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Sample records for conditions multivariate biasing

  1. ibr: Iterative bias reduction multivariate smoothing

    SciTech Connect

    Hengartner, Nicholas W; Cornillon, Pierre-andre; Matzner - Lober, Eric

    2009-01-01

    Regression is a fundamental data analysis tool for relating a univariate response variable Y to a multivariate predictor X {element_of} E R{sup d} from the observations (X{sub i}, Y{sub i}), i = 1,...,n. Traditional nonparametric regression use the assumption that the regression function varies smoothly in the independent variable x to locally estimate the conditional expectation m(x) = E[Y|X = x]. The resulting vector of predicted values {cflx Y}{sub i} at the observed covariates X{sub i} is called a regression smoother, or simply a smoother, because the predicted values {cflx Y}{sub i} are less variable than the original observations Y{sub i}. Linear smoothers are linear in the response variable Y and are operationally written as {cflx m} = X{sub {lambda}}Y, where S{sub {lambda}} is a n x n smoothing matrix. The smoothing matrix S{sub {lambda}} typically depends on a tuning parameter which we denote by {lambda}, and that governs the tradeoff between the smoothness of the estimate and the goodness-of-fit of the smoother to the data by controlling the effective size of the local neighborhood over which the responses are averaged. We parameterize the smoothing matrix such that large values of {lambda} are associated to smoothers that averages over larger neighborhood and produce very smooth curves, while small {lambda} are associated to smoothers that average over smaller neighborhood to produce a more wiggly curve that wants to interpolate the data. The parameter {lambda} is the bandwidth for kernel smoother, the span size for running-mean smoother, bin smoother, and the penalty factor {lambda} for spline smoother.

  2. Diamond nucleation under bias conditions

    SciTech Connect

    Stoeckel, R.; Stammler, M.; Janischowsky, K.; Ley, L.; Albrecht, M.; Strunk, H.P.

    1998-01-01

    The so-called bias pretreatment allows the growth of heteroepitaxial diamond films by plasma chemical vapor deposition on silicon (100) surfaces. We present plan-view and cross-sectional transmission electron micrographs of the substrate surface at different phases of the bias pretreatment. These observations are augmented by measurements of the etch rates of Si, SiC, and different carbon modifications under plasma conditions and the size distribution of oriented diamond crystals grown after bias pretreatment. Based on these results a new model for diamond nucleation under bias conditions is proposed. First, a closed layer of nearly epitaxially oriented cubic SiC with a thickness of about 10 nm is formed. Subplantation of carbon into this SiC layer causes a supersaturation with carbon and results in the subcutaneous formation of epitaxially oriented nucleation centers in the SiC layer. Etching of the SiC during the bias pretreatment as well as during diamond growth brings these nucleation centers to the sample surface and causes the growth of diamonds epitaxially oriented on the Si/SiC substrate. {copyright} {ital 1998 American Institute of Physics.}

  3. A direct-gradient multivariate index of biotic condition

    USGS Publications Warehouse

    Miranda, Leandro E.; Aycock, J.N.; Killgore, K. J.

    2012-01-01

    Multimetric indexes constructed by summing metric scores have been criticized despite many of their merits. A leading criticism is the potential for investigator bias involved in metric selection and scoring. Often there is a large number of competing metrics equally well correlated with environmental stressors, requiring a judgment call by the investigator to select the most suitable metrics to include in the index and how to score them. Data-driven procedures for multimetric index formulation published during the last decade have reduced this limitation, yet apprehension remains. Multivariate approaches that select metrics with statistical algorithms may reduce the level of investigator bias and alleviate a weakness of multimetric indexes. We investigated the suitability of a direct-gradient multivariate procedure to derive an index of biotic condition for fish assemblages in oxbow lakes in the Lower Mississippi Alluvial Valley. Although this multivariate procedure also requires that the investigator identify a set of suitable metrics potentially associated with a set of environmental stressors, it is different from multimetric procedures because it limits investigator judgment in selecting a subset of biotic metrics to include in the index and because it produces metric weights suitable for computation of index scores. The procedure, applied to a sample of 35 competing biotic metrics measured at 50 oxbow lakes distributed over a wide geographical region in the Lower Mississippi Alluvial Valley, selected 11 metrics that adequately indexed the biotic condition of five test lakes. Because the multivariate index includes only metrics that explain the maximum variability in the stressor variables rather than a balanced set of metrics chosen to reflect various fish assemblage attributes, it is fundamentally different from multimetric indexes of biotic integrity with advantages and disadvantages. As such, it provides an alternative to multimetric procedures.

  4. Multivariate Bias Correction Procedures for Improving Water Quality Predictions using Mechanistic Models

    NASA Astrophysics Data System (ADS)

    Libera, D.; Arumugam, S.

    2015-12-01

    Water quality observations are usually not available on a continuous basis because of the expensive cost and labor requirements so calibrating and validating a mechanistic model is often difficult. Further, any model predictions inherently have bias (i.e., under/over estimation) and require techniques that preserve the long-term mean monthly attributes. This study suggests and compares two multivariate bias-correction techniques to improve the performance of the SWAT model in predicting daily streamflow, TN Loads across the southeast based on split-sample validation. The first approach is a dimension reduction technique, canonical correlation analysis that regresses the observed multivariate attributes with the SWAT model simulated values. The second approach is from signal processing, importance weighting, that applies a weight based off the ratio of the observed and model densities to the model data to shift the mean, variance, and cross-correlation towards the observed values. These procedures were applied to 3 watersheds chosen from the Water Quality Network in the Southeast Region; specifically watersheds with sufficiently large drainage areas and number of observed data points. The performance of these two approaches are also compared with independent estimates from the USGS LOADEST model. Uncertainties in the bias-corrected estimates due to limited water quality observations are also discussed.

  5. Atmospheric conditions, lunar phases, and childbirth: a multivariate analysis.

    PubMed

    Ochiai, Angela Megumi; Gonçalves, Fabio Luiz Teixeira; Ambrizzi, Tercio; Florentino, Lucia Cristina; Wei, Chang Yi; Soares, Alda Valeria Neves; De Araujo, Natalucia Matos; Gualda, Dulce Maria Rosa

    2012-07-01

    Our objective was to assess extrinsic influences upon childbirth. In a cohort of 1,826 days containing 17,417 childbirths among them 13,252 spontaneous labor admissions, we studied the influence of environment upon the high incidence of labor (defined by 75th percentile or higher), analyzed by logistic regression. The predictors of high labor admission included increases in outdoor temperature (odds ratio: 1.742, P = 0.045, 95%CI: 1.011 to 3.001), and decreases in atmospheric pressure (odds ratio: 1.269, P = 0.029, 95%CI: 1.055 to 1.483). In contrast, increases in tidal range were associated with a lower probability of high admission (odds ratio: 0.762, P = 0.030, 95%CI: 0.515 to 0.999). Lunar phase was not a predictor of high labor admission (P = 0.339). Using multivariate analysis, increases in temperature and decreases in atmospheric pressure predicted high labor admission, and increases of tidal range, as a measurement of the lunar gravitational force, predicted a lower probability of high admission.

  6. Atmospheric conditions, lunar phases, and childbirth: a multivariate analysis

    NASA Astrophysics Data System (ADS)

    Ochiai, Angela Megumi; Gonçalves, Fabio Luiz Teixeira; Ambrizzi, Tercio; Florentino, Lucia Cristina; Wei, Chang Yi; Soares, Alda Valeria Neves; De Araujo, Natalucia Matos; Gualda, Dulce Maria Rosa

    2012-07-01

    Our objective was to assess extrinsic influences upon childbirth. In a cohort of 1,826 days containing 17,417 childbirths among them 13,252 spontaneous labor admissions, we studied the influence of environment upon the high incidence of labor (defined by 75th percentile or higher), analyzed by logistic regression. The predictors of high labor admission included increases in outdoor temperature (odds ratio: 1.742, P = 0.045, 95%CI: 1.011 to 3.001), and decreases in atmospheric pressure (odds ratio: 1.269, P = 0.029, 95%CI: 1.055 to 1.483). In contrast, increases in tidal range were associated with a lower probability of high admission (odds ratio: 0.762, P = 0.030, 95%CI: 0.515 to 0.999). Lunar phase was not a predictor of high labor admission ( P = 0.339). Using multivariate analysis, increases in temperature and decreases in atmospheric pressure predicted high labor admission, and increases of tidal range, as a measurement of the lunar gravitational force, predicted a lower probability of high admission.

  7. Conditional bias-penalized optimal estimation for QPE

    NASA Astrophysics Data System (ADS)

    Seo, D.

    2011-12-01

    Most precipitation estimation techniques employ some form of optimal estimation, which usually targets unbiasedness and minimum error variance. Because these properties generally hold only in the unconditional sense, the resulting estimates are subject to conditional biases that may be unacceptably large. A prime example is precipitation analysis using rain gauge data for which, e.g., kriging may significantly underestimate heavy precipitation and, albeit less consequentially, overestimate very light precipitation. In this presentation, we introduce an extremely simple extension to the widely used optimal estimation techniques of simple and ordinary kriging, referred to herein as conditional bias-penalized kriging (CBPK), which minimizes explicitly conditional bias in addition to unconditional error variance. To understand the properties and performance characteristics of CBPK, we carried out numerical experiments in which normal and lognormal random fields of varying spatial correlation scale and rain gauge network density are synthetically generated, and the estimates are cross-validated; the results are summarized in this presentation. Also presented are generalization of CBPK in the framework of classical optimal linear estimation theory, and how it may be used in multisensor QPE.

  8. Stochastic bias from non-Gaussian initial conditions

    SciTech Connect

    Baumann, Daniel; Ferraro, Simone; Smith, Kendrick M.; Green, Daniel E-mail: sferraro@princeton.edu E-mail: kmsmith@astro.princeton.edu

    2013-05-01

    In this article, we show that a stochastic form of scale-dependent halo bias arises in multi-source inflationary models, where multiple fields determine the initial curvature perturbation. We derive this effect for general non-Gaussian initial conditions and study various examples, such as curvaton models and quasi-single field inflation. We present a general formula for both the stochastic and the non-stochastic parts of the halo bias, in terms of the N-point cumulants of the curvature perturbation at the end of inflation. At lowest order, the stochasticity arises if the collapsed limit of the four-point function is boosted relative to the square of the three-point function in the squeezed limit. We derive all our results in two ways, using the barrier crossing formalism and the peak-background split method. In a companion paper [1], we prove that these two approaches are mathematically equivalent.

  9. Visual Bias Predicts Gait Adaptability in Novel Sensory Discordant Conditions

    NASA Technical Reports Server (NTRS)

    Brady, Rachel A.; Batson, Crystal D.; Peters, Brian T.; Mulavara, Ajitkumar P.; Bloomberg, Jacob J.

    2010-01-01

    We designed a gait training study that presented combinations of visual flow and support-surface manipulations to investigate the response of healthy adults to novel discordant sensorimotor conditions. We aimed to determine whether a relationship existed between subjects visual dependence and their postural stability and cognitive performance in a new discordant environment presented at the conclusion of training (Transfer Test). Our training system comprised a treadmill placed on a motion base facing a virtual visual scene that provided a variety of sensory challenges. Ten healthy adults completed 3 training sessions during which they walked on a treadmill at 1.1 m/s while receiving discordant support-surface and visual manipulations. At the first visit, in an analysis of normalized torso translation measured in a scene-movement-only condition, 3 of 10 subjects were classified as visually dependent. During the Transfer Test, all participants received a 2-minute novel exposure. In a combined measure of stride frequency and reaction time, the non-visually dependent subjects showed improved adaptation on the Transfer Test compared to their visually dependent counterparts. This finding suggests that individual differences in the ability to adapt to new sensorimotor conditions may be explained by individuals innate sensory biases. An accurate preflight assessment of crewmembers biases for visual dependence could be used to predict their propensities to adapt to novel sensory conditions. It may also facilitate the development of customized training regimens that could expedite adaptation to alternate gravitational environments.

  10. Novelty, conditioning and attentional bias to sexual rewards

    PubMed Central

    Banca, Paula; Morris, Laurel S.; Mitchell, Simon; Harrison, Neil A.; Potenza, Marc N.; Voon, Valerie

    2016-01-01

    The Internet provides a large source of novel and rewarding stimuli, particularly with respect to sexually explicit materials. Novelty-seeking and cue-conditioning are fundamental processes underlying preference and approach behaviors implicated in disorders of addiction. Here we examine these processes in individuals with compulsive sexual behaviors (CSB), hypothesizing a greater preference for sexual novelty and stimuli conditioned to sexual rewards relative to healthy volunteers. Twenty-two CSB males and forty age-matched male volunteers were tested in two separate behavioral tasks focusing on preferences for novelty and conditioned stimuli. Twenty subjects from each group were also assessed in a third conditioning and extinction task using functional magnetic resonance imaging. CSB was associated with enhanced novelty preference for sexual, as compared to control images, and a generalized preference for cues conditioned to sexual and monetary versus neutral outcomes compared to healthy volunteers. CSB individuals also had greater dorsal cingulate habituation to repeated sexual versus monetary images with the degree of habituation correlating with enhanced preference for sexual novelty. Approach behaviors to sexually conditioned cues dissociable from novelty preference were associated with an early attentional bias to sexual images. This study shows that CSB individuals have a dysfunctional enhanced preference for sexual novelty possibly mediated by greater cingulate habituation along with a generalized enhancement of conditioning to rewards. We further emphasize a dissociable role for cue-conditioning and novelty preference on the early attentional bias for sexual cues. These findings have wider relevance as the Internet provides a broad range of novel and potentially rewarding stimuli. PMID:26606725

  11. Novelty, conditioning and attentional bias to sexual rewards.

    PubMed

    Banca, Paula; Morris, Laurel S; Mitchell, Simon; Harrison, Neil A; Potenza, Marc N; Voon, Valerie

    2016-01-01

    The Internet provides a large source of novel and rewarding stimuli, particularly with respect to sexually explicit materials. Novelty-seeking and cue-conditioning are fundamental processes underlying preference and approach behaviors implicated in disorders of addiction. Here we examine these processes in individuals with compulsive sexual behaviors (CSB), hypothesizing a greater preference for sexual novelty and stimuli conditioned to sexual rewards relative to healthy volunteers. Twenty-two CSB males and forty age-matched male volunteers were tested in two separate behavioral tasks focusing on preferences for novelty and conditioned stimuli. Twenty subjects from each group were also assessed in a third conditioning and extinction task using functional magnetic resonance imaging. CSB was associated with enhanced novelty preference for sexual, as compared to control images, and a generalized preference for cues conditioned to sexual and monetary versus neutral outcomes compared to healthy volunteers. CSB individuals also had greater dorsal cingulate habituation to repeated sexual versus monetary images with the degree of habituation correlating with enhanced preference for sexual novelty. Approach behaviors to sexually conditioned cues dissociable from novelty preference were associated with an early attentional bias to sexual images. This study shows that CSB individuals have a dysfunctional enhanced preference for sexual novelty possibly mediated by greater cingulate habituation along with a generalized enhancement of conditioning to rewards. We further emphasize a dissociable role for cue-conditioning and novelty preference on the early attentional bias for sexual cues. These findings have wider relevance as the Internet provides a broad range of novel and potentially rewarding stimuli. PMID:26606725

  12. Measurement bias detection with Kronecker product restricted models for multivariate longitudinal data: an illustration with health-related quality of life data from thirteen measurement occasions

    PubMed Central

    Verdam, Mathilde G. E.; Oort, Frans J.

    2014-01-01

    Highlights Application of Kronecker product to construct parsimonious structural equation models for multivariate longitudinal data. A method for the investigation of measurement bias with Kronecker product restricted models. Application of these methods to health-related quality of life data from bone metastasis patients, collected at 13 consecutive measurement occasions. The use of curves to facilitate substantive interpretation of apparent measurement bias. Assessment of change in common factor means, after accounting for apparent measurement bias. Longitudinal measurement invariance is usually investigated with a longitudinal factor model (LFM). However, with multiple measurement occasions, the number of parameters to be estimated increases with a multiple of the number of measurement occasions. To guard against too low ratios of numbers of subjects and numbers of parameters, we can use Kronecker product restrictions to model the multivariate longitudinal structure of the data. These restrictions can be imposed on all parameter matrices, including measurement invariance restrictions on factor loadings and intercepts. The resulting models are parsimonious and have attractive interpretation, but require different methods for the investigation of measurement bias. Specifically, additional parameter matrices are introduced to accommodate possible violations of measurement invariance. These additional matrices consist of measurement bias parameters that are either fixed at zero or free to be estimated. In cases of measurement bias, it is also possible to model the bias over time, e.g., with linear or non-linear curves. Measurement bias detection with Kronecker product restricted models will be illustrated with multivariate longitudinal data from 682 bone metastasis patients whose health-related quality of life (HRQL) was measured at 13 consecutive weeks. PMID:25295016

  13. Multi-variable bias correction: application of forest fire risk in present and future climate in Sweden

    NASA Astrophysics Data System (ADS)

    Yang, W.; Gardelin, M.; Olsson, J.; Bosshard, T.

    2015-09-01

    As the risk of a forest fire is largely influenced by weather, evaluating its tendency under a changing climate becomes important for management and decision making. Currently, biases in climate models make it difficult to realistically estimate the future climate and consequent impact on fire risk. A distribution-based scaling (DBS) approach was developed as a post-processing tool that intends to correct systematic biases in climate modelling outputs. In this study, we used two projections, one driven by historical reanalysis (ERA40) and one from a global climate model (ECHAM5) for future projection, both having been dynamically downscaled by a regional climate model (RCA3). The effects of the post-processing tool on relative humidity and wind speed were studied in addition to the primary variables precipitation and temperature. Finally, the Canadian Fire Weather Index system was used to evaluate the influence of changing meteorological conditions on the moisture content in fuel layers and the fire-spread risk. The forest fire risk results using DBS are proven to better reflect risk using observations than that using raw climate outputs. For future periods, southern Sweden is likely to have a higher fire risk than today, whereas northern Sweden will have a lower risk of forest fire.

  14. Effect of altered sensory conditions on multivariate descriptors of human postural sway

    NASA Technical Reports Server (NTRS)

    Kuo, A. D.; Speers, R. A.; Peterka, R. J.; Horak, F. B.; Peterson, B. W. (Principal Investigator)

    1998-01-01

    Multivariate descriptors of sway were used to test whether altered sensory conditions result not only in changes in amount of sway but also in postural coordination. Eigenvalues and directions of eigenvectors of the covariance of shnk and hip angles were used as a set of multivariate descriptors. These quantities were measured in 14 healthy adult subjects performing the Sensory Organization test, which disrupts visual and somatosensory information used for spatial orientation. Multivariate analysis of variance and discriminant analysis showed that resulting sway changes were at least bivariate in character, with visual and somatosensory conditions producing distinct changes in postural coordination. The most significant changes were found when somatosensory information was disrupted by sway-referencing of the support surface (P = 3.2 x 10(-10)). The resulting covariance measurements showed that subjects not only swayed more but also used increased hip motion analogous to the hip strategy. Disruption of vision, by either closing the eyes or sway-referencing the visual surround, also resulted in altered sway (P = 1.7 x 10(-10)), with proportionately more motion of the center of mass than with platform sway-referencing. As shown by discriminant analysis, an optimal univariate measure could explain at most 90% of the behavior due to altered sensory conditions. The remaining 10%, while smaller, are highly significant changes in posture control that depend on sensory conditions. The results imply that normal postural coordination of the trunk and legs requires both somatosensory and visual information and that each sensory modality makes a unique contribution to posture control. Descending postural commands are multivariate in nature, and the motion at each joint is affected uniquely by input from multiple sensors.

  15. A multivariate conditional model for streamflow prediction and spatial precipitation refinement

    NASA Astrophysics Data System (ADS)

    Liu, Zhiyong; Zhou, Ping; Chen, Xiuzhi; Guan, Yinghui

    2015-10-01

    The effective prediction and estimation of hydrometeorological variables are important for water resources planning and management. In this study, we propose a multivariate conditional model for streamflow prediction and the refinement of spatial precipitation estimates. This model consists of high dimensional vine copulas, conditional bivariate copula simulations, and a quantile-copula function. The vine copula is employed because of its flexibility in modeling the high dimensional joint distribution of multivariate data by building a hierarchy of conditional bivariate copulas. We investigate two cases to evaluate the performance and applicability of the proposed approach. In the first case, we generate one month ahead streamflow forecasts that incorporate multiple predictors including antecedent precipitation and streamflow records in a basin located in South China. The prediction accuracy of the vine-based model is compared with that of traditional data-driven models such as the support vector regression (SVR) and the adaptive neuro-fuzzy inference system (ANFIS). The results indicate that the proposed model produces more skillful forecasts than SVR and ANFIS. Moreover, this probabilistic model yields additional information concerning the predictive uncertainty. The second case involves refining spatial precipitation estimates derived from the tropical rainfall measuring mission precipitationproduct for the Yangtze River basin by incorporating remotely sensed soil moisture data and the observed precipitation from meteorological gauges over the basin. The validation results indicate that the proposed model successfully refines the spatial precipitation estimates. Although this model is tested for specific cases, it can be extended to other hydrometeorological variables for predictions and spatial estimations.

  16. Large-scale bias and efficient generation of initial conditions for nonlocal primordial non-Gaussianity

    NASA Astrophysics Data System (ADS)

    Scoccimarro, Román; Hui, Lam; Manera, Marc; Chan, Kwan Chuen

    2012-04-01

    We study the scale dependence of halo bias in generic (nonlocal) primordial non-Gaussian (PNG) initial conditions of the type motivated by inflation, parametrized by an arbitrary quadratic kernel. We first show how to generate nonlocal PNG initial conditions with minimal overhead compared to local PNG models for a general class of primordial bispectra that can be written as linear combinations of separable templates. We run cosmological simulations for the local, and nonlocal equilateral and orthogonal models and present results on the scale dependence of halo bias. We also derive a general formula for the Fourier-space bias using the peak-background split in the context of the excursion-set approach to halos and discuss the difference and similarities with the known corresponding result from local bias models. Our peak-background split bias formula generalizes previous results in the literature to include non-Markovian effects and nonuniversality of the mass function and are in better agreement with measurements in numerical simulations than previous results for a variety of halo masses, redshifts and halo definitions. We also derive for the first time quadratic bias results for arbitrary nonlocal PNG, and show that nonlinear bias loops give small corrections at large scales. The resulting well-behaved perturbation theory paves the way to constrain nonlocal PNG from measurements of the power spectrum and bispectrum in galaxy redshift surveys.

  17. Generation of multivariate near shore extreme wave conditions based on an extreme value copula for offshore boundary conditions.

    NASA Astrophysics Data System (ADS)

    Leyssen, Gert; Mercelis, Peter; De Schoesitter, Philippe; Blanckaert, Joris

    2013-04-01

    Near shore extreme wave conditions, used as input for numerical wave agitation simulations and for the dimensioning of coastal defense structures, need to be determined at a harbour entrance situated at the French North Sea coast. To obtain significant wave heights, the numerical wave model SWAN has been used. A multivariate approach was used to account for the joint probabilities. Considered variables are: wind velocity and direction, water level and significant offshore wave height and wave period. In a first step a univariate extreme value distribution has been determined for the main variables. By means of a technique based on the mean excess function, an appropriate member of the GPD is selected. An optimal threshold for peak over threshold selection is determined by maximum likelihood optimization. Next, the joint dependency structure for the primary random variables is modeled by an extreme value copula. Eventually the multivariate domain of variables was stratified in different classes, each of which representing a combination of variable quantiles with a joint probability, which are used for model simulation. The main variable is the wind velocity, as in the area of concern extreme wave conditions are wind driven. The analysis is repeated for 9 different wind directions. The secondary variable is water level. In shallow waters extreme waves will be directly affected by water depth. Hence the joint probability of occurrence for water level and wave height is of major importance for design of coastal defense structures. Wind velocity and water levels are only dependent for some wind directions (wind induced setup). Dependent directions are detected using a Kendall and Spearman test and appeared to be those with the longest fetch. For these directions, wind velocity and water level extreme value distributions are multivariately linked through a Gumbel Copula. These distributions are stratified into classes of which the frequency of occurrence can be

  18. Influence of growth conditions on exchange bias of NiMn-based spin valves

    SciTech Connect

    Wienecke, Anja; Kruppe, Rahel; Rissing, Lutz

    2015-05-07

    As shown in previous investigations, a correlation between a NiMn-based spin valve's thermal stability and its inherent exchange bias exists, even if the blocking temperature of the antiferromagnet is clearly above the heating temperature and the reason for thermal degradation is mainly diffusion and not the loss of exchange bias. Samples with high exchange bias are thermally more stable than samples with low exchange bias. Those structures promoting a high exchange bias are seemingly the same suppressing thermally induced diffusion processes (A. Wienecke and L. Rissing, “Relationship between thermal stability and layer-stack/structure of NiMn-based GMR systems,” in IEEE Transaction on Magnetic Conference (EMSA 2014)). Many investigations were carried out on the influence of the sputtering parameters as well as the layer thickness on the magnetoresistive effect. The influence of these parameters on the exchange bias and the sample's thermal stability, respectively, was hardly taken into account. The investigation described here concentrates on the last named issue. The focus lies on the influence of the sputtering parameters and layer thickness of the “starting layers” in the stack and the layers forming the (synthetic) antiferromagnet. This paper includes a guideline for the evaluated sputtering conditions and layer thicknesses to realize a high exchange bias and presumably good thermal stability for NiMn-based spin valves with a synthetic antiferromagnet.

  19. Influence of growth conditions on exchange bias of NiMn-based spin valves

    NASA Astrophysics Data System (ADS)

    Wienecke, Anja; Kruppe, Rahel; Rissing, Lutz

    2015-05-01

    As shown in previous investigations, a correlation between a NiMn-based spin valve's thermal stability and its inherent exchange bias exists, even if the blocking temperature of the antiferromagnet is clearly above the heating temperature and the reason for thermal degradation is mainly diffusion and not the loss of exchange bias. Samples with high exchange bias are thermally more stable than samples with low exchange bias. Those structures promoting a high exchange bias are seemingly the same suppressing thermally induced diffusion processes (A. Wienecke and L. Rissing, "Relationship between thermal stability and layer-stack/structure of NiMn-based GMR systems," in IEEE Transaction on Magnetic Conference (EMSA 2014)). Many investigations were carried out on the influence of the sputtering parameters as well as the layer thickness on the magnetoresistive effect. The influence of these parameters on the exchange bias and the sample's thermal stability, respectively, was hardly taken into account. The investigation described here concentrates on the last named issue. The focus lies on the influence of the sputtering parameters and layer thickness of the "starting layers" in the stack and the layers forming the (synthetic) antiferromagnet. This paper includes a guideline for the evaluated sputtering conditions and layer thicknesses to realize a high exchange bias and presumably good thermal stability for NiMn-based spin valves with a synthetic antiferromagnet.

  20. Schema bias in source monitoring varies with encoding conditions: support for a probability-matching account.

    PubMed

    Kuhlmann, Beatrice G; Vaterrodt, Bianca; Bayen, Ute J

    2012-09-01

    Two experiments examined reliance on schematic knowledge in source monitoring. Based on a probability-matching account of source guessing, a schema bias will only emerge if participants do not have a representation of the source-item contingency in the study list, or if the perceived contingency is consistent with schematic expectations. Thus, the account predicts that encoding conditions that affect contingency detection also affect schema bias. In Experiment 1, the schema bias commonly found when schematic information about the sources is not provided before encoding was diminished by an intentional source-memory instruction. In Experiment 2, the depth of processing of schema-consistent and schema-inconsistent source-item pairings was manipulated. Participants consequently overestimated the occurrence of the pairing type they processed in a deep manner, and their source guessing reflected this biased contingency perception. Results support the probability-matching account of source guessing.

  1. Observational fear conditioning in the acquisition and extinction of attentional bias for threat: an experimental evaluation.

    PubMed

    Kelly, Megan M; Forsyth, John P

    2007-05-01

    Anxious persons show automatic and strategic attentional biases for threatening information. Yet, the mechanisms and processes that underlie such biases remain unclear. The central aim of the present study was to elucidate the relation between observational threat learning and the acquisition and extinction of biased threat processing by integrating emotional Stroop color naming tasks within an observational differential fear conditioning procedure. Forty-three healthy female participants underwent several consecutive observational fear conditioning phases. During acquisition, participants watched a confederate displaying mock panic attacks (UCS) paired with a verbal stimulus (CS+), but not with a second nonreinforced verbal stimulus (CS-). As expected, participants showed greater magnitude electrodermal and verbal-evaluative (e.g., distress, fear) conditioned responses to the CS+ over the CS- word. Participants also demonstrated slower color-naming latencies to CS+ compared to the CS- word following acquisition and showed attenuation of this preferential processing bias for threat following extinction. Findings are discussed broadly in the context of the interplay between fear learning and processing biases for threat as observed in persons suffering from anxiety disorders. PMID:17516811

  2. Effect of bias condition on heavy ion radiation in bipolar junction transistors

    NASA Astrophysics Data System (ADS)

    Liu, Chao-Ming; Li, Xing-Ji; Geng, Hong-Bin; Yang, De-Zhuang; He, Shi-Yu

    2012-08-01

    The characteristic degradations in a silicon NPN bipolar junction transistor (BJT) of 3DG142 type are examined under irradiation with 40-MeV chlorine (Cl) ions under forward, grounded, and reverse bias conditions, respectively. Different electrical parameters are in-situ measured during the exposure under each bias condition. From the experimental data, a larger variation of base current (IB) is observed after irradiation at a given value of base-emitter voltage (VBE), while the collector current is slightly affected by irradiation at a given VBE. The gain degradation is affected mostly by the behaviour of the base current. From the experimental data, the variation of current gain in the case of forward bias is much smaller than that in the other conditions. Moreover, for 3DG142 BJT, the current gain degradation in the case of reverse bias is more severe than that in the grounded case at low fluence, while at high fluence, the gain degradation in the reverse bias case becomes smaller than that in the grounded case.

  3. The potential for social contextual and group biases in team decision-making: biases, conditions and psychological mechanisms.

    PubMed

    Jones, P E; Roelofsma, P H

    2000-08-01

    This paper provides a critical review of social contextual and group biases that are relevant to team decision-making in command and control situations. Motivated by the insufficient level of attention this area has received, the purpose of the paper is to provide an insight into the potential that these types of biases have to affect the decision-making of such teams. The biases considered are: false consensus, groupthink, group polarization and group escalation of commitment. For each bias the following four questions are addressed. What is the descriptive nature of the bias? What factors induce the bias? What psychological mechanisms underlie the bias? What is the relevance of the bias to command and control teams? The analysis suggests that these biases have a strong potential to affect team decisions. Consistent with the nature of team decision-making in command and control situations, all of the biases considered tend to be associated with those decisions that are important or novel and are promoted by time pressure and high levels of uncertainty. A concept unifying these biases is that of the shared mental model, but whereas false consensus emanates from social projection tendencies, the rest emanate from social influence factors. The authors also discuss the 'tricky' distinction between teams and groups and propose a revised definition for command and control team. Finally, the authors emphasize the need for future empirical research in this area to pay additional attention to the social side of cognition and the potential that social biases have to affect team decision-making.

  4. Ion-ion Instability with Biased Mesh Grid as Boundary Condition

    NASA Astrophysics Data System (ADS)

    Matsukuma, Masaaki; Kawai, Yoshinobu

    2001-10-01

    We studied chaotic behavior of ion-ion instability experimentally. It was reported[1] that the system with a certain boundary condition became chaotic. This is interpreted as follows: the boundary condition decreases the degrees of the system's freedom and as a result the turbulent state becomes chaos. However, it is not understood exactly that the boundary condition influences the system. We also reported[2] that the ion-ion instability saturated due to a particle trapping effect. The dynamics of the trapped particles is affected by the amplitude of waves. Therefore it is expected that controlling the bias potential to the mesh grid affect the unstable wave system. Time-series and power spectra were measured for various configurations of bias potentials. It was found that the system with a positively biased mesh grid became more periodic than other case. In the system with a strongly negative biased separation grid, the chaotic oscillation excited in a sheath region was observed. Reference [1] M. Matsukuma et.al.: J. Phys. Soc. Jpn. 69 (2000) 303. [2] M. Matsukuma et.al.: Proc. ICPP2000 1 300.

  5. Exploration of Temporal ICD Coding Bias Related to Acute Diabetic Conditions

    PubMed Central

    McKillop, Mollie; Polubriaginof, Fernanda; Weng, Chunhua

    2015-01-01

    Electronic Health Records (EHRs) hold great promise for secondary data reuse but have been reported to contain severe biases. The temporal characteristics of coding biases remain unclear. This study used a survival analysis approach to reveal temporal bias trends for coding acute diabetic conditions among 268 diabetes patients. For glucose-controlled ketoacidosis patients we found it took an average of 7.5 months for the incorrect code to be removed, while for glucose-controlled hypoglycemic patients it took an average of 9 months. We also examined blood glucose lab values and performed a case review to confirm the validity of our findings. We discuss the implications of our findings and propose future work. PMID:26958300

  6. The development of an attentional bias for angry faces following Pavlovian fear conditioning.

    PubMed

    Pischek-Simpson, Leah K; Boschen, Mark J; Neumann, David L; Waters, Allison M

    2009-04-01

    Although it is well documented that fear responses develop following aversive Pavlovian conditioning, it is unclear whether fear learning also manifests in the form of attentional biases for fear-related stimuli. Boschen, Parker, and Neumann (Boschen, M. J., Parker, I., & Neumann, D. L. (2007). Changes in implicit associations do not occur simultaneously to Pavlovian conditioning of physiological anxiety responses. Journal of Anxiety Disorders, 21, 788-803.) showed that despite the acquisition of differential skin conductance conditioned responses to angry faces paired (CS+) and unpaired (CS-) with an aversive shock, development of implicit associations was not subsequently observed on the Implicit Association Test. In the present study, participants (N=76) were assigned either to a Shock or NoShock group and completed a similar aversive Pavlovian conditioning procedure with angry face CS+ and CS- stimuli. Participants next completed a visual probe task in which the angry face CS+ and CS- stimuli were paired with angry face control stimuli and neutral faces. Results confirmed that differential fear conditioning was observed in the Shock group but not in the NoShock group, and that the Shock group subsequently showed a selective attentional bias for the angry face CS+ compared with the CS- and control stimuli during the visual probe task. The findings confirm the interplay between learning-based mechanisms and cognitive processes, such as attentional biases, in models of fear acquisition and have implications for treatment of the anxiety disorders.

  7. N-body simulations with generic non-Gaussian initial conditions II: halo bias

    NASA Astrophysics Data System (ADS)

    Wagner, Christian; Verde, Licia

    2012-03-01

    We present N-body simulations for generic non-Gaussian initial conditions with the aim of exploring and modelling the scale-dependent halo bias. This effect is evident on very large scales requiring large simulation boxes. In addition, the previously available prescription to implement generic non-Gaussian initial conditions has been improved to keep under control higher-order terms which were spoiling the power spectrum on large scales. We pay particular attention to the differences between physical, inflation-motivated primordial bispectra and their factorizable templates, and to the operational definition of the non-Gaussian halo bias (which has both a scale-dependent and an approximately scale-independent contributions). We find that analytic predictions for both the non-Gaussian halo mass function and halo bias work well once a fudge factor (which was introduced before but still lacks convincing physical explanation) is calibrated on simulations. The halo bias remains therefore an extremely promising tool to probe primordial non-Gaussianity and thus to give insights into the physical mechanism that generated the primordial perturbations. The simulation outputs and tables of the analytic predictions will be made publicly available via the non-Gaussian comparison project web site http://icc.ub.edu/~liciaverde/NGSCP.html.

  8. Commentary: Biases that affect the decision to conditionally release an insanity acquittee.

    PubMed

    Fox, Patrick K

    2008-01-01

    The care and management of hospitalized insanity acquittees can be quite challenging. As patients progress in treatment, clinicians must invariably address whether the patient is ready to be returned to the community, balancing the liberty interests of the acquittee with the protection of society. The process by which this determination is made is far from simple and involves review of clinical interview and collateral information, identification of indicators of outcome post-discharge, and the use of structured risk assessment instruments. The decision to release an acquittee conditionally is also influenced by an array of factors that emanate from within the clinician, within the institution, the mental health system, the courts, and the broader society. While such biases affect a clinician's objectivity, they are also a natural part of the evaluation process. Their identification is essential so that the degree to which such biases influence the conditional release decision can be more fully understood and addressed.

  9. Radiation induced deep level defects in bipolar junction transistors under various bias conditions

    NASA Astrophysics Data System (ADS)

    Liu, Chaoming; Yang, Jianqun; Li, Xingji; Ma, Guoliang; Xiao, Liyi; Bollmann, Joachim

    2015-12-01

    Bipolar junction transistor (BJT) is sensitive to ionization and displacement radiation effects in space. In this paper, 35 MeV Si ions were used as irradiation source to research the radiation damage on NPN and PNP bipolar transistors. The changing of electrical parameters of transistors was in situ measured with increasing irradiation fluence of 35 MeV Si ions. Using deep level transient spectroscopy (DLTS), defects in the bipolar junction transistors under various bias conditions are measured after irradiation. Based on the in situ electrical measurement and DLTS spectra, it is clearly that the bias conditions can affect the concentration of deep level defects, and the radiation damage induced by heavy ions.

  10. Multivariate probability distribution for sewer system vulnerability assessment under data-limited conditions.

    PubMed

    Del Giudice, G; Padulano, R; Siciliano, D

    2016-01-01

    The lack of geometrical and hydraulic information about sewer networks often excludes the adoption of in-deep modeling tools to obtain prioritization strategies for funds management. The present paper describes a novel statistical procedure for defining the prioritization scheme for preventive maintenance strategies based on a small sample of failure data collected by the Sewer Office of the Municipality of Naples (IT). Novelty issues involve, among others, considering sewer parameters as continuous statistical variables and accounting for their interdependences. After a statistical analysis of maintenance interventions, the most important available factors affecting the process are selected and their mutual correlations identified. Then, after a Box-Cox transformation of the original variables, a methodology is provided for the evaluation of a vulnerability map of the sewer network by adopting a joint multivariate normal distribution with different parameter sets. The goodness-of-fit is eventually tested for each distribution by means of a multivariate plotting position. The developed methodology is expected to assist municipal engineers in identifying critical sewers, prioritizing sewer inspections in order to fulfill rehabilitation requirements. PMID:26901717

  11. A Poisson-lognormal conditional-autoregressive model for multivariate spatial analysis of pedestrian crash counts across neighborhoods.

    PubMed

    Wang, Yiyi; Kockelman, Kara M

    2013-11-01

    This work examines the relationship between 3-year pedestrian crash counts across Census tracts in Austin, Texas, and various land use, network, and demographic attributes, such as land use balance, residents' access to commercial land uses, sidewalk density, lane-mile densities (by roadway class), and population and employment densities (by type). The model specification allows for region-specific heterogeneity, correlation across response types, and spatial autocorrelation via a Poisson-based multivariate conditional auto-regressive (CAR) framework and is estimated using Bayesian Markov chain Monte Carlo methods. Least-squares regression estimates of walk-miles traveled per zone serve as the exposure measure. Here, the Poisson-lognormal multivariate CAR model outperforms an aspatial Poisson-lognormal multivariate model and a spatial model (without cross-severity correlation), both in terms of fit and inference. Positive spatial autocorrelation emerges across neighborhoods, as expected (due to latent heterogeneity or missing variables that trend in space, resulting in spatial clustering of crash counts). In comparison, the positive aspatial, bivariate cross correlation of severe (fatal or incapacitating) and non-severe crash rates reflects latent covariates that have impacts across severity levels but are more local in nature (such as lighting conditions and local sight obstructions), along with spatially lagged cross correlation. Results also suggest greater mixing of residences and commercial land uses is associated with higher pedestrian crash risk across different severity levels, ceteris paribus, presumably since such access produces more potential conflicts between pedestrian and vehicle movements. Interestingly, network densities show variable effects, and sidewalk provision is associated with lower severe-crash rates. PMID:24036167

  12. Condition bias of hunter-shot ring-necked ducks exposed to lead

    USGS Publications Warehouse

    McCracken, K.G.; Afton, A.D.; Peters, M.S.

    2000-01-01

    We evaluated the condition bias hypothesis for ring-necked ducks (Aythya collaris) exposed to lead by testing the null hypothesis that ducks shot by hunters do not differ in physiological condition from those collected randomly from the same location. After adjusting for structural body size and log(e) concentration of blood lead, we found that overall body condition differed significantly between collection types and age classes, and marginally between sexes. Ingesta-free body mass of ring-necked ducks sampled randomly averaged 8.8% greater than those shot over decoys, and 99% of this difference was accounted for by lipid reserves. Ingesta, ash, and protein did not differ between collection types; however, after-hatching-year (AHY) birds had 5.1% more ash and 4.8% more protein than did hatching-year (HY) birds. The only sex difference was that males had 4.1% more protein than did females. Ingesta-free body mass, lipids, and protein were negatively related to concentration of blood lead. Collection type-by-concentration of blood lead and age-by-sex-by-concentration of blood lead interactions were not significant. To the extent that lead pellets persist as a cause of disease or mortality, waterfowl biologists should account for lead exposure as a possible source of condition bias when estimating population parameters and modeling survival of ring-necked ducks and other waterfowl species prone to ingest lead. These findings further underscore the problem that ingested lead shotgun pellets pose for waterfowl.

  13. Extinction of cocaine-induced place approach in rats: a validation of the "biased" conditioning procedure.

    PubMed

    Calcagnetti, D J; Schechter, M D

    1993-01-01

    It has often been demonstrated that when a rat is conditioned in a cue-specific environment that has been repeatedly paired with cocaine injections, it will spend more time in that environment than it does in a saline-paired environment. This behavioral procedure is commonly known as the conditioned place preference (CPP)-test. At present, a firm theoretical understanding of the mechanisms underlying the production of a CPP are unknown. It is insufficient merely to know that a CPP can result after repeated drug pairings. Rather, it is necessary that the procedure is validated within a learning theory framework. The objective of the present study was, therefore, to establish that what is observed in place preference studies was, indeed, conditioning. This was accomplished by determining whether a cocaine-induced increase in time spent in a drug-paired environment was subject to attenuation following extinction trials. Rats were tested for their initial bias in spending more time in one of two stimulus-specific chambers of a place-conditioning apparatus. On four occasions, rats were injected with 2.5 mg/kg cocaine and confined to their less-preferred chamber whereas, on four alternating sessions, they were conditioned with saline (vehicle) in their preferred chamber. Subsequent testing in the nondrugged state revealed that these rats displayed a significant increase in the time spent in their initially least-preferred environment compared to baseline measurements. Following establishment of this cocaine-induced CPP, the rats were injected only with saline and conditioned for an equal number of sessions (i.e., four).(ABSTRACT TRUNCATED AT 250 WORDS)

  14. Conditioned social dominance threat: observation of others' social dominance biases threat learning.

    PubMed

    Haaker, Jan; Molapour, Tanaz; Olsson, Andreas

    2016-10-01

    Social groups are organized along dominance hierarchies, which determine how we respond to threats posed by dominant and subordinate others. The persuasive impact of these dominance threats on mental and physical well-being has been well described but it is unknown how dominance rank of others bias our experience and learning in the first place. We introduce a model of conditioned social dominance threat in humans, where the presence of a dominant other is paired with an aversive event. Participants first learned about the dominance rank of others by observing their dyadic confrontations. During subsequent fear learning, the dominant and subordinate others were equally predictive of an aversive consequence (mild electric shock) to the participant. In three separate experiments, we show that participants' eye-blink startle responses and amygdala reactivity adaptively tracked dominance of others during observation of confrontation. Importantly, during fear learning dominant vs subordinate others elicited stronger and more persistent learned threat responses as measured by physiological arousal and amygdala activity. Our results characterize the neural basis of learning through observing conflicts between others, and how this affects subsequent learning through direct, personal experiences.

  15. Degradation of Leakage Currents in Solid Tantalum Capacitors Under Steady-State Bias Conditions

    NASA Technical Reports Server (NTRS)

    Teverovsky, Alexander A.

    2010-01-01

    Degradation of leakage currents in various types of solid tantalum capacitors under steady-state bias conditions was investigated at temperatures from 105 oC to 170 oC and voltages up to two times the rated voltage. Variations of leakage currents with time under highly accelerated life testing (HALT) and annealing, thermally stimulated depolarization currents, and I-V characteristics were measured to understand the conduction mechanism and the reason for current degradation. During HALT the currents increase gradually up to three orders of magnitude in some cases, and then stabilize with time. This degradation is reversible and annealing can restore the initial levels of leakage currents. The results are attributed to migration of positively charged oxygen vacancies in tantalum pentoxide films that diminish the Schottky barrier at the MnO2/Ta2O5 interface and increase electron injection. A simple model allows for estimation of concentration and mobility of oxygen vacancies based on the level of current degradation.

  16. Charge Accumulation Effects on Breakdown Condition of Capacitive Discharges in DC-biased RF Field

    NASA Astrophysics Data System (ADS)

    Shoji, M.; Sato, M.

    1998-10-01

    Breakdown characteristics of capacitively coupled argon dc-biased rf (13.56 MHz) discharges are measured using an insulated electrode (IE) system made from glass-covered aluminum disk plates. In the IE system under the influence of a dc-biased rf field, charged particles generated in the discharge space will accumulate at the glass surface without leakage, which may weaken the dc electric field strength. After the dc-biased rf voltage is applied, a time lag Tl until breakdown is observed and the rf breakdown voltage V_rf is considerably lowered. For example, V_rf decreases by more than 10 % at Tl = 1000 sec. The values of V_rf which cause breakdown within Tl = 20 sec. in the IE system are compared with those for the bare metal electrode (BME) system for which no charge accumulation takes place. At low dc biases, they are almost the same for both systems. As the dc bias is increased, V_rf of the BME system becomes much smaller than that of the IE system. The decrease in V_rf can be explained by the occurring of secondary electron emission from the metal surface.

  17. Hysteresis loss analysis of soft magnetic materials under direct current bias conditions

    NASA Astrophysics Data System (ADS)

    Turgut, Zafer; Kosai, Hiroyuki; Bixel, Tyler; Scofield, James; Semiatin, S. Lee; Horwath, John

    2015-05-01

    Direct current bias related hysteresis loss characteristics of three commercially available magnetic materials: (1) an iron based Metglas tape core, (2) a Sendust powder core, and (3) a Mn-Zn based ferrite in both un-gapped and gapped configurations were studied. The measurements are conducted for a fixed external field Hext, a fixed flux swing (ΔB), and a fixed maximum forward magnetization (Bmax) as a function of the external bias field. In all the measurements, a direct correlation is found between permeability and measured loss values as a function of dc bias field. Increased hysteresis losses are measured in the magnetization rotation region in which classical domain theory predicts minimal losses. The observed trends are discussed within the frame work of classical domain theory.

  18. Operating conditions for the generation of stable anode spot plasma in front of a positively biased electrode

    SciTech Connect

    Park, Yeong-Shin; Lee, Yuna; Dang, Jeong-Jeung; Chung, Kyoung-Jae; Hwang, Y. S.

    2014-02-15

    Stability of an anode spot plasma, which is an additional high density plasma generated in front of a positively biased electrode immersed in ambient plasma, is a critical issue for its utilization to various types of ion sources. In this study, operating conditions for the generation of stable anode spot plasmas are experimentally investigated. Diagnostics of the bias current flowing into the positively biased electrode and the properties of ambient plasma reveal that unstable nature of the anode spot is deeply associated with the reduction of double layer potential between the anode spot plasma and the ambient plasma. It is found that stability of the anode spot plasma can be improved with increasing the ionization rate in ambient plasma so as to compensate the loss of electrons across the double layer or with enlarging the area of the biased electrode to prevent electron accumulation inside the anode spot. The results obtained from the present study give the guideline for operating conditions of anode spot plasmas as an ion source with high brightness.

  19. Regional vertical total electron content (VTEC) modeling together with satellite and receiver differential code biases (DCBs) using semi-parametric multivariate adaptive regression B-splines (SP-BMARS)

    NASA Astrophysics Data System (ADS)

    Durmaz, Murat; Karslioglu, Mahmut Onur

    2015-04-01

    There are various global and regional methods that have been proposed for the modeling of ionospheric vertical total electron content (VTEC). Global distribution of VTEC is usually modeled by spherical harmonic expansions, while tensor products of compactly supported univariate B-splines can be used for regional modeling. In these empirical parametric models, the coefficients of the basis functions as well as differential code biases (DCBs) of satellites and receivers can be treated as unknown parameters which can be estimated from geometry-free linear combinations of global positioning system observables. In this work we propose a new semi-parametric multivariate adaptive regression B-splines (SP-BMARS) method for the regional modeling of VTEC together with satellite and receiver DCBs, where the parametric part of the model is related to the DCBs as fixed parameters and the non-parametric part adaptively models the spatio-temporal distribution of VTEC. The latter is based on multivariate adaptive regression B-splines which is a non-parametric modeling technique making use of compactly supported B-spline basis functions that are generated from the observations automatically. This algorithm takes advantage of an adaptive scale-by-scale model building strategy that searches for best-fitting B-splines to the data at each scale. The VTEC maps generated from the proposed method are compared numerically and visually with the global ionosphere maps (GIMs) which are provided by the Center for Orbit Determination in Europe (CODE). The VTEC values from SP-BMARS and CODE GIMs are also compared with VTEC values obtained through calibration using local ionospheric model. The estimated satellite and receiver DCBs from the SP-BMARS model are compared with the CODE distributed DCBs. The results show that the SP-BMARS algorithm can be used to estimate satellite and receiver DCBs while adaptively and flexibly modeling the daily regional VTEC.

  20. Multivariate statistical analysis of water chemistry conditions in three wastewater stabilization ponds with algae blooms and pH fluctuations.

    PubMed

    Wallace, Jack; Champagne, Pascale; Hall, Geof

    2016-06-01

    The wastewater stabilization ponds (WSPs) at a wastewater treatment facility in eastern Ontario, Canada, have experienced excessive algae growth and high pH levels in the summer months. A full range of parameters were sampled from the system and the chemical dynamics in the three WSPs were assessed through multivariate statistical analysis. The study presents a novel approach for exploratory analysis of a comprehensive water chemistry dataset, incorporating principal components analysis (PCA) and principal components (PC) and partial least squares (PLS) regressions. The analyses showed strong correlations between chl-a and sunlight, temperature, organic matter, and nutrients, and weak and negative correlations between chl-a and pH and chl-a and DO. PCA reduced the data from 19 to 8 variables, with a good fit to the original data matrix (similarity measure of 0.73). Multivariate regressions to model system pH in terms of these key parameters were performed on the reduced variable set and the PCs generated, for which strong fits (R(2) > 0.79 with all data) were observed. The methodologies presented in this study are applicable to a wide range of natural and engineered systems where a large number of water chemistry parameters are monitored resulting in the generation of large data sets.

  1. Multivariate statistical analysis of water chemistry conditions in three wastewater stabilization ponds with algae blooms and pH fluctuations.

    PubMed

    Wallace, Jack; Champagne, Pascale; Hall, Geof

    2016-06-01

    The wastewater stabilization ponds (WSPs) at a wastewater treatment facility in eastern Ontario, Canada, have experienced excessive algae growth and high pH levels in the summer months. A full range of parameters were sampled from the system and the chemical dynamics in the three WSPs were assessed through multivariate statistical analysis. The study presents a novel approach for exploratory analysis of a comprehensive water chemistry dataset, incorporating principal components analysis (PCA) and principal components (PC) and partial least squares (PLS) regressions. The analyses showed strong correlations between chl-a and sunlight, temperature, organic matter, and nutrients, and weak and negative correlations between chl-a and pH and chl-a and DO. PCA reduced the data from 19 to 8 variables, with a good fit to the original data matrix (similarity measure of 0.73). Multivariate regressions to model system pH in terms of these key parameters were performed on the reduced variable set and the PCs generated, for which strong fits (R(2) > 0.79 with all data) were observed. The methodologies presented in this study are applicable to a wide range of natural and engineered systems where a large number of water chemistry parameters are monitored resulting in the generation of large data sets. PMID:27038585

  2. Terahertz responsivity of field-effect transistors under arbitrary biasing conditions

    NASA Astrophysics Data System (ADS)

    Földesy, Péter

    2013-09-01

    Current biased photoresponse model of long channel field-effect transistor (FET) detectors is introduced to describe the low frequency behavior in complex circuit environment. The model is applicable in all FET working regions, including subthreshold, linear, saturated modes, includes bulk potential variations, and handles the simultaneous gate-source and drain-source detection or source-driven topologies. The model is based on the phenomenological representation that links the photoresponse to the gate transconductance over drain current ratio (gm/ID) and circuit theory. A derived method is provided to analyze the detector behavior, to characterize existing antenna coupled detectors, and to predict the photoresponse in a complex circuit. The model is validated by measurements of 180 nm gate length silicon and GaAs high electron mobility FETs.

  3. Non-random temporary emigration and the robust design: Conditions for bias at the end of a time series: Section VIII

    USGS Publications Warehouse

    Langtimm, Catherine A.

    2008-01-01

    Knowing the extent and magnitude of the potential bias can help in making decisions as to what time frame provides the best estimates or the most reliable opportunity to model and test hypotheses about factors affecting survival probability. To assess bias, truncating the capture histories to shorter time frames and reanalyzing the data to compare time-specific estimates may help identify spurious effects. Running simulations that mimic the parameter values and movement conditions in the real situation can provide estimates of standardized bias that can be used to identify those annual estimates that are biased to the point where the 95% confidence intervals are inadequate in describing the uncertainty of the estimates.

  4. Multivariate Analysis in Metabolomics

    PubMed Central

    Worley, Bradley; Powers, Robert

    2015-01-01

    Metabolomics aims to provide a global snapshot of all small-molecule metabolites in cells and biological fluids, free of observational biases inherent to more focused studies of metabolism. However, the staggeringly high information content of such global analyses introduces a challenge of its own; efficiently forming biologically relevant conclusions from any given metabolomics dataset indeed requires specialized forms of data analysis. One approach to finding meaning in metabolomics datasets involves multivariate analysis (MVA) methods such as principal component analysis (PCA) and partial least squares projection to latent structures (PLS), where spectral features contributing most to variation or separation are identified for further analysis. However, as with any mathematical treatment, these methods are not a panacea; this review discusses the use of multivariate analysis for metabolomics, as well as common pitfalls and misconceptions. PMID:26078916

  5. Sensitivity and Bias under Conditions of Equal and Unequal Academic Task Difficulty

    ERIC Educational Resources Information Center

    Reed, Derek D.; Martens, Brian K.

    2008-01-01

    We conducted an experimental analysis of children's relative problem-completion rates across two workstations under conditions of equal (Experiment 1) and unequal (Experiment 2) problem difficulty. Results were described using the generalized matching equation and were evaluated for degree of schedule versus stimulus control. Experiment 1 involved…

  6. An Alternative Flight Software Trigger Paradigm: Applying Multivariate Logistic Regression to Sense Trigger Conditions Using Inaccurate or Scarce Information

    NASA Technical Reports Server (NTRS)

    Smith, Kelly M.; Gay, Robert S.; Stachowiak, Susan J.

    2013-01-01

    In late 2014, NASA will fly the Orion capsule on a Delta IV-Heavy rocket for the Exploration Flight Test-1 (EFT-1) mission. For EFT-1, the Orion capsule will be flying with a new GPS receiver and new navigation software. Given the experimental nature of the flight, the flight software must be robust to the loss of GPS measurements. Once the high-speed entry is complete, the drogue parachutes must be deployed within the proper conditions to stabilize the vehicle prior to deploying the main parachutes. When GPS is available in nominal operations, the vehicle will deploy the drogue parachutes based on an altitude trigger. However, when GPS is unavailable, the navigated altitude errors become excessively large, driving the need for a backup barometric altimeter to improve altitude knowledge. In order to increase overall robustness, the vehicle also has an alternate method of triggering the parachute deployment sequence based on planet-relative velocity if both the GPS and the barometric altimeter fail. However, this backup trigger results in large altitude errors relative to the targeted altitude. Motivated by this challenge, this paper demonstrates how logistic regression may be employed to semi-automatically generate robust triggers based on statistical analysis. Logistic regression is used as a ground processor pre-flight to develop a statistical classifier. The classifier would then be implemented in flight software and executed in real-time. This technique offers improved performance even in the face of highly inaccurate measurements. Although the logistic regression-based trigger approach will not be implemented within EFT-1 flight software, the methodology can be carried forward for future missions and vehicles.

  7. An Alternative Flight Software Paradigm: Applying Multivariate Logistic Regression to Sense Trigger Conditions using Inaccurate or Scarce Information

    NASA Technical Reports Server (NTRS)

    Smith, Kelly; Gay, Robert; Stachowiak, Susan

    2013-01-01

    In late 2014, NASA will fly the Orion capsule on a Delta IV-Heavy rocket for the Exploration Flight Test-1 (EFT-1) mission. For EFT-1, the Orion capsule will be flying with a new GPS receiver and new navigation software. Given the experimental nature of the flight, the flight software must be robust to the loss of GPS measurements. Once the high-speed entry is complete, the drogue parachutes must be deployed within the proper conditions to stabilize the vehicle prior to deploying the main parachutes. When GPS is available in nominal operations, the vehicle will deploy the drogue parachutes based on an altitude trigger. However, when GPS is unavailable, the navigated altitude errors become excessively large, driving the need for a backup barometric altimeter to improve altitude knowledge. In order to increase overall robustness, the vehicle also has an alternate method of triggering the parachute deployment sequence based on planet-relative velocity if both the GPS and the barometric altimeter fail. However, this backup trigger results in large altitude errors relative to the targeted altitude. Motivated by this challenge, this paper demonstrates how logistic regression may be employed to semi-automatically generate robust triggers based on statistical analysis. Logistic regression is used as a ground processor pre-flight to develop a statistical classifier. The classifier would then be implemented in flight software and executed in real-time. This technique offers improved performance even in the face of highly inaccurate measurements. Although the logistic regression-based trigger approach will not be implemented within EFT-1 flight software, the methodology can be carried forward for future missions and vehicles

  8. An Alternative Flight Software Trigger Paradigm: Applying Multivariate Logistic Regression to Sense Trigger Conditions using Inaccurate or Scarce Information

    NASA Technical Reports Server (NTRS)

    Smith, Kelly M.; Gay, Robert S.; Stachowiak, Susan J.

    2013-01-01

    In late 2014, NASA will fly the Orion capsule on a Delta IV-Heavy rocket for the Exploration Flight Test-1 (EFT-1) mission. For EFT-1, the Orion capsule will be flying with a new GPS receiver and new navigation software. Given the experimental nature of the flight, the flight software must be robust to the loss of GPS measurements. Once the high-speed entry is complete, the drogue parachutes must be deployed within the proper conditions to stabilize the vehicle prior to deploying the main parachutes. When GPS is available in nominal operations, the vehicle will deploy the drogue parachutes based on an altitude trigger. However, when GPS is unavailable, the navigated altitude errors become excessively large, driving the need for a backup barometric altimeter. In order to increase overall robustness, the vehicle also has an alternate method of triggering the drogue parachute deployment based on planet-relative velocity if both the GPS and the barometric altimeter fail. However, this velocity-based trigger results in large altitude errors relative to the targeted altitude. Motivated by this challenge, this paper demonstrates how logistic regression may be employed to automatically generate robust triggers based on statistical analysis. Logistic regression is used as a ground processor pre-flight to develop a classifier. The classifier would then be implemented in flight software and executed in real-time. This technique offers excellent performance even in the face of highly inaccurate measurements. Although the logistic regression-based trigger approach will not be implemented within EFT-1 flight software, the methodology can be carried forward for future missions and vehicles.

  9. Multivariate normality

    NASA Technical Reports Server (NTRS)

    Crutcher, H. L.; Falls, L. W.

    1976-01-01

    Sets of experimentally determined or routinely observed data provide information about the past, present and, hopefully, future sets of similarly produced data. An infinite set of statistical models exists which may be used to describe the data sets. The normal distribution is one model. If it serves at all, it serves well. If a data set, or a transformation of the set, representative of a larger population can be described by the normal distribution, then valid statistical inferences can be drawn. There are several tests which may be applied to a data set to determine whether the univariate normal model adequately describes the set. The chi-square test based on Pearson's work in the late nineteenth and early twentieth centuries is often used. Like all tests, it has some weaknesses which are discussed in elementary texts. Extension of the chi-square test to the multivariate normal model is provided. Tables and graphs permit easier application of the test in the higher dimensions. Several examples, using recorded data, illustrate the procedures. Tests of maximum absolute differences, mean sum of squares of residuals, runs and changes of sign are included in these tests. Dimensions one through five with selected sample sizes 11 to 101 are used to illustrate the statistical tests developed.

  10. SEMICONDUCTOR DEVICES: Dose-rate effects of p-channel metal oxide semiconductor field-effect transistors at various biasing conditions

    NASA Astrophysics Data System (ADS)

    Bo, Lan; Qi, Guo; Jing, Sun; Jiangwei, Cui; Maoshun, Li; Rui, Chen; Wuxiong, Fei; Yun, Zhao

    2010-05-01

    The total-dose response and annealing effect of p-channel metal oxide semiconductor field-effect transistors (PMOSFETs) were investigated at various dose rates and biasing conditions. The results show that the shift of threshold voltage is more obvious when the dose rate is decreased. Under the various dose rates and biasing conditions, some have exhibited a time-dependent effect and others showed enhanced low-dose-rate sensitivity (ELDRS). Finally, using the subthreshold-separating method, the threshold-voltage shift is separated into shifts due to interface states and oxide-trapped charges, and the underlying mechanisms of the observed effects are discussed. It has been indicated that the ELDRS effect results from the different quantities of the interface states generated at high and low dose rates.

  11. Bias from conditioning on live birth in pregnancy cohorts: an illustration based on neurodevelopment in children after prenatal exposure to organic pollutants

    PubMed Central

    Liew, Zeyan; Olsen, Jørn; Cui, Xin; Ritz, Beate; Arah, Onyebuchi A

    2015-01-01

    Only 60–70% of fertilized eggs may result in a live birth, and very early fetal loss mainly goes unnoticed. Outcomes that can only be ascertained in live-born children will be missing for those who do not survive till birth. In this article, we illustrate a common bias structure (leading to ‘live-birth bias’) that arises from studying the effects of prenatal exposure to environmental factors on long-term health outcomes among live births only in pregnancy cohorts. To illustrate this we used prenatal exposure to perfluoroalkyl substances (PFAS) and attention-deficit/hyperactivity disorder (ADHD) in school-aged children as an example. PFAS are persistent organic pollutants that may impact human fecundity and be toxic for neurodevelopment. We simulated several hypothetical scenarios based on characteristics from the Danish National Birth Cohort and found that a weak inverse association may appear even if PFAS do not cause ADHD but have a considerable effect on fetal survival. The magnitude of the negative bias was generally small, and adjusting for common causes of the outcome and fetal loss can reduce the bias. Our example highlights the need to identify the determinants of pregnancy loss and the importance of quantifying bias arising from conditioning on live birth in observational studies. PMID:25604449

  12. Housing conditions affect rat responses to two types of ambiguity in a reward–reward discrimination cognitive bias task

    PubMed Central

    Parker, Richard M.A.; Paul, Elizabeth S.; Burman, Oliver H.P.; Browne, William J.; Mendl, Michael

    2014-01-01

    Decision-making under ambiguity in cognitive bias tasks is a promising new indicator of affective valence in animals. Rat studies support the hypothesis that animals in a negative affective state evaluate ambiguous cues negatively. Prior automated operant go/go judgement bias tasks have involved training rats that an auditory cue of one frequency predicts a Reward and a cue of a different frequency predicts a Punisher (RP task), and then measuring whether ambiguous cues of intermediate frequency are judged as predicting reward (‘optimism’) or punishment (‘pessimism’). We investigated whether an automated Reward–Reward (RR) task yielded similar results to, and was faster to train than, RP tasks. We also introduced a new ambiguity test (simultaneous presentation of the two training cues) alongside the standard single ambiguous cue test. Half of the rats experienced an unpredictable housing treatment (UHT) designed to induce a negative state. Control rats were relatively ‘pessimistic’, whilst UHT rats were quicker, but no less accurate, in their responses in the RR test, and showed less anxiety-like behaviour in independent tests. A possible reason for these findings is that rats adapted to and were stimulated by UHT, whilst control rats in a predictable environment were more sensitive to novelty and change. Responses in the new ambiguity test correlated positively with those in single ambiguous cue tests, and may provide a measure of attention bias. The RR task was quicker to train than previous automated RP tasks. Together, they could be used to disentangle how reward and punishment processes underpin affect-induced cognitive biases. PMID:25106739

  13. Housing conditions affect rat responses to two types of ambiguity in a reward-reward discrimination cognitive bias task.

