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Sample records for learning latent variable

  1. Accuracy of latent-variable estimation in Bayesian semi-supervised learning.

    Science.gov (United States)

    Yamazaki, Keisuke

    2015-09-01

    Hierarchical probabilistic models, such as Gaussian mixture models, are widely used for unsupervised learning tasks. These models consist of observable and latent variables, which represent the observable data and the underlying data-generation process, respectively. Unsupervised learning tasks, such as cluster analysis, are regarded as estimations of latent variables based on the observable ones. The estimation of latent variables in semi-supervised learning, where some labels are observed, will be more precise than that in unsupervised, and one of the concerns is to clarify the effect of the labeled data. However, there has not been sufficient theoretical analysis of the accuracy of the estimation of latent variables. In a previous study, a distribution-based error function was formulated, and its asymptotic form was calculated for unsupervised learning with generative models. It has been shown that, for the estimation of latent variables, the Bayes method is more accurate than the maximum-likelihood method. The present paper reveals the asymptotic forms of the error function in Bayesian semi-supervised learning for both discriminative and generative models. The results show that the generative model, which uses all of the given data, performs better when the model is well specified. Copyright © 2015 Elsevier Ltd. All rights reserved.

  2. Learning Latent Variable and Predictive Models of Dynamical Systems

    Science.gov (United States)

    2009-10-01

    Huijbregts. The ICSI RT07s Speaker Diarization System. Springer-Verlag, 2008. 4.5 [57] Gal Elidan and Nir Friedman. Learning the dimensionality of hidden...13, 435 and a test set of size 1, 771. VOWEL: This data set consists of multiple utterances of a particular Japanese vowel by nine male speakers . We...classification based on cultural style [51]; audio diarization , i.e., extraction of speech segments in long audio signals from background sounds [52]; audio

  3. Longitudinal Research with Latent Variables

    CERN Document Server

    van Montfort, Kees; Satorra, Albert

    2010-01-01

    This book combines longitudinal research and latent variable research, i.e. it explains how longitudinal studies with objectives formulated in terms of latent variables should be carried out, with an emphasis on detailing how the methods are applied. Because longitudinal research with latent variables currently utilizes different approaches with different histories, different types of research questions, and different computer programs to perform the analysis, the book is divided into nine chapters. Starting from some background information about the specific approach, short history and the ma

  4. Variable importance in latent variable regression models

    NARCIS (Netherlands)

    Kvalheim, O.M.; Arneberg, R.; Bleie, O.; Rajalahti, T.; Smilde, A.K.; Westerhuis, J.A.

    2014-01-01

    The quality and practical usefulness of a regression model are a function of both interpretability and prediction performance. This work presents some new graphical tools for improved interpretation of latent variable regression models that can also assist in improved algorithms for variable

  5. Latent variable models are network models.

    Science.gov (United States)

    Molenaar, Peter C M

    2010-06-01

    Cramer et al. present an original and interesting network perspective on comorbidity and contrast this perspective with a more traditional interpretation of comorbidity in terms of latent variable theory. My commentary focuses on the relationship between the two perspectives; that is, it aims to qualify the presumed contrast between interpretations in terms of networks and latent variables.

  6. Latent variables and route choice behavior

    DEFF Research Database (Denmark)

    Prato, Carlo Giacomo; Bekhor, Shlomo; Pronello, Cristina

    2012-01-01

    In the last decade, a broad array of disciplines has shown a general interest in enhancing discrete choice models by considering the incorporation of psychological factors affecting decision making. This paper provides insight into the comprehension of the determinants of route choice behavior...... and bound algorithm. A hybrid model consists of measurement equations, which relate latent variables to measurement indicators and utilities to choice indicators, and structural equations, which link travelers’ observable characteristics to latent variables and explanatory variables to utilities. Estimation...

  7. Handbook of latent variable and related models

    CERN Document Server

    Lee, Sik-Yum

    2011-01-01

    This Handbook covers latent variable models, which are a flexible class of models for modeling multivariate data to explore relationships among observed and latent variables.- Covers a wide class of important models- Models and statistical methods described provide tools for analyzing a wide spectrum of complicated data- Includes illustrative examples with real data sets from business, education, medicine, public health and sociology.- Demonstrates the use of a wide variety of statistical, computational, and mathematical techniques.

  8. Learning Latent Vector Spaces for Product Search

    NARCIS (Netherlands)

    Van Gysel, C.; de Rijke, M.; Kanoulas, E.

    2016-01-01

    We introduce a novel latent vector space model that jointly learns the latent representations of words, e-commerce products and a mapping between the two without the need for explicit annotations. The power of the model lies in its ability to directly model the discriminative relation between

  9. Generalized latent variable modeling multilevel, longitudinal, and structural equation models

    CERN Document Server

    Skrondal, Anders; Rabe-Hesketh, Sophia

    2004-01-01

    This book unifies and extends latent variable models, including multilevel or generalized linear mixed models, longitudinal or panel models, item response or factor models, latent class or finite mixture models, and structural equation models.

  10. Investigating IT Faculty Resistance to Learning Management System Adoption Using Latent Variables in an Acceptance Technology Model.

    Science.gov (United States)

    Bousbahi, Fatiha; Alrazgan, Muna Saleh

    2015-01-01

    To enhance instruction in higher education, many universities in the Middle East have chosen to introduce learning management systems (LMS) to their institutions. However, this new educational technology is not being used at its full potential and faces resistance from faculty members. To investigate this phenomenon, we conducted an empirical research study to uncover factors influencing faculty members' acceptance of LMS. Thus, in the Fall semester of 2014, Information Technology faculty members were surveyed to better understand their perceptions of the incorporation of LMS into their courses. The results showed that personal factors such as motivation, load anxiety, and organizational support play important roles in the perception of the usefulness of LMS among IT faculty members. These findings suggest adding these constructs in order to extend the Technology acceptance model (TAM) for LMS acceptance, which can help stakeholders of the university to implement the use of this system. This may assist in planning and evaluating the use of e-learning.

  11. Gene Variants Associated with Antisocial Behaviour: A Latent Variable Approach

    Science.gov (United States)

    Bentley, Mary Jane; Lin, Haiqun; Fernandez, Thomas V.; Lee, Maria; Yrigollen, Carolyn M.; Pakstis, Andrew J.; Katsovich, Liliya; Olds, David L.; Grigorenko, Elena L.; Leckman, James F.

    2013-01-01

    Objective: The aim of this study was to determine if a latent variable approach might be useful in identifying shared variance across genetic risk alleles that is associated with antisocial behaviour at age 15 years. Methods: Using a conventional latent variable approach, we derived an antisocial phenotype in 328 adolescents utilizing data from a…

  12. Learning Latent Structure in Complex Networks

    DEFF Research Database (Denmark)

    Mørup, Morten; Hansen, Lars Kai

    such as the Modularity, it has recently been shown that latent structure in complex networks is learnable by Bayesian generative link distribution models (Airoldi et al., 2008, Hofman and Wiggins, 2008). In this paper we propose a new generative model that allows representation of latent community structure......Latent structure in complex networks, e.g., in the form of community structure, can help understand network dynamics, identify heterogeneities in network properties, and predict ‘missing’ links. While most community detection algorithms are based on optimizing heuristic clustering objectives...... as in the previous Bayesian approaches and in addition allows learning of node specific link properties similar to that in the modularity objective. We employ a new relaxation method for efficient inference in these generative models that allows us to learn the behavior of very large networks. We compare the link...

  13. On the Integrity of Online Testing for Introductory Statistics Courses: A Latent Variable Approach

    Directory of Open Access Journals (Sweden)

    Alan Fask

    2015-04-01

    Full Text Available There has been a remarkable growth in distance learning courses in higher education. Despite indications that distance learning courses are more vulnerable to cheating behavior than traditional courses, there has been little research studying whether online exams facilitate a relatively greater level of cheating. This article examines this issue by developing an approach using a latent variable to measure student cheating. This latent variable is linked to both known student mastery related variables and variables unrelated to student mastery. Grade scores from a proctored final exam and an unproctored final exam are used to test for increased cheating behavior in the unproctored exam

  14. A Non-Gaussian Spatial Generalized Linear Latent Variable Model

    KAUST Repository

    Irincheeva, Irina

    2012-08-03

    We consider a spatial generalized linear latent variable model with and without normality distributional assumption on the latent variables. When the latent variables are assumed to be multivariate normal, we apply a Laplace approximation. To relax the assumption of marginal normality in favor of a mixture of normals, we construct a multivariate density with Gaussian spatial dependence and given multivariate margins. We use the pairwise likelihood to estimate the corresponding spatial generalized linear latent variable model. The properties of the resulting estimators are explored by simulations. In the analysis of an air pollution data set the proposed methodology uncovers weather conditions to be a more important source of variability than air pollution in explaining all the causes of non-accidental mortality excluding accidents. © 2012 International Biometric Society.

  15. A Non-Gaussian Spatial Generalized Linear Latent Variable Model

    KAUST Repository

    Irincheeva, Irina; Cantoni, Eva; Genton, Marc G.

    2012-01-01

    We consider a spatial generalized linear latent variable model with and without normality distributional assumption on the latent variables. When the latent variables are assumed to be multivariate normal, we apply a Laplace approximation. To relax the assumption of marginal normality in favor of a mixture of normals, we construct a multivariate density with Gaussian spatial dependence and given multivariate margins. We use the pairwise likelihood to estimate the corresponding spatial generalized linear latent variable model. The properties of the resulting estimators are explored by simulations. In the analysis of an air pollution data set the proposed methodology uncovers weather conditions to be a more important source of variability than air pollution in explaining all the causes of non-accidental mortality excluding accidents. © 2012 International Biometric Society.

  16. Classification criteria of syndromes by latent variable models

    DEFF Research Database (Denmark)

    Petersen, Janne

    2010-01-01

    patient's characteristics. These methods may erroneously reduce multiplicity either by combining markers of different phenotypes or by mixing HALS with other processes such as aging. Latent class models identify homogenous groups of patients based on sets of variables, for example symptoms. As no gold......The thesis has two parts; one clinical part: studying the dimensions of human immunodeficiency virus associated lipodystrophy syndrome (HALS) by latent class models, and a more statistical part: investigating how to predict scores of latent variables so these can be used in subsequent regression...... standard exists for diagnosing HALS the normally applied diagnostic models cannot be used. Latent class models, which have never before been used to diagnose HALS, make it possible, under certain assumptions, to: statistically evaluate the number of phenotypes, test for mixing of HALS with other processes...

  17. How to get rid of W: a latent variables approach to modelling spatially lagged variables

    NARCIS (Netherlands)

    Folmer, H.; Oud, J.

    2008-01-01

    In this paper we propose a structural equation model (SEM) with latent variables to model spatial dependence. Rather than using the spatial weights matrix W, we propose to use latent variables to represent spatial dependence and spillover effects, of which the observed spatially lagged variables are

  18. How to get rid of W : a latent variables approach to modelling spatially lagged variables

    NARCIS (Netherlands)

    Folmer, Henk; Oud, Johan

    2008-01-01

    In this paper we propose a structural equation model (SEM) with latent variables to model spatial dependence. Rather than using the spatial weights matrix W, we propose to use latent variables to represent spatial dependence and spillover effects, of which the observed spatially lagged variables are

  19. Gene variants associated with antisocial behaviour: a latent variable approach.

    Science.gov (United States)

    Bentley, Mary Jane; Lin, Haiqun; Fernandez, Thomas V; Lee, Maria; Yrigollen, Carolyn M; Pakstis, Andrew J; Katsovich, Liliya; Olds, David L; Grigorenko, Elena L; Leckman, James F

    2013-10-01

    The aim of this study was to determine if a latent variable approach might be useful in identifying shared variance across genetic risk alleles that is associated with antisocial behaviour at age 15 years. Using a conventional latent variable approach, we derived an antisocial phenotype in 328 adolescents utilizing data from a 15-year follow-up of a randomized trial of a prenatal and infancy nurse-home visitation programme in Elmira, New York. We then investigated, via a novel latent variable approach, 450 informative genetic polymorphisms in 71 genes previously associated with antisocial behaviour, drug use, affiliative behaviours and stress response in 241 consenting individuals for whom DNA was available. Haplotype and Pathway analyses were also performed. Eight single-nucleotide polymorphisms (SNPs) from eight genes contributed to the latent genetic variable that in turn accounted for 16.0% of the variance within the latent antisocial phenotype. The number of risk alleles was linearly related to the latent antisocial variable scores. Haplotypes that included the putative risk alleles for all eight genes were also associated with higher latent antisocial variable scores. In addition, 33 SNPs from 63 of the remaining genes were also significant when added to the final model. Many of these genes interact on a molecular level, forming molecular networks. The results support a role for genes related to dopamine, norepinephrine, serotonin, glutamate, opioid and cholinergic signalling as well as stress response pathways in mediating susceptibility to antisocial behaviour. This preliminary study supports use of relevant behavioural indicators and latent variable approaches to study the potential 'co-action' of gene variants associated with antisocial behaviour. It also underscores the cumulative relevance of common genetic variants for understanding the aetiology of complex behaviour. If replicated in future studies, this approach may allow the identification of a

  20. Avoiding and Correcting Bias in Score-Based Latent Variable Regression with Discrete Manifest Items

    Science.gov (United States)

    Lu, Irene R. R.; Thomas, D. Roland

    2008-01-01

    This article considers models involving a single structural equation with latent explanatory and/or latent dependent variables where discrete items are used to measure the latent variables. Our primary focus is the use of scores as proxies for the latent variables and carrying out ordinary least squares (OLS) regression on such scores to estimate…

  1. Iron appetite and latent learning in rats.

    Science.gov (United States)

    Woods, S C; Vasselli, J R; Milam, K M

    1977-11-01

    Two experiments are reported which show that rats are capable of forming an association between the presence of iron in a solution when it is not specifically needed and a subsequent state of iron deficiency. Specifically, rats were trained to lever press for water while thirsty. One group received ferrous ions in addition to the water. When these rats were subsequently rendered iron deficient, they lever pressed more under extinction conditions as a graded function of lower hemoglobin levels. Controls that either did not receive ferrous ions during training or received solutions other than ferrous solutions during training did not respond this way under extinction conditions. This is therefore a type of latent learning previously demonstrated only for sodium appetite.

  2. On the explaining-away phenomenon in multivariate latent variable models.

    Science.gov (United States)

    van Rijn, Peter; Rijmen, Frank

    2015-02-01

    Many probabilistic models for psychological and educational measurements contain latent variables. Well-known examples are factor analysis, item response theory, and latent class model families. We discuss what is referred to as the 'explaining-away' phenomenon in the context of such latent variable models. This phenomenon can occur when multiple latent variables are related to the same observed variable, and can elicit seemingly counterintuitive conditional dependencies between latent variables given observed variables. We illustrate the implications of explaining away for a number of well-known latent variable models by using both theoretical and real data examples. © 2014 The British Psychological Society.

  3. Linear latent variable models: the lava-package

    DEFF Research Database (Denmark)

    Holst, Klaus Kähler; Budtz-Jørgensen, Esben

    2013-01-01

    are implemented including robust standard errors for clustered correlated data, multigroup analyses, non-linear parameter constraints, inference with incomplete data, maximum likelihood estimation with censored and binary observations, and instrumental variable estimators. In addition an extensive simulation......An R package for specifying and estimating linear latent variable models is presented. The philosophy of the implementation is to separate the model specification from the actual data, which leads to a dynamic and easy way of modeling complex hierarchical structures. Several advanced features...

  4. Explicit estimating equations for semiparametric generalized linear latent variable models

    KAUST Repository

    Ma, Yanyuan

    2010-07-05

    We study generalized linear latent variable models without requiring a distributional assumption of the latent variables. Using a geometric approach, we derive consistent semiparametric estimators. We demonstrate that these models have a property which is similar to that of a sufficient complete statistic, which enables us to simplify the estimating procedure and explicitly to formulate the semiparametric estimating equations. We further show that the explicit estimators have the usual root n consistency and asymptotic normality. We explain the computational implementation of our method and illustrate the numerical performance of the estimators in finite sample situations via extensive simulation studies. The advantage of our estimators over the existing likelihood approach is also shown via numerical comparison. We employ the method to analyse a real data example from economics. © 2010 Royal Statistical Society.

  5. Hidden Markov latent variable models with multivariate longitudinal data.

    Science.gov (United States)

    Song, Xinyuan; Xia, Yemao; Zhu, Hongtu

    2017-03-01

    Cocaine addiction is chronic and persistent, and has become a major social and health problem in many countries. Existing studies have shown that cocaine addicts often undergo episodic periods of addiction to, moderate dependence on, or swearing off cocaine. Given its reversible feature, cocaine use can be formulated as a stochastic process that transits from one state to another, while the impacts of various factors, such as treatment received and individuals' psychological problems on cocaine use, may vary across states. This article develops a hidden Markov latent variable model to study multivariate longitudinal data concerning cocaine use from a California Civil Addict Program. The proposed model generalizes conventional latent variable models to allow bidirectional transition between cocaine-addiction states and conventional hidden Markov models to allow latent variables and their dynamic interrelationship. We develop a maximum-likelihood approach, along with a Monte Carlo expectation conditional maximization (MCECM) algorithm, to conduct parameter estimation. The asymptotic properties of the parameter estimates and statistics for testing the heterogeneity of model parameters are investigated. The finite sample performance of the proposed methodology is demonstrated by simulation studies. The application to cocaine use study provides insights into the prevention of cocaine use. © 2016, The International Biometric Society.

  6. Defining a Family of Cognitive Diagnosis Models Using Log-Linear Models with Latent Variables

    Science.gov (United States)

    Henson, Robert A.; Templin, Jonathan L.; Willse, John T.

    2009-01-01

    This paper uses log-linear models with latent variables (Hagenaars, in "Loglinear Models with Latent Variables," 1993) to define a family of cognitive diagnosis models. In doing so, the relationship between many common models is explicitly defined and discussed. In addition, because the log-linear model with latent variables is a general model for…

  7. Inverse Ising problem in continuous time: A latent variable approach

    Science.gov (United States)

    Donner, Christian; Opper, Manfred

    2017-12-01

    We consider the inverse Ising problem: the inference of network couplings from observed spin trajectories for a model with continuous time Glauber dynamics. By introducing two sets of auxiliary latent random variables we render the likelihood into a form which allows for simple iterative inference algorithms with analytical updates. The variables are (1) Poisson variables to linearize an exponential term which is typical for point process likelihoods and (2) Pólya-Gamma variables, which make the likelihood quadratic in the coupling parameters. Using the augmented likelihood, we derive an expectation-maximization (EM) algorithm to obtain the maximum likelihood estimate of network parameters. Using a third set of latent variables we extend the EM algorithm to sparse couplings via L1 regularization. Finally, we develop an efficient approximate Bayesian inference algorithm using a variational approach. We demonstrate the performance of our algorithms on data simulated from an Ising model. For data which are simulated from a more biologically plausible network with spiking neurons, we show that the Ising model captures well the low order statistics of the data and how the Ising couplings are related to the underlying synaptic structure of the simulated network.

  8. Partial Granger causality--eliminating exogenous inputs and latent variables.

    Science.gov (United States)

    Guo, Shuixia; Seth, Anil K; Kendrick, Keith M; Zhou, Cong; Feng, Jianfeng

    2008-07-15

    Attempts to identify causal interactions in multivariable biological time series (e.g., gene data, protein data, physiological data) can be undermined by the confounding influence of environmental (exogenous) inputs. Compounding this problem, we are commonly only able to record a subset of all related variables in a system. These recorded variables are likely to be influenced by unrecorded (latent) variables. To address this problem, we introduce a novel variant of a widely used statistical measure of causality--Granger causality--that is inspired by the definition of partial correlation. Our 'partial Granger causality' measure is extensively tested with toy models, both linear and nonlinear, and is applied to experimental data: in vivo multielectrode array (MEA) local field potentials (LFPs) recorded from the inferotemporal cortex of sheep. Our results demonstrate that partial Granger causality can reveal the underlying interactions among elements in a network in the presence of exogenous inputs and latent variables in many cases where the existing conditional Granger causality fails.

  9. The Integration of Continuous and Discrete Latent Variable Models: Potential Problems and Promising Opportunities

    Science.gov (United States)

    Bauer, Daniel J.; Curran, Patrick J.

    2004-01-01

    Structural equation mixture modeling (SEMM) integrates continuous and discrete latent variable models. Drawing on prior research on the relationships between continuous and discrete latent variable models, the authors identify 3 conditions that may lead to the estimation of spurious latent classes in SEMM: misspecification of the structural model,…

  10. Reduction of Non-stationary Noise using a Non-negative Latent Variable Decomposition

    DEFF Research Database (Denmark)

    Schmidt, Mikkel Nørgaard; Larsen, Jan

    2008-01-01

    We present a method for suppression of non-stationary noise in single channel recordings of speech. The method is based on a non-negative latent variable decomposition model for the speech and noise signals, learned directly from a noisy mixture. In non-speech regions an over complete basis...... is learned for the noise that is then used to jointly estimate the speech and the noise from the mixture. We compare the method to the classical spectral subtraction approach, where the noise spectrum is estimated as the average over non-speech frames. The proposed method significantly outperforms...

  11. Integrated Multiscale Latent Variable Regression and Application to Distillation Columns

    Directory of Open Access Journals (Sweden)

    Muddu Madakyaru

    2013-01-01

    Full Text Available Proper control of distillation columns requires estimating some key variables that are challenging to measure online (such as compositions, which are usually estimated using inferential models. Commonly used inferential models include latent variable regression (LVR techniques, such as principal component regression (PCR, partial least squares (PLS, and regularized canonical correlation analysis (RCCA. Unfortunately, measured practical data are usually contaminated with errors, which degrade the prediction abilities of inferential models. Therefore, noisy measurements need to be filtered to enhance the prediction accuracy of these models. Multiscale filtering has been shown to be a powerful feature extraction tool. In this work, the advantages of multiscale filtering are utilized to enhance the prediction accuracy of LVR models by developing an integrated multiscale LVR (IMSLVR modeling algorithm that integrates modeling and feature extraction. The idea behind the IMSLVR modeling algorithm is to filter the process data at different decomposition levels, model the filtered data from each level, and then select the LVR model that optimizes a model selection criterion. The performance of the developed IMSLVR algorithm is illustrated using three examples, one using synthetic data, one using simulated distillation column data, and one using experimental packed bed distillation column data. All examples clearly demonstrate the effectiveness of the IMSLVR algorithm over the conventional methods.

  12. Labour market participants’ competitiveness assessment based on latent variables theory

    Directory of Open Access Journals (Sweden)

    T. V. Sabetova

    2017-01-01

    Full Text Available The article suggests innovative model for assessment of labour market subjects’ competitiveness, or successfulness. The authors state that general complex indicator for individual competitiveness within the labour market cannot be identified. Instead, precise enough assessment of such competitiveness can be based on some variables, though different for in-house and external labour market. The model of latent variables’ assessment based on Rasch’s method was selected as the base for the suggested method. The assessment model gives unbiased generalized values of subjects’ competitiveness on the linear non-dimensional scale based on the partial estimates of the selected criteria. The free choice of these criteria allows the model’s appliance for various labour market segments. The article demonstrates the mathematical grounding for the model; methodic of the assessment criteria selection; the way of assessment performance using MS Excel. It also analyses the features of the obtained estimates and shows their comparison with the estimates obtained by traditional methods. The model suggested by the authors can introduce any quantitative parameter of competitiveness as a variable after analysis of the factors affecting it. The quantitative estimates of these factors become the model’s criteria, but the assessment precision does not alter.

  13. On the Latent Variable Interpretation in Sum-Product Networks.

    Science.gov (United States)

    Peharz, Robert; Gens, Robert; Pernkopf, Franz; Domingos, Pedro

    2017-10-01

    One of the central themes in Sum-Product networks (SPNs) is the interpretation of sum nodes as marginalized latent variables (LVs). This interpretation yields an increased syntactic or semantic structure, allows the application of the EM algorithm and to efficiently perform MPE inference. In literature, the LV interpretation was justified by explicitly introducing the indicator variables corresponding to the LVs' states. However, as pointed out in this paper, this approach is in conflict with the completeness condition in SPNs and does not fully specify the probabilistic model. We propose a remedy for this problem by modifying the original approach for introducing the LVs, which we call SPN augmentation. We discuss conditional independencies in augmented SPNs, formally establish the probabilistic interpretation of the sum-weights and give an interpretation of augmented SPNs as Bayesian networks. Based on these results, we find a sound derivation of the EM algorithm for SPNs. Furthermore, the Viterbi-style algorithm for MPE proposed in literature was never proven to be correct. We show that this is indeed a correct algorithm, when applied to selective SPNs, and in particular when applied to augmented SPNs. Our theoretical results are confirmed in experiments on synthetic data and 103 real-world datasets.

  14. Tensor Decompositions for Learning Latent Variable Models

    Science.gov (United States)

    2012-12-08

    and eigenvectors of tensors is generally significantly more complicated than their matrix counterpart (both algebraically [Qi05, CS11, Lim05] and...The reduction First, let W ∈ Rd×k be a linear transformation such that M2(W,W ) = W M2W = I where I is the k × k identity matrix (i.e., W whitens ...approximate the whitening matrix W ∈ Rd×k from second-moment matrix M2 ∈ Rd×d. To do this, one first multiplies M2 by a random matrix R ∈ Rd×k′ for some k′ ≥ k

  15. Internet Gamblers Differ on Social Variables: A Latent Class Analysis.

    Science.gov (United States)

    Khazaal, Yasser; Chatton, Anne; Achab, Sophia; Monney, Gregoire; Thorens, Gabriel; Dufour, Magali; Zullino, Daniele; Rothen, Stephane

    2017-09-01

    Online gambling has gained popularity in the last decade, leading to an important shift in how consumers engage in gambling and in the factors related to problem gambling and prevention. Indebtedness and loneliness have previously been associated with problem gambling. The current study aimed to characterize online gamblers in relation to indebtedness, loneliness, and several in-game social behaviors. The data set was obtained from 584 Internet gamblers recruited online through gambling websites and forums. Of these gamblers, 372 participants completed all study assessments and were included in the analyses. Questionnaires included those on sociodemographics and social variables (indebtedness, loneliness, in-game social behaviors), as well as the Gambling Motives Questionnaire, Gambling Related Cognitions Scale, Internet Addiction Test, Problem Gambling Severity Index, Short Depression-Happiness Scale, and UPPS-P Impulsive Behavior Scale. Social variables were explored with a latent class model. The clusters obtained were compared for psychological measures and three clusters were found: lonely indebted gamblers (cluster 1: 6.5%), not lonely not indebted gamblers (cluster 2: 75.4%), and not lonely indebted gamblers (cluster 3: 18%). Participants in clusters 1 and 3 (particularly in cluster 1) were at higher risk of problem gambling than were those in cluster 2. The three groups differed on most assessed variables, including the Problem Gambling Severity Index, the Short Depression-Happiness Scale, and the UPPS-P subscales (except the sensation seeking subscore). Results highlight significant between-group differences, suggesting that Internet gamblers are not a homogeneous group. Specific intervention strategies could be implemented for groups at risk.

  16. Measurement Uncertainty in Racial and Ethnic Identification among Adolescents of Mixed Ancestry: A Latent Variable Approach

    Science.gov (United States)

    Tracy, Allison J.; Erkut, Sumru; Porche, Michelle V.; Kim, Jo; Charmaraman, Linda; Grossman, Jennifer M.; Ceder, Ineke; Garcia, Heidie Vazquez

    2010-01-01

    In this article, we operationalize identification of mixed racial and ethnic ancestry among adolescents as a latent variable to (a) account for measurement uncertainty, and (b) compare alternative wording formats for racial and ethnic self-categorization in surveys. Two latent variable models were fit to multiple mixed-ancestry indicator data from…

  17. A Composite Likelihood Inference in Latent Variable Models for Ordinal Longitudinal Responses

    Science.gov (United States)

    Vasdekis, Vassilis G. S.; Cagnone, Silvia; Moustaki, Irini

    2012-01-01

    The paper proposes a composite likelihood estimation approach that uses bivariate instead of multivariate marginal probabilities for ordinal longitudinal responses using a latent variable model. The model considers time-dependent latent variables and item-specific random effects to be accountable for the interdependencies of the multivariate…

  18. Modeling Psychological Attributes in Psychology – An Epistemological Discussion: Network Analysis vs. Latent Variables

    Science.gov (United States)

    Guyon, Hervé; Falissard, Bruno; Kop, Jean-Luc

    2017-01-01

    Network Analysis is considered as a new method that challenges Latent Variable models in inferring psychological attributes. With Network Analysis, psychological attributes are derived from a complex system of components without the need to call on any latent variables. But the ontological status of psychological attributes is not adequately defined with Network Analysis, because a psychological attribute is both a complex system and a property emerging from this complex system. The aim of this article is to reappraise the legitimacy of latent variable models by engaging in an ontological and epistemological discussion on psychological attributes. Psychological attributes relate to the mental equilibrium of individuals embedded in their social interactions, as robust attractors within complex dynamic processes with emergent properties, distinct from physical entities located in precise areas of the brain. Latent variables thus possess legitimacy, because the emergent properties can be conceptualized and analyzed on the sole basis of their manifestations, without exploring the upstream complex system. However, in opposition with the usual Latent Variable models, this article is in favor of the integration of a dynamic system of manifestations. Latent Variables models and Network Analysis thus appear as complementary approaches. New approaches combining Latent Network Models and Network Residuals are certainly a promising new way to infer psychological attributes, placing psychological attributes in an inter-subjective dynamic approach. Pragmatism-realism appears as the epistemological framework required if we are to use latent variables as representations of psychological attributes. PMID:28572780

  19. Investigating Factorial Invariance of Latent Variables Across Populations When Manifest Variables Are Missing Completely.

    Science.gov (United States)

    Widaman, Keith F; Grimm, Kevin J; Early, Dawnté R; Robins, Richard W; Conger, Rand D

    2013-07-01

    Difficulties arise in multiple-group evaluations of factorial invariance if particular manifest variables are missing completely in certain groups. Ad hoc analytic alternatives can be used in such situations (e.g., deleting manifest variables), but some common approaches, such as multiple imputation, are not viable. At least 3 solutions to this problem are viable: analyzing differing sets of variables across groups, using pattern mixture approaches, and a new method using random number generation. The latter solution, proposed in this article, is to generate pseudo-random normal deviates for all observations for manifest variables that are missing completely in a given sample and then to specify multiple-group models in a way that respects the random nature of these values. An empirical example is presented in detail comparing the 3 approaches. The proposed solution can enable quantitative comparisons at the latent variable level between groups using programs that require the same number of manifest variables in each group.

  20. STATUS SOSIAL EKONOMI DAN FERTILITAS: A Latent Variable Approach

    Directory of Open Access Journals (Sweden)

    Suandi -

    2012-11-01

    Full Text Available The main problems faced by developing countries including Indonesia are not onlyeconomic problems that tend to harm, but still met the high fertility rate. The purpose ofwriting to find out the relationship between socioeconomic status to the level of fertilitythrough the "A Latent Variable Approach." The study adopts the approach of fertility oneconomic development. Economic development based on the theories of Malthus: anincrease in "income" is slower than the increase in births (fertility and is the root ofpeople falling into poverty. However, Becker made linkage model or the influence ofchildren income and price. According to Becker, viewed from the aspect of demand thatthe price of children is greater than the income effect.The study shows that (1 level of education correlates positively on income andnegatively affect fertility, (2 age structure of women (control contraceptives adverselyaffect fertility. That is, the older the age, the level of individual productivity and lowerfertility or declining, and (3 husband's employment status correlated positively to theearnings (income. Through a permanent factor income or household income referred toas a negative influence on fertility. There are differences in value orientation of childrenbetween advanced society (rich with a backward society (the poor. The poor, forexample, the value of children is more production of goods. That is, children born moreemphasis on aspects of the number or the number of children owned (quantity, numberof children born by the poor is expected to help their parents at the age of retirement orno longer productive so that the child is expected to assist them in economic, security,and social security (insurance, while the developed (rich children are moreconsumption value or quality of the child.

  1. Confidence Intervals for a Semiparametric Approach to Modeling Nonlinear Relations among Latent Variables

    Science.gov (United States)

    Pek, Jolynn; Losardo, Diane; Bauer, Daniel J.

    2011-01-01

    Compared to parametric models, nonparametric and semiparametric approaches to modeling nonlinearity between latent variables have the advantage of recovering global relationships of unknown functional form. Bauer (2005) proposed an indirect application of finite mixtures of structural equation models where latent components are estimated in the…

  2. The application of seasonal latent variable in forecasting electricity demand as an alternative method

    International Nuclear Information System (INIS)

    Sumer, Kutluk Kagan; Goktas, Ozlem; Hepsag, Aycan

    2009-01-01

    In this study, we used ARIMA, seasonal ARIMA (SARIMA) and alternatively the regression model with seasonal latent variable in forecasting electricity demand by using data that belongs to 'Kayseri and Vicinity Electricity Joint-Stock Company' over the 1997:1-2005:12 periods. This study tries to examine the advantages of forecasting with ARIMA, SARIMA methods and with the model has seasonal latent variable to each other. The results support that ARIMA and SARIMA models are unsuccessful in forecasting electricity demand. The regression model with seasonal latent variable used in this study gives more successful results than ARIMA and SARIMA models because also this model can consider seasonal fluctuations and structural breaks

  3. Latent variable modeling%建立隐性变量模型

    Institute of Scientific and Technical Information of China (English)

    蔡力

    2012-01-01

    @@ A latent variable model, as the name suggests,is a statistical model that contains latent, that is, unobserved, variables.Their roots go back to Spearman's 1904 seminal work[1] on factor analysis,which is arguably the first well-articulated latent variable model to be widely used in psychology, mental health research, and allied disciplines.Because of the association of factor analysis with early studies of human intelligence, the fact that key variables in a statistical model are, on occasion, unobserved has been a point of lingering contention and controversy.The reader is assured, however, that a latent variable,defined in the broadest manner, is no more mysterious than an error term in a normal theory linear regression model or a random effect in a mixed model.

  4. Temporal analysis of text data using latent variable models

    DEFF Research Database (Denmark)

    Mølgaard, Lasse Lohilahti; Larsen, Jan; Goutte, Cyril

    2009-01-01

    Detecting and tracking of temporal data is an important task in multiple applications. In this paper we study temporal text mining methods for Music Information Retrieval. We compare two ways of detecting the temporal latent semantics of a corpus extracted from Wikipedia, using a stepwise...

  5. poLCA: An R Package for Polytomous Variable Latent Class Analysis

    Directory of Open Access Journals (Sweden)

    Drew A. Linzer

    2011-08-01

    Full Text Available poLCA is a software package for the estimation of latent class and latent class regression models for polytomous outcome variables, implemented in the R statistical computing environment. Both models can be called using a single simple command line. The basic latent class model is a finite mixture model in which the component distributions are assumed to be multi-way cross-classification tables with all variables mutually independent. The latent class regression model further enables the researcher to estimate the effects of covariates on predicting latent class membership. poLCA uses expectation-maximization and Newton-Raphson algorithms to find maximum likelihood estimates of the model parameters.

  6. Latent variable models an introduction to factor, path, and structural equation analysis

    CERN Document Server

    Loehlin, John C

    2004-01-01

    This fourth edition introduces multiple-latent variable models by utilizing path diagrams to explain the underlying relationships in the models. The book is intended for advanced students and researchers in the areas of social, educational, clinical, ind

  7. Assessing Factors Related to Waist Circumference and Obesity: Application of a Latent Variable Model

    OpenAIRE

    Dalvand, Sahar; Koohpayehzadeh, Jalil; Karimlou, Masoud; Asgari, Fereshteh; Rafei, Ali; Seifi, Behjat; Niksima, Seyed Hassan; Bakhshi, Enayatollah

    2015-01-01

    Background. Because the use of BMI (Body Mass Index) alone as a measure of adiposity has been criticized, in the present study our aim was to fit a latent variable model to simultaneously examine the factors that affect waist circumference (continuous outcome) and obesity (binary outcome) among Iranian adults. Methods. Data included 18,990 Iranian individuals aged 20–65 years that are derived from the third National Survey of Noncommunicable Diseases Risk Factors in Iran. Using latent variabl...

  8. Perturbative corrections for approximate inference in gaussian latent variable models

    DEFF Research Database (Denmark)

    Opper, Manfred; Paquet, Ulrich; Winther, Ole

    2013-01-01

    Expectation Propagation (EP) provides a framework for approximate inference. When the model under consideration is over a latent Gaussian field, with the approximation being Gaussian, we show how these approximations can systematically be corrected. A perturbative expansion is made of the exact b...... illustrate on tree-structured Ising model approximations. Furthermore, they provide a polynomial-time assessment of the approximation error. We also provide both theoretical and practical insights on the exactness of the EP solution. © 2013 Manfred Opper, Ulrich Paquet and Ole Winther....

  9. Stereotype Threat and College Academic Performance: A Latent Variables Approach*

    Science.gov (United States)

    Owens, Jayanti; Massey, Douglas S.

    2013-01-01

    Stereotype threat theory has gained experimental and survey-based support in helping explain the academic underperformance of minority students at selective colleges and universities. Stereotype threat theory states that minority students underperform because of pressures created by negative stereotypes about their racial group. Past survey-based studies, however, are characterized by methodological inefficiencies and potential biases: key theoretical constructs have only been measured using summed indicators and predicted relationships modeled using ordinary least squares. Using the National Longitudinal Survey of Freshman, this study overcomes previous methodological shortcomings by developing a latent construct model of stereotype threat. Theoretical constructs and equations are estimated simultaneously from multiple indicators, yielding a more reliable, valid, and parsimonious test of key propositions. Findings additionally support the view that social stigma can indeed have strong negative effects on the academic performance of pejoratively stereotyped racial-minority group members, not only in laboratory settings, but also in the real world. PMID:23950616

  10. New approaches for examining associations with latent categorical variables: applications to substance abuse and aggression.

    Science.gov (United States)

    Feingold, Alan; Tiberio, Stacey S; Capaldi, Deborah M

    2014-03-01

    Assessments of substance use behaviors often include categorical variables that are frequently related to other measures using logistic regression or chi-square analysis. When the categorical variable is latent (e.g., extracted from a latent class analysis [LCA]), classification of observations is often used to create an observed nominal variable from the latent one for use in a subsequent analysis. However, recent simulation studies have found that this classical 3-step analysis championed by the pioneers of LCA produces underestimates of the associations of latent classes with other variables. Two preferable but underused alternatives for examining such linkages-each of which is most appropriate under certain conditions-are (a) 3-step analysis, which corrects the underestimation bias of the classical approach, and (b) 1-step analysis. The purpose of this article is to dissuade researchers from conducting classical 3-step analysis and to promote the use of the 2 newer approaches that are described and compared. In addition, the applications of these newer models-for use when the independent, the dependent, or both categorical variables are latent-are illustrated through substantive analyses relating classes of substance abusers to classes of intimate partner aggressors.

  11. Micro-macro multilevel latent class models with multiple discrete individual-level variables

    NARCIS (Netherlands)

    Bennink, M.; Croon, M.A.; Kroon, B.; Vermunt, J.K.

    2016-01-01

    An existing micro-macro method for a single individual-level variable is extended to the multivariate situation by presenting two multilevel latent class models in which multiple discrete individual-level variables are used to explain a group-level outcome. As in the univariate case, the

  12. Latent vs. Observed Variables : Analysis of Irrigation Water Efficiency Using SEM and SUR

    NARCIS (Netherlands)

    Tang, Jianjun; Folmer, Henk

    In this paper we compare conceptualising single factor technical and allocative efficiency as indicators of a single latent variable, or as separate observed variables. In the former case, the impacts on both efficiency types are analysed by means of structural equationmodeling (SEM), in the latter

  13. Behaviorism, latent learning, and cognitive maps: needed revisions in introductory psychology textbooks.

    Science.gov (United States)

    Jensen, Robert

    2006-01-01

    This paper critically assesses the scholarship in introductory psychology textbooks in relation to the topic of latent learning. A review of the treatment of latent learning in 48 introductory psychology textbooks published between 1948 and 2004, with 21 of these texts published since 1999, reveals that the scholarship on the topic of latent learning demonstrated in introductory textbooks warrants improvement. Errors that persist in textbooks include the assertion that the latent learning experiments demonstrate unequivocally that reinforcement was not necessary for learning to occur, that behavioral theories could not account for the results of the latent learning experiments, that B. F. Skinner was an S-R association behaviorist who argued that reinforcement is necessary for learning to occur, and that because behavioral theories (including that of B. F. Skinner) were unable explain the results of the latent learning experiments the cognitive map invoked by Edward Tolman is the only explanation for latent learning. Finally, the validity of the cognitive map is typically accepted without question. Implications of the presence of these errors for students and the discipline are considered. Lastly, remedies are offered to improve the scholarship found in introductory psychology textbooks.

  14. Generalized Network Psychometrics : Combining Network and Latent Variable Models

    NARCIS (Netherlands)

    Epskamp, S.; Rhemtulla, M.; Borsboom, D.

    2017-01-01

    We introduce the network model as a formal psychometric model, conceptualizing the covariance between psychometric indicators as resulting from pairwise interactions between observable variables in a network structure. This contrasts with standard psychometric models, in which the covariance between

  15. Causal Effect Inference with Deep Latent-Variable Models

    NARCIS (Netherlands)

    Louizos, C; Shalit, U.; Mooij, J.; Sontag, D.; Zemel, R.; Welling, M.

    2017-01-01

    Learning individual-level causal effects from observational data, such as inferring the most effective medication for a specific patient, is a problem of growing importance for policy makers. The most important aspect of inferring causal effects from observational data is the handling of

  16. Realist identification of group-level latent variables for perinatal social epidemiology theory building.

    Science.gov (United States)

    Eastwood, John Graeme; Jalaludin, Bin Badrudin; Kemp, Lynn Ann; Phung, Hai Ngoc

    2014-01-01

    We have previously reported in this journal on an ecological study of perinatal depressive symptoms in South Western Sydney. In that article, we briefly reported on a factor analysis that was utilized to identify empirical indicators for analysis. In this article, we report on the mixed method approach that was used to identify those latent variables. Social epidemiology has been slow to embrace a latent variable approach to the study of social, political, economic, and cultural structures and mechanisms, partly for philosophical reasons. Critical realist ontology and epistemology have been advocated as an appropriate methodological approach to both theory building and theory testing in the health sciences. We describe here an emergent mixed method approach that uses qualitative methods to identify latent constructs followed by factor analysis using empirical indicators chosen to measure identified qualitative codes. Comparative analysis of the findings is reported together with a limited description of realist approaches to abstract reasoning.

  17. Latent variable method for automatic adaptation to background states in motor imagery BCI

    Science.gov (United States)

    Dagaev, Nikolay; Volkova, Ksenia; Ossadtchi, Alexei

    2018-02-01

    Objective. Brain-computer interface (BCI) systems are known to be vulnerable to variabilities in background states of a user. Usually, no detailed information on these states is available even during the training stage. Thus there is a need in a method which is capable of taking background states into account in an unsupervised way. Approach. We propose a latent variable method that is based on a probabilistic model with a discrete latent variable. In order to estimate the model’s parameters, we suggest to use the expectation maximization algorithm. The proposed method is aimed at assessing characteristics of background states without any corresponding data labeling. In the context of asynchronous motor imagery paradigm, we applied this method to the real data from twelve able-bodied subjects with open/closed eyes serving as background states. Main results. We found that the latent variable method improved classification of target states compared to the baseline method (in seven of twelve subjects). In addition, we found that our method was also capable of background states recognition (in six of twelve subjects). Significance. Without any supervised information on background states, the latent variable method provides a way to improve classification in BCI by taking background states into account at the training stage and then by making decisions on target states weighted by posterior probabilities of background states at the prediction stage.

  18. A Latent-Variable Causal Model of Faculty Reputational Ratings.

    Science.gov (United States)

    King, Suzanne; Wolfle, Lee M.

    A reanalysis was conducted of Saunier's research (1985) on sources of variation in the National Research Council (NRC) reputational ratings of university faculty. Saunier conducted a stepwise regression analysis using 12 predictor variables. Due to problems with multicollinearity and because of the atheoretical nature of stepwise regression,…

  19. Evaluation of Validity and Reliability for Hierarchical Scales Using Latent Variable Modeling

    Science.gov (United States)

    Raykov, Tenko; Marcoulides, George A.

    2012-01-01

    A latent variable modeling method is outlined, which accomplishes estimation of criterion validity and reliability for a multicomponent measuring instrument with hierarchical structure. The approach provides point and interval estimates for the scale criterion validity and reliability coefficients, and can also be used for testing composite or…

  20. Global Convergence of the EM Algorithm for Unconstrained Latent Variable Models with Categorical Indicators

    Science.gov (United States)

    Weissman, Alexander

    2013-01-01

    Convergence of the expectation-maximization (EM) algorithm to a global optimum of the marginal log likelihood function for unconstrained latent variable models with categorical indicators is presented. The sufficient conditions under which global convergence of the EM algorithm is attainable are provided in an information-theoretic context by…

  1. Cognitive Preconditions of Early Reading and Spelling: A Latent-Variable Approach with Longitudinal Data

    Science.gov (United States)

    Preßler, Anna-Lena; Könen, Tanja; Hasselhorn, Marcus; Krajewski, Kristin

    2014-01-01

    The aim of the present study was to empirically disentangle the interdependencies of the impact of nonverbal intelligence, working memory capacities, and phonological processing skills on early reading decoding and spelling within a latent variable approach. In a sample of 127 children, these cognitive preconditions were assessed before the onset…

  2. Standard Errors of Estimated Latent Variable Scores with Estimated Structural Parameters

    Science.gov (United States)

    Hoshino, Takahiro; Shigemasu, Kazuo

    2008-01-01

    The authors propose a concise formula to evaluate the standard error of the estimated latent variable score when the true values of the structural parameters are not known and must be estimated. The formula can be applied to factor scores in factor analysis or ability parameters in item response theory, without bootstrap or Markov chain Monte…

  3. A Second-Order Conditionally Linear Mixed Effects Model with Observed and Latent Variable Covariates

    Science.gov (United States)

    Harring, Jeffrey R.; Kohli, Nidhi; Silverman, Rebecca D.; Speece, Deborah L.

    2012-01-01

    A conditionally linear mixed effects model is an appropriate framework for investigating nonlinear change in a continuous latent variable that is repeatedly measured over time. The efficacy of the model is that it allows parameters that enter the specified nonlinear time-response function to be stochastic, whereas those parameters that enter in a…

  4. The Relationship between Executive Functions and Language Abilities in Children: A Latent Variables Approach

    Science.gov (United States)

    Kaushanskaya, Margarita; Park, Ji Sook; Gangopadhyay, Ishanti; Davidson, Meghan M.; Weismer, Susan Ellis

    2017-01-01

    Purpose: We aimed to outline the latent variables approach for measuring nonverbal executive function (EF) skills in school-age children, and to examine the relationship between nonverbal EF skills and language performance in this age group. Method: Seventy-one typically developing children, ages 8 through 11, participated in the study. Three EF…

  5. Intraclass Correlation Coefficients in Hierarchical Designs: Evaluation Using Latent Variable Modeling

    Science.gov (United States)

    Raykov, Tenko

    2011-01-01

    Interval estimation of intraclass correlation coefficients in hierarchical designs is discussed within a latent variable modeling framework. A method accomplishing this aim is outlined, which is applicable in two-level studies where participants (or generally lower-order units) are clustered within higher-order units. The procedure can also be…

  6. Classification criteria of syndromes by latent variable models

    DEFF Research Database (Denmark)

    Petersen, Janne

    2010-01-01

    , although this is often desired. I have proposed a new method for predicting class membership that, in contrast to methods based on posterior probabilities of class membership, yields consistent estimates when regressed on explanatory variables in a subsequent analysis. There are four different basic models...... analyses. Part 1: HALS engages different phenotypic changes of peripheral lipoatrophy and central lipohypertrophy.  There are several different definitions of HALS and no consensus on the number of phenotypes. Many of the definitions consist of counting fulfilled criteria on markers and do not include...

  7. Bayesian leave-one-out cross-validation approximations for Gaussian latent variable models

    DEFF Research Database (Denmark)

    Vehtari, Aki; Mononen, Tommi; Tolvanen, Ville

    2016-01-01

    The future predictive performance of a Bayesian model can be estimated using Bayesian cross-validation. In this article, we consider Gaussian latent variable models where the integration over the latent values is approximated using the Laplace method or expectation propagation (EP). We study...... the properties of several Bayesian leave-one-out (LOO) cross-validation approximations that in most cases can be computed with a small additional cost after forming the posterior approximation given the full data. Our main objective is to assess the accuracy of the approximative LOO cross-validation estimators...

  8. A Comparison of Approaches for the Analysis of Interaction Effects between Latent Variables Using Partial Least Squares Path Modeling

    Science.gov (United States)

    Henseler, Jorg; Chin, Wynne W.

    2010-01-01

    In social and business sciences, the importance of the analysis of interaction effects between manifest as well as latent variables steadily increases. Researchers using partial least squares (PLS) to analyze interaction effects between latent variables need an overview of the available approaches as well as their suitability. This article…

  9. INCLUSION OF THE LATENT PERSONALITY VARIABLE IN MULTINOMIAL LOGIT MODELS USING THE 16PF PSYCHOMETRIC TEST

    Directory of Open Access Journals (Sweden)

    JORGE E. CÓRDOBA MAQUILÓN

    2012-01-01

    Full Text Available Los modelos de demanda de viajes utilizan principalmente los atributos modales y las características socioeconómicas como variables explicativas. También se ha establecido que las actitudes y percepciones influyen en el comportamiento de los usuarios. Sin embargo, las variables psicológicas del individuo condicionan la conducta del usuario. En este estudio se incluyó la variable latente personalidad, en la estimación del modelo híbrido de elección discreta, el cual constituye una buena alternativa para incorporar los efectos de los factores subjetivos. La variable latente personalidad se evaluó con la prueba psicométrica 16PF de validez internacional. El artículo analiza los resultados de la aplicación de este modelo a una población de empleados y docentes universitarios, y también propone un camino para la utilización de pruebas psicométricas en los modelos híbridos de elección discreta. Nuestros resultados muestran que los modelos híbridos que incluyen variables latentes psicológicas son superiores a los modelos tradicionales que ignoran los efectos de la conducta de los usuarios.

  10. Abstract: Inference and Interval Estimation for Indirect Effects With Latent Variable Models.

    Science.gov (United States)

    Falk, Carl F; Biesanz, Jeremy C

    2011-11-30

    Models specifying indirect effects (or mediation) and structural equation modeling are both popular in the social sciences. Yet relatively little research has compared methods that test for indirect effects among latent variables and provided precise estimates of the effectiveness of different methods. This simulation study provides an extensive comparison of methods for constructing confidence intervals and for making inferences about indirect effects with latent variables. We compared the percentile (PC) bootstrap, bias-corrected (BC) bootstrap, bias-corrected accelerated (BC a ) bootstrap, likelihood-based confidence intervals (Neale & Miller, 1997), partial posterior predictive (Biesanz, Falk, and Savalei, 2010), and joint significance tests based on Wald tests or likelihood ratio tests. All models included three reflective latent variables representing the independent, dependent, and mediating variables. The design included the following fully crossed conditions: (a) sample size: 100, 200, and 500; (b) number of indicators per latent variable: 3 versus 5; (c) reliability per set of indicators: .7 versus .9; (d) and 16 different path combinations for the indirect effect (α = 0, .14, .39, or .59; and β = 0, .14, .39, or .59). Simulations were performed using a WestGrid cluster of 1680 3.06GHz Intel Xeon processors running R and OpenMx. Results based on 1,000 replications per cell and 2,000 resamples per bootstrap method indicated that the BC and BC a bootstrap methods have inflated Type I error rates. Likelihood-based confidence intervals and the PC bootstrap emerged as methods that adequately control Type I error and have good coverage rates.

  11. A fully Bayesian latent variable model for integrative clustering analysis of multi-type omics data.

    Science.gov (United States)

    Mo, Qianxing; Shen, Ronglai; Guo, Cui; Vannucci, Marina; Chan, Keith S; Hilsenbeck, Susan G

    2018-01-01

    Identification of clinically relevant tumor subtypes and omics signatures is an important task in cancer translational research for precision medicine. Large-scale genomic profiling studies such as The Cancer Genome Atlas (TCGA) Research Network have generated vast amounts of genomic, transcriptomic, epigenomic, and proteomic data. While these studies have provided great resources for researchers to discover clinically relevant tumor subtypes and driver molecular alterations, there are few computationally efficient methods and tools for integrative clustering analysis of these multi-type omics data. Therefore, the aim of this article is to develop a fully Bayesian latent variable method (called iClusterBayes) that can jointly model omics data of continuous and discrete data types for identification of tumor subtypes and relevant omics features. Specifically, the proposed method uses a few latent variables to capture the inherent structure of multiple omics data sets to achieve joint dimension reduction. As a result, the tumor samples can be clustered in the latent variable space and relevant omics features that drive the sample clustering are identified through Bayesian variable selection. This method significantly improve on the existing integrative clustering method iClusterPlus in terms of statistical inference and computational speed. By analyzing TCGA and simulated data sets, we demonstrate the excellent performance of the proposed method in revealing clinically meaningful tumor subtypes and driver omics features. © The Author 2017. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

  12. Incorporating Latent Variables into Discrete Choice Models - A Simultaneous Estimation Approach Using SEM Software

    Directory of Open Access Journals (Sweden)

    Dirk Temme

    2008-12-01

    Full Text Available Integrated choice and latent variable (ICLV models represent a promising new class of models which merge classic choice models with the structural equation approach (SEM for latent variables. Despite their conceptual appeal, applications of ICLV models in marketing remain rare. We extend previous ICLV applications by first estimating a multinomial choice model and, second, by estimating hierarchical relations between latent variables. An empirical study on travel mode choice clearly demonstrates the value of ICLV models to enhance the understanding of choice processes. In addition to the usually studied directly observable variables such as travel time, we show how abstract motivations such as power and hedonism as well as attitudes such as a desire for flexibility impact on travel mode choice. Furthermore, we show that it is possible to estimate such a complex ICLV model with the widely available structural equation modeling package Mplus. This finding is likely to encourage more widespread application of this appealing model class in the marketing field.

