadmill at 60% and 100% of their walk-to-run preferred transition speed at 0% and 7% treadmill grade. The Teager-Kaiser energy operator was employed to improve the muscle activity detection and statistical non-parametric mapping based on paired t-tests was used to highlight statistical differences in the EMG patterns corresponding to different trials. The activation amplitude of all trunk muscles increased at high speed, while no differences were recorded at 7% treadmill grade. ES and MU appeared to support the upper body at the heel-strike during both W and PW, with the latter resulting in elevated recruitment of EO and RA as required to control for the longer stride and the push of the pole. Accordingly, the greater activity of the abdominal muscles and the comparable intervention of the spine extensors supports the use of poles by walkers seeking higher engagement of the lower trunk region. Copyright © 2017 Elsevier Ltd. All rights reserved.
Comparison of non-parametric methods for ungrouping coarsely aggregated data.
Rizzi, Silvia; Thinggaard, Mikael; Engholm, Gerda; Christensen, Niels; Johannesen, Tom Børge; Vaupel, James W; Lindahl-Jacobsen, Rune
2016-05-23
Histograms are a common tool to estimate densities non-parametrically. They are extensively encountered in health sciences to summarize data in a compact format. Examples are age-specific distributions of death or onset of diseases grouped in 5-years age classes with an open-ended age group at the highest ages. When histogram intervals are too coarse, information is lost and comparison between histograms with different boundaries is arduous. In these cases it is useful to estimate detailed distributions from grouped data. From an extensive literature search we identify five methods for ungrouping count data. We compare the performance of two spline interpolation methods, two kernel density estimators and a penalized composite link model first via a simulation study and then with empirical data obtained from the NORDCAN Database. All methods analyzed can be used to estimate differently shaped distributions; can handle unequal interval length; and allow stretches of 0 counts. The methods show similar performance when the grouping scheme is relatively narrow, i.e. 5-years age classes. With coarser age intervals, i.e. in the presence of open-ended age groups, the penalized composite link model performs the best. We give an overview and test different methods to estimate detailed distributions from grouped count data. Health researchers can benefit from these versatile methods, which are ready for use in the statistical software R. We recommend using the penalized composite link model when data are grouped in wide age classes.
A Non-parametric Approach to the Overall Estimate of Cognitive Load Using NIRS Time Series
Keshmiri, Soheil; Sumioka, Hidenobu; Yamazaki, Ryuji; Ishiguro, Hiroshi
2017-01-01
We present a non-parametric approach to prediction of the n-back n ∈ {1, 2} task as a proxy measure of mental workload using Near Infrared Spectroscopy (NIRS) data. In particular, we focus on measuring the mental workload through hemodynamic responses in the brain induced by these tasks, thereby realizing the potential that they can offer for their detection in real world scenarios (e.g., difficulty of a conversation). Our approach takes advantage of intrinsic linearity that is inherent in the components of the NIRS time series to adopt a one-step regression strategy. We demonstrate the correctness of our approach through its mathematical analysis. Furthermore, we study the performance of our model in an inter-subject setting in contrast with state-of-the-art techniques in the literature to show a significant improvement on prediction of these tasks (82.50 and 86.40% for female and male participants, respectively). Moreover, our empirical analysis suggest a gender difference effect on the performance of the classifiers (with male data exhibiting a higher non-linearity) along with the left-lateralized activation in both genders with higher specificity in females. PMID:28217088
Non-Parametric Evolutionary Algorithm for Estimating Root Zone Soil Moisture
NASA Astrophysics Data System (ADS)
Mohanty, B.; Shin, Y.; Ines, A. M.
2013-12-01
Prediction of root zone soil moisture is critical for water resources management. In this study, we explored a non-parametric evolutionary algorithm for estimating root zone soil moisture from a time series of spatially-distributed rainfall across multiple weather locations under two different hydro-climatic regions. A new genetic algorithm-based hidden Markov model (HMMGA) was developed to estimate long-term root zone soil moisture dynamics at different soil depths. Also, we analyzed rainfall occurrence probabilities and dry/wet spell lengths reproduced by this approach. The HMMGA was used to estimate the optimal state sequences (weather states) based on the precipitation history. Historical root zone soil moisture statistics were then determined based on the weather state conditions. To test the new approach, we selected two different soil moisture fields, Oklahoma (130 km x 130 km) and Illinois (300 km x 500 km), during 1995 to 2009 and 1994 to 2010, respectively. We found that the newly developed framework performed well in predicting root zone soil moisture dynamics at both the spatial scales. Also, the reproduced rainfall occurrence probabilities and dry/wet spell lengths matched well with the observations at the spatio-temporal scales. Since the proposed algorithm requires only precipitation and historical soil moisture data from existing, established weather stations, it can serve an attractive alternative for predicting root zone soil moisture in the future using climate change scenarios and root zone soil moisture history.
Deformable mirror models for open-loop adaptive optics using non-parametric estimation techniques
NASA Astrophysics Data System (ADS)
Guzmán, Dani; De Cos Juez, Francisco Javier; Myers, Richard; Sánchez Lasheras, Fernando; Young, Laura K.; Guesalaga, Andrés
2010-07-01
Open-loop adaptive optics is a technique in which the turbulent wavefront is measured before it hits the deformable mirror for correction; therefore the correct control of the mirror in open-loop is key in achieving the expected level of correction. In this paper, we present non-parametric estimation techniques to model deformable mirrors working in open-loop. We have results with mirrors characterized by non-linear behavior: a Xinetics electrostrictive mirror and a Boston Micromachines MEMS mirror. The inputs for these models are the wavefront corrections to apply to the mirror and the outputs are the set of voltages to shape the mirror. We have performed experiments on both mirrors, achieving Go-To errors relative to peak-to-peak wavefront excursion in the order of 1 % RMS for the Xinetics mirror and 3 % RMS for the Boston mirror . These techniques are trained with interferometric data from the mirror under control; therefore they do not depend on the physical parameters of the device.
Non-parametric estimation of a time-dependent predictive accuracy curve.
Saha-Chaudhuri, P; Heagerty, P J
2013-01-01
A major biomedical goal associated with evaluating a candidate biomarker or developing a predictive model score for event-time outcomes is to accurately distinguish between incident cases from the controls surviving beyond t throughout the entire study period. Extensions of standard binary classification measures like time-dependent sensitivity, specificity, and receiver operating characteristic (ROC) curves have been developed in this context (Heagerty, P. J., and others, 2000. Time-dependent ROC curves for censored survival data and a diagnostic marker. Biometrics 56, 337-344). We propose a direct, non-parametric method to estimate the time-dependent Area under the curve (AUC) which we refer to as the weighted mean rank (WMR) estimator. The proposed estimator performs well relative to the semi-parametric AUC curve estimator of Heagerty and Zheng (2005. Survival model predictive accuracy and ROC curves. Biometrics 61, 92-105). We establish the asymptotic properties of the proposed estimator and show that the accuracy of markers can be compared very simply using the difference in the WMR statistics. Estimators of pointwise standard errors are provided.
A Non-parametric Approach to the Overall Estimate of Cognitive Load Using NIRS Time Series.
Keshmiri, Soheil; Sumioka, Hidenobu; Yamazaki, Ryuji; Ishiguro, Hiroshi
2017-01-01
We present a non-parametric approach to prediction of the n-back n ∈ {1, 2} task as a proxy measure of mental workload using Near Infrared Spectroscopy (NIRS) data. In particular, we focus on measuring the mental workload through hemodynamic responses in the brain induced by these tasks, thereby realizing the potential that they can offer for their detection in real world scenarios (e.g., difficulty of a conversation). Our approach takes advantage of intrinsic linearity that is inherent in the components of the NIRS time series to adopt a one-step regression strategy. We demonstrate the correctness of our approach through its mathematical analysis. Furthermore, we study the performance of our model in an inter-subject setting in contrast with state-of-the-art techniques in the literature to show a significant improvement on prediction of these tasks (82.50 and 86.40% for female and male participants, respectively). Moreover, our empirical analysis suggest a gender difference effect on the performance of the classifiers (with male data exhibiting a higher non-linearity) along with the left-lateralized activation in both genders with higher specificity in females.
Two non-parametric methods for derivation of constraints from radiotherapy dose-histogram data
NASA Astrophysics Data System (ADS)
Ebert, M. A.; Gulliford, S. L.; Buettner, F.; Foo, K.; Haworth, A.; Kennedy, A.; Joseph, D. J.; Denham, J. W.
2014-07-01
Dose constraints based on histograms provide a convenient and widely-used method for informing and guiding radiotherapy treatment planning. Methods of derivation of such constraints are often poorly described. Two non-parametric methods for derivation of constraints are described and investigated in the context of determination of dose-specific cut-points—values of the free parameter (e.g., percentage volume of the irradiated organ) which best reflect resulting changes in complication incidence. A method based on receiver operating characteristic (ROC) analysis and one based on a maximally-selected standardized rank sum are described and compared using rectal toxicity data from a prostate radiotherapy trial. Multiple test corrections are applied using a free step-down resampling algorithm, which accounts for the large number of tests undertaken to search for optimal cut-points and the inherent correlation between dose-histogram points. Both methods provide consistent significant cut-point values, with the rank sum method displaying some sensitivity to the underlying data. The ROC method is simple to implement and can utilize a complication atlas, though an advantage of the rank sum method is the ability to incorporate all complication grades without the need for grade dichotomization.
NASA Astrophysics Data System (ADS)
Dimas, George; Iakovidis, Dimitris K.; Karargyris, Alexandros; Ciuti, Gastone; Koulaouzidis, Anastasios
2017-09-01
Wireless capsule endoscopy is a non-invasive screening procedure of the gastrointestinal (GI) tract performed with an ingestible capsule endoscope (CE) of the size of a large vitamin pill. Such endoscopes are equipped with a usually low-frame-rate color camera which enables the visualization of the GI lumen and the detection of pathologies. The localization of the commercially available CEs is performed in the 3D abdominal space using radio-frequency (RF) triangulation from external sensor arrays, in combination with transit time estimation. State-of-the-art approaches, such as magnetic localization, which have been experimentally proved more accurate than the RF approach, are still at an early stage. Recently, we have demonstrated that CE localization is feasible using solely visual cues and geometric models. However, such approaches depend on camera parameters, many of which are unknown. In this paper the authors propose a novel non-parametric visual odometry (VO) approach to CE localization based on a feed-forward neural network architecture. The effectiveness of this approach in comparison to state-of-the-art geometric VO approaches is validated using a robotic-assisted in vitro experimental setup.
Alternative methods of marginal abatement cost estimation: Non- parametric distance functions
Boyd, G.; Molburg, J.; Prince, R.
1996-12-31
This project implements a economic methodology to measure the marginal abatement costs of pollution by measuring the lost revenue implied by an incremental reduction in pollution. It utilizes observed performance, or `best practice`, of facilities to infer the marginal abatement cost. The initial stage of the project is to use data from an earlier published study on productivity trends and pollution in electric utilities to test this approach and to provide insights on its implementation to issues of cost-benefit analysis studies needed by the Department of Energy. The basis for this marginal abatement cost estimation is a relationship between the outputs and the inputs of a firm or plant. Given a fixed set of input resources, including quasi-fixed inputs like plant and equipment and variable inputs like labor and fuel, a firm is able to produce a mix of outputs. This paper uses this theoretical view of the joint production process to implement a methodology and obtain empirical estimates of marginal abatement costs. These estimates are compared to engineering estimates.
Ocampo-Duque, William; Osorio, Carolina; Piamba, Christian; Schuhmacher, Marta; Domingo, José L
2013-02-01
The integration of water quality monitoring variables is essential in environmental decision making. Nowadays, advanced techniques to manage subjectivity, imprecision, uncertainty, vagueness, and variability are required in such complex evaluation process. We here propose a probabilistic fuzzy hybrid model to assess river water quality. Fuzzy logic reasoning has been used to compute a water quality integrative index. By applying a Monte Carlo technique, based on non-parametric probability distributions, the randomness of model inputs was estimated. Annual histograms of nine water quality variables were built with monitoring data systematically collected in the Colombian Cauca River, and probability density estimations using the kernel smoothing method were applied to fit data. Several years were assessed, and river sectors upstream and downstream the city of Santiago de Cali, a big city with basic wastewater treatment and high industrial activity, were analyzed. The probabilistic fuzzy water quality index was able to explain the reduction in water quality, as the river receives a larger number of agriculture, domestic, and industrial effluents. The results of the hybrid model were compared to traditional water quality indexes. The main advantage of the proposed method is that it considers flexible boundaries between the linguistic qualifiers used to define the water status, being the belongingness of water quality to the diverse output fuzzy sets or classes provided with percentiles and histograms, which allows classify better the real water condition. The results of this study show that fuzzy inference systems integrated to stochastic non-parametric techniques may be used as complementary tools in water quality indexing methodologies.
NASA Astrophysics Data System (ADS)
Hassani, Hossein; Huang, Xu; Gupta, Rangan; Ghodsi, Mansi
2016-10-01
In a recent paper, Gupta et al., (2015), analyzed whether sunspot numbers cause global temperatures based on monthly data covering the period 1880:1-2013:9. The authors find that standard time domain Granger causality test fails to reject the null hypothesis that sunspot numbers do not cause global temperatures for both full and sub-samples, namely 1880:1-1936:2, 1936:3-1986:11 and 1986:12-2013:9 (identified based on tests of structural breaks). However, frequency domain causality test detects predictability for the full-sample at short (2-2.6 months) cycle lengths, but not the sub-samples. But since, full-sample causality cannot be relied upon due to structural breaks, Gupta et al., (2015) conclude that the evidence of causality running from sunspot numbers to global temperatures is weak and inconclusive. Given the importance of the issue of global warming, our current paper aims to revisit this issue of whether sunspot numbers cause global temperatures, using the same data set and sub-samples used by Gupta et al., (2015), based on an nonparametric Singular Spectrum Analysis (SSA)-based causality test. Based on this test, we however, show that sunspot numbers have predictive ability for global temperatures for the three sub-samples, over and above the full-sample. Thus, generally speaking, our non-parametric SSA-based causality test outperformed both time domain and frequency domain causality tests and highlighted that sunspot numbers have always been important in predicting global temperatures.
Semi-parametric and non-parametric methods for clinical trials with incomplete data.
O'Brien, Peter C; Zhang, David; Bailey, Kent R
2005-02-15
Last observation carried forward (LOCF) and analysis using only data from subjects who complete a trial (Completers) are commonly used techniques for analysing data in clinical trials with incomplete data when the endpoint is change from baseline at last scheduled visit. We propose two alternative methods. The semi-parametric method, which cumulates changes observed between consecutive time points, is conceptually similar to the familiar life-table method and corresponding Kaplan-Meier estimation when the primary endpoint is time to event. A non-parametric analogue of LOCF is obtained by carrying forward, not the observed value, but the rank of the change from baseline at the last observation for each subject. We refer to this method as the LRCF method. Both procedures retain the simplicity of LOCF and Completers analyses and, like these methods, do not require data imputation or modelling assumptions. In the absence of any incomplete data they reduce to the usual two-sample tests. In simulations intended to reflect chronic diseases that one might encounter in practice, LOCF was observed to produce markedly biased estimates and markedly inflated type I error rates when censoring was unequal in the two treatment arms. These problems did not arise with the Completers, Cumulative Change, or LRCF methods. Cumulative Change and LRCF were more powerful than Completers, and the Cumulative Change test provided more efficient estimates than the Completers analysis, in all simulations. We conclude that the Cumulative Change and LRCF methods are preferable to LOCF and Completers analyses. Mixed model repeated measures (MMRM) performed similarly to Cumulative Change and LRCF and makes somewhat less restrictive assumptions about missingness mechanisms, so that it is also a reasonable alternative to LOCF and Completers analyses.
Parametric vs. non-parametric daily weather generator: validation and comparison
NASA Astrophysics Data System (ADS)
Dubrovsky, Martin
2016-04-01
As the climate models (GCMs and RCMs) fail to satisfactorily reproduce the real-world surface weather regime, various statistical methods are applied to downscale GCM/RCM outputs into site-specific weather series. The stochastic weather generators are among the most favourite downscaling methods capable to produce realistic (observed like) meteorological inputs for agrological, hydrological and other impact models used in assessing sensitivity of various ecosystems to climate change/variability. To name their advantages, the generators may (i) produce arbitrarily long multi-variate synthetic weather series representing both present and changed climates (in the latter case, the generators are commonly modified by GCM/RCM-based climate change scenarios), (ii) be run in various time steps and for multiple weather variables (the generators reproduce the correlations among variables), (iii) be interpolated (and run also for sites where no weather data are available to calibrate the generator). This contribution will compare two stochastic daily weather generators in terms of their ability to reproduce various features of the daily weather series. M&Rfi is a parametric generator: Markov chain model is used to model precipitation occurrence, precipitation amount is modelled by the Gamma distribution, and the 1st order autoregressive model is used to generate non-precipitation surface weather variables. The non-parametric GoMeZ generator is based on the nearest neighbours resampling technique making no assumption on the distribution of the variables being generated. Various settings of both weather generators will be assumed in the present validation tests. The generators will be validated in terms of (a) extreme temperature and precipitation characteristics (annual and 30 years extremes and maxima of duration of hot/cold/dry/wet spells); (b) selected validation statistics developed within the frame of VALUE project. The tests will be based on observational weather series
Revisiting the Distance Duality Relation using a non-parametric regression method
NASA Astrophysics Data System (ADS)
Rana, Akshay; Jain, Deepak; Mahajan, Shobhit; Mukherjee, Amitabha
2016-07-01
The interdependence of luminosity distance, DL and angular diameter distance, DA given by the distance duality relation (DDR) is very significant in observational cosmology. It is very closely tied with the temperature-redshift relation of Cosmic Microwave Background (CMB) radiation. Any deviation from η(z)≡ DL/DA (1+z)2 =1 indicates a possible emergence of new physics. Our aim in this work is to check the consistency of these relations using a non-parametric regression method namely, LOESS with SIMEX. This technique avoids dependency on the cosmological model and works with a minimal set of assumptions. Further, to analyze the efficiency of the methodology, we simulate a dataset of 020 points of η (z) data based on a phenomenological model η(z)= (1+z)epsilon. The error on the simulated data points is obtained by using the temperature of CMB radiation at various redshifts. For testing the distance duality relation, we use the JLA SNe Ia data for luminosity distances, while the angular diameter distances are obtained from radio galaxies datasets. Since the DDR is linked with CMB temperature-redshift relation, therefore we also use the CMB temperature data to reconstruct η (z). It is important to note that with CMB data, we are able to study the evolution of DDR upto a very high redshift z = 2.418. In this analysis, we find no evidence of deviation from η=1 within a 1σ region in the entire redshift range used in this analysis (0 < z <= 2.418).
Johnson, H.O.; Gupta, S.C.; Vecchia, A.V.; Zvomuya, F.
2009-01-01
Excessive loading of sediment and nutrients to rivers is a major problem in many parts of the United States. In this study, we tested the non-parametric Seasonal Kendall (SEAKEN) trend model and the parametric USGS Quality of Water trend program (QWTREND) to quantify trends in water quality of the Minnesota River at Fort Snelling from 1976 to 2003. Both methods indicated decreasing trends in flow-adjusted concentrations of total suspended solids (TSS), total phosphorus (TP), and orthophosphorus (OP) and a generally increasing trend in flow-adjusted nitrate plus nitrite-nitrogen (NO3-N) concentration. The SEAKEN results were strongly influenced by the length of the record as well as extreme years (dry or wet) earlier in the record. The QWTREND results, though influenced somewhat by the same factors, were more stable. The magnitudes of trends between the two methods were somewhat different and appeared to be associated with conceptual differences between the flow-adjustment processes used and with data processing methods. The decreasing trends in TSS, TP, and OP concentrations are likely related to conservation measures implemented in the basin. However, dilution effects from wet climate or additional tile drainage cannot be ruled out. The increasing trend in NO3-N concentrations was likely due to increased drainage in the basin. Since the Minnesota River is the main source of sediments to the Mississippi River, this study also addressed the rapid filling of Lake Pepin on the Mississippi River and found the likely cause to be increased flow due to recent wet climate in the region. Copyright ?? 2009 by the American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America. All rights reserved.
Lanza, L G; Stagi, L
2012-01-01
The analysis of counting and catching errors of both catching and non-catching types of rain intensity gauges was recently possible over a wide variety of measuring principles and instrument design solutions, based on the work performed during the recent Field Intercomparison of Rainfall Intensity Gauges promoted by World Meteorological Organization (WMO). The analysis reported here concerns the assessment of accuracy and precision of various types of instruments based on extensive calibration tests performed in the laboratory during the first phase of this WMO Intercomparison. The non-parametric analysis of relative errors allowed us to conclude that the accuracy of the investigated RI gauges is generally high, after assuming that it should be at least contained within the limits set forth by WMO in this respect. The measuring principle exploited by the instrument is generally not very decisive in obtaining such good results in the laboratory. Rather, the attention paid by the manufacturer to suitably accounting and correcting for systematic errors and time-constant related effects was demonstrated to be influential. The analysis of precision showed that the observed frequency distribution of relative errors around their mean value is not indicative of an underlying Gaussian population, being much more peaked in most cases than can be expected from samples extracted from a Gaussian distribution. The analysis of variance (one-way ANOVA), assuming the instrument model as the only potentially affecting factor, does not confirm the hypothesis of a single common underlying distribution for all instruments. Pair-wise multiple comparison analysis revealed cases in which significant differences could be observed.
A Non-Parametric Surrogate-based Test of Significance for T-Wave Alternans Detection
Nemati, Shamim; Abdala, Omar; Bazán, Violeta; Yim-Yeh, Susie; Malhotra, Atul; Clifford, Gari
2010-01-01
We present a non-parametric adaptive surrogate test that allows for the differentiation of statistically significant T-Wave Alternans (TWA) from alternating patterns that can be solely explained by the statistics of noise. The proposed test is based on estimating the distribution of noise induced alternating patterns in a beat sequence from a set of surrogate data derived from repeated reshuffling of the original beat sequence. Thus, in assessing the significance of the observed alternating patterns in the data no assumptions are made about the underlying noise distribution. In addition, since the distribution of noise-induced alternans magnitudes is calculated separately for each sequence of beats within the analysis window, the method is robust to data non-stationarities in both noise and TWA. The proposed surrogate method for rejecting noise was compared to the standard noise rejection methods used with the Spectral Method (SM) and the Modified Moving Average (MMA) techniques. Using a previously described realistic multi-lead model of TWA, and real physiological noise, we demonstrate the proposed approach reduces false TWA detections, while maintaining a lower missed TWA detection compared with all the other methods tested. A simple averaging-based TWA estimation algorithm was coupled with the surrogate significance testing and was evaluated on three public databases; the Normal Sinus Rhythm Database (NRSDB), the Chronic Heart Failure Database (CHFDB) and the Sudden Cardiac Death Database (SCDDB). Differences in TWA amplitudes between each database were evaluated at matched heart rate (HR) intervals from 40 to 120 beats per minute (BPM). Using the two-sample Kolmogorov-Smirnov test, we found that significant differences in TWA levels exist between each patient group at all decades of heart rates. The most marked difference was generally found at higher heart rates, and the new technique resulted in a larger margin of separability between patient populations than
NASA Astrophysics Data System (ADS)
Butler, John S.; Molloy, Anna; Williams, Laura; Kimmich, Okka; Quinlivan, Brendan; O'Riordan, Sean; Hutchinson, Michael; Reilly, Richard B.
2015-08-01
Objective. Recent studies have proposed that the temporal discrimination threshold (TDT), the shortest detectable time period between two stimuli, is a possible endophenotype for adult onset idiopathic isolated focal dystonia (AOIFD). Patients with AOIFD, the third most common movement disorder, and their first-degree relatives have been shown to have abnormal visual and tactile TDTs. For this reason it is important to fully characterize each participant’s data. To date the TDT has only been reported as a single value. Approach. Here, we fit individual participant data with a cumulative Gaussian to extract the mean and standard deviation of the distribution. The mean represents the point of subjective equality (PSE), the inter-stimulus interval at which participants are equally likely to respond that two stimuli are one stimulus (synchronous) or two different stimuli (asynchronous). The standard deviation represents the just noticeable difference (JND) which is how sensitive participants are to changes in temporal asynchrony around the PSE. We extended this method by submitting the data to a non-parametric bootstrapped analysis to get 95% confidence intervals on individual participant data. Main results. Both the JND and PSE correlate with the TDT value but are independent of each other. Hence this suggests that they represent different facets of the TDT. Furthermore, we divided groups by age and compared the TDT, PSE, and JND values. The analysis revealed a statistical difference for the PSE which was only trending for the TDT. Significance. The analysis method will enable deeper analysis of the TDT to leverage subtle differences within and between control and patient groups, not apparent in the standard TDT measure.
Population pharmacokinetics of nevirapine in Malaysian HIV patients: a non-parametric approach.
Mustafa, Suzana; Yusuf, Wan Nazirah Wan; Woillard, Jean Baptiste; Choon, Tan Soo; Hassan, Norul Badriah
2016-07-01
Nevirapine is the first non-nucleoside reverse-transcriptase inhibitor approved and is widely used in combination therapy to treat HIV-1 infection. The pharmacokinetics of nevirapine was extensively studied in various populations with a parametric approach. Hence, this study was aimed to determine population pharmacokinetic parameters in Malaysian HIV-infected patients with a non-parametric approach which allows detection of outliers or non-normal distribution contrary to the parametric approach. Nevirapine population pharmacokinetics was modelled with Pmetrics. A total of 708 observations from 112 patients were included in the model building and validation analysis. Evaluation of the model was based on a visual inspection of observed versus predicted (population and individual) concentrations and plots weighted residual error versus concentrations. Accuracy and robustness of the model were evaluated by visual predictive check (VPC). The median parameters' estimates obtained from the final model were used to predict individual nevirapine plasma area-under-curve (AUC) in the validation dataset. The Bland-Altman plot was used to compare the AUC predicted with trapezoidal AUC. The median nevirapine clearance was of 2.92 L/h, the median rate of absorption was 2.55/h and the volume of distribution was 78.23 L. Nevirapine pharmacokinetics were best described by one-compartmental with first-order absorption model and a lag-time. Weighted residuals for the model selected were homogenously distributed over the concentration and time range. The developed model adequately estimated AUC. In conclusion, a model to describe the pharmacokinetics of nevirapine was developed. The developed model adequately describes nevirapine population pharmacokinetics in HIV-infected patients in Malaysia.
Yang, Hai; Wei, Qiang; Zhong, Xue; Yang, Hushan; Li, Bingshan
2017-02-15
Comprehensive catalogue of genes that drive tumor initiation and progression in cancer is key to advancing diagnostics, therapeutics and treatment. Given the complexity of cancer, the catalogue is far from complete yet. Increasing evidence shows that driver genes exhibit consistent aberration patterns across multiple-omics in tumors. In this study, we aim to leverage complementary information encoded in each of the omics data to identify novel driver genes through an integrative framework. Specifically, we integrated mutations, gene expression, DNA copy numbers, DNA methylation and protein abundance, all available in The Cancer Genome Atlas (TCGA) and developed iDriver, a non-parametric Bayesian framework based on multivariate statistical modeling to identify driver genes in an unsupervised fashion. iDriver captures the inherent clusters of gene aberrations and constructs the background distribution that is used to assess and calibrate the confidence of driver genes identified through multi-dimensional genomic data. We applied the method to 4 cancer types in TCGA and identified candidate driver genes that are highly enriched with known drivers. (e.g.: P < 3.40 × 10 -36 for breast cancer). We are particularly interested in novel genes and observed multiple lines of supporting evidence. Using systematic evaluation from multiple independent aspects, we identified 45 candidate driver genes that were not previously known across these 4 cancer types. The finding has important implications that integrating additional genomic data with multivariate statistics can help identify cancer drivers and guide the next stage of cancer genomics research. The C ++ source code is freely available at https://medschool.vanderbilt.edu/cgg/ . hai.yang@vanderbilt.edu or bingshan.li@Vanderbilt.Edu. Supplementary data are available at Bioinformatics online.
A Non-parametric approach to measuring the K- pi+ amplitudes in D+ ---> K- K+ pi+ decay
Link, J.M.; Yager, P.M.; Anjos, J.C.; Bediaga, I.; Castromonte, C.; Machado, A.A.; Magnin, J.; Massafferri, A.; de Miranda, J.M.; Pepe, I.M.; Polycarpo, E.; /Rio de Janeiro, CBPF /CINVESTAV, IPN /Colorado U. /Fermilab /Frascati /Guanajuato U. /Illinois U., Urbana /Indiana U. /Korea U. /Kyungpook Natl. U. /INFN, Milan /Milan U.
2006-12-01
Using a large sample of D{sup +} {yields} K{sup -}K{sup +}{pi}{sup +} decays collected by the FOCUS photoproduction experiment at Fermilab, we present the first non-parametric analysis of the K{sup -}{pi}{sup +} amplitudes in D{sup +} {yields} K{sup -}K{sup +}{pi}{sup +} decay. The technique is similar to the technique used for our non-parametric measurements of the D{sup +} {yields} {bar K}*{sup 0} e{sup +}{nu} form factors. Although these results are in rough agreement with those of E687, we observe a wider S-wave contribution for the {bar K}*{sub 0}{sup 0}(1430) contribution than the standard, PDG [1] Breit-Wigner parameterization. We have some weaker evidence for the existence of a new, D-wave component at low values of the K{sup -}{pi}{sup +} mass.
A non-parametric approach to measuring the k- pi+ amplitudes in d+ --> k- k+ pi+ decay
Link, J.M.
2006-12-01
Using a large sample of D{sup +} {yields} K{sup -}K{sup +}{pi}{sup +} decays collected by the FOCUS photoproduction experiment at Fermilab, we present the first non-parametric analysis of the K{sup -} {pi}{sup +} amplitudes in D{sup +} {yields} K{sup -}K{sup +}{pi}{sup +} decay. The technique is similar to the technique used for our non-parametric measurements of the D{sup +} {yields} {bar K}*{sup 0} e{sup +}{nu} form factors. Although these results are in rough agreement with those of E687, we observe a wider S-wave contribution for the {bar K}*{sub 0}{sup 0}(1430) contribution than the standard, PDG [1] Breit-Wigner parameterization. We have some weaker evidence for the existence of a new, D-wave component at low values of the K{sup -} {pi}{sup +} mass.
Computing functions by approximating the input
NASA Astrophysics Data System (ADS)
Goldberg, Mayer
2012-12-01
In computing real-valued functions, it is ordinarily assumed that the input to the function is known, and it is the output that we need to approximate. In this work, we take the opposite approach: we show how to compute the values of some transcendental functions by approximating the input to these functions, and obtaining exact answers for their output. Our approach assumes only the most rudimentary knowledge of algebra and trigonometry, and makes no use of calculus.
Wang, Ying; Wu, Fengchang; Giesy, John P; Feng, Chenglian; Liu, Yuedan; Qin, Ning; Zhao, Yujie
2015-09-01
Due to use of different parametric models for establishing species sensitivity distributions (SSDs), comparison of water quality criteria (WQC) for metals of the same group or period in the periodic table is uncertain and results can be biased. To address this inadequacy, a new probabilistic model, based on non-parametric kernel density estimation was developed and optimal bandwidths and testing methods are proposed. Zinc (Zn), cadmium (Cd), and mercury (Hg) of group IIB of the periodic table are widespread in aquatic environments, mostly at small concentrations, but can exert detrimental effects on aquatic life and human health. With these metals as target compounds, the non-parametric kernel density estimation method and several conventional parametric density estimation methods were used to derive acute WQC of metals for protection of aquatic species in China that were compared and contrasted with WQC for other jurisdictions. HC5 values for protection of different types of species were derived for three metals by use of non-parametric kernel density estimation. The newly developed probabilistic model was superior to conventional parametric density estimations for constructing SSDs and for deriving WQC for these metals. HC5 values for the three metals were inversely proportional to atomic number, which means that the heavier atoms were more potent toxicants. The proposed method provides a novel alternative approach for developing SSDs that could have wide application prospects in deriving WQC and use in assessment of risks to ecosystems.
Ruiz-Sanchez, Eduardo
2015-12-01
The Neotropical woody bamboo genus Otatea is one of five genera in the subtribe Guaduinae. Of the eight described Otatea species, seven are endemic to Mexico and one is also distributed in Central and South America. Otatea acuminata has the widest geographical distribution of the eight species, and two of its recently collected populations do not match the known species morphologically. Parametric and non-parametric methods were used to delimit the species in Otatea using five chloroplast markers, one nuclear marker, and morphological characters. The parametric coalescent method and the non-parametric analysis supported the recognition of two distinct evolutionary lineages. Molecular clock estimates were used to estimate divergence times in Otatea. The results for divergence time in Otatea estimated the origin of the speciation events from the Late Miocene to Late Pleistocene. The species delimitation analyses (parametric and non-parametric) identified that the two populations of O. acuminata from Chiapas and Hidalgo are from two separate evolutionary lineages and these new species have morphological characters that separate them from O. acuminata s.s. The geological activity of the Trans-Mexican Volcanic Belt and the Isthmus of Tehuantepec may have isolated populations and limited the gene flow between Otatea species, driving speciation. Based on the results found here, I describe Otatea rzedowskiorum and Otatea victoriae as two new species, morphologically different from O. acuminata.
Tan, Qihua; Zhao, J H; Iachine, I; Hjelmborg, J; Vach, W; Vaupel, J W; Christensen, K; Kruse, T A
2004-04-01
This report investigates the power issue in applying the non-parametric linkage analysis of affected sib-pairs (ASP) [Kruglyak and Lander, 1995: Am J Hum Genet 57:439-454] to localize genes that contribute to human longevity using long-lived sib-pairs. Data were simulated by introducing a recently developed statistical model for measuring marker-longevity associations [Yashin et al., 1999: Am J Hum Genet 65:1178-1193], enabling direct power comparison between linkage and association approaches. The non-parametric linkage (NPL) scores estimated in the region harboring the causal allele are evaluated to assess the statistical power for different genetic (allele frequency and risk) and heterogeneity parameters under various sampling schemes (age-cut and sample size). Based on the genotype-specific survival function, we derived a heritability calculation as an overall measurement for the effect of causal genes with different parameter settings so that the power can be compared for different modes (dominant, recessive) of inheritance. Our results show that the ASP approach is a powerful tool in mapping very strong effect genes, both dominant and recessive. To map a rare dominant genetic variation that reduces hazard of death by half, a large sample (above 600 pairs) with at least one extremely long-lived (over age 99) sib in each pair is needed. Again, with large sample size and high age cut-off, the method is able to localize recessive genes with relatively small effects, but the power is very limited in case of a dominant effect. Although the power issue may depend heavily on the true genetic nature in maintaining survival, our study suggests that results from small-scale sib-pair investigations should be referred with caution, given the complexity of human longevity. Copyright 2004 Wiley-Liss, Inc.
Computational Modeling of Mitochondrial Function
Cortassa, Sonia; Aon, Miguel A.
2012-01-01
The advent of techniques with the ability to scan massive changes in cellular makeup (genomics, proteomics, etc.) has revealed the compelling need for analytical methods to interpret and make sense of those changes. Computational models built on sound physico-chemical mechanistic basis are unavoidable at the time of integrating, interpreting, and simulating high-throughput experimental data. Another powerful role of computational models is predicting new behavior provided they are adequately validated. Mitochondrial energy transduction has been traditionally studied with thermodynamic models. More recently, kinetic or thermo-kinetic models have been proposed, leading the path toward an understanding of the control and regulation of mitochondrial energy metabolism and its interaction with cytoplasmic and other compartments. In this work, we outline the methods, step-by-step, that should be followed to build a computational model of mitochondrial energetics in isolation or integrated to a network of cellular processes. Depending on the question addressed by the modeler, the methodology explained herein can be applied with different levels of detail, from the mitochondrial energy producing machinery in a network of cellular processes to the dynamics of a single enzyme during its catalytic cycle. PMID:22057575
Validation of two (parametric vs non-parametric) daily weather generators
NASA Astrophysics Data System (ADS)
Dubrovsky, M.; Skalak, P.
2015-12-01
As the climate models (GCMs and RCMs) fail to satisfactorily reproduce the real-world surface weather regime, various statistical methods are applied to downscale GCM/RCM outputs into site-specific weather series. The stochastic weather generators are among the most favourite downscaling methods capable to produce realistic (observed-like) meteorological inputs for agrological, hydrological and other impact models used in assessing sensitivity of various ecosystems to climate change/variability. To name their advantages, the generators may (i) produce arbitrarily long multi-variate synthetic weather series representing both present and changed climates (in the latter case, the generators are commonly modified by GCM/RCM-based climate change scenarios), (ii) be run in various time steps and for multiple weather variables (the generators reproduce the correlations among variables), (iii) be interpolated (and run also for sites where no weather data are available to calibrate the generator). This contribution will compare two stochastic daily weather generators in terms of their ability to reproduce various features of the daily weather series. M&Rfi is a parametric generator: Markov chain model is used to model precipitation occurrence, precipitation amount is modelled by the Gamma distribution, and the 1st order autoregressive model is used to generate non-precipitation surface weather variables. The non-parametric GoMeZ generator is based on the nearest neighbours resampling technique making no assumption on the distribution of the variables being generated. Various settings of both weather generators will be assumed in the present validation tests. The generators will be validated in terms of (a) extreme temperature and precipitation characteristics (annual and 30-years extremes and maxima of duration of hot/cold/dry/wet spells); (b) selected validation statistics developed within the frame of VALUE project. The tests will be based on observational weather series
Evaluation of world's largest social welfare scheme: An assessment using non-parametric approach.
Singh, Sanjeet
2016-08-01
Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA) is the world's largest social welfare scheme in India for the poverty alleviation through rural employment generation. This paper aims to evaluate and rank the performance of the states in India under MGNREGA scheme. A non-parametric approach, Data Envelopment Analysis (DEA) is used to calculate the overall technical, pure technical, and scale efficiencies of states in India. The sample data is drawn from the annual official reports published by the Ministry of Rural Development, Government of India. Based on three selected input parameters (expenditure indicators) and five output parameters (employment generation indicators), I apply both input and output oriented DEA models to estimate how well the states utilize their resources and generate outputs during the financial year 2013-14. The relative performance evaluation has been made under the assumption of constant returns and also under variable returns to scale to assess the impact of scale on performance. The results indicate that the main source of inefficiency is both technical and managerial practices adopted. 11 states are overall technically efficient and operate at the optimum scale whereas 18 states are pure technical or managerially efficient. It has been found that for some states it necessary to alter scheme size to perform at par with the best performing states. For inefficient states optimal input and output targets along with the resource savings and output gains are calculated. Analysis shows that if all inefficient states operate at optimal input and output levels, on an average 17.89% of total expenditure and a total amount of $780million could have been saved in a single year. Most of the inefficient states perform poorly when it comes to the participation of women and disadvantaged sections (SC&ST) in the scheme. In order to catch up with the performance of best performing states, inefficient states on an average need to enhance
Non-parametric PCM to ADM conversion. [Pulse Code to Adaptive Delta Modulation
NASA Technical Reports Server (NTRS)
Locicero, J. L.; Schilling, D. L.