    PubMed

    Parker, Richard M A; Paul, Elizabeth S; Burman, Oliver H P; Browne, William J; Mendl, Michael

    2014-11-01

    Decision-making under ambiguity in cognitive bias tasks is a promising new indicator of affective valence in animals. Rat studies support the hypothesis that animals in a negative affective state evaluate ambiguous cues negatively. Prior automated operant go/go judgement bias tasks have involved training rats that an auditory cue of one frequency predicts a Reward and a cue of a different frequency predicts a Punisher (RP task), and then measuring whether ambiguous cues of intermediate frequency are judged as predicting reward ('optimism') or punishment ('pessimism'). We investigated whether an automated Reward-Reward (RR) task yielded similar results to, and was faster to train than, RP tasks. We also introduced a new ambiguity test (simultaneous presentation of the two training cues) alongside the standard single ambiguous cue test. Half of the rats experienced an unpredictable housing treatment (UHT) designed to induce a negative state. Control rats were relatively 'pessimistic', whilst UHT rats were quicker, but no less accurate, in their responses in the RR test, and showed less anxiety-like behaviour in independent tests. A possible reason for these findings is that rats adapted to and were stimulated by UHT, whilst control rats in a predictable environment were more sensitive to novelty and change. Responses in the new ambiguity test correlated positively with those in single ambiguous cue tests, and may provide a measure of attention bias. The RR task was quicker to train than previous automated RP tasks. Together, they could be used to disentangle how reward and punishment processes underpin affect-induced cognitive biases.

  14. Housing conditions affect rat responses to two types of ambiguity in a reward-reward discrimination cognitive bias task.

    PubMed

    Parker, Richard M A; Paul, Elizabeth S; Burman, Oliver H P; Browne, William J; Mendl, Michael

    2014-11-01

    Decision-making under ambiguity in cognitive bias tasks is a promising new indicator of affective valence in animals. Rat studies support the hypothesis that animals in a negative affective state evaluate ambiguous cues negatively. Prior automated operant go/go judgement bias tasks have involved training rats that an auditory cue of one frequency predicts a Reward and a cue of a different frequency predicts a Punisher (RP task), and then measuring whether ambiguous cues of intermediate frequency are judged as predicting reward ('optimism') or punishment ('pessimism'). We investigated whether an automated Reward-Reward (RR) task yielded similar results to, and was faster to train than, RP tasks. We also introduced a new ambiguity test (simultaneous presentation of the two training cues) alongside the standard single ambiguous cue test. Half of the rats experienced an unpredictable housing treatment (UHT) designed to induce a negative state. Control rats were relatively 'pessimistic', whilst UHT rats were quicker, but no less accurate, in their responses in the RR test, and showed less anxiety-like behaviour in independent tests. A possible reason for these findings is that rats adapted to and were stimulated by UHT, whilst control rats in a predictable environment were more sensitive to novelty and change. Responses in the new ambiguity test correlated positively with those in single ambiguous cue tests, and may provide a measure of attention bias. The RR task was quicker to train than previous automated RP tasks. Together, they could be used to disentangle how reward and punishment processes underpin affect-induced cognitive biases. PMID:25106739

  15. Estimating the decomposition of predictive information in multivariate systems

    NASA Astrophysics Data System (ADS)

    Faes, Luca; Kugiumtzis, Dimitris; Nollo, Giandomenico; Jurysta, Fabrice; Marinazzo, Daniele

    2015-03-01

    In the study of complex systems from observed multivariate time series, insight into the evolution of one system may be under investigation, which can be explained by the information storage of the system and the information transfer from other interacting systems. We present a framework for the model-free estimation of information storage and information transfer computed as the terms composing the predictive information about the target of a multivariate dynamical process. The approach tackles the curse of dimensionality employing a nonuniform embedding scheme that selects progressively, among the past components of the multivariate process, only those that contribute most, in terms of conditional mutual information, to the present target process. Moreover, it computes all information-theoretic quantities using a nearest-neighbor technique designed to compensate the bias due to the different dimensionality of individual entropy terms. The resulting estimators of prediction entropy, storage entropy, transfer entropy, and partial transfer entropy are tested on simulations of coupled linear stochastic and nonlinear deterministic dynamic processes, demonstrating the superiority of the proposed approach over the traditional estimators based on uniform embedding. The framework is then applied to multivariate physiologic time series, resulting in physiologically well-interpretable information decompositions of cardiovascular and cardiorespiratory interactions during head-up tilt and of joint brain-heart dynamics during sleep.

  16. Improved analysis of bias in Monte Carlo criticality safety

    NASA Astrophysics Data System (ADS)

    Haley, Thomas C.

    2000-08-01

    Criticality safety, the prevention of nuclear chain reactions, depends on Monte Carlo computer codes for most commercial applications. One major shortcoming of these codes is the limited accuracy of the atomic and nuclear data files they depend on. In order to apply a code and its data files to a given criticality safety problem, the code must first be benchmarked against similar problems for which the answer is known. The difference between a code prediction and the known solution is termed the "bias" of the code. Traditional calculations of the bias for application to commercial criticality problems are generally full of assumptions and lead to large uncertainties which must be conservatively factored into the bias as statistical tolerances. Recent trends in storing commercial nuclear fuel---narrowed regulatory margins of safety, degradation of neutron absorbers, the desire to use higher enrichment fuel, etc.---push the envelope of criticality safety. They make it desirable to minimize uncertainty in the bias to accommodate these changes, and they make it vital to understand what assumptions are safe to make under what conditions. A set of improved procedures is proposed for (1) developing multivariate regression bias models, and (2) applying multivariate regression bias models. These improved procedures lead to more accurate estimates of the bias and much smaller uncertainties about this estimate, while also generally providing more conservative results. The drawback is that the procedures are not trivial and are highly labor intensive to implement. The payback in savings in margin to criticality and conservatism for calculations near regulatory and safety limits may be worth this cost. To develop these procedures, a bias model using the statistical technique of weighted least squares multivariate regression is developed in detail. Problems that can occur from a weak statistical analysis are highlighted, and a solid statistical method for developing the bias

  17. Post-training re-exposure to fear conditioned stimuli enhances memory consolidation and biases rats toward the use of dorsolateral striatum-dependent response learning.

    PubMed

    Leong, Kah-Chung; Goodman, Jarid; Packard, Mark G

    2015-09-15

    In a dual-solution task that can be acquired using either hippocampus-dependent "place" or dorsolateral striatum-dependent "response" learning, emotional arousal induced by unconditioned stimuli (e.g. anxiogenic drug injections or predator odor exposure) biases rats toward response learning. In the present experiments emotionally-arousing conditioned stimuli were used to modulate the relative use of multiple memory systems. In Experiment 1, adult male Long-Evans rats initially received three standard fear-conditioning trials in which a tone (2 kHz, 75 dB) was paired with a brief electrical shock (1 mA, 2s). On day 2, the rats were trained in a dual-solution plus-maze task to swim from the same start arm (South) to a hidden escape platform always located in the same goal arm (East). Immediately following training, rats received post-training re-exposure to the fear-conditioned stimuli (i.e. tone and context) without shock. On day 3, the relative use of place or response learning was assessed on a probe trial in which rats were started from the opposite start arm (North). Post-training re-exposure to fear-conditioned stimuli produced preferential use of a response strategy. In Experiment 2, different rats received fear conditioning and were then trained in a single-solution task that required the use of response learning. Immediately following training, rats received post-training re-exposure to the fear-conditioned stimuli without shock. Re-exposure to fear-conditioned stimuli enhanced memory consolidation in the response learning task. Thus, re-exposure to fear-conditioned stimuli biases rats toward the use of dorsolateral striatum-dependent response learning and enhances memory consolidation of response learning.

  18. On the use of multivariate statistical methods for combining in-stream monitoring data and spatial analysis to characterize water quality conditions in the White River basin, Indiana, USA.

    PubMed

    Gamble, Andrew; Babbar-Sebens, Meghna

    2012-01-01

    Mechanistic hydrologic and water quality models provide useful alternatives for estimating water quality in unmonitored streams. However, developing these elaborate models for large watersheds can be time-consuming and expensive, in addition to challenges that arise during calibration when there is limited spatial and/or temporal monitored in-stream water quality data. The main objective of this research was to investigate different approaches for developing multivariate analysis models as alternative methods for rapidly assessing relationships between spatio-temporal physical attributes of the watershed and water quality conditions in monitored streams, and then using the developed relationships for estimating water quality conditions in unmonitored streams. The study compares the use of various statistical estimates (mean, geometric mean, trimmed mean, and median) of monitored water quality variables to represent annual and seasonal water quality conditions. The relationship between these estimates and the spatial data is then modeled via linear and non-linear multivariate methods. Overall, the non-linear techniques for classification outperformed the linear techniques with an average cross-validation accuracy of 79.7%. Additionally, the geometric mean based models outperformed models based on other statistical indicators with an average cross-validation accuracy of 80.2%. Dividing the data into annual and quarterly datasets also offered important insights into the behavior of certain water quality variables impacted by seasonal variations. The research provides useful guidance on the use and interpretation of the various statistical estimates and statistical models for multivariate water quality analyses.

  19. Small Sample Properties of Bayesian Multivariate Autoregressive Time Series Models

    ERIC Educational Resources Information Center

    Price, Larry R.

    2012-01-01

    The aim of this study was to compare the small sample (N = 1, 3, 5, 10, 15) performance of a Bayesian multivariate vector autoregressive (BVAR-SEM) time series model relative to frequentist power and parameter estimation bias. A multivariate autoregressive model was developed based on correlated autoregressive time series vectors of varying…

  20. Are Early-Life Socioeconomic Conditions Directly Related to Birth Outcomes? Grandmaternal Education, Grandchild Birth Weight, and Associated Bias Analyses.

    PubMed

    Huang, Jonathan Y; Gavin, Amelia R; Richardson, Thomas S; Rowhani-Rahbar, Ali; Siscovick, David S; Enquobahrie, Daniel A

    2015-10-01

    Grandmaternal education may be related to grandchild birth weight (GBW) through maternal early-life development; however, conventional regression models may be endogenously confounded. Alternative models employing explicit structural assumptions may provide incrementally clearer evidence. We used data from the US National Longitudinal Study of Adolescent to Adult Health (1995-2009; 1,681 mother-child pairs) to estimate "direct effects" of grandmaternal educational level (less than high school, high school diploma or equivalent, or college degree) at the time of the mother's birth on GBW, adjusted for maternal life-course factors: maltreatment as a child, education and income as an adult, prepregnancy overweight, and prenatal smoking. Using conventional and marginal structural model (MSM) approaches, we estimated 54-g (95% confidence interval: -14.0, 122.1) and 87-g (95% confidence interval: 10.9, 162.5) higher GBWs per increase in educational level, respectively. The MSM allowed simultaneous mediation by and adjustment for prepregnancy overweight. Estimates were insensitive to alternate structural assumptions and mediator parameterizations. Bias analysis suggested that a single unmeasured confounder would have to have a strong influence on GBW (approximately 150 g) or be greatly imbalanced across exposure groups (approximately 25%) to completely explain the findings. Coupling an MSM with sensitivity analyses provides some evidence that maternal early-life socioeconomic environment is directly associated with offspring birth weight.

  1. Analytical description of the injection ratio of self-biased bipolar transistors under the very high injection conditions of ESD events

    NASA Astrophysics Data System (ADS)

    Gendron, A.; Renaud, P.; Bafleur, M.; Nolhier, N.

    2008-05-01

    This paper proposes a 1D-analytical description of the injection ratio of a self-biased bipolar transistor under very high current injection conditions. Starting from an expression of the current gain based on the stored charge into the emitter and base regions, we derive a new analytical expression of the current injection ratio. This analytical description demonstrates the presence of an asymptotic limit for the injection ratio at very high current densities, as the ratio of electron/hole mobilities in the case of an NPN transistor and to the ratio of hole/electron saturation velocities for a PNP. Moreover, for the first time, a base narrowing effect is demonstrated and explained in the case of a self-biased PNP, in contrast with the base widening effect (Kirk effect [Kirk CT, A theory of transistor cutoff frequency (fT) falloff at high current densities, IRE Trans Electr Dev 1961: p. 164-73]) reported for lower current density. These results are validated by numerical simulation and show a good agreement with experimental characterizations of transistors especially designed to operate under extreme condition such as electrostatic discharge (ESD) events.

  2. Sensory Bias Predicts Postural Stability, Anxiety, and Cognitive Performance in Healthy Adults Walking in Novel Discordant Conditions

    NASA Technical Reports Server (NTRS)

    Brady, Rachel A.; Batson, Crystal D.; Peters, Brian T.; Mulavara, Ajitkumar P.; Bloomberg, Jacob J.

    2010-01-01

    We designed a gait training study that presented combinations of visual flow and support surface manipulations to investigate the response of healthy adults to novel discordant sensorimotor conditions. We aimed to determine whether a relationship existed between subjects visual dependence and their scores on a collective measure of anxiety, cognition, and postural stability in a new discordant environment presented at the conclusion of training (Transfer Test). A treadmill was mounted to a motion base platform positioned 2 m behind a large visual screen. Training consisted of three walking sessions, each within a week of the previous visit, that presented four 5-minute exposures to various combinations of support surface and visual scene manipulations, all lateral sinusoids. The conditions were scene translation only, support surface translation only, simultaneous scene and support surface translations in-phase, and simultaneous scene and support surface translations 180 out-of-phase. During the Transfer Test, the trained participants received a 2-minute novel exposure. A visual sinusoidal roll perturbation, with twice the original flow rate, was superimposed on a sinusoidal support surface roll perturbation that was 90 out of phase with the scene. A high correlation existed between normalized torso translation, measured in the scene-only condition at the first visit, and a combined measure of normalized heart rate, stride frequency, and reaction time at the transfer test. Results suggest that visually dependent participants experience decreased postural stability, increased anxiety, and increased reaction times compared to their less visually dependent counterparts when negotiating novel discordant conditions.

  3. Correcting acoustic Doppler current profiler discharge measurement bias from moving-bed conditions without global positioning during the 2004 Glen Canyon Dam controlled flood on the Colorado River

    USGS Publications Warehouse

    Gartner, J.W.; Ganju, N.K.

    2007-01-01

    Discharge measurements were made by acoustic Doppler current profiler at two locations on the Colorado River during the 2004 controlled flood from Glen Canyon Dam, Arizona. Measurement hardware and software have constantly improved from the 1980s such that discharge measurements by acoustic profiling instruments are now routinely made over a wide range of hydrologic conditions. However, measurements made with instruments deployed from moving boats require reliable boat velocity data for accurate measurements of discharge. This is normally accomplished by using special acoustic bottom track pings that sense instrument motion over bottom. While this method is suitable for most conditions, high current flows that produce downstream bed sediment movement create a condition known as moving bed that will bias velocities and discharge to lower than actual values. When this situation exists, one solution is to determine boat velocity with satellite positioning information. Another solution is to use a lower frequency instrument. Discharge measurements made during the 2004 Glen Canyon controlled flood were subject to moving-bed conditions and frequent loss of bottom track. Due to site conditions and equipment availability, the measurements were conducted without benefit of external positioning information or lower frequency instruments. This paper documents and evaluates several techniques used to correct the resulting underestimated discharge measurements. One technique produces discharge values in good agreement with estimates from numerical model and measured hydrographs during the flood. ?? 2007, by the American Society of Limnology and Oceanography, Inc.

  4. Multivariate optimization and supplementation strategies for the simultaneous production of amylases, cellulases, xylanases, and proteases by Aspergillus awamori under solid-state fermentation conditions.

    PubMed

    de Castro, Aline Machado; Castilho, Leda R; Freire, Denise Maria Guimarães

    2015-02-01

    The production of extracts containing a pool of enzymes for extensive biomass deconstruction can lead to significant advantages in biorefinery applications. In this work, a strain of Aspergillus awamori IOC-3914 was used for the simultaneous production of five groups of hydrolases by solid-state fermentation of babassu cake. Sequential experimental design strategies and multivariate optimization using the desirability function were first used to study temperature, moisture content, and granulometry. After that, further improvements in product yields were achieved by supplementation with other agro-industrial materials. At the end of the study, the production of enzymes was up to 3.3-fold increased, and brewer's spent grains and babassu flour showed to be the best supplements. Maximum activities for endoamylases, exoamylases, cellulases (CMCases), xylanases, and proteases achieved were 197, 106, 20, 835, and 57 U g(-1), respectively. The strain was also able to produce β-glucosidases and debranching amylases (up to 35 and 43 U g(-1), respectively), indicating the potential of its enzyme pool for cellulose and starch degradation.

  5. Multivariable PID control by decoupling

    NASA Astrophysics Data System (ADS)

    Garrido, Juan; Vázquez, Francisco; Morilla, Fernando

    2016-04-01

    This paper presents a new methodology to design multivariable proportional-integral-derivative (PID) controllers based on decoupling control. The method is presented for general n × n processes. In the design procedure, an ideal decoupling control with integral action is designed to minimise interactions. It depends on the desired open-loop processes that are specified according to realisability conditions and desired closed-loop performance specifications. These realisability conditions are stated and three common cases to define the open-loop processes are studied and proposed. Then, controller elements are approximated to PID structure. From a practical point of view, the wind-up problem is also considered and a new anti-wind-up scheme for multivariable PID controller is proposed. Comparisons with other works demonstrate the effectiveness of the methodology through the use of several simulation examples and an experimental lab process.

  6. Problems with Multivariate Normality: Can the Multivariate Bootstrap Help?

    ERIC Educational Resources Information Center

    Thompson, Bruce

    Multivariate normality is required for some statistical tests. This paper explores the implications of violating the assumption of multivariate normality and illustrates a graphical procedure for evaluating multivariate normality. The logic for using the multivariate bootstrap is presented. The multivariate bootstrap can be used when distribution…

  7. MBIS: multivariate Bayesian image segmentation tool.

    PubMed

    Esteban, Oscar; Wollny, Gert; Gorthi, Subrahmanyam; Ledesma-Carbayo, María-J; Thiran, Jean-Philippe; Santos, Andrés; Bach-Cuadra, Meritxell

    2014-07-01

    We present MBIS (Multivariate Bayesian Image Segmentation tool), a clustering tool based on the mixture of multivariate normal distributions model. MBIS supports multichannel bias field correction based on a B-spline model. A second methodological novelty is the inclusion of graph-cuts optimization for the stationary anisotropic hidden Markov random field model. Along with MBIS, we release an evaluation framework that contains three different experiments on multi-site data. We first validate the accuracy of segmentation and the estimated bias field for each channel. MBIS outperforms a widely used segmentation tool in a cross-comparison evaluation. The second experiment demonstrates the robustness of results on atlas-free segmentation of two image sets from scan-rescan protocols on 21 healthy subjects. Multivariate segmentation is more replicable than the monospectral counterpart on T1-weighted images. Finally, we provide a third experiment to illustrate how MBIS can be used in a large-scale study of tissue volume change with increasing age in 584 healthy subjects. This last result is meaningful as multivariate segmentation performs robustly without the need for prior knowledge.

  8. Field-aligned neutral wind bias correction scheme for global ionospheric modeling at midlatitudes by assimilating FORMOSAT-3/COSMIC hmF2 data under geomagnetically quiet conditions

    NASA Astrophysics Data System (ADS)

    Sun, Yang-Yi; Matsuo, Tomoko; Maruyama, Naomi; Liu, Jann-Yenq

    2015-04-01

    This study demonstrates the usage of a data assimilation procedure, which ingests the FORMOSAT-3/COSMIC (F3/C) hmF2 observations to correct the model wind biases to enhance the capability of the new global Ionosphere Plasmasphere Electrodynamics (IPE) model under geomagnetically quiet conditions. The IPE model is built upon the field line interhemispheric plasma model with a realistic geomagnetic field model and empirical model drivers. The hmF2 observed by the F3/C radio occultation technique is utilized to adjust global thermospheric field-aligned neutral winds (i.e., a component of the thermospheric neutral wind parallel to the magnetic field) at midlatitudes according to a linear relationship between time differentials of the field-aligned wind and hmF2. The adjusted winds are further applied to drive the IPE model. The comparison of the modeled electron density with the observations of F3/C and ground-based GPS receivers at the 2012 March equinox suggests that the modeled electron density can be significantly improved in the midlatitude regions of the Southern Hemisphere, if the wind correction scheme is applied. Moreover, the F3/C observation, the IPE model, and the wind bias correction scheme are applied to study the 2012 Southern Hemisphere Midlatitude Summer Nighttime Anomaly (southern MSNA)/Weddell Sea Anomaly (WSA) event at December solstice for examining the role of the neutral winds in controlling the longitudinal variation of the southern MSNA/WSA behavior. With the help of the wind bias correction scheme, the IPE model better tracks the F3/C-observed eastward movement of the southern MSNA/WSA feature. The apparent eastward movement of the southern MSNA/WSA features in the local time coordinate is primarily caused by the longitudinal variation in the declination angle of the geomagnetic field that controls the field-aligned projection of both geographic meridional and zonal components of the neutral wind. Both the IPE simulations and the F3/C

  9. A general, multivariate definition of causal effects in epidemiology.

    PubMed

    Flanders, W Dana; Klein, Mitchel

    2015-07-01

    Population causal effects are often defined as contrasts of average individual-level counterfactual outcomes, comparing different exposure levels. Common examples include causal risk difference and risk ratios. These and most other examples emphasize effects on disease onset, a reflection of the usual epidemiological interest in disease occurrence. Exposure effects on other health characteristics, such as prevalence or conditional risk of a particular disability, can be important as well, but contrasts involving these other measures may often be dismissed as non-causal. For example, an observed prevalence ratio might often viewed as an estimator of a causal incidence ratio and hence subject to bias. In this manuscript, we provide and evaluate a definition of causal effects that generalizes those previously available. A key part of the generalization is that contrasts used in the definition can involve multivariate, counterfactual outcomes, rather than only univariate outcomes. An important consequence of our generalization is that, using it, one can properly define causal effects based on a wide variety of additional measures. Examples include causal prevalence ratios and differences and causal conditional risk ratios and differences. We illustrate how these additional measures can be useful, natural, easily estimated, and of public health importance. Furthermore, we discuss conditions for valid estimation of each type of causal effect, and how improper interpretation or inferences for the wrong target population can be sources of bias.

  10. A multivariate CAR model for mismatched lattices.

    PubMed

    Porter, Aaron T; Oleson, Jacob J

    2014-10-01

    In this paper, we develop a multivariate Gaussian conditional autoregressive model for use on mismatched lattices. Most current multivariate CAR models are designed for each multivariate outcome to utilize the same lattice structure. In many applications, a change of basis will allow different lattices to be utilized, but this is not always the case, because a change of basis is not always desirable or even possible. Our multivariate CAR model allows each outcome to have a different neighborhood structure which can utilize different lattices for each structure. The model is applied in two real data analysis. The first is a Bayesian learning example in mapping the 2006 Iowa Mumps epidemic, which demonstrates the importance of utilizing multiple channels of infection flow in mapping infectious diseases. The second is a multivariate analysis of poverty levels and educational attainment in the American Community Survey. PMID:25457598

  11. Multivariate Regression with Calibration*

    PubMed Central

    Liu, Han; Wang, Lie; Zhao, Tuo

    2014-01-01

    We propose a new method named calibrated multivariate regression (CMR) for fitting high dimensional multivariate regression models. Compared to existing methods, CMR calibrates the regularization for each regression task with respect to its noise level so that it is simultaneously tuning insensitive and achieves an improved finite-sample performance. Computationally, we develop an efficient smoothed proximal gradient algorithm which has a worst-case iteration complexity O(1/ε), where ε is a pre-specified numerical accuracy. Theoretically, we prove that CMR achieves the optimal rate of convergence in parameter estimation. We illustrate the usefulness of CMR by thorough numerical simulations and show that CMR consistently outperforms other high dimensional multivariate regression methods. We also apply CMR on a brain activity prediction problem and find that CMR is as competitive as the handcrafted model created by human experts. PMID:25620861

  12. Patterns of ectoparasitism in North American red squirrels (Tamiasciurus hudsonicus): Sex-biases, seasonality, age, and effects on male body condition

    PubMed Central

    Patterson, Jesse E.H.; Neuhaus, Peter; Kutz, Susan J.; Ruckstuhl, Kathreen E.

    2015-01-01

    Within many species, males are often more heavily parasitised than females. Several hypotheses have been proposed to explain this phenomenon, including immunocompetence handicaps, sexual size dimorphism and behavioural differences. Here we set out to test the latter two hypotheses and make inferences about the former by assessing patterns of ectoparasitism across various life-history stages in a population of North American red squirrels (Tamiasciurus hudsonicus). We also conducted an ectoparasite removal experiment to investigate the effects of ectoparasites on male body condition. We found that males were more intensely parasitized than females, but only during the mating period. There was no difference in ectoparasite intensity between male and female juveniles at birth or at emergence, suggesting that ectoparasites do not exploit male red squirrels for longer-range natal dispersal. Male red squirrels in our population were slightly heavier than females, however we did not find any evidence that this dimorphism drives male-biased ectoparasitism. Finally, we could not detect an effect of ectoparasite removal on male body mass. Our results lend support to the hypothesis that ectoparasites exploit their male hosts for transmission and that male red squirrels are important for the transmission dynamics of ectoparasites in this population; however, the mechanisms (i.e., immunocompetence, testosterone) are not known. PMID:26236631

  13. Examination of Ion Beam Acceleration and Self-Bias Effect in the Modified MadHeX Plasma Source with Conducting and Insulating Upstream Boundary Conditions

    NASA Astrophysics Data System (ADS)

    Sung, Yung-Ta; Devinney, Michael; Scharer, John

    2013-10-01

    The MadHeX experiment consists of a Pyrex tube connected to a stainless steel magnetic field expansion chamber (expansion ratio RE = 4.5) has been upgraded with an axial magnetic mirror field and an additional magnet in the transition region. This configuration enhances electron temperature and ionization fraction and minimizes neutral reflux. A half-turn double-helix antenna is used to excite electrostatic or inductive regime waves in the source. An ion beam of energy, E = 160 eV at 500 W RF power, has been observed in a low pressure (0.3 mtorr) argon plasma formed in the expansion region with a 340 G magnetic field with a R = 1.4 nozzle. The effects of upstream end plate boundary conditions on the plasma self-bias and ion beam acceleration are discussed. The effect of lower flow rates and pressures, higher RF powers (500 W-8 kW) and magnetic field strength dependence on the ion beam acceleration, plasma potential, electron density and temperature are explored. The axial ion velocity distribution function and temperatures at higher powers are observed by argon 668 nm laser induced fluorescence with density measurements obtained by mm wave interferometry. The EEDF and non-Maxwellian tail are examined using optical emission spectroscopy. Research supported by the University of Wisconsin-Madison.

  14. Multivariate bubbles and antibubbles

    NASA Astrophysics Data System (ADS)

    Fry, John

    2014-08-01

    In this paper we develop models for multivariate financial bubbles and antibubbles based on statistical physics. In particular, we extend a rich set of univariate models to higher dimensions. Changes in market regime can be explicitly shown to represent a phase transition from random to deterministic behaviour in prices. Moreover, our multivariate models are able to capture some of the contagious effects that occur during such episodes. We are able to show that declining lending quality helped fuel a bubble in the US stock market prior to 2008. Further, our approach offers interesting insights into the spatial development of UK house prices.

  15. Multivariate Data EXplorer (MDX)

    SciTech Connect

    Steed, Chad Allen

    2012-08-01

    The MDX toolkit facilitates exploratory data analysis and visualization of multivariate datasets. MDX provides and interactive graphical user interface to load, explore, and modify multivariate datasets stored in tabular forms. MDX uses an extended version of the parallel coordinates plot and scatterplots to represent the data. The user can perform rapid visual queries using mouse gestures in the visualization panels to select rows or columns of interest. The visualization panel provides coordinated multiple views whereby selections made in one plot are propagated to the other plots. Users can also export selected data or reconfigure the visualization panel to explore relationships between columns and rows in the data.

  16. Protein phosphorylation stoichiometry by simultaneous ICP-QMS determination of phosphorus and sulfur oxide ions: a multivariate optimization of plasma operating conditions.

    PubMed

    Ciavardelli, Domenico; Sacchetta, Paolo; Federici, Giorgio; Di Ilio, Carmine; Urbani, Andrea

    2010-02-15

    Molecular mass spectrometry (MS) analysis of protein phosphorylation is partially limited by the molecular specie specificity of the analytical responses that might impair both qualitative and quantitative performances. Elemental MS, such as inductively coupled plasma mass spectrometry (ICP-MS) can overcome these drawbacks; in fact, analytical performance is theoretically independent of the molecular structure of a target analyte naturally containing the elements of interest. Nevertheless, isobaric interferences derived from sample matrix and laboratory environment can hinder the quantitative determination of both phosphorus (P) and sulfur (S) as (31)P(+) and (32)S(+) by inductively coupled plasma quadrupole mass spectrometry (ICP-QMS) under standard plasma conditions. These interferences may be overcome by quantifying P and S as oxide ions (31)P(16)O(+) and (32)S(16)O(+), respectively. In this study, we present a systematic investigation on the effect of plasma instrumental conditions on the oxide ion responses by a design of experiment approach for the simultaneous ICP-QMS determination of P and S ((31)P(16)O(+) and (32)S(16)O(+), respectively) in protein samples without the use of dynamic reaction, collision reaction cells or pre-addition of oxygen as reactant gas in the torch. The proposed method was evaluated in terms of limit of detection, limit of quantification, linearity, repeatability, and trueness. Moreover, detection and quantification capabilities of the optimized method were compared to the standard plasma mode for determination of (31)P(+) and (34)S(+). Spectral and non-spectral interferences affecting the quantification of (31)P(+), (31)P(16)O(+) and (32)S(16)O(+) were also studied. The suitability of inorganic elemental standards for P and S quantification in proteins was assessed. The method was applied to quantify the phosphorylation stoichiometry of commercially available caseins (bovine beta-casein, native and dephosphorylated alpha-casein) and

  17. Halo velocity bias

    NASA Astrophysics Data System (ADS)

    Biagetti, Matteo; Desjacques, Vincent; Kehagias, Alex; Riotto, Antonio

    2014-11-01

    It has been recently shown that any halo velocity bias present in the initial conditions does not decay to unity, in agreement with predictions from peak theory. However, this is at odds with the standard formalism based on the coupled-fluids approximation for the coevolution of dark matter and halos. Starting from conservation laws in phase space, we discuss why the fluid momentum conservation equation for the biased tracers needs to be modified in accordance with the change advocated in Baldauf et al. Our findings indicate that a correct description of the halo properties should properly take into account peak constraints when starting from the Vlasov-Boltzmann equation.

  18. Elements of a pragmatic approach for dealing with bias and uncertainty in experiments through predictions : experiment design and data conditioning; %22real space%22 model validation and conditioning; hierarchical modeling and extrapolative prediction.

    SciTech Connect

    Romero, Vicente Jose

    2011-11-01

    This report explores some important considerations in devising a practical and consistent framework and methodology for utilizing experiments and experimental data to support modeling and prediction. A pragmatic and versatile 'Real Space' approach is outlined for confronting experimental and modeling bias and uncertainty to mitigate risk in modeling and prediction. The elements of experiment design and data analysis, data conditioning, model conditioning, model validation, hierarchical modeling, and extrapolative prediction under uncertainty are examined. An appreciation can be gained for the constraints and difficulties at play in devising a viable end-to-end methodology. Rationale is given for the various choices underlying the Real Space end-to-end approach. The approach adopts and refines some elements and constructs from the literature and adds pivotal new elements and constructs. Crucially, the approach reflects a pragmatism and versatility derived from working many industrial-scale problems involving complex physics and constitutive models, steady-state and time-varying nonlinear behavior and boundary conditions, and various types of uncertainty in experiments and models. The framework benefits from a broad exposure to integrated experimental and modeling activities in the areas of heat transfer, solid and structural mechanics, irradiated electronics, and combustion in fluids and solids.

  19. Electrical, optical, and material characterizations of blue InGaN light emitting diodes submitted to reverse-bias stress in water vapor condition

    SciTech Connect

    Chen, Hsiang Chu, Yu-Cheng; Chen, Yun-Ti; Chen, Chian-You; Shei, Shih-Chang

    2014-09-07

    In this paper, we investigate degradation of InGaN/GaN light emitting diodes (LEDs) under reverse-bias operations in water vapor and dry air. To examine failure origins, electrical characterizations including current-voltage, breakdown current profiles, optical measurement, and multiple material analyses were performed. Our findings indicate that the diffusion of indium atoms in water vapor can expedite degradation. Investigation of reverse-bias stress can help provide insight into the effects of water vapor on LEDs.

  20. Multivariate Data EXplorer (MDX)

    2012-08-01

    The MDX toolkit facilitates exploratory data analysis and visualization of multivariate datasets. MDX provides and interactive graphical user interface to load, explore, and modify multivariate datasets stored in tabular forms. MDX uses an extended version of the parallel coordinates plot and scatterplots to represent the data. The user can perform rapid visual queries using mouse gestures in the visualization panels to select rows or columns of interest. The visualization panel provides coordinated multiple views wherebymore » selections made in one plot are propagated to the other plots. Users can also export selected data or reconfigure the visualization panel to explore relationships between columns and rows in the data.« less

  1. Biased Allostery.

    PubMed

    Edelstein, Stuart J; Changeux, Jean-Pierre

    2016-09-01

    G-protein-coupled receptors (GPCRs) constitute a large group of integral membrane proteins that transduce extracellular signals from a wide range of agonists into targeted intracellular responses. Although the responses can vary depending on the category of G-proteins activated by a particular receptor, responses were also found to be triggered by interactions of the receptor with β-arrestins. It was subsequently discovered that for the same receptor molecule (e.g., the β-adrenergic receptor), some agonists have a propensity to specifically favor responses by G-proteins, others by β-arrestins, as has now been extensively studied. This feature of the GPCR system is known as biased agonism and is subject to various interpretations, including agonist-induced conformational change versus selective stabilization of preexisting active conformations. Here, we explore a complete allosteric framework for biased agonism based on alternative preexisting conformations that bind more strongly, but nonexclusively, either G-proteins or β-arrestins. The framework incorporates reciprocal effects among all interacting molecules. As a result, G-proteins and β-arrestins are in steric competition for binding to the cytoplasmic surface of either the G-protein-favoring or β-arrestin-favoring GPCR conformation. Moreover, through linkage relations, the strength of the interactions of G-proteins or β-arrestins with the corresponding active conformation potentiates the apparent affinity for the agonist, effectively equating these two proteins to allosteric modulators. The balance between response alternatives can also be influenced by the physiological concentrations of either G-proteins or β-arrestins, as well as by phosphorylation or interactions with positive or negative allosteric modulators. The nature of the interactions in the simulations presented suggests novel experimental tests to distinguish more fully among alternative mechanisms. PMID:27602718

  2. Multivariate Meta-Analysis Using Individual Participant Data

    ERIC Educational Resources Information Center

    Riley, R. D.; Price, M. J.; Jackson, D.; Wardle, M.; Gueyffier, F.; Wang, J.; Staessen, J. A.; White, I. R.

    2015-01-01

    When combining results across related studies, a multivariate meta-analysis allows the joint synthesis of correlated effect estimates from multiple outcomes. Joint synthesis can improve efficiency over separate univariate syntheses, may reduce selective outcome reporting biases, and enables joint inferences across the outcomes. A common issue is…

  3. Introduction to multivariate discrimination

    NASA Astrophysics Data System (ADS)

    Kégl, Balázs

    2013-07-01

    Multivariate discrimination or classification is one of the best-studied problem in machine learning, with a plethora of well-tested and well-performing algorithms. There are also several good general textbooks [1-9] on the subject written to an average engineering, computer science, or statistics graduate student; most of them are also accessible for an average physics student with some background on computer science and statistics. Hence, instead of writing a generic introduction, we concentrate here on relating the subject to a practitioner experimental physicist. After a short introduction on the basic setup (Section 1) we delve into the practical issues of complexity regularization, model selection, and hyperparameter optimization (Section 2), since it is this step that makes high-complexity non-parametric fitting so different from low-dimensional parametric fitting. To emphasize that this issue is not restricted to classification, we illustrate the concept on a low-dimensional but non-parametric regression example (Section 2.1). Section 3 describes the common algorithmic-statistical formal framework that unifies the main families of multivariate classification algorithms. We explain here the large-margin principle that partly explains why these algorithms work. Section 4 is devoted to the description of the three main (families of) classification algorithms, neural networks, the support vector machine, and AdaBoost. We do not go into the algorithmic details; the goal is to give an overview on the form of the functions these methods learn and on the objective functions they optimize. Besides their technical description, we also make an attempt to put these algorithm into a socio-historical context. We then briefly describe some rather heterogeneous applications to illustrate the pattern recognition pipeline and to show how widespread the use of these methods is (Section 5). We conclude the chapter with three essentially open research problems that are either

  4. Multivariate respiratory motion prediction

    NASA Astrophysics Data System (ADS)

    Dürichen, R.; Wissel, T.; Ernst, F.; Schlaefer, A.; Schweikard, A.

    2014-10-01

    In extracranial robotic radiotherapy, tumour motion is compensated by tracking external and internal surrogates. To compensate system specific time delays, time series prediction of the external optical surrogates is used. We investigate whether the prediction accuracy can be increased by expanding the current clinical setup by an accelerometer, a strain belt and a flow sensor. Four previously published prediction algorithms are adapted to multivariate inputs—normalized least mean squares (nLMS), wavelet-based least mean squares (wLMS), support vector regression (SVR) and relevance vector machines (RVM)—and evaluated for three different prediction horizons. The measurement involves 18 subjects and consists of two phases, focusing on long term trends (M1) and breathing artefacts (M2). To select the most relevant and least redundant sensors, a sequential forward selection (SFS) method is proposed. Using a multivariate setting, the results show that the clinically used nLMS algorithm is susceptible to large outliers. In the case of irregular breathing (M2), the mean root mean square error (RMSE) of a univariate nLMS algorithm is 0.66 mm and can be decreased to 0.46 mm by a multivariate RVM model (best algorithm on average). To investigate the full potential of this approach, the optimal sensor combination was also estimated on the complete test set. The results indicate that a further decrease in RMSE is possible for RVM (to 0.42 mm). This motivates further research about sensor selection methods. Besides the optical surrogates, the sensors most frequently selected by the algorithms are the accelerometer and the strain belt. These sensors could be easily integrated in the current clinical setup and would allow a more precise motion compensation.

  5. Multivariate Hypergeometric Similarity Measure

    PubMed Central

    Kaddi, Chanchala D.; Parry, R. Mitchell; Wang, May D.

    2016-01-01

    We propose a similarity measure based on the multivariate hypergeometric distribution for the pairwise comparison of images and data vectors. The formulation and performance of the proposed measure are compared with other similarity measures using synthetic data. A method of piecewise approximation is also implemented to facilitate application of the proposed measure to large samples. Example applications of the proposed similarity measure are presented using mass spectrometry imaging data and gene expression microarray data. Results from synthetic and biological data indicate that the proposed measure is capable of providing meaningful discrimination between samples, and that it can be a useful tool for identifying potentially related samples in large-scale biological data sets. PMID:24407308

  6. Observational biases for transiting planets

    NASA Astrophysics Data System (ADS)

    Kipping, David M.; Sandford, Emily

    2016-09-01

    Observational biases distort our view of nature, such that the patterns we see within a surveyed population of interest are often unrepresentative of the truth we seek. Transiting planets currently represent the most informative data set on the ensemble properties of exoplanets within 1 AU of their star. However, the transit method is inherently biased due to both geometric and detection-driven effects. In this work, we derive the overall observational biases affecting the most basic transit parameters from first principles. By assuming a trapezoidal transit and using conditional probability, we infer the expected distribution of these terms both as a joint distribution and in a marginalized form. These general analytic results provide a baseline against which to compare trends predicted by mission-tailored injection/recovery simulations and offer a simple way to correct for observational bias. Our results explain why the observed population of transiting planets displays a non-uniform impact parameter distribution, with a bias towards near-equatorial geometries. We also find that the geometric bias towards observed planets transiting near periastron is attenuated by the longer durations which occur near apoastron. Finally, we predict that the observational bias with respect to ratio-of-radii is super-quadratic, scaling as (RP/R⋆)5/2, driven by an enhanced geometric transit probability and modestly longer durations.

  7. Assessment of bias for MRI diffusion tensor imaging using SIMEX.

    PubMed

    Lauzon, Carolyn B; Asman, Andrew J; Crainiceanu, Ciprian; Caffo, Brian C; Landman, Bennett A

    2011-01-01

    Diffusion Tensor Imaging (DTI) is a Magnetic Resonance Imaging method for measuring water diffusion in vivo. One powerful DTI contrast is fractional anisotropy (FA). FA reflects the strength of water's diffusion directional preference and is a primary metric for neuronal fiber tracking. As with other DTI contrasts, FA measurements are obscured by the well established presence of bias. DTI bias has been challenging to assess because it is a multivariable problem including SNR, six tensor parameters, and the DTI collection and processing method used. SIMEX is a modem statistical technique that estimates bias by tracking measurement error as a function of added noise. Here, we use SIMEX to assess bias in FA measurements and show the method provides; i) accurate FA bias estimates, ii) representation of FA bias that is data set specific and accessible to non-statisticians, and iii) a first time possibility for incorporation of bias into DTI data analysis. PMID:21995019

  8. Divertor bias experiments

    NASA Astrophysics Data System (ADS)

    Staebler, G. M.

    1994-06-01

    Electrical biasing of the divertor target plates has recently been implemented on several tokamaks. The results of these experiments to date will be reviewed in this paper. The bias electrode configuration is unique in each experiment. The effects of biasing on the scrape-off layer (SOL) plasma also differ. By comparing results between machines, and using theoretical models, an understanding of the basic physics of biasing begins to emerge. Divertor biasing has been demonstrated to have a strong influence on the particle and energy transport within the SOL. The ability to externally control the SOL plasma with biasing has promising applications to future tokamak reactors.

  9. Recursive bias estimation for high dimensional smoothers

    SciTech Connect

    Hengartner, Nicolas W; Matzner-lober, Eric; Cornillon, Pierre - Andre

    2008-01-01

    In multivariate nonparametric analysis, sparseness of the covariates also called curse of dimensionality, forces one to use large smoothing parameters. This leads to biased smoothers. Instead of focusing on optimally selecting the smoothing parameter, we fix it to some reasonably large value to ensure an over-smoothing of the data. The resulting smoother has a small variance but a substantial bias. In this paper, we propose to iteratively correct the bias initial estimator by an estimate of the latter obtained by smoothing the residuals. We examine in detail the convergence of the iterated procedure for classical smoothers and relate our procedure to L{sub 2}-Boosting. We apply our method to simulated and real data and show that our method compares favorably with existing procedures.

  10. Demonstrating the Correspondence Bias

    ERIC Educational Resources Information Center

    Howell, Jennifer L.; Shepperd, James A.

    2011-01-01

    Among the best-known and most robust biases in person perception is the correspondence bias--the tendency for people to make dispositional, rather than situational, attributions for an actor's behavior. The correspondence bias appears in virtually every social psychology textbook and in many introductory psychology textbooks, yet the authors'…

  11. Bias in Grading

    ERIC Educational Resources Information Center

    Malouff, John

    2008-01-01

    Bias in grading can be conscious or unconscious. The author describes different types of bias, such as those based on student attractiveness or performance in prior courses, and a variety of methods of reducing bias, including keeping students anonymous during grading and using detailed criteria for subjective grading.

  12. Solar array/spacecraft biasing

    NASA Technical Reports Server (NTRS)

    Fitzgerald, D. J.

    1981-01-01

    Biasing techniques and their application to the control of spacecraft potential is discussed. Normally when a spacecraft is operated with ion thrusters, the spacecraft will be 10-20 volts negative of the surrounding plasma. This will affect scientific measurements and will allow ions from the charge-exchange plasma to bombard the spacecraft surfaces with a few tens of volts of energy. This condition may not be tolerable. A proper bias system is described that can bring the spacecraft to or near the potential of the surrounding plasma.

  13. Dopamine antagonism does not impair learning of Pavlovian conditioned approach to manipulable or non-manipulable cues but biases responding towards goal tracking.

    PubMed

    Scülfort, Stefanie A; Bartsch, Dusan; Enkel, Thomas

    2016-11-01

    Dopamine's (DA) role in reward-processing is currently discussed as either providing a teaching signal to guide learning or mediating the transfer of incentive salience (i.e. motivational aspects) from unconditioned stimuli (US) to conditioned stimuli (CS). We used a Pavlovian conditioned approach (PCA) procedure to further investigate DAs contribution to these processes. Experiment 1 assessed the acquisition of PCA to a manipulable lever cue for 7days under DA-blockade with Flupenthixol (FLU; 225μg/kg) or Saline (SAL) treatment, followed by 6-days off-drug testing. FLU decreased the number of conditioned responses (CR) during the treatment phase, but cessation of treatment resulted in an immediate increase in CR to levels comparable to SAL controls; notably, CR in FLU-treated rats were restricted to goal tracking behaviour. During continued off-drug testing, rats from the FLU group developed sign tracking with a similar temporal pattern as controls. In experiment 2, acquisition of PCA to a non-manipulable auditory cue was investigated. FLU reduced the number of CR during treatment, and removing DA antagonism resulted in a similar rapid increase of CR as seen in experiment 1. These data complement other reports by demonstrating that, independently from the physical properties of the CS, DA is not required for learning predictive aspects of a CS-US relationship but for the development of behaviour (namely sign tracking) which is based on the motivational aspects of a CS-US relationship. PMID:27478141

  14. Queries for Bias Testing

    NASA Technical Reports Server (NTRS)

    Gordon, Diana F.

    1992-01-01

    Selecting a good bias prior to concept learning can be difficult. Therefore, dynamic bias adjustment is becoming increasingly popular. Current dynamic bias adjustment systems, however, are limited in their ability to identify erroneous assumptions about the relationship between the bias and the target concept. Without proper diagnosis, it is difficult to identify and then remedy faulty assumptions. We have developed an approach that makes these assumptions explicit, actively tests them with queries to an oracle, and adjusts the bias based on the test results.

  15. MULTIVARIATE KERNEL PARTITION PROCESS MIXTURES

    PubMed Central

    Dunson, David B.

    2013-01-01

    Mixtures provide a useful approach for relaxing parametric assumptions. Discrete mixture models induce clusters, typically with the same cluster allocation for each parameter in multivariate cases. As a more flexible approach that facilitates sparse nonparametric modeling of multivariate random effects distributions, this article proposes a kernel partition process (KPP) in which the cluster allocation varies for different parameters. The KPP is shown to be the driving measure for a multivariate ordered Chinese restaurant process that induces a highly-flexible dependence structure in local clustering. This structure allows the relative locations of the random effects to inform the clustering process, with spatially-proximal random effects likely to be assigned the same cluster index. An exact block Gibbs sampler is developed for posterior computation, avoiding truncation of the infinite measure. The methods are applied to hormone curve data, and a dependent KPP is proposed for classification from functional predictors. PMID:24478563

  16. Multivariate Model of Infant Competence.

    ERIC Educational Resources Information Center

    Kierscht, Marcia Selland; Vietze, Peter M.

    This paper describes a multivariate model of early infant competence formulated from variables representing infant-environment transaction including: birthweight, habituation index, personality ratings of infant social orientation and task orientation, ratings of maternal responsiveness to infant distress and social signals, and observational…

  17. Parameter Sensitivity in Multivariate Methods

    ERIC Educational Resources Information Center

    Green, Bert F., Jr.

    1977-01-01

    Interpretation of multivariate models requires knowing how much the fit of the model is impaired by changes in the parameters. The relation of parameter change to loss of goodness of fit can be called parameter sensitivity. Formulas are presented for assessing the sensitivity of multiple regression and principal component weights. (Author/JKS)

  18. Interpretation biases in paranoia.

    PubMed

    Savulich, George; Freeman, Daniel; Shergill, Sukhi; Yiend, Jenny

    2015-01-01

    Information in the environment is frequently ambiguous in meaning. Emotional ambiguity, such as the stare of a stranger, or the scream of a child, encompasses possible good or bad emotional consequences. Those with elevated vulnerability to affective disorders tend to interpret such material more negatively than those without, a phenomenon known as "negative interpretation bias." In this study we examined the relationship between vulnerability to psychosis, measured by trait paranoia, and interpretation bias. One set of material permitted broadly positive/negative (valenced) interpretations, while another allowed more or less paranoid interpretations, allowing us to also investigate the content specificity of interpretation biases associated with paranoia. Regression analyses (n=70) revealed that trait paranoia, trait anxiety, and cognitive inflexibility predicted paranoid interpretation bias, whereas trait anxiety and cognitive inflexibility predicted negative interpretation bias. In a group comparison those with high levels of trait paranoia were negatively biased in their interpretations of ambiguous information relative to those with low trait paranoia, and this effect was most pronounced for material directly related to paranoid concerns. Together these data suggest that a negative interpretation bias occurs in those with elevated vulnerability to paranoia, and that this bias may be strongest for material matching paranoid beliefs. We conclude that content-specific biases may be important in the cause and maintenance of paranoid symptoms.

  19. Causal Inference and Omitted Variable Bias in Financial Aid Research: Assessing Solutions

    ERIC Educational Resources Information Center

    Riegg, Stephanie K.

    2008-01-01

    This article highlights the problem of omitted variable bias in research on the causal effect of financial aid on college-going. I first describe the problem of self-selection and the resulting bias from omitted variables. I then assess and explore the strengths and weaknesses of random assignment, multivariate regression, proxy variables, fixed…

  20. Measuring discriminability when there are multiple sources of bias.

    PubMed

    Brown, Glenn S; White, K Geoffrey

    2009-02-01

    Performance measures such as log d and d' aim to measure stimulus discriminability independently of response bias in conditional discrimination tasks, including the yes/no signal-detection procedure. However, they assume only one dimension of bias (e.g., response color) and do not account for bias on additional dimensions (e.g., response side). Such bias reduces log d, thus violating the statistical independence of discriminability and bias measurements. We modified log d to account for side bias and reanalyzed previous side-biased data. With strong side bias, the modified log d differed enough from the standard log d to potentially alter the conclusions of an experiment. Simulations showed that the modified log d produces discriminability estimates that are more accurate and bias-independent than the standard log d calculation.

  1. Estimation and correction of model bias in the NASA/GMAO GEOS5 data assimilation system: Sequential implementation

    NASA Astrophysics Data System (ADS)

    Zhang, Banglin; Tallapragada, Vijay; Weng, Fuzhong; Sippel, Jason; Ma, Zaizhong

    2016-06-01

    This study presents a simplified multivariate bias correction scheme that is sequentially implemented in the GEOS5 data assimilation system and compared against a control experiment without model bias correction. The results show considerable improvement in terms of the mean biases of rawinsonde observation-minus-background (OmB) residuals for observed water vapor, wind and temperature variables. The time series spectral analysis shows whitening of bias-corrected OmB residuals, and mean biases for rawinsonde observation-minus-analysis (OmA) are also improved. Some wind and temperature biases in the control experiment near the equatorial tropopause nearly vanish from the bias-corrected experiment. Despite the analysis improvement, the bias correction scheme has only a moderate impact on forecast skill. Significant interaction is also found among quality-control, satellite observation bias correction, and background bias correction, and the latter positively impacts satellite bias correction.

  2. Political bias is tenacious.

    PubMed

    Ditto, Peter H; Wojcik, Sean P; Chen, Eric Evan; Grady, Rebecca Hofstein; Ringel, Megan M

    2015-01-01

    Duarte et al. are right to worry about political bias in social psychology but they underestimate the ease of correcting it. Both liberals and conservatives show partisan bias that often worsens with cognitive sophistication. More non-liberals in social psychology is unlikely to speed our convergence upon the truth, although it may broaden the questions we ask and the data we collect.

  3. Investigating Test Bias.

    ERIC Educational Resources Information Center

    Hoepfner, Ralph; Strickland, Guy P.

    This study investigates the question of test bias to develop an index of the appropriateness of a test to a particular socioeconomic or racial-ethnic group. Bias is defined as an item by race interaction in an analysis-of-variance design. The sample of 172 third graders at two integrated schools in a large California school district, included 26…

  4. Sampler bias -- Phase 1

    SciTech Connect

    Blanchard, R.J.

    1995-03-07

    This documents Phase 1 determinations on sampler induced bias for four sampler types used in tank characterization. Each sampler, grab sampler or bottle-on-a-string, auger sampler, sludge sampler and universal sampler, is briefly discussed and their physical limits noted. Phase 2 of this document will define additional testing and analysis to further define Sampler Bias.

  5. Multichannel hierarchical image classification using multivariate copulas

    NASA Astrophysics Data System (ADS)

    Voisin, Aurélie; Krylov, Vladimir A.; Moser, Gabriele; Serpico, Sebastiano B.; Zerubia, Josiane

    2012-03-01

    This paper focuses on the classification of multichannel images. The proposed supervised Bayesian classification method applied to histological (medical) optical images and to remote sensing (optical and synthetic aperture radar) imagery consists of two steps. The first step introduces the joint statistical modeling of the coregistered input images. For each class and each input channel, the class-conditional marginal probability density functions are estimated by finite mixtures of well-chosen parametric families. For optical imagery, the normal distribution is a well-known model. For radar imagery, we have selected generalized gamma, log-normal, Nakagami and Weibull distributions. Next, the multivariate d-dimensional Clayton copula, where d can be interpreted as the number of input channels, is applied to estimate multivariate joint class-conditional statistics. As a second step, we plug the estimated joint probability density functions into a hierarchical Markovian model based on a quadtree structure. Multiscale features are extracted by discrete wavelet transforms, or by using input multiresolution data. To obtain the classification map, we integrate an exact estimator of the marginal posterior mode.

  6. Multivariate statistical analysis of environmental monitoring data

    SciTech Connect

    Ross, D.L.

    1997-11-01

    EPA requires statistical procedures to determine whether soil or ground water adjacent to or below waste units is contaminated. These statistical procedures are often based on comparisons between two sets of data: one representing background conditions, and one representing site conditions. Since statistical requirements were originally promulgated in the 1980s, EPA has made several improvements and modifications. There are, however, problems which remain. One problem is that the regulations do not require a minimum probability that contaminated sites will be correctly identified. Another problems is that the effect of testing several correlated constituents on the probable outcome of the statistical tests has not been quantified. Results from computer simulations to determine power functions for realistic monitoring situations are presented here. Power functions for two different statistical procedures: the Student`s t-test, and the multivariate Hotelling`s T{sup 2} test, are compared. The comparisons indicate that the multivariate test is often more powerful when the tests are applied with significance levels to control the probability of falsely identifying clean sites as contaminated. This program could also be used to verify that statistical procedures achieve some minimum power standard at a regulated waste unit.

  7. Adaptive Variable Bias Magnetic Bearing Control

    NASA Technical Reports Server (NTRS)

    Johnson, Dexter; Brown, Gerald V.; Inman, Daniel J.

    1998-01-01

    Most magnetic bearing control schemes use a bias current with a superimposed control current to linearize the relationship between the control current and the force it delivers. With the existence of the bias current, even in no load conditions, there is always some power consumption. In aerospace applications, power consumption becomes an important concern. In response to this concern, an alternative magnetic bearing control method, called Adaptive Variable Bias Control (AVBC), has been developed and its performance examined. The AVBC operates primarily as a proportional-derivative controller with a relatively slow, bias current dependent, time-varying gain. The AVBC is shown to reduce electrical power loss, be nominally stable, and provide control performance similar to conventional bias control. Analytical, computer simulation, and experimental results are presented in this paper.

  8. An Examination of the Statistical Properties of a Multivariate Measure of Strength of Relationship. Final Report.

    ERIC Educational Resources Information Center

    Tatsuoka, Maurice M.

    A computer-simulated study was made of the sampling distribution of omega squared, a measure of strength of relationship in multivariate analysis of variance which had earlier been proposed by the author. It was found that this measure was highly positively biased when the number of variables is large and the sample size is small. A correction…

  9. Remote Impact of Extratropical Thermal Bias on Tropical Biases in the Norwegian Earth System Model

    NASA Astrophysics Data System (ADS)

    Koseki, Shunya; Losada, Teresa; Keenlyside, Noel; Toniazzo, Thomas; Castano-Tierno, Antonio; Rodriguez-Fonseca, Belen; Demissie, Teferi; Mechoso, Roberto

    2016-04-01

    One of large biases exhibited by most state-of-the-art coupled general circulation models (CGCMs) is warm sea surface temperature (SST) in the tropical ocean. Due to the warm SST bias, CGCMs fails to represent the location of intertropical convergence zone (ITCZ) realistically. Other common bias is warm SST over the Southern Ocean partly because of less reproduction of stratocumulus over the Southern Ocean. Some previous studies show that the ITCZ position is affected by the extratropical thermal condition. In this study, we explore a connection between the extratropical warm SST bias and tropical biases in the Norwegian Earth System Model (NorESM). The control simulation of NorESM has the common tropical biases and warm bias over the Southern Ocean. NorESM overestimates the downward shortwave radiation flux over the Southern Ocean and underestimates the low-level cloud formation (in particular, between 40S and 30S). The more incoming shortwave radiation is consistent with the warm SST bias over the Southern Ocean. We conduct a sensitivity experiment in which the incoming shortwave radiation at the top of atmosphere is reduced artificially only between 30S and 60S. The reduced shortwave radiation cools the SST in the Southern Ocean. Interestingly, the annual-mean rainfall over the tropics is reduced (amplified) to the south (north) of the equator. Especially, the double-ITCZ over the tropical Pacific Ocean is diminished in the sensitivity experiment. Moreover, warm SST biases in the tropical ocean are also reduced. Over the tropical Atlantic, the reduction of biases is more remarkable in MAM and JJA: westerly bias over the equatorial Atlantic is reduced and SST is cooler compared to control simulation. Consequently, the rainfall increases (decreases) in the north (south) of the equator, that is, the sensitivity experiment shows more realistic climatological state. This result indicates that a part of tropical biases in NorESM is associated with the warm SST bias in

  10. Biased predecision processing.

    PubMed

    Brownstein, Aaron L

    2003-07-01

    Decision makers conduct biased predecision processing when they restructure their mental representation of the decision environment to favor one alternative before making their choice. The question of whether biased predecision processing occurs has been controversial since L. Festinger (1957) maintained that it does not occur. The author reviews relevant research in sections on theories of cognitive dissonance, decision conflict, choice certainty, action control, action phases, dominance structuring, differentiation and consolidation, constructive processing, motivated reasoning, and groupthink. Some studies did not find evidence of biased predecision processing, but many did. In the Discussion section, the moderators are summarized and used to assess the theories. PMID:12848220

  11. Multivariate Strategies in Functional Magnetic Resonance Imaging

    ERIC Educational Resources Information Center

    Hansen, Lars Kai

    2007-01-01

    We discuss aspects of multivariate fMRI modeling, including the statistical evaluation of multivariate models and means for dimensional reduction. In a case study we analyze linear and non-linear dimensional reduction tools in the context of a "mind reading" predictive multivariate fMRI model.

  12. An Integrated Multivariable Artificial Pancreas Control System

    PubMed Central

    Turksoy, Kamuran; Quinn, Lauretta T.; Littlejohn, Elizabeth

    2014-01-01

    The objective was to develop a closed-loop (CL) artificial pancreas (AP) control system that uses continuous measurements of glucose concentration and physiological variables, integrated with a hypoglycemia early alarm module to regulate glucose concentration and prevent hypoglycemia. Eleven open-loop (OL) and 9 CL experiments were performed. A multivariable adaptive artificial pancreas (MAAP) system was used for the first 6 CL experiments. An integrated multivariable adaptive artificial pancreas (IMAAP) system consisting of MAAP augmented with a hypoglycemia early alarm system was used during the last 3 CL experiments. Glucose values and physical activity information were measured and transferred to the controller every 10 minutes and insulin suggestions were entered to the pump manually. All experiments were designed to be close to real-life conditions. Severe hypoglycemic episodes were seen several times during the OL experiments. With the MAAP system, the occurrence of severe hypoglycemia was decreased significantly (P < .01). No hypoglycemia was seen with the IMAAP system. There was also a significant difference (P < .01) between OL and CL experiments with regard to percentage of glucose concentration (54% vs 58%) that remained within target range (70-180 mg/dl). Integration of an adaptive control and hypoglycemia early alarm system was able to keep glucose concentration values in target range in patients with type 1 diabetes. Postprandial hypoglycemia and exercise-induced hypoglycemia did not occur when this system was used. Physical activity information improved estimation of the blood glucose concentration and effectiveness of the control system. PMID:24876613

  13. An integrated multivariable artificial pancreas control system.

    PubMed

    Turksoy, Kamuran; Quinn, Lauretta T; Littlejohn, Elizabeth; Cinar, Ali

    2014-05-01

    The objective was to develop a closed-loop (CL) artificial pancreas (AP) control system that uses continuous measurements of glucose concentration and physiological variables, integrated with a hypoglycemia early alarm module to regulate glucose concentration and prevent hypoglycemia. Eleven open-loop (OL) and 9 CL experiments were performed. A multivariable adaptive artificial pancreas (MAAP) system was used for the first 6 CL experiments. An integrated multivariable adaptive artificial pancreas (IMAAP) system consisting of MAAP augmented with a hypoglycemia early alarm system was used during the last 3 CL experiments. Glucose values and physical activity information were measured and transferred to the controller every 10 minutes and insulin suggestions were entered to the pump manually. All experiments were designed to be close to real-life conditions. Severe hypoglycemic episodes were seen several times during the OL experiments. With the MAAP system, the occurrence of severe hypoglycemia was decreased significantly (P < .01). No hypoglycemia was seen with the IMAAP system. There was also a significant difference (P < .01) between OL and CL experiments with regard to percentage of glucose concentration (54% vs 58%) that remained within target range (70-180 mg/dl). Integration of an adaptive control and hypoglycemia early alarm system was able to keep glucose concentration values in target range in patients with type 1 diabetes. Postprandial hypoglycemia and exercise-induced hypoglycemia did not occur when this system was used. Physical activity information improved estimation of the blood glucose concentration and effectiveness of the control system.

  14. Multivariate residues and maximal unitarity

    NASA Astrophysics Data System (ADS)

    Søgaard, Mads; Zhang, Yang

    2013-12-01

    We extend the maximal unitarity method to amplitude contributions whose cuts define multidimensional algebraic varieties. The technique is valid to all orders and is explicitly demonstrated at three loops in gauge theories with any number of fermions and scalars in the adjoint representation. Deca-cuts realized by replacement of real slice integration contours by higher-dimensional tori encircling the global poles are used to factorize the planar triple box onto a product of trees. We apply computational algebraic geometry and multivariate complex analysis to derive unique projectors for all master integral coefficients and obtain compact analytic formulae in terms of tree-level data.

  15. Software For Multivariate Bayesian Classification

    NASA Technical Reports Server (NTRS)

    Saul, Ronald; Laird, Philip; Shelton, Robert

    1996-01-01

    PHD general-purpose classifier computer program. Uses Bayesian methods to classify vectors of real numbers, based on combination of statistical techniques that include multivariate density estimation, Parzen density kernels, and EM (Expectation Maximization) algorithm. By means of simple graphical interface, user trains classifier to recognize two or more classes of data and then use it to identify new data. Written in ANSI C for Unix systems and optimized for online classification applications. Embedded in another program, or runs by itself using simple graphical-user-interface. Online help files makes program easy to use.

  16. Introduction to Unconscious Bias

    NASA Astrophysics Data System (ADS)

    Schmelz, Joan T.