  13. Bayesian modeling of ChIP-chip data using latent variables.

    KAUST Repository

    Wu, Mingqi

    2009-10-26

    BACKGROUND: The ChIP-chip technology has been used in a wide range of biomedical studies, such as identification of human transcription factor binding sites, investigation of DNA methylation, and investigation of histone modifications in animals and plants. Various methods have been proposed in the literature for analyzing the ChIP-chip data, such as the sliding window methods, the hidden Markov model-based methods, and Bayesian methods. Although, due to the integrated consideration of uncertainty of the models and model parameters, Bayesian methods can potentially work better than the other two classes of methods, the existing Bayesian methods do not perform satisfactorily. They usually require multiple replicates or some extra experimental information to parametrize the model, and long CPU time due to involving of MCMC simulations. RESULTS: In this paper, we propose a Bayesian latent model for the ChIP-chip data. The new model mainly differs from the existing Bayesian models, such as the joint deconvolution model, the hierarchical gamma mixture model, and the Bayesian hierarchical model, in two respects. Firstly, it works on the difference between the averaged treatment and control samples. This enables the use of a simple model for the data, which avoids the probe-specific effect and the sample (control/treatment) effect. As a consequence, this enables an efficient MCMC simulation of the posterior distribution of the model, and also makes the model more robust to the outliers. Secondly, it models the neighboring dependence of probes by introducing a latent indicator vector. A truncated Poisson prior distribution is assumed for the latent indicator variable, with the rationale being justified at length. CONCLUSION: The Bayesian latent method is successfully applied to real and ten simulated datasets, with comparisons with some of the existing Bayesian methods, hidden Markov model methods, and sliding window methods. The numerical results indicate that the

  14. Latent memory facilitates relearning through molecular signaling mechanisms that are distinct from original learning.

    Science.gov (United States)

    Menges, Steven A; Riepe, Joshua R; Philips, Gary T

    2015-09-01

    A highly conserved feature of memory is that it can exist in a latent, non-expressed state which is revealed during subsequent learning by its ability to significantly facilitate (savings) or inhibit (latent inhibition) subsequent memory formation. Despite the ubiquitous nature of latent memory, the mechanistic nature of the latent memory trace and its ability to influence subsequent learning remains unclear. The model organism Aplysia californica provides the unique opportunity to make strong links between behavior and underlying cellular and molecular mechanisms. Using Aplysia, we have studied the mechanisms of savings due to latent memory for a prior, forgotten experience. We previously reported savings in the induction of three distinct temporal domains of memory: short-term (10min), intermediate-term (2h) and long-term (24h). Here we report that savings memory formation utilizes molecular signaling pathways that are distinct from original learning: whereas the induction of both original intermediate- and long-term memory in naïve animals requires mitogen activated protein kinase (MAPK) activation and ongoing protein synthesis, 2h savings memory is not disrupted by inhibitors of MAPK or protein synthesis, and 24h savings memory is not dependent on MAPK activation. Collectively, these findings reveal that during forgetting, latent memory for the original experience can facilitate relearning through molecular signaling mechanisms that are distinct from original learning. Copyright © 2015 Elsevier Inc. All rights reserved.

  15. A latent class distance association model for cross-classified data with a categorical response variable.

    Science.gov (United States)

    Vera, José Fernando; de Rooij, Mark; Heiser, Willem J

    2014-11-01

    In this paper we propose a latent class distance association model for clustering in the predictor space of large contingency tables with a categorical response variable. The rows of such a table are characterized as profiles of a set of explanatory variables, while the columns represent a single outcome variable. In many cases such tables are sparse, with many zero entries, which makes traditional models problematic. By clustering the row profiles into a few specific classes and representing these together with the categories of the response variable in a low-dimensional Euclidean space using a distance association model, a parsimonious prediction model can be obtained. A generalized EM algorithm is proposed to estimate the model parameters and the adjusted Bayesian information criterion statistic is employed to test the number of mixture components and the dimensionality of the representation. An empirical example highlighting the advantages of the new approach and comparing it with traditional approaches is presented. © 2014 The British Psychological Society.

  16. Assessing factors related to waist circumference and obesity: application of a latent variable model.

    Science.gov (United States)

    Dalvand, Sahar; Koohpayehzadeh, Jalil; Karimlou, Masoud; Asgari, Fereshteh; Rafei, Ali; Seifi, Behjat; Niksima, Seyed Hassan; Bakhshi, Enayatollah

    2015-01-01

    Because the use of BMI (Body Mass Index) alone as a measure of adiposity has been criticized, in the present study our aim was to fit a latent variable model to simultaneously examine the factors that affect waist circumference (continuous outcome) and obesity (binary outcome) among Iranian adults. Data included 18,990 Iranian individuals aged 20-65 years that are derived from the third National Survey of Noncommunicable Diseases Risk Factors in Iran. Using latent variable model, we estimated the relation of two correlated responses (waist circumference and obesity) with independent variables including age, gender, PR (Place of Residence), PA (physical activity), smoking status, SBP (Systolic Blood Pressure), DBP (Diastolic Blood Pressure), CHOL (cholesterol), FBG (Fasting Blood Glucose), diabetes, and FHD (family history of diabetes). All variables were related to both obesity and waist circumference (WC). Older age, female sex, being an urban resident, physical inactivity, nonsmoking, hypertension, hypercholesterolemia, hyperglycemia, diabetes, and having family history of diabetes were significant risk factors that increased WC and obesity. Findings from this study of Iranian adult settings offer more insights into factors associated with high WC and high prevalence of obesity in this population.

  17. Assessing Factors Related to Waist Circumference and Obesity: Application of a Latent Variable Model

    Directory of Open Access Journals (Sweden)

    Sahar Dalvand

    2015-01-01

    Full Text Available Background. Because the use of BMI (Body Mass Index alone as a measure of adiposity has been criticized, in the present study our aim was to fit a latent variable model to simultaneously examine the factors that affect waist circumference (continuous outcome and obesity (binary outcome among Iranian adults. Methods. Data included 18,990 Iranian individuals aged 20–65 years that are derived from the third National Survey of Noncommunicable Diseases Risk Factors in Iran. Using latent variable model, we estimated the relation of two correlated responses (waist circumference and obesity with independent variables including age, gender, PR (Place of Residence, PA (physical activity, smoking status, SBP (Systolic Blood Pressure, DBP (Diastolic Blood Pressure, CHOL (cholesterol, FBG (Fasting Blood Glucose, diabetes, and FHD (family history of diabetes. Results. All variables were related to both obesity and waist circumference (WC. Older age, female sex, being an urban resident, physical inactivity, nonsmoking, hypertension, hypercholesterolemia, hyperglycemia, diabetes, and having family history of diabetes were significant risk factors that increased WC and obesity. Conclusions. Findings from this study of Iranian adult settings offer more insights into factors associated with high WC and high prevalence of obesity in this population.

  18. Structural identifiability of cyclic graphical models of biological networks with latent variables.

    Science.gov (United States)

    Wang, Yulin; Lu, Na; Miao, Hongyu

    2016-06-13

    Graphical models have long been used to describe biological networks for a variety of important tasks such as the determination of key biological parameters, and the structure of graphical model ultimately determines whether such unknown parameters can be unambiguously obtained from experimental observations (i.e., the identifiability problem). Limited by resources or technical capacities, complex biological networks are usually partially observed in experiment, which thus introduces latent variables into the corresponding graphical models. A number of previous studies have tackled the parameter identifiability problem for graphical models such as linear structural equation models (SEMs) with or without latent variables. However, the limited resolution and efficiency of existing approaches necessarily calls for further development of novel structural identifiability analysis algorithms. An efficient structural identifiability analysis algorithm is developed in this study for a broad range of network structures. The proposed method adopts the Wright's path coefficient method to generate identifiability equations in forms of symbolic polynomials, and then converts these symbolic equations to binary matrices (called identifiability matrix). Several matrix operations are introduced for identifiability matrix reduction with system equivalency maintained. Based on the reduced identifiability matrices, the structural identifiability of each parameter is determined. A number of benchmark models are used to verify the validity of the proposed approach. Finally, the network module for influenza A virus replication is employed as a real example to illustrate the application of the proposed approach in practice. The proposed approach can deal with cyclic networks with latent variables. The key advantage is that it intentionally avoids symbolic computation and is thus highly efficient. Also, this method is capable of determining the identifiability of each single parameter and

  19. Large-scale weakly supervised object localization via latent category learning.

    Science.gov (United States)

    Chong Wang; Kaiqi Huang; Weiqiang Ren; Junge Zhang; Maybank, Steve

    2015-04-01

    Localizing objects in cluttered backgrounds is challenging under large-scale weakly supervised conditions. Due to the cluttered image condition, objects usually have large ambiguity with backgrounds. Besides, there is also a lack of effective algorithm for large-scale weakly supervised localization in cluttered backgrounds. However, backgrounds contain useful latent information, e.g., the sky in the aeroplane class. If this latent information can be learned, object-background ambiguity can be largely reduced and background can be suppressed effectively. In this paper, we propose the latent category learning (LCL) in large-scale cluttered conditions. LCL is an unsupervised learning method which requires only image-level class labels. First, we use the latent semantic analysis with semantic object representation to learn the latent categories, which represent objects, object parts or backgrounds. Second, to determine which category contains the target object, we propose a category selection strategy by evaluating each category's discrimination. Finally, we propose the online LCL for use in large-scale conditions. Evaluation on the challenging PASCAL Visual Object Class (VOC) 2007 and the large-scale imagenet large-scale visual recognition challenge 2013 detection data sets shows that the method can improve the annotation precision by 10% over previous methods. More importantly, we achieve the detection precision which outperforms previous results by a large margin and can be competitive to the supervised deformable part model 5.0 baseline on both data sets.

  20. ltm: An R Package for Latent Variable Modeling and Item Response Analysis

    Directory of Open Access Journals (Sweden)

    Dimitris Rizopoulos

    2006-11-01

    Full Text Available The R package ltm has been developed for the analysis of multivariate dichotomous and polytomous data using latent variable models, under the Item Response Theory approach. For dichotomous data the Rasch, the Two-Parameter Logistic, and Birnbaum's Three-Parameter models have been implemented, whereas for polytomous data Semejima's Graded Response model is available. Parameter estimates are obtained under marginal maximum likelihood using the Gauss-Hermite quadrature rule. The capabilities and features of the package are illustrated using two real data examples.

  1. Childhood malnutrition in Egypt using geoadditive Gaussian and latent variable models.

    Science.gov (United States)

    Khatab, Khaled

    2010-04-01

    Major progress has been made over the last 30 years in reducing the prevalence of malnutrition amongst children less than 5 years of age in developing countries. However, approximately 27% of children under the age of 5 in these countries are still malnourished. This work focuses on the childhood malnutrition in one of the biggest developing countries, Egypt. This study examined the association between bio-demographic and socioeconomic determinants and the malnutrition problem in children less than 5 years of age using the 2003 Demographic and Health survey data for Egypt. In the first step, we use separate geoadditive Gaussian models with the continuous response variables stunting (height-for-age), underweight (weight-for-age), and wasting (weight-for-height) as indicators of nutritional status in our case study. In a second step, based on the results of the first step, we apply the geoadditive Gaussian latent variable model for continuous indicators in which the 3 measurements of the malnutrition status of children are assumed as indicators for the latent variable "nutritional status".

  2. Mixture simultaneous factor analysis for capturing differences in latent variables between higher level units of multilevel data

    NARCIS (Netherlands)

    De Roover, K.; Vermunt, J.K.; Timmerman, Marieke E.; Ceulemans, Eva

    2017-01-01

    Given multivariate data, many research questions pertain to the covariance structure: whether and how the variables (for example, personality measures) covary. Exploratory factor analysis (EFA) is often used to look for latent variables that may explain the covariances among variables; for example,

  3. Modeling change in learning strategies throughout higher education: a multi-indicator latent growth perspective.

    Science.gov (United States)

    Coertjens, Liesje; Donche, Vincent; De Maeyer, Sven; Vanthournout, Gert; Van Petegem, Peter

    2013-01-01

    The change in learning strategies during higher education is an important topic of research in the Student Approaches to Learning field. Although the studies on this topic are increasingly longitudinal, analyses have continued to rely primarily on traditional statistical methods. The present research is innovative in the way it uses a multi-indicator latent growth analysis in order to more accurately estimate the general and differential development in learning strategy scales. Moreover, the predictive strength of the latent growth models are estimated. The sample consists of one cohort of Flemish University College students, 245 of whom participated in the three measurement waves by filling out the processing and regulation strategies scales of the Inventory of Learning Styles--Short Versions. Independent-samples t-tests revealed that the longitudinal group is a non-random subset of students starting University College. For each scale, a multi-indicator latent growth model is estimated using Mplus 6.1. Results suggest that, on average, during higher education, students persisting in their studies in a non-delayed manner seem to shift towards high-quality learning and away from undirected and surface-oriented learning. Moreover, students from the longitudinal group are found to vary in their initial levels, while, unexpectedly, not in their change over time. Although the growth models fit the data well, significant residual variances in the latent factors remain.

  4. Modeling change in learning strategies throughout higher education: a multi-indicator latent growth perspective.

    Directory of Open Access Journals (Sweden)

    Liesje Coertjens

    Full Text Available The change in learning strategies during higher education is an important topic of research in the Student Approaches to Learning field. Although the studies on this topic are increasingly longitudinal, analyses have continued to rely primarily on traditional statistical methods. The present research is innovative in the way it uses a multi-indicator latent growth analysis in order to more accurately estimate the general and differential development in learning strategy scales. Moreover, the predictive strength of the latent growth models are estimated. The sample consists of one cohort of Flemish University College students, 245 of whom participated in the three measurement waves by filling out the processing and regulation strategies scales of the Inventory of Learning Styles--Short Versions. Independent-samples t-tests revealed that the longitudinal group is a non-random subset of students starting University College. For each scale, a multi-indicator latent growth model is estimated using Mplus 6.1. Results suggest that, on average, during higher education, students persisting in their studies in a non-delayed manner seem to shift towards high-quality learning and away from undirected and surface-oriented learning. Moreover, students from the longitudinal group are found to vary in their initial levels, while, unexpectedly, not in their change over time. Although the growth models fit the data well, significant residual variances in the latent factors remain.

  5. Using multiple biomarkers and determinants to obtain a better measurement of oxidative stress: a latent variable structural equation model approach.

    Science.gov (United States)

    Eldridge, Ronald C; Flanders, W Dana; Bostick, Roberd M; Fedirko, Veronika; Gross, Myron; Thyagarajan, Bharat; Goodman, Michael

    2017-09-01

    Since oxidative stress involves a variety of cellular changes, no single biomarker can serve as a complete measure of this complex biological process. The analytic technique of structural equation modeling (SEM) provides a possible solution to this problem by modelling a latent (unobserved) variable constructed from the covariance of multiple biomarkers. Using three pooled datasets, we modelled a latent oxidative stress variable from five biomarkers related to oxidative stress: F 2 -isoprostanes (FIP), fluorescent oxidation products, mitochondrial DNA copy number, γ-tocopherol (Gtoc) and C-reactive protein (CRP, an inflammation marker closely linked to oxidative stress). We validated the latent variable by assessing its relation to pro- and anti-oxidant exposures. FIP, Gtoc and CRP characterized the latent oxidative stress variable. Obesity, smoking, aspirin use and β-carotene were statistically significantly associated with oxidative stress in the theorized directions; the same exposures were weakly and inconsistently associated with the individual biomarkers. Our results suggest that using SEM with latent variables decreases the biomarker-specific variability, and may produce a better measure of oxidative stress than do single variables. This methodology can be applied to similar areas of research in which a single biomarker is not sufficient to fully describe a complex biological phenomenon.

  6. An introduction to latent variable growth curve modeling concepts, issues, and application

    CERN Document Server

    Duncan, Terry E; Strycker, Lisa A

    2013-01-01

    This book provides a comprehensive introduction to latent variable growth curve modeling (LGM) for analyzing repeated measures. It presents the statistical basis for LGM and its various methodological extensions, including a number of practical examples of its use. It is designed to take advantage of the reader's familiarity with analysis of variance and structural equation modeling (SEM) in introducing LGM techniques. Sample data, syntax, input and output, are provided for EQS, Amos, LISREL, and Mplus on the book's CD. Throughout the book, the authors present a variety of LGM techniques that are useful for many different research designs, and numerous figures provide helpful diagrams of the examples.Updated throughout, the second edition features three new chapters-growth modeling with ordered categorical variables, growth mixture modeling, and pooled interrupted time series LGM approaches. Following a new organization, the book now covers the development of the LGM, followed by chapters on multiple-group is...

  7. Less is more: latent learning is maximized by shorter training sessions in auditory perceptual learning.

    Science.gov (United States)

    Molloy, Katharine; Moore, David R; Sohoglu, Ediz; Amitay, Sygal

    2012-01-01

    The time course and outcome of perceptual learning can be affected by the length and distribution of practice, but the training regimen parameters that govern these effects have received little systematic study in the auditory domain. We asked whether there was a minimum requirement on the number of trials within a training session for learning to occur, whether there was a maximum limit beyond which additional trials became ineffective, and whether multiple training sessions provided benefit over a single session. We investigated the efficacy of different regimens that varied in the distribution of practice across training sessions and in the overall amount of practice received on a frequency discrimination task. While learning was relatively robust to variations in regimen, the group with the shortest training sessions (∼8 min) had significantly faster learning in early stages of training than groups with longer sessions. In later stages, the group with the longest training sessions (>1 hr) showed slower learning than the other groups, suggesting overtraining. Between-session improvements were inversely correlated with performance; they were largest at the start of training and reduced as training progressed. In a second experiment we found no additional longer-term improvement in performance, retention, or transfer of learning for a group that trained over 4 sessions (∼4 hr in total) relative to a group that trained for a single session (∼1 hr). However, the mechanisms of learning differed; the single-session group continued to improve in the days following cessation of training, whereas the multi-session group showed no further improvement once training had ceased. Shorter training sessions were advantageous because they allowed for more latent, between-session and post-training learning to emerge. These findings suggest that efficient regimens should use short training sessions, and optimized spacing between sessions.

  8. a Latent Variable Path Analysis Model of Secondary Physics Enrollments in New York State.

    Science.gov (United States)

    Sobolewski, Stanley John

    The Percentage of Enrollment in Physics (PEP) at the secondary level nationally has been approximately 20% for the past few decades. For a more scientifically literate citizenry as well as specialists to continue scientific research and development, it is desirable that more students enroll in physics. Some of the predictor variables for physics enrollment and physics achievement that have been identified previously includes a community's socioeconomic status, the availability of physics, the sex of the student, the curriculum, as well as teacher and student data. This study isolated and identified predictor variables for PEP of secondary schools in New York. Data gathered by the State Education Department for the 1990-1991 school year was used. The source of this data included surveys completed by teachers and administrators on student characteristics and school facilities. A data analysis similar to that done by Bryant (1974) was conducted to determine if the relationships between a set of predictor variables related to physics enrollment had changed in the past 20 years. Variables which were isolated included: community, facilities, teacher experience, number of type of science courses, school size and school science facilities. When these variables were isolated, latent variable path diagrams were proposed and verified by the Linear Structural Relations computer modeling program (LISREL). These diagrams differed from those developed by Bryant in that there were more manifest variables used which included achievement scores in the form of Regents exam results. Two criterion variables were used, percentage of students enrolled in physics (PEP) and percent of students enrolled passing the Regents physics exam (PPP). The first model treated school and community level variables as exogenous while the second model treated only the community level variables as exogenous. The goodness of fit indices for the models was 0.77 for the first model and 0.83 for the second

  9. Evaluating measurement models in clinical research: covariance structure analysis of latent variable models of self-conception.

    Science.gov (United States)

    Hoyle, R H

    1991-02-01

    Indirect measures of psychological constructs are vital to clinical research. On occasion, however, the meaning of indirect measures of psychological constructs is obfuscated by statistical procedures that do not account for the complex relations between items and latent variables and among latent variables. Covariance structure analysis (CSA) is a statistical procedure for testing hypotheses about the relations among items that indirectly measure a psychological construct and relations among psychological constructs. This article introduces clinical researchers to the strengths and limitations of CSA as a statistical procedure for conceiving and testing structural hypotheses that are not tested adequately with other statistical procedures. The article is organized around two empirical examples that illustrate the use of CSA for evaluating measurement models with correlated error terms, higher-order factors, and measured and latent variables.

  10. Study The role of latent variables in lost working days by Structural Equation Modeling Approach

    Directory of Open Access Journals (Sweden)

    Meysam Heydari

    2016-12-01

    Full Text Available Background: Based on estimations, each year about 250 million work-related injuries and many temporary or permanent disabilities occur which most are preventable. Oil and Gas industries are among industries with high incidence of injuries in the world. The aim of this study has investigated  the role and effect of different risk management variables on lost working days (LWD in the seismic projects. Methods: This study was a retrospective, cross-sectional and systematic analysis, which was carried out on occupational accidents between 2008-2015(an 8 years period in different seismic projects for oilfield exploration at Dana Energy (Iranian Seismic Company. The preliminary sample size of the study were 487accidents. A systems analysis approach were applied by using root case analysis (RCA and structural equation modeling (SEM. Tools for the data analysis were included, SPSS23 and AMOS23  software. Results: The mean of lost working days (LWD, was calculated 49.57, the final model of structural equation modeling showed that latent variables of, safety and health training factor(-0.33, risk assessment factor(-0.55 and risk control factor (-0.61 as direct causes significantly affected of lost working days (LWD in the seismic industries (p< 0.05. Conclusion: The finding of present study revealed that combination of variables affected in lost working days (LWD. Therefore,the role of these variables in accidents should be investigated and suitable programs should be considered for them.

  11. Conceptualising computerized adaptive testing for measurement of latent variables associated with physical objects

    International Nuclear Information System (INIS)

    Camargo, F R; Henson, B

    2015-01-01

    The notion of that more or less of a physical feature affects in different degrees the users' impression with regard to an underlying attribute of a product has frequently been applied in affective engineering. However, those attributes exist only as a premise that cannot directly be measured and, therefore, inferences based on their assessment are error-prone. To establish and improve measurement of latent attributes it is presented in this paper the concept of a stochastic framework using the Rasch model for a wide range of independent variables referred to as an item bank. Based on an item bank, computerized adaptive testing (CAT) can be developed. A CAT system can converge into a sequence of items bracketing to convey information at a user's particular endorsement level. It is through item banking and CAT that the financial benefits of using the Rasch model in affective engineering can be realised

  12. Application of latent variable model in Rosenberg self-esteem scale.

    Science.gov (United States)

    Leung, Shing-On; Wu, Hui-Ping

    2013-01-01

    Latent Variable Models (LVM) are applied to Rosenberg Self-Esteem Scale (RSES). Parameter estimations automatically give negative signs hence no recoding is necessary for negatively scored items. Bad items can be located through parameter estimate, item characteristic curves and other measures. Two factors are extracted with one on self-esteem and the other on the degree to take moderate views, with the later not often being covered in previous studies. A goodness-of-fit measure based on two-way margins is used but more works are needed. Results show that scaling provided by models with more formal statistical ground correlated highly with conventional method, which may provide justification for usual practice.

  13. Measuring behaviours for escaping from house fires: use of latent variable models to summarise multiple behaviours.

    Science.gov (United States)

    Ploubidis, G B; Edwards, P; Kendrick, D

    2015-12-15

    This paper reports the development and testing of a construct measuring parental fire safety behaviours for planning escape from a house fire. Latent variable modelling of data on parental-reported fire safety behaviours and plans for escaping from a house fire and multivariable logistic regression to quantify the association between groups defined by the latent variable modelling and parental-report of having a plan for escaping from a house fire. Data comes from 1112 participants in a cluster randomised controlled trial set in children's centres in 4 study centres in the UK. A two class model provided the best fit to the data, combining responses to five fire safety planning behaviours. The first group ('more behaviours for escaping from a house fire') comprised 86% of participants who were most likely to have a torch, be aware of how their smoke alarm sounds, to have external door and window keys accessible, and exits clear. The second group ('fewer behaviours for escaping from a house fire') comprised 14% of participants who were less likely to report these five behaviours. After adjusting for potential confounders, participants allocated to the 'more behaviours for escaping from a house fire group were 2.5 times more likely to report having an escape plan (OR 2.48; 95% CI 1.59-3.86) than those in the "fewer behaviours for escaping from a house fire" group. Multiple fire safety behaviour questions can be combined into a single binary summary measure of fire safety behaviours for escaping from a house fire. Our findings will be useful to future studies wishing to use a single measure of fire safety planning behaviour as measures of outcome or exposure. NCT 01452191. Date of registration 13/10/2011.

  14. Can Social History Variables Predict Prison Inmates’ Risk for Latent Tuberculosis Infection?

    Directory of Open Access Journals (Sweden)

    Tyler E. Weant

    2012-01-01

    Full Text Available Improved screening and treatment of latent tuberculosis infection (LTBI in correctional facilities may improve TB control. The Ohio Department of Rehabilitation and Correction (ODRC consists of 32 prisons. Inmates are screened upon entry to ODRC and yearly thereafter. The objective of the study was to determine if social history factors such as tobacco, alcohol, and drug use are significant predictors of LTBI and treatment outcomes. We reviewed the medical charts of inmates and randomly selected age-matched controls at one ODRC facility for 2009. We used a conditional logistic regression to assess associations between selected social history variables and LTBI diagnosis. Eighty-nine inmates with a history of LTBI and 88 controls were identified. No social history variable was a significant predictor of LTBI. Medical comorbidities such as asthma, rheumatoid arthritis, and hepatitis C were significantly higher in inmates with LTBI. 84% of inmates diagnosed with LTBI had either completed or were on treatment. Annual TB screening may not be cost-effective in all inmate populations. Identification of factors to help target screening populations at risk for TB is critical. Social history variables did not predict LTBI in our inmate population. Additional studies are needed to identify inmates for the targeted TB testing.

  15. Interaction between Helicobacter pylori and latent toxoplasmosis and demographic variables on cognitive function in young to middle-aged adults.

    Directory of Open Access Journals (Sweden)

    Shawn D Gale

    Full Text Available Helicobacter pylori and latent toxoplasmosis are widespread diseases that have been associated with cognitive deficits and Alzheimer's disease. We sought to determine whether interactions between Helicobacter pylori and latent toxoplasmosis, age, race-ethnicity, educational attainment, economic status, and general health predict cognitive function in young and middle-aged adults. To do so, we used multivariable regression and multivariate models to analyze data obtained from the United States' National Health and Nutrition Examination Survey from the Centers for Disease Control and Prevention, which can be weighted to represent the US population. In this sample, we found that 31.6 percent of women and 36.2 percent of men of the overall sample had IgG Antibodies against Helicobacter pylori, although the seroprevalence of Helicobacter pylori varied with sociodemographic variables. There were no main effects for Helicobacter pylori or latent toxoplasmosis for any of the cognitive measures in models adjusting for age, sex, race-ethnicity, educational attainment, economic standing, and self-rated health predicting cognitive function. However, interactions between Helicobacter pylori and race-ethnicity, educational attainment, latent toxoplasmosis in the fully adjusted models predicted cognitive function. People seropositive for both Helicobacter pylori and latent toxoplasmosis - both of which appear to be common in the general population - appear to be more susceptible to cognitive deficits than are people seropositive for either Helicobacter pylori and or latent toxoplasmosis alone, suggesting a synergistic effect between these two infectious diseases on cognition in young to middle-aged adults.

  16. Interaction between Helicobacter pylori and latent toxoplasmosis and demographic variables on cognitive function in young to middle-aged adults.

    Science.gov (United States)

    Gale, Shawn D; Erickson, Lance D; Brown, Bruce L; Hedges, Dawson W

    2015-01-01

    Helicobacter pylori and latent toxoplasmosis are widespread diseases that have been associated with cognitive deficits and Alzheimer's disease. We sought to determine whether interactions between Helicobacter pylori and latent toxoplasmosis, age, race-ethnicity, educational attainment, economic status, and general health predict cognitive function in young and middle-aged adults. To do so, we used multivariable regression and multivariate models to analyze data obtained from the United States' National Health and Nutrition Examination Survey from the Centers for Disease Control and Prevention, which can be weighted to represent the US population. In this sample, we found that 31.6 percent of women and 36.2 percent of men of the overall sample had IgG Antibodies against Helicobacter pylori, although the seroprevalence of Helicobacter pylori varied with sociodemographic variables. There were no main effects for Helicobacter pylori or latent toxoplasmosis for any of the cognitive measures in models adjusting for age, sex, race-ethnicity, educational attainment, economic standing, and self-rated health predicting cognitive function. However, interactions between Helicobacter pylori and race-ethnicity, educational attainment, latent toxoplasmosis in the fully adjusted models predicted cognitive function. People seropositive for both Helicobacter pylori and latent toxoplasmosis - both of which appear to be common in the general population - appear to be more susceptible to cognitive deficits than are people seropositive for either Helicobacter pylori and or latent toxoplasmosis alone, suggesting a synergistic effect between these two infectious diseases on cognition in young to middle-aged adults.

  17. Scalable learning of probabilistic latent models for collaborative filtering

    DEFF Research Database (Denmark)

    Langseth, Helge; Nielsen, Thomas Dyhre

    2015-01-01

    variational Bayes learning and inference algorithm for these types of models. Empirical results show that the proposed algorithm achieves significantly better accuracy results than other straw-men models evaluated on a collection of well-known data sets. We also demonstrate that the algorithm has a highly...

  18. Indentifying Latent Classes and Testing Their Determinants in Early Adolescents' Use of Computers and Internet for Learning

    Science.gov (United States)

    Heo, Gyun

    2013-01-01

    The purpose of the present study was to identify latent classes resting on early adolescents' change trajectory patterns in using computers and the Internet for learning and to test the effects of gender, self-control, self-esteem, and game use in South Korea. Latent growth mixture modeling (LGMM) was used to identify subpopulations in the Korea…

  19. Use of latent variables representing psychological motivation to explore citizens’ intentions with respect to congestion charging reform in Jakarta

    Directory of Open Access Journals (Sweden)

    Sugiarto Sugiarto

    2015-01-01

    Full Text Available The aim of this paper is to investigate the intentions of Jakarta citizens with respect to the electronic road pricing (ERP reform proposed by the city government. Utilizing data from a stated preference survey conducted in 2013, we construct six variables representing latent psychological motivations (appropriateness of ERP adoption; recognition that ERP can mitigate congestion and improve the environment; car dependency (CDC; awareness of the problems of cars in society; inhibition of freedom movement caused by ERP; and doubts about the ability of ERP to mitigate congestion and environment problems. A multiple-indicators multiple-causes (MIMIC model is developed to investigate the effects of respondents’ socio-demographics (causes on the latent constructs in order to gain better understanding of the relationship between respondents’ intentions and the observed individual’s responses (indicators obtained from the stated preference survey. The MIMIC model offers a good account of whether and how socio-demographic attributes and individual indicators predict the latent variables of psychological motivation constructs. Then, we further verify the influences of the latent variables, combining them with levy rate patterns and daily mobility attributes to investigate significant determining factors for social acceptance of the ERP proposal. A latent variable representations based on the generalized ordered response model are employed in our investigations to allow more flexibility in parameter estimation across outcomes. The results confirm that there is a strong correlation between latent psychological motivations and daily mobility attributes and the level of social acceptance for the ERP proposal. This empirical investigation demonstrates that the latent variables play more substantial role in determining scheme’s acceptance. Moreover, elasticity measures show that latent attributes are more sensitive compared to levies and daily mobility

  20. Monoamine Oxidase A (MAOA Gene and Personality Traits from Late Adolescence through Early Adulthood: A Latent Variable Investigation

    Directory of Open Access Journals (Sweden)

    Man K. Xu

    2017-10-01

    Full Text Available Very few molecular genetic studies of personality traits have used longitudinal phenotypic data, therefore molecular basis for developmental change and stability of personality remains to be explored. We examined the role of the monoamine oxidase A gene (MAOA on extraversion and neuroticism from adolescence to adulthood, using modern latent variable methods. A sample of 1,160 male and 1,180 female participants with complete genotyping data was drawn from a British national birth cohort, the MRC National Survey of Health and Development (NSHD. The predictor variable was based on a latent variable representing genetic variations of the MAOA gene measured by three SNPs (rs3788862, rs5906957, and rs979606. Latent phenotype variables were constructed using psychometric methods to represent cross-sectional and longitudinal phenotypes of extraversion and neuroticism measured at ages 16 and 26. In males, the MAOA genetic latent variable (AAG was associated with lower extraversion score at age 16 (β = −0.167; CI: −0.289, −0.045; p = 0.007, FDRp = 0.042, as well as greater increase in extraversion score from 16 to 26 years (β = 0.197; CI: 0.067, 0.328; p = 0.003, FDRp = 0.036. No genetic association was found for neuroticism after adjustment for multiple testing. Although, we did not find statistically significant associations after multiple testing correction in females, this result needs to be interpreted with caution due to issues related to x-inactivation in females. The latent variable method is an effective way of modeling phenotype- and genetic-based variances and may therefore improve the methodology of molecular genetic studies of complex psychological traits.

  1. Latent Variable Regression 4-Level Hierarchical Model Using Multisite Multiple-Cohorts Longitudinal Data. CRESST Report 801

    Science.gov (United States)

    Choi, Kilchan

    2011-01-01

    This report explores a new latent variable regression 4-level hierarchical model for monitoring school performance over time using multisite multiple-cohorts longitudinal data. This kind of data set has a 4-level hierarchical structure: time-series observation nested within students who are nested within different cohorts of students. These…

  2. Impact of marriage on HIV/AIDS risk behaviors among impoverished, at-risk couples: a multilevel latent variable approach.

    Science.gov (United States)

    Stein, Judith A; Nyamathi, Adeline; Ullman, Jodie B; Bentler, Peter M

    2007-01-01

    Studies among normative samples generally demonstrate a positive impact of marriage on health behaviors and other related attitudes. In this study, we examine the impact of marriage on HIV/AIDS risk behaviors and attitudes among impoverished, highly stressed, homeless couples, many with severe substance abuse problems. A multilevel analysis of 368 high-risk sexually intimate married and unmarried heterosexual couples assessed individual and couple-level effects on social support, substance use problems, HIV/AIDS knowledge, perceived HIV/AIDS risk, needle-sharing, condom use, multiple sex partners, and HIV/AIDS testing. More variance was explained in the protective and risk variables by couple-level latent variable predictors than by individual latent variable predictors, although some gender effects were found (e.g., more alcohol problems among men). The couple-level variable of marriage predicted lower perceived risk, less deviant social support, and fewer sex partners but predicted more needle-sharing.

  3. Visualizing Confidence Bands for Semiparametrically Estimated Nonlinear Relations among Latent Variables

    Science.gov (United States)

    Pek, Jolynn; Chalmers, R. Philip; Kok, Bethany E.; Losardo, Diane

    2015-01-01

    Structural equation mixture models (SEMMs), when applied as a semiparametric model (SPM), can adequately recover potentially nonlinear latent relationships without their specification. This SPM is useful for exploratory analysis when the form of the latent regression is unknown. The purpose of this article is to help users familiar with structural…

  4. Behavior problems at ages 6 and 11 and high school academic achievement: longitudinal latent variable modeling.

    Science.gov (United States)

    Breslau, Naomi; Breslau, Joshua; Miller, Elizabeth; Raykov, Tenko

    2011-02-28

    Previous studies documented long-run effects of behavior problems at the start of school on academic achievement. However, these studies did not examine whether the observed effects of early behavior problems are explained by more proximate behavior problems, given the tendency of children's behavior problems to persist. Latent variable modeling was applied to estimate the effects of behavior problems at ages 6 and 11 on academic achievement at age 17, using data from a longitudinal study (n=823). Behavior problems at ages 6 and 11, each stage independently of the other, predicted lower math and reading test scores at age 17, controlling for intelligence quotient (IQ), birth weight, maternal characteristics, family and community environment, and taking into account behavior problems at age 17. Behavior problems at the start of school, independent of later behavior problems, exert lingering effects on achievement by impeding the acquisition of cognitive skills that are the foundation for later academic progress. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

  5. Latent variables underlying the memory beliefs of Chartered Clinical Psychologists, Hypnotherapists and undergraduate students.

    Science.gov (United States)

    Ost, James; Easton, Simon; Hope, Lorraine; French, Christopher C; Wright, Daniel B

    2017-01-01

    In courts in the United Kingdom, understanding of memory phenomena is often assumed to be a matter of common sense. To test this assumption 337 UK respondents, consisting of 125 Chartered Clinical Psychologists, 88 individuals who advertised their services as Hypnotherapists (HTs) in a classified directory, the Yellow Pages TM , and 124 first year undergraduate psychology students, completed a questionnaire that assessed their knowledge of 10 memory phenomena about which there is a broad scientific consensus. HTs' responses were the most inconsistent with the scientific consensus, scoring lowest on six of these ten items. Principal Components Analysis indicated two latent variables - reflecting beliefs about memory quality and malleability - underlying respondents' responses. In addition, respondents were asked to rate their own knowledge of the academic memory literature in general. There was no significant relationship between participants' self reported knowledge and their actual knowledge (as measured by their responses to the 10-item questionnaire). There was evidence of beliefs among the HTs that could give rise to some concern (e.g., that early memories from the first year of life are accurately stored and are retrievable).

  6. Analysis on the public acceptance of nuclear energy using structural equation model with latent variables

    International Nuclear Information System (INIS)

    Lee, Young Eal

    1996-02-01

    Comparison of the effect of education and public information on the public acceptance of nuclear energy is carried out. For the increase of public acceptance, the correct understanding on the nuclear energy via proper regular school education would be the first basis and the appropriate public information services by utility and unbiased mass media would be the second basis. Subjects that which is more effect in education or information and how much effective quantitatively to improve the public acceptance are derived. Structural Equation Model (SEM) with Latent Variables (LVs) in social science to public attitudes towards nuclear energy is developed. Questionnaire is conducted to respondents who took part in the program of visiting the nuclear power plant opened by OKAEA in 1995. As a result of the analysis, effect of education for correct awareness of nuclear energy is more sensitive to public acceptance than that of information. It is shown that the susceptibility in education factor in influence of radiation on human body and that in information factor persons consider nuclear power plant as an environmental polluter. It is concluded that radiation treatment should be a 'Hand on Experience' and general principle of nuclear power generation should be contained in the educational text book. Education and information should not been independently performed but been carried out simultaneously and mutually aided. It is shown that this modeling approach is useful to make the decision for the long-term nuclear energy policy transparent and successful

  7. Uncovering state-dependent relationships in shallow lakes using Bayesian latent variable regression.

    Science.gov (United States)

    Vitense, Kelsey; Hanson, Mark A; Herwig, Brian R; Zimmer, Kyle D; Fieberg, John

    2018-03-01

    Ecosystems sometimes undergo dramatic shifts between contrasting regimes. Shallow lakes, for instance, can transition between two alternative stable states: a clear state dominated by submerged aquatic vegetation and a turbid state dominated by phytoplankton. Theoretical models suggest that critical nutrient thresholds differentiate three lake types: highly resilient clear lakes, lakes that may switch between clear and turbid states following perturbations, and highly resilient turbid lakes. For effective and efficient management of shallow lakes and other systems, managers need tools to identify critical thresholds and state-dependent relationships between driving variables and key system features. Using shallow lakes as a model system for which alternative stable states have been demonstrated, we developed an integrated framework using Bayesian latent variable regression (BLR) to classify lake states, identify critical total phosphorus (TP) thresholds, and estimate steady state relationships between TP and chlorophyll a (chl a) using cross-sectional data. We evaluated the method using data simulated from a stochastic differential equation model and compared its performance to k-means clustering with regression (KMR). We also applied the framework to data comprising 130 shallow lakes. For simulated data sets, BLR had high state classification rates (median/mean accuracy >97%) and accurately estimated TP thresholds and state-dependent TP-chl a relationships. Classification and estimation improved with increasing sample size and decreasing noise levels. Compared to KMR, BLR had higher classification rates and better approximated the TP-chl a steady state relationships and TP thresholds. We fit the BLR model to three different years of empirical shallow lake data, and managers can use the estimated bifurcation diagrams to prioritize lakes for management according to their proximity to thresholds and chance of successful rehabilitation. Our model improves upon

  8. Learning dynamic Bayesian networks with mixed variables

    DEFF Research Database (Denmark)

    Bøttcher, Susanne Gammelgaard

    This paper considers dynamic Bayesian networks for discrete and continuous variables. We only treat the case, where the distribution of the variables is conditional Gaussian. We show how to learn the parameters and structure of a dynamic Bayesian network and also how the Markov order can be learned...

  9. Class Evolution Tree: A Graphical Tool to Support Decisions on the Number of Classes in Exploratory Categorical Latent Variable Modeling for Rehabilitation Research

    Science.gov (United States)

    Kriston, Levente; Melchior, Hanne; Hergert, Anika; Bergelt, Corinna; Watzke, Birgit; Schulz, Holger; von Wolff, Alessa

    2011-01-01

    The aim of our study was to develop a graphical tool that can be used in addition to standard statistical criteria to support decisions on the number of classes in explorative categorical latent variable modeling for rehabilitation research. Data from two rehabilitation research projects were used. In the first study, a latent profile analysis was…

  10. Decomposing the heterogeneity of depression at the person-, symptom-, and time-level : Latent variable models versus multimode principal component analysis

    NARCIS (Netherlands)

    de Vos, Stijn; Wardenaar, Klaas J.; Bos, Elisabeth H.; Wit, Ernst C.; de Jonge, Peter

    2015-01-01

    Background: Heterogeneity of psychopathological concepts such as depression hampers progress in research and clinical practice. Latent Variable Models (LVMs) have been widely used to reduce this problem by identification of more homogeneous factors or subgroups. However, heterogeneity exists at

  11. Illustration of Step-Wise Latent Class Modeling With Covariates and Taxometric Analysis in Research Probing Children's Mental Models in Learning Sciences.

    Science.gov (United States)

    Stamovlasis, Dimitrios; Papageorgiou, George; Tsitsipis, Georgios; Tsikalas, Themistoklis; Vaiopoulou, Julie

    2018-01-01

    This paper illustrates two psychometric methods, latent class analysis (LCA) and taxometric analysis (TA) using empirical data from research probing children's mental representation in science learning. LCA is used to obtain a typology based on observed variables and to further investigate how the encountered classes might be related to external variables, where the effectiveness of classification process and the unbiased estimations of parameters become the main concern. In the step-wise LCA, the class membership is assigned and subsequently its relationship with covariates is established. This leading-edge modeling approach suffers from severe downward-biased estimations. The illustration of LCA is focused on alternative bias correction approaches and demonstrates the effect of modal and proportional class-membership assignment along with BCH and ML correction procedures. The illustration of LCA is presented with three covariates, which are psychometric variables operationalizing formal reasoning, divergent thinking and field dependence-independence, respectively. Moreover, taxometric analysis, a method designed to detect the type of the latent structural model, categorical or dimensional, is introduced, along with the relevant basic concepts and tools. TA was applied complementarily in the same data sets to answer the fundamental hypothesis about children's naïve knowledge on the matters under study and it comprises an additional asset in building theory which is fundamental for educational practices. Taxometric analysis provided results that were ambiguous as far as the type of the latent structure. This finding initiates further discussion and sets a problematization within this framework rethinking fundamental assumptions and epistemological issues.

  12. affective variables of language learning

    Institute of Scientific and Technical Information of China (English)

    李文敬

    2011-01-01

    why people enjoy different degrees of success in second language learning,given similar opportunities.in the presence of overly negative emotions such as anxiety,fear,stress,anger or depression,our optimal learning potential maybe compromised.the affective domain refers to the emotional domain that has to do with the emotional behavior of human beings.it includes such factors as self-confidence,extroversion,anxiety,attitudes and motivation.three major factors are introduced here:self-confidence,anxiety and motivation.

  13. Mastery Learning and the Decreasing Variability Hypothesis.

    Science.gov (United States)

    Livingston, Jennifer A.; Gentile, J. Ronald

    1996-01-01

    This report results from studies that tested two variations of Bloom's decreasing variability hypothesis using performance on successive units of achievement in four graduate classrooms that used mastery learning procedures. Data do not support the decreasing variability hypothesis; rather, they show no change over time. (SM)

  14. Machine learning search for variable stars

    Science.gov (United States)

    Pashchenko, Ilya N.; Sokolovsky, Kirill V.; Gavras, Panagiotis

    2018-04-01

    Photometric variability detection is often considered as a hypothesis testing problem: an object is variable if the null hypothesis that its brightness is constant can be ruled out given the measurements and their uncertainties. The practical applicability of this approach is limited by uncorrected systematic errors. We propose a new variability detection technique sensitive to a wide range of variability types while being robust to outliers and underestimated measurement uncertainties. We consider variability detection as a classification problem that can be approached with machine learning. Logistic Regression (LR), Support Vector Machines (SVM), k Nearest Neighbours (kNN), Neural Nets (NN), Random Forests (RF), and Stochastic Gradient Boosting classifier (SGB) are applied to 18 features (variability indices) quantifying scatter and/or correlation between points in a light curve. We use a subset of Optical Gravitational Lensing Experiment phase two (OGLE-II) Large Magellanic Cloud (LMC) photometry (30 265 light curves) that was searched for variability using traditional methods (168 known variable objects) as the training set and then apply the NN to a new test set of 31 798 OGLE-II LMC light curves. Among 205 candidates selected in the test set, 178 are real variables, while 13 low-amplitude variables are new discoveries. The machine learning classifiers considered are found to be more efficient (select more variables and fewer false candidates) compared to traditional techniques using individual variability indices or their linear combination. The NN, SGB, SVM, and RF show a higher efficiency compared to LR and kNN.

  15. Are depression and frailty overlapping syndromes in mid- and late-life? A latent variable analysis.

    Science.gov (United States)

    Mezuk, Briana; Lohman, Matthew; Dumenci, Levent; Lapane, Kate L

    2013-06-01

    Depression and frailty both predict disability and morbidity in later life. However, it is unclear to what extent these common geriatric syndromes represent overlapping constructs. To examine the joint relationship between the constructs of depression and frailty. Data come from 2004-2005 wave of the Baltimore Epidemiologic Catchment Area Study, and the analysis is limited to participants 40 years and older, with complete data on frailty and depression indicators (N = 683). Depression was measured using the Diagnostic Interview Schedule, and frailty was indexed by modified Fried criteria. A series of confirmatory latent class analyses were used to assess the degree to which depression and frailty syndromes identify the same populations. A latent kappa coefficient (κl) was also estimated between the constructs. Confirmatory latent class analyses indicated that depression and frailty represent distinct syndromes rather than a single construct. The joint modeling of the two constructs supported a three-class solution for depression and two-class solution for frailty, with 2.9% categorized as severely depressed, 19.4% as mildly depressed, and 77.7% as not depressed, and 21.1% categorized as frail and 78.9% as not frail. The chance-corrected agreement statistic indicated moderate correspondence between the depression and frailty constructs (κl: 66, 95% confidence interval: 0.58-0.74). Results suggest that depression and frailty are interrelated concepts, yet their operational criteria identify substantively overlapping subpopulations. These findings have implications for understanding factors that contribute to the etiology and prognosis of depression and frailty in later life. Copyright © 2013 American Association for Geriatric Psychiatry. Published by Elsevier Inc. All rights reserved.

  16. Automatic Evaluation for E-Learning Using Latent Semantic Analysis: A Use Case

    Directory of Open Access Journals (Sweden)

    Mireia Farrús

    2013-03-01

    Full Text Available Assessment in education allows for obtaining, organizing, and presenting information about how much and how well the student is learning. The current paper aims at analysing and discussing some of the most state-of-the-art assessment systems in education. Later, this work presents a specific use case developed for the Universitat Oberta de Catalunya, which is an online university. An automatic evaluation tool is proposed that allows the student to evaluate himself anytime and receive instant feedback. This tool is a web-based platform, and it has been designed for engineering subjects (i.e., with math symbols and formulas in Catalan and Spanish. Particularly, the technique used for automatic assessment is latent semantic analysis. Although the experimental framework from the use case is quite challenging, results are promising.

  17. Presentations and recorded keynotes of the First European Workshop on Latent Semantic Analysis in Technology Enhanced Learning

    NARCIS (Netherlands)

    Several

    2007-01-01

    Presentations and recorded keynotes at the 1st European Workshop on Latent Semantic Analysis in Technology-Enhanced Learning, March, 29-30, 2007. Heerlen, The Netherlands: The Open University of the Netherlands. Please see the conference website for more information:

  18. Visual variability affects early verb learning.

    Science.gov (United States)

    Twomey, Katherine E; Lush, Lauren; Pearce, Ruth; Horst, Jessica S

    2014-09-01

    Research demonstrates that within-category visual variability facilitates noun learning; however, the effect of visual variability on verb learning is unknown. We habituated 24-month-old children to a novel verb paired with an animated star-shaped actor. Across multiple trials, children saw either a single action from an action category (identical actions condition, for example, travelling while repeatedly changing into a circle shape) or multiple actions from that action category (variable actions condition, for example, travelling while changing into a circle shape, then a square shape, then a triangle shape). Four test trials followed habituation. One paired the habituated verb with a new action from the habituated category (e.g., 'dacking' + pentagon shape) and one with a completely novel action (e.g., 'dacking' + leg movement). The others paired a new verb with a new same-category action (e.g., 'keefing' + pentagon shape), or a completely novel category action (e.g., 'keefing' + leg movement). Although all children discriminated novel verb/action pairs, children in the identical actions condition discriminated trials that included the completely novel verb, while children in the variable actions condition discriminated the out-of-category action. These data suggest that - as in noun learning - visual variability affects verb learning and children's ability to form action categories. © 2014 The British Psychological Society.

  19. On the Integrity of Online Testing for Introductory Statistics Courses: A Latent Variable Approach

    Science.gov (United States)

    Fask, Alan; Englander, Fred; Wang, Zhaobo

    2015-01-01

    There has been a remarkable growth in distance learning courses in higher education. Despite indications that distance learning courses are more vulnerable to cheating behavior than traditional courses, there has been little research studying whether online exams facilitate a relatively greater level of cheating. This article examines this issue…

  20. Using latent variable approach to estimate China's economy-wide energy rebound effect over 1954–2010

    International Nuclear Information System (INIS)

    Shao, Shuai; Huang, Tao; Yang, Lili

    2014-01-01

    The energy rebound effect has been a significant issue in China, which is undergoing economic transition, since it reflects the effectiveness of energy-saving policy relying on improved energy efficiency. Based on the IPAT equation and Brookes' explanation of the rebound effect, this paper develops an alternative estimation model of the rebound effect. By using the estimation model and latent variable approach, which is achieved through a time-varying coefficient state space model, we estimate China's economy-wide energy rebound effect over 1954–2010. The results show that the rebound effect evidently exists in China as a result of the annual average of 39.73% over 1954–2010. Before and after the implementation of China's reform and opening-up policy in 1978, the rebound effects are 47.24% and 37.32%, with a strong fluctuation and a circuitously downward trend, respectively, indicating that a stable political environment and the development of market economy system facilitate the effectiveness of energy-saving policy. Although the energy-saving effect of improving energy efficiency has been partly realised, there remains a large energy-saving potential in China. - Highlights: • We present an improved estimation methodology of economy-wide energy rebound effect. • We use the latent variable approach to estimate China's economy-wide rebound effect. • The rebound exists in China and varies before and after reform and opening-up. • After 1978, the average rebound is 37.32% with a circuitously downward trend. • Traditional Solow remainder method underestimates the rebound in most cases

  1. Illustration of Step-Wise Latent Class Modeling With Covariates and Taxometric Analysis in Research Probing Children's Mental Models in Learning Sciences

    Directory of Open Access Journals (Sweden)

    Dimitrios Stamovlasis

    2018-04-01

    Full Text Available This paper illustrates two psychometric methods, latent class analysis (LCA and taxometric analysis (TA using empirical data from research probing children's mental representation in science learning. LCA is used to obtain a typology based on observed variables and to further investigate how the encountered classes might be related to external variables, where the effectiveness of classification process and the unbiased estimations of parameters become the main concern. In the step-wise LCA, the class membership is assigned and subsequently its relationship with covariates is established. This leading-edge modeling approach suffers from severe downward-biased estimations. The illustration of LCA is focused on alternative bias correction approaches and demonstrates the effect of modal and proportional class-membership assignment along with BCH and ML correction procedures. The illustration of LCA is presented with three covariates, which are psychometric variables operationalizing formal reasoning, divergent thinking and field dependence-independence, respectively. Moreover, taxometric analysis, a method designed to detect the type of the latent structural model, categorical or dimensional, is introduced, along with the relevant basic concepts and tools. TA was applied complementarily in the same data sets to answer the fundamental hypothesis about children's naïve knowledge on the matters under study and it comprises an additional asset in building theory which is fundamental for educational practices. Taxometric analysis provided results that were ambiguous as far as the type of the latent structure. This finding initiates further discussion and sets a problematization within this framework rethinking fundamental assumptions and epistemological issues.

  2. Shared Gaussian Process Latent Variable Model for Multi-view Facial Expression Recognition

    NARCIS (Netherlands)

    Eleftheriadis, Stefanos; Rudovic, Ognjen; Pantic, Maja

    Facial-expression data often appear in multiple views either due to head-movements or the camera position. Existing methods for multi-view facial expression recognition perform classification of the target expressions either by using classifiers learned separately for each view or by using a single

  3. Synthetic aperture radar ship discrimination, generation and latent variable extraction using information maximizing generative adversarial networks

    CSIR Research Space (South Africa)

    Schwegmann, Colin P

    2017-07-01

    Full Text Available such as Synthetic Aperture Radar imagery. To aid in the creation of improved machine learning-based ship detection and discrimination methods this paper applies a type of neural network known as an Information Maximizing Generative Adversarial Network. Generative...

  4. Identification of children with mathematics learning disabilities (MLDs) using latent class growth analysis.

    Science.gov (United States)

    Wong, Terry T-Y; Ho, Connie S-H; Tang, Joey

    2014-11-01

    The traditional way of identifying children with mathematics learning disabilities (MLDs) using the low-achievement method with one-off assessment suffers from several limitations (e.g., arbitrary cutoff, measurement error, lacking consideration of growth). The present study attempted to identify children with MLD using the latent growth modelling approach, which minimizes the above potential problems. Two hundred and ten Chinese-speaking children were classified into five classes based on their arithmetic performance over 3 years. Their performance on various number-related cognitive measures was also assessed. A potential MLD class was identified, which demonstrated poor achievement over the 3 years and showed smaller improvement over time compared with the average-achieving class. This class had deficits in all number-related cognitive skills, hence supporting the number sense deficit hypothesis. On the other hand, another low-achieving class, which showed little improvement in arithmetic skills over time, was also identified. This class had an average cognitive profile but a low SES. Interventions should be provided to both low-achieving classes according to their needs. Copyright © 2014 Elsevier Ltd. All rights reserved.

  5. Semi-supervised learning based probabilistic latent semantic analysis for automatic image annotation

    Institute of Scientific and Technical Information of China (English)

    Tian Dongping

    2017-01-01

    In recent years, multimedia annotation problem has been attracting significant research attention in multimedia and computer vision areas, especially for automatic image annotation, whose purpose is to provide an efficient and effective searching environment for users to query their images more easily.In this paper, a semi-supervised learning based probabilistic latent semantic analysis ( PL-SA) model for automatic image annotation is presenred.Since it' s often hard to obtain or create la-beled images in large quantities while unlabeled ones are easier to collect, a transductive support vector machine ( TSVM) is exploited to enhance the quality of the training image data.Then, differ-ent image features with different magnitudes will result in different performance for automatic image annotation.To this end, a Gaussian normalization method is utilized to normalize different features extracted from effective image regions segmented by the normalized cuts algorithm so as to reserve the intrinsic content of images as complete as possible.Finally, a PLSA model with asymmetric mo-dalities is constructed based on the expectation maximization( EM) algorithm to predict a candidate set of annotations with confidence scores.Extensive experiments on the general-purpose Corel5k dataset demonstrate that the proposed model can significantly improve performance of traditional PL-SA for the task of automatic image annotation.