1977-01-01
An all-digital technique to convert pulse code modulated (PCM) signals into adaptive delta modulation (ADM) format is presented. The converter developed is shown to be independent of the statistical parameters of the encoded signal and can be constructed with only standard digital hardware. The structure of the converter is simple enough to be fabricated on a large scale integrated circuit where the advantages of reliability and cost can be optimized. A concise evaluation of this PCM to ADM translation technique is presented and several converters are simulated on a digital computer. A family of performance curves is given which displays the signal-to-noise ratio for sinusoidal test signals subjected to the conversion process, as a function of input signal power for several ratios of ADM rate to Nyquist rate.
On computation of Hough functions
NASA Astrophysics Data System (ADS)
Wang, Houjun; Boyd, John P.; Akmaev, Rashid A.
2016-04-01
Hough functions are the eigenfunctions of the Laplace tidal equation governing fluid motion on a rotating sphere with a resting basic state. Several numerical methods have been used in the past. In this paper, we compare two of those methods: normalized associated Legendre polynomial expansion and Chebyshev collocation. Both methods are not widely used, but both have some advantages over the commonly used unnormalized associated Legendre polynomial expansion method. Comparable results are obtained using both methods. For the first method we note some details on numerical implementation. The Chebyshev collocation method was first used for the Laplace tidal problem by Boyd (1976) and is relatively easy to use. A compact MATLAB code is provided for this method. We also illustrate the importance and effect of including a parity factor in Chebyshev polynomial expansions for modes with odd zonal wave numbers.
Porter, W R; Trager, W F
1977-01-01
The theoretical basis for the direct linear plot [Eisenthal & Cornish-Bowden (1974) Biochem. J. 139, 715-720], a non-parametric statistical method for the analysis of data-fitting the Michaelis-Menten equation, was reinvestigated in order to accommodate additional experimental designs and to provide estimates of precision more directly comparable with those obtained by parametric statistical methods. Methods are given for calculating upper and lower confidence limits for the estimated parameters, for accommodating replicate measurements and for comparing the results of two separate experiments. Factors that influence the proper design of experiments are discussed. PMID:849264
Schatzki, T F; Vandercook, C E
1978-07-01
The composition of organic constituents (total sugars, reactive phenols, total amino acids, arginine, and gamma-aminobutyric acid) has been measured in a large (360 samples) selection of concentrated orange juice for manufacturing and orange pulp wash in the U.S. trade. The detection of adulteration with sugar, reducing sugars, and citric acid addition has been investigated by using non-parametric nearest neighbor classification techniques in the 4-space of log ratios of the compositions. The results show that such detection is possible with a type 1=type 2 error rate of 10% for 20% adulteration if at least 7 samples are taken. The assumptions of such samplings are discussed.
Neelakantan, S; Veng-Pedersen, P
2005-11-01
A novel numerical deconvolution method is presented that enables the estimation of drug absorption rates under time-variant disposition conditions. The method involves two components. (1) A disposition decomposition-recomposition (DDR) enabling exact changes in the unit impulse response (UIR) to be constructed based on centrally based clearance changes iteratively determined. (2) A non-parametric, end-constrained cubic spline (ECS) input response function estimated by cross-validation. The proposed DDR-ECS method compensates for disposition changes between the test and the reference administrations by using a "beta" clearance correction based on DDR analysis. The representation of the input response by the ECS method takes into consideration the complex absorption process and also ensures physiologically realistic approximations of the response. The stability of the new method to noisy data was evaluated by comprehensive simulations that considered different UIRs, various input functions, clearance changes and a novel scaling of the input function that includes the "flip-flop" absorption phenomena. The simulated input response was also analysed by two other methods and all three methods were compared for their relative performances. The DDR-ECS method provides better estimation of the input profile under significant clearance changes but tends to overestimate the input when there were only small changes in the clearance.
Harlander, Niklas; Rosenkranz, Tobias; Hohmann, Volker
2012-08-01
Single channel noise reduction has been well investigated and seems to have reached its limits in terms of speech intelligibility improvement, however, the quality of such schemes can still be advanced. This study tests to what extent novel model-based processing schemes might improve performance in particular for non-stationary noise conditions. Two prototype model-based algorithms, a speech-model-based, and a auditory-model-based algorithm were compared to a state-of-the-art non-parametric minimum statistics algorithm. A speech intelligibility test, preference rating, and listening effort scaling were performed. Additionally, three objective quality measures for the signal, background, and overall distortions were applied. For a better comparison of all algorithms, particular attention was given to the usage of the similar Wiener-based gain rule. The perceptual investigation was performed with fourteen hearing-impaired subjects. The results revealed that the non-parametric algorithm and the auditory model-based algorithm did not affect speech intelligibility, whereas the speech-model-based algorithm slightly decreased intelligibility. In terms of subjective quality, both model-based algorithms perform better than the unprocessed condition and the reference in particular for highly non-stationary noise environments. Data support the hypothesis that model-based algorithms are promising for improving performance in non-stationary noise conditions.
Frepoli, Cesare; Oriani, Luca
2006-07-01
In recent years, non-parametric or order statistics methods have been widely used to assess the impact of the uncertainties within Best-Estimate LOCA evaluation models. The bounding of the uncertainties is achieved with a direct Monte Carlo sampling of the uncertainty attributes, with the minimum trial number selected to 'stabilize' the estimation of the critical output values (peak cladding temperature (PCT), local maximum oxidation (LMO), and core-wide oxidation (CWO A non-parametric order statistics uncertainty analysis was recently implemented within the Westinghouse Realistic Large Break LOCA evaluation model, also referred to as 'Automated Statistical Treatment of Uncertainty Method' (ASTRUM). The implementation or interpretation of order statistics in safety analysis is not fully consistent within the industry. This has led to an extensive public debate among regulators and researchers which can be found in the open literature. The USNRC-approved Westinghouse method follows a rigorous implementation of the order statistics theory, which leads to the execution of 124 simulations within a Large Break LOCA analysis. This is a solid approach which guarantees that a bounding value (at 95% probability) of the 95{sup th} percentile for each of the three 10 CFR 50.46 ECCS design acceptance criteria (PCT, LMO and CWO) is obtained. The objective of this paper is to provide additional insights on the ASTRUM statistical approach, with a more in-depth analysis of pros and cons of the order statistics and of the Westinghouse approach in the implementation of this statistical methodology. (authors)
Soltanian, Ali Reza; Hossein, Mahjub
2012-01-01
Kernel smoothing method is a non-parametric or graphical method for statistical estimation. In the present study was used a kernel smoothing method for finding the death hazard rates of patients with acute myocardial infarction. By employing non-parametric regression methods, the curve estimation, may have some complexity. In this article, four indices of Epanechnikov, Biquadratic, Triquadratic and Rectangle kernels were used under local and k-nearest neighbors' bandwidth. For comparing the models, were employed mean integrated squared error. To illustrate in the study, was used the dataset of acute myocardial infraction patients in Bushehr port, in the south of Iran. To obtain proper bandwidth, was used generalized cross-validation method. Corresponding to a low bandwidth value, the curve is unreadable and the regression curve is so roughly. In the event of increasing bandwidth value, the distribution has more readable and smooth. In this study, estimate of death hazard rate for the patients based on Epanechnikov kernel under local bandwidth was 1.011 x 10(-11), which had the lowest mean square error compared to k-nearest neighbors bandwidth. We obtained the death hazard rate in 10 and 30 months after the first acute myocardial infraction using Epanechnikov kernelas were 0.0031 and 0.0012, respectively. The Epanechnikov kernel for obtaining death hazard rate of patients with acute myocardial infraction has minimum mean integrated squared error compared to the other kernels. In addition, the mortality hazard rate of acute myocardial infraction in the study was low.
Maydeu-Olivares, Albert
2005-04-01
Chernyshenko, Stark, Chan, Drasgow, and Williams (2001) investigated the fit of Samejima's logistic graded model and Levine's non-parametric MFS model to the scales of two personality questionnaires and found that the graded model did not fit well. We attribute the poor fit of the graded model to small amounts of multidimensionality present in their data. To verify this conjecture, we compare the fit of these models to the Social Problem Solving Inventory-Revised, whose scales were designed to be unidimensional. A calibration and a cross-validation sample of new observations were used. We also included the following parametric models in the comparison: Bock's nominal model, Masters' partial credit model, and Thissen and Steinberg's extension of the latter. All models were estimated using full information maximum likelihood. We also included in the comparison a normal ogive model version of Samejima's model estimated using limited information estimation. We found that for all scales Samejima's model outperformed all other parametric IRT models in both samples, regardless of the estimation method employed. The non-parametric model outperformed all parametric models in the calibration sample. However, the graded model outperformed MFS in the cross-validation sample in some of the scales. We advocate employing the graded model estimated using limited information methods in modeling Likert-type data, as these methods are more versatile than full information methods to capture the multidimensionality that is generally present in personality data.
Jin, Qian; He, Li-Jun; Zhang, Ai-Bing
2012-01-01
In the recent worldwide campaign for the global biodiversity inventory via DNA barcoding, a simple and easily used measure of confidence for assigning sequences to species in DNA barcoding has not been established so far, although the likelihood ratio test and the bayesian approach had been proposed to address this issue from a statistical point of view. The TDR (Two Dimensional non-parametric Resampling) measure newly proposed in this study offers users a simple and easy approach to evaluate the confidence of species membership in DNA barcoding projects. We assessed the validity and robustness of the TDR approach using datasets simulated under coalescent models, and an empirical dataset, and found that TDR measure is very robust in assessing species membership of DNA barcoding. In contrast to the likelihood ratio test and bayesian approach, the TDR method stands out due to simplicity in both concepts and calculations, with little in the way of restrictive population genetic assumptions. To implement this approach we have developed a computer program package (TDR1.0beta) freely available from ftp://202.204.209.200/education/video/TDR1.0beta.rar.
CADDIS Volume 4. Data Analysis: PECBO Appendix - R Scripts for Non-Parametric Regressions
Script for computing nonparametric regression analysis. Overview of using scripts to infer environmental conditions from biological observations, statistically estimating species-environment relationships, statistical scripts.
Approximate Bayesian computation with functional statistics.
Soubeyrand, Samuel; Carpentier, Florence; Guiton, François; Klein, Etienne K
2013-03-26
Functional statistics are commonly used to characterize spatial patterns in general and spatial genetic structures in population genetics in particular. Such functional statistics also enable the estimation of parameters of spatially explicit (and genetic) models. Recently, Approximate Bayesian Computation (ABC) has been proposed to estimate model parameters from functional statistics. However, applying ABC with functional statistics may be cumbersome because of the high dimension of the set of statistics and the dependences among them. To tackle this difficulty, we propose an ABC procedure which relies on an optimized weighted distance between observed and simulated functional statistics. We applied this procedure to a simple step model, a spatial point process characterized by its pair correlation function and a pollen dispersal model characterized by genetic differentiation as a function of distance. These applications showed how the optimized weighted distance improved estimation accuracy. In the discussion, we consider the application of the proposed ABC procedure to functional statistics characterizing non-spatial processes.
Jewell, Nicholas P; Lei, Xiudong; Ghani, Azra C; Donnelly, Christl A; Leung, Gabriel M; Ho, Lai-Ming; Cowling, Benjamin J; Hedley, Anthony J
2007-04-30
For diseases with some level of associated mortality, the case fatality ratio measures the proportion of diseased individuals who die from the disease. In principle, it is straightforward to estimate this quantity from individual follow-up data that provides times from onset to death or recovery. In particular, in a competing risks context, the case fatality ratio is defined by the limiting value of the sub-distribution function, F(1)(t) = Pr(T
Proficiency Scaling Based on Conditional Probability Functions for Attributes
1993-10-01
4.1 Non-parametric regression estimates as probability functions for attributes Non- parametric estimation of the unknown density function f from a plot...as construction of confidence intervals for PFAs and further improvement of non- parametric estimation methods are not discussed in this paper. The... parametric estimation of PFAs will be illustrated with the attribute mastery patterns of SAT M Section 4. In the next section, analysis results will be
Dynamics and computation in functional shifts
NASA Astrophysics Data System (ADS)
Namikawa, Jun; Hashimoto, Takashi
2004-07-01
We introduce a new type of shift dynamics as an extended model of symbolic dynamics, and investigate the characteristics of shift spaces from the viewpoints of both dynamics and computation. This shift dynamics is called a functional shift, which is defined by a set of bi-infinite sequences of some functions on a set of symbols. To analyse the complexity of functional shifts, we measure them in terms of topological entropy, and locate their languages in the Chomsky hierarchy. Through this study, we argue that considering functional shifts from the viewpoints of both dynamics and computation gives us opposite results about the complexity of systems. We also describe a new class of shift spaces whose languages are not recursively enumerable.
Computer Games Functioning as Motivation Stimulants
ERIC Educational Resources Information Center
Lin, Grace Hui Chin; Tsai, Tony Kung Wan; Chien, Paul Shih Chieh
2011-01-01
Numerous scholars have recommended computer games can function as influential motivation stimulants of English learning, showing benefits as learning tools (Clarke and Dede, 2007; Dede, 2009; Klopfer and Squire, 2009; Liu and Chu, 2010; Mitchell, Dede & Dunleavy, 2009). This study aimed to further test and verify the above suggestion,…
NASA Astrophysics Data System (ADS)
Li, Xiao-Dong; Lv, Mang-Mang; Ho, John K. L.
2016-07-01
In this article, two adaptive iterative learning control (ILC) algorithms are presented for nonlinear continuous systems with non-parametric uncertainties. Unlike general ILC techniques, the proposed adaptive ILC algorithms allow that both the initial error at each iteration and the reference trajectory are iteration-varying in the ILC process, and can achieve non-repetitive trajectory tracking beyond a small initial time interval. Compared to the neural network or fuzzy system-based adaptive ILC schemes and the classical ILC methods, in which the number of iterative variables is generally larger than or equal to the number of control inputs, the first adaptive ILC algorithm proposed in this paper uses just two iterative variables, while the second even uses a single iterative variable provided that some bound information on system dynamics is known. As a result, the memory space in real-time ILC implementations is greatly reduced.
Sodano, Sandro M; Tracey, Terence J G; Hafkenscheid, Anton
2014-01-01
Non-Parametric IRT methods were applied to 127 clinicians' ratings of their patients using the Dutch Impact Message Inventory-Circumplex (IMI-C) to develop the IMI-C Short. Four items from each octant subscale of the IMI-C were selected to maximally differentiate individuals along the continuum of impact messages. Using larger samples (patients: N = 700, 812; raters: N = 42, 85, respectively), IRT-based reliability was generally comparable between the brief and parent subscales. Classical Test Theory-based reliability was adequate for the brief subscales and they converged with their parent subscales. Since the IMI-C is purported to represent the circular arrangement of impact messages, the fits of the parent and abbreviated scales were assessed and found to be good and not differ significantly. The new IMI-C Short offers advantages over the full-length IMI-C.
Shi, Yang; Chinnaiyan, Arul M; Jiang, Hui
2015-07-01
High-throughput sequencing of transcriptomes (RNA-Seq) has become a powerful tool to study gene expression. Here we present an R package, rSeqNP, which implements a non-parametric approach to test for differential expression and splicing from RNA-Seq data. rSeqNP uses permutation tests to access statistical significance and can be applied to a variety of experimental designs. By combining information across isoforms, rSeqNP is able to detect more differentially expressed or spliced genes from RNA-Seq data. The R package with its source code and documentation are freely available at http://www-personal.umich.edu/∼jianghui/rseqnp/. jianghui@umich.edu Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.
Andersson, Jesper L R; Graham, Mark S; Zsoldos, Enikő; Sotiropoulos, Stamatios N
2016-11-01
Despite its great potential in studying brain anatomy and structure, diffusion magnetic resonance imaging (dMRI) is marred by artefacts more than any other commonly used MRI technique. In this paper we present a non-parametric framework for detecting and correcting dMRI outliers (signal loss) caused by subject motion. Signal loss (dropout) affecting a whole slice, or a large connected region of a slice, is frequently observed in diffusion weighted images, leading to a set of unusable measurements. This is caused by bulk (subject or physiological) motion during the diffusion encoding part of the imaging sequence. We suggest a method to detect slices affected by signal loss and replace them by a non-parametric prediction, in order to minimise their impact on subsequent analysis. The outlier detection and replacement, as well as correction of other dMRI distortions (susceptibility-induced distortions, eddy currents (EC) and subject motion) are performed within a single framework, allowing the use of an integrated approach for distortion correction. Highly realistic simulations have been used to evaluate the method with respect to its ability to detect outliers (types 1 and 2 errors), the impact of outliers on retrospective correction of movement and distortion and the impact on estimation of commonly used diffusion tensor metrics, such as fractional anisotropy (FA) and mean diffusivity (MD). Data from a large imaging project studying older adults (the Whitehall Imaging sub-study) was used to demonstrate the utility of the method when applied to datasets with severe subject movement. The results indicate high sensitivity and specificity for detecting outliers and that their deleterious effects on FA and MD can be almost completely corrected.
NASA Astrophysics Data System (ADS)
Thomas, M. A.
2016-12-01
The Waste Isolation Pilot Plant (WIPP) is the only deep geological repository for transuranic waste in the United States. As the Science Advisor for the WIPP, Sandia National Laboratories annually evaluates site data against trigger values (TVs), metrics whose violation is indicative of conditions that may impact long-term repository performance. This study focuses on a groundwater-quality dataset used to redesign a TV for the Culebra Dolomite Member (Culebra) of the Permian-age Rustler Formation. Prior to this study, a TV violation occurred if the concentration of a major ion fell outside a range defined as the mean +/- two standard deviations. The ranges were thought to denote conditions that 95% of future values would fall within. Groundwater-quality data used in evaluating compliance, however, are rarely normally distributed. To create a more robust Culebra groundwater-quality TV, this study employed the randomization test, a non-parametric permutation method. Recent groundwater compositions considered TV violations under the original ion concentration ranges are now interpreted as false positives in light of the insignificant p-values calculated with the randomization test. This work highlights that the normality assumption can weaken as the size of a groundwater-quality dataset grows over time. Non-parametric permutation methods are an attractive option because no assumption about the statistical distribution is required and calculating all combinations of the data is an increasingly tractable problem with modern workstations. Sandia National Laboratories is a multi-program laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Corporation, for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-AC04-94AL85000. This research is funded by WIPP programs administered by the Office of Environmental Management (EM) of the U.S. Department of Energy. SAND2016-7306A
2008-06-01
December 2006. 7. M. Fritz, B. Leibe, B. Caputo, and B. Schiele . Integrating representative and discriminant models for object category detection. 8... Schiele . Robust object detection with interleaved categorization and segmentation. Int. J. of Computer Vision, 2007 (in press). 18. D. G. Lowe. Distinctive
Distributed Non-Parametric Representations for Vital Filtering: UW at TREC KBA 2014
2014-11-01
Large-scale cross-document coreference using distributed inference and hierarchical models. In Association for Computational Linguistics ( ACL ), 2011...and Bengio, Yoshua. Word repre- sentations: A simple and general method for semisupervised learning. In ACL , pp. 384–394, 2010. Wang, Jingang, Song
The Sleipnir library for computational functional genomics.
Huttenhower, Curtis; Schroeder, Mark; Chikina, Maria D; Troyanskaya, Olga G
2008-07-01
Biological data generation has accelerated to the point where hundreds or thousands of whole-genome datasets of various types are available for many model organisms. This wealth of data can lead to valuable biological insights when analyzed in an integrated manner, but the computational challenge of managing such large data collections is substantial. In order to mine these data efficiently, it is necessary to develop methods that use storage, memory and processing resources carefully. The Sleipnir C++ library implements a variety of machine learning and data manipulation algorithms with a focus on heterogeneous data integration and efficiency for very large biological data collections. Sleipnir allows microarray processing, functional ontology mining, clustering, Bayesian learning and inference and support vector machine tasks to be performed for heterogeneous data on scales not previously practical. In addition to the library, which can easily be integrated into new computational systems, prebuilt tools are provided to perform a variety of common tasks. Many tools are multithreaded for parallelization in desktop or high-throughput computing environments, and most tasks can be performed in minutes for hundreds of datasets using a standard personal computer. Source code (C++) and documentation are available at http://function.princeton.edu/sleipnir and compiled binaries are available from the authors on request.
New Computer Simulations of Macular Neural Functioning
NASA Technical Reports Server (NTRS)
Ross, Muriel D.; Doshay, D.; Linton, S.; Parnas, B.; Montgomery, K.; Chimento, T.
1994-01-01
We use high performance graphics workstations and supercomputers to study the functional significance of the three-dimensional (3-D) organization of gravity sensors. These sensors have a prototypic architecture foreshadowing more complex systems. Scaled-down simulations run on a Silicon Graphics workstation and scaled-up, 3-D versions run on a Cray Y-MP supercomputer. A semi-automated method of reconstruction of neural tissue from serial sections studied in a transmission electron microscope has been developed to eliminate tedious conventional photography. The reconstructions use a mesh as a step in generating a neural surface for visualization. Two meshes are required to model calyx surfaces. The meshes are connected and the resulting prisms represent the cytoplasm and the bounding membranes. A finite volume analysis method is employed to simulate voltage changes along the calyx in response to synapse activation on the calyx or on calyceal processes. The finite volume method insures that charge is conserved at the calyx-process junction. These and other models indicate that efferent processes act as voltage followers, and that the morphology of some afferent processes affects their functioning. In a final application, morphological information is symbolically represented in three dimensions in a computer. The possible functioning of the connectivities is tested using mathematical interpretations of physiological parameters taken from the literature. Symbolic, 3-D simulations are in progress to probe the functional significance of the connectivities. This research is expected to advance computer-based studies of macular functioning and of synaptic plasticity.
New Computer Simulations of Macular Neural Functioning
NASA Technical Reports Server (NTRS)
Ross, Muriel D.; Doshay, D.; Linton, S.; Parnas, B.; Montgomery, K.; Chimento, T.
1994-01-01
We use high performance graphics workstations and supercomputers to study the functional significance of the three-dimensional (3-D) organization of gravity sensors. These sensors have a prototypic architecture foreshadowing more complex systems. Scaled-down simulations run on a Silicon Graphics workstation and scaled-up, 3-D versions run on a Cray Y-MP supercomputer. A semi-automated method of reconstruction of neural tissue from serial sections studied in a transmission electron microscope has been developed to eliminate tedious conventional photography. The reconstructions use a mesh as a step in generating a neural surface for visualization. Two meshes are required to model calyx surfaces. The meshes are connected and the resulting prisms represent the cytoplasm and the bounding membranes. A finite volume analysis method is employed to simulate voltage changes along the calyx in response to synapse activation on the calyx or on calyceal processes. The finite volume method insures that charge is conserved at the calyx-process junction. These and other models indicate that efferent processes act as voltage followers, and that the morphology of some afferent processes affects their functioning. In a final application, morphological information is symbolically represented in three dimensions in a computer. The possible functioning of the connectivities is tested using mathematical interpretations of physiological parameters taken from the literature. Symbolic, 3-D simulations are in progress to probe the functional significance of the connectivities. This research is expected to advance computer-based studies of macular functioning and of synaptic plasticity.
The emerging discipline of Computational Functional Anatomy
Miller, Michael I.; Qiu, Anqi
2010-01-01
Computational Functional Anatomy (CFA) is the study of functional and physiological response variables in anatomical coordinates. For this we focus on two things: (i) the construction of bijections (via diffeomorphisms) between the coordinatized manifolds of human anatomy, and (ii) the transfer (group action and parallel transport) of functional information into anatomical atlases via these bijections. We review advances in the unification of the bijective comparison of anatomical submanifolds via point-sets including points, curves and surface triangulations as well as dense imagery. We examine the transfer via these bijections of functional response variables into anatomical coordinates via group action on scalars and matrices in DTI as well as parallel transport of metric information across multiple templates which preserves the inner product. PMID:19103297
Analysis of Ventricular Function by Computed Tomography
Rizvi, Asim; Deaño, Roderick C.; Bachman, Daniel P.; Xiong, Guanglei; Min, James K.; Truong, Quynh A.
2014-01-01
The assessment of ventricular function, cardiac chamber dimensions and ventricular mass is fundamental for clinical diagnosis, risk assessment, therapeutic decisions, and prognosis in patients with cardiac disease. Although cardiac computed tomography (CT) is a noninvasive imaging technique often used for the assessment of coronary artery disease, it can also be utilized to obtain important data about left and right ventricular function and morphology. In this review, we will discuss the clinical indications for the use of cardiac CT for ventricular analysis, review the evidence on the assessment of ventricular function compared to existing imaging modalities such cardiac MRI and echocardiography, provide a typical cardiac CT protocol for image acquisition and post-processing for ventricular analysis, and provide step-by-step instructions to acquire multiplanar cardiac views for ventricular assessment from the standard axial, coronal, and sagittal planes. Furthermore, both qualitative and quantitative assessments of ventricular function as well as sample reporting are detailed. PMID:25576407
Neutron monitor yield function: New improved computations
NASA Astrophysics Data System (ADS)
Mishev, A. L.; Usoskin, I. G.; Kovaltsov, G. A.
2013-06-01
A ground-based neutron monitor (NM) is a standard tool to measure cosmic ray (CR) variability near Earth, and it is crucially important to know its yield function for primary CRs. Although there are several earlier theoretically calculated yield functions, none of them agrees with experimental data of latitude surveys of sea-level NMs, thus suggesting for an inconsistency. A newly computed yield function of the standard sea-level 6NM64 NM is presented here separately for primary CR protons and α-particles, the latter representing also heavier species of CRs. The computations have been done using the GEANT-4 PLANETOCOSMICS Monte-Carlo tool and a realistic curved atmospheric model. For the first time, an effect of the geometrical correction of the NM effective area, related to the finite lateral expansion of the CR induced atmospheric cascade, is considered, which was neglected in the previous studies. This correction slightly enhances the relative impact of higher-energy CRs (energy above 5-10 GeV/nucleon) in NM count rate. The new computation finally resolves the long-standing problem of disagreement between the theoretically calculated spatial variability of CRs over the globe and experimental latitude surveys. The newly calculated yield function, corrected for this geometrical factor, appears fully consistent with the experimental latitude surveys of NMs performed during three consecutive solar minima in 1976-1977, 1986-1987, and 1996-1997. Thus, we provide a new yield function of the standard sea-level NM 6NM64 that is validated against experimental data.
Computer network defense through radial wave functions
NASA Astrophysics Data System (ADS)
Malloy, Ian J.
The purpose of this research is to synthesize basic and fundamental findings in quantum computing, as applied to the attack and defense of conventional computer networks. The concept focuses on uses of radio waves as a shield for, and attack against traditional computers. A logic bomb is analogous to a landmine in a computer network, and if one was to implement it as non-trivial mitigation, it will aid computer network defense. As has been seen in kinetic warfare, the use of landmines has been devastating to geopolitical regions in that they are severely difficult for a civilian to avoid triggering given the unknown position of a landmine. Thus, the importance of understanding a logic bomb is relevant and has corollaries to quantum mechanics as well. The research synthesizes quantum logic phase shifts in certain respects using the Dynamic Data Exchange protocol in software written for this work, as well as a C-NOT gate applied to a virtual quantum circuit environment by implementing a Quantum Fourier Transform. The research focus applies the principles of coherence and entanglement from quantum physics, the concept of expert systems in artificial intelligence, principles of prime number based cryptography with trapdoor functions, and modeling radio wave propagation against an event from unknown parameters. This comes as a program relying on the artificial intelligence concept of an expert system in conjunction with trigger events for a trapdoor function relying on infinite recursion, as well as system mechanics for elliptic curve cryptography along orbital angular momenta. Here trapdoor both denotes the form of cipher, as well as the implied relationship to logic bombs.
Non-parametric estimation of relative risk in survival and associated tests.
Wakounig, Samo; Heinze, Georg; Schemper, Michael
2015-12-01
We extend the Tarone and Ware scheme of weighted log-rank tests to cover the associated weighted Mantel-Haenszel estimators of relative risk. Weighting functions previously employed are critically reviewed. The notion of an average hazard ratio is defined and its connection to the effect size measure P(Y > X) is emphasized. The connection makes estimation of P(Y > X) possible also under censoring. Two members of the extended Tarone-Ware scheme accomplish the estimation of intuitively interpretable average hazard ratios, also under censoring and time-varying relative risk which is achieved by an inverse probability of censoring weighting. The empirical properties of the members of the extended Tarone-Ware scheme are demonstrated by a Monte Carlo study. The differential role of the weighting functions considered is illustrated by a comparative analysis of four real data sets.
2014-10-02
Mokhtar et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits...the identification of the changes to the distribution, which signifies when a change in the sys - tem’s conditions have occurred, i.e. the measured...line disruption. The system operates in a harsh environment where high temperatures and fuel impurities can lead to sys - tem degradation and functional
Propensity score method: a non-parametric technique to reduce model dependence
2017-01-01
Propensity score analysis (PSA) is a powerful technique that it balances pretreatment covariates, making the causal effect inference from observational data as reliable as possible. The use of PSA in medical literature has increased exponentially in recent years, and the trend continue to rise. The article introduces rationales behind PSA, followed by illustrating how to perform PSA in R with MatchIt package. There are a variety of methods available for PS matching such as nearest neighbors, full matching, exact matching and genetic matching. The task can be easily done by simply assigning a string value to the method argument in the matchit() function. The generic summary() and plot() functions can be applied to an object of class matchit to check covariate balance after matching. Furthermore, there is a useful package PSAgraphics that contains several graphical functions to check covariate balance between treatment groups across strata. If covariate balance is not achieved, one can modify model specifications or use other techniques such as random forest and recursive partitioning to better represent the underlying structure between pretreatment covariates and treatment assignment. The process can be repeated until the desirable covariate balance is achieved. PMID:28164092
Sazykina, Tatiana G; Kryshev, A I; Sanina, K D
2009-11-01
Databases on effects of chronic low-LET radiation exposure were analyzed by non-parametric statistical methods, to estimate the threshold dose rates above which radiation effects can be expected in vertebrate organisms. Data were grouped under three umbrella endpoints: effects on morbidity, reproduction, and life shortening. The data sets were compiled on a simple 'yes' or 'no' basis. Each data set included dose rates at which effects were reported without further details about the size or peculiarity of the effects. In total, the data sets include 84 values for endpoint "morbidity", 77 values for reproduction, and 41 values for life shortening. The dose rates in each set were ranked from low to higher values. The threshold TDR5 for radiation effects of a given umbrella type was estimated as a dose rate below which only a small percentage (5%) of data reported statistically significant radiation effects. The statistical treatment of the data sets was performed using non-parametric order statistics, and the bootstrap method. The resulting thresholds estimated by the order statistics are for morbidity effects 8.1 x 10(-4) Gy day(-1) (2.0 x 10(-4)-1.0 x 10(-3)), reproduction effects 6.0 x 10(-4) Gy day(-1) (4.0 x 10(-4)-1.5 x 10(-3)), and life shortening 3.0 x 10(-3) Gy day(-1) (1.0 x 10(-3)-6.0 x 10(-3)), respectively. The bootstrap method gave slightly lower values: 2.1 x 10(-4) Gy day(-1) (1.4 x 10(-4)-3.2 x 10(-4)) (morbidity), 4.1 x 10(-4) Gy day(-1) (3.0 x 10(-4)-5.7 x 10(-4)) (reproduction), and 1.1 x 10(-3) Gy day(-1) (7.9 x 10(-4)-1.3 x 10(-3)) (life shortening), respectively. The generic threshold dose rate (based on all umbrella types of effects) was estimated at 1.0 x 10(-3) Gy day(-1).
Computational functions in biochemical reaction networks.
Arkin, A; Ross, J
1994-01-01
In prior work we demonstrated the implementation of logic gates, sequential computers (universal Turing machines), and parallel computers by means of the kinetics of chemical reaction mechanisms. In the present article we develop this subject further by first investigating the computational properties of several enzymatic (single and multiple) reaction mechanisms: we show their steady states are analogous to either Boolean or fuzzy logic gates. Nearly perfect digital function is obtained only in the regime in which the enzymes are saturated with their substrates. With these enzymatic gates, we construct combinational chemical networks that execute a given truth-table. The dynamic range of a network's output is strongly affected by "input/output matching" conditions among the internal gate elements. We find a simple mechanism, similar to the interconversion of fructose-6-phosphate between its two bisphosphate forms (fructose-1,6-bisphosphate and fructose-2,6-bisphosphate), that functions analogously to an AND gate. When the simple model is supplanted with one in which the enzyme rate laws are derived from experimental data, the steady state of the mechanism functions as an asymmetric fuzzy aggregation operator with properties akin to a fuzzy AND gate. The qualitative behavior of the mechanism does not change when situated within a large model of glycolysis/gluconeogenesis and the TCA cycle. The mechanism, in this case, switches the pathway's mode from glycolysis to gluconeogenesis in response to chemical signals of low blood glucose (cAMP) and abundant fuel for the TCA cycle (acetyl coenzyme A). Images FIGURE 3 FIGURE 4 FIGURE 5 FIGURE 7 FIGURE 10 FIGURE 12 FIGURE 13 FIGURE 14 FIGURE 15 FIGURE 16 PMID:7948674
Out-of-Sample Extensions for Non-Parametric Kernel Methods.
Pan, Binbin; Chen, Wen-Sheng; Chen, Bo; Xu, Chen; Lai, Jianhuang
2017-02-01
Choosing suitable kernels plays an important role in the performance of kernel methods. Recently, a number of studies were devoted to developing nonparametric kernels. Without assuming any parametric form of the target kernel, nonparametric kernel learning offers a flexible scheme to utilize the information of the data, which may potentially characterize the data similarity better. The kernel methods using nonparametric kernels are referred to as nonparametric kernel methods. However, many nonparametric kernel methods are restricted to transductive learning, where the prediction function is defined only over the data points given beforehand. They have no straightforward extension for the out-of-sample data points, and thus cannot be applied to inductive learning. In this paper, we show how to make the nonparametric kernel methods applicable to inductive learning. The key problem of out-of-sample extension is how to extend the nonparametric kernel matrix to the corresponding kernel function. A regression approach in the hyper reproducing kernel Hilbert space is proposed to solve this problem. Empirical results indicate that the out-of-sample performance is comparable to the in-sample performance in most cases. Experiments on face recognition demonstrate the superiority of our nonparametric kernel method over the state-of-the-art parametric kernel methods.
Discrete Wigner functions and quantum computational speedup
Galvao, Ernesto F.
2005-04-01
Gibbons et al. [Phys. Rev. A 70, 062101 (2004)] have recently defined a class of discrete Wigner functions W to represent quantum states in a finite Hilbert space dimension d. I characterize the set C{sub d} of states having non-negative W simultaneously in all definitions of W in this class. For d{<=}5 I show C{sub d} is the convex hull of stabilizer states. This supports the conjecture that negativity of W is necessary for exponential speedup in pure-state quantum computation.
Algorithms for Computing the Lag Function.
1981-03-27
and S. J. Giner Subject: Algorithms for Computing the Lag Function References: See p . 27 Abstract: This memorandum provides a scheme for the numerical...highly oscillatory, and with singularities at the end points. j -3- 27 March 1981 GHP:SJG:Ihz TABLE OF CONTENTS P age Abstract...0 -9 16 -9 1) 1 11 1 1 -8 3 -1 -t I -8 8 -1 -1 1i 1 2 -6 2 1 1 2 -6 2 1 1 1 3 -3 -1 1 3 -3 -1 1i 1 4 1 1 4 1 -10- 27 March 1981 (1- P : SJG: 1hz The
Pataky, Todd C; Vanrenterghem, Jos; Robinson, Mark A
2015-05-01
Biomechanical processes are often manifested as one-dimensional (1D) trajectories. It has been shown that 1D confidence intervals (CIs) are biased when based on 0D statistical procedures, and the non-parametric 1D bootstrap CI has emerged in the Biomechanics literature as a viable solution. The primary purpose of this paper was to clarify that, for 1D biomechanics datasets, the distinction between 0D and 1D methods is much more important than the distinction between parametric and non-parametric procedures. A secondary purpose was to demonstrate that a parametric equivalent to the 1D bootstrap exists in the form of a random field theory (RFT) correction for multiple comparisons. To emphasize these points we analyzed six datasets consisting of force and kinematic trajectories in one-sample, paired, two-sample and regression designs. Results showed, first, that the 1D bootstrap and other 1D non-parametric CIs were qualitatively identical to RFT CIs, and all were very different from 0D CIs. Second, 1D parametric and 1D non-parametric hypothesis testing results were qualitatively identical for all six datasets. Last, we highlight the limitations of 1D CIs by demonstrating that they are complex, design-dependent, and thus non-generalizable. These results suggest that (i) analyses of 1D data based on 0D models of randomness are generally biased unless one explicitly identifies 0D variables before the experiment, and (ii) parametric and non-parametric 1D hypothesis testing provide an unambiguous framework for analysis when one׳s hypothesis explicitly or implicitly pertains to whole 1D trajectories.
Mathematical models for non-parametric inferences from line transect data
Burnham, K.P.; Anderson, D.R.
1976-01-01
A general mathematical theory of line transects is developed which supplies a framework for nonparametric density estimation based on either right angle or sighting distances. The probability of observing a point given its right angle distance (y) from the line is generalized to an arbitrary function g(y). Given only that g(0) = 1, it is shown there are nonparametric approaches to density estimation using the observed right angle distances. The model is then generalized to include sighting distances (r). Let f(y I r) be the conditional distribution of right angle distance given sighting distance. It is shown that nonparametric estimation based only on sighting distances requires we know the transformation of r given by f(0 I r).
Non-parametric graphnet-regularized representation of dMRI in space and time.
Fick, Rutger H J; Petiet, Alexandra; Santin, Mathieu; Philippe, Anne-Charlotte; Lehericy, Stephane; Deriche, Rachid; Wassermann, Demian
2017-09-14
Effective representation of the four-dimensional diffusion MRI signal - varying over three-dimensional q-space and diffusion time τ - is a sought-after and still unsolved challenge in diffusion MRI (dMRI). We propose a functional basis approach that is specifically designed to represent the dMRI signal in this qτ-space. Following recent terminology, we refer to our qτ-functional basis as "qτ-dMRI". qτ-dMRI can be seen as a time-dependent realization of q-space imaging by Paul Callaghan and colleagues. We use GraphNet regularization - imposing both signal smoothness and sparsity - to drastically reduce the number of diffusion-weighted images (DWIs) that is needed to represent the dMRI signal in the qτ-space. As the main contribution, qτ-dMRI provides the framework to - without making biophysical assumptions - represent the qτ-space signal and estimate time-dependent q-space indices (qτ-indices), providing a new means for studying diffusion in nervous tissue. We validate our method on both in-silico generated data using Monte-Carlo simulations and an in-vivo test-retest study of two C57Bl6 wild-type mice, where we found good reproducibility of estimated qτ-index values and trends. In the hopes of opening up new τ-dependent venues of studying nervous tissues, qτ-dMRI is the first of its kind in being specifically designed to provide open interpretation of the qτ-diffusion signal. Copyright © 2017 Elsevier B.V. All rights reserved.