    2010-05-01

    We all have biases, and we are (for the most part) unaware of them. In general, men and women BOTH unconsciously devalue the contributions of women. This can have a detrimental effect on grant proposals, job applications, and performance reviews. Sociology is way ahead of astronomy in these studies. When evaluating identical application packages, male and female University psychology professors preferred 2:1 to hire "Brian” over "Karen” as an assistant professor. When evaluating a more experienced record (at the point of promotion to tenure), reservations were expressed four times more often when the name was female. This unconscious bias has a repeated negative effect on Karen's career. This talk will introduce the concept of unconscious bias and also give recommendations on how to address it using an example for a faculty search committee. The process of eliminating unconscious bias begins with awareness, then moves to policy and practice, and ends with accountability.

  17. Estimating Bias Error Distributions

    NASA Technical Reports Server (NTRS)

    Liu, Tian-Shu; Finley, Tom D.

    2001-01-01

    This paper formulates the general methodology for estimating the bias error distribution of a device in a measuring domain from less accurate measurements when a minimal number of standard values (typically two values) are available. A new perspective is that the bias error distribution can be found as a solution of an intrinsic functional equation in a domain. Based on this theory, the scaling- and translation-based methods for determining the bias error distribution arc developed. These methods are virtually applicable to any device as long as the bias error distribution of the device can be sufficiently described by a power series (a polynomial) or a Fourier series in a domain. These methods have been validated through computational simulations and laboratory calibration experiments for a number of different devices.

  18. Political bias is tenacious.

    PubMed

    Ditto, Peter H; Wojcik, Sean P; Chen, Eric Evan; Grady, Rebecca Hofstein; Ringel, Megan M

    2015-01-01

    Duarte et al. are right to worry about political bias in social psychology but they underestimate the ease of correcting it. Both liberals and conservatives show partisan bias that often worsens with cognitive sophistication. More non-liberals in social psychology is unlikely to speed our convergence upon the truth, although it may broaden the questions we ask and the data we collect. PMID:26786070

  19. Eye Movements while Reading Biased Homographs: Effects of Prior Encounter and Biasing Context on Reducing the Subordinate Bias Effect

    PubMed Central

    Leinenger, Mallorie; Rayner, Keith

    2013-01-01

    Readers experience processing difficulties when reading biased homographs preceded by subordinate-biasing contexts. Attempts to overcome this processing deficit have often failed to reduce the subordinate bias effect (SBE). In the present studies, we examined the processing of biased homographs preceded by single-sentence, subordinate-biasing contexts, and varied whether this preceding context contained a prior instance of the homograph or a control word/phrase. Having previously encountered the homograph earlier in the sentence reduced the SBE for the subsequent encounter, while simply instantiating the subordinate meaning produced processing difficulty. We compared these reductions in reading times to differences in processing time between dominant-biased repeated and non-repeated conditions in order to verify that the reductions observed in the subordinate cases did not simply reflect a general repetition benefit. Our results indicate that a strong, subordinate-biasing context can interact during lexical access to overcome the activation from meaning frequency and reduce the SBE during reading. PMID:24073328

  20. Linear models of coregionalization for multivariate lattice data: Order-dependent and order-free cMCARs.

    PubMed

    MacNab, Ying C

    2016-08-01

    This paper concerns with multivariate conditional autoregressive models defined by linear combination of independent or correlated underlying spatial processes. Known as linear models of coregionalization, the method offers a systematic and unified approach for formulating multivariate extensions to a broad range of univariate conditional autoregressive models. The resulting multivariate spatial models represent classes of coregionalized multivariate conditional autoregressive models that enable flexible modelling of multivariate spatial interactions, yielding coregionalization models with symmetric or asymmetric cross-covariances of different spatial variation and smoothness. In the context of multivariate disease mapping, for example, they facilitate borrowing strength both over space and cross variables, allowing for more flexible multivariate spatial smoothing. Specifically, we present a broadened coregionalization framework to include order-dependent, order-free, and order-robust multivariate models; a new class of order-free coregionalized multivariate conditional autoregressives is introduced. We tackle computational challenges and present solutions that are integral for Bayesian analysis of these models. We also discuss two ways of computing deviance information criterion for comparison among competing hierarchical models with or without unidentifiable prior parameters. The models and related methodology are developed in the broad context of modelling multivariate data on spatial lattice and illustrated in the context of multivariate disease mapping. The coregionalization framework and related methods also present a general approach for building spatially structured cross-covariance functions for multivariate geostatistics. PMID:27566769

  1. The Optimization of Multivariate Generalizability Studies with Budget Constraints.

    ERIC Educational Resources Information Center

    Marcoulides, George A.; Goldstein, Zvi

    1992-01-01

    A method is presented for determining the optimal number of conditions to use in multivariate-multifacet generalizability designs when resource constraints are imposed. A decision maker can determine the number of observations needed to obtain the largest possible generalizability coefficient. The procedure easily applies to the univariate case.…

  2. Cytometric fingerprinting: quantitative characterization of multivariate distributions.

    PubMed

    Rogers, Wade T; Moser, Allan R; Holyst, Herbert A; Bantly, Andrew; Mohler, Emile R; Scangas, George; Moore, Jonni S

    2008-05-01

    Recent technological advances in flow cytometry instrumentation provide the basis for high-dimensionality and high-throughput biological experimentation in a heterogeneous cellular context. Concomitant advances in scalable computational algorithms are necessary to better utilize the information that is contained in these high-complexity experiments. The development of such tools has the potential to expand the utility of flow cytometric analysis from a predominantly hypothesis-driven mode to one of discovery, or hypothesis-generating research. A new method of analysis of flow cytometric data called Cytometric Fingerprinting (CF) has been developed. CF captures the set of multivariate probability distribution functions corresponding to list-mode data and then "flattens" them into a computationally efficient fingerprint representation that facilitates quantitative comparisons of samples. An experimental and synthetic data were generated to act as reference sets for evaluating CF. Without the introduction of prior knowledge, CF was able to "discover" the location and concentration of spiked cells in ungated analyses over a concentration range covering four orders of magnitude, to a lower limit on the order of 10 spiked events in a background of 100,000 events. We describe a new method for quantitative analysis of list-mode cytometric data. CF includes a novel algorithm for space subdivision that improves estimation of the probability density function by dividing space into nonrectangular polytopes. Additionally it renders a multidimensional distribution in the form of a one-dimensional multiresolution hierarchical fingerprint that creates a computationally efficient representation of high dimensionality distribution functions. CF supports both the generation and testing of hypotheses, eliminates sources of operator bias, and provides an increased level of automation of data analysis.

  3. Biases in Visual, Auditory, and Audiovisual Perception of Space.

    PubMed

    Odegaard, Brian; Wozny, David R; Shams, Ladan

    2015-12-01

    Localization of objects and events in the environment is critical for survival, as many perceptual and motor tasks rely on estimation of spatial location. Therefore, it seems reasonable to assume that spatial localizations should generally be accurate. Curiously, some previous studies have reported biases in visual and auditory localizations, but these studies have used small sample sizes and the results have been mixed. Therefore, it is not clear (1) if the reported biases in localization responses are real (or due to outliers, sampling bias, or other factors), and (2) whether these putative biases reflect a bias in sensory representations of space or a priori expectations (which may be due to the experimental setup, instructions, or distribution of stimuli). Here, to address these questions, a dataset of unprecedented size (obtained from 384 observers) was analyzed to examine presence, direction, and magnitude of sensory biases, and quantitative computational modeling was used to probe the underlying mechanism(s) driving these effects. Data revealed that, on average, observers were biased towards the center when localizing visual stimuli, and biased towards the periphery when localizing auditory stimuli. Moreover, quantitative analysis using a Bayesian Causal Inference framework suggests that while pre-existing spatial biases for central locations exert some influence, biases in the sensory representations of both visual and auditory space are necessary to fully explain the behavioral data. How are these opposing visual and auditory biases reconciled in conditions in which both auditory and visual stimuli are produced by a single event? Potentially, the bias in one modality could dominate, or the biases could interact/cancel out. The data revealed that when integration occurred in these conditions, the visual bias dominated, but the magnitude of this bias was reduced compared to unisensory conditions. Therefore, multisensory integration not only improves the

  4. Biases in Visual, Auditory, and Audiovisual Perception of Space.

    PubMed

    Odegaard, Brian; Wozny, David R; Shams, Ladan

    2015-12-01

    Localization of objects and events in the environment is critical for survival, as many perceptual and motor tasks rely on estimation of spatial location. Therefore, it seems reasonable to assume that spatial localizations should generally be accurate. Curiously, some previous studies have reported biases in visual and auditory localizations, but these studies have used small sample sizes and the results have been mixed. Therefore, it is not clear (1) if the reported biases in localization responses are real (or due to outliers, sampling bias, or other factors), and (2) whether these putative biases reflect a bias in sensory representations of space or a priori expectations (which may be due to the experimental setup, instructions, or distribution of stimuli). Here, to address these questions, a dataset of unprecedented size (obtained from 384 observers) was analyzed to examine presence, direction, and magnitude of sensory biases, and quantitative computational modeling was used to probe the underlying mechanism(s) driving these effects. Data revealed that, on average, observers were biased towards the center when localizing visual stimuli, and biased towards the periphery when localizing auditory stimuli. Moreover, quantitative analysis using a Bayesian Causal Inference framework suggests that while pre-existing spatial biases for central locations exert some influence, biases in the sensory representations of both visual and auditory space are necessary to fully explain the behavioral data. How are these opposing visual and auditory biases reconciled in conditions in which both auditory and visual stimuli are produced by a single event? Potentially, the bias in one modality could dominate, or the biases could interact/cancel out. The data revealed that when integration occurred in these conditions, the visual bias dominated, but the magnitude of this bias was reduced compared to unisensory conditions. Therefore, multisensory integration not only improves the

  5. Reduced susceptibility to confirmation bias in schizophrenia.

    PubMed

    Doll, Bradley B; Waltz, James A; Cockburn, Jeffrey; Brown, Jaime K; Frank, Michael J; Gold, James M

    2014-06-01

    Patients with schizophrenia (SZ) show cognitive impairments on a wide range of tasks, with clear deficiencies in tasks reliant on prefrontal cortex function and less consistently observed impairments in tasks recruiting the striatum. This study leverages tasks hypothesized to differentially recruit these neural structures to assess relative deficiencies of each. Forty-eight patients and 38 controls completed two reinforcement learning tasks hypothesized to interrogate prefrontal and striatal functions and their interaction. In each task, participants learned reward discriminations by trial and error and were tested on novel stimulus combinations to assess learned values. In the task putatively assessing fronto-striatal interaction, participants were (inaccurately) instructed that one of the stimuli was valuable. Consistent with prior reports and a model of confirmation bias, this manipulation resulted in overvaluation of the instructed stimulus after its true value had been experienced. Patients showed less susceptibility to this confirmation bias effect than did controls. In the choice bias task hypothesized to more purely assess striatal function, biases in endogenously and exogenously chosen actions were assessed. No group differences were observed. In the subset of participants who showed learning in both tasks, larger group differences were observed in the confirmation bias task than in the choice bias task. In the confirmation bias task, patients also showed impairment in the task conditions with no prior instruction. This deficit was most readily observed on the most deterministic discriminations. Taken together, these results suggest impairments in fronto-striatal interaction in SZ, rather than in striatal function per se.

  6. Statistical analysis of multivariate atmospheric variables. [cloud cover

    NASA Technical Reports Server (NTRS)

    Tubbs, J. D.

    1979-01-01

    Topics covered include: (1) estimation in discrete multivariate distributions; (2) a procedure to predict cloud cover frequencies in the bivariate case; (3) a program to compute conditional bivariate normal parameters; (4) the transformation of nonnormal multivariate to near-normal; (5) test of fit for the extreme value distribution based upon the generalized minimum chi-square; (6) test of fit for continuous distributions based upon the generalized minimum chi-square; (7) effect of correlated observations on confidence sets based upon chi-square statistics; and (8) generation of random variates from specified distributions.

  7. Method of multivariate spectral analysis

    DOEpatents

    Keenan, Michael R.; Kotula, Paul G.

    2004-01-06

    A method of determining the properties of a sample from measured spectral data collected from the sample by performing a multivariate spectral analysis. The method can include: generating a two-dimensional matrix A containing measured spectral data; providing a weighted spectral data matrix D by performing a weighting operation on matrix A; factoring D into the product of two matrices, C and S.sup.T, by performing a constrained alternating least-squares analysis of D=CS.sup.T, where C is a concentration intensity matrix and S is a spectral shapes matrix; unweighting C and S by applying the inverse of the weighting used previously; and determining the properties of the sample by inspecting C and S. This method can be used to analyze X-ray spectral data generated by operating a Scanning Electron Microscope (SEM) with an attached Energy Dispersive Spectrometer (EDS).

  8. Thinking in Black and White: Conscious thought increases racially biased judgments through biased face memory.

    PubMed

    Strick, Madelijn; Stoeckart, Peter F; Dijksterhuis, Ap

    2015-11-01

    It is a common research finding that conscious thought helps people to avoid racial discrimination. These three experiments, however, illustrate that conscious thought may increase biased face memory, which leads to increased judgment bias (i.e., preferring White to Black individuals). In Experiments 1 and 2, university students formed impressions of Black and White housemate candidates. They judged the candidates either immediately (immediate decision condition), thought about their judgments for a few minutes (conscious thought condition), or performed an unrelated task for a few minutes (unconscious thought condition). Conscious thinkers and immediate decision-makers showed a stronger face memory bias than unconscious thinkers, and this mediated increased judgment bias, although not all results were significant. Experiment 3 used a new, different paradigm and showed that a Black male was remembered as darker after a period of conscious thought than after a period of unconscious thought. Implications for racial prejudice are discussed. PMID:26164254

  9. Thinking in Black and White: Conscious thought increases racially biased judgments through biased face memory.

    PubMed

    Strick, Madelijn; Stoeckart, Peter F; Dijksterhuis, Ap

    2015-11-01

    It is a common research finding that conscious thought helps people to avoid racial discrimination. These three experiments, however, illustrate that conscious thought may increase biased face memory, which leads to increased judgment bias (i.e., preferring White to Black individuals). In Experiments 1 and 2, university students formed impressions of Black and White housemate candidates. They judged the candidates either immediately (immediate decision condition), thought about their judgments for a few minutes (conscious thought condition), or performed an unrelated task for a few minutes (unconscious thought condition). Conscious thinkers and immediate decision-makers showed a stronger face memory bias than unconscious thinkers, and this mediated increased judgment bias, although not all results were significant. Experiment 3 used a new, different paradigm and showed that a Black male was remembered as darker after a period of conscious thought than after a period of unconscious thought. Implications for racial prejudice are discussed.

  10. A normality bias in legal decision making.

    PubMed

    Prentice, Robert A; Koehler, Jonathan J

    2003-03-01

    It is important to understand how legal fact finders determine causation and assign blame. However, this process is poorly understood. Among the psychological factors that affect decision makers are an omission bias (a tendency to blame actions more than inactions [omissions] for bad results), and a normality bias (a tendency to react more strongly to bad outcomes that spring from abnormal rather than normal circumstances). The omission and normality biases often reinforce one another when inaction preserves the normal state and when action creates an abnormal state. But what happens when these biases push in opposite directions as they would when inaction promotes an abnormal state or when action promotes a normal state? Which bias exerts the stronger influence on the judgments and behaviors of legal decision makers? The authors address this issue in two controlled experiments. One experiment involves medical malpractice and the other involves stockbroker negligence. They find that jurors pay much more attention to the normality of conditions than to whether those conditions arose through acts or omissions. Defendants who followed a nontraditional medical treatment regime or who chose a nontraditional stock portfolio received more blame and more punishment for bad outcomes than did defendants who obtained equally poor results after recommending a traditional medical regime or a traditional stock portfolio. Whether these recommendations entailed an action or an omission was essentially irrelevant. The Article concludes with a discussion of the implications of a robust normality bias for American jurisprudence.

  11. Assessing Projection Bias in Consumers' Food Preferences.

    PubMed

    de-Magistris, Tiziana; Gracia, Azucena

    2016-01-01

    The aim of this study is to test whether projection bias exists in consumers' purchasing decisions for food products. To achieve our aim, we used a non-hypothetical experiment (i.e., experimental auction), where hungry and non-hungry participants were incentivized to reveal their willingness to pay (WTP). The results confirm the existence of projection bias when consumers made their decisions on food products. In particular, projection bias existed because currently hungry participants were willing to pay a higher price premium for cheeses than satiated ones, both in hungry and satiated future states. Moreover, participants overvalued the food product more when they were delivered in the future hungry condition than in the satiated one. Our study provides clear, quantitative and meaningful evidence of projection bias because our findings are based on economic valuation of food preferences. Indeed, the strength of this study is that findings are expressed in terms of willingness to pay which is an interpretable amount of money.

  12. Own Variety Bias

    PubMed Central

    García, Andrea Ariza

    2015-01-01

    In a language identification task, native Belgian French and native Swiss French speakers identified French from France as their own variety. However, Canadian French was not subject to this bias. Canadian and French listeners didn’t claim a different variety as their own.

  13. Biased to Learn Language

    ERIC Educational Resources Information Center

    Sebastian-Galles, Nuria

    2007-01-01

    Some recent publications that explore the foundations of early language development are reviewed in this article. The review adopts the pivotal idea that infants' advancements are helped by the existence of different types of biases. The infant's discovery of the phonological properties of the language of the environment, as well as their learning…

  14. Optically biased laser gyro

    SciTech Connect

    Anderson, D.Z.; Chow, W.W.; Scully, M.O.; Sanders, V.E.

    1980-10-01

    We describe a four-mode ring laser that exhibits none of the mode-locking characteristics that plague laser gyros. This laser is characterized by a bias that changes sign with a change in the direction of rotation and prevents the counterpropagating modes from locking. A theoretical analysis explaining the experimental results is outlined.

  15. Own Variety Bias.

    PubMed

    Sloos, Marjoleine; García, Andrea Ariza

    2015-10-01

    In a language identification task, native Belgian French and native Swiss French speakers identified French from France as their own variety. However, Canadian French was not subject to this bias. Canadian and French listeners didn't claim a different variety as their own. PMID:27648211

  16. Multivariate meta-analysis: potential and promise.

    PubMed

    Jackson, Dan; Riley, Richard; White, Ian R

    2011-09-10

    The multivariate random effects model is a generalization of the standard univariate model. Multivariate meta-analysis is becoming more commonly used and the techniques and related computer software, although continually under development, are now in place. In order to raise awareness of the multivariate methods, and discuss their advantages and disadvantages, we organized a one day 'Multivariate meta-analysis' event at the Royal Statistical Society. In addition to disseminating the most recent developments, we also received an abundance of comments, concerns, insights, critiques and encouragement. This article provides a balanced account of the day's discourse. By giving others the opportunity to respond to our assessment, we hope to ensure that the various view points and opinions are aired before multivariate meta-analysis simply becomes another widely used de facto method without any proper consideration of it by the medical statistics community. We describe the areas of application that multivariate meta-analysis has found, the methods available, the difficulties typically encountered and the arguments for and against the multivariate methods, using four representative but contrasting examples. We conclude that the multivariate methods can be useful, and in particular can provide estimates with better statistical properties, but also that these benefits come at the price of making more assumptions which do not result in better inference in every case. Although there is evidence that multivariate meta-analysis has considerable potential, it must be even more carefully applied than its univariate counterpart in practice.

  17. DC Self Bias Trends in Dual Frequency PECVD Deposition Systems

    NASA Astrophysics Data System (ADS)

    Keil, D. L.; Augustyniak, E.; Leeser, C.; Galli, F.

    2011-10-01

    Capacitively coupled plasma (CCP) etch systems commonly report the DC auto or self bias developed as a consequence of capacitively coupling RF to the plasma. Frequently, these systems employ wafer pedestals comprised of electrostatic chucks which must monitor the self bias as part of their normal operation. DC self bias is often found to correlate with various etch process behaviors or system states. It is less common, however, to find CCP deposition systems that report DC self bias. This work reports results of a study of DC self bias trends due to chamber pressure, chamber conditioning and aging, and changes in wafer pedestal hardware. In particular, chamber film accumulation is found to correlate to certain DC bias trends. The applicability of these results for process tracking and system monitoring is discussed. Additionally, the DC self bias response to deliberate perturbations to the RF system are examined. These perturbations include those not normally encountered during commercial deposition such as `bleeding' current to ground.

  18. Multivariate Time Series Similarity Searching

    PubMed Central

    Wang, Jimin; Zhu, Yuelong; Li, Shijin; Wan, Dingsheng; Zhang, Pengcheng

    2014-01-01

    Multivariate time series (MTS) datasets are very common in various financial, multimedia, and hydrological fields. In this paper, a dimension-combination method is proposed to search similar sequences for MTS. Firstly, the similarity of single-dimension series is calculated; then the overall similarity of the MTS is obtained by synthesizing each of the single-dimension similarity based on weighted BORDA voting method. The dimension-combination method could use the existing similarity searching method. Several experiments, which used the classification accuracy as a measure, were performed on six datasets from the UCI KDD Archive to validate the method. The results show the advantage of the approach compared to the traditional similarity measures, such as Euclidean distance (ED), cynamic time warping (DTW), point distribution (PD), PCA similarity factor (SPCA), and extended Frobenius norm (Eros), for MTS datasets in some ways. Our experiments also demonstrate that no measure can fit all datasets, and the proposed measure is a choice for similarity searches. PMID:24895665

  19. Multivariate time series similarity searching.

    PubMed

    Wang, Jimin; Zhu, Yuelong; Li, Shijin; Wan, Dingsheng; Zhang, Pengcheng

    2014-01-01

    Multivariate time series (MTS) datasets are very common in various financial, multimedia, and hydrological fields. In this paper, a dimension-combination method is proposed to search similar sequences for MTS. Firstly, the similarity of single-dimension series is calculated; then the overall similarity of the MTS is obtained by synthesizing each of the single-dimension similarity based on weighted BORDA voting method. The dimension-combination method could use the existing similarity searching method. Several experiments, which used the classification accuracy as a measure, were performed on six datasets from the UCI KDD Archive to validate the method. The results show the advantage of the approach compared to the traditional similarity measures, such as Euclidean distance (ED), cynamic time warping (DTW), point distribution (PD), PCA similarity factor (SPCA), and extended Frobenius norm (Eros), for MTS datasets in some ways. Our experiments also demonstrate that no measure can fit all datasets, and the proposed measure is a choice for similarity searches. PMID:24895665

  20. Attentional biases in geometric form perception.

    PubMed

    Latimer, C; Stevens, C; Irish, M; Webber, L

    2000-08-01

    This paper reports the operation of robust attentional bias to the top and right during perception of small, single geometric forms. Same/different judgements of successively presented standard and comparison forms are faster when local differences are located at top and right rather than in other regions of the forms. The bias persists when form size is reduced to approximately one degree of visual angle, and it is unaffected by saccadic eye movements and by instructions to attend to other reliably differentiating regions of the forms. Results lend support in various degrees to two of the possible explanations of the bias: (1) a static, skewed distribution of attentional resources around eye fixation; and (2) biased, covert scanning that commences invariably at the top and right of stimulus forms. Origins of the bias in terms of possible left-hemispheric capacity for constructing representations of visual stimuli from parts, as well as in terms of reading experience and prevailing optic flow during locomotion through space are considered. Recent investigations of conditions under which the bias can be maintained or reduced are mentioned.

  1. Affective Biases in Humans and Animals.

    PubMed

    Robinson, E S J; Roiser, J P

    2016-01-01

    Depression is one of the most common but poorly understood psychiatric conditions. Although drug treatments and psychological therapies are effective in some patients, many do not achieve full remission and some patients receive no apparent benefit. Developing new improved treatments requires a better understanding of the aetiology of symptoms and evaluation of novel therapeutic targets in pre-clinical studies. Recent developments in our understanding of the basic cognitive processes that may contribute to the development of depression and its treatment offer new opportunities for both clinical and pre-clinical research. This chapter discusses the clinical evidence supporting a cognitive neuropsychological model of depression and antidepressant efficacy, and how this information may be usefully translated to pre-clinical investigation. Studies using neuropsychological tests in depressed patients and at risk populations have revealed basic negative emotional biases and disrupted reward and punishment processing, which may also impact on non-affective cognition. These affective biases are sensitive to antidepressant treatments with early onset effects observed, suggesting an important role in recovery. This clinical work into affective biases has also facilitated back-translation to animals and the development of assays to study affective biases in rodents. These animal studies suggest that, similar to humans, rodents in putative negative affective states exhibit negative affective biases on decision-making and memory tasks. Antidepressant treatments also induce positive biases in these rodent tasks, supporting the translational validity of this approach. Although still in the early stages of development and validation, affective biases in depression have the potential to offer new insights into the clinical condition, as well as facilitating the development of more translational approaches for pre-clinical studies. PMID:27660073

  2. Mardia's Multivariate Kurtosis with Missing Data

    ERIC Educational Resources Information Center

    Yuan, Ke-Hai; Lambert, Paul L.; Fouladi, Rachel T.

    2004-01-01

    Mardia's measure of multivariate kurtosis has been implemented in many statistical packages commonly used by social scientists. It provides important information on whether a commonly used multivariate procedure is appropriate for inference. Many statistical packages also have options for missing data. However, there is no procedure for applying…

  3. Multivariate pluvial flood damage models

    SciTech Connect

    Van Ootegem, Luc; Verhofstadt, Elsy; Van Herck, Kristine; Creten, Tom

    2015-09-15

    Depth–damage-functions, relating the monetary flood damage to the depth of the inundation, are commonly used in the case of fluvial floods (floods caused by a river overflowing). We construct four multivariate damage models for pluvial floods (caused by extreme rainfall) by differentiating on the one hand between ground floor floods and basement floods and on the other hand between damage to residential buildings and damage to housing contents. We do not only take into account the effect of flood-depth on damage, but also incorporate the effects of non-hazard indicators (building characteristics, behavioural indicators and socio-economic variables). By using a Tobit-estimation technique on identified victims of pluvial floods in Flanders (Belgium), we take into account the effect of cases of reported zero damage. Our results show that the flood depth is an important predictor of damage, but with a diverging impact between ground floor floods and basement floods. Also non-hazard indicators are important. For example being aware of the risk just before the water enters the building reduces content damage considerably, underlining the importance of warning systems and policy in this case of pluvial floods. - Highlights: • Prediction of damage of pluvial floods using also non-hazard information • We include ‘no damage cases’ using a Tobit model. • The damage of flood depth is stronger for ground floor than for basement floods. • Non-hazard indicators are especially important for content damage. • Potential gain of policies that increase awareness of flood risks.

  4. Temperature trend biases

    NASA Astrophysics Data System (ADS)

    Venema, Victor; Lindau, Ralf

    2016-04-01

    In an accompanying talk we show that well-homogenized national dataset warm more than temperatures from global collections averaged over the region of common coverage. In this poster we want to present auxiliary work about possible biases in the raw observations and on how well relative statistical homogenization can remove trend biases. There are several possible causes of cooling biases, which have not been studied much. Siting could be an important factor. Urban stations tend to move away from the centre to better locations. Many stations started inside of urban areas and are nowadays more outside. Even for villages the temperature difference between the centre and edge can be 0.5°C. When a city station moves to an airport, which often happened around WWII, this takes the station (largely) out of the urban heat island. During the 20th century the Stevenson screen was established as the dominant thermometer screen. This screen protected the thermometer much better against radiation than earlier designs. Deficits of earlier measurement methods have artificially warmed the temperatures in the 19th century. Newer studies suggest we may have underestimated the size of this bias. Currently we are in a transition to Automatic Weather Stations. The net global effect of this transition is not clear at this moment. Irrigation on average decreases the 2m-temperature by about 1 degree centigrade. At the same time, irrigation has increased significantly during the last century. People preferentially live in irrigated areas and weather stations serve agriculture. Thus it is possible that there is a higher likelihood that weather stations are erected in irrigated areas than elsewhere. In this case irrigation could lead to a spurious cooling trend. In the Parallel Observations Science Team of the International Surface Temperature Initiative (ISTI-POST) we are studying influence of the introduction of Stevenson screens and Automatic Weather Stations using parallel measurements

  5. The Psychological Price of Media Bias

    ERIC Educational Resources Information Center

    Babad, Elisha

    2005-01-01

    Media bias was investigated through the effects of a TV interviewer's preferential behavior on the image of the interviewee in the eyes of the viewers. Judges viewed a political interview with either a friendly or a hostile interviewer then rated their impressions of the interviewed politician, whose behavior was identical in all conditions. The…

  6. Multivariate gene-set testing based on graphical models.

    PubMed

    Städler, Nicolas; Mukherjee, Sach

    2015-01-01

    The identification of predefined groups of genes ("gene-sets") which are differentially expressed between two conditions ("gene-set analysis", or GSA) is a very popular analysis in bioinformatics. GSA incorporates biological knowledge by aggregating over genes that are believed to be functionally related. This can enhance statistical power over analyses that consider only one gene at a time. However, currently available GSA approaches are based on univariate two-sample comparison of single genes. This means that they cannot test for multivariate hypotheses such as differences in covariance structure between the two conditions. Yet interplay between genes is a central aspect of biological investigation and it is likely that such interplay may differ between conditions. This paper proposes a novel approach for gene-set analysis that allows for truly multivariate hypotheses, in particular differences in gene-gene networks between conditions. Testing hypotheses concerning networks is challenging due the nature of the underlying estimation problem. Our starting point is a recent, general approach for high-dimensional two-sample testing. We refine the approach and show how it can be used to perform multivariate, network-based gene-set testing. We validate the approach in simulated examples and show results using high-throughput data from several studies in cancer biology.

  7. Joint Bias Correction of Multiple Climate Model Outputs for Impacts

    NASA Astrophysics Data System (ADS)

    McGinnis, S. A.; Sain, S. R.; Mearns, L. O.

    2014-12-01

    Climate model output often contains significant biases that can hinder its use in impacts analysis. Recent work has shown that, of the many bias correction methods in use, the best overall performance is provided by distribution mapping. Distribution mapping corrects bias via a transfer function that adjusts data points such that the cumulative distribution function (CDF) of the model output matches the CDF of the observational data. However, this method is not guaranteed to preserve the relationships between variables when applied to the variables individually.We present a new method of bias-correcting multiple variables jointly based on simultaneous diagonalization of the covariance matrices. This process transforms the variables into an uncorrelated form, where each component can be corrected independently using distribution mapping; the corrected variables are then transformed back into their original form, restoring the correlations of their joint distribution.We apply the method to model output from NARCCAP (the North American Regional Climate Change Assessment Program), bias-correcting 7 impacts-relevant variables (daily minimum and maximum temperature, precipitation, incoming solar radiation, specific humidity, and u- and v-winds) to match the University of Idaho's METDATA, which combines NLDAS-2 reanalysis with PRISM observations to derive a high-resolution daily gridded observational dataset for the contiguous United States. This joint multivariate bias correction produces results that better capture regional climate processes, such as the seasonal pattern of differences in moisture flux on dry vs rainy days and seasonal changes in diurnal heating on clear vs cloudy days.

  8. Generalized error-dependent prediction uncertainty in multivariate calibration.

    PubMed

    Allegrini, Franco; Wentzell, Peter D; Olivieri, Alejandro C

    2016-01-15

    Most of the current expressions used to calculate figures of merit in multivariate calibration have been derived assuming independent and identically distributed (iid) measurement errors. However, it is well known that this condition is not always valid for real data sets, where the existence of many external factors can lead to correlated and/or heteroscedastic noise structures. In this report, the influence of the deviations from the classical iid paradigm is analyzed in the context of error propagation theory. New expressions have been derived to calculate sample dependent prediction standard errors under different scenarios. These expressions allow for a quantitative study of the influence of the different sources of instrumental error affecting the system under analysis. Significant differences are observed when the prediction error is estimated in each of the studied scenarios using the most popular first-order multivariate algorithms, under both simulated and experimental conditions.

  9. Inclusion of Dominance Effects in the Multivariate GBLUP Model.

    PubMed

    dos Santos, Jhonathan Pedroso Rigal; Vasconcellos, Renato Coelho de Castro; Pires, Luiz Paulo Miranda; Balestre, Marcio; Von Pinho, Renzo Garcia

    2016-01-01

    New proposals for models and applications of prediction processes with data on molecular markers may help reduce the financial costs of and identify superior genotypes in maize breeding programs. Studies evaluating Genomic Best Linear Unbiased Prediction (GBLUP) models including dominance effects have not been performed in the univariate and multivariate context in the data analysis of this crop. A single cross hybrid construction procedure was performed in this study using phenotypic data and actual molecular markers of 4,091 maize lines from the public database Panzea. A total of 400 simple hybrids resulting from this process were analyzed using the univariate and multivariate GBLUP model considering only additive effects additive plus dominance effects. Historic heritability scenarios of five traits and other genetic architecture settings were used to compare models, evaluating the predictive ability and estimation of variance components. Marginal differences were detected between the multivariate and univariate models. The main explanation for the small discrepancy between models is the low- to moderate-magnitude correlations between the traits studied and moderate heritabilities. These conditions do not favor the advantages of multivariate analysis. The inclusion of dominance effects in the models was an efficient strategy to improve the predictive ability and estimation quality of variance components. PMID:27074056

  10. Learning Adaptive Forecasting Models from Irregularly Sampled Multivariate Clinical Data

    PubMed Central

    Liu, Zitao; Hauskrecht, Milos

    2016-01-01

    Building accurate predictive models of clinical multivariate time series is crucial for understanding of the patient condition, the dynamics of a disease, and clinical decision making. A challenging aspect of this process is that the model should be flexible and adaptive to reflect well patient-specific temporal behaviors and this also in the case when the available patient-specific data are sparse and short span. To address this problem we propose and develop an adaptive two-stage forecasting approach for modeling multivariate, irregularly sampled clinical time series of varying lengths. The proposed model (1) learns the population trend from a collection of time series for past patients; (2) captures individual-specific short-term multivariate variability; and (3) adapts by automatically adjusting its predictions based on new observations. The proposed forecasting model is evaluated on a real-world clinical time series dataset. The results demonstrate the benefits of our approach on the prediction tasks for multivariate, irregularly sampled clinical time series, and show that it can outperform both the population based and patient-specific time series prediction models in terms of prediction accuracy. PMID:27525189

  11. Assessing Bias in Search Engines.

    ERIC Educational Resources Information Center

    Mowshowitz, Abbe; Kawaguchi, Akira

    2002-01-01

    Addresses the measurement of bias in search engines on the Web, defining bias as the balance and representation of items in a collection retrieved from a database for a set of queries. Assesses bias by measuring the deviation from the ideal of the distribution produced by a particular search engine. (Author/LRW)

  12. Negativity bias and basic values.

    PubMed

    Schwartz, Shalom H

    2014-06-01

    Basic values explain more variance in political attitudes and preferences than other personality and sociodemographic variables. The values most relevant to the political domain are those likely to reflect the degree of negativity bias. Value conflicts that represent negativity bias clarify differences between what worries conservatives and liberals and suggest that relations between ideology and negativity bias are linear. PMID:24970450

  13. Terahertz Bloch oscillator with a modulated bias.

    PubMed

    Hyart, Timo; Alexeeva, Natalia V; Mattas, Jussi; Alekseev, Kirill N

    2009-04-10

    Electrons performing Bloch oscillations in an energy band of a dc-biased superlattice in the presence of weak dissipation can potentially generate THz fields at room temperature. The realization of such a Bloch oscillator is a long-standing problem due to the instability of a homogeneous electric field in conditions of negative differential conductivity. We establish the theoretical feasibility of stable THz gain in a long superlattice device in which the bias is quasistatically modulated by microwave fields. The modulation waveforms must have at least two harmonics in their spectra.

  14. The distinct effects of internalizing weight bias: An experimental study.

    PubMed

    Pearl, Rebecca L; Puhl, Rebecca M

    2016-06-01

    Both experiencing and internalizing weight bias are associated with negative mental and physical health outcomes, but internalization may be a more potent predictor of these outcomes. The current study aimed to differentiate between causal effects of experiencing versus internalizing weight bias on emotional responses and psychological well-being. Adults with overweight/obesity (N=260) completed an online experiment in which they were randomly assigned to focus on either the experience or internalization of weight bias, and completed measures of affect, self-esteem, and body dissatisfaction. Results indicated that the Internalization condition led to more negative affect, less positive affect, and lower self-esteem than the Experience condition. The Internalization condition also led to heightened body dissatisfaction among men, but not women. These findings suggest that weight bias internalization may be a stronger predictor of poor mental and physical health than experiences alone, and carry implications for developing weight bias interventions. PMID:26927688

  15. The distinct effects of internalizing weight bias: An experimental study.

    PubMed

    Pearl, Rebecca L; Puhl, Rebecca M

    2016-06-01

    Both experiencing and internalizing weight bias are associated with negative mental and physical health outcomes, but internalization may be a more potent predictor of these outcomes. The current study aimed to differentiate between causal effects of experiencing versus internalizing weight bias on emotional responses and psychological well-being. Adults with overweight/obesity (N=260) completed an online experiment in which they were randomly assigned to focus on either the experience or internalization of weight bias, and completed measures of affect, self-esteem, and body dissatisfaction. Results indicated that the Internalization condition led to more negative affect, less positive affect, and lower self-esteem than the Experience condition. The Internalization condition also led to heightened body dissatisfaction among men, but not women. These findings suggest that weight bias internalization may be a stronger predictor of poor mental and physical health than experiences alone, and carry implications for developing weight bias interventions.

  16. Audibility and visual biasing in speech perception

    NASA Astrophysics Data System (ADS)

    Clement, Bart Richard

    Although speech perception has been considered a predominantly auditory phenomenon, large benefits from vision in degraded acoustic conditions suggest integration of audition and vision. More direct evidence of this comes from studies of audiovisual disparity that demonstrate vision can bias and even dominate perception (McGurk & MacDonald, 1976). It has been observed that hearing-impaired listeners demonstrate more visual biasing than normally hearing listeners (Walden et al., 1990). It is argued here that stimulus audibility must be equated across groups before true differences can be established. In the present investigation, effects of visual biasing on perception were examined as audibility was degraded for 12 young normally hearing listeners. Biasing was determined by quantifying the degree to which listener identification functions for a single synthetic auditory /ba-da-ga/ continuum changed across two conditions: (1)an auditory-only listening condition; and (2)an auditory-visual condition in which every item of the continuum was synchronized with visual articulations of the consonant-vowel (CV) tokens /ba/ and /ga/, as spoken by each of two talkers. Audibility was altered by presenting the conditions in quiet and in noise at each of three signal-to- noise (S/N) ratios. For the visual-/ba/ context, large effects of audibility were found. As audibility decreased, visual biasing increased. A large talker effect also was found, with one talker eliciting more biasing than the other. An independent lipreading measure demonstrated that this talker was more visually intelligible than the other. For the visual-/ga/ context, audibility and talker effects were less robust, possibly obscured by strong listener effects, which were characterized by marked differences in perceptual processing patterns among participants. Some demonstrated substantial biasing whereas others demonstrated little, indicating a strong reliance on audition even in severely degraded acoustic

  17. Multivariate Models of Adult Pacific Salmon Returns

    PubMed Central

    Burke, Brian J.; Peterson, William T.; Beckman, Brian R.; Morgan, Cheryl; Daly, Elizabeth A.; Litz, Marisa

    2013-01-01

    Most modeling and statistical approaches encourage simplicity, yet ecological processes are often complex, as they are influenced by numerous dynamic environmental and biological factors. Pacific salmon abundance has been highly variable over the last few decades and most forecasting models have proven inadequate, primarily because of a lack of understanding of the processes affecting variability in survival. Better methods and data for predicting the abundance of returning adults are therefore required to effectively manage the species. We combined 31 distinct indicators of the marine environment collected over an 11-year period into a multivariate analysis to summarize and predict adult spring Chinook salmon returns to the Columbia River in 2012. In addition to forecasts, this tool quantifies the strength of the relationship between various ecological indicators and salmon returns, allowing interpretation of ecosystem processes. The relative importance of indicators varied, but a few trends emerged. Adult returns of spring Chinook salmon were best described using indicators of bottom-up ecological processes such as composition and abundance of zooplankton and fish prey as well as measures of individual fish, such as growth and condition. Local indicators of temperature or coastal upwelling did not contribute as much as large-scale indicators of temperature variability, matching the spatial scale over which salmon spend the majority of their ocean residence. Results suggest that effective management of Pacific salmon requires multiple types of data and that no single indicator can represent the complex early-ocean ecology of salmon. PMID:23326586

  18. Meta-Regression Approximations to Reduce Publication Selection Bias

    ERIC Educational Resources Information Center

    Stanley, T. D.; Doucouliagos, Hristos

    2014-01-01

    Publication selection bias is a serious challenge to the integrity of all empirical sciences. We derive meta-regression approximations to reduce this bias. Our approach employs Taylor polynomial approximations to the conditional mean of a truncated distribution. A quadratic approximation without a linear term, precision-effect estimate with…

  19. Recognition bias and the physical attractiveness stereotype.

    PubMed

    Rohner, Jean-Christophe; Rasmussen, Anders

    2012-06-01

    Previous studies have found a recognition bias for information consistent with the physical attractiveness stereotype (PAS), in which participants believe that they remember that attractive individuals have positive qualities and that unattractive individuals have negative qualities, regardless of what information actually occurred. The purpose of this research was to examine whether recognition bias for PAS congruent information is replicable and invariant across a variety of conditions (i.e. generalizable). The effects of nine different moderator variables were examined in two experiments. With a few exceptions, the effect of PAS congruence on recognition bias was independent of the moderator variables. The results suggest that the tendency to believe that one remembers information consistent with the physical attractiveness stereotype is a robust phenomenon.

  20. Hindsight and confirmation biases in an exercise in telepathy.

    PubMed

    Rudski, Jeffrey M

    2002-12-01

    Belief in the paranormal or claims of paranormal experiences may be, at least in part, associated with systematic cognitive biases. 48 undergraduate college students engaged in an exercise in telepathy in which the color of cards was 'sent' to them by the experimenter under two conditions. In a Hindsight-possible condition, participants recorded whether their choice was correct following the revelation of the color. In the Control condition participants committed to a particular response by writing it down before receiving feedback, thus eliminating ability to alter retrospectively what 'was known all along'. Consistent with a hindsight bias, participants performed significantly better under the Hindsight-possible condition. Moreover, a statisically significant correlation was found between paranormal belief assessed on Tobacyk's 1988 Revised Paranormal Belief Scale in the Hindsight-possible but not in the Control condition, suggesting a confirmation bias. Results are discussed in terms of interactions between hindsight and confirmation biases and how they might relate to paranormal beliefs.

  1. Towards identification of relevant variables in the observed aerosol optical depth bias between MODIS and AERONET observations

    NASA Astrophysics Data System (ADS)

    Malakar, N. K.; Lary, D. J.; Gencaga, D.; Albayrak, A.; Wei, J.

    2013-08-01

    Measurements made by satellite remote sensing, Moderate Resolution Imaging Spectroradiometer (MODIS), and globally distributed Aerosol Robotic Network (AERONET) are compared. Comparison of the two datasets measurements for aerosol optical depth values show that there are biases between the two data products. In this paper, we present a general framework towards identifying relevant set of variables responsible for the observed bias. We present a general framework to identify the possible factors influencing the bias, which might be associated with the measurement conditions such as the solar and sensor zenith angles, the solar and sensor azimuth, scattering angles, and surface reflectivity at the various measured wavelengths, etc. Specifically, we performed analysis for remote sensing Aqua-Land data set, and used machine learning technique, neural network in this case, to perform multivariate regression between the ground-truth and the training data sets. Finally, we used mutual information between the observed and the predicted values as the measure of similarity to identify the most relevant set of variables. The search is brute force method as we have to consider all possible combinations. The computations involves a huge number crunching exercise, and we implemented it by writing a job-parallel program.

  2. Two success-biased social learning strategies.

    PubMed

    Baldini, Ryan

    2013-06-01

    I compare the evolutionary dynamics of two success-biased social learning strategies, which, by definition, use the success of others to inform one's social learning decisions. The first, "Compare Means", causes a learner to adopt cultural variants with highest mean payoff in her sample. The second, "Imitate the Best", causes a learner to imitate the single most successful individual in her sample. I summarize conditions under which each strategy performs well or poorly, and investigate their evolution via a gene-culture coevolutionary model. Despite the adaptive appeal of these strategies, both encounter conditions under which they systematically perform worse than simply imitating at random. Compare Means performs worst when the optimal cultural variant is usually at high frequency, while Imitate the Best performs worst when suboptimal variants sometimes produce high payoffs. The extent to which it is optimal to use success-biased social learning depends strongly on the payoff distributions and environmental conditions that human social learners face.

  3. Suppression of attentional bias in PTSD.

    PubMed

    Constans, Joseph I; McCloskey, Michael S; Vasterling, Jennifer J; Brailey, Kevin; Mathews, Andrew

    2004-05-01

    Sixty combat veterans with posttraumatic stress disorder performed an emotional Stroop task under 1 of 4 contextual conditions designed to test theoretical explanations for an attentional bias suppression effect. Results revealed that when the emotional Stroop task was performed under conditions involving a future threat of either watching a combat video or giving a speech, attentional bias was inhibited. There was limited support for the prediction that the suppression effect was strongest when stressor content matched word content on the Stroop. In contrast to participants in the threat conditions, veterans who believed that they would receive additional compensation for speeded color naming or who believed that they would have no other experimental demands were slower when color naming combat-threat words. Potential theoretical explanations of the findings are discussed.

  4. Multivariate Longitudinal Analysis with Bivariate Correlation Test.

    PubMed

    Adjakossa, Eric Houngla; Sadissou, Ibrahim; Hounkonnou, Mahouton Norbert; Nuel, Gregory

    2016-01-01

    In the context of multivariate multilevel data analysis, this paper focuses on the multivariate linear mixed-effects model, including all the correlations between the random effects when the dimensional residual terms are assumed uncorrelated. Using the EM algorithm, we suggest more general expressions of the model's parameters estimators. These estimators can be used in the framework of the multivariate longitudinal data analysis as well as in the more general context of the analysis of multivariate multilevel data. By using a likelihood ratio test, we test the significance of the correlations between the random effects of two dependent variables of the model, in order to investigate whether or not it is useful to model these dependent variables jointly. Simulation studies are done to assess both the parameter recovery performance of the EM estimators and the power of the test. Using two empirical data sets which are of longitudinal multivariate type and multivariate multilevel type, respectively, the usefulness of the test is illustrated. PMID:27537692

  5. Multivariate Longitudinal Analysis with Bivariate Correlation Test

    PubMed Central

    Adjakossa, Eric Houngla; Sadissou, Ibrahim; Hounkonnou, Mahouton Norbert; Nuel, Gregory

    2016-01-01

    In the context of multivariate multilevel data analysis, this paper focuses on the multivariate linear mixed-effects model, including all the correlations between the random effects when the dimensional residual terms are assumed uncorrelated. Using the EM algorithm, we suggest more general expressions of the model’s parameters estimators. These estimators can be used in the framework of the multivariate longitudinal data analysis as well as in the more general context of the analysis of multivariate multilevel data. By using a likelihood ratio test, we test the significance of the correlations between the random effects of two dependent variables of the model, in order to investigate whether or not it is useful to model these dependent variables jointly. Simulation studies are done to assess both the parameter recovery performance of the EM estimators and the power of the test. Using two empirical data sets which are of longitudinal multivariate type and multivariate multilevel type, respectively, the usefulness of the test is illustrated. PMID:27537692

  6. Auditory perception bias in speech imitation

    PubMed Central

    Postma-Nilsenová, Marie; Postma, Eric

    2013-01-01

    In an experimental study, we explored the role of auditory perception bias in vocal pitch imitation. Psychoacoustic tasks involving a missing fundamental indicate that some listeners are attuned to the relationship between all the higher harmonics present in the signal, which supports their perception of the fundamental frequency (the primary acoustic correlate of pitch). Other listeners focus on the lowest harmonic constituents of the complex sound signal which may hamper the perception of the fundamental. These two listener types are referred to as fundamental and spectral listeners, respectively. We hypothesized that the individual differences in speakers' capacity to imitate F0 found in earlier studies, may at least partly be due to the capacity to extract information about F0 from the speech signal. Participants' auditory perception bias was determined with a standard missing fundamental perceptual test. Subsequently, speech data were collected in a shadowing task with two conditions, one with a full speech signal and one with high-pass filtered speech above 300 Hz. The results showed that perception bias toward fundamental frequency was related to the degree of F0 imitation. The effect was stronger in the condition with high-pass filtered speech. The experimental outcomes suggest advantages for fundamental listeners in communicative situations where F0 imitation is used as a behavioral cue. Future research needs to determine to what extent auditory perception bias may be related to other individual properties known to improve imitation, such as phonetic talent. PMID:24204361

  7. Multivariate and Multiscale Data Assimilation in Terrestrial Systems: A Review

    PubMed Central

    Montzka, Carsten; Pauwels, Valentijn R. N.; Franssen, Harrie-Jan Hendricks; Han, Xujun; Vereecken, Harry

    2012-01-01

    simulation model. Existing approaches can be used to simultaneously update several model states and model parameters if applicable. In other words, the basic principles for multivariate data assimilation are already available. We argue that a better understanding of the measurement errors for different observation types, improved estimates of observation bias and improved multiscale assimilation methods for data which scale nonlinearly is important to properly weight them in multiscale multivariate data assimilation. In this context, improved cross-validation of different data types, and increased ground truth verification of remote sensing products are required. PMID:23443380

  8. Multivariate and multiscale data assimilation in terrestrial systems: a review.

    PubMed

    Montzka, Carsten; Pauwels, Valentijn R N; Franssen, Harrie-Jan Hendricks; Han, Xujun; Vereecken, Harry

    2012-11-26

    simulation model. Existing approaches can be used to simultaneously update several model states and model parameters if applicable. In other words, the basic principles for multivariate data assimilation are already available. We argue that a better understanding of the measurement errors for different observation types, improved estimates of observation bias and improved multiscale assimilation methods for data which scale nonlinearly is important to properly weight them in multiscale multivariate data assimilation. In this context, improved cross-validation of different data types, and increased ground truth verification of remote sensing products are required.

  9. Enhancing scientific reasoning by refining students' models of multivariable causality

    NASA Astrophysics Data System (ADS)

    Keselman, Alla

    Inquiry learning as an educational method is gaining increasing support among elementary and middle school educators. In inquiry activities at the middle school level, students are typically asked to conduct investigations and infer causal relationships about multivariable causal systems. In these activities, students usually demonstrate significant strategic weaknesses and insufficient metastrategic understanding of task demands. Present work suggests that these weaknesses arise from students' deficient mental models of multivariable causality, in which effects of individual features are neither additive, nor constant. This study is an attempt to develop an intervention aimed at enhancing scientific reasoning by refining students' models of multivariable causality. Three groups of students engaged in a scientific investigation activity over seven weekly sessions. By creating unique combinations of five features potentially involved in earthquake mechanism and observing associated risk meter readings, students had to find out which of the features were causal, and to learn to predict earthquake risk. Additionally, students in the instructional and practice groups engaged in self-directed practice in making scientific predictions. The instructional group also participated in weekly instructional sessions on making predictions based on multivariable causality. Students in the practice and instructional conditions showed small to moderate improvement in their attention to the evidence and in their metastrategic ability to recognize effective investigative strategies in the work of other students. They also demonstrated a trend towards making a greater number of valid inferences than the control group students. Additionally, students in the instructional condition showed significant improvement in their ability to draw inferences based on multiple records. They also developed more accurate knowledge about non-causal features of the system. These gains were maintained

  10. The intentionality bias and schizotypy.

    PubMed

    Moore, J W; Pope, A

    2014-01-01

    The "intentionality bias" refers to our automatic tendency to judge other people's actions to be intentional. In this experiment we extended research on this effect in two key ways. First, we developed a novel nonlinguistic task for assessing the intentionality bias. This task used video stimuli of ambiguous movements. Second, we investigated the relationship between the strength of this bias and schizotypy (schizophrenia-like symptoms in healthy individuals). Our results showed that the intentionality bias was replicated for the video stimuli and also that this bias is stronger in those individuals scoring higher on the schizotypy rating scales. Overall these findings lend further support for the existence of the intentionality bias. We also discuss the possible relevance of these findings for our understanding of certain symptoms of schizophrenic illness.

  11. A Study of Item Bias for Attitudinal Measurement Using Maximum Likelihood Factor Analysis.

    ERIC Educational Resources Information Center

    Mayberry, Paul W.

    A technique for detecting item bias that is responsive to attitudinal measurement considerations is a maximum likelihood factor analysis procedure comparing multivariate factor structures across various subpopulations, often referred to as SIFASP. The SIFASP technique allows for factorial model comparisons in the testing of various hypotheses…

  12. Recursive bias estimation for high dimensional regression smoothers

    SciTech Connect

    Hengartner, Nicolas W; Cornillon, Pierre - Andre; Matzner - Lober, Eric

    2009-01-01

    In multivariate nonparametric analysis, sparseness of the covariates also called curse of dimensionality, forces one to use large smoothing parameters. This leads to biased smoother. Instead of focusing on optimally selecting the smoothing parameter, we fix it to some reasonably large value to ensure an over-smoothing of the data. The resulting smoother has a small variance but a substantial bias. In this paper, we propose to iteratively correct of the bias initial estimator by an estimate of the latter obtained by smoothing the residuals. We examine in details the convergence of the iterated procedure for classical smoothers and relate our procedure to L{sub 2}-Boosting, For multivariate thin plate spline smoother, we proved that our procedure adapts to the correct and unknown order of smoothness for estimating an unknown function m belonging to H({nu}) (Sobolev space where m should be bigger than d/2). We apply our method to simulated and real data and show that our method compares favorably with existing procedures.

  13. Multivariate Statistical Modelling of Drought and Heat Wave Events

    NASA Astrophysics Data System (ADS)

    Manning, Colin; Widmann, Martin; Vrac, Mathieu; Maraun, Douglas; Bevaqua, Emanuele

    2016-04-01

    Multivariate Statistical Modelling of Drought and Heat Wave Events C. Manning1,2, M. Widmann1, M. Vrac2, D. Maraun3, E. Bevaqua2,3 1. School of Geography, Earth and Environmental Sciences, University of Birmingham, Edgbaston, Birmingham, UK 2. Laboratoire des Sciences du Climat et de l'Environnement, (LSCE-IPSL), Centre d'Etudes de Saclay, Gif-sur-Yvette, France 3. Wegener Center for Climate and Global Change, University of Graz, Brandhofgasse 5, 8010 Graz, Austria Compound extreme events are a combination of two or more contributing events which in themselves may not be extreme but through their joint occurrence produce an extreme impact. Compound events are noted in the latest IPCC report as an important type of extreme event that have been given little attention so far. As part of the CE:LLO project (Compound Events: muLtivariate statisticaL mOdelling) we are developing a multivariate statistical model to gain an understanding of the dependence structure of certain compound events. One focus of this project is on the interaction between drought and heat wave events. Soil moisture has both a local and non-local effect on the occurrence of heat waves where it strongly controls the latent heat flux affecting the transfer of sensible heat to the atmosphere. These processes can create a feedback whereby a heat wave maybe amplified or suppressed by the soil moisture preconditioning, and vice versa, the heat wave may in turn have an effect on soil conditions. An aim of this project is to capture this dependence in order to correctly describe the joint probabilities of these conditions and the resulting probability of their compound impact. We will show an application of Pair Copula Constructions (PCCs) to study the aforementioned compound event. PCCs allow in theory for the formulation of multivariate dependence structures in any dimension where the PCC is a decomposition of a multivariate distribution into a product of bivariate components modelled using copulas. A

  14. [A multivariate nonlinear model for quantitative analysis in laser-induced breakdown spectroscopy].

    PubMed

    Chen, Xing-Long; Fu, Hong-Bo; Wang, Jing-Ge; Ni, Zhi-Bo; He, Wen-Gan; Xu, Jun; Rao Rui-zhong; Dong, Rui-Zhong

    2014-11-01

    Most quantitative models used in laser-induced breakdown spectroscopy (LIBS) are based on the hypothesis that laser-induced plasma approaches the state of local thermal equilibrium (LTE). However, the local equilibrium is possible only at a specific time segment during the evolution. As the populations of each energy level does not follow Boltzmann distribution in non-LTE condition, those quantitative models using single spectral line would be inaccurate. A multivariate nonlinear model, in which the LTE is not required, was proposed in this article to reduce the signal fluctuation and improve the accuracy of quantitative analysis. This multivariate nonlinear model was compared with the internal calibration model which is based on the LTE condition. The content of Mn in steel samples was determined by using the two models, respectively. A minor error and a minor relative standard deviation (RSD) were observed in multivariate nonlinear model. This result demonstrates that multivariate nonlinear model can improve measurement accuracy and repeatability.

  15. A New Multivariate Approach in Generating Ensemble Meteorological Forcings for Hydrological Forecasting

    NASA Astrophysics Data System (ADS)

    Khajehei, Sepideh; Moradkhani, Hamid

    2015-04-01

    Producing reliable and accurate hydrologic ensemble forecasts are subject to various sources of uncertainty, including meteorological forcing, initial conditions, model structure, and model parameters. Producing reliable and skillful precipitation ensemble forecasts is one approach to reduce the total uncertainty in hydrological applications. Currently, National Weather Prediction (NWP) models are developing ensemble forecasts for various temporal ranges. It is proven that raw products from NWP models are biased in mean and spread. Given the above state, there is a need for methods that are able to generate reliable ensemble forecasts for hydrological applications. One of the common techniques is to apply statistical procedures in order to generate ensemble forecast from NWP-generated single-value forecasts. The procedure is based on the bivariate probability distribution between the observation and single-value precipitation forecast. However, one of the assumptions of the current method is fitting Gaussian distribution to the marginal distributions of observed and modeled climate variable. Here, we have described and evaluated a Bayesian approach based on Copula functions to develop an ensemble precipitation forecast from the conditional distribution of single-value precipitation forecasts. Copula functions are known as the multivariate joint distribution of univariate marginal distributions, which are presented as an alternative procedure in capturing the uncertainties related to meteorological forcing. Copulas are capable of modeling the joint distribution of two variables with any level of correlation and dependency. This study is conducted over a sub-basin in the Columbia River Basin in USA using the monthly precipitation forecasts from Climate Forecast System (CFS) with 0.5x0.5 Deg. spatial resolution to reproduce the observations. The verification is conducted on a different period and the superiority of the procedure is compared with Ensemble Pre

  16. Classical least squares multivariate spectral analysis

    DOEpatents

    Haaland, David M.

    2002-01-01

    An improved classical least squares multivariate spectral analysis method that adds spectral shapes describing non-calibrated components and system effects (other than baseline corrections) present in the analyzed mixture to the prediction phase of the method. These improvements decrease or eliminate many of the restrictions to the CLS-type methods and greatly extend their capabilities, accuracy, and precision. One new application of PACLS includes the ability to accurately predict unknown sample concentrations when new unmodeled spectral components are present in the unknown samples. Other applications of PACLS include the incorporation of spectrometer drift into the quantitative multivariate model and the maintenance of a calibration on a drifting spectrometer. Finally, the ability of PACLS to transfer a multivariate model between spectrometers is demonstrated.

  17. Multivariate analysis: A statistical approach for computations

    NASA Astrophysics Data System (ADS)

    Michu, Sachin; Kaushik, Vandana

    2014-10-01

    Multivariate analysis is a type of multivariate statistical approach commonly used in, automotive diagnosis, education evaluating clusters in finance etc and more recently in the health-related professions. The objective of the paper is to provide a detailed exploratory discussion about factor analysis (FA) in image retrieval method and correlation analysis (CA) of network traffic. Image retrieval methods aim to retrieve relevant images from a collected database, based on their content. The problem is made more difficult due to the high dimension of the variable space in which the images are represented. Multivariate correlation analysis proposes an anomaly detection and analysis method based on the correlation coefficient matrix. Anomaly behaviors in the network include the various attacks on the network like DDOs attacks and network scanning.

  18. Snow multivariable data assimilation for hydrological predictions in mountain areas

    NASA Astrophysics Data System (ADS)

    Piazzi, Gaia; Campo, Lorenzo; Gabellani, Simone; Rudari, Roberto; Castelli, Fabio; Cremonese, Edoardo; Morra di Cella, Umberto; Stevenin, Hervé; Ratto, Sara Maria

    2016-04-01

    The seasonal presence of snow on alpine catchments strongly impacts both surface energy balance and water resource. Thus, the knowledge of the snowpack dynamics is of critical importance for several applications, such as water resource management, floods prediction and hydroelectric power production. Several independent data sources provide information about snowpack state: ground-based measurements, satellite data and physical models. Although all these data types are reliable, each of them is affected by specific flaws and errors (respectively dependency on local conditions, sensor biases and limitations, initialization and poor quality forcing data). Moreover, there are physical factors that make an exhaustive reconstruction of snow dynamics complicated: snow intermittence in space and time, stratification and slow phenomena like metamorphism processes, uncertainty in snowfall evaluation, wind transportation, etc. Data Assimilation (DA) techniques provide an objective methodology to combine observational and modeled information to obtain the most likely estimate of snowpack state. Indeed, by combining all the available sources of information, the implementation of DA schemes can quantify and reduce the uncertainties of the estimations. This study presents SMASH (Snow Multidata Assimilation System for Hydrology), a multi-layer snow dynamic model, strengthened by a robust multivariable data assimilation algorithm. The model is physically based on mass and energy balances and can be used to reproduce the main physical processes occurring within the snowpack: accumulation, density dynamics, melting, sublimation, radiative balance, heat and mass exchanges. The model is driven by observed forcing meteorological data (air temperature, wind velocity, relative air humidity, precipitation and incident solar radiation) to provide a complete estimate of snowpack state. The implementation of an Ensemble Kalman Filter (EnKF) scheme enables to assimilate simultaneously ground

  19. Assessing Projection Bias in Consumers’ Food Preferences

    PubMed Central

    de-Magistris, Tiziana; Gracia, Azucena

    2016-01-01

    The aim of this study is to test whether projection bias exists in consumers’ purchasing decisions for food products. To achieve our aim, we used a non-hypothetical experiment (i.e., experimental auction), where hungry and non-hungry participants were incentivized to reveal their willingness to pay (WTP). The results confirm the existence of projection bias when consumers made their decisions on food products. In particular, projection bias existed because currently hungry participants were willing to pay a higher price premium for cheeses than satiated ones, both in hungry and satiated future states. Moreover, participants overvalued the food product more when they were delivered in the future hungry condition than in the satiated one. Our study provides clear, quantitative and meaningful evidence of projection bias because our findings are based on economic valuation of food preferences. Indeed, the strength of this study is that findings are expressed in terms of willingness to pay which is an interpretable amount of money. PMID:26828930

  20. Multivariate data analysis for outcome studies.

    PubMed

    Spector, P E

    1981-02-01

    The use of multivariate statistical techniques for analyzing the complex data often gathered in outcome studies is discussed. The multivariate analysis of variance (MANOVA) is suggested for multiple group studies common to outcome studies. This technique can be utilized for a large number of specific research designs whenever multiple outcome measures are collected. MANOVA offers two specific advantages over more familiar univariate approaches: it presents better control over Type 1 error rates while preserving statistical power, and it allows more thorough analysis of complex data. PMID:7223728

  1. Sequential biases in accumulating evidence

    PubMed Central

    Huggins, Richard; Dogo, Samson Henry

    2015-01-01

    Whilst it is common in clinical trials to use the results of tests at one phase to decide whether to continue to the next phase and to subsequently design the next phase, we show that this can lead to biased results in evidence synthesis. Two new kinds of bias associated with accumulating evidence, termed ‘sequential decision bias’ and ‘sequential design bias’, are identified. Both kinds of bias are the result of making decisions on the usefulness of a new study, or its design, based on the previous studies. Sequential decision bias is determined by the correlation between the value of the current estimated effect and the probability of conducting an additional study. Sequential design bias arises from using the estimated value instead of the clinically relevant value of an effect in sample size calculations. We considered both the fixed‐effect and the random‐effects models of meta‐analysis and demonstrated analytically and by simulations that in both settings the problems due to sequential biases are apparent. According to our simulations, the sequential biases increase with increased heterogeneity. Minimisation of sequential biases arises as a new and important research area necessary for successful evidence‐based approaches to the development of science. © 2015 The Authors. Research Synthesis Methods Published by John Wiley & Sons Ltd. PMID:26626562

  2. Classifying sex biased congenital anomalies

    SciTech Connect

    Lubinsky, M.S.