  6. Understanding Digital Learning and Its Variable Effects

    Science.gov (United States)

    Means, B.

    2016-12-01

    An increasing proportion of undergraduate courses use an online or blended learning format. This trend signals major changes in the kind of instruction students receive in their STEM courses, yet evidence about the effectiveness of these new approaches is sparse. Existing syntheses and meta-analyses summarize outcomes from experimental or quasi-experimental studies of online and blended courses and document how few studies incorporate proper controls for differences in student characteristics, instructor behaviors, and other course conditions. The evidence that is available suggests that on average blended courses are equal to or better than traditional face-to-face courses and that online courses are equivalent in terms of learning outcomes. But these averages conceal a tremendous underlying variability. Results vary markedly from course to course, even when the same technology is used in both. Some research suggests that online instruction puts lower-achieving students at a disadvantage. It is clear that introducing digital learning per se is no guarantee that student engagement and learning will be enhanced. Getting more consistently positive impacts out of learning technologies is going to require systematic characterization of the features of learning technologies and associated instructional practices as well as attention to context and student characteristics. This presentation will present a framework for characterizing essential features of digital learning resources, implementation practices, and conditions. It will also summarize the research evidence with respect to the learning impacts of specific technology features including spaced practice, immediate feedback, mastery learning based pacing, visualizations and simulations, gaming features, prompts for explanations and reflection, and tools for online collaboration.

  7. Self-Consciousness and Assertiveness as Explanatory Variables of L2 Oral Ability: A Latent Variable Approach

    Science.gov (United States)

    Ockey, Gary

    2011-01-01

    Drawing on current theories in personality, second-language (L2) oral ability, and psychometrics, this study investigates the extent to which self-consciousness and assertiveness are explanatory variables of L2 oral ability. Three hundred sixty first-year Japanese university students who were studying English as a foreign language participated in…

  8. Collinear Latent Variables in Multilevel Confirmatory Factor Analysis : A Comparison of Maximum Likelihood and Bayesian Estimation

    NARCIS (Netherlands)

    Can, Seda; van de Schoot, Rens|info:eu-repo/dai/nl/304833207; Hox, Joop|info:eu-repo/dai/nl/073351431

    2015-01-01

    Because variables may be correlated in the social and behavioral sciences, multicollinearity might be problematic. This study investigates the effect of collinearity manipulated in within and between levels of a two-level confirmatory factor analysis by Monte Carlo simulation. Furthermore, the

  9. The "g" Factor and Cognitive Test Session Behavior: Using a Latent Variable Approach in Examining Measurement Invariance Across Age Groups on the WJ III

    Science.gov (United States)

    Frisby, Craig L.; Wang, Ze

    2016-01-01

    Data from the standardization sample of the Woodcock-Johnson Psychoeducational Battery--Third Edition (WJ III) Cognitive standard battery and Test Session Observation Checklist items were analyzed to understand the relationship between g (general mental ability) and test session behavior (TSB; n = 5,769). Latent variable modeling methods were used…

  10. High-Performance Psychometrics: The Parallel-E Parallel-M Algorithm for Generalized Latent Variable Models. Research Report. ETS RR-16-34

    Science.gov (United States)

    von Davier, Matthias

    2016-01-01

    This report presents results on a parallel implementation of the expectation-maximization (EM) algorithm for multidimensional latent variable models. The developments presented here are based on code that parallelizes both the E step and the M step of the parallel-E parallel-M algorithm. Examples presented in this report include item response…

  11. Cognitive psychology meets psychometric theory: on the relation between process models for decision making and latent variable models for individual differences

    NARCIS (Netherlands)

    van der Maas, H.L.J.; Molenaar, D.; Maris, G.; Kievit, R.A.; Borsboom, D.

    2011-01-01

    This article analyzes latent variable models from a cognitive psychology perspective. We start by discussing work by Tuerlinckx and De Boeck (2005), who proved that a diffusion model for 2-choice response processes entails a 2-parameter logistic item response theory (IRT) model for individual

  12. Cognitive Psychology Meets Psychometric Theory: On the Relation between Process Models for Decision Making and Latent Variable Models for Individual Differences

    Science.gov (United States)

    van der Maas, Han L. J.; Molenaar, Dylan; Maris, Gunter; Kievit, Rogier A.; Borsboom, Denny

    2011-01-01

    This article analyzes latent variable models from a cognitive psychology perspective. We start by discussing work by Tuerlinckx and De Boeck (2005), who proved that a diffusion model for 2-choice response processes entails a 2-parameter logistic item response theory (IRT) model for individual differences in the response data. Following this line…

  13. A new model of wheezing severity in young children using the validated ISAAC wheezing module: A latent variable approach with validation in independent cohorts.

    Science.gov (United States)

    Brunwasser, Steven M; Gebretsadik, Tebeb; Gold, Diane R; Turi, Kedir N; Stone, Cosby A; Datta, Soma; Gern, James E; Hartert, Tina V

    2018-01-01

    The International Study of Asthma and Allergies in Children (ISAAC) Wheezing Module is commonly used to characterize pediatric asthma in epidemiological studies, including nearly all airway cohorts participating in the Environmental Influences on Child Health Outcomes (ECHO) consortium. However, there is no consensus model for operationalizing wheezing severity with this instrument in explanatory research studies. Severity is typically measured using coarsely-defined categorical variables, reducing power and potentially underestimating etiological associations. More precise measurement approaches could improve testing of etiological theories of wheezing illness. We evaluated a continuous latent variable model of pediatric wheezing severity based on four ISAAC Wheezing Module items. Analyses included subgroups of children from three independent cohorts whose parents reported past wheezing: infants ages 0-2 in the INSPIRE birth cohort study (Cohort 1; n = 657), 6-7-year-old North American children from Phase One of the ISAAC study (Cohort 2; n = 2,765), and 5-6-year-old children in the EHAAS birth cohort study (Cohort 3; n = 102). Models were estimated using structural equation modeling. In all cohorts, covariance patterns implied by the latent variable model were consistent with the observed data, as indicated by non-significant χ2 goodness of fit tests (no evidence of model misspecification). Cohort 1 analyses showed that the latent factor structure was stable across time points and child sexes. In both cohorts 1 and 3, the latent wheezing severity variable was prospectively associated with wheeze-related clinical outcomes, including physician asthma diagnosis, acute corticosteroid use, and wheeze-related outpatient medical visits when adjusting for confounders. We developed an easily applicable continuous latent variable model of pediatric wheezing severity based on items from the well-validated ISAAC Wheezing Module. This model prospectively associates with

  14. Impulsivity, Working Memory, and Impaired Control over Alcohol: A Latent Variable Analysis

    Science.gov (United States)

    Wardell, Jeffrey D.; Quilty, Lena C.; Hendershot, Christian S.

    2017-01-01

    Impaired control over alcohol is an important risk factor for heavy drinking among young adults and may mediate, in part, the association between personality risk and alcohol problems. Research suggests that trait impulsivity is associated with impaired control over alcohol; however, few studies of this association have included a range of impulsivity facets. The purpose of this study was to examine specific pathways from higher-order impulsivity factors to alcohol problems mediated via impaired control over alcohol. We also examined the moderating role of working memory in these associations. Young heavy drinkers (N=300) completed two multidimensional impulsivity measures (UPPS-P and BIS-11) along with self-report measures of impaired control over alcohol, alcohol use, and alcohol problems. Working memory was assessed using a computerized digit span task. Results showed that the impulsivity facets loaded onto two higher-order factors that were labeled response and reflection impulsivity. Response impulsivity predicted unique variance in self-reported impaired control and alcohol problems, whereas reflection impulsivity predicted unique variance in heavy drinking frequency only. Further, significant indirect associations were observed from response and reflection impulsivity to alcohol problems mediated via impaired control and heavy drinking frequency, respectively. Working memory and sensation seeking were not uniquely associated with the alcohol variables, and no support was found for the moderating role of working memory. The results help to clarify associations among impulsivity, impaired control, and alcohol problems, suggesting that impaired control may play a specific role in the pathway to alcohol problems from response impulsivity but not from reflection impulsivity. PMID:27269291

  15. Mode choice models' ability to express intention to change travel behaviour considering non-compensatory rules and latent variables

    Directory of Open Access Journals (Sweden)

    Nobuhiro Sanko

    2013-03-01

    Full Text Available Disaggregate behaviour choice models have been improved in many aspects, but they are rarely evaluated from the viewpoint of their ability to express intention to change travel behaviour. This study compared various models, including objective and latent models and compensatory and non-compensatory decision-making models. Latent models contain latent factors calculated using the LISREL (linear structural relations model. Non-compensatory models are based on a lexicographic-semiorder heuristic. This paper proposes ‘probability increment’ and ‘joint probability increment’ as indicators for evaluating the ability of these models to express intention to change travel behaviour. The application to commuting travel data in the Chukyo metropolitan area in Japan showed that the appropriate non-compensatory and latent models outperform other models.

  16. Development of a scale to measure adherence to self-monitoring of blood glucose with latent variable measurement.

    Science.gov (United States)

    Wagner, J A; Schnoll, R A; Gipson, M T

    1998-07-01

    Adherence to self-monitoring of blood glucose (SMBG) is problematic for many people with diabetes. Self-reports of adherence have been found to be unreliable, and existing paper-and-pencil measures have limitations. This study developed a brief measure of SMBG adherence with good psychometric properties and a useful factor structure that can be used in research and in practice. A total of 216 adults with diabetes responded to 30 items rated on a 9-point Likert scale that asked about blood monitoring habits. In part I of the study, items were evaluated and retained based on their psychometric properties. The sample was divided into exploratory and confirmatory halves. Using the exploratory half, items with acceptable psychometric properties were subjected to a principal components analysis. In part II of the study, structural equation modeling was used to confirm the component solution with the entire sample. Structural modeling was also used to test the relationship between these components. It was hypothesized that the scale would produce four correlated factors. Principal components analysis suggested a two-component solution, and confirmatory factor analysis confirmed this solution. The first factor measures the degree to which patients rely on others to help them test and thus was named "social influence." The second component measures the degree to which patients use physical symptoms of blood glucose levels to help them test and thus was named "physical influence." Results of the structural model show that the components are correlated and make up the higher-order latent variable adherence. The resulting 15-item scale provides a short, reliable way to assess patient adherence to SMBG. Despite the existence of several aspects of adherence, this study indicates that the construct consists of only two components. This scale is an improvement on previous measures of adherence because of its good psychometric properties, its interpretable factor structure, and its

  17. Predictive Variable Gain Iterative Learning Control for PMSM

    Directory of Open Access Journals (Sweden)

    Huimin Xu

    2015-01-01

    Full Text Available A predictive variable gain strategy in iterative learning control (ILC is introduced. Predictive variable gain iterative learning control is constructed to improve the performance of trajectory tracking. A scheme based on predictive variable gain iterative learning control for eliminating undesirable vibrations of PMSM system is proposed. The basic idea is that undesirable vibrations of PMSM system are eliminated from two aspects of iterative domain and time domain. The predictive method is utilized to determine the learning gain in the ILC algorithm. Compression mapping principle is used to prove the convergence of the algorithm. Simulation results demonstrate that the predictive variable gain is superior to constant gain and other variable gains.

  18. Improvement in latent variable indirect response modeling of multiple categorical clinical endpoints: application to modeling of guselkumab treatment effects in psoriatic patients.

    Science.gov (United States)

    Hu, Chuanpu; Randazzo, Bruce; Sharma, Amarnath; Zhou, Honghui

    2017-10-01

    Exposure-response modeling plays an important role in optimizing dose and dosing regimens during clinical drug development. The modeling of multiple endpoints is made possible in part by recent progress in latent variable indirect response (IDR) modeling for ordered categorical endpoints. This manuscript aims to investigate the level of improvement achievable by jointly modeling two such endpoints in the latent variable IDR modeling framework through the sharing of model parameters. This is illustrated with an application to the exposure-response of guselkumab, a human IgG1 monoclonal antibody in clinical development that blocks IL-23. A Phase 2b study was conducted in 238 patients with psoriasis for which disease severity was assessed using Psoriasis Area and Severity Index (PASI) and Physician's Global Assessment (PGA) scores. A latent variable Type I IDR model was developed to evaluate the therapeutic effect of guselkumab dosing on 75, 90 and 100% improvement of PASI scores from baseline and PGA scores, with placebo effect empirically modeled. The results showed that the joint model is able to describe the observed data better with fewer parameters compared with the common approach of separately modeling the endpoints.

  19. Latent memory of unattended stimuli reactivated by practice: an FMRI study on the role of consciousness and attention in learning.

    Science.gov (United States)

    Meuwese, Julia D I; Scholte, H Steven; Lamme, Victor A F

    2014-01-01

    Although we can only report about what is in the focus of our attention, much more than that is actually processed. And even when attended, stimuli may not always be reportable, for instance when they are masked. A stimulus can thus be unreportable for different reasons: the absence of attention or the absence of a conscious percept. But to what extent does the brain learn from exposure to these unreportable stimuli? In this fMRI experiment subjects were exposed to textured figure-ground stimuli, of which reportability was manipulated either by masking (which only interferes with consciousness) or with an inattention paradigm (which only interferes with attention). One day later learning was assessed neurally and behaviorally. Positive neural learning effects were found for stimuli presented in the inattention paradigm; for attended yet masked stimuli negative adaptation effects were found. Interestingly, these inattentional learning effects only became apparent in a second session after a behavioral detection task had been administered during which performance feedback was provided. This suggests that the memory trace that is formed during inattention is latent until reactivated by behavioral practice. However, no behavioral learning effects were found, therefore we cannot conclude that perceptual learning has taken place for these unattended stimuli.

  20. Latent memory of unattended stimuli reactivated by practice: an FMRI study on the role of consciousness and attention in learning.

    Directory of Open Access Journals (Sweden)

    Julia D I Meuwese

    Full Text Available Although we can only report about what is in the focus of our attention, much more than that is actually processed. And even when attended, stimuli may not always be reportable, for instance when they are masked. A stimulus can thus be unreportable for different reasons: the absence of attention or the absence of a conscious percept. But to what extent does the brain learn from exposure to these unreportable stimuli? In this fMRI experiment subjects were exposed to textured figure-ground stimuli, of which reportability was manipulated either by masking (which only interferes with consciousness or with an inattention paradigm (which only interferes with attention. One day later learning was assessed neurally and behaviorally. Positive neural learning effects were found for stimuli presented in the inattention paradigm; for attended yet masked stimuli negative adaptation effects were found. Interestingly, these inattentional learning effects only became apparent in a second session after a behavioral detection task had been administered during which performance feedback was provided. This suggests that the memory trace that is formed during inattention is latent until reactivated by behavioral practice. However, no behavioral learning effects were found, therefore we cannot conclude that perceptual learning has taken place for these unattended stimuli.

  1. Variables influencing medical student learning in the operating room.

    Science.gov (United States)

    Schwind, Cathy J; Boehler, Margaret L; Rogers, David A; Williams, Reed G; Dunnington, Gary; Folse, Roland; Markwell, Stephen J

    2004-02-01

    The operating room (OR) is an important venue where surgeons do much of medical student teaching and yet there has been little work evaluating variables that influence learning in this unique environment. We designed this study to identify variables that affected medical student learning in the OR. We developed a questionnaire based on surgery faculty observations of learning in the OR. The medical students completed the questionnaire on 114 learning episodes in the OR. Pearson correlation coefficient was used to establish the strength of association between various variables and the student's overall perception of learning. The students evaluated 27 variables that might impact their learning in the OR. Strong correlations were identified between the attending physician's attitude, interactions and teaching ability in the OR and the environment being conducive to learning. Surgical faculty behavior is a powerful determinant of student perceptions of what provides for a favorable learning environment in the OR.

  2. The Effect of Latent Binary Variables on the Uncertainty of the Prediction of a Dichotomous Outcome Using Logistic Regression Based Propensity Score Matching.

    Science.gov (United States)

    Szekér, Szabolcs; Vathy-Fogarassy, Ágnes

    2018-01-01

    Logistic regression based propensity score matching is a widely used method in case-control studies to select the individuals of the control group. This method creates a suitable control group if all factors affecting the output variable are known. However, if relevant latent variables exist as well, which are not taken into account during the calculations, the quality of the control group is uncertain. In this paper, we present a statistics-based research in which we try to determine the relationship between the accuracy of the logistic regression model and the uncertainty of the dependent variable of the control group defined by propensity score matching. Our analyses show that there is a linear correlation between the fit of the logistic regression model and the uncertainty of the output variable. In certain cases, a latent binary explanatory variable can result in a relative error of up to 70% in the prediction of the outcome variable. The observed phenomenon calls the attention of analysts to an important point, which must be taken into account when deducting conclusions.

  3. Randomization-Based Inference about Latent Variables from Complex Samples: The Case of Two-Stage Sampling

    Science.gov (United States)

    Li, Tiandong

    2012-01-01

    In large-scale assessments, such as the National Assessment of Educational Progress (NAEP), plausible values based on Multiple Imputations (MI) have been used to estimate population characteristics for latent constructs under complex sample designs. Mislevy (1991) derived a closed-form analytic solution for a fixed-effect model in creating…

  4. Beyond IQ: A Latent State-Trait Analysis of General Intelligence, Dynamic Decision Making, and Implicit Learning

    Science.gov (United States)

    Danner, Daniel; Hagemann, Dirk; Schankin, Andrea; Hager, Marieke; Funke, Joachim

    2011-01-01

    The present study investigated cognitive performance measures beyond IQ. In particular, we investigated the psychometric properties of dynamic decision making variables and implicit learning variables and their relation with general intelligence and professional success. N = 173 employees from different companies and occupational groups completed…

  5. Discrimination learning with variable stimulus 'salience'

    Directory of Open Access Journals (Sweden)

    Treviño Mario

    2011-08-01

    Full Text Available Abstract Background In nature, sensory stimuli are organized in heterogeneous combinations. Salient items from these combinations 'stand-out' from their surroundings and determine what and how we learn. Yet, the relationship between varying stimulus salience and discrimination learning remains unclear. Presentation of the hypothesis A rigorous formulation of the problem of discrimination learning should account for varying salience effects. We hypothesize that structural variations in the environment where the conditioned stimulus (CS is embedded will be a significant determinant of learning rate and retention level. Testing the hypothesis Using numerical simulations, we show how a modified version of the Rescorla-Wagner model, an influential theory of associative learning, predicts relevant interactions between varying salience and discrimination learning. Implications of the hypothesis If supported by empirical data, our model will help to interpret critical experiments addressing the relations between attention, discrimination and learning.

  6. Social isolation induces deficit of latent learning performance in mice: a putative animal model of attention deficit/hyperactivity disorder.

    Science.gov (United States)

    Ouchi, Hirofumi; Ono, Kazuya; Murakami, Yukihisa; Matsumoto, Kinzo

    2013-02-01

    Social isolation of rodents (SI) elicits a variety of stress responses such as increased aggressiveness, hyper-locomotion, and reduced susceptibility to pentobarbital. To obtain a better understanding of the relevance of SI-induced behavioral abnormalities to psychiatric disorders, we examined the effect of SI on latent learning as an index of spatial attention, and discussed the availability of SI as an epigenetic model of attention deficit hyperactivity disorder (ADHD). Except in specially stated cases, 4-week-old male mice were housed in a group or socially isolated for 3-70 days before experiments. The animals socially isolated for 1 week or more exhibited spatial attention deficit in the water-finding test. Re-socialized rearing for 5 weeks after 1-week SI failed to attenuate the spatial attention deficit. The effect of SI on spatial attention showed no gender difference or correlation with increased aggressive behavior. Moreover, SI had no effect on cognitive performance elucidated in a modified Y-maze or an object recognition test, but it significantly impaired contextual and conditional fear memory elucidated in the fear-conditioning test. Drugs used for ADHD therapy, methylphenidate (1-10 mg/kg, i.p.) and caffeine (0.5-1 mg/kg, i.p.), improved SI-induced latent learning deficit in a manner reversible with cholinergic but not dopaminergic antagonists. Considering the behavioral features of SI mice together with their susceptibility to ADHD drugs, the present findings suggest that SI provides an epigenetic animal model of ADHD and that central cholinergic systems play a role in the effect of methylphenidate on SI-induced spatial attention deficit. Copyright © 2012 Elsevier B.V. All rights reserved.

  7. Tracking influence between naive Bayes models using score-based structure learning

    CSIR Research Space (South Africa)

    Ajoodha, R

    2017-11-01

    Full Text Available Current structure learning practices in Bayesian networks have been developed to learn the structure between observable variables and learning latent parameters independently. One exception establishes a variant of EM for learning the structure...

  8. Intra-individual variability as a predictor of learning

    Directory of Open Access Journals (Sweden)

    Matija Svetina

    2004-05-01

    Full Text Available Learning is one of the most important aspects of children's behaviour. A new theory that emerged from evolutionary principles and information-processing models assumes learning to be run by two basic mechanisms: variability and selection. The theory is based on the underlying assumption that intra-individual variability of strategies that children use to solve a problem, is a core mechanism of learning change. This assumption was tested in the case of multiple classification (MC task. 30 6-year-old children were tested for intelligence, short-term memory, and MC. Procedure followed classical pre-test/learning/post-test scheme. Amount of learning was measured through percentage of correct answers before and after learning sessions, whereas intra-individual variability was assessed through children's explanations of their answers on MC problems. The results yielded intra-individual variability to explain learning changes beyond inter-individual differences in intelligence or short-term memory. Although the results rose some new questions to be considered in further research, the data supported the hypothesis of intra-individual variability as predictor of learning change.

  9. Testing Group Mean Differences of Latent Variables in Multilevel Data Using Multiple-Group Multilevel CFA and Multilevel MIMIC Modeling.

    Science.gov (United States)

    Kim, Eun Sook; Cao, Chunhua

    2015-01-01

    Considering that group comparisons are common in social science, we examined two latent group mean testing methods when groups of interest were either at the between or within level of multilevel data: multiple-group multilevel confirmatory factor analysis (MG ML CFA) and multilevel multiple-indicators multiple-causes modeling (ML MIMIC). The performance of these methods were investigated through three Monte Carlo studies. In Studies 1 and 2, either factor variances or residual variances were manipulated to be heterogeneous between groups. In Study 3, which focused on within-level multiple-group analysis, six different model specifications were considered depending on how to model the intra-class group correlation (i.e., correlation between random effect factors for groups within cluster). The results of simulations generally supported the adequacy of MG ML CFA and ML MIMIC for multiple-group analysis with multilevel data. The two methods did not show any notable difference in the latent group mean testing across three studies. Finally, a demonstration with real data and guidelines in selecting an appropriate approach to multilevel multiple-group analysis are provided.

  10. Cross-Language Plagiarism Detection System Using Latent Semantic Analysis and Learning Vector Quantization

    Directory of Open Access Journals (Sweden)

    Anak Agung Putri Ratna

    2017-06-01

    Full Text Available Computerized cross-language plagiarism detection has recently become essential. With the scarcity of scientific publications in Bahasa Indonesia, many Indonesian authors frequently consult publications in English in order to boost the quantity of scientific publications in Bahasa Indonesia (which is currently rising. Due to the syntax disparity between Bahasa Indonesia and English, most of the existing methods for automated cross-language plagiarism detection do not provide satisfactory results. This paper analyses the probability of developing Latent Semantic Analysis (LSA for a computerized cross-language plagiarism detector for two languages with different syntax. To improve performance, various alterations in LSA are suggested. By using a linear vector quantization (LVQ classifier in the LSA and taking into account the Frobenius norm, output has reached up to 65.98% in accuracy. The results of the experiments showed that the best accuracy achieved is 87% with a document size of 6 words, and the document definition size must be kept below 10 words in order to maintain high accuracy. Additionally, based on experimental results, this paper suggests utilizing the frequency occurrence method as opposed to the binary method for the term–document matrix construction.

  11. MODIS-Based Estimation of Terrestrial Latent Heat Flux over North America Using Three Machine Learning Algorithms

    Directory of Open Access Journals (Sweden)

    Xuanyu Wang

    2017-12-01

    Full Text Available Terrestrial latent heat flux (LE is a key component of the global terrestrial water, energy, and carbon exchanges. Accurate estimation of LE from moderate resolution imaging spectroradiometer (MODIS data remains a major challenge. In this study, we estimated the daily LE for different plant functional types (PFTs across North America using three machine learning algorithms: artificial neural network (ANN; support vector machines (SVM; and, multivariate adaptive regression spline (MARS driven by MODIS and Modern Era Retrospective Analysis for Research and Applications (MERRA meteorology data. These three predictive algorithms, which were trained and validated using observed LE over the period 2000–2007, all proved to be accurate. However, ANN outperformed the other two algorithms for the majority of the tested configurations for most PFTs and was the only method that arrived at 80% precision for LE estimation. We also applied three machine learning algorithms for MODIS data and MERRA meteorology to map the average annual terrestrial LE of North America during 2002–2004 using a spatial resolution of 0.05°, which proved to be useful for estimating the long-term LE over North America.

  12. Latent Class Analysis of Students' Mathematics Learning Strategies and the Relationship between Learning Strategy and Mathematical Literacy

    Science.gov (United States)

    Lin, Su-Wei; Tai, Wen-Chun

    2015-01-01

    This study investigated how various mathematics learning strategies affect the mathematical literacy of students. The data for this study were obtained from the 2012 Programme for International Student Assessment (PISA) data of Taiwan. The PISA learning strategy survey contains three types of learning strategies: elaboration, control, and…

  13. Machine learning techniques to select variable stars

    Directory of Open Access Journals (Sweden)

    García-Varela Alejandro

    2017-01-01

    Full Text Available In order to perform a supervised classification of variable stars, we propose and evaluate a set of six features extracted from the magnitude density of the light curves. They are used to train automatic classification systems using state-of-the-art classifiers implemented in the R statistical computing environment. We find that random forests is the most successful method to select variables.

  14. Microgenetic patterns of children’s multiplication learning: Confirming the overlapping waves model by latent growth modeling

    NARCIS (Netherlands)

    van der Ven, S.H.G.; Boom, J.; Kroesbergen, E.H.; Leseman, P.P.M.

    2012-01-01

    Variability in strategy selection is an important characteristic of learning new skills such as mathematical skills. Strategies gradually come and go during this development. In 1996, Siegler described this phenomenon as "overlapping waves." In the current microgenetic study, we attempted to model

  15. Input Variability Facilitates Unguided Subcategory Learning in Adults

    Science.gov (United States)

    Eidsvåg, Sunniva Sørhus; Austad, Margit; Plante, Elena; Asbjørnsen, Arve E.

    2015-01-01

    Purpose: This experiment investigated whether input variability would affect initial learning of noun gender subcategories in an unfamiliar, natural language (Russian), as it is known to assist learning of other grammatical forms. Method: Forty adults (20 men, 20 women) were familiarized with examples of masculine and feminine Russian words. Half…

  16. Modelling the Success of Learning Management Systems: Application of Latent Class Segmentation Using FIMIX-PLS

    Science.gov (United States)

    Arenas-Gaitán, Jorge; Rondán-Cataluña, Francisco Javier; Ramírez-Correa, Patricio E.

    2018-01-01

    There is not a unique attitude towards the implementation of digital technology in educational sceneries. This paper aims to validate an adaptation of the DeLone and McLean information systems success model in the context of a learning management system. Furthermore, this study means to prove (1) the necessity of segmenting students in order to…

  17. A unified framework of image latent feature learning on Sina microblog

    Science.gov (United States)

    Wei, Jinjin; Jin, Zhigang; Zhou, Yuan; Zhang, Rui

    2015-10-01

    Large-scale user-contributed images with texts are rapidly increasing on the social media websites, such as Sina microblog. However, the noise and incomplete correspondence between the images and the texts give rise to the difficulty in precise image retrieval and ranking. In this paper, a hypergraph-based learning framework is proposed for image ranking, which simultaneously utilizes visual feature, textual content and social link information to estimate the relevance between images. Representing each image as a vertex in the hypergraph, complex relationship between images can be reflected exactly. Then updating the weight of hyperedges throughout the hypergraph learning process, the effect of different edges can be adaptively modulated in the constructed hypergraph. Furthermore, the popularity degree of the image is employed to re-rank the retrieval results. Comparative experiments on a large-scale Sina microblog data-set demonstrate the effectiveness of the proposed approach.

  18. Dynamic Latent Classification Model

    DEFF Research Database (Denmark)

    Zhong, Shengtong; Martínez, Ana M.; Nielsen, Thomas Dyhre

    as possible. Motivated by this problem setting, we propose a generative model for dynamic classification in continuous domains. At each time point the model can be seen as combining a naive Bayes model with a mixture of factor analyzers (FA). The latent variables of the FA are used to capture the dynamics...

  19. Sensitivity analysis for linear structural equation models, longitudinal mediation with latent growth models and blended learning in biostatistics education

    Science.gov (United States)

    Sullivan, Adam John

    In chapter 1, we consider the biases that may arise when an unmeasured confounder is omitted from a structural equation model (SEM) and sensitivity analysis techniques to correct for such biases. We give an analysis of which effects in an SEM are and are not biased by an unmeasured confounder. It is shown that a single unmeasured confounder will bias not just one but numerous effects in an SEM. We present sensitivity analysis techniques to correct for biases in total, direct, and indirect effects when using SEM analyses, and illustrate these techniques with a study of aging and cognitive function. In chapter 2, we consider longitudinal mediation with latent growth curves. We define the direct and indirect effects using counterfactuals and consider the assumptions needed for identifiability of those effects. We develop models with a binary treatment/exposure followed by a model where treatment/exposure changes with time allowing for treatment/exposure-mediator interaction. We thus formalize mediation analysis with latent growth curve models using counterfactuals, makes clear the assumptions and extends these methods to allow for exposure mediator interactions. We present and illustrate the techniques with a study on Multiple Sclerosis(MS) and depression. In chapter 3, we report on a pilot study in blended learning that took place during the Fall 2013 and Summer 2014 semesters here at Harvard. We blended the traditional BIO 200: Principles of Biostatistics and created ID 200: Principles of Biostatistics and epidemiology. We used materials from the edX course PH207x: Health in Numbers: Quantitative Methods in Clinical & Public Health Research and used. These materials were used as a video textbook in which students would watch a given number of these videos prior to class. Using surveys as well as exam data we informally assess these blended classes from the student's perspective as well as a comparison of these students with students in another course, BIO 201

  20. Variability in Second Language Learning: The Roles of Individual Differences, Learning Conditions, and Linguistic Complexity

    Science.gov (United States)

    Tagarelli, Kaitlyn M.; Ruiz, Simón; Vega, José Luis Moreno; Rebuschat, Patrick

    2016-01-01

    Second language learning outcomes are highly variable, due to a variety of factors, including individual differences, exposure conditions, and linguistic complexity. However, exactly how these factors interact to influence language learning is unknown. This article examines the relationship between these three variables in language learners.…

  1. Acute alcohol effects on set-shifting and its moderation by baseline individual differences: a latent variable analysis.

    Science.gov (United States)

    Korucuoglu, Ozlem; Sher, Kenneth J; Wood, Phillip K; Saults, John Scott; Altamirano, Lee; Miyake, Akira; Bartholow, Bruce D

    2017-03-01

    To compare the acute effects of alcohol on set-shifting task performance (relative to sober baseline performance) during ascending and descending limb breath alcohol concentration (BrAC), as well as possible moderation of these effects by baseline individual differences. Shifting performance was tested during an initial baseline and a subsequent drinking session, during which participants were assigned randomly to one of three beverage conditions (alcohol, placebo or control) and one of two BrAC limb conditions [ascending and descending (A/D) or descending-only (D-only)]. A human experimental laboratory on the University of Missouri campus in Columbia, MO, USA. A total of 222 moderate-drinking adults (ages 21-30 years) recruited from Columbia, MO and tested between 2010 and 2013. The outcome measure was performance on set-shifting tasks under the different beverage and limb conditions. Shifting performance assessed at baseline was a key moderator. Although performance improved across sessions, this improvement was reduced in the alcohol compared with no-alcohol groups (post-drink latent mean comparison across groups, all Ps ≤ 0.05), and this effect was more pronounced in individuals with lower pre-drink performance (comparison of pre- to post-drink path coefficients across groups, all Ps ≤ 0.05). In the alcohol group, performance was better on descending compared with ascending limb (P ≤ 0.001), but descending limb performance did not differ across the A/D and D-only groups. Practising tasks before drinking moderates the acute effects of alcohol on the ability to switch between tasks. Greater impairment in shifting ability on descending compared with ascending breath alcohol concentration is not related to task practice. © 2016 Society for the Study of Addiction.

  2. Using Machine Learning to Discover Latent Social Phenotypes in Free-Ranging Macaques

    Science.gov (United States)

    Madlon-Kay, Seth; Brent, Lauren J. N.; Heller, Katherine A.; Platt, Michael L.

    2017-01-01

    Investigating the biological bases of social phenotypes is challenging because social behavior is both high-dimensional and richly structured, and biological factors are more likely to influence complex patterns of behavior rather than any single behavior in isolation. The space of all possible patterns of interactions among behaviors is too large to investigate using conventional statistical methods. In order to quantitatively define social phenotypes from natural behavior, we developed a machine learning model to identify and measure patterns of behavior in naturalistic observational data, as well as their relationships to biological, environmental, and demographic sources of variation. We applied this model to extensive observations of natural behavior in free-ranging rhesus macaques, and identified behavioral states that appeared to capture periods of social isolation, competition over food, conflicts among groups, and affiliative coexistence. Phenotypes, represented as the rate of being in each state for a particular animal, were strongly and broadly influenced by dominance rank, sex, and social group membership. We also identified two states for which variation in rates had a substantial genetic component. We discuss how this model can be extended to identify the contributions to social phenotypes of particular genetic pathways. PMID:28754001

  3. Measuring the surgical 'learning curve': methods, variables and competency.

    Science.gov (United States)

    Khan, Nuzhath; Abboudi, Hamid; Khan, Mohammed Shamim; Dasgupta, Prokar; Ahmed, Kamran

    2014-03-01

    To describe how learning curves are measured and what procedural variables are used to establish a 'learning curve' (LC). To assess whether LCs are a valuable measure of competency. A review of the surgical literature pertaining to LCs was conducted using the Medline and OVID databases. Variables should be fully defined and when possible, patient-specific variables should be used. Trainee's prior experience and level of supervision should be quantified; the case mix and complexity should ideally be constant. Logistic regression may be used to control for confounding variables. Ideally, a learning plateau should reach a predefined/expert-derived competency level, which should be fully defined. When the group splitting method is used, smaller cohorts should be used in order to narrow the range of the LC. Simulation technology and competence-based objective assessments may be used in training and assessment in LC studies. Measuring the surgical LC has potential benefits for patient safety and surgical education. However, standardisation in the methods and variables used to measure LCs is required. Confounding variables, such as participant's prior experience, case mix, difficulty of procedures and level of supervision, should be controlled. Competency and expert performance should be fully defined. © 2013 The Authors. BJU International © 2013 BJU International.

  4. Evaluating aggregate effects of rare and common variants in the 1000 Genomes Project exon sequencing data using latent variable structural equation modeling.

    Science.gov (United States)

    Nock, Nl; Zhang, Lx

    2011-11-29

    Methods that can evaluate aggregate effects of rare and common variants are limited. Therefore, we applied a two-stage approach to evaluate aggregate gene effects in the 1000 Genomes Project data, which contain 24,487 single-nucleotide polymorphisms (SNPs) in 697 unrelated individuals from 7 populations. In stage 1, we identified potentially interesting genes (PIGs) as those having at least one SNP meeting Bonferroni correction using univariate, multiple regression models. In stage 2, we evaluate aggregate PIG effects on trait, Q1, by modeling each gene as a latent construct, which is defined by multiple common and rare variants, using the multivariate statistical framework of structural equation modeling (SEM). In stage 1, we found that PIGs varied markedly between a randomly selected replicate (replicate 137) and 100 other replicates, with the exception of FLT1. In stage 1, collapsing rare variants decreased false positives but increased false negatives. In stage 2, we developed a good-fitting SEM model that included all nine genes simulated to affect Q1 (FLT1, KDR, ARNT, ELAV4, FLT4, HIF1A, HIF3A, VEGFA, VEGFC) and found that FLT1 had the largest effect on Q1 (βstd = 0.33 ± 0.05). Using replicate 137 estimates as population values, we found that the mean relative bias in the parameters (loadings, paths, residuals) and their standard errors across 100 replicates was on average, less than 5%. Our latent variable SEM approach provides a viable framework for modeling aggregate effects of rare and common variants in multiple genes, but more elegant methods are needed in stage 1 to minimize type I and type II error.

  5. Are Anxiety and Depression Just as Stable as Personality During Late Adolescence? Results From a Three-Year Longitudinal Latent Variable Study

    Science.gov (United States)

    Prenoveau, Jason M.; Craske, Michelle G.; Zinbarg, Richard E.; Mineka, Susan; Rose, Raphael D.; Griffith, James W.

    2012-01-01

    Although considerable evidence shows that affective symptoms and personality traits demonstrate moderate to high relative stabilities during adolescence and early adulthood, there has been little work done to examine differential stability among these constructs or to study the manner in which the stability of these constructs is expressed. The present study used a three-year longitudinal design in an adolescent/young adult sample to examine the stability of depression symptoms, social phobia symptoms, specific phobia symptoms, neuroticism, and extraversion. When considering one-, two-, and three-year durations, anxiety and personality stabilities were generally similar and typically greater than the stability of depression. Comparison of various representations of a latent variable trait-state-occasion (TSO) model revealed that whereas the full TSO model was the best representation for depression, a trait stability model was the most parsimonious of the best-fitting models for the anxiety and personality constructs. Over three years, the percentages of variance explained by the trait component for the anxiety and personality constructs (73– 84%) were significantly greater than that explained by the trait component for depression (46%). These findings indicate that symptoms of depression are more episodic in nature, whereas symptoms of anxiety are more similar to personality variables in their expression of stability. PMID:21604827

  6. Handwriting generates variable visual output to facilitate symbol learning.

    Science.gov (United States)

    Li, Julia X; James, Karin H

    2016-03-01

    Recent research has demonstrated that handwriting practice facilitates letter categorization in young children. The present experiments investigated why handwriting practice facilitates visual categorization by comparing 2 hypotheses: that handwriting exerts its facilitative effect because of the visual-motor production of forms, resulting in a direct link between motor and perceptual systems, or because handwriting produces variable visual instances of a named category in the environment that then changes neural systems. We addressed these issues by measuring performance of 5-year-old children on a categorization task involving novel, Greek symbols across 6 different types of learning conditions: 3 involving visual-motor practice (copying typed symbols independently, tracing typed symbols, tracing handwritten symbols) and 3 involving visual-auditory practice (seeing and saying typed symbols of a single typed font, of variable typed fonts, and of handwritten examples). We could therefore compare visual-motor production with visual perception both of variable and similar forms. Comparisons across the 6 conditions (N = 72) demonstrated that all conditions that involved studying highly variable instances of a symbol facilitated symbol categorization relative to conditions where similar instances of a symbol were learned, regardless of visual-motor production. Therefore, learning perceptually variable instances of a category enhanced performance, suggesting that handwriting facilitates symbol understanding by virtue of its environmental output: supporting the notion of developmental change though brain-body-environment interactions. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

  7. Handwriting generates variable visual input to facilitate symbol learning

    Science.gov (United States)

    Li, Julia X.; James, Karin H.

    2015-01-01

    Recent research has demonstrated that handwriting practice facilitates letter categorization in young children. The present experiments investigated why handwriting practice facilitates visual categorization by comparing two hypotheses: That handwriting exerts its facilitative effect because of the visual-motor production of forms, resulting in a direct link between motor and perceptual systems, or because handwriting produces variable visual instances of a named category in the environment that then changes neural systems. We addressed these issues by measuring performance of 5 year-old children on a categorization task involving novel, Greek symbols across 6 different types of learning conditions: three involving visual-motor practice (copying typed symbols independently, tracing typed symbols, tracing handwritten symbols) and three involving visual-auditory practice (seeing and saying typed symbols of a single typed font, of variable typed fonts, and of handwritten examples). We could therefore compare visual-motor production with visual perception both of variable and similar forms. Comparisons across the six conditions (N=72) demonstrated that all conditions that involved studying highly variable instances of a symbol facilitated symbol categorization relative to conditions where similar instances of a symbol were learned, regardless of visual-motor production. Therefore, learning perceptually variable instances of a category enhanced performance, suggesting that handwriting facilitates symbol understanding by virtue of its environmental output: supporting the notion of developmental change though brain-body-environment interactions. PMID:26726913

  8. A numeric comparison of variable selection algorithms for supervised learning

    International Nuclear Information System (INIS)

    Palombo, G.; Narsky, I.

    2009-01-01

    Datasets in modern High Energy Physics (HEP) experiments are often described by dozens or even hundreds of input variables. Reducing a full variable set to a subset that most completely represents information about data is therefore an important task in analysis of HEP data. We compare various variable selection algorithms for supervised learning using several datasets such as, for instance, imaging gamma-ray Cherenkov telescope (MAGIC) data found at the UCI repository. We use classifiers and variable selection methods implemented in the statistical package StatPatternRecognition (SPR), a free open-source C++ package developed in the HEP community ( (http://sourceforge.net/projects/statpatrec/)). For each dataset, we select a powerful classifier and estimate its learning accuracy on variable subsets obtained by various selection algorithms. When possible, we also estimate the CPU time needed for the variable subset selection. The results of this analysis are compared with those published previously for these datasets using other statistical packages such as R and Weka. We show that the most accurate, yet slowest, method is a wrapper algorithm known as generalized sequential forward selection ('Add N Remove R') implemented in SPR.

  9. A hybrid choice model with nonlinear utility functions and bounded distributions for latent variables : application to purchase intention decisions of electric cars

    NARCIS (Netherlands)

    Kim, J.; Rasouli, S.; Timmermans, H.J.P.

    2014-01-01

    The hybrid choice model (HCM) provides a powerful framework to account for heterogeneity across decision-makers in terms of different underlying latent attitudes. Typically, effects of the latent attitudes have been represented assuming linear utility functions. In contributing to the further

  10. A hybrid choice model with a nonlinear utility function and bounded distribution for latent variables : application to purchase intention decisions of electric cars

    NARCIS (Netherlands)

    Kim, J.; Rasouli, S.; Timmermans, H.J.P.

    2016-01-01

    The hybrid choice model (HCM) provides a powerful framework to account for heterogeneity across decision-makers in terms of different underlying latent attitudes. Typically, effects of the latent attitudes have been represented assuming linear utility functions. In contributing to the further

  11. Evaluation of Several Learning Environment Variables at Secondary Institutions

    Directory of Open Access Journals (Sweden)

    Murat Tuncer

    2012-06-01

    Full Text Available Health is an issue whose importance needs to be focused in the learning environment and learning activities in education. The level of teaching and learning is known to effect health of learners. Learning environments are teeming with many variables. Ambient temperature, noise, humidity and illumination are a few of them. If these variables are outside the specified limits for ambient levels this may need to a loss of learning and adversely affect the health of learners. This research was conducted to evaluate this aspect at institutions of secondary education in Turkey. The literature discusses the findings of various measurements that were taken with a variety of devices such as the Environment Meter-DT 8820, GMI PN 66094 and AARONIA AG SPECTRAN at randomly selected schools and classes. The temperature and carbon dioxide values in the classrooms were outside the defined limits according to research findings. In addition, many classrooms had noise levels above limits which could impair human health and some color selections in classrooms were made incorrectly. When the results of the findings are analyzed, we find the learner’s metabolism is negatively affected; attention loss and serious health problems may be experienced in the long run. It is highly recommended that laws and regulations regarding school construction and settlement be enacted and that precise limits be defined in those laws. In addition, it is thought establishing electromechanical systems to measure indoor and outdoor air quality in classrooms would bring benefits

  12. Measurement and structural relations of an authoritative school climate model: A multi-level latent variable investigation.

    Science.gov (United States)

    Konold, Timothy R; Cornell, Dewey

    2015-12-01

    This study tested a conceptual model of school climate in which two key elements of an authoritative school, structure and support variables, are associated with student engagement in school and lower levels of peer aggression. Multilevel multivariate structural modeling was conducted in a statewide sample of 48,027 students in 323 public high schools who completed the Authoritative School Climate Survey. As hypothesized, two measures of structure (Disciplinary Structure and Academic Expectations) and two measures of support (Respect for Students and Willingness to Seek Help) were associated with higher student engagement (Affective Engagement and Cognitive Engagement) and lower peer aggression (Prevalence of Teasing and Bullying) on both student and school levels of analysis, controlling for the effects of school demographics (school size, percentage of minority students, and percentage of low income students). These results support the extension of authoritative school climate model to high school and guide further research on the conditions for a positive school climate. Copyright © 2015 Society for the Study of School Psychology. Published by Elsevier Ltd. All rights reserved.

  13. The Effects of Educational Diversity in a National Sample of Law Students: Fitting Multilevel Latent Variable Models in Data With Categorical Indicators.

    Science.gov (United States)

    Gottfredson, Nisha C; Panter, A T; Daye, Charles E; Allen, Walter F; Wightman, Linda F

    2009-01-01

    Controversy surrounding the use of race-conscious admissions can be partially resolved with improved empirical knowledge of the effects of racial diversity in educational settings. We use a national sample of law students nested in 64 law schools to test the complex and largely untested theory regarding the effects of educational diversity on student outcomes. Social scientists who study these outcomes frequently encounter both latent variables and nested data within a single analysis. Yet, until recently, an appropriate modeling technique has been computationally infeasible, and consequently few applied researchers have estimated appropriate models to test their theories, sometimes limiting the scope of their research question. Our results, based on disaggregated multilevel structural equation models, show that racial diversity is related to a reduction in prejudiced attitudes and increased perceived exposure to diverse ideas and that these effects are mediated by more frequent interpersonal contact with diverse peers. These findings provide support for the idea that administrative manipulation of educational diversity may lead to improved student outcomes. Admitting a racially/ethnically diverse student body provides an educational experience that encourages increased exposure to diverse ideas and belief systems.

  14. cn.FARMS: a latent variable model to detect copy number variations in microarray data with a low false discovery rate.

    Science.gov (United States)

    Clevert, Djork-Arné; Mitterecker, Andreas; Mayr, Andreas; Klambauer, Günter; Tuefferd, Marianne; De Bondt, An; Talloen, Willem; Göhlmann, Hinrich; Hochreiter, Sepp

    2011-07-01

    Cost-effective oligonucleotide genotyping arrays like the Affymetrix SNP 6.0 are still the predominant technique to measure DNA copy number variations (CNVs). However, CNV detection methods for microarrays overestimate both the number and the size of CNV regions and, consequently, suffer from a high false discovery rate (FDR). A high FDR means that many CNVs are wrongly detected and therefore not associated with a disease in a clinical study, though correction for multiple testing takes them into account and thereby decreases the study's discovery power. For controlling the FDR, we propose a probabilistic latent variable model, 'cn.FARMS', which is optimized by a Bayesian maximum a posteriori approach. cn.FARMS controls the FDR through the information gain of the posterior over the prior. The prior represents the null hypothesis of copy number 2 for all samples from which the posterior can only deviate by strong and consistent signals in the data. On HapMap data, cn.FARMS clearly outperformed the two most prevalent methods with respect to sensitivity and FDR. The software cn.FARMS is publicly available as a R package at http://www.bioinf.jku.at/software/cnfarms/cnfarms.html.

  15. Using a latent variable model with non-constant factor loadings to examine PM2.5 constituents related to secondary inorganic aerosols.

    Science.gov (United States)

    Zhang, Zhenzhen; O'Neill, Marie S; Sánchez, Brisa N

    2016-04-01

    Factor analysis is a commonly used method of modelling correlated multivariate exposure data. Typically, the measurement model is assumed to have constant factor loadings. However, from our preliminary analyses of the Environmental Protection Agency's (EPA's) PM 2.5 fine speciation data, we have observed that the factor loadings for four constituents change considerably in stratified analyses. Since invariance of factor loadings is a prerequisite for valid comparison of the underlying latent variables, we propose a factor model that includes non-constant factor loadings that change over time and space using P-spline penalized with the generalized cross-validation (GCV) criterion. The model is implemented using the Expectation-Maximization (EM) algorithm and we select the multiple spline smoothing parameters by minimizing the GCV criterion with Newton's method during each iteration of the EM algorithm. The algorithm is applied to a one-factor model that includes four constituents. Through bootstrap confidence bands, we find that the factor loading for total nitrate changes across seasons and geographic regions.

  16. Cognitive psychology meets psychometric theory: on the relation between process models for decision making and latent variable models for individual differences.

    Science.gov (United States)

    van der Maas, Han L J; Molenaar, Dylan; Maris, Gunter; Kievit, Rogier A; Borsboom, Denny

    2011-04-01

    This article analyzes latent variable models from a cognitive psychology perspective. We start by discussing work by Tuerlinckx and De Boeck (2005), who proved that a diffusion model for 2-choice response processes entails a 2-parameter logistic item response theory (IRT) model for individual differences in the response data. Following this line of reasoning, we discuss the appropriateness of IRT for measuring abilities and bipolar traits, such as pro versus contra attitudes. Surprisingly, if a diffusion model underlies the response processes, IRT models are appropriate for bipolar traits but not for ability tests. A reconsideration of the concept of ability that is appropriate for such situations leads to a new item response model for accuracy and speed based on the idea that ability has a natural zero point. The model implies fundamentally new ways to think about guessing, response speed, and person fit in IRT. We discuss the relation between this model and existing models as well as implications for psychology and psychometrics. 2011 APA, all rights reserved

  17. Understanding the variable effect of instructional innovations on student learning

    Science.gov (United States)

    Iverson, Heidi L.

    2012-02-01

    As a result of dissatisfaction with the traditional lecture-based model of education a large number of reform-oriented instructional innovations have been developed, enacted, and studied in undergraduate physics courses. While previous work has shown that the impact of instructional innovations on student learning has been overwhelmingly positive, it has also been highly variable. The purpose of this analysis is to investigate this variability. For this analysis, 79 published studies on undergraduate physics instructional innovations were analyzed with respect to the types of innovations used and the methodological characteristics of the studies themselves. The findings of this analysis have indicated that nearly half of the variability in effect size can be accounted for by study design characteristics rather than by the characteristics of the innovations used. However, a subsequent analysis illustrated that one specific innovation, Workshop/Studio Physics, appears to be particularly effective within the observed sample of studies.

  18. Latent Memory of Unattended Stimuli Reactivated by Practice: An fMRI Study on the Role of Consciousness and Attention in Learning

    Science.gov (United States)

    Meuwese, Julia D. I.; Scholte, H. Steven; Lamme, Victor A. F.

    2014-01-01

    Although we can only report about what is in the focus of our attention, much more than that is actually processed. And even when attended, stimuli may not always be reportable, for instance when they are masked. A stimulus can thus be unreportable for different reasons: the absence of attention or the absence of a conscious percept. But to what extent does the brain learn from exposure to these unreportable stimuli? In this fMRI experiment subjects were exposed to textured figure-ground stimuli, of which reportability was manipulated either by masking (which only interferes with consciousness) or with an inattention paradigm (which only interferes with attention). One day later learning was assessed neurally and behaviorally. Positive neural learning effects were found for stimuli presented in the inattention paradigm; for attended yet masked stimuli negative adaptation effects were found. Interestingly, these inattentional learning effects only became apparent in a second session after a behavioral detection task had been administered during which performance feedback was provided. This suggests that the memory trace that is formed during inattention is latent until reactivated by behavioral practice. However, no behavioral learning effects were found, therefore we cannot conclude that perceptual learning has taken place for these unattended stimuli. PMID:24603676

  19. Learning effect and test-retest variability of pulsar perimetry.

    Science.gov (United States)

    Salvetat, Maria Letizia; Zeppieri, Marco; Parisi, Lucia; Johnson, Chris A; Sampaolesi, Roberto; Brusini, Paolo

    2013-03-01

    To assess Pulsar Perimetry learning effect and test-retest variability (TRV) in normal (NORM), ocular hypertension (OHT), glaucomatous optic neuropathy (GON), and primary open-angle glaucoma (POAG) eyes. This multicenter prospective study included 43 NORM, 38 OHT, 33 GON, and 36 POAG patients. All patients underwent standard automated perimetry and Pulsar Contrast Perimetry using white stimuli modulated in phase and counterphase at 30 Hz (CP-T30W test). The learning effect and TRV for Pulsar Perimetry were assessed for 3 consecutive visual fields (VFs). The learning effect were evaluated by comparing results from the first session with the other 2. TRV was assessed by calculating the mean of the differences (in absolute value) between retests for each combination of single tests. TRV was calculated for Mean Sensitivity, Mean Defect, and single Mean Sensitivity for each 66 test locations. Influence of age, VF eccentricity, and loss severity on TRV were assessed using linear regression analysis and analysis of variance. The learning effect was not significant in any group (analysis of variance, P>0.05). TRV for Mean Sensitivity and Mean Defect was significantly lower in NORM and OHT (0.6 ± 0.5 spatial resolution contrast units) than in GON and POAG (0.9 ± 0.5 and 1.0 ± 0.8 spatial resolution contrast units, respectively) (Kruskal-Wallis test, P=0.04); however, the differences in NORM among age groups was not significant (Kruskal-Wallis test, P>0.05). Slight significant differences were found for the single Mean Sensitivity TRV among single locations (Duncan test, PPulsar Perimetry CP-T30W test did not show significant learning effect in patients with standard automated perimetry experience. TRV for global indices was generally low, and was not related to patient age; it was only slightly affected by VF defect eccentricity, and significantly influenced by VF loss severity.

  20. Clustering-based Feature Learning on Variable Stars

    Science.gov (United States)

    Mackenzie, Cristóbal; Pichara, Karim; Protopapas, Pavlos

    2016-04-01

    The success of automatic classification of variable stars depends strongly on the lightcurve representation. Usually, lightcurves are represented as a vector of many descriptors designed by astronomers called features. These descriptors are expensive in terms of computing, require substantial research effort to develop, and do not guarantee a good classification. Today, lightcurve representation is not entirely automatic; algorithms must be designed and manually tuned up for every survey. The amounts of data that will be generated in the future mean astronomers must develop scalable and automated analysis pipelines. In this work we present a feature learning algorithm designed for variable objects. Our method works by extracting a large number of lightcurve subsequences from a given set, which are then clustered to find common local patterns in the time series. Representatives of these common patterns are then used to transform lightcurves of a labeled set into a new representation that can be used to train a classifier. The proposed algorithm learns the features from both labeled and unlabeled lightcurves, overcoming the bias using only labeled data. We test our method on data sets from the Massive Compact Halo Object survey and the Optical Gravitational Lensing Experiment; the results show that our classification performance is as good as and in some cases better than the performance achieved using traditional statistical features, while the computational cost is significantly lower. With these promising results, we believe that our method constitutes a significant step toward the automation of the lightcurve classification pipeline.