Chiou, Jeng-Min; Liang, Kung-Yee; Chiu, Yen-Feng
2005-01-01
Multipoint linkage analysis using sibpair designs remains a common approach to help investigators to narrow chromosomal regions for traits (either qualitative or quantitative) of interest. Despite its popularity, the success of this approach depends heavily on how issues such as genetic heterogeneity, gene-gene, and gene-environment interactions are properly handled. If addressed properly, the likelihood of detecting genetic linkage and of efficiently estimating the location of the trait locus would be enhanced, sometimes drastically. Previously, we have proposed an approach to deal with these issues by modeling the genetic effect of the target trait locus as a function of covariates pertained to the sibpairs. Here the genetic effect is simply the probability that a sibpair shares the same allele at the trait locus from their parents. Such modeling helps to divide the sibpairs into more homogeneous subgroups, which in turn helps to enhance the chance to detect linkage. One limitation of this approach is the need to categorize the covariates so that a small and fixed number of genetic effect parameters are introduced. In this report, we take advantage of the fact that nowadays multiple markers are readily available for genotyping simultaneously. This suggests that one could estimate the dependence of the generic effect on the covariates nonparametrically. We present an iterative procedure to estimate (1) the genetic effect nonparametrically and (2) the location of the trait locus through estimating functions developed by Liang et al. ([2001a] Hum Hered 51:67-76). We apply this new method to the linkage study of schizophrenia to illustrate how the onset ages of each sibpair may help to address the issue of genetic heterogeneity. This analysis sheds new light on the dependence of the trait effect on onset ages from affected sibpairs, an observation not revealed previously. In addition, we have carried out some simulation work, which suggests that this method provides
Non-parametric Bayesian graph models reveal community structure in resting state fMRI.
Andersen, Kasper Winther; Madsen, Kristoffer H; Siebner, Hartwig Roman; Schmidt, Mikkel N; Mørup, Morten; Hansen, Lars Kai
2014-10-15
Modeling of resting state functional magnetic resonance imaging (rs-fMRI) data using network models is of increasing interest. It is often desirable to group nodes into clusters to interpret the communication patterns between nodes. In this study we consider three different nonparametric Bayesian models for node clustering in complex networks. In particular, we test their ability to predict unseen data and their ability to reproduce clustering across datasets. The three generative models considered are the Infinite Relational Model (IRM), Bayesian Community Detection (BCD), and the Infinite Diagonal Model (IDM). The models define probabilities of generating links within and between clusters and the difference between the models lies in the restrictions they impose upon the between-cluster link probabilities. IRM is the most flexible model with no restrictions on the probabilities of links between clusters. BCD restricts the between-cluster link probabilities to be strictly lower than within-cluster link probabilities to conform to the community structure typically seen in social networks. IDM only models a single between-cluster link probability, which can be interpreted as a background noise probability. These probabilistic models are compared against three other approaches for node clustering, namely Infomap, Louvain modularity, and hierarchical clustering. Using 3 different datasets comprising healthy volunteers' rs-fMRI we found that the BCD model was in general the most predictive and reproducible model. This suggests that rs-fMRI data exhibits community structure and furthermore points to the significance of modeling heterogeneous between-cluster link probabilities.
Farcomeni, Alessio; Serranti, Silvia; Bonifazi, Giuseppe
2008-01-01
Glass ceramic detection in glass recycling plants represents a still unsolved problem, as glass ceramic material looks like normal glass and is usually detected only by specialized personnel. The presence of glass-like contaminants inside waste glass products, resulting from both industrial and differentiated urban waste collection, increases process production costs and reduces final product quality. In this paper an innovative approach for glass ceramic recognition, based on the non-parametric analysis of infrared spectra, is proposed and investigated. The work was specifically addressed to the spectral classification of glass and glass ceramic fragments collected in an actual recycling plant from three different production lines: flat glass, colored container-glass and white container-glass. The analyses, carried out in the near and mid-infrared (NIR-MIR) spectral field (1280-4480 nm), show that glass ceramic and glass fragments can be recognized by applying a wavelet transform, with a small classification error. Moreover, a method for selecting only a small subset of relevant wavelength ratios is suggested, allowing the conduct of a fast recognition of the two classes of materials. The results show how the proposed approach can be utilized to develop a classification engine to be integrated inside a hardware and software sorting architecture for fast "on-line" ceramic glass recognition and separation.
Koohbor, Behrad; Kidane, Addis; Lu, Wei -Yang; Sutton, Michael A.
2016-01-25
Dynamic stress–strain response of rigid closed-cell polymeric foams is investigated in this work by subjecting high toughness polyurethane foam specimens to direct impact with different projectile velocities and quantifying their deformation response with high speed stereo-photography together with 3D digital image correlation. The measured transient displacement field developed in the specimens during high stain rate loading is used to calculate the transient axial acceleration field throughout the specimen. A simple mathematical formulation based on conservation of mass is also proposed to determine the local change of density in the specimen during deformation. By obtaining the full-field acceleration and density distributions, the inertia stresses at each point in the specimen are determined through a non-parametric analysis and superimposed on the stress magnitudes measured at specimen ends to obtain the full-field stress distribution. Furthermore, the process outlined above overcomes a major challenge in high strain rate experiments with low impedance polymeric foam specimens, i.e. the delayed equilibrium conditions can be quantified.
Koohbor, Behrad; Kidane, Addis; Lu, Wei -Yang; ...
2016-01-25
Dynamic stress–strain response of rigid closed-cell polymeric foams is investigated in this work by subjecting high toughness polyurethane foam specimens to direct impact with different projectile velocities and quantifying their deformation response with high speed stereo-photography together with 3D digital image correlation. The measured transient displacement field developed in the specimens during high stain rate loading is used to calculate the transient axial acceleration field throughout the specimen. A simple mathematical formulation based on conservation of mass is also proposed to determine the local change of density in the specimen during deformation. By obtaining the full-field acceleration and density distributions,more » the inertia stresses at each point in the specimen are determined through a non-parametric analysis and superimposed on the stress magnitudes measured at specimen ends to obtain the full-field stress distribution. Furthermore, the process outlined above overcomes a major challenge in high strain rate experiments with low impedance polymeric foam specimens, i.e. the delayed equilibrium conditions can be quantified.« less
Cabrieto, Jedelyn; Tuerlinckx, Francis; Kuppens, Peter; Grassmann, Mariel; Ceulemans, Eva
2017-06-01
Change point detection in multivariate time series is a complex task since next to the mean, the correlation structure of the monitored variables may also alter when change occurs. DeCon was recently developed to detect such changes in mean and\\or correlation by combining a moving windows approach and robust PCA. However, in the literature, several other methods have been proposed that employ other non-parametric tools: E-divisive, Multirank, and KCP. Since these methods use different statistical approaches, two issues need to be tackled. First, applied researchers may find it hard to appraise the differences between the methods. Second, a direct comparison of the relative performance of all these methods for capturing change points signaling correlation changes is still lacking. Therefore, we present the basic principles behind DeCon, E-divisive, Multirank, and KCP and the corresponding algorithms, to make them more accessible to readers. We further compared their performance through extensive simulations using the settings of Bulteel et al. (Biological Psychology, 98 (1), 29-42, 2014) implying changes in mean and in correlation structure and those of Matteson and James (Journal of the American Statistical Association, 109 (505), 334-345, 2014) implying different numbers of (noise) variables. KCP emerged as the best method in almost all settings. However, in case of more than two noise variables, only DeCon performed adequately in detecting correlation changes.
NASA Astrophysics Data System (ADS)
Petrosian, Vahe
2016-07-01
We have developed an inversion method for determination of the characteristics of the acceleration mechanism directly and non-parametrically from observations, in contrast to the usual forward fitting of parametric model variables to observations. This is done in the frame work of the so-called leaky box model of acceleration, valid for isotropic momentum distribution and for volume integrated characteristics in a finite acceleration site. We consider both acceleration by shocks and stochastic acceleration where turbulence plays the primary role to determine the acceleration, scattering and escape rates. Assuming a knowledge of the background plasma the model has essentially two unknown parameters, namely the momentum and pitch angle scattering diffusion coefficients, which can be evaluated given two independent spectral observations. These coefficients are obtained directly from the spectrum of radiation from the supernova remnants (SNRs), which gives the spectrum of accelerated particles, and the observed spectrum of cosmic rays (CRs), which are related to the spectrum of particles escaping the SNRs. The results obtained from application of this method will be presented.
NASA Astrophysics Data System (ADS)
Rezaei Kh., S.; Bailer-Jones, C. A. L.; Hanson, R. J.; Fouesneau, M.
2017-02-01
We present a non-parametric model for inferring the three-dimensional (3D) distribution of dust density in the Milky Way. Our approach uses the extinction measured towards stars at different locations in the Galaxy at approximately known distances. Each extinction measurement is proportional to the integrated dust density along its line of sight (LoS). Making simple assumptions about the spatial correlation of the dust density, we can infer the most probable 3D distribution of dust across the entire observed region, including along sight lines which were not observed. This is possible because our model employs a Gaussian process to connect all LoS. We demonstrate the capability of our model to capture detailed dust density variations using mock data and simulated data from the Gaia Universe Model Snapshot. We then apply our method to a sample of giant stars observed by APOGEE and Kepler to construct a 3D dust map over a small region of the Galaxy. Owing to our smoothness constraint and its isotropy, we provide one of the first maps which does not show the "fingers of God" effect.
Mayr, Andreas; Hothorn, Torsten; Fenske, Nora
2012-01-25
The construction of prediction intervals (PIs) for future body mass index (BMI) values of individual children based on a recent German birth cohort study with n = 2007 children is problematic for standard parametric approaches, as the BMI distribution in childhood is typically skewed depending on age. We avoid distributional assumptions by directly modelling the borders of PIs by additive quantile regression, estimated by boosting. We point out the concept of conditional coverage to prove the accuracy of PIs. As conditional coverage can hardly be evaluated in practical applications, we conduct a simulation study before fitting child- and covariate-specific PIs for future BMI values and BMI patterns for the present data. The results of our simulation study suggest that PIs fitted by quantile boosting cover future observations with the predefined coverage probability and outperform the benchmark approach. For the prediction of future BMI values, quantile boosting automatically selects informative covariates and adapts to the age-specific skewness of the BMI distribution. The lengths of the estimated PIs are child-specific and increase, as expected, with the age of the child. Quantile boosting is a promising approach to construct PIs with correct conditional coverage in a non-parametric way. It is in particular suitable for the prediction of BMI patterns depending on covariates, since it provides an interpretable predictor structure, inherent variable selection properties and can even account for longitudinal data structures.
A new measure for gene expression biclustering based on non-parametric correlation.
Flores, Jose L; Inza, Iñaki; Larrañaga, Pedro; Calvo, Borja
2013-12-01
One of the emerging techniques for performing the analysis of the DNA microarray data known as biclustering is the search of subsets of genes and conditions which are coherently expressed. These subgroups provide clues about the main biological processes. Until now, different approaches to this problem have been proposed. Most of them use the mean squared residue as quality measure but relevant and interesting patterns can not be detected such as shifting, or scaling patterns. Furthermore, recent papers show that there exist new coherence patterns involved in different kinds of cancer and tumors such as inverse relationships between genes which can not be captured. The proposed measure is called Spearman's biclustering measure (SBM) which performs an estimation of the quality of a bicluster based on the non-linear correlation among genes and conditions simultaneously. The search of biclusters is performed by using a evolutionary technique called estimation of distribution algorithms which uses the SBM measure as fitness function. This approach has been examined from different points of view by using artificial and real microarrays. The assessment process has involved the use of quality indexes, a set of bicluster patterns of reference including new patterns and a set of statistical tests. It has been also examined the performance using real microarrays and comparing to different algorithmic approaches such as Bimax, CC, OPSM, Plaid and xMotifs. SBM shows several advantages such as the ability to recognize more complex coherence patterns such as shifting, scaling and inversion and the capability to selectively marginalize genes and conditions depending on the statistical significance. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.
Computing dispersion interactions in density functional theory
NASA Astrophysics Data System (ADS)
Cooper, V. R.; Kong, L.; Langreth, D. C.
2010-02-01
In this article techniques for including dispersion interactions within density functional theory are examined. In particular comparisons are made between four popular methods: dispersion corrected DFT, pseudopotential correction schemes, symmetry adapted perturbation theory, and a non-local density functional - the so called Rutgers-Chalmers van der Waals density functional (vdW-DF). The S22 benchmark data set is used to evaluate the relative accuracy of these methods and factors such as scalability and transferability are also discussed. We demonstrate that vdW-DF presents an excellent compromise between computational speed and accuracy and lends most easily to full scale application in solid materials. This claim is supported through a brief discussion of a recent large scale application to H2 in a prototype metal organic framework material (MOF), Zn2BDC2TED. The vdW-DF shows overwhelming promise for first-principles studies of physisorbed molecules in porous extended systems; thereby having broad applicability for studies as diverse as molecular adsorption and storage, battery technology, catalysis and gas separations.
Computational Functional Analysis of Lipid Metabolic Enzymes.
Bagnato, Carolina; Have, Arjen Ten; Prados, María B; Beligni, María V
2017-01-01
The computational analysis of enzymes that participate in lipid metabolism has both common and unique challenges when compared to the whole protein universe. Some of the hurdles that interfere with the functional annotation of lipid metabolic enzymes that are common to other pathways include the definition of proper starting datasets, the construction of reliable multiple sequence alignments, the definition of appropriate evolutionary models, and the reconstruction of phylogenetic trees with high statistical support, particularly for large datasets. Most enzymes that take part in lipid metabolism belong to complex superfamilies with many members that are not involved in lipid metabolism. In addition, some enzymes that do not have sequence similarity catalyze similar or even identical reactions. Some of the challenges that, albeit not unique, are more specific to lipid metabolism refer to the high compartmentalization of the routes, the catalysis in hydrophobic environments and, related to this, the function near or in biological membranes.In this work, we provide guidelines intended to assist in the proper functional annotation of lipid metabolic enzymes, based on previous experiences related to the phospholipase D superfamily and the annotation of the triglyceride synthesis pathway in algae. We describe a pipeline that starts with the definition of an initial set of sequences to be used in similarity-based searches and ends in the reconstruction of phylogenies. We also mention the main issues that have to be taken into consideration when using tools to analyze subcellular localization, hydrophobicity patterns, or presence of transmembrane domains in lipid metabolic enzymes.
A frequentist approach to computer model calibration
Wong, Raymond K. W.; Storlie, Curtis Byron; Lee, Thomas C. M.
2016-05-05
The paper considers the computer model calibration problem and provides a general frequentist solution. Under the framework proposed, the data model is semiparametric with a non-parametric discrepancy function which accounts for any discrepancy between physical reality and the computer model. In an attempt to solve a fundamentally important (but often ignored) identifiability issue between the computer model parameters and the discrepancy function, the paper proposes a new and identifiable parameterization of the calibration problem. It also develops a two-step procedure for estimating all the relevant quantities under the new parameterization. This estimation procedure is shown to enjoy excellent rates of convergence and can be straightforwardly implemented with existing software. For uncertainty quantification, bootstrapping is adopted to construct confidence regions for the quantities of interest. As a result, the practical performance of the methodology is illustrated through simulation examples and an application to a computational fluid dynamics model.
A frequentist approach to computer model calibration
Wong, Raymond K. W.; Storlie, Curtis Byron; Lee, Thomas C. M.
2016-05-05
The paper considers the computer model calibration problem and provides a general frequentist solution. Under the framework proposed, the data model is semiparametric with a non-parametric discrepancy function which accounts for any discrepancy between physical reality and the computer model. In an attempt to solve a fundamentally important (but often ignored) identifiability issue between the computer model parameters and the discrepancy function, the paper proposes a new and identifiable parameterization of the calibration problem. It also develops a two-step procedure for estimating all the relevant quantities under the new parameterization. This estimation procedure is shown to enjoy excellent rates ofmore » convergence and can be straightforwardly implemented with existing software. For uncertainty quantification, bootstrapping is adopted to construct confidence regions for the quantities of interest. As a result, the practical performance of the methodology is illustrated through simulation examples and an application to a computational fluid dynamics model.« less
Computational based functional analysis of Bacillus phytases.
Verma, Anukriti; Singh, Vinay Kumar; Gaur, Smriti
2016-02-01
Phytase is an enzyme which catalyzes the total hydrolysis of phytate to less phosphorylated myo-inositol derivatives and inorganic phosphate and digests the undigestable phytate part present in seeds and grains and therefore provides digestible phosphorus, calcium and other mineral nutrients. Phytases are frequently added to the feed of monogastric animals so that bioavailability of phytic acid-bound phosphate increases, ultimately enhancing the nutritional value of diets. The Bacillus phytase is very suitable to be used in animal feed because of its optimum pH with excellent thermal stability. Present study is aimed to perform an in silico comparative characterization and functional analysis of phytases from Bacillus amyloliquefaciens to explore physico-chemical properties using various bio-computational tools. All proteins are acidic and thermostable and can be used as suitable candidates in the feed industry.
Ford, Eric B.; Fabrycky, Daniel C.; Steffen, Jason H.; Carter, Joshua A.; Fressin, Francois; Holman, Matthew J.; Lissauer, Jack J.; Moorhead, Althea V.; Morehead, Robert C.; Ragozzine, Darin; Rowe, Jason F.; /NASA, Ames /SETI Inst., Mtn. View /San Diego State U., Astron. Dept.
2012-01-01
We present a new method for confirming transiting planets based on the combination of transit timing variations (TTVs) and dynamical stability. Correlated TTVs provide evidence that the pair of bodies are in the same physical system. Orbital stability provides upper limits for the masses of the transiting companions that are in the planetary regime. This paper describes a non-parametric technique for quantifying the statistical significance of TTVs based on the correlation of two TTV data sets. We apply this method to an analysis of the transit timing variations of two stars with multiple transiting planet candidates identified by Kepler. We confirm four transiting planets in two multiple planet systems based on their TTVs and the constraints imposed by dynamical stability. An additional three candidates in these same systems are not confirmed as planets, but are likely to be validated as real planets once further observations and analyses are possible. If all were confirmed, these systems would be near 4:6:9 and 2:4:6:9 period commensurabilities. Our results demonstrate that TTVs provide a powerful tool for confirming transiting planets, including low-mass planets and planets around faint stars for which Doppler follow-up is not practical with existing facilities. Continued Kepler observations will dramatically improve the constraints on the planet masses and orbits and provide sensitivity for detecting additional non-transiting planets. If Kepler observations were extended to eight years, then a similar analysis could likely confirm systems with multiple closely spaced, small transiting planets in or near the habitable zone of solar-type stars.
Yang, H; Plevy, S; Taylor, K; Tyan, D; Fischel-Ghodsian, N; McElree, C; Targan, S; Rotter, J
1999-01-01
BACKGROUND—There is evidence for genetic susceptibility to Crohn's disease, and a tentative association with tumour necrosis factor (TNF) and HLA class II alleles. AIMS—To examine the potential of genetic linkage between Crohn's disease and the MHC region on chromosome 6p. METHODS—TNF microsatellite markers and, for some families, additional HLA antigens were typed for 323 individuals from 49Crohn's disease multiplex families to generate informative haplotypes. Non-parametric linkage analysis methods, including sib pair and affected relative pair methods, were used. RESULTS—Increased sharing of haplotypes was observed in affected sib pairs: 92% (48/52) shared one or two haplotypes versus an expected 75% if linkage did not exist (p=0.004). After other affected relative pairs were included, the significance level reached 0.001. The mean proportion of haplotype sharing was increased for both concordant affected (π=0.60, p=0.002) and unaffected sib pairs (π=0.58, p=0.031) compared with the expected value (π=0.5). In contrast, sharing in discordant sib pairs was significantly decreased (π=0.42, p=0.007). Linear regression analysis using all three types of sib pairs yielded a slope of −0.38 at p=0.00003. It seemed that the HLA effect was stronger in non-Jewish families than in Jewish families. CONCLUSIONS—All available analytical methods support linkage of Crohn's disease to the MHC region in these Crohn's disease families. This region is estimated to contribute approximately 10-33% of the total genetic risk to Crohn's disease. Keywords: Crohn's disease; HLA; linkage; inflammatory bowel disease; tumour necrosis factor; genetics PMID:10075959
NASA Astrophysics Data System (ADS)
Erkal, Denis; Koposov, Sergey E.; Belokurov, Vasily
2017-09-01
Only in the Milky Way is it possible to conduct an experiment that uses stellar streams to detect low-mass dark matter subhaloes. In smooth and static host potentials, tidal tails of disrupting satellites appear highly symmetric. However, perturbations from dark subhaloes, as well as from GMCs and the Milky Way bar, can induce density fluctuations that destroy this symmetry. Motivated by the recent release of unprecedentedly deep and wide imaging data around the Pal 5 stellar stream, we develop a new probabilistic, adaptive and non-parametric technique that allows us to bring the cluster's tidal tails into clear focus. Strikingly, we uncover a stream whose density exhibits visible changes on a variety of angular scales. We detect significant bumps and dips, both narrow and broad: two peaks on either side of the progenitor, each only a fraction of a degree across, and two gaps, ∼2° and ∼9° wide, the latter accompanied by a gargantuan lump of debris. This largest density feature results in a pronounced intertail asymmetry which cannot be made consistent with an unperturbed stream according to a suite of simulations we have produced. We conjecture that the sharp peaks around Pal 5 are epicyclic overdensities, while the two dips are consistent with impacts by subhaloes. Assuming an age of 3.4 Gyr for Pal 5, these two gaps would correspond to the characteristic size of gaps created by subhaloes in the mass range of 106-107 M⊙ and 107-108 M⊙, respectively. In addition to dark substructure, we find that the bar of the Milky Way can plausibly produce the asymmetric density seen in Pal 5 and that GMCs could cause the smaller gap.
Wang, Ying; Feng, Chenglian; Liu, Yuedan; Zhao, Yujie; Li, Huixian; Zhao, Tianhui; Guo, Wenjing
2017-02-01
Transition metals in the fourth period of the periodic table of the elements are widely widespread in aquatic environments. They could often occur at certain concentrations to cause adverse effects on aquatic life and human health. Generally, parametric models are mostly used to construct species sensitivity distributions (SSDs), which result in comparison for water quality criteria (WQC) of elements in the same period or group of the periodic table might be inaccurate and the results could be biased. To address this inadequacy, the non-parametric kernel density estimation (NPKDE) with its optimal bandwidths and testing methods were developed for establishing SSDs. The NPKDE was better fit, more robustness and better predicted than conventional normal and logistic parametric density estimations for constructing SSDs and deriving acute HC5 and WQC for transition metals in the fourth period of the periodic table. The decreasing sequence of HC5 values for the transition metals in the fourth period was Ti > Mn > V > Ni > Zn > Cu > Fe > Co > Cr(VI), which were not proportional to atomic number in the periodic table, and for different metals the relatively sensitive species were also different. The results indicated that except for physical and chemical properties there are other factors affecting toxicity mechanisms of transition metals. The proposed method enriched the methodological foundation for WQC. Meanwhile, it also provided a relatively innovative, accurate approach for the WQC derivation and risk assessment of the same group and period metals in aquatic environments to support protection of aquatic organisms. Copyright © 2016 Elsevier Ltd. All rights reserved.
Ford, Eric B.; Moorhead, Althea V.; Morehead, Robert C.; Fabrycky, Daniel C.; Carter, Joshua A.; Fressin, Francois; Holman, Matthew J.; Ragozzine, Darin; Charbonneau, David; Lissauer, Jack J.; Rowe, Jason F.; Borucki, William J.; Bryson, Stephen T.; Burke, Christopher J.; Caldwell, Douglas A.; Welsh, William F.; Allen, Christopher; Buchhave, Lars A.; Collaboration: Kepler Science Team; and others
2012-05-10
We present a new method for confirming transiting planets based on the combination of transit timing variations (TTVs) and dynamical stability. Correlated TTVs provide evidence that the pair of bodies is in the same physical system. Orbital stability provides upper limits for the masses of the transiting companions that are in the planetary regime. This paper describes a non-parametric technique for quantifying the statistical significance of TTVs based on the correlation of two TTV data sets. We apply this method to an analysis of the TTVs of two stars with multiple transiting planet candidates identified by Kepler. We confirm four transiting planets in two multiple-planet systems based on their TTVs and the constraints imposed by dynamical stability. An additional three candidates in these same systems are not confirmed as planets, but are likely to be validated as real planets once further observations and analyses are possible. If all were confirmed, these systems would be near 4:6:9 and 2:4:6:9 period commensurabilities. Our results demonstrate that TTVs provide a powerful tool for confirming transiting planets, including low-mass planets and planets around faint stars for which Doppler follow-up is not practical with existing facilities. Continued Kepler observations will dramatically improve the constraints on the planet masses and orbits and provide sensitivity for detecting additional non-transiting planets. If Kepler observations were extended to eight years, then a similar analysis could likely confirm systems with multiple closely spaced, small transiting planets in or near the habitable zone of solar-type stars.
Korany, Mohamed A; Maher, Hadir M; Galal, Shereen M; Fahmy, Ossama T; Ragab, Marwa A A
2010-11-15
This manuscript discusses the application of chemometrics to the handling of HPLC response data using the internal standard method (ISM). This was performed on a model mixture containing terbutaline sulphate, guaiphenesin, bromhexine HCl, sodium benzoate and propylparaben as an internal standard. Derivative treatment of chromatographic response data of analyte and internal standard was followed by convolution of the resulting derivative curves using 8-points sin x(i) polynomials (discrete Fourier functions). The response of each analyte signal, its corresponding derivative and convoluted derivative data were divided by that of the internal standard to obtain the corresponding ratio data. This was found beneficial in eliminating different types of interferences. It was successfully applied to handle some of the most common chromatographic problems and non-ideal conditions, namely: overlapping chromatographic peaks and very low analyte concentrations. For example, a significant change in the correlation coefficient of sodium benzoate, in case of overlapping peaks, went from 0.9975 to 0.9998 on applying normal conventional peak area and first derivative under Fourier functions methods, respectively. Also a significant improvement in the precision and accuracy for the determination of synthetic mixtures and dosage forms in non-ideal cases was achieved. For example, in the case of overlapping peaks guaiphenesin mean recovery% and RSD% went from 91.57, 9.83 to 100.04, 0.78 on applying normal conventional peak area and first derivative under Fourier functions methods, respectively. This work also compares the application of Theil's method, a non-parametric regression method, in handling the response ratio data, with the least squares parametric regression method, which is considered the de facto standard method used for regression. Theil's method was found to be superior to the method of least squares as it assumes that errors could occur in both x- and y-directions and
Dundar, Murat; Akova, Ferit; Yerebakan, Halid Z; Rajwa, Bartek
2014-09-24
Flow cytometry (FC)-based computer-aided diagnostics is an emerging technique utilizing modern multiparametric cytometry systems.The major difficulty in using machine-learning approaches for classification of FC data arises from limited access to a wide variety of anomalous samples for training. In consequence, any learning with an abundance of normal cases and a limited set of specific anomalous cases is biased towards the types of anomalies represented in the training set. Such models do not accurately identify anomalies, whether previously known or unknown, that may exist in future samples tested. Although one-class classifiers trained using only normal cases would avoid such a bias, robust sample characterization is critical for a generalizable model. Owing to sample heterogeneity and instrumental variability, arbitrary characterization of samples usually introduces feature noise that may lead to poor predictive performance. Herein, we present a non-parametric Bayesian algorithm called ASPIRE (anomalous sample phenotype identification with random effects) that identifies phenotypic differences across a batch of samples in the presence of random effects. Our approach involves simultaneous clustering of cellular measurements in individual samples and matching of discovered clusters across all samples in order to recover global clusters using probabilistic sampling techniques in a systematic way. We demonstrate the performance of the proposed method in identifying anomalous samples in two different FC data sets, one of which represents a set of samples including acute myeloid leukemia (AML) cases, and the other a generic 5-parameter peripheral-blood immunophenotyping. Results are evaluated in terms of the area under the receiver operating characteristics curve (AUC). ASPIRE achieved AUCs of 0.99 and 1.0 on the AML and generic blood immunophenotyping data sets, respectively. These results demonstrate that anomalous samples can be identified by ASPIRE with almost
NASA Astrophysics Data System (ADS)
Van Steenbergen, N.; Willems, P.
2012-04-01
Reliable flood forecasts are the most important non-structural measures to reduce the impact of floods. However flood forecasting systems are subject to uncertainty originating from the input data, model structure and model parameters of the different hydraulic and hydrological submodels. To quantify this uncertainty a non-parametric data-based approach has been developed. This approach analyses the historical forecast residuals (differences between the predictions and the observations at river gauging stations) without using a predefined statistical error distribution. Because the residuals are correlated with the value of the forecasted water level and the lead time, the residuals are split up into discrete classes of simulated water levels and lead times. For each class, percentile values are calculated of the model residuals and stored in a 'three dimensional error' matrix. By 3D interpolation in this error matrix, the uncertainty in new forecasted water levels can be quantified. In addition to the quantification of the uncertainty, the communication of this uncertainty is equally important. The communication has to be done in a consistent way, reducing the chance of misinterpretation. Also, the communication needs to be adapted to the audience; the majority of the larger public is not interested in in-depth information on the uncertainty on the predicted water levels, but only is interested in information on the likelihood of exceedance of certain alarm levels. Water managers need more information, e.g. time dependent uncertainty information, because they rely on this information to undertake the appropriate flood mitigation action. There are various ways in presenting uncertainty information (numerical, linguistic, graphical, time (in)dependent, etc.) each with their advantages and disadvantages for a specific audience. A useful method to communicate uncertainty of flood forecasts is by probabilistic flood mapping. These maps give a representation of the
NASA Astrophysics Data System (ADS)
Brown, M. G. L.; He, T.; Liang, S.
2016-12-01
Satellite-derived estimates of incident photosynthetically active radiation (PAR) can be used to monitor global change, are required by most terrestrial ecosystem models, and can be used to estimate primary production according to the theory of light use efficiency. Compared with parametric approaches, non-parametric techniques that include an artificial neural network (ANN), support vector machine regression (SVM), an artificial bee colony (ABC), and a look-up table (LUT) do not require many ancillary data as inputs for the estimation of PAR from satellite data. In this study, a selection of machine learning methods to estimate PAR from MODIS top of atmosphere (TOA) radiances are compared to a LUT approach to determine which techniques might best handle the nonlinear relationship between TOA radiance and incident PAR. Evaluation of these methods (ANN, SVM, and LUT) is performed with ground measurements at seven SURFRAD sites. Due to the design of the ANN, it can handle the nonlinear relationship between TOA radiance and PAR better than linearly interpolating between the values in the LUT; however, training the ANN has to be carried out on an angular-bin basis, which results in a LUT of ANNs. The SVM model may be better for incorporating multiple viewing angles than the ANN; however, both techniques require a large amount of training data, which may introduce a regional bias based on where the most training and validation data are available. Based on the literature, the ABC is a promising alternative to an ANN, SVM regression and a LUT, but further development for this application is required before concrete conclusions can be drawn. For now, the LUT method outperforms the machine-learning techniques, but future work should be directed at developing and testing the ABC method. A simple, robust method to estimate direct and diffuse incident PAR, with minimal inputs and a priori knowledge, would be very useful for monitoring global change of primary production
Chung, Clement; Emili, Andrew; Frey, Brendan J
2013-04-01
Tandem mass spectrometry (MS/MS) is a dominant approach for large-scale high-throughput post-translational modification (PTM) profiling. Although current state-of-the-art blind PTM spectral analysis algorithms can predict thousands of modified peptides (PTM predictions) in an MS/MS experiment, a significant percentage of these predictions have inaccurate modification mass estimates and false modification site assignments. This problem can be addressed by post-processing the PTM predictions with a PTM refinement algorithm. We developed a novel PTM refinement algorithm, iPTMClust, which extends a recently introduced PTM refinement algorithm PTMClust and uses a non-parametric Bayesian model to better account for uncertainties in the quantity and identity of PTMs in the input data. The use of this new modeling approach enables iPTMClust to provide a confidence score per modification site that allows fine-tuning and interpreting resulting PTM predictions. The primary goal behind iPTMClust is to improve the quality of the PTM predictions. First, to demonstrate that iPTMClust produces sensible and accurate cluster assignments, we compare it with k-means clustering, mixtures of Gaussians (MOG) and PTMClust on a synthetically generated PTM dataset. Second, in two separate benchmark experiments using PTM data taken from a phosphopeptide and a yeast proteome study, we show that iPTMClust outperforms state-of-the-art PTM prediction and refinement algorithms, including PTMClust. Finally, we illustrate the general applicability of our new approach on a set of human chromatin protein complex data, where we are able to identify putative novel modified peptides and modification sites that may be involved in the formation and regulation of protein complexes. Our method facilitates accurate PTM profiling, which is an important step in understanding the mechanisms behind many biological processes and should be an integral part of any proteomic study. Our algorithm is implemented in
Contextuality and Wigner-function negativity in qubit quantum computation
NASA Astrophysics Data System (ADS)
Raussendorf, Robert; Browne, Dan E.; Delfosse, Nicolas; Okay, Cihan; Bermejo-Vega, Juan
2017-05-01
We describe schemes of quantum computation with magic states on qubits for which contextuality and negativity of the Wigner function are necessary resources possessed by the magic states. These schemes satisfy a constraint. Namely, the non-negativity of Wigner functions must be preserved under all available measurement operations. Furthermore, we identify stringent consistency conditions on such computational schemes, revealing the general structure by which negativity of Wigner functions, hardness of classical simulation of the computation, and contextuality are connected.
A Tutorial on Analog Computation: Computing Functions over the Reals
NASA Astrophysics Data System (ADS)
Campagnolo, Manuel Lameiras
The best known programmable analog computing device is the differential analyser. The concept for the device dates back to Lord Kelvin and his brother James Thomson in 1876, and was constructed in 1932 at MIT under the supervision of Vannevar Bush. The MIT differential analyser used wheel-and-disk mechanical integrators and was able to solve sixth-order differential equations. During the 1930’s, more powerful differential analysers were built. In 1941 Claude Shannon showed that given a sufficient numbers of integrators the machines could, in theory, precisely generate the solutions of all differentially algebraic equations. Shannon’s mathematical model of the differential analyser is known as the GPAC.
Computational Interpretations of Analysis via Products of Selection Functions
NASA Astrophysics Data System (ADS)
Escardó, Martín; Oliva, Paulo
We show that the computational interpretation of full comprehension via two well-known functional interpretations (dialectica and modified realizability) corresponds to two closely related infinite products of selection functions.
Computer method for identification of boiler transfer functions
NASA Technical Reports Server (NTRS)
Miles, J. H.
1972-01-01
Iterative computer aided procedure was developed which provides for identification of boiler transfer functions using frequency response data. Method uses frequency response data to obtain satisfactory transfer function for both high and low vapor exit quality data.
Computer program for Bessel and Hankel functions
NASA Technical Reports Server (NTRS)
Kreider, Kevin L.; Saule, Arthur V.; Rice, Edward J.; Clark, Bruce J.
1991-01-01
A set of FORTRAN subroutines for calculating Bessel and Hankel functions is presented. The routines calculate Bessel and Hankel functions of the first and second kinds, as well as their derivatives, for wide ranges of integer order and real or complex argument in single or double precision. Depending on the order and argument, one of three evaluation methods is used: the power series definition, an Airy function expansion, or an asymptotic expansion. Routines to calculate Airy functions and their derivatives are also included.
Functions and Statistics with Computers at the College Level.
ERIC Educational Resources Information Center
Hollowell, Kathleen A.; Duch, Barbara J.
An experimental college level course, Functions and Statistics with Computers, was designed using the textbook "Functions, Statistics, and Trigonometry with Computers" developed by the University of Chicago School Mathematics Project. Students in the experimental course were compared with students in the traditional course based on attitude toward…
Computer Use and the Relation between Age and Cognitive Functioning
ERIC Educational Resources Information Center
Soubelet, Andrea
2012-01-01
This article investigates whether computer use for leisure could mediate or moderate the relations between age and cognitive functioning. Findings supported smaller age differences in measures of cognitive functioning for people who reported spending more hours using a computer. Because of the cross-sectional design of the study, two alternative…
Computer Use and the Relation between Age and Cognitive Functioning
ERIC Educational Resources Information Center
Soubelet, Andrea
2012-01-01
This article investigates whether computer use for leisure could mediate or moderate the relations between age and cognitive functioning. Findings supported smaller age differences in measures of cognitive functioning for people who reported spending more hours using a computer. Because of the cross-sectional design of the study, two alternative…
Scarpazza, Cristina; Nichols, Thomas E.; Seramondi, Donato; Maumet, Camille; Sartori, Giuseppe; Mechelli, Andrea
2016-01-01
In recent years, an increasing number of studies have used Voxel Based Morphometry (VBM) to compare a single patient with a psychiatric or neurological condition of interest against a group of healthy controls. However, the validity of this approach critically relies on the assumption that the single patient is drawn from a hypothetical population with a normal distribution and variance equal to that of the control group. In a previous investigation, we demonstrated that family-wise false positive error rate (i.e., the proportion of statistical comparisons yielding at least one false positive) in single case VBM are much higher than expected (Scarpazza et al., 2013). Here, we examine whether the use of non-parametric statistics, which does not rely on the assumptions of normal distribution and equal variance, would enable the investigation of single subjects with good control of false positive risk. We empirically estimated false positive rates (FPRs) in single case non-parametric VBM, by performing 400 statistical comparisons between a single disease-free individual and a group of 100 disease-free controls. The impact of smoothing (4, 8, and 12 mm) and type of pre-processing (Modulated, Unmodulated) was also examined, as these factors have been found to influence FPRs in previous investigations using parametric statistics. The 400 statistical comparisons were repeated using two independent, freely available data sets in order to maximize the generalizability of the results. We found that the family-wise error rate was 5% for increases and 3.6% for decreases in one data set; and 5.6% for increases and 6.3% for decreases in the other data set (5% nominal). Further, these results were not dependent on the level of smoothing and modulation. Therefore, the present study provides empirical evidence that single case VBM studies with non-parametric statistics are not susceptible to high false positive rates. The critical implication of this finding is that VBM can be used
Scarpazza, Cristina; Nichols, Thomas E; Seramondi, Donato; Maumet, Camille; Sartori, Giuseppe; Mechelli, Andrea
2016-01-01
In recent years, an increasing number of studies have used Voxel Based Morphometry (VBM) to compare a single patient with a psychiatric or neurological condition of interest against a group of healthy controls. However, the validity of this approach critically relies on the assumption that the single patient is drawn from a hypothetical population with a normal distribution and variance equal to that of the control group. In a previous investigation, we demonstrated that family-wise false positive error rate (i.e., the proportion of statistical comparisons yielding at least one false positive) in single case VBM are much higher than expected (Scarpazza et al., 2013). Here, we examine whether the use of non-parametric statistics, which does not rely on the assumptions of normal distribution and equal variance, would enable the investigation of single subjects with good control of false positive risk. We empirically estimated false positive rates (FPRs) in single case non-parametric VBM, by performing 400 statistical comparisons between a single disease-free individual and a group of 100 disease-free controls. The impact of smoothing (4, 8, and 12 mm) and type of pre-processing (Modulated, Unmodulated) was also examined, as these factors have been found to influence FPRs in previous investigations using parametric statistics. The 400 statistical comparisons were repeated using two independent, freely available data sets in order to maximize the generalizability of the results. We found that the family-wise error rate was 5% for increases and 3.6% for decreases in one data set; and 5.6% for increases and 6.3% for decreases in the other data set (5% nominal). Further, these results were not dependent on the level of smoothing and modulation. Therefore, the present study provides empirical evidence that single case VBM studies with non-parametric statistics are not susceptible to high false positive rates. The critical implication of this finding is that VBM can be used
The Computer and Its Functions; How to Communicate with the Computer.