    1997-03-31

    The reasons for sex biases in congenital anomalies that arise before structural or hormonal dimorphisms are established has long been unclear. A review of such disorders shows that patterning and tissue anomalies are female biased, and structural findings are more common in males. This suggests different gender dependent susceptibilities to developmental disturbances, with female vulnerabilities focused on early blastogenesis/determination, while males are more likely to involve later organogenesis/morphogenesis. A dual origin for some anomalies explains paradoxical reductions of sex biases with greater severity (i.e., multiple rather than single malformations), presumably as more severe events increase the involvement of an otherwise minor process with opposite biases to those of the primary mechanism. The cause for these sex differences is unknown, but early dimorphisms, such as differences in growth or presence of H-Y antigen, may be responsible. This model provides a useful rationale for understanding and classifying sex-biased congenital anomalies. 42 refs., 7 tabs.

  3. Implicit and Explicit Weight Bias in a National Sample of 4732 Medical Students: The Medical Student CHANGES Study

    PubMed Central

    Phelan, Sean M.; Dovidio, John F.; Puhl, Rebecca M.; Burgess, Diana J.; Nelson, David B.; Yeazel, Mark W.; Hardeman, Rachel; Perry, Sylvia; van Ryn, Michelle

    2014-01-01

    Objective To examine the magnitude of explicit and implicit weight biases compared to biases against other groups; and identify student factors predicting bias in a large national sample of medical students. Design and Methods A web-based survey was completed by 4732 1st year medical students from 49 medical schools as part of a longitudinal study of medical education. The survey included a validated measure of implicit weight bias, the implicit association test, and 2 measures of explicit bias: a feeling thermometer and the anti-fat attitudes test. Results A majority of students exhibited implicit (74%) and explicit (67%) weight bias. Implicit weight bias scores were comparable to reported bias against racial minorities. Explicit attitudes were more negative toward obese people than toward racial minorities, gays, lesbians, and poor people. In multivariate regression models, implicit and explicit weight bias was predicted by lower BMI, male sex, and non-Black race. Either implicit or explicit bias was also predicted by age, SES, country of birth, and specialty choice. Conclusions Implicit and explicit weight bias is common among 1st year medical students, and varies across student factors. Future research should assess implications of biases and test interventions to reduce their impact. PMID:24375989

  4. Comparison of the single channel and multichannel (multivariate) concepts of selectivity in analytical chemistry.

    PubMed

    Dorkó, Zsanett; Verbić, Tatjana; Horvai, George

    2015-07-01

    Different measures of selectivity are in use for single channel and multichannel linear analytical measurements, respectively. It is important to understand that these two measures express related but still distinctly different features of the respective measurements. These relationships are clarified by introducing new arguments. The most widely used selectivity measure of multichannel linear methods (which is based on the net analyte signal, NAS, concept) expresses the sensitivity to random errors of a determination where all bias from interferents is computationally eliminated using pure component spectra. The conventional selectivity measure of single channel linear measurements, on the other hand, helps to estimate the bias caused by an interferent in a biased measurement. In single channel methods expert knowledge about the samples is used to limit the possible range of interferent concentrations. The same kind of expert knowledge allows improved (lower mean squared error, MSE) analyte determinations also in "classical" multichannel measurements if those are intractable due to perfect collinearity or to high noise inflation. To achieve this goal bias variance tradeoff is employed, hence there remains some bias in the results and therefore the concept of single channel selectivity can be extended in a natural way to multichannel measurements. This extended definition and the resulting selectivity measure can also be applied to the so-called inverse multivariate methods like partial least squares regression (PLSR), principal component regression (PCR) and ridge regression (RR). PMID:25882406

  5. Comparison of the single channel and multichannel (multivariate) concepts of selectivity in analytical chemistry.

    PubMed

    Dorkó, Zsanett; Verbić, Tatjana; Horvai, George

    2015-07-01

    Different measures of selectivity are in use for single channel and multichannel linear analytical measurements, respectively. It is important to understand that these two measures express related but still distinctly different features of the respective measurements. These relationships are clarified by introducing new arguments. The most widely used selectivity measure of multichannel linear methods (which is based on the net analyte signal, NAS, concept) expresses the sensitivity to random errors of a determination where all bias from interferents is computationally eliminated using pure component spectra. The conventional selectivity measure of single channel linear measurements, on the other hand, helps to estimate the bias caused by an interferent in a biased measurement. In single channel methods expert knowledge about the samples is used to limit the possible range of interferent concentrations. The same kind of expert knowledge allows improved (lower mean squared error, MSE) analyte determinations also in "classical" multichannel measurements if those are intractable due to perfect collinearity or to high noise inflation. To achieve this goal bias variance tradeoff is employed, hence there remains some bias in the results and therefore the concept of single channel selectivity can be extended in a natural way to multichannel measurements. This extended definition and the resulting selectivity measure can also be applied to the so-called inverse multivariate methods like partial least squares regression (PLSR), principal component regression (PCR) and ridge regression (RR).

  6. DUALITY IN MULTIVARIATE RECEPTOR MODEL. (R831078)

    EPA Science Inventory

    Multivariate receptor models are used for source apportionment of multiple observations of compositional data of air pollutants that obey mass conservation. Singular value decomposition of the data leads to two sets of eigenvectors. One set of eigenvectors spans a space in whi...

  7. Using Matlab in a Multivariable Calculus Course.

    ERIC Educational Resources Information Center

    Schlatter, Mark D.

    The benefits of high-level mathematics packages such as Matlab include both a computer algebra system and the ability to provide students with concrete visual examples. This paper discusses how both capabilities of Matlab were used in a multivariate calculus class. Graphical user interfaces which display three-dimensional surfaces, contour plots,…

  8. Vertical and bisection bias in active touch.

    PubMed

    Millar, S; al-Attar, Z

    2000-01-01

    We investigated the conditions that underlie the vertical and bisection illusion in touch, in order to understand the basis of their similarity to visual illusions, and the means of reducing the biases in length perception by active touch. Movement, speed, and spatial reference cues were tested. Movements in scanning L-shapes in ipsilateral and contralateral (across the body midline) table-top space produced significant underestimation of the vertical line with the right hand, but not with the left hand. Right-handed scanning of L-shapes showed no significant bias when the vertical line in the figure was aligned to the body midline, suggesting that spatial cues were involved. The vertical line was overestimated in inverted T-shapes, but underestimated in rotated T-shapes, implicating line bisection. Holding scanning latencies constant reduced the vertical error for inverted T-shapes, but could not explain the bisection bias. Sectioning biases were predicted by the location of junctions on sectioned lines, showing that junction points act as misleading anchor cues for movement extents. The illusion was significantly reduced when reference information was added by instructing subjects to relate two-handed scanning of the figure to an external frame and to body-centred cues. It is argued that disparities in spatial reference (anchor) cues for movement extents are involved in vertical and bisection biases in active touch. The hypothesis that length illusions depend on disparities in spatial reference information can also account for the similarity of the tactile to the visual horizontal-vertical illusion.

  9. Multivariable control systems with saturating actuators antireset windup strategies

    NASA Technical Reports Server (NTRS)

    Kapasouris, P.; Athans, M.

    1985-01-01

    Preliminary, promising, results for introducing antireset windup (ARW) properties in multivariable feedback control systems with multiple saturating actuator nonlinearities and integrating actions are presented. The ARW method introduces simple nonlinear feedback around the integrators. The multiloop circle criterion is used to derive sufficient conditions for closed-loop stability that employ frequency-domain singular value tests. The improvement in transient response due to the ARW feedback is demonstrated using a 2-input 2-outpurt control system based upon F-404 jet engine dynamics.

  10. Quantifying Multivariate Classification Performance - the Problem of Overfitting

    SciTech Connect

    Stallard, Brian R.; Taylor, John G.

    1999-08-09

    We have been studying the use of spectral imagery to locate targets in spectrally interfering backgrounds. In making performance estimates for various sensors it has become evident that some calculations are unreliable because of overflying. Hence, we began a thorough study of the problem of overfitting in multivariate classification. In this paper we present some model based results describing the problem. From the model we know the ideal covariance matrix, the ideal discriminant vector, and the ideal classification performance. We then investigate how experimental conditions such as noise, number of bands, and number of samples cause discrepancies from the ideal results. We also suggest ways to discover and alleviate overfitting.

  11. Direction specific biases in human visual and vestibular heading perception.

    PubMed

    Crane, Benjamin T

    2012-01-01

    Heading direction is determined from visual and vestibular cues. Both sensory modalities have been shown to have better direction discrimination for headings near straight ahead. Previous studies of visual heading estimation have not used the full range of stimuli, and vestibular heading estimation has not previously been reported. The current experiments measure human heading estimation in the horizontal plane to vestibular, visual, and spoken stimuli. The vestibular and visual tasks involved 16 cm of platform or visual motion. The spoken stimulus was a voice command speaking a heading angle. All conditions demonstrated direction dependent biases in perceived headings such that biases increased with headings further from the fore-aft axis. The bias was larger with the visual stimulus when compared with the vestibular stimulus in all 10 subjects. For the visual and vestibular tasks precision was best for headings near fore-aft. The spoken headings had the least bias, and the variation in precision was less dependent on direction. In a separate experiment when headings were limited to ± 45°, the biases were much less, demonstrating the range of headings influences perception. There was a strong and highly significant correlation between the bias curves for visual and spoken stimuli in every subject. The correlation between visual-vestibular and vestibular-spoken biases were weaker but remained significant. The observed biases in both visual and vestibular heading perception qualitatively resembled predictions of a recent population vector decoder model (Gu et al., 2010) based on the known distribution of neuronal sensitivities.

  12. Direction Specific Biases in Human Visual and Vestibular Heading Perception

    PubMed Central

    Crane, Benjamin T.

    2012-01-01

    Heading direction is determined from visual and vestibular cues. Both sensory modalities have been shown to have better direction discrimination for headings near straight ahead. Previous studies of visual heading estimation have not used the full range of stimuli, and vestibular heading estimation has not previously been reported. The current experiments measure human heading estimation in the horizontal plane to vestibular, visual, and spoken stimuli. The vestibular and visual tasks involved 16 cm of platform or visual motion. The spoken stimulus was a voice command speaking a heading angle. All conditions demonstrated direction dependent biases in perceived headings such that biases increased with headings further from the fore-aft axis. The bias was larger with the visual stimulus when compared with the vestibular stimulus in all 10 subjects. For the visual and vestibular tasks precision was best for headings near fore-aft. The spoken headings had the least bias, and the variation in precision was less dependent on direction. In a separate experiment when headings were limited to ±45°, the biases were much less, demonstrating the range of headings influences perception. There was a strong and highly significant correlation between the bias curves for visual and spoken stimuli in every subject. The correlation between visual-vestibular and vestibular-spoken biases were weaker but remained significant. The observed biases in both visual and vestibular heading perception qualitatively resembled predictions of a recent population vector decoder model (Gu et al., 2010) based on the known distribution of neuronal sensitivities. PMID:23236490

  13. Characterization of the desiccation of wheat kernels by multivariate imaging.

    PubMed

    Jaillais, B; Perrin, E; Mangavel, C; Bertrand, D

    2011-06-01

    Variations in the quality of wheat kernels can be an important problem in the cereal industry. In particular, desiccation conditions play an essential role in both the technological characteristics of the kernel and its ability to sprout. In planta desiccation constitutes a key stage in the determinism of the functional properties of seeds. The impact of desiccation on the endosperm texture of seed is presented in this work. A simple imaging system had previously been developed to acquire multivariate images to characterize the heterogeneity of food materials. A special algorithm for the use under principal component analysis (PCA) was developed to process the acquired multivariate images. Wheat grains were collected at physiological maturity, and were subjected to two types of drying conditions that induced different kinetics of water loss. A data set containing 24 images (dimensioned 702 × 524 pixels) corresponding to the different desiccation stages of wheat kernels was acquired at different wavelengths and then analyzed. A comparison of the images of kernel sections highlighted changes in kernel texture as a function of their drying conditions. Slow drying led to a floury texture, whereas fast drying caused a glassy texture. The automated imaging system thus developed is sufficiently rapid and economical to enable the characterization in large collections of grain texture as a function of time and water content.

  14. Experimental evidence for multivariate stabilizing sexual selection.

    PubMed

    Brooks, Robert; Hunt, John; Blows, Mark W; Smith, Michael J; Bussière, Luc F; Jennions, Michael D

    2005-04-01

    Stabilizing selection is a fundamental concept in evolutionary biology. In the presence of a single intermediate optimum phenotype (fitness peak) on the fitness surface, stabilizing selection should cause the population to evolve toward such a peak. This prediction has seldom been tested, particularly for suites of correlated traits. The lack of tests for an evolutionary match between population means and adaptive peaks may be due, at least in part, to problems associated with empirically detecting multivariate stabilizing selection and with testing whether population means are at the peak of multivariate fitness surfaces. Here we show how canonical analysis of the fitness surface, combined with the estimation of confidence regions for stationary points on quadratic response surfaces, may be used to define multivariate stabilizing selection on a suite of traits and to establish whether natural populations reside on the multivariate peak. We manufactured artificial advertisement calls of the male cricket Teleogryllus commodus and played them back to females in laboratory phonotaxis trials to estimate the linear and nonlinear sexual selection that female phonotactic choice imposes on male call structure. Significant nonlinear selection on the major axes of the fitness surface was convex in nature and displayed an intermediate optimum, indicating multivariate stabilizing selection. The mean phenotypes of four independent samples of males, from the same population as the females used in phonotaxis trials, were within the 95% confidence region for the fitness peak. These experiments indicate that stabilizing sexual selection may play an important role in the evolution of male call properties in natural populations of T. commodus.

  15. Cognitive Bias in Systems Verification

    NASA Technical Reports Server (NTRS)

    Larson, Steve

    2012-01-01

    Working definition of cognitive bias: Patterns by which information is sought and interpreted that can lead to systematic errors in decisions. Cognitive bias is used in diverse fields: Economics, Politics, Intelligence, Marketing, to name a few. Attempts to ground cognitive science in physical characteristics of the cognitive apparatus exceed our knowledge. Studies based on correlations; strict cause and effect is difficult to pinpoint. Effects cited in the paper and discussed here have been replicated many times over, and appear sound. Many biases have been described, but it is still unclear whether they are all distinct. There may only be a handful of fundamental biases, which manifest in various ways. Bias can effect system verification in many ways . Overconfidence -> Questionable decisions to deploy. Availability -> Inability to conceive critical tests. Representativeness -> Overinterpretation of results. Positive Test Strategies -> Confirmation bias. Debiasing at individual level very difficult. The potential effect of bias on the verification process can be managed, but not eliminated. Worth considering at key points in the process.

  16. Bias and spread in EVT performance tests.

    NASA Technical Reports Server (NTRS)

    Smith, J. G.

    1971-01-01

    Performance tests (error probability measurements) of communications systems characterized by low bit rates and high reliability requirements frequently utilize classical extreme value theory (EVT) to avoid the excessive test times encountered with bit error rate (BER) tests. If the underlying noise is Gaussian or perturbed Gaussian, the EVT error estimates have either excessive bias or excessive variance if an insufficient number of test samples is used. EVT is examined to explain the cause of this bias and spread. Experimental verification is made by testing a known Gaussian source, and procedures that minimize these effects are described. It seems apparent that even under the best of conditions the EVT test results are not particularly better than those of BER tests.

  17. Multivariate optimization of the hollow fibre liquid phase microextraction of muscimol in human urine samples.

    PubMed

    Ncube, Somandla; Poliwoda, Anna; Tutu, Hlanganani; Wieczorek, Piotr; Chimuka, Luke

    2016-10-15

    A liquid phase microextraction based on hollow fibre followed by liquid chromatographic determination was developed for the extraction and quantitation of the hallucinogenic muscimol from urine samples. Method applicability on polar hallucinogens was also tested on two alkaloids, a psychedelic hallucinogen, tryptamine and a polar amino acid, tryptophan which exists in its charged state in the entire pH range. A multivariate design of experiments was used in which a half fractional factorial approach was applied to screen six factors (donor phase pH, acceptor phase HCl concentration, carrier composition, stirring rate, extraction time and salt content) for their extent of vitality in carrier mediated liquid microextractions. Four factors were deemed essential for the effective extraction of each analyte. The vital factors were further optimized for the extraction of single-spiked analyte solutions using a central composite design. When the simultaneous extraction of analytes was performed under universal factor conditions biased towards maximizing the enrichment of muscimol, a good composite desirability value of 0.687 was obtained. The method was finally applied on spiked urine samples with acceptable enrichments of 4.1, 19.7 and 24.1 obtained for muscimol, tryptophan and tryptamine respectively. Matrix-based calibration curves were used to address matrix effects. The r(2) values of the matrix-based linear regression prediction models ranged from 0.9933 to 0.9986. The linearity of the regression line of the matrix-based calibration curves for each analyte was directly linked to the analyte enrichment repeatability which ranged from an RSD value of 8.3-13.1%. Limits of detection for the developed method were 5.12, 3.10 and 0.21ngmL(-1) for muscimol, tryptophan and tryptamine respectively. The developed method has proven to offer a viable alternative for the quantitation of muscimol in human urine samples.

  18. Usual Dietary Intakes: SAS Macros for Fitting Multivariate Measurement Error Models & Estimating Multivariate Usual Intake Distributions

    Cancer.gov

    The following SAS macros can be used to create a multivariate usual intake distribution for multiple dietary components that are consumed nearly every day or episodically. A SAS macro for performing balanced repeated replication (BRR) variance estimation is also included.

  19. The intentionality bias in schizophrenia.

    PubMed

    Peyroux, Elodie; Strickland, Brent; Tapiero, Isabelle; Franck, Nicolas

    2014-11-30

    The tendency to over-interpret events of daily life as resulting from voluntary or intentional actions is one of the key aspects of schizophrenia with persecutory delusions. Here, we ask whether this characteristic may emerge from the abnormal activity of a basic cognitive process found in healthy adults and children: the intentionality bias, which refers to the implicit and automatic inclination to interpret human actions as intentional (Rosset, 2008, Cognition 108, 771-780). In our experiment, patients with schizophrenia and healthy controls were shown sentences describing human actions in various linguistic contexts, and were asked to indicate whether the action was intentional or not. The results indicated that people with schizophrenia exhibited a striking bias to over attribute intentionality regardless of linguistic context, contrary to healthy controls who did not exhibit such a general intentionality bias. Moreover, this study provides some insight into the cognitive mechanisms underlying this bias: an inability to inhibit the automatic attribution of intentionality.

  20. Magnetic bearings with zero bias

    NASA Technical Reports Server (NTRS)

    Brown, Gerald V.; Grodsinsky, Carlos M.

    1991-01-01

    A magnetic bearing operating without a bias field has supported a shaft rotating at speeds up to 12,000 rpm with the usual four power supplies and with only two. A magnetic bearing is commonly operated with a bias current equal to half of the maximum current allowable in its coils. This linearizes the relation between net force and control current and improves the force slewing rate and hence the band width. The steady bias current dissipates power, even when no force is required from the bearing. The power wasted is equal to two-thirds of the power at maximum force output. Examined here is the zero bias idea. The advantages and disadvantages are noted.

  1. Learning multivariate distributions by competitive assembly of marginals.

    PubMed

    Sánchez-Vega, Francisco; Younes, Laurent; Geman, Donald

    2013-02-01

    We present a new framework for learning high-dimensional multivariate probability distributions from estimated marginals. The approach is motivated by compositional models and Bayesian networks, and designed to adapt to small sample sizes. We start with a large, overlapping set of elementary statistical building blocks, or "primitives," which are low-dimensional marginal distributions learned from data. Each variable may appear in many primitives. Subsets of primitives are combined in a Lego-like fashion to construct a probabilistic graphical model; only a small fraction of the primitives will participate in any valid construction. Since primitives can be precomputed, parameter estimation and structure search are separated. Model complexity is controlled by strong biases; we adapt the primitives to the amount of training data and impose rules which restrict the merging of them into allowable compositions. The likelihood of the data decomposes into a sum of local gains, one for each primitive in the final structure. We focus on a specific subclass of networks which are binary forests. Structure optimization corresponds to an integer linear program and the maximizing composition can be computed for reasonably large numbers of variables. Performance is evaluated using both synthetic data and real datasets from natural language processing and computational biology.

  2. Influence of SST biases on future climate change projections

    SciTech Connect

    Ashfaq, Moetasim; Skinner, Chris B; Cherkauer, Keith

    2010-01-01

    We use a quantile-based bias correction technique and a multi-member ensemble of the atmospheric component of NCAR CCSM3 (CAM3) simulations to investigate the influence of sea surface temperature (SST) biases on future climate change projections. The simulations, which cover 1977 1999 in the historical period and 2077 2099 in the future (A1B) period, use the CCSM3-generated SSTs as prescribed boundary conditions. Bias correction is applied to the monthly time-series of SSTs so that the simulated changes in SST mean and variability are preserved. Our comparison of CAM3 simulations with and without SST correction shows that the SST biases affect the precipitation distribution in CAM3 over many regions by introducing errors in atmospheric moisture content and upper-level (lower-level) divergence (convergence). Also, bias correction leads to significantly different precipitation and surface temperature changes over many oceanic and terrestrial regions (predominantly in the tropics) in response to the future anthropogenic increases in greenhouse forcing. The differences in the precipitation response from SST bias correction occur both in the mean and the percent change, and are independent of the ocean atmosphere coupling. Many of these differences are comparable to or larger than the spread of future precipitation changes across the CMIP3 ensemble. Such biases can affect the simulated terrestrial feedbacks and thermohaline circulations in coupled climate model integrations through changes in the hydrological cycle and ocean salinity. Moreover, biases in CCSM3-generated SSTs are generally similar to the biases in CMIP3 ensemble mean SSTs, suggesting that other GCMs may display a similar sensitivity of projected climate change to SST errors. These results help to quantify the influence of climate model biases on the simulated climate change, and therefore should inform the effort to further develop approaches for reliable climate change projection.

  3. Reducing bias in survival under non-random temporary emigration

    USGS Publications Warehouse

    Peñaloza, Claudia L.; Kendall, William L.; Langtimm, Catherine Ann

    2014-01-01

    Despite intensive monitoring, temporary emigration from the sampling area can induce bias severe enough for managers to discard life-history parameter estimates toward the terminus of the times series (terminal bias). Under random temporary emigration unbiased parameters can be estimated with CJS models. However, unmodeled Markovian temporary emigration causes bias in parameter estimates and an unobservable state is required to model this type of emigration. The robust design is most flexible when modeling temporary emigration, and partial solutions to mitigate bias have been identified, nonetheless there are conditions were terminal bias prevails. Long-lived species with high adult survival and highly variable non-random temporary emigration present terminal bias in survival estimates, despite being modeled with the robust design and suggested constraints. Because this bias is due to uncertainty about the fate of individuals that are undetected toward the end of the time series, solutions should involve using additional information on survival status or location of these individuals at that time. Using simulation, we evaluated the performance of models that jointly analyze robust design data and an additional source of ancillary data (predictive covariate on temporary emigration, telemetry, dead recovery, or auxiliary resightings) in reducing terminal bias in survival estimates. The auxiliary resighting and predictive covariate models reduced terminal bias the most. Additional telemetry data was effective at reducing terminal bias only when individuals were tracked for a minimum of two years. High adult survival of long-lived species made the joint model with recovery data ineffective at reducing terminal bias because of small-sample bias. The naïve constraint model (last and penultimate temporary emigration parameters made equal), was the least efficient, though still able to reduce terminal bias when compared to an unconstrained model. Joint analysis of several

  4. Multivariate Mapping of Environmental Data Using Extreme Learning Machines

    NASA Astrophysics Data System (ADS)

    Leuenberger, Michael; Kanevski, Mikhail

    2014-05-01

    In most real cases environmental data are multivariate, highly variable at several spatio-temporal scales, and are generated by nonlinear and complex phenomena. Mapping - spatial predictions of such data, is a challenging problem. Machine learning algorithms, being universal nonlinear tools, have demonstrated their efficiency in modelling of environmental spatial and space-time data (Kanevski et al. 2009). Recently, a new approach in machine learning - Extreme Learning Machine (ELM), has gained a great popularity. ELM is a fast and powerful approach being a part of the machine learning algorithm category. Developed by G.-B. Huang et al. (2006), it follows the structure of a multilayer perceptron (MLP) with one single-hidden layer feedforward neural networks (SLFNs). The learning step of classical artificial neural networks, like MLP, deals with the optimization of weights and biases by using gradient-based learning algorithm (e.g. back-propagation algorithm). Opposed to this optimization phase, which can fall into local minima, ELM generates randomly the weights between the input layer and the hidden layer and also the biases in the hidden layer. By this initialization, it optimizes just the weight vector between the hidden layer and the output layer in a single way. The main advantage of this algorithm is the speed of the learning step. In a theoretical context and by growing the number of hidden nodes, the algorithm can learn any set of training data with zero error. To avoid overfitting, cross-validation method or "true validation" (by randomly splitting data into training, validation and testing subsets) are recommended in order to find an optimal number of neurons. With its universal property and solid theoretical basis, ELM is a good machine learning algorithm which can push the field forward. The present research deals with an extension of ELM to multivariate output modelling and application of ELM to the real data case study - pollution of the sediments in

  5. Nonlinear aerodynamic modeling using multivariate orthogonal functions

    NASA Technical Reports Server (NTRS)

    Morelli, Eugene A.

    1993-01-01

    A technique was developed for global modeling of nonlinear aerodynamic coefficients using multivariate orthogonal functions based on the data. Each orthogonal function retained in the model was decomposed into an expansion of ordinary polynomials in the independent variables, so that the final model could be interpreted as selectively retained terms from a multivariable power series expansion. A predicted squared-error metric was used to determine the orthogonal functions to be retained in the model; analytical derivatives were easily computed. The approach was demonstrated on the Z-body axis aerodynamic force coefficient (Cz) wind tunnel data for an F-18 research vehicle which came from a tabular wind tunnel and covered the entire subsonic flight envelope. For a realistic case, the analytical model predicted experimental values of Cz very well. The modeling technique is shown to be capable of generating a compact, global analytical representation of nonlinear aerodynamics. The polynomial model has good predictive capability, global validity, and analytical differentiability.

  6. Multivariate Approaches to Classification in Extragalactic Astronomy

    NASA Astrophysics Data System (ADS)

    Fraix-Burnet, Didier; Thuillard, Marc; Chattopadhyay, Asis Kumar

    2015-08-01

    Clustering objects into synthetic groups is a natural activity of any science. Astrophysics is not an exception and is now facing a deluge of data. For galaxies, the one-century old Hubble classification and the Hubble tuning fork are still largely in use, together with numerous mono- or bivariate classifications most often made by eye. However, a classification must be driven by the data, and sophisticated multivariate statistical tools are used more and more often. In this paper we review these different approaches in order to situate them in the general context of unsupervised and supervised learning. We insist on the astrophysical outcomes of these studies to show that multivariate analyses provide an obvious path toward a renewal of our classification of galaxies and are invaluable tools to investigate the physics and evolution of galaxies.

  7. Hybrid least squares multivariate spectral analysis methods

    DOEpatents

    Haaland, David M.

    2004-03-23

    A set of hybrid least squares multivariate spectral analysis methods in which spectral shapes of components or effects not present in the original calibration step are added in a following prediction or calibration step to improve the accuracy of the estimation of the amount of the original components in the sampled mixture. The hybrid method herein means a combination of an initial calibration step with subsequent analysis by an inverse multivariate analysis method. A spectral shape herein means normally the spectral shape of a non-calibrated chemical component in the sample mixture but can also mean the spectral shapes of other sources of spectral variation, including temperature drift, shifts between spectrometers, spectrometer drift, etc. The shape can be continuous, discontinuous, or even discrete points illustrative of the particular effect.

  8. Hybrid least squares multivariate spectral analysis methods

    DOEpatents

    Haaland, David M.

    2002-01-01

    A set of hybrid least squares multivariate spectral analysis methods in which spectral shapes of components or effects not present in the original calibration step are added in a following estimation or calibration step to improve the accuracy of the estimation of the amount of the original components in the sampled mixture. The "hybrid" method herein means a combination of an initial classical least squares analysis calibration step with subsequent analysis by an inverse multivariate analysis method. A "spectral shape" herein means normally the spectral shape of a non-calibrated chemical component in the sample mixture but can also mean the spectral shapes of other sources of spectral variation, including temperature drift, shifts between spectrometers, spectrometer drift, etc. The "shape" can be continuous, discontinuous, or even discrete points illustrative of the particular effect.

  9. Multivariate temporal dictionary learning for EEG.

    PubMed

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

    2013-04-30

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

  10. Role of information asymmetry and situational salience in reducing intergroup bias: the case of ultimatum games.

    PubMed

    Valenzuela, Ana; Srivastava, Joydeep

    2012-12-01

    While majority of the literature documents the preponderance of social identity-related biases in favor of in-group members, this research investigates factors that may attenuate the bias. Examining intergroup bias within the realm of information availability and accessibility, this research highlights malleability of judgments and decisions as a function of social identity in both complete and incomplete information situations in the context of ultimatum games. Study 1 replicates the positive bias toward in-group members even in situations where individuals know that the counterpart is behaving unfairly. Study 2 shows that the intergroup bias is attenuated for relatively unfavorable offers in incomplete information situations. However, the intergroup bias is persistent for relatively favorable offers. Study 3 shows that making situational constraints salient also attenuates the intergroup bias for relatively favorable offers. Together, the findings identify conditions, based on information availability and accessibility, under which the intergroup bias can be corrected.

  11. The evolution of multivariate maternal effects.

    PubMed

    Kuijper, Bram; Johnstone, Rufus A; Townley, Stuart

    2014-04-01

    There is a growing interest in predicting the social and ecological contexts that favor the evolution of maternal effects. Most predictions focus, however, on maternal effects that affect only a single character, whereas the evolution of maternal effects is poorly understood in the presence of suites of interacting traits. To overcome this, we simulate the evolution of multivariate maternal effects (captured by the matrix M) in a fluctuating environment. We find that the rate of environmental fluctuations has a substantial effect on the properties of M: in slowly changing environments, offspring are selected to have a multivariate phenotype roughly similar to the maternal phenotype, so that M is characterized by positive dominant eigenvalues; by contrast, rapidly changing environments favor Ms with dominant eigenvalues that are negative, as offspring favor a phenotype which substantially differs from the maternal phenotype. Moreover, when fluctuating selection on one maternal character is temporally delayed relative to selection on other traits, we find a striking pattern of cross-trait maternal effects in which maternal characters influence not only the same character in offspring, but also other offspring characters. Additionally, when selection on one character contains more stochastic noise relative to selection on other traits, large cross-trait maternal effects evolve from those maternal traits that experience the smallest amounts of noise. The presence of these cross-trait maternal effects shows that individual maternal effects cannot be studied in isolation, and that their study in a multivariate context may provide important insights about the nature of past selection. Our results call for more studies that measure multivariate maternal effects in wild populations.

  12. Multivariate linear recurrences and power series division

    PubMed Central

    Hauser, Herwig; Koutschan, Christoph

    2012-01-01

    Bousquet-Mélou and Petkovšek investigated the generating functions of multivariate linear recurrences with constant coefficients. We will give a reinterpretation of their results by means of division theorems for formal power series, which clarifies the structural background and provides short, conceptual proofs. In addition, extending the division to the context of differential operators, the case of recurrences with polynomial coefficients can be treated in an analogous way. PMID:23482936

  13. Bias and design in software specifications

    NASA Technical Reports Server (NTRS)

    Straub, Pablo A.; Zelkowitz, Marvin V.

    1990-01-01

    Implementation bias in a specification is an arbitrary constraint in the solution space. Presented here is a model of bias in software specifications. Bias is defined in terms of the specification process and a classification of the attributes of the software product. Our definition of bias provides insight into both the origin and the consequences of bias. It also shows that bias is relative and essentially unavoidable. Finally, we describe current work on defining a measure of bias, formalizing our model, and relating bias to software defects.

  14. Water quality change detection: multivariate algorithms

    NASA Astrophysics Data System (ADS)

    Klise, Katherine A.; McKenna, Sean A.

    2006-05-01

    In light of growing concern over the safety and security of our nation's drinking water, increased attention has been focused on advanced monitoring of water distribution systems. The key to these advanced monitoring systems lies in the combination of real time data and robust statistical analysis. Currently available data streams from sensors provide near real time information on water quality. Combining these data streams with change detection algorithms, this project aims to develop automated monitoring techniques that will classify real time data and denote anomalous water types. Here, water quality data in 1 hour increments over 3000 hours at 4 locations are used to test multivariate algorithms to detect anomalous water quality events. The algorithms use all available water quality sensors to measure deviation from expected water quality. Simulated anomalous water quality events are added to the measured data to test three approaches to measure this deviation. These approaches include multivariate distance measures to 1) the previous observation, 2) the closest observation in multivariate space, and 3) the closest cluster of previous water quality observations. Clusters are established using kmeans classification. Each approach uses a moving window of previous water quality measurements to classify the current measurement as normal or anomalous. Receiver Operating Characteristic (ROC) curves test the ability of each approach to discriminate between normal and anomalous water quality using a variety of thresholds and simulated anomalous events. These analyses result in a better understanding of the deviation from normal water quality that is necessary to sound an alarm.

  15. Regional dissociated heterochrony in multivariate analysis.

    PubMed

    Mitteroecker, P; Gunz, P; Weber, G W; Bookstein, F L

    2004-12-01

    Heterochrony, the classic framework to study ontogeny and phylogeny, in essence relies on a univariate concept of shape. Though principal component plots of multivariate shape data seem to resemble classical bivariate allometric plots, the language of heterochrony cannot be translated directly into general multivariate methodology. We simulate idealized multivariate ontogenetic trajectories and demonstrate their behavior in principal component plots in shape space and in size-shape space. The concept of "dissociation", which is conventionally regarded as a change in the relationship between shape change and size change, appears to be algebraically the same as regional dissociation - the variation of apparent heterochrony by region. Only if the trajectories of two related species lie along exactly the same path in shape space can the classic terminology of heterochrony apply so that pure dissociation of size change against shape change can be detected. We demonstrate a geometric morphometric approach to these issues using adult and subadult crania of 48 Pan paniscus and 47 P. troglodytes. On each specimen we digitized 47 landmarks and 144 semilandmarks on ridge curves and the external neurocranial surface. The relation between these two species' growth trajectories is too complex for a simple summary in terms of global heterochrony.

  16. Assessing causality in multivariate accident models.

    PubMed

    Elvik, Rune

    2011-01-01

    This paper discusses the application of operational criteria of causality to multivariate statistical models developed to identify sources of systematic variation in accident counts, in particular the effects of variables representing safety treatments. Nine criteria of causality serving as the basis for the discussion have been developed. The criteria resemble criteria that have been widely used in epidemiology. To assess whether the coefficients estimated in a multivariate accident prediction model represent causal relationships or are non-causal statistical associations, all criteria of causality are relevant, but the most important criterion is how well a model controls for potentially confounding factors. Examples are given to show how the criteria of causality can be applied to multivariate accident prediction models in order to assess the relationships included in these models. It will often be the case that some of the relationships included in a model can reasonably be treated as causal, whereas for others such an interpretation is less supported. The criteria of causality are indicative only and cannot provide a basis for stringent logical proof of causality.

  17. A multivariate Poisson-lognormal regression model for prediction of crash counts by severity, using Bayesian methods.

    PubMed

    Ma, Jianming; Kockelman, Kara M; Damien, Paul

    2008-05-01

    Numerous efforts have been devoted to investigating crash occurrence as related to roadway design features, environmental factors and traffic conditions. However, most of the research has relied on univariate count models; that is, traffic crash counts at different levels of severity are estimated separately, which may neglect shared information in unobserved error terms, reduce efficiency in parameter estimates, and lead to potential biases in sample databases. This paper offers a multivariate Poisson-lognormal (MVPLN) specification that simultaneously models crash counts by injury severity. The MVPLN specification allows for a more general correlation structure as well as overdispersion. This approach addresses several questions that are difficult to answer when estimating crash counts separately. Thanks to recent advances in crash modeling and Bayesian statistics, parameter estimation is done within the Bayesian paradigm, using a Gibbs Sampler and the Metropolis-Hastings (M-H) algorithms for crashes on Washington State rural two-lane highways. Estimation results from the MVPLN approach show statistically significant correlations between crash counts at different levels of injury severity. The non-zero diagonal elements suggest overdispersion in crash counts at all levels of severity. The results lend themselves to several recommendations for highway safety treatments and design policies. For example, wide lanes and shoulders are key for reducing crash frequencies, as are longer vertical curves. PMID:18460364

  18. Challenges of guarantee-time bias.

    PubMed

    Giobbie-Hurder, Anita; Gelber, Richard D; Regan, Meredith M

    2013-08-10

    The potential for guarantee-time bias (GTB), also known as immortal time bias, exists whenever an analysis that is timed from enrollment or random assignment, such as disease-free or overall survival, is compared across groups defined by a classifying event occurring sometime during follow-up. The types of events associated with GTB are varied and may include the occurrence of objective disease response, onset of toxicity, or seroconversion. However, comparative analyses using these types of events as predictors are different from analyses using baseline characteristics that are specified completely before the occurrence of any outcome event. Recognizing the potential for GTB is not always straightforward, and it can be challenging to know when GTB is influencing the results of an analysis. This article defines GTB, provides examples of GTB from several published articles, and discusses three analytic techniques that can be used to remove the bias: conditional landmark analysis, extended Cox model, and inverse probability weighting. The strengths and limitations of each technique are presented. As an example, we explore the effect of bisphosphonate use on disease-free survival (DFS) using data from the BIG (Breast International Group) 1-98 randomized clinical trial. An analysis using a naive approach showed substantial benefit for patients who received bisphosphonate therapy. In contrast, analyses using the three methods known to remove GTB showed no statistical evidence of a reduction in risk of a DFS event with bisphosphonate therapy.

  19. Cognitive Biases Questionnaire for Psychosis

    PubMed Central

    Peters, Emmanuelle R.

    2014-01-01

    Objective: The Cognitive Biases Questionnaire for psychosis (CBQp) was developed to capture 5 cognitive distortions (jumping to conclusions, intentionalising, catastrophising, emotional reasoning, and dichotomous thinking), which are considered important for the pathogenesis of psychosis. Vignettes were adapted from the Cognitive Style Test (CST),1 relating to “Anomalous Perceptions” and “Threatening Events” themes. Method: Scale structure, reliability, and validity were investigated in a psychosis group, and CBQp scores were compared with those of depressed and healthy control samples. Results: The CBQp showed good internal consistency and test-retest reliability. The 5 biases were not independent, with a 2-related factor scale providing the best fit. This structure suggests that the CBQp assesses a general thinking bias rather than distinct cognitive errors, while Anomalous Perception and Threatening Events theme scores can be used separately. Total CBQp scores showed good convergent validity with the CST, but individual biases were not related to existing tasks purporting to assess similar reasoning biases. Psychotic and depressed populations scored higher than healthy controls, and symptomatic psychosis patients scored higher than their nonsymptomatic counterparts, with modest relationships between CBQp scores and symptom severity once emotional disorders were partialled out. Anomalous Perception theme and Intentionalising bias scores showed some specificity to psychosis. Conclusions: Overall, the CBQp has good psychometric properties, although it is likely that it measures a different construct to existing tasks, tentatively suggested to represent a bias of interpretation rather than reasoning, judgment or decision-making processes. It is a potentially useful tool in both research and clinical arenas. PMID:23413104

  20. Finite difference grid generation by multivariate blending function interpolation

    NASA Technical Reports Server (NTRS)

    Anderson, P. G.; Spradley, L. W.

    1980-01-01

    The General Interpolants Method (GIM) code which solves the multidimensional Navier-Stokes equations for arbitrary geometric domains is described. The geometry module in the GIM code generates two and three dimensional grids over specified flow regimes, establishes boundary condition information and computes finite difference analogs for use in the GIM code numerical solution module. The technique can be classified as an algebraic equation approach. The geometry package uses multivariate blending function interpolation of vector-values functions which define the shapes of the edges and surfaces bounding the flow domain. By employing blending functions which conform to the cardinality conditions the flow domain may be mapped onto a unit square (2-D) or unit cube (3-D), thus producing an intrinsic coordinate system for the region of interest. The intrinsic coordinate system facilitates grid spacing control to allow for optimum distribution of nodes in the flow domain.

  1. The effect of cognitive bias modification for interpretation on avoidance of pain during an acute experimental pain task.

    PubMed

    Jones, Emma Blaisdale; Sharpe, Louise

    2014-08-01

    Research confirms that patients with chronic pain show a tendency to interpret ambiguous stimuli as pain related. However, whether modifying these interpretive pain biases impacts pain outcomes is unknown. This study aimed to demonstrate that interpretation biases towards pain can be modified, and that changing these biases influences pain outcomes in the cold pressor task. One hundred and six undergraduate students were randomly allocated to receive either threatening or reassuring information regarding the cold pressor. They also were randomly allocated to 1 of 2 conditions in the Ambiguous Scenarios Task, in which they were trained to have either a threatening interpretation of pain (pain bias condition) or a nonthreatening interpretation of pain (no pain bias condition). Therefore, the study had a 2 (threat/reassuring)×2 (pain bias/no pain bias) design. Analyses showed that a bias was induced contingent on condition, and that the threat manipulation was effective. Participants in the pain bias condition hesitated more before doing the cold pressor task than those in the no pain bias condition, as did those in the threat compared with the reassurance condition. The major finding was that interpretive bias mediated the relationship between bias condition and hesitance time, supporting the causal role of interpretive biases for avoidance behaviors in current chronic pain models. No differences were found on other pain outcomes regarding bias or threat, and the efficacy of the bias modification was not impacted by different levels of threat. These results suggest that cognitive bias modification should be further explored as a potential intervention in pain.

  2. Piecewise aggregate representations and lower-bound distance functions for multivariate time series

    NASA Astrophysics Data System (ADS)

    Li, Hailin

    2015-06-01

    Dimensionality reduction is one of the most important methods to improve the efficiency of the techniques that are applied to the field of multivariate time series data mining. Due to multivariate time series with the variable-based and time-based dimensions, the reduction techniques must take both of them into consideration. To achieve this goal, we use a center sequence to represent a multivariate time series so that the new sequence can be seen as a univariate time series. Thus two sophisticated piecewise aggregate representations, including piecewise aggregate approximation and symbolization applied to univariate time series, are used to further represent the extended sequence that is derived from the center one. Furthermore, some distance functions are designed to measure the similarity between two representations. Through being proven by some related mathematical analysis, the proposed functions are lower bound on Euclidean distance and dynamic time warping. In this way, false dismissals can be avoided when they are used to index the time series. In addition, multivariate time series with different lengths can be transformed into the extended sequences with equal length, and their corresponding distance functions can measure the similarity between two unequal-length multivariate time series. The experimental results demonstrate that the proposed methods can reduce the dimensionality, and their corresponding distance functions satisfy the lower-bound condition, which can speed up the calculation of similarity search and indexing in the multivariate time series datasets.

  3. Effect of the 2010 Chilean Earthquake on Posttraumatic Stress Reducing Sensitivity to Unmeasured Bias Through Study Design

    PubMed Central

    Zubizarreta, José R.; Cerdá, Magdalena; Rosenbaum, Paul R.

    2013-01-01

    In 2010, a magnitude 8.8 earthquake hit Chile, devastating parts of the country. Having just completed its national socioeconomic survey, the Chilean government reinterviewed a subsample of respondents, creating unusual longitudinal data about the same persons before and after a major disaster. The follow-up evaluated posttraumatic stress symptoms (PTSS) using Davidson’s Trauma Scale. We use these data with two goals in mind. Most studies of PTSS after disasters rely on recall to characterize the state of affairs before the disaster. We are able to use prospective data on preexposure conditions, free of recall bias, to study the effects of the earthquake. Second, we illustrate recent developments in statistical methodology for the design and analysis of observational studies. In particular, we use new and recent methods for multivariate matching to control 46 covariates that describe demographic variables, housing quality, wealth, health, and health insurance before the earthquake. We use the statistical theory of design sensitivity to select a study design with findings expected to be insensitive to small or moderate biases from failure to control some unmeasured covariate. PTSS were dramatically but unevenly elevated among residents of strongly shaken areas of Chile when compared with similar persons in largely untouched parts of the country. In 96% of exposed-control pairs exhibiting substantial PTSS, it was the exposed person who experienced stronger symptoms (95% confidence interval = 0.91–1.00). PMID:23222557

  4. Multi-application controls: Robust nonlinear multivariable aerospace controls applications

    NASA Technical Reports Server (NTRS)

    Enns, Dale F.; Bugajski, Daniel J.; Carter, John; Antoniewicz, Bob

    1994-01-01

    This viewgraph presentation describes the general methodology used to apply Honywell's Multi-Application Control (MACH) and the specific application to the F-18 High Angle-of-Attack Research Vehicle (HARV) including piloted simulation handling qualities evaluation. The general steps include insertion of modeling data for geometry and mass properties, aerodynamics, propulsion data and assumptions, requirements and specifications, e.g. definition of control variables, handling qualities, stability margins and statements for bandwidth, control power, priorities, position and rate limits. The specific steps include choice of independent variables for least squares fits to aerodynamic and propulsion data, modifications to the management of the controls with regard to integrator windup and actuation limiting and priorities, e.g. pitch priority over roll, and command limiting to prevent departures and/or undesirable inertial coupling or inability to recover to a stable trim condition. The HARV control problem is characterized by significant nonlinearities and multivariable interactions in the low speed, high angle-of-attack, high angular rate flight regime. Systematic approaches to the control of vehicle motions modeled with coupled nonlinear equations of motion have been developed. This paper will discuss the dynamic inversion approach which explicity accounts for nonlinearities in the control design. Multiple control effectors (including aerodynamic control surfaces and thrust vectoring control) and sensors are used to control the motions of the vehicles in several degrees-of-freedom. Several maneuvers will be used to illustrate performance of MACH in the high angle-of-attack flight regime. Analytical methods for assessing the robust performance of the multivariable control system in the presence of math modeling uncertainty, disturbances, and commands have reached a high level of maturity. The structured singular value (mu) frequency response methodology is presented

  5. Maintenance of motility bias during cyanobacterial phototaxis.

    PubMed

    Chau, Rosanna Man Wah; Ursell, Tristan; Wang, Shuo; Huang, Kerwyn Casey; Bhaya, Devaki

    2015-04-01

    Signal transduction in bacteria is complex, ranging across scales from molecular signal detectors and effectors to cellular and community responses to stimuli. The unicellular, photosynthetic cyanobacterium Synechocystis sp. PCC6803 transduces a light stimulus into directional movement known as phototaxis. This response occurs via a biased random walk toward or away from a directional light source, which is sensed by intracellular photoreceptors and mediated by Type IV pili. It is unknown how quickly cells can respond to changes in the presence or directionality of light, or how photoreceptors affect single-cell motility behavior. In this study, we use time-lapse microscopy coupled with quantitative single-cell tracking to investigate the timescale of the cellular response to various light conditions and to characterize the contribution of the photoreceptor TaxD1 (PixJ1) to phototaxis. We first demonstrate that a community of cells exhibits both spatial and population heterogeneity in its phototactic response. We then show that individual cells respond within minutes to changes in light conditions, and that movement directionality is conferred only by the current light directionality, rather than by a long-term memory of previous conditions. Our measurements indicate that motility bias likely results from the polarization of pilus activity, yielding variable levels of movement in different directions. Experiments with a photoreceptor (taxD1) mutant suggest a supplementary role of TaxD1 in enhancing movement directionality, in addition to its previously identified role in promoting positive phototaxis. Motivated by the behavior of the taxD1 mutant, we demonstrate using a reaction-diffusion model that diffusion anisotropy is sufficient to produce the observed changes in the pattern of collective motility. Taken together, our results establish that single-cell tracking can be used to determine the factors that affect motility bias, which can then be coupled with

  6. High resolution WRF ensemble forecasting for irrigation: Multi-variable evaluation

    NASA Astrophysics Data System (ADS)

    Kioutsioukis, Ioannis; de Meij, Alexander; Jakobs, Hermann; Katragkou, Eleni; Vinuesa, Jean-Francois; Kazantzidis, Andreas

    2016-01-01

    An ensemble of meteorological simulations with the WRF model at convection-allowing resolution (2 km) is analysed in a multi-variable evaluation framework over Europe. Besides temperature and precipitation, utilized variables are relative humidity, boundary layer height, shortwave radiation, wind speed, convective and large-scale precipitation in view of explaining some of the biases. Furthermore, the forecast skill of evapotranspiration and irrigation water need is ultimately assessed. It is found that the modelled temperature exhibits a small but significant negative bias during the cold period in the snow-covered northeast regions. Total precipitation exhibits positive bias during all seasons but autumn, peaking in the spring months. The varying physics configurations resulted in significant differences for the simulated minimum temperature, summer rainfall, relative humidity, solar radiation and planetary boundary layer height. The interaction of the temperature and moisture profiles with the different microphysics schemes, results in excess convective precipitation using MYJ/WSM6 compared to YSU/Thompson. With respect to evapotranspiration and irrigation need, the errors using the MYJ configuration were in opposite directions and eventually cancel out, producing overall smaller biases. WRF was able to dynamically downscale global forecast data into finer resolutions in space and time for hydro-meteorological applications such as the irrigation management. Its skill was sensitive to the geographical location and physical configuration, driven by the variable relative importance of evapotranspiration and rainfall.

  7. Biased signaling at chemokine receptors.

    PubMed

    Corbisier, Jenny; Galès, Céline; Huszagh, Alexandre; Parmentier, Marc; Springael, Jean-Yves

    2015-04-10

    The ability of G protein-coupled receptors (GPCRs) to activate selective signaling pathways according to the conformation stabilized by bound ligands (signaling bias) is a challenging concept in the GPCR field. Signaling bias has been documented for several GPCRs, including chemokine receptors. However, most of these studies examined the global signaling bias between G protein- and arrestin-dependent pathways, leaving unaddressed the potential bias between particular G protein subtypes. Here, we investigated the coupling selectivity of chemokine receptors CCR2, CCR5, and CCR7 in response to various ligands with G protein subtypes by using bioluminescence resonance energy transfer biosensors monitoring directly the activation of G proteins. We also compared data obtained with the G protein biosensors with those obtained with other functional readouts, such as β-arrestin-2 recruitment, cAMP accumulation, and calcium mobilization assays. We showed that the binding of chemokines to CCR2, CCR5, and CCR7 activated the three Gαi subtypes (Gαi1, Gαi2, and Gαi3) and the two Gαo isoforms (Gαoa and Gαob) with potencies that generally correlate to their binding affinities. In addition, we showed that the binding of chemokines to CCR5 and CCR2 also activated Gα12, but not Gα13. For each receptor, we showed that the relative potency of various agonist chemokines was not identical in all assays, supporting the notion that signaling bias exists at chemokine receptors.

  8. On a Family of Multivariate Modified Humbert Polynomials

    PubMed Central

    Aktaş, Rabia; Erkuş-Duman, Esra

    2013-01-01

    This paper attempts to present a multivariable extension of generalized Humbert polynomials. The results obtained here include various families of multilinear and multilateral generating functions, miscellaneous properties, and also some special cases for these multivariable polynomials. PMID:23935411

  9. Adaptation to high throughput batch chromatography enhances multivariate screening.

    PubMed

    Barker, Gregory A; Calzada, Joseph; Herzer, Sibylle; Rieble, Siegfried

    2015-09-01

    High throughput process development offers unique approaches to explore complex process design spaces with relatively low material consumption. Batch chromatography is one technique that can be used to screen chromatographic conditions in a 96-well plate. Typical batch chromatography workflows examine variations in buffer conditions or comparison of multiple resins in a given process, as opposed to the assessment of protein loading conditions in combination with other factors. A modification to the batch chromatography paradigm is described here where experimental planning, programming, and a staggered loading approach increase the multivariate space that can be explored with a liquid handling system. The iterative batch chromatography (IBC) approach is described, which treats every well in a 96-well plate as an individual experiment, wherein protein loading conditions can be varied alongside other factors such as wash and elution buffer conditions. As all of these factors are explored in the same experiment, the interactions between them are characterized and the number of follow-up confirmatory experiments is reduced. This in turn improves statistical power and throughput. Two examples of the IBC method are shown and the impact of the load conditions are assessed in combination with the other factors explored.

  10. Does suggestive information cause a confirmation bias in bullet comparisons?

    PubMed

    Kerstholt, Jose; Eikelboom, Aletta; Dijkman, Tjisse; Stoel, Reinoud; Hermsen, Rob; van Leuven, Bert

    2010-05-20

    Several researchers have argued that the confirmation bias, the tendency to selectively gather and process information such that it fits existing beliefs, is a main threat to objective forensic examinations. The goal of the present study was to empirically investigate whether examiners making bullet comparisons are indeed vulnerable to this bias. In the first experiment, six qualified examiners evaluated 6 sets of bullets that were presented to them twice. In the neutral task condition it was mentioned in the case description that there were two perpetrators and two crime scenes, whereas in the potentially biasing task condition it was mentioned that there was only one perpetrator and one crime scene. The results showed no effect of biased information on the decision outcome. An exploratory analysis revealed rather large individual differences in two cases. In a second study we compared the conclusions of first and second examiners of actual cases that were conducted in the period between 1997 and 2006. As the second examiner mostly has no context information it may be expected that the conclusion of the first examiner should be more extreme when he or she would have become prey to a confirmation bias. The results indicate an effect in the opposite direction: the first examiner gave less extreme ratings than the second one. In all, our results indicate that examiners were not affected by biased information the case description.

  11. Social Anxiety and Interpretation Bias: Effect of Positive Priming.

    PubMed

    Wang, Xiaoling; Qian, Mingyi; Yu, Hongyu; Sun, Yang; Li, Songwei; Yang, Peng; Lin, Muyu; Yao, Nishao; Zhang, Xilin

    2016-10-01

    This study examined how positive-scale assessment of ambiguous social stimuli affects interpretation bias in social anxiety. Participants with high and low social anxiety (N = 60) performed a facial expression discrimination task to assess interpretation bias. Participants were then randomly assigned to assess the emotion of briefly presented faces either on a negative or on a positive scale. They subsequently repeated the facial expression discrimination task. Participants with high versus low social anxiety made more negative interpretations of ambiguous facial expressions. However, those in the positive-scale assessment condition subsequently showed reduced negative interpretations of ambiguous facial expressions. These results suggest that interpretation bias in social anxiety could be mediated by positive priming rather than an outright negative bias.

  12. Essentialism promotes children's inter-ethnic bias

    PubMed Central

    Diesendruck, Gil; Menahem, Roni

    2015-01-01

    The present study investigated the developmental foundation of the relation between social essentialism and attitudes. Forty-eight Jewish Israeli secular 6-year-olds were exposed to either a story emphasizing essentialism about ethnicity, or stories controlling for the salience of ethnicity or essentialism per se. After listening to a story, children's attitudes were assessed in a drawing and in an IAT task. Compared to the control conditions, children in the ethnic essentialism condition drew a Jewish and an Arab character as farther apart from each other, and the Jewish character with a more positive affect than the Arab character. Moreover, boys in the ethnic essentialism condition manifested a stronger bias in the IAT. These findings reveal an early link between essentialism and inter-group attitudes. PMID:26321992

  13. Heuristic-biased stochastic sampling

    SciTech Connect

    Bresina, J.L.

    1996-12-31

    This paper presents a search technique for scheduling problems, called Heuristic-Biased Stochastic Sampling (HBSS). The underlying assumption behind the HBSS approach is that strictly adhering to a search heuristic often does not yield the best solution and, therefore, exploration off the heuristic path can prove fruitful. Within the HBSS approach, the balance between heuristic adherence and exploration can be controlled according to the confidence one has in the heuristic. By varying this balance, encoded as a bias function, the HBSS approach encompasses a family of search algorithms of which greedy search and completely random search are extreme members. We present empirical results from an application of HBSS to the realworld problem of observation scheduling. These results show that with the proper bias function, it can be easy to outperform greedy search.

  14. Anchoring bias in online voting

    NASA Astrophysics Data System (ADS)

    Yang, Zimo; Zhang, Zi-Ke; Zhou, Tao

    2012-12-01

    Voting online with explicit ratings could largely reflect people's preferences and objects' qualities, but ratings are always irrational, because they may be affected by many unpredictable factors like mood, weather and other people's votes. By analyzing two real systems, this paper reveals a systematic bias embedding in the individual decision-making processes, namely people tend to give a low rating after a low rating, as well as a high rating following a high rating. This so-called anchoring bias is validated via extensive comparisons with null models, and numerically speaking, the extent of bias decays with voting interval in a logarithmic form. Our findings could be applied in the design of recommender systems and considered as important complementary materials to previous knowledge about anchoring effects on financial trades, performance judgments, auctions, and so on.

  15. Measurement of Receptor Signaling Bias.

    PubMed

    Kenakin, Terry

    2016-01-01

    G protein-coupled receptors (GPCRs) are often pleiotropically linked to numerous cellular signaling mechanisms in cells, and it is now known that many agonists differentially activate some signaling pathways at the expense of others. The mechanism for this effect is the stabilization of different active receptor states by different agonists, and it leads to varying qualities of efficacy for different agonists. Agonist bias is a powerful mechanism to amplify beneficial signals and diminish harmful signals, and thus improve the overall profile of agonist ligands. This unit describes a method to quantify agonist bias with a scale that enables medicinal chemists to amplify or reduce these effects in new molecules. The method is based on the Black/Leff operational model and yields a statistical estimate of the confidence for bias measurements. © 2016 by John Wiley & Sons, Inc. PMID:27636109

  16. Unpacking the Evidence of Gender Bias

    ERIC Educational Resources Information Center

    Fulmer, Connie L.

    2010-01-01

    The purpose of this study was to investigate gender bias in pre-service principals using the Gender-Leader Implicit Association Test. Analyses of student-learning narratives revealed how students made sense of gender bias (biased or not-biased) and how each reacted to evidence (surprised or not-surprised). Two implications were: (1) the need for…

  17. Measurement Bias Detection through Factor Analysis

    ERIC Educational Resources Information Center

    Barendse, M. T.; Oort, F. J.; Werner, C. S.; Ligtvoet, R.; Schermelleh-Engel, K.

    2012-01-01

    Measurement bias is defined as a violation of measurement invariance, which can be investigated through multigroup factor analysis (MGFA), by testing across-group differences in intercepts (uniform bias) and factor loadings (nonuniform bias). Restricted factor analysis (RFA) can also be used to detect measurement bias. To also enable nonuniform…

  18. A Reconsideration of Bias in the News.

    ERIC Educational Resources Information Center

    Stevenson, Robert L.; Greene, Mark T.

    This paper discusses three conceptual problems--point of view, unit of bias, and behavioral response--with using content analysis to study news bias. The paper shows that the point of view of the content analyst is not appropriate if one wants to see how news consumers define and react to bias, that the unit of bias should be the specific instance…

  19. Collection Development and the Psychology of Bias

    ERIC Educational Resources Information Center

    Quinn, Brian

    2012-01-01

    The library literature addressing the role of bias in collection development emphasizes a philosophical approach. It is based on the notion that bias can be controlled by the conscious act of believing in certain values and adhering to a code of ethics. It largely ignores the psychological research on bias, which suggests that bias is a more…

  20. The Truth and Bias Model of Judgment

    ERIC Educational Resources Information Center

    West, Tessa V.; Kenny, David A.

    2011-01-01

    We present a new model for the general study of how the truth and biases affect human judgment. In the truth and bias model, judgments about the world are pulled by 2 primary forces, the truth force and the bias force, and these 2 forces are interrelated. The truth and bias model differentiates force and value, where the force is the strength of…

  1. Without Bias: A Guidebook for Nondiscriminatory Communication.

    ERIC Educational Resources Information Center

    Pickens, Judy E., Ed.; And Others

    This guidebook discusses ways to eliminate various types of discrimination from business communications. Separately authored chapters discuss eliminating racial and ethnic bias; eliminating sexual bias; achieving communication sensitive about handicaps of disabled persons; eliminating bias from visual media; eliminating bias from meetings,…

  2. Analyzing Multivariate Repeated Measures Designs When Covariance Matrices Are Heterogeneous.

    ERIC Educational Resources Information Center

    Lix, Lisa M.; And Others

    Methods for the analysis of within-subjects effects in multivariate groups by trials repeated measures designs are considered in the presence of heteroscedasticity of the group variance-covariance matrices and multivariate nonnormality. Under a doubly multivariate model approach to hypothesis testing, within-subjects main and interaction effect…

  3. Multivariate Models for Normal and Binary Responses in Intervention Studies

    ERIC Educational Resources Information Center

    Pituch, Keenan A.; Whittaker, Tiffany A.; Chang, Wanchen

    2016-01-01

    Use of multivariate analysis (e.g., multivariate analysis of variance) is common when normally distributed outcomes are collected in intervention research. However, when mixed responses--a set of normal and binary outcomes--are collected, standard multivariate analyses are no longer suitable. While mixed responses are often obtained in…

  4. Multivariate Analysis of Genotype-Phenotype Association.

    PubMed

    Mitteroecker, Philipp; Cheverud, James M; Pavlicev, Mihaela

    2016-04-01

    With the advent of modern imaging and measurement technology, complex phenotypes are increasingly represented by large numbers of measurements, which may not bear biological meaning one by one. For such multivariate phenotypes, studying the pairwise associations between all measurements and all alleles is highly inefficient and prevents insight into the genetic pattern underlying the observed phenotypes. We present a new method for identifying patterns of allelic variation (genetic latent variables) that are maximally associated-in terms of effect size-with patterns of phenotypic variation (phenotypic latent variables). This multivariate genotype-phenotype mapping (MGP) separates phenotypic features under strong genetic control from less genetically determined features and thus permits an analysis of the multivariate structure of genotype-phenotype association, including its dimensionality and the clustering of genetic and phenotypic variables within this association. Different variants of MGP maximize different measures of genotype-phenotype association: genetic effect, genetic variance, or heritability. In an application to a mouse sample, scored for 353 SNPs and 11 phenotypic traits, the first dimension of genetic and phenotypic latent variables accounted for >70% of genetic variation present in all 11 measurements; 43% of variation in this phenotypic pattern was explained by the corresponding genetic latent variable. The first three dimensions together sufficed to account for almost 90% of genetic variation in the measurements and for all the interpretable genotype-phenotype association. Each dimension can be tested as a whole against the hypothesis of no association, thereby reducing the number of statistical tests from 7766 to 3-the maximal number of meaningful independent tests. Important alleles can be selected based on their effect size (additive or nonadditive effect on the phenotypic latent variable). This low dimensionality of the genotype-phenotype map

  5. Time varying, multivariate volume data reduction

    SciTech Connect

    Ahrens, James P; Fout, Nathaniel; Ma, Kwan - Liu

    2010-01-01

    Large-scale supercomputing is revolutionizing the way science is conducted. A growing challenge, however, is understanding the massive quantities of data produced by large-scale simulations. The data, typically time-varying, multivariate, and volumetric, can occupy from hundreds of gigabytes to several terabytes of storage space. Transferring and processing volume data of such sizes is prohibitively expensive and resource intensive. Although it may not be possible to entirely alleviate these problems, data compression should be considered as part of a viable solution, especially when the primary means of data analysis is volume rendering. In this paper we present our study of multivariate compression, which exploits correlations among related variables, for volume rendering. Two configurations for multidimensional compression based on vector quantization are examined. We emphasize quality reconstruction and interactive rendering, which leads us to a solution using graphics hardware to perform on-the-fly decompression during rendering. In this paper we present a solution which addresses the need for data reduction in large supercomputing environments where data resulting from simulations occupies tremendous amounts of storage. Our solution employs a lossy encoding scheme to acrueve data reduction with several options in terms of rate-distortion behavior. We focus on encoding of multiple variables together, with optional compression in space and time. The compressed volumes can be rendered directly with commodity graphics cards at interactive frame rates and rendering quality similar to that of static volume renderers. Compression results using a multivariate time-varying data set indicate that encoding multiple variables results in acceptable performance in the case of spatial and temporal encoding as compared to independent compression of variables. The relative performance of spatial vs. temporal compression is data dependent, although temporal compression has the

  6. Multivariate linkage analysis of specific language impairment (SLI).

    PubMed

    Monaco, Anthony P

    2007-09-01

    Specific language impairment (SLI) is defined as an inability to develop appropriate language skills without explanatory medical conditions, low intelligence or lack of opportunity. Previously, a genome scan of 98 families affected by SLI was completed by the SLI Consortium, resulting in the identification of two quantitative trait loci (QTL) on chromosomes 16q (SLI1) and 19q (SLI2). This was followed by a replication of both regions in an additional 86 families. Both these studies applied linkage methods to one phenotypic trait at a time. However, investigations have suggested that simultaneous analysis of several traits may offer more power. The current study therefore applied a multivariate variance-components approach to the SLI Consortium dataset using additional phenotypic data. A multivariate genome scan was completed and supported the importance of the SLI1 and SLI2 loci, whilst highlighting a possible novel QTL on chromosome 10. Further investigation implied that the effect of SLI1 on non-word repetition was equally as strong on reading and spelling phenotypes. In contrast, SLI2 appeared to have influences on a selection of expressive and receptive language phenotypes in addition to non-word repetition, but did not show linkage to literacy phenotypes.