  1. CLUSTERING-BASED FEATURE LEARNING ON VARIABLE STARS

    International Nuclear Information System (INIS)

    Mackenzie, Cristóbal; Pichara, Karim; Protopapas, Pavlos

    2016-01-01

    The success of automatic classification of variable stars depends strongly on the lightcurve representation. Usually, lightcurves are represented as a vector of many descriptors designed by astronomers called features. These descriptors are expensive in terms of computing, require substantial research effort to develop, and do not guarantee a good classification. Today, lightcurve representation is not entirely automatic; algorithms must be designed and manually tuned up for every survey. The amounts of data that will be generated in the future mean astronomers must develop scalable and automated analysis pipelines. In this work we present a feature learning algorithm designed for variable objects. Our method works by extracting a large number of lightcurve subsequences from a given set, which are then clustered to find common local patterns in the time series. Representatives of these common patterns are then used to transform lightcurves of a labeled set into a new representation that can be used to train a classifier. The proposed algorithm learns the features from both labeled and unlabeled lightcurves, overcoming the bias using only labeled data. We test our method on data sets from the Massive Compact Halo Object survey and the Optical Gravitational Lensing Experiment; the results show that our classification performance is as good as and in some cases better than the performance achieved using traditional statistical features, while the computational cost is significantly lower. With these promising results, we believe that our method constitutes a significant step toward the automation of the lightcurve classification pipeline

  2. CLUSTERING-BASED FEATURE LEARNING ON VARIABLE STARS

    Energy Technology Data Exchange (ETDEWEB)

    Mackenzie, Cristóbal; Pichara, Karim [Computer Science Department, Pontificia Universidad Católica de Chile, Santiago (Chile); Protopapas, Pavlos [Institute for Applied Computational Science, Harvard University, Cambridge, MA (United States)

    2016-04-01

    The success of automatic classification of variable stars depends strongly on the lightcurve representation. Usually, lightcurves are represented as a vector of many descriptors designed by astronomers called features. These descriptors are expensive in terms of computing, require substantial research effort to develop, and do not guarantee a good classification. Today, lightcurve representation is not entirely automatic; algorithms must be designed and manually tuned up for every survey. The amounts of data that will be generated in the future mean astronomers must develop scalable and automated analysis pipelines. In this work we present a feature learning algorithm designed for variable objects. Our method works by extracting a large number of lightcurve subsequences from a given set, which are then clustered to find common local patterns in the time series. Representatives of these common patterns are then used to transform lightcurves of a labeled set into a new representation that can be used to train a classifier. The proposed algorithm learns the features from both labeled and unlabeled lightcurves, overcoming the bias using only labeled data. We test our method on data sets from the Massive Compact Halo Object survey and the Optical Gravitational Lensing Experiment; the results show that our classification performance is as good as and in some cases better than the performance achieved using traditional statistical features, while the computational cost is significantly lower. With these promising results, we believe that our method constitutes a significant step toward the automation of the lightcurve classification pipeline.

  3. Fitting a Mixture Rasch Model to English as a Foreign Language Listening Tests: The Role of Cognitive and Background Variables in Explaining Latent Differential Item Functioning

    Science.gov (United States)

    Aryadoust, Vahid

    2015-01-01

    The present study uses a mixture Rasch model to examine latent differential item functioning in English as a foreign language listening tests. Participants (n = 250) took a listening and lexico-grammatical test and completed the metacognitive awareness listening questionnaire comprising problem solving (PS), planning and evaluation (PE), mental…

  4. High variability impairs motor learning regardless of whether it affects task performance.

    Science.gov (United States)

    Cardis, Marco; Casadio, Maura; Ranganathan, Rajiv

    2018-01-01

    Motor variability plays an important role in motor learning, although the exact mechanisms of how variability affects learning are not well understood. Recent evidence suggests that motor variability may have different effects on learning in redundant tasks, depending on whether it is present in the task space (where it affects task performance) or in the null space (where it has no effect on task performance). We examined the effect of directly introducing null and task space variability using a manipulandum during the learning of a motor task. Participants learned a bimanual shuffleboard task for 2 days, where their goal was to slide a virtual puck as close as possible toward a target. Critically, the distance traveled by the puck was determined by the sum of the left- and right-hand velocities, which meant that there was redundancy in the task. Participants were divided into five groups, based on both the dimension in which the variability was introduced and the amount of variability that was introduced during training. Results showed that although all groups were able to reduce error with practice, learning was affected more by the amount of variability introduced rather than the dimension in which variability was introduced. Specifically, groups with higher movement variability during practice showed larger errors at the end of practice compared with groups that had low variability during learning. These results suggest that although introducing variability can increase exploration of new solutions, this may adversely affect the ability to retain the learned solution. NEW & NOTEWORTHY We examined the role of introducing variability during motor learning in a redundant task. The presence of redundancy allows variability to be introduced in different dimensions: the task space (where it affects task performance) or the null space (where it does not affect task performance). We found that introducing variability affected learning adversely, but the amount of

  5. Latent Transition Analysis with a Mixture Item Response Theory Measurement Model

    Science.gov (United States)

    Cho, Sun-Joo; Cohen, Allan S.; Kim, Seock-Ho; Bottge, Brian

    2010-01-01

    A latent transition analysis (LTA) model was described with a mixture Rasch model (MRM) as the measurement model. Unlike the LTA, which was developed with a latent class measurement model, the LTA-MRM permits within-class variability on the latent variable, making it more useful for measuring treatment effects within latent classes. A simulation…

  6. Learning Path Recommendation Based on Modified Variable Length Genetic Algorithm

    Science.gov (United States)

    Dwivedi, Pragya; Kant, Vibhor; Bharadwaj, Kamal K.

    2018-01-01

    With the rapid advancement of information and communication technologies, e-learning has gained a considerable attention in recent years. Many researchers have attempted to develop various e-learning systems with personalized learning mechanisms for assisting learners so that they can learn more efficiently. In this context, curriculum sequencing…

  7. Some Cognitive Variables in Meaningful Learning of the Physics Concepts of Work and Energy: A Study of Ausubelian Learning Model.

    Science.gov (United States)

    Talisayon, Vivien Millan

    This study is an empirical investigation of Ausubel's paradigm of meaningful learning, applied specifically to the learning of high school physics students. In the first phase of the study path analysis and multiple regression techniques were used to describe the Ausubelian learning variables: available relevant ideas in learner's cognitive…

  8. A hypothesis on improving foreign accents by optimizing variability in vocal learning brain circuits

    OpenAIRE

    Simmonds, Anna J.

    2015-01-01

    Rapid vocal motor learning is observed when acquiring a language in early childhood, or learning to speak another language later in life. Accurate pronunciation is one of the hardest things for late learners to master and they are almost always left with a non-native accent. Here, I propose a novel hypothesis that this accent could be improved by optimizing variability in vocal learning brain circuits during learning. Much of the neurobiology of human vocal motor learning has been inferred fr...

  9. Individual and Contextual Variables in Municipal Officers' Workplace Learning

    Science.gov (United States)

    Moraes, Valéria Vieira; Borges-Andrade, Jairo Eduardo

    2015-01-01

    Purpose: The purpose of this paper is to investigate workplace learning among municipal officers in the high-learning-demanding organizational context of their work practice in the first year of mandate. Design/methodology/approach: A before-and-after quasi-experimental design was used to assess the effect of time of work practice on learning work…

  10. Psychological and Organizational Variables Associated with Workplace Learning in Small and Medium Manufacturing Businesses in Korea

    Science.gov (United States)

    Moon, Se-Yeon; Na, Seung-Il

    2009-01-01

    The purpose of this study was to determine the relationship between workplace learning and psychological variables, such as learning competency, motivation, curiosity, self-esteem and locus of control, and organizational variables, such as centralization of power, formality, merit system and communication. The studied population consisted entirely…

  11. Effects of visual feedback-induced variability on motor learning of handrim wheelchair propulsion.

    Science.gov (United States)

    Leving, Marika T; Vegter, Riemer J K; Hartog, Johanneke; Lamoth, Claudine J C; de Groot, Sonja; van der Woude, Lucas H V

    2015-01-01

    It has been suggested that a higher intra-individual variability benefits the motor learning of wheelchair propulsion. The present study evaluated whether feedback-induced variability on wheelchair propulsion technique variables would also enhance the motor learning process. Learning was operationalized as an improvement in mechanical efficiency and propulsion technique, which are thought to be closely related during the learning process. 17 Participants received visual feedback-based practice (feedback group) and 15 participants received regular practice (natural learning group). Both groups received equal practice dose of 80 min, over 3 weeks, at 0.24 W/kg at a treadmill speed of 1.11 m/s. To compare both groups the pre- and post-test were performed without feedback. The feedback group received real-time visual feedback on seven propulsion variables with instruction to manipulate the presented variable to achieve the highest possible variability (1st 4-min block) and optimize it in the prescribed direction (2nd 4-min block). To increase motor exploration the participants were unaware of the exact variable they received feedback on. Energy consumption and the propulsion technique variables with their respective coefficient of variation were calculated to evaluate the amount of intra-individual variability. The feedback group, which practiced with higher intra-individual variability, improved the propulsion technique between pre- and post-test to the same extent as the natural learning group. Mechanical efficiency improved between pre- and post-test in the natural learning group but remained unchanged in the feedback group. These results suggest that feedback-induced variability inhibited the improvement in mechanical efficiency. Moreover, since both groups improved propulsion technique but only the natural learning group improved mechanical efficiency, it can be concluded that the improvement in mechanical efficiency and propulsion technique do not always appear

  12. Data on the interexaminer variation of minutia markup on latent fingerprints.

    Science.gov (United States)

    Ulery, Bradford T; Hicklin, R Austin; Roberts, Maria Antonia; Buscaglia, JoAnn

    2016-09-01

    The data in this article supports the research paper entitled "Interexaminer variation of minutia markup on latent fingerprints" [1]. The data in this article describes the variability in minutia markup during both analysis of the latents and comparison between latents and exemplars. The data was collected in the "White Box Latent Print Examiner Study," in which each of 170 volunteer latent print examiners provided detailed markup documenting their examinations of latent-exemplar pairs of prints randomly assigned from a pool of 320 pairs. Each examiner examined 22 latent-exemplar pairs; an average of 12 examiners marked each latent.

  13. Effects of Example Variability and Prior Knowledge in How Students Learn to Solve Equations

    Science.gov (United States)

    Guo, Jian-Peng; Yang, Ling-Yan; Ding, Yi

    2014-01-01

    Researchers have consistently demonstrated that multiple examples are better than one example in facilitating learning because the comparison evoked by multiple examples supports learning and transfer. However, research outcomes are unclear regarding the effects of example variability and prior knowledge on learning from comparing multiple…

  14. Students’ attitudes towards learning communication skills : correlating attitudes, demographic and metacognitive variables

    OpenAIRE

    Lumma-Sellenthin, Antje

    2012-01-01

    Objectives: This study aimed at exploring the relationship of students' attitudes towards learning communication skills to demographic variables, metacognitive skills, and to the appreciation of patient-oriented care. Methods: The cross-sectional survey study involved first- and third-term students from two traditional and two problem-based curricula (N= 351). Demographic variables, attitudes towards communication skills learning, patient orientation, and awareness of learning strategies were...

  15. Exact solution for a two-phase Stefan problem with variable latent heat and a convective boundary condition at the fixed face

    Science.gov (United States)

    Bollati, Julieta; Tarzia, Domingo A.

    2018-04-01

    Recently, in Tarzia (Thermal Sci 21A:1-11, 2017) for the classical two-phase Lamé-Clapeyron-Stefan problem an equivalence between the temperature and convective boundary conditions at the fixed face under a certain restriction was obtained. Motivated by this article we study the two-phase Stefan problem for a semi-infinite material with a latent heat defined as a power function of the position and a convective boundary condition at the fixed face. An exact solution is constructed using Kummer functions in case that an inequality for the convective transfer coefficient is satisfied generalizing recent works for the corresponding one-phase free boundary problem. We also consider the limit to our problem when that coefficient goes to infinity obtaining a new free boundary problem, which has been recently studied in Zhou et al. (J Eng Math 2017. https://doi.org/10.1007/s10665-017-9921-y).

  16. Discriminative latent models for recognizing contextual group activities.

    Science.gov (United States)

    Lan, Tian; Wang, Yang; Yang, Weilong; Robinovitch, Stephen N; Mori, Greg

    2012-08-01

    In this paper, we go beyond recognizing the actions of individuals and focus on group activities. This is motivated from the observation that human actions are rarely performed in isolation; the contextual information of what other people in the scene are doing provides a useful cue for understanding high-level activities. We propose a novel framework for recognizing group activities which jointly captures the group activity, the individual person actions, and the interactions among them. Two types of contextual information, group-person interaction and person-person interaction, are explored in a latent variable framework. In particular, we propose three different approaches to model the person-person interaction. One approach is to explore the structures of person-person interaction. Differently from most of the previous latent structured models, which assume a predefined structure for the hidden layer, e.g., a tree structure, we treat the structure of the hidden layer as a latent variable and implicitly infer it during learning and inference. The second approach explores person-person interaction in the feature level. We introduce a new feature representation called the action context (AC) descriptor. The AC descriptor encodes information about not only the action of an individual person in the video, but also the behavior of other people nearby. The third approach combines the above two. Our experimental results demonstrate the benefit of using contextual information for disambiguating group activities.

  17. A hypothesis on improving foreign accents by optimizing variability in vocal learning brain circuits.

    Science.gov (United States)

    Simmonds, Anna J

    2015-01-01

    Rapid vocal motor learning is observed when acquiring a language in early childhood, or learning to speak another language later in life. Accurate pronunciation is one of the hardest things for late learners to master and they are almost always left with a non-native accent. Here, I propose a novel hypothesis that this accent could be improved by optimizing variability in vocal learning brain circuits during learning. Much of the neurobiology of human vocal motor learning has been inferred from studies on songbirds. Jarvis (2004) proposed the hypothesis that as in songbirds there are two pathways in humans: one for learning speech (the striatal vocal learning pathway), and one for production of previously learnt speech (the motor pathway). Learning new motor sequences necessary for accurate non-native pronunciation is challenging and I argue that in late learners of a foreign language the vocal learning pathway becomes inactive prematurely. The motor pathway is engaged once again and learners maintain their original native motor patterns for producing speech, resulting in speaking with a foreign accent. Further, I argue that variability in neural activity within vocal motor circuitry generates vocal variability that supports accurate non-native pronunciation. Recent theoretical and experimental work on motor learning suggests that variability in the motor movement is necessary for the development of expertise. I propose that there is little trial-by-trial variability when using the motor pathway. When using the vocal learning pathway variability gradually increases, reflecting an exploratory phase in which learners try out different ways of pronouncing words, before decreasing and stabilizing once the "best" performance has been identified. The hypothesis proposed here could be tested using behavioral interventions that optimize variability and engage the vocal learning pathway for longer, with the prediction that this would allow learners to develop new motor

  18. Latent lifestyle preferences and household location decisions

    Science.gov (United States)

    Walker, Joan L.; Li, Jieping

    2007-04-01

    Lifestyle, indicating preferences towards a particular way of living, is a key driver of the decision of where to live. We employ latent class choice models to represent this behavior, where the latent classes are the lifestyles and the choice model is the choice of residential location. Thus, we simultaneously estimate lifestyle groups and how lifestyle impacts location decisions. Empirical results indicate three latent lifestyle segments: suburban dwellers, urban dwellers, and transit-riders. The suggested lifestyle segments have intriguing policy implications. Lifecycle characteristics are used to predict lifestyle preferences, although there remain significant aspects that cannot be explained by observable variables.

  19. Heteroscedastic Latent Trait Models for Dichotomous Data.

    Science.gov (United States)

    Molenaar, Dylan

    2015-09-01

    Effort has been devoted to account for heteroscedasticity with respect to observed or latent moderator variables in item or test scores. For instance, in the multi-group generalized linear latent trait model, it could be tested whether the observed (polychoric) covariance matrix differs across the levels of an observed moderator variable. In the case that heteroscedasticity arises across the latent trait itself, existing models commonly distinguish between heteroscedastic residuals and a skewed trait distribution. These models have valuable applications in intelligence, personality and psychopathology research. However, existing approaches are only limited to continuous and polytomous data, while dichotomous data are common in intelligence and psychopathology research. Therefore, in present paper, a heteroscedastic latent trait model is presented for dichotomous data. The model is studied in a simulation study, and applied to data pertaining alcohol use and cognitive ability.

  20. The Perceived Success of Tutoring Students with Learning Disabilities: Relations to Tutee and Tutoring Variables

    Science.gov (United States)

    Michael, Rinat

    2016-01-01

    The current study examined the contribution of two types of variables to the perceived success of a tutoring project for college students with learning disabilities (LD): tutoring-related variables (the degree of engagement in different tutoring activities and difficulties encountered during tutoring), and tutee-related variables (learning…

  1. Variable learning performance: the levels of behaviour organization.

    Science.gov (United States)

    Csányi, V; Altbäcker, V

    1990-01-01

    Our experiments were focused on some special aspects of learning in the paradise fish. Passive avoidance conditioning method was used with different success depending on the complexity of the learning tasks. In the case of simple behavioural elements various "constrains" on avoidance learning were found. In a small, covered place the fish were ready to perform freezing reaction and mild punishment increased the frequency and duration of the freezing bouts very substantially. However, it was very difficult to enhance the frequency of freezing by punishment in a tank with transparent walls, where the main response to punishment was escape. The most easily learned tasks were the complex ones which had several different solutions. The fish learned to avoid either side of an aquarium very easily because they could use various behavioural elements to solve the problem. These findings could be interpreted within the framework of different organizational levels of behaviour.

  2. Dissociable effects of practice variability on learning motor and timing skills.

    Science.gov (United States)

    Caramiaux, Baptiste; Bevilacqua, Frédéric; Wanderley, Marcelo M; Palmer, Caroline

    2018-01-01

    Motor skill acquisition inherently depends on the way one practices the motor task. The amount of motor task variability during practice has been shown to foster transfer of the learned skill to other similar motor tasks. In addition, variability in a learning schedule, in which a task and its variations are interweaved during practice, has been shown to help the transfer of learning in motor skill acquisition. However, there is little evidence on how motor task variations and variability schedules during practice act on the acquisition of complex motor skills such as music performance, in which a performer learns both the right movements (motor skill) and the right time to perform them (timing skill). This study investigated the impact of rate (tempo) variability and the schedule of tempo change during practice on timing and motor skill acquisition. Complete novices, with no musical training, practiced a simple musical sequence on a piano keyboard at different rates. Each novice was assigned to one of four learning conditions designed to manipulate the amount of tempo variability across trials (large or small tempo set) and the schedule of tempo change (randomized or non-randomized order) during practice. At test, the novices performed the same musical sequence at a familiar tempo and at novel tempi (testing tempo transfer), as well as two novel (but related) sequences at a familiar tempo (testing spatial transfer). We found that practice conditions had little effect on learning and transfer performance of timing skill. Interestingly, practice conditions influenced motor skill learning (reduction of movement variability): lower temporal variability during practice facilitated transfer to new tempi and new sequences; non-randomized learning schedule improved transfer to new tempi and new sequences. Tempo (rate) and the sequence difficulty (spatial manipulation) affected performance variability in both timing and movement. These findings suggest that there is a

  3. Cognitive and motivational variables that shape academic learning: A preliminary study

    Directory of Open Access Journals (Sweden)

    Palos, Ramona

    2013-07-01

    Full Text Available The aim of this pilot study was to capture the relationship between cognitive and motivational variables and the student learning. 102 students from the Psychology specialization, license cycle, took part in the study. The following tools were used: the Rational-Experiential Inventory (Paccini & Epstein, 1999; the Intellectual development level questionnaire (Paloş, 2009, the Motivated Strategies for Learning Questionnaire (Rao & Sachs, 1999. The results indicated that the motivational and learning strategies used by students are influenced by their intellectual development level and their information processing style. Knowing the cognitive and motivational variables play an important role in devising the educational experiences and in making learning more efficient.

  4. Exemplar variability facilitates rapid learning of an otherwise unlearnable grammar by individuals with language-based learning disability.

    Science.gov (United States)

    von Koss Torkildsen, Janne; Dailey, Natalie S; Aguilar, Jessica M; Gómez, Rebecca; Plante, Elena

    2013-04-01

    Even without explicit instruction, learners are able to extract information about the form of a language simply by attending to input that reflects the underlying grammar. In this study, the authors explored the role of variability in this learning by asking whether varying the number of unique exemplars heard by the learner affects learning of an artificial syntactic form. Learners with normal language (n = 16) and language-based learning disability (LLD; n = 16) were exposed to strings of nonwords that represented an underlying grammar. Half of the learners heard 3 exemplars 16 times each (low variability group), and the other half of the learners heard 24 exemplars twice each (high variability group). Learners were then tested for recognition of items heard and generalization of the grammar with new nonword strings. Only those learners with LLD who were in the high variability group were able to demonstrate generalization of the underlying grammar. For learners with normal language, both those in the high and the low variability groups showed generalization of the grammar, but relative effect sizes suggested a larger learning effect in the high variability group. The results demonstrate that the structure of the learning context can determine the ability to generalize from specific training items to novel cases.

  5. A multiplicative reinforcement learning model capturing learning dynamics and interindividual variability in mice

    OpenAIRE

    Bathellier, Brice; Tee, Sui Poh; Hrovat, Christina; Rumpel, Simon

    2013-01-01

    Learning speed can strongly differ across individuals. This is seen in humans and animals. Here, we measured learning speed in mice performing a discrimination task and developed a theoretical model based on the reinforcement learning framework to account for differences between individual mice. We found that, when using a multiplicative learning rule, the starting connectivity values of the model strongly determine the shape of learning curves. This is in contrast to current learning models ...

  6. Study preferences for exemplar variability in self-regulated category learning.

    Science.gov (United States)

    Wahlheim, Christopher N; DeSoto, K Andrew

    2017-02-01

    Increasing exemplar variability during category learning can enhance classification of novel exemplars from studied categories. Four experiments examined whether participants preferred variability when making study choices with the goal of later classifying novel exemplars. In Experiments 1-3, participants were familiarised with exemplars of birds from multiple categories prior to making category-level assessments of learning and subsequent choices about whether to receive more variability or repetitions of exemplars during study. After study, participants classified novel exemplars from studied categories. The majority of participants showed a consistent preference for variability in their study, but choices were not related to category-level assessments of learning. Experiment 4 provided evidence that study preferences were based primarily on theoretical beliefs in that most participants indicated a preference for variability on questionnaires that did not include prior experience with exemplars. Potential directions for theoretical development and applications to education are discussed.

  7. Examining Self Regulated Learning in Relation to Certain Selected Variables

    Science.gov (United States)

    Johnson, N.

    2012-01-01

    Self-regulation is the controlling of a process or activity by the students who are involved in Problem solving in Physics rather than by an external agency (Johnson, 2011). Selfregulated learning consists of three main components: cognition, metacognition, and motivation. Cognition includes skills necessary to encode, memorise, and recall…

  8. Mode transition and change in variable use in perceptual learning

    NARCIS (Netherlands)

    Hajnal, A; Grocki, M; Jacobs, DM; Zaal, FTJM; Michaels, CF

    2006-01-01

    Runeson, Justin, and Olsson (2000) proposed (a) that perceptual learning entails a transition from an inferential to a direct-perceptual mode of apprehension, and (b) that relative confidence-the difference between estimated and actual performance-indicates whether apprehension is inferential or

  9. Mode transition and change in variable use in perceptual learning

    NARCIS (Netherlands)

    Hajnal, A.; Grocki, M.; Jacobs, D.M.; Zaal, F.T.J.M.; Michaels, C.F.

    2006-01-01

    Runeson, Juslin, and Olsson (2000) proposed (a) that perceptual learning entails a transition from an inferential to a direct-perceptual mode of apprehension, and (b) that relative confidence - the difference between estimated and actual performance - indicates whether apprehension is inferential or

  10. Variability of Performance: A "Signature" Characteristic of Learning Disabled Children?

    Science.gov (United States)

    Fuchs, Douglas; And Others

    Two studies were conducted to compare the performance instability of children (grades 3-9) labeled learning disabled/brain injured (LD/BI) to the performance instability of emotionally handicapped (EH) children. In the first study, 50 LD/BI and 37 EH students were measured on three third grade reading passages twice, once within one sitting and…

  11. Effect Of Variable Practice On The Motor Learning Process In Manual Wheelchair Propulsion

    NARCIS (Netherlands)

    Leving, Marika T; Vegter, Riemer J K; de Groot, Sonja; van der Woude, Lucas H V

    Handrim wheelchair propulsion is a cyclic skill that needs to be learned during rehabilitation. It has been suggested that a higher intra-individual variability benefits the motor learning process of wheelchair propulsion. PURPOSE: The goal of the current study was to determine the effect of

  12. Child predictors of learning to control variables via instruction or self-discovery

    NARCIS (Netherlands)

    Wagensveld, B.; Segers, P.C.J.; Kleemans, M.A.J.; Verhoeven, L.T.W.

    2015-01-01

    We examined the role child factors on the acquisition and transfer of learning the control of variables strategy (CVS) via instruction or self-discovery. Seventy-six fourth graders and 43 sixth graders were randomly assigned to a group receiving direct CVS instruction or a discovery learning group.

  13. Evaluating the Effectiveness Roles of Variables in the Novice Programmers Learning

    Science.gov (United States)

    Shi, Nianfeng; Cui, Wen; Zhang, Ping; Sun, Ximing

    2018-01-01

    This research applies the roles of variables to the novice programmers in the C language programming. The results are evaluated using the Structure of Observed Learning Outcomes (SOLO) taxonomy. The participants were divided into an experimental group and a control group. The students from the control group learned programming in the traditional…

  14. Effects of visual feedback-induced variability on motor learning of handrim wheelchair propulsion.

    Directory of Open Access Journals (Sweden)

    Marika T Leving

    Full Text Available It has been suggested that a higher intra-individual variability benefits the motor learning of wheelchair propulsion. The present study evaluated whether feedback-induced variability on wheelchair propulsion technique variables would also enhance the motor learning process. Learning was operationalized as an improvement in mechanical efficiency and propulsion technique, which are thought to be closely related during the learning process.17 Participants received visual feedback-based practice (feedback group and 15 participants received regular practice (natural learning group. Both groups received equal practice dose of 80 min, over 3 weeks, at 0.24 W/kg at a treadmill speed of 1.11 m/s. To compare both groups the pre- and post-test were performed without feedback. The feedback group received real-time visual feedback on seven propulsion variables with instruction to manipulate the presented variable to achieve the highest possible variability (1st 4-min block and optimize it in the prescribed direction (2nd 4-min block. To increase motor exploration the participants were unaware of the exact variable they received feedback on. Energy consumption and the propulsion technique variables with their respective coefficient of variation were calculated to evaluate the amount of intra-individual variability.The feedback group, which practiced with higher intra-individual variability, improved the propulsion technique between pre- and post-test to the same extent as the natural learning group. Mechanical efficiency improved between pre- and post-test in the natural learning group but remained unchanged in the feedback group.These results suggest that feedback-induced variability inhibited the improvement in mechanical efficiency. Moreover, since both groups improved propulsion technique but only the natural learning group improved mechanical efficiency, it can be concluded that the improvement in mechanical efficiency and propulsion technique do not

  15. Evolution of learning strategies in temporally and spatially variable environments: a review of theory.

    Science.gov (United States)

    Aoki, Kenichi; Feldman, Marcus W

    2014-02-01

    The theoretical literature from 1985 to the present on the evolution of learning strategies in variable environments is reviewed, with the focus on deterministic dynamical models that are amenable to local stability analysis, and on deterministic models yielding evolutionarily stable strategies. Individual learning, unbiased and biased social learning, mixed learning, and learning schedules are considered. A rapidly changing environment or frequent migration in a spatially heterogeneous environment favors individual learning over unbiased social learning. However, results are not so straightforward in the context of learning schedules or when biases in social learning are introduced. The three major methods of modeling temporal environmental change--coevolutionary, two-timescale, and information decay--are compared and shown to sometimes yield contradictory results. The so-called Rogers' paradox is inherent in the two-timescale method as originally applied to the evolution of pure strategies, but is often eliminated when the other methods are used. Moreover, Rogers' paradox is not observed for the mixed learning strategies and learning schedules that we review. We believe that further theoretical work is necessary on learning schedules and biased social learning, based on models that are logically consistent and empirically pertinent. Copyright © 2013 Elsevier Inc. All rights reserved.

  16. Evolution of learning strategies in temporally and spatially variable environments: A review of theory

    Science.gov (United States)

    Aoki, Kenichi; Feldman, Marcus W.

    2013-01-01

    The theoretical literature from 1985 to the present on the evolution of learning strategies in variable environments is reviewed, with the focus on deterministic dynamical models that are amenable to local stability analysis, and on deterministic models yielding evolutionarily stable strategies. Individual learning, unbiased and biased social learning, mixed learning, and learning schedules are considered. A rapidly changing environment or frequent migration in a spatially heterogeneous environment favors individual learning over unbiased social learning. However, results are not so straightforward in the context of learning schedules or when biases in social learning are introduced. The three major methods of modeling temporal environmental change – coevolutionary, two-timescale, and information decay – are compared and shown to sometimes yield contradictory results. The so-called Rogers’ paradox is inherent in the two-timescale method as originally applied to the evolution of pure strategies, but is often eliminated when the other methods are used. Moreover, Rogers’ paradox is not observed for the mixed learning strategies and learning schedules that we review. We believe that further theoretical work is necessary on learning schedules and biased social learning, based on models that are logically consistent and empirically pertinent. PMID:24211681

  17. Latent semantics as cognitive components

    DEFF Research Database (Denmark)

    Petersen, Michael Kai; Mørup, Morten; Hansen, Lars Kai

    2010-01-01

    Cognitive component analysis, defined as an unsupervised learning of features resembling human comprehension, suggests that the sensory structures we perceive might often be modeled by reducing dimensionality and treating objects in space and time as linear mixtures incorporating sparsity...... emotional responses can be encoded in words, we propose a simplified cognitive approach to model how we perceive media. Representing song lyrics in a vector space of reduced dimensionality using LSA, we combine bottom-up defined term distances with affective adjectives, that top-down constrain the latent......, which we suggest might function as cognitive components for perceiving the underlying structure in lyrics....

  18. Evaluation of Online Log Variables That Estimate Learners' Time Management in a Korean Online Learning Context

    Science.gov (United States)

    Jo, Il-Hyun; Park, Yeonjeong; Yoon, Meehyun; Sung, Hanall

    2016-01-01

    The purpose of this study was to identify the relationship between the psychological variables and online behavioral patterns of students, collected through a learning management system (LMS). As the psychological variable, time and study environment management (TSEM), one of the sub-constructs of MSLQ, was chosen to verify a set of time-related…

  19. Lessons learned from ESA INTEGRAL: cataclysmic variables and blazars

    Czech Academy of Sciences Publication Activity Database

    Hudec, René; Gális, R.; Kocka, Matúš

    2010-01-01

    Roč. 81, č. 1 (2010), s. 320-325 ISSN 0037-8720. [Multifrequency behaviour of high energy cosmic sources. Vulcano, 25.05.2009-30.05. 2009] Institutional research plan: CEZ:AV0Z10030501 Keywords : high-energy sources * cataclysmic variables * INTEGRAL Subject RIV: BN - Astronomy, Celestial Mechanics, Astrophysics

  20. Latent palmprint matching.

    Science.gov (United States)

    Jain, Anil K; Feng, Jianjiang

    2009-06-01

    The evidential value of palmprints in forensic applications is clear as about 30 percent of the latents recovered from crime scenes are from palms. While biometric systems for palmprint-based personal authentication in access control type of applications have been developed, they mostly deal with low-resolution (about 100 ppi) palmprints and only perform full-to-full palmprint matching. We propose a latent-to-full palmprint matching system that is needed in forensic applications. Our system deals with palmprints captured at 500 ppi (the current standard in forensic applications) or higher resolution and uses minutiae as features to be compatible with the methodology used by latent experts. Latent palmprint matching is a challenging problem because latent prints lifted at crime scenes are of poor image quality, cover only a small area of the palm, and have a complex background. Other difficulties include a large number of minutiae in full prints (about 10 times as many as fingerprints), and the presence of many creases in latents and full prints. A robust algorithm to reliably estimate the local ridge direction and frequency in palmprints is developed. This facilitates the extraction of ridge and minutiae features even in poor quality palmprints. A fixed-length minutia descriptor, MinutiaCode, is utilized to capture distinctive information around each minutia and an alignment-based minutiae matching algorithm is used to match two palmprints. Two sets of partial palmprints (150 live-scan partial palmprints and 100 latent palmprints) are matched to a background database of 10,200 full palmprints to test the proposed system. Despite the inherent difficulty of latent-to-full palmprint matching, rank-1 recognition rates of 78.7 and 69 percent, respectively, were achieved in searching live-scan partial palmprints and latent palmprints against the background database.

  1. A Particle Swarm Optimization Variant with an Inner Variable Learning Strategy

    Directory of Open Access Journals (Sweden)

    Guohua Wu

    2014-01-01

    Full Text Available Although Particle Swarm Optimization (PSO has demonstrated competitive performance in solving global optimization problems, it exhibits some limitations when dealing with optimization problems with high dimensionality and complex landscape. In this paper, we integrate some problem-oriented knowledge into the design of a certain PSO variant. The resulting novel PSO algorithm with an inner variable learning strategy (PSO-IVL is particularly efficient for optimizing functions with symmetric variables. Symmetric variables of the optimized function have to satisfy a certain quantitative relation. Based on this knowledge, the inner variable learning (IVL strategy helps the particle to inspect the relation among its inner variables, determine the exemplar variable for all other variables, and then make each variable learn from the exemplar variable in terms of their quantitative relations. In addition, we design a new trap detection and jumping out strategy to help particles escape from local optima. The trap detection operation is employed at the level of individual particles whereas the trap jumping out strategy is adaptive in its nature. Experimental simulations completed for some representative optimization functions demonstrate the excellent performance of PSO-IVL. The effectiveness of the PSO-IVL stresses a usefulness of augmenting evolutionary algorithms by problem-oriented domain knowledge.

  2. Latent classification models

    DEFF Research Database (Denmark)

    Langseth, Helge; Nielsen, Thomas Dyhre

    2005-01-01

    parametric family ofdistributions.  In this paper we propose a new set of models forclassification in continuous domains, termed latent classificationmodels. The latent classification model can roughly be seen ascombining the \\NB model with a mixture of factor analyzers,thereby relaxing the assumptions...... classification model, and wedemonstrate empirically that the accuracy of the proposed model issignificantly higher than the accuracy of other probabilisticclassifiers....

  3. Longitudinal Examination of Procrastination and Anxiety, and Their Relation to Self-Efficacy for Self- Regulated Learning: Latent Growth Curve Modeling

    Science.gov (United States)

    Yerdelen, Sündüs; McCaffrey, Adam; Klassen, Robert M.

    2016-01-01

    This study investigated the longitudinal association between students' anxiety and procrastination and the relation of self-efficacy for self-regulation to these constructs. Latent Growth Curve Modeling was used to analyze data gathered from 182 undergraduate students (134 female, 48 male) at 4 times during a semester. Our results showed that…

  4. The role of socio-cognitive variables in predicting learning satisfaction in smart schools

    Directory of Open Access Journals (Sweden)

    Mohammad Reza Firoozi

    2017-03-01

    Full Text Available The present study aimed to investigate the role of Socio-Cognitive variables in predicting learning satisfaction in Smart Schools. The population was all the primary school students studying in smart schools in the city of Shiraz in the school year 2014-2015. The sample, randomly chosen through multi-stage cluster sampling, was 383 primary school students studying in smart schools in Shiraz. The instruments were the Computer Self-Efficiency Questionnaire developed by Torkzadeh (2003, Performance Expectation Questionnaire developed by Compeau and Higgins (1995, System Functionality and Content Feature Questionnaire developed by Pituch and Lee (2006, Interaction Questionnaire developed by Johnston, Killion and Oomen (2005, Learning Climate Questionnaire developed by Chou` and Liu (2005 and Learning Satisfaction Questionnaire developed by Chou and Liu (2005. In order to determine the possible relationship between variables and to predict the changes in the degree of satisfaction, we made use of correlational procedures and step-wise regression analysis. The results indicated that all the socio-cognitive variables have a positive and significant correlation with learning satisfaction. Out of the socio-cognitive variables in question, Computer Self-Efficiency, Performance Expectation and Learning Climate significantly explained 53% of the variance of learning satisfaction.

  5. The Role of Socio-Cognitive Variables in Predicting Learning Satisfaction in Smart Schools

    Directory of Open Access Journals (Sweden)

    Mohammad Reza FIROOZI

    2017-03-01

    Full Text Available The present study aimed to investigate the role of Socio-Cognitive variables in predicting learning satisfaction in Smart Schools. The population was all the primary school students studying in smart schools in the city of Shiraz in the school year 2014-2015. The sample, randomly chosen through multi-stage cluster sampling, was 383 primary school students studying in smart schools in Shiraz. The instruments were the Computer Self-Efficiency Questionnaire developed by Torkzadeh (2003, Performance Expectation Questionnaire developed by Compeau and Higgins (1995, System Functionality and Content Feature Questionnaire developed by Pituch and Lee (2006, Interaction Questionnaire developed by Johnston, Killion and Oomen (2005, Learning Climate Questionnaire developed by Chou` and Liu (2005 and Learning Satisfaction Questionnaire developed by Chou and Liu (2005. In order to determine the possible relationship between variables and to predict the changes in the degree of satisfaction, we made use of correlational procedures and step-wise regression analysis. The results indicated that all the socio-cognitive variables have a positive and significant correlation with learning satisfaction. Out of the socio-cognitive variables in question, Computer Self-Efficiency, Performance Expectation and Learning Climate significantly explained 53% of the variance of learning satisfaction.

  6. Exploration of joint redundancy but not task space variability facilitates supervised motor learning.

    Science.gov (United States)

    Singh, Puneet; Jana, Sumitash; Ghosal, Ashitava; Murthy, Aditya

    2016-12-13

    The number of joints and muscles in a human arm is more than what is required for reaching to a desired point in 3D space. Although previous studies have emphasized how such redundancy and the associated flexibility may play an important role in path planning, control of noise, and optimization of motion, whether and how redundancy might promote motor learning has not been investigated. In this work, we quantify redundancy space and investigate its significance and effect on motor learning. We propose that a larger redundancy space leads to faster learning across subjects. We observed this pattern in subjects learning novel kinematics (visuomotor adaptation) and dynamics (force-field adaptation). Interestingly, we also observed differences in the redundancy space between the dominant hand and nondominant hand that explained differences in the learning of dynamics. Taken together, these results provide support for the hypothesis that redundancy aids in motor learning and that the redundant component of motor variability is not noise.

  7. Confirmatory Factor Analysis untuk Mengetahui Pemanfaatan Multimedia Learning pada Perguruan Tinggi Swasta di Kota Semarang

    Directory of Open Access Journals (Sweden)

    Achmad Solechan

    2014-07-01

    Full Text Available Dominant indicator of the use of multimedia in learning needs to be studied using the Confirmatory Factor Analysis. This study aims to determine the most dominant factor affecting the use of multimedia in learning, this study uses the Technology Acceptance Modelling theory. This study uses the technique of Judgment Sampling Area or a sampling technique that is based on the determination of the research area. Determination of the study area are four private universities in Semarang, namely USM, Udinus, STMIK Provisi and Unisbank, so the overall number of respondents as many as 200 students. This study shows that: 1-the greatest contribution value of the Perceived Usefulness latent variable that is multimedia learning increase the effectiveness of learning in the classroom, 2-the greatest contribution value of the Confirmation latent variable, that is Lecturer Services using multimedia learning is preferred, 3-the greatest contribution value of the Perceived Ease of Use latent variable that is teaching materials using multimedia learning is something that is easy for students to understand. This study also shows that : 4-the greatest contribution value of the Satisfaction latent variable that is multimedia learning used by Lecturer in teaching and learning in the classroom is able to provide information in accordance with the information that students need, and 5-the greatest contribution value of the Continued IT Usage Intention latent variable that is students interested in understanding the material, which is taught by Lecturer, if Lecturer teaching using multimedia based than conventional models

  8. Scaling prediction errors to reward variability benefits error-driven learning in humans.

    Science.gov (United States)

    Diederen, Kelly M J; Schultz, Wolfram

    2015-09-01

    Effective error-driven learning requires individuals to adapt learning to environmental reward variability. The adaptive mechanism may involve decays in learning rate across subsequent trials, as shown previously, and rescaling of reward prediction errors. The present study investigated the influence of prediction error scaling and, in particular, the consequences for learning performance. Participants explicitly predicted reward magnitudes that were drawn from different probability distributions with specific standard deviations. By fitting the data with reinforcement learning models, we found scaling of prediction errors, in addition to the learning rate decay shown previously. Importantly, the prediction error scaling was closely related to learning performance, defined as accuracy in predicting the mean of reward distributions, across individual participants. In addition, participants who scaled prediction errors relative to standard deviation also presented with more similar performance for different standard deviations, indicating that increases in standard deviation did not substantially decrease "adapters'" accuracy in predicting the means of reward distributions. However, exaggerated scaling beyond the standard deviation resulted in impaired performance. Thus efficient adaptation makes learning more robust to changing variability. Copyright © 2015 the American Physiological Society.

  9. The Impacts of Demographic Variables on Technological and Contextual Challenges of E-learning Implementation

    Science.gov (United States)

    Aldowah, Hanan; Ghazal, Samar; Naufal Umar, Irfan; Muniandy, Balakrishnan

    2017-09-01

    Information technology has achieved robust growth which has made it possible for learning to occur quickly. The rapid development of information, communication and technologies (ICT) has initiated an unparalleled transformation in universities all over the world. This development of technology and learning is offering new techniques to represent knowledge, new practices, and new global communities of learners. As a result, today’s economic and social changes force universities to try to find new learning approaches and systems. E-learning seems to be an appropriate approach in this aspect. However, the implementation of e-learning systems in universities is not an easy task because of some challenges related to context, technology, and other challenges. This paper studied the impacts of demographic data and reported the critical points for the decision makers to consider when planning and implementing e-learning in universities. A quantitative approach was used to study the effects of technological and contextual challenges on e-learning implementation in which a questionnaire was used for the data collection. According to the findings of the study, the most important challenges of the implementation of e-learning are related either to organizational (Contextual) and technological (technical) issues. The demographic variables have been found to play a direct and indirect role with the technological and contextual challenges of implementing e-learning. This paper showed that there are some significant differences in the two challenges faced by instructors in terms of the demographic variables. The result revealed that some significant differences exist between demographic variables and the two challenges of e-learning in terms of gender, age, teaching experience, ICT experience and e-learning experience. However, there is no significant difference in terms of e-learning experience. The obtained data, from such study, can provide information about what academic

  10. Children's Learning in Scientific Thinking: Instructional Approaches and Roles of Variable Identification and Executive Function

    Science.gov (United States)

    Blums, Angela

    The present study examines instructional approaches and cognitive factors involved in elementary school children's thinking and learning the Control of Variables Strategy (CVS), a critical aspect of scientific reasoning. Previous research has identified several features related to effective instruction of CVS, including using a guided learning approach, the use of self-reflective questions, and learning in individual and group contexts. The current study examined the roles of procedural and conceptual instruction in learning CVS and investigated the role of executive function in the learning process. Additionally, this study examined how learning to identify variables is a part of the CVS process. In two studies (individual and classroom experiments), 139 third, fourth, and fifth grade students participated in hands-on and paper and pencil CVS learning activities and, in each study, were assigned to either a procedural instruction, conceptual instruction, or control (no instruction) group. Participants also completed a series of executive function tasks. The study was carried out with two parts--Study 1 used an individual context and Study 2 was carried out in a group setting. Results indicated that procedural and conceptual instruction were more effective than no instruction, and the ability to identify variables was identified as a key piece to the CVS process. Executive function predicted ability to identify variables and predicted success on CVS tasks. Developmental differences were present, in that older children outperformed younger children on CVS tasks, and that conceptual instruction was slightly more effective for older children. Some differences between individual and group instruction were found, with those in the individual context showing some advantage over the those in the group setting in learning CVS concepts. Conceptual implications about scientific thinking and practical implications in science education are discussed.

  11. Robust total energy demand estimation with a hybrid Variable Neighborhood Search – Extreme Learning Machine algorithm

    International Nuclear Information System (INIS)

    Sánchez-Oro, J.; Duarte, A.; Salcedo-Sanz, S.

    2016-01-01

    Highlights: • The total energy demand in Spain is estimated with a Variable Neighborhood algorithm. • Socio-economic variables are used, and one year ahead prediction horizon is considered. • Improvement of the prediction with an Extreme Learning Machine network is considered. • Experiments are carried out in real data for the case of Spain. - Abstract: Energy demand prediction is an important problem whose solution is evaluated by policy makers in order to take key decisions affecting the economy of a country. A number of previous approaches to improve the quality of this estimation have been proposed in the last decade, the majority of them applying different machine learning techniques. In this paper, the performance of a robust hybrid approach, composed of a Variable Neighborhood Search algorithm and a new class of neural network called Extreme Learning Machine, is discussed. The Variable Neighborhood Search algorithm is focused on obtaining the most relevant features among the set of initial ones, by including an exponential prediction model. While previous approaches consider that the number of macroeconomic variables used for prediction is a parameter of the algorithm (i.e., it is fixed a priori), the proposed Variable Neighborhood Search method optimizes both: the number of variables and the best ones. After this first step of feature selection, an Extreme Learning Machine network is applied to obtain the final energy demand prediction. Experiments in a real case of energy demand estimation in Spain show the excellent performance of the proposed approach. In particular, the whole method obtains an estimation of the energy demand with an error lower than 2%, even when considering the crisis years, which are a real challenge.

  12. Elementary Students' Affective Variables in a Networked Learning Environment Supported by a Blog: A Case Study

    Science.gov (United States)

    Allaire, Stéphane; Thériault, Pascale; Gagnon, Vincent; Lalancette, Evelyne

    2013-01-01

    This study documents to what extent writing on a blog in a networked learning environment could influence the affective variables of elementary-school students' writing. The framework is grounded more specifically in theory of self-determination (Deci & Ryan, 1985), relationship to writing (Chartrand & Prince, 2009) and the transactional…

  13. Analysis of Primary School Student's Science Learning Anxiety According to Some Variables

    Science.gov (United States)

    Karakaya, Ferhat; Avgin, Sakine Serap; Kumperli, Ethem

    2016-01-01

    On this research, it is analyzed if the science learning anxiety level shows difference according to variables which are gender, grade level, science lesson grade, mother education, father education level. Scanning Design is used for this study. Research working group is consisted of 294 primary school from 6th, 7th and 8th graders on 2015-2016…

  14. French Nursery Schools and German Kindergartens: Effects of Individual and Contextual Variables on Early Learning

    Science.gov (United States)

    Tazouti, Youssef; Viriot-Goeldel, Caroline; Matter, Cornelie; Geiger-Jaillet, Anemone; Carol, Rita; Deviterne, Dominique

    2011-01-01

    The present article investigates the effects of individual and contextual variables on children's early learning in French nursery schools and German kindergartens. Our study of 552 children at preschools in France (299 children from French nursery schools) and Germany (253 children from German kindergartens) measured skills that facilitate the…

  15. Psychosocial Variables as Predictors of School Adjustment of Gifted Students with Learning Disabilities in Nigeria

    Science.gov (United States)

    Fakolade, O. A.; Oyedokun, S. O.

    2015-01-01

    The paper considered several psychosocial variables as predictors of school adjustment of 40 gifted students with learning disabilities in Junior Secondary School in Ikenne Local Government Council Area of Ogun State, Nigeria. Purposeful random sampling was employed to select four schools from 13 junior secondary schools in the area, six…

  16. New supervised learning theory applied to cerebellar modeling for suppression of variability of saccade end points.

    Science.gov (United States)

    Fujita, Masahiko

    2013-06-01

    A new supervised learning theory is proposed for a hierarchical neural network with a single hidden layer of threshold units, which can approximate any continuous transformation, and applied to a cerebellar function to suppress the end-point variability of saccades. In motor systems, feedback control can reduce noise effects if the noise is added in a pathway from a motor center to a peripheral effector; however, it cannot reduce noise effects if the noise is generated in the motor center itself: a new control scheme is necessary for such noise. The cerebellar cortex is well known as a supervised learning system, and a novel theory of cerebellar cortical function developed in this study can explain the capability of the cerebellum to feedforwardly reduce noise effects, such as end-point variability of saccades. This theory assumes that a Golgi-granule cell system can encode the strength of a mossy fiber input as the state of neuronal activity of parallel fibers. By combining these parallel fiber signals with appropriate connection weights to produce a Purkinje cell output, an arbitrary continuous input-output relationship can be obtained. By incorporating such flexible computation and learning ability in a process of saccadic gain adaptation, a new control scheme in which the cerebellar cortex feedforwardly suppresses the end-point variability when it detects a variation in saccadic commands can be devised. Computer simulation confirmed the efficiency of such learning and showed a reduction in the variability of saccadic end points, similar to results obtained from experimental data.

  17. The Effect of Visual Variability on the Learning of Academic Concepts.

    Science.gov (United States)

    Bourgoyne, Ashley; Alt, Mary

    2017-06-10

    The purpose of this study was to identify effects of variability of visual input on development of conceptual representations of academic concepts for college-age students with normal language (NL) and those with language-learning disabilities (LLD). Students with NL (n = 11) and LLD (n = 11) participated in a computer-based training for introductory biology course concepts. Participants were trained on half the concepts under a low-variability condition and half under a high-variability condition. Participants completed a posttest in which they were asked to identify and rate the accuracy of novel and trained visual representations of the concepts. We performed separate repeated measures analyses of variance to examine the accuracy of identification and ratings. Participants were equally accurate on trained and novel items in the high-variability condition, but were less accurate on novel items only in the low-variability condition. The LLD group showed the same pattern as the NL group; they were just less accurate. Results indicated that high-variability visual input may facilitate the acquisition of academic concepts in college students with NL and LLD. High-variability visual input may be especially beneficial for generalization to novel representations of concepts. Implicit learning methods may be harnessed by college courses to provide students with basic conceptual knowledge when they are entering courses or beginning new units.

  18. Research on Open-Closed-Loop Iterative Learning Control with Variable Forgetting Factor of Mobile Robots

    Directory of Open Access Journals (Sweden)

    Hongbin Wang

    2016-01-01

    Full Text Available We propose an iterative learning control algorithm (ILC that is developed using a variable forgetting factor to control a mobile robot. The proposed algorithm can be categorized as an open-closed-loop iterative learning control, which produces control instructions by using both previous and current data. However, introducing a variable forgetting factor can weaken the former control output and its variance in the control law while strengthening the robustness of the iterative learning control. If it is applied to the mobile robot, this will reduce position errors in robot trajectory tracking control effectively. In this work, we show that the proposed algorithm guarantees tracking error bound convergence to a small neighborhood of the origin under the condition of state disturbances, output measurement noises, and fluctuation of system dynamics. By using simulation, we demonstrate that the controller is effective in realizing the prefect tracking.

  19. Dimensions of organizational learning: contextual variables in companies under lean manufacturing implementation

    Directory of Open Access Journals (Sweden)

    Guilherme Luz Tortorella

    2014-08-01

    Full Text Available The Lean Production (LP is an approach that encompasses a variety of management practices to reduce losses and improve operational efficiency. Due to this fact, the ability to innovate, change and learn continuously presents itself as a key element in the implementation of the LP. Several contextual variables were mentioned in the literature as potential impediments to implementing lean. However, little is known about the influence of these variables on the dimensions of Organizational Learning (OL. This study aims to examine the relationship between six contextual variables and the frequency of occurrence of problems in companies that are implementing the LP. Furthermore, the identification of relevant relationships between dimensions of OL and contextual variables contribute to the identification of the contexts in which problems can be expected to occur. The sample contains thirteen companies implementing the LP. The results indicate that the same contextual variables, which are deemed as influential to implement LP, have a different influence on the ability of organizational learning.

  20. Robust Measurement via A Fused Latent and Graphical Item Response Theory Model.

    Science.gov (United States)

    Chen, Yunxiao; Li, Xiaoou; Liu, Jingchen; Ying, Zhiliang

    2018-03-12

    Item response theory (IRT) plays an important role in psychological and educational measurement. Unlike the classical testing theory, IRT models aggregate the item level information, yielding more accurate measurements. Most IRT models assume local independence, an assumption not likely to be satisfied in practice, especially when the number of items is large. Results in the literature and simulation studies in this paper reveal that misspecifying the local independence assumption may result in inaccurate measurements and differential item functioning. To provide more robust measurements, we propose an integrated approach by adding a graphical component to a multidimensional IRT model that can offset the effect of unknown local dependence. The new model contains a confirmatory latent variable component, which measures the targeted latent traits, and a graphical component, which captures the local dependence. An efficient proximal algorithm is proposed for the parameter estimation and structure learning of the local dependence. This approach can substantially improve the measurement, given no prior information on the local dependence structure. The model can be applied to measure both a unidimensional latent trait and multidimensional latent traits.

  1. Investigation of Mediational Processes Using Parallel Process Latent Growth Curve Modeling

    Science.gov (United States)

    Cheong, JeeWon; MacKinnon, David P.; Khoo, Siek Toon

    2010-01-01

    This study investigated a method to evaluate mediational processes using latent growth curve modeling. The mediator and the outcome measured across multiple time points were viewed as 2 separate parallel processes. The mediational process was defined as the independent variable influencing the growth of the mediator, which, in turn, affected the growth of the outcome. To illustrate modeling procedures, empirical data from a longitudinal drug prevention program, Adolescents Training and Learning to Avoid Steroids, were used. The program effects on the growth of the mediator and the growth of the outcome were examined first in a 2-group structural equation model. The mediational process was then modeled and tested in a parallel process latent growth curve model by relating the prevention program condition, the growth rate factor of the mediator, and the growth rate factor of the outcome. PMID:20157639

  2. Human θ burst stimulation enhances subsequent motor learning and increases performance variability.

    Science.gov (United States)

    Teo, James T H; Swayne, Orlando B C; Cheeran, Binith; Greenwood, Richard J; Rothwell, John C

    2011-07-01

    Intermittent theta burst stimulation (iTBS) transiently increases motor cortex excitability in healthy humans by a process thought to involve synaptic long-term potentiation (LTP), and this is enhanced by nicotine. Acquisition of a ballistic motor task is likewise accompanied by increased excitability and presumed intracortical LTP. Here, we test how iTBS and nicotine influences subsequent motor learning. Ten healthy subjects participated in a double-blinded placebo-controlled trial testing the effects of iTBS and nicotine. iTBS alone increased the rate of learning but this increase was blocked by nicotine. We then investigated factors other than synaptic strengthening that may play a role. Behavioral analysis and modeling suggested that iTBS increased performance variability, which correlated with learning outcome. A control experiment confirmed the increase in motor output variability by showing that iTBS increased the dispersion of involuntary transcranial magnetic stimulation-evoked thumb movements. We suggest that in addition to the effect on synaptic plasticity, iTBS may have facilitated performance by increasing motor output variability; nicotine negated this effect on variability perhaps via increasing the signal-to-noise ratio in cerebral cortex.