ERIC Educational Resources Information Center
Ward, Peggy M.
A brief discussion of why it is important for students to be familiar with computers and their functions and a list of some practical applications introduce this two-part paper. Focusing on how the computer works, the first part explains the various components of the computer, different kinds of memory storage devices, disk operating systems, and…
Singular Function Integration in Computational Physics
NASA Astrophysics Data System (ADS)
Hasbun, Javier
2009-03-01
In teaching computational methods in the undergraduate physics curriculum, standard integration approaches taught include the rectangular, trapezoidal, Simpson, Romberg, and others. Over time, these techniques have proven to be invaluable and students are encouraged to employ the most efficient method that is expected to perform best when applied to a given problem. However, some physics research applications require techniques that can handle singularities. While decreasing the step size in traditional approaches is an alternative, this may not always work and repetitive processes make this route even more inefficient. Here, I present two existing integration rules designed to handle singular integrals. I compare them to traditional rules as well as to the exact analytic results. I suggest that it is perhaps time to include such approaches in the undergraduate computational physics course.
Pair correlation function integrals: Computation and use
NASA Astrophysics Data System (ADS)
Wedberg, Rasmus; O'Connell, John P.; Peters, Günther H.; Abildskov, Jens
2011-08-01
We describe a method for extending radial distribution functions obtained from molecular simulations of pure and mixed molecular fluids to arbitrary distances. The method allows total correlation function integrals to be reliably calculated from simulations of relatively small systems. The long-distance behavior of radial distribution functions is determined by requiring that the corresponding direct correlation functions follow certain approximations at long distances. We have briefly described the method and tested its performance in previous communications [R. Wedberg, J. P. O'Connell, G. H. Peters, and J. Abildskov, Mol. Simul. 36, 1243 (2010);, 10.1080/08927020903536366 Fluid Phase Equilib. 302, 32 (2011)], 10.1016/j.fluid.2010.10.004, but describe here its theoretical basis more thoroughly and derive long-distance approximations for the direct correlation functions. We describe the numerical implementation of the method in detail, and report numerical tests complementing previous results. Pure molecular fluids are here studied in the isothermal-isobaric ensemble with isothermal compressibilities evaluated from the total correlation function integrals and compared with values derived from volume fluctuations. For systems where the radial distribution function has structure beyond the sampling limit imposed by the system size, the integration is more reliable, and usually more accurate, than simple integral truncation.
Bent Function Discovery by Reconfigurable Computer
2010-09-01
0 1 0 0 0 1 0 0 0 1 1 1 1 0). It is also convenient to represent an n-variable function in its algebraic normal form. Specifically, Definition 2.1...The algebraic normal form (ANF) of a function f is f = ∑ a∈F2 cax a1 1 x a2 2 . . . x an n , where ∑ is the exclusive OR, a = (a1, a2, . . . , an), ca...0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0). (End of Example) The algebraic normal form of a function is also known as the positive polarity Reed-Muller form
Using fit functions in computational dielectric spectroscopy.
Schröder, Christian; Steinhauser, Othmar
2010-06-28
This work deals with the development of an appropriate set of fit functions for describing dielectric spectra based on simulated raw data. All these fit functions are of exponential character with properly chosen cofunctions. The type of the cofunctions is different for translation, rotation and their coupling. As an alternative to multiexponential fits we also discuss Kohlrausch-Williams-Watts functions. Since the corresponding Fourier-Laplace series for these stretched exponentials has severe convergence problems, we represent their Fourier-Laplace spectrum as a Havriliak-Negami expression with properly chosen parameters. A general relation between the parameter of the Kohlrausch-Williams-Watts and the Havriliak-Negami parameters is given. The set of fit functions is applied to the concrete simulation of the hydrated ionic liquid 1-ethyl-3-methyl-imidazolium triflate with H(2)O. The systematic variation of the water mole fraction permits to study the gradual transition from a neutral molecular liquid to molecular ionic liquids.
Computing Partial Transposes and Related Entanglement Functions
NASA Astrophysics Data System (ADS)
Maziero, Jonas
2016-12-01
The partial transpose (PT) is an important function for entanglement testing and quantification and also for the study of geometrical aspects of the quantum state space. In this article, considering general bipartite and multipartite discrete systems, explicit formulas ready for the numerical implementation of the PT and of related entanglement functions are presented and the Fortran code produced for that purpose is described. What is more, we obtain an analytical expression for the Hilbert-Schmidt entanglement of two-qudit systems and for the associated closest separable state. In contrast to previous works on this matter, we only use the properties of the PT, not applying Lagrange multipliers.
Basic mathematical function libraries for scientific computation
NASA Technical Reports Server (NTRS)
Galant, David C.
1989-01-01
Ada packages implementing selected mathematical functions for the support of scientific and engineering applications were written. The packages provide the Ada programmer with the mathematical function support found in the languages Pascal and FORTRAN as well as an extended precision arithmetic and a complete complex arithmetic. The algorithms used are fully described and analyzed. Implementation assumes that the Ada type FLOAT objects fully conform to the IEEE 754-1985 standard for single binary floating-point arithmetic, and that INTEGER objects are 32-bit entities. Codes for the Ada packages are included as appendixes.
Computer-aided design of functional protein interactions.
Mandell, Daniel J; Kortemme, Tanja
2009-11-01
Predictive methods for the computational design of proteins search for amino acid sequences adopting desired structures that perform specific functions. Typically, design of 'function' is formulated as engineering new and altered binding activities into proteins. Progress in the design of functional protein-protein interactions is directed toward engineering proteins to precisely control biological processes by specifically recognizing desired interaction partners while avoiding competitors. The field is aiming for strategies to harness recent advances in high-resolution computational modeling-particularly those exploiting protein conformational variability-to engineer new functions and incorporate many functional requirements simultaneously.
Enumeration of Bent Boolean Functions by Reconfigurable Computer
2010-05-01
Publishing Company, 1986. [10] D. H. Knuth , The Art of Computer Programming, 2nd Ed., Addison- Wesley Publishing Co., Reading, Menlo Park, London...Enumeration of Bent Boolean Functions by Reconfigurable Computer J. L. Shafer S. W. Schneider J. T. Butler P. Stănică ECE Department Department of ...it yields a new realization of the transeunt triangle that has less complexity and delay. Finally, we show computational results from a
Mura, Maria Chiara; De Felice, Marco; Morlino, Roberta; Fuselli, Sergio
2010-01-01
In step with the need to develop statistical procedures to manage small-size environmental samples, in this work we have used concentration values of benzene (C6H6), concurrently detected by seven outdoor and indoor monitoring stations over 12 000 minutes, in order to assess the representativeness of collected data and the impact of the pollutant on indoor environment. Clearly, the former issue is strictly connected to sampling-site geometry, which proves critical to correctly retrieving information from analysis of pollutants of sanitary interest. Therefore, according to current criteria for network-planning, single stations have been interpreted as nodes of a set of adjoining triangles; then, a) node pairs have been taken into account in order to estimate pollutant stationarity on triangle sides, as well as b) node triplets, to statistically associate data from air-monitoring with the corresponding territory area, and c) node sextuplets, to assess the impact probability of the outdoor pollutant on indoor environment for each area. Distributions from the various node combinations are all non-Gaussian, in the consequently, Kruskal-Wallis (KW) non-parametric statistics has been exploited to test variability on continuous density function from each pair, triplet and sextuplet. Results from the above-mentioned statistical analysis have shown randomness of site selection, which has not allowed a reliable generalization of monitoring data to the entire selected territory, except for a single "forced" case (70%); most important, they suggest a possible procedure to optimize network design.
Computer-Intensive Algebra and Students' Conceptual Knowledge of Functions.
ERIC Educational Resources Information Center
O'Callaghan, Brian R.
1998-01-01
Describes a research project that examined the effects of the Computer-Intensive Algebra (CIA) and traditional algebra curricula on students' (N=802) understanding of the function concept. Results indicate that CIA students achieved a better understanding of functions and were better at the components of modeling, interpreting, and translating.…
Evaluation of Computer Games for Learning about Mathematical Functions
ERIC Educational Resources Information Center
Tüzün, Hakan; Arkun, Selay; Bayirtepe-Yagiz, Ezgi; Kurt, Funda; Yermeydan-Ugur, Benlihan
2008-01-01
In this study, researchers evaluated the usability of game environments for teaching and learning about mathematical functions. A 3-Dimensional multi-user computer game called as "Quest Atlantis" has been used, and an educational game about mathematical functions has been developed in parallel to the Quest Atlantis' technical and…
Positive Wigner Functions Render Classical Simulation of Quantum Computation Efficient
NASA Astrophysics Data System (ADS)
Mari, A.; Eisert, J.
2012-12-01
We show that quantum circuits where the initial state and all the following quantum operations can be represented by positive Wigner functions can be classically efficiently simulated. This is true both for continuous-variable as well as discrete variable systems in odd prime dimensions, two cases which will be treated on entirely the same footing. Noting the fact that Clifford and Gaussian operations preserve the positivity of the Wigner function, our result generalizes the Gottesman-Knill theorem. Our algorithm provides a way of sampling from the output distribution of a computation or a simulation, including the efficient sampling from an approximate output distribution in the case of sampling imperfections for initial states, gates, or measurements. In this sense, this work highlights the role of the positive Wigner function as separating classically efficiently simulable systems from those that are potentially universal for quantum computing and simulation, and it emphasizes the role of negativity of the Wigner function as a computational resource.
Positive Wigner functions render classical simulation of quantum computation efficient.
Mari, A; Eisert, J
2012-12-07
We show that quantum circuits where the initial state and all the following quantum operations can be represented by positive Wigner functions can be classically efficiently simulated. This is true both for continuous-variable as well as discrete variable systems in odd prime dimensions, two cases which will be treated on entirely the same footing. Noting the fact that Clifford and Gaussian operations preserve the positivity of the Wigner function, our result generalizes the Gottesman-Knill theorem. Our algorithm provides a way of sampling from the output distribution of a computation or a simulation, including the efficient sampling from an approximate output distribution in the case of sampling imperfections for initial states, gates, or measurements. In this sense, this work highlights the role of the positive Wigner function as separating classically efficiently simulable systems from those that are potentially universal for quantum computing and simulation, and it emphasizes the role of negativity of the Wigner function as a computational resource.
Significant Digit Computation of the Incomplete Beta Function Ratios
1988-11-01
Profile, Archive for History of Exact Sciences, 24, 1981, 11-29. 8. Henrici P., Applied and Computational Complex Analysis (Vol. 1), John Wiley and Sons...SHrTITI E 5 FUINPING ,IRS Significant Digit Computation of the Incomplete Beta Function Ratios 6 AtITImOR(S) Armido I. DiI)onato Alfred H. Morris, Jr. I...ASSII l,) UNCLASSIFIED IN i i FOREWORD I he work described in this report was done in the Space and Surface Systems Division and tile Computer and
Local-basis-function approach to computed tomography
NASA Astrophysics Data System (ADS)
Hanson, K. M.; Wecksung, G. W.
1985-12-01
In the local basis-function approach, a reconstruction is represented as a linear expansion of basis functions, which are arranged on a rectangular grid and possess a local region of support. The basis functions considered here are positive and may overlap. It is found that basis functions based on cubic B-splines offer significant improvements in the calculational accuracy that can be achieved with iterative tomographic reconstruction algorithms. By employing repetitive basis functions, the computational effort involved in these algorithms can be minimized through the use of tabulated values for the line or strip integrals over a single-basis function. The local nature of the basis functions reduces the difficulties associated with applying local constraints on reconstruction values, such as upper and lower limits. Since a reconstruction is specified everywhere by a set of coefficients, display of a coarsely represented image does not require an arbitrary choice of an interpolation function.
Tempel, David G; Aspuru-Guzik, Alán
2012-01-01
We prove that the theorems of TDDFT can be extended to a class of qubit Hamiltonians that are universal for quantum computation. The theorems of TDDFT applied to universal Hamiltonians imply that single-qubit expectation values can be used as the basic variables in quantum computation and information theory, rather than wavefunctions. From a practical standpoint this opens the possibility of approximating observables of interest in quantum computations directly in terms of single-qubit quantities (i.e. as density functionals). Additionally, we also demonstrate that TDDFT provides an exact prescription for simulating universal Hamiltonians with other universal Hamiltonians that have different, and possibly easier-to-realize two-qubit interactions. This establishes the foundations of TDDFT for quantum computation and opens the possibility of developing density functionals for use in quantum algorithms.
Tempel, David G.; Aspuru-Guzik, Alán
2012-01-01
We prove that the theorems of TDDFT can be extended to a class of qubit Hamiltonians that are universal for quantum computation. The theorems of TDDFT applied to universal Hamiltonians imply that single-qubit expectation values can be used as the basic variables in quantum computation and information theory, rather than wavefunctions. From a practical standpoint this opens the possibility of approximating observables of interest in quantum computations directly in terms of single-qubit quantities (i.e. as density functionals). Additionally, we also demonstrate that TDDFT provides an exact prescription for simulating universal Hamiltonians with other universal Hamiltonians that have different, and possibly easier-to-realize two-qubit interactions. This establishes the foundations of TDDFT for quantum computation and opens the possibility of developing density functionals for use in quantum algorithms. PMID:22553483
Computer method for identification of boiler transfer functions
NASA Technical Reports Server (NTRS)
Miles, J. H.
1971-01-01
An iterative computer method is described for identifying boiler transfer functions using frequency response data. An objective penalized performance measure and a nonlinear minimization technique are used to cause the locus of points generated by a transfer function to resemble the locus of points obtained from frequency response measurements. Different transfer functions can be tried until a satisfactory empirical transfer function to the system is found. To illustrate the method, some examples and some results from a study of a set of data consisting of measurements of the inlet impedance of a single tube forced flow boiler with inserts are given.
A large-scale evaluation of computational protein function prediction.
Radivojac, Predrag; Clark, Wyatt T; Oron, Tal Ronnen; Schnoes, Alexandra M; Wittkop, Tobias; Sokolov, Artem; Graim, Kiley; Funk, Christopher; Verspoor, Karin; Ben-Hur, Asa; Pandey, Gaurav; Yunes, Jeffrey M; Talwalkar, Ameet S; Repo, Susanna; Souza, Michael L; Piovesan, Damiano; Casadio, Rita; Wang, Zheng; Cheng, Jianlin; Fang, Hai; Gough, Julian; Koskinen, Patrik; Törönen, Petri; Nokso-Koivisto, Jussi; Holm, Liisa; Cozzetto, Domenico; Buchan, Daniel W A; Bryson, Kevin; Jones, David T; Limaye, Bhakti; Inamdar, Harshal; Datta, Avik; Manjari, Sunitha K; Joshi, Rajendra; Chitale, Meghana; Kihara, Daisuke; Lisewski, Andreas M; Erdin, Serkan; Venner, Eric; Lichtarge, Olivier; Rentzsch, Robert; Yang, Haixuan; Romero, Alfonso E; Bhat, Prajwal; Paccanaro, Alberto; Hamp, Tobias; Kaßner, Rebecca; Seemayer, Stefan; Vicedo, Esmeralda; Schaefer, Christian; Achten, Dominik; Auer, Florian; Boehm, Ariane; Braun, Tatjana; Hecht, Maximilian; Heron, Mark; Hönigschmid, Peter; Hopf, Thomas A; Kaufmann, Stefanie; Kiening, Michael; Krompass, Denis; Landerer, Cedric; Mahlich, Yannick; Roos, Manfred; Björne, Jari; Salakoski, Tapio; Wong, Andrew; Shatkay, Hagit; Gatzmann, Fanny; Sommer, Ingolf; Wass, Mark N; Sternberg, Michael J E; Škunca, Nives; Supek, Fran; Bošnjak, Matko; Panov, Panče; Džeroski, Sašo; Šmuc, Tomislav; Kourmpetis, Yiannis A I; van Dijk, Aalt D J; ter Braak, Cajo J F; Zhou, Yuanpeng; Gong, Qingtian; Dong, Xinran; Tian, Weidong; Falda, Marco; Fontana, Paolo; Lavezzo, Enrico; Di Camillo, Barbara; Toppo, Stefano; Lan, Liang; Djuric, Nemanja; Guo, Yuhong; Vucetic, Slobodan; Bairoch, Amos; Linial, Michal; Babbitt, Patricia C; Brenner, Steven E; Orengo, Christine; Rost, Burkhard; Mooney, Sean D; Friedberg, Iddo
2013-03-01
Automated annotation of protein function is challenging. As the number of sequenced genomes rapidly grows, the overwhelming majority of protein products can only be annotated computationally. If computational predictions are to be relied upon, it is crucial that the accuracy of these methods be high. Here we report the results from the first large-scale community-based critical assessment of protein function annotation (CAFA) experiment. Fifty-four methods representing the state of the art for protein function prediction were evaluated on a target set of 866 proteins from 11 organisms. Two findings stand out: (i) today's best protein function prediction algorithms substantially outperform widely used first-generation methods, with large gains on all types of targets; and (ii) although the top methods perform well enough to guide experiments, there is considerable need for improvement of currently available tools.
Efficient and accurate computation of the incomplete Airy functions
NASA Technical Reports Server (NTRS)
Constantinides, E. D.; Marhefka, R. J.
1993-01-01
The incomplete Airy integrals serve as canonical functions for the uniform ray optical solutions to several high-frequency scattering and diffraction problems that involve a class of integrals characterized by two stationary points that are arbitrarily close to one another or to an integration endpoint. Integrals with such analytical properties describe transition region phenomena associated with composite shadow boundaries. An efficient and accurate method for computing the incomplete Airy functions would make the solutions to such problems useful for engineering purposes. In this paper a convergent series solution for the incomplete Airy functions is derived. Asymptotic expansions involving several terms are also developed and serve as large argument approximations. The combination of the series solution with the asymptotic formulae provides for an efficient and accurate computation of the incomplete Airy functions. Validation of accuracy is accomplished using direct numerical integration data.
Computational approaches for rational design of proteins with novel functionalities
Tiwari, Manish Kumar; Singh, Ranjitha; Singh, Raushan Kumar; Kim, In-Won; Lee, Jung-Kul
2012-01-01
Proteins are the most multifaceted macromolecules in living systems and have various important functions, including structural, catalytic, sensory, and regulatory functions. Rational design of enzymes is a great challenge to our understanding of protein structure and physical chemistry and has numerous potential applications. Protein design algorithms have been applied to design or engineer proteins that fold, fold faster, catalyze, catalyze faster, signal, and adopt preferred conformational states. The field of de novo protein design, although only a few decades old, is beginning to produce exciting results. Developments in this field are already having a significant impact on biotechnology and chemical biology. The application of powerful computational methods for functional protein designing has recently succeeded at engineering target activities. Here, we review recently reported de novo functional proteins that were developed using various protein design approaches, including rational design, computational optimization, and selection from combinatorial libraries, highlighting recent advances and successes. PMID:24688643
A large-scale evaluation of computational protein function prediction
Radivojac, Predrag; Clark, Wyatt T; Ronnen Oron, Tal; Schnoes, Alexandra M; Wittkop, Tobias; Sokolov, Artem; Graim, Kiley; Funk, Christopher; Verspoor, Karin; Ben-Hur, Asa; Pandey, Gaurav; Yunes, Jeffrey M; Talwalkar, Ameet S; Repo, Susanna; Souza, Michael L; Piovesan, Damiano; Casadio, Rita; Wang, Zheng; Cheng, Jianlin; Fang, Hai; Gough, Julian; Koskinen, Patrik; Törönen, Petri; Nokso-Koivisto, Jussi; Holm, Liisa; Cozzetto, Domenico; Buchan, Daniel W A; Bryson, Kevin; Jones, David T; Limaye, Bhakti; Inamdar, Harshal; Datta, Avik; Manjari, Sunitha K; Joshi, Rajendra; Chitale, Meghana; Kihara, Daisuke; Lisewski, Andreas M; Erdin, Serkan; Venner, Eric; Lichtarge, Olivier; Rentzsch, Robert; Yang, Haixuan; Romero, Alfonso E; Bhat, Prajwal; Paccanaro, Alberto; Hamp, Tobias; Kassner, Rebecca; Seemayer, Stefan; Vicedo, Esmeralda; Schaefer, Christian; Achten, Dominik; Auer, Florian; Böhm, Ariane; Braun, Tatjana; Hecht, Maximilian; Heron, Mark; Hönigschmid, Peter; Hopf, Thomas; Kaufmann, Stefanie; Kiening, Michael; Krompass, Denis; Landerer, Cedric; Mahlich, Yannick; Roos, Manfred; Björne, Jari; Salakoski, Tapio; Wong, Andrew; Shatkay, Hagit; Gatzmann, Fanny; Sommer, Ingolf; Wass, Mark N; Sternberg, Michael J E; Škunca, Nives; Supek, Fran; Bošnjak, Matko; Panov, Panče; Džeroski, Sašo; Šmuc, Tomislav; Kourmpetis, Yiannis A I; van Dijk, Aalt D J; ter Braak, Cajo J F; Zhou, Yuanpeng; Gong, Qingtian; Dong, Xinran; Tian, Weidong; Falda, Marco; Fontana, Paolo; Lavezzo, Enrico; Di Camillo, Barbara; Toppo, Stefano; Lan, Liang; Djuric, Nemanja; Guo, Yuhong; Vucetic, Slobodan; Bairoch, Amos; Linial, Michal; Babbitt, Patricia C; Brenner, Steven E; Orengo, Christine; Rost, Burkhard; Mooney, Sean D; Friedberg, Iddo
2013-01-01
Automated annotation of protein function is challenging. As the number of sequenced genomes rapidly grows, the overwhelming majority of protein products can only be annotated computationally. If computational predictions are to be relied upon, it is crucial that the accuracy of these methods be high. Here we report the results from the first large-scale community-based Critical Assessment of protein Function Annotation (CAFA) experiment. Fifty-four methods representing the state-of-the-art for protein function prediction were evaluated on a target set of 866 proteins from eleven organisms. Two findings stand out: (i) today’s best protein function prediction algorithms significantly outperformed widely-used first-generation methods, with large gains on all types of targets; and (ii) although the top methods perform well enough to guide experiments, there is significant need for improvement of currently available tools. PMID:23353650
The flight telerobotic servicer: From functional architecture to computer architecture
NASA Technical Reports Server (NTRS)
Lumia, Ronald; Fiala, John
1989-01-01
After a brief tutorial on the NASA/National Bureau of Standards Standard Reference Model for Telerobot Control System Architecture (NASREM) functional architecture, the approach to its implementation is shown. First, interfaces must be defined which are capable of supporting the known algorithms. This is illustrated by considering the interfaces required for the SERVO level of the NASREM functional architecture. After interface definition, the specific computer architecture for the implementation must be determined. This choice is obviously technology dependent. An example illustrating one possible mapping of the NASREM functional architecture to a particular set of computers which implements it is shown. The result of choosing the NASREM functional architecture is that it provides a technology independent paradigm which can be mapped into a technology dependent implementation capable of evolving with technology in the laboratory and in space.
Robust Computation of Morse-Smale Complexes of Bilinear Functions
Norgard, G; Bremer, P T
2010-11-30
The Morse-Smale (MS) complex has proven to be a useful tool in extracting and visualizing features from scalar-valued data. However, existing algorithms to compute the MS complex are restricted to either piecewise linear or discrete scalar fields. This paper presents a new combinatorial algorithm to compute MS complexes for two dimensional piecewise bilinear functions defined on quadrilateral meshes. We derive a new invariant of the gradient flow within a bilinear cell and use it to develop a provably correct computation which is unaffected by numerical instabilities. This includes a combinatorial algorithm to detect and classify critical points as well as a way to determine the asymptotes of cell-based saddles and their intersection with cell edges. Finally, we introduce a simple data structure to compute and store integral lines on quadrilateral meshes which by construction prevents intersections and enables us to enforce constraints on the gradient flow to preserve known invariants.
Computing exact bundle compliance control charts via probability generating functions.
Chen, Binchao; Matis, Timothy; Benneyan, James
2016-06-01
Compliance to evidenced-base practices, individually and in 'bundles', remains an important focus of healthcare quality improvement for many clinical conditions. The exact probability distribution of composite bundle compliance measures used to develop corresponding control charts and other statistical tests is based on a fairly large convolution whose direct calculation can be computationally prohibitive. Various series expansions and other approximation approaches have been proposed, each with computational and accuracy tradeoffs, especially in the tails. This same probability distribution also arises in other important healthcare applications, such as for risk-adjusted outcomes and bed demand prediction, with the same computational difficulties. As an alternative, we use probability generating functions to rapidly obtain exact results and illustrate the improved accuracy and detection over other methods. Numerical testing across a wide range of applications demonstrates the computational efficiency and accuracy of this approach.
Computational design of proteins with novel structure and functions
NASA Astrophysics Data System (ADS)
Wei, Yang; Lu-Hua, Lai
2016-01-01
Computational design of proteins is a relatively new field, where scientists search the enormous sequence space for sequences that can fold into desired structure and perform desired functions. With the computational approach, proteins can be designed, for example, as regulators of biological processes, novel enzymes, or as biotherapeutics. These approaches not only provide valuable information for understanding of sequence-structure-function relations in proteins, but also hold promise for applications to protein engineering and biomedical research. In this review, we briefly introduce the rationale for computational protein design, then summarize the recent progress in this field, including de novo protein design, enzyme design, and design of protein-protein interactions. Challenges and future prospects of this field are also discussed. Project supported by the National Basic Research Program of China (Grant No. 2015CB910300), the National High Technology Research and Development Program of China (Grant No. 2012AA020308), and the National Natural Science Foundation of China (Grant No. 11021463).
Functional Characteristics of Intelligent Computer-Assisted Instruction: Intelligent Features.
ERIC Educational Resources Information Center
Park, Ok-choon
1988-01-01
Examines the functional characteristics of intelligent computer assisted instruction (ICAI) and discusses the requirements of a multidisciplinary cooperative effort of its development. A typical ICAI model is presented and intelligent features of ICAI systems are described, including modeling the student's learning process, qualitative decision…
Supporting Executive Functions during Children's Preliteracy Learning with the Computer
ERIC Educational Resources Information Center
Van de Sande, E.; Segers, E.; Verhoeven, L.
2016-01-01
The present study examined how embedded activities to support executive functions helped children to benefit from a computer intervention that targeted preliteracy skills. Three intervention groups were compared on their preliteracy gains in a randomized controlled trial design: an experimental group that worked with software to stimulate early…
Supporting Executive Functions during Children's Preliteracy Learning with the Computer
ERIC Educational Resources Information Center
Van de Sande, E.; Segers, E.; Verhoeven, L.
2016-01-01
The present study examined how embedded activities to support executive functions helped children to benefit from a computer intervention that targeted preliteracy skills. Three intervention groups were compared on their preliteracy gains in a randomized controlled trial design: an experimental group that worked with software to stimulate early…
SNAP: A computer program for generating symbolic network functions
NASA Technical Reports Server (NTRS)
Lin, P. M.; Alderson, G. E.
1970-01-01
The computer program SNAP (symbolic network analysis program) generates symbolic network functions for networks containing R, L, and C type elements and all four types of controlled sources. The program is efficient with respect to program storage and execution time. A discussion of the basic algorithms is presented, together with user's and programmer's guides.
Andersson, Jesper L R; Sotiropoulos, Stamatios N
2015-11-15
Diffusion MRI offers great potential in studying the human brain microstructure and connectivity. However, diffusion images are marred by technical problems, such as image distortions and spurious signal loss. Correcting for these problems is non-trivial and relies on having a mechanism that predicts what to expect. In this paper we describe a novel way to represent and make predictions about diffusion MRI data. It is based on a Gaussian process on one or several spheres similar to the Geostatistical method of "Kriging". We present a choice of covariance function that allows us to accurately predict the signal even from voxels with complex fibre patterns. For multi-shell data (multiple non-zero b-values) the covariance function extends across the shells which means that data from one shell is used when making predictions for another shell.
Andersson, Jesper L.R.; Sotiropoulos, Stamatios N.
2015-01-01
Diffusion MRI offers great potential in studying the human brain microstructure and connectivity. However, diffusion images are marred by technical problems, such as image distortions and spurious signal loss. Correcting for these problems is non-trivial and relies on having a mechanism that predicts what to expect. In this paper we describe a novel way to represent and make predictions about diffusion MRI data. It is based on a Gaussian process on one or several spheres similar to the Geostatistical method of “Kriging”. We present a choice of covariance function that allows us to accurately predict the signal even from voxels with complex fibre patterns. For multi-shell data (multiple non-zero b-values) the covariance function extends across the shells which means that data from one shell is used when making predictions for another shell. PMID:26236030
Computer program for calculating and fitting thermodynamic functions
NASA Technical Reports Server (NTRS)
Mcbride, Bonnie J.; Gordon, Sanford
1992-01-01
A computer program is described which (1) calculates thermodynamic functions (heat capacity, enthalpy, entropy, and free energy) for several optional forms of the partition function, (2) fits these functions to empirical equations by means of a least-squares fit, and (3) calculates, as a function of temperture, heats of formation and equilibrium constants. The program provides several methods for calculating ideal gas properties. For monatomic gases, three methods are given which differ in the technique used for truncating the partition function. For diatomic and polyatomic molecules, five methods are given which differ in the corrections to the rigid-rotator harmonic-oscillator approximation. A method for estimating thermodynamic functions for some species is also given.
Loucoubar, Cheikh; Paul, Richard; Bar-Hen, Avner; Huret, Augustin; Tall, Adama; Sokhna, Cheikh; Trape, Jean-François; Ly, Alioune Badara; Faye, Joseph; Badiane, Abdoulaye; Diakhaby, Gaoussou; Sarr, Fatoumata Diène; Diop, Aliou; Sakuntabhai, Anavaj; Bureau, Jean-François
2011-01-01
Complex, high-dimensional data sets pose significant analytical challenges in the post-genomic era. Such data sets are not exclusive to genetic analyses and are also pertinent to epidemiology. There has been considerable effort to develop hypothesis-free data mining and machine learning methodologies. However, current methodologies lack exhaustivity and general applicability. Here we use a novel non-parametric, non-euclidean data mining tool, HyperCube®, to explore exhaustively a complex epidemiological malaria data set by searching for over density of events in m-dimensional space. Hotspots of over density correspond to strings of variables, rules, that determine, in this case, the occurrence of Plasmodium falciparum clinical malaria episodes. The data set contained 46,837 outcome events from 1,653 individuals and 34 explanatory variables. The best predictive rule contained 1,689 events from 148 individuals and was defined as: individuals present during 1992-2003, aged 1-5 years old, having hemoglobin AA, and having had previous Plasmodium malariae malaria parasite infection ≤10 times. These individuals had 3.71 times more P. falciparum clinical malaria episodes than the general population. We validated the rule in two different cohorts. We compared and contrasted the HyperCube® rule with the rules using variables identified by both traditional statistical methods and non-parametric regression tree methods. In addition, we tried all possible sub-stratified quantitative variables. No other model with equal or greater representativity gave a higher Relative Risk. Although three of the four variables in the rule were intuitive, the effect of number of P. malariae episodes was not. HyperCube® efficiently sub-stratified quantitative variables to optimize the rule and was able to identify interactions among the variables, tasks not easy to perform using standard data mining methods. Search of local over density in m-dimensional space, explained by easily
A Survey of Computational Intelligence Techniques in Protein Function Prediction
Tiwari, Arvind Kumar; Srivastava, Rajeev
2014-01-01
During the past, there was a massive growth of knowledge of unknown proteins with the advancement of high throughput microarray technologies. Protein function prediction is the most challenging problem in bioinformatics. In the past, the homology based approaches were used to predict the protein function, but they failed when a new protein was different from the previous one. Therefore, to alleviate the problems associated with homology based traditional approaches, numerous computational intelligence techniques have been proposed in the recent past. This paper presents a state-of-the-art comprehensive review of various computational intelligence techniques for protein function predictions using sequence, structure, protein-protein interaction network, and gene expression data used in wide areas of applications such as prediction of DNA and RNA binding sites, subcellular localization, enzyme functions, signal peptides, catalytic residues, nuclear/G-protein coupled receptors, membrane proteins, and pathway analysis from gene expression datasets. This paper also summarizes the result obtained by many researchers to solve these problems by using computational intelligence techniques with appropriate datasets to improve the prediction performance. The summary shows that ensemble classifiers and integration of multiple heterogeneous data are useful for protein function prediction. PMID:25574395
A survey of computational intelligence techniques in protein function prediction.
Tiwari, Arvind Kumar; Srivastava, Rajeev
2014-01-01
During the past, there was a massive growth of knowledge of unknown proteins with the advancement of high throughput microarray technologies. Protein function prediction is the most challenging problem in bioinformatics. In the past, the homology based approaches were used to predict the protein function, but they failed when a new protein was different from the previous one. Therefore, to alleviate the problems associated with homology based traditional approaches, numerous computational intelligence techniques have been proposed in the recent past. This paper presents a state-of-the-art comprehensive review of various computational intelligence techniques for protein function predictions using sequence, structure, protein-protein interaction network, and gene expression data used in wide areas of applications such as prediction of DNA and RNA binding sites, subcellular localization, enzyme functions, signal peptides, catalytic residues, nuclear/G-protein coupled receptors, membrane proteins, and pathway analysis from gene expression datasets. This paper also summarizes the result obtained by many researchers to solve these problems by using computational intelligence techniques with appropriate datasets to improve the prediction performance. The summary shows that ensemble classifiers and integration of multiple heterogeneous data are useful for protein function prediction.
Computing the hadronic vacuum polarization function by analytic continuation
Feng, Xu; Hashimoto, Shoji; Hotzel, Grit; ...
2013-08-29
We propose a method to compute the hadronic vacuum polarization function on the lattice at continuous values of photon momenta bridging between the spacelike and timelike regions. We provide two independent demonstrations to show that this method leads to the desired hadronic vacuum polarization function in Minkowski spacetime. We present with the example of the leading-order QCD correction to the muon anomalous magnetic moment that this approach can provide a valuable alternative method for calculations of physical quantities where the hadronic vacuum polarization function enters.
Environment parameters and basic functions for floating-point computation
NASA Technical Reports Server (NTRS)
Brown, W. S.; Feldman, S. I.
1978-01-01
A language-independent proposal for environment parameters and basic functions for floating-point computation is presented. Basic functions are proposed to analyze, synthesize, and scale floating-point numbers. The model provides a small set of parameters and a small set of axioms along with sharp measures of roundoff error. The parameters and functions can be used to write portable and robust codes that deal intimately with the floating-point representation. Subject to underflow and overflow constraints, a number can be scaled by a power of the floating-point radix inexpensively and without loss of precision. A specific representation for FORTRAN is included.
Variance computations for functional of absolute risk estimates.
Pfeiffer, R M; Petracci, E
2011-07-01
We present a simple influence function based approach to compute the variances of estimates of absolute risk and functions of absolute risk. We apply this approach to criteria that assess the impact of changes in the risk factor distribution on absolute risk for an individual and at the population level. As an illustration we use an absolute risk prediction model for breast cancer that includes modifiable risk factors in addition to standard breast cancer risk factors. Influence function based variance estimates for absolute risk and the criteria are compared to bootstrap variance estimates.
Variance computations for functional of absolute risk estimates
Pfeiffer, R.M.; Petracci, E.
2011-01-01
We present a simple influence function based approach to compute the variances of estimates of absolute risk and functions of absolute risk. We apply this approach to criteria that assess the impact of changes in the risk factor distribution on absolute risk for an individual and at the population level. As an illustration we use an absolute risk prediction model for breast cancer that includes modifiable risk factors in addition to standard breast cancer risk factors. Influence function based variance estimates for absolute risk and the criteria are compared to bootstrap variance estimates. PMID:21643476
Community-Wide Evaluation of Computational Function Prediction.
Friedberg, Iddo; Radivojac, Predrag
2017-01-01
A biological experiment is the most reliable way of assigning function to a protein. However, in the era of high-throughput sequencing, scientists are unable to carry out experiments to determine the function of every single gene product. Therefore, to gain insights into the activity of these molecules and guide experiments, we must rely on computational means to functionally annotate the majority of sequence data. To understand how well these algorithms perform, we have established a challenge involving a broad scientific community in which we evaluate different annotation methods according to their ability to predict the associations between previously unannotated protein sequences and Gene Ontology terms. Here we discuss the rationale, benefits, and issues associated with evaluating computational methods in an ongoing community-wide challenge.
Corvaisier, S; Bleyzac, N; Confesson, M A; Bureau, C; Maire, P
1997-01-01
To establish a reference for MAP Bayesian adaptive control of amikacin therapy in non-insulin-dependent diabetic patients, 30 patients (age: 63.5 +/- 10.1 years) were studied. Weight (84.2 +/- 15.4 kg) and body mass index (28.0 +/- 4.3 kg/m2 for males and 30.5 +/- 6.4 kg/m2 for females) were stable during treatment. Creatinine clearance (CCr) was 70.3 +/- 27.2 ml/min/1.73 m2 before treatment and 69.6 +/- 24.3 ml/min/1.73 m2 (NS) at the end of treatment (2 to 15 days). 129 serum concentrations were drawn (4.8 +/- 2.6 levels per patient). The one-compartment model was parameterized as having Vs (l.kg-1) and Kslope (min/ml.h) for each unit of CCr (Kel = Kintercept + Kslope x CCr). The non-renal Kintercept was fixed at 0.00693 h-1. The NPEM computes the joint probability densities. The mean, median, and SD were respectively: Vs = 0.3574, 0.3654, 0.0825 l.kg-1; Kslope = 0.0026, 0.0027, 0.0007 min/ml.h. For the a priori first doses determination, precision is higher with the new population. No difference in adaptive control was observed. In additive, the full joint density probability should be used to develop stochastic multiple model linear quadratic (MMLQ) adaptive control strategies.
Efficient Computation of Exchange Energy Density with Gaussian Basis Functions.