  7. The Threshold of Embedded M Collider Bias and Confounding Bias

    ERIC Educational Resources Information Center

    Kelcey, Benjamin; Carlisle, Joanne

    2011-01-01

    Of particular import to this study, is collider bias originating from stratification on retreatment variables forming an embedded M or bowtie structural design. That is, rather than assume an M structural design which suggests that "X" is a collider but not a confounder, the authors adopt what they consider to be a more reasonable position and…

  8. Threat-Related Attentional Bias in Anxious and Nonanxious Individuals: A Meta-Analytic Study

    ERIC Educational Resources Information Center

    Bar-Haim, Yair; Lamy, Dominique; Pergamin, Lee; Bakermans-Kranenburg, Marian J.; van IJzendoorn, Marinus H.

    2007-01-01

    This meta-analysis of 172 studies (N = 2,263 anxious, N = 1,768 nonanxious) examined the boundary conditions of threat-related attentional biases in anxiety. Overall, the results show that the bias is reliably demonstrated with different experimental paradigms and under a variety of experimental conditions, but that it is only an effect size of d…

  9. Multivariate curve-fitting in GAUSS

    USGS Publications Warehouse

    Bunck, C.M.; Pendleton, G.W.

    1988-01-01

    Multivariate curve-fitting techniques for repeated measures have been developed and an interactive program has been written in GAUSS. The program implements not only the one-factor design described in Morrison (1967) but also includes pairwise comparisons of curves and rates, a two-factor design, and other options. Strategies for selecting the appropriate degree for the polynomial are provided. The methods and program are illustrated with data from studies of the effects of environmental contaminants on ducklings, nesting kestrels and quail.

  10. Multivariate postprocessing techniques for probabilistic hydrological forecasting

    NASA Astrophysics Data System (ADS)

    Hemri, Stephan; Lisniak, Dmytro; Klein, Bastian

    2016-04-01

    Hydrologic ensemble forecasts driven by atmospheric ensemble prediction systems need statistical postprocessing in order to account for systematic errors in terms of both mean and spread. Runoff is an inherently multivariate process with typical events lasting from hours in case of floods to weeks or even months in case of droughts. This calls for multivariate postprocessing techniques that yield well calibrated forecasts in univariate terms and ensure a realistic temporal dependence structure at the same time. To this end, the univariate ensemble model output statistics (EMOS; Gneiting et al., 2005) postprocessing method is combined with two different copula approaches that ensure multivariate calibration throughout the entire forecast horizon. These approaches comprise ensemble copula coupling (ECC; Schefzik et al., 2013), which preserves the dependence structure of the raw ensemble, and a Gaussian copula approach (GCA; Pinson and Girard, 2012), which estimates the temporal correlations from training observations. Both methods are tested in a case study covering three subcatchments of the river Rhine that represent different sizes and hydrological regimes: the Upper Rhine up to the gauge Maxau, the river Moselle up to the gauge Trier, and the river Lahn up to the gauge Kalkofen. The results indicate that both ECC and GCA are suitable for modelling the temporal dependences of probabilistic hydrologic forecasts (Hemri et al., 2015). References Gneiting, T., A. E. Raftery, A. H. Westveld, and T. Goldman (2005), Calibrated probabilistic forecasting using ensemble model output statistics and minimum CRPS estimation, Monthly Weather Review, 133(5), 1098-1118, DOI: 10.1175/MWR2904.1. Hemri, S., D. Lisniak, and B. Klein, Multivariate postprocessing techniques for probabilistic hydrological forecasting, Water Resources Research, 51(9), 7436-7451, DOI: 10.1002/2014WR016473. Pinson, P., and R. Girard (2012), Evaluating the quality of scenarios of short-term wind power

  11. Multivariate Lipschitz optimization: Survey and computational comparison

    SciTech Connect

    Hansen, P.; Gourdin, E.; Jaumard, B.

    1994-12-31

    Many methods have been proposed to minimize a multivariate Lipschitz function on a box. They pertain the three approaches: (i) reduction to the univariate case by projection (Pijavskii) or by using a space-filling curve (Strongin); (ii) construction and refinement of a single upper bounding function (Pijavskii, Mladineo, Mayne and Polak, Jaumard Hermann and Ribault, Wood...); (iii) branch and bound with local upper bounding functions (Galperin, Pint{acute e}r, Meewella and Mayne, the present authors). A survey is made, stressing similarities of algorithms, expressed when possible within a unified framework. Moreover, an extensive computational comparison is reported on.

  12. Algorithms for computing the multivariable stability margin

    NASA Technical Reports Server (NTRS)

    Tekawy, Jonathan A.; Safonov, Michael G.; Chiang, Richard Y.

    1989-01-01

    Stability margin for multiloop flight control systems has become a critical issue, especially in highly maneuverable aircraft designs where there are inherent strong cross-couplings between the various feedback control loops. To cope with this issue, we have developed computer algorithms based on non-differentiable optimization theory. These algorithms have been developed for computing the Multivariable Stability Margin (MSM). The MSM of a dynamical system is the size of the smallest structured perturbation in component dynamics that will destabilize the system. These algorithms have been coded and appear to be reliable. As illustrated by examples, they provide the basis for evaluating the robustness and performance of flight control systems.

  13. F100 Multivariable Control Synthesis Program. Computer Implementation of the F100 Multivariable Control Algorithm

    NASA Technical Reports Server (NTRS)

    Soeder, J. F.

    1983-01-01

    As turbofan engines become more complex, the development of controls necessitate the use of multivariable control techniques. A control developed for the F100-PW-100(3) turbofan engine by using linear quadratic regulator theory and other modern multivariable control synthesis techniques is described. The assembly language implementation of this control on an SEL 810B minicomputer is described. This implementation was then evaluated by using a real-time hybrid simulation of the engine. The control software was modified to run with a real engine. These modifications, in the form of sensor and actuator failure checks and control executive sequencing, are discussed. Finally recommendations for control software implementations are presented.

  14. Bias in Dynamic Monte Carlo Alpha Calculations

    SciTech Connect

    Sweezy, Jeremy Ed; Nolen, Steven Douglas; Adams, Terry R.; Trahan, Travis John

    2015-02-06

    A 1/N bias in the estimate of the neutron time-constant (commonly denoted as α) has been seen in dynamic neutronic calculations performed with MCATK. In this paper we show that the bias is most likely caused by taking the logarithm of a stochastic quantity. We also investigate the known bias due to the particle population control method used in MCATK. We conclude that this bias due to the particle population control method is negligible compared to other sources of bias.

  15. Biased Signaling of Protease-Activated Receptors

    PubMed Central

    Zhao, Peishen; Metcalf, Matthew; Bunnett, Nigel W.

    2014-01-01

    In addition to their role in protein degradation and digestion, proteases can also function as hormone-like signaling molecules that regulate vital patho-physiological processes, including inflammation, hemostasis, pain, and repair mechanisms. Certain proteases can signal to cells by cleaving protease-activated receptors (PARs), a family of four G protein-coupled receptors. PARs are expressed by almost all cell types, control important physiological and disease-relevant processes, and are an emerging therapeutic target for major diseases. Most information about PAR activation and function derives from studies of a few proteases, for example thrombin in the case of PAR1, PAR3, and PAR4, and trypsin in the case of PAR2 and PAR4. These proteases cleave PARs at established sites with the extracellular N-terminal domains, and expose tethered ligands that stabilize conformations of the cleaved receptors that activate the canonical pathways of G protein- and/or β-arrestin-dependent signaling. However, a growing number of proteases have been identified that cleave PARs at divergent sites to activate distinct patterns of receptor signaling and trafficking. The capacity of these proteases to trigger distinct signaling pathways is referred to as biased signaling, and can lead to unique patho-physiological outcomes. Given that a different repertoire of proteases are activated in various patho-physiological conditions that may activate PARs by different mechanisms, signaling bias may account for the divergent actions of proteases and PARs. Moreover, therapies that target disease-relevant biased signaling pathways may be more effective and selective approaches for the treatment of protease- and PAR-driven diseases. Thus, rather than mediating the actions of a few proteases, PARs may integrate the biological actions of a wide spectrum of proteases in different patho-physiological conditions. PMID:24860547

  16. Multivariate intralocus sexual conflict in seed beetles.

    PubMed

    Berger, David; Berg, Elena C; Widegren, William; Arnqvist, Göran; Maklakov, Alexei A

    2014-12-01

    Intralocus sexual conflict (IaSC) is pervasive because males and females experience differences in selection but share much of the same genome. Traits with integrated genetic architecture should be reservoirs of sexually antagonistic genetic variation for fitness, but explorations of multivariate IaSC are scarce. Previously, we showed that upward artificial selection on male life span decreased male fitness but increased female fitness compared with downward selection in the seed beetle Callosobruchus maculatus. Here, we use these selection lines to investigate sex-specific evolution of four functionally integrated traits (metabolic rate, locomotor activity, body mass, and life span) that collectively define a sexually dimorphic life-history syndrome in many species. Male-limited selection for short life span led to correlated evolution in females toward a more male-like multivariate phenotype. Conversely, males selected for long life span became more female-like, implying that IaSC results from genetic integration of this suite of traits. However, while life span, metabolism, and body mass showed correlated evolution in the sexes, activity did not evolve in males but, surprisingly, did so in females. This led to sexual monomorphism in locomotor activity in short-life lines associated with detrimental effects in females. Our results thus support the general tenet that widespread pleiotropy generates IaSC despite sex-specific genetic architecture.

  17. Adaptable Multivariate Calibration Models for Spectral Applications

    SciTech Connect

    THOMAS,EDWARD V.

    1999-12-20

    Multivariate calibration techniques have been used in a wide variety of spectroscopic situations. In many of these situations spectral variation can be partitioned into meaningful classes. For example, suppose that multiple spectra are obtained from each of a number of different objects wherein the level of the analyte of interest varies within each object over time. In such situations the total spectral variation observed across all measurements has two distinct general sources of variation: intra-object and inter-object. One might want to develop a global multivariate calibration model that predicts the analyte of interest accurately both within and across objects, including new objects not involved in developing the calibration model. However, this goal might be hard to realize if the inter-object spectral variation is complex and difficult to model. If the intra-object spectral variation is consistent across objects, an effective alternative approach might be to develop a generic intra-object model that can be adapted to each object separately. This paper contains recommendations for experimental protocols and data analysis in such situations. The approach is illustrated with an example involving the noninvasive measurement of glucose using near-infrared reflectance spectroscopy. Extensions to calibration maintenance and calibration transfer are discussed.

  18. A multivariate Baltic Sea environmental index.

    PubMed

    Dippner, Joachim W; Kornilovs, Georgs; Junker, Karin

    2012-11-01

    Since 2001/2002, the correlation between North Atlantic Oscillation index and biological variables in the North Sea and Baltic Sea fails, which might be addressed to a global climate regime shift. To understand inter-annual and inter-decadal variability in environmental variables, a new multivariate index for the Baltic Sea is developed and presented here. The multivariate Baltic Sea Environmental (BSE) index is defined as the 1st principal component score of four z-transformed time series: the Arctic Oscillation index, the salinity between 120 and 200 m in the Gotland Sea, the integrated river runoff of all rivers draining into the Baltic Sea, and the relative vorticity of geostrophic wind over the Baltic Sea area. A statistical downscaling technique has been applied to project different climate indices to the sea surface temperature in the Gotland, to the Landsort gauge, and the sea ice extent. The new BSE index shows a better performance than all other climate indices and is equivalent to the Chen index for physical properties. An application of the new index to zooplankton time series from the central Baltic Sea (Latvian EEZ) shows an excellent skill in potential predictability of environmental time series.

  19. Fast Multivariate Search on Large Aviation Datasets

    NASA Technical Reports Server (NTRS)

    Bhaduri, Kanishka; Zhu, Qiang; Oza, Nikunj C.; Srivastava, Ashok N.

    2010-01-01

    Multivariate Time-Series (MTS) are ubiquitous, and are generated in areas as disparate as sensor recordings in aerospace systems, music and video streams, medical monitoring, and financial systems. Domain experts are often interested in searching for interesting multivariate patterns from these MTS databases which can contain up to several gigabytes of data. Surprisingly, research on MTS search is very limited. Most existing work only supports queries with the same length of data, or queries on a fixed set of variables. In this paper, we propose an efficient and flexible subsequence search framework for massive MTS databases, that, for the first time, enables querying on any subset of variables with arbitrary time delays between them. We propose two provably correct algorithms to solve this problem (1) an R-tree Based Search (RBS) which uses Minimum Bounding Rectangles (MBR) to organize the subsequences, and (2) a List Based Search (LBS) algorithm which uses sorted lists for indexing. We demonstrate the performance of these algorithms using two large MTS databases from the aviation domain, each containing several millions of observations Both these tests show that our algorithms have very high prune rates (>95%) thus needing actual

  20. Augmented Classical Least Squares Multivariate Spectral Analysis

    DOEpatents

    Haaland, David M.; Melgaard, David K.

    2005-01-11

    A method of multivariate spectral analysis, termed augmented classical least squares (ACLS), provides an improved CLS calibration model when unmodeled sources of spectral variation are contained in a calibration sample set. The ACLS methods use information derived from component or spectral residuals during the CLS calibration to provide an improved calibration-augmented CLS model. The ACLS methods are based on CLS so that they retain the qualitative benefits of CLS, yet they have the flexibility of PLS and other hybrid techniques in that they can define a prediction model even with unmodeled sources of spectral variation that are not explicitly included in the calibration model. The unmodeled sources of spectral variation may be unknown constituents, constituents with unknown concentrations, nonlinear responses, non-uniform and correlated errors, or other sources of spectral variation that are present in the calibration sample spectra. Also, since the various ACLS methods are based on CLS, they can incorporate the new prediction-augmented CLS (PACLS) method of updating the prediction model for new sources of spectral variation contained in the prediction sample set without having to return to the calibration process. The ACLS methods can also be applied to alternating least squares models. The ACLS methods can be applied to all types of multivariate data.

  1. Augmented Classical Least Squares Multivariate Spectral Analysis

    DOEpatents

    Haaland, David M.; Melgaard, David K.

    2005-07-26

    A method of multivariate spectral analysis, termed augmented classical least squares (ACLS), provides an improved CLS calibration model when unmodeled sources of spectral variation are contained in a calibration sample set. The ACLS methods use information derived from component or spectral residuals during the CLS calibration to provide an improved calibration-augmented CLS model. The ACLS methods are based on CLS so that they retain the qualitative benefits of CLS, yet they have the flexibility of PLS and other hybrid techniques in that they can define a prediction model even with unmodeled sources of spectral variation that are not explicitly included in the calibration model. The unmodeled sources of spectral variation may be unknown constituents, constituents with unknown concentrations, nonlinear responses, non-uniform and correlated errors, or other sources of spectral variation that are present in the calibration sample spectra. Also, since the various ACLS methods are based on CLS, they can incorporate the new prediction-augmented CLS (PACLS) method of updating the prediction model for new sources of spectral variation contained in the prediction sample set without having to return to the calibration process. The ACLS methods can also be applied to alternating least squares models. The ACLS methods can be applied to all types of multivariate data.

  2. Augmented classical least squares multivariate spectral analysis

    DOEpatents

    Haaland, David M.; Melgaard, David K.

    2004-02-03

    A method of multivariate spectral analysis, termed augmented classical least squares (ACLS), provides an improved CLS calibration model when unmodeled sources of spectral variation are contained in a calibration sample set. The ACLS methods use information derived from component or spectral residuals during the CLS calibration to provide an improved calibration-augmented CLS model. The ACLS methods are based on CLS so that they retain the qualitative benefits of CLS, yet they have the flexibility of PLS and other hybrid techniques in that they can define a prediction model even with unmodeled sources of spectral variation that are not explicitly included in the calibration model. The unmodeled sources of spectral variation may be unknown constituents, constituents with unknown concentrations, nonlinear responses, non-uniform and correlated errors, or other sources of spectral variation that are present in the calibration sample spectra. Also, since the various ACLS methods are based on CLS, they can incorporate the new prediction-augmented CLS (PACLS) method of updating the prediction model for new sources of spectral variation contained in the prediction sample set without having to return to the calibration process. The ACLS methods can also be applied to alternating least squares models. The ACLS methods can be applied to all types of multivariate data.

  3. Network structure of multivariate time series.

    PubMed

    Lacasa, Lucas; Nicosia, Vincenzo; Latora, Vito

    2015-10-21

    Our understanding of a variety of phenomena in physics, biology and economics crucially depends on the analysis of multivariate time series. While a wide range tools and techniques for time series analysis already exist, the increasing availability of massive data structures calls for new approaches for multidimensional signal processing. We present here a non-parametric method to analyse multivariate time series, based on the mapping of a multidimensional time series into a multilayer network, which allows to extract information on a high dimensional dynamical system through the analysis of the structure of the associated multiplex network. The method is simple to implement, general, scalable, does not require ad hoc phase space partitioning, and is thus suitable for the analysis of large, heterogeneous and non-stationary time series. We show that simple structural descriptors of the associated multiplex networks allow to extract and quantify nontrivial properties of coupled chaotic maps, including the transition between different dynamical phases and the onset of various types of synchronization. As a concrete example we then study financial time series, showing that a multiplex network analysis can efficiently discriminate crises from periods of financial stability, where standard methods based on time-series symbolization often fail.

  4. Network structure of multivariate time series

    NASA Astrophysics Data System (ADS)

    Lacasa, Lucas; Nicosia, Vincenzo; Latora, Vito

    2015-10-01

    Our understanding of a variety of phenomena in physics, biology and economics crucially depends on the analysis of multivariate time series. While a wide range tools and techniques for time series analysis already exist, the increasing availability of massive data structures calls for new approaches for multidimensional signal processing. We present here a non-parametric method to analyse multivariate time series, based on the mapping of a multidimensional time series into a multilayer network, which allows to extract information on a high dimensional dynamical system through the analysis of the structure of the associated multiplex network. The method is simple to implement, general, scalable, does not require ad hoc phase space partitioning, and is thus suitable for the analysis of large, heterogeneous and non-stationary time series. We show that simple structural descriptors of the associated multiplex networks allow to extract and quantify nontrivial properties of coupled chaotic maps, including the transition between different dynamical phases and the onset of various types of synchronization. As a concrete example we then study financial time series, showing that a multiplex network analysis can efficiently discriminate crises from periods of financial stability, where standard methods based on time-series symbolization often fail.

  5. Benchmarking a reduced multivariate polynomial pattern classifier.

    PubMed

    Toh, Kar-Ann; Tran, Quoc-Long; Srinivasan, Dipti

    2004-06-01

    A novel method using a reduced multivariate polynomial model has been developed for biometric decision fusion where simplicity and ease of use could be a concern. However, much to our surprise, the reduced model was found to have good classification accuracy for several commonly used data sets from the Web. In this paper, we extend the single output model to a multiple outputs model to handle multiple class problems. The method is particularly suitable for problems with small number of features and large number of examples. Basic component of this polynomial model boils down to construction of new pattern features which are sums of the original features and combination of these new and original features using power and product terms. A linear regularized least-squares predictor is then built using these constructed features. The number of constructed feature terms varies linearly with the order of the polynomial, instead of having a power law in the case of full multivariate polynomials. The method is simple as it amounts to only a few lines of Matlab code. We perform extensive experiments on this reduced model using 42 data sets. Our results compared remarkably well with best reported results of several commonly used algorithms from the literature. Both the classification accuracy and efficiency aspects are reported for this reduced model.

  6. Network structure of multivariate time series

    PubMed Central

    Lacasa, Lucas; Nicosia, Vincenzo; Latora, Vito

    2015-01-01

    Our understanding of a variety of phenomena in physics, biology and economics crucially depends on the analysis of multivariate time series. While a wide range tools and techniques for time series analysis already exist, the increasing availability of massive data structures calls for new approaches for multidimensional signal processing. We present here a non-parametric method to analyse multivariate time series, based on the mapping of a multidimensional time series into a multilayer network, which allows to extract information on a high dimensional dynamical system through the analysis of the structure of the associated multiplex network. The method is simple to implement, general, scalable, does not require ad hoc phase space partitioning, and is thus suitable for the analysis of large, heterogeneous and non-stationary time series. We show that simple structural descriptors of the associated multiplex networks allow to extract and quantify nontrivial properties of coupled chaotic maps, including the transition between different dynamical phases and the onset of various types of synchronization. As a concrete example we then study financial time series, showing that a multiplex network analysis can efficiently discriminate crises from periods of financial stability, where standard methods based on time-series symbolization often fail. PMID:26487040

  7. Attributional Bias and Course Evaluations.

    ERIC Educational Resources Information Center

    Gigliotti, Richard J.; Buchtel, Foster S.

    1990-01-01

    How self-serving bias affects evaluations of college courses was tested for 691 students by comparing a model predicting that evaluations reflect actual grades with a model predicting that evaluations reflect confirmation or disconfirmation of expectations. Results support course evaluation validity by indicating a minimal effect of self-serving…

  8. Attentional bias in math anxiety.

    PubMed

    Rubinsten, Orly; Eidlin, Hili; Wohl, Hadas; Akibli, Orly

    2015-01-01

    Cognitive theory from the field of general anxiety suggests that the tendency to display attentional bias toward negative information results in anxiety. Accordingly, the current study aims to investigate whether attentional bias is involved in math anxiety (MA) as well (i.e., a persistent negative reaction to math). Twenty seven participants (14 with high levels of MA and 13 with low levels of MA) were presented with a novel computerized numerical version of the well established dot probe task. One of six types of prime stimuli, either math related or typically neutral, was presented on one side of a computer screen. The prime was preceded by a probe (either one or two asterisks) that appeared in either the prime or the opposite location. Participants had to discriminate probe identity (one or two asterisks). Math anxious individuals reacted faster when the probe was at the location of the numerical related stimuli. This suggests the existence of attentional bias in MA. That is, for math anxious individuals, the cognitive system selectively favored the processing of emotionally negative information (i.e., math related words). These findings suggest that attentional bias is linked to unduly intense MA symptoms.

  9. Combating Anti-Muslim Bias

    ERIC Educational Resources Information Center

    Shah, Nirvi

    2011-01-01

    America's 2.5 million Muslims make up less than 1% of the U.S. population, according to the Pew Research Center. Many Muslim students face discrimination and some cases have warranted investigation by the U.S. Department of Education's Office of Civil Rights. Muslim groups have reported widespread bias as well. For many Muslim…

  10. Stereotype Formation: Biased by Association

    ERIC Educational Resources Information Center

    Le Pelley, Mike E.; Reimers, Stian J.; Calvini, Guglielmo; Spears, Russell; Beesley, Tom; Murphy, Robin A.

    2010-01-01

    We propose that biases in attitude and stereotype formation might arise as a result of learned differences in the extent to which social groups have previously been predictive of behavioral or physical properties. Experiments 1 and 2 demonstrate that differences in the experienced predictiveness of groups with respect to evaluatively neutral…

  11. Attentional bias in math anxiety.

    PubMed

    Rubinsten, Orly; Eidlin, Hili; Wohl, Hadas; Akibli, Orly

    2015-01-01

    Cognitive theory from the field of general anxiety suggests that the tendency to display attentional bias toward negative information results in anxiety. Accordingly, the current study aims to investigate whether attentional bias is involved in math anxiety (MA) as well (i.e., a persistent negative reaction to math). Twenty seven participants (14 with high levels of MA and 13 with low levels of MA) were presented with a novel computerized numerical version of the well established dot probe task. One of six types of prime stimuli, either math related or typically neutral, was presented on one side of a computer screen. The prime was preceded by a probe (either one or two asterisks) that appeared in either the prime or the opposite location. Participants had to discriminate probe identity (one or two asterisks). Math anxious individuals reacted faster when the probe was at the location of the numerical related stimuli. This suggests the existence of attentional bias in MA. That is, for math anxious individuals, the cognitive system selectively favored the processing of emotionally negative information (i.e., math related words). These findings suggest that attentional bias is linked to unduly intense MA symptoms. PMID:26528208

  12. Key Words in Instruction. Bias

    ERIC Educational Resources Information Center

    Callison, Daniel

    2005-01-01

    Two challenging criteria for judging information involve bias and authority. In both cases, judgments may not be clearly possible. In both cases, there may be degrees or levels of acceptability. For students to gain experience and to demonstrate skills in making judgments, they need opportunities to consider a wide spectrum of resources under a…

  13. Attentional bias in math anxiety

    PubMed Central

    Rubinsten, Orly; Eidlin, Hili; Wohl, Hadas; Akibli, Orly

    2015-01-01

    Cognitive theory from the field of general anxiety suggests that the tendency to display attentional bias toward negative information results in anxiety. Accordingly, the current study aims to investigate whether attentional bias is involved in math anxiety (MA) as well (i.e., a persistent negative reaction to math). Twenty seven participants (14 with high levels of MA and 13 with low levels of MA) were presented with a novel computerized numerical version of the well established dot probe task. One of six types of prime stimuli, either math related or typically neutral, was presented on one side of a computer screen. The prime was preceded by a probe (either one or two asterisks) that appeared in either the prime or the opposite location. Participants had to discriminate probe identity (one or two asterisks). Math anxious individuals reacted faster when the probe was at the location of the numerical related stimuli. This suggests the existence of attentional bias in MA. That is, for math anxious individuals, the cognitive system selectively favored the processing of emotionally negative information (i.e., math related words). These findings suggest that attentional bias is linked to unduly intense MA symptoms. PMID:26528208

  14. A Multivariate Approach for Comparing and Classifying Streamwater Quality

    NASA Astrophysics Data System (ADS)

    Hooper, R. P.; McGlynn, B. L.; Hjerdt, K. N.; McDonnell, J. J.

    2001-05-01

    Few measures exist for objectively comparing the chemistry of streams. We develop a multivariate technique, based on an eigenvalue analysis of streamwater concentrations, to facilitate comparison of water quality among sites across basin scales. A correlation matrix is constructed to include only solutes that mix conservatively. An eigenvalue analysis of this matrix is performed at each site to determine the approximate rank of the data set. If the ranks of all sites are roughly equal, one site is chosen as the reference site. The reduced set of eigenvectors from this site is chosen as the basis for a new, lower dimensional coordinate system and the data from the other sites are projected into this coordinate system. To assess the relative orientation of data from the reference site to all of the other sites, the relative bias (RB) and relative root mean square error (RRMSE) are calculated between the original and the projected points. The new technique was applied to multiple sites within three experimental watersheds to assess the consistency of water quality across the basin scale. The three watersheds were: Panola Mountain, Georgia, USA (6 solutes, 8 sites, 3 to 1000 ha); Sleepers River, Vermont, USA (5 solutes, 7 sites, 3 to 840 ha); and Maimai, South Island, New Zealand (4 solutes, 4 sites, 3 to 300 ha). Data from all sites were roughly planar with the first two eigenvectors explaining more than 90% of the variation. The RRMSEs for the reference site were generally between 5 and 10% with <0.1% RB. At Maimai, the RRMSE was roughly equivalent between the test sites and the 17-ha reference site, 5-8%; the RB was less than 4% at all sites. At Sleepers River, Ca and Mg had larger RRMSE at smaller basins relative to the 41 ha reference site; there was no consistent pattern to the RB for these solutes. Mg, Na, and SiO2 exhibited larger RRMSE (10-20%) and had substantial bias (10%, -20%, and 10%, respectively) at the 840-ha site compared with the 41-ha site. At

  15. Product Correlation When Original Variables Are Jointly Distributed Multivariate Normal: A Comparison with Sum Correlation

    ERIC Educational Resources Information Center

    Sockloff, Alan L.

    1977-01-01

    Product correlation was studied under the condition that the original variables are jointly distributed multivariate normal with equal coefficients of variation. Product correlation was shown to range between the two extremes of sum correlation and either the value zero or an unfamiliar function of the intercorrelations of the original variables.…

  16. Correcting for Visuo-Haptic Biases in 3D Haptic Guidance

    PubMed Central

    Kuling, Irene A.; Brenner, Eli; Bergmann Tiest, Wouter M.; Kappers, Astrid M. L.

    2016-01-01

    Visuo-haptic biases are observed when bringing your unseen hand to a visual target. The biases are different between, but consistent within participants. We investigated the usefulness of adjusting haptic guidance to these user-specific biases in aligning haptic and visual perception. By adjusting haptic guidance according to the biases, we aimed to reduce the conflict between the modalities. We first measured the biases using an adaptive procedure. Next, we measured performance in a pointing task using three conditions: 1) visual images that were adjusted to user-specific biases, without haptic guidance, 2) veridical visual images combined with haptic guidance, and 3) shifted visual images combined with haptic guidance. Adding haptic guidance increased precision. Combining haptic guidance with user-specific visual information yielded the highest accuracy and the lowest level of conflict with the guidance at the end point. These results show the potential of correcting for user-specific perceptual biases when designing haptic guidance. PMID:27438009

  17. Reducing Muslim/Arab stereotypes through evaluative conditioning.

    PubMed

    French, Andrea R; Franz, Timothy M; Phelan, Laura L; Blaine, Bruce E

    2013-01-01

    This study replicated and extended Olson and Fazio (2006) by testing whether evaluative conditioning is a means to reduce negative stereotypes about Muslim and other Arab persons. Specifically, evaluative conditioning was hypothesized to lower implicit biases against Muslim and Arab persons. The FreeIAT was used to measure implicit biases. Participants in the evaluative conditioning group showed a significant lowering in implicit biases. Explicit measures of bias were not affected by the conditioning procedure.

  18. Detection and Attribution of Simulated Climatic Extreme Events and Impacts: High Sensitivity to Bias Correction

    NASA Astrophysics Data System (ADS)

    Sippel, S.; Otto, F. E. L.; Forkel, M.; Allen, M. R.; Guillod, B. P.; Heimann, M.; Reichstein, M.; Seneviratne, S. I.; Kirsten, T.; Mahecha, M. D.

    2015-12-01

    Understanding, quantifying and attributing the impacts of climatic extreme events and variability is crucial for societal adaptation in a changing climate. However, climate model simulations generated for this purpose typically exhibit pronounced biases in their output that hinders any straightforward assessment of impacts. To overcome this issue, various bias correction strategies are routinely used to alleviate climate model deficiencies most of which have been criticized for physical inconsistency and the non-preservation of the multivariate correlation structure. We assess how biases and their correction affect the quantification and attribution of simulated extremes and variability in i) climatological variables and ii) impacts on ecosystem functioning as simulated by a terrestrial biosphere model. Our study demonstrates that assessments of simulated climatic extreme events and impacts in the terrestrial biosphere are highly sensitive to bias correction schemes with major implications for the detection and attribution of these events. We introduce a novel ensemble-based resampling scheme based on a large regional climate model ensemble generated by the distributed weather@home setup[1], which fully preserves the physical consistency and multivariate correlation structure of the model output. We use extreme value statistics to show that this procedure considerably improves the representation of climatic extremes and variability. Subsequently, biosphere-atmosphere carbon fluxes are simulated using a terrestrial ecosystem model (LPJ-GSI) to further demonstrate the sensitivity of ecosystem impacts to the methodology of bias correcting climate model output. We find that uncertainties arising from bias correction schemes are comparable in magnitude to model structural and parameter uncertainties. The present study consists of a first attempt to alleviate climate model biases in a physically consistent way and demonstrates that this yields improved simulations of

  19. Biased Brownian dynamics for rate constant calculation.

    PubMed

    Zou, G; Skeel, R D; Subramaniam, S

    2000-08-01

    An enhanced sampling method-biased Brownian dynamics-is developed for the calculation of diffusion-limited biomolecular association reaction rates with high energy or entropy barriers. Biased Brownian dynamics introduces a biasing force in addition to the electrostatic force between the reactants, and it associates a probability weight with each trajectory. A simulation loses weight when movement is along the biasing force and gains weight when movement is against the biasing force. The sampling of trajectories is then biased, but the sampling is unbiased when the trajectory outcomes are multiplied by their weights. With a suitable choice of the biasing force, more reacted trajectories are sampled. As a consequence, the variance of the estimate is reduced. In our test case, biased Brownian dynamics gives a sevenfold improvement in central processing unit (CPU) time with the choice of a simple centripetal biasing force.

  20. Biased Brownian dynamics for rate constant calculation.

    PubMed

    Zou, G; Skeel, R D; Subramaniam, S

    2000-08-01

    An enhanced sampling method-biased Brownian dynamics-is developed for the calculation of diffusion-limited biomolecular association reaction rates with high energy or entropy barriers. Biased Brownian dynamics introduces a biasing force in addition to the electrostatic force between the reactants, and it associates a probability weight with each trajectory. A simulation loses weight when movement is along the biasing force and gains weight when movement is against the biasing force. The sampling of trajectories is then biased, but the sampling is unbiased when the trajectory outcomes are multiplied by their weights. With a suitable choice of the biasing force, more reacted trajectories are sampled. As a consequence, the variance of the estimate is reduced. In our test case, biased Brownian dynamics gives a sevenfold improvement in central processing unit (CPU) time with the choice of a simple centripetal biasing force. PMID:10919998

  1. Unsupervised classification of multivariate geostatistical data: Two algorithms

    NASA Astrophysics Data System (ADS)

    Romary, Thomas; Ors, Fabien; Rivoirard, Jacques; Deraisme, Jacques

    2015-12-01

    With the increasing development of remote sensing platforms and the evolution of sampling facilities in mining and oil industry, spatial datasets are becoming increasingly large, inform a growing number of variables and cover wider and wider areas. Therefore, it is often necessary to split the domain of study to account for radically different behaviors of the natural phenomenon over the domain and to simplify the subsequent modeling step. The definition of these areas can be seen as a problem of unsupervised classification, or clustering, where we try to divide the domain into homogeneous domains with respect to the values taken by the variables in hand. The application of classical clustering methods, designed for independent observations, does not ensure the spatial coherence of the resulting classes. Image segmentation methods, based on e.g. Markov random fields, are not adapted to irregularly sampled data. Other existing approaches, based on mixtures of Gaussian random functions estimated via the expectation-maximization algorithm, are limited to reasonable sample sizes and a small number of variables. In this work, we propose two algorithms based on adaptations of classical algorithms to multivariate geostatistical data. Both algorithms are model free and can handle large volumes of multivariate, irregularly spaced data. The first one proceeds by agglomerative hierarchical clustering. The spatial coherence is ensured by a proximity condition imposed for two clusters to merge. This proximity condition relies on a graph organizing the data in the coordinates space. The hierarchical algorithm can then be seen as a graph-partitioning algorithm. Following this interpretation, a spatial version of the spectral clustering algorithm is also proposed. The performances of both algorithms are assessed on toy examples and a mining dataset.

  2. The nondiscriminating heart: lovingkindness meditation training decreases implicit intergroup bias.

    PubMed

    Kang, Yoona; Gray, Jeremy R; Dovidio, John F

    2014-06-01

    Although meditation is increasingly accepted as having personal benefits, less is known about the broader impact of meditation on social and intergroup relations. We tested the effect of lovingkindness meditation training on improving implicit attitudes toward members of 2 stigmatized social outgroups: Blacks and homeless people. Healthy non-Black, nonhomeless adults (N = 101) were randomly assigned to 1 of 3 conditions: 6-week lovingkindness practice, 6-week lovingkindness discussion (a closely matched active control), or waitlist control. Decreases in implicit bias against stigmatized outgroups (as measured by Implicit Association Test) were observed only in the lovingkindness practice condition. Reduced psychological stress mediated the effect of lovingkindness practice on implicit bias against homeless people, but it did not mediate the reduced bias against Black people. These results suggest that lovingkindness meditation can improve automatically activated, implicit attitudes toward stigmatized social groups and that this effect occurs through distinctive mechanisms for different stigmatized social groups.

  3. Mapping Species Distributions with MAXENT Using a Geographically Biased Sample of Presence Data: A Performance Assessment of Methods for Correcting Sampling Bias

    PubMed Central

    Fourcade, Yoan; Engler, Jan O.; Rödder, Dennis; Secondi, Jean

    2014-01-01

    MAXENT is now a common species distribution modeling (SDM) tool used by conservation practitioners for predicting the distribution of a species from a set of records and environmental predictors. However, datasets of species occurrence used to train the model are often biased in the geographical space because of unequal sampling effort across the study area. This bias may be a source of strong inaccuracy in the resulting model and could lead to incorrect predictions. Although a number of sampling bias correction methods have been proposed, there is no consensual guideline to account for it. We compared here the performance of five methods of bias correction on three datasets of species occurrence: one “virtual” derived from a land cover map, and two actual datasets for a turtle (Chrysemys picta) and a salamander (Plethodon cylindraceus). We subjected these datasets to four types of sampling biases corresponding to potential types of empirical biases. We applied five correction methods to the biased samples and compared the outputs of distribution models to unbiased datasets to assess the overall correction performance of each method. The results revealed that the ability of methods to correct the initial sampling bias varied greatly depending on bias type, bias intensity and species. However, the simple systematic sampling of records consistently ranked among the best performing across the range of conditions tested, whereas other methods performed more poorly in most cases. The strong effect of initial conditions on correction performance highlights the need for further research to develop a step-by-step guideline to account for sampling bias. However, this method seems to be the most efficient in correcting sampling bias and should be advised in most cases. PMID:24818607

  4. Multivariate analysis applied to tomato hybrid production.

    PubMed

    Balasch, S; Nuez, F; Palomares, G; Cuartero, J

    1984-11-01

    Twenty characters were measured on 60 tomato varieties cultivated in the open-air and in polyethylene plastic-house. Data were analyzed by means of principal components, factorial discriminant methods, Mahalanobis D(2) distances and principal coordinate techniques. Factorial discriminant and Mahalanobis D(2) distances methods, both of which require collecting data plant by plant, lead to similar conclusions as the principal components method that only requires taking data by plots. Characters that make up the principal components in both environments studied are the same, although the relative importance of each one of them varies within the principal components. By combining information supplied by multivariate analysis with the inheritance mode of characters, crossings among cultivars can be experimented with that will produce heterotic hybrids showing characters within previously established limits.

  5. Bayesian Local Contamination Models for Multivariate Outliers

    PubMed Central

    Page, Garritt L.; Dunson, David B.

    2013-01-01

    In studies where data are generated from multiple locations or sources it is common for there to exist observations that are quite unlike the majority. Motivated by the application of establishing a reference value in an inter-laboratory setting when outlying labs are present, we propose a local contamination model that is able to accommodate unusual multivariate realizations in a flexible way. The proposed method models the process level of a hierarchical model using a mixture with a parametric component and a possibly nonparametric contamination. Much of the flexibility in the methodology is achieved by allowing varying random subsets of the elements in the lab-specific mean vectors to be allocated to the contamination component. Computational methods are developed and the methodology is compared to three other possible approaches using a simulation study. We apply the proposed method to a NIST/NOAA sponsored inter-laboratory study which motivated the methodological development. PMID:24363465

  6. Response Surface Modeling Using Multivariate Orthogonal Functions

    NASA Technical Reports Server (NTRS)

    Morelli, Eugene A.; DeLoach, Richard

    2001-01-01

    A nonlinear modeling technique was used to characterize response surfaces for non-dimensional longitudinal aerodynamic force and moment coefficients, based on wind tunnel data from a commercial jet transport model. Data were collected using two experimental procedures - one based on modem design of experiments (MDOE), and one using a classical one factor at a time (OFAT) approach. The nonlinear modeling technique used multivariate orthogonal functions generated from the independent variable data as modeling functions in a least squares context to characterize the response surfaces. Model terms were selected automatically using a prediction error metric. Prediction error bounds computed from the modeling data alone were found to be- a good measure of actual prediction error for prediction points within the inference space. Root-mean-square model fit error and prediction error were less than 4 percent of the mean response value in all cases. Efficacy and prediction performance of the response surface models identified from both MDOE and OFAT experiments were investigated.

  7. Acute proliferative retrolental fibroplasia: multivariate risk analysis.

    PubMed Central

    Flynn, J T

    1983-01-01

    This study has presented a two-way analysis of a data set consisting of demographic, diagnostic, and therapeutic variables against the risk of occurrence of APRLF and its location in the retina in a population of 639 infants in birthweights ranging from 600 to 1500 gm. Univariate and multivariate risk analysis techniques were employed to analyze the data. As established from previous studies, birthweight was a powerful predictor of the outcome variable. Oxygen therapy as defined and quantified in this study was not. Duration of ventilatory assistance did seem associated. The population was not uniform. Infants below 1000 gm birthweight had such a high incidence of APRLF that no other exogenous risk factors seemed of significance. Above 1000 gm birthweight, certain factors, particularly duration of ventilation, seemed of predictive strength and significance. Images FIGURE 5 A FIGURE 5 B FIGURE 4 A FIGURE 4 B PMID:6689564

  8. Inferring phase equations from multivariate time series.

    PubMed

    Tokuda, Isao T; Jain, Swati; Kiss, István Z; Hudson, John L

    2007-08-10

    An approach is presented for extracting phase equations from multivariate time series data recorded from a network of weakly coupled limit cycle oscillators. Our aim is to estimate important properties of the phase equations including natural frequencies and interaction functions between the oscillators. Our approach requires the measurement of an experimental observable of the oscillators; in contrast with previous methods it does not require measurements in isolated single or two-oscillator setups. This noninvasive technique can be advantageous in biological systems, where extraction of few oscillators may be a difficult task. The method is most efficient when data are taken from the nonsynchronized regime. Applicability to experimental systems is demonstrated by using a network of electrochemical oscillators; the obtained phase model is utilized to predict the synchronization diagram of the system.

  9. Age differences in the correction processes of context-induced biases: when correction succeeds.

    PubMed

    Wang, Mo; Chen, Yiwei

    2004-09-01

    Previous research has demonstrated that older adults are more susceptible than young adults to context-induced biases in social judgments. The primary goal of this study was to examine the conditions under which older adults could or could not correct their biases. Young and older adults completed a social judgment task that normally would produce contrast biases in 3 correction cue conditions: no cue, subtle cue, and blatant cue. It was found that both young and older adults corrected their biases in the blatant cue condition, but only young adults corrected in the subtle cue condition. The results suggest that older adults may need more environmental support in correcting their biases. PMID:15383003

  10. Exploration of new multivariate spectral calibration algorithms.

    SciTech Connect

    Van Benthem, Mark Hilary; Haaland, David Michael; Melgaard, David Kennett; Martin, Laura Elizabeth; Wehlburg, Christine Marie; Pell, Randy J.; Guenard, Robert D.

    2004-03-01

    A variety of multivariate calibration algorithms for quantitative spectral analyses were investigated and compared, and new algorithms were developed in the course of this Laboratory Directed Research and Development project. We were able to demonstrate the ability of the hybrid classical least squares/partial least squares (CLSIPLS) calibration algorithms to maintain calibrations in the presence of spectrometer drift and to transfer calibrations between spectrometers from the same or different manufacturers. These methods were found to be as good or better in prediction ability as the commonly used partial least squares (PLS) method. We also present the theory for an entirely new class of algorithms labeled augmented classical least squares (ACLS) methods. New factor selection methods are developed and described for the ACLS algorithms. These factor selection methods are demonstrated using near-infrared spectra collected from a system of dilute aqueous solutions. The ACLS algorithm is also shown to provide improved ease of use and better prediction ability than PLS when transferring calibrations between near-infrared calibrations from the same manufacturer. Finally, simulations incorporating either ideal or realistic errors in the spectra were used to compare the prediction abilities of the new ACLS algorithm with that of PLS. We found that in the presence of realistic errors with non-uniform spectral error variance across spectral channels or with spectral errors correlated between frequency channels, ACLS methods generally out-performed the more commonly used PLS method. These results demonstrate the need for realistic error structure in simulations when the prediction abilities of various algorithms are compared. The combination of equal or superior prediction ability and the ease of use of the ACLS algorithms make the new ACLS methods the preferred algorithms to use for multivariate spectral calibrations.

  11. Sampling bias in an internet treatment trial for depression.

    PubMed

    Donkin, L; Hickie, I B; Christensen, H; Naismith, S L; Neal, B; Cockayne, N L; Glozier, N

    2012-10-23

    Internet psychological interventions are efficacious and may reduce traditional access barriers. No studies have evaluated whether any sampling bias exists in these trials that may limit the translation of the results of these trials into real-world application. We identified 7999 potentially eligible trial participants from a community-based health cohort study and invited them to participate in a randomized controlled trial of an online cognitive behavioural therapy programme for people with depression. We compared those who consented to being assessed for trial inclusion with nonconsenters on demographic, clinical and behavioural indicators captured in the health study. Any potentially biasing factors were then assessed for their association with depression outcome among trial participants to evaluate the existence of sampling bias. Of the 35 health survey variables explored, only 4 were independently associated with higher likelihood of consenting-female sex (odds ratio (OR) 1.11, 95% confidence interval (CI) 1.05-1.19), speaking English at home (OR 1.48, 95% CI 1.15-1.90) higher education (OR 1.67, 95% CI 1.46-1.92) and a prior diagnosis of depression (OR 1.37, 95% CI 1.22-1.55). The multivariate model accounted for limited variance (C-statistic 0.6) in explaining participation. These four factors were not significantly associated with either the primary trial outcome measure or any differential impact by intervention arm. This demonstrates that, among eligible trial participants, few factors were associated with the consent to participate. There was no indication that such self-selection biased the trial results or would limit the generalizability and translation into a public or clinical setting.

  12. Multivariate spatial models of excess crash frequency at area level: case of Costa Rica.

    PubMed

    Aguero-Valverde, Jonathan

    2013-10-01

    Recently, areal models of crash frequency have being used in the analysis of various area-wide factors affecting road crashes. On the other hand, disease mapping methods are commonly used in epidemiology to assess the relative risk of the population at different spatial units. A natural next step is to combine these two approaches to estimate the excess crash frequency at area level as a measure of absolute crash risk. Furthermore, multivariate spatial models of crash severity are explored in order to account for both frequency and severity of crashes and control for the spatial correlation frequently found in crash data. This paper aims to extent the concept of safety performance functions to be used in areal models of crash frequency. A multivariate spatial model is used for that purpose and compared to its univariate counterpart. Full Bayes hierarchical approach is used to estimate the models of crash frequency at canton level for Costa Rica. An intrinsic multivariate conditional autoregressive model is used for modeling spatial random effects. The results show that the multivariate spatial model performs better than its univariate counterpart in terms of the penalized goodness-of-fit measure Deviance Information Criteria. Additionally, the effects of the spatial smoothing due to the multivariate spatial random effects are evident in the estimation of excess equivalent property damage only crashes.

  13. Meta-regression approximations to reduce publication selection bias.

    PubMed

    Stanley, T D; Doucouliagos, Hristos

    2014-03-01

    Publication selection bias is a serious challenge to the integrity of all empirical sciences. We derive meta-regression approximations to reduce this bias. Our approach employs Taylor polynomial approximations to the conditional mean of a truncated distribution. A quadratic approximation without a linear term, precision-effect estimate with standard error (PEESE), is shown to have the smallest bias and mean squared error in most cases and to outperform conventional meta-analysis estimators, often by a great deal. Monte Carlo simulations also demonstrate how a new hybrid estimator that conditionally combines PEESE and the Egger regression intercept can provide a practical solution to publication selection bias. PEESE is easily expanded to accommodate systematic heterogeneity along with complex and differential publication selection bias that is related to moderator variables. By providing an intuitive reason for these approximations, we can also explain why the Egger regression works so well and when it does not. These meta-regression methods are applied to several policy-relevant areas of research including antidepressant effectiveness, the value of a statistical life, the minimum wage, and nicotine replacement therapy. PMID:26054026

  14. Variable-bias coin tossing

    NASA Astrophysics Data System (ADS)

    Colbeck, Roger; Kent, Adrian

    2006-03-01

    Alice is a charismatic quantum cryptographer who believes her parties are unmissable; Bob is a (relatively) glamorous string theorist who believes he is an indispensable guest. To prevent possibly traumatic collisions of self-perception and reality, their social code requires that decisions about invitation or acceptance be made via a cryptographically secure variable-bias coin toss (VBCT). This generates a shared random bit by the toss of a coin whose bias is secretly chosen, within a stipulated range, by one of the parties; the other party learns only the random bit. Thus one party can secretly influence the outcome, while both can save face by blaming any negative decisions on bad luck. We describe here some cryptographic VBCT protocols whose security is guaranteed by quantum theory and the impossibility of superluminal signaling, setting our results in the context of a general discussion of secure two-party computation. We also briefly discuss other cryptographic applications of VBCT.

  15. Belief bias and relational reasoning.

    PubMed

    Roberts, Maxwell J; Sykes, Elizabeth D A

    2003-01-01

    When people evaluate categorical syllogisms, they tend to reject unbelievable conclusions and accept believable ones irrespective of their validity. Typically, this effect is particularly marked for invalid conclusions that are possible, but do not necessarily follow, given the premises. However, smaller believability effects can also be detected for other types of conclusion. Three experiments are reported here, in which an attempt was made to determine whether belief bias effects can manifest themselves on the relational inference task. Subjects evaluated the validity of conclusions such as William the Conqueror was king after the Pyramids were built (temporal task) or Manchester is north of Bournemouth (spatial task) with respect to their premises. All of the major findings for equivalent categorical syllogism tasks were replicated. However, the overall size of the main effect of believability appears to be related to task presentation, a phenomenon not previously identified for categorical syllogisms and which current theories of belief bias have difficulty explaining.

  16. Casuistry and social category bias.

    PubMed

    Norton, Michael I; Vandello, Joseph A; Darley, John M

    2004-12-01

    This research explored cases where people are drawn to make judgments between individuals based on questionable criteria, in particular those individuals' social group memberships. We suggest that individuals engage in casuistry to mask biased decision making, by recruiting more acceptable criteria to justify such decisions. We present 6 studies that demonstrate how casuistry licenses people to judge on the basis of social category information but appear unbiased--to both others and themselves--while doing so. In 2 domains (employment and college admissions decisions), with 2 social categories (gender and race), and with 2 motivations (favoring an in-group or out-group), the present studies explored how participants justify decisions biased by social category information by arbitrarily inflating the relative value of their preferred candidates' qualifications over those of competitors.

  17. Girl child and gender bias.

    PubMed

    Chowdhry, D P

    1995-01-01

    This article identifies gender bias against female children and youth in India. Gender bias is based on centuries-old religious beliefs and sayings from ancient times. Discrimination is reflected in denial or ignorance of female children's educational, health, nutrition, and recreational needs. Female infanticide and selective abortion of female fetuses are other forms of discrimination. The task of eliminating or reducing gender bias will involve legal, developmental, political, and administrative measures. Public awareness needs to be created. There is a need to reorient the education and health systems and to advocate for gender equality. The government of India set the following goals for the 1990s: to protect the survival of the girl child and practice safe motherhood; to develop the girl child in general; and to protect vulnerable girl children in different circumstances and in special groups. The Health Authorities should monitor the laws carefully to assure marriage after the minimum age, ban sex determination of the fetus, and monitor the health and nutrition of pre-school girls and nursing and pregnant mothers. Mothers need to be encouraged to breast feed, and to breast feed equally between genders. Every village and slum area needs a mini health center. Maternal mortality must decline. Primary health centers and hospitals need more women's wards. Education must be universally accessible. Enrollments should be increased by educating rural tribal and slum parents, reducing distances between home and school, making curriculum more relevant to girls, creating more female teachers, and providing facilities and incentives for meeting the needs of girl students. Supplementary income could be provided to families for sending girls to school. Recreational activities must be free of gender bias. Dowry, sati, and devdasi systems should be banned.

  18. Self regulating body bias generator

    NASA Technical Reports Server (NTRS)

    Hass, Kenneth (Inventor)

    2004-01-01

    The back bias voltage on a functional circuit is controlled through a closed loop process. A delay element receives a clock pulse and produces a delay output. The delay element is advantageously constructed of the same materials as the functional circuit so that the aging and degradation of the delay element parallels the degradation of the functional circuit. As the delay element degrades, the transistor switching time increases, increasing the time delay of the delay output. An AND gate compares a clock pulse to an output pulse of the delay element, the AND output forming a control pulse. A duty cycle of the control pulse is determined by the delay time between the clock pulse and the delay element output. The control pulse is received at the input of a charge pump. The charge pump produces a back bias voltage which is then applied to the delay element and to the functional circuit. If the time delay produced by the delay element exceeds the optimal delay, the duty cycle of the control pulse is shortened, and the back bias voltage is lowered, thereby increasing the switching speed of the transistors in the delay element and reducing the time delay. If the throughput of the delay element is too fast, the duty cycle of the control pulse is lengthened, raising the back bias voltage produced by the charge pump. This, in turn, lowers the switching speed of the transistors in both the delay element and the functional circuit. The slower switching speed in the delay element increases time delay. In this manner, the switching speed of the delay element, and of the functional circuit, is maintained at a constant level over the life of the circuit.

  19. Arctic Clouds and Sea Ice Inhomogeneities and Plane-parallel Biases

    NASA Astrophysics Data System (ADS)

    Rozwadowska, A.; Cahalan, R. F.

    Monte Carlo simulations of the expected influence of non-uniformity in cloud struc- ture and surface albedo on shortwave radiative fluxes in the Arctic atmosphere are presented. In particular, plane-parallel biases in cloud albedo and transmittance are studied for non-absorbing low-level all-liquid stratus clouds over sea ice. The "abso- lute bias" is defined as the difference between the cloud albedo or transmittance for the uniform or plane-parallel case, and the albedo or transmittance for nonuniform conditions with the same mean cloud optical thickness and the same mean surface albedo, averaged over a given area (i.e. bias > 0 means plane-parallel overestimates). Ranges of means and standard deviations of input parameters typical of Arctic con- ditions are determined from the FIRE-ACE/SHEBA/ARM experiment. We determine the sensitivity of the bias with respect to the following: domain averaged means and spatial variances of cloud optical thickness and surface albedo, shape of the surface reflectance function, presence of a scattering layer under the clouds, and solar zenith angle. The simulations show that the biases in Arctic conditions are generally lower than in subtropical stratocumulus. The magnitudes of the absolute biases are unlikely to exceed 0.02 for albedo and 0.05 for transmittance. The "relative bias" expresses the absolute bias as a percentage of the actual cloud albedo or transmittance. The mag- nitude of the relative bias in albedo is typically below 2% over the reflective Arctic surface, while the magnitude of the relative bias in transmittance can exceed 10% . Over ice free ocean, it is well known that the albedo bias is strictly positive but in the Arctic it can change sign when the surface bias contribution dominates over the cloud contribution. On the other hand, the transmittance bias remains strictly negative in the Arctic, regardless of surface conditions. The influence of cloud variability on the bi- ases strongly decreases with an

  20. SEASAT altimeter timing bias estimation

    NASA Astrophysics Data System (ADS)

    Marsh, J. G.; Williamson, R. G.

    1982-04-01

    The calibration of the altimeter observation time tags to the millisecond level of accuracy is fundamental to the processing of the data. Initial analyses of the SEASAT altimeter data indicated the presence of a time calibration bias which produced altimeter measurement errors in excess of a meter. A technique has been developed for the solution of the time tag bias based upon the analysis of sea surface height discrepancies at ground track intersections. This technique has permitted very good separation of the dominant once per revolution ephemeris error, which amounts to about 1.5 m rms, from the timing error signature. Furthermore, the technique does not depend upon the availability of precise geoid data. The application of this technique to a global set of SEASAT altimeter data covering the time period of July 28-August 9, 1978, has resulted in a value of -81.0±2 ms for the time tag bias. This value agrees to within 2.9 ms of the value derived at the University of Texas from a similar analysis of the altimeter data. Furthermore, these values corroborate the revised value of -79.4 ms derived at NASA/Wallops Flight Center and the Johns Hopkins University/Applied Physics Lab from a reexamination of the internal instrument time delays. The modeling of oceanic tides and the orbit computations are the major error sources in these analyses.

  1. Generalization of the FRAM's Bias

    SciTech Connect

    Duc T. Vo

    2005-10-01

    The Fixed-Energy Response-Function Analysis with Multiple Efficiency (FRAM) code was developed at Los Alamos National Laboratory to measure the gamma-ray spectrometry of the isotopic composition of plutonium, uranium, and other actinides. Its reported uncertainties of the results come from the propagation of the statistics in the peak areas only. No systematic error components are included in the reported uncertainties. We have done several studies and found that the FRAM's statistical precision can be reasonably represented by its reported uncertainties. The FRAM's biases or systematic uncertainties can come from a variety of sources and can be difficult to determine. We carefully examined the FRAM analytical results of the archival plutonium data and of the data specifically acquired for this isotopic uncertainty analysis project and found the relationship between the bias and other parameters. We worked out the equations representing the biases of the measured isotopes from each measurement using the internal parameters in the spectrum such as peak resolution and shape, region of analysis, and burnup (for plutonium) or enrichment (for uranium).

  2. Response bias in plaintiffs' histories.

    PubMed

    Lees-Haley, P R; Williams, C W; Zasler, N D; Marguilies, S; English, L T; Stevens, K B

    1997-11-01

    This study investigated response bias in self-reported history of factors relevant to the assessment of traumatic brain injury, toxic brain injury and related emotional distress. Response bias refers to systematic error in self-report data. A total of 446 subjects (comprising 131 litigating and 315 non-litigating adults from five locations in the United States) completed a symptom questionnaire. Data were obtained from university faculty and students, from patients in clinics specializing in physiatry neurology, and family medicine, and from plaintiffs undergoing forensic neuropsychological evaluations. Comparisons were made for litigant and non litigant ratings of their past and current cognitive and emotional functioning, including life in general, ability to concentrate, memory, depression, anxiety, alcohol, drugs, ability to work or attend school, irritability, headaches, confusion, self-esteem, and fatigue. Although there is no basis for hypothesizing plaintiffs to be healthier than the general population, plaintiffs rated their pre-injury functioning superior to non-plaintiffs. These findings suggest that response biases need to be taken into account by forensic examiners when relying on litigants' self-reports of pre-injury status.

  3. The Existence of Implicit Racial Bias in Nursing Faculty

    ERIC Educational Resources Information Center

    Fitzsimmons, Kathleen A.

    2009-01-01

    This study examined the existence of implicit racial bias in nursing faculty using the Implicit Association Test (IAT). It was conducted within a critical race theory framework where race was seen as a permanent, pervasive, and systemic condition, not an individual process. The study was fueled by data showing continued disparate academic and…

  4. Categorical Biases in Spatial Memory: The Role of Certainty

    ERIC Educational Resources Information Center

    Holden, Mark P.; Newcombe, Nora S.; Shipley, Thomas F.

    2015-01-01

    Memories for spatial locations often show systematic errors toward the central value of the surrounding region. The Category Adjustment (CA) model suggests that this bias is due to a Bayesian combination of categorical and metric information, which offers an optimal solution under conditions of uncertainty (Huttenlocher, Hedges, & Duncan,…

  5. Water resources climate change projections using supervised nonlinear and multivariate soft computing techniques

    NASA Astrophysics Data System (ADS)

    Sarhadi, Ali; Burn, Donald H.; Johnson, Fiona; Mehrotra, Raj; Sharma, Ashish

    2016-05-01

    Accurate projection of global warming on the probabilistic behavior of hydro-climate variables is one of the main challenges in climate change impact assessment studies. Due to the complexity of climate-associated processes, different sources of uncertainty influence the projected behavior of hydro-climate variables in regression-based statistical downscaling procedures. The current study presents a comprehensive methodology to improve the predictive power of the procedure to provide improved projections. It does this by minimizing the uncertainty sources arising from the high-dimensionality of atmospheric predictors, the complex and nonlinear relationships between hydro-climate predictands and atmospheric predictors, as well as the biases that exist in climate model simulations. To address the impact of the high dimensional feature spaces, a supervised nonlinear dimensionality reduction algorithm is presented that is able to capture the nonlinear variability among projectors through extracting a sequence of principal components that have maximal dependency with the target hydro-climate variables. Two soft-computing nonlinear machine-learning methods, Support Vector Regression (SVR) and Relevance Vector Machine (RVM), are engaged to capture the nonlinear relationships between predictand and atmospheric predictors. To correct the spatial and temporal biases over multiple time scales in the GCM predictands, the Multivariate Recursive Nesting Bias Correction (MRNBC) approach is used. The results demonstrate that this combined approach significantly improves the downscaling procedure in terms of precipitation projection.

  6. A multivariate joint hydrological drought indicator using vine copula

    NASA Astrophysics Data System (ADS)

    Liu, Zhiyong; Menzel, Lucas

    2016-04-01

    We present a multivariate joint hydrological drought indicator using the high-dimensional vine copula. This hydrological indicator is based on the concept of the standardized index (SI) (the version of this algorithm for streamflow is called the standardized streamflow index, simply the SSI). Unlike the single SSI n-month scales (e.g., SSI 1-month or 6-month), this indicator is done without focusing on a certain time window. This means that all different time windows from 1- to 12-months (i.e., the SSI-1 month, SSI 2-month, ..., SSI 12-month) are considered together when developing this hydrological drought indicator. Therefore, in this study, a 12-dimensional joint function is modeled to join the multivariate margins (the distribution functions of the SSI-1 month, SSI 2-month, ..., SSI 12-month) for all time windows based on the copula algorithm. We then used the C-vine copulas to construct the joint dependence of the multivariate margins with window sizes from 1-month to 12-months. To construct the C-vine copula, five bivariate copulas (i.e., Gaussian, Clayton, Frank, Gumbel, and Joe copulas) were considered as the potential pair-copulas (building blocks). Based on well-fitted marginal distributions, a 12-d C-vine copula was used to join the margins, model the joint dependence structure and generate this 12-variate hydrological indicator (named joint streamflow drought indicator, simply JSDI). We tested the performance of this indicator using two hydrological stations in Germany. The results indicate that the JSDI generally combines the strengths of the short-term drought index in capturing the drought onset and medium-term drought index in reflecting the drought duration or persistence. Therefore, it provides a more comprehensive assessment of drought and could be more competitive than other traditional hydrological drought indices (e.g., the SSI). This attractive feature is attributed to the fact that the JSDI describes the overall drought conditions based on

  7. Electronic properties of a biased graphene bilayer.

    PubMed

    Castro, Eduardo V; Novoselov, K S; Morozov, S V; Peres, N M R; Lopes dos Santos, J M B; Nilsson, Johan; Guinea, F; Geim, A K; Castro Neto, A H

    2010-05-01

    We study, within the tight-binding approximation, the electronic properties of a graphene bilayer in the presence of an external electric field applied perpendicular to the system-a biased bilayer. The effect of the perpendicular electric field is included through a parallel plate capacitor model, with screening correction at the Hartree level. The full tight-binding description is compared with its four-band and two-band continuum approximations, and the four-band model is shown to always be a suitable approximation for the conditions realized in experiments. The model is applied to real biased bilayer devices, made out of either SiC or exfoliated graphene, and good agreement with experimental results is found, indicating that the model is capturing the key ingredients, and that a finite gap is effectively being controlled externally. Analysis of experimental results regarding the electrical noise and cyclotron resonance further suggests that the model can be seen as a good starting point for understanding the electronic properties of graphene bilayer. Also, we study the effect of electron-hole asymmetry terms, such as the second-nearest-neighbour hopping energies t' (in-plane) and γ(4) (inter-layer), and the on-site energy Δ.