  3. A Probability Distribution over Latent Causes, in the Orbitofrontal Cortex.

    Science.gov (United States)

    Chan, Stephanie C Y; Niv, Yael; Norman, Kenneth A

    2016-07-27

    The orbitofrontal cortex (OFC) has been implicated in both the representation of "state," in studies of reinforcement learning and decision making, and also in the representation of "schemas," in studies of episodic memory. Both of these cognitive constructs require a similar inference about the underlying situation or "latent cause" that generates our observations at any given time. The statistically optimal solution to this inference problem is to use Bayes' rule to compute a posterior probability distribution over latent causes. To test whether such a posterior probability distribution is represented in the OFC, we tasked human participants with inferring a probability distribution over four possible latent causes, based on their observations. Using fMRI pattern similarity analyses, we found that BOLD activity in the OFC is best explained as representing the (log-transformed) posterior distribution over latent causes. Furthermore, this pattern explained OFC activity better than other task-relevant alternatives, such as the most probable latent cause, the most recent observation, or the uncertainty over latent causes. Our world is governed by hidden (latent) causes that we cannot observe, but which generate the observations we see. A range of high-level cognitive processes require inference of a probability distribution (or "belief distribution") over the possible latent causes that might be generating our current observations. This is true for reinforcement learning and decision making (where the latent cause comprises the true "state" of the task), and for episodic memory (where memories are believed to be organized by the inferred situation or "schema"). Using fMRI, we show that this belief distribution over latent causes is encoded in patterns of brain activity in the orbitofrontal cortex, an area that has been separately implicated in the representations of both states and schemas. Copyright © 2016 the authors 0270-6474/16/367817-12$15.00/0.

  4. Song practice promotes acute vocal variability at a key stage of sensorimotor learning.

    Directory of Open Access Journals (Sweden)

    Julie E Miller

    Full Text Available BACKGROUND: Trial by trial variability during motor learning is a feature encoded by the basal ganglia of both humans and songbirds, and is important for reinforcement of optimal motor patterns, including those that produce speech and birdsong. Given the many parallels between these behaviors, songbirds provide a useful model to investigate neural mechanisms underlying vocal learning. In juvenile and adult male zebra finches, endogenous levels of FoxP2, a molecule critical for language, decrease two hours after morning song onset within area X, part of the basal ganglia-forebrain pathway dedicated to song. In juveniles, experimental 'knockdown' of area X FoxP2 results in abnormally variable song in adulthood. These findings motivated our hypothesis that low FoxP2 levels increase vocal variability, enabling vocal motor exploration in normal birds. METHODOLOGY/PRINCIPAL FINDINGS: After two hours in either singing or non-singing conditions (previously shown to produce differential area X FoxP2 levels, phonological and sequential features of the subsequent songs were compared across conditions in the same bird. In line with our prediction, analysis of songs sung by 75 day (75d birds revealed that syllable structure was more variable and sequence stereotypy was reduced following two hours of continuous practice compared to these features following two hours of non-singing. Similar trends in song were observed in these birds at 65d, despite higher overall within-condition variability at this age. CONCLUSIONS/SIGNIFICANCE: Together with previous work, these findings point to the importance of behaviorally-driven acute periods during song learning that allow for both refinement and reinforcement of motor patterns. Future work is aimed at testing the observation that not only does vocal practice influence expression of molecular networks, but that these networks then influence subsequent variability in these skills.

  5. Variable complexity online sequential extreme learning machine, with applications to streamflow prediction

    Science.gov (United States)

    Lima, Aranildo R.; Hsieh, William W.; Cannon, Alex J.

    2017-12-01

    In situations where new data arrive continually, online learning algorithms are computationally much less costly than batch learning ones in maintaining the model up-to-date. The extreme learning machine (ELM), a single hidden layer artificial neural network with random weights in the hidden layer, is solved by linear least squares, and has an online learning version, the online sequential ELM (OSELM). As more data become available during online learning, information on the longer time scale becomes available, so ideally the model complexity should be allowed to change, but the number of hidden nodes (HN) remains fixed in OSELM. A variable complexity VC-OSELM algorithm is proposed to dynamically add or remove HN in the OSELM, allowing the model complexity to vary automatically as online learning proceeds. The performance of VC-OSELM was compared with OSELM in daily streamflow predictions at two hydrological stations in British Columbia, Canada, with VC-OSELM significantly outperforming OSELM in mean absolute error, root mean squared error and Nash-Sutcliffe efficiency at both stations.

  6. Nonlinear Synchronization for Automatic Learning of 3D Pose Variability in Human Motion Sequences

    Directory of Open Access Journals (Sweden)

    Mozerov M

    2010-01-01

    Full Text Available A dense matching algorithm that solves the problem of synchronizing prerecorded human motion sequences, which show different speeds and accelerations, is proposed. The approach is based on minimization of MRF energy and solves the problem by using Dynamic Programming. Additionally, an optimal sequence is automatically selected from the input dataset to be a time-scale pattern for all other sequences. The paper utilizes an action specific model which automatically learns the variability of 3D human postures observed in a set of training sequences. The model is trained using the public CMU motion capture dataset for the walking action, and a mean walking performance is automatically learnt. Additionally, statistics about the observed variability of the postures and motion direction are also computed at each time step. The synchronized motion sequences are used to learn a model of human motion for action recognition and full-body tracking purposes.

  7. Resting heart rate variability predicts safety learning and fear extinction in an interoceptive fear conditioning paradigm.

    Directory of Open Access Journals (Sweden)

    Meike Pappens

    Full Text Available This study aimed to investigate whether interindividual differences in autonomic inhibitory control predict safety learning and fear extinction in an interoceptive fear conditioning paradigm. Data from a previously reported study (N = 40 were extended (N = 17 and re-analyzed to test whether healthy participants' resting heart rate variability (HRV - a proxy of cardiac vagal tone - predicts learning performance. The conditioned stimulus (CS was a slight sensation of breathlessness induced by a flow resistor, the unconditioned stimulus (US was an aversive short-lasting suffocation experience induced by a complete occlusion of the breathing circuitry. During acquisition, the paired group received 6 paired CS-US presentations; the control group received 6 explicitly unpaired CS-US presentations. In the extinction phase, both groups were exposed to 6 CS-only presentations. Measures included startle blink EMG, skin conductance responses (SCR and US-expectancy ratings. Resting HRV significantly predicted the startle blink EMG learning curves both during acquisition and extinction. In the unpaired group, higher levels of HRV at rest predicted safety learning to the CS during acquisition. In the paired group, higher levels of HRV were associated with better extinction. Our findings suggest that the strength or integrity of prefrontal inhibitory mechanisms involved in safety- and extinction learning can be indexed by HRV at rest.

  8. Vocabulary relearning in semantic dementia: Positive and negative consequences of increasing variability in the learning experience.

    Science.gov (United States)

    Hoffman, Paul; Clarke, Natasha; Jones, Roy W; Noonan, Krist A

    2015-09-01

    Anomia therapy typically aims to improve patients' communication ability through targeted practice in naming a set of particular items. For such interventions to be of maximum benefit, the use of trained (or relearned) vocabulary must generalise from the therapy setting into novel situations. We investigated relearning in three patients with semantic dementia, a condition that has been associated with poor generalisation of relearned vocabulary. We tested two manipulations designed to improve generalisation of relearned words by introducing greater variation into the learning experience. In the first study, we found that trained items were retained more successfully when they were presented in a variety of different sequences during learning. In the second study, we found that training items using a range of different pictured exemplars improved the patients' ability to generalise words to novel instances of the same object. However, in one patient this came at the cost of inappropriate over-generalisations, in which trained words were incorrectly used to name semantically or visually similar objects. We propose that more variable learning experiences benefit patients because they shift responsibility for learning away from the inflexible hippocampal learning system and towards the semantic system. The success of this approach therefore depends critically on the integrity of the semantic representations of the items being trained. Patients with naming impairments in the context of relatively mild comprehension deficits are most likely to benefit from this approach, while avoiding the negative consequences of over-generalisation. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

  9. ACTIVE LEARNING TO OVERCOME SAMPLE SELECTION BIAS: APPLICATION TO PHOTOMETRIC VARIABLE STAR CLASSIFICATION

    Energy Technology Data Exchange (ETDEWEB)

    Richards, Joseph W.; Starr, Dan L.; Miller, Adam A.; Bloom, Joshua S.; Butler, Nathaniel R.; Berian James, J. [Astronomy Department, University of California, Berkeley, CA 94720-7450 (United States); Brink, Henrik [Dark Cosmology Centre, Juliane Maries Vej 30, 2100 Copenhagen O (Denmark); Long, James P.; Rice, John, E-mail: jwrichar@stat.berkeley.edu [Statistics Department, University of California, Berkeley, CA 94720-7450 (United States)

    2012-01-10

    Despite the great promise of machine-learning algorithms to classify and predict astrophysical parameters for the vast numbers of astrophysical sources and transients observed in large-scale surveys, the peculiarities of the training data often manifest as strongly biased predictions on the data of interest. Typically, training sets are derived from historical surveys of brighter, more nearby objects than those from more extensive, deeper surveys (testing data). This sample selection bias can cause catastrophic errors in predictions on the testing data because (1) standard assumptions for machine-learned model selection procedures break down and (2) dense regions of testing space might be completely devoid of training data. We explore possible remedies to sample selection bias, including importance weighting, co-training, and active learning (AL). We argue that AL-where the data whose inclusion in the training set would most improve predictions on the testing set are queried for manual follow-up-is an effective approach and is appropriate for many astronomical applications. For a variable star classification problem on a well-studied set of stars from Hipparcos and Optical Gravitational Lensing Experiment, AL is the optimal method in terms of error rate on the testing data, beating the off-the-shelf classifier by 3.4% and the other proposed methods by at least 3.0%. To aid with manual labeling of variable stars, we developed a Web interface which allows for easy light curve visualization and querying of external databases. Finally, we apply AL to classify variable stars in the All Sky Automated Survey, finding dramatic improvement in our agreement with the ASAS Catalog of Variable Stars, from 65.5% to 79.5%, and a significant increase in the classifier's average confidence for the testing set, from 14.6% to 42.9%, after a few AL iterations.

  10. ACTIVE LEARNING TO OVERCOME SAMPLE SELECTION BIAS: APPLICATION TO PHOTOMETRIC VARIABLE STAR CLASSIFICATION

    International Nuclear Information System (INIS)

    Richards, Joseph W.; Starr, Dan L.; Miller, Adam A.; Bloom, Joshua S.; Butler, Nathaniel R.; Berian James, J.; Brink, Henrik; Long, James P.; Rice, John

    2012-01-01

    Despite the great promise of machine-learning algorithms to classify and predict astrophysical parameters for the vast numbers of astrophysical sources and transients observed in large-scale surveys, the peculiarities of the training data often manifest as strongly biased predictions on the data of interest. Typically, training sets are derived from historical surveys of brighter, more nearby objects than those from more extensive, deeper surveys (testing data). This sample selection bias can cause catastrophic errors in predictions on the testing data because (1) standard assumptions for machine-learned model selection procedures break down and (2) dense regions of testing space might be completely devoid of training data. We explore possible remedies to sample selection bias, including importance weighting, co-training, and active learning (AL). We argue that AL—where the data whose inclusion in the training set would most improve predictions on the testing set are queried for manual follow-up—is an effective approach and is appropriate for many astronomical applications. For a variable star classification problem on a well-studied set of stars from Hipparcos and Optical Gravitational Lensing Experiment, AL is the optimal method in terms of error rate on the testing data, beating the off-the-shelf classifier by 3.4% and the other proposed methods by at least 3.0%. To aid with manual labeling of variable stars, we developed a Web interface which allows for easy light curve visualization and querying of external databases. Finally, we apply AL to classify variable stars in the All Sky Automated Survey, finding dramatic improvement in our agreement with the ASAS Catalog of Variable Stars, from 65.5% to 79.5%, and a significant increase in the classifier's average confidence for the testing set, from 14.6% to 42.9%, after a few AL iterations.

  11. Active Learning to Overcome Sample Selection Bias: Application to Photometric Variable Star Classification

    Science.gov (United States)

    Richards, Joseph W.; Starr, Dan L.; Brink, Henrik; Miller, Adam A.; Bloom, Joshua S.; Butler, Nathaniel R.; James, J. Berian; Long, James P.; Rice, John

    2012-01-01

    Despite the great promise of machine-learning algorithms to classify and predict astrophysical parameters for the vast numbers of astrophysical sources and transients observed in large-scale surveys, the peculiarities of the training data often manifest as strongly biased predictions on the data of interest. Typically, training sets are derived from historical surveys of brighter, more nearby objects than those from more extensive, deeper surveys (testing data). This sample selection bias can cause catastrophic errors in predictions on the testing data because (1) standard assumptions for machine-learned model selection procedures break down and (2) dense regions of testing space might be completely devoid of training data. We explore possible remedies to sample selection bias, including importance weighting, co-training, and active learning (AL). We argue that AL—where the data whose inclusion in the training set would most improve predictions on the testing set are queried for manual follow-up—is an effective approach and is appropriate for many astronomical applications. For a variable star classification problem on a well-studied set of stars from Hipparcos and Optical Gravitational Lensing Experiment, AL is the optimal method in terms of error rate on the testing data, beating the off-the-shelf classifier by 3.4% and the other proposed methods by at least 3.0%. To aid with manual labeling of variable stars, we developed a Web interface which allows for easy light curve visualization and querying of external databases. Finally, we apply AL to classify variable stars in the All Sky Automated Survey, finding dramatic improvement in our agreement with the ASAS Catalog of Variable Stars, from 65.5% to 79.5%, and a significant increase in the classifier's average confidence for the testing set, from 14.6% to 42.9%, after a few AL iterations.

  12. Potentiation of latent inhibition by haloperidol and clozapine is attenuated in Dopamine D2 receptor (Drd-2)-deficient mice: Do antipsychotics influence learning to ignore irrelevant stimuli via both Drd-2 and non-Drd-2 mechanisms?

    Science.gov (United States)

    O’Callaghan, Matthew J; Bay-Richter, Cecilie; O’Tuathaigh, Colm MP; Heery, David M; Waddington, John L; Moran, Paula M

    2014-01-01

    Whether the dopamine Drd-2 receptor is necessary for the behavioural action of antipsychotic drugs is an important question, as Drd-2 antagonism is responsible for their debilitating motor side effects. Using Drd-2 null mice (Drd2 -/-) it has previously been shown that Drd-2 is not necessary for antipsychotic drugs to reverse D-amphetamine disruption of latent inhibition (LI), a behavioural measure of learning to ignore irrelevant stimuli. Weiner’s ‘two-headed’ model indicates that antipsychotics not only reverse LI disruption, ‘disrupted LI’, but also potentiate LI when low/absent in controls, ‘persistent’ LI. We investigated whether antipsychotic drugs haloperidol or clozapine potentiated LI in wild-type controls or Drd2 -/-. Both drugs potentiated LI in wild-type but not in Drd2-/- mice, suggesting moderation of this effect of antipsychotics in the absence of Drd-2. Haloperidol potentiated LI similarly in both Drd1-/- and wild-type mice, indicating no such moderation in Drd1-/-. These data suggest that antipsychotic drugs can have either Drd-2 or non-Drd-2 effects on learning to ignore irrelevant stimuli, depending on how the abnormality is produced. Identification of the non-Drd-2 mechanism may help to identify novel non-Drd2 based therapeutic strategies for psychosis. PMID:25122042

  13. Latent semantic analysis.

    Science.gov (United States)

    Evangelopoulos, Nicholas E

    2013-11-01

    This article reviews latent semantic analysis (LSA), a theory of meaning as well as a method for extracting that meaning from passages of text, based on statistical computations over a collection of documents. LSA as a theory of meaning defines a latent semantic space where documents and individual words are represented as vectors. LSA as a computational technique uses linear algebra to extract dimensions that represent that space. This representation enables the computation of similarity among terms and documents, categorization of terms and documents, and summarization of large collections of documents using automated procedures that mimic the way humans perform similar cognitive tasks. We present some technical details, various illustrative examples, and discuss a number of applications from linguistics, psychology, cognitive science, education, information science, and analysis of textual data in general. WIREs Cogn Sci 2013, 4:683-692. doi: 10.1002/wcs.1254 CONFLICT OF INTEREST: The author has declared no conflicts of interest for this article. For further resources related to this article, please visit the WIREs website. © 2013 John Wiley & Sons, Ltd.

  14. Verbal Knowledge, Working Memory, and Processing Speed as Predictors of Verbal Learning in Older Adults

    Science.gov (United States)

    Rast, Philippe

    2011-01-01

    The present study aimed at modeling individual differences in a verbal learning task by means of a latent structured growth curve approach based on an exponential function that yielded 3 parameters: initial recall, learning rate, and asymptotic performance. Three cognitive variables--speed of information processing, verbal knowledge, working…

  15. AUTOCLASSIFICATION OF THE VARIABLE 3XMM SOURCES USING THE RANDOM FOREST MACHINE LEARNING ALGORITHM

    International Nuclear Information System (INIS)

    Farrell, Sean A.; Murphy, Tara; Lo, Kitty K.

    2015-01-01

    In the current era of large surveys and massive data sets, autoclassification of astrophysical sources using intelligent algorithms is becoming increasingly important. In this paper we present the catalog of variable sources in the Third XMM-Newton Serendipitous Source catalog (3XMM) autoclassified using the Random Forest machine learning algorithm. We used a sample of manually classified variable sources from the second data release of the XMM-Newton catalogs (2XMMi-DR2) to train the classifier, obtaining an accuracy of ∼92%. We also evaluated the effectiveness of identifying spurious detections using a sample of spurious sources, achieving an accuracy of ∼95%. Manual investigation of a random sample of classified sources confirmed these accuracy levels and showed that the Random Forest machine learning algorithm is highly effective at automatically classifying 3XMM sources. Here we present the catalog of classified 3XMM variable sources. We also present three previously unidentified unusual sources that were flagged as outlier sources by the algorithm: a new candidate supergiant fast X-ray transient, a 400 s X-ray pulsar, and an eclipsing 5 hr binary system coincident with a known Cepheid.

  16. Retrieving Tract Variables From Acoustics: A Comparison of Different Machine Learning Strategies.

    Science.gov (United States)

    Mitra, Vikramjit; Nam, Hosung; Espy-Wilson, Carol Y; Saltzman, Elliot; Goldstein, Louis

    2010-09-13

    Many different studies have claimed that articulatory information can be used to improve the performance of automatic speech recognition systems. Unfortunately, such articulatory information is not readily available in typical speaker-listener situations. Consequently, such information has to be estimated from the acoustic signal in a process which is usually termed "speech-inversion." This study aims to propose and compare various machine learning strategies for speech inversion: Trajectory mixture density networks (TMDNs), feedforward artificial neural networks (FF-ANN), support vector regression (SVR), autoregressive artificial neural network (AR-ANN), and distal supervised learning (DSL). Further, using a database generated by the Haskins Laboratories speech production model, we test the claim that information regarding constrictions produced by the distinct organs of the vocal tract (vocal tract variables) is superior to flesh-point information (articulatory pellet trajectories) for the inversion process.

  17. On diffusion processes with variable drift rates as models for decision making during learning

    International Nuclear Information System (INIS)

    Eckhoff, P; Holmes, P; Law, C; Connolly, P M; Gold, J I

    2008-01-01

    We investigate Ornstein-Uhlenbeck and diffusion processes with variable drift rates as models of evidence accumulation in a visual discrimination task. We derive power-law and exponential drift-rate models and characterize how parameters of these models affect the psychometric function describing performance accuracy as a function of stimulus strength and viewing time. We fit the models to psychophysical data from monkeys learning the task to identify parameters that best capture performance as it improves with training. The most informative parameter was the overall drift rate describing the signal-to-noise ratio of the sensory evidence used to form the decision, which increased steadily with training. In contrast, secondary parameters describing the time course of the drift during motion viewing did not exhibit steady trends. The results indicate that relatively simple versions of the diffusion model can fit behavior over the course of training, thereby giving a quantitative account of learning effects on the underlying decision process

  18. Latent heat coldness storage; Stockage du froid par chaleur latente

    Energy Technology Data Exchange (ETDEWEB)

    Dumas, J.P. [Pau Univ., Lab. de Thermodynamique et Energetique, LTE, 64 (France)

    2002-07-01

    This article presents the advantages of latent heat storage systems which use the solid-liquid phase transformation of a pure substance or of a solution. The three main methods of latent heat storage of coldness are presented: ice boxes, encapsulated nodules, and ice flows: 1 - definition of the thermal energy storage (sensible heat, latent heat, thermochemical storage); 2 - advantages and drawbacks of latent heat storage; 3 - choice criteria for a phase-change material; 4 - phenomenological aspect of liquid-solid transformations (phase equilibrium, crystallisation and surfusion); 5 - different latent heat storage processes (ice boxes, encapsulated nodules, two-phase refrigerating fluids); 6 - ice boxes (internal and external melting, loop, air injection, measurement of ice thickness); 7 - encapsulated nodules (nodules, tank, drainage, advantage and drawbacks, charge and discharge); 8 - two-phase refrigerating fluids (composition, ice fabrication, flow circulation, flow storage, exchangers). (J.S.)

  19. Statistical Learning and Adaptive Decision-Making Underlie Human Response Time Variability in Inhibitory Control

    Directory of Open Access Journals (Sweden)

    Ning eMa

    2015-08-01

    Full Text Available Response time (RT is an oft-reported behavioral measure in psychological and neurocognitive experiments, but the high level of observed trial-to-trial variability in this measure has often limited its usefulness. Here, we combine computational modeling and psychophysics to examine the hypothesis that fluctuations in this noisy measure reflect dynamic computations in human statistical learning and corresponding cognitive adjustments. We present data from the stop-signal task, in which subjects respond to a go stimulus on each trial, unless instructed not to by a subsequent, infrequently presented stop signal. We model across-trial learning of stop signal frequency, P(stop, and stop-signal onset time, SSD (stop-signal delay, with a Bayesian hidden Markov model, and within-trial decision-making with an optimal stochastic control model. The combined model predicts that RT should increase with both expected P(stop and SSD. The human behavioral data (n=20 bear out this prediction, showing P(stop and SSD both to be significant, independent predictors of RT, with P(stop being a more prominent predictor in 75% of the subjects, and SSD being more prominent in the remaining 25%. The results demonstrate that humans indeed readily internalize environmental statistics and adjust their cognitive/behavioral strategy accordingly, and that subtle patterns in RT variability can serve as a valuable tool for validating models of statistical learning and decision-making. More broadly, the modeling tools presented in this work can be generalized to a large body of behavioral paradigms, in order to extract insights about cognitive and neural processing from apparently quite noisy behavioral measures. We also discuss how this behaviorally validated model can then be used to conduct model-based analysis of neural data, in order to help identify specific brain areas for representing and encoding key computational quantities in learning and decision-making.

  20. Statistical learning and adaptive decision-making underlie human response time variability in inhibitory control.

    Science.gov (United States)

    Ma, Ning; Yu, Angela J

    2015-01-01

    Response time (RT) is an oft-reported behavioral measure in psychological and neurocognitive experiments, but the high level of observed trial-to-trial variability in this measure has often limited its usefulness. Here, we combine computational modeling and psychophysics to examine the hypothesis that fluctuations in this noisy measure reflect dynamic computations in human statistical learning and corresponding cognitive adjustments. We present data from the stop-signal task (SST), in which subjects respond to a go stimulus on each trial, unless instructed not to by a subsequent, infrequently presented stop signal. We model across-trial learning of stop signal frequency, P(stop), and stop-signal onset time, SSD (stop-signal delay), with a Bayesian hidden Markov model, and within-trial decision-making with an optimal stochastic control model. The combined model predicts that RT should increase with both expected P(stop) and SSD. The human behavioral data (n = 20) bear out this prediction, showing P(stop) and SSD both to be significant, independent predictors of RT, with P(stop) being a more prominent predictor in 75% of the subjects, and SSD being more prominent in the remaining 25%. The results demonstrate that humans indeed readily internalize environmental statistics and adjust their cognitive/behavioral strategy accordingly, and that subtle patterns in RT variability can serve as a valuable tool for validating models of statistical learning and decision-making. More broadly, the modeling tools presented in this work can be generalized to a large body of behavioral paradigms, in order to extract insights about cognitive and neural processing from apparently quite noisy behavioral measures. We also discuss how this behaviorally validated model can then be used to conduct model-based analysis of neural data, in order to help identify specific brain areas for representing and encoding key computational quantities in learning and decision-making.

  1. Multilevel Latent Class Analysis: Parametric and Nonparametric Models

    Science.gov (United States)

    Finch, W. Holmes; French, Brian F.

    2014-01-01

    Latent class analysis is an analytic technique often used in educational and psychological research to identify meaningful groups of individuals within a larger heterogeneous population based on a set of variables. This technique is flexible, encompassing not only a static set of variables but also longitudinal data in the form of growth mixture…

  2. Latent factors and route choice behaviour

    DEFF Research Database (Denmark)

    Prato, Carlo Giacomo

    . A reliable dataset was prepared through measures of internal consistency and sampling adequacy, and data were analyzed with a proper application of factor analysis to the route choice context. For the dataset obtained from the survey, six latent constructs affecting driver behaviour were extracted and scores...... on each factor for each survey participant were calculated. Path generation algorithms were examined with respect to observed behaviour, through a measure of reproduction with deterministic techniques of the routes indicated in the answers to the survey. Results presented evidence that the majority...... and Link Nested Logit. Estimates were produced from model specifications that considered level-of-service, label and facility dummy variables. Moreover, a modelling framework was designed to represent drivers’ choices as affected by the latent constructs extracted with factor analysis. Previous experience...

  3. Impact of Climate Variability on Maize Production in Pakistan using Remote Sensing and Machine Learning

    Science.gov (United States)

    Richetti, J.; Ahmad, I.; Aristizabal, F.; Judge, J.

    2017-12-01

    Determining maize agricultural production under climate variability is valuable to policy makers in Pakistan since maize is the third most produced crop by area after wheat and rice. This study aims to predict the maize production under climate variability. Two-hundred ground truth points of both maize and non-maize land covers were collected from the Faisalabad district during the growing seasons of 2015 and 2016. Landsat-8 images taken in second week of May which correspond spatially and temporally to the local, peak growing season for maize were gathered. For classifying the region training data was constructed for a variety of machine learning algorithms by sampling the second, third, and fourth bands of the Landsat-8 imagery at these reference locations. Cross validation was used for parameter tuning as well as estimating the generalized performances. All the classifiers resulted in overall accuracies of greater than 90% for both years and a support vector machine with a radial basis kernel recorded the maximum accuracy of 97%. The tuned models were used to determine the spatial distribution of maize fields for both growing seasons in the Faisalabad district using parallel processing to improve computation time. The overall classified maize growing area represented 12% difference than that reported by the Crop Reporting Service (CRS) of Punjab Pakistan for both 2015 and 2016. For the agricultural production normalized difference vegetation index from Landsat-8 and climate indicators from ground stations will be used as inputs in a variety of machine learning regression algorithms. The expected results will be compared to actual yield from 64 commercial farms. To verify the impact of climate variability in the maize agricultural production historical climate data from previous 30 years will be used in the developed model to asses the impact of climate variability on the maize production.

  4. Elevated dopamine alters consummatory pattern generation and increases behavioral variability during learning

    Directory of Open Access Journals (Sweden)

    Mark A. Rossi

    2015-05-01

    Full Text Available The role of dopamine in controlling behavior remains poorly understood. In this study we examined licking behavior in an established hyperdopaminergic mouse model—dopamine transporter knockout (DAT KO mice. DAT KO mice showed higher rates of licking, which is due to increased perseveration of licking in a bout. By contrast, they showed increased individual lick durations, and reduced inter-lick-intervals. During extinction, both KO and control mice transiently increased variability in lick pattern generation while reducing licking rate, yet they showed very different behavioral patterns. Control mice gradually increased lick duration as well as variability. By contrast, DAT KO mice exhibited more immediate (within 10 licks adjustments—an immediate increase in lick duration variability, as well as more rapid extinction. These results suggest that the level of dopamine can modulate the persistence and pattern generation of a highly stereotyped consummatory behavior like licking, as well as new learning in response to changes in environmental feedback. Increased dopamine in DAT KO mice not only increased perseveration of bouts and individual lick duration, but also increased the behavioral variability in response to the extinction contingency and the rate of extinction.

  5. The Latent Structure of Dictionaries.

    Science.gov (United States)

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

    2016-07-01

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

  6. Motivation in foreign language learning: a look at type of school environment as a contextual variable

    Directory of Open Access Journals (Sweden)

    Pavičić Takać Višnja

    2014-12-01

    Full Text Available Impelled by the observation that motivation might be one of the most important factors within the affective domain influencing foreign language learning (FLL, the field of second language acquisition (SLA has seen an intense worldwide interest in empirical research in motivational issues. The studies have been rooted in different theories and methodologies, (most notably those advanced by Gardner and Dörnyei and their respective associates that have given precedence to a number of variables assumed to play an important role in understanding the phenomenon of FLL motivation. The present study is set between the macroperspective of the social-psychological period–by giving a general view of second language motivation–and the situation-specific period–by taking into consideration the immediate learning context. It focuses on exploring the nature of FLL motivation in Croatia at secondary education level where FLL is part of core curriculum. In particular, it explores the role of one specific contextual variable that has been largely ignored in SLA motivational research, i.e. type of school environment, and its interaction with gender and success in FLL.

  7. Robust Machine Learning Variable Importance Analyses of Medical Conditions for Health Care Spending.

    Science.gov (United States)

    Rose, Sherri

    2018-03-11

    To propose nonparametric double robust machine learning in variable importance analyses of medical conditions for health spending. 2011-2012 Truven MarketScan database. I evaluate how much more, on average, commercially insured enrollees with each of 26 of the most prevalent medical conditions cost per year after controlling for demographics and other medical conditions. This is accomplished within the nonparametric targeted learning framework, which incorporates ensemble machine learning. Previous literature studying the impact of medical conditions on health care spending has almost exclusively focused on parametric risk adjustment; thus, I compare my approach to parametric regression. My results demonstrate that multiple sclerosis, congestive heart failure, severe cancers, major depression and bipolar disorders, and chronic hepatitis are the most costly medical conditions on average per individual. These findings differed from those obtained using parametric regression. The literature may be underestimating the spending contributions of several medical conditions, which is a potentially critical oversight. If current methods are not capturing the true incremental effect of medical conditions, undesirable incentives related to care may remain. Further work is needed to directly study these issues in the context of federal formulas. © Health Research and Educational Trust.

  8. How do task characteristics affect learning and performance? The roles of variably mapped and dynamic tasks.

    Science.gov (United States)

    Macnamara, Brooke N; Frank, David J

    2018-05-01

    For well over a century, scientists have investigated individual differences in performance. The majority of studies have focused on either differences in practice, or differences in cognitive resources. However, the predictive ability of either practice or cognitive resources varies considerably across tasks. We are the first to examine task characteristics' impact on learning and performance in a complex task while controlling for other task characteristics. In 2 experiments we test key theoretical task characteristic thought to moderate the relationship between practice, cognitive resources, and performance. We devised a task where each of several key task characteristics can be manipulated independently. Participants played 5 rounds of a game similar to the popular tower defense videogame Plants vs. Zombies where both cognitive load and game characteristics were manipulated. In Experiment 1, participants either played a consistently mapped version-the stimuli and the associated meaning of their properties were constant across the 5 rounds-or played a variably mapped version-the stimuli and the associated meaning of their properties changed every few minutes. In Experiment 2, participants either played a static version-that is, turn taking with no time pressure-or played a dynamic version-that is, the stimuli moved regardless of participants' response rates. In Experiment 1, participants' accuracy and efficiency were substantially hindered in the variably mapped conditions. In Experiment 2, learning and performance accuracy were hindered in the dynamic conditions, especially when under cognitive load. Our results suggest that task characteristics impact the relative importance of cognitive resources and practice on predicting learning and performance. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

  9. Variables that impact the implementation of project-based learning in high school science

    Science.gov (United States)

    Cunningham, Kellie

    Wagner and colleagues (2006) state the mediocrity of teaching and instructional leadership is the central problem that must be addressed if we are to improve student achievement. Educational reform efforts have been initiated to improve student performance and to hold teachers and school leaders accountable for student achievement (Wagner et al., 2006). Specifically, in the area of science, goals for improving student learning have led reformers to establish standards for what students should know and be able to do, as well as what instructional methods should be used. Key concepts and principles have been identified for student learning. Additionally, reformers recommend student-centered, inquiry-based practices that promote a deep understanding of how science is embedded in the everyday world. These new approaches to science education emphasize inquiry as an essential element for student learning (Schneider, Krajcik, Marx, & Soloway, 2002). Project-based learning (PBL) is an inquiry-based instructional approach that addresses these recommendations for science education reform. The objective of this research was to study the implementation of project-based learning (PBL) in an urban school undergoing reform efforts and identify the variables that positively or negatively impacted the PBL implementation process and its outcomes. This study responded to the need to change how science is taught by focusing on the implementation of project-based learning as an instructional approach to improve student achievement in science and identify the role of both school leaders and teachers in the creation of a school environment that supports project-based learning. A case study design using a mixed-method approach was used in this study. Data were collected through individual interviews with the school principal, science instructional coach, and PBL facilitator. A survey, classroom observations and interviews involving three high school science teachers teaching grades 9

  10. Interexaminer variation of minutia markup on latent fingerprints.

    Science.gov (United States)

    Ulery, Bradford T; Hicklin, R Austin; Roberts, Maria Antonia; Buscaglia, JoAnn

    2016-07-01

    Latent print examiners often differ in the number of minutiae they mark during analysis of a latent, and also during comparison of a latent with an exemplar. Differences in minutia counts understate interexaminer variability: examiners' markups may have similar minutia counts but differ greatly in which specific minutiae were marked. We assessed variability in minutia markup among 170 volunteer latent print examiners. Each provided detailed markup documenting their examinations of 22 latent-exemplar pairs of prints randomly assigned from a pool of 320 pairs. An average of 12 examiners marked each latent. The primary factors associated with minutia reproducibility were clarity, which regions of the prints examiners chose to mark, and agreement on value or comparison determinations. In clear areas (where the examiner was "certain of the location, presence, and absence of all minutiae"), median reproducibility was 82%; in unclear areas, median reproducibility was 46%. Differing interpretations regarding which regions should be marked (e.g., when there is ambiguity in the continuity of a print) contributed to variability in minutia markup: especially in unclear areas, marked minutiae were often far from the nearest minutia marked by a majority of examiners. Low reproducibility was also associated with differences in value or comparison determinations. Lack of standardization in minutia markup and unfamiliarity with test procedures presumably contribute to the variability we observed. We have identified factors accounting for interexaminer variability; implementing standards for detailed markup as part of documentation and focusing future training efforts on these factors may help to facilitate transparency and reduce subjectivity in the examination process. Published by Elsevier Ireland Ltd.

  11. Chaos Synchronization Using Adaptive Dynamic Neural Network Controller with Variable Learning Rates

    Directory of Open Access Journals (Sweden)

    Chih-Hong Kao

    2011-01-01

    Full Text Available This paper addresses the synchronization of chaotic gyros with unknown parameters and external disturbance via an adaptive dynamic neural network control (ADNNC system. The proposed ADNNC system is composed of a neural controller and a smooth compensator. The neural controller uses a dynamic RBF (DRBF network to online approximate an ideal controller. The DRBF network can create new hidden neurons online if the input data falls outside the hidden layer and prune the insignificant hidden neurons online if the hidden neuron is inappropriate. The smooth compensator is designed to compensate for the approximation error between the neural controller and the ideal controller. Moreover, the variable learning rates of the parameter adaptation laws are derived based on a discrete-type Lyapunov function to speed up the convergence rate of the tracking error. Finally, the simulation results which verified the chaotic behavior of two nonlinear identical chaotic gyros can be synchronized using the proposed ADNNC scheme.

  12. Latent Inhibition in an Insect: The Role of Aminergic Signaling

    Science.gov (United States)

    Fernandez, Vanesa M.; Giurfa, Martin; Devaud, Jean-Marc; Farina, Walter M.

    2012-01-01

    Latent inhibition (LI) is a decrement in learning performance that results from the nonreinforced pre-exposure of the to-be-conditioned stimulus, in both vertebrates and invertebrates. In vertebrates, LI development involves dopamine and serotonin; in invertebrates there is yet no information. We studied differential olfactory conditioning of the…

  13. Relations of some sociocultural variables and attitudes and motivations of young Arab students learning English as a second language.

    Science.gov (United States)

    Lori, A A; al-Ansari, S H

    2001-02-01

    This paper examined a number of variables pertaining to the sociocultural outlooks of 412 young Arab students learning English as a foreign language and the relation of their attitudes and motivations prior to their learning of the language. Analysis indicated clearly that certain variables appeared to be correlated with their attitudes and motivations more than others. Most of the students had maids in their homes, and the presence of a maid was associated with most of the psycholinguistic variables tested. Their previous learning experience of the language was positively correlated as was their knowledge of English stories. Having some sort of English games had the highest correlations (.25 to .41). Potential pedagogical implications of these results were discussed.

  14. Do cooperative learning and family involvement improve variables linked to academic performance?

    Science.gov (United States)

    Santos Rego, Miguel A; Ferraces Otero, María J; Godas Otero, Agustín; Lorenzo Moledo, María M

    2018-05-01

    One of the most serious problems in the Spanish education system is the high percentage of school failure in Compulsory Secondary Education. The aim of this study is to analyze the influence of a socio-educational program based on cooperative learning and family involvement on a series of variables related to academic performance, paying particular attention to the differences between retained and non-retained students. A two-group quasi-experimental design incorporating pre-testing and post-testing was used. The study involved 146 students in the experimental group and 123 in the control group, 8 teachers, and 89 parents or other family members. The program was observed to have a positive effect on self-image, study habits, satisfaction with the subject, maternal support and control, and opinions about the school. In addition, the results for non-retained students are better. Cooperative work and family involvement in education affect the variables which research links to improving school performance.

  15. Building latent class trees, with an application to a study of social capital

    NARCIS (Netherlands)

    van den Bergh, M.; Schmittmann, V.D.; Vermunt, J.K.

    2017-01-01

    Researchers use latent class (LC) analysis to derive meaningful clusters from sets of categorical variables. However, especially when the number of classes required to obtain a good fit is large, interpretation of the latent classes may not be straightforward. To overcome this problem, we propose an

  16. Piecewise Linear-Linear Latent Growth Mixture Models with Unknown Knots

    Science.gov (United States)

    Kohli, Nidhi; Harring, Jeffrey R.; Hancock, Gregory R.

    2013-01-01

    Latent growth curve models with piecewise functions are flexible and useful analytic models for investigating individual behaviors that exhibit distinct phases of development in observed variables. As an extension of this framework, this study considers a piecewise linear-linear latent growth mixture model (LGMM) for describing segmented change of…

  17. Longitudinal mixed-effects models for latent cognitive function

    NARCIS (Netherlands)

    van den Hout, Ardo; Fox, Gerardus J.A.; Muniz-Terrera, Graciela

    2015-01-01

    A mixed-effects regression model with a bent-cable change-point predictor is formulated to describe potential decline of cognitive function over time in the older population. For the individual trajectories, cognitive function is considered to be a latent variable measured through an item response

  18. Latent cluster analysis of ALS phenotypes identifies prognostically differing groups.

    Directory of Open Access Journals (Sweden)

    Jeban Ganesalingam

    2009-09-01

    Full Text Available Amyotrophic lateral sclerosis (ALS is a degenerative disease predominantly affecting motor neurons and manifesting as several different phenotypes. Whether these phenotypes correspond to different underlying disease processes is unknown. We used latent cluster analysis to identify groupings of clinical variables in an objective and unbiased way to improve phenotyping for clinical and research purposes.Latent class cluster analysis was applied to a large database consisting of 1467 records of people with ALS, using discrete variables which can be readily determined at the first clinic appointment. The model was tested for clinical relevance by survival analysis of the phenotypic groupings using the Kaplan-Meier method.The best model generated five distinct phenotypic classes that strongly predicted survival (p<0.0001. Eight variables were used for the latent class analysis, but a good estimate of the classification could be obtained using just two variables: site of first symptoms (bulbar or limb and time from symptom onset to diagnosis (p<0.00001.The five phenotypic classes identified using latent cluster analysis can predict prognosis. They could be used to stratify patients recruited into clinical trials and generating more homogeneous disease groups for genetic, proteomic and risk factor research.

  19. Latent geometry of bipartite networks

    Science.gov (United States)

    Kitsak, Maksim; Papadopoulos, Fragkiskos; Krioukov, Dmitri

    2017-03-01

    Despite the abundance of bipartite networked systems, their organizing principles are less studied compared to unipartite networks. Bipartite networks are often analyzed after projecting them onto one of the two sets of nodes. As a result of the projection, nodes of the same set are linked together if they have at least one neighbor in common in the bipartite network. Even though these projections allow one to study bipartite networks using tools developed for unipartite networks, one-mode projections lead to significant loss of information and artificial inflation of the projected network with fully connected subgraphs. Here we pursue a different approach for analyzing bipartite systems that is based on the observation that such systems have a latent metric structure: network nodes are points in a latent metric space, while connections are more likely to form between nodes separated by shorter distances. This approach has been developed for unipartite networks, and relatively little is known about its applicability to bipartite systems. Here, we fully analyze a simple latent-geometric model of bipartite networks and show that this model explains the peculiar structural properties of many real bipartite systems, including the distributions of common neighbors and bipartite clustering. We also analyze the geometric information loss in one-mode projections in this model and propose an efficient method to infer the latent pairwise distances between nodes. Uncovering the latent geometry underlying real bipartite networks can find applications in diverse domains, ranging from constructing efficient recommender systems to understanding cell metabolism.

  20. Variability in University Students' Use of Technology: An "Approaches to Learning" Perspective

    Science.gov (United States)

    Mimirinis, Mike

    2016-01-01

    This study reports the results of a cross-case study analysis of how students' approaches to learning are demonstrated in blended learning environments. It was initially propositioned that approaches to learning as key determinants of the quality of student learning outcomes are demonstrated specifically in how students utilise technology in…

  1. Machine learning from hard x-ray surveys: applications to magnetic cataclysmic variable studies

    Science.gov (United States)

    Scaringi, Simone

    2009-11-01

    Within this thesis are discussed two main topics of contemporary astrophysics. The first is that of machine learning algorithms for astronomy whilst the second is that of magnetic cataclysmic variables (mCVs). To begin, an overview is given of ISINA: INTEGRAL Scouce Identifiction Network Algorithm. This machine learning algorithm, using random forests, is applied to the IBIS/ISGRI data set in order to ease the production of unbiased future soft gamma-ray source catalogues. The feature extraction process on an initial candidate list is described together with feature merging. Three trainng and testing sets are created in order to deal with the diverse time-scales encountered when dealing with the gamma-ray sky: one dealing with faint persistent source recognition, one dealing with strong persistent sources and a final one dealing with transients. For the latter, a new transient detection technique is introduced and described: the transient matrix. Finally the performance of the network is assessed and discussed using the testing set and some illustrative source examples. ISINA is also compared to the more conventional approach of visual inspection. Next mCVs are discussed, and in particular the properties arising from a hard X-ray selected sample which has proven remarkably efficient in detecting intermediate polars and asynchronous polars, two of the rarest type of cataclysmic variables (CVs). This thesis focuses particularly on the link between hard X-ray properties and spin/orbital periods. To this end, a new sample of these objects is constructed by cross-corelating candidate sources detected in INTEGRAL/IBIS observations against catalogues of known CVs. Also included in the analysis are hard X-ray Observations from Swift/BAT and SUZAKU/HXD in order to make the study more complete. It is found that most hard X-ray detected mCVs have Pspin/Porb<0.1 above the period gap. In this respect, attention is given to the very low number of detected systems in any ban

  2. A Spreadsheet-Based Visualized Mindtool for Improving Students' Learning Performance in Identifying Relationships between Numerical Variables

    Science.gov (United States)

    Lai, Chiu-Lin; Hwang, Gwo-Jen

    2015-01-01

    In this study, a spreadsheet-based visualized Mindtool was developed for improving students' learning performance when finding relationships between numerical variables by engaging them in reasoning and decision-making activities. To evaluate the effectiveness of the proposed approach, an experiment was conducted on the "phenomena of climate…

  3. Foreign Language Learning in a "Monoglot Culture": Motivational Variables amongst Students of French and Spanish at an English University

    Science.gov (United States)

    Oakes, Leigh

    2013-01-01

    The study on which this article is based investigated reasons for learning a foreign language at university in a predominantly English-speaking environment (the UK). It examined the relative importance of motivational variables as theorised in the field of second language (L2) motivation, and the effect of first language (L1) and linguistic…

  4. Biomarkers of latent TB infection

    DEFF Research Database (Denmark)

    Ruhwald, Morten; Ravn, Pernille

    2009-01-01

    For the last 100 years, the tuberculin skin test (TST) has been the only diagnostic tool available for latent TB infection (LTBI) and no biomarker per se is available to diagnose the presence of LTBI. With the introduction of M. tuberculosis-specific IFN-gamma release assays (IGRAs), a new area...... of in vitro immunodiagnostic tests for LTBI based on biomarker readout has become a reality. In this review, we discuss existing evidence on the clinical usefulness of IGRAs and the indefinite number of potential new biomarkers that can be used to improve diagnosis of latent TB infection. We also present...... early data suggesting that the monocyte-derived chemokine inducible protein-10 may be useful as a novel biomarker for the immunodiagnosis of latent TB infection....

  5. Estimators for longitudinal latent exposure models: examining measurement model assumptions.

    Science.gov (United States)

    Sánchez, Brisa N; Kim, Sehee; Sammel, Mary D

    2017-06-15

    Latent variable (LV) models are increasingly being used in environmental epidemiology as a way to summarize multiple environmental exposures and thus minimize statistical concerns that arise in multiple regression. LV models may be especially useful when multivariate exposures are collected repeatedly over time. LV models can accommodate a variety of assumptions but, at the same time, present the user with many choices for model specification particularly in the case of exposure data collected repeatedly over time. For instance, the user could assume conditional independence of observed exposure biomarkers given the latent exposure and, in the case of longitudinal latent exposure variables, time invariance of the measurement model. Choosing which assumptions to relax is not always straightforward. We were motivated by a study of prenatal lead exposure and mental development, where assumptions of the measurement model for the time-changing longitudinal exposure have appreciable impact on (maximum-likelihood) inferences about the health effects of lead exposure. Although we were not particularly interested in characterizing the change of the LV itself, imposing a longitudinal LV structure on the repeated multivariate exposure measures could result in high efficiency gains for the exposure-disease association. We examine the biases of maximum likelihood estimators when assumptions about the measurement model for the longitudinal latent exposure variable are violated. We adapt existing instrumental variable estimators to the case of longitudinal exposures and propose them as an alternative to estimate the health effects of a time-changing latent predictor. We show that instrumental variable estimators remain unbiased for a wide range of data generating models and have advantages in terms of mean squared error. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

  6. Dynamic sensorimotor planning during long-term sequence learning: the role of variability, response chunking and planning errors.

    Science.gov (United States)

    Verstynen, Timothy; Phillips, Jeff; Braun, Emily; Workman, Brett; Schunn, Christian; Schneider, Walter

    2012-01-01

    Many everyday skills are learned by binding otherwise independent actions into a unified sequence of responses across days or weeks of practice. Here we looked at how the dynamics of action planning and response binding change across such long timescales. Subjects (N = 23) were trained on a bimanual version of the serial reaction time task (32-item sequence) for two weeks (10 days total). Response times and accuracy both showed improvement with time, but appeared to be learned at different rates. Changes in response speed across training were associated with dynamic changes in response time variability, with faster learners expanding their variability during the early training days and then contracting response variability late in training. Using a novel measure of response chunking, we found that individual responses became temporally correlated across trials and asymptoted to set sizes of approximately 7 bound responses at the end of the first week of training. Finally, we used a state-space model of the response planning process to look at how predictive (i.e., response anticipation) and error-corrective (i.e., post-error slowing) processes correlated with learning rates for speed, accuracy and chunking. This analysis yielded non-monotonic association patterns between the state-space model parameters and learning rates, suggesting that different parts of the response planning process are relevant at different stages of long-term learning. These findings highlight the dynamic modulation of response speed, variability, accuracy and chunking as multiple movements become bound together into a larger set of responses during sequence learning.

  7. Latent Fundamentals Arbitrage with a Mixed Effects Factor Model

    Directory of Open Access Journals (Sweden)

    Andrei Salem Gonçalves

    2012-09-01

    Full Text Available We propose a single-factor mixed effects panel data model to create an arbitrage portfolio that identifies differences in firm-level latent fundamentals. Furthermore, we show that even though the characteristics that affect returns are unknown variables, it is possible to identify the strength of the combination of these latent fundamentals for each stock by following a simple approach using historical data. As a result, a trading strategy that bought the stocks with the best fundamentals (strong fundamentals portfolio and sold the stocks with the worst ones (weak fundamentals portfolio realized significant risk-adjusted returns in the U.S. market for the period between July 1986 and June 2008. To ensure robustness, we performed sub period and seasonal analyses and adjusted for trading costs and we found further empirical evidence that using a simple investment rule, that identified these latent fundamentals from the structure of past returns, can lead to profit.

  8. Association between latent toxoplasmosis and cognition in adults: a cross-sectional study.

    Science.gov (United States)

    Gale, S D; Brown, B L; Erickson, L D; Berrett, A; Hedges, D W

    2015-04-01

    Latent infection from Toxoplasma gondii (T. gondii) is widespread worldwide and has been associated with cognitive deficits in some but not all animal models and in humans. We tested the hypothesis that latent toxoplasmosis is associated with decreased cognitive function in a large cross-sectional dataset, the National Health and Nutrition Examination Survey (NHANES). There were 4178 participants aged 20-59 years, of whom 19.1% had IgG antibodies against T. gondii. Two ordinary least squares (OLS) regression models adjusted for the NHANES complex sampling design and weighted to represent the US population were estimated for simple reaction time, processing speed and short-term memory or attention. The first model included only main effects of latent toxoplasmosis and demographic control variables, and the second added interaction terms between latent toxoplasmosis and the poverty-to-income ratio (PIR), educational attainment and race-ethnicity. We also used multivariate models to assess all three cognitive outcomes in the same model. Although the models evaluating main effects only demonstrated no association between latent toxoplasmosis and the cognitive outcomes, significant interactions between latent toxoplasmosis and the PIR, between latent toxoplasmosis and educational attainment, and between latent toxoplasmosis and race-ethnicity indicated that latent toxoplasmosis may adversely affect cognitive function in certain groups.

  9. Latent class models for classification

    NARCIS (Netherlands)

    Vermunt, J.K.; Magidson, J.

    2003-01-01

    An overview is provided of recent developments in the use of latent class (LC) and other types of finite mixture models for classification purposes. Several extensions of existing models are presented. Two basic types of LC models for classification are defined: supervised and unsupervised

  10. The sequential hypothesis of sleep function. IV. A correlative analysis of sleep variables in learning and nonlearning rats.

    Science.gov (United States)

    Langella, M; Colarieti, L; Ambrosini, M V; Giuditta, A

    1992-02-01

    Female adult rats were trained for a two-way active avoidance task (4 h), and allowed free sleep (3 h). Control rats (C) were left in their home cages during the acquisition period. Dural electrodes and an intraventricular cannula, implanted one week in advance, were used for EEG recording during the period of sleep and for the injection of [3H]thymidine at the beginning of the training session, respectively. Rats were killed at the end of the sleep period, and the DNA-specific activity was determined in the main brain regions and in liver. Correlations among sleep, behavioral and biochemical variables were assessed using Spearman's nonparametric method. In learning rats (L), the number of avoidances was negatively correlated with SS-W variables, and positively correlated with SS-PS variables (episodes of synchronized sleep followed by wakefulness or paradoxical sleep, respectively) and with PS variables. An inverse pattern of correlations was shown by the number of escapes or freezings. No correlations occurred in rats unable to achieve the learning criterion (NL). In L rats, the specific activity of brain DNA was negatively correlated with SS-W variables and positively correlated with SS-PS variables, while essentially no correlation concerned PS variables. On the other hand, in NL rats, comparable correlations were positive with SS-W variables and negative with SS-PS and PS variables. Few and weak correlations occurred in C rats. The data support a role of SS in brain information processing, as postulated by the sequential hypothesis on the function of sleep. In addition, they suggest that the elimination of nonadaptive memory traces may require several SS-W episodes and a terminal SS-PS episode. During PS episodes, adaptive memory traces cleared of nonadaptive components may be copied in more suitable brain sites.

  11. Where's the Noise? Key Features of Spontaneous Activity and Neural Variability Arise through Learning in a Deterministic Network.

    Directory of Open Access Journals (Sweden)

    Christoph Hartmann

    2015-12-01

    Full Text Available Even in the absence of sensory stimulation the brain is spontaneously active. This background "noise" seems to be the dominant cause of the notoriously high trial-to-trial variability of neural recordings. Recent experimental observations have extended our knowledge of trial-to-trial variability and spontaneous activity in several directions: 1. Trial-to-trial variability systematically decreases following the onset of a sensory stimulus or the start of a motor act. 2. Spontaneous activity states in sensory cortex outline the region of evoked sensory responses. 3. Across development, spontaneous activity aligns itself with typical evoked activity patterns. 4. The spontaneous brain activity prior to the presentation of an ambiguous stimulus predicts how the stimulus will be interpreted. At present it is unclear how these observations relate to each other and how they arise in cortical circuits. Here we demonstrate that all of these phenomena can be accounted for by a deterministic self-organizing recurrent neural network model (SORN, which learns a predictive model of its sensory environment. The SORN comprises recurrently coupled populations of excitatory and inhibitory threshold units and learns via a combination of spike-timing dependent plasticity (STDP and homeostatic plasticity mechanisms. Similar to balanced network architectures, units in the network show irregular activity and variable responses to inputs. Additionally, however, the SORN exhibits sequence learning abilities matching recent findings from visual cortex and the network's spontaneous activity reproduces the experimental findings mentioned above. Intriguingly, the network's behaviour is reminiscent of sampling-based probabilistic inference, suggesting that correlates of sampling-based inference can develop from the interaction of STDP and homeostasis in deterministic networks. We conclude that key observations on spontaneous brain activity and the variability of neural

  12. On Latent Growth Models for Composites and Their Constituents.

    Science.gov (United States)

    Hancock, Gregory R; Mao, Xiulin; Kher, Hemant

    2013-09-01

    Over the last decade and a half, latent growth modeling has become an extremely popular and versatile technique for evaluating longitudinal change and its determinants. Most common among the models applied are those for a single measured variable over time. This model has been extended in a variety of ways, most relevant for the current work being the multidomain and the second-order latent growth models. Whereas the former allows for growth function characteristics to be modeled for multiple outcomes simultaneously, with the degree of growth characteristics' relations assessed within the model (e.g., cross-domain slope factor correlations), the latter models growth in latent outcomes, each of which has effect indicators repeated over time. But what if one has an outcome that is believed to be formative relative to its indicator variables rather than latent? In this case, where the outcome is a composite of multiple constituents, modeling change over time is less straightforward. This article provides analytical and applied details for simultaneously modeling growth in composites and their constituent elements, including a real data example using a general computer self-efficacy questionnaire.