Liu, Fenglai; Kong, Jing
2017-06-13
Density functional theory (DFT) is widely applied in chemistry and physics. Still it fails to correctly predict quantitatively or even qualitatively for systems with significant nondynamic correlation. Several DFT functionals were proposed in recent years to treat the nondynamic correlation, most of which added the exact exchange energy density as a new variable. This quantity, calculated as Hartree-Fock (HF) exchange energy density, is the computational bottleneck for calculations with these new functionals. We present an implementation of an efficient seminumerical algorithm in this paper as a solution for this computational bottleneck. The method scales quadratically with respect to the molecular size and the basis set size. The scheme, exact for the purpose of computing the HF exchange energy density, is favored for medium-sized basis sets and can be competitive even for large basis sets with efficient grids when compared with our previous approximate resolution-of-identity scheme. It can also be used as a seminumerical integration scheme to compute the HF exchange energy and matrix on a standard atom-centered grid. Calculations on a series of alanine peptides show that for large basis sets the seminumerical scheme becomes competitive to the conventional analytical method and can be about six times faster for aug-cc-pvtz basis. The practicality of the algorithm is demonstrated through a local hybrid self-consistent calculation of the acenes-20 molecule.
Computation of three-dimensional flows using two stream functions
NASA Technical Reports Server (NTRS)
Greywall, Mahesh S.
1991-01-01
An approach to compute 3-D flows using two stream functions is presented. The method generates a boundary fitted grid as part of its solution. Commonly used two steps for computing the flow fields are combined into a single step in the present approach: (1) boundary fitted grid generation; and (2) solution of Navier-Stokes equations on the generated grid. The presented method can be used to directly compute 3-D viscous flows, or the potential flow approximation of this method can be used to generate grids for other algorithms to compute 3-D viscous flows. The independent variables used are chi, a spatial coordinate, and xi and eta, values of stream functions along two sets of suitably chosen intersecting stream surfaces. The dependent variables used are the streamwise velocity, and two functions that describe the stream surfaces. Since for a 3-D flow there is no unique way to define two sets of intersecting stream surfaces to cover the given flow, different types of two sets of intersecting stream surfaces are considered. First, the metric of the (chi, xi, eta) curvilinear coordinate system associated with each type is presented. Next, equations for the steady state transport of mass, momentum, and energy are presented in terms of the metric of the (chi, xi, eta) coordinate system. Also included are the inviscid and the parabolized approximations to the general transport equations.
Ong, Lee-Ling S; Wang, Mengmeng; Dauwels, Justin; Asada, H Harry
2014-01-01
An approach to jointly estimate 3D shapes and poses of stained nuclei from confocal microscopy images, using statistical prior information, is presented. Extracting nuclei boundaries from our experimental images of cell migration is challenging due to clustered nuclei and variations in their shapes. This issue is formulated as a maximum a posteriori estimation problem. By incorporating statistical prior models of 3D nuclei shapes into level set functions, the active contour evolutions applied on the images is constrained. A 3D alignment algorithm is developed to build the training databases and to match contours obtained from the images to them. To address the issue of aligning the model over multiple clustered nuclei, a watershed-like technique is used to detect and separate clustered regions prior to active contour evolution. Our method is tested on confocal images of endothelial cells in microfluidic devices, compared with existing approaches.
Structure, function, and behaviour of computational models in systems biology
2013-01-01
Background Systems Biology develops computational models in order to understand biological phenomena. The increasing number and complexity of such “bio-models” necessitate computer support for the overall modelling task. Computer-aided modelling has to be based on a formal semantic description of bio-models. But, even if computational bio-models themselves are represented precisely in terms of mathematical expressions their full meaning is not yet formally specified and only described in natural language. Results We present a conceptual framework – the meaning facets – which can be used to rigorously specify the semantics of bio-models. A bio-model has a dual interpretation: On the one hand it is a mathematical expression which can be used in computational simulations (intrinsic meaning). On the other hand the model is related to the biological reality (extrinsic meaning). We show that in both cases this interpretation should be performed from three perspectives: the meaning of the model’s components (structure), the meaning of the model’s intended use (function), and the meaning of the model’s dynamics (behaviour). In order to demonstrate the strengths of the meaning facets framework we apply it to two semantically related models of the cell cycle. Thereby, we make use of existing approaches for computer representation of bio-models as much as possible and sketch the missing pieces. Conclusions The meaning facets framework provides a systematic in-depth approach to the semantics of bio-models. It can serve two important purposes: First, it specifies and structures the information which biologists have to take into account if they build, use and exchange models. Secondly, because it can be formalised, the framework is a solid foundation for any sort of computer support in bio-modelling. The proposed conceptual framework establishes a new methodology for modelling in Systems Biology and constitutes a basis for computer-aided collaborative research
Integrated command, control, communications and computation system functional architecture
NASA Technical Reports Server (NTRS)
Cooley, C. G.; Gilbert, L. E.
1981-01-01
The functional architecture for an integrated command, control, communications, and computation system applicable to the command and control portion of the NASA End-to-End Data. System is described including the downlink data processing and analysis functions required to support the uplink processes. The functional architecture is composed of four elements: (1) the functional hierarchy which provides the decomposition and allocation of the command and control functions to the system elements; (2) the key system features which summarize the major system capabilities; (3) the operational activity threads which illustrate the interrelationahip between the system elements; and (4) the interfaces which illustrate those elements that originate or generate data and those elements that use the data. The interfaces also provide a description of the data and the data utilization and access techniques.
Fresno, Cristóbal; González, Germán Alexis; Merino, Gabriela Alejandra; Flesia, Ana Georgina; Podhajcer, Osvaldo Luis; Llera, Andrea Sabina; Fernández, Elmer Andrés
2017-03-01
The PAM50 classifier is used to assign patients to the highest correlated breast cancer subtype irrespectively of the obtained value. Nonetheless, all subtype correlations are required to build the risk of recurrence (ROR) score, currently used in therapeutic decisions. Present subtype uncertainty estimations are not accurate, seldom considered or require a population-based approach for this context. Here we present a novel single-subject non-parametric uncertainty estimation based on PAM50's gene label permutations. Simulations results ( n = 5228) showed that only 61% subjects can be reliably 'Assigned' to the PAM50 subtype, whereas 33% should be 'Not Assigned' (NA), leaving the rest to tight 'Ambiguous' correlations between subtypes. The NA subjects exclusion from the analysis improved survival subtype curves discrimination yielding a higher proportion of low and high ROR values. Conversely, all NA subjects showed similar survival behaviour regardless of the original PAM50 assignment. We propose to incorporate our PAM50 uncertainty estimation to support therapeutic decisions. Source code can be found in 'pbcmc' R package at Bioconductor. cristobalfresno@gmail.com or efernandez@bdmg.com.ar. Supplementary data are available at Bioinformatics online.
NASA Astrophysics Data System (ADS)
Schutte, Willem D.; Swanepoel, Jan W. H.
2016-09-01
An automated tool to derive the off-pulse interval of a light curve originating from a pulsar is needed. First, we derive a powerful and accurate non-parametric sequential estimation technique to estimate the off-pulse interval of a pulsar light curve in an objective manner. This is in contrast to the subjective `eye-ball' (visual) technique, and complementary to the Bayesian Block method which is currently used in the literature. The second aim involves the development of a statistical package, necessary for the implementation of our new estimation technique. We develop a statistical procedure to estimate the off-pulse interval in the presence of noise. It is based on a sequential application of p-values obtained from goodness-of-fit tests for uniformity. The Kolmogorov-Smirnov, Cramér-von Mises, Anderson-Darling and Rayleigh test statistics are applied. The details of the newly developed statistical package SOPIE (Sequential Off-Pulse Interval Estimation) are discussed. The developed estimation procedure is applied to simulated and real pulsar data. Finally, the SOPIE estimated off-pulse intervals of two pulsars are compared to the estimates obtained with the Bayesian Block method and yield very satisfactory results. We provide the code to implement the SOPIE package, which is publicly available at http://CRAN.R-project.org/package=SOPIE (Schutte).
NASA Astrophysics Data System (ADS)
Chen, Chin-Wei; Côté, Patrick; West, Andrew A.; Peng, Eric W.; Ferrarese, Laura
2010-11-01
We present photometric and structural parameters for 100 ACS Virgo Cluster Survey (ACSVCS) galaxies based on homogeneous, multi-wavelength (ugriz), wide-field SDSS (DR5) imaging. These early-type galaxies, which trace out the red sequence in the Virgo Cluster, span a factor of nearly ~103 in g-band luminosity. We describe an automated pipeline that generates background-subtracted mosaic images, masks field sources and measures mean shapes, total magnitudes, effective radii, and effective surface brightnesses using a model-independent approach. A parametric analysis of the surface brightness profiles is also carried out to obtain Sérsic-based structural parameters and mean galaxy colors. We compare the galaxy parameters to those in the literature, including those from the ACSVCS, finding good agreement in most cases, although the sizes of the brightest, and most extended, galaxies are found to be most uncertain and model dependent. Our photometry provides an external measurement of the random errors on total magnitudes from the widely used Virgo Cluster Catalog, which we estimate to be σ(BT )≈ 0.13 mag for the brightest galaxies, rising to ≈ 0.3 mag for galaxies at the faint end of our sample (BT ≈ 16). The distribution of axial ratios of low-mass ("dwarf") galaxies bears a strong resemblance to the one observed for the higher-mass ("giant") galaxies. The global structural parameters for the full galaxy sample—profile shape, effective radius, and mean surface brightness—are found to vary smoothly and systematically as a function of luminosity, with unmistakable evidence for changes in structural homology along the red sequence. As noted in previous studies, the ugriz galaxy colors show a nonlinear but smooth variation over a ~7 mag range in absolute magnitude, with an enhanced scatter for the faintest systems that is likely the signature of their more diverse star formation histories.
Chen, Chin-Wei; Cote, Patrick; Ferrarese, Laura; West, Andrew A.; Peng, Eric W.
2010-11-15
We present photometric and structural parameters for 100 ACS Virgo Cluster Survey (ACSVCS) galaxies based on homogeneous, multi-wavelength (ugriz), wide-field SDSS (DR5) imaging. These early-type galaxies, which trace out the red sequence in the Virgo Cluster, span a factor of nearly {approx}10{sup 3} in g-band luminosity. We describe an automated pipeline that generates background-subtracted mosaic images, masks field sources and measures mean shapes, total magnitudes, effective radii, and effective surface brightnesses using a model-independent approach. A parametric analysis of the surface brightness profiles is also carried out to obtain Sersic-based structural parameters and mean galaxy colors. We compare the galaxy parameters to those in the literature, including those from the ACSVCS, finding good agreement in most cases, although the sizes of the brightest, and most extended, galaxies are found to be most uncertain and model dependent. Our photometry provides an external measurement of the random errors on total magnitudes from the widely used Virgo Cluster Catalog, which we estimate to be {sigma}(B{sub T}){approx} 0.13 mag for the brightest galaxies, rising to {approx} 0.3 mag for galaxies at the faint end of our sample (B{sub T} {approx} 16). The distribution of axial ratios of low-mass ('dwarf') galaxies bears a strong resemblance to the one observed for the higher-mass ('giant') galaxies. The global structural parameters for the full galaxy sample-profile shape, effective radius, and mean surface brightness-are found to vary smoothly and systematically as a function of luminosity, with unmistakable evidence for changes in structural homology along the red sequence. As noted in previous studies, the ugriz galaxy colors show a nonlinear but smooth variation over a {approx}7 mag range in absolute magnitude, with an enhanced scatter for the faintest systems that is likely the signature of their more diverse star formation histories.
Optimization of removal function in computer controlled optical surfacing
NASA Astrophysics Data System (ADS)
Chen, Xi; Guo, Peiji; Ren, Jianfeng
2010-10-01
The technical principle of computer controlled optical surfacing (CCOS) and the common method of optimizing removal function that is used in CCOS are introduced in this paper. A new optimizing method time-sharing synthesis of removal function is proposed to solve problems of the removal function being far away from Gaussian type and slow approaching of the removal function error that encountered in the mode of planet motion or translation-rotation. Detailed time-sharing synthesis of using six removal functions is discussed. For a given region on the workpiece, six positions are selected as the centers of the removal function; polishing tool controlled by the executive system of CCOS revolves around each centre to complete a cycle in proper order. The overall removal function obtained by the time-sharing process is the ratio of total material removal in six cycles to time duration of the six cycles, which depends on the arrangement and distribution of the six removal functions. Simulations on the synthesized overall removal functions under two different modes of motion, i.e., planet motion and translation-rotation are performed from which the optimized combination of tool parameters and distribution of time-sharing synthesis removal functions are obtained. The evaluation function when optimizing is determined by an approaching factor which is defined as the ratio of the material removal within the area of half of the polishing tool coverage from the polishing center to the total material removal within the full polishing tool coverage area. After optimization, it is found that the optimized removal function obtained by time-sharing synthesis is closer to the ideal Gaussian type removal function than those by the traditional methods. The time-sharing synthesis method of the removal function provides an efficient way to increase the convergence speed of the surface error in CCOS for the fabrication of aspheric optical surfaces, and to reduce the intermediate- and high
Computational design of receptor and sensor proteins with novel functions
NASA Astrophysics Data System (ADS)
Looger, Loren L.; Dwyer, Mary A.; Smith, James J.; Hellinga, Homme W.
2003-05-01
The formation of complexes between proteins and ligands is fundamental to biological processes at the molecular level. Manipulation of molecular recognition between ligands and proteins is therefore important for basic biological studies and has many biotechnological applications, including the construction of enzymes, biosensors, genetic circuits, signal transduction pathways and chiral separations. The systematic manipulation of binding sites remains a major challenge. Computational design offers enormous generality for engineering protein structure and function. Here we present a structure-based computational method that can drastically redesign protein ligand-binding specificities. This method was used to construct soluble receptors that bind trinitrotoluene, L-lactate or serotonin with high selectivity and affinity. These engineered receptors can function as biosensors for their new ligands; we also incorporated them into synthetic bacterial signal transduction pathways, regulating gene expression in response to extracellular trinitrotoluene or L-lactate. The use of various ligands and proteins shows that a high degree of control over biomolecular recognition has been established computationally. The biological and biosensing activities of the designed receptors illustrate potential applications of computational design.
Predictive computation of genomic logic processing functions in embryonic development
Peter, Isabelle S.; Faure, Emmanuel; Davidson, Eric H.
2012-01-01
Gene regulatory networks (GRNs) control the dynamic spatial patterns of regulatory gene expression in development. Thus, in principle, GRN models may provide system-level, causal explanations of developmental process. To test this assertion, we have transformed a relatively well-established GRN model into a predictive, dynamic Boolean computational model. This Boolean model computes spatial and temporal gene expression according to the regulatory logic and gene interactions specified in a GRN model for embryonic development in the sea urchin. Additional information input into the model included the progressive embryonic geometry and gene expression kinetics. The resulting model predicted gene expression patterns for a large number of individual regulatory genes each hour up to gastrulation (30 h) in four different spatial domains of the embryo. Direct comparison with experimental observations showed that the model predictively computed these patterns with remarkable spatial and temporal accuracy. In addition, we used this model to carry out in silico perturbations of regulatory functions and of embryonic spatial organization. The model computationally reproduced the altered developmental functions observed experimentally. Two major conclusions are that the starting GRN model contains sufficiently complete regulatory information to permit explanation of a complex developmental process of gene expression solely in terms of genomic regulatory code, and that the Boolean model provides a tool with which to test in silico regulatory circuitry and developmental perturbations. PMID:22927416
Evaluation of tablet computers for visual function assessment.
Bodduluri, Lakshmi; Boon, Mei Ying; Dain, Stephen J
2017-04-01
Recent advances in technology and the increased use of tablet computers for mobile health applications such as vision testing necessitate an understanding of the behavior of the displays of such devices, to facilitate the reproduction of existing or the development of new vision assessment tests. The purpose of this study was to investigate the physical characteristics of one model of tablet computer (iPad mini Retina display) with regard to display consistency across a set of devices (15) and their potential application as clinical vision assessment tools. Once the tablet computer was switched on, it required about 13 min to reach luminance stability, while chromaticity remained constant. The luminance output of the device remained stable until a battery level of 5%. Luminance varied from center to peripheral locations of the display and with viewing angle, whereas the chromaticity did not vary. A minimal (1%) variation in luminance was observed due to temperature, and once again chromaticity remained constant. Also, these devices showed good temporal stability of luminance and chromaticity. All 15 tablet computers showed gamma functions approximating the standard gamma (2.20) and showed similar color gamut sizes, except for the blue primary, which displayed minimal variations. The physical characteristics across the 15 devices were similar and are known, thereby facilitating the use of this model of tablet computer as visual stimulus displays.
Efficient Computation of Functional Brain Networks: toward Real-Time Functional Connectivity
García-Prieto, Juan; Bajo, Ricardo; Pereda, Ernesto
2017-01-01
Functional Connectivity has demonstrated to be a key concept for unraveling how the brain balances functional segregation and integration properties while processing information. This work presents a set of open-source tools that significantly increase computational efficiency of some well-known connectivity indices and Graph-Theory measures. PLV, PLI, ImC, and wPLI as Phase Synchronization measures, Mutual Information as an information theory based measure, and Generalized Synchronization indices are computed much more efficiently than prior open-source available implementations. Furthermore, network theory related measures like Strength, Shortest Path Length, Clustering Coefficient, and Betweenness Centrality are also implemented showing computational times up to thousands of times faster than most well-known implementations. Altogether, this work significantly expands what can be computed in feasible times, even enabling whole-head real-time network analysis of brain function. PMID:28220071
Efficient Computation of Functional Brain Networks: toward Real-Time Functional Connectivity.
García-Prieto, Juan; Bajo, Ricardo; Pereda, Ernesto
2017-01-01
Functional Connectivity has demonstrated to be a key concept for unraveling how the brain balances functional segregation and integration properties while processing information. This work presents a set of open-source tools that significantly increase computational efficiency of some well-known connectivity indices and Graph-Theory measures. PLV, PLI, ImC, and wPLI as Phase Synchronization measures, Mutual Information as an information theory based measure, and Generalized Synchronization indices are computed much more efficiently than prior open-source available implementations. Furthermore, network theory related measures like Strength, Shortest Path Length, Clustering Coefficient, and Betweenness Centrality are also implemented showing computational times up to thousands of times faster than most well-known implementations. Altogether, this work significantly expands what can be computed in feasible times, even enabling whole-head real-time network analysis of brain function.
Computations involving differential operators and their actions on functions
NASA Technical Reports Server (NTRS)
Crouch, Peter E.; Grossman, Robert; Larson, Richard
1991-01-01
The algorithms derived by Grossmann and Larson (1989) are further developed for rewriting expressions involving differential operators. The differential operators involved arise in the local analysis of nonlinear dynamical systems. These algorithms are extended in two different directions: the algorithms are generalized so that they apply to differential operators on groups and the data structures and algorithms are developed to compute symbolically the action of differential operators on functions. Both of these generalizations are needed for applications.
Computer simulation of functioning of elements of security systems
NASA Astrophysics Data System (ADS)
Godovykh, A. V.; Stepanov, B. P.; Sheveleva, A. A.
2017-01-01
The article is devoted to issues of development of the informational complex for simulation of functioning of the security system elements. The complex is described from the point of view of main objectives, a design concept and an interrelation of main elements. The proposed conception of the computer simulation provides an opportunity to simulate processes of security system work for training security staff during normal and emergency operation.
Efficient quantum algorithm for computing n-time correlation functions.
Pedernales, J S; Di Candia, R; Egusquiza, I L; Casanova, J; Solano, E
2014-07-11
We propose a method for computing n-time correlation functions of arbitrary spinorial, fermionic, and bosonic operators, consisting of an efficient quantum algorithm that encodes these correlations in an initially added ancillary qubit for probe and control tasks. For spinorial and fermionic systems, the reconstruction of arbitrary n-time correlation functions requires the measurement of two ancilla observables, while for bosonic variables time derivatives of the same observables are needed. Finally, we provide examples applicable to different quantum platforms in the frame of the linear response theory.
Computational prediction of functional abortive RNA in E. coli.
Marcus, Jeremy I; Hassoun, Soha; Nair, Nikhil U
2017-03-24
Failure by RNA polymerase to break contacts with promoter DNA results in release of bound RNA and re-initiation of transcription. These abortive RNAs were assumed to be non-functional but have recently been shown to affect termination in bacteriophage T7. Little is known about the functional role of these RNA in other genetic models. Using a computational approach, we investigated whether abortive RNA could exert function in E. coli. Fragments generated from 3780 transcription units were used as query sequences within their respective transcription units to search for possible binding sites. Sites that fell within known regulatory features were then ranked based upon the free energy of hybridization to the abortive. We further hypothesize about mechanisms of regulatory action for a select number of likely matches. Future experimental validation of these putative abortive-mRNA pairs may confirm our findings and promote exploration of functional abortive RNAs (faRNAs) in natural and synthetic systems.
INTEGRATING COMPUTATIONAL PROTEIN FUNCTION PREDICTION INTO DRUG DISCOVERY INITIATIVES
Grant, Marianne A.
2014-01-01
Pharmaceutical researchers must evaluate vast numbers of protein sequences and formulate innovative strategies for identifying valid targets and discovering leads against them as a way of accelerating drug discovery. The ever increasing number and diversity of novel protein sequences identified by genomic sequencing projects and the success of worldwide structural genomics initiatives have spurred great interest and impetus in the development of methods for accurate, computationally empowered protein function prediction and active site identification. Previously, in the absence of direct experimental evidence, homology-based protein function annotation remained the gold-standard for in silico analysis and prediction of protein function. However, with the continued exponential expansion of sequence databases, this approach is not always applicable, as fewer query protein sequences demonstrate significant homology to protein gene products of known function. As a result, several non-homology based methods for protein function prediction that are based on sequence features, structure, evolution, biochemical and genetic knowledge have emerged. Herein, we review current bioinformatic programs and approaches for protein function prediction/annotation and discuss their integration into drug discovery initiatives. The development of such methods to annotate protein functional sites and their application to large protein functional families is crucial to successfully utilizing the vast amounts of genomic sequence information available to drug discovery and development processes. PMID:25530654
Computational approaches for inferring the functions of intrinsically disordered proteins
Varadi, Mihaly; Vranken, Wim; Guharoy, Mainak; Tompa, Peter
2015-01-01
Intrinsically disordered proteins (IDPs) are ubiquitously involved in cellular processes and often implicated in human pathological conditions. The critical biological roles of these proteins, despite not adopting a well-defined fold, encouraged structural biologists to revisit their views on the protein structure-function paradigm. Unfortunately, investigating the characteristics and describing the structural behavior of IDPs is far from trivial, and inferring the function(s) of a disordered protein region remains a major challenge. Computational methods have proven particularly relevant for studying IDPs: on the sequence level their dependence on distinct characteristics determined by the local amino acid context makes sequence-based prediction algorithms viable and reliable tools for large scale analyses, while on the structure level the in silico integration of fundamentally different experimental data types is essential to describe the behavior of a flexible protein chain. Here, we offer an overview of the latest developments and computational techniques that aim to uncover how protein function is connected to intrinsic disorder. PMID:26301226
Wendland radial basis functions applied as filters on computed tomography
NASA Astrophysics Data System (ADS)
Aguilar, Juan C.; Berriel-Valdos, L. R.; Aguilar, J. Felix
2011-08-01
Wendland radial basis functions are applied as an alternative solution to the interpolation problem when the filtered back projection algorithm is used in computed tomography. Since we have a regular grid of data points and these functions are compactly supported, the interpolation can be made as a fast filtering process rather than solving a typical linear system of equations. This allows us to apply the Error Kernel method, which gives details of the approximation quality in the frequency domain, when we make interpolation with basis functions such as the B-splines. The Error Kernel provides us a direct comparison between Wendland functions and B-splines. The comparison shows that the Wendland functions can offer the same interpolation quality of the B-splines when the support is large, but with a small support the performance is poor. We see this behavior making tomographic reconstructions with different Wendland functions and also with different supports. A numerical experiment consisting of successive image rotations to an image was performed to verify the similarities between the Wendland functions and B-splines.
Multiple von Neumann computers: an evolutionary approach to functional emergence.
Suzuki, H
1997-01-01
A novel system composed of multiple von Neumann computers and an appropriate problem environment is proposed and simulated. Each computer has a memory to store the machine instruction program, and when a program is executed, a series of machine codes in the memory is sequentially decoded, leading to register operations in the central processing unit (CPU). By means of these operations, the computer not only can handle its generally used registers but also can read and write the environmental database. Simulation is driven by genetic algorithms (GAs) performed on the population of program memories. Mutation and crossover create program diversity in the memory, and selection facilitates the reproduction of appropriate programs. Through these evolutionary operations, advantageous combinations of machine codes are created and fixed in the population one by one, and the higher function, which enables the computer to calculate an appropriate number from the environment, finally emerges in the program memory. In the latter half of the article, the performance of GAs on this system is studied. Under different sets of parameters, the evolutionary speed, which is determined by the time until the domination of the final program, is examined and the conditions for faster evolution are clarified. At an intermediate mutation rate and at an intermediate population size, crossover helps create novel advantageous sets of machine codes and evidently accelerates optimization by GAs.
Structure-based Methods for Computational Protein Functional Site Prediction
Dukka, B KC
2013-01-01
Due to the advent of high throughput sequencing techniques and structural genomic projects, the number of gene and protein sequences has been ever increasing. Computational methods to annotate these genes and proteins are even more indispensable. Proteins are important macromolecules and study of the function of proteins is an important problem in structural bioinformatics. This paper discusses a number of methods to predict protein functional site especially focusing on protein ligand binding site prediction. Initially, a short overview is presented on recent advances in methods for selection of homologous sequences. Furthermore, a few recent structural based approaches and sequence-and-structure based approaches for protein functional sites are discussed in details. PMID:24688745
On the Hydrodynamic Function of Sharkskin: A Computational Investigation
NASA Astrophysics Data System (ADS)
Boomsma, Aaron; Sotiropoulos, Fotis
2014-11-01
Denticles (placoid scales) are small structures that cover the epidermis of some sharks. The hydrodynamic function of denticles is unclear. Because they resemble riblets, they have been thought to passively reduce skin-friction-for which there is some experimental evidence. Others have experimentally shown that denticles increase skin-friction and have hypothesized that denticles act as vortex generators to delay separation. To help clarify their function, we use high-resolution large eddy and direct numerical simulations, with an immersed boundary method, to simulate flow patterns past and calculate the drag force on Mako Short Fin denticles. Simulations are carried out for the denticles placed in a canonical turbulent boundary layer as well as in the vicinity of a separation bubble. The computed results elucidate the three-dimensional structure of the flow around denticles and provide insights into the hydrodynamic function of sharkskin.
21 CFR 870.1435 - Single-function, preprogrammed diagnostic computer.
Code of Federal Regulations, 2010 CFR
2010-04-01
... 21 Food and Drugs 8 2010-04-01 2010-04-01 false Single-function, preprogrammed diagnostic computer... Single-function, preprogrammed diagnostic computer. (a) Identification. A single-function, preprogrammed diagnostic computer is a hard-wired computer that calculates a specific physiological or blood-flow parameter...
21 CFR 870.1435 - Single-function, preprogrammed diagnostic computer.
Code of Federal Regulations, 2012 CFR
2012-04-01
... 21 Food and Drugs 8 2012-04-01 2012-04-01 false Single-function, preprogrammed diagnostic computer... Single-function, preprogrammed diagnostic computer. (a) Identification. A single-function, preprogrammed diagnostic computer is a hard-wired computer that calculates a specific physiological or blood-flow parameter...
21 CFR 870.1435 - Single-function, preprogrammed diagnostic computer.
Code of Federal Regulations, 2014 CFR
2014-04-01
... 21 Food and Drugs 8 2014-04-01 2014-04-01 false Single-function, preprogrammed diagnostic computer... Single-function, preprogrammed diagnostic computer. (a) Identification. A single-function, preprogrammed diagnostic computer is a hard-wired computer that calculates a specific physiological or blood-flow parameter...
21 CFR 870.1435 - Single-function, preprogrammed diagnostic computer.
Code of Federal Regulations, 2013 CFR
2013-04-01
... 21 Food and Drugs 8 2013-04-01 2013-04-01 false Single-function, preprogrammed diagnostic computer... Single-function, preprogrammed diagnostic computer. (a) Identification. A single-function, preprogrammed diagnostic computer is a hard-wired computer that calculates a specific physiological or blood-flow parameter...
21 CFR 870.1435 - Single-function, preprogrammed diagnostic computer.
Code of Federal Regulations, 2011 CFR
2011-04-01
... 21 Food and Drugs 8 2011-04-01 2011-04-01 false Single-function, preprogrammed diagnostic computer... Single-function, preprogrammed diagnostic computer. (a) Identification. A single-function, preprogrammed diagnostic computer is a hard-wired computer that calculates a specific physiological or blood-flow parameter...
NASA Astrophysics Data System (ADS)
Soberaski, J.; Moysey, S.; Bedient, P.
2007-05-01
Remote sensing data and GIS tools have opened the door to simplify the parameterization of distributed watershed models. However, decisions about the spatial homogeneity of model parameters should also be based on the actual response of a basin to rainfall. For the last 75 years, hydrologists have relied on the unit hydrograph (UH) as a key tool for analyzing watersheds because its shape is directly related to important attributes of the drainage basin controlling runoff (e.g., topography, land use, soil properties, stream network, etc.). Deconvolution of excess rainfall from direct runoff can provide non-parametric estimates of the UH that capture the effects of sub-basin heterogeneity, thereby making these hydrographs particularly useful tools for comparing and classifying watersheds. Due to the mathematical instability of deconvolution, it is unclear whether meaningful UH estimates can be obtained for the purpose of inter-basin comparisons, particularly when processes controlling excess precipitation and direct runoff within the watershed are uncertain. This study evaluates the sensitivity of non-parametric UH's to uncertainty in watershed properties for six gauged sub-basins of the Cypress Creek Watershed, TX. We have used MATLAB to conduct a rainfall-runoff analysis of the Cypress Creek Watershed, TX over a 17 day period during Tropical Storm Allison in 2001. For the six basins analyzed, discharges for Cypress Creek are available at the outflow of each sub-basin and NEXRAD rainfall data are available throughout the watershed. To determine the direct runoff contributed by each sub-basin, incoming upstream flows were routed by simple advection and then subtracted from the downstream discharge record. Excess precipitation was calculated by applying the Green & Ampt infiltration model to the rainfall record for each basin after accounting for initial abstractions and direct losses due to impervious surfaces. In each step of this procedure, the parameters
Analog computation of auto and cross-correlation functions
NASA Technical Reports Server (NTRS)
1974-01-01
For analysis of the data obtained from the cross beam systems it was deemed desirable to compute the auto- and cross-correlation functions by both digital and analog methods to provide a cross-check of the analysis methods and an indication as to which of the two methods would be most suitable for routine use in the analysis of such data. It is the purpose of this appendix to provide a concise description of the equipment and procedures used for the electronic analog analysis of the cross beam data. A block diagram showing the signal processing and computation set-up used for most of the analog data analysis is provided. The data obtained at the field test sites were recorded on magnetic tape using wide-band FM recording techniques. The data as recorded were band-pass filtered by electronic signal processing in the data acquisition systems.
NASA Astrophysics Data System (ADS)
Constantinescu, C. C.; Yoder, K. K.; Kareken, D. A.; Bouman, C. A.; O'Connor, S. J.; Normandin, M. D.; Morris, E. D.
2008-03-01
We previously developed a model-independent technique (non-parametric ntPET) for extracting the transient changes in neurotransmitter concentration from paired (rest & activation) PET studies with a receptor ligand. To provide support for our method, we introduced three hypotheses of validation based on work by Endres and Carson (1998 J. Cereb. Blood Flow Metab. 18 1196-210) and Yoder et al (2004 J. Nucl. Med. 45 903-11), and tested them on experimental data. All three hypotheses describe relationships between the estimated free (synaptic) dopamine curves (FDA(t)) and the change in binding potential (ΔBP). The veracity of the FDA(t) curves recovered by nonparametric ntPET is supported when the data adhere to the following hypothesized behaviors: (1) ΔBP should decline with increasing DA peak time, (2) ΔBP should increase as the strength of the temporal correlation between FDA(t) and the free raclopride (FRAC(t)) curve increases, (3) ΔBP should decline linearly with the effective weighted availability of the receptor sites. We analyzed regional brain data from 8 healthy subjects who received two [11C]raclopride scans: one at rest, and one during which unanticipated IV alcohol was administered to stimulate dopamine release. For several striatal regions, nonparametric ntPET was applied to recover FDA(t), and binding potential values were determined. Kendall rank-correlation analysis confirmed that the FDA(t) data followed the expected trends for all three validation hypotheses. Our findings lend credence to our model-independent estimates of FDA(t). Application of nonparametric ntPET may yield important insights into how alterations in timing of dopaminergic neurotransmission are involved in the pathologies of addiction and other psychiatric disorders.
Breitling, Rainer; Herzyk, Pawel
2005-10-01
We have recently introduced a rank-based test statistic, RankProducts (RP), as a new non-parametric method for detecting differentially expressed genes in microarray experiments. It has been shown to generate surprisingly good results with biological datasets. The basis for this performance and the limits of the method are, however, little understood. Here we explore the performance of such rank-based approaches under a variety of conditions using simulated microarray data, and compare it with classical Wilcoxon rank sums and t-statistics, which form the basis of most alternative differential gene expression detection techniques. We show that for realistic simulated microarray datasets, RP is more powerful and accurate for sorting genes by differential expression than t-statistics or Wilcoxon rank sums - in particular for replicate numbers below 10, which are most commonly used in biological experiments. Its relative performance is particularly strong when the data are contaminated by non-normal random noise or when the samples are very inhomogenous, e.g. because they come from different time points or contain a mixture of affected and unaffected cells. However, RP assumes equal measurement variance for all genes and tends to give overly optimistic p-values when this assumption is violated. It is therefore essential that proper variance stabilizing normalization is performed on the data before calculating the RP values. Where this is impossible, another rank-based variant of RP (average ranks) provides a useful alternative with very similar overall performance. The Perl scripts implementing the simulation and evaluation are available upon request. Implementations of the RP method are available for download from the authors website (http://www.brc.dcs.gla.ac.uk/glama).
Computational models of basal-ganglia pathway functions: focus on functional neuroanatomy
Schroll, Henning; Hamker, Fred H.
2013-01-01
Over the past 15 years, computational models have had a considerable impact on basal-ganglia research. Most of these models implement multiple distinct basal-ganglia pathways and assume them to fulfill different functions. As there is now a multitude of different models, it has become complex to keep track of their various, sometimes just marginally different assumptions on pathway functions. Moreover, it has become a challenge to oversee to what extent individual assumptions are corroborated or challenged by empirical data. Focusing on computational, but also considering non-computational models, we review influential concepts of pathway functions and show to what extent they are compatible with or contradict each other. Moreover, we outline how empirical evidence favors or challenges specific model assumptions and propose experiments that allow testing assumptions against each other. PMID:24416002
Computational models of basal-ganglia pathway functions: focus on functional neuroanatomy.
Schroll, Henning; Hamker, Fred H
2013-12-30
Over the past 15 years, computational models have had a considerable impact on basal-ganglia research. Most of these models implement multiple distinct basal-ganglia pathways and assume them to fulfill different functions. As there is now a multitude of different models, it has become complex to keep track of their various, sometimes just marginally different assumptions on pathway functions. Moreover, it has become a challenge to oversee to what extent individual assumptions are corroborated or challenged by empirical data. Focusing on computational, but also considering non-computational models, we review influential concepts of pathway functions and show to what extent they are compatible with or contradict each other. Moreover, we outline how empirical evidence favors or challenges specific model assumptions and propose experiments that allow testing assumptions against each other.
Complete RNA inverse folding: computational design of functional hammerhead ribozymes
Dotu, Ivan; Garcia-Martin, Juan Antonio; Slinger, Betty L.; Mechery, Vinodh; Meyer, Michelle M.; Clote, Peter
2014-01-01
Nanotechnology and synthetic biology currently constitute one of the most innovative, interdisciplinary fields of research, poised to radically transform society in the 21st century. This paper concerns the synthetic design of ribonucleic acid molecules, using our recent algorithm, RNAiFold, which can determine all RNA sequences whose minimum free energy secondary structure is a user-specified target structure. Using RNAiFold, we design ten cis-cleaving hammerhead ribozymes, all of which are shown to be functional by a cleavage assay. We additionally use RNAiFold to design a functional cis-cleaving hammerhead as a modular unit of a synthetic larger RNA. Analysis of kinetics on this small set of hammerheads suggests that cleavage rate of computationally designed ribozymes may be correlated with positional entropy, ensemble defect, structural flexibility/rigidity and related measures. Artificial ribozymes have been designed in the past either manually or by SELEX (Systematic Evolution of Ligands by Exponential Enrichment); however, this appears to be the first purely computational design and experimental validation of novel functional ribozymes. RNAiFold is available at http://bioinformatics.bc.edu/clotelab/RNAiFold/. PMID:25209235
Computer Modeling of the Earliest Cellular Structures and Functions
NASA Technical Reports Server (NTRS)
Pohorille, Andrew; Chipot, Christophe; Schweighofer, Karl
2000-01-01
In the absence of extinct or extant record of protocells (the earliest ancestors of contemporary cells). the most direct way to test our understanding of the origin of cellular life is to construct laboratory models of protocells. Such efforts are currently underway in the NASA Astrobiology Program. They are accompanied by computational studies aimed at explaining self-organization of simple molecules into ordered structures and developing designs for molecules that perform proto-cellular functions. Many of these functions, such as import of nutrients, capture and storage of energy. and response to changes in the environment are carried out by proteins bound to membrane< We will discuss a series of large-scale, molecular-level computer simulations which demonstrate (a) how small proteins (peptides) organize themselves into ordered structures at water-membrane interfaces and insert into membranes, (b) how these peptides aggregate to form membrane-spanning structures (eg. channels), and (c) by what mechanisms such aggregates perform essential proto-cellular functions, such as proton transport of protons across cell walls, a key step in cellular bioenergetics. The simulations were performed using the molecular dynamics method, in which Newton's equations of motion for each item in the system are solved iteratively. The problems of interest required simulations on multi-nanosecond time scales, which corresponded to 10(exp 6)-10(exp 8) time steps.
Computer Modeling of the Earliest Cellular Structures and Functions
NASA Technical Reports Server (NTRS)
Pohorille, Andrew; Chipot, Christophe; Schweighofer, Karl
2000-01-01
In the absence of extinct or extant record of protocells (the earliest ancestors of contemporary cells). the most direct way to test our understanding of the origin of cellular life is to construct laboratory models of protocells. Such efforts are currently underway in the NASA Astrobiology Program. They are accompanied by computational studies aimed at explaining self-organization of simple molecules into ordered structures and developing designs for molecules that perform proto-cellular functions. Many of these functions, such as import of nutrients, capture and storage of energy. and response to changes in the environment are carried out by proteins bound to membrane< We will discuss a series of large-scale, molecular-level computer simulations which demonstrate (a) how small proteins (peptides) organize themselves into ordered structures at water-membrane interfaces and insert into membranes, (b) how these peptides aggregate to form membrane-spanning structures (eg. channels), and (c) by what mechanisms such aggregates perform essential proto-cellular functions, such as proton transport of protons across cell walls, a key step in cellular bioenergetics. The simulations were performed using the molecular dynamics method, in which Newton's equations of motion for each item in the system are solved iteratively. The problems of interest required simulations on multi-nanosecond time scales, which corresponded to 10(exp 6)-10(exp 8) time steps.