  8. Bayesian ROC curve estimation under verification bias.

    PubMed

    Gu, Jiezhun; Ghosal, Subhashis; Kleiner, David E

    2014-12-20

    Receiver operating characteristic (ROC) curve has been widely used in medical science for its ability to measure the accuracy of diagnostic tests under the gold standard. However, in a complicated medical practice, a gold standard test can be invasive, expensive, and its result may not always be available for all the subjects under study. Thus, a gold standard test is implemented only when it is necessary and possible. This leads to the so-called 'verification bias', meaning that subjects with verified disease status (also called label) are not selected in a completely random fashion. In this paper, we propose a new Bayesian approach for estimating an ROC curve based on continuous data following the popular semiparametric binormal model in the presence of verification bias. By using a rank-based likelihood, and following Gibbs sampling techniques, we compute the posterior distribution of the binormal parameters intercept and slope, as well as the area under the curve by imputing the missing labels within Markov Chain Monte-Carlo iterations. Consistency of the resulting posterior under mild conditions is also established. We compare the new method with other comparable methods and conclude that our estimator performs well in terms of accuracy. PMID:25269427

  9. The Probability Distribution for a Biased Spinner

    ERIC Educational Resources Information Center

    Foster, Colin

    2012-01-01

    This article advocates biased spinners as an engaging context for statistics students. Calculating the probability of a biased spinner landing on a particular side makes valuable connections between probability and other areas of mathematics. (Contains 2 figures and 1 table.)

  10. Estimation of attitude sensor timetag biases

    NASA Technical Reports Server (NTRS)

    Sedlak, J.

    1995-01-01

    This paper presents an extended Kalman filter for estimating attitude sensor timing errors. Spacecraft attitude is determined by finding the mean rotation from a set of reference vectors in inertial space to the corresponding observed vectors in the body frame. Any timing errors in the observations can lead to attitude errors if either the spacecraft is rotating or the reference vectors themselves vary with time. The state vector here consists of the attitude quaternion, timetag biases, and, optionally, gyro drift rate biases. The filter models the timetags as random walk processes: their expectation values propagate as constants and white noise contributes to their covariance. Thus, this filter is applicable to cases where the true timing errors are constant or slowly varying. The observability of the state vector is studied first through an examination of the algebraic observability condition and then through several examples with simulated star tracker timing errors. The examples use both simulated and actual flight data from the Extreme Ultraviolet Explorer (EUVE). The flight data come from times when EUVE had a constant rotation rate, while the simulated data feature large angle attitude maneuvers. The tests include cases with timetag errors on one or two sensors, both constant and time-varying, and with and without gyro bias errors. Due to EUVE's sensor geometry, the observability of the state vector is severely limited when the spacecraft rotation rate is constant. In the absence of attitude maneuvers, the state elements are highly correlated, and the state estimate is unreliable. The estimates are particularly sensitive to filter mistuning in this case. The EUVE geometry, though, is a degenerate case having coplanar sensors and rotation vector. Observability is much improved and the filter performs well when the rate is either varying or noncoplanar with the sensors, as during a slew. Even with bad geometry and constant rates, if gyro biases are

  11. The Nonverbal Transmission of Intergroup Bias: A Model of Bias Contagion with Implications for Social Policy

    PubMed Central

    Weisbuch, Max; Pauker, Kristin

    2013-01-01

    Social and policy interventions over the last half-century have achieved laudable reductions in blatant discrimination. Yet members of devalued social groups continue to face subtle discrimination. In this article, we argue that decades of anti-discrimination interventions have failed to eliminate intergroup bias because such bias is contagious. We present a model of bias contagion in which intergroup bias is subtly communicated through nonverbal behavior. Exposure to such nonverbal bias “infects” observers with intergroup bias. The model we present details two means by which nonverbal bias can be expressed—either as a veridical index of intergroup bias or as a symptom of worry about appearing biased. Exposure to this nonverbal bias can increase perceivers’ own intergroup biases through processes of implicit learning, informational influence, and normative influence. We identify critical moderators that may interfere with these processes and consequently propose several social and educational interventions based on these moderators. PMID:23997812

  12. CONDITIONAL DISTANCE CORRELATION

    PubMed Central

    Wang, Xueqin; Pan, Wenliang; Hu, Wenhao; Tian, Yuan; Zhang, Heping

    2015-01-01

    Statistical inference on conditional dependence is essential in many fields including genetic association studies and graphical models. The classic measures focus on linear conditional correlations, and are incapable of characterizing non-linear conditional relationship including non-monotonic relationship. To overcome this limitation, we introduces a nonparametric measure of conditional dependence for multivariate random variables with arbitrary dimensions. Our measure possesses the necessary and intuitive properties as a correlation index. Briefly, it is zero almost surely if and only if two multivariate random variables are conditionally independent given a third random variable. More importantly, the sample version of this measure can be expressed elegantly as the root of a V or U-process with random kernels and has desirable theoretical properties. Based on the sample version, we propose a test for conditional independence, which is proven to be more powerful than some recently developed tests through our numerical simulations. The advantage of our test is even greater when the relationship between the multivariate random variables given the third random variable cannot be expressed in a linear or monotonic function of one random variable versus the other. We also show that the sample measure is consistent and weakly convergent, and the test statistic is asymptotically normal. By applying our test in a real data analysis, we are able to identify two conditionally associated gene expressions, which otherwise cannot be revealed. Thus, our measure of conditional dependence is not only an ideal concept, but also has important practical utility. PMID:26877569

  13. Examining Event-Related Potential (ERP) Correlates of Decision Bias in Recognition Memory Judgments

    PubMed Central

    Hill, Holger; Windmann, Sabine

    2014-01-01

    Memory judgments can be based on accurate memory information or on decision bias (the tendency to report that an event is part of episodic memory when one is in fact unsure). Event related potentials (ERP) correlates are important research tools for elucidating the dynamics underlying memory judgments but so far have been established only for investigations of accurate old/new discrimination. To identify the ERP correlates of bias, and observe how these interact with ERP correlates of memory, we conducted three experiments that manipulated decision bias within participants via instructions during recognition memory tests while their ERPs were recorded. In Experiment 1, the bias manipulation was performed between blocks of trials (automatized bias) and compared to trial-by-trial shifts of bias in accord with an external cue (flexibly controlled bias). In Experiment 2, the bias manipulation was performed at two different levels of accurate old/new discrimination as the memory strength of old (studied) items was varied. In Experiment 3, the bias manipulation was added to another, bottom-up driven manipulation of bias induced via familiarity. In the first two Experiments, and in the low familiarity condition of Experiment 3, we found evidence of an early frontocentral ERP component at 320 ms poststimulus (the FN320) that was sensitive to the manipulation of bias via instruction, with more negative amplitudes indexing more liberal bias. By contrast, later during the trial (500–700 ms poststimulus), bias effects interacted with old/new effects across all three experiments. Results suggest that the decision criterion is typically activated early during recognition memory trials, and is integrated with retrieved memory signals and task-specific processing demands later during the trial. More generally, the findings demonstrate how ERPs can help to specify the dynamics of recognition memory processes under top-down and bottom-up controlled retrieval conditions. PMID

  14. Skin Temperature Analysis and Bias Correction in a Coupled Land-Atmosphere Data Assimilation System

    NASA Technical Reports Server (NTRS)

    Bosilovich, Michael G.; Radakovich, Jon D.; daSilva, Arlindo; Todling, Ricardo; Verter, Frances

    2006-01-01

    In an initial investigation, remotely sensed surface temperature is assimilated into a coupled atmosphere/land global data assimilation system, with explicit accounting for biases in the model state. In this scheme, an incremental bias correction term is introduced in the model's surface energy budget. In its simplest form, the algorithm estimates and corrects a constant time mean bias for each gridpoint; additional benefits are attained with a refined version of the algorithm which allows for a correction of the mean diurnal cycle. The method is validated against the assimilated observations, as well as independent near-surface air temperature observations. In many regions, not accounting for the diurnal cycle of bias caused degradation of the diurnal amplitude of background model air temperature. Energy fluxes collected through the Coordinated Enhanced Observing Period (CEOP) are used to more closely inspect the surface energy budget. In general, sensible heat flux is improved with the surface temperature assimilation, and two stations show a reduction of bias by as much as 30 Wm(sup -2) Rondonia station in Amazonia, the Bowen ratio changes direction in an improvement related to the temperature assimilation. However, at many stations the monthly latent heat flux bias is slightly increased. These results show the impact of univariate assimilation of surface temperature observations on the surface energy budget, and suggest the need for multivariate land data assimilation. The results also show the need for independent validation data, especially flux stations in varied climate regimes.

  15. A Multivariate Analysis of Extratropical Cyclone Environmental Sensitivity

    NASA Astrophysics Data System (ADS)

    Tierney, G.; Posselt, D. J.; Booth, J. F.

    2015-12-01

    The implications of a changing climate system include more than a simple temperature increase. A changing climate also modifies atmospheric conditions responsible for shaping the genesis and evolution of atmospheric circulations. In the mid-latitudes, the effects of climate change on extratropical cyclones (ETCs) can be expressed through changes in bulk temperature, horizontal and vertical temperature gradients (leading to changes in mean state winds) as well as atmospheric moisture content. Understanding how these changes impact ETC evolution and dynamics will help to inform climate mitigation and adaptation strategies, and allow for better informed weather emergency planning. However, our understanding is complicated by the complex interplay between a variety of environmental influences, and their potentially opposing effects on extratropical cyclone strength. Attempting to untangle competing influences from a theoretical or observational standpoint is complicated by nonlinear responses to environmental perturbations and a lack of data. As such, numerical models can serve as a useful tool for examining this complex issue. We present results from an analysis framework that combines the computational power of idealized modeling with the statistical robustness of multivariate sensitivity analysis. We first establish control variables, such as baroclinicity, bulk temperature, and moisture content, and specify a range of values that simulate possible changes in a future climate. The Weather Research and Forecasting (WRF) model serves as the link between changes in climate state and ETC relevant outcomes. A diverse set of output metrics (e.g., sea level pressure, average precipitation rates, eddy kinetic energy, and latent heat release) facilitates examination of storm dynamics, thermodynamic properties, and hydrologic cycles. Exploration of the multivariate sensitivity of ETCs to changes in control parameters space is performed via an ensemble of WRF runs coupled with

  16. Multivariate mixtures of Erlangs for density estimation under censoring.

    PubMed

    Verbelen, Roel; Antonio, Katrien; Claeskens, Gerda

    2016-07-01

    Multivariate mixtures of Erlang distributions form a versatile, yet analytically tractable, class of distributions making them suitable for multivariate density estimation. We present a flexible and effective fitting procedure for multivariate mixtures of Erlangs, which iteratively uses the EM algorithm, by introducing a computationally efficient initialization and adjustment strategy for the shape parameter vectors. We furthermore extend the EM algorithm for multivariate mixtures of Erlangs to be able to deal with randomly censored and fixed truncated data. The effectiveness of the proposed algorithm is demonstrated on simulated as well as real data sets.

  17. Gender Bias: Recent Research and Interventions.

    ERIC Educational Resources Information Center

    New Jersey Research Bulletin, 1996

    1996-01-01

    This annotated bibliography lists 14 publications about recent research on gender bias and interventions to reduce gender bias in schools. The bibliography is divided into two sections: current research and intervention. The first includes descriptions of studies examining the following topics: gender bias in U.S. schools and its effects;…

  18. Outcome-Reporting Bias in Education Research

    ERIC Educational Resources Information Center

    Pigott, Therese D.; Valentine, Jeffrey C.; Polanin, Joshua R.; Williams, Ryan T.; Canada, Dericka D.

    2013-01-01

    Outcome-reporting bias occurs when primary studies do not include information about all outcomes measured in a study. When studies omit findings on important measures, efforts to synthesize the research using systematic review techniques will be biased and interpretations of individual studies will be incomplete. Outcome-reporting bias has been…

  19. Attentional Bias for Exercise-Related Images

    ERIC Educational Resources Information Center

    Berry, Tanya R.; Spence, John C.; Stolp, Sean M.

    2011-01-01

    This research examined attentional bias toward exercise-related images using a visual probe task. It was hypothesized that more-active participants would display attentional bias toward the exercise-related images. The results showed that men displayed attentional bias for the exercise images. There was a significant interaction of activity level…

  20. Using Newspapers to Study Media Bias.

    ERIC Educational Resources Information Center

    Kirman, Joseph M.

    1992-01-01

    Suggests that students can learn to recognize media bias by studying media reports of current events or historical topics. Describes a study unit using media coverage of the second anniversary of the Palestinian uprising against Israel. Discusses lesson objectives, planning, defining bias teaching procedures, and criteria for determining bias. (DK)

  1. Culturally Biased Assumptions in Counseling Psychology

    ERIC Educational Resources Information Center

    Pedersen, Paul B.

    2003-01-01

    Eight clusters of culturally biased assumptions are identified for further discussion from Leong and Ponterotto's (2003) article. The presence of cultural bias demonstrates that cultural bias is so robust and pervasive that is permeates the profession of counseling psychology, even including those articles that effectively attack cultural bias…

  2. Hybrid nonlinearity supported by nonconventionally biased photorefractive crystals

    NASA Astrophysics Data System (ADS)

    Zhang, P.; Liu, S.; Lou, C.; Gao, Y.; Zhao, J.; Xu, J.; Chen, Z.

    2009-06-01

    We theoretically and experimentally demonstrate that a nonconventionally biased photorefractive crystal can support hybrid nonlinearity, i.e., coexistence of self-focusing and self-defocusing nonlinearities under an identical bias condition. It is revealed that the nonlinearity experienced by a one-dimensional (stripe) beam can be switched between self-focusing and self-defocusing solely by changing the beam orientation. For a two-dimensional beam, the hybrid nonlinearity leads to unusual nonlinear beam dynamics with enhanced anisotropy and nonlocality.

  3. Flexible Linked Axes for multivariate data visualization.

    PubMed

    Claessen, Jarry H T; van Wijk, Jarke J

    2011-12-01

    Multivariate data visualization is a classic topic, for which many solutions have been proposed, each with its own strengths and weaknesses. In standard solutions the structure of the visualization is fixed, we explore how to give the user more freedom to define visualizations. Our new approach is based on the usage of Flexible Linked Axes: The user is enabled to define a visualization by drawing and linking axes on a canvas. Each axis has an associated attribute and range, which can be adapted. Links between pairs of axes are used to show data in either scatter plot- or Parallel Coordinates Plot-style. Flexible Linked Axes enable users to define a wide variety of different visualizations. These include standard methods, such as scatter plot matrices, radar charts, and PCPs [11]; less well known approaches, such as Hyperboxes [1], TimeWheels [17], and many-to-many relational parallel coordinate displays [14]; and also custom visualizations, consisting of combinations of scatter plots and PCPs. Furthermore, our method allows users to define composite visualizations that automatically support brushing and linking. We have discussed our approach with ten prospective users, who found the concept easy to understand and highly promising.

  4. A Gibbs sampler for multivariate linear regression

    NASA Astrophysics Data System (ADS)

    Mantz, Adam B.

    2016-04-01

    Kelly described an efficient algorithm, using Gibbs sampling, for performing linear regression in the fairly general case where non-zero measurement errors exist for both the covariates and response variables, where these measurements may be correlated (for the same data point), where the response variable is affected by intrinsic scatter in addition to measurement error, and where the prior distribution of covariates is modelled by a flexible mixture of Gaussians rather than assumed to be uniform. Here, I extend the Kelly algorithm in two ways. First, the procedure is generalized to the case of multiple response variables. Secondly, I describe how to model the prior distribution of covariates using a Dirichlet process, which can be thought of as a Gaussian mixture where the number of mixture components is learned from the data. I present an example of multivariate regression using the extended algorithm, namely fitting scaling relations of the gas mass, temperature, and luminosity of dynamically relaxed galaxy clusters as a function of their mass and redshift. An implementation of the Gibbs sampler in the R language, called LRGS, is provided.

  5. Composite density maps for multivariate trajectories.

    PubMed

    Scheepens, Roeland; Willems, Niels; van de Wetering, Huub; Andrienko, Gennady; Andrienko, Natalia; van Wijk, Jarke J

    2011-12-01

    We consider moving objects as multivariate time-series. By visually analyzing the attributes, patterns may appear that explain why certain movements have occurred. Density maps as proposed by Scheepens et al. [25] are a way to reveal these patterns by means of aggregations of filtered subsets of trajectories. Since filtering is often not sufficient for analysts to express their domain knowledge, we propose to use expressions instead. We present a flexible architecture for density maps to enable custom, versatile exploration using multiple density fields. The flexibility comes from a script, depicted in this paper as a block diagram, which defines an advanced computation of a density field. We define six different types of blocks to create, compose, and enhance trajectories or density fields. Blocks are customized by means of expressions that allow the analyst to model domain knowledge. The versatility of our architecture is demonstrated with several maritime use cases developed with domain experts. Our approach is expected to be useful for the analysis of objects in other domains. PMID:22034373

  6. Apparatus and system for multivariate spectral analysis

    DOEpatents

    Keenan, Michael R.; Kotula, Paul G.

    2003-06-24

    An apparatus and system for determining the properties of a sample from measured spectral data collected from the sample by performing a method of multivariate spectral analysis. The method can include: generating a two-dimensional matrix A containing measured spectral data; providing a weighted spectral data matrix D by performing a weighting operation on matrix A; factoring D into the product of two matrices, C and S.sup.T, by performing a constrained alternating least-squares analysis of D=CS.sup.T, where C is a concentration intensity matrix and S is a spectral shapes matrix; unweighting C and S by applying the inverse of the weighting used previously; and determining the properties of the sample by inspecting C and S. This method can be used by a spectrum analyzer to process X-ray spectral data generated by a spectral analysis system that can include a Scanning Electron Microscope (SEM) with an Energy Dispersive Detector and Pulse Height Analyzer.

  7. Multivariate volume visualization through dynamic projections

    SciTech Connect

    Liu, Shusen; Wang, Bei; Thiagarajan, Jayaraman J.; Bremer, Peer -Timo; Pascucci, Valerio

    2014-11-01

    We propose a multivariate volume visualization framework that tightly couples dynamic projections with a high-dimensional transfer function design for interactive volume visualization. We assume that the complex, high-dimensional data in the attribute space can be well-represented through a collection of low-dimensional linear subspaces, and embed the data points in a variety of 2D views created as projections onto these subspaces. Through dynamic projections, we present animated transitions between different views to help the user navigate and explore the attribute space for effective transfer function design. Our framework not only provides a more intuitive understanding of the attribute space but also allows the design of the transfer function under multiple dynamic views, which is more flexible than being restricted to a single static view of the data. For large volumetric datasets, we maintain interactivity during the transfer function design via intelligent sampling and scalable clustering. As a result, using examples in combustion and climate simulations, we demonstrate how our framework can be used to visualize interesting structures in the volumetric space.

  8. Multivariate sensitivity to voice during auditory categorization

    PubMed Central

    Peelle, Jonathan E.; Kraemer, David; Lloyd, Samuel; Granger, Richard

    2015-01-01

    Past neuroimaging studies have documented discrete regions of human temporal cortex that are more strongly activated by conspecific voice sounds than by nonvoice sounds. However, the mechanisms underlying this voice sensitivity remain unclear. In the present functional MRI study, we took a novel approach to examining voice sensitivity, in which we applied a signal detection paradigm to the assessment of multivariate pattern classification among several living and nonliving categories of auditory stimuli. Within this framework, voice sensitivity can be interpreted as a distinct neural representation of brain activity that correctly distinguishes human vocalizations from other auditory object categories. Across a series of auditory categorization tests, we found that bilateral superior and middle temporal cortex consistently exhibited robust sensitivity to human vocal sounds. Although the strongest categorization was in distinguishing human voice from other categories, subsets of these regions were also able to distinguish reliably between nonhuman categories, suggesting a general role in auditory object categorization. Our findings complement the current evidence of cortical sensitivity to human vocal sounds by revealing that the greatest sensitivity during categorization tasks is devoted to distinguishing voice from nonvoice categories within human temporal cortex. PMID:26245316

  9. Multivariate sensitivity to voice during auditory categorization.

    PubMed

    Lee, Yune Sang; Peelle, Jonathan E; Kraemer, David; Lloyd, Samuel; Granger, Richard

    2015-09-01

    Past neuroimaging studies have documented discrete regions of human temporal cortex that are more strongly activated by conspecific voice sounds than by nonvoice sounds. However, the mechanisms underlying this voice sensitivity remain unclear. In the present functional MRI study, we took a novel approach to examining voice sensitivity, in which we applied a signal detection paradigm to the assessment of multivariate pattern classification among several living and nonliving categories of auditory stimuli. Within this framework, voice sensitivity can be interpreted as a distinct neural representation of brain activity that correctly distinguishes human vocalizations from other auditory object categories. Across a series of auditory categorization tests, we found that bilateral superior and middle temporal cortex consistently exhibited robust sensitivity to human vocal sounds. Although the strongest categorization was in distinguishing human voice from other categories, subsets of these regions were also able to distinguish reliably between nonhuman categories, suggesting a general role in auditory object categorization. Our findings complement the current evidence of cortical sensitivity to human vocal sounds by revealing that the greatest sensitivity during categorization tasks is devoted to distinguishing voice from nonvoice categories within human temporal cortex. PMID:26245316

  10. Sex-biased transcriptome evolution in Drosophila.

    PubMed

    Assis, Raquel; Zhou, Qi; Bachtrog, Doris

    2012-01-01

    Sex-biased genes are thought to drive phenotypic differences between males and females. The recent availability of high-throughput gene expression data for many related species has led to a burst of investigations into the genomic and evolutionary properties of sex-biased genes. In Drosophila, a number of studies have found that X chromosomes are deficient in male-biased genes (demasculinized) and enriched for female-biased genes (feminized) and that male-biased genes evolve faster than female-biased genes. However, studies have yielded vastly different conclusions regarding the numbers of sex-biased genes and forces shaping their evolution. Here, we use RNA-seq data from multiple tissues of Drosophila melanogaster and D. pseudoobscura, a species with a recently evolved X chromosome, to explore the evolution of sex-biased genes in Drosophila. First, we compare several independent metrics for classifying sex-biased genes and find that the overlap of genes identified by different metrics is small, particularly for female-biased genes. Second, we investigate genome-wide expression patterns and uncover evidence of demasculinization and feminization of both ancestral and new X chromosomes, demonstrating that gene content on sex chromosomes evolves rapidly. Third, we examine the evolutionary rates of sex-biased genes and show that male-biased genes evolve much faster than female-biased genes, which evolve at similar rates to unbiased genes. Analysis of gene expression among tissues reveals that this trend may be partially due to pleiotropic effects of female-biased genes, which limits their evolutionary potential. Thus, our findings illustrate the importance of accurately identifying sex-biased genes and provide insight into their evolutionary dynamics in Drosophila.

  11. The impact bias is alive and well.

    PubMed

    Wilson, Timothy D; Gilbert, Daniel T

    2013-11-01

    A substantial body of research on affective forecasting has found that people often overestimate the affective impact of future events. Levine, Lench, Kaplan, and Safer (2012) argued that whereas people may overestimate the duration of their emotional responses, they do not overestimate the initial intensity of these responses as much as previous research has suggested. We suggest that Levine et al. (a) failed to review or include in their meta-analysis many studies that directly contradict their claim, (b) used a faulty classification scheme, (c) collapsed across conditions that were meant to (and did) produce opposing effects, and (d) miscoded some of the studies they did include. When these errors are corrected, their claim is clearly not supported. Levine et al. also reported the results of 4 studies, which are open to alternative explanations. The impact bias is alive and well.

  12. On the Misuse of Manifest Variables in the Detection of Measurement Bias.

    ERIC Educational Resources Information Center

    Meredith, William; Millsap, Roger E.

    1992-01-01

    A unified treatment is presented for conditions that should allow detection of measurement bias using statistical procedures involving only observed or manifest variables. Computational results demonstrate that methods for studying bias that rely exclusively on manifest variables are not generally diagnostic of the presence or absence of…

  13. Error Biases in Inner and Overt Speech: Evidence from Tongue Twisters

    ERIC Educational Resources Information Center

    Corley, Martin; Brocklehurst, Paul H.; Moat, H. Susannah

    2011-01-01

    To compare the properties of inner and overt speech, Oppenheim and Dell (2008) counted participants' self-reported speech errors when reciting tongue twisters either overtly or silently and found a bias toward substituting phonemes that resulted in words in both conditions, but a bias toward substituting similar phonemes only when speech was…

  14. Fluid simulation of the bias effect in inductive/capacitive discharges

    SciTech Connect

    Zhang, Yu-Ru; Gao, Fei; Li, Xue-Chun; Wang, You-Nian; Bogaerts, Annemie

    2015-11-15

    Computer simulations are performed for an argon inductively coupled plasma (ICP) with a capacitive radio-frequency bias power, to investigate the bias effect on the discharge mode transition and on the plasma characteristics at various ICP currents, bias voltages, and bias frequencies. When the bias frequency is fixed at 13.56 MHz and the ICP current is low, e.g., 6 A, the spatiotemporal averaged plasma density increases monotonically with bias voltage, and the bias effect is already prominent at a bias voltage of 90 V. The maximum of the ionization rate moves toward the bottom electrode, which indicates clearly the discharge mode transition in inductive/capacitive discharges. At higher ICP currents, i.e., 11 and 13 A, the plasma density decreases first and then increases with bias voltage, due to the competing mechanisms between the ion acceleration power dissipation and the capacitive power deposition. At 11 A, the bias effect is still important, but it is noticeable only at higher bias voltages. At 13 A, the ionization rate is characterized by a maximum at the reactor center near the dielectric window at all selected bias voltages, which indicates that the ICP power, instead of the bias power, plays a dominant role under this condition, and no mode transition is observed. Indeed, the ratio of the bias power to the total power is lower than 0.4 over a wide range of bias voltages, i.e., 0–300 V. Besides the effect of ICP current, also the effect of various bias frequencies is investigated. It is found that the modulation of the bias power to the spatiotemporal distributions of the ionization rate at 2 MHz is strikingly different from the behavior observed at higher bias frequencies. Furthermore, the minimum of the plasma density appears at different bias voltages, i.e., 120 V at 2 MHz and 90 V at 27.12 MHz.

  15. An experimental verification of laser-velocimeter sampling bias and its correction

    NASA Technical Reports Server (NTRS)

    Johnson, D. A.; Modarress, D.; Owen, F. K.

    1982-01-01

    The existence of 'sampling bias' in individual-realization laser velocimeter measurements is experimentally verified and shown to be independent of sample rate. The experiments were performed in a simple two-stream mixing shear flow with the standard for comparison being laser-velocimeter results obtained under continuous-wave conditions. It is also demonstrated that the errors resulting from sampling bias can be removed by a proper interpretation of the sampling statistics. In addition, data obtained in a shock-induced separated flow and in the near-wake of airfoils are presented, both bias-corrected and uncorrected, to illustrate the effects of sampling bias in the extreme.

  16. Biased selection within the social health insurance market in Colombia.

    PubMed

    Castano, Ramon; Zambrano, Andres

    2006-12-01

    Reducing the impact of insurance market failures with regulations such as community-rated premiums, standardized benefit packages and open enrolment, yield limited effect because they create room for selection bias. The Colombian social health insurance system started a market approach in 1993 expecting to improve performance of preexisting monopolistic insurance funds by exposing them to competition by new entrants. This paper tests the hypothesis that market failures would lead to biased selection favoring new entrants. Two household surveys are analyzed using Self-Reported Health Status and the presence of chronic conditions as prospective indicators of individual risk. Biased selection is found to take place, leading to adverse selection among incumbents, and favorable selection among new entrants. This pattern is absent in 1997 but is evident in 2003. Given that the two incumbents analyzed are public organizations, the fiscal implications of the findings in terms of government bailouts, are analyzed. PMID:16516333

  17. Some More Sensitive Measures of Sensitivity and Response Bias

    NASA Technical Reports Server (NTRS)

    Balakrishnan, J. D.

    1998-01-01

    In this article, the author proposes a new pair of sensitivity and response bias indices and compares them to other measures currently available, including d' and Beta of signal detection theory. Unlike d' and Beta, these new performance measures do not depend on specific distributional assumptions or assumptions about the transformation from stimulus information to a discrimination judgment with simulated and empirical data, the new sensitivity index is shown to be more accurate than d' and 16 other indices when these measures are used to compare the sensitivity levels of 2 experimental conditions. Results from a perceptual discrimination experiment demonstrate the feasibility of the new distribution-free bias index and suggest that biases of the type defined within the signal detection theory framework (i.e., the placement of a decision criterion) do not exist, even under an asymmetric payoff manipulation.

  18. Omission bias and perceived intention in children and adults.

    PubMed

    Hayashi, Hajimu

    2015-06-01

    Omission bias refers to the tendency to judge acts of commission as morally worse than equivalent acts of omission. Children aged 7-8 and 11-12 years, as well as adults, made moral judgements about acts of commission and omission in two conditions in which the protagonist obtained a self-directed benefit. In the antisocial condition, the other person was harmed; in the selfish condition, the other person was not harmed. The results showed that adults and both age groups of children judged that the agent who did something (act of commission) was morally worse than the agent who did nothing (omission) for both antisocial and selfish conditions, although this judgement tendency was clearer in the selfish condition than in the antisocial condition. Agent intention was held constant across commission and omission, but most participants rated the intention of the agent who did something as stronger than that of the agent who did nothing. These results suggest that omission bias occurs regardless of differences in age and situation. In addition, perceived intention appears to change in conjunction with omission bias.

  19. Charge amplifier with bias compensation

    DOEpatents

    Johnson, Gary W.

    2002-01-01

    An ion beam uniformity monitor for very low beam currents using a high-sensitivity charge amplifier with bias compensation. The ion beam monitor is used to assess the uniformity of a raster-scanned ion beam, such as used in an ion implanter, and utilizes four Faraday cups placed in the geometric corners of the target area. Current from each cup is integrated with respect to time, thus measuring accumulated dose, or charge, in Coulombs. By comparing the dose at each corner, a qualitative assessment of ion beam uniformity is made possible. With knowledge of the relative area of the Faraday cups, the ion flux and areal dose can also be obtained.

  20. Attentional bias towards threatening stimuli in children with anxiety: A meta-analysis.

    PubMed

    Dudeney, Joanne; Sharpe, Louise; Hunt, Caroline

    2015-08-01

    Although it is well known that anxious adults show selective attention to threatening stimuli, research investigating attentional bias in children with anxiety has produced mixed results. The purpose of this paper is to provide a comprehensive analysis of studies investigating attentional bias in children with anxiety. Using a systematic search for articles which included both children with anxiety and reported data suitable for a meta-analysis, 38 articles were identified involving 4221 subjects (anxiety n=2222). We used a random effects meta-analysis with standardized mean difference as our primary outcome to estimate between- and within-group effects of attentional bias towards threat-related information in children with anxiety. Overall, children with anxiety showed a significantly greater bias to threat-related stimuli, compared to controls (d=0.21). Children with anxiety also showed a significant bias to threat-related stimuli, over neutral stimuli (d=0.54), which was greater than the bias shown by control children (d=0.15). Specific variables in attentional bias were also explored, with varying results. The review concluded that anxious children do show a similar bias towards threatening stimuli as has been documented in adults, albeit to a lesser degree and this bias is moderated by age, such that the difference between anxious and control children increases with age. Given the small number of studies in some areas, future research is needed to understand the precise conditions under which anxious children exhibit selective attentional biases to threat-related stimuli.

  1. Squeezing the halo bispectrum: a test of bias models

    NASA Astrophysics Data System (ADS)

    Moradinezhad Dizgah, Azadeh; Chan, Kwan Chuen; Noreña, Jorge; Biagetti, Matteo; Desjacques, Vincent

    2016-09-01

    We study the halo-matter cross bispectrum in the presence of primordial non-Gaussianity of the local type. We restrict ourselves to the squeezed limit, for which the calculation are straightforward, and perform the measurements in the initial conditions of N-body simulations, to mitigate the contamination induced by nonlinear gravitational evolution. Interestingly, the halo-matter cross bispectrum is not trivial even in this simple limit as it is strongly sensitive to the scale-dependence of the quadratic and third-order halo bias. Therefore, it can be used to test biasing prescriptions. We consider three different prescription for halo clustering: excursion set peaks (ESP), local bias and a model in which the halo bias parameters are explicitly derived from a peak-background split. In all cases, the model parameters are fully constrained with statistics other than the cross bispectrum. We measure the cross bispectrum involving one halo fluctuation field and two mass overdensity fields for various halo masses and collapse redshifts. We find that the ESP is in reasonably good agreement with the numerical data, while the other alternatives we consider fail in various cases. This suggests that the scale-dependence of halo bias also is a crucial ingredient to the squeezed limit of the halo bispectrum.

  2. Optimal procedures for detecting analytic bias using patient samples.

    PubMed

    Smith, F A; Kroft, S H

    1997-09-01

    We recently described the performance characteristics of the exponentially adjusted moving mean (EAMM), a patient-data, moving block mean procedure, which is a generalized algorithm that unifies Bull's algorithm and the classic average of normals (AON) procedure. Herein we describe the trend EAMM (TEAMM), a continuous signal analog of the EAMM procedure related to classic trend analysis. Using computer simulation, we have compared EAMM and TEAMM over a range of biases for various sample sizes (N or equivalent smoothing factor alpha) and exponential parameters (P) under conditions of equivalent false rejection (fixed on a per patient sample basis). We found optimal pairs of N and P for each level of bias by determination of minimum mean patient samples to rejection. Overall optimal algorithms were determined through calculation of undetected lost medical utility (ULMU), a novel function that quantifies the medical damage due to analytic bias. The ULMU function was calculated based on lost test specificity in a normal population. We found that optimized TEAMM was superior to optimized EAMM for all levels of analytic bias. If these observations hold true for non-Gaussian populations, TEAMM procedures are the method of choice for detecting bias using patient samples or as an event gauge to trigger use of known-value control materials.

  3. Spatial Bias in Field-Estimated Unsaturated Hydraulic Properties

    SciTech Connect

    HOLT,ROBERT M.; WILSON,JOHN L.; GLASS JR.,ROBERT J.

    2000-12-21

    Hydraulic property measurements often rely on non-linear inversion models whose errors vary between samples. In non-linear physical measurement systems, bias can be directly quantified and removed using calibration standards. In hydrologic systems, field calibration is often infeasible and bias must be quantified indirectly. We use a Monte Carlo error analysis to indirectly quantify spatial bias in the saturated hydraulic conductivity, K{sub s}, and the exponential relative permeability parameter, {alpha}, estimated using a tension infiltrometer. Two types of observation error are considered, along with one inversion-model error resulting from poor contact between the instrument and the medium. Estimates of spatial statistics, including the mean, variance, and variogram-model parameters, show significant bias across a parameter space representative of poorly- to well-sorted silty sand to very coarse sand. When only observation errors are present, spatial statistics for both parameters are best estimated in materials with high hydraulic conductivity, like very coarse sand. When simple contact errors are included, the nature of the bias changes dramatically. Spatial statistics are poorly estimated, even in highly conductive materials. Conditions that permit accurate estimation of the statistics for one of the parameters prevent accurate estimation for the other; accurate regions for the two parameters do not overlap in parameter space. False cross-correlation between estimated parameters is created because estimates of K{sub s} also depend on estimates of {alpha} and both parameters are estimated from the same data.

  4. Challenges in bias correcting climate change simulations

    NASA Astrophysics Data System (ADS)

    Maraun, Douglas; Shepherd, Ted; Zappa, Giuseppe; Gutierrez, Jose; Widmann, Martin; Hagemann, Stefan; Richter, Ingo; Soares, Pedro; Mearns, Linda

    2016-04-01

    Biases in climate model simulations - if these are directly used as input for impact models - will introduce further biases in subsequent impact simulations. In response to this issue, so-called bias correction methods have been developed to post-process climate model output. These methods are now widely used and a crucial component in the generation of high resolution climate change projections. Bias correction is conceptually similar to model output statistics, which has been successfully used for several decades in numerical weather prediction. Yet in climate science, some authors outrightly dismiss any form of bias correction. Starting from this seeming contradiction, we highlight differences between the two contexts and infer consequences and limitations for the applicability of bias correction to climate change projections. We first show that cross validation approaches successfully used to evaluate weather forecasts are fundamentally insufficient to evaluate climate change bias correction. We further demonstrate that different types of model mismatches with observations require different solutions, and some may not sensibly be mitigated. In particular we consider the influence of large-scale circulation biases, biases in the persistence of weather regimes, and regional biases caused by an insufficient representation of the flow-topography interaction. We conclude with a list of recommendations and suggestions for future research to reduce, to post-process, and to cope with climate model biases.

  5. Numeracy and framing bias in epilepsy.

    PubMed

    Choi, Hyunmi; Wong, John B; Mendiratta, Anil; Heiman, Gary A; Hamberger, Marla J

    2011-01-01

    Patients with epilepsy are frequently confronted with complex treatment decisions. Communicating treatment risks is often difficult because patients may have difficulty with basic statistical concepts (i.e., low numeracy) or might misconceive the statistical information based on the way information is presented, a phenomenon known as "framing bias." We assessed numeracy and framing bias in 95 adults with chronic epilepsy and explored cognitive correlates of framing bias. Compared with normal controls, patients with epilepsy had significantly poorer performance on the Numeracy scale (P=0.02), despite a higher level of education than normal controls (P<0.001). Compared with patients with higher numeracy, patients with lower numeracy were significantly more likely to exhibit framing bias. Abstract problem solving performance correlated with the degree of framing bias (r=0.631, P<0.0001), suggesting a relationship between aspects of executive functioning and framing bias. Poor numeracy and susceptibility framing bias place patients with epilepsy at risk for uninformed decisions.

  6. Terror mismanagement: evidence that mortality salience exacerbates attentional bias in social anxiety.

    PubMed

    Finch, Emma C; Iverach, Lisa; Menzies, Ross G; Jones, Mark

    2016-11-01

    Death anxiety is a basic fear underlying a range of psychological conditions, and has been found to increase avoidance in social anxiety. Given that attentional bias is a core feature of social anxiety, the aim of the present study was to examine the impact of mortality salience (MS) on attentional bias in social anxiety. Participants were 36 socially anxious and 37 non-socially anxious individuals, randomly allocated to a MS or control condition. An eye-tracking procedure assessed initial bias towards, and late-stage avoidance of, socially threatening facial expressions. As predicted, socially anxious participants in the MS condition demonstrated significantly more initial bias to social threat than non-socially anxious participants in the MS condition and socially anxious participants in the control condition. However, this effect was not found for late-stage avoidance of social threat. These findings suggest that reminders of death may heighten initial vigilance towards social threat.

  7. Terror mismanagement: evidence that mortality salience exacerbates attentional bias in social anxiety

    PubMed Central

    Finch, Emma C.; Iverach, Lisa; Menzies, Ross G.; Jones, Mark

    2016-01-01

    ABSTRACT Death anxiety is a basic fear underlying a range of psychological conditions, and has been found to increase avoidance in social anxiety. Given that attentional bias is a core feature of social anxiety, the aim of the present study was to examine the impact of mortality salience (MS) on attentional bias in social anxiety. Participants were 36 socially anxious and 37 non-socially anxious individuals, randomly allocated to a MS or control condition. An eye-tracking procedure assessed initial bias towards, and late-stage avoidance of, socially threatening facial expressions. As predicted, socially anxious participants in the MS condition demonstrated significantly more initial bias to social threat than non-socially anxious participants in the MS condition and socially anxious participants in the control condition. However, this effect was not found for late-stage avoidance of social threat. These findings suggest that reminders of death may heighten initial vigilance towards social threat. PMID:26211552

  8. Multivariate Generalizations of Student's t-Distribution. ONR Technical Report. [Biometric Lab Report No. 90-3.

    ERIC Educational Resources Information Center

    Gibbons, Robert D.; And Others

    In the process of developing a conditionally-dependent item response theory (IRT) model, the problem arose of modeling an underlying multivariate normal (MVN) response process with general correlation among the items. Without the assumption of conditional independence, for which the underlying MVN cdf takes on comparatively simple forms and can be…

  9. Causal diagrams and multivariate analysis II: precision work.

    PubMed

    Jupiter, Daniel C

    2014-01-01

    In this Investigators' Corner, I continue my discussion of when and why we researchers should include variables in multivariate regression. My examination focuses on studies comparing treatment groups and situations for which we can either exclude variables from multivariate analyses or include them for reasons of precision.

  10. Evaluating Univariate, Bivariate, and Multivariate Normality Using Graphical Procedures.

    ERIC Educational Resources Information Center

    Burdenski, Thomas K., Jr.

    This paper reviews graphical and nongraphical procedures for evaluating multivariate normality by guiding the reader through univariate and bivariate procedures that are necessary, but insufficient, indications of a multivariate normal distribution. A data set using three dependent variables for two groups provided by D. George and P. Mallery…

  11. Multivariate Analysis of Ipsative Data: Problems and Solutions.

    ERIC Educational Resources Information Center

    McLean, James E.; Chissom, Brad S.

    The term "ipsative" refers to measurement based on intra-individual comparisons. The research literature in the social sciences contains many cautions about using ipsative data in multivariate analysis. The purpose of this paper is to identify the problems associated with the multivariate and regression analyses of ipsative data and to provide…

  12. Simulating Multivariate Nonnormal Data Using an Iterative Algorithm

    ERIC Educational Resources Information Center

    Ruscio, John; Kaczetow, Walter

    2008-01-01

    Simulating multivariate nonnormal data with specified correlation matrices is difficult. One especially popular method is Vale and Maurelli's (1983) extension of Fleishman's (1978) polynomial transformation technique to multivariate applications. This requires the specification of distributional moments and the calculation of an intermediate…

  13. Relationship between Multiple Regression and Selected Multivariable Methods.

    ERIC Educational Resources Information Center

    Schumacker, Randall E.

    The relationship of multiple linear regression to various multivariate statistical techniques is discussed. The importance of the standardized partial regression coefficient (beta weight) in multiple linear regression as it is applied in path, factor, LISREL, and discriminant analyses is emphasized. The multivariate methods discussed in this paper…

  14. Exploratory Multivariate Analysis of Variance: Contrasts and Variables.

    ERIC Educational Resources Information Center

    Barcikowski, Robert S.; Elliott, Ronald S.

    The contribution of individual variables to overall multivariate significance in a multivariate analysis of variance (MANOVA) is investigated using a combination of canonical discriminant analysis and Roy-Bose simultaneous confidence intervals. Difficulties with this procedure are discussed, and its advantages are illustrated using examples based…

  15. Bioharness™ Multivariable Monitoring Device: Part. II: Reliability

    PubMed Central

    Johnstone, James A.; Ford, Paul A.; Hughes, Gerwyn; Watson, Tim; Garrett, Andrew T.

    2012-01-01

    The Bioharness™ monitoring system may provide physiological information on human performance but the reliability of this data is fundamental for confidence in the equipment being used. The objective of this study was to assess the reliability of each of the 5 Bioharness™ variables using a treadmill based protocol. 10 healthy males participated. A between and within subject design to assess the reliability of Heart rate (HR), Breathing Frequency (BF), Accelerometry (ACC) and Infra-red skin temperature (ST) was completed via a repeated, discontinuous, incremental treadmill protocol. Posture (P) was assessed by a tilt table, moved through 160°. Between subject data reported low Coefficient of Variation (CV) and strong correlations(r) for ACC and P (CV< 7.6; r = 0.99, p < 0.01). In contrast, HR and BF (CV~19.4; r~0.70, p < 0.01) and ST (CV 3.7; r = 0.61, p < 0.01), present more variable data. Intra and inter device data presented strong relationships (r > 0.89, p < 0.01) and low CV (<10.1) for HR, ACC, P and ST. BF produced weaker relationships (r < 0.72) and higher CV (<17.4). In comparison to the other variables BF variable consistently presents less reliability. Global results suggest that the Bioharness™ is a reliable multivariable monitoring device during laboratory testing within the limits presented. Key pointsHeart rate and breathing frequency data increased in variance at higher velocities (i.e. ≥ 10 km.h-1)In comparison to the between subject testing, the intra and inter reliability presented good reliability in data suggesting placement or position of device relative to performer could be important for data collectionUnderstanding a devices variability in measurement is important before it can be used within an exercise testing or monitoring setting PMID:24149347

  16. Multivariate statistical analysis of wildfires in Portugal

    NASA Astrophysics Data System (ADS)

    Costa, Ricardo; Caramelo, Liliana; Pereira, Mário

    2013-04-01

    Several studies demonstrate that wildfires in Portugal present high temporal and spatial variability as well as cluster behavior (Pereira et al., 2005, 2011). This study aims to contribute to the characterization of the fire regime in Portugal with the multivariate statistical analysis of the time series of number of fires and area burned in Portugal during the 1980 - 2009 period. The data used in the analysis is an extended version of the Rural Fire Portuguese Database (PRFD) (Pereira et al, 2011), provided by the National Forest Authority (Autoridade Florestal Nacional, AFN), the Portuguese Forest Service, which includes information for more than 500,000 fire records. There are many multiple advanced techniques for examining the relationships among multiple time series at the same time (e.g., canonical correlation analysis, principal components analysis, factor analysis, path analysis, multiple analyses of variance, clustering systems). This study compares and discusses the results obtained with these different techniques. Pereira, M.G., Trigo, R.M., DaCamara, C.C., Pereira, J.M.C., Leite, S.M., 2005: "Synoptic patterns associated with large summer forest fires in Portugal". Agricultural and Forest Meteorology. 129, 11-25. Pereira, M. G., Malamud, B. D., Trigo, R. M., and Alves, P. I.: The history and characteristics of the 1980-2005 Portuguese rural fire database, Nat. Hazards Earth Syst. Sci., 11, 3343-3358, doi:10.5194/nhess-11-3343-2011, 2011 This work is supported by European Union Funds (FEDER/COMPETE - Operational Competitiveness Programme) and by national funds (FCT - Portuguese Foundation for Science and Technology) under the project FCOMP-01-0124-FEDER-022692, the project FLAIR (PTDC/AAC-AMB/104702/2008) and the EU 7th Framework Program through FUME (contract number 243888).

  17. A Multivariate Analysis of Galaxy Cluster Properties

    NASA Astrophysics Data System (ADS)

    Ogle, P. M.; Djorgovski, S.

    1993-05-01

    We have assembled from the literature a data base on on 394 clusters of galaxies, with up to 16 parameters per cluster. They include optical and x-ray luminosities, x-ray temperatures, galaxy velocity dispersions, central galaxy and particle densities, optical and x-ray core radii and ellipticities, etc. In addition, derived quantities, such as the mass-to-light ratios and x-ray gas masses are included. Doubtful measurements have been identified, and deleted from the data base. Our goal is to explore the correlations between these parameters, and interpret them in the framework of our understanding of evolution of clusters and large-scale structure, such as the Gott-Rees scaling hierarchy. Among the simple, monovariate correlations we found, the most significant include those between the optical and x-ray luminosities, x-ray temperatures, cluster velocity dispersions, and central galaxy densities, in various mutual combinations. While some of these correlations have been discussed previously in the literature, generally smaller samples of objects have been used. We will also present the results of a multivariate statistical analysis of the data, including a principal component analysis (PCA). Such an approach has not been used previously for studies of cluster properties, even though it is much more powerful and complete than the simple monovariate techniques which are commonly employed. The observed correlations may lead to powerful constraints for theoretical models of formation and evolution of galaxy clusters. P.M.O. was supported by a Caltech graduate fellowship. S.D. acknowledges a partial support from the NASA contract NAS5-31348 and the NSF PYI award AST-9157412.

  18. Bias correction of the CCSM4 for improved regional climate modeling of the North American monsoon

    NASA Astrophysics Data System (ADS)

    Meyer, Jonathan D. D.; Jin, Jiming

    2016-05-01

    This study investigates how a form of bias correction using linear regression improves the limitations of the community climate system model (CCSM) version 4 when it is dynamically downscaled with the Weather Research and Forecasting (WRF) model for the North American monsoon (NAM). Long-term biases in the CCSM dataset were removed using the climate forecast system reanalysis (CFSR) dataset as a baseline, from which a physically consistent set of bias-corrected variables were created. To quantitatively identify the effects of CCSM data on the NAM simulations, three 32-year climatologies were generated with WRF driven by (1) CFSR, (2) original CCSM, and (3) bias-corrected CCSM data. The WRF-CFSR simulations serve as a baseline for comparison. With the bias correction, onset dates simulated by WRF bias-corrected CCSM data were generally within a week of the WRF-CFSR climatology, while WRF using the original CCSM data occur up to 3-4 weeks too early over the core of the NAM. Additionally, bias-correction led to improvements in the mature phase of the NAM, reducing August root-mean-square-error values by 26 % over the core of the NAM and 36 % over the northern periphery. Comparison of the CFSR and the bias-corrected CCSM climatologies showed marked consistency in the general evolution of the NAM system. Dry biases in the NAM precipitation existed in each climatology with the original CCSM performing the poorest when compared to observations. The poor performance of the original CCSM simulations stem from biases in the thermodynamic profile supplied to the model through lateral boundary conditions. Bias-correction improved the excessive capping inversions, and mid-level mixing ratio dry biases (2-3 g kg-1) present in the CCSM simulations. Improvements in the bias-corrected CCSM data resulted in greater convective activity and a more representative seasonal distribution of precipitation.

  19. Reporting bias in medical research - a narrative review.

    PubMed

    McGauran, Natalie; Wieseler, Beate; Kreis, Julia; Schüler, Yvonne-Beatrice; Kölsch, Heike; Kaiser, Thomas

    2010-01-01

    Reporting bias represents a major problem in the assessment of health care interventions. Several prominent cases have been described in the literature, for example, in the reporting of trials of antidepressants, Class I anti-arrhythmic drugs, and selective COX-2 inhibitors. The aim of this narrative review is to gain an overview of reporting bias in the medical literature, focussing on publication bias and selective outcome reporting. We explore whether these types of bias have been shown in areas beyond the well-known cases noted above, in order to gain an impression of how widespread the problem is. For this purpose, we screened relevant articles on reporting bias that had previously been obtained by the German Institute for Quality and Efficiency in Health Care in the context of its health technology assessment reports and other research work, together with the reference lists of these articles.We identified reporting bias in 40 indications comprising around 50 different pharmacological, surgical (e.g. vacuum-assisted closure therapy), diagnostic (e.g. ultrasound), and preventive (e.g. cancer vaccines) interventions. Regarding pharmacological interventions, cases of reporting bias were, for example, identified in the treatment of the following conditions: depression, bipolar disorder, schizophrenia, anxiety disorder, attention-deficit hyperactivity disorder, Alzheimer's disease, pain, migraine, cardiovascular disease, gastric ulcers, irritable bowel syndrome, urinary incontinence, atopic dermatitis, diabetes mellitus type 2, hypercholesterolaemia, thyroid disorders, menopausal symptoms, various types of cancer (e.g. ovarian cancer and melanoma), various types of infections (e.g. HIV, influenza and Hepatitis B), and acute trauma. Many cases involved the withholding of study data by manufacturers and regulatory agencies or the active attempt by manufacturers to suppress publication. The ascertained effects of reporting bias included the overestimation of efficacy

  20. Target frequency influences antisaccade endpoint bias: evidence for perceptual averaging.

    PubMed

    Gillen, Caitlin; Heath, Matthew

    2014-12-01

    Perceptual judgments related to stimulus-sets are represented computationally different than individual items. In particular, the perceptual averaging hypothesis contends that the visual system represents target properties (e.g., eccentricity) via a statistical summary of the individual targets included within a stimulus-set. Here we sought to determine whether perceptual averaging governs the visual information mediating an oculomotor task requiring top-down control (i.e., antisaccade). To that end, participants completed antisaccades (i.e., saccade mirror-symmetrical to a target) – and complementary prosaccades (i.e., saccade to veridical target location) – to different target eccentricities (10.5°, 15.5° and 20.5°) located left and right of a common fixation. Importantly, trials were completed in blocks wherein eccentricities were presented with equal frequency (i.e., control condition) and when the ‘proximal’ (10.5°: i.e., proximal-weighting condition) and ‘distal’ (20.5°: i.e., distal-weighting condition) targets were respectively presented five times as often as the other eccentricities. If antisaccades are governed by a statistical summary then amplitudes should be biased in the direction of the most frequently presented target within a block. As expected, pro- and antisaccade across each target eccentricity were associated with an undershooting bias and prosaccades were refractory to the manipulation of target frequency. Most notably, antisaccades in the proximal-weighting condition had a larger undershooting bias than the control condition, whereas the converse was true for the distal-weighing condition; that is, antisaccades were biased in the direction of the most frequently presented target. Thus, we propose that perceptual averaging extends to motor tasks requiring top-down cognitive control.

  1. Longitudinal assessment of treatment effects on pulmonary ventilation using 1H/3He MRI multivariate templates

    NASA Astrophysics Data System (ADS)

    Tustison, Nicholas J.; Contrella, Benjamin; Altes, Talissa A.; Avants, Brian B.; de Lange, Eduard E.; Mugler, John P.

    2013-03-01

    The utitlity of pulmonary functional imaging techniques, such as hyperpolarized 3He MRI, has encouraged their inclusion in research studies for longitudinal assessment of disease progression and the study of treatment effects. We present methodology for performing voxelwise statistical analysis of ventilation maps derived from hyper­ polarized 3He MRI which incorporates multivariate template construction using simultaneous acquisition of IH and 3He images. Additional processing steps include intensity normalization, bias correction, 4-D longitudinal segmentation, and generation of expected ventilation maps prior to voxelwise regression analysis. Analysis is demonstrated on a cohort of eight individuals with diagnosed cystic fibrosis (CF) undergoing treatment imaged five times every two weeks with a prescribed treatment schedule.

  2. An analytic comparison of Herrnstein's equations and a multivariate rate equation.

    PubMed

    McDowell, J J

    1980-05-01

    Herrnstein's equations are approximations of the multivariate rate equation at ordinary rates of reinforcement and responding. The rate equation is the result of a linear system analysis of variable-interval performance. Rate equation matching is more comprehensive than ordinary matching because it predicts and specifies the nature of concurrent bias, and predicts a tendency toward undermatching, which is sometimes observed in concurrent situations. The rate equation contradicts one feature of Herrnstein's hyperbola, viz., the theoretically required constancy of k. According to the rate equation, Herrnstein's k should vary directly with parameters of reinforcement such as amount or immediacy. Because of this prediction, the rate equation asserts that the conceptual framework of matching does not apply to single alternative responding. The issue of the constancy of k provides empirical grounds for distinguishing between Herrnstein's account and a linear system analysis of single alternative variable-interval responding.

  3. A laboratory study of the electromagnetic bias of rough surface scattering by water waves

    NASA Technical Reports Server (NTRS)

    Parsons, Chester L.; Miller, Lee S.

    1990-01-01

    The design, development, and use of a focused-beam radar to measure the electromagnetic bias introduced by the scattering of radar waves by a roughened water surface are discussed. The bias measurements were made over wide ranges of environmental conditions in a wavetank laboratory. Wave-elevation data were provided by standard laboratory capacitance probes. Backscattered radar power measurements coincident in time and space with the elevation data were produced by the radar. The two data sets are histogrammed to produce probability density functions for elevation and radar reflectivity, from which the electromagnetic bias is computed. The experimental results demonstrate that the electromagnetic bias is quite variable over the wide range of environmental conditions that can be produced in the laboratory. The data suggest that the bias is dependent upon the local wind field and on the amplitude and frequency of any background wave field that is present.

  4. Multivariate distributions of soil hydraulic parameters

    NASA Astrophysics Data System (ADS)

    Qu, Wei; Pachepsky, Yakov; Huisman, Johan Alexander; Martinez, Gonzalo; Bogena, Heye; Vereecken, Harry

    2014-05-01

    on pedotransfer relationships not only within a given textural class but also on pedotransfer relationships within other textural classes since the pedotransfer relationships are developed across the database containing data for several textural classes. Therefore, joint multivariate parameter distributions for a specific class may not be sufficiently accurate. Currently PTF may give the best prediction of the parameter itself, but they are not designed to estimate correlations between parameters. Covariance matrices for soil hydraulic parameters present an additional type of pedotransfer information that needs to be acquired and used whenever random sets of those parameters are to be generated.

  5. Multivariate test power approximations for balanced linear mixed models in studies with missing data.

    PubMed

    Ringham, Brandy M; Kreidler, Sarah M; Muller, Keith E; Glueck, Deborah H

    2016-07-30

    Multilevel and longitudinal studies are frequently subject to missing data. For example, biomarker studies for oral cancer may involve multiple assays for each participant. Assays may fail, resulting in missing data values that can be assumed to be missing completely at random. Catellier and Muller proposed a data analytic technique to account for data missing at random in multilevel and longitudinal studies. They suggested modifying the degrees of freedom for both the Hotelling-Lawley trace F statistic and its null case reference distribution. We propose parallel adjustments to approximate power for this multivariate test in studies with missing data. The power approximations use a modified non-central F statistic, which is a function of (i) the expected number of complete cases, (ii) the expected number of non-missing pairs of responses, or (iii) the trimmed sample size, which is the planned sample size reduced by the anticipated proportion of missing data. The accuracy of the method is assessed by comparing the theoretical results to the Monte Carlo simulated power for the Catellier and Muller multivariate test. Over all experimental conditions, the closest approximation to the empirical power of the Catellier and Muller multivariate test is obtained by adjusting power calculations with the expected number of complete cases. The utility of the method is demonstrated with a multivariate power analysis for a hypothetical oral cancer biomarkers study. We describe how to implement the method using standard, commercially available software products and give example code. Copyright © 2015 John Wiley & Sons, Ltd. PMID:26603500

  6. Multivariate crash modeling for motor vehicle and non-motorized modes at the macroscopic level.

    PubMed

    Lee, Jaeyoung; Abdel-Aty, Mohamed; Jiang, Ximiao

    2015-05-01

    Macroscopic traffic crash analyses have been conducted to incorporate traffic safety into long-term transportation planning. This study aims at developing a multivariate Poisson lognormal conditional autoregressive model at the macroscopic level for crashes by different transportation modes such as motor vehicle, bicycle, and pedestrian crashes. Many previous studies have shown the presence of common unobserved factors across different crash types. Thus, it was expected that adopting multivariate model structure would show a better modeling performance since it can capture shared unobserved features across various types. The multivariate model and univariate model were estimated based on traffic analysis zones (TAZs) and compared. It was found that the multivariate model significantly outperforms the univariate model. It is expected that the findings from this study can contribute to more reliable traffic crash modeling, especially when focusing on different modes. Also, variables that are found significant for each mode can be used to guide traffic safety policy decision makers to allocate resources more efficiently for the zones with higher risk of a particular transportation mode.

  7. The Multivariate Largest Lyapunov Exponent as an Age-Related Metric of Quiet Standing Balance

    PubMed Central

    Liu, Kun; Wang, Hongrui; Xiao, Jinzhuang

    2015-01-01

    The largest Lyapunov exponent has been researched as a metric of the balance ability during human quiet standing. However, the sensitivity and accuracy of this measurement method are not good enough for clinical use. The present research proposes a metric of the human body's standing balance ability based on the multivariate largest Lyapunov exponent which can quantify the human standing balance. The dynamic multivariate time series of ankle, knee, and hip were measured by multiple electrical goniometers. Thirty-six normal people of different ages participated in the test. With acquired data, the multivariate largest Lyapunov exponent was calculated. Finally, the results of the proposed approach were analysed and compared with the traditional method, for which the largest Lyapunov exponent and power spectral density from the centre of pressure were also calculated. The following conclusions can be obtained. The multivariate largest Lyapunov exponent has a higher degree of differentiation in differentiating balance in eyes-closed conditions. The MLLE value reflects the overall coordination between multisegment movements. Individuals of different ages can be distinguished by their MLLE values. The standing stability of human is reduced with the increment of age. PMID:26064182

  8. Multivariate test power approximations for balanced linear mixed models in studies with missing data.

    PubMed

    Ringham, Brandy M; Kreidler, Sarah M; Muller, Keith E; Glueck, Deborah H

    2016-07-30

    Multilevel and longitudinal studies are frequently subject to missing data. For example, biomarker studies for oral cancer may involve multiple assays for each participant. Assays may fail, resulting in missing data values that can be assumed to be missing completely at random. Catellier and Muller proposed a data analytic technique to account for data missing at random in multilevel and longitudinal studies. They suggested modifying the degrees of freedom for both the Hotelling-Lawley trace F statistic and its null case reference distribution. We propose parallel adjustments to approximate power for this multivariate test in studies with missing data. The power approximations use a modified non-central F statistic, which is a function of (i) the expected number of complete cases, (ii) the expected number of non-missing pairs of responses, or (iii) the trimmed sample size, which is the planned sample size reduced by the anticipated proportion of missing data. The accuracy of the method is assessed by comparing the theoretical results to the Monte Carlo simulated power for the Catellier and Muller multivariate test. Over all experimental conditions, the closest approximation to the empirical power of the Catellier and Muller multivariate test is obtained by adjusting power calculations with the expected number of complete cases. The utility of the method is demonstrated with a multivariate power analysis for a hypothetical oral cancer biomarkers study. We describe how to implement the method using standard, commercially available software products and give example code. Copyright © 2015 John Wiley & Sons, Ltd.

  9. Social reward shapes attentional biases.

    PubMed

    Anderson, Brian A

    2016-01-01

    Paying attention to stimuli that predict a reward outcome is important for an organism to survive and thrive. When visual stimuli are associated with tangible, extrinsic rewards such as money or food, these stimuli acquire high attentional priority and come to automatically capture attention. In humans and other primates, however, many behaviors are not motivated directly by such extrinsic rewards, but rather by the social feedback that results from performing those behaviors. In the present study, I examine whether positive social feedback can similarly influence attentional bias. The results show that stimuli previously associated with a high probability of positive social feedback elicit value-driven attentional capture, much like stimuli associated with extrinsic rewards. Unlike with extrinsic rewards, however, such stimuli also influence task-specific motivation. My findings offer a potential mechanism by which social reward shapes the information that we prioritize when perceiving the world around us. PMID:25941868

  10. Increased hindsight bias in schizophrenia.

    PubMed

    Woodward, Todd S; Moritz, Steffen; Arnold, Michelle M; Cuttler, Carrie; Whitman, Jennifer C; Lindsay, D Stephen

    2006-07-01

    An underlying theme common to prominent theoretical accounts of cognition in schizophrenia is that information processing is disproportionately influenced by recently/currently encountered information relative to the influence of previously learned information. In this study, the authors tested this account by using the hindsight bias or knew-it-all-along (KIA) paradigm, which demonstrates that newly acquired knowledge influences recall of past events. In line with the account that patients with schizophrenia display a disproportionately strong influence of recently encountered information relative to the influence of previously learned information, patients displayed a KIA effect that was significantly greater than in controls. This result is discussed in the context of the cognitive underpinnings of the KIA effect and delusion formation. PMID:16846264

  11. Symmetry as Bias: Rediscovering Special Relativity

    NASA Technical Reports Server (NTRS)

    Lowry, Michael R.

    1992-01-01

    This paper describes a rational reconstruction of Einstein's discovery of special relativity, validated through an implementation: the Erlanger program. Einstein's discovery of special relativity revolutionized both the content of physics and the research strategy used by theoretical physicists. This research strategy entails a mutual bootstrapping process between a hypothesis space for biases, defined through different postulated symmetries of the universe, and a hypothesis space for physical theories. The invariance principle mutually constrains these two spaces. The invariance principle enables detecting when an evolving physical theory becomes inconsistent with its bias, and also when the biases for theories describing different phenomena are inconsistent. Structural properties of the invariance principle facilitate generating a new bias when an inconsistency is detected. After a new bias is generated. this principle facilitates reformulating the old, inconsistent theory by treating the latter as a limiting approximation. The structural properties of the invariance principle can be suitably generalized to other types of biases to enable primal-dual learning.