  13. Latent class analysis of early developmental trajectory in baby siblings of children with autism.

    Science.gov (United States)

    Landa, Rebecca J; Gross, Alden L; Stuart, Elizabeth A; Bauman, Margaret

    2012-09-01

    Siblings of children with autism (sibs-A) are at increased genetic risk for autism spectrum disorders (ASD) and milder impairments. To elucidate diversity and contour of early developmental trajectories exhibited by sibs-A, regardless of diagnostic classification, latent class modeling was used. Sibs-A (N = 204) were assessed with the Mullen Scales of Early Learning from age 6 to 36 months. Mullen T scores served as dependent variables. Outcome classifications at age 36 months included: ASD (N = 52); non-ASD social/communication delay (broader autism phenotype; BAP; N = 31); and unaffected (N = 121). Child-specific patterns of performance were studied using latent class growth analysis. Latent class membership was then related to diagnostic outcome through estimation of within-class proportions of children assigned to each diagnostic classification. A 4-class model was favored. Class 1 represented accelerated development and consisted of 25.7% of the sample, primarily unaffected children. Class 2 (40.0% of the sample), was characterized by normative development with above-average nonverbal cognitive outcome. Class 3 (22.3% of the sample) was characterized by receptive language, and gross and fine motor delay. Class 4 (12.0% of the sample), was characterized by widespread delayed skill acquisition, reflected by declining trajectories. Children with an outcome diagnosis of ASD were spread across Classes 2, 3, and 4. Results support a category of ASD that involves slowing in early non-social development. Receptive language and motor development is vulnerable to early delay in sibs-A with and without ASD outcomes. Non-ASD sibs-A are largely distributed across classes depicting average or accelerated development. Developmental trajectories of motor, language, and cognition appear independent of communication and social delays in non-ASD sibs-A. © 2012 The Authors. Journal of Child Psychology and Psychiatry © 2012 Association for Child and Adolescent Mental Health.

  14. Inter-Labeler and Intra-Labeler Variability of Condition Severity Classification Models Using Active and Passive Learning Methods

    Science.gov (United States)

    Nissim, Nir; Shahar, Yuval; Boland, Mary Regina; Tatonetti, Nicholas P; Elovici, Yuval; Hripcsak, George; Moskovitch, Robert

    2018-01-01

    Background and Objectives Labeling instances by domain experts for classification is often time consuming and expensive. To reduce such labeling efforts, we had proposed the application of active learning (AL) methods, introduced our CAESAR-ALE framework for classifying the severity of clinical conditions, and shown its significant reduction of labeling efforts. The use of any of three AL methods (one well known [SVM-Margin], and two that we introduced [Exploitation and Combination_XA]) significantly reduced (by 48% to 64%) condition labeling efforts, compared to standard passive (random instance-selection) SVM learning. Furthermore, our new AL methods achieved maximal accuracy using 12% fewer labeled cases than the SVM-Margin AL method. However, because labelers have varying levels of expertise, a major issue associated with learning methods, and AL methods in particular, is how to best to use the labeling provided by a committee of labelers. First, we wanted to know, based on the labelers’ learning curves, whether using AL methods (versus standard passive learning methods) has an effect on the Intra-labeler variability (within the learning curve of each labeler) and inter-labeler variability (among the learning curves of different labelers). Then, we wanted to examine the effect of learning (either passively or actively) from the labels created by the majority consensus of a group of labelers. Methods We used our CAESAR-ALE framework for classifying the severity of clinical conditions, the three AL methods and the passive learning method, as mentioned above, to induce the classifications models. We used a dataset of 516 clinical conditions and their severity labeling, represented by features aggregated from the medical records of 1.9 million patients treated at Columbia University Medical Center. We analyzed the variance of the classification performance within (intra-labeler), and especially among (inter-labeler) the classification models that were induced by

  15. Inter-labeler and intra-labeler variability of condition severity classification models using active and passive learning methods.

    Science.gov (United States)

    Nissim, Nir; Shahar, Yuval; Elovici, Yuval; Hripcsak, George; Moskovitch, Robert

    2017-09-01

    Labeling instances by domain experts for classification is often time consuming and expensive. To reduce such labeling efforts, we had proposed the application of active learning (AL) methods, introduced our CAESAR-ALE framework for classifying the severity of clinical conditions, and shown its significant reduction of labeling efforts. The use of any of three AL methods (one well known [SVM-Margin], and two that we introduced [Exploitation and Combination_XA]) significantly reduced (by 48% to 64%) condition labeling efforts, compared to standard passive (random instance-selection) SVM learning. Furthermore, our new AL methods achieved maximal accuracy using 12% fewer labeled cases than the SVM-Margin AL method. However, because labelers have varying levels of expertise, a major issue associated with learning methods, and AL methods in particular, is how to best to use the labeling provided by a committee of labelers. First, we wanted to know, based on the labelers' learning curves, whether using AL methods (versus standard passive learning methods) has an effect on the Intra-labeler variability (within the learning curve of each labeler) and inter-labeler variability (among the learning curves of different labelers). Then, we wanted to examine the effect of learning (either passively or actively) from the labels created by the majority consensus of a group of labelers. We used our CAESAR-ALE framework for classifying the severity of clinical conditions, the three AL methods and the passive learning method, as mentioned above, to induce the classifications models. We used a dataset of 516 clinical conditions and their severity labeling, represented by features aggregated from the medical records of 1.9 million patients treated at Columbia University Medical Center. We analyzed the variance of the classification performance within (intra-labeler), and especially among (inter-labeler) the classification models that were induced by using the labels provided by seven

  16. Latent Space Embedding for Retrieval in Question-Answer Archives

    OpenAIRE

    Padmanabhan, Deepak; Garg, Dinesh; Shevade, Shirish

    2017-01-01

    Community-driven Question Answering (CQA) systems such as Yahoo! Answers have become valuable sources of reusable information. CQA retrieval enables usage of historical CQA archives to solve new questions posed by users. This task has received much recent attention, with methods building upon literature from translation models, topic models, and deep learning. In this paper, we devise a CQA retrieval technique, LASER-QA, that embeds question-answer pairs within a unified latent space preservi...

  17. Developing models to predict 8th grade students' achievement levels on timss science based on opportunity-to-learn variables

    Science.gov (United States)

    Whitford, Melinda M.

    Science educational reforms have placed major emphasis on improving science classroom instruction and it is therefore vital to study opportunity-to-learn (OTL) variables related to student science learning experiences and teacher teaching practices. This study will identify relationships between OTL and student science achievement and will identify OTL predictors of students' attainment at various distinct achievement levels (low/intermediate/high/advanced). Specifically, the study (a) address limitations of previous studies by examining a large number of independent and control variables that may impact students' science achievement and (b) it will test hypotheses of structural relations to how the identified predictors and mediating factors impact on student achievement levels. The study will follow a multi-stage and integrated bottom-up and top-down approach to identify predictors of students' achievement levels on standardized tests using TIMSS 2011 dataset. Data mining or pattern recognition, a bottom-up approach will identify the most prevalent association patterns between different student achievement levels and variables related to student science learning experiences, teacher teaching practices and home and school environments. The second stage is a top-down approach, testing structural equation models of relations between the significant predictors and students' achievement levels according.

  18. Logic Learning Machine and standard supervised methods for Hodgkin's lymphoma prognosis using gene expression data and clinical variables.

    Science.gov (United States)

    Parodi, Stefano; Manneschi, Chiara; Verda, Damiano; Ferrari, Enrico; Muselli, Marco

    2018-03-01

    This study evaluates the performance of a set of machine learning techniques in predicting the prognosis of Hodgkin's lymphoma using clinical factors and gene expression data. Analysed samples from 130 Hodgkin's lymphoma patients included a small set of clinical variables and more than 54,000 gene features. Machine learning classifiers included three black-box algorithms ( k-nearest neighbour, Artificial Neural Network, and Support Vector Machine) and two methods based on intelligible rules (Decision Tree and the innovative Logic Learning Machine method). Support Vector Machine clearly outperformed any of the other methods. Among the two rule-based algorithms, Logic Learning Machine performed better and identified a set of simple intelligible rules based on a combination of clinical variables and gene expressions. Decision Tree identified a non-coding gene ( XIST) involved in the early phases of X chromosome inactivation that was overexpressed in females and in non-relapsed patients. XIST expression might be responsible for the better prognosis of female Hodgkin's lymphoma patients.

  19. Latent Class Analysis of Criminal Social Identity in a Prison Sample

    Directory of Open Access Journals (Sweden)

    Boduszek Daniel

    2014-06-01

    Full Text Available This study aimed to examine the number of latent classes of criminal social identity that exist among male recidivistic prisoners. Latent class analysis was used to identify homogeneous groups of criminal social identity. Multinomial logistic regression was used to interpret the nature of the latent classes, or groups, by estimating the associationsto number of police arrests, recidivism, and violent offending while controlling for current age. The best fitting latent class model was a five-class solution: ‘High criminal social identity’ (17%, ‘High Centrality, Moderate Affect, Low Ties’ (21.7%, ‘Low Centrality, Moderate Affect, High Ties’ (13.3%,‘Low Cognitive, High Affect, Low Ties’ (24.6%, and ‘Low criminal social identity’ (23.4%. Each of the latent classes was predicted by differing external variables. Criminal social identity is best explained by five homogenous classes that display qualitative and quantitative differences.

  20. Searching for Variables and Models to Investigate Mediators of Learning from Multiple Representations

    Science.gov (United States)

    Rau, Martina A.; Scheines, Richard

    2012-01-01

    Although learning from multiple representations has been shown to be effective in a variety of domains, little is known about the mechanisms by which it occurs. We analyzed log data on error-rate, hint-use, and time-spent obtained from two experiments with a Cognitive Tutor for fractions. The goal of the experiments was to compare learning from…

  1. Treatment of Latent Tuberculosis Infection

    OpenAIRE

    Tang, Patrick; Johnston, James

    2017-01-01

    Opinion statement The treatment of latent tuberculosis infection (LTBI) is an essential component of tuberculosis (TB) elimination in regions that have a low incidence of TB. However, the decision to treat individuals with LTBI must consider the limitations of current diagnostic tests for LTBI, the risk of developing active TB disease, the potential adverse effects from chemoprophylactic therapy, and the importance of treatment adherence. When an individual has been diagnosed with LTBI and ac...

  2. The sign learning theory

    African Journals Online (AJOL)

    KING OF DAWN

    The sign learning theory also holds secrets that could be exploited in accomplishing motor tasks. ... Introduction ... In his classic work: Cognitive Map in Rats and Men (1948),Tolman talked about five groups of experiments viz: latent learning ...

  3. Prediction and early detection of delirium in the intensive care unit by using heart rate variability and machine learning.

    Science.gov (United States)

    Oh, Jooyoung; Cho, Dongrae; Park, Jaesub; Na, Se Hee; Kim, Jongin; Heo, Jaeseok; Shin, Cheung Soo; Kim, Jae-Jin; Park, Jin Young; Lee, Boreom

    2018-03-27

    Delirium is an important syndrome found in patients in the intensive care unit (ICU), however, it is usually under-recognized during treatment. This study was performed to investigate whether delirious patients can be successfully distinguished from non-delirious patients by using heart rate variability (HRV) and machine learning. Electrocardiography data of 140 patients was acquired during daily ICU care, and HRV data were analyzed. Delirium, including its type, severity, and etiologies, was evaluated daily by trained psychiatrists. HRV data and various machine learning algorithms including linear support vector machine (SVM), SVM with radial basis function (RBF) kernels, linear extreme learning machine (ELM), ELM with RBF kernels, linear discriminant analysis, and quadratic discriminant analysis were utilized to distinguish delirium patients from non-delirium patients. HRV data of 4797 ECGs were included, and 39 patients had delirium at least once during their ICU stay. The maximum classification accuracy was acquired using SVM with RBF kernels. Our prediction method based on HRV with machine learning was comparable to previous delirium prediction models using massive amounts of clinical information. Our results show that autonomic alterations could be a significant feature of patients with delirium in the ICU, suggesting the potential for the automatic prediction and early detection of delirium based on HRV with machine learning.

  4. Variability in Dopamine Genes Dissociates Model-Based and Model-Free Reinforcement Learning.

    Science.gov (United States)

    Doll, Bradley B; Bath, Kevin G; Daw, Nathaniel D; Frank, Michael J

    2016-01-27

    Considerable evidence suggests that multiple learning systems can drive behavior. Choice can proceed reflexively from previous actions and their associated outcomes, as captured by "model-free" learning algorithms, or flexibly from prospective consideration of outcomes that might occur, as captured by "model-based" learning algorithms. However, differential contributions of dopamine to these systems are poorly understood. Dopamine is widely thought to support model-free learning by modulating plasticity in striatum. Model-based learning may also be affected by these striatal effects, or by other dopaminergic effects elsewhere, notably on prefrontal working memory function. Indeed, prominent demonstrations linking striatal dopamine to putatively model-free learning did not rule out model-based effects, whereas other studies have reported dopaminergic modulation of verifiably model-based learning, but without distinguishing a prefrontal versus striatal locus. To clarify the relationships between dopamine, neural systems, and learning strategies, we combine a genetic association approach in humans with two well-studied reinforcement learning tasks: one isolating model-based from model-free behavior and the other sensitive to key aspects of striatal plasticity. Prefrontal function was indexed by a polymorphism in the COMT gene, differences of which reflect dopamine levels in the prefrontal cortex. This polymorphism has been associated with differences in prefrontal activity and working memory. Striatal function was indexed by a gene coding for DARPP-32, which is densely expressed in the striatum where it is necessary for synaptic plasticity. We found evidence for our hypothesis that variations in prefrontal dopamine relate to model-based learning, whereas variations in striatal dopamine function relate to model-free learning. Decisions can stem reflexively from their previously associated outcomes or flexibly from deliberative consideration of potential choice outcomes

  5. Learning multiple variable-speed sequences in striatum via cortical tutoring.

    Science.gov (United States)

    Murray, James M; Escola, G Sean

    2017-05-08

    Sparse, sequential patterns of neural activity have been observed in numerous brain areas during timekeeping and motor sequence tasks. Inspired by such observations, we construct a model of the striatum, an all-inhibitory circuit where sequential activity patterns are prominent, addressing the following key challenges: (i) obtaining control over temporal rescaling of the sequence speed, with the ability to generalize to new speeds; (ii) facilitating flexible expression of distinct sequences via selective activation, concatenation, and recycling of specific subsequences; and (iii) enabling the biologically plausible learning of sequences, consistent with the decoupling of learning and execution suggested by lesion studies showing that cortical circuits are necessary for learning, but that subcortical circuits are sufficient to drive learned behaviors. The same mechanisms that we describe can also be applied to circuits with both excitatory and inhibitory populations, and hence may underlie general features of sequential neural activity pattern generation in the brain.

  6. Deep Processing Strategies and Critical Thinking: Developmental Trajectories Using Latent Growth Analyses

    Science.gov (United States)

    Phan, Huy P.

    2011-01-01

    The author explored the developmental courses of deep learning approach and critical thinking over a 2-year period. Latent growth curve modeling (LGM) procedures were used to test and trace the trajectories of both theoretical frameworks over time. Participants were 264 (119 women, 145 men) university undergraduates. The Deep Learning subscale of…

  7. Climate variability and change in southern Mali : Learning from farmer perceptions and on-farm trials

    NARCIS (Netherlands)

    Traore, B.; Wijk, van M.T.; Descheemaeker, K.K.E.; Corbeels, M.; Rufino, M.C.; Giller, K.E.

    2015-01-01

    Agricultural production in the Sudano–Sahelian zone of west Africa is highly vulnerable to the impacts of climate variability and climate change. The present study aimed to understand farmers’ perceptions of climate variability and change and to evaluate adaptation options together with farmers,

  8. Alexithymia and psychosocial problems among Italian preadolescents. A latent class analysis approach.

    Science.gov (United States)

    Mannarini, Stefania; Balottin, Laura; Toldo, Irene; Gatta, Michela

    2016-10-01

    The study, conducted on Italian preadolscents aged 11 to 13 belonging to the general population, aims to investigate the relationship between the emotional functioning, namely, alexithymia, and the risk of developing behavioral and emotional problems measured using the Strength and Difficulty Questionnaire. The latent class analysis approach allowed to identify two latent variables, accounting for the internalizing (emotional symptoms and difficulties in emotional awareness) and for the externalizing problems (conduct problems and hyperactivity, problematic relationships with peers, poor prosocial behaviors and externally oriented thinking). The two latent variables featured two latent classes: the difficulty in dealing with problems and the strength to face problems that was representative of most of the healthy participants with specific gender differences. Along with the analysis of psychopathological behaviors, the study of resilience and strengths can prove to be a key step in order to develop valuable preventive approaches to tackle psychiatric disorders. © 2016 Scandinavian Psychological Associations and John Wiley & Sons Ltd.

  9. Stochastic weather inputs for improved urban water demand forecasting: application of nonlinear input variable selection and machine learning methods

    Science.gov (United States)

    Quilty, J.; Adamowski, J. F.

    2015-12-01

    Urban water supply systems are often stressed during seasonal outdoor water use as water demands related to the climate are variable in nature making it difficult to optimize the operation of the water supply system. Urban water demand forecasts (UWD) failing to include meteorological conditions as inputs to the forecast model may produce poor forecasts as they cannot account for the increase/decrease in demand related to meteorological conditions. Meteorological records stochastically simulated into the future can be used as inputs to data-driven UWD forecasts generally resulting in improved forecast accuracy. This study aims to produce data-driven UWD forecasts for two different Canadian water utilities (Montreal and Victoria) using machine learning methods by first selecting historical UWD and meteorological records derived from a stochastic weather generator using nonlinear input variable selection. The nonlinear input variable selection methods considered in this work are derived from the concept of conditional mutual information, a nonlinear dependency measure based on (multivariate) probability density functions and accounts for relevancy, conditional relevancy, and redundancy from a potential set of input variables. The results of our study indicate that stochastic weather inputs can improve UWD forecast accuracy for the two sites considered in this work. Nonlinear input variable selection is suggested as a means to identify which meteorological conditions should be utilized in the forecast.

  10. Use of Machine Learning Techniques for Identification of Robust Teleconnections to East African Rainfall Variability

    Science.gov (United States)

    Roberts, J. Brent; Robertson, F. R.; Funk, C.

    2014-01-01

    Hidden Markov models can be used to investigate structure of subseasonal variability. East African short rain variability has connections to large-scale tropical variability. MJO - Intraseasonal variations connected with appearance of "wet" and "dry" states. ENSO/IOZM SST and circulation anomalies are apparent during years of anomalous residence time in the subseasonal "wet" state. Similar results found in previous studies, but we can interpret this with respect to variations of subseasonal wet and dry modes. Reveal underlying connections between MJO/IOZM/ENSO with respect to East African rainfall.

  11. Repeatability and reproducibility of decisions by latent fingerprint examiners.

    Directory of Open Access Journals (Sweden)

    Bradford T Ulery

    Full Text Available The interpretation of forensic fingerprint evidence relies on the expertise of latent print examiners. We tested latent print examiners on the extent to which they reached consistent decisions. This study assessed intra-examiner repeatability by retesting 72 examiners on comparisons of latent and exemplar fingerprints, after an interval of approximately seven months; each examiner was reassigned 25 image pairs for comparison, out of total pool of 744 image pairs. We compare these repeatability results with reproducibility (inter-examiner results derived from our previous study. Examiners repeated 89.1% of their individualization decisions, and 90.1% of their exclusion decisions; most of the changed decisions resulted in inconclusive decisions. Repeatability of comparison decisions (individualization, exclusion, inconclusive was 90.0% for mated pairs, and 85.9% for nonmated pairs. Repeatability and reproducibility were notably lower for comparisons assessed by the examiners as "difficult" than for "easy" or "moderate" comparisons, indicating that examiners' assessments of difficulty may be useful for quality assurance. No false positive errors were repeated (n = 4; 30% of false negative errors were repeated. One percent of latent value decisions were completely reversed (no value even for exclusion vs. of value for individualization. Most of the inter- and intra-examiner variability concerned whether the examiners considered the information available to be sufficient to reach a conclusion; this variability was concentrated on specific image pairs such that repeatability and reproducibility were very high on some comparisons and very low on others. Much of the variability appears to be due to making categorical decisions in borderline cases.

  12. Variability and practice load in motor learning. [Variabilidad y carga de práctica en el aprendizaje motor].

    Directory of Open Access Journals (Sweden)

    Francisco Javier Moreno

    2015-01-01

    Full Text Available Previous studies have pointed out the convenience of taking the characteristics of the skill to be learned and the intrinsic characteristics of the learners into account when designing practice tasks. Nevertheless, few studies have manipulated the amount of variable practice. The ability to adapt, as an inherent feature of biological systems, can be an adequate framework to explain and predict motor learning processes. This paper is based on adaption processes explained under the theory of allostasis and the general adaption syndrome and shares the background of the Dynamic Systems Theory, to propose the concept of practice load as a useful tool to quantify variability of practice in motor learning. From this standpoint, the conditions of variable practice are reviewed to be a stimulus in an adequate magnitude and direction to take the learner to a higher level of performance and hence to optimize motor learning. Resumen Muchos autores han recomendado la conveniencia de ajustar los niveles de práctica variable teniendo en cuenta las características de la tarea y la variabilidad intrínseca que muestra el aprendiz en la ejecución de la habilidad. Sin embargo, no son numerosos los trabajos que han manipulado varios niveles de cantidad de variabilidad al practicar. La capacidad de adaptación, como rasgo de los sistemas biológicos puede resultar un marco adecuado para afrontar esta cuestión. En este trabajo, apoyado en los procesos de adaptación explicados bajo las teorías de alostasis y el síndrome general de adaptación (GAS, y bajo presupuestos compartidos por la Teoría General de Sistemas Dinámicos, propondrá el concepto de carga de práctica como una herramienta para cuantificar la práctica en el aprendizaje motor. Bajo esta perspectiva se revisan las condiciones en las que la práctica en variabilidad debe modularse, para suponer una estimulación que facilite al aprendiz una adaptación a un nivel de rendimiento superior y con

  13. Exploring the Relationship between School Growth Mindset and Organizational Learning Variables: Implications for Multicultural Education

    Science.gov (United States)

    Hanson, Janet; Bangert, Arthur; Ruff, William

    2016-01-01

    According to school growth mindset theory a school's organizational structure influences teachers' beliefs in their collective ability to help all students grow and learn; including those from diverse cultural, religious, identity, and socioeconomic demographics. The implicit theory of growth mindset has been quantified for a school's culture on…

  14. Use of Machine Learning Techniques for Iidentification of Robust Teleconnections to East African Rainfall Variability in Observations and Models

    Science.gov (United States)

    Roberts, J. Brent; Robertson, Franklin R.; Funk, Chris

    2014-01-01

    Providing advance warning of East African rainfall variations is a particular focus of several groups including those participating in the Famine Early Warming Systems Network. Both seasonal and long-term model projections of climate variability are being used to examine the societal impacts of hydrometeorological variability on seasonal to interannual and longer time scales. The NASA / USAID SERVIR project, which leverages satellite and modeling-based resources for environmental decision making in developing nations, is focusing on the evaluation of both seasonal and climate model projections to develop downscaled scenarios for using in impact modeling. The utility of these projections is reliant on the ability of current models to capture the embedded relationships between East African rainfall and evolving forcing within the coupled ocean-atmosphere-land climate system. Previous studies have posited relationships between variations in El Niño, the Walker circulation, Pacific decadal variability (PDV), and anthropogenic forcing. This study applies machine learning methods (e.g. clustering, probabilistic graphical model, nonlinear PCA) to observational datasets in an attempt to expose the importance of local and remote forcing mechanisms of East African rainfall variability. The ability of the NASA Goddard Earth Observing System (GEOS5) coupled model to capture the associated relationships will be evaluated using Coupled Model Intercomparison Project Phase 5 (CMIP5) simulations.

  15. The Latent Structure of Autistic Traits: A Taxometric, Latent Class and Latent Profile Analysis of the Adult Autism Spectrum Quotient

    Science.gov (United States)

    James, Richard J.; Dubey, Indu; Smith, Danielle; Ropar, Danielle; Tunney, Richard J.

    2016-01-01

    Autistic traits are widely thought to operate along a continuum. A taxometric analysis of Adult Autism Spectrum Quotient data was conducted to test this assumption, finding little support but identifying a high severity taxon. To understand this further, latent class and latent profile models were estimated that indicated the presence of six…

  16. Identifying Pertinent Variables for Nonresponse Follow-Up Surveys. Lessons Learned from 4 Cases in Switzerland

    Directory of Open Access Journals (Sweden)

    Caroline Vandenplas

    2015-12-01

    Full Text Available All social surveys suffer from different types of errors, of which one of the most studied is non-response bias. Non-response bias is a systematic error that occurs because individuals differ in their accessibility and propensity to participate in a survey according to their own characteristics as well as those from the survey itself. The extent of the problem heavily depends on the correlation between response mechanisms and key survey variables. However, non-response bias is difficult to measure or to correct for due to the lack of relevant data about the whole target population or sample. In this paper, non-response follow-up surveys are considered as a possible source of information about non-respondents. Non-response follow-ups, however, suffer from two methodological issues: they themselves operate through a response mechanism that can cause potential non-response bias, and they pose a problem of comparability of measure, mostly because the survey design differs between main survey and non-response follow-up. In order to detect possible bias, the survey variables included in non-response surveys have to be related to the mechanism of participation, but not be sensitive to measurement effects due to the different designs. Based on accumulated experience of four similar non-response follow-ups, we studied the survey variables that fulfill these conditions. We differentiated socio-demographic variables that are measurement-invariant but have a lower correlation with non-response and variables that measure attitudes, such as trust, social participation, or integration in the public sphere, which are more sensitive to measurement effects but potentially more appropriate to account for the non-response mechanism. Our results show that education level, work status, and living alone, as well as political interest, satisfaction with democracy, and trust in institutions are pertinent variables to include in non-response follow-ups of general social

  17. Tufted capuchin monkeys (Sapajus sp) learning how to crack nuts: does variability decline throughout development?

    Science.gov (United States)

    Resende, Briseida Dogo; Nagy-Reis, Mariana Baldy; Lacerda, Fernanda Neves; Pagnotta, Murillo; Savalli, Carine

    2014-11-01

    We investigated the process of nut-cracking acquisition in a semi-free population of tufted capuchin monkeys (Sapajus sp) in São Paulo, Brazil. We analyzed the cracking episodes from monkeys of different ages and found that variability of actions related to cracking declined. Inept movements were more frequent in juveniles, which also showed an improvement on efficient striking. The most effective behavioral sequence for cracking was more frequently used by the most experienced monkeys, which also used non-optimal sequences. Variability in behavior sequences and actions may allow adaptive changes to behavior under changing environmental conditions. Copyright © 2014 Elsevier B.V. All rights reserved.

  18. Improving Semi-Supervised Learning with Auxiliary Deep Generative Models

    DEFF Research Database (Denmark)

    Maaløe, Lars; Sønderby, Casper Kaae; Sønderby, Søren Kaae

    Deep generative models based upon continuous variational distributions parameterized by deep networks give state-of-the-art performance. In this paper we propose a framework for extending the latent representation with extra auxiliary variables in order to make the variational distribution more...... expressive for semi-supervised learning. By utilizing the stochasticity of the auxiliary variable we demonstrate how to train discriminative classifiers resulting in state-of-the-art performance within semi-supervised learning exemplified by an 0.96% error on MNIST using 100 labeled data points. Furthermore...

  19. The Latent Curriculum: Breaking Conceptual Barriers to Information Architecture

    Directory of Open Access Journals (Sweden)

    Catherine Boden

    2012-05-01

    Full Text Available In online instruction there is a physical and temporal distance between students and instructors that is not present in face-to-face instruction, which has implications for developing online curricula. This paper examines information literacy components of Introduction to Systematic Reviews, an online graduate-level course offered at the University of Saskatchewan. Course evaluation suggested that, although the screencast tutorials were well accepted by the students as a method of learning, there was need to enhance their content. Through grading of assignments, consultations with the students, and evaluation of the final search strategies, the authors identified common aspects of search strategy development with which the students struggled throughout the course. There was a need to unpack the curriculum to more clearly identify specific areas that needed to be expanded or improved. Bloom’s Revised Taxonomy was utilized as the construct to identify information literacy learning objectives at a relatively granular level. Comparison of learning objectives and the content of the screencast tutorials revealed disparities between desired outcomes and the curriculum (particularly for high-level thinking – the latent curriculum. Analyzing curricula using a tool like Bloom’s Revised Taxonomy will help information literacy librarians recognize hidden or latent learning objectives.

  20. Time-lagged autoencoders: Deep learning of slow collective variables for molecular kinetics

    Science.gov (United States)

    Wehmeyer, Christoph; Noé, Frank

    2018-06-01

    Inspired by the success of deep learning techniques in the physical and chemical sciences, we apply a modification of an autoencoder type deep neural network to the task of dimension reduction of molecular dynamics data. We can show that our time-lagged autoencoder reliably finds low-dimensional embeddings for high-dimensional feature spaces which capture the slow dynamics of the underlying stochastic processes—beyond the capabilities of linear dimension reduction techniques.

  1. Effects of Variability in Fundamental Frequency on L2 Vocabulary Learning: A Comparison between Learners Who Do and Do Not Speak a Tone Language

    Science.gov (United States)

    Barcroft, Joe; Sommers, Mitchell S.

    2014-01-01

    Previous studies (Barcroft & Sommers, 2005; Sommers & Barcroft, 2007) have demonstrated that variability in talker, speaking style, and speaking rate positively affect second language vocabulary learning, whereas variability in overall amplitude and fundamental frequency (F0) do not, at least for native English speakers. Sommers and…

  2. Poly(A) motif prediction using spectral latent features from human DNA sequences

    KAUST Repository

    Xie, Bo; Jankovic, Boris R.; Bajic, Vladimir B.; Song, Le; Gao, Xin

    2013-01-01

    Motivation: Polyadenylation is the addition of a poly(A) tail to an RNA molecule. Identifying DNA sequence motifs that signal the addition of poly(A) tails is essential to improved genome annotation and better understanding of the regulatory mechanisms and stability of mRNA.Existing poly(A) motif predictors demonstrate that information extracted from the surrounding nucleotide sequences of candidate poly(A) motifs can differentiate true motifs from the false ones to a great extent. A variety of sophisticated features has been explored, including sequential, structural, statistical, thermodynamic and evolutionary properties. However, most of these methods involve extensive manual feature engineering, which can be time-consuming and can require in-depth domain knowledge.Results: We propose a novel machine-learning method for poly(A) motif prediction by marrying generative learning (hidden Markov models) and discriminative learning (support vector machines). Generative learning provides a rich palette on which the uncertainty and diversity of sequence information can be handled, while discriminative learning allows the performance of the classification task to be directly optimized. Here, we used hidden Markov models for fitting the DNA sequence dynamics, and developed an efficient spectral algorithm for extracting latent variable information from these models. These spectral latent features were then fed into support vector machines to fine-tune the classification performance.We evaluated our proposed method on a comprehensive human poly(A) dataset that consists of 14 740 samples from 12 of the most abundant variants of human poly(A) motifs. Compared with one of the previous state-of-the-art methods in the literature (the random forest model with expert-crafted features), our method reduces the average error rate, false-negative rate and false-positive rate by 26, 15 and 35%, respectively. Meanwhile, our method makes ?30% fewer error predictions relative to the other

  3. Poly(A) motif prediction using spectral latent features from human DNA sequences

    KAUST Repository

    Xie, Bo

    2013-06-21

    Motivation: Polyadenylation is the addition of a poly(A) tail to an RNA molecule. Identifying DNA sequence motifs that signal the addition of poly(A) tails is essential to improved genome annotation and better understanding of the regulatory mechanisms and stability of mRNA.Existing poly(A) motif predictors demonstrate that information extracted from the surrounding nucleotide sequences of candidate poly(A) motifs can differentiate true motifs from the false ones to a great extent. A variety of sophisticated features has been explored, including sequential, structural, statistical, thermodynamic and evolutionary properties. However, most of these methods involve extensive manual feature engineering, which can be time-consuming and can require in-depth domain knowledge.Results: We propose a novel machine-learning method for poly(A) motif prediction by marrying generative learning (hidden Markov models) and discriminative learning (support vector machines). Generative learning provides a rich palette on which the uncertainty and diversity of sequence information can be handled, while discriminative learning allows the performance of the classification task to be directly optimized. Here, we used hidden Markov models for fitting the DNA sequence dynamics, and developed an efficient spectral algorithm for extracting latent variable information from these models. These spectral latent features were then fed into support vector machines to fine-tune the classification performance.We evaluated our proposed method on a comprehensive human poly(A) dataset that consists of 14 740 samples from 12 of the most abundant variants of human poly(A) motifs. Compared with one of the previous state-of-the-art methods in the literature (the random forest model with expert-crafted features), our method reduces the average error rate, false-negative rate and false-positive rate by 26, 15 and 35%, respectively. Meanwhile, our method makes ?30% fewer error predictions relative to the other

  4. Performance Variables and Professional Experience in Simulated Laparoscopy: A Two-Group Learning Curve Study

    NARCIS (Netherlands)

    Luursema, J.M.; Rovers, Maroeska M.; Groenier, Marleen; van Goor, Harry

    2014-01-01

    Objective Virtual reality simulators are increasingly used in laparoscopy training. Such simulators allow objective assessment of performance. However, both low-level variables and overall scores generated by the simulator can be hard to interpret. We present a method to generate intermediate

  5. Performance variables and professional experience in simulated laparoscopy: a two-group learning curve study

    NARCIS (Netherlands)

    Luursema, J.M.; Rovers, M.M.; Groenier, M.; Goor, H. van

    2014-01-01

    OBJECTIVE: Virtual reality simulators are increasingly used in laparoscopy training. Such simulators allow objective assessment of performance. However, both low-level variables and overall scores generated by the simulator can be hard to interpret. We present a method to generate intermediate

  6. Challenge in Enhancing the Teaching and Learning of Variable Measurements in Quantitative Research

    Science.gov (United States)

    Kee, Chang Peng; Osman, Kamisah; Ahmad, Fauziah

    2013-01-01

    Statistical analysis is one component that cannot be avoided in a quantitative research. Initial observations noted that students in higher education institution faced difficulty analysing quantitative data which were attributed to the confusions of various variable measurements. This paper aims to compare the outcomes of two approaches applied in…

  7. Understanding diagnostic variability in breast pathology: lessons learned from an expert consensus review panel

    Science.gov (United States)

    Allison, Kimberly H; Reisch, Lisa M; Carney, Patricia A; Weaver, Donald L; Schnitt, Stuart J; O’Malley, Frances P; Geller, Berta M; Elmore, Joann G

    2015-01-01

    Aims To gain a better understanding of the reasons for diagnostic variability, with the aim of reducing the phenomenon. Methods and results In preparation for a study on the interpretation of breast specimens (B-PATH), a panel of three experienced breast pathologists reviewed 336 cases to develop consensus reference diagnoses. After independent assessment, cases coded as diagnostically discordant were discussed at consensus meetings. By the use of qualitative data analysis techniques, transcripts of 16 h of consensus meetings for a subset of 201 cases were analysed. Diagnostic variability could be attributed to three overall root causes: (i) pathologist-related; (ii) diagnostic coding/study methodology-related; and (iii) specimen-related. Most pathologist-related root causes were attributable to professional differences in pathologists’ opinions about whether the diagnostic criteria for a specific diagnosis were met, most frequently in cases of atypia. Diagnostic coding/study methodology-related root causes were primarily miscategorizations of descriptive text diagnoses, which led to the development of a standardized electronic diagnostic form (BPATH-Dx). Specimen-related root causes included artefacts, limited diagnostic material, and poor slide quality. After re-review and discussion, a consensus diagnosis could be assigned in all cases. Conclusions Diagnostic variability is related to multiple factors, but consensus conferences, standardized electronic reporting formats and comments on suboptimal specimen quality can be used to reduce diagnostic variability. PMID:24511905

  8. Investigation Two Type of Absolute and Coordination Variability of Upper Limb Joints through Learning

    Directory of Open Access Journals (Sweden)

    Zahra Entezari Khorasani

    2018-03-01

    Conclusion:  Overall, it seems that wrist movement and wrist-elbow coordination are very important in free throw shooting skill. Also, the findings of this study showed that it is necessary to isolate mechanical and dynamical degrees of freedom in the measuring and discussing the Theories regarding the movement variability.

  9. Effects of robotically modulating kinematic variability on motor skill learning and motivation.

    Science.gov (United States)

    Duarte, Jaime E; Reinkensmeyer, David J

    2015-04-01

    It is unclear how the variability of kinematic errors experienced during motor training affects skill retention and motivation. We used force fields produced by a haptic robot to modulate the kinematic errors of 30 healthy adults during a period of practice in a virtual simulation of golf putting. On day 1, participants became relatively skilled at putting to a near and far target by first practicing without force fields. On day 2, they warmed up at the task without force fields, then practiced with force fields that either reduced or augmented their kinematic errors and were finally assessed without the force fields active. On day 3, they returned for a long-term assessment, again without force fields. A control group practiced without force fields. We quantified motor skill as the variability in impact velocity at which participants putted the ball. We quantified motivation using a self-reported, standardized scale. Only individuals who were initially less skilled benefited from training; for these people, practicing with reduced kinematic variability improved skill more than practicing in the control condition. This reduced kinematic variability also improved self-reports of competence and satisfaction. Practice with increased kinematic variability worsened these self-reports as well as enjoyment. These negative motivational effects persisted on day 3 in a way that was uncorrelated with actual skill. In summary, robotically reducing kinematic errors in a golf putting training session improved putting skill more for less skilled putters. Robotically increasing kinematic errors had no performance effect, but decreased motivation in a persistent way. Copyright © 2015 the American Physiological Society.

  10. Power and type I error of local fit statistics in multilevel latent class analysis

    NARCIS (Netherlands)

    Nagelkerke, E.; Oberski, D.L.; Vermunt, J.K.

    2017-01-01

    In the social and behavioral sciences, variables are often categorical and people are often nested in groups. Models for such data, such as multilevel logistic regression or the multilevel latent class model, should account for not only the categorical nature of the variables, but also the nested

  11. Age is associated with latent tuberculosis in nurses

    Directory of Open Access Journals (Sweden)

    Naesinee Chaiear

    2016-12-01

    Full Text Available Objective: To evaluate risk factors for developing latent tuberculosis (LTB in Thai nurses. Methods: A comparison study was conducted at Srinagarind Hospital, Khon Kaen, Thailand. Clinical factors were compared between persons with tuberculin conversion and those without tuberculin conversion identified by tuberculin skin test. Results: There were 173 eligible persons with the LTB (34.7%. There were five workplaces where participants worked regularly including the general ward, surgical ward, pediatric ward, medical ward and critical care ward. In a multivariate model, two factors were significantly associated with LTB including age and history of tuberculosis in colleagues. The adjusted odds ratio (95% confidence interval of both variables were 1.056 (1.004–1.104 and 0.202 (0.044– 0.941. Conclusions: Older age is associated with latent tuberculosis in nurses. LTB should be screened routinely and treated if diagnosed for nurses.

  12. Plutonium and latent nuclear proliferation

    International Nuclear Information System (INIS)

    Quester, G.H.

    1992-01-01

    A country producing nuclear electric power acquires an ability to produce atomic bombs quite easily and without taking many steps beyond that which would be perfectly normal for civilian purposes. The role of plutonium in the three fold list of the gains that must be sought in arms control formulated by Schelling and Halpevin are discussed. On the first, that we should seek to reduce the likelihood of war, it can be argued that plutonium reduces the likelihood in some cases. The second, that we should seek to reduce the destruction in war, is made worse by plutonium. On the third criterion, that we should seek to reduce the burdens in peacetime of everyone's being prepared for war, the situation is confusing and depends on the prospects for nuclear electrical power. It is concluded that latent capability to produce nuclear weapons may be sufficient without the need for actual detonations and deployment of bombs. (UK)

  13. Stability of latent class segments over time

    DEFF Research Database (Denmark)

    Mueller, Simone

    2011-01-01

    Dynamic stability, as the degree to which identified segments at a given time remain unchanged over time in terms of number, size and profile, is a desirable segment property which has received limited attention so far. This study addresses the question to what degree latent classes identified from...... logit model suggests significant changes in the price sensitivity and the utility from environmental claims between both experimental waves. A pooled scale adjusted latent class model is estimated jointly over both waves and the relative size of latent classes is compared across waves, resulting...... in significant differences in the size of two out of seven classes. These differences can largely be accounted for by the changes on the aggregated level. The relative size of latent classes is correlated at 0.52, suggesting a fair robustness. An ex-post characterisation of latent classes by behavioural...

  14. Self-Learning Variable Structure Control for a Class of Sensor-Actuator Systems

    Science.gov (United States)

    Chen, Sanfeng; Li, Shuai; Liu, Bo; Lou, Yuesheng; Liang, Yongsheng

    2012-01-01

    Variable structure strategy is widely used for the control of sensor-actuator systems modeled by Euler-Lagrange equations. However, accurate knowledge on the model structure and model parameters are often required for the control design. In this paper, we consider model-free variable structure control of a class of sensor-actuator systems, where only the online input and output of the system are available while the mathematic model of the system is unknown. The problem is formulated from an optimal control perspective and the implicit form of the control law are analytically obtained by using the principle of optimality. The control law and the optimal cost function are explicitly solved iteratively. Simulations demonstrate the effectiveness and the efficiency of the proposed method. PMID:22778633

  15. Statistical learning from nonrecurrent experience with discrete input variables and recursive-error-minimization equations

    Science.gov (United States)

    Carter, Jeffrey R.; Simon, Wayne E.

    1990-08-01

    Neural networks are trained using Recursive Error Minimization (REM) equations to perform statistical classification. Using REM equations with continuous input variables reduces the required number of training experiences by factors of one to two orders of magnitude over standard back propagation. Replacing the continuous input variables with discrete binary representations reduces the number of connections by a factor proportional to the number of variables reducing the required number of experiences by another order of magnitude. Undesirable effects of using recurrent experience to train neural networks for statistical classification problems are demonstrated and nonrecurrent experience used to avoid these undesirable effects. 1. THE 1-41 PROBLEM The statistical classification problem which we address is is that of assigning points in ddimensional space to one of two classes. The first class has a covariance matrix of I (the identity matrix) the covariance matrix of the second class is 41. For this reason the problem is known as the 1-41 problem. Both classes have equal probability of occurrence and samples from both classes may appear anywhere throughout the ddimensional space. Most samples near the origin of the coordinate system will be from the first class while most samples away from the origin will be from the second class. Since the two classes completely overlap it is impossible to have a classifier with zero error. The minimum possible error is known as the Bayes error and

  16. The latent rationality of risky decisions

    Energy Technology Data Exchange (ETDEWEB)

    Japp, K.P. [Bielefeld Univ. (Germany). Faculty for Sociology

    1999-12-01

    The general question of rationality has changed from the old-fashioned difference of means and ends to the modern difference of system and environment. Organizations as social systems producing and reproducing decisions translate this difference into the difference of stability and variety. The question then is: In which way can the difference between stability and variety express rationality? - In the temporal dimension of risk-taking, re-entries may be expressed as 'present futures' or 'future presences'. These expressions indicate both: The irresolvable uncertainty of any risk-taking, indicated by open futures, and its boundedness by self-application of distinctions, e.g. projected futures from the background of a known past. - In the material dimension of risk-taking, re-entries may be expressed as 'stable flexibility' or 'flexible stability'. Again, these expressions indicate both: The irresolvable uncertainty of any risk-taking, indicted by open flexibilities, and its boundedness by self-application of distinctions, e.g. flexibility and stability after learning the respective costs of the single options. In the social dimension of risk-taking, re-entries may be expressed as 'pragmatic dissent' or 'controversial pragmatism'. Again, these expressions indicate both: The irresolvable uncertainty of any risk-taking, indicated by open dissent or controversies, and its boundedness by self-application of distinctions, e.g. pragmatic agreements and irresolvable dissent. Again, all three asymmetries represent re-entries. The built-in preferences simply do not work without the subtleties of re-entries, at least when these processes are described by sociologically informed observers. Who else should know that he or she is operating on the basis of something called re-entries? In everyday life communication, no one sees a thing like that since every observation has an in-built bias for one side of a distinction

  17. The latent rationality of risky decisions

    International Nuclear Information System (INIS)

    Japp, K.P.

    1999-01-01

    The general question of rationality has changed from the old-fashioned difference of means and ends to the modern difference of system and environment. Organizations as social systems producing and reproducing decisions translate this difference into the difference of stability and variety. The question then is: In which way can the difference between stability and variety express rationality? - In the temporal dimension of risk-taking, re-entries may be expressed as 'present futures' or 'future presences'. These expressions indicate both: The irresolvable uncertainty of any risk-taking, indicated by open futures, and its boundedness by self-application of distinctions, e.g. projected futures from the background of a known past. - In the material dimension of risk-taking, re-entries may be expressed as 'stable flexibility' or 'flexible stability'. Again, these expressions indicate both: The irresolvable uncertainty of any risk-taking, indicted by open flexibilities, and its boundedness by self-application of distinctions, e.g. flexibility and stability after learning the respective costs of the single options. In the social dimension of risk-taking, re-entries may be expressed as 'pragmatic dissent' or 'controversial pragmatism'. Again, these expressions indicate both: The irresolvable uncertainty of any risk-taking, indicated by open dissent or controversies, and its boundedness by self-application of distinctions, e.g. pragmatic agreements and irresolvable dissent. Again, all three asymmetries represent re-entries. The built-in preferences simply do not work without the subtleties of re-entries, at least when these processes are described by sociologically informed observers. Who else should know that he or she is operating on the basis of something called re-entries? In everyday life communication, no one sees a thing like that since every observation has an in-built bias for one side of a distinction. So rationality will stay latent as the operation of re

  18. A tale of four surveys:What have we learned about the variable sky?

    Science.gov (United States)

    Howell, S. B.

    2008-03-01

    Four tales concerning a set of photometric imaging surveys are spun. The reader is lead through a brief description of each survey and major results are presented. The four surveys are summarized in a few simple "rules": 1) The fraction of point sources that are variable with respect to those that are found to be constant, increases as a power law as the photometric precision of the survey improves, and 2) This fact can be simply formulated as a power law function granting the user a predictive power.

  19. The Concept Framework of Structural Equation model of Mobile Cloud Learning Acceptance for Higher Education Students in the 21st Century

    Directory of Open Access Journals (Sweden)

    Thanyatorn Amornkitpinyo

    2017-08-01

    Full Text Available This research’s part is in the structural equation model of mobile cloud learning acceptance for higher education students in the 21st century as its objective is to synthesize and design the framework of this model. The methods of this research are divided into 2 parts which are synthesis, combining it to process the mode and designing framework concept. The findings of this research are as the following: 1. Basic digital literacy, Information Quality and Social Cloud are included in the model as the exogenous latent variables. 2. Satisfaction and TAM model (perceived usefulness and perceived ease of use are included as the mediating latent variables. 3. Actual Use is the outcome of the model’s latent variable.

  20. A Latent Variable Clustering Method for Wireless Sensor Networks

    DEFF Research Database (Denmark)

    Vasilev, Vladislav; Iliev, Georgi; Poulkov, Vladimir

    2016-01-01

    In this paper we derive a clustering method based on the Hidden Conditional Random Field (HCRF) model in order to maximizes the performance of a wireless sensor. Our novel approach to clustering in this paper is in the application of an index invariant graph that we defined in a previous work and...

  1. Latent variables definition for a new mobility model in Barcelona

    OpenAIRE

    Puignau, Sara; Di Ciommo, Floridea; Saurí, Sergi

    2016-01-01

    [EN] In many European cities, mobility patterns are changing mainly due to advances in information and communications technology. Besides from this, people living in urban areas have now more transport mode alternatives to travel and the ownership of a vehicle is losing relevance to the modern world. In addition, the new generations value the time spent in the trip in a different way and they take advantage of travel time by means of connectivity (i.e. multitasking). As a re...

  2. Latent variables definition for a new mobility model in Barcelona

    Energy Technology Data Exchange (ETDEWEB)

    Puignau, S.A.; Ciommo, F. di; Sauri, S.

    2016-07-01

    Based on the recent travel behaviour literature, time and space perceptions and the awareness of shared economy seem to gain importance in mobility patterns. The objective of this article is to evaluate how far the behaviour of new generations brings about different mobility patterns in Barcelona. For this purpose, we have designed a web-based survey that provides innovative revealed-preference data. (Author)

  3. Explicit estimating equations for semiparametric generalized linear latent variable models

    KAUST Repository

    Ma, Yanyuan; Genton, Marc G.

    2010-01-01

    which is similar to that of a sufficient complete statistic, which enables us to simplify the estimating procedure and explicitly to formulate the semiparametric estimating equations. We further show that the explicit estimators have the usual root n

  4. On Direction of Dependence in Latent Variable Contexts

    Science.gov (United States)

    von Eye, Alexander; Wiedermann, Wolfgang

    2014-01-01

    Approaches to determining direction of dependence in nonexperimental data are based on the relation between higher-than second-order moments on one side and correlation and regression models on the other. These approaches have experienced rapid development and are being applied in contexts such as research on partner violence, attention deficit…

  5. Visualization of pairwise and multilocus linkage disequilibrium structure using latent forests.

    Directory of Open Access Journals (Sweden)

    Raphaël Mourad

    Full Text Available Linkage disequilibrium study represents a major issue in statistical genetics as it plays a fundamental role in gene mapping and helps us to learn more about human history. The linkage disequilibrium complex structure makes its exploratory data analysis essential yet challenging. Visualization methods, such as the triangular heat map implemented in Haploview, provide simple and useful tools to help understand complex genetic patterns, but remain insufficient to fully describe them. Probabilistic graphical models have been widely recognized as a powerful formalism allowing a concise and accurate modeling of dependences between variables. In this paper, we propose a method for short-range, long-range and chromosome-wide linkage disequilibrium visualization using forests of hierarchical latent class models. Thanks to its hierarchical nature, our method is shown to provide a compact view of both pairwise and multilocus linkage disequilibrium spatial structures for the geneticist. Besides, a multilocus linkage disequilibrium measure has been designed to evaluate linkage disequilibrium in hierarchy clusters. To learn the proposed model, a new scalable algorithm is presented. It constrains the dependence scope, relying on physical positions, and is able to deal with more than one hundred thousand single nucleotide polymorphisms. The proposed algorithm is fast and does not require phase genotypic data.

  6. Reasoning about variables in 11 to 18 year olds: informal, schooled and formal expression in learning about functions

    Science.gov (United States)

    Ayalon, Michal; Watson, Anne; Lerman, Steve

    2016-09-01

    This study examines expressions of reasoning by some higher achieving 11 to 18 year-old English students responding to a survey consisting of function tasks developed in collaboration with their teachers. We report on 70 students, 10 from each of English years 7-13. Iterative and comparative analysis identified capabilities and difficulties of students and suggested conjectures concerning links between the affordances of the tasks, the curriculum, and students' responses. The paper focuses on five of the survey tasks and highlights connections between informal and formal expressions of reasoning about variables in learning. We introduce the notion of `schooled' expressions of reasoning, neither formal nor informal, to emphasise the role of the formatting tools introduced in school that shape future understanding and reasoning.

  7. What Can We Learn about GRB from the Variability Timescale Related Correlations?

    Energy Technology Data Exchange (ETDEWEB)

    Xie, Wei; Lei, Wei-Hua; Wang, Ding-Xiong, E-mail: leiwh@hust.edu.cn [School of Physics, Huazhong University of Science and Technology, Wuhan 430074 (China)

    2017-04-01

    Recently, two empirical correlations related to the minimum variability timescale (MTS) of the light curves are discovered in gamma-ray bursts (GRBs). One is the anti-correlation between MTS and Lorentz factor Γ, and the other is the anti-correlation between the MTS and gamma-ray luminosity L {sub γ}. Both of the two correlations might be used to explore the activity of the central engine of GRBs. In this paper, we try to understand these empirical correlations by combining two popular black hole central engine models (namely, the Blandford and Znajek mechanism (BZ) and the neutrino-dominated accretion flow (NDAF)). By taking the MTS as the timescale of viscous instability of the NDAF, we find that these correlations favor the scenario in which the jet is driven by the BZ mechanism.

  8. What Can We Learn about GRB from the Variability Timescale Related Correlations?

    International Nuclear Information System (INIS)

    Xie, Wei; Lei, Wei-Hua; Wang, Ding-Xiong

    2017-01-01

    Recently, two empirical correlations related to the minimum variability timescale (MTS) of the light curves are discovered in gamma-ray bursts (GRBs). One is the anti-correlation between MTS and Lorentz factor Γ, and the other is the anti-correlation between the MTS and gamma-ray luminosity L γ . Both of the two correlations might be used to explore the activity of the central engine of GRBs. In this paper, we try to understand these empirical correlations by combining two popular black hole central engine models (namely, the Blandford and Znajek mechanism (BZ) and the neutrino-dominated accretion flow (NDAF)). By taking the MTS as the timescale of viscous instability of the NDAF, we find that these correlations favor the scenario in which the jet is driven by the BZ mechanism.

  9. The R package "sperrorest" : Parallelized spatial error estimation and variable importance assessment for geospatial machine learning

    Science.gov (United States)

    Schratz, Patrick; Herrmann, Tobias; Brenning, Alexander

    2017-04-01

    Computational and statistical prediction methods such as the support vector machine have gained popularity in remote-sensing applications in recent years and are often compared to more traditional approaches like maximum-likelihood classification. However, the accuracy assessment of such predictive models in a spatial context needs to account for the presence of spatial autocorrelation in geospatial data by using spatial cross-validation and bootstrap strategies instead of their now more widely used non-spatial equivalent. The R package sperrorest by A. Brenning [IEEE International Geoscience and Remote Sensing Symposium, 1, 374 (2012)] provides a generic interface for performing (spatial) cross-validation of any statistical or machine-learning technique available in R. Since spatial statistical models as well as flexible machine-learning algorithms can be computationally expensive, parallel computing strategies are required to perform cross-validation efficiently. The most recent major release of sperrorest therefore comes with two new features (aside from improved documentation): The first one is the parallelized version of sperrorest(), parsperrorest(). This function features two parallel modes to greatly speed up cross-validation runs. Both parallel modes are platform independent and provide progress information. par.mode = 1 relies on the pbapply package and calls interactively (depending on the platform) parallel::mclapply() or parallel::parApply() in the background. While forking is used on Unix-Systems, Windows systems use a cluster approach for parallel execution. par.mode = 2 uses the foreach package to perform parallelization. This method uses a different way of cluster parallelization than the parallel package does. In summary, the robustness of parsperrorest() is increased with the implementation of two independent parallel modes. A new way of partitioning the data in sperrorest is provided by partition.factor.cv(). This function gives the user the

  10. Extraction of latent images from printed media

    Science.gov (United States)

    Sergeyev, Vladislav; Fedoseev, Victor

    2015-12-01

    In this paper we propose an automatic technology for extraction of latent images from printed media such as documents, banknotes, financial securities, etc. This technology includes image processing by adaptively constructed Gabor filter bank for obtaining feature images, as well as subsequent stages of feature selection, grouping and multicomponent segmentation. The main advantage of the proposed technique is versatility: it allows to extract latent images made by different texture variations. Experimental results showing performance of the method over another known system for latent image extraction are given.