The Post Processing Functions of a Database Computer.
1979-07-01
the set. L ~~ 1 •1 ,~; I .p•• •i 11 SECURiTY CLASSIF ICATION OF THIS PACF(IIhrn / ) n t s 1: nfr ,,- d) IL...design of a database computer known as DBC. The architecture of • DBC is shown in Figure 1. This report deals with the post processing functions of DBC...this section, we shall consider a method for implicit join that is used in the database machine CASSM [6, 7], CASSM has a cellular architecture in which
Non-functioning adrenal adenomas discovered incidentally on computed tomography
Mitnick, J.S.; Bosniak, M.A.; Megibow, A.J.; Naidich, D.P.
1983-08-01
Eighteen patients with unilateral non-metastatic non-functioning adrenal masses were studied with computed tomography (CT). Pathological examination in cases revealed benign adrenal adenomas. The others were followed up with serial CT scans and found to show no change in tumor size over a period of six months to three years. On the basis of these findings, the authors suggest certain criteria of a benign adrenal mass, including (a) diameter less than 5 cm, (b) smooth contour, (c) well-defined margin, and (d) no change in size on follow-up. Serial CT scanning can be used as an alternative to surgery in the management of many of these patients.
Material reconstruction for spectral computed tomography with detector response function
NASA Astrophysics Data System (ADS)
Liu, Jiulong; Gao, Hao
2016-11-01
Different from conventional computed tomography (CT), spectral CT using energy-resolved photon-counting detectors is able to provide the unprecedented material compositions. However accurate spectral CT needs to account for the detector response function (DRF), which is often distorted by factors such as pulse pileup and charge-sharing. In this work, we propose material reconstruction methods for spectral CT with DRF. The simulation results suggest that the proposed methods reconstructed more accurate material compositions than the conventional method without DRF. Moreover, the proposed linearized method with linear data fidelity from spectral resampling had improved reconstruction quality from the nonlinear method directly based on nonlinear data fidelity.
Computation of the lattice Green function for a dislocation
NASA Astrophysics Data System (ADS)
Tan, Anne Marie Z.; Trinkle, Dallas R.
2016-08-01
Modeling isolated dislocations is challenging due to their long-ranged strain fields. Flexible boundary condition methods capture the correct long-range strain field of a defect by coupling the defect core to an infinite harmonic bulk through the lattice Green function (LGF). To improve the accuracy and efficiency of flexible boundary condition methods, we develop a numerical method to compute the LGF specifically for a dislocation geometry; in contrast to previous methods, where the LGF was computed for the perfect bulk as an approximation for the dislocation. Our approach directly accounts for the topology of a dislocation, and the errors in the LGF computation converge rapidly for edge dislocations in a simple cubic model system as well as in BCC Fe with an empirical potential. When used within the flexible boundary condition approach, the dislocation LGF relaxes dislocation core geometries in fewer iterations than when the perfect bulk LGF is used as an approximation for the dislocation, making a flexible boundary condition approach more efficient.
CMB anisotropy in compact hyperbolic universes. I. Computing correlation functions
NASA Astrophysics Data System (ADS)
Bond, J. Richard; Pogosyan, Dmitry; Souradeep, Tarun
2000-08-01
Cosmic microwave background (CMB) anisotropy measurements have brought the issue of global topology of the universe from the realm of theoretical possibility to within the grasp of observations. The global topology of the universe modifies the correlation properties of cosmic fields. In particular, strong correlations are predicted in CMB anisotropy patterns on the largest observable scales if the size of the universe is comparable to the distance to the CMB last scattering surface. We describe in detail our completely general scheme using a regularized method of images for calculating such correlation functions in models with nontrivial topology, and apply it to the computationally challenging compact hyperbolic spaces. Our procedure directly sums over images within a specified radius, ideally many times the diameter of the space, effectively treats more distant images in a continuous approximation, and uses Cesaro resummation to further sharpen the results. At all levels of approximation the symmetries of the space are preserved in the correlation function. This new technique eliminates the need for the difficult task of spatial eigenmode decomposition on these spaces. Although the eigenspectrum can be obtained by this method if desired, at a given level of approximation the correlation functions are more accurately determined. We use the 3-torus example to demonstrate that the method works very well. We apply it to power spectrum as well as correlation function evaluations in a number of compact hyperbolic (CH) spaces. Application to the computation of CMB anisotropy correlations on CH spaces, and the observational constraints following from them, are given in a companion paper.
An Atomistic Statistically Effective Energy Function for Computational Protein Design.
Topham, Christopher M; Barbe, Sophie; André, Isabelle
2016-08-09
Shortcomings in the definition of effective free-energy surfaces of proteins are recognized to be a major contributory factor responsible for the low success rates of existing automated methods for computational protein design (CPD). The formulation of an atomistic statistically effective energy function (SEEF) suitable for a wide range of CPD applications and its derivation from structural data extracted from protein domains and protein-ligand complexes are described here. The proposed energy function comprises nonlocal atom-based and local residue-based SEEFs, which are coupled using a novel atom connectivity number factor to scale short-range, pairwise, nonbonded atomic interaction energies and a surface-area-dependent cavity energy term. This energy function was used to derive additional SEEFs describing the unfolded-state ensemble of any given residue sequence based on computed average energies for partially or fully solvent-exposed fragments in regions of irregular structure in native proteins. Relative thermal stabilities of 97 T4 bacteriophage lysozyme mutants were predicted from calculated energy differences for folded and unfolded states with an average unsigned error (AUE) of 0.84 kcal mol(-1) when compared to experiment. To demonstrate the utility of the energy function for CPD, further validation was carried out in tests of its capacity to recover cognate protein sequences and to discriminate native and near-native protein folds, loop conformers, and small-molecule ligand binding poses from non-native benchmark decoys. Experimental ligand binding free energies for a diverse set of 80 protein complexes could be predicted with an AUE of 2.4 kcal mol(-1) using an additional energy term to account for the loss in ligand configurational entropy upon binding. The atomistic SEEF is expected to improve the accuracy of residue-based coarse-grained SEEFs currently used in CPD and to extend the range of applications of extant atom-based protein statistical
Enzymatic Halogenases and Haloperoxidases: Computational Studies on Mechanism and Function.
Timmins, Amy; de Visser, Sam P
2015-01-01
Despite the fact that halogenated compounds are rare in biology, a number of organisms have developed processes to utilize halogens and in recent years, a string of enzymes have been identified that selectively insert halogen atoms into, for instance, a CH aliphatic bond. Thus, a number of natural products, including antibiotics, contain halogenated functional groups. This unusual process has great relevance to the chemical industry for stereoselective and regiospecific synthesis of haloalkanes. Currently, however, industry utilizes few applications of biological haloperoxidases and halogenases, but efforts are being worked on to understand their catalytic mechanism, so that their catalytic function can be upscaled. In this review, we summarize experimental and computational studies on the catalytic mechanism of a range of haloperoxidases and halogenases with structurally very different catalytic features and cofactors. This chapter gives an overview of heme-dependent haloperoxidases, nonheme vanadium-dependent haloperoxidases, and flavin adenine dinucleotide-dependent haloperoxidases. In addition, we discuss the S-adenosyl-l-methionine fluoridase and nonheme iron/α-ketoglutarate-dependent halogenases. In particular, computational efforts have been applied extensively for several of these haloperoxidases and halogenases and have given insight into the essential structural features that enable these enzymes to perform the unusual halogen atom transfer to substrates.
Numerical computation of aeroacoustic transfer functions for realistic airfoils
NASA Astrophysics Data System (ADS)
Miotto, Renato Fuzaro; Wolf, William Roberto; de Santana, Leandro Dantas
2017-10-01
Based on Amiet's theory formalism, we propose a numerical framework to compute the aeroacoustic transfer function of realistic airfoil geometries. The aeroacoustic transfer function relates the amplitude and phase of an incoming periodic gust to the respective unsteady lift response permitting, therefore, the application of Curle's analogy to compute the radiated noise. The methodology is focused on the airfoil leading-edge noise problem being able to also consider the trailing-edge back-scattering and, consequently, airfoil compactness effects. The approach is valid for compressible subsonic flows and the airfoil blade is assumed of large aspect ratio subjected to three-dimensional periodic gusts with supersonic velocity trace at the airfoil leading edge (i.e. supercritical gusts). This work proposes the iterative application of the boundary element method to numerically solve the boundary value problem prescribed by the linearized airfoil theory. Details of the numerical implementation are discussed and include the application of boundary conditions in different steps of the iterative procedure, treatment of derivatives in the implementation of the Kutta condition and accurate representation of singularities present at the leading- and trailing-edges. This study validates the numerical approach by comparing results with Amiet's theory obtained analytically. Subsequently, effects of realistic airfoil geometries on the leading-edge airfoil radiated noise are presented.
Functional Connectivity’s Degenerate View of Brain Computation
Giron, Alain; Rudrauf, David
2016-01-01
Brain computation relies on effective interactions between ensembles of neurons. In neuroimaging, measures of functional connectivity (FC) aim at statistically quantifying such interactions, often to study normal or pathological cognition. Their capacity to reflect a meaningful variety of patterns as expected from neural computation in relation to cognitive processes remains debated. The relative weights of time-varying local neurophysiological dynamics versus static structural connectivity (SC) in the generation of FC as measured remains unsettled. Empirical evidence features mixed results: from little to significant FC variability and correlation with cognitive functions, within and between participants. We used a unified approach combining multivariate analysis, bootstrap and computational modeling to characterize the potential variety of patterns of FC and SC both qualitatively and quantitatively. Empirical data and simulations from generative models with different dynamical behaviors demonstrated, largely irrespective of FC metrics, that a linear subspace with dimension one or two could explain much of the variability across patterns of FC. On the contrary, the variability across BOLD time-courses could not be reduced to such a small subspace. FC appeared to strongly reflect SC and to be partly governed by a Gaussian process. The main differences between simulated and empirical data related to limitations of DWI-based SC estimation (and SC itself could then be estimated from FC). Above and beyond the limited dynamical range of the BOLD signal itself, measures of FC may offer a degenerate representation of brain interactions, with limited access to the underlying complexity. They feature an invariant common core, reflecting the channel capacity of the network as conditioned by SC, with a limited, though perhaps meaningful residual variability. PMID:27736900
Filter design for molecular factor computing using wavelet functions.
Li, Xiaoyong; Xu, Zhihong; Cai, Wensheng; Shao, Xueguang
2015-06-23
Molecular factor computing (MFC) is a new strategy that employs chemometric methods in an optical instrument to obtain analytical results directly using an appropriate filter without data processing. In the present contribution, a method for designing an MFC filter using wavelet functions was proposed for spectroscopic analysis. In this method, the MFC filter is designed as a linear combination of a set of wavelet functions. A multiple linear regression model relating the concentration to the wavelet coefficients is constructed, so that the wavelet coefficients are obtained by projecting the spectra onto the selected wavelet functions. These wavelet functions are selected by optimizing the model using a genetic algorithm (GA). Once the MFC filter is obtained, the concentration of a sample can be calculated directly by projecting the spectrum onto the filter. With three NIR datasets of corn, wheat and blood, it was shown that the performance of the designed filter is better than that of the optimized partial least squares models, and commonly used signal processing methods, such as background correction and variable selection, were not needed. More importantly, the designed filter can be used as an MFC filter in designing MFC-based instruments.
Stochl, Jan; Jones, Peter B; Croudace, Tim J
2012-06-11
Mokken scaling techniques are a useful tool for researchers who wish to construct unidimensional tests or use questionnaires that comprise multiple binary or polytomous items. The stochastic cumulative scaling model offered by this approach is ideally suited when the intention is to score an underlying latent trait by simple addition of the item response values. In our experience, the Mokken model appears to be less well-known than for example the (related) Rasch model, but is seeing increasing use in contemporary clinical research and public health. Mokken's method is a generalisation of Guttman scaling that can assist in the determination of the dimensionality of tests or scales, and enables consideration of reliability, without reliance on Cronbach's alpha. This paper provides a practical guide to the application and interpretation of this non-parametric item response theory method in empirical research with health and well-being questionnaires. Scalability of data from 1) a cross-sectional health survey (the Scottish Health Education Population Survey) and 2) a general population birth cohort study (the National Child Development Study) illustrate the method and modeling steps for dichotomous and polytomous items respectively. The questionnaire data analyzed comprise responses to the 12 item General Health Questionnaire, under the binary recoding recommended for screening applications, and the ordinal/polytomous responses to the Warwick-Edinburgh Mental Well-being Scale. After an initial analysis example in which we select items by phrasing (six positive versus six negatively worded items) we show that all items from the 12-item General Health Questionnaire (GHQ-12)--when binary scored--were scalable according to the double monotonicity model, in two short scales comprising six items each (Bech's "well-being" and "distress" clinical scales). An illustration of ordinal item analysis confirmed that all 14 positively worded items of the Warwick-Edinburgh Mental
2012-01-01
Background Mokken scaling techniques are a useful tool for researchers who wish to construct unidimensional tests or use questionnaires that comprise multiple binary or polytomous items. The stochastic cumulative scaling model offered by this approach is ideally suited when the intention is to score an underlying latent trait by simple addition of the item response values. In our experience, the Mokken model appears to be less well-known than for example the (related) Rasch model, but is seeing increasing use in contemporary clinical research and public health. Mokken's method is a generalisation of Guttman scaling that can assist in the determination of the dimensionality of tests or scales, and enables consideration of reliability, without reliance on Cronbach's alpha. This paper provides a practical guide to the application and interpretation of this non-parametric item response theory method in empirical research with health and well-being questionnaires. Methods Scalability of data from 1) a cross-sectional health survey (the Scottish Health Education Population Survey) and 2) a general population birth cohort study (the National Child Development Study) illustrate the method and modeling steps for dichotomous and polytomous items respectively. The questionnaire data analyzed comprise responses to the 12 item General Health Questionnaire, under the binary recoding recommended for screening applications, and the ordinal/polytomous responses to the Warwick-Edinburgh Mental Well-being Scale. Results and conclusions After an initial analysis example in which we select items by phrasing (six positive versus six negatively worded items) we show that all items from the 12-item General Health Questionnaire (GHQ-12) – when binary scored – were scalable according to the double monotonicity model, in two short scales comprising six items each (Bech’s “well-being” and “distress” clinical scales). An illustration of ordinal item analysis confirmed that all 14
Couvy-Duchesne, Baptiste; Davenport, Tracey A; Martin, Nicholas G; Wright, Margaret J; Hickie, Ian B
2017-08-01
The Somatic and Psychological HEalth REport (SPHERE) is a 34-item self-report questionnaire that assesses symptoms of mental distress and persistent fatigue. As it was developed as a screening instrument for use mainly in primary care-based clinical settings, its validity and psychometric properties have not been studied extensively in population-based samples. We used non-parametric Item Response Theory to assess scale validity and item properties of the SPHERE-34 scales, collected through four waves of the Brisbane Longitudinal Twin Study (N = 1707, mean age = 12, 51% females; N = 1273, mean age = 14, 50% females; N = 1513, mean age = 16, 54% females, N = 1263, mean age = 18, 56% females). We estimated the heritability of the new scores, their genetic correlation, and their predictive ability in a sub-sample (N = 1993) who completed the Composite International Diagnostic Interview. After excluding items most responsible for noise, sex or wave bias, the SPHERE-34 questionnaire was reduced to 21 items (SPHERE-21), comprising a 14-item scale for anxiety-depression and a 10-item scale for chronic fatigue (3 items overlapping). These new scores showed high internal consistency (alpha > 0.78), moderate three months reliability (ICC = 0.47-0.58) and item scalability (Hi > 0.23), and were positively correlated (phenotypic correlations r = 0.57-0.70; rG = 0.77-1.00). Heritability estimates ranged from 0.27 to 0.51. In addition, both scores were associated with later DSM-IV diagnoses of MDD, social anxiety and alcohol dependence (OR in 1.23-1.47). Finally, a post-hoc comparison showed that several psychometric properties of the SPHERE-21 were similar to those of the Beck Depression Inventory. The scales of SPHERE-21 measure valid and comparable constructs across sex and age groups (from 9 to 28 years). SPHERE-21 scores are heritable, genetically correlated and show good predictive ability of mental health in an Australian-based population
NASA Astrophysics Data System (ADS)
Ruppin, F.; Adam, R.; Comis, B.; Ade, P.; André, P.; Arnaud, M.; Beelen, A.; Benoît, A.; Bideaud, A.; Billot, N.; Bourrion, O.; Calvo, M.; Catalano, A.; Coiffard, G.; D'Addabbo, A.; De Petris, M.; Désert, F.-X.; Doyle, S.; Goupy, J.; Kramer, C.; Leclercq, S.; Macías-Pérez, J. F.; Mauskopf, P.; Mayet, F.; Monfardini, A.; Pajot, F.; Pascale, E.; Perotto, L.; Pisano, G.; Pointecouteau, E.; Ponthieu, N.; Pratt, G. W.; Revéret, V.; Ritacco, A.; Rodriguez, L.; Romero, C.; Schuster, K.; Sievers, A.; Triqueneaux, S.; Tucker, C.; Zylka, R.
2017-01-01
The determination of the thermodynamic properties of clusters of galaxies at intermediate and high redshift can bring new insights into the formation of large-scale structures. It is essential for a robust calibration of the mass-observable scaling relations and their scatter, which are key ingredients for precise cosmology using cluster statistics. Here we illustrate an application of high resolution (<20 arcsec) thermal Sunyaev-Zel'dovich (tSZ) observations by probing the intracluster medium (ICM) of the Planck-discovered galaxy cluster PSZ1 G045.85+57.71 at redshift z = 0.61, using tSZ data obtained with the NIKA camera, which is a dual-band (150 and 260 GHz) instrument operated at the IRAM 30-m telescope. We deproject jointly NIKA and Planck data to extract the electronic pressure distribution from the cluster core (R 0.02 R500) to its outskirts (R 3 R500) non-parametrically for the first time at intermediate redshift. The constraints on the resulting pressure profile allow us to reduce the relative uncertainty on the integrated Compton parameter by a factor of two compared to the Planck value. Combining the tSZ data and the deprojected electronic density profile from XMM-Newton allows us to undertake a hydrostatic mass analysis, for which we study the impact of a spherical model assumption on the total mass estimate. We also investigate the radial temperature and entropy distributions. These data indicate that PSZ1 G045.85+57.71 is a massive (M500 5.5 × 1014M⊙) cool-core cluster. This work is part of a pilot study aiming at optimizing the treatment of the NIKA2 tSZ large program dedicated to the follow-up of SZ-discovered clusters at intermediate and high redshifts. This study illustrates the potential of NIKA2 to put constraints on thethermodynamic properties and tSZ-scaling relations of these clusters, and demonstrates the excellent synergy between tSZ and X-ray observations of similar angular resolution.
Dismuke, C E; Sena, V
1999-05-01
The use of Diagnosis Related Groups (DRG) as a mechanism for hospital financing is a currently debated topic in Portugal. The DRG system was scheduled to be initiated by the Health Ministry of Portugal on January 1, 1990 as an instrument for the allocation of public hospital budgets funded by the National Health Service (NHS), and as a method of payment for other third party payers (e.g., Public Employees (ADSE), private insurers, etc.). Based on experience from other countries such as the United States, it was expected that implementation of this system would result in more efficient hospital resource utilisation and a more equitable distribution of hospital budgets. However, in order to minimise the potentially adverse financial impact on hospitals, the Portuguese Health Ministry decided to gradually phase in the use of the DRG system for budget allocation by using blended hospital-specific and national DRG case-mix rates. Since implementation in 1990, the percentage of each hospital's budget based on hospital specific costs was to decrease, while the percentage based on DRG case-mix was to increase. This was scheduled to continue until 1995 when the plan called for allocating yearly budgets on a 50% national and 50% hospital-specific cost basis. While all other non-NHS third party payers are currently paying based on DRGs, the adoption of DRG case-mix as a National Health Service budget setting tool has been slower than anticipated. There is now some argument in both the political and academic communities as to the appropriateness of DRGs as a budget setting criterion as well as to their impact on hospital efficiency in Portugal. This paper uses a two-stage procedure to assess the impact of actual DRG payment on the productivity (through its components, i.e., technological change and technical efficiency change) of diagnostic technology in Portuguese hospitals during the years 1992-1994, using both parametric and non-parametric frontier models. We find evidence
Assessing executive function using a computer game: computational modeling of cognitive processes.
Hagler, Stuart; Jimison, Holly Brugge; Pavel, Misha
2014-07-01
Early and reliable detection of cognitive decline is one of the most important challenges of current healthcare. In this project, we developed an approach whereby a frequently played computer game can be used to assess a variety of cognitive processes and estimate the results of the pen-and-paper trail making test (TMT)--known to measure executive function, as well as visual pattern recognition, speed of processing, working memory, and set-switching ability. We developed a computational model of the TMT based on a decomposition of the test into several independent processes, each characterized by a set of parameters that can be estimated from play of a computer game designed to resemble the TMT. An empirical evaluation of the model suggests that it is possible to use the game data to estimate the parameters of the underlying cognitive processes and using the values of the parameters to estimate the TMT performance. Cognitive measures and trends in these measures can be used to identify individuals for further assessment, to provide a mechanism for improving the early detection of neurological problems, and to provide feedback and monitoring for cognitive interventions in the home.
Computer Modeling of Protocellular Functions: Peptide Insertion in Membranes
NASA Technical Reports Server (NTRS)
Rodriquez-Gomez, D.; Darve, E.; Pohorille, A.
2006-01-01
Lipid vesicles became the precursors to protocells by acquiring the capabilities needed to survive and reproduce. These include transport of ions, nutrients and waste products across cell walls and capture of energy and its conversion into a chemically usable form. In modem organisms these functions are carried out by membrane-bound proteins (about 30% of the genome codes for this kind of proteins). A number of properties of alpha-helical peptides suggest that their associations are excellent candidates for protobiological precursors of proteins. In particular, some simple a-helical peptides can aggregate spontaneously and form functional channels. This process can be described conceptually by a three-step thermodynamic cycle: 1 - folding of helices at the water-membrane interface, 2 - helix insertion into the lipid bilayer and 3 - specific interactions of these helices that result in functional tertiary structures. Although a crucial step, helix insertion has not been adequately studied because of the insolubility and aggregation of hydrophobic peptides. In this work, we use computer simulation methods (Molecular Dynamics) to characterize the energetics of helix insertion and we discuss its importance in an evolutionary context. Specifically, helices could self-assemble only if their interactions were sufficiently strong to compensate the unfavorable Free Energy of insertion of individual helices into membranes, providing a selection mechanism for protobiological evolution.
An Evolutionary Computation Approach to Examine Functional Brain Plasticity.
Roy, Arnab; Campbell, Colin; Bernier, Rachel A; Hillary, Frank G
2016-01-01
One common research goal in systems neurosciences is to understand how the functional relationship between a pair of regions of interest (ROIs) evolves over time. Examining neural connectivity in this way is well-suited for the study of developmental processes, learning, and even in recovery or treatment designs in response to injury. For most fMRI based studies, the strength of the functional relationship between two ROIs is defined as the correlation between the average signal representing each region. The drawback to this approach is that much information is lost due to averaging heterogeneous voxels, and therefore, the functional relationship between a ROI-pair that evolve at a spatial scale much finer than the ROIs remain undetected. To address this shortcoming, we introduce a novel evolutionary computation (EC) based voxel-level procedure to examine functional plasticity between an investigator defined ROI-pair by simultaneously using subject-specific BOLD-fMRI data collected from two sessions seperated by finite duration of time. This data-driven procedure detects a sub-region composed of spatially connected voxels from each ROI (a so-called sub-regional-pair) such that the pair shows a significant gain/loss of functional relationship strength across the two time points. The procedure is recursive and iteratively finds all statistically significant sub-regional-pairs within the ROIs. Using this approach, we examine functional plasticity between the default mode network (DMN) and the executive control network (ECN) during recovery from traumatic brain injury (TBI); the study includes 14 TBI and 12 healthy control subjects. We demonstrate that the EC based procedure is able to detect functional plasticity where a traditional averaging based approach fails. The subject-specific plasticity estimates obtained using the EC-procedure are highly consistent across multiple runs. Group-level analyses using these plasticity estimates showed an increase in the strength
An Evolutionary Computation Approach to Examine Functional Brain Plasticity
Roy, Arnab; Campbell, Colin; Bernier, Rachel A.; Hillary, Frank G.
2016-01-01
One common research goal in systems neurosciences is to understand how the functional relationship between a pair of regions of interest (ROIs) evolves over time. Examining neural connectivity in this way is well-suited for the study of developmental processes, learning, and even in recovery or treatment designs in response to injury. For most fMRI based studies, the strength of the functional relationship between two ROIs is defined as the correlation between the average signal representing each region. The drawback to this approach is that much information is lost due to averaging heterogeneous voxels, and therefore, the functional relationship between a ROI-pair that evolve at a spatial scale much finer than the ROIs remain undetected. To address this shortcoming, we introduce a novel evolutionary computation (EC) based voxel-level procedure to examine functional plasticity between an investigator defined ROI-pair by simultaneously using subject-specific BOLD-fMRI data collected from two sessions seperated by finite duration of time. This data-driven procedure detects a sub-region composed of spatially connected voxels from each ROI (a so-called sub-regional-pair) such that the pair shows a significant gain/loss of functional relationship strength across the two time points. The procedure is recursive and iteratively finds all statistically significant sub-regional-pairs within the ROIs. Using this approach, we examine functional plasticity between the default mode network (DMN) and the executive control network (ECN) during recovery from traumatic brain injury (TBI); the study includes 14 TBI and 12 healthy control subjects. We demonstrate that the EC based procedure is able to detect functional plasticity where a traditional averaging based approach fails. The subject-specific plasticity estimates obtained using the EC-procedure are highly consistent across multiple runs. Group-level analyses using these plasticity estimates showed an increase in the strength
Using computational biophysics to understand protein evolution and function
NASA Astrophysics Data System (ADS)
Ytreberg, F. Marty
2010-10-01
Understanding how proteins evolve and function is vital for human health (e.g., developing better drugs, predicting the outbreak of disease, etc.). In spite of its importance, little is known about the underlying molecular mechanisms behind these biological processes. Computational biophysics has emerged as a useful tool in this area due to its unique ability to obtain a detailed, atomistic view of proteins and how they interact. I will give two examples from our studies where computational biophysics has provided valuable insight: (i) Protein evolution in viruses. Our results suggest that the amino acid changes that occur during high temperature evolution of a virus decrease the binding free energy of the capsid, i.e., these changes increase capsid stability. (ii) Determining realistic structural ensembles for intrinsically disordered proteins. Most methods for determining protein structure rely on the protein folding into a single conformation, and thus are not suitable for disordered proteins. I will describe a new approach that combines experiment and simulation to generate structures for disordered proteins.
Computational Effective Fault Detection by Means of Signature Functions
Baranski, Przemyslaw; Pietrzak, Piotr
2016-01-01
The paper presents a computationally effective method for fault detection. A system’s responses are measured under healthy and ill conditions. These signals are used to calculate so-called signature functions that create a signal space. The current system’s response is projected into this space. The signal location in this space easily allows to determine the fault. No classifier such as a neural network, hidden Markov models, etc. is required. The advantage of this proposed method is its efficiency, as computing projections amount to calculating dot products. Therefore, this method is suitable for real-time embedded systems due to its simplicity and undemanding processing capabilities which permit the use of low-cost hardware and allow rapid implementation. The approach performs well for systems that can be considered linear and stationary. The communication presents an application, whereby an industrial process of moulding is supervised. The machine is composed of forms (dies) whose alignment must be precisely set and maintained during the work. Typically, the process is stopped periodically to manually control the alignment. The applied algorithm allows on-line monitoring of the device by analysing the acceleration signal from a sensor mounted on a die. This enables to detect failures at an early stage thus prolonging the machine’s life. PMID:26949942
Optimizing high performance computing workflow for protein functional annotation.
Stanberry, Larissa; Rekepalli, Bhanu; Liu, Yuan; Giblock, Paul; Higdon, Roger; Montague, Elizabeth; Broomall, William; Kolker, Natali; Kolker, Eugene
2014-09-10
Functional annotation of newly sequenced genomes is one of the major challenges in modern biology. With modern sequencing technologies, the protein sequence universe is rapidly expanding. Newly sequenced bacterial genomes alone contain over 7.5 million proteins. The rate of data generation has far surpassed that of protein annotation. The volume of protein data makes manual curation infeasible, whereas a high compute cost limits the utility of existing automated approaches. In this work, we present an improved and optmized automated workflow to enable large-scale protein annotation. The workflow uses high performance computing architectures and a low complexity classification algorithm to assign proteins into existing clusters of orthologous groups of proteins. On the basis of the Position-Specific Iterative Basic Local Alignment Search Tool the algorithm ensures at least 80% specificity and sensitivity of the resulting classifications. The workflow utilizes highly scalable parallel applications for classification and sequence alignment. Using Extreme Science and Engineering Discovery Environment supercomputers, the workflow processed 1,200,000 newly sequenced bacterial proteins. With the rapid expansion of the protein sequence universe, the proposed workflow will enable scientists to annotate big genome data.
Optimizing high performance computing workflow for protein functional annotation
Stanberry, Larissa; Rekepalli, Bhanu; Liu, Yuan; Giblock, Paul; Higdon, Roger; Montague, Elizabeth; Broomall, William; Kolker, Natali; Kolker, Eugene
2014-01-01
Functional annotation of newly sequenced genomes is one of the major challenges in modern biology. With modern sequencing technologies, the protein sequence universe is rapidly expanding. Newly sequenced bacterial genomes alone contain over 7.5 million proteins. The rate of data generation has far surpassed that of protein annotation. The volume of protein data makes manual curation infeasible, whereas a high compute cost limits the utility of existing automated approaches. In this work, we present an improved and optmized automated workflow to enable large-scale protein annotation. The workflow uses high performance computing architectures and a low complexity classification algorithm to assign proteins into existing clusters of orthologous groups of proteins. On the basis of the Position-Specific Iterative Basic Local Alignment Search Tool the algorithm ensures at least 80% specificity and sensitivity of the resulting classifications. The workflow utilizes highly scalable parallel applications for classification and sequence alignment. Using Extreme Science and Engineering Discovery Environment supercomputers, the workflow processed 1,200,000 newly sequenced bacterial proteins. With the rapid expansion of the protein sequence universe, the proposed workflow will enable scientists to annotate big genome data. PMID:25313296
Imaging local brain function with emission computed tomography
Kuhl, D.E.
1984-03-01
Positron emission tomography (PET) using /sup 18/F-fluorodeoxyglucose (FDG) was used to map local cerebral glucose utilization in the study of local cerebral function. This information differs fundamentally from structural assessment by means of computed tomography (CT). In normal human volunteers, the FDG scan was used to determine the cerebral metabolic response to conrolled sensory stimulation and the effects of aging. Cerebral metabolic patterns are distinctive among depressed and demented elderly patients. The FDG scan appears normal in the depressed patient, studded with multiple metabolic defects in patients with multiple infarct dementia, and in the patients with Alzheimer disease, metabolism is particularly reduced in the parietal cortex, but only slightly reduced in the caudate and thalamus. The interictal FDG scan effectively detects hypometabolic brain zones that are sites of onset for seizures in patients with partial epilepsy, even though these zones usually appear normal on CT scans. The future prospects of PET are discussed.
The Impact of Computer Use on Learning of Quadratic Functions
ERIC Educational Resources Information Center
Pihlap, Sirje
2017-01-01
Studies of the impact of various types of computer use on the results of learning and student motivation have indicated that the use of computers can increase learning motivation, and that computers can have a positive effect, a negative effect, or no effect at all on learning outcomes. Some results indicate that it is not computer use itself that…
Garashchuk, Sophya
2007-04-21
The de Broglie-Bohm formulation of the Schrodinger equation implies conservation of the wave function probability density associated with each quantum trajectory in closed systems. This conservation property greatly simplifies numerical implementations of the quantum trajectory dynamics and increases its accuracy. The reconstruction of a wave function, however, becomes expensive or inaccurate as it requires fitting or interpolation procedures. In this paper we present a method of computing wave packet correlation functions and wave function projections, which typically contain all the desired information about dynamics, without the full knowledge of the wave function by making quadratic expansions of the wave function phase and amplitude near each trajectory similar to expansions used in semiclassical methods. Computation of the quantities of interest in this procedure is linear with respect to the number of trajectories. The introduced approximations are consistent with approximate quantum potential dynamics method. The projection technique is applied to model chemical systems and to the H+H(2) exchange reaction in three dimensions.
A computer vision based candidate for functional balance test.
Nalci, Alican; Khodamoradi, Alireza; Balkan, Ozgur; Nahab, Fatta; Garudadri, Harinath
2015-08-01
Balance in humans is a motor skill based on complex multimodal sensing, processing and control. Ability to maintain balance in activities of daily living (ADL) is compromised due to aging, diseases, injuries and environmental factors. Center for Disease Control and Prevention (CDC) estimate of the costs of falls among older adults was $34 billion in 2013 and is expected to reach $54.9 billion in 2020. In this paper, we present a brief review of balance impairments followed by subjective and objective tools currently used in clinical settings for human balance assessment. We propose a novel computer vision (CV) based approach as a candidate for functional balance test. The test will take less than a minute to administer and expected to be objective, repeatable and highly discriminative in quantifying ability to maintain posture and balance. We present an informal study with preliminary data from 10 healthy volunteers, and compare performance with a balance assessment system called BTrackS Balance Assessment Board. Our results show high degree of correlation with BTrackS. The proposed system promises to be a good candidate for objective functional balance tests and warrants further investigations to assess validity in clinical settings, including acute care, long term care and assisted living care facilities. Our long term goals include non-intrusive approaches to assess balance competence during ADL in independent living environments.
Chemical Visualization of Boolean Functions: A Simple Chemical Computer
NASA Astrophysics Data System (ADS)
Blittersdorf, R.; Müller, J.; Schneider, F. W.
1995-08-01
We present a chemical realization of the Boolean functions AND, OR, NAND, and NOR with a neutralization reaction carried out in three coupled continuous flow stirred tank reactors (CSTR). Two of these CSTR's are used as input reactors, the third reactor marks the output. The chemical reaction is the neutralization of hydrochloric acid (HCl) with sodium hydroxide (NaOH) in the presence of phenolphtalein as an indicator, which is red in alkaline solutions and colorless in acidic solutions representing the two binary states 1 and 0, respectively. The time required for a "chemical computation" is determined by the flow rate of reactant solutions into the reactors since the neutralization reaction itself is very fast. While the acid flow to all reactors is equal and constant, the flow rate of NaOH solution controls the states of the input reactors. The connectivities between the input and output reactors determine the flow rate of NaOH solution into the output reactor, according to the chosen Boolean function. Thus the state of the output reactor depends on the states of the input reactors.
Computing black hole partition functions from quasinormal modes
Arnold, Peter; Szepietowski, Phillip; Vaman, Diana
2016-07-07
We propose a method of computing one-loop determinants in black hole space-times (with emphasis on asymptotically anti-de Sitter black holes) that may be used for numerics when completely-analytic results are unattainable. The method utilizes the expression for one-loop determinants in terms of quasinormal frequencies determined by Denef, Hartnoll and Sachdev in [1]. A numerical evaluation must face the fact that the sum over the quasinormal modes, indexed by momentum and overtone numbers, is divergent. A necessary ingredient is then a regularization scheme to handle the divergent contributions of individual fixed-momentum sectors to the partition function. To this end, we formulatemore » an effective two-dimensional problem in which a natural refinement of standard heat kernel techniques can be used to account for contributions to the partition function at fixed momentum. We test our method in a concrete case by reproducing the scalar one-loop determinant in the BTZ black hole background. Furthermore, we then discuss the application of such techniques to more complicated spacetimes.« less
Computing black hole partition functions from quasinormal modes
Arnold, Peter; Szepietowski, Phillip; Vaman, Diana
2016-07-07
We propose a method of computing one-loop determinants in black hole space-times (with emphasis on asymptotically anti-de Sitter black holes) that may be used for numerics when completely-analytic results are unattainable. The method utilizes the expression for one-loop determinants in terms of quasinormal frequencies determined by Denef, Hartnoll and Sachdev in [1]. A numerical evaluation must face the fact that the sum over the quasinormal modes, indexed by momentum and overtone numbers, is divergent. A necessary ingredient is then a regularization scheme to handle the divergent contributions of individual fixed-momentum sectors to the partition function. To this end, we formulate an effective two-dimensional problem in which a natural refinement of standard heat kernel techniques can be used to account for contributions to the partition function at fixed momentum. We test our method in a concrete case by reproducing the scalar one-loop determinant in the BTZ black hole background. Furthermore, we then discuss the application of such techniques to more complicated spacetimes.
Computing black hole partition functions from quasinormal modes
NASA Astrophysics Data System (ADS)
Arnold, Peter; Szepietowski, Phillip; Vaman, Diana
2016-07-01
We propose a method of computing one-loop determinants in black hole space-times (with emphasis on asymptotically anti-de Sitter black holes) that may be used for numerics when completely-analytic results are unattainable. The method utilizes the expression for one-loop determinants in terms of quasinormal frequencies determined by Denef, Hartnoll and Sachdev in [1]. A numerical evaluation must face the fact that the sum over the quasinormal modes, indexed by momentum and overtone numbers, is divergent. A necessary ingredient is then a regularization scheme to handle the divergent contributions of individual fixed-momentum sectors to the partition function. To this end, we formulate an effective two-dimensional problem in which a natural refinement of standard heat kernel techniques can be used to account for contributions to the partition function at fixed momentum. We test our method in a concrete case by reproducing the scalar one-loop determinant in the BTZ black hole background. We then discuss the application of such techniques to more complicated spacetimes.
Goovaerts, P.
2008-01-01
Indicator kriging provides a flexible interpolation approach that is well suited for datasets where: 1) many observations are below the detection limit, 2) the histogram is strongly skewed, or 3) specific classes of attribute values are better connected in space than others (e.g. low pollutant concentrations). To apply indicator kriging at its full potential requires, however, the tedious inference and modeling of multiple indicator semivariograms, as well as the post-processing of the results to retrieve attribute estimates and associated measures of uncertainty. This paper presents a computer code that performs automatically the following tasks: selection of thresholds for binary coding of continuous data, computation and modeling of indicator semivariograms, modeling of probability distributions at unmonitored locations (regular or irregular grids), and estimation of the mean and variance of these distributions. The program also offers tools for quantifying the goodness of the model of uncertainty within a cross-validation and jack-knife frameworks. The different functionalities are illustrated using heavy metal concentrations from the well-known soil Jura dataset. A sensitivity analysis demonstrates the benefit of using more thresholds when indicator kriging is implemented with a linear interpolation model, in particular for variables with positively skewed histograms. PMID:20161335
Goovaerts, P
2009-06-01
Indicator kriging provides a flexible interpolation approach that is well suited for datasets where: 1) many observations are below the detection limit, 2) the histogram is strongly skewed, or 3) specific classes of attribute values are better connected in space than others (e.g. low pollutant concentrations). To apply indicator kriging at its full potential requires, however, the tedious inference and modeling of multiple indicator semivariograms, as well as the post-processing of the results to retrieve attribute estimates and associated measures of uncertainty. This paper presents a computer code that performs automatically the following tasks: selection of thresholds for binary coding of continuous data, computation and modeling of indicator semivariograms, modeling of probability distributions at unmonitored locations (regular or irregular grids), and estimation of the mean and variance of these distributions. The program also offers tools for quantifying the goodness of the model of uncertainty within a cross-validation and jack-knife frameworks. The different functionalities are illustrated using heavy metal concentrations from the well-known soil Jura dataset. A sensitivity analysis demonstrates the benefit of using more thresholds when indicator kriging is implemented with a linear interpolation model, in particular for variables with positively skewed histograms.