  12. Publication Bias in Methodological Computational Research

    PubMed Central

    Boulesteix, Anne-Laure; Stierle, Veronika; Hapfelmeier, Alexander

    2015-01-01

    The problem of publication bias has long been discussed in research fields such as medicine. There is a consensus that publication bias is a reality and that solutions should be found to reduce it. In methodological computational research, including cancer informatics, publication bias may also be at work. The publication of negative research findings is certainly also a relevant issue, but has attracted very little attention to date. The present paper aims at providing a new formal framework to describe the notion of publication bias in the context of methodological computational research, facilitate and stimulate discussions on this topic, and increase awareness in the scientific community. We report an exemplary pilot study that aims at gaining experiences with the collection and analysis of information on unpublished research efforts with respect to publication bias, and we outline the encountered problems. Based on these experiences, we try to formalize the notion of publication bias. PMID:26508827

  13. Professional Culture and Climate: Addressing Unconscious Bias

    NASA Astrophysics Data System (ADS)

    Knezek, Patricia

    2016-10-01

    Unconscious bias reflects expectations or stereotypes that influence our judgments of others (regardless of our own group). Everyone has unconscious biases. The end result of unconscious bias can be an accumulation of advantage or disadvantage that impacts the long term career success of individuals, depending on which biases they are subject to. In order to foster a professional culture and climate, being aware of these unconscious biases and mitigating against them is a first step. This is particularly important when judgements are needed, such as in cases for recruitment, choice of speakers for conferences, and even reviewing papers submitted for publication. This presentation will cover how unconscious bias manifests itself, what evidence exists to demonstrate it exists, and ways it can be addressed.

  14. Quantum Criticality in the Biased Dicke Model

    PubMed Central

    Zhu, Hanjie; Zhang, Guofeng; Fan, Heng

    2016-01-01

    The biased Dicke model describes a system of biased two-level atoms coupled to a bosonic field, and is expected to produce new phenomena that are not present in the original Dicke model. In this paper, we study the critical properties of the biased Dicke model in the classical oscillator limits. For the finite-biased case in this limit, We present analytical results demonstrating that the excitation energy does not vanish for arbitrary coupling. This indicates that the second order phase transition is avoided in the biased Dicke model, which contrasts to the original Dicke model. We also analyze the squeezing and the entanglement in the ground state, and find that a finite bias will strongly modify their behaviors in the vicinity of the critical coupling point. PMID:26786239

  15. Interventions That Affect Gender Bias in Hiring: A Systematic Review

    PubMed Central

    Isaac, Carol; Lee, Barbara; Carnes, Molly

    2015-01-01

    Purpose To systematically review experimental evidence for interventions mitigating gender bias in employment. Unconscious endorsement of gender stereotypes can undermine academic medicine's commitment to gender equity. Method The authors performed electronic and hand searches for randomized controlled studies since 1973 of interventions that affect gender differences in evaluation of job applicants. Twenty-seven studies met all inclusion criteria. Interventions fell into three categories: application information, applicant features, and rating conditions. Results The studies identified gender bias as the difference in ratings or perceptions of men and women with identical qualifications. Studies reaffirmed negative bias against women being evaluated for positions traditionally or predominantly held by men (male sex-typed jobs). The assessments of male and female raters rarely differed. Interventions that provided raters with clear evidence of job-relevant competencies were effective. However, clearly competent women were rated lower than equivalent men for male sex-typed jobs unless evidence of communal qualities was also provided. A commitment to the value of credentials before review of applicants and women's presence at above 25% of the applicant pool eliminated bias against women. Two studies found unconscious resistance to “antibias” training, which could be overcome with distraction or an intervening task. Explicit employment equity policies and an attractive appearance benefited men more than women, whereas repeated employment gaps were more detrimental to men. Masculine-scented perfume favored the hiring of both sexes. Negative bias occurred against women who expressed anger or who were perceived as self-promoting. Conclusions High-level evidence exists for strategies to mitigate gender bias in hiring. PMID:19881440

  16. Modeling the relationship between climate oscillations and drought by a multivariate GARCH model

    NASA Astrophysics Data System (ADS)

    Modarres, R.; Ouarda, T. B. M. J.

    2014-01-01

    Typical multivariate time series models may exhibit comovement in mean but not in variance of hydrologic and climatic variables. This paper introduces multivariate generalized autoregressive conditional heteroscedasticity (GARCH) models to capture the comovement of the variance or the conditional covariance between two hydroclimatic time series. The diagonal vectorized and Baba-Engle-Kroft-Kroner models are developed to evaluate the covariance between drought and two atmospheric circulations, Southern Oscillation Index (SOI) and North Atlantic Oscillation (NAO) time series during 1954-2000. The univariate generalized autoregressive conditional heteroscedasticity model indicates a strong persistency level in conditional variance for NAO and a moderate persistency level for SOI. The conditional variance of short-term drought index indicates low level of persistency, while the long-term index drought indicates high level of persistency in conditional variance. The estimated conditional covariance between drought and atmospheric indices is shown to be weak and negative. It is also observed that the covariance between drought and atmospheric indices is largely dependent on short-run variance of atmospheric indices rather than their long-run variance. The nonlinearity and stationarity tests show that the conditional covariances are nonlinear but stationary. However, the degree of nonlinearity is higher for the covariance between long-term drought and atmospheric indices. It is also observed that the nonlinearity of NAO is higher than that for SOI, in contrast to the stationarity which is stronger for SOI time series.

  17. Cognitive Biases and Nonverbal Cue Availability in Detecting Deception

    ERIC Educational Resources Information Center

    Burgoon, Judee K.; Blair, J. Pete; Strom, Renee E.

    2008-01-01

    In potentially deceptive situations, people rely on mental shortcuts to help process information. These heuristic judgments are often biased and result in inaccurate assessments of sender veracity. Four such biases--truth bias, visual bias, demeanor bias, and expectancy violation bias--were examined in a judgment experiment that varied nonverbal…

  18. Visual classification of very fine-grained sediments: Evaluation through univariate and multivariate statistics

    USGS Publications Warehouse

    Hohn, M. Ed; Nuhfer, E.B.; Vinopal, R.J.; Klanderman, D.S.

    1980-01-01

    Classifying very fine-grained rocks through fabric elements provides information about depositional environments, but is subject to the biases of visual taxonomy. To evaluate the statistical significance of an empirical classification of very fine-grained rocks, samples from Devonian shales in four cored wells in West Virginia and Virginia were measured for 15 variables: quartz, illite, pyrite and expandable clays determined by X-ray diffraction; total sulfur, organic content, inorganic carbon, matrix density, bulk density, porosity, silt, as well as density, sonic travel time, resistivity, and ??-ray response measured from well logs. The four lithologic types comprised: (1) sharply banded shale, (2) thinly laminated shale, (3) lenticularly laminated shale, and (4) nonbanded shale. Univariate and multivariate analyses of variance showed that the lithologic classification reflects significant differences for the variables measured, difference that can be detected independently of stratigraphic effects. Little-known statistical methods found useful in this work included: the multivariate analysis of variance with more than one effect, simultaneous plotting of samples and variables on canonical variates, and the use of parametric ANOVA and MANOVA on ranked data. ?? 1980 Plenum Publishing Corporation.

  19. Visual classification of very fine-grained sediments: evaluation through univariate and multivariate statistics

    SciTech Connect

    Hohn, M.E.; Nuhfer, E.B.; Vinopal, R.J.; Klanderman, D.S.

    1980-01-01

    Classifying very fine-grained rocks through fabric elements provides information about depositional environments, but is subject to the biases of visual taxonomy. To evaluate the statistical significance of an empirical classification of very fine-grained rocks, samples from Devonian shales in four cored wells in West Virginia and Virginia were measured for 15 variables: quartz, illite, pyrite and expandable clays determined by X-ray diffraction; total sulfur, organic content, inorganic carbon, matrix density, bulk density, porosity, silt, as well as density, sonic travel time, resistivity, and ..gamma..-ray response measured from well logs. The four lithologic types comprised: (1) sharply banded shale, (2) thinly laminated shale, (3) lenticularly laminated shale, and (4) nonbanded shale. Univariate and multivariate analyses of variance showed that the lithologic classification reflects significant differences for the variables measured, difference that can be detected independently of stratigraphic effects. Little-known statistical methods found useful in this work included: the multivariate analysis of variance with more than one effect, simultaneous plotting of samples and variables on canonical variates, and the use of parametric ANOVA and MANOVA on ranked data.

  20. Measuring and comparing evolvability and constraint in multivariate characters.

    PubMed

    Hansen, T F; Houle, D

    2008-09-01

    The Lande equation forms the basis for our understanding of the short-term evolution of quantitative traits in a multivariate context. It predicts the response to selection as the product of an additive genetic variance matrix and a selection gradient. The selection gradient approximates the force and direction of selection, and the genetic variance matrix quantifies the role of the genetic system in evolution. Attempts to understand the evolutionary significance of the genetic variance matrix are hampered by the fact that the majority of the methods used to characterize and compare variance matrices have not been derived in an explicit theoretical context. We use the Lande equation to derive new measures of the ability of a variance matrix to allow or constrain evolution in any direction in phenotype space. Evolvability captures the ability of a population to evolve in the direction of selection when stabilizing selection is absent. Conditional evolvability captures the ability of a population to respond to directional selection in the presence of stabilizing selection on other trait combinations. We then derive measures of character autonomy and integration from these evolvabilities. We study the properties of these measures and show how they can be used to interpret and compare variance matrices. As an illustration, we show that divergence of wing shape in the dipteran family Drosophilidae has proceeded in directions that have relatively high evolvabilities.

  1. Multivariable Techniques for High-Speed Research Flight Control Systems

    NASA Technical Reports Server (NTRS)

    Newman, Brett A.

    1999-01-01

    This report describes the activities and findings conducted under contract with NASA Langley Research Center. Subject matter is the investigation of suitable multivariable flight control design methodologies and solutions for large, flexible high-speed vehicles. Specifically, methodologies are to address the inner control loops used for stabilization and augmentation of a highly coupled airframe system possibly involving rigid-body motion, structural vibrations, unsteady aerodynamics, and actuator dynamics. Design and analysis techniques considered in this body of work are both conventional-based and contemporary-based, and the vehicle of interest is the High-Speed Civil Transport (HSCT). Major findings include: (1) control architectures based on aft tail only are not well suited for highly flexible, high-speed vehicles, (2) theoretical underpinnings of the Wykes structural mode control logic is based on several assumptions concerning vehicle dynamic characteristics, and if not satisfied, the control logic can break down leading to mode destabilization, (3) two-loop control architectures that utilize small forward vanes with the aft tail provide highly attractive and feasible solutions to the longitudinal axis control challenges, and (4) closed-loop simulation sizing analyses indicate the baseline vane model utilized in this report is most likely oversized for normal loading conditions.

  2. Optimal mapping of site-specific multivariate soil properties.

    PubMed

    Burrough, P A; Swindell, J

    1997-01-01

    This paper demonstrates how geostatistics and fuzzy k-means classification can be used together to improve our practical understanding of crop yield-site response. Two aspects of soil are important for precision farming: (a) sensible classes for a given crop, and (b) their spatial variation. Local site classifications are more sensitive than general taxonomies and can be provided by the method of fuzzy k-means to transform a multivariate data set with i attributes measured at n sites into k overlapping classes; each site has a membership value mk for each class in the range 0-1. Soil variation is of interest when conditions vary over patches manageable by agricultural machinery. The spatial variation of each of the k classes can be analysed by computing the variograms of mk over the n sites. Memberships for each of the k classes can be mapped by ordinary kriging. Areas of class dominance and the transition zones between them can be identified by an inter-class confusion index; reducing the zones to boundaries gives crisp maps of dominant soil groups that can be used to guide precision farming equipment. Automation of the procedure is straightforward given sufficient data. Time variations in soil properties can be automatically incorporated in the computation of membership values. The procedures are illustrated with multi-year crop yield data collected from a 5 ha demonstration field at the Royal Agricultural College in Cirencester, UK. PMID:9573478

  3. Multivariable Dynamic Ankle Mechanical Impedance With Active Muscles

    PubMed Central

    Lee, Hyunglae; Krebs, Hermano Igo; Hogan, Neville

    2015-01-01

    Multivariable dynamic ankle mechanical impedance in two coupled degrees-of-freedom (DOFs) was quantified when muscles were active. Measurements were performed at five different target activation levels of tibialis anterior and soleus, from 10% to 30% of maximum voluntary contraction (MVC) with increments of 5% MVC. Interestingly, several ankle behaviors characterized in our previous study of the relaxed ankle were observed with muscles active: ankle mechanical impedance in joint coordinates showed responses largely consistent with a second-order system consisting of inertia, viscosity, and stiffness; stiffness was greater in the sagittal plane than in the frontal plane at all activation conditions for all subjects; and the coupling between dorsiflexion–plantarflexion and inversion–eversion was small—the two DOF measurements were well explained by a strictly diagonal impedance matrix. In general, ankle stiffness increased linearly with muscle activation in all directions in the 2-D space formed by the sagittal and frontal planes, but more in the sagittal than in the frontal plane, resulting in an accentuated “peanut shape.” This characterization of young healthy subjects’ ankle mechanical impedance with active muscles will serve as a baseline to investigate pathophysiological ankle behaviors of biomechanically and/or neurologically impaired patients. PMID:25203497

  4. When Do Children Exhibit a "Yes" Bias?

    ERIC Educational Resources Information Center

    Okanda, Mako; Itakura, Shoji

    2010-01-01

    This study investigated whether one hundred and thirty-five 3- to 6-year-old children exhibit a yes bias to various yes-no questions and whether their knowledge status affects the production of a yes bias. Three-year-olds exhibited a yes bias to all yes-no questions such as "preference-object" and "knowledge-object" questions pertaining to…

  5. Multivariate dice recognition using invariant features

    NASA Astrophysics Data System (ADS)

    Hsu, Gee-Sern; Peng, Hsiao-Chia; Yeh, Shang-Min; Lin, Chyi-Yeu

    2013-04-01

    A system is proposed for automatic reading of the number of dots on dice in general table game settings. Different from previous dice recognition systems that recognize dice of a specific color using a single top-view camera in an enclosure with controlled settings, the proposed one uses multiple cameras to recognize dice of various colors and under uncontrolled conditions. It is composed of three modules. Module-1 locates the dice using the gradient-conditioned color segmentation, proposed, to segment dice of arbitrary colors from the background. Module-2 exploits the local invariant features good for building homographies, giving a solution to segment the top faces of the dice. To identify the dots on the segmented top faces, a maximally stable extremal region detector is embedded in module-3 for its consistency in locating the dot region. Experiments show that the proposed system performs satisfactorily in various test conditions.

  6. Evaluation of bias associated with high-multiplex, target-specific pre-amplification

    PubMed Central

    Okino, Steven T.; Kong, Michelle; Sarras, Haya; Wang, Yan

    2015-01-01

    We developed a novel PCR-based pre-amplification (PreAmp) technology that can increase the abundance of over 350 target genes one million-fold. To assess potential bias introduced by PreAmp we utilized ERCC RNA reference standards, a model system that quantifies measurement error in RNA analysis. We assessed three types of bias: amplification bias, dynamic range bias and fold-change bias. We show that our PreAmp workflow introduces only minimal amplification and fold-change bias under stringent conditions. We do detect dynamic range bias if a target gene is highly abundant and PreAmp occurred for 16 or more PCR cycles; however, this type of bias is easily correctable. To assess PreAmp bias in a gene expression profiling experiment, we analyzed a panel of genes that are regulated during differentiation using the NTera2 stem cell model system. We find that results generated using PreAmp are similar to results obtained using standard qPCR (without the pre-amplification step). Importantly, PreAmp maintains patterns of gene expression changes across samples; the same biological insights would be derived from a PreAmp experiment as with a standard gene expression profiling experiment. We conclude that our PreAmp technology can facilitate analysis of extremely limited samples in gene expression quantification experiments. PMID:27077043

  7. Evaluation of bias associated with high-multiplex, target-specific pre-amplification.

    PubMed

    Okino, Steven T; Kong, Michelle; Sarras, Haya; Wang, Yan

    2016-01-01

    We developed a novel PCR-based pre-amplification (PreAmp) technology that can increase the abundance of over 350 target genes one million-fold. To assess potential bias introduced by PreAmp we utilized ERCC RNA reference standards, a model system that quantifies measurement error in RNA analysis. We assessed three types of bias: amplification bias, dynamic range bias and fold-change bias. We show that our PreAmp workflow introduces only minimal amplification and fold-change bias under stringent conditions. We do detect dynamic range bias if a target gene is highly abundant and PreAmp occurred for 16 or more PCR cycles; however, this type of bias is easily correctable. To assess PreAmp bias in a gene expression profiling experiment, we analyzed a panel of genes that are regulated during differentiation using the NTera2 stem cell model system. We find that results generated using PreAmp are similar to results obtained using standard qPCR (without the pre-amplification step). Importantly, PreAmp maintains patterns of gene expression changes across samples; the same biological insights would be derived from a PreAmp experiment as with a standard gene expression profiling experiment. We conclude that our PreAmp technology can facilitate analysis of extremely limited samples in gene expression quantification experiments.

  8. The central tendency bias in color perception: effects of internal and external noise.

    PubMed

    Olkkonen, Maria; McCarthy, Patrice F; Allred, Sarah R

    2014-09-05

    Perceptual estimates can be biased by previously seen stimuli in delayed estimation tasks. These biases are often toward the mean of the whole stimulus set. Recently, we demonstrated such a central tendency bias in delayed color estimation. In the Bayesian framework of perceptual inference, perceptual biases arise when noisy sensory measurements are combined with prior information about the world. Here, we investigate this idea in color perception by manipulating stimulus range and stimulus noise while characterizing delayed color estimates. First, we manipulated the experimental prior for stimulus color by embedding stimuli in collections with different hue ranges. Stimulus range affected hue bias: Hue estimates were always biased toward the mean of the current set. Next, we studied the effect of internal and external noise on the amount of hue bias. Internal noise was manipulated by increasing the delay between the reference and test from 0.4 to 4 s. External noise was manipulated by increasing the amount of chromatic noise in the reference stimulus, while keeping the delay between the reference and test constant at 2 s. Both noise manipulations had a reliable effect on the strength of the central tendency bias. Furthermore, there was a tendency for a positive relationship between variability of the estimates and bias in both noise conditions. In conclusion, observers are able to learn an experimental hue prior, and the weight on the prior can be manipulated by introducing noise in the estimation process.

  9. The truth and bias model of judgment.

    PubMed

    West, Tessa V; Kenny, David A

    2011-04-01

    We present a new model for the general study of how the truth and biases affect human judgment. In the truth and bias model, judgments about the world are pulled by 2 primary forces, the truth force and the bias force, and these 2 forces are interrelated. The truth and bias model differentiates force and value, where the force is the strength of the attraction and the value is the location toward which the judgment is attracted. The model also makes a formal theoretical distinction between bias and moderator variables. Two major classes of biases are discussed: biases that are measured with variables (e.g., assumed similarity) and directional bias, which refers to the extent to which judgments are pulled toward 1 end of the judgment continuum. Moderator variables are conceptualized as variables that affect the accuracy and bias forces but that do not affect judgments directly. We illustrate the model with 4 examples. We discuss the theoretical, empirical, methodological, measurement, and design implications of the model. PMID:21480740

  10. The truth and bias model of judgment.

    PubMed

    West, Tessa V; Kenny, David A

    2011-04-01

    We present a new model for the general study of how the truth and biases affect human judgment. In the truth and bias model, judgments about the world are pulled by 2 primary forces, the truth force and the bias force, and these 2 forces are interrelated. The truth and bias model differentiates force and value, where the force is the strength of the attraction and the value is the location toward which the judgment is attracted. The model also makes a formal theoretical distinction between bias and moderator variables. Two major classes of biases are discussed: biases that are measured with variables (e.g., assumed similarity) and directional bias, which refers to the extent to which judgments are pulled toward 1 end of the judgment continuum. Moderator variables are conceptualized as variables that affect the accuracy and bias forces but that do not affect judgments directly. We illustrate the model with 4 examples. We discuss the theoretical, empirical, methodological, measurement, and design implications of the model.

  11. Chronic and acute biases in perceptual stabilization

    PubMed Central

    Al-Dossari, Munira; Blake, Randolph; Brascamp, Jan W.; Freeman, Alan W.

    2015-01-01

    When perceptually ambiguous stimuli are presented intermittently, the percept on one presentation tends to be the same as that on the previous presentation. The role of short-term, acute biases in the production of this perceptual stability is relatively well understood. In addition, however, long-lasting, chronic bias may also contribute to stability. In this paper we develop indices for both biases and for stability, and show that stability can be expressed as a sum of contributions from the two types of bias. We then apply this analytical procedure to binocular rivalry, showing that adjustment of the monocular contrasts can alter the relative contributions of the two biases. Stability is mainly determined by chronic bias when the contrasts are equal, but acute bias dominates stability when right-eye contrast is set lower than left-eye contrast. Finally, we show that the right-eye bias persists in continuous binocular rivalry. Our findings reveal a previously unappreciated contribution of chronic bias to stable perception. PMID:26641947

  12. Deterministic photon bias in speckle imaging

    NASA Technical Reports Server (NTRS)

    Beletic, James W.

    1989-01-01

    A method for determining photo bias terms in speckle imaging is presented, and photon bias is shown to be a deterministic quantity that can be calculated without the use of the expectation operator. The quantities obtained are found to be identical to previous results. The present results have extended photon bias calculations to the important case of the bispectrum where photon events are assigned different weights, in which regime the bias is a frequency dependent complex quantity that must be calculated for each frame.

  13. delta-biased Josephson tunnel junctions

    SciTech Connect

    Monaco, R.; Mygind, J.; Koshelets, V. P.; Dmitriev, P.

    2010-02-01

    The behavior of a long Josephson tunnel junction drastically depends on the distribution of the dc bias current. We investigate the case in which the bias current is fed in the central point of a one-dimensional junction. Such junction configuration has been recently used to detect the persistent currents circulating in a superconducting loop. Analytical and numerical results indicate that the presence of fractional vortices leads to remarkable differences from the conventional case of uniformly distributed dc bias current. The theoretical findings are supported by detailed measurements on a number of delta-biased samples having different electrical and geometrical parameters.

  14. How Do Biases in General Circulation Models Affect Projections of Aridity and Drought?

    NASA Astrophysics Data System (ADS)

    Ficklin, D. L.; Abatzoglou, J. T.; Robeson, S. M.; Dufficy, A. L.

    2015-12-01

    Unless corrected, biases in General Circulation Models (GCMs) can affect hydroclimatological applications and projections. Compared to a raw GCM ensemble (direct GCM output), bias-corrected GCM inputs correct for systematic errors and can produce high-resolution projections that are useful for impact analyses. By examining the difference between raw and bias-corrected GCMs for the continental United States, this work highlights how GCM biases can affect projections of aridity (defined as precipitation (P)/potential evapotranspiration (PET)) and drought (using the Palmer Drought Severity Index (PDSI)). At the annual time scale for spatial averages over the continental United States, the raw GCM ensemble median has a historical positive precipitation bias (+24%) and negative PET bias (-7%) compared to the bias-corrected output. While both GCM ensembles (raw and bias-corrected) result in drier conditions in the future, the bias-corrected GCMs produce enhanced aridity (number of months with PET>P) in the late 21st century (2070-2099) compared to the historical climate (1950-1979). For the western United States, the bias-corrected GCM ensemble estimates much less humid and sub-humid conditions (based on P/PET categorical values) than the raw GCM ensemble. However, using June, July, and August PDSI, the bias-corrected GCM ensemble projects less acute decreases for the southwest United States compared to the raw GCM ensemble (1 to 2 PDSI units higher) as a result of larger decreases in projected precipitation in the raw GCM ensemble. A number of examples and ecological implications of this work for the western United States will be presented.

  15. Comparison of projection skills of deterministic ensemble methods using pseudo-simulation data generated from multivariate Gaussian distribution

    NASA Astrophysics Data System (ADS)

    Oh, Seok-Geun; Suh, Myoung-Seok

    2016-03-01

    The projection skills of five ensemble methods were analyzed according to simulation skills, training period, and ensemble members, using 198 sets of pseudo-simulation data (PSD) produced by random number generation assuming the simulated temperature of regional climate models. The PSD sets were classified into 18 categories according to the relative magnitude of bias, variance ratio, and correlation coefficient, where each category had 11 sets (including 1 truth set) with 50 samples. The ensemble methods used were as follows: equal weighted averaging without bias correction (EWA_NBC), EWA with bias correction (EWA_WBC), weighted ensemble averaging based on root mean square errors and correlation (WEA_RAC), WEA based on the Taylor score (WEA_Tay), and multivariate linear regression (Mul_Reg). The projection skills of the ensemble methods improved generally as compared with the best member for each category. However, their projection skills are significantly affected by the simulation skills of the ensemble member. The weighted ensemble methods showed better projection skills than non-weighted methods, in particular, for the PSD categories having systematic biases and various correlation coefficients. The EWA_NBC showed considerably lower projection skills than the other methods, in particular, for the PSD categories with systematic biases. Although Mul_Reg showed relatively good skills, it showed strong sensitivity to the PSD categories, training periods, and number of members. On the other hand, the WEA_Tay and WEA_RAC showed relatively superior skills in both the accuracy and reliability for all the sensitivity experiments. This indicates that WEA_Tay and WEA_RAC are applicable even for simulation data with systematic biases, a short training period, and a small number of ensemble members.

  16. Interpretable Early Classification of Multivariate Time Series

    ERIC Educational Resources Information Center

    Ghalwash, Mohamed F.

    2013-01-01

    Recent advances in technology have led to an explosion in data collection over time rather than in a single snapshot. For example, microarray technology allows us to measure gene expression levels in different conditions over time. Such temporal data grants the opportunity for data miners to develop algorithms to address domain-related problems,…

  17. Effects of Poverty and Lack of Insurance on Perceptions of Racial and Ethnic Bias in Health Care

    PubMed Central

    Stepanikova, Irena; Cook, Karen S

    2008-01-01

    Objective To investigate whether poverty and lack of insurance are associated with perceived racial and ethnic bias in health care. Data Source 2001 Survey on Disparities in Quality of Health Care, a nationally representative telephone survey. We use data on black, Hispanic, and white adults who have a regular physician (N=4,556). Study Design We estimate multivariate logistic regression models to examine the effects of poverty and lack of health insurance on perceived racial and ethnic bias in health care for all respondents and by racial, ethnic, and language groups. Principal Findings Controlling for sociodemographic and other factors, uninsured blacks and Hispanics interviewed in English are more likely to report racial and ethnic bias in health care compared with their privately insured counterparts. Poor whites are more likely to report racial and ethnic bias in health care compared with other whites. Good physician–patient communication is negatively associated with perceived racial and ethnic bias. Conclusions Compared with their more socioeconomically advantaged counterparts, poor whites, uninsured blacks, and some uninsured Hispanics are more likely to perceive that racial and ethnic bias operates in the health care they receive. Providing health insurance for the uninsured may help reduce this perceived bias among some minority groups. PMID:18546546

  18. Gender bias in the evaluation of new age music.

    PubMed

    Colley, Ann; North, Adrian; Hargreaves, David J

    2003-04-01

    Eminent composers in Western European art music continue to be predominantly male and eminence in contemporary pop music is similarly male dominated. One contributing factor may be the continuing under-valuation of women's music. Possible anti-female bias in a contemporary genre was investigated using the Goldberg paradigm to elicit judgments of New Age compositions. Since stronger stereotyping effects occur when information provided about individuals is sparse, fictitious male and female composers were presented either by name only or by name with a brief biography. Evidence for anti-female bias was found in the name-only condition and was stronger when liking for the music was controlled. Other findings were the tendency for females to give higher ratings, and the association of gender differences in liking of the music with ratings of quality in the name-only condition. These results are relevant to the design of formal assessment procedures for musical composition.

  19. Cognitive bias in rats is not influenced by oxytocin

    PubMed Central

    McGuire, Molly C.; Williams, Keith L.; Welling, Lisa L. M.; Vonk, Jennifer

    2015-01-01

    The effect of oxytocin on cognitive bias was investigated in rats in a modified conditioned place preference paradigm. Fifteen male rats were trained to discriminate between two different cue combinations, one paired with palatable foods (reward training), and the other paired with unpalatable food (aversive training). Next, their reactions to two ambiguous cue combinations were evaluated and their latency to contact the goal pot recorded. Rats were injected with either oxytocin (OT) or saline with the prediction that rats administered OT would display a shorter average latency to approach on ambiguous trials. There was no significant difference between latencies to approach on ambiguous trials compared to reward trials, but the rats were significantly slower on the aversive compared to the ambiguous conditions. Oxytocin did not affect approach time; however, it was unclear, after follow-up testing, whether the OT doses tested were sufficient to produce the desired effects on cognitive bias. Future research should consider this possibility. PMID:26388811

  20. A general framework for multivariate multi-index drought prediction based on Multivariate Ensemble Streamflow Prediction (MESP)

    NASA Astrophysics Data System (ADS)

    Hao, Zengchao; Hao, Fanghua; Singh, Vijay P.

    2016-08-01

    Drought is among the costliest natural hazards worldwide and extreme drought events in recent years have caused huge losses to various sectors. Drought prediction is therefore critically important for providing early warning information to aid decision making to cope with drought. Due to the complicated nature of drought, it has been recognized that the univariate drought indicator may not be sufficient for drought characterization and hence multivariate drought indices have been developed for drought monitoring. Alongside the substantial effort in drought monitoring with multivariate drought indices, it is of equal importance to develop a drought prediction method with multivariate drought indices to integrate drought information from various sources. This study proposes a general framework for multivariate multi-index drought prediction that is capable of integrating complementary prediction skills from multiple drought indices. The Multivariate Ensemble Streamflow Prediction (MESP) is employed to sample from historical records for obtaining statistical prediction of multiple variables, which is then used as inputs to achieve multivariate prediction. The framework is illustrated with a linearly combined drought index (LDI), which is a commonly used multivariate drought index, based on climate division data in California and New York in the United States with different seasonality of precipitation. The predictive skill of LDI (represented with persistence) is assessed by comparison with the univariate drought index and results show that the LDI prediction skill is less affected by seasonality than the meteorological drought prediction based on SPI. Prediction results from the case study show that the proposed multivariate drought prediction outperforms the persistence prediction, implying a satisfactory performance of multivariate drought prediction. The proposed method would be useful for drought prediction to integrate drought information from various sources

  1. Multivariable disturbance observer-based H2 analytical decoupling control design for multivariable systems

    NASA Astrophysics Data System (ADS)

    Zhang, Wei; Wang, Yagang; Liu, Yurong; Zhang, Weidong

    2016-01-01

    In this paper, an H2 analytical decoupling control scheme with multivariable disturbance observer for both stable and unstable multi-input/multi-output (MIMO) systems with multiple time delays is proposed. Compared with conventional control strategies, the main merit is that the proposed control scheme can improve the system performances effectively when the MIMO processes with severe model mismatches and strong external disturbances. Besides, the design method has three additional advantages. First, the derived controller and observer are given in analytical forms, the design procedure is simple. Second, the orders of the designed controller and observer are low, they can be implemented easily in practice. Finally, the performance and robustness can be adjusted easily by tuning the parameters in the designed controller and observer. It is useful for practical application. Simulations are provided to illustrate the effectiveness of the proposed control scheme.

  2. Plane-parallel biases computed from inhomogeneous Arctic clouds and sea ice

    NASA Astrophysics Data System (ADS)

    Rozwadowska, Anna; Cahalan, Robert F.

    2002-10-01

    Monte Carlo simulations of the expected influence of nonuniformity in cloud structure and surface albedo on shortwave radiative fluxes in the Arctic atmosphere are presented. In particular, plane-parallel biases in cloud albedo and transmittance are studied for nonabsorbing, low-level, all-liquid stratus clouds over sea ice. The "absolute bias" is defined as the difference between the cloud albedo or transmittance for the uniform or plane-parallel case, and the albedo or transmittance for nonuniform conditions with the same mean cloud optical thickness and the same mean surface albedo, averaged over a given area (i.e., bias > 0 means plane-parallel overestimates). Ranges of means and standard deviations of input parameters typical of Arctic conditions are determined from the First International Satellite Cloud Climatology Project (ISCCP) Regional Experiment Artic Cloud Experiment (FIRE/ACE)/Surface Heat Budget of the Arctic Ocean (SHEBA)/Atmospheric Radiation Measurement Program (ARM) experiment, a cooperative effort of the Department of Energy, NASA, NSF, the National Oceanic and Atmospheric Administration, the Office of Naval Research, and the Atmospheric Environment Service. We determine the sensitivity of the bias with respect to the following: domain averaged means and spatial variances of cloud optical thickness and surface albedo, shape of the surface reflectance function, presence of a scattering layer under the clouds, and solar zenith angle. The simulations show that the biases in Arctic conditions are generally lower than in subtropical stratocumulus. The magnitudes of the absolute biases are unlikely to exceed 0.02 for albedo and 0.05 for transmittance. The "relative bias" expresses the absolute bias as a percentage of the actual cloud albedo or transmittance. The magnitude of the relative bias in albedo is typically below 2% over the reflective Arctic surface, while the magnitude of the relative bias in transmittance can exceed 10%.

  3. Bias reduction in decadal predictions of West African monsoon rainfall using regional climate models

    NASA Astrophysics Data System (ADS)

    Paxian, A.; Sein, D.; Panitz, H.-J.; Warscher, M.; Breil, M.; Engel, T.; Tödter, J.; Krause, A.; Cabos Narvaez, W. D.; Fink, A. H.; Ahrens, B.; Kunstmann, H.; Jacob, D.; Paeth, H.

    2016-02-01

    The West African monsoon rainfall is essential for regional food production, and decadal predictions are necessary for policy makers and farmers. However, predictions with global climate models reveal precipitation biases. This study addresses the hypotheses that global prediction biases can be reduced by dynamical downscaling with a multimodel ensemble of three regional climate models (RCMs), a RCM coupled to a global ocean model and a RCM applying more realistic soil initialization and boundary conditions, i.e., aerosols, sea surface temperatures (SSTs), vegetation, and land cover. Numerous RCM predictions have been performed with REMO, COSMO-CLM (CCLM), and Weather Research and Forecasting (WRF) in various versions and for different decades. Global predictions reveal typical positive and negative biases over the Guinea Coast and the Sahel, respectively, related to a southward shifted Intertropical Convergence Zone (ITCZ) and a positive tropical Atlantic SST bias. These rainfall biases are reduced by some regional predictions in the Sahel but aggravated by all RCMs over the Guinea Coast, resulting from the inherited SST bias, increased westerlies and evaporation over the tropical Atlantic and shifted African easterly waves. The coupled regional predictions simulate high-resolution atmosphere-ocean interactions strongly improving the SST bias, the ITCZ shift and the Guinea Coast and Central Sahel precipitation biases. Some added values in rainfall bias are found for more realistic SST and land cover boundary conditions over the Guinea Coast and improved vegetation in the Central Sahel. Thus, the ability of RCMs and improved boundary conditions to reduce rainfall biases for climate impact research depends on the considered West African region.

  4. Describing the Elephant: Structure and Function in Multivariate Data.

    ERIC Educational Resources Information Center

    McDonald, Roderick P.

    1986-01-01

    There is a unity underlying the diversity of models for the analysis of multivariate data. Essentially, they constitute a family of models, most generally nonlinear, for structural/functional relations between variables drawn from a behavior domain. (Author)

  5. A unifying modeling framework for highly multivariate disease mapping.

    PubMed

    Botella-Rocamora, P; Martinez-Beneito, M A; Banerjee, S

    2015-04-30

    Multivariate disease mapping refers to the joint mapping of multiple diseases from regionally aggregated data and continues to be the subject of considerable attention for biostatisticians and spatial epidemiologists. The key issue is to map multiple diseases accounting for any correlations among themselves. Recently, Martinez-Beneito (2013) provided a unifying framework for multivariate disease mapping. While attractive in that it colligates a variety of existing statistical models for mapping multiple diseases, this and other existing approaches are computationally burdensome and preclude the multivariate analysis of moderate to large numbers of diseases. Here, we propose an alternative reformulation that accrues substantial computational benefits enabling the joint mapping of tens of diseases. Furthermore, the approach subsumes almost all existing classes of multivariate disease mapping models and offers substantial insight into the properties of statistical disease mapping models. PMID:25645551

  6. Multivariate Generalizability Models for Tests Developed from Tables of Specifications.

    ERIC Educational Resources Information Center

    Jarjoura, David; Brennan, Robert L.

    1983-01-01

    Multivariate generalizability techniques are used to bridge the gap between psychometric constraints and the tables of specifications needed in test development. Techniques are illustrated with results from the American College Testing Assessment Program. (Author/PN)

  7. A unifying modeling framework for highly multivariate disease mapping.

    PubMed

    Botella-Rocamora, P; Martinez-Beneito, M A; Banerjee, S

    2015-04-30

    Multivariate disease mapping refers to the joint mapping of multiple diseases from regionally aggregated data and continues to be the subject of considerable attention for biostatisticians and spatial epidemiologists. The key issue is to map multiple diseases accounting for any correlations among themselves. Recently, Martinez-Beneito (2013) provided a unifying framework for multivariate disease mapping. While attractive in that it colligates a variety of existing statistical models for mapping multiple diseases, this and other existing approaches are computationally burdensome and preclude the multivariate analysis of moderate to large numbers of diseases. Here, we propose an alternative reformulation that accrues substantial computational benefits enabling the joint mapping of tens of diseases. Furthermore, the approach subsumes almost all existing classes of multivariate disease mapping models and offers substantial insight into the properties of statistical disease mapping models.

  8. Constructing multivariate distributions with generalized marginals and t-copulas

    NASA Astrophysics Data System (ADS)

    Dass, Sarat C.; Huang, Wenmei; Muthuvalu, Mohana S.

    2014-10-01

    Generalized distributions are probability distributions that have both discrete and continuous components. In this paper, a method is proposed for constructing flexible multivariate distributions based on arbitrarily pre-specified generalized marginals and t-copulas. We give theoretical results establishing identifiability of the parameters of the multivariate distribution. These distributions are useful for modeling real data that show non-Gaussian characteristics such as disease trajectories (i.e., malaria and dengue) over time and space.

  9. Pattern recognition used to investigate multivariate data in analytical chemistry

    SciTech Connect

    Jurs, P.C.

    1986-06-06

    Pattern recognition and allied multivariate methods provide an approach to the interpretation of the multivariate data often encountered in analytical chemistry. Widely used methods include mapping and display, discriminant development, clustering, and modeling. Each has been applied to a variety of chemical problems, and examples are given. The results of two recent studies are shown, a classification of subjects as normal or cystic fibrosis heterozygotes and simulation of chemical shifts of carbon-13 nuclear magnetic resonance spectra by linear model equations.

  10. Multivariate analysis of environmental data for two hydrographic basins

    SciTech Connect

    Andrade, J.M.; Prada, D.; Muniategui, S.; Gonzalez, E.; Alonso, E. )

    1992-02-01

    A multivariate study (PCA Analysis and Cluster analysis) of two Spanish hydrographic basins (The Mandeo and Mero basins) was made to achieve reliable conclusions about their actual physico-chemical environmental situation. Two police-samples' are defined, their effects explained, and are introduced in Cluster analysis as a way to examine sample quality. The multivariate analysis shows different qualities in the two hydrographic basins.

  11. Biased Perception of Mean Emotion in Abstinent Heroin Abusers.

    PubMed

    Zhang, Meng; Wang, Xuan; Hu, Chun; Liao, Huayu; Yang, Tong; Shen, Mowei

    2015-01-01

    Although evidence suggests that drug abusers exhibit biases when coding individual emotional facial expressions, little is known about how they process multiple expressions simultaneously. The present study evaluated the mean emotions perceived by abstinent heroin abusers. Male abstinent heroin abusers (AHs) and healthy controls (HCs) were randomly assigned into three emotional conditions (happy, sad, or angry), viewed sets of four faces (Experiment 1) or individual faces (Experiment 2) that varied in emotionality (neutral to happy/sad/angry), and judged whether a test face presented later was more/less emotional than the preceding stimuli. Average points of subjective equality were calculated to reflect participants' biases in perceiving emotions of sets or single faces. Relative to HCs, AHs overestimated mean emotions for sad and angry faces in Experiment 1; however, no such biases were found in Experiment 2. This suggests biased ensemble coding towards negative emotional facial expressions in AHs. Furthermore, when controlling for depression and anxiety, AHs' enhanced perception of mean emotion for angry or sad faces in Experiment 1 decreased, indicating a possible mediating effect of these psychopathological variables in the relationship between drug addiction history and abnormal ensemble processing for sets of emotional expressions. PMID:26595559

  12. Biased Perception of Mean Emotion in Abstinent Heroin Abusers.

    PubMed

    Zhang, Meng; Wang, Xuan; Hu, Chun; Liao, Huayu; Yang, Tong; Shen, Mowei

    2015-01-01

    Although evidence suggests that drug abusers exhibit biases when coding individual emotional facial expressions, little is known about how they process multiple expressions simultaneously. The present study evaluated the mean emotions perceived by abstinent heroin abusers. Male abstinent heroin abusers (AHs) and healthy controls (HCs) were randomly assigned into three emotional conditions (happy, sad, or angry), viewed sets of four faces (Experiment 1) or individual faces (Experiment 2) that varied in emotionality (neutral to happy/sad/angry), and judged whether a test face presented later was more/less emotional than the preceding stimuli. Average points of subjective equality were calculated to reflect participants' biases in perceiving emotions of sets or single faces. Relative to HCs, AHs overestimated mean emotions for sad and angry faces in Experiment 1; however, no such biases were found in Experiment 2. This suggests biased ensemble coding towards negative emotional facial expressions in AHs. Furthermore, when controlling for depression and anxiety, AHs' enhanced perception of mean emotion for angry or sad faces in Experiment 1 decreased, indicating a possible mediating effect of these psychopathological variables in the relationship between drug addiction history and abnormal ensemble processing for sets of emotional expressions.

  13. Zero-bias spin separation

    NASA Astrophysics Data System (ADS)

    Ganichev, Sergey D.; Bel'Kov, Vasily V.; Tarasenko, Sergey A.; Danilov, Sergey N.; Giglberger, Stephan; Hoffmann, Christoph; Ivchenko, Eougenious L.; Weiss, Dieter; Wegscheider, Werner; Gerl, Christian; Schuh, Dieter; Stahl, Joachim; de Boeck, Jo; Borghs, Gustaaf; Prettl, Wilhelm

    2006-09-01

    The generation, manipulation and detection of spin-polarized electrons in low-dimensional semiconductors are at the heart of spintronics. Pure spin currents, that is, fluxes of magnetization without charge current, are quite attractive in this respect. A paradigmatic example is the spin Hall effect, where an electrical current drives a transverse spin current and causes a non-equilibrium spin accumulation observed near the sample boundary. Here we provide evidence for an another effect causing spin currents which is fundamentally different from the spin Hall effect. In contrast to the spin Hall effect, it does not require an electric current to flow: without bias the spin separation is achieved by spin-dependent scattering of electrons in media with suitable symmetry. We show, by free-carrier absorption of terahertz (THz) radiation, that spin currents flow in a wide range of temperatures. Moreover, the experimental results provide evidence that simple electron gas heating by any means is already sufficient to yield spin separation due to spin-dependent energy-relaxation processes.

  14. Haploinsufficiency predictions without study bias

    PubMed Central

    Steinberg, Julia; Honti, Frantisek; Meader, Stephen; Webber, Caleb

    2015-01-01

    Any given human individual carries multiple genetic variants that disrupt protein-coding genes, through structural variation, as well as nucleotide variants and indels. Predicting the phenotypic consequences of a gene disruption remains a significant challenge. Current approaches employ information from a range of biological networks to predict which human genes are haploinsufficient (meaning two copies are required for normal function) or essential (meaning at least one copy is required for viability). Using recently available study gene sets, we show that these approaches are strongly biased towards providing accurate predictions for well-studied genes. By contrast, we derive a haploinsufficiency score from a combination of unbiased large-scale high-throughput datasets, including gene co-expression and genetic variation in over 6000 human exomes. Our approach provides a haploinsufficiency prediction for over twice as many genes currently unassociated with papers listed in Pubmed as three commonly-used approaches, and outperforms these approaches for predicting haploinsufficiency for less-studied genes. We also show that fine-tuning the predictor on a set of well-studied ‘gold standard’ haploinsufficient genes does not improve the prediction for less-studied genes. This new score can readily be used to prioritize gene disruptions resulting from any genetic variant, including copy number variants, indels and single-nucleotide variants. PMID:26001969

  15. Sampling effort affects multivariate comparisons of stream assemblages

    USGS Publications Warehouse

    Cao, Y.; Larsen, D.P.; Hughes, R.M.; Angermeier, P.L.; Patton, T.M.

    2002-01-01

    Multivariate analyses are used widely for determining patterns of assemblage structure, inferring species-environment relationships and assessing human impacts on ecosystems. The estimation of ecological patterns often depends on sampling effort, so the degree to which sampling effort affects the outcome of multivariate analyses is a concern. We examined the effect of sampling effort on site and group separation, which was measured using a mean similarity method. Two similarity measures, the Jaccard Coefficient and Bray-Curtis Index were investigated with 1 benthic macroinvertebrate and 2 fish data sets. Site separation was significantly improved with increased sampling effort because the similarity between replicate samples of a site increased more rapidly than between sites. Similarly, the faster increase in similarity between sites of the same group than between sites of different groups caused clearer separation between groups. The strength of site and group separation completely stabilized only when the mean similarity between replicates reached 1. These results are applicable to commonly used multivariate techniques such as cluster analysis and ordination because these multivariate techniques start with a similarity matrix. Completely stable outcomes of multivariate analyses are not feasible. Instead, we suggest 2 criteria for estimating the stability of multivariate analyses of assemblage data: 1) mean within-site similarity across all sites compared, indicating sample representativeness, and 2) the SD of within-site similarity across sites, measuring sample comparability.

  16. Assessing threat responses towards the symptoms and diagnosis of schizophrenia using visual perceptual biases.

    PubMed

    Heenan, Adam; Best, Michael W; Ouellette, Sarah J; Meiklejohn, Erin; Troje, Nikolaus F; Bowie, Christopher R

    2014-10-01

    Stigma towards individuals diagnosed with schizophrenia continues despite increasing public knowledge about the disorder. Questionnaires are used almost exclusively to assess stigma despite self-report biases affecting their validity. The purpose of this experiment was to implicitly assess stigma towards individuals with schizophrenia by measuring visual perceptual biases immediately after participants conversed with a confederate. We manipulated both the diagnostic label attributed to the confederate (peer vs. schizophrenia) and the presence of behavioural symptoms (present vs. absent). Immediately before and after conversing with the confederate, we measured participants' facing-the-viewer (FTV) biases (the preference to perceive depth-ambiguous stick-figure walkers as facing towards them). As studies have suggested that the FTV bias is sensitive to the perception of threat, we hypothesized that FTV biases would be greater after participants conversed with someone that they believed had schizophrenia, and also after they conversed with someone who presented symptoms of schizophrenia. We found partial support for these hypotheses. Participants had significantly greater FTV biases in the Peer Label/Symptoms Present condition. Interestingly, while FTV biases were lowest in the Schizophrenia Label/Symptoms Present condition, participants in this condition were most likely to believe that people with schizophrenia should face social restrictions. Our findings support that both implicit and explicit beliefs help develop and sustain stigma.

  17. Understanding Implicit Bias: What Educators Should Know

    ERIC Educational Resources Information Center

    Staats, Cheryl

    2016-01-01

    The desire to ensure the best for children is precisely why educators should become aware of the concept of implicit bias: the attitudes or stereotypes that affect our understanding, actions, and decisions in an unconscious manner. Operating outside of our conscious awareness, implicit biases are pervasive, and they can challenge even the most…

  18. The Antifeminist Bias in Traditional Criticism.

    ERIC Educational Resources Information Center

    Rogers, Katharine M.

    Ten recent articles and books are cited in this paper as examples of a continuing antifeminist bias in literary criticism. Several forms of this bias are discussed, including an imperviousness to the feminist awareness, a refusal to recognize it, and open irritation by some critics that women are now finding a voice in literary criticism. A…

  19. Biases in Children's and Adults' Moral Judgments

    ERIC Educational Resources Information Center

    Powell, Nina L.; Derbyshire, Stuart W. G.; Guttentag, Robert E.

    2012-01-01

    Two experiments examined biases in children's (5/6- and 7/8-year-olds) and adults' moral judgments. Participants at all ages judged that it was worse to produce harm when harm occurred (a) through action rather than inaction (omission bias), (b) when physical contact with the victim was involved (physical contact principle), and (c) when the harm…

  20. Gender Bias in Lebanese Language Classes

    ERIC Educational Resources Information Center

    Mougharbel, Ghada M.; Bahous, Rima

    2010-01-01

    Gender bias, though often implicit and unnoticed, exists in many forms and in different situations. The purpose of this study is to investigate whether gender bias exists in Lebanese language classrooms. Semi-structured interviews, questionnaires, and nonparticipant observational techniques were used for data collection. Results reveal…

  1. A Reconsideration of Bias in the News.

    ERIC Educational Resources Information Center

    Stevenson, Robert L.; Greene, Mark T.

    1980-01-01

    Discusses conceptual problems with the traditional approach to the study of news bias; reports on a study conducted with 73 college students, which yielded data supporting the thesis that what news consumers see as biased news is often material that is discrepant with what they already believe. (GT)

  2. Hindsight Bias and Developing Theories of Mind

    ERIC Educational Resources Information Center

    Bernstein, Daniel M.; Atance, Cristina; Meltzoff, Andrew N.; Loftus, Geoffrey R.

    2007-01-01

    Although "hindsight bias" (the "I knew it all along" phenomenon) has been documented in adults, its development has not been investigated. This is despite the fact that hindsight bias errors closely resemble the errors children make on theory of mind (ToM) tasks. Two main goals of the present work were to (a) create a battery of hindsight tasks…

  3. Distinctive characteristics of sexual orientation bias crimes.

    PubMed

    Stacey, Michele

    2011-10-01

    Despite increased attention in the area of hate crime research in the past 20 years, sexual orientation bias crimes have rarely been singled out for study. When these types of crimes are looked at, the studies are typically descriptive in nature. This article seeks to increase our knowledge of sexual orientation bias by answering the question: What are the differences between sexual orientation motivated bias crimes and racial bias crimes? This question is examined using data from the National Incident Based Reporting System (NIBRS) and multiple regression techniques. This analysis draws on the strengths of NIBRS to look at the incident characteristics of hate crimes and distinguishing characteristics of sexual orientation crimes. Specifically this analysis looks at the types and seriousness of offenses motivated by sexual orientation bias as opposed to race bias as well as victim and offender characteristics. The findings suggest that there are differences between these two types of bias crimes, suggesting a need for further separation of the bias types in policy and research.

  4. Response Bias in Needs Assessment Studies.

    ERIC Educational Resources Information Center

    Calsyn, Robert J.; Klinkenberg, W. Dean

    1995-01-01

    Agencies conducting needs assessments in which respondents are asked about their awareness of the agency must be alert to a bias that inflates awareness (agency awareness acquiescence). A study with 157 college students demonstrated such awareness bias, which was related to the impression management component of social desirability. (SLD)

  5. Understanding Unconscious Bias and Unintentional Racism

    ERIC Educational Resources Information Center

    Moule, Jean

    2009-01-01

    Unconscious biases affect one's relationships, whether they are fleeting relationships in airports or longer term relationships between teachers and students, teachers and parents, teachers and other educators. In this article, the author argues that understanding one's possible biases is essential for developing community in schools.…

  6. Framing Bias among Expert and Novice Physicians.

    ERIC Educational Resources Information Center

    Christensen, Caryn; And Others

    1991-01-01

    A study explored the responses of medical students, resident physicians, and experienced physicians to 12 vignettes describing hypothetical patients to determine the relationship between clinical experience and susceptibility to bias in treatment decisions resulting from presentation of possible outcomes. Framing bias was most evident in the…

  7. Distinctive Characteristics of Sexual Orientation Bias Crimes

    ERIC Educational Resources Information Center

    Stacey, Michele

    2011-01-01

    Despite increased attention in the area of hate crime research in the past 20 years, sexual orientation bias crimes have rarely been singled out for study. When these types of crimes are looked at, the studies are typically descriptive in nature. This article seeks to increase our knowledge of sexual orientation bias by answering the question:…

  8. The Battle over Studies of Faculty Bias

    ERIC Educational Resources Information Center

    Gravois, John

    2007-01-01

    The American Federation of Teachers (AFT) recently commissioned a study to review the research that finds liberal bias run amok in academe. Believing that the AFT is not a dispassionate observer of this debate, this article provides "The Chronicle of Higher Education's" survey of the genre. The studies reviewed include: (1) "Political Bias in the…

  9. Racially Biased Policing: Determinants of Citizen Perceptions

    ERIC Educational Resources Information Center

    Weitzer, Ronald; Tuch, Steven A.

    2005-01-01

    The current controversy surrounding racial profiling in America has focused renewed attention on the larger issue of racial bias by the police. Yet little is known about the extent of police racial bias and even less about public perceptions of the problem. This article analyzes recent national survey data on citizens' views of and reported…

  10. Exploratory Studies of Bias in Achievement Tests.

    ERIC Educational Resources Information Center

    Green, Donald Ross; Draper, John F.

    This paper considers the question of bias in group administered academic achievement tests, bias which is inherent in the instruments themselves. A body of data on the test of performance of three disadvantaged minority groups--northern, urban black; southern, rural black; and, southwestern, Mexican-Americans--as tryout samples in contrast to…

  11. Sex Bias in Job Evaluation Procedures.

    ERIC Educational Resources Information Center

    Arvey, Richard D.

    1986-01-01

    Outlines issues pertaining to possible sex bias in job evaluation procedures and reviews relevant research. Gives attention to possible sex bias in job analysis procedures, choice and weighting of factors, and reliability and validity issues. Discusses future research needs, particularly reliability and validity aspects of job evaluation…

  12. How Many Hindsight Biases Are There?

    ERIC Educational Resources Information Center

    Blank, Hartmut; Nestler, Steffen; von Collani, Gernot; Fischer, Volkhard

    2008-01-01

    The answer is three: questioning a conceptual default assumption in hindsight bias research, we argue that the hindsight bias is not a unitary phenomenon but consists of three separable and partially independent subphenomena or components, namely, memory distortions, impressions of foreseeability and impressions of necessity. Following a detailed…

  13. The meaning of the bias uncertainty measure.

    PubMed

    Bartley, David L

    2008-08-01

    Characterization of measurement uncertainty in terms of root sums of squares of both unknown systematic as well as random error components is given meaning in the sense of prediction intervals. Both types of errors are commonly encountered with industrial hygiene air monitoring of hazardous substances. Two extreme types of measurement methods are presented for illustrating how confidence levels may be ascribed to prediction intervals defined by such uncertainty values. In the case of method calibration at each measurement, systematic error or bias may enter from a biased calibrant. At another extreme, a single initial method evaluation may leave residual bias owing to random error in the evaluation itself or to the use of a biased reference method. Analysis is simplified through new simple approximations to probabilistic limits (quantiles) on the magnitude of a non-central Student t-distributed random variable. Connection is established between traditional confidence limits, accuracy measures in the case of bias minimization and an uncertainty measure.

  14. Are all biases missing data problems?

    PubMed Central

    Howe, Chanelle J.; Cain, Lauren E.; Hogan, Joseph W.

    2015-01-01

    Estimating causal effects is a frequent goal of epidemiologic studies. Traditionally, there have been three established systematic threats to consistent estimation of causal effects. These three threats are bias due to confounders, selection, and measurement error. Confounding, selection, and measurement bias have typically been characterized as distinct types of biases. However, each of these biases can also be characterized as missing data problems that can be addressed with missing data solutions. Here we describe how the aforementioned systematic threats arise from missing data as well as review methods and their related assumptions for reducing each bias type. We also link the assumptions made by the reviewed methods to the missing completely at random (MCAR) and missing at random (MAR) assumptions made in the missing data framework that allow for valid inferences to be made based on the observed, incomplete data. PMID:26576336

  15. Exploring the various interpretations of "test bias".

    PubMed

    Warne, Russell T; Yoon, Myeongsun; Price, Chris J

    2014-10-01

    Test bias is a hotly debated topic in society, especially as it relates to diverse groups of examinees who often score low on standardized tests. However, the phrase "test bias" has a multitude of interpretations that many people are not aware of. In this article, we explain five different meanings of "test bias" and summarize the empirical and theoretical evidence related to each interpretation. The five meanings are as follows: (a) mean group differences, (b) differential predictive validity, (c) differential item functioning, (d) differing factor structures of tests, and (e) unequal consequences of test use for various groups. We explain in this article why meanings (a) and (e) are not actual forms of test bias and that there are serious concerns about (b). In our conclusion, we discuss the benefits of standardized testing for diverse examinees and urge readers to be careful and precise in their use of the phrase "test bias."

  16. Leaf physiognomy and climate: A multivariate analysis

    NASA Astrophysics Data System (ADS)

    Davis, J. M.; Taylor, S. E.

    1980-11-01

    Research has demonstrated that leaf physiognomy is representative of the local or microclimate conditions under which plants grow. The physiognomy of leaf samples from Oregon, Michigan, Missouri, Tennessee, and the Panama Canal Zone has been related to the microclimate using Walter diagrams and Thornthwaite water-budget data. A technique to aid paleoclimatologists in identifying the nature of the microclimate from leaf physiognomy utilizes statistical procedures to classify leaf samples into one of six microclimate regimes based on leaf physiognomy information available from fossilized samples.

  17. Velocity distribution of ions incident on a radio-frequency biased wafer

    NASA Astrophysics Data System (ADS)

    Wakayama, G.; Nanbu, K.

    2001-08-01

    The ion velocity distribution (IVD) is important in plasma etching of microfeatures. IVD at a rf biased wafer is studied, first analytically using probability theory and then numerically by using a particle simulation method. The analytic expression shows that IVD is governed by the parameter qVrf/miωl, where q is the charge of ion, Vrf is the rf bias amplitude, ω is the rf bias angular frequency, l is the penetration depth of bias potential, and mi is the mass of ion. The analytical expression is applicable to the case when the ion collisions in the penetration depth are negligibly few and the rf period of biasing is much shorter than the time that ions take in traversing the depth l. The IVDs for general conditions are also examined using the self-consistent particle-in-cell/Monte Carlo simulation.

  18. Effects of prism adaptation on motor-intentional spatial bias in neglect

    PubMed Central

    Fortis, Paola; Chen, Peii; Goedert, Kelly M.; Barrett, Anna M.

    2011-01-01

    Prism adaptation may alleviate some symptoms of spatial neglect. However, the mechanism through which this technique works is still unclear. The current study investigated whether prism adaptation differentially affects dysfunction in perceptual-attentional “where” versus motor-intentional “aiming” bias. Five neglect patients performed a line bisection task in which lines were viewed under both normal and right-left reversed viewing conditions, allowing for the fractionation of “where” and “aiming” spatial bias components. Following two consecutive days of prism adaptation, participants demonstrated a significant improvement in “aiming” spatial bias, with no effect on “where” spatial bias. These findings suggest that prism adaptation may primarily affect motor-intentional “aiming” bias in post-stroke spatial neglect patients. PMID:21817924

  19. Medical journal peer review: process and bias.

    PubMed

    Manchikanti, Laxmaiah; Kaye, Alan D; Boswell, Mark V; Hirsch, Joshua A

    2015-01-01

    Scientific peer review is pivotal in health care research in that it facilitates the evaluation of findings for competence, significance, and originality by qualified experts. While the origins of peer review can be traced to the societies of the eighteenth century, it became an institutionalized part of the scholarly process in the latter half of the twentieth century. This was a response to the growth of research and greater subject specialization. With the current increase in the number of specialty journals, the peer review process continues to evolve to meet the needs of patients, clinicians, and policy makers. The peer review process itself faces challenges. Unblinded peer review might suffer from positive or negative bias towards certain authors, specialties, and institutions. Peer review can also suffer when editors and/or reviewers might be unable to understand the contents of the submitted manuscript. This can result in an inability to detect major flaws, or revelations of major flaws after acceptance of publication by the editors. Other concerns include potentially long delays in publication and challenges uncovering plagiarism, duplication, corruption and scientific misconduct. Conversely, a multitude of these challenges have led to claims of scientific misconduct and an erosion of faith. These challenges have invited criticism of the peer review process itself. However, despite its imperfections, the peer review process enjoys widespread support in the scientific community. Peer review bias is one of the major focuses of today's scientific assessment of the literature. Various types of peer review bias include content-based bias, confirmation bias, bias due to conservatism, bias against interdisciplinary research, publication bias, and the bias of conflicts of interest. Consequently, peer review would benefit from various changes and improvements with appropriate training of reviewers to provide quality reviews to maintain the quality and integrity of

  20. F100 multivariable control synthesis program: Evaluation of a multivariable control using a real-time engine simulation

    NASA Technical Reports Server (NTRS)

    Szuch, J. R.; Soeder, J. F.; Seldner, K.; Cwynar, D. S.

    1977-01-01

    The design, evaluation, and testing of a practical, multivariable, linear quadratic regulator control for the F100 turbofan engine were accomplished. NASA evaluation of the multivariable control logic and implementation are covered. The evaluation utilized a real time, hybrid computer simulation of the engine. Results of the evaluation are presented, and recommendations concerning future engine testing of the control are made. Results indicated that the engine testing of the control should be conducted as planned.

  1. A test to identify judgement bias in mice.

    PubMed

    Boleij, Hetty; van't Klooster, José; Lavrijsen, Marla; Kirchhoff, Susanne; Arndt, Saskia S; Ohl, Frauke

    2012-07-15

    Emotional states are known to affect cognitive processes. For example highly anxious individuals interpret ambiguous stimuli more negatively than low anxious people, an effect called negative judgement bias. Recently, the measurement of judgement bias has been used to try and indicate emotional states in animals. In the present experiment a potential test for judgement bias in mice was examined. Mice were trained with two distinct odour cues (vanilla or apple) predicting either a palatable or an unpalatable almond piece. Subsequently their reaction to mixtures of both odours, the ambiguous stimuli, was investigated. Mice of the BALB/cJ and 129P3/J inbred mouse strains (high initial anxiety and low initial anxiety phenotypes respectively) were tested. While BALB/cJ mice showed odour association learning and showed intermediate reactions to the ambiguous cues, 129P3/J mice did not discriminate between the cues. Additionally BALB/cJ mice that were tested under more aversive white light conditions revealed a higher latency to approach the almond piece than mice tested under less aversive red light conditions. The ambiguous stimulus however was interpreted as negative under both test conditions. Brain c-Fos expression levels (a marker for neuronal activity) differed between the BALB/c/J and 129P3/J in the lateral amygdala and the prelimbic cortex, indicating differences in ambiguous information processing between the strains. The behavioural results suggest that the present judgement bias test might be used to assess emotional states in at least BALB/c mice, however further research on both behaviour and on the involved brain mechanisms is necessary to confirm this idea.

  2. Exploring biases in exoplanet spectroscopy retrievals

    NASA Astrophysics Data System (ADS)

    Feng, Ying; Fortney, Jonathan; Line, Michael R.; Morley, Caroline

    2015-12-01

    Spectra from the atmospheres of transiting planets and imaged planets are now being routinely achieved. The interpretation of these spectra gives us a window into the physics and chemistry of these atmospheres, as well as a better understanding of planet formation. Over the past several years retrievals of exoplanet spectra have been used to obtain chemical abundances and thermal structures, along with assessments of uncertainties on these quantities. However, any potential biases inherent in these methods have yet to be fully explored. The atmospheres we observe are inherently three-dimensional (3D) structures that feature gradients in temperatures and chemical abundances, as well as hot spots, cold spots, storms, and cloud patchiness. How well does the assumption of retrieving 1D hemispheric average conditions represent the true state of a 3D atmosphere? Can we be led astray? Answering these questions are important today, and will only become more important as data quality improves. Using a descendent of the CHIMERA retrieval code, here I present the results of how retrievals perform on more complex scenarios. We start with the emitted spectrum from the average of two pressure-temperature profiles, one warmer, one colder, and move on to more sophisticated, but still realistic, scenarios. Our work makes use of a new code we have developed to construct spectra from arbitrary 3D model atmospheres.

  3. An algorithm for multivariate weak stochastic dominance

    SciTech Connect

    Mosler, K.