  11. Bayesian Latent Class Analysis Tutorial.

    Science.gov (United States)

    Li, Yuelin; Lord-Bessen, Jennifer; Shiyko, Mariya; Loeb, Rebecca

    2018-01-01

    This article is a how-to guide on Bayesian computation using Gibbs sampling, demonstrated in the context of Latent Class Analysis (LCA). It is written for students in quantitative psychology or related fields who have a working knowledge of Bayes Theorem and conditional probability and have experience in writing computer programs in the statistical language R . The overall goals are to provide an accessible and self-contained tutorial, along with a practical computation tool. We begin with how Bayesian computation is typically described in academic articles. Technical difficulties are addressed by a hypothetical, worked-out example. We show how Bayesian computation can be broken down into a series of simpler calculations, which can then be assembled together to complete a computationally more complex model. The details are described much more explicitly than what is typically available in elementary introductions to Bayesian modeling so that readers are not overwhelmed by the mathematics. Moreover, the provided computer program shows how Bayesian LCA can be implemented with relative ease. The computer program is then applied in a large, real-world data set and explained line-by-line. We outline the general steps in how to extend these considerations to other methodological applications. We conclude with suggestions for further readings.

  12. Variable training does not lead to better motor learning compared to repetitive training in children with and without DCD when exposed to active video games.

    Science.gov (United States)

    Bonney, Emmanuel; Jelsma, Dorothee; Ferguson, Gillian; Smits-Engelsman, Bouwien

    2017-03-01

    Little is known about the influence of practice schedules on motor learning and skills transfer in children with and without developmental coordination disorder (DCD). Understanding how practice schedules affect motor learning is necessary for motor skills development and rehabilitation. The study investigated whether active video games (exergames) training delivered under variable practice led to better learning and transfer than repetitive practice. 111 children aged 6-10 years (M=8.0, SD=1.0) with no active exergaming experience were randomized to receive exergames training delivered under variable (Variable Game Group (VGG), n=56) or repetitive practice schedule (Repetitive Game Group (RGG), n=55). Half the participants were identified as DCD using the DSM-5 criteria, while the rest were typically developing (TD), age-matched children. Both groups participated in two 20min sessions per week for 5 weeks. Both participant groups (TD and DCD) improved equally well on game performance. There was no significant difference in positive transfer to balance tasks between practice schedules (Repetitive and Variable) and participant groups (TD and DCD). Children with and without DCD learn balance skills quite well when exposed to exergames. Gains in learning and transfer are similar regardless of the form of practice schedule employed. This is the first paper to compare the effect of practice schedules on learning in children with DCD and those with typical development. No differences in motor learning were found between repetitive and variable practice schedules. When children with and without DCD spend the same amount of time on exergames, they do not show any differences in acquisition of motor skills. Transfer of motor skills is similar in children with and without DCD regardless of differences in practice schedules. Copyright © 2017 Elsevier Ltd. All rights reserved.

  13. Semi-Supervised Learning of Lift Optimization of Multi-Element Three-Segment Variable Camber Airfoil

    Science.gov (United States)

    Kaul, Upender K.; Nguyen, Nhan T.

    2017-01-01

    This chapter describes a new intelligent platform for learning optimal designs of morphing wings based on Variable Camber Continuous Trailing Edge Flaps (VCCTEF) in conjunction with a leading edge flap called the Variable Camber Krueger (VCK). The new platform consists of a Computational Fluid Dynamics (CFD) methodology coupled with a semi-supervised learning methodology. The CFD component of the intelligent platform comprises of a full Navier-Stokes solution capability (NASA OVERFLOW solver with Spalart-Allmaras turbulence model) that computes flow over a tri-element inboard NASA Generic Transport Model (GTM) wing section. Various VCCTEF/VCK settings and configurations were considered to explore optimal design for high-lift flight during take-off and landing. To determine globally optimal design of such a system, an extremely large set of CFD simulations is needed. This is not feasible to achieve in practice. To alleviate this problem, a recourse was taken to a semi-supervised learning (SSL) methodology, which is based on manifold regularization techniques. A reasonable space of CFD solutions was populated and then the SSL methodology was used to fit this manifold in its entirety, including the gaps in the manifold where there were no CFD solutions available. The SSL methodology in conjunction with an elastodynamic solver (FiDDLE) was demonstrated in an earlier study involving structural health monitoring. These CFD-SSL methodologies define the new intelligent platform that forms the basis for our search for optimal design of wings. Although the present platform can be used in various other design and operational problems in engineering, this chapter focuses on the high-lift study of the VCK-VCCTEF system. Top few candidate design configurations were identified by solving the CFD problem in a small subset of the design space. The SSL component was trained on the design space, and was then used in a predictive mode to populate a selected set of test points outside

  14. Latent Heat Storage Through Phase Change Materials

    Indian Academy of Sciences (India)

    IAS Admin

    reducing storage volume for different materials. The examples are numerous: ... Latent heat is an attractive way to store solar heat as it provides high energy storage density, .... Maintenance of the PCM treated fabric is easy. The melted PCM.

  15. New Treatment Regimen for Latent Tuberculosis Infection

    Centers for Disease Control (CDC) Podcasts

    In this podcast, Dr. Kenneth Castro, Director of the Division of Tuberculosis Elimination, discusses the December 9, 2011 CDC guidelines for the use of a new regimen for the treatment of persons with latent tuberculosis infection.

  16. UNSOLVED AND LATENT CRIME: DIFFERENCES AND SIMILARITIES

    Directory of Open Access Journals (Sweden)

    Mikhail Kleymenov

    2017-01-01

    Full Text Available УДК 343Purpose of the article is to study the specific legal and informational nature of the unsolved crime in comparison with the phenomenon of delinquency, special study and analysis to improve the efficiency of law enforcement.Methods of research are abstract-logical, systematic, statistical, study of documents. The main results of research. Unsolved crime has specific legal, statistical and informational na-ture as the crime phenomenon, which is expressed in cumulative statistical population of unsolved crimes. An array of unsolved crimes is the sum of the number of acts, things of which is suspended and not terminated. The fault of the perpetrator in these cases is not proven, they are not considered by the court, it is not a conviction. Unsolved crime must be registered. Latent crime has a different informational nature. The main symptom of latent crimes is the uncertainty for the subjects of law enforcement, which delegated functions of identification, registration and accounting. Latent crime is not recorded. At the same time, there is a "border" area between the latent and unsolved crimes, which includes covered from the account of the crime. In modern Russia the majority of crimes covered from accounting by passing the decision about refusal in excitation of criminal case. Unsolved crime on their criminogenic consequences represents a significant danger to the public is higher compared to latent crime.It is conducted in the article a special analysis of the differences and similarities in the unsolved latent crime for the first time in criminological literature.The analysis proves the need for radical changes in the current Russian assessment of the state of crime and law enforcement to solve crimes. The article argues that an unsolved crime is a separate and, in contrast to latent crime, poorly understood phenomenon. However unsolved latent crime and have common features and areas of interaction.

  17. Exposing Latent Information in Folksonomies for Reasoning

    Science.gov (United States)

    2010-01-14

    1.73 $.") http://www.w3.org/2006/07/SWD/ SKOS /reference/20081001/ Spiteri, L.F. (2007) "The structure and form of folksonomy tags: The road to the...Exposing Latent Information in Folksonomies for Reasoning January 14, 2010 Sponsored by Defense Advanced Research Projects Agency (DOD...DATES COVERED (From - To! 4/14/2009-12/23/2009 4. TITLE AND SUBTITLE Exposing Latent Information in Folksonomies for Reasoning Sa. CONTRACT

  18. Original article Latent classes of criminal intent associated with criminal behaviour

    Directory of Open Access Journals (Sweden)

    Daniel Boduszek

    2014-07-01

    Full Text Available Background This study aimed to examine the number of latent classes of criminal intent that exist among prisoners and to look at the associations with recidivism, number of police arrests, type of offending (robbery, violent offences, murder, and multiple offences, and age. Participants and procedure Latent class analysis was used to identify homogeneous subgroups of prisoners based on their responses to the 10 questions reflecting criminal intent. Participants were 309 male recidivistic prisoners incarcerated in a maximum security prison. Multinomial logistic regression was used to interpret the nature of the latent classes, or groups, by estimating the association between recidivism and latent classes of criminal intent while controlling for offence type (robbery, violent offences, murder, and multiple offences, number of arrests, and age. Results The best fitting latent class model was a three-class solution: ‘High criminal intent’ (49.3%, ‘Intermediate criminal intent’ (41.3%, and ‘Low criminal intent’ (9.4%. The latent classes were differentially related to the external variables (recidivism, violent offences, and age. Conclusions Criminal intent is best explained by three homogeneous classes that appear to represent an underlying continuum. Future work is needed to identify whether these distinct classes of criminal intent may predict engagement in various types of criminal behaviour.

  19. PET CT Identifies Reactivation Risk in Cynomolgus Macaques with Latent M. tuberculosis.

    Directory of Open Access Journals (Sweden)

    Philana Ling Lin

    2016-07-01

    Full Text Available Mycobacterium tuberculosis infection presents across a spectrum in humans, from latent infection to active tuberculosis. Among those with latent tuberculosis, it is now recognized that there is also a spectrum of infection and this likely contributes to the variable risk of reactivation tuberculosis. Here, functional imaging with 18F-fluorodeoxygluose positron emission tomography and computed tomography (PET CT of cynomolgus macaques with latent M. tuberculosis infection was used to characterize the features of reactivation after tumor necrosis factor (TNF neutralization and determine which imaging characteristics before TNF neutralization distinguish reactivation risk. PET CT was performed on latently infected macaques (n = 26 before and during the course of TNF neutralization and a separate set of latently infected controls (n = 25. Reactivation occurred in 50% of the latently infected animals receiving TNF neutralizing antibody defined as development of at least one new granuloma in adjacent or distant locations including extrapulmonary sites. Increased lung inflammation measured by PET and the presence of extrapulmonary involvement before TNF neutralization predicted reactivation with 92% sensitivity and specificity. To define the biologic features associated with risk of reactivation, we used these PET CT parameters to identify latently infected animals at high risk for reactivation. High risk animals had higher cumulative lung bacterial burden and higher maximum lesional bacterial burdens, and more T cells producing IL-2, IL-10 and IL-17 in lung granulomas as compared to low risk macaques. In total, these data support that risk of reactivation is associated with lung inflammation and higher bacterial burden in macaques with latent Mtb infection.

  20. Improving knowledge management systems with latent semantic analysis

    International Nuclear Information System (INIS)

    Sebok, A.; Plott, C.; LaVoie, N.

    2006-01-01

    Latent Semantic Analysis (LSA) offers a technique for improving lessons learned and knowledge management systems. These systems are expected to become more widely used in the nuclear industry, as experienced personnel leave and are replaced by younger, less-experienced workers. LSA is a machine learning technology that allows searching of text based on meaning rather than predefined keywords or categories. Users can enter and retrieve data using their own words, rather than relying on constrained language lists or navigating an artificially structured database. LSA-based tools can greatly enhance the usability and usefulness of knowledge management systems and thus provide a valuable tool to assist nuclear industry personnel in gathering and transferring worker expertise. (authors)

  1. Learning and Memory

    OpenAIRE

    1999-01-01

    Under various circumstances and in different species the outward expression of learning varies considerably, and this has led to the classification of different categories of learning. Just as there is no generally agreed on definition of learning, there is no one system of classification. Types of learning commonly recognized are: Habituation, sensitization, classical conditioning, operant conditioning, trial and error, taste aversion, latent learning, cultural learning, imprinting, insight ...

  2. Lecture Hall and Learning Design: A Survey of Variables, Parameters, Criteria and Interrelationships for Audio-Visual Presentation Systems and Audience Reception.

    Science.gov (United States)

    Justin, J. Karl

    Variables and parameters affecting architectural planning and audiovisual systems selection for lecture halls and other learning spaces are surveyed. Interrelationships of factors are discussed, including--(1) design requirements for modern educational techniques as differentiated from cinema, theater or auditorium design, (2) general hall…

  3. Latent-Trait Latent-Class Analysis of Self-disclosure in the Work Environment

    NARCIS (Netherlands)

    Maij - de Meij, A.M.; Kelderman, H.; van der Flier, H.

    2005-01-01

    Based on the literature about self-disclosure, it was hypothesized that different groups of subjects differ in their pattern of self-disclosure with respect to different areas of social interaction. An extended latent-trait latent-class model was proposed to describe these general patterns of

  4. Latent-trait latent-class analysis of selfdisclosure in the work environment

    NARCIS (Netherlands)

    Maij - de Meij, A.M.; Kelderman, H.; van der Flier, H.

    2006-01-01

    Based on the literature about self-disclosure, it was hypothesized that different groups of subjects differ in their pattern of self-disclosure with respect to different areas of social interaction. An extended latent-trait latent-class model was proposed to describe these general patterns of

  5. Latent-Trait Latent-Class Analysis of Self-Disclosure in the Work Environment

    Science.gov (United States)

    Maij-de Meij, Annette M.; Kelderman, Henk; van der Flier, Henk

    2005-01-01

    Based on the literature about self-disclosure, it was hypothesized that different groups of subjects differ in their pattern of self-disclosure with respect to different areas of social interaction. An extended latent-trait latent-class model was proposed to describe these general patterns of self-disclosure. The model was used to analyze the data…

  6. Latent-trait latent-class analysis of selfdisclosure in the work environment

    NARCIS (Netherlands)

    Maij - de Meij, A.M.; Kelderman, H.; van der Flier, H.

    2005-01-01

    Based on the literature about self-disclosure, it was hypothesized that different groups of subjects differ in their pattern of self-disclosure with respect to different areas of social interaction. An extended latent-trait latent-class model was proposed to describe these general patterns of

  7. Latent and actual entrepreneurship in Europe and the US: some recent developments

    NARCIS (Netherlands)

    I. Grilo (Isabel); A.R. Thurik (Roy)

    2005-01-01

    textabstractThis paper uses 2004 survey data from the 15 old EU member states and the US to explain country differences in latent and actual entrepreneurship. Other than demographic variables such as gender, age and education, the set of covariates includes the perception by respondents of

  8. Conditional High-Order Boltzmann Machines for Supervised Relation Learning.

    Science.gov (United States)

    Huang, Yan; Wang, Wei; Wang, Liang; Tan, Tieniu

    2017-09-01

    Relation learning is a fundamental problem in many vision tasks. Recently, high-order Boltzmann machine and its variants have shown their great potentials in learning various types of data relation in a range of tasks. But most of these models are learned in an unsupervised way, i.e., without using relation class labels, which are not very discriminative for some challenging tasks, e.g., face verification. In this paper, with the goal to perform supervised relation learning, we introduce relation class labels into conventional high-order multiplicative interactions with pairwise input samples, and propose a conditional high-order Boltzmann Machine (CHBM), which can learn to classify the data relation in a binary classification way. To be able to deal with more complex data relation, we develop two improved variants of CHBM: 1) latent CHBM, which jointly performs relation feature learning and classification, by using a set of latent variables to block the pathway from pairwise input samples to output relation labels and 2) gated CHBM, which untangles factors of variation in data relation, by exploiting a set of latent variables to multiplicatively gate the classification of CHBM. To reduce the large number of model parameters generated by the multiplicative interactions, we approximately factorize high-order parameter tensors into multiple matrices. Then, we develop efficient supervised learning algorithms, by first pretraining the models using joint likelihood to provide good parameter initialization, and then finetuning them using conditional likelihood to enhance the discriminant ability. We apply the proposed models to a series of tasks including invariant recognition, face verification, and action similarity labeling. Experimental results demonstrate that by exploiting supervised relation labels, our models can greatly improve the performance.

  9. Assessing the Influence of Precipitation Variability on the Vegetation Dynamics of the Mediterranean Rangelands using NDVI and Machine Learning

    Science.gov (United States)

    Daliakopoulos, Ioannis; Tsanis, Ioannis

    2017-04-01

    Mitigating the vulnerability of Mediterranean rangelands against degradation is limited by our ability to understand and accurately characterize those impacts in space and time. The Normalized Difference Vegetation Index (NDVI) is a radiometric measure of the photosynthetically active radiation absorbed by green vegetation canopy chlorophyll and is therefore a good surrogate measure of vegetation dynamics. On the other hand, meteorological indices such as the drought assessing Standardised Precipitation Index (SPI) are can be easily estimated from historical and projected datasets at the global scale. This work investigates the potential of driving Random Forest (RF) models with meteorological indices to approximate NDVI-based vegetation dynamics. A sufficiently large number of RF models are trained using random subsets of the dataset as predictors, in a bootstrapping approach to account for the uncertainty introduced by the subset selection. The updated E-OBS-v13.1 dataset of the ENSEMBLES EU FP6 program provides observed monthly meteorological input to estimate SPI over the Mediterranean rangelands. RF models are trained to depict vegetation dynamics using the latest version (3g.v1) of the third generation GIMMS NDVI generated from NOAA's Advanced Very High Resolution Radiometer (AVHRR) sensors. Analysis is conducted for the period 1981-2015 at a gridded spatial resolution of 25 km. Preliminary results demonstrate the potential of machine learning algorithms to effectively mimic the underlying physical relationship of drought and Earth Observation vegetation indices to provide estimates based on precipitation variability.

  10. The relationship of document and quantitative literacy with learning styles and selected personal variables for aerospace technology students at Indiana State University

    Science.gov (United States)

    Martin, Royce Ann

    The purpose of this study was to determine the extent that student scores on a researcher-constructed quantitative and document literacy test, the Aviation Documents Delineator (ADD), were associated with (a) learning styles (imaginative, analytic, common sense, dynamic, and undetermined), as identified by the Learning Type Measure, (b) program curriculum (aerospace administration, professional pilot, both aerospace administration and professional pilot, other, or undeclared), (c) overall cumulative grade point average at Indiana State University, and (d) year in school (freshman, sophomore, junior, or senior). The Aviation Documents Delineator (ADD) was a three-part, 35 question survey that required students to interpret graphs, tables, and maps. Tasks assessed in the ADD included (a) locating, interpreting, and describing specific data displayed in the document, (b) determining data for a specified point on the table through interpolation, (c) comparing data for a string of variables representing one aspect of aircraft performance to another string of variables representing a different aspect of aircraft performance, (d) interpreting the documents to make decisions regarding emergency situations, and (e) performing single and/or sequential mathematical operations on a specified set of data. The Learning Type Measure (LTM) was a 15 item self-report survey developed by Bernice McCarthy (1995) to profile an individual's processing and perception tendencies in order to reveal different individual approaches to learning. The sample used in this study included 143 students enrolled in Aerospace Technology Department courses at Indiana State University in the fall of 1996. The ADD and the LTM were administered to each subject. Data collected in this investigation were analyzed using a stepwise multiple regression analysis technique. Results of the study revealed that the variables, year in school and GPA, were significant predictors of the criterion variables, document

  11. An exploratory analysis of personality, attitudes, and study skills on the learning curve within a team-based learning environment.

    Science.gov (United States)

    Persky, Adam M; Henry, Teague; Campbell, Ashley

    2015-03-25

    To examine factors that determine the interindividual variability of learning within a team-based learning environment. Students in a pharmacokinetics course were given 4 interim, low-stakes cumulative assessments throughout the semester and a cumulative final examination. Students' Myers-Briggs personality type was assessed, as well as their study skills, motivations, and attitudes towards team-learning. A latent curve model (LCM) was applied and various covariates were assessed to improve the regression model. A quadratic LCM was applied for the first 4 assessments to predict final examination performance. None of the covariates examined significantly impacted the regression model fit except metacognitive self-regulation, which explained some of the variability in the rate of learning. There were some correlations between personality type and attitudes towards team learning, with introverts having a lower opinion of team-learning than extroverts. The LCM could readily describe the learning curve. Extroverted and introverted personality types had the same learning performance even though preference for team-learning was lower in introverts. Other personality traits, study skills, or practice did not significantly contribute to the learning variability in this course.

  12. Feature and Region Selection for Visual Learning.

    Science.gov (United States)

    Zhao, Ji; Wang, Liantao; Cabral, Ricardo; De la Torre, Fernando

    2016-03-01

    Visual learning problems, such as object classification and action recognition, are typically approached using extensions of the popular bag-of-words (BoWs) model. Despite its great success, it is unclear what visual features the BoW model is learning. Which regions in the image or video are used to discriminate among classes? Which are the most discriminative visual words? Answering these questions is fundamental for understanding existing BoW models and inspiring better models for visual recognition. To answer these questions, this paper presents a method for feature selection and region selection in the visual BoW model. This allows for an intermediate visualization of the features and regions that are important for visual learning. The main idea is to assign latent weights to the features or regions, and jointly optimize these latent variables with the parameters of a classifier (e.g., support vector machine). There are four main benefits of our approach: 1) our approach accommodates non-linear additive kernels, such as the popular χ(2) and intersection kernel; 2) our approach is able to handle both regions in images and spatio-temporal regions in videos in a unified way; 3) the feature selection problem is convex, and both problems can be solved using a scalable reduced gradient method; and 4) we point out strong connections with multiple kernel learning and multiple instance learning approaches. Experimental results in the PASCAL VOC 2007, MSR Action Dataset II and YouTube illustrate the benefits of our approach.

  13. Latent Growth and Dynamic Structural Equation Models.

    Science.gov (United States)

    Grimm, Kevin J; Ram, Nilam

    2018-05-07

    Latent growth models make up a class of methods to study within-person change-how it progresses, how it differs across individuals, what are its determinants, and what are its consequences. Latent growth methods have been applied in many domains to examine average and differential responses to interventions and treatments. In this review, we introduce the growth modeling approach to studying change by presenting different models of change and interpretations of their model parameters. We then apply these methods to examining sex differences in the development of binge drinking behavior through adolescence and into adulthood. Advances in growth modeling methods are then discussed and include inherently nonlinear growth models, derivative specification of growth models, and latent change score models to study stochastic change processes. We conclude with relevant design issues of longitudinal studies and considerations for the analysis of longitudinal data.

  14. Lanthanide mixed ligand chelates for DNA profiling and latent fingerprint detection

    Science.gov (United States)

    Menzel, E. R.; Allred, Clay

    1997-02-01

    It is our aim to develop a universally applicable latent fingerprint detection method using lanthanide (rare-earth) complexes as a source of luminescence. Use of these lanthanide complexes offers advantages on several fronts, including benefits from large Stokes shifts, long luminescence lifetimes, narrow emissions, ability of sequential assembly of complexes, and chemical variability of the ligands. Proper exploitation of these advantages would lead to a latent fingerprint detection method superior to any currently available. These same characteristics also lend themselves to many of the problems associated with DNA processing in the forensic science context.

  15. Examination of the change in latent statuses in bullying behaviors across time.

    Science.gov (United States)

    Ryoo, Ji Hoon; Wang, Cixin; Swearer, Susan M

    2015-03-01

    Involvement in bullying and victimization has been mostly studied using cross-sectional data from 1 time point. As such, much of our understanding of bullying and victimization has not captured the dynamic experiences of youth over time. To examine the change of latent statuses in bullying and victimization, we applied latent transition analysis examining self-reported bullying involvement from 1,180 students in 5th through 9th grades across 3 time points. We identified unobserved heterogeneous subgroups (i.e., latent statuses) and investigated how students transition between the unobserved subgroups over time. For victimization, 4 latent statuses were identified: frequent victim (11.23%), occasional traditional victim (28.86%), occasional cyber and traditional victim (10.34%), and infrequent victim (49.57%). For bullying behavior, 3 latent statuses were identified: frequent perpetrator (5.12%), occasional verbal/relational perpetrator (26.04%), and infrequent perpetrator (68.84%). The characteristics of the transitions were examined. The multiple-group effects of gender, grade, and first language learned on transitions across statuses were also investigated. The infrequent victim and infrequent perpetrator groups were the most stable, and the frequent victim and frequent perpetrator groups were the least stable. These findings suggest instability in perpetration and victimization over time, as well as significant changes, especially during school transition years. Findings suggest that school-based interventions need to address the heterogeneity in perpetrator and victim experiences in adolescence.

  16. Longitudinal Effects of Student-Perceived Classroom Support on Motivation - A Latent Change Model.

    Science.gov (United States)

    Lazarides, Rebecca; Raufelder, Diana

    2017-01-01

    This two-wave longitudinal study examined how developmental changes in students' mastery goal orientation, academic effort, and intrinsic motivation were predicted by student-perceived support of motivational support (support for autonomy, competence, and relatedness) in secondary classrooms. The study extends previous knowledge that showed that support for motivational support in class is related to students' intrinsic motivation as it focused on the developmental changes of a set of different motivational variables and the relations of these changes to student-perceived motivational support in class. Thus, differential classroom effects on students' motivational development were investigated. A sample of 1088 German students was assessed in the beginning of the school year when students were in grade 8 ( Mean age = 13.70, SD = 0.53, 54% girls) and again at the end of the next school year when students were in grade 9. Results of latent change models showed a tendency toward decline in mastery goal orientation and a significant decrease in academic effort from grade 8 to 9. Intrinsic motivation did not decrease significantly across time. Student-perceived support of competence in class predicted the level and change in students' academic effort. The findings emphasized that it is beneficial to create classroom learning environments that enhance students' perceptions of competence in class when aiming to enhance students' academic effort in secondary school classrooms.

  17. Longitudinal Effects of Student-Perceived Classroom Support on Motivation – A Latent Change Model

    Science.gov (United States)

    Lazarides, Rebecca; Raufelder, Diana

    2017-01-01

    This two-wave longitudinal study examined how developmental changes in students’ mastery goal orientation, academic effort, and intrinsic motivation were predicted by student-perceived support of motivational support (support for autonomy, competence, and relatedness) in secondary classrooms. The study extends previous knowledge that showed that support for motivational support in class is related to students’ intrinsic motivation as it focused on the developmental changes of a set of different motivational variables and the relations of these changes to student-perceived motivational support in class. Thus, differential classroom effects on students’ motivational development were investigated. A sample of 1088 German students was assessed in the beginning of the school year when students were in grade 8 (Mean age = 13.70, SD = 0.53, 54% girls) and again at the end of the next school year when students were in grade 9. Results of latent change models showed a tendency toward decline in mastery goal orientation and a significant decrease in academic effort from grade 8 to 9. Intrinsic motivation did not decrease significantly across time. Student-perceived support of competence in class predicted the level and change in students’ academic effort. The findings emphasized that it is beneficial to create classroom learning environments that enhance students’ perceptions of competence in class when aiming to enhance students’ academic effort in secondary school classrooms. PMID:28382012

  18. LATENT STRUCTURE OF MOTOR ABILITIES AND SKILLS OF DEAF CHILDREN

    Directory of Open Access Journals (Sweden)

    Husnija Hasanbegović

    2012-04-01

    Full Text Available In this work surveys of latent motility abilities and skills of school children are shown. The sample for this survey was consisted of two subsamples. First one has consisted of deaf children N=29, and the second one has consisted hearing children of same age N=69. Subsamples of deaf is chosen according to model of applied sample, and subsample is chosen randomly, so two stages group sample N=90 has been created. After quantitative differences have been discovered between subsamples, hearing pupils have shown statistically better results at motility skills and techniques than deaf children and cumulative results have been subjected to inter correlation of variables. The target of using this method was determination of saturation of common variability through saturation of variables and their correlation by Ortoblique rotation for determination of latent information that are going to serve as practical guides at education and deaf children treatment, because of improvement of their motility abilities and skills according to hearing children. Three factors have been singled out as main preview of measurement on manifest variables. According to first review of measuring it has been established that at deaf children is needed to work on improving of physical abilities and mobility and then developed motility abilities and skills. Their information has been gained most probably by non system fluctuations as information about ability of balance maintaining which is most probably non dependable of motility abilities and skills as at deaf and hearing children too. According to this survey by entering the structure of measuring instrument it is possible to create programs for improving motility abilities and skills at deaf children.

  19. A Multinomial Probit Model with Latent Factors

    DEFF Research Database (Denmark)

    Piatek, Rémi; Gensowski, Miriam

    2017-01-01

    be meaningfully linked to an economic model. We provide sufficient conditions that make this structure identified and interpretable. For inference, we design a Markov chain Monte Carlo sampler based on marginal data augmentation. A simulation exercise shows the good numerical performance of our sampler......We develop a parametrization of the multinomial probit model that yields greater insight into the underlying decision-making process, by decomposing the error terms of the utilities into latent factors and noise. The latent factors are identified without a measurement system, and they can...

  20. A Bayesian approach to estimate sensible and latent heat over vegetated land surface

    Directory of Open Access Journals (Sweden)

    C. van der Tol

    2009-06-01

    Full Text Available Sensible and latent heat fluxes are often calculated from bulk transfer equations combined with the energy balance. For spatial estimates of these fluxes, a combination of remotely sensed and standard meteorological data from weather stations is used. The success of this approach depends on the accuracy of the input data and on the accuracy of two variables in particular: aerodynamic and surface conductance. This paper presents a Bayesian approach to improve estimates of sensible and latent heat fluxes by using a priori estimates of aerodynamic and surface conductance alongside remote measurements of surface temperature. The method is validated for time series of half-hourly measurements in a fully grown maize field, a vineyard and a forest. It is shown that the Bayesian approach yields more accurate estimates of sensible and latent heat flux than traditional methods.

  1. Numeric Input Relations for Relational Learning with Applications to Community Structure Analysis

    DEFF Research Database (Denmark)

    Jiang, Jiuchuan; Jaeger, Manfred

    2015-01-01

    distribution is defined by the model from numerical input variables that are only used for conditioning the distribution of discrete response variables. We show how numerical input relations can very easily be used in the Relational Bayesian Network framework, and that existing inference and learning methods......Most work in the area of statistical relational learning (SRL) is focussed on discrete data, even though a few approaches for hybrid SRL models have been proposed that combine numerical and discrete variables. In this paper we distinguish numerical random variables for which a probability...... use the augmented RBN framework to define probabilistic models for multi-relational (social) networks in which the probability of a link between two nodes depends on numeric latent feature vectors associated with the nodes. A generic learning procedure can be used to obtain a maximum-likelihood fit...

  2. Detection of latent prints by Raman imaging

    Science.gov (United States)

    Lewis, Linda Anne [Andersonville, TN; Connatser, Raynella Magdalene [Knoxville, TN; Lewis, Sr., Samuel Arthur

    2011-01-11

    The present invention relates to a method for detecting a print on a surface, the method comprising: (a) contacting the print with a Raman surface-enhancing agent to produce a Raman-enhanced print; and (b) detecting the Raman-enhanced print using a Raman spectroscopic method. The invention is particularly directed to the imaging of latent fingerprints.

  3. Statistical inference based on latent ability estimates

    NARCIS (Netherlands)

    Hoijtink, H.J.A.; Boomsma, A.

    The quality of approximations to first and second order moments (e.g., statistics like means, variances, regression coefficients) based on latent ability estimates is being discussed. The ability estimates are obtained using either the Rasch, oi the two-parameter logistic model. Straightforward use

  4. Residual Structures in Latent Growth Curve Modeling

    Science.gov (United States)

    Grimm, Kevin J.; Widaman, Keith F.

    2010-01-01

    Several alternatives are available for specifying the residual structure in latent growth curve modeling. Two specifications involve uncorrelated residuals and represent the most commonly used residual structures. The first, building on repeated measures analysis of variance and common specifications in multilevel models, forces residual variances…

  5. Forensic Chemistry: The Revelation of Latent Fingerprints

    Science.gov (United States)

    Friesen, J. Brent

    2015-01-01

    The visualization of latent fingerprints often involves the use of a chemical substance that creates a contrast between the fingerprint residues and the surface on which the print was deposited. The chemical-aided visualization techniques can be divided into two main categories: those that chemically react with the fingerprint residue and those…

  6. Endogenous Opioid-Masked Latent Pain Sensitization

    DEFF Research Database (Denmark)

    Pereira, Manuel P; Donahue, Renee R; Dahl, Jørgen B

    2015-01-01

    UNLABELLED: Following the resolution of a severe inflammatory injury in rodents, administration of mu-opioid receptor inverse agonists leads to reinstatement of pain hypersensitivity. The mechanisms underlying this form of latent pain sensitization (LS) likely contribute to the development of chr...

  7. Altered intrinsic functional connectivity in the latent period of epileptogenesis in a temporal lobe epilepsy model.

    Science.gov (United States)

    Lee, Hyoin; Jung, Seungmoon; Lee, Peter; Jeong, Yong

    2017-10-01

    The latent period, a seizure-free phase, is the duration between brain injury and the onset of spontaneous recurrent seizures (SRSs) during epileptogenesis. The latent period is thought to involve several progressive pathophysiological events that lead to the evolution of the chronic epilepsy phase. Hence, it is vital to investigate the changes in the latent period during epileptogenesis in order to better understand temporal lobe epilepsy (TLE), and to achieve early diagnosis and appropriate management of the condition. Accordingly, recent studies with patients with TLE using resting-state functional magnetic resonance imaging (rs-fMRI) have reported that alterations of resting-state functional connectivity (rsFC) during the chronic period are associated with some clinical manifestations, including learning and memory impairments, emotional instability, and social behavior deficits, in addition to repetitive seizure episodes. In contrast, the changes in the intrinsic rsFC during epileptogenesis, particularly during the latent period, remain unclear. In this study, we investigated the alterations in intrinsic rsFC during the latent and chronic periods in a pilocarpine-induced TLE mouse model using intrinsic optical signal imaging (IOSI). This technique can monitor the changes in the local hemoglobin concentration according to neuronal activity and can help investigate large-scale brain intrinsic networks. After seeding on the anatomical regions of interest (ROIs) and calculating the correlation coefficients between each ROI, we established and compared functional correlation matrices and functional connectivity maps during the latent and chronic periods of epilepsy. We found a decrease in the interhemispheric rsFC at the frontal and temporal regions during both the latent and chronic periods. Furthermore, a significant decrease in the interhemispheric rsFC was observed in the somatosensory area during the chronic period. Changes in network configurations during

  8. Incorporating direct marketing activity into latent attrition models

    NARCIS (Netherlands)

    Schweidel, David A.; Knox, George

    2013-01-01

    When defection is unobserved, latent attrition models provide useful insights about customer behavior and accurate forecasts of customer value. Yet extant models ignore direct marketing efforts. Response models incorporate the effects of direct marketing, but because they ignore latent attrition,

  9. Five years of lesson modification to implement non-traditional learning sessions in a traditional-delivery curriculum: A retrospective assessment using applied implementation variables.

    Science.gov (United States)

    Gleason, Shaun E; McNair, Bryan; Kiser, Tyree H; Franson, Kari L

    Non-traditional learning (NTL), including aspects of self-directed learning (SDL), may address self-awareness development needs. Many factors can impact successful implementation of NTL. To share our multi-year experience with modifications that aim to improve NTL sessions in a traditional curriculum. To improve understanding of applied implementation variables (some of which were based on successful SDL implementation components) that impact NTL. We delivered a single lesson in a traditional-delivery curriculum once annually for five years, varying delivery annually in response to student learning and reaction-to-learning results. At year 5, we compared student learning and reaction-to-learning to applied implementation factors using logistic regression. Higher instructor involvement and overall NTL levels predicted correct exam responses (p=0.0007 and ptraditional and highest overall NTL deliveries. Students rated instructor presentation skills and teaching methods higher when greater instructor involvement (pmethods were most effective when lower student involvement and higher technology levels (ptraditional-delivery curriculum, instructor involvement appears essential, while the impact of student involvement and educational technology levels varies. Copyright © 2017 Elsevier Inc. All rights reserved.

  10. Latent log-linear models for handwritten digit classification.

    Science.gov (United States)

    Deselaers, Thomas; Gass, Tobias; Heigold, Georg; Ney, Hermann

    2012-06-01

    We present latent log-linear models, an extension of log-linear models incorporating latent variables, and we propose two applications thereof: log-linear mixture models and image deformation-aware log-linear models. The resulting models are fully discriminative, can be trained efficiently, and the model complexity can be controlled. Log-linear mixture models offer additional flexibility within the log-linear modeling framework. Unlike previous approaches, the image deformation-aware model directly considers image deformations and allows for a discriminative training of the deformation parameters. Both are trained using alternating optimization. For certain variants, convergence to a stationary point is guaranteed and, in practice, even variants without this guarantee converge and find models that perform well. We tune the methods on the USPS data set and evaluate on the MNIST data set, demonstrating the generalization capabilities of our proposed models. Our models, although using significantly fewer parameters, are able to obtain competitive results with models proposed in the literature.

  11. The correlation between effective factors of e-learning and demographic variables in a post-graduate program of virtual medical education in Tehran University of Medical Sciences.

    Science.gov (United States)

    Golband, Farnoosh; Hosseini, Agha Fatemeh; Mojtahedzadeh, Rita; Mirhosseini, Fakhrossadat; Bigdeli, Shoaleh

    2014-01-01

    E-learning as an educational approach has been adopted by diverse educational and academic centers worldwide as it facilitates learning in facing the challenges of the new era in education. Considering the significance of virtual education and its growing practice, it is of vital importance to examine its components for promoting and maintaining success. This analytical cross-sectional study was an attempt to determine the relationship between four factors of content, educator, learner and system, and effective e-learning in terms of demographic variables, including age, gender, educational background, and marital status of postgraduate master's students (MSc) studying at virtual faculty of Tehran University of Medical Sciences. The sample was selected by census (n=60); a demographic data gathering tool and a researcher-made questionnaire were used to collect data. The face and content validity of both tools were confirmed and the results were analyzed by descriptive statistics (frequency, percentile, standard deviation and mean) and inferential statistics (independent t-test, Scheffe's test, one-way ANOVA and Pearson correlation test) by using SPSS (V.16). The present study revealed that There was no statistically significant relationship between age and marital status and effective e-learning (P>0.05); whereas, there was a statistically significant difference between gender and educational background with effective e-learning (Pe-learning can help managers and designers to make the right decisions about educational components of e-learning, i.e. content, educator, system and learner and improve them to create a more productive learning environment for learners.

  12. The learning environment as a mediating variable between self-directed learning readiness and academic performance of a sample of saudi nursing and medical emergency students.

    Science.gov (United States)

    Alotaibi, Khaled N

    2016-01-01

    There has been some ground-breaking research on self-directed learning (SDL) in nursing education which reveals the superiority of SDL to traditional learning methods in terms of students' academic performance and the development of positive attitudes toward the learning process on the part of both students and teachers. The relationship between students' self-directed learning readiness (SDLR) and students' academic performance, and the mediating role of students' perceptions of the learning environment needs further investigation. In this study, it is proposed that students' perceptions of their learning environment could enhance their SDLR and thus boost their academic performance (in terms of their GPA). A descriptive design was used to examine the relationships between the domains of SDLR, which are self-management, desire to learn and self-control and students' perceptions of the learning environment (SPLE) and students' GPA. A survey involving 342 [Corrected] Saudi students from nursing and emergency medical services undergraduate programs in King Saud University was used for this research. The results showed that SDLR level positively influenced students' academic performance positively, and that students' perceptions of their learning environment played a significant role in determining their level of SDLR and academic performance. It is recommended that nursing and emergency medical services educators provide a supportive learning environment in terms of good teaching, clear goals and standards, appropriate assessment, appropriate workload, and emphasis on independence to encourage students to engage in the process of SDL which can, in turn, enhance their academic performance. Copyright © 2015 Elsevier Ltd. All rights reserved.

  13. Shallow and Deep Latent Heating Modes Over Tropical Oceans Observed with TRMM PR Spectral Latent Heating Data

    Science.gov (United States)

    Takayabu, Yukari N.; Shige, Shoichi; Tao, Wei-Kuo; Hirota, Nagio

    2010-01-01

    The global hydrological cycle is central to the Earth's climate system, with rainfall and the physics of its formation acting as the key links in the cycle. Two-thirds of global rainfall occurs in the Tropics. Associated with this rainfall is a vast amount of heat, which is known as latent heat. It arises mainly due to the phase change of water vapor condensing into liquid droplets; three-fourths of the total heat energy available to the Earth's atmosphere comes from tropical rainfall. In addition, fresh water provided by tropical rainfall and its variability exerts a large impact upon the structure and motions of the upper ocean layer. Three-dimensional distributions of latent heating estimated from Tropical Rainfall Measuring Mission Precipitation Radar (TRMM PR)utilizing the Spectral Latent Heating (SLH) algorithm are analyzed. Mass-weighted and vertically integrated latent heating averaged over the tropical oceans is estimated as approx.72.6 J/s (approx.2.51 mm/day), and that over tropical land is approx.73.7 J/s (approx.2.55 mm/day), for 30degN-30degS. It is shown that non-drizzle precipitation over tropical and subtropical oceans consists of two dominant modes of rainfall systems, deep systems and congestus. A rough estimate of shallow mode contribution against the total heating is about 46.7 % for the average tropical oceans, which is substantially larger than 23.7 % over tropical land. While cumulus congestus heating linearly correlates with the SST, deep mode is dynamically bounded by large-scale subsidence. It is notable that substantial amount of rain, as large as 2.38 mm day-1 in average, is brought from congestus clouds under the large-scale subsiding circulation. It is also notable that even in the region with SST warmer than 28 oC, large-scale subsidence effectively suppresses the deep convection, remaining the heating by congestus clouds. Our results support that the entrainment of mid-to-lower-tropospheric dry air, which accompanies the large

  14. A Study Module in the Logical Structure of Cognitive Process in the Context of Variable-Based Blended Learning

    Science.gov (United States)

    Smirnova, Galina I.; Katashev, Valery G.

    2017-01-01

    Blended learning is increasingly gaining importance in all levels of educational system, particularly in tertiary education. In engineering profiles the core blended learning activity is students' independent work, the efficiency of which is defined by the degree of students' active involvement into the educational process, their ability to absorb…

  15. Identification of Chinese medicine syndromes in persistent insomnia associated with major depressive disorder: a latent tree analysis.

    Science.gov (United States)

    Yeung, Wing-Fai; Chung, Ka-Fai; Zhang, Nevin Lian-Wen; Zhang, Shi Ping; Yung, Kam-Ping; Chen, Pei-Xian; Ho, Yan-Yee

    2016-01-01

    Chinese medicine (CM) syndrome (zheng) differentiation is based on the co-occurrence of CM manifestation profiles, such as signs and symptoms, and pulse and tongue features. Insomnia is a symptom that frequently occurs in major depressive disorder despite adequate antidepressant treatment. This study aims to identify co-occurrence patterns in participants with persistent insomnia and major depressive disorder from clinical feature data using latent tree analysis, and to compare the latent variables with relevant CM syndromes. One hundred and forty-two participants with persistent insomnia and a history of major depressive disorder completed a standardized checklist (the Chinese Medicine Insomnia Symptom Checklist) specially developed for CM syndrome classification of insomnia. The checklist covers symptoms and signs, including tongue and pulse features. The clinical features assessed by the checklist were analyzed using Lantern software. CM practitioners with relevant experience compared the clinical feature variables under each latent variable with reference to relevant CM syndromes, based on a previous review of CM syndromes. The symptom data were analyzed to build the latent tree model and the model with the highest Bayes information criterion score was regarded as the best model. This model contained 18 latent variables, each of which divided participants into two clusters. Six clusters represented more than 50 % of the sample. The clinical feature co-occurrence patterns of these six clusters were interpreted as the CM syndromes Liver qi stagnation transforming into fire, Liver fire flaming upward, Stomach disharmony, Hyperactivity of fire due to yin deficiency, Heart-kidney noninteraction, and Qi deficiency of the heart and gallbladder. The clinical feature variables that contributed significant cumulative information coverage (at least 95 %) were identified. Latent tree model analysis on a sample of depressed participants with insomnia revealed 13 clinical

  16. Modeling Nonlinear Change via Latent Change and Latent Acceleration Frameworks: Examining Velocity and Acceleration of Growth Trajectories

    Science.gov (United States)

    Grimm, Kevin; Zhang, Zhiyong; Hamagami, Fumiaki; Mazzocco, Michele

    2013-01-01

    We propose the use of the latent change and latent acceleration frameworks for modeling nonlinear growth in structural equation models. Moving to these frameworks allows for the direct identification of "rates of change" and "acceleration" in latent growth curves--information available indirectly through traditional growth…

  17. Latent Class Models in action: bridging social capital & Internet usage.

    Science.gov (United States)

    Neves, Barbara Barbosa; Fonseca, Jaime R S

    2015-03-01

    This paper explores how Latent Class Models (LCM) can be applied in social research, when the basic assumptions of regression models cannot be validated. We examine the usefulness of this method with data collected from a study on the relationship between bridging social capital and the Internet. Social capital is defined here as the resources that are potentially available in one's social ties. Bridging is a dimension of social capital, usually related to weak ties (acquaintances), and a source of instrumental resources such as information. The study surveyed a stratified random sample of 417 inhabitants of Lisbon, Portugal. We used LCM to create the variable bridging social capital, but also to estimate the relationship between bridging social capital and Internet usage when we encountered convergence problems with the logistic regression analysis. We conclude by showing a positive relationship between bridging and Internet usage, and by discussing the potential of LCM for social science research. Copyright © 2014 Elsevier Inc. All rights reserved.

  18. The Mixed Effects of Phonetic Input Variability on Relative Ease of L2 Learning: Evidence from English Learners’ Production of French and Spanish Stop-Rhotic Clusters

    Directory of Open Access Journals (Sweden)

    Laura Colantoni

    2018-04-01

    Full Text Available We examined the consequences of within-category phonetic variability in the input on non-native learners’ production accuracy. Following previous empirical research on the L2 acquisition of phonetics and the lexicon, we tested the hypothesis that phonetic variability facilitates learning by analyzing English-speaking learners’ production of French and Spanish word-medial stop-rhotic clusters, which differ from their English counterparts in terms of stop and rhotic voicing and manner. Crucially, for both the stops and rhotics, there are differences in within-language variability. Twenty native speakers per language and 39 L1 English-learners of French (N = 20 and Spanish (N = 19 of intermediate and advanced proficiency performed a carrier-sentence reading task. A given parameter was deemed to have been acquired when the learners’ production fell within the range of attested native speaker values. An acoustic analysis of the data partially supports the facilitative effect of phonetic variability. To account for the unsupported hypotheses, we discuss a number of issues, including the difficulty of measuring variability, the need to determine the extent to which learners’ perception shapes intake, and the challenge of teasing apart the effects of input variability from those of transferred L1 articulatory patterns.

  19. Accounting for standard errors of vision-specific latent trait in regression models.

    Science.gov (United States)

    Wong, Wan Ling; Li, Xiang; Li, Jialiang; Wong, Tien Yin; Cheng, Ching-Yu; Lamoureux, Ecosse L

    2014-07-11

    To demonstrate the effectiveness of Hierarchical Bayesian (HB) approach in a modeling framework for association effects that accounts for SEs of vision-specific latent traits assessed using Rasch analysis. A systematic literature review was conducted in four major ophthalmic journals to evaluate Rasch analysis performed on vision-specific instruments. The HB approach was used to synthesize the Rasch model and multiple linear regression model for the assessment of the association effects related to vision-specific latent traits. The effectiveness of this novel HB one-stage "joint-analysis" approach allows all model parameters to be estimated simultaneously and was compared with the frequently used two-stage "separate-analysis" approach in our simulation study (Rasch analysis followed by traditional statistical analyses without adjustment for SE of latent trait). Sixty-six reviewed articles performed evaluation and validation of vision-specific instruments using Rasch analysis, and 86.4% (n = 57) performed further statistical analyses on the Rasch-scaled data using traditional statistical methods; none took into consideration SEs of the estimated Rasch-scaled scores. The two models on real data differed for effect size estimations and the identification of "independent risk factors." Simulation results showed that our proposed HB one-stage "joint-analysis" approach produces greater accuracy (average of 5-fold decrease in bias) with comparable power and precision in estimation of associations when compared with the frequently used two-stage "separate-analysis" procedure despite accounting for greater uncertainty due to the latent trait. Patient-reported data, using Rasch analysis techniques, do not take into account the SE of latent trait in association analyses. The HB one-stage "joint-analysis" is a better approach, producing accurate effect size estimations and information about the independent association of exposure variables with vision-specific latent traits

  20. Latent class models in financial data analysis

    Directory of Open Access Journals (Sweden)

    Attilio Gardini

    2007-10-01

    Full Text Available This paper deals with optimal international portfolio choice by developing a latent class approach based on the distinction between international and non-international investors. On the basis of micro data, we analyze the effects of many social, demographic, economic and financial characteristics on the probability to be an international investor. Traditional measures of equity home bias do not allow for the existence of international investment rationing operators. On the contrary, by resorting to latent class analysis it is possible to detect the unobservable distinction between international investors and investors who are precluded from operating into international financial markets and, therefore, to evaluate the role of these unobservable constraints on equity home bias.

  1. Exploring galaxy evolution with latent space walks

    Science.gov (United States)

    Schawinski, Kevin; Turp, Dennis; Zhang, Ce

    2018-01-01

    We present a new approach using artificial intelligence to perform data-driven forward models of astrophysical phenomena. We describe how a variational autoencoder can be used to encode galaxies to latent space, independently manipulate properties such as the specific star formation rate, and return it to real space. Such transformations can be used for forward modeling phenomena using data as the only constraints. We demonstrate the utility of this approach using the question of the quenching of star formation in galaxies.

  2. New Treatment Regimen for Latent Tuberculosis Infection

    Centers for Disease Control (CDC) Podcasts

    2012-03-15

    In this podcast, Dr. Kenneth Castro, Director of the Division of Tuberculosis Elimination, discusses the December 9, 2011 CDC guidelines for the use of a new regimen for the treatment of persons with latent tuberculosis infection.  Created: 3/15/2012 by National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention (NCHHSTP).   Date Released: 3/15/2012.

  3. Barcelona - Talent Latent 09 / Ahto Sooaru

    Index Scriptorium Estoniae

    Sooaru, Ahto

    2010-01-01

    Fotonäitusest "Talent Latent 09" Barcelonas Arts Santa Monica kunstikeskuses. Loetletud näitusel eksponeeritud fotode autorid. Pikemalt Rafael Milach'i (sünd. 1978), Lucia Ganieva, Javier Marquerie Thomas'i (sünd. 1986), Amaury da Cunha (sünd. 1976) töödest. Lühidalt ka teistest näitustest Arts Santa Monica kunstikeskuses

  4. Variable density management in riparian reserves: lessons learned from an operational study in managed forests of western Oregon, USA.

    Science.gov (United States)

    Samuel Chan; Paul Anderson; John Cissel; Larry Lateen; Charley Thompson

    2004-01-01

    A large-scale operational study has been undertaken to investigate variable density management in conjunction with riparian buffers as a means to accelerate development of late-seral habitat, facilitate rare species management, and maintain riparian functions in 40-70 year-old headwater forests in western Oregon, USA. Upland variable retention treatments include...

  5. Estimating Classification Errors Under Edit Restrictions in Composite Survey-Register Data Using Multiple Imputation Latent Class Modelling (MILC

    Directory of Open Access Journals (Sweden)

    Boeschoten Laura

    2017-12-01

    Full Text Available Both registers and surveys can contain classification errors. These errors can be estimated by making use of a composite data set. We propose a new method based on latent class modelling to estimate the number of classification errors across several sources while taking into account impossible combinations with scores on other variables. Furthermore, the latent class model, by multiply imputing a new variable, enhances the quality of statistics based on the composite data set. The performance of this method is investigated by a simulation study, which shows that whether or not the method can be applied depends on the entropy R2 of the latent class model and the type of analysis a researcher is planning to do. Finally, the method is applied to public data from Statistics Netherlands.

  6. Latent change models of adult cognition: are changes in processing speed and working memory associated with changes in episodic memory?

    Science.gov (United States)

    Hertzog, Christopher; Dixon, Roger A; Hultsch, David F; MacDonald, Stuart W S

    2003-12-01

    The authors used 6-year longitudinal data from the Victoria Longitudinal Study (VLS) to investigate individual differences in amount of episodic memory change. Latent change models revealed reliable individual differences in cognitive change. Changes in episodic memory were significantly correlated with changes in other cognitive variables, including speed and working memory. A structural equation model for the latent change scores showed that changes in speed and working memory predicted changes in episodic memory, as expected by processing resource theory. However, these effects were best modeled as being mediated by changes in induction and fact retrieval. Dissociations were detected between cross-sectional ability correlations and longitudinal changes. Shuffling the tasks used to define the Working Memory latent variable altered patterns of change correlations.

  7. An Analysis of the Relationship between the Learning Process and Learning Motivation Profiles of Japanese Pharmacy Students Using Structural Equation Modeling

    Directory of Open Access Journals (Sweden)

    Shigeo Yamamura

    2018-04-01

    Full Text Available Pharmacy students in Japan have to maintain strong motivation to learn for six years during their education. The authors explored the students’ learning structure. All pharmacy students in their 4th through to 6th year at Josai International University participated in the survey. The revised two factor study process questionnaire and science motivation questionnaire II were used to assess their learning process and learning motivation profiles, respectively. Structural equation modeling (SEM was used to examine a causal relationship between the latent variables in the learning process and those in the learning motivation profile. The learning structure was modeled on the idea that the learning process affects the learning motivation profile of respondents. In the multi-group SEM, the estimated mean of the deep learning to learning motivation profile increased just after their clinical clerkship for 6th year students. This indicated that the clinical experience benefited students’ deep learning, which is probably because the experience of meeting with real patients encourages meaningful learning in pharmacy studies.

  8. An Analysis of the Relationship between the Learning Process and Learning Motivation Profiles of Japanese Pharmacy Students Using Structural Equation Modeling.

    Science.gov (United States)

    Yamamura, Shigeo; Takehira, Rieko

    2018-04-23

    Pharmacy students in Japan have to maintain strong motivation to learn for six years during their education. The authors explored the students’ learning structure. All pharmacy students in their 4th through to 6th year at Josai International University participated in the survey. The revised two factor study process questionnaire and science motivation questionnaire II were used to assess their learning process and learning motivation profiles, respectively. Structural equation modeling (SEM) was used to examine a causal relationship between the latent variables in the learning process and those in the learning motivation profile. The learning structure was modeled on the idea that the learning process affects the learning motivation profile of respondents. In the multi-group SEM, the estimated mean of the deep learning to learning motivation profile increased just after their clinical clerkship for 6th year students. This indicated that the clinical experience benefited students’ deep learning, which is probably because the experience of meeting with real patients encourages meaningful learning in pharmacy studies.