NASA Astrophysics Data System (ADS)
Goovaerts, P.
2009-06-01
Indicator kriging (IK) provides a flexible interpolation approach that is well suited for datasets where: (1) many observations are below the detection limit, (2) the histogram is strongly skewed, or (3) specific classes of attribute values are better connected in space than others (e.g. low pollutant concentrations). To apply indicator kriging at its full potential requires, however, the tedious inference and modeling of multiple indicator semivariograms, as well as the post-processing of the results to retrieve attribute estimates and associated measures of uncertainty. This paper presents a computer code that performs automatically the following tasks: selection of thresholds for binary coding of continuous data, computation and modeling of indicator semivariograms, modeling of probability distributions at unmonitored locations (regular or irregular grids), and estimation of the mean and variance of these distributions. The program also offers tools for quantifying the goodness of the model of uncertainty within a cross-validation and jack-knife frameworks. The different functionalities are illustrated using heavy metal concentrations from the well-known soil Jura dataset. A sensitivity analysis demonstrates the benefit of using more thresholds when indicator kriging is implemented with a linear interpolation model, in particular for variables with positively skewed histograms.
Carr, Steven M.; Duggan, Ana T.; Stenson, Garry B.; Marshall, H. Dawn
2015-01-01
-stone biogeographic models, but not a simple 1-step trans-Atlantic model. Plots of the cumulative pairwise sequence difference curves among seals in each of the four populations provide continuous proxies for phylogenetic diversification within each. Non-parametric Kolmogorov-Smirnov (K-S) tests of maximum pairwise differences between these curves indicates that the Greenland Sea population has a markedly younger phylogenetic structure than either the White Sea population or the two Northwest Atlantic populations, which are of intermediate age and homogeneous structure. The Monte Carlo and K-S assessments provide sensitive quantitative tests of within-species mitogenomic phylogeography. This is the first study to indicate that the White Sea and Greenland Sea populations have different population genetic histories. The analysis supports the hypothesis that Harp Seals comprises three genetically distinguishable breeding populations, in the White Sea, Greenland Sea, and Northwest Atlantic. Implications for an ice-dependent species during ongoing climate change are discussed. PMID:26301872
Carr, Steven M; Duggan, Ana T; Stenson, Garry B; Marshall, H Dawn
2015-01-01
-stone biogeographic models, but not a simple 1-step trans-Atlantic model. Plots of the cumulative pairwise sequence difference curves among seals in each of the four populations provide continuous proxies for phylogenetic diversification within each. Non-parametric Kolmogorov-Smirnov (K-S) tests of maximum pairwise differences between these curves indicates that the Greenland Sea population has a markedly younger phylogenetic structure than either the White Sea population or the two Northwest Atlantic populations, which are of intermediate age and homogeneous structure. The Monte Carlo and K-S assessments provide sensitive quantitative tests of within-species mitogenomic phylogeography. This is the first study to indicate that the White Sea and Greenland Sea populations have different population genetic histories. The analysis supports the hypothesis that Harp Seals comprises three genetically distinguishable breeding populations, in the White Sea, Greenland Sea, and Northwest Atlantic. Implications for an ice-dependent species during ongoing climate change are discussed.
A statistically-augmented computational platform for evaluating meniscal function
Guo, Hongqiang; Santner, Thomas J.; Chen, Tony; Wang, Hongsheng; Brial, Caroline; Gilbert, Susannah L.; Koff, Matthew F.; Lerner, Amy L.; Maher, Suzanne A.
2015-01-01
Meniscal implants have been developed in an attempt to provide pain relief and prevent pathological degeneration of articular cartilage. However, as yet there has been no systematic and comprehensive analysis of the effects of the meniscal design variables on meniscal function across a wide patient population, and there are no clear design criteria to ensure the functional performance of candidate meniscal implants. Our aim was to develop a statistically-augmented, experimentally-validated, computational platform to assess the effect of meniscal properties and patient variables on knee joint contact mechanics during the activity of walking. Our analysis used Finite Element Models (FEMs) that represented the geometry, kinematics as based on simulated gait and contact mechanics of three laboratory tested human cadaveric knees. The FEMs were subsequently programmed to represent prescribed meniscal variables (circumferential and radial/axial moduli - Ecm, Erm, stiffness of the meniscal attachments - Slpma, Slamp) and patient variables (varus/valgus alignment – VVA, and articular cartilage modulus - Ec). The contact mechanics data generated from the FEM runs were used as training data to a statistical interpolator which estimated joint contact data for untested configurations of input variables. Our data suggested that while Ecm and Erm of a meniscus are critical in determining knee joint mechanics in early and late stance (peak 1 and peak 3 of the gait cycle), for some knees that have greater laxity in the mid-stance phase of gait, the stiffness of the articular cartilage, Ec, can influence force distribution across the tibial plateau. We found that the medial meniscus plays a dominant load-carrying role in the early stance phase and less so in late stance, while the lateral meniscus distributes load throughout gait. Joint contact mechanics in the medial compartment are more sensitive to Ecm than those in the lateral compartment. Finally, throughout stance, varus
A statistically-augmented computational platform for evaluating meniscal function.
Guo, Hongqiang; Santner, Thomas J; Chen, Tony; Wang, Hongsheng; Brial, Caroline; Gilbert, Susannah L; Koff, Matthew F; Lerner, Amy L; Maher, Suzanne A
2015-06-01
Meniscal implants have been developed in an attempt to provide pain relief and prevent pathological degeneration of articular cartilage. However, as yet there has been no systematic and comprehensive analysis of the effects of the meniscal design variables on meniscal function across a wide patient population, and there are no clear design criteria to ensure the functional performance of candidate meniscal implants. Our aim was to develop a statistically-augmented, experimentally-validated, computational platform to assess the effect of meniscal properties and patient variables on knee joint contact mechanics during the activity of walking. Our analysis used Finite Element Models (FEMs) that represented the geometry, kinematics as based on simulated gait and contact mechanics of three laboratory tested human cadaveric knees. The FEMs were subsequently programmed to represent prescribed meniscal variables (circumferential and radial/axial moduli-Ecm, Erm, stiffness of the meniscal attachments-Slpma, Slamp) and patient variables (varus/valgus alignment-VVA, and articular cartilage modulus-Ec). The contact mechanics data generated from the FEM runs were used as training data to a statistical interpolator which estimated joint contact data for untested configurations of input variables. Our data suggested that while Ecm and Erm of a meniscus are critical in determining knee joint mechanics in early and late stance (peak 1 and peak 3 of the gait cycle), for some knees that have greater laxity in the mid-stance phase of gait, the stiffness of the articular cartilage, Ec, can influence force distribution across the tibial plateau. We found that the medial meniscus plays a dominant load-carrying role in the early stance phase and less so in late stance, while the lateral meniscus distributes load throughout gait. Joint contact mechanics in the medial compartment are more sensitive to Ecm than those in the lateral compartment. Finally, throughout stance, varus
Computer simulations of crystal structures using density functional theory
NASA Astrophysics Data System (ADS)
Zeng, Yueping
During past several decades, first principles pseudopotential methods based on density functional theory have provided the basis for the majority of first-principles calculations of the ground state electronic properties of a wide variety of condensed matter systems. However, as the numerical accuracy of these calculations has improved, it has become apparent that there are some sizable discrepancies between the calculations and the experimental measurements of the ground state properties of many of these materials. Part of the difficulty comes in determining the form of the exchange-correlation interaction, the local-density approximation (LDA) and generalized-gradient approximation (GGA) being the most common forms. It is important to explore the limits of density-functional theory and of the LDA and GGA forms of the exchange-correlation functional. Another difficulty lies in determining the accuracy of the frozen core approximation which is the basis of first-principles pseudopotential methods. In this context, simulations of the transition metal material FeSsb2 and of the SiC (100) surface are very important. This thesis describes an effort to simulate the ground state properties of materials using density functional theory (DFT) with focus on investigation of the exchange-correlation functional and the frozen-core approximation. Comparison of the calculations with experiments for FeSsb2 and for the SiC (100) surface using first-principles pseudopotential and the all-electron linearized-augmented-plane-wave (LAPW) methods are analyzed. Advantages of using the recently developed new projector-augmented-wave (PAW) ideas, which reduce the gap between the all-electron LAPW and first-principles pseudopotential methods are discussed. Test results for bulk materials silicon, diamond, and SiC are presented. Analysis of the reliability of the frozen-core approximation and of the pseudopotential theory for the cohesive energy indicates that for materials containing the
Computer Center CDC Libraries.
1984-06-01
SCALE STATISTICS G8 NON-PARAMETRIC METHODS AND STATISTICAL TESTS G9 STATISTICAL INFERENCE q JUNE 1984 CDC CYBER PAGE 1-4 HO OPERATIONS RESEARCH...PREPROCESSING L5 DISASSEMBLY AND DERELATIVIZING • L6 RELATIVIZING L7 COMPUTER LANGUAGE TRANSLATORS MO DATA HANDLING Mi SORTING * M2 CONVERSION AND/OR SCALING M3...PBETA IS/MI XIRAND IS/MI/ 9JUNE 1984 CDC CYBER G7-19 PAGE 1-16 G7 MULTIVARIATE ANALYSIS AND SCALE STATISTICS AFACT /S/Il/ CANCORR/DISI / OFHARR IS/l
Enhancing functionality and performance in the PVM network computing system
Sunderam, V.
1996-09-01
The research funded by this grant is part of an ongoing research project in heterogeneous distributed computing with the PVM system, at Emory as well as at Oak Ridge Labs and the University of Tennessee. This grant primarily supports research at Emory that continues to evolve new concepts and systems in distributed computing, but it also includes the PI`s ongoing interaction with the other groups in terms of collaborative research as well as software systems development and maintenance. We have continued our second year efforts (July 1995 - June 1996), on the same topics as during the first year, namely (a) visualization of PVM programs to complement XPVM displays; (b) I/O and generalized distributed computing in PVM; and (c) evolution of a multithreaded concurrent computing model. 12 refs.
NASA Technical Reports Server (NTRS)
Kennedy, J. R.; Fitzpatrick, W. S.
1971-01-01
The computer executive functional system design concepts derived from study of the Space Station/Base are presented. Information Management System hardware configuration as directly influencing the executive design is reviewed. The hardware configuration and generic executive design requirements are considered in detail in a previous report (System Configuration and Executive Requirements Specifications for Reusable Shuttle and Space Station/Base, 9/25/70). This report defines basic system primitives and delineates processes and process control. Supervisor states are considered for describing basic multiprogramming and multiprocessing systems. A high-level computer executive including control of scheduling, allocation of resources, system interactions, and real-time supervisory functions is defined. The description is oriented to provide a baseline for a functional simulation of the computer executive system.
Accurate Computation of Divided Differences of the Exponential Function,
1983-06-01
compute A is by its Taylor series A = Aexp = e = Because of the special structure of Z,. there is an extremely elegant algo- rithm for the first row...of steps 2 and 3. In general we cannot avoid using a 2-dimensioned array to form Fe unless F has some special structure . 2.4.2. Back filling the... structure of Z, will be des- troyed by the reduction and therefore some modifications of the algorithm TS are needed. The work for the whole computation
NASA Technical Reports Server (NTRS)
Curran, R. T.; Hornfeck, W. A.
1972-01-01
The functional requirements for the design of an interpretive simulator for the space ultrareliable modular computer (SUMC) are presented. A review of applicable existing computer simulations is included along with constraints on the SUMC simulator functional design. Input requirements, output requirements, and language requirements for the simulator are discussed in terms of a SUMC configuration which may vary according to the application.
Dose spread functions in computed tomography: A Monte Carlo study
Boone, John M.
2009-10-15
Purpose: Current CT dosimetry employing CTDI methodology has come under fire in recent years, partially in response to the increasing width of collimated x-ray fields in modern CT scanners. This study was conducted to provide a better understanding of the radiation dose distributions in CT. Methods: Monte Carlo simulations were used to evaluate radiation dose distributions along the z axis arising from CT imaging in cylindrical phantoms. Mathematical cylinders were simulated with compositions of water, polymethyl methacrylate (PMMA), and polyethylene. Cylinder diameters from 10 to 50 cm were studied. X-ray spectra typical of several CT manufacturers (80, 100, 120, and 140 kVp) were used. In addition to no bow tie filter, the head and body bow tie filters from modern General Electric and Siemens CT scanners were evaluated. Each cylinder was divided into three concentric regions of equal volume such that the energy deposited is proportional to dose for each region. Two additional dose assessment regions, central and edge locations 10 mm in diameter, were included for comparisons to CTDI{sub 100} measurements. Dose spread functions (DSFs) were computed for a wide number of imaging parameters. Results: DSFs generally exhibit a biexponential falloff from the z=0 position. For a very narrow primary beam input (<<1 mm), DSFs demonstrated significant low amplitude long range scatter dose tails. For body imaging conditions (30 cm diameter in water), the DSF at the center showed {approx}160 mm at full width at tenth maximum (FWTM), while at the edge the FWTM was {approx}80 mm. Polyethylene phantoms exhibited wider DSFs than PMMA or water, as did higher tube voltages in any material. The FWTM were 80, 180, and 250 mm for 10, 30, and 50 cm phantom diameters, respectively, at the center in water at 120 kVp with a typical body bow tie filter. Scatter to primary dose ratios (SPRs) increased with phantom diameter from 4 at the center (1 cm diameter) for a 16 cm diameter cylinder
Recursive Definitions of Partial Functions and Their Computations
1972-03-01
allows ist fa iliir limplifIcetion rules, such as: fa tor the iipquentiel ’it-then- elee ’ connective! ’ :J_ 1 then A...B If »ic Now, if*then* elee ’ only has one x-sct in g, namelv ;1,’j. This means intuitively that computing in
Reading and Flowcharting: Interfacing Functions of Computer Literacy.
ERIC Educational Resources Information Center
Wepner, Shelley B.
Flowcharting, a skill used to program computers, can be used to teach reading skills. Like programing, flowcharting requires knowledge of a particular content area and an understanding of how to process the information. Skills such as identifying the main idea and supporting details, sequencing ideas or statements, and distinguishing relevant from…
Computer routines for probability distributions, random numbers, and related functions
Kirby, W.
1983-01-01
Use of previously coded and tested subroutines simplifies and speeds up program development and testing. This report presents routines that can be used to calculate various probability distributions and other functions of importance in statistical hydrology. The routines are designed as general-purpose Fortran subroutines and functions to be called from user-written main progress. The probability distributions provided include the beta, chi-square, gamma, Gaussian (normal), Pearson Type III (tables and approximation), and Weibull. Also provided are the distributions of the Grubbs-Beck outlier test, Kolmogorov 's and Smirnov 's D, Student 's t, noncentral t (approximate), and Snedecor F. Other mathematical functions include the Bessel function, I sub o, gamma and log-gamma functions, error functions, and exponential integral. Auxiliary services include sorting and printer-plotting. Random number generators for uniform and normal numbers are provided and may be used with some of the above routines to generate numbers from other distributions. (USGS)
Computer routines for probability distributions, random numbers, and related functions
Kirby, W.H.
1980-01-01
Use of previously codes and tested subroutines simplifies and speeds up program development and testing. This report presents routines that can be used to calculate various probability distributions and other functions of importance in statistical hydrology. The routines are designed as general-purpose Fortran subroutines and functions to be called from user-written main programs. The probability distributions provided include the beta, chisquare, gamma, Gaussian (normal), Pearson Type III (tables and approximation), and Weibull. Also provided are the distributions of the Grubbs-Beck outlier test, Kolmogorov 's and Smirnov 's D, Student 's t, noncentral t (approximate), and Snedecor F tests. Other mathematical functions include the Bessel function I (subzero), gamma and log-gamma functions, error functions and exponential integral. Auxiliary services include sorting and printer plotting. Random number generators for uniform and normal numbers are provided and may be used with some of the above routines to generate numbers from other distributions. (USGS)
NASA Astrophysics Data System (ADS)
Francisco, E.; Pendás, A. Martín; Blanco, M. A.
2008-04-01
Given an N-electron molecule and an exhaustive partition of the real space ( R) into m arbitrary regions Ω,Ω,…,Ω ( ⋃i=1mΩ=R), the edf program computes all the probabilities P(n,n,…,n) of having exactly n electrons in Ω, n electrons in Ω,…, and n electrons ( n+n+⋯+n=N) in Ω. Each Ω may correspond to a single basin (atomic domain) or several such basins (functional group). In the later case, each atomic domain must belong to a single Ω. The program can manage both single- and multi-determinant wave functions which are read in from an aimpac-like wave function description ( .wfn) file (T.A. Keith et al., The AIMPAC95 programs, http://www.chemistry.mcmaster.ca/aimpac, 1995). For multi-determinantal wave functions a generalization of the original .wfn file has been introduced. The new format is completely backwards compatible, adding to the previous structure a description of the configuration interaction (CI) coefficients and the determinants of correlated wave functions. Besides the .wfn file, edf only needs the overlap integrals over all the atomic domains between the molecular orbitals (MO). After the P(n,n,…,n) probabilities are computed, edf obtains from them several magnitudes relevant to chemical bonding theory, such as average electronic populations and localization/delocalization indices. Regarding spin, edf may be used in two ways: with or without a splitting of the P(n,n,…,n) probabilities into α and β spin components. Program summaryProgram title: edf Catalogue identifier: AEAJ_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEAJ_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.html No. of lines in distributed program, including test data, etc.: 5387 No. of bytes in distributed program, including test data, etc.: 52 381 Distribution format: tar.gz Programming language: Fortran 77 Computer
Introduction to Classical Density Functional Theory by a Computational Experiment
ERIC Educational Resources Information Center
Jeanmairet, Guillaume; Levy, Nicolas; Levesque, Maximilien; Borgis, Daniel
2014-01-01
We propose an in silico experiment to introduce the classical density functional theory (cDFT). Density functional theories, whether quantum or classical, rely on abstract concepts that are nonintuitive; however, they are at the heart of powerful tools and active fields of research in both physics and chemistry. They led to the 1998 Nobel Prize in…
On the numerical computation of the Mittag-Leffler function
NASA Astrophysics Data System (ADS)
Valério, Duarte; Tenreiro Machado, José
2014-10-01
Recently simple limiting functions establishing upper and lower bounds on the Mittag-Leffler function were found. This paper follows those expressions to design an efficient algorithm for the approximate calculation of expressions usual in fractional-order control systems. The numerical experiments demonstrate the superior efficiency of the proposed method.
Introduction to Classical Density Functional Theory by a Computational Experiment
ERIC Educational Resources Information Center
Jeanmairet, Guillaume; Levy, Nicolas; Levesque, Maximilien; Borgis, Daniel
2014-01-01
We propose an in silico experiment to introduce the classical density functional theory (cDFT). Density functional theories, whether quantum or classical, rely on abstract concepts that are nonintuitive; however, they are at the heart of powerful tools and active fields of research in both physics and chemistry. They led to the 1998 Nobel Prize in…
A brain-computer interface to support functional recovery.
Kjaer, Troels W; Sørensen, Helge B
2013-01-01
Brain-computer interfaces (BCI) register changes in brain activity and utilize this to control computers. The most widely used method is based on registration of electrical signals from the cerebral cortex using extracranially placed electrodes also called electroencephalography (EEG). The features extracted from the EEG may, besides controlling the computer, also be fed back to the patient for instance as visual input. This facilitates a learning process. BCI allow us to utilize brain activity in the rehabilitation of patients after stroke. The activity of the cerebral cortex varies with the type of movement we imagine, and by letting the patient know the type of brain activity best associated with the intended movement the rehabilitation process may be faster and more efficient. The focus of BCI utilization in medicine has changed in recent years. While we previously focused on devices facilitating communication in the rather few patients with locked-in syndrome, much interest is now devoted to the therapeutic use of BCI in rehabilitation. For this latter group of patients, the device is not intended to be a lifelong assistive companion but rather a 'teacher' during the rehabilitation period. Copyright © 2013 S. Karger AG, Basel.
Computer Corner: Spreadsheets, Power Series, Generating Functions, and Integers.
ERIC Educational Resources Information Center
Snow, Donald R.
1989-01-01
Implements a table algorithm on a spreadsheet program and obtains functions for several number sequences such as the Fibonacci and Catalan numbers. Considers other applications of the table algorithm to integers represented in various number bases. (YP)
Multiple multiresolution representation of functions and calculus for fast computation
Fann, George I; Harrison, Robert J; Hill, Judith C; Jia, Jun; Galindo, Diego A
2010-01-01
We describe the mathematical representations, data structure and the implementation of the numerical calculus of functions in the software environment multiresolution analysis environment for scientific simulations, MADNESS. In MADNESS, each smooth function is represented using an adaptive pseudo-spectral expansion using the multiwavelet basis to a arbitrary but finite precision. This is an extension of the capabilities of most of the existing net, mesh and spectral based methods where the discretization is based on a single adaptive mesh, or expansions.
Evaluation of computing systems using functionals of a Stochastic process
NASA Technical Reports Server (NTRS)
Meyer, J. F.; Wu, L. T.
1980-01-01
An intermediate model was used to represent the probabilistic nature of a total system at a level which is higher than the base model and thus closer to the performance variable. A class of intermediate models, which are generally referred to as functionals of a Markov process, were considered. A closed form solution of performability for the case where performance is identified with the minimum value of a functional was developed.
COMPUTATIONAL STRATEGIES FOR THE DESIGN OF NEW ENZYMATIC FUNCTIONS
Świderek, K; Tuñón, I.; Moliner, V.; Bertran, J.
2015-01-01
In this contribution, recent developments in the design of biocatalysts are reviewed with particular emphasis in the de novo strategy. Studies based on three different reactions, Kemp elimination, Diels-Alder and retro-aldolase, are used to illustrate different success achieved during the last years. Finally, a section is devoted to the particular case of designed metalloenzymes. As a general conclusion, the interplay between new and more sophisticated engineering protocols and computational methods, based on molecular dynamics simulations with Quantum Mechanics/Molecular Mechanics potentials and fully flexible models, seems to constitute the bed rock for present and future successful design strategies. PMID:25797438
Computational strategies for the design of new enzymatic functions.
Świderek, K; Tuñón, I; Moliner, V; Bertran, J
2015-09-15
In this contribution, recent developments in the design of biocatalysts are reviewed with particular emphasis in the de novo strategy. Studies based on three different reactions, Kemp elimination, Diels-Alder and Retro-Aldolase, are used to illustrate different success achieved during the last years. Finally, a section is devoted to the particular case of designed metalloenzymes. As a general conclusion, the interplay between new and more sophisticated engineering protocols and computational methods, based on molecular dynamics simulations with Quantum Mechanics/Molecular Mechanics potentials and fully flexible models, seems to constitute the bed rock for present and future successful design strategies.
A Functional Level Preprocessor for Computer Aided Digital Design.
1980-12-01
the parsing of ucer input, is based on that for the computer language, PASCAL [J2,1J. The procedure is tle author’s original design Each line of input...NIKLAUS WIR~iN. PASCAL -USER:I MANUAL AmD REPORT. NEW YORK, NY: SPRINGER-VERLAG 1978 Li LANCAST17R, DOIN. CMOS CoORBOLK(A. IND)IANAPOLIS, IND): HOWAI(D...34flGS 0151, OtTAll me:;genera ted by SISL, duri -ne iti; last run. Each message is of the foriiat: SutiWur Uk ;L ATLNG M1:SSA(;lE-, )’URIAT NUMBELR, and
Bread dough rheology: Computing with a damage function model
NASA Astrophysics Data System (ADS)
Tanner, Roger I.; Qi, Fuzhong; Dai, Shaocong
2015-01-01
We describe an improved damage function model for bread dough rheology. The model has relatively few parameters, all of which can easily be found from simple experiments. Small deformations in the linear region are described by a gel-like power-law memory function. A set of large non-reversing deformations - stress relaxation after a step of shear, steady shearing and elongation beginning from rest, and biaxial stretching, is used to test the model. With the introduction of a revised strain measure which includes a Mooney-Rivlin term, all of these motions can be well described by the damage function described in previous papers. For reversing step strains, larger amplitude oscillatory shearing and recoil reasonable predictions have been found. The numerical methods used are discussed and we give some examples.
Efficient and Flexible Computation of Many-Electron Wave Function Overlaps
2016-01-01
A new algorithm for the computation of the overlap between many-electron wave functions is described. This algorithm allows for the extensive use of recurring intermediates and thus provides high computational efficiency. Because of the general formalism employed, overlaps can be computed for varying wave function types, molecular orbitals, basis sets, and molecular geometries. This paves the way for efficiently computing nonadiabatic interaction terms for dynamics simulations. In addition, other application areas can be envisaged, such as the comparison of wave functions constructed at different levels of theory. Aside from explaining the algorithm and evaluating the performance, a detailed analysis of the numerical stability of wave function overlaps is carried out, and strategies for overcoming potential severe pitfalls due to displaced atoms and truncated wave functions are presented. PMID:26854874
Efficient and Flexible Computation of Many-Electron Wave Function Overlaps.
Plasser, Felix; Ruckenbauer, Matthias; Mai, Sebastian; Oppel, Markus; Marquetand, Philipp; González, Leticia
2016-03-08
A new algorithm for the computation of the overlap between many-electron wave functions is described. This algorithm allows for the extensive use of recurring intermediates and thus provides high computational efficiency. Because of the general formalism employed, overlaps can be computed for varying wave function types, molecular orbitals, basis sets, and molecular geometries. This paves the way for efficiently computing nonadiabatic interaction terms for dynamics simulations. In addition, other application areas can be envisaged, such as the comparison of wave functions constructed at different levels of theory. Aside from explaining the algorithm and evaluating the performance, a detailed analysis of the numerical stability of wave function overlaps is carried out, and strategies for overcoming potential severe pitfalls due to displaced atoms and truncated wave functions are presented.
Linger, Richard C; Pleszkoch, Mark G; Prowell, Stacy J; Sayre, Kirk D; Ankrum, Scott
2013-01-01
Organizations maintaining mainframe legacy software can benefit from code modernization and incorporation of security capabilities to address the current threat environment. Oak Ridge National Laboratory is developing the Hyperion system to compute the behavior of software as a means to gain understanding of software functionality and security properties. Computation of functionality is critical to revealing security attributes, which are in fact specialized functional behaviors of software. Oak Ridge is collaborating with MITRE Corporation to conduct a demonstration project to compute behavior of legacy IBM Assembly Language code for a federal agency. The ultimate goal is to understand functionality and security vulnerabilities as a basis for code modernization. This paper reports on the first phase, to define functional semantics for IBM Assembly instructions and conduct behavior computation experiments.
A general computational framework for modeling cellular structure and function.
Schaff, J; Fink, C C; Slepchenko, B; Carson, J H; Loew, L M
1997-01-01
The "Virtual Cell" provides a general system for testing cell biological mechanisms and creates a framework for encapsulating the burgeoning knowledge base comprising the distribution and dynamics of intracellular biochemical processes. It approaches the problem by associating biochemical and electrophysiological data describing individual reactions with experimental microscopic image data describing their subcellular localizations. Individual processes are collected within a physical and computational infrastructure that accommodates any molecular mechanism expressible as rate equations or membrane fluxes. An illustration of the method is provided by a dynamic simulation of IP3-mediated Ca2+ release from endoplasmic reticulum in a neuronal cell. The results can be directly compared to experimental observations and provide insight into the role of experimentally inaccessible components of the overall mechanism. Images FIGURE 1 FIGURE 2 FIGURE 4 FIGURE 5 PMID:9284281
Pedotransfer functions for permeability: A computational study at pore scales
NASA Astrophysics Data System (ADS)
Hyman, Jeffrey D.; Smolarkiewicz, Piotr K.; Larrabee Winter, C.
2013-04-01
Three phenomenological power law models for the permeability of porous media are derived from computational experiments with flow through explicit pore spaces. The pore spaces are represented by three-dimensional pore networks in 63 virtual porous media along with 15 physical pore networks. The power laws relate permeability to (i) porosity, (ii) squared mean hydraulic radius of pores, and (iii) their product. Their performance is compared to estimates derived via the Kozeny equation, which also uses the product of porosity with squared mean hydraulic pore radius to estimate permeability. The power laws provide tighter estimates than the Kozeny equation even after adjusting for the extra parameter they each require. The best fit is with the power law based on the Kozeny predictor, that is, the product of porosity with the square of mean hydraulic pore radius.
A suggestion for computing objective function in model calibration
Wu, Yiping; Liu, Shuguang
2014-01-01
A parameter-optimization process (model calibration) is usually required for numerical model applications, which involves the use of an objective function to determine the model cost (model-data errors). The sum of square errors (SSR) has been widely adopted as the objective function in various optimization procedures. However, ‘square error’ calculation was found to be more sensitive to extreme or high values. Thus, we proposed that the sum of absolute errors (SAR) may be a better option than SSR for model calibration. To test this hypothesis, we used two case studies—a hydrological model calibration and a biogeochemical model calibration—to investigate the behavior of a group of potential objective functions: SSR, SAR, sum of squared relative deviation (SSRD), and sum of absolute relative deviation (SARD). Mathematical evaluation of model performance demonstrates that ‘absolute error’ (SAR and SARD) are superior to ‘square error’ (SSR and SSRD) in calculating objective function for model calibration, and SAR behaved the best (with the least error and highest efficiency). This study suggests that SSR might be overly used in real applications, and SAR may be a reasonable choice in common optimization implementations without emphasizing either high or low values (e.g., modeling for supporting resources management).
Determining Roots of Complex Functions with Computer Graphics.
ERIC Educational Resources Information Center
Skala, Helen; Kowalski, Robert
1990-01-01
Describes a graphical method of approximating roots of complex functions that uses the multicolor display capabilities of microcomputers. Theorems and proofs are presented that illustrate the method, and uses in undergraduate mathematics courses are suggested, including numerical analysis and complex variables. (six references) (LRW)
ERIC Educational Resources Information Center
Sarfo, Frederick Kwaku; Amankwah, Francis; Konin, Daniel
2017-01-01
The study is aimed at investigating 1) the level of computer self-efficacy among public senior high school (SHS) teachers in Ghana and 2) the functionality of teachers' age, gender, and computer experiences on their computer self-efficacy. Four hundred and Seven (407) SHS teachers were used for the study. The "Computer Self-Efficacy"…
NASA Technical Reports Server (NTRS)
Curran, R. T.
1971-01-01
A flight computer functional executive design for the reusable shuttle is presented. The design is given in the form of functional flowcharts and prose description. Techniques utilized in the regulation of process flow to accomplish activation, resource allocation, suspension, termination, and error masking based on process primitives are considered. Preliminary estimates of main storage utilization by the Executive are furnished. Conclusions and recommendations for timely, effective software-hardware integration in the reusable shuttle avionics system are proposed.
Toward high-resolution computational design of helical membrane protein structure and function
Barth, Patrick; Senes, Alessandro
2016-01-01
The computational design of α-helical membrane proteins is still in its infancy but has made important progress. De novo design has produced stable, specific and active minimalistic oligomeric systems. Computational re-engineering can improve stability and modulate the function of natural membrane proteins. Currently, the major hurdle for the field is not computational, but the experimental characterization of the designs. The emergence of new structural methods for membrane proteins will accelerate progress PMID:27273630
Frequency domain transfer function identification using the computer program SYSFIT
Trudnowski, D.J.
1992-12-01
Because the primary application of SYSFIT for BPA involves studying power system dynamics, this investigation was geared toward simulating the effects that might be encountered in studying electromechanical oscillations in power systems. Although the intended focus of this work is power system oscillations, the studies are sufficiently genetic that the results can be applied to many types of oscillatory systems with closely-spaced modes. In general, there are two possible ways of solving the optimization problem. One is to use a least-squares optimization function and to write the system in such a form that the problem becomes one of linear least-squares. The solution can then be obtained using a standard least-squares technique. The other method involves using a search method to obtain the optimal model. This method allows considerably more freedom in forming the optimization function and model, but it requires an initial guess of the system parameters. SYSFIT employs this second approach. Detailed investigations were conducted into three main areas: (1) fitting to exact frequency response data of a linear system; (2) fitting to the discrete Fourier transformation of noisy data; and (3) fitting to multi-path systems. The first area consisted of investigating the effects of alternative optimization cost function options; using different optimization search methods; incorrect model order, missing response data; closely-spaced poles; and closely-spaced pole-zero pairs. Within the second area, different noise colorations and levels were studied. In the third area, methods were investigated for improving fitting results by incorporating more than one system path. The following is a list of guidelines and properties developed from the study for fitting a transfer function to the frequency response of a system using optimization search methods.
A computational interactome and functional annotation for the human proteome
Garzón, José Ignacio; Deng, Lei; Murray, Diana; Shapira, Sagi; Petrey, Donald; Honig, Barry
2016-01-01
We present a database, PrePPI (Predicting Protein-Protein Interactions), of more than 1.35 million predicted protein-protein interactions (PPIs). Of these at least 127,000 are expected to constitute direct physical interactions although the actual number may be much larger (~500,000). The current PrePPI, which contains predicted interactions for about 85% of the human proteome, is related to an earlier version but is based on additional sources of interaction evidence and is far larger in scope. The use of structural relationships allows PrePPI to infer numerous previously unreported interactions. PrePPI has been subjected to a series of validation tests including reproducing known interactions, recapitulating multi-protein complexes, analysis of disease associated SNPs, and identifying functional relationships between interacting proteins. We show, using Gene Set Enrichment Analysis (GSEA), that predicted interaction partners can be used to annotate a protein’s function. We provide annotations for most human proteins, including many annotated as having unknown function. DOI: http://dx.doi.org/10.7554/eLife.18715.001 PMID:27770567
Fair and Square Computation of Inverse "Z"-Transforms of Rational Functions
ERIC Educational Resources Information Center
Moreira, M. V.; Basilio, J. C.
2012-01-01
All methods presented in textbooks for computing inverse "Z"-transforms of rational functions have some limitation: 1) the direct division method does not, in general, provide enough information to derive an analytical expression for the time-domain sequence "x"("k") whose "Z"-transform is "X"("z"); 2) computation using the inversion integral…
Effects of Computer versus Paper Administration of an Adult Functional Writing Assessment
ERIC Educational Resources Information Center
Chen, Jing; White, Sheida; McCloskey, Michael; Soroui, Jaleh; Chun, Young
2011-01-01
This study investigated the comparability of paper and computer versions of a functional writing assessment administered to adults 16 and older. Three writing tasks were administered in both paper and computer modes to volunteers in the field test of an assessment of adult literacy in 2008. One set of analyses examined mode effects on scoring by…
Effects of Computer versus Paper Administration of an Adult Functional Writing Assessment
ERIC Educational Resources Information Center
Chen, Jing; White, Sheida; McCloskey, Michael; Soroui, Jaleh; Chun, Young
2011-01-01
This study investigated the comparability of paper and computer versions of a functional writing assessment administered to adults 16 and older. Three writing tasks were administered in both paper and computer modes to volunteers in the field test of an assessment of adult literacy in 2008. One set of analyses examined mode effects on scoring by…
Fair and Square Computation of Inverse "Z"-Transforms of Rational Functions
ERIC Educational Resources Information Center
Moreira, M. V.; Basilio, J. C.
2012-01-01
All methods presented in textbooks for computing inverse "Z"-transforms of rational functions have some limitation: 1) the direct division method does not, in general, provide enough information to derive an analytical expression for the time-domain sequence "x"("k") whose "Z"-transform is "X"("z"); 2) computation using the inversion integral…
A Systematic Approach for Understanding Slater-Gaussian Functions in Computational Chemistry
ERIC Educational Resources Information Center
Stewart, Brianna; Hylton, Derrick J.; Ravi, Natarajan
2013-01-01
A systematic way to understand the intricacies of quantum mechanical computations done by a software package known as "Gaussian" is undertaken via an undergraduate research project. These computations involve the evaluation of key parameters in a fitting procedure to express a Slater-type orbital (STO) function in terms of the linear…
Performance of a computer-based assessment of cognitive function measures in two cohorts of seniors
USDA-ARS?s Scientific Manuscript database
Computer-administered assessment of cognitive function is being increasingly incorporated in clinical trials, however its performance in these settings has not been systematically evaluated. The Seniors Health and Activity Research Program (SHARP) pilot trial (N=73) developed a computer-based tool f...
A Systematic Approach for Understanding Slater-Gaussian Functions in Computational Chemistry
ERIC Educational Resources Information Center
Stewart, Brianna; Hylton, Derrick J.; Ravi, Natarajan
2013-01-01
A systematic way to understand the intricacies of quantum mechanical computations done by a software package known as "Gaussian" is undertaken via an undergraduate research project. These computations involve the evaluation of key parameters in a fitting procedure to express a Slater-type orbital (STO) function in terms of the linear…
Computer programs for calculation of thermodynamic functions of mixing in crystalline solutions
NASA Technical Reports Server (NTRS)
Comella, P. A.; Saxena, S. K.
1972-01-01
The computer programs Beta, GEGIM, REGSOL1, REGSOL2, Matrix, and Quasi are presented. The programs are useful in various calculations for the thermodynamic functions of mixing and the activity-composition relations in rock forming minerals.
SCAIEF, C.C.
1999-12-16
This functions, requirements and specifications document defines the baseline requirements and criteria for the design, purchase, fabrication, construction, installation, and operation of the system to replace the Computer Automated Surveillance System (CASS) alarm monitoring.
Computational characterization of sodium selenite using density functional theory.
Barraza-Jiménez, Diana; Flores-Hidalgo, Manuel Alberto; Galvan, Donald H; Sánchez, Esteban; Glossman-Mitnik, Daniel
2011-04-01
In this theoretical study we used density functional theory to calculate the molecular and crystalline structures of sodium selenite. Our structural results were compared with experimental data. From the molecular structure we determined the ionization potential, electronic affinity, and global reactivity parameters like electronegativity, hardness, softness and global electrophilic index. A significant difference in the IP and EA values was observed, and this difference was dependent on the calculation method used (employing either vertical or adiabatic energies). Thus, values obtained for the electrophilic index (2.186 eV from vertical energies and 2.188 eV from adiabatic energies) were not significantly different. Selectivity was calculated using the Fukui functions. Since the Mulliken charge study predicted a negative value, it is recommended that AIM should be used in selectivity characterization. It was evident from the selectivity index that sodium atoms are the most sensitive sites to nucleophilic attack. The results obtained in this work provide data that will aid the characterization of compounds used in crop biofortification.
Density functional computations for inner-shell excitation spectroscopy
NASA Astrophysics Data System (ADS)
Hu, Ching-Han; Chong, Delano P.