    1994-12-31

    The talk addresses the computational problem of comparing two given probability distributions in n-space with respect to several stochastic orderings. The orderings investigated are weak first degree stochastic dominance, weak second degree stochastic dominance, and their dual ordering relations. For each of the four dominance relations we present conditions which are necessary and sufficient for dominance of F over G when F and G have finite support in n-space. An algorithm is proposed which operates efficiently on the join-semilattice generated by their joint support. If F and G are empirical distribution functions, and {anti F} and {anti G}denote the underlying probability laws, significance tests can be performed on {anti F} = {anti G} against the alternative that {anti F} {ne} {anti G} and {anti F} dominates {anti G} in one of the four orderings. Other applications are found in decision theory, applied probability, operations research, and economics.

  4. Large biases in regression-based constituent flux estimates: causes and diagnostic tools

    USGS Publications Warehouse

    Hirsch, Robert M.

    2014-01-01

    It has been documented in the literature that, in some cases, widely used regression-based models can produce severely biased estimates of long-term mean river fluxes of various constituents. These models, estimated using sample values of concentration, discharge, and date, are used to compute estimated fluxes for a multiyear period at a daily time step. This study compares results of the LOADEST seven-parameter model, LOADEST five-parameter model, and the Weighted Regressions on Time, Discharge, and Season (WRTDS) model using subsampling of six very large datasets to better understand this bias problem. This analysis considers sample datasets for dissolved nitrate and total phosphorus. The results show that LOADEST-7 and LOADEST-5, although they often produce very nearly unbiased results, can produce highly biased results. This study identifies three conditions that can give rise to these severe biases: (1) lack of fit of the log of concentration vs. log discharge relationship, (2) substantial differences in the shape of this relationship across seasons, and (3) severely heteroscedastic residuals. The WRTDS model is more resistant to the bias problem than the LOADEST models but is not immune to them. Understanding the causes of the bias problem is crucial to selecting an appropriate method for flux computations. Diagnostic tools for identifying the potential for bias problems are introduced, and strategies for resolving bias problems are described.

  5. Reward sensitivity predicts ice cream-related attentional bias assessed by inattentional blindness.

    PubMed

    Li, Xiaoming; Tao, Qian; Fang, Ya; Cheng, Chen; Hao, Yangyang; Qi, Jianjun; Li, Yu; Zhang, Wei; Wang, Ying; Zhang, Xiaochu

    2015-06-01

    The cognitive mechanism underlying the association between individual differences in reward sensitivity and food craving is unknown. The present study explored the mechanism by examining the role of reward sensitivity in attentional bias toward ice cream cues. Forty-nine college students who displayed high level of ice cream craving (HICs) and 46 who displayed low level of ice cream craving (LICs) performed an inattentional blindness (IB) task which was used to assess attentional bias for ice cream. In addition, reward sensitivity and coping style were assessed by the Behavior Inhibition System/Behavior Activation System Scales and Simplified Coping Style Questionnaire. Results showed significant higher identification rate of the critical stimulus in the HICs than LICs, suggesting greater attentional bias for ice cream in the HICs. It was indicated that attentional bias for food cues persisted even under inattentional condition. Furthermore, a significant correlation was found between the attentional bias and reward sensitivity after controlling for coping style, and reward sensitivity predicted attentional bias for food cues. The mediation analyses showed that attentional bias mediated the relationship between reward sensitivity and food craving. Those findings suggest that the association between individual differences in reward sensitivity and food craving may be attributed to attentional bias for food-related cues.

  6. Development of bias in analytical predictions based on behavior of platforms during hurricanes

    SciTech Connect

    Aggarwal, R.K.; Dolan, D.K.; Cornell, C.A.

    1996-12-31

    A Joint Industry Project (JIP) was initiated by 13 oil companies and the US Minerals Management Service (MMS), wherein a methodology was developed to use information from observed platform conditions resulting from Andrew and the hurricane hindcast data with capacity, reliability, and Bayesian updating analyses to determine a measure of differences (biases) in the analytical predictions and field observations. The procedures used for structural integrity analysis were also improved as a result of this study. Phase 1 of this project completed in October 1993 defined a global bias factor. A study of foundation behavior was completed following Phase 1 and determined bias factors specific to foundation failure modes. This paper presents the approach followed in the most recent phase of this project in which bias factors specific to jacket and two foundation failure modes (lateral and axial) were developed. This study utilized an updated storm hindcast, improved analysis models, and a more detailed calibration procedure. The three bias factors were developed and were found to differ significantly. The bias factors developed through this study have provided means to further improve procedures used in the assessment of existing platforms. The proper use of these new analytical methodologies and bias factors will produce more appropriate and cost-effective mitigation measures for safe platform operations. The methodology for establishing bias factors developed and proven in these projects is applicable to other offshore regions and production systems with specific environmental, geotechnical, material and structure features.

  7. Mulstiscale Stochastic Generator of Multivariate Met-Ocean Time Series

    NASA Astrophysics Data System (ADS)

    Guanche, Yanira; Mínguez, Roberto; Méndez, Fernando J.

    2013-04-01

    The design of maritime structures requires information on sea state conditions that influence its behavior during its life cycle. In the last decades, there has been a increasing development of sea databases (buoys, reanalysis, satellite) that allow an accurate description of the marine climate and its interaction with a given structure in terms of functionality and stability. However, these databases have a limited timelength, and its appliance entails an associated uncertainty. To avoid this limitation, engineers try to sample synthetically generated time series, statistically consistent, which allow the simulation of longer time periods. The present work proposes a hybrid methodology to deal with this issue. It is based in the combination of clustering algorithms (k-means) and an autoregressive logistic regression model (logit). Since the marine climate is directly related to the atmospheric conditions at a synoptic scale, the proposed methodology takes both systems into account; generating simultaneously circulation patterns (weather types) time series and the sea state time series related. The generation of these time series can be summarized in three steps: (1) By applying the clustering technique k-means the atmospheric conditions are classified into a representative number of synoptical patterns (2) Taking into account different covariates involved (such as seasonality, interannual variability, trends or autoregressive term) the autoregressive logistic model is adjusted (3) Once the model is able to simulate weather types time series the last step is to generate multivariate hourly metocean parameters related to these weather types. This is done by an autoregressive model (ARMA) for each variable, including cross-correlation between them. To show the goodness of the proposed method the following data has been used: Sea Level Pressure (SLP) databases from NCEP-NCAR and Global Ocean Wave (GOW) reanalysis from IH Cantabria. The synthetical met-ocean hourly

  8. The choice of prior distribution for a covariance matrix in multivariate meta-analysis: a simulation study.

    PubMed

    Hurtado Rúa, Sandra M; Mazumdar, Madhu; Strawderman, Robert L

    2015-12-30

    Bayesian meta-analysis is an increasingly important component of clinical research, with multivariate meta-analysis a promising tool for studies with multiple endpoints. Model assumptions, including the choice of priors, are crucial aspects of multivariate Bayesian meta-analysis (MBMA) models. In a given model, two different prior distributions can lead to different inferences about a particular parameter. A simulation study was performed in which the impact of families of prior distributions for the covariance matrix of a multivariate normal random effects MBMA model was analyzed. Inferences about effect sizes were not particularly sensitive to prior choice, but the related covariance estimates were. A few families of prior distributions with small relative biases, tight mean squared errors, and close to nominal coverage for the effect size estimates were identified. Our results demonstrate the need for sensitivity analysis and suggest some guidelines for choosing prior distributions in this class of problems. The MBMA models proposed here are illustrated in a small meta-analysis example from the periodontal field and a medium meta-analysis from the study of stroke. Copyright © 2015 John Wiley & Sons, Ltd.

  9. The choice of prior distribution for a covariance matrix in multivariate meta-analysis: a simulation study.

    PubMed

    Hurtado Rúa, Sandra M; Mazumdar, Madhu; Strawderman, Robert L

    2015-12-30

    Bayesian meta-analysis is an increasingly important component of clinical research, with multivariate meta-analysis a promising tool for studies with multiple endpoints. Model assumptions, including the choice of priors, are crucial aspects of multivariate Bayesian meta-analysis (MBMA) models. In a given model, two different prior distributions can lead to different inferences about a particular parameter. A simulation study was performed in which the impact of families of prior distributions for the covariance matrix of a multivariate normal random effects MBMA model was analyzed. Inferences about effect sizes were not particularly sensitive to prior choice, but the related covariance estimates were. A few families of prior distributions with small relative biases, tight mean squared errors, and close to nominal coverage for the effect size estimates were identified. Our results demonstrate the need for sensitivity analysis and suggest some guidelines for choosing prior distributions in this class of problems. The MBMA models proposed here are illustrated in a small meta-analysis example from the periodontal field and a medium meta-analysis from the study of stroke. Copyright © 2015 John Wiley & Sons, Ltd. PMID:26303671

  10. Learning biases underlying individual differences in sensitivity to social rejection

    PubMed Central

    Olsson, Andreas; Carmona, Susanna; Downey, Geraldine; Bolger, Niall; Ochsner, Kevin N.

    2014-01-01

    People vary greatly in their dispositions to anxiously expect, readily perceive, and strongly react to social rejection (rejection sensitivity, RS) with implications for social functioning and health. Here, we examined how RS influences learning about social threat. Using a classical fear conditioning task, we established that high as compared to low (HRS vs. LRS) individuals displayed a resistance to extinction of the conditioned response to angry faces, but not to neutral faces or non-social stimuli. Our findings suggest that RS biases the flexible updating of acquired expectations for threat, which helps to explain how RS operates as a self-fulfilling prophecy. PMID:23914767

  11. Classification of Malaysia aromatic rice using multivariate statistical analysis

    SciTech Connect

    Abdullah, A. H.; Adom, A. H.; Shakaff, A. Y. Md; Masnan, M. J.; Zakaria, A.; Rahim, N. A.; Omar, O.

    2015-05-15

    Aromatic rice (Oryza sativa L.) is considered as the best quality premium rice. The varieties are preferred by consumers because of its preference criteria such as shape, colour, distinctive aroma and flavour. The price of aromatic rice is higher than ordinary rice due to its special needed growth condition for instance specific climate and soil. Presently, the aromatic rice quality is identified by using its key elements and isotopic variables. The rice can also be classified via Gas Chromatography Mass Spectrometry (GC-MS) or human sensory panels. However, the uses of human sensory panels have significant drawbacks such as lengthy training time, and prone to fatigue as the number of sample increased and inconsistent. The GC–MS analysis techniques on the other hand, require detailed procedures, lengthy analysis and quite costly. This paper presents the application of in-house developed Electronic Nose (e-nose) to classify new aromatic rice varieties. The e-nose is used to classify the variety of aromatic rice based on the samples odour. The samples were taken from the variety of rice. The instrument utilizes multivariate statistical data analysis, including Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and K-Nearest Neighbours (KNN) to classify the unknown rice samples. The Leave-One-Out (LOO) validation approach is applied to evaluate the ability of KNN to perform recognition and classification of the unspecified samples. The visual observation of the PCA and LDA plots of the rice proves that the instrument was able to separate the samples into different clusters accordingly. The results of LDA and KNN with low misclassification error support the above findings and we may conclude that the e-nose is successfully applied to the classification of the aromatic rice varieties.

  12. Classification of Malaysia aromatic rice using multivariate statistical analysis

    NASA Astrophysics Data System (ADS)

    Abdullah, A. H.; Adom, A. H.; Shakaff, A. Y. Md; Masnan, M. J.; Zakaria, A.; Rahim, N. A.; Omar, O.

    2015-05-01

    Aromatic rice (Oryza sativa L.) is considered as the best quality premium rice. The varieties are preferred by consumers because of its preference criteria such as shape, colour, distinctive aroma and flavour. The price of aromatic rice is higher than ordinary rice due to its special needed growth condition for instance specific climate and soil. Presently, the aromatic rice quality is identified by using its key elements and isotopic variables. The rice can also be classified via Gas Chromatography Mass Spectrometry (GC-MS) or human sensory panels. However, the uses of human sensory panels have significant drawbacks such as lengthy training time, and prone to fatigue as the number of sample increased and inconsistent. The GC-MS analysis techniques on the other hand, require detailed procedures, lengthy analysis and quite costly. This paper presents the application of in-house developed Electronic Nose (e-nose) to classify new aromatic rice varieties. The e-nose is used to classify the variety of aromatic rice based on the samples odour. The samples were taken from the variety of rice. The instrument utilizes multivariate statistical data analysis, including Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and K-Nearest Neighbours (KNN) to classify the unknown rice samples. The Leave-One-Out (LOO) validation approach is applied to evaluate the ability of KNN to perform recognition and classification of the unspecified samples. The visual observation of the PCA and LDA plots of the rice proves that the instrument was able to separate the samples into different clusters accordingly. The results of LDA and KNN with low misclassification error support the above findings and we may conclude that the e-nose is successfully applied to the classification of the aromatic rice varieties.

  13. Near infrared spectroscopy, cluster and multivariate analysis hyphenated to thin layer chromatography for the analysis of amino acids.

    PubMed

    Heigl, N; Huck, C W; Rainer, M; Najam-Ul-Haq, M; Bonn, G K

    2006-07-01

    A method based on near-infrared spectroscopy (NIRS) was developed for the rapid and non-destructive determination and quantification of solid and dissolved amino acids. The statistical results obtained after optimisation of measurement conditions were evaluated on the basis of statistical parameters, Q-value (quality of calibrations), R(2), standard error of estimation (SEE), standard error of prediction (SEP), BIAS applying cluster and different multivariate analytical procedures. Experimental optimisation comprised the selection of the highest suitable optical thin-layer (0.5, 1.0, 1.5, 2.0, 2.5, 3.0 mm), sample temperature (10-30 degrees C), measurement option (light fibre, 0.5 mm optical thin-layer; boiling point tube; different types of cuvettes) and sample concentration in the range between 100 and 500 ppm. Applying the optimised conditions and a 115-QS Suprasil cuvette (V = 400 microl), the established qualitative model enabled to distinguish between different dissolved amino acids with a Q-value of 0.9555. Solid amino acids were investigated in the transflectance mode, allowing to differentiate them with a Q-value of 0.9155. For the qualitative and quantitative analysis of amino acids in complex matrices NIRS was established as a detection system directly onto the plate after prior separation on cellulose based thin-layer chromatography (TLC) sheets employing n-butanol, acetic acid and distilled water at a ratio of 8:4:2 (v/v/v) as an optimised mobile phase. Due to the prior separation step, the established calibration curve was found to be more stable than the one calculated from the dissolved amino acids. The found lower limit of detection was 0.01 mg/ml. Finally, this optimised TLC-NIRS method was successfully applied for the qualitative and quantitative analysis of L-lysine in apple juice. NIRS is shown not only to offer a fast, non-destructive detection tool but also to provide an easy-to-use alternative to more complicated detection methods such as

  14. Oceanic origin of southeast tropical Atlantic biases

    NASA Astrophysics Data System (ADS)

    Xu, Zhao; Li, Mingkui; Patricola, Christina M.; Chang, Ping

    2014-12-01

    Most coupled general circulation models suffer from a prominent warm sea surface temperature bias in the southeast tropical Atlantic Ocean off the coast of Africa. The origin of the bias is not understood and remains highly controversial. Previous studies suggest that the origin of the bias stems from systematic errors of atmospheric models in simulating surface heat flux and coastal wind, or poorly simulated coastal upwelling. In this study, we show, using different reanalysis and observational data sets combined with a set of eddy-resolving regional ocean model simulations, that systematic errors in ocean models also make a significant contribution to the bias problem. In particular (1) the strong warm bias at the Angola-Benguela front that is maintained by the local wind and the convergence of Angola and Benguela Currents is caused by an overshooting of the Angola Current in ocean models and (2) the alongshore warm bias to the south of the front is caused by ocean model deficiencies in simulating the sharp thermocline along the Angola coast, which is linked to biases in the equatorial thermocline, and the complex circulation system within the Benguela upwelling zone.

  15. Statistical framework for estimating GNSS bias

    NASA Astrophysics Data System (ADS)

    Vierinen, Juha; Coster, Anthea J.; Rideout, William C.; Erickson, Philip J.; Norberg, Johannes

    2016-03-01

    We present a statistical framework for estimating global navigation satellite system (GNSS) non-ionospheric differential time delay bias. The biases are estimated by examining differences of measured line-integrated electron densities (total electron content: TEC) that are scaled to equivalent vertical integrated densities. The spatiotemporal variability, instrumentation-dependent errors, and errors due to inaccurate ionospheric altitude profile assumptions are modeled as structure functions. These structure functions determine how the TEC differences are weighted in the linear least-squares minimization procedure, which is used to produce the bias estimates. A method for automatic detection and removal of outlier measurements that do not fit into a model of receiver bias is also described. The same statistical framework can be used for a single receiver station, but it also scales to a large global network of receivers. In addition to the Global Positioning System (GPS), the method is also applicable to other dual-frequency GNSS systems, such as GLONASS (Globalnaya Navigazionnaya Sputnikovaya Sistema). The use of the framework is demonstrated in practice through several examples. A specific implementation of the methods presented here is used to compute GPS receiver biases for measurements in the MIT Haystack Madrigal distributed database system. Results of the new algorithm are compared with the current MIT Haystack Observatory MAPGPS (MIT Automated Processing of GPS) bias determination algorithm. The new method is found to produce estimates of receiver bias that have reduced day-to-day variability and more consistent coincident vertical TEC values.

  16. Weight Bias in University Health Professions Students.

    PubMed

    Blanton, Cynthia; Brooks, Jennifer K; McKnight, Laura

    2016-01-01

    Negative attitudes toward people with high body weight have been documented in pre-professional health students, prompting concern that such feelings may manifest as poor patient care in professional practice. This study assessed weight bias in university students in the non-physician health professions. A convenience sample of 206 students completed an online survey composed of a validated 14-item scale (1-5 lowest to highest weight bias) and questions regarding personal experiences of weight bias. Respondents were grouped by discipline within graduate and undergraduate levels. Weight bias was present in a majority of respondents. Overall, the percentage of responses indicative of weight bias was 92.7%. The mean total score was 3.65. ± 0.52, and the rating exceeded 3 for all 14 scale descriptors of high-weight people. In graduate students, discipline had a significant main effect on total score (p=0.01), with lower scores in dietetics (3.17 ± 0.46) vs audiology/sign language/speech language pathology (3.84 ± 0.41) and physician assistant students (3.78 ± 0.51; p<0.05). These findings show that weight bias is prevalent in health professions students at a mountain west university. Well-controlled studies that track students into professional practice would help determine whether bias-reduction interventions in college improve provider behaviors and clinical outcomes. PMID:27585618

  17. [Practical considerations on detection of publication bias].

    PubMed

    Palma Pérez, Silvia; Delgado Rodríguez, Miguel

    2006-12-01

    The present review aims to answer 3 questions: does publication bias need to be assessed in meta-analyses?; what procedures, not requiring complex statistical approaches, can be applied to detect it?; and should other factors be taken into account when interpreting the procedures? The first question is easy to answer. Publication bias is a potential threat to the validity of the conclusions of meta-analyses. Therefore, both the MOOSE and QUOROM statements include publication bias in their guidelines; nevertheless, many meta-analyses do not use these statements (e.g., meta-analyses conducted by the Cochrane Collaboration), perhaps because they use a comprehensive search strategy. There are many methods to assess publication bias. The most frequently used are funnel plots or , (which allow the effects of bias to be estimated), and methods based upon regression on plots, such as Egger's method and funnel plot regression. An advantage of these methods is that they can only be applied using published data. However, agreement between these methods in detecting bias is often poor. Therefore, application of more than one method to detect publication bias is recommended. To correctly interpret the results, the number of pooled studies should be more than 10 and the existence of heterogeneity in the pooled estimate must be taken into account.

  18. Modality transition-based network from multivariate time series for characterizing horizontal oil-water flow patterns

    NASA Astrophysics Data System (ADS)

    Ding, Mei-Shuang; Jin, Ning-De; Gao, Zhong-Ke

    2015-11-01

    The simultaneous flow of oil and water through a horizontal pipe is a common occurrence during petroleum industrial processes. Characterizing the flow behavior underlying horizontal oil-water flows is a challenging problem of significant importance. In order to solve this problem, we carry out experiment to measure multivariate signals from different flow patterns and then propose a novel modality transition-based network to analyze the multivariate signals. The results suggest that the local betweenness centrality and weighted shortest path of the constructed network can characterize the transitions of flow conditions and further allow quantitatively distinguishing and uncovering the dynamic flow behavior underlying different horizontal oil-water flow patterns.

  19. Removing Malmquist bias from linear regressions

    NASA Technical Reports Server (NTRS)

    Verter, Frances

    1993-01-01

    Malmquist bias is present in all astronomical surveys where sources are observed above an apparent brightness threshold. Those sources which can be detected at progressively larger distances are progressively more limited to the intrinsically luminous portion of the true distribution. This bias does not distort any of the measurements, but distorts the sample composition. We have developed the first treatment to correct for Malmquist bias in linear regressions of astronomical data. A demonstration of the corrected linear regression that is computed in four steps is presented.

  20. Exchange bias effect in alloys and compounds.

    PubMed

    Giri, S; Patra, M; Majumdar, S

    2011-02-23

    The phenomenology of exchange bias effects observed in structurally single-phase alloys and compounds but composed of a variety of coexisting magnetic phases such as ferromagnetic, antiferromagnetic, ferrimagnetic, spin-glass, cluster-glass and disordered magnetic states are reviewed. The investigations on exchange bias effects are discussed in diverse types of alloys and compounds where qualitative and quantitative aspects of magnetism are focused based on macroscopic experimental tools such as magnetization and magnetoresistance measurements. Here, we focus on improvement of fundamental issues of the exchange bias effects rather than on their technological importance.

  1. Multivariate refutation of aetiological hypotheses in non-experimental epidemiology.

    PubMed

    Maclure, M

    1990-12-01

    Extension of Karl Popper's logic of refutation from the realm of contingency tables to multivariate modelling leads to the conclusion that rigorously scientific multivariate analysis in non-experimental epidemiology differs from the traditional quasi-scientific approach. Instead of aiming for high sensitivity in detecting aetiological agents, the goal in refutation is high specificity--to give the best defence of the 'innocence' of every exposure hypothesized as being a cause. Instead of 'forward selection' or 'backward elimination', multivariate refutation uses the method of 'forward elimination'. This entails a likelihood approach (which may be complemented by, but should be demarcated from, Bayesian methods) not only for statistical inference but also, by analogy, for study design and conduct: one starts with the conclusion (the estimate or hypothesis) and works backwards to the observations (the likelihood of the data or the design of the study). Differences in practice can sometimes be large, as illustrated by a study of hypothesized triggers of myocardial infarction. Multivariate refutation should replace the concept of multivariate modelling in non-experimental epidemiology.

  2. The statistical analysis of multivariate serological frequency data.

    PubMed

    Reyment, Richard A

    2005-11-01

    Data occurring in the form of frequencies are common in genetics-for example, in serology. Examples are provided by the AB0 group, the Rhesus group, and also DNA data. The statistical analysis of tables of frequencies is carried out using the available methods of multivariate analysis with usually three principal aims. One of these is to seek meaningful relationships between the components of a data set, the second is to examine relationships between populations from which the data have been obtained, the third is to bring about a reduction in dimensionality. This latter aim is usually realized by means of bivariate scatter diagrams using scores computed from a multivariate analysis. The multivariate statistical analysis of tables of frequencies cannot safely be carried out by standard multivariate procedures because they represent compositions and are therefore embedded in simplex space, a subspace of full space. Appropriate procedures for simplex space are compared and contrasted with simple standard methods of multivariate analysis ("raw" principal component analysis). The study shows that the differences between a log-ratio model and a simple logarithmic transformation of proportions may not be very great, particularly as regards graphical ordinations, but important discrepancies do occur. The divergencies between logarithmically based analyses and raw data are, however, great. Published data on Rhesus alleles observed for Italian populations are used to exemplify the subject. PMID:16024067

  3. Brushing of attribute clouds for the visualization of multivariate data.

    PubMed

    Jänicke, Heike; Böttinger, Michael; Scheuermann, Gerik

    2008-01-01

    The visualization and exploration of multivariate data is still a challenging task. Methods either try to visualize all variables simultaneously at each position using glyph-based approaches or use linked views for the interaction between attribute space and physical domain such as brushing of scatterplots. Most visualizations of the attribute space are either difficult to understand or suffer from visual clutter. We propose a transformation of the high-dimensional data in attribute space to 2D that results in a point cloud, called attribute cloud, such that points with similar multivariate attributes are located close to each other. The transformation is based on ideas from multivariate density estimation and manifold learning. The resulting attribute cloud is an easy to understand visualization of multivariate data in two dimensions. We explain several techniques to incorporate additional information into the attribute cloud, that help the user get a better understanding of multivariate data. Using different examples from fluid dynamics and climate simulation, we show how brushing can be used to explore the attribute cloud and find interesting structures in physical space.

  4. Multivariate calibration applied to the quantitative analysis of infrared spectra

    SciTech Connect

    Haaland, D.M.

    1991-01-01

    Multivariate calibration methods are very useful for improving the precision, accuracy, and reliability of quantitative spectral analyses. Spectroscopists can more effectively use these sophisticated statistical tools if they have a qualitative understanding of the techniques involved. A qualitative picture of the factor analysis multivariate calibration methods of partial least squares (PLS) and principal component regression (PCR) is presented using infrared calibrations based upon spectra of phosphosilicate glass thin films on silicon wafers. Comparisons of the relative prediction abilities of four different multivariate calibration methods are given based on Monte Carlo simulations of spectral calibration and prediction data. The success of multivariate spectral calibrations is demonstrated for several quantitative infrared studies. The infrared absorption and emission spectra of thin-film dielectrics used in the manufacture of microelectronic devices demonstrate rapid, nondestructive at-line and in-situ analyses using PLS calibrations. Finally, the application of multivariate spectral calibrations to reagentless analysis of blood is presented. We have found that the determination of glucose in whole blood taken from diabetics can be precisely monitored from the PLS calibration of either mind- or near-infrared spectra of the blood. Progress toward the non-invasive determination of glucose levels in diabetics is an ultimate goal of this research. 13 refs., 4 figs.

  5. A multivariate prediction model for microarray cross-hybridization

    PubMed Central

    Chen, Yian A; Chou, Cheng-Chung; Lu, Xinghua; Slate, Elizabeth H; Peck, Konan; Xu, Wenying; Voit, Eberhard O; Almeida, Jonas S

    2006-01-01

    Background Expression microarray analysis is one of the most popular molecular diagnostic techniques in the post-genomic era. However, this technique faces the fundamental problem of potential cross-hybridization. This is a pervasive problem for both oligonucleotide and cDNA microarrays; it is considered particularly problematic for the latter. No comprehensive multivariate predictive modeling has been performed to understand how multiple variables contribute to (cross-) hybridization. Results We propose a systematic search strategy using multiple multivariate models [multiple linear regressions, regression trees, and artificial neural network analyses (ANNs)] to select an effective set of predictors for hybridization. We validate this approach on a set of DNA microarrays with cytochrome p450 family genes. The performance of our multiple multivariate models is compared with that of a recently proposed third-order polynomial regression method that uses percent identity as the sole predictor. All multivariate models agree that the 'most contiguous base pairs between probe and target sequences,' rather than percent identity, is the best univariate predictor. The predictive power is improved by inclusion of additional nonlinear effects, in particular target GC content, when regression trees or ANNs are used. Conclusion A systematic multivariate approach is provided to assess the importance of multiple sequence features for hybridization and of relationships among these features. This approach can easily be applied to larger datasets. This will allow future developments of generalized hybridization models that will be able to correct for false-positive cross-hybridization signals in expression experiments. PMID:16509965

  6. Multicomponent seismic noise attenuation with multivariate order statistic filters

    NASA Astrophysics Data System (ADS)

    Wang, Chao; Wang, Yun; Wang, Xiaokai; Xun, Chao

    2016-10-01

    The vector relationship between multicomponent seismic data is highly important for multicomponent processing and interpretation, but this vector relationship could be damaged when each component is processed individually. To overcome the drawback of standard component-by-component filtering, multivariate order statistic filters are introduced and extended to attenuate the noise of multicomponent seismic data by treating such dataset as a vector wavefield rather than a set of scalar fields. According to the characteristics of seismic signals, we implement this type of multivariate filtering along local events. First, the optimal local events are recognized according to the similarity between the vector signals which are windowed from neighbouring seismic traces with a sliding time window along each trial trajectory. An efficient strategy is used to reduce the computational cost of similarity measurement for vector signals. Next, one vector sample each from the neighbouring traces are extracted along the optimal local event as the input data for a multivariate filter. Different multivariate filters are optimal for different noise. The multichannel modified trimmed mean (MTM) filter, as one of the multivariate order statistic filters, is applied to synthetic and field multicomponent seismic data to test its performance for attenuating white Gaussian noise. The results indicate that the multichannel MTM filter can attenuate noise while preserving the relative amplitude information of multicomponent seismic data more effectively than a single-channel filter.

  7. The statistical analysis of multivariate serological frequency data.

    PubMed

    Reyment, Richard A

    2005-11-01

    Data occurring in the form of frequencies are common in genetics-for example, in serology. Examples are provided by the AB0 group, the Rhesus group, and also DNA data. The statistical analysis of tables of frequencies is carried out using the available methods of multivariate analysis with usually three principal aims. One of these is to seek meaningful relationships between the components of a data set, the second is to examine relationships between populations from which the data have been obtained, the third is to bring about a reduction in dimensionality. This latter aim is usually realized by means of bivariate scatter diagrams using scores computed from a multivariate analysis. The multivariate statistical analysis of tables of frequencies cannot safely be carried out by standard multivariate procedures because they represent compositions and are therefore embedded in simplex space, a subspace of full space. Appropriate procedures for simplex space are compared and contrasted with simple standard methods of multivariate analysis ("raw" principal component analysis). The study shows that the differences between a log-ratio model and a simple logarithmic transformation of proportions may not be very great, particularly as regards graphical ordinations, but important discrepancies do occur. The divergencies between logarithmically based analyses and raw data are, however, great. Published data on Rhesus alleles observed for Italian populations are used to exemplify the subject.

  8. KERNEL-SMOOTHED CONDITIONAL QUANTILES OF CORRELATED BIVARIATE DISCRETE DATA

    PubMed Central

    De Gooijer, Jan G.; Yuan, Ao

    2012-01-01

    Socio-economic variables are often measured on a discrete scale or rounded to protect confidentiality. Nevertheless, when exploring the effect of a relevant covariate on the outcome distribution of a discrete response variable, virtually all common quantile regression methods require the distribution of the covariate to be continuous. This paper departs from this basic requirement by presenting an algorithm for nonparametric estimation of conditional quantiles when both the response variable and the covariate are discrete. Moreover, we allow the variables of interest to be pairwise correlated. For computational efficiency, we aggregate the data into smaller subsets by a binning operation, and make inference on the resulting prebinned data. Specifically, we propose two kernel-based binned conditional quantile estimators, one for untransformed discrete response data and one for rank-transformed response data. We establish asymptotic properties of both estimators. A practical procedure for jointly selecting band- and binwidth parameters is also presented. Simulation results show excellent estimation accuracy in terms of bias, mean squared error, and confidence interval coverage. Typically prebinning the data leads to considerable computational savings when large datasets are under study, as compared to direct (un)conditional quantile kernel estimation of multivariate data. With this in mind, we illustrate the proposed methodology with an application to a large dataset concerning US hospital patients with congestive heart failure. PMID:23667297

  9. Drivers' biased perceptions of speed and safety campaign messages.

    PubMed

    Walton, D; McKeown, P C

    2001-09-01

    One hundred and thirteen drivers were surveyed for their perceptions of driving speed to compare self-reported average speed, perceived average-other speed and the actual average speed, in two conditions (50 and 100 kph zones). These contrasts were used to evaluate whether public safety messages concerning speeding effectively reach their target audience. Evidence is presented supporting the hypothesis that drivers who have a biased perception of their own speed relative to others are more likely to ignore advertising campaigns encouraging people not to speed. A method of self-other-actual comparisons detects biased perceptions when the standard method of self-other comparison does not. In particular, drivers exaggerate the perceived speed of others and this fact is masked using traditional methods. The method of manipulation is proposed as a way to evaluate the effect of future advertising campaigns, and a strategy for such campaigns is proposed based on the results of the self-other comparisons. PMID:11491243

  10. Assessing Attentional Biases with Stuttering

    ERIC Educational Resources Information Center

    Lowe, Robyn; Menzies, Ross; Packman, Ann; O'Brian, Sue; Jones, Mark; Onslow, Mark

    2016-01-01

    Background: Many adults who stutter presenting for speech treatment experience social anxiety disorder. The presence of mental health disorders in adults who stutter has been implicated in a failure to maintain speech treatment benefits. Contemporary theories of social anxiety disorder propose that the condition is maintained by negative…

  11. Matrix and position correction of shuffler assays by application of the alternating conditional expectation algorithm to shuffler data

    SciTech Connect

    Pickrell, M M; Rinard, P M

    1992-01-01

    The {sup 252}Cf shuffler assays fissile uranium and plutonium using active neutron interrogation and then counting the induced delayed neutrons. Using the shuffler, we conducted over 1700 assays of 55-gal. drums with 28 different matrices and several different fissionable materials. We measured the drums to dispose the matrix and position effects on {sup 252}Cf shuffler assays. We used several neutron flux monitors during irradiation and kept statistics on the count rates of individual detector banks. The intent of these measurements was to gauge the effect of the matrix independently from the uranium assay. Although shufflers have previously been equipped neutron monitors, the functional relationship between the flux monitor sepals and the matrix-induced perturbation has been unknown. There are several flux monitors so the problem is multivariate, and the response is complicated. Conventional regression techniques cannot address complicated multivariate problems unless the underlying functional form and approximate parameter values are known in advance. Neither was available in this case. To address this problem, we used a new technique called alternating conditional expectations (ACE), which requires neither the functional relationship nor the initial parameters. The ACE algorithm develops the functional form and performs a numerical regression from only the empirical data. We applied the ACE algorithm to the shuffler-assay and flux-monitor data and developed an analytic function for the matrix correction. This function was optimized using conventional multivariate techniques. We were able to reduce the matrix-induced-bias error for homogeneous samples to 12.7%. The bias error for inhomogeneous samples was reduced to 13.5%. These results used only a few adjustable parameters compared to the number of available data points; the data were not over fit,'' but rather the results are general and robust.

  12. Selection of orthogonal reversed-phase HPLC systems by univariate and auto-associative multivariate regression trees.

    PubMed

    Put, R; Van Gyseghem, E; Coomans, D; Vander Heyden, Y

    2005-11-25

    In order to select chromatographic starting conditions to be optimized during further method development of the separation of a given mixture, so-called generic orthogonal chromatographic systems could be explored in parallel. In this paper the use of univariate and multivariate regression trees (MRT) was studied to define the most orthogonal subset from a given set of chromatographic systems. Two data sets were considered, which contain the retention data of 68 structurally diversive drugs on sets of 32 and 38 chromatographic systems, respectively. For both the univariate and multivariate approaches no other data but the measured retention factors are needed to build the decision trees. Since multivariate regression trees are used in an unsupervised way, they are called auto-associative multivariate regression trees (AAMRT). For all decision trees used, a variable importance list of the predictor variables can be derived. It was concluded that based on these ranked lists, both for univariate and multivariate regression trees, a selection of the most orthogonal systems from a given set of systems can be obtained in a user-friendly and fast way.

  13. An update on multivariate return periods in hydrology

    NASA Astrophysics Data System (ADS)

    Gräler, Benedikt; Petroselli, Andrea; Grimaldi, Salvatore; De Baets, Bernard; Verhoest, Niko

    2016-05-01

    Many hydrological studies are devoted to the identification of events that are expected to occur on average within a certain time span. While this topic is well established in the univariate case, recent advances focus on a multivariate characterization of events based on copulas. Following a previous study, we show how the definition of the survival Kendall return period fits into the set of multivariate return periods.Moreover, we preliminary investigate the ability of the multivariate return period definitions to select maximal events from a time series. Starting from a rich simulated data set, we show how similar the selection of events from a data set is. It can be deduced from the study and theoretically underpinned that the strength of correlation in the sample influences the differences between the selection of maximal events.

  14. Generalized Enhanced Multivariance Product Representation for Data Partitioning: Constancy Level

    SciTech Connect

    Tunga, M. Alper; Demiralp, Metin

    2011-09-14

    Enhanced Multivariance Product Representation (EMPR) method is used to represent multivariate functions in terms of less-variate structures. The EMPR method extends the HDMR expansion by inserting some additional support functions to increase the quality of the approximants obtained for dominantly or purely multiplicative analytical structures. This work aims to develop the generalized form of the EMPR method to be used in multivariate data partitioning approaches. For this purpose, the Generalized HDMR philosophy is taken into consideration to construct the details of the Generalized EMPR at constancy level as the introductory steps and encouraging results are obtained in data partitioning problems by using our new method. In addition, to examine this performance, a number of numerical implementations with concluding remarks are given at the end of this paper.

  15. Properties of multivariable root loci. M.S. Thesis

    NASA Technical Reports Server (NTRS)

    Yagle, A. E.

    1981-01-01

    Various properties of multivariable root loci are analyzed from a frequency domain point of view by using the technique of Newton polygons, and some generalizations of the SISO root locus rules to the multivariable case are pointed out. The behavior of the angles of arrival and departure is related to the Smith-MacMillan form of G(s) and explicit equations for these angles are obtained. After specializing to first order and a restricted class of higher order poles and zeros, some simple equations for these angles that are direct generalizations of the SISO equations are found. The unusual behavior of root loci on the real axis at branch points is studied. The SISO root locus rules for break-in and break-out points are shown to generalize directly to the multivariable case. Some methods for computing both types of points are presented.

  16. Autobiographical memory bias in social anxiety.

    PubMed

    Krans, Julie; de Bree, June; Bryant, Richard A

    2014-01-01

    In social anxiety the psychological self is closely related to the feared stimulus. Socially anxious individuals are, by definition, concerned about how the self is perceived and evaluated by others. As autobiographical memory is strongly related to views of the self it follows that biases in autobiographical memory play an important role in social anxiety. In the present study high (n = 19) and low (n = 29) socially anxious individuals were compared on autobiographical memory bias, current goals, and self-discrepancy. Individuals high in social anxiety showed a bias towards recalling more negative and more social anxiety-related autobiographical memories, reported more current goals related to overcoming social anxiety, and showed larger self-discrepancies. The pattern of results is largely in line with earlier research in individuals with PTSD and complicated grief. This suggests that the relation between autobiographical memory bias and the self is a potentially valuable trans-diagnostic factor.

  17. FIP bias in a sigmoidal active region

    NASA Astrophysics Data System (ADS)

    Baker, D.; Brooks, D. H.; Démoulin, P.; van Driel-Gesztelyi, Lidia; Green, L. M.; Steed, K.; Carlyle, J.

    2014-01-01

    We investigate first ionization potential (FIP) bias levels in an anemone active region (AR) - coronal hole (CH) complex using an abundance map derived from Hinode/EIS spectra. The detailed, spatially resolved abundance map has a large field of view covering 359'' × 485''. Plasma with high FIP bias, or coronal abundances, is concentrated at the footpoints of the AR loops whereas the surrounding CH has a low FIP bias, ~1, i.e. photospheric abundances. A channel of low FIP bias is located along the AR's main polarity inversion line containing a filament where ongoing flux cancellation is observed, indicating a bald patch magnetic topology characteristic of a sigmoid/flux rope configuration.

  18. Neurocognition and cognitive biases in schizophrenia.

    PubMed

    Garcia, Cristina P; Sacks, Stephanie A; Weisman de Mamani, Amy G

    2012-08-01

    Individuals with schizophrenia have been found to exhibit a number of information processing biases that may play a role in the development and exacerbation of symptoms and may impair overall functioning. However, little is known about the factors that are associated with these cognitive biases. Recently, researchers have begun to consider whether neurocognitive deficits, common in schizophrenia, may be risk factors for the development of cognitive biases. In the present study, we assessed neurocognition (verbal learning, delayed verbal recall memory, and verbal recognition memory) and cognitive biases (knowledge corruption and impaired cognitive insight) in 72 individuals with schizophrenia or schizoaffective disorder. As hypothesized, poorer delayed verbal recall memory was associated with increased knowledge corruption. Contrary to expectations, verbal learning and verbal memory were not associated with cognitive insight. These findings suggest that an inadequate recall memory system may put patients with schizophrenia at greater risk for cognitive distortions.

  19. Gender bias in the force concept inventory?

    NASA Astrophysics Data System (ADS)

    Dietz, R. D.; Pearson, R. H.; Semak, M. R.; Willis, C. W.

    2012-02-01

    Could the well-established fact that males tend to score higher than females on the Force Concept Inventory (FCI) be due to gender bias in the questions? The eventual answer to the question hinges on the definition of bias. We assert that a question is biased only if a factor other than ability (in this case gender) affects the likelihood that a student will answer the question correctly. The statistical technique of differential item functioning allows us to control for ability in our analysis of student performance on each of the thirty FCI questions. This method uses the total score on the FCI as the measure of ability. We conclude that the evidence for gender bias in the FCI questions is marginal at best.

  20. Social influence protects collective decision making from equality bias.

    PubMed

    Hertz, Uri; Romand-Monnier, Margaux; Kyriakopoulou, Konstantina; Bahrami, Bahador

    2016-02-01

    A basic tenet of research on wisdom of the crowds-and key assumption of Condercet's (1785) Jury Theorem-is the independence of voters' opinions before votes are aggregated. However, we often look for others' opinions before casting our vote. Such social influence can push groups toward herding, leading to "madness of the crowds." To investigate the role of social influence in joint decision making, in Experiment 1 we had dyads of participants perform a visual oddball search task together. In the Independent (IND) condition participants initially made a private decision. If they disagreed, discussion and collective decision ensued. In the Influence (INF) condition no private decisions were made and collective decision was immediately negotiated. Dyads that did not accrue collective benefit under the IND condition improved with added social influence under the INF condition. In Experiment 2, covertly, we added noise to 1 of the dyad members' visual search display. The resulting increased heterogeneity in dyad members' performances impaired the dyadic performance under the IND condition (Bahrami et al., 2010). Importantly, dyadic performance improved with social influence under the INF condition, replicating results in Experiment 1. Further analyses revealed that under the IND condition, dyads exercised equality bias (Mahmoodi et al., 2015) by granting undue credit to the less-reliable partner. Under the INF condition, however, the more-reliable partner (correctly) dominated the joint decisions. Although social influence may impede collective success under ideal conditions, our results demonstrate how it can help the group members overcome factors such as equality bias, which could potentially lead to catastrophic failure. (PsycINFO Database Record

  1. Social influence protects collective decision making from equality bias.

    PubMed

    Hertz, Uri; Romand-Monnier, Margaux; Kyriakopoulou, Konstantina; Bahrami, Bahador

    2016-02-01

    A basic tenet of research on wisdom of the crowds-and key assumption of Condercet's (1785) Jury Theorem-is the independence of voters' opinions before votes are aggregated. However, we often look for others' opinions before casting our vote. Such social influence can push groups toward herding, leading to "madness of the crowds." To investigate the role of social influence in joint decision making, in Experiment 1 we had dyads of participants perform a visual oddball search task together. In the Independent (IND) condition participants initially made a private decision. If they disagreed, discussion and collective decision ensued. In the Influence (INF) condition no private decisions were made and collective decision was immediately negotiated. Dyads that did not accrue collective benefit under the IND condition improved with added social influence under the INF condition. In Experiment 2, covertly, we added noise to 1 of the dyad members' visual search display. The resulting increased heterogeneity in dyad members' performances impaired the dyadic performance under the IND condition (Bahrami et al., 2010). Importantly, dyadic performance improved with social influence under the INF condition, replicating results in Experiment 1. Further analyses revealed that under the IND condition, dyads exercised equality bias (Mahmoodi et al., 2015) by granting undue credit to the less-reliable partner. Under the INF condition, however, the more-reliable partner (correctly) dominated the joint decisions. Although social influence may impede collective success under ideal conditions, our results demonstrate how it can help the group members overcome factors such as equality bias, which could potentially lead to catastrophic failure. (PsycINFO Database Record PMID:26436525

  2. Operational modal analysis approach based on multivariable transmissibility with different transferring outputs

    NASA Astrophysics Data System (ADS)

    Gómez Araújo, Iván; Laier, Jose Elias

    2015-09-01

    In recent years, transmissibility functions have been used as alternatives to identify the modal parameters of structures under operating conditions. The scalar power spectrum density transmissibility (PSDT), which relates only two responses, was proposed to extract modal parameters by combining different PSDTs with different transferring outputs. In this sense, this paper proposes extending the scalar PSDT concept to multivariable PSDT by relating multiple responses instead of only two. This extension implies the definition of a transmissibility matrix, relating the cross-spectral density matrix among the responses at coordinates Z and U with the cross-spectral density matrix among the responses at coordinates Z and K. The coordinates in Z are known as the transferring outputs. By defining the same coordinates K and U, but with different transferring outputs Z, we prove that the multivariable PSDT converges to the same matrix when it approaches the system poles. This property is used to define only one matrix with different multivariable PSDTs with same coordinates K and U, but with different transferring outputs. The resulting matrix is singular at the system poles, meaning that by applying the inverse of the matrix, the modal parameters can be identified. Here, a numeric example of a beam model subjected to excitations and data from an operational vibration bridge test shows that the proposed method is capable of identifying modal parameters. Furthermore, the results demonstrate the possibility of estimating the same modal parameters by changing only the coordinates K and U, providing greater reliability during modal parameter identification.

  3. Atomic-scale phase composition through multivariate statistical analysis of atom probe tomography data.

    PubMed

    Keenan, Michael R; Smentkowski, Vincent S; Ulfig, Robert M; Oltman, Edward; Larson, David J; Kelly, Thomas F

    2011-06-01

    We demonstrate for the first time that multivariate statistical analysis techniques can be applied to atom probe tomography data to estimate the chemical composition of a sample at the full spatial resolution of the atom probe in three dimensions. Whereas the raw atom probe data provide the specific identity of an atom at a precise location, the multivariate results can be interpreted in terms of the probabilities that an atom representing a particular chemical phase is situated there. When aggregated to the size scale of a single atom (∼0.2 nm), atom probe spectral-image datasets are huge and extremely sparse. In fact, the average spectrum will have somewhat less than one total count per spectrum due to imperfect detection efficiency. These conditions, under which the variance in the data is completely dominated by counting noise, test the limits of multivariate analysis, and an extensive discussion of how to extract the chemical information is presented. Efficient numerical approaches to performing principal component analysis (PCA) on these datasets, which may number hundreds of millions of individual spectra, are put forward, and it is shown that PCA can be computed in a few seconds on a typical laptop computer.

  4. A model-based examination of multivariate physical modes in the Gulf of Alaska

    NASA Astrophysics Data System (ADS)

    Hermann, A. J.; Ladd, C.; Cheng, W.; Curchitser, E. N.; Hedstrom, K.

    2016-10-01

    We use multivariate output from a hydrodynamic model of the Gulf of Alaska (GOA) to explore the covariance among its physical state and air/sea fluxes. We attempt to summarize this coupled variability using a limited set of patterns, and examine their correlation to three large-scale climate indices relevant to the Northeast Pacific. This analysis is focused on perturbations from monthly climatology of the following attributes of the GOA: sea surface temperature, sea surface height, mixed layer depth, sea surface salinity, latent heat flux, sensible heat flux, shortwave irradiance, net long wave irradiance, currents at 40 m depth, and wind stress. We identified two multivariate modes, both substantially correlated with the Pacific Decadal Oscillation (PDO) and Multivariate El Nino (MEI) indices on interannual timescales, which together account for ~30% of the total normalized variance of the perturbation time series. These two modes indicate the following covarying events during periods of positive PDO/MEI: (1) anomalously warm, wet and windy conditions (typically in winter), with elevated coastal SSH, followed 2-5 months later by (2) reduced cloud cover, with emerging shelf-break eddies. Similar modes are found when the analysis is performed separately on the eastern and western GOA; in general, modal amplitudes appear stronger in the western GOA.

  5. Comparative evaluation of spectroscopic models using different multivariate statistical tools in a multicancer scenario

    NASA Astrophysics Data System (ADS)

    Ghanate, A. D.; Kothiwale, S.; Singh, S. P.; Bertrand, Dominique; Krishna, C. Murali

    2011-02-01

    Cancer is now recognized as one of the major causes of morbidity and mortality. Histopathological diagnosis, the gold standard, is shown to be subjective, time consuming, prone to interobserver disagreement, and often fails to predict prognosis. Optical spectroscopic methods are being contemplated as adjuncts or alternatives to conventional cancer diagnostics. The most important aspect of these approaches is their objectivity, and multivariate statistical tools play a major role in realizing it. However, rigorous evaluation of the robustness of spectral models is a prerequisite. The utility of Raman spectroscopy in the diagnosis of cancers has been well established. Until now, the specificity and applicability of spectral models have been evaluated for specific cancer types. In this study, we have evaluated the utility of spectroscopic models representing normal and malignant tissues of the breast, cervix, colon, larynx, and oral cavity in a broader perspective, using different multivariate tests. The limit test, which was used in our earlier study, gave high sensitivity but suffered from poor specificity. The performance of other methods such as factorial discriminant analysis and partial least square discriminant analysis are at par with more complex nonlinear methods such as decision trees, but they provide very little information about the classification model. This comparative study thus demonstrates not just the efficacy of Raman spectroscopic models but also the applicability and limitations of different multivariate tools for discrimination under complex conditions such as the multicancer scenario.

  6. Questions of bias in climate models

    SciTech Connect

    Smith, Steven J.; Wigley, Tom M.; Meinshausen, Malte; Rogelj, Joeri

    2014-08-27

    The recent work by Shindell usefully contributes to the debate over estimating climate sensitivity by highlighting an important aspect of the climate system: that climate forcings that occur over land result in a more rapid temperature response than forcings that are distributed more uniformly over the globe. While, as noted in this work, simple climate models may be biased by assuming the same temperature response for all forcing agents, the implication that the MAGICC model is biased in this way is not correct.

  7. Quantifying Biogenic Bias in Screening Libraries

    PubMed Central

    Hert, Jérôme; Irwin, John J.; Laggner, Christian; Keiser, Michael J.; Shoichet, Brian K.

    2009-01-01

    In lead discovery, libraries of 106 molecules are screened for biological activity. Given the over 1060 drug-like molecules thought possible, such screens might never succeed. That they do, even occasionally, implies a biased selection of library molecules. Here a method is developed to quantify the bias in screening libraries towards biogenic molecules. With this approach, we consider what is missing from screening libraries and how they can be optimized. PMID:19483698

  8. A translational rodent assay of affective biases in depression and antidepressant therapy.

    PubMed

    Stuart, Sarah A; Butler, Paul; Munafò, Marcus R; Nutt, David J; Robinson, Emma Sj

    2013-08-01

    The subjective measures used to study mood disorders in humans cannot be replicated in animals; however, the increasing application of objective neuropsychological methods provides opportunities to develop translational animal tasks. Here we describe a novel behavioral approach, which has enabled us to investigate similar affective biases in rodents. In our affective bias test (ABT), rats encounter two independent positive experiences--the association between food reward and specific digging substrate--during discrimination learning sessions. These are performed on separate days under either neutral conditions or during a pharmacological or affective state manipulation. Affective bias is then quantified using a preference test where both previously rewarded substrates are presented together and the rat's choices recorded. The absolute value of the experience is kept consistent and all other factors are counterbalanced so that any bias at recall can be attributed to treatment. Replicating previous findings from studies in healthy volunteers, we observe significant positive affective biases following acute treatment with typical (fluoxetine, citalopram, reboxetine, venlafaxine, clomipramine) and atypical antidepressants (agomelatine, mirtazapine), and significant negative affective biases following treatment with drugs associated with inducing negative affective states in humans (FG7142, rimonabant, 13-cis retinoic acid). We also observed that acute psychosocial stress and environmental enrichment induce significant negative and positive affective biases, respectively, and provide evidence that these affective biases involve memory consolidation. The positive and negative affective biases induced in our test also mirror the antidepressant and pro-depressant effects of these drugs in patients suggesting our test has both translational and predictive validity. Our results suggest that cognitive affective biases could contribute to drug- or stress-induced mood changes

  9. A translational rodent assay of affective biases in depression and antidepressant therapy.

    PubMed

    Stuart, Sarah A; Butler, Paul; Munafò, Marcus R; Nutt, David J; Robinson, Emma Sj

    2013-08-01

    The subjective measures used to study mood disorders in humans cannot be replicated in animals; however, the increasing application of objective neuropsychological methods provides opportunities to develop translational animal tasks. Here we describe a novel behavioral approach, which has enabled us to investigate similar affective biases in rodents. In our affective bias test (ABT), rats encounter two independent positive experiences--the association between food reward and specific digging substrate--during discrimination learning sessions. These are performed on separate days under either neutral conditions or during a pharmacological or affective state manipulation. Affective bias is then quantified using a preference test where both previously rewarded substrates are presented together and the rat's choices recorded. The absolute value of the experience is kept consistent and all other factors are counterbalanced so that any bias at recall can be attributed to treatment. Replicating previous findings from studies in healthy volunteers, we observe significant positive affective biases following acute treatment with typical (fluoxetine, citalopram, reboxetine, venlafaxine, clomipramine) and atypical antidepressants (agomelatine, mirtazapine), and significant negative affective biases following treatment with drugs associated with inducing negative affective states in humans (FG7142, rimonabant, 13-cis retinoic acid). We also observed that acute psychosocial stress and environmental enrichment induce significant negative and positive affective biases, respectively, and provide evidence that these affective biases involve memory consolidation. The positive and negative affective biases induced in our test also mirror the antidepressant and pro-depressant effects of these drugs in patients suggesting our test has both translational and predictive validity. Our results suggest that cognitive affective biases could contribute to drug- or stress-induced mood changes

  10. Undesirable Choice Biases with Small Differences in the Spatial Structure of Chance Stimulus Sequences

    PubMed Central

    Herrera, David; Treviño, Mario

    2015-01-01

    In two-alternative discrimination tasks, experimenters usually randomize the location of the rewarded stimulus so that systematic behavior with respect to irrelevant stimuli can only produce chance performance on the learning curves. One way to achieve this is to use random numbers derived from a discrete binomial distribution to create a 'full random training schedule' (FRS). When using FRS, however, sporadic but long laterally-biased training sequences occur by chance and such 'input biases' are thought to promote the generation of laterally-biased choices (i.e., 'output biases'). As an alternative, a 'Gellerman-like training schedule' (GLS) can be used. It removes most input biases by prohibiting the reward from appearing on the same location for more than three consecutive trials. The sequence of past rewards obtained from choosing a particular discriminative stimulus influences the probability of choosing that same stimulus on subsequent trials. Assuming that the long-term average ratio of choices matches the long-term average ratio of reinforcers, we hypothesized that a reduced amount of input biases in GLS compared to FRS should lead to a reduced production of output biases. We compared the choice patterns produced by a 'Rational Decision Maker' (RDM) in response to computer-generated FRS and GLS training sequences. To create a virtual RDM, we implemented an algorithm that generated choices based on past rewards. Our simulations revealed that, although the GLS presented fewer input biases than the FRS, the virtual RDM produced more output biases with GLS than with FRS under a variety of test conditions. Our results reveal that the statistical and temporal properties of training sequences interacted with the RDM to influence the production of output biases. Thus, discrete changes in the training paradigms did not translate linearly into modifications in the pattern of choices generated by a RDM. Virtual RDMs could be further employed to guide the selection of

  11. Multivariate concentration determination using principal component regression with residual analysis

    PubMed Central

    Keithley, Richard B.; Heien, Michael L.; Wightman, R. Mark

    2009-01-01

    Data analysis is an essential tenet of analytical chemistry, extending the possible information obtained from the measurement of chemical phenomena. Chemometric methods have grown considerably in recent years, but their wide use is hindered because some still consider them too complicated. The purpose of this review is to describe a multivariate chemometric method, principal component regression, in a simple manner from the point of view of an analytical chemist, to demonstrate the need for proper quality-control (QC) measures in multivariate analysis and to advocate the use of residuals as a proper QC method. PMID:20160977

  12. Multivariate optimization of capillary electrophoresis methods: a critical review.

    PubMed

    Orlandini, Serena; Gotti, Roberto; Furlanetto, Sandra

    2014-01-01

    In this article a review on the recent applications of multivariate techniques for optimization of electromigration methods, is presented. Papers published in the period from August 2007 to February 2013, have been taken into consideration. Upon a brief description of each of the involved CE operative modes, the characteristics of the chemometric strategies (type of design, factors and responses) applied to face a number of analytical challenges, are presented. Finally, a critical discussion, giving some practical advices and pointing out the most common issues involved in multivariate set-up of CE methods, is provided.

  13. Fixed order dynamic compensation for multivariable linear systems

    NASA Technical Reports Server (NTRS)

    Kramer, F. S.; Calise, A. J.

    1986-01-01

    This paper considers the design of fixed order dynamic compensators for multivariable time invariant linear systems, minimizing a linear quadratic performance cost functional. Attention is given to robustness issues in terms of multivariable frequency domain specifications. An output feedback formulation is adopted by suitably augmenting the system description to include the compensator states. Either a controller or observer canonical form is imposed on the compensator description to reduce the number of free parameters to its minimal number. The internal structure of the compensator is prespecified by assigning a set of ascending feedback invariant indices, thus forming a Brunovsky structure for the nominal compensator.

  14. Multivariate Chemical Image Fusion of Vibrational Spectroscopic Imaging Modalities.

    PubMed

    Gowen, Aoife A; Dorrepaal, Ronan M

    2016-01-01

    Chemical image fusion refers to the combination of chemical images from different modalities for improved characterisation of a sample. Challenges associated with existing approaches include: difficulties with imaging the same sample area or having identical pixels across microscopic modalities, lack of prior knowledge of sample composition and lack of knowledge regarding correlation between modalities for a given sample. In addition, the multivariate structure of chemical images is often overlooked when fusion is carried out. We address these challenges by proposing a framework for multivariate chemical image fusion of vibrational spectroscopic imaging modalities, demonstrating the approach for image registration, fusion and resolution enhancement of chemical images obtained with IR and Raman microscopy. PMID:27384549

  15. Multivariate geometry as an approach to algal community analysis

    USGS Publications Warehouse

    Allen, T.F.H.; Skagen, S.

    1973-01-01

    Multivariate analyses are put in the context of more usual approaches to phycological investigations. The intuitive common-sense involved in methods of ordination, classification and discrimination are emphasised by simple geometric accounts which avoid jargon and matrix algebra. Warnings are given that artifacts result from technique abuses by the naive or over-enthusiastic. An analysis of a simple periphyton data set is presented as an example of the approach. Suggestions are made as to situations in phycological investigations, where the techniques could be appropriate. The discipline is reprimanded for its neglect of the multivariate approach.

  16. Steady-state decoupling and design of linear multivariable systems

    NASA Technical Reports Server (NTRS)

    Thaler, G. J.

    1974-01-01

    A constructive criterion for decoupling the steady states of a linear time-invariant multivariable system is presented. This criterion consists of a set of inequalities which, when satisfied, will cause the steady states of a system to be decoupled. Stability analysis and a new design technique for such systems are given. A new and simple connection between single-loop and multivariable cases is found. These results are then applied to the compensation design for NASA STOL C-8A aircraft. Both steady-state decoupling and stability are justified through computer simulations.

  17. Dynamic stimuli: accentuating aesthetic preference biases.

    PubMed

    Friedrich, Trista E; Harms, Victoria L; Elias, Lorin J

    2014-01-01

    Despite humans' preference for symmetry, artwork often portrays asymmetrical characteristics that influence the viewer's aesthetic preference for the image. When presented with asymmetrical images, aesthetic preference is often given to images whose content flows from left-to-right and whose mass is located on the right of the image. Cerebral lateralization has been suggested to account for the left-to-right directionality bias; however, the influence of cultural factors, such as scanning habits, on aesthetic preference biases is debated. The current research investigates aesthetic preference for mobile objects and landscapes, as previous research has found contrasting preference for the two image types. Additionally, the current experiment examines the effects of dynamic movement on directionality preference to test the assumption that static images are perceived as aesthetically equivalent to dynamic images. After viewing mirror-imaged pairs of pictures and videos, right-to-left readers failed to show a preference bias, whereas left-to-right readers preferred stimuli with left-to-right directionality regardless of the location of the mass. The directionality bias in both reading groups was accentuated by the videos, but the bias was significantly stronger in left-to-right readers. The findings suggest that scanning habits moderate the leftward bias resulting from hemispheric specialization and that dynamic stimuli further fluent visual processing.

  18. Hot-hand bias in rhesus monkeys.

    PubMed

    Blanchard, Tommy C; Wilke, Andreas; Hayden, Benjamin Y

    2014-07-01

    Human decision-makers often exhibit the hot-hand phenomenon, a tendency to perceive positive serial autocorrelations in independent sequential events. The term is named after the observation that basketball fans and players tend to perceive streaks of high accuracy shooting when they are demonstrably absent. That is, both observing fans and participating players tend to hold the belief that a player's chance of hitting a shot are greater following a hit than following a miss. We hypothesize that this bias reflects a strong and stable tendency among primates (including humans) to perceive positive autocorrelations in temporal sequences, that this bias is an adaptation to clumpy foraging environments, and that it may even be ecologically rational. Several studies support this idea in humans, but a stronger test would be to determine whether nonhuman primates also exhibit a hot-hand bias. Here we report behavior of 3 monkeys performing a novel gambling task in which correlation between sequential gambles (i.e., temporal clumpiness) is systematically manipulated. We find that monkeys have better performance (meaning, more optimal behavior) for clumped (positively correlated) than for dispersed (negatively correlated) distributions. These results identify and quantify a new bias in monkeys' risky decisions, support accounts that specifically incorporate cognitive biases into risky choice, and support the suggestion that the hot-hand phenomenon is an evolutionary ancient bias.

  19. Neural correlates of attentional bias in addiction.

    PubMed

    Hester, Robert; Luijten, Maartje

    2014-06-01

    A small but growing neuroimaging literature has begun to examine the neural mechanisms underlying the difficulty that substance-use dependent (SUD) groups have with ignoring salient, drug-related stimuli. Drug-related attentional bias appears to implicate the countermanding forces of cognitive control and reward salience. Basic cognitive neuroscience research suggests that ignoring emotionally evocative stimuli in our environment requires both up-regulation of control networks and down-regulation of processing in emotion and reward regions. Research to date suggests that attentional biases for drug-related stimuli emerge from a failure to sufficiently increase control of attention over salient, but task-irrelevant stimuli. While SUD samples have typically shown increased activity in the cognitive control regions (ie, lateral prefrontal and dorsal anterior cingulate), during attentional bias such increases appear to have been insufficient for the concomitant increases in processing by the emotion/reward regions (ie, amygdala, insula, and striatum). Given the potential contribution of attentional biases to perpetuating drug use and the development of interventions (both pharmaceutical and cognitive-behavioral) to treat biases, understanding the neural basis of successfully reducing bias remains an important, but as yet unanswered, question for our field.

  20. RF cavities with transversely biased ferrite tuning

    SciTech Connect

    Smythe, W.R.; Brophy, T.G.; Carlini, R.D.; Friedrichs, C.C.; Grisham, D.L.; Spalek, G.; Wilkerson, L.C.

    1985-10-01

    Earley et al. suggested that ferrite tuned rf cavities have lower ferrite power dissipation if the ferrite bias field is perpendicular rather than parallel to the rf magnetic field. A 50-84 MHz cavity has been constructed in which ferrite can be biased either way. Low power measurements of six microwave ferrites show that the magnetic Q's of these ferrites under perpendicular bias are much higher than under parallel bias, and that the high Q region extends over a much wider range of rf permeability. TDK Y-5 ferrite was found to have a magnetic Q of 10,800, 4,800, 1,200 and 129 at rf permeabilities of 1.2, 2.4, 3.7 and 4.5, respectively. Measurements of perpendicularly biased ferrite at various power levels were made in a coaxial line cavity. The Q of Y-5 ferrite was found to decrease by less than a factor of 2 as the power density in the ferrite was increased to 1.3 W/cmT. A cavity design for a 6 GeV, high current, rapid cycling synchrotron using transversely biased ferrite tuning is described.