1996-11-01
The 1 s → π ∗ inner-shell excitation spectra of seven molecules have been studied using density functional theory along with the unrestricted generalized transition state (uGTS) approach. The exchange-correlation potential is based on a combined functional of Becke's exchange (B88) and Perdew's correlation (P86). A scaling procedure based on Clementi and Raimondi's rules for atomic screening is applied to the cc-pVTZ basis set of atoms where a partial core-hole is created in the uGTS calculations. The average absolute deviation between our predicted 1 s → π ∗ excitations eneergies and experimental values is only 0.16 eV. Singlet-triplet splittings of C 1 s → π ∗ transitions of CO, C 2H 2, C 2H 4, and C 6H 6 also agree with experimental observations. The average absolute deviation of our predicted core-electron binding energies and term values is 0.23 and 0.29 eV, respectively.
Computer Aided Design Package Incorporating Computer Aided Real-Time Control Function
NASA Astrophysics Data System (ADS)
Furuta, K.; Ohyama, Y.
1987-10-01
This paper presents CAD system named DPACS, which has functions not only CAD but also real time contro] and test. For the CAD, it has functions of control system analysis, design, simulation, identifcation and data handling and management. A real time control program based on CAD data is produced automatically and the digital control is possible by using A/D and D/A converter even if an operator doesn't know the programing technique or any programing language at al]. The details of CAD system is explained by the example of designing a controller for the inverted pendulum system.
A Computer Program for the Computation of Running Gear Temperatures Using Green's Function
NASA Technical Reports Server (NTRS)
Koshigoe, S.; Murdock, J. W.; Akin, L. S.; Townsend, D. P.
1996-01-01
A new technique has been developed to study two dimensional heat transfer problems in gears. This technique consists of transforming the heat equation into a line integral equation with the use of Green's theorem. The equation is then expressed in terms of eigenfunctions that satisfy the Helmholtz equation, and their corresponding eigenvalues for an arbitrarily shaped region of interest. The eigenfunction are obtalned by solving an intergral equation. Once the eigenfunctions are found, the temperature is expanded in terms of the eigenfunctions with unknown time dependent coefficients that can be solved by using Runge Kutta methods. The time integration is extremely efficient. Therefore, any changes in the time dependent coefficients or source terms in the boundary conditions do not impose a great computational burden on the user. The method is demonstrated by applying it to a sample gear tooth. Temperature histories at representative surface locatons are given.
Computational identification of functional RNA homologs in metagenomic data
Nawrocki, Eric P.; Eddy, Sean R.
2013-01-01
A key step toward understanding a metagenomics data set is the identification of functional sequence elements within it, such as protein coding genes and structural RNAs. Relative to protein coding genes, structural RNAs are more difficult to identify because of their reduced alphabet size, lack of open reading frames, and short length. Infernal is a software package that implements “covariance models” (CMs) for RNA homology search, which harness both sequence and structural conservation when searching for RNA homologs. Thanks to the added statistical signal inherent in the secondary structure conservation of many RNA families, Infernal is more powerful than sequence-only based methods such as BLAST and profile HMMs. Together with the Rfam database of CMs, Infernal is a useful tool for identifying RNAs in metagenomics data sets. PMID:23722291
Computation of Schenberg response function by using finite element modelling
NASA Astrophysics Data System (ADS)
Frajuca, C.; Bortoli, F. S.; Magalhaes, N. S.
2016-05-01
Schenberg is a detector of gravitational waves resonant mass type, with a central frequency of operation of 3200 Hz. Transducers located on the surface of the resonating sphere, according to a distribution half-dodecahedron, are used to monitor a strain amplitude. The development of mechanical impedance matchers that act by increasing the coupling of the transducers with the sphere is a major challenge because of the high frequency and small in size. The objective of this work is to study the Schenberg response function obtained by finite element modeling (FEM). Finnaly, the result is compared with the result of the simplified model for mass spring type system modeling verifying if that is suitable for the determination of sensitivity detector, as the conclusion the both modeling give the same results.
NASA Astrophysics Data System (ADS)
Borgis, Daniel; Assaraf, Roland; Rotenberg, Benjamin; Vuilleumier, Rodolphe
2013-12-01
No fancy statistical objects here, we go back to the computation of one of the most basic and fundamental quantities in the statistical mechanics of fluids, namely the pair distribution functions. Those functions are usually computed in molecular simulations by using histogram techniques. We show here that they can be estimated using a global information on the instantaneous forces acting on the particles, and that this leads to a reduced variance compared to the standard histogram estimators. The technique is extended successfully to the computation of three-dimensional solvent densities around tagged molecular solutes, quantities that are noisy and very long to converge, using histograms.
Fast computation of functional networks from fMRI activity: a multi-platform comparison
NASA Astrophysics Data System (ADS)
Rao, A. Ravishankar; Bordawekar, Rajesh; Cecchi, Guillermo
2011-03-01
The recent deployment of functional networks to analyze fMRI images has been very promising. In this method, the spatio-temporal fMRI data is converted to a graph-based representation, where the nodes are voxels and edges indicate the relationship between the nodes, such as the strength of correlation or causality. Graph-theoretic measures can then be used to compare different fMRI scans. However, there is a significant computational bottleneck, as the computation of functional networks with directed links takes several hours on conventional machines with single CPUs. The study in this paper shows that a GPU can be advantageously used to accelerate the computation, such that the network computation takes a few minutes. Though GPUs have been used for the purposes of displaying fMRI images, their use in computing functional networks is novel. We describe specific techniques such as load balancing, and the use of a large number of threads to achieve the desired speedup. Our experience in utilizing the GPU for functional network computations should prove useful to the scientific community investigating fMRI as GPUs are a low-cost platform for addressing the computational bottleneck.
Astrocytes, Synapses and Brain Function: A Computational Approach
NASA Astrophysics Data System (ADS)
Nadkarni, Suhita
2006-03-01
Modulation of synaptic reliability is one of the leading mechanisms involved in long- term potentiation (LTP) and long-term depression (LTD) and therefore has implications in information processing in the brain. A recently discovered mechanism for modulating synaptic reliability critically involves recruitments of astrocytes - star- shaped cells that outnumber the neurons in most parts of the central nervous system. Astrocytes until recently were thought to be subordinate cells merely participating in supporting neuronal functions. New evidence, however, made available by advances in imaging technology has changed the way we envision the role of these cells in synaptic transmission and as modulator of neuronal excitability. We put forward a novel mathematical framework based on the biophysics of the bidirectional neuron-astrocyte interactions that quantitatively accounts for two distinct experimental manifestation of recruitment of astrocytes in synaptic transmission: a) transformation of a low fidelity synapse transforms into a high fidelity synapse and b) enhanced postsynaptic spontaneous currents when astrocytes are activated. Such a framework is not only useful for modeling neuronal dynamics in a realistic environment but also provides a conceptual basis for interpreting experiments. Based on this modeling framework, we explore the role of astrocytes for neuronal network behavior such as synchrony and correlations and compare with experimental data from cultured networks.
Passive Dendrites Enable Single Neurons to Compute Linearly Non-separable Functions
Cazé, Romain Daniel; Humphries, Mark; Gutkin, Boris
2013-01-01
Local supra-linear summation of excitatory inputs occurring in pyramidal cell dendrites, the so-called dendritic spikes, results in independent spiking dendritic sub-units, which turn pyramidal neurons into two-layer neural networks capable of computing linearly non-separable functions, such as the exclusive OR. Other neuron classes, such as interneurons, may possess only a few independent dendritic sub-units, or only passive dendrites where input summation is purely sub-linear, and where dendritic sub-units are only saturating. To determine if such neurons can also compute linearly non-separable functions, we enumerate, for a given parameter range, the Boolean functions implementable by a binary neuron model with a linear sub-unit and either a single spiking or a saturating dendritic sub-unit. We then analytically generalize these numerical results to an arbitrary number of non-linear sub-units. First, we show that a single non-linear dendritic sub-unit, in addition to the somatic non-linearity, is sufficient to compute linearly non-separable functions. Second, we analytically prove that, with a sufficient number of saturating dendritic sub-units, a neuron can compute all functions computable with purely excitatory inputs. Third, we show that these linearly non-separable functions can be implemented with at least two strategies: one where a dendritic sub-unit is sufficient to trigger a somatic spike; another where somatic spiking requires the cooperation of multiple dendritic sub-units. We formally prove that implementing the latter architecture is possible with both types of dendritic sub-units whereas the former is only possible with spiking dendrites. Finally, we show how linearly non-separable functions can be computed by a generic two-compartment biophysical model and a realistic neuron model of the cerebellar stellate cell interneuron. Taken together our results demonstrate that passive dendrites are sufficient to enable neurons to compute linearly non
A mesh-decoupled height function method for computing interface curvature
NASA Astrophysics Data System (ADS)
Owkes, Mark; Desjardins, Olivier
2015-01-01
In this paper, a mesh-decoupled height function method is proposed and tested. The method is based on computing height functions within columns that are not aligned with the underlying mesh and have variable dimensions. Because they are decoupled from the computational mesh, the columns can be aligned with the interface normal vector, which is found to improve the curvature calculation for under-resolved interfaces where the standard height function method often fails. A computational geometry toolbox is used to compute the heights in the complex geometry that is formed at the intersection of the computational mesh and the columns. The toolbox reduces the complexity of the problem to a series of straightforward geometric operations using simplices. The proposed scheme is shown to compute more accurate curvatures than the standard height function method on coarse meshes. A combined method that uses the standard height function where it is well defined and the proposed scheme in under-resolved regions is tested. This approach achieves accurate and robust curvatures for under-resolved interface features and second-order converging curvatures for well-resolved interfaces.
NASA Astrophysics Data System (ADS)
Schuch, Norbert; Verstraete, Frank
2009-10-01
One of the central problems in quantum mechanics is to determine the ground-state properties of a system of electrons interacting through the Coulomb potential. Since its introduction, density functional theory has become the most widely used and successful method for simulating systems of interacting electrons. Here, we show that the field of computational complexity imposes fundamental limitations on density functional theory. In particular, if the associated `universal functional' could be found efficiently, this would imply that any problem in the computational complexity class Quantum Merlin Arthur could be solved efficiently. Quantum Merlin Arthur is the quantum version of the class NP and thus any problem in NP could be solved in polynomial time. This is considered highly unlikely. Our result follows from the fact that finding the ground-state energy of the Hubbard model in an external magnetic field is a hard problem even for a quantum computer, but, given the universal functional, it can be computed efficiently using density functional theory. This work illustrates how the field of quantum computing could be useful even if quantum computers were never built.
PERFORMANCE OF A COMPUTER-BASED ASSESSMENT OF COGNITIVE FUNCTION MEASURES IN TWO COHORTS OF SENIORS
Espeland, Mark A.; Katula, Jeffrey A.; Rushing, Julia; Kramer, Arthur F.; Jennings, Janine M.; Sink, Kaycee M.; Nadkarni, Neelesh K.; Reid, Kieran F.; Castro, Cynthia M.; Church, Timothy; Kerwin, Diana R.; Williamson, Jeff D.; Marottoli, Richard A.; Rushing, Scott; Marsiske, Michael; Rapp, Stephen R.
2013-01-01
Background Computer-administered assessment of cognitive function is being increasingly incorporated in clinical trials, however its performance in these settings has not been systematically evaluated. Design The Seniors Health and Activity Research Program (SHARP) pilot trial (N=73) developed a computer-based tool for assessing memory performance and executive functioning. The Lifestyle Interventions and Independence for Seniors (LIFE) investigators incorporated this battery in a full scale multicenter clinical trial (N=1635). We describe relationships that test scores have with those from interviewer-administered cognitive function tests and risk factors for cognitive deficits and describe performance measures (completeness, intra-class correlations). Results Computer-based assessments of cognitive function had consistent relationships across the pilot and full scale trial cohorts with interviewer-administered assessments of cognitive function, age, and a measure of physical function. In the LIFE cohort, their external validity was further demonstrated by associations with other risk factors for cognitive dysfunction: education, hypertension, diabetes, and physical function. Acceptable levels of data completeness (>83%) were achieved on all computer-based measures, however rates of missing data were higher among older participants (odds ratio=1.06 for each additional year; p<0.001) and those who reported no current computer use (odds ratio=2.71; p<0.001). Intra-class correlations among clinics were at least as low (ICC≤0.013) as for interviewer measures (ICC≤0.023), reflecting good standardization. All cognitive measures loaded onto the first principal component (global cognitive function), which accounted for 40% of the overall variance. Conclusion Our results support the use of computer-based tools for assessing cognitive function in multicenter clinical trials of older individuals. PMID:23589390
2003-06-01
This thesis extends the Computer Aided Software Evolution System (CASES) with Quality Function Deployment (QFD) to enhance dependency traceability...type and degree) between software development artifacts. Embedding Quality Function Deployment (QFD) in the Relational Hypergraph Software Evolution ...to define and manage any software evolution process. These major contributions allow a software engineer to: (1) Input, modify, and analyze
ERIC Educational Resources Information Center
Tumthong, Suwut; Piriyasurawong, Pullop; Jeerangsuwan, Namon
2016-01-01
This research proposes a functional competency development model for academic personnel based on international professional qualification standards in computing field and examines the appropriateness of the model. Specifically, the model consists of three key components which are: 1) functional competency development model, 2) blended training…
Computation of turbulent boundary layers employing the defect wall-function method. M.S. Thesis
NASA Technical Reports Server (NTRS)
Brown, Douglas L.
1994-01-01
In order to decrease overall computational time requirements of spatially-marching parabolized Navier-Stokes finite-difference computer code when applied to turbulent fluid flow, a wall-function methodology, originally proposed by R. Barnwell, was implemented. This numerical effort increases computational speed and calculates reasonably accurate wall shear stress spatial distributions and boundary-layer profiles. Since the wall shear stress is analytically determined from the wall-function model, the computational grid near the wall is not required to spatially resolve the laminar-viscous sublayer. Consequently, a substantially increased computational integration step size is achieved resulting in a considerable decrease in net computational time. This wall-function technique is demonstrated for adiabatic flat plate test cases from Mach 2 to Mach 8. These test cases are analytically verified employing: (1) Eckert reference method solutions, (2) experimental turbulent boundary-layer data of Mabey, and (3) finite-difference computational code solutions with fully resolved laminar-viscous sublayers. Additionally, results have been obtained for two pressure-gradient cases: (1) an adiabatic expansion corner and (2) an adiabatic compression corner.
Computation of fractional integrals via functions of hypergeometric and Bessel type
NASA Astrophysics Data System (ADS)
Kilbas, A. A.; Trujillo, J. J.
2000-06-01
The paper is devoted to computation of the fractional integrals of power exponential functions. It is considered a function [lambda][gamma],[sigma]([beta])(z) defined bywith positive [beta] and complex [gamma], [sigma] and z such that Re([gamma])>(1/[beta])-1 and Re(z)>0. The special cases are discussed when [lambda][gamma],[sigma]([beta])(z) is expressed in terms of the Tricomi confluent hypergeometric function [Psi](a,c;x) and of modified Bessel function of the third kind K[gamma](x). Representations of these functions via fractional integrals are proved. The results obtained apply to compute fractional integrals of power exponential functions in terms of [lambda][gamma],[sigma]([beta])(x), [Psi](a,c;x) and K[gamma](x). Examples are considered.
Peak functions for modeling high resolution soil profile data
USDA-ARS?s Scientific Manuscript database
Parametric and non-parametric depth functions have been used to estimate continuous soil profile properties. However, some soil properties, such as those seen in weathered loess, have complex peaked and anisotropic depth distributions. These distributions are poorly handled by common parametric func...
Estimation of distribution function in bivariate competing risk models.
Sankaran, P G; Lawless, J F; Abraham, B; Antony, Ansa Alphonsa
2006-06-01
We consider lifetime data involving pairs of study individuals with more than one possible cause of failure for each individual. Non-parametric estimation of cause-specific distribution functions is considered under independent censoring. Properties of the estimators are discussed and an illustration of their application is given.
Computations for group sequential boundaries using the Lan-DeMets spending function method.
Reboussin, D M; DeMets, D L; Kim, K M; Lan, K K
2000-06-01
We describe an interactive Fortran program which performs computations related to the design and analysis of group sequential clinical trials using Lan-DeMets spending functions. Many clinical trials include interim analyses of accumulating data and rely on group sequential methods to avoid consequent inflation of the type I error rate. The computations are appropriate for interim test statistics whose distribution or limiting distribution is multivariate normal with independent increments. Recent theoretical results indicate that virtually any design likely to be used in a clinical trial will fall into this category. Interim analyses need not be equally spaced, and their number need not be specified in advance. In addition to determining sequential boundaries using an alpha spending function, the program can perform power computations, compute probabilities associated with a given set of boundaries, and generate confidence intervals.
Locating and computing in parallel all the simple roots of special functions using PVM
NASA Astrophysics Data System (ADS)
Plagianakos, V. P.; Nousis, N. K.; Vrahatis, M. N.
2001-08-01
An algorithm is proposed for locating and computing in parallel and with certainty all the simple roots of any twice continuously differentiable function in any specific interval. To compute with certainty all the roots, the proposed method is heavily based on the knowledge of the total number of roots within the given interval. To obtain this information we use results from topological degree theory and, in particular, the Kronecker-Picard approach. This theory gives a formula for the computation of the total number of roots of a system of equations within a given region, which can be computed in parallel. With this tool in hand, we construct a parallel procedure for the localization and isolation of all the roots by dividing the given region successively and applying the above formula to these subregions until the final domains contain at the most one root. The subregions with no roots are discarded, while for the rest a modification of the well-known bisection method is employed for the computation of the contained root. The new aspect of the present contribution is that the computation of the total number of zeros using the Kronecker-Picard integral as well as the localization and computation of all the roots is performed in parallel using the parallel virtual machine (PVM). PVM is an integrated set of software tools and libraries that emulates a general-purpose, flexible, heterogeneous concurrent computing framework on interconnected computers of varied architectures. The proposed algorithm has large granularity and low synchronization, and is robust. It has been implemented and tested and our experience is that it can massively compute with certainty all the roots in a certain interval. Performance information from massive computations related to a recently proposed conjecture due to Elbert (this issue, J. Comput. Appl. Math. 133 (2001) 65-83) is reported.
ERIC Educational Resources Information Center
Hughes, John; And Others
This report provides a description of a Computer Aided Training System Development and Management (CATSDM) environment based on state-of-the-art hardware and software technology, and including recommendations for off the shelf systems to be utilized as a starting point in addressing the particular systematic training and instruction design and…
Druskin, V.; Lee, Ping; Knizhnerman, L.
1996-12-31
There is now a growing interest in the area of using Krylov subspace approximations to compute the actions of matrix functions. The main application of this approach is the solution of ODE systems, obtained after discretization of partial differential equations by method of lines. In the event that the cost of computing the matrix inverse is relatively inexpensive, it is sometimes attractive to solve the ODE using the extended Krylov subspaces, originated by actions of both positive and negative matrix powers. Examples of such problems can be found frequently in computational electromagnetics.
N S Andreasen Struijk, Lotte; Lontis, Eugen R; Gaihede, Michael; Caltenco, Hector A; Lund, Morten Enemark; Schioeler, Henrik; Bentsen, Bo
2017-08-01
Individuals with tetraplegia depend on alternative interfaces in order to control computers and other electronic equipment. Current interfaces are often limited in the number of available control commands, and may compromise the social identity of an individual due to their undesirable appearance. The purpose of this study was to implement an alternative computer interface, which was fully embedded into the oral cavity and which provided multiple control commands. The development of a wireless, intraoral, inductive tongue computer was described. The interface encompassed a 10-key keypad area and a mouse pad area. This system was embedded wirelessly into the oral cavity of the user. The functionality of the system was demonstrated in two tetraplegic individuals and two able-bodied individuals Results: The system was invisible during use and allowed the user to type on a computer using either the keypad area or the mouse pad. The maximal typing rate was 1.8 s for repetitively typing a correct character with the keypad area and 1.4 s for repetitively typing a correct character with the mouse pad area. The results suggest that this inductive tongue computer interface provides an esthetically acceptable and functionally efficient environmental control for a severely disabled user. Implications for Rehabilitation New Design, Implementation and detection methods for intra oral assistive devices. Demonstration of wireless, powering and encapsulation techniques suitable for intra oral embedment of assistive devices. Demonstration of the functionality of a rechargeable and fully embedded intra oral tongue controlled computer input device.
NASA Technical Reports Server (NTRS)
King, H. F.; Komornicki, A.
1986-01-01
Formulas are presented relating Taylor series expansion coefficients of three functions of several variables, the energy of the trial wave function (W), the energy computed using the optimized variational wave function (E), and the response function (lambda), under certain conditions. Partial derivatives of lambda are obtained through solution of a recursive system of linear equations, and solution through order n yields derivatives of E through order 2n + 1, extending Puley's application of Wigner's 2n + 1 rule to partial derivatives in couple perturbation theory. An examination of numerical accuracy shows that the usual two-term second derivative formula is less stable than an alternative four-term formula, and that previous claims that energy derivatives are stationary properties of the wave function are fallacious. The results have application to quantum theoretical methods for the computation of derivative properties such as infrared frequencies and intensities.
NASA Technical Reports Server (NTRS)
King, H. F.; Komornicki, A.
1986-01-01
Formulas are presented relating Taylor series expansion coefficients of three functions of several variables, the energy of the trial wave function (W), the energy computed using the optimized variational wave function (E), and the response function (lambda), under certain conditions. Partial derivatives of lambda are obtained through solution of a recursive system of linear equations, and solution through order n yields derivatives of E through order 2n + 1, extending Puley's application of Wigner's 2n + 1 rule to partial derivatives in couple perturbation theory. An examination of numerical accuracy shows that the usual two-term second derivative formula is less stable than an alternative four-term formula, and that previous claims that energy derivatives are stationary properties of the wave function are fallacious. The results have application to quantum theoretical methods for the computation of derivative properties such as infrared frequencies and intensities.
Liu, Jia; Yan, Zhengzheng; Pu, Yuehua; Shiu, Wen-Shin; Wu, Jianhuang; Chen, Rongliang; Leng, Xinyi; Qin, Haiqiang; Liu, Xin; Jia, Baixue; Song, Ligang; Wang, Yilong; Miao, Zhongrong; Wang, Yongjun; Liu, Liping; Cai, Xiao-Chuan
2017-07-01
The fractional pressure ratio is introduced to quantitatively assess the hemodynamic significance of severe intracranial stenosis. A computational fluid dynamics-based method is proposed to non-invasively compute the FPRCFD and compared against fractional pressure ratio measured by an invasive technique. Eleven patients with severe intracranial stenosis considered for endovascular intervention were recruited and an invasive procedure was performed to measure the distal and the aortic pressure ( Pd and Pa). The fractional pressure ratio was calculated as [Formula: see text]. The computed tomography angiography was used to reconstruct three-dimensional (3D) arteries for each patient. Cerebral hemodynamics was then computed for the arteries using a mathematical model governed by Navier-Stokes equations and with the outflow conditions imposed by a model of distal resistance and compliance. The non-invasive [Formula: see text], [Formula: see text], and FPRCFD were then obtained from the computational fluid dynamics calculation using a 16-core parallel computer. The invasive and non-invasive parameters were tested by statistical analysis. For this group of patients, the computational fluid dynamics method achieved comparable results with the invasive measurements. The fractional pressure ratio and FPRCFD are very close and highly correlated, but not linearly proportional, with the percentage of stenosis. The proposed computational fluid dynamics method can potentially be useful in assessing the functional alteration of cerebral stenosis.
Computation of determinant expansion coefficients within the graphically contracted function method.
Gidofalvi, Gergely; Shepard, Ron
2009-11-30
Most electronic structure methods express the wavefunction as an expansion of N-electron basis functions that are chosen to be either Slater determinants or configuration state functions. Although the expansion coefficient of a single determinant may be readily computed from configuration state function coefficients for small wavefunction expansions, traditional algorithms are impractical for systems with a large number of electrons and spatial orbitals. In this work, we describe an efficient algorithm for the evaluation of a single determinant expansion coefficient for wavefunctions expanded as a linear combination of graphically contracted functions. Each graphically contracted function has significant multiconfigurational character and depends on a relatively small number of variational parameters called arc factors. Because the graphically contracted function approach expresses the configuration state function coefficients as products of arc factors, a determinant expansion coefficient may be computed recursively more efficiently than with traditional configuration interaction methods. Although the cost of computing determinant coefficients scales exponentially with the number of spatial orbitals for traditional methods, the algorithm presented here exploits two levels of recursion and scales polynomially with system size. Hence, as demonstrated through applications to systems with hundreds of electrons and orbitals, it may readily be applied to very large systems.
Computationally efficient algorithms for the two-dimensional Kolmogorov Smirnov test
NASA Astrophysics Data System (ADS)
Lopes, R. H. C.; Hobson, P. R.; Reid, I. D.
2008-07-01
Goodness-of-fit statistics measure the compatibility of random samples against some theoretical or reference probability distribution function. The classical one-dimensional Kolmogorov-Smirnov test is a non-parametric statistic for comparing two empirical distributions which defines the largest absolute difference between the two cumulative distribution functions as a measure of disagreement. Adapting this test to more than one dimension is a challenge because there are 2d-1 independent ways of ordering a cumulative distribution function in d dimensions. We discuss Peacock's version of the Kolmogorov-Smirnov test for two-dimensional data sets which computes the differences between cumulative distribution functions in 4n2 quadrants. We also examine Fasano and Franceschini's variation of Peacock's test, Cooke's algorithm for Peacock's test, and ROOT's version of the two-dimensional Kolmogorov-Smirnov test. We establish a lower-bound limit on the work for computing Peacock's test of Ω(n2lgn), introducing optimal algorithms for both this and Fasano and Franceschini's test, and show that Cooke's algorithm is not a faithful implementation of Peacock's test. We also discuss and evaluate parallel algorithms for Peacock's test.
NASA Technical Reports Server (NTRS)
Almroth, B. O.; Stehlin, P.; Brogan, F. A.
1981-01-01
A method for improving the efficiency of nonlinear structural analysis by the use of global displacement functions is presented. The computer programs include options to define the global functions as input or let the program automatically select and update these functions. The program was applied to a number of structures: (1) 'pear-shaped cylinder' in compression, (2) bending of a long cylinder, (3) spherical shell subjected to point force, (4) panel with initial imperfections, (5) cylinder with cutouts. The sample cases indicate the usefulness of the procedure in the solution of nonlinear structural shell problems by the finite element method. It is concluded that the use of global functions for extrapolation will lead to savings in computer time.
Analysis and selection of optimal function implementations in massively parallel computer
Archer, Charles Jens; Peters, Amanda; Ratterman, Joseph D.
2011-05-31
An apparatus, program product and method optimize the operation of a parallel computer system by, in part, collecting performance data for a set of implementations of a function capable of being executed on the parallel computer system based upon the execution of the set of implementations under varying input parameters in a plurality of input dimensions. The collected performance data may be used to generate selection program code that is configured to call selected implementations of the function in response to a call to the function under varying input parameters. The collected performance data may be used to perform more detailed analysis to ascertain the comparative performance of the set of implementations of the function under the varying input parameters.
Networks of spiking neurons that compute linear functions using action potential timing
NASA Astrophysics Data System (ADS)
Ruf, Berthold
1999-03-01
For fast neural computations within the brain it is very likely that the timing of single firing events is relevant. Recently Maass has shown that under certain weak assumptions a weighted sum can be computed in temporal coding by leaky integrate-and-fire neurons. This construction can be extended to approximate arbitrary functions. In comparison to integrate-and-fire neurons there are several sources in biologically more realistic neurons for additional nonlinear effects like e.g. the spatial and temporal interaction of postsynaptic potentials or voltage-gated ion channels at the soma. Here we demonstrate with the help of computer simulations using GENESIS that despite of these nonlinearities such neurons can compute linear functions in a natural and straightforward way based on the main principles of the construction given by Maass. One only has to assume that a neuron receives all its inputs in a time interval of approximately the length of the rising segment of its excitatory postsynaptic potentials. We also show that under certain assumptions there exists within this construction some type of activation function being computed by such neurons. Finally we demonstrate that on the basis of these results it is possible to realize in a simple way pattern analysis with spiking neurons. It allows the analysis of a mixture of several learned patterns within a few milliseconds.
Computer-assisted assessment of depression and function in older primary care patients
Kurt, Reyis; Bogner, Hillary R.; Straton, Joseph B.; Tien, Allen Y.; Gallo, Joseph J.
2010-01-01
Summary We wanted to test the psychometric reliability and validity of self-reported information on psychological and functional status gathered by computer in a sample of primary care outpatients. Persons aged 65 years and older visiting a primary care medical practice in Baltimore (n = 240) were approached. Complete baseline data were obtained for 54 patients and 34 patients completed 1-week retest follow-up. Standard instruments were administered by computer and also given as paper and pencil tests. Test–retest reliability estimates were calculated and comparisons across mode of administration were made. Separately, an interviewer administered a questionnaire to gauge patient attitudes and feelings after using the computer. Most participants (72%) reported no previous computer use. Nevertheless, inter-method reliability of the GDS15 at baseline (0.719, n = 47), intra-method reliability of the computer in time (0.797, n = 31), inter-method reliability of the CESDR20 at baseline (0.740, n = 53), and the correlation between the CESDR20 computer version at baseline and follow-up (0.849, n = 34) were all excellent. The inter-method reliability of the CESDR20 at follow-up (0.615, n = 37) was lower but still acceptable. Although 28% were anxious prior to using the computer testing system, that percent decreased to 19% while using the system. The efficiency and reliability in comparison to the paper instruments were good or better. Even though most participants had not ever used a computer prior to participating in the study, they had generally favorable attitudes toward the use of computers, and also reported having favorable experience with the computer testing system. PMID:14757259
Non-Parametric Model Drift Detection
2016-07-01
Drift Detection for the Machine Translation Task .................................................................. 15 CONCLUSIONS...framework on two tasks in NLP domain, topic modeling, and machine translation. Our main findings are summarized as follows: • We can measure important...Introduction Most machine learning methods operate under the assumption that the training and the test data are sampled from the same distribution
Non Parametric Classification Using Learning Vector Quantization
1990-08-21
0.(0) = a. Then for every finite T and 7 > 0 lim P isup e,, - 6.(t,)I > 7) 0. (2.18)all 10 (t,. <T This result is proved in Section 2.3. The second...91 References A. Benveniste, M. Metivier & P. Priouret [1987], Algorithmes Adaptatifs et Ap- proximations Stochastiques, Mason, Paris . P. Billingsley
MRIVIEW: An interactive computational tool for investigation of brain structure and function
Ranken, D.; George, J.
1993-12-31
MRIVIEW is a software system which uses image processing and visualization to provide neuroscience researchers with an integrated environment for combining functional and anatomical information. Key features of the software include semi-automated segmentation of volumetric head data and an interactive coordinate reconciliation method which utilizes surface visualization. The current system is a precursor to a computational brain atlas. We describe features this atlas will incorporate, including methods under development for visualizing brain functional data obtained from several different research modalities.
NASA Technical Reports Server (NTRS)
Trosset, Michael W.
1999-01-01
Comprehensive computational experiments to assess the performance of algorithms for numerical optimization require (among other things) a practical procedure for generating pseudorandom nonlinear objective functions. We propose a procedure that is based on the convenient fiction that objective functions are realizations of stochastic processes. This report details the calculations necessary to implement our procedure for the case of certain stationary Gaussian processes and presents a specific implementation in the statistical programming language S-PLUS.
A fast computation method for MUSIC spectrum function based on circular arrays
NASA Astrophysics Data System (ADS)
Du, Zhengdong; Wei, Ping
2015-02-01
The large computation amount of multiple signal classification (MUSIC) spectrum function seriously affects the timeliness of direction finding system using MUSIC algorithm, especially in the two-dimensional directions of arrival (DOA) estimation of azimuth and elevation with a large antenna array. This paper proposes a fast computation method for MUSIC spectrum. It is suitable for any circular array. First, the circular array is transformed into a virtual uniform circular array, in the process of calculating MUSIC spectrum, for the cyclic characteristics of steering vector, the inner product in the calculation of spatial spectrum is realised by cyclic convolution. The computational amount of MUSIC spectrum is obviously less than that of the conventional method. It is a very practical way for MUSIC spectrum computation in circular arrays.
Implementation of linear-scaling plane wave density functional theory on parallel computers
NASA Astrophysics Data System (ADS)
Skylaris, Chris-Kriton; Haynes, Peter D.; Mostofi, Arash A.; Payne, Mike C.
We describe the algorithms we have developed for linear-scaling plane wave density functional calculations on parallel computers as implemented in the onetep program. We outline how onetep achieves plane wave accuracy with a computational cost which increases only linearly with the number of atoms by optimising directly the single-particle density matrix expressed in a psinc basis set. We describe in detail the novel algorithms we have developed for computing with the psinc basis set the quantities needed in the evaluation and optimisation of the total energy within our approach. For our parallel computations we use the general Message Passing Interface (MPI) library of subroutines to exchange data between processors. Accordingly, we have developed efficient schemes for distributing data and computational load to processors in a balanced manner. We describe these schemes in detail and in relation to our algorithms for computations with a psinc basis. Results of tests on different materials show that onetep is an efficient parallel code that should be able to take advantage of a wide range of parallel computer architectures.
Maple (Computer Algebra System) in Teaching Pre-Calculus: Example of Absolute Value Function
ERIC Educational Resources Information Center
Tuluk, Güler
2014-01-01
Modules in Computer Algebra Systems (CAS) make Mathematics interesting and easy to understand. The present study focused on the implementation of the algebraic, tabular (numerical), and graphical approaches used for the construction of the concept of absolute value function in teaching mathematical content knowledge along with Maple 9. The study…
ERIC Educational Resources Information Center
Zwick, Rebecca; And Others
Simulated data were used to investigate the performance of modified versions of the Mantel-Haenszel and standardization methods of differential item functioning (DIF) analysis in computer-adaptive tests (CATs). Each "examinee" received 25 items out of a 75-item pool. A three-parameter logistic item response model was assumed, and…
ERIC Educational Resources Information Center
Hetzroni, Orit E.; Tannous, Juman
2004-01-01
This study investigated the use of computer-based intervention for enhancing communication functions of children with autism. The software program was developed based on daily life activities in the areas of play, food, and hygiene. The following variables were investigated: delayed echolalia, immediate echolalia, irrelevant speech, relevant…
A Computational Model Quantifies the Effect of Anatomical Variability on Velopharyngeal Function
ERIC Educational Resources Information Center
Inouye, Joshua M.; Perry, Jamie L.; Lin, Kant Y.; Blemker, Silvia S.
2015-01-01
Purpose: This study predicted the effects of velopharyngeal (VP) anatomical parameters on VP function to provide a greater understanding of speech mechanics and aid in the treatment of speech disorders. Method: We created a computational model of the VP mechanism using dimensions obtained from magnetic resonance imaging measurements of 10 healthy…
Identifying Differential Item Functioning in Multi-Stage Computer Adaptive Testing
ERIC Educational Resources Information Center
Gierl, Mark J.; Lai, Hollis; Li, Johnson
2013-01-01
The purpose of this study is to evaluate the performance of CATSIB (Computer Adaptive Testing-Simultaneous Item Bias Test) for detecting differential item functioning (DIF) when items in the matching and studied subtest are administered adaptively in the context of a realistic multi-stage adaptive test (MST). MST was simulated using a 4-item…
Identifying Differential Item Functioning in Multi-Stage Computer Adaptive Testing
ERIC Educational Resources Information Center
Gierl, Mark J.; Lai, Hollis; Li, Johnson
2013-01-01
The purpose of this study is to evaluate the performance of CATSIB (Computer Adaptive Testing-Simultaneous Item Bias Test) for detecting differential item functioning (DIF) when items in the matching and studied subtest are administered adaptively in the context of a realistic multi-stage adaptive test (MST). MST was simulated using a 4-item…
ERIC Educational Resources Information Center
Hetzroni, Orit E.; Tannous, Juman
2004-01-01
This study investigated the use of computer-based intervention for enhancing communication functions of children with autism. The software program was developed based on daily life activities in the areas of play, food, and hygiene. The following variables were investigated: delayed echolalia, immediate echolalia, irrelevant speech, relevant…
The nonverbal communication functions of emoticons in computer-mediated communication.
Lo, Shao-Kang
2008-10-01
Most past studies assume that computer-mediated communication (CMC) lacks nonverbal communication cues. However, Internet users have devised and learned to use emoticons to assist their communications. This study examined emoticons as a communication tool that, although presented as verbal cues, perform nonverbal communication functions. We therefore termed emoticons quasi-nonverbal cues.
ERIC Educational Resources Information Center
Man, Yiu-Kwong
2012-01-01
In this note, a new method for computing the partial fraction decomposition of rational functions with irreducible quadratic factors in the denominators is presented. This method involves polynomial divisions and substitutions only, without having to solve for the complex roots of the irreducible quadratic polynomial or to solve a system of linear…
Maple (Computer Algebra System) in Teaching Pre-Calculus: Example of Absolute Value Function
ERIC Educational Resources Information Center
Tuluk, Güler
2014-01-01
Modules in Computer Algebra Systems (CAS) make Mathematics interesting and easy to understand. The present study focused on the implementation of the algebraic, tabular (numerical), and graphical approaches used for the construction of the concept of absolute value function in teaching mathematical content knowledge along with Maple 9. The study…
A Computational Model Quantifies the Effect of Anatomical Variability on Velopharyngeal Function
ERIC Educational Resources Information Center
Inouye, Joshua M.; Perry, Jamie L.; Lin, Kant Y.; Blemker, Silvia S.
2015-01-01
Purpose: This study predicted the effects of velopharyngeal (VP) anatomical parameters on VP function to provide a greater understanding of speech mechanics and aid in the treatment of speech disorders. Method: We created a computational model of the VP mechanism using dimensions obtained from magnetic resonance imaging measurements of 10 healthy…
ERIC Educational Resources Information Center
Man, Yiu-Kwong
2012-01-01
In this note, a new method for computing the partial fraction decomposition of rational functions with irreducible quadratic factors in the denominators is presented. This method involves polynomial divisions and substitutions only, without having to solve for the complex roots of the irreducible quadratic polynomial or to solve a system of linear…
Computation of non-monotonic Lyapunov functions for continuous-time systems
NASA Astrophysics Data System (ADS)
Li, Huijuan; Liu, AnPing
2017-09-01
In this paper, we propose two methods to compute non-monotonic Lyapunov functions for continuous-time systems which are asymptotically stable. The first method is to solve a linear optimization problem on a compact and bounded set. The proposed linear programming based algorithm delivers a CPA1
PuFT: Computer-Assisted Program for Pulmonary Function Tests.
ERIC Educational Resources Information Center
Boyle, Joseph
1983-01-01
PuFT computer program (Microsoft Basic) is designed to help in understanding/interpreting pulmonary function tests (PFT). The program provides predicted values for common PFT after entry of patient data, calculates/plots graph simulating force vital capacity (FVC), and allows observations of effects on predicted PFT values and FVC curve when…