  9. The Multidimensionality of Multicultural Service Learning: The Variable Effects of Social Identity, Context and Pedagogy on Pre-Service Teachers' Learning

    Science.gov (United States)

    Chang, Shih-pei; Anagnostopoulos, Dorothea; Omae, Hilda

    2011-01-01

    Multicultural service learning (MSL) seeks to develop pre-service teachers' capacities and commitment to teach diverse student populations. We use multiple regression analyses of survey data collected from 212 pre-service teachers engaged in 22 MSL sites to assess the effects of pre-service teachers' social identities, MSL contexts, and university…

  10. Mental toughness latent profiles in endurance athletes.

    Directory of Open Access Journals (Sweden)

    Joanna S Zeiger

    Full Text Available Mental toughness in endurance athletes, while an important factor for success, has been scarcely studied. An online survey was used to examine eight mental toughness factors in endurance athletes. The study aim was to determine mental toughness profiles via latent profile analysis in endurance athletes and whether associations exist between the latent profiles and demographics and sports characteristics. Endurance athletes >18 years of age were recruited via social media outlets (n = 1245, 53% female. Mental toughness was measured using the Sports Mental Toughness Questionnaire (SMTQ, Psychological Performance Inventory-Alternative (PPI-A, and self-esteem was measured using the Rosenberg Self-Esteem Scale (RSE. A three-class solution emerged, designated as high mental toughness (High MT, moderate mental toughness (Moderate MT and low mental toughness (Low MT. ANOVA tests showed significant differences between all three classes on all 8 factors derived from the SMTQ, PPI-A and the RSE. There was an increased odds of being in the High MT class compared to the Low MT class for males (OR = 1.99; 95% CI, 1.39, 2.83; P<0.001, athletes who were over 55 compared to those who were 18-34 (OR = 2.52; 95% CI, 1.37, 4.62; P<0.01, high sports satisfaction (OR = 8.17; 95% CI, 5.63, 11.87; P<0.001, and high division placement (OR = 2.18; 95% CI, 1.46,3.26; P<0.001. The data showed that mental toughness latent profiles exist in endurance athletes. High MT is associated with demographics and sports characteristics. Mental toughness screening in athletes may help direct practitioners with mental skills training.

  11. Using existing questionnaires in latent class analysis

    DEFF Research Database (Denmark)

    Nielsen, Anne Molgaard; Vach, Werner; Kent, Peter

    2016-01-01

    BACKGROUND: Latent class analysis (LCA) is increasingly being used in health research, but optimal approaches to handling complex clinical data are unclear. One issue is that commonly used questionnaires are multidimensional, but expressed as summary scores. Using the example of low back pain (LBP......), the aim of this study was to explore and descriptively compare the application of LCA when using questionnaire summary scores and when using single items to subgrouping of patients based on multidimensional data. MATERIALS AND METHODS: Baseline data from 928 LBP patients in an observational study were...

  12. Bayesian inference for an illness-death model for stroke with cognition as a latent time-dependent risk factor

    NARCIS (Netherlands)

    Hout A. van den; Fox J.P.; Klein Entink R.H.

    2015-01-01

    Longitudinal data can be used to estimate the transition intensities between healthy and unhealthy states prior to death. An illness-death model for history of stroke is presented, where time-dependent transition intensities are regressed on a latent variable representing cognitive function. The

  13. Food deprivation enhances both autoshaping and autoshaping impairment by a latent inhibition procedure.

    Science.gov (United States)

    Sparber, S B; Bollweg, G L; Messing, R B

    1991-02-01

    The influence of food deprivation on acquisition of autoshaped operant behavior was measured. In one study separate groups of young, male rats that were deprived to 75%, 80%, 85%, 90%, and 95% of ad lib weight were subjected to an autoshaping procedure in which a 6 s delay was interposed between lever retraction (which occurred when rats made a lever touch, or automatically after 15 s) and food pellet delivery. In a second study, groups of rats were deprived to 80% or 90% of ad lib weight prior to testing in a latent inhibition variation of the same autoshaping procedure. This was done to determine if greater food deprivation would enhance learning which, because of the latent inhibition manipulation, is manifest as less lever-directed behavior. Greater food deprivation was associated both with fast acquisition of autoshaped lever responding and with more reliable failure to increase lever responding in the latent inhibition paradigm. Thus, increasing food deprivation was associated with enhanced acquisition regardless of whether the required performance was an increase or a failure to increase the same behavior, indicating a specific effect on learning. Copyright © 1991. Published by Elsevier B.V.

  14. Closing the gap between behavior and models in route choice: The role of spatiotemporal constraints and latent traits in choice set formation

    DEFF Research Database (Denmark)

    Kaplan, Sigal; Prato, Carlo Giacomo

    2012-01-01

    not account for individual-related spatiotemporal constraints. This paper reduces the gap by proposing a route choice model incorporating spatiotemporal constraints and latent traits. The proposed approach combines stochastic route generation with a latent variable semi-compensatory model representing......A considerable gap exists between the behavioral paradigm of choice set formation in route choice and its representation in route choice modeling. While travelers form their viable choice set by retaining routes that satisfy spatiotemporal constraints, existing route generation techniques do...

  15. Supervised Learning of Two-Layer Perceptron under the Existence of External Noise — Learning Curve of Boolean Functions of Two Variables in Tree-Like Architecture —

    Science.gov (United States)

    Uezu, Tatsuya; Kiyokawa, Shuji

    2016-06-01

    We investigate the supervised batch learning of Boolean functions expressed by a two-layer perceptron with a tree-like structure. We adopt continuous weights (spherical model) and the Gibbs algorithm. We study the Parity and And machines and two types of noise, input and output noise, together with the noiseless case. We assume that only the teacher suffers from noise. By using the replica method, we derive the saddle point equations for order parameters under the replica symmetric (RS) ansatz. We study the critical value αC of the loading rate α above which the learning phase exists for cases with and without noise. We find that αC is nonzero for the Parity machine, while it is zero for the And machine. We derive the exponents barβ of order parameters expressed as (α - α C)bar{β} when α is near to αC. Furthermore, in the Parity machine, when noise exists, we find a spin glass solution, in which the overlap between the teacher and student vectors is zero but that between student vectors is nonzero. We perform Markov chain Monte Carlo simulations by simulated annealing and also by exchange Monte Carlo simulations in both machines. In the Parity machine, we study the de Almeida-Thouless stability, and by comparing theoretical and numerical results, we find that there exist parameter regions where the RS solution is unstable, and that the spin glass solution is metastable or unstable. We also study asymptotic learning behavior for large α and derive the exponents hat{β } of order parameters expressed as α - hat{β } when α is large in both machines. By simulated annealing simulations, we confirm these results and conclude that learning takes place for the input noise case with any noise amplitude and for the output noise case when the probability that the teacher's output is reversed is less than one-half.

  16. Aspects of physicochemical methods for the detection of latent fingerprints

    International Nuclear Information System (INIS)

    Knowles, A.M.

    1978-01-01

    This paper reviews physicochemical methods of detecting latent finger-prints on a wide range of materials commonly found at the scene of a crime, with particular emphasis placed on the newer autoradiographic techniques. This is set against a description of studies on the fundamental nature of the latent fingerprint and its host substrate, with a brief review of the history of reagents used in latent fingerprint examination. (author)

  17. Optimal calibration of variable biofuel blend dual-injection engines using sparse Bayesian extreme learning machine and metaheuristic optimization

    International Nuclear Information System (INIS)

    Wong, Ka In; Wong, Pak Kin

    2017-01-01

    Highlights: • A new calibration method is proposed for dual-injection engines under biofuel blends. • Sparse Bayesian extreme learning machine and flower pollination algorithm are employed in the proposed method. • An SI engine is retrofitted for operating under dual-injection strategy. • The proposed method is verified experimentally under the two idle speed conditions. • Comparison with other machine learning methods and optimization algorithms is conducted. - Abstract: Although many combinations of biofuel blends are available in the market, it is more beneficial to vary the ratio of biofuel blends at different engine operating conditions for optimal engine performance. Dual-injection engines have the potential to implement such function. However, while optimal engine calibration is critical for achieving high performance, the use of two injection systems, together with other modern engine technologies, leads the calibration of the dual-injection engines to a very complicated task. Traditional trial-and-error-based calibration approach can no longer be adopted as it would be time-, fuel- and labor-consuming. Therefore, a new and fast calibration method based on sparse Bayesian extreme learning machine (SBELM) and metaheuristic optimization is proposed to optimize the dual-injection engines operating with biofuels. A dual-injection spark-ignition engine fueled with ethanol and gasoline is employed for demonstration purpose. The engine response for various parameters is firstly acquired, and an engine model is then constructed using SBELM. With the engine model, the optimal engine settings are determined based on recently proposed metaheuristic optimization methods. Experimental results validate the optimal settings obtained with the proposed methodology, indicating that the use of machine learning and metaheuristic optimization for dual-injection engine calibration is effective and promising.

  18. Effect of Progesterone on Latent Phase Prolongation in Patients With Preterm Premature Rupture of Membranes

    Directory of Open Access Journals (Sweden)

    Fatemeh Abdali

    2018-01-01

    Full Text Available Preterm premature rupture of membranes (PPROM is a condition leading to an increased risk of maternal and neonatal morbidity and mortality in pregnant women. To prevent this complication, some studies have proposed using prophylactic progesterone. However, due to lack of sufficient relevant data, there is still need for further studies in this regard. This study was performed to determine the effect of rectal progesterone on the latent phase and maternal and neonatal outcome variables in females with PPROM. During the present randomized clinical trial study (IRCT201512077676N4, a total of 120 patients with PPROM at pregnancy ages between 26 and 32 weeks were randomly assigned to 2 equal intervention and control groups. In the intervention group, progesterone suppositories (400 mg per night were administered until delivery or completion of the 34th gestational week and was compared with placebo effect in control group. The latent phase and maternal and neonatal outcome variables were compared between the two groups. The mean age of patients was 29.56±5.66 (19-42 and 29.88±5.57 (17-40 years in the intervention and control group, respectively. The two groups were almost identical in the confounding factors. The median latent phase was 8.5 days in the intervention group vs. 5 days in the control group in the 28th-30th weeks of gestation, which was significantly higher in the intervention group (P=0.001. Among maternal and neonatal outcome variables, only the mean birth-weight was significantly higher in the intervention group than that in the controls (1609.92±417.28 gr vs. 1452.03±342.35 gr, P=0.03. Administration of progesterone suppository in patients with PPROM at gestational ages of 28 to 30 weeks is effective in elongating the latent phase and increasing birth-weight with no significant complications.

  19. Connecting Lines of Research on Task Model Variables, Automatic Item Generation, and Learning Progressions in Game-Based Assessment

    Science.gov (United States)

    Graf, Edith Aurora

    2014-01-01

    In "How Task Features Impact Evidence from Assessments Embedded in Simulations and Games," Almond, Kim, Velasquez, and Shute have prepared a thought-provoking piece contrasting the roles of task model variables in a traditional assessment of mathematics word problems to their roles in "Newton's Playground," a game designed…

  20. An Application of Supervised Learning Methods to Search for Variable Stars in a Selected Field of the VVV Survey

    Science.gov (United States)

    Rodríguez-Feliciano, B.; García-Varela, A.; Pérez-Ortiz, M. F.; Sabogal, B. E.; Minniti, D.

    2017-07-01

    We characterize properties of time series of variable stars in the B278 field of the VVV survey, using robust statistics. Using random forest and support vector machines classifiers we propose 47 candidates to RR Lyraae, and 12 candidates to WU Ursae Majoris eclipsing binaries.

  1. An Empirical Study of Presage Variables in the Teaching-Learning of Statistics, in the Light of Research on Competencies

    Science.gov (United States)

    Rodriguez, Clemente; Gutierrez-Perez, Jose; Pozo, Teresa

    2010-01-01

    Introduction: This research seeks to determine the influence exercised by a set of presage and process variables (students' pre-existing opinion towards statistics, their dedication to mastery of statistics content, assessment of the teaching materials, and the teacher's effort in the teaching of statistics) in students' resolution of activities…

  2. PENILAIAN PROGRAM PRAKTIKUM TERHADAP PENINGKATAN KUALITI GURU PRA PERKHIDMATAN: ANALISIS BERDASARKAN LATENT GROWTH CURVE MODELLING

    Directory of Open Access Journals (Sweden)

    Azizah Sarkowi

    2015-12-01

    Full Text Available Practicum is an important component in teacher education programs. This study identify the improvement in the quality of pre-service teachers for three phases practicum. Multi-point prospective panel study has been conducted on a 541 pre-service teachers at the Institute of Teacher Education. Teacher’s quality is measured based on the achievement of program learning outcomes. Based on matching last six digit identification card number for three studies series, 337 questionnaires were analyzed using a latent growth curve model using AMOS 18.0. Latent Analysis shows that the model achieve goodness of fit. There is a linear trend of improvement in the performance of the three phases of the practicum. This increase varies between individuals and the rate of growth depends on the level of achievement at practicum phase I. Studies indicate that the increase in the practicum period of teacher education policy should be continued.

  3. A Framework for Reproducible Latent Fingerprint Enhancements.

    Science.gov (United States)

    Carasso, Alfred S

    2014-01-01

    Photoshop processing of latent fingerprints is the preferred methodology among law enforcement forensic experts, but that appproach is not fully reproducible and may lead to questionable enhancements. Alternative, independent, fully reproducible enhancements, using IDL Histogram Equalization and IDL Adaptive Histogram Equalization, can produce better-defined ridge structures, along with considerable background information. Applying a systematic slow motion smoothing procedure to such IDL enhancements, based on the rapid FFT solution of a Lévy stable fractional diffusion equation, can attenuate background detail while preserving ridge information. The resulting smoothed latent print enhancements are comparable to, but distinct from, forensic Photoshop images suitable for input into automated fingerprint identification systems, (AFIS). In addition, this progressive smoothing procedure can be reexamined by displaying the suite of progressively smoother IDL images. That suite can be stored, providing an audit trail that allows monitoring for possible loss of useful information, in transit to the user-selected optimal image. Such independent and fully reproducible enhancements provide a valuable frame of reference that may be helpful in informing, complementing, and possibly validating the forensic Photoshop methodology.

  4. Mental toughness latent profiles in endurance athletes.

    Science.gov (United States)

    Zeiger, Joanna S; Zeiger, Robert S

    2018-01-01

    Mental toughness in endurance athletes, while an important factor for success, has been scarcely studied. An online survey was used to examine eight mental toughness factors in endurance athletes. The study aim was to determine mental toughness profiles via latent profile analysis in endurance athletes and whether associations exist between the latent profiles and demographics and sports characteristics. Endurance athletes >18 years of age were recruited via social media outlets (n = 1245, 53% female). Mental toughness was measured using the Sports Mental Toughness Questionnaire (SMTQ), Psychological Performance Inventory-Alternative (PPI-A), and self-esteem was measured using the Rosenberg Self-Esteem Scale (RSE). A three-class solution emerged, designated as high mental toughness (High MT), moderate mental toughness (Moderate MT) and low mental toughness (Low MT). ANOVA tests showed significant differences between all three classes on all 8 factors derived from the SMTQ, PPI-A and the RSE. There was an increased odds of being in the High MT class compared to the Low MT class for males (OR = 1.99; 95% CI, 1.39, 2.83; Pathletes who were over 55 compared to those who were 18-34 (OR = 2.52; 95% CI, 1.37, 4.62; Pathletes. High MT is associated with demographics and sports characteristics. Mental toughness screening in athletes may help direct practitioners with mental skills training.

  5. Latent heat of traffic moving from rest

    Science.gov (United States)

    Farzad Ahmadi, S.; Berrier, Austin S.; Doty, William M.; Greer, Pat G.; Habibi, Mohammad; Morgan, Hunter A.; Waterman, Josam H. C.; Abaid, Nicole; Boreyko, Jonathan B.

    2017-11-01

    Contrary to traditional thinking and driver intuition, here we show that there is no benefit to ground vehicles increasing their packing density at stoppages. By systematically controlling the packing density of vehicles queued at a traffic light on a Smart Road, drone footage revealed that the benefit of an initial increase in displacement for close-packed vehicles is completely offset by the lag time inherent to changing back into a ‘liquid phase’ when flow resumes. This lag is analogous to the thermodynamic concept of the latent heat of fusion, as the ‘temperature’ (kinetic energy) of the vehicles cannot increase until the traffic ‘melts’ into the liquid phase. These findings suggest that in situations where gridlock is not an issue, drivers should not decrease their spacing during stoppages in order to lessen the likelihood of collisions with no loss in flow efficiency. In contrast, motion capture experiments of a line of people walking from rest showed higher flow efficiency with increased packing densities, indicating that the importance of latent heat becomes trivial for slower moving systems.

  6. Bayesian Analysis for Dynamic Generalized Linear Latent Model with Application to Tree Survival Rate

    Directory of Open Access Journals (Sweden)

    Yu-sheng Cheng

    2014-01-01

    Full Text Available Logistic regression model is the most popular regression technique, available for modeling categorical data especially for dichotomous variables. Classic logistic regression model is typically used to interpret relationship between response variables and explanatory variables. However, in real applications, most data sets are collected in follow-up, which leads to the temporal correlation among the data. In order to characterize the different variables correlations, a new method about the latent variables is introduced in this study. At the same time, the latent variables about AR (1 model are used to depict time dependence. In the framework of Bayesian analysis, parameters estimates and statistical inferences are carried out via Gibbs sampler with Metropolis-Hastings (MH algorithm. Model comparison, based on the Bayes factor, and forecasting/smoothing of the survival rate of the tree are established. A simulation study is conducted to assess the performance of the proposed method and a pika data set is analyzed to illustrate the real application. Since Bayes factor approaches vary significantly, efficiency tests have been performed in order to decide which solution provides a better tool for the analysis of real relational data sets.

  7. Application of a latent class analysis to empirically define eating disorder phenotypes.

    Science.gov (United States)

    Keel, Pamela K; Fichter, Manfred; Quadflieg, Norbert; Bulik, Cynthia M; Baxter, Mark G; Thornton, Laura; Halmi, Katherine A; Kaplan, Allan S; Strober, Michael; Woodside, D Blake; Crow, Scott J; Mitchell, James E; Rotondo, Alessandro; Mauri, Mauro; Cassano, Giovanni; Treasure, Janet; Goldman, David; Berrettini, Wade H; Kaye, Walter H

    2004-02-01

    Diagnostic criteria for eating disorders influence how we recognize, research, and treat eating disorders, and empirically valid phenotypes are required for revealing their genetic bases. To empirically define eating disorder phenotypes. Data regarding eating disorder symptoms and features from 1179 individuals with clinically significant eating disorders were submitted to a latent class analysis. The resulting latent classes were compared on non-eating disorder variables in a series of validation analyses. Multinational, collaborative study with cases ascertained through diverse clinical settings (inpatient, outpatient, and community). Members of affected relative pairs recruited for participation in genetic studies of eating disorders in which probands met DSM-IV-TR criteria for anorexia nervosa (AN) or bulimia nervosa and had at least 1 biological relative with a clinically significant eating disorder. Main Outcome Measure Number and clinical characterization of latent classes. A 4-class solution provided the best fit. Latent class 1 (LC1) resembled restricting AN; LC2, AN and bulimia nervosa with the use of multiple methods of purging; LC3, restricting AN without obsessive-compulsive features; and LC4, bulimia nervosa with self-induced vomiting as the sole form of purging. Biological relatives were significantly likely to belong to the same latent class. Across validation analyses, LC2 demonstrated the highest levels of psychological disturbance, and LC3 demonstrated the lowest. The presence of obsessive-compulsive features differentiates among individuals with restricting AN. Similarly, the combination of low weight and multiple methods of purging distinguishes among individuals with binge eating and purging behaviors. These results support some of the distinctions drawn within the DSM-IV-TR among eating disorder subtypes, while introducing new features to define phenotypes.

  8. Adolescent substance use behavior and suicidal behavior for boys and girls: a cross-sectional study by latent analysis approach.

    Science.gov (United States)

    Wang, Peng-Wei; Yen, Cheng-Fang

    2017-12-08

    Adolescent suicidal behavior may consist of different symptoms, including suicidal ideation, suicidal planning and suicidal attempts. Adolescent substance use behavior may contribute to adolescent suicidal behavior. However, research on the relationships between specific substance use and individual suicidal behavior is insufficient, as adolescents may not use only one substance or develop only one facet of suicidal behavior. Latent variables permit us to describe the relationships between clusters of related behaviors more accurately than studying the relationships between specific behaviors. Thus, the aim of this study was to explore how adolescent substance use behavior contributes to suicidal behavior using latent variables representing adolescent suicidal and substance use behaviors. A total of 13,985 adolescents were recruited using a stratified random sampling strategy. The participants indicated whether they had experienced suicidal ideation, planning and attempts and reported their cigarette, alcohol, ketamine and MDMA use during the past year. Latent analysis was used to examine the relationship between substance use and suicidal behavior. Adolescents who used any one of the above substances exhibited more suicidal behavior. The results of latent variables analysis revealed that adolescent substance use contributed to suicidal behavior and that boys exhibited more severe substance use behavior than girls. However, there was no gender difference in the association between substance use and suicidal behavior. Substance use behavior in adolescents is related to more suicidal behavior. In addition, the contribution of substance use to suicidal behavior does not differ between genders.

  9. Symptom Cluster Research With Biomarkers and Genetics Using Latent Class Analysis.

    Science.gov (United States)

    Conley, Samantha

    2017-12-01

    The purpose of this article is to provide an overview of latent class analysis (LCA) and examples from symptom cluster research that includes biomarkers and genetics. A review of LCA with genetics and biomarkers was conducted using Medline, Embase, PubMed, and Google Scholar. LCA is a robust latent variable model used to cluster categorical data and allows for the determination of empirically determined symptom clusters. Researchers should consider using LCA to link empirically determined symptom clusters to biomarkers and genetics to better understand the underlying etiology of symptom clusters. The full potential of LCA in symptom cluster research has not yet been realized because it has been used in limited populations, and researchers have explored limited biologic pathways.

  10. Bayesian modeling of measurement error in predictor variables

    NARCIS (Netherlands)

    Fox, Gerardus J.A.; Glas, Cornelis A.W.

    2003-01-01

    It is shown that measurement error in predictor variables can be modeled using item response theory (IRT). The predictor variables, that may be defined at any level of an hierarchical regression model, are treated as latent variables. The normal ogive model is used to describe the relation between

  11. Evaluation of Deep Learning Models for Predicting CO2 Flux

    Science.gov (United States)

    Halem, M.; Nguyen, P.; Frankel, D.

    2017-12-01

    Artificial neural networks have been employed to calculate surface flux measurements from station data because they are able to fit highly nonlinear relations between input and output variables without knowing the detail relationships between the variables. However, the accuracy in performing neural net estimates of CO2 flux from observations of CO2 and other atmospheric variables is influenced by the architecture of the neural model, the availability, and complexity of interactions between physical variables such as wind, temperature, and indirect variables like latent heat, and sensible heat, etc. We evaluate two deep learning models, feed forward and recurrent neural network models to learn how they each respond to the physical measurements, time dependency of the measurements of CO2 concentration, humidity, pressure, temperature, wind speed etc. for predicting the CO2 flux. In this paper, we focus on a) building neural network models for estimating CO2 flux based on DOE data from tower Atmospheric Radiation Measurement data; b) evaluating the impact of choosing the surface variables and model hyper-parameters on the accuracy and predictions of surface flux; c) assessing the applicability of the neural network models on estimate CO2 flux by using OCO-2 satellite data; d) studying the efficiency of using GPU-acceleration for neural network performance using IBM Power AI deep learning software and packages on IBM Minsky system.

  12. A Latent Class Approach to Estimating Test-Score Reliability

    Science.gov (United States)

    van der Ark, L. Andries; van der Palm, Daniel W.; Sijtsma, Klaas

    2011-01-01

    This study presents a general framework for single-administration reliability methods, such as Cronbach's alpha, Guttman's lambda-2, and method MS. This general framework was used to derive a new approach to estimating test-score reliability by means of the unrestricted latent class model. This new approach is the latent class reliability…

  13. Intercept Centering and Time Coding in Latent Difference Score Models

    Science.gov (United States)

    Grimm, Kevin J.

    2012-01-01

    Latent difference score (LDS) models combine benefits derived from autoregressive and latent growth curve models allowing for time-dependent influences and systematic change. The specification and descriptions of LDS models include an initial level of ability or trait plus an accumulation of changes. A limitation of this specification is that the…

  14. A Review of the Latent and Manifest Benefits (LAMB) Scale

    Science.gov (United States)

    Muller, Juanita; Waters, Lea

    2012-01-01

    The latent and manifest benefits (LAMB) scale (Muller, Creed, Waters & Machin, 2005) was designed to measure the latent and manifest benefits of employment and provide a single scale to test Jahoda's (1981) and Fryer's (1986) theories of unemployment. Since its publication in 2005 there have been 13 studies that have used the scale with 5692…

  15. Prevalence and risk factors of latent Tuberculosis among ...

    African Journals Online (AJOL)

    Background: Latent Tuberculosis treatment is a key tuberculosis control intervention. Adolescents are a high risk group that is not routinely treated in low income countries. Knowledge of latent Tuberculosis (TB) burden among adolescents may influence policy. Objectives: We determined the prevalence and risk factors of ...

  16. Latent class analysis of the Yale-Brown Obsessive-Compulsive Scale symptoms in obsessive-compulsive disorder.

    Science.gov (United States)

    Delucchi, Kevin L; Katerberg, Hilga; Stewart, S Evelyn; Denys, Damiaan A J P; Lochner, Christine; Stack, Denise E; den Boer, Johan A; van Balkom, Anton J L M; Jenike, Michael A; Stein, Dan J; Cath, Danielle C; Mathews, Carol A

    2011-01-01

    Obsessive-compulsive disorder (OCD) is phenomenologically heterogeneous, and findings of underlying structure classification based on symptom grouping have been ambiguous to date. Variable-centered approaches, primarily factor analysis, have been used to identify homogeneous groups of symptoms; but person-centered latent methods have seen little use. This study was designed to uncover sets of homogeneous groupings within 1611 individuals with OCD based on symptoms. Latent class analysis models using 61 obsessive-compulsive symptoms collected from the Yale-Brown Obsessive-Compulsive Scale were fit. Relationships between latent class membership and treatment response, sex, symptom severity, and comorbid tic disorders were tested for relationship to class membership. Latent class analysis models of best fit yielded 3 classes. Classes differed only in frequency of symptom endorsement. Classes with higher symptom endorsement were associated with earlier age of onset, being male, higher Yale-Brown Obsessive-Compulsive Scale symptom severity scores, and comorbid tic disorders. There were no differences in treatment response between classes. These results provide support for the validity of a single underlying latent OCD construct, in addition to the distinct symptom factors identified previously via factor analyses. Copyright © 2011 Elsevier Inc. All rights reserved.

  17. Tweets clustering using latent semantic analysis

    Science.gov (United States)

    Rasidi, Norsuhaili Mahamed; Bakar, Sakhinah Abu; Razak, Fatimah Abdul

    2017-04-01

    Social media are becoming overloaded with information due to the increasing number of information feeds. Unlike other social media, Twitter users are allowed to broadcast a short message called as `tweet". In this study, we extract tweets related to MH370 for certain of time. In this paper, we present overview of our approach for tweets clustering to analyze the users' responses toward tragedy of MH370. The tweets were clustered based on the frequency of terms obtained from the classification process. The method we used for the text classification is Latent Semantic Analysis. As a result, there are two types of tweets that response to MH370 tragedy which is emotional and non-emotional. We show some of our initial results to demonstrate the effectiveness of our approach.

  18. Latent Virus Reactivation in Space Shuttle Astronauts

    Science.gov (United States)

    Mehta, S. K.; Crucian, B. E.; Stowe, R. P.; Sams, C.; Castro, V. A.; Pierson, D. L.

    2011-01-01

    Latent virus reactivation was measured in 17 astronauts (16 male and 1 female) before, during, and after short-duration Space Shuttle missions. Blood, urine, and saliva samples were collected 2-4 months before launch, 10 days before launch (L-10), 2-3 hours after landing (R+0), 3 days after landing (R+14), and 120 days after landing (R+120). Epstein-Barr virus (EBV) DNA was measured in these samples by quantitative polymerase chain reaction. Varicella-zoster virus (VZV) DNA was measured in the 381 saliva samples and cytomegalovirus (CMV) DNA in the 66 urine samples collected from these subjects. Fourteen astronauts shed EBV DNA in 21% of their saliva samples before, during, and after flight, and 7 astronauts shed VZV in 7.4% of their samples during and after flight. It was interesting that shedding of both EBV and VZV increased during the flight phase relative to before or after flight. In the case of CMV, 32% of urine samples from 8 subjects contained DNA of this virus. In normal healthy control subjects, EBV shedding was found in 3% and VZV and CMV were found in less than 1% of the samples. The circadian rhythm of salivary cortisol measured before, during, and after space flight did not show any significant difference between flight phases. These data show that increased reactivation of latent herpes viruses may be associated with decreased immune system function, which has been reported in earlier studies as well as in these same subjects (data not reported here).

  19. ENDOGENOUS ANALGESIA, DEPENDENCE, AND LATENT PAIN SENSITIZATION

    Science.gov (United States)

    Taylor, Bradley K; Corder, Gregory

    2015-01-01

    Endogenous activation of μ-opioid receptors (MORs) provides relief from acute pain. Recent studies have established that tissue inflammation produces latent pain sensitization (LS) that is masked by spinal MOR signaling for months, even after complete recovery from injury and re-establishment of normal pain thresholds. Disruption with MOR inverse agonists reinstates pain and precipitates cellular, somatic and aversive signs of physical withdrawal; this phenomenon requires N-methyl-D-aspartate receptor-mediated activation of calcium-sensitive adenylyl cyclase type 1 (AC1). In this review, we present a new conceptual model of the transition from acute to chronic pain, based on the delicate balance between LS and endogenous analgesia that develops after painful tissue injury. First, injury activates pain pathways. Second, the spinal cord establishes MOR constitutive activity (MORCA) as it attempts to control pain. Third, over time, the body becomes dependent on MORCA, which paradoxically sensitizes pain pathways. Stress or injury escalates opposing inhibitory and excitatory influences on nociceptive processing as a pathological consequence of increased endogenous opioid tone. Pain begets MORCA begets pain vulnerability in a vicious cycle. The final result is a silent insidious state characterized by the escalation of two opposing excitatory and inhibitory influences on pain transmission: LS mediated by AC1 (which maintains accelerator), and pain inhibition mediated by MORCA (which maintains the brake). This raises the prospect that opposing homeostatic interactions between MORCA analgesia and latent NMDAR–AC1-mediated pain sensitization create a lasting vulnerability to develop chronic pain. Thus, chronic pain syndromes may result from a failure in constitutive signaling of spinal MORs and a loss of endogenous analgesic control. An overarching long-term therapeutic goal of future research is to alleviate chronic pain by either: a) facilitating endogenous opioid

  20. Laser interrogation of latent vehicle registration number

    Energy Technology Data Exchange (ETDEWEB)

    Russo, R.E. [Lawrence Berkeley Lab., CA (United States). Energy and Environment Div.]|[Lawrence Livermore National Lab., CA (United States). Forensic Science Center; Pelkey, G.E. [City of Livermore Police Dept., CA (United States); Grant, P.; Whipple, R.E.; Andresen, B.D. [Lawrence Livermore National Lab., CA (United States). Forensic Science Center

    1994-09-01

    A recent investigation involved automobile registration numbers as important evidentiary specimens. In California, as in most states, small, thin metallic decals are issued to owners of vehicles each year as the registration is renewed. The decals are applied directly to the license plate of the vehicle and typically on top of the previous year`s expired decal. To afford some degree of security, the individual registration decals have been designed to tear easily; they cannot be separated from each other, but can be carefully removed intact from the metal license plate by using a razor blade. In September 1993, the City of Livermore Police Department obtained a blue 1993 California decal that had been placed over an orange 1992 decal. The two decals were being investigated as possible evidence in a case involving vehicle registration fraud. To confirm the suspicion and implicate a suspect, the department needed to known the registration number on the bottom (completely covered) 1992 decal. The authors attempted to use intense and directed light to interrogate the colored stickers. Optical illumination using a filtered white-light source partially identified the latent number. However, the most successful technique used a tunable dye laser pumped by a pulsed Nd:YAG laser. By selectively tuning the wavelength and intensity of the dye laser, backlit illumination of the decals permitted visualization of the underlying registration number through the surface of the top sticker. With optimally-tuned wavelength and intensity, 100% accuracy was obtained in identifying the sequence of latent characters. The advantage of optical techniques is their completely nondestructive nature, thus preserving the evidence for further interrogation or courtroom presentation.

  1. Latent segmentation based count models: Analysis of bicycle safety in Montreal and Toronto.

    Science.gov (United States)

    Yasmin, Shamsunnahar; Eluru, Naveen

    2016-10-01

    The study contributes to literature on bicycle safety by building on the traditional count regression models to investigate factors affecting bicycle crashes at the Traffic Analysis Zone (TAZ) level. TAZ is a traffic related geographic entity which is most frequently used as spatial unit for macroscopic crash risk analysis. In conventional count models, the impact of exogenous factors is restricted to be the same across the entire region. However, it is possible that the influence of exogenous factors might vary across different TAZs. To accommodate for the potential variation in the impact of exogenous factors we formulate latent segmentation based count models. Specifically, we formulate and estimate latent segmentation based Poisson (LP) and latent segmentation based Negative Binomial (LNB) models to study bicycle crash counts. In our latent segmentation approach, we allow for more than two segments and also consider a large set of variables in segmentation and segment specific models. The formulated models are estimated using bicycle-motor vehicle crash data from the Island of Montreal and City of Toronto for the years 2006 through 2010. The TAZ level variables considered in our analysis include accessibility measures, exposure measures, sociodemographic characteristics, socioeconomic characteristics, road network characteristics and built environment. A policy analysis is also conducted to illustrate the applicability of the proposed model for planning purposes. This macro-level research would assist decision makers, transportation officials and community planners to make informed decisions to proactively improve bicycle safety - a prerequisite to promoting a culture of active transportation. Copyright © 2016 Elsevier Ltd. All rights reserved.

  2. A latent low-dimensional common input drives a pool of motor neurons: a probabilistic latent state-space model.

    Science.gov (United States)

    Feeney, Daniel F; Meyer, François G; Noone, Nicholas; Enoka, Roger M

    2017-10-01

    Motor neurons appear to be activated with a common input signal that modulates the discharge activity of all neurons in the motor nucleus. It has proven difficult for neurophysiologists to quantify the variability in a common input signal, but characterization of such a signal may improve our understanding of how the activation signal varies across motor tasks. Contemporary methods of quantifying the common input to motor neurons rely on compiling discrete action potentials into continuous time series, assuming the motor pool acts as a linear filter, and requiring signals to be of sufficient duration for frequency analysis. We introduce a space-state model in which the discharge activity of motor neurons is modeled as inhomogeneous Poisson processes and propose a method to quantify an abstract latent trajectory that represents the common input received by motor neurons. The approach also approximates the variation in synaptic noise in the common input signal. The model is validated with four data sets: a simulation of 120 motor units, a pair of integrate-and-fire neurons with a Renshaw cell providing inhibitory feedback, the discharge activity of 10 integrate-and-fire neurons, and the discharge times of concurrently active motor units during an isometric voluntary contraction. The simulations revealed that a latent state-space model is able to quantify the trajectory and variability of the common input signal across all four conditions. When compared with the cumulative spike train method of characterizing common input, the state-space approach was more sensitive to the details of the common input current and was less influenced by the duration of the signal. The state-space approach appears to be capable of detecting rather modest changes in common input signals across conditions. NEW & NOTEWORTHY We propose a state-space model that explicitly delineates a common input signal sent to motor neurons and the physiological noise inherent in synaptic signal

  3. Assessing Trust and Effectiveness in Virtual Teams: Latent Growth Curve and Latent Change Score Models

    Directory of Open Access Journals (Sweden)

    Michael D. Coovert

    2017-08-01

    Full Text Available Trust plays a central role in the effectiveness of work groups and teams. This is the case for both face-to-face and virtual teams. Yet little is known about the development of trust in virtual teams. We examined cognitive and affective trust and their relationship to team effectiveness as reflected through satisfaction with one’s team and task performance. Latent growth curve analysis reveals both trust types start at a significant level with individual differences in that initial level. Cognitive trust follows a linear growth pattern while affective trust is overall non-linear, but becomes linear once established. Latent change score models are utilized to examine change in trust and also its relationship with satisfaction with the team and team performance. In examining only change in trust and its relationship to satisfaction there appears to be a straightforward influence of trust on satisfaction and satisfaction on trust. However, when incorporated into a bivariate coupling latent change model the dynamics of the relationship are revealed. A similar pattern holds for trust and task performance; however, in the bivariate coupling change model a more parsimonious representation is preferred.

  4. Learning in artistic gymnastics. An experimental study with children analysing some variables in that process Aprendizaje en gimnasia artística. Un estudio experimental con niños que analiza ciertas variables del proceso

    Directory of Open Access Journals (Sweden)

    J. López

    2010-09-01

    Full Text Available

    Today the training of sports skills has lead to the conception of new approaches to attain maximum results. In practice, many teaching methods are used, yet most of the articles on motor learning or sports training refer to the total or global and the partial or analytic methods, both of interest in the field of gymnastics, offering a number of important combinations between either extreme.

    Opinions differ concerning effectiveness, and such differences also exist in gymnastics. Carrasco (1977, nevertheless, proposes "mini-circuits" as the ideal teaching method in gymnastics. In looking for a practical solution to global or analytical teaching, one experimental group study was undertaken with children participating in Sports Schools between the ages of 9 and 11. The aim was to compare the effect of three training sessions (analytical training, "mini-circuit" training, mixed training on the learning and recall of gymnastic skills.

    Interested in both final performance as well as the teaching process, the following variables were studied: motor activity time, waiting time, total number of global movements, total number of feedbacks emitted by the teacher (amount and direction, and total number of spot checks. A pre-test, post-test and re- test design was used with three groups to assess the three training sessions. Each group was trained to learn the same variable-dependent outcome.

    The results of the study showed that the "mini-circuit" training was the most effective learning and recall method. The most highly-influence process variables were both the type of aids and type of feedback provided. Overall, it is worth highlighting the importance of using the "mini-circuit" method with children. From a pedagogic perspective, this is an important finding to take into consideration, which could yield important results during schooling.
    KEY WORDS

  5. Radar subpixel-scale rainfall variability and uncertainty: lessons learned from observations of a dense rain-gauge network

    Directory of Open Access Journals (Sweden)

    N. Peleg

    2013-06-01

    Full Text Available Runoff and flash flood generation are very sensitive to rainfall's spatial and temporal variability. The increasing use of radar and satellite data in hydrological applications, due to the sparse distribution of rain gauges over most catchments worldwide, requires furthering our knowledge of the uncertainties of these data. In 2011, a new super-dense network of rain gauges containing 14 stations, each with two side-by-side gauges, was installed within a 4 km2 study area near Kibbutz Galed in northern Israel. This network was established for a detailed exploration of the uncertainties and errors regarding rainfall variability within a common pixel size of data obtained from remote sensing systems for timescales of 1 min to daily. In this paper, we present the analysis of the first year's record collected from this network and from the Shacham weather radar, located 63 km from the study area. The gauge–rainfall spatial correlation and uncertainty were examined along with the estimated radar error. The nugget parameter of the inter-gauge rainfall correlations was high (0.92 on the 1 min scale and increased as the timescale increased. The variance reduction factor (VRF, representing the uncertainty from averaging a number of rain stations per pixel, ranged from 1.6% for the 1 min timescale to 0.07% for the daily scale. It was also found that at least three rain stations are needed to adequately represent the rainfall (VRF < 5% on a typical radar pixel scale. The difference between radar and rain gauge rainfall was mainly attributed to radar estimation errors, while the gauge sampling error contributed up to 20% to the total difference. The ratio of radar rainfall to gauge-areal-averaged rainfall, expressed by the error distribution scatter parameter, decreased from 5.27 dB for 3 min timescale to 3.21 dB for the daily scale. The analysis of the radar errors and uncertainties suggest that a temporal scale of at least 10 min should be used for

  6. Variable memory strategy use in children's adaptive intratask learning behavior: developmental changes and working memory influences in free recall.

    Science.gov (United States)

    Lehmann, Martin; Hasselhorn, Marcus

    2007-01-01

    Variability in strategy use within single trials in free recall was analyzed longitudinally from second to fourth grades (ages 8-10 years). To control for practice effects another sample of fourth graders was included (age 10 years). Video analyses revealed that children employed different strategies when preparing for free recall. A gradual shift from labeling to cumulative rehearsal was present both with increasing age and across different list positions. Whereas cumulative rehearsal was frequent at early list positions, labeling was dominant at later list portions. Working memory capacity predicted the extent of cumulative rehearsal usage, which became more efficient with increasing age. Results are discussed in the context of the adaptive strategy choice model.

  7. Latent Virus Reactivation in Astronauts and Shingles Patients

    Science.gov (United States)

    Mehta, Satish K.; Cohrs, Randall J.; Gilden, Donald H.; Tyring, Stephen K.; Castro, Victoria A.; Ott, C. Mark; Pierson, Duane L.

    2010-01-01

    Spaceflight is a uniquely stressful environment with astronauts experiencing a variety of stressors including: isolation and confinement, psychosocial, noise, sleep deprivation, anxiety, variable gravitational forces, and increased radiation. These stressors are manifested through the HPA and SAM axes resulting in increased stress hormones. Diminished T-lymphocyte functions lead to reactivation of latent herpesviruses in astronauts during spaceflight. Herpes simplex virus reactivated with symptoms during spaceflight whereas Epstein-Barr virus (EBV), cytomegalovirus (CMV), and varicella zoster virus (VZV) reactivate and are shed without symptoms. EBV and VZV are shed in saliva and CMV in the urine. The levels of EBV shed in astronauts increased 10-fold during the flight; CMV and VZV are not typically shed in low stressed individuals, but both were shed in astronauts during spaceflight. All herpes viruses were detected by polymerase chain reaction (PCR) assay. Culturing revealed that VZV shed in saliva was infectious virus. The PCR technology was extended to test saliva of 54 shingles patients. All shingles patients shed VZV in their saliva, and the levels followed the course of the disease. Viremia was also found to be common during shingles. The technology may be used before zoster lesions appear allowing for prevention of disease. The technology may be used for rapid detection of VZV in doctors offices. These studies demonstrated the value of applying technologies designed for astronauts to people on Earth.

  8. Sex Differences in Fluid Reasoning: Manifest and Latent Estimates from the Cognitive Abilities Test

    Directory of Open Access Journals (Sweden)

    Joni M. Lakin

    2014-06-01

    Full Text Available The size and nature of sex differences in cognitive ability continues to be a source of controversy. Conflicting findings result from the selection of measures, samples, and methods used to estimate sex differences. Existing sex differences work on the Cognitive Abilities Test (CogAT has analyzed manifest variables, leaving open questions about sex differences in latent narrow cognitive abilities and the underlying broad ability of fluid reasoning (Gf. This study attempted to address these questions. A confirmatory bifactor model was used to estimate Gf and three residual narrow ability factors (verbal, quantitative, and figural. We found that latent mean differences were larger than manifest estimates for all three narrow abilities. However, mean differences in Gf were trivial, consistent with previous research. In estimating group variances, the Gf factor showed substantially greater male variability (around 20% greater. The narrow abilities varied: verbal reasoning showed small variability differences while quantitative and figural showed substantial differences in variance (up to 60% greater. These results add precision and nuance to the study of the variability and masking hypothesis.

  9. Morphometry of latent palmprints as a function of time.

    Science.gov (United States)

    Barros, Rodrigo M; Faria, Bruna E F; Kuckelhaus, Selma A S

    2013-12-01

    In many crimes, the elapsed time between production and collecting fingermark traces is crucial. and a method able to detect the aging of latent prints would represent an improvement in forensic procedures. Considering that as the latent print gets older, substantial changes in the relative proportion of individual components secreted by skin glands could affect the morphology of ridges, morphometry could be a potential tool to assess the aging of latent fingermarks. Then, considering the very limited research in the field, the present work aims to evaluate the morphometry of latent palmprint ridges, as a function of time, in order to identify an aging pattern. The latent marks were deposited by 20 donors on glass microscope slides considering pressure and contact angle, and then were maintained under controlled environmental conditions. The morphometric study was conducted on marks developed with magnetic powder in 7 different time intervals after deposition (0, 5, 10, 15, 20, 25 or 30 days); 60 ridges were evaluated for each developed mark. The results showed that: 1) the method for the replacement and mixing of skin secretions on the palm was appropriate to ensure reproducibility of latent prints, and 2) considering the studied group, there was a time-dependent reduction in the width of ridges and on the percentage of visible ridges over 30 days. Results suggest the possibility of using the morphometric method to determine an aging profile of latent palmprints on glass surface, aiming for forensic purposes. © 2013.

  10. Tropical Gravity Wave Momentum Fluxes and Latent Heating Distributions

    Science.gov (United States)

    Geller, Marvin A.; Zhou, Tiehan; Love, Peter T.

    2015-01-01

    Recent satellite determinations of global distributions of absolute gravity wave (GW) momentum fluxes in the lower stratosphere show maxima over the summer subtropical continents and little evidence of GW momentum fluxes associated with the intertropical convergence zone (ITCZ). This seems to be at odds with parameterizations forGWmomentum fluxes, where the source is a function of latent heating rates, which are largest in the region of the ITCZ in terms of monthly averages. The authors have examined global distributions of atmospheric latent heating, cloud-top-pressure altitudes, and lower-stratosphere absolute GW momentum fluxes and have found that monthly averages of the lower-stratosphere GW momentum fluxes more closely resemble the monthly mean cloud-top altitudes rather than the monthly mean rates of latent heating. These regions of highest cloud-top altitudes occur when rates of latent heating are largest on the time scale of cloud growth. This, plus previously published studies, suggests that convective sources for stratospheric GW momentum fluxes, being a function of the rate of latent heating, will require either a climate model to correctly model this rate of latent heating or some ad hoc adjustments to account for shortcomings in a climate model's land-sea differences in convective latent heating.

  11. Latent effectiveness of desiccant wheel: A silica gels- water system

    International Nuclear Information System (INIS)

    Rabah, A. A.; Mohamed, S. A.

    2009-01-01

    A latent heat effectiveness model in term of dimensionless groups? =f (NTU, m * ,Crm * ) for energy wheel has been analytically derived. The energy wheel is divided into humidification and dehumidification sections. For each section macroscopic mass differential equations for gas and the matrix were applied. In this process local latent effectiveness (? c ,? h ) for the humidification and dehumidification section of the wheel were obtained. The Latent effectiveness of the wheel is then derived form local effectiveness [? =f (? c ,? h)]. The model is compared with the existing experimental investigation and manufacturer data for energy wheel. More than 90% of the experimental data within a confidence limit of 95%. (Author)

  12. Towards an HIV-1 cure: measuring the latent reservoir

    Science.gov (United States)

    Bruner, Katherine M.; Hosmane, Nina N.; Siliciano, Robert F.

    2015-01-01

    The latent reservoir of HIV-1 in resting memory CD4+ T cells serves as a major barrier to curing HIV-1 infection. While many PCR- and culture-based assays have been used to measure the size of the latent reservoir, correlation between results of different assays is poor and recent studies indicate that no available assay provides an accurate measurement of reservoir size. The discrepancies between assays are a hurdle to clinical trials that aim to measure the efficacy of HIV-1 eradication strategies. Here we describe the advantages and disadvantages of various approaches to measure the latent reservoir. PMID:25747663

  13. Radiographer use of anatomical side markers and the latent conditions affecting their use in practice

    International Nuclear Information System (INIS)

    Titley, Anna G.; Cosson, Philip

    2014-01-01

    Background: Patient safety is a primary concern within the NHS. It has been reported that anatomical side marker (ASM) use in radiography does not meet the ‘best practice’ standard. Case reports suggest this may be a contributing factor to adverse events in healthcare. Purpose: This study aimed to investigate the latent conditions contributing to poor ASM practice; communities of practice, time of image acquisition and competing priorities with collimation practice. Method: Proxy variables of projection and laterality were used to measure communities of practice. ASM practice on 330 examinations (170 lumbar spine, 160 finger) was retrospectively observed using a data collection tool. Aggregate scores were calculated from the two images in each examination. Data was analysed using descriptive statistics, chi-square tests (projection) and Mann–Whitney U tests (laterality, time of acquisition and collimation practice). Results: ‘Best practice’ ASM use was met on one examination. Correct ASM were observed within the primary collimation in 32.0% images. Projection, laterality and collimation practice were associated with ASM use. Time of acquisition was not found to be associated. Discussion: Communities of practice and competing priorities impact on ASM use. Logistic regression to determine a primary latent condition was not possible. However, comparison with previous research suggests this is likely to be specific to each radiography department. Conclusion: Latent conditions are associated with poor ASM practice. These must be identified and addressed in each individual radiography department, to improve patient safety and uphold NHS Constitutional standards

  14. Characteristics of cyclist crashes in Italy using latent class analysis and association rule mining.

    Directory of Open Access Journals (Sweden)

    Gabriele Prati

    Full Text Available The factors associated with severity of the bicycle crashes may differ across different bicycle crash patterns. Therefore, it is important to identify distinct bicycle crash patterns with homogeneous attributes. The current study aimed at identifying subgroups of bicycle crashes in Italy and analyzing separately the different bicycle crash types. The present study focused on bicycle crashes that occurred in Italy during the period between 2011 and 2013. We analyzed categorical indicators corresponding to the characteristics of infrastructure (road type, road signage, and location type, road user (i.e., opponent vehicle and cyclist's maneuver, type of collision, age and gender of the cyclist, vehicle (type of opponent vehicle, and the environmental and time period variables (time of the day, day of the week, season, pavement condition, and weather. To identify homogenous subgroups of bicycle crashes, we used latent class analysis. Using latent class analysis, the bicycle crash data set was segmented into 19 classes, which represents 19 different bicycle crash types. Logistic regression analysis was used to identify the association between class membership and severity of the bicycle crashes. Finally, association rules were conducted for each of the latent classes to uncover the factors associated with an increased likelihood of severity. Association rules highlighted different crash characteristics associated with an increased likelihood of severity for each of the 19 bicycle crash types.

  15. Measuring what latent fingerprint examiners consider sufficient information for individualization determinations.

    Directory of Open Access Journals (Sweden)

    Bradford T Ulery

    Full Text Available Latent print examiners use their expertise to determine whether the information present in a comparison of two fingerprints (or palmprints is sufficient to conclude that the prints were from the same source (individualization. When fingerprint evidence is presented in court, it is the examiner's determination--not an objective metric--that is presented. This study was designed to ascertain the factors that explain examiners' determinations of sufficiency for individualization. Volunteer latent print examiners (n = 170 were each assigned 22 pairs of latent and exemplar prints for examination, and annotated features, correspondence of features, and clarity. The 320 image pairs were selected specifically to control clarity and quantity of features. The predominant factor differentiating annotations associated with individualization and inconclusive determinations is the count of corresponding minutiae; other factors such as clarity provided minimal additional discriminative value. Examiners' counts of corresponding minutiae were strongly associated with their own determinations; however, due to substantial variation of both annotations and determinations among examiners, one examiner's annotation and determination on a given comparison is a relatively weak predictor of whether another examiner would individualize. The extensive variability in annotations also means that we must treat any individual examiner's minutia counts as interpretations of the (unknowable information content of the prints: saying "the prints had N corresponding minutiae marked" is not the same as "the prints had N corresponding minutiae." More consistency in annotations, which could be achieved through standardization and training, should lead to process improvements and provide greater transparency in casework.

  16. Soil Organic Carbon Variability in High-Andean Ecosystems: Bringing Together Machine Learning and Proximal Soil Sensing

    Science.gov (United States)

    Gavilan, C.; Grunwald, S.; Quiroz, R.

    2017-12-01

    compared to PLSR. These findings suggest that integrating VNIR and MIR spectroscopy with machine learning algorithms constitutes a promising approach for assessing SOC content in high-Andean ecosystems.

  17. Solar thermoelectricity via advanced latent heat storage

    Science.gov (United States)

    Olsen, M. L.; Rea, J.; Glatzmaier, G. C.; Hardin, C.; Oshman, C.; Vaughn, J.; Roark, T.; Raade, J. W.; Bradshaw, R. W.; Sharp, J.; Avery, A. D.; Bobela, D.; Bonner, R.; Weigand, R.; Campo, D.; Parilla, P. A.; Siegel, N. P.; Toberer, E. S.; Ginley, D. S.

    2016-05-01

    We report on a new modular, dispatchable, and cost-effective solar electricity-generating technology. Solar ThermoElectricity via Advanced Latent heat Storage (STEALS) integrates several state-of-the-art technologies to provide electricity on demand. In the envisioned STEALS system, concentrated sunlight is converted to heat at a solar absorber. The heat is then delivered to either a thermoelectric (TE) module for direct electricity generation, or to charge a phase change material for thermal energy storage, enabling subsequent generation during off-sun hours, or both for simultaneous electricity production and energy storage. The key to making STEALS a dispatchable technology lies in the development of a "thermal valve," which controls when heat is allowed to flow through the TE module, thus controlling when electricity is generated. The current project addresses each of the three major subcomponents, (i) the TE module, (ii) the thermal energy storage system, and (iii) the thermal valve. The project also includes system-level and techno- economic modeling of the envisioned integrated system and will culminate in the demonstration of a laboratory-scale STEALS prototype capable of generating 3kWe.

  18. Reinforcing Saccadic Amplitude Variability

    Science.gov (United States)

    Paeye, Celine; Madelain, Laurent

    2011-01-01

    Saccadic endpoint variability is often viewed as the outcome of neural noise occurring during sensorimotor processing. However, part of this variability might result from operant learning. We tested this hypothesis by reinforcing dispersions of saccadic amplitude distributions, while maintaining constant their medians. In a first experiment we…

  19. Dissociative Experiences are Associated with Obsessive-Compulsive Symptoms in a Non-clinical Sample: A Latent Profile Analysis

    Science.gov (United States)

    BOYSAN, Murat

    2014-01-01

    Introduction There has been a burgeoning literature considering the significant associations between obsessive-compulsive symptoms and dissociative experiences. In this study, the relationsips between dissociative symtomotology and dimensions of obsessive-compulsive symptoms were examined in homogeneous sub-groups obtained with latent class algorithm in an undergraduate Turkish sample. Method Latent profile analysis, a recently developed classification method based on latent class analysis, was applied to the Dissociative Experiences Scale (DES) item-response data from 2976 undergraduates. Differences in severity of obsessive-compulsive symptoms, anxiety and depression across groups were evaluated by running multinomial logistic regression analyses. Associations between latent class probabilities and psychological variables in terms of obsessive-compulsive sub-types, anxiety, and depression were assessed by computing Pearson’s product-moment correlation coefficients. Results The findings of the latent profile analysis supported further evidence for discontinuity model of dissociative experiences. The analysis empirically justified the distinction among three sub-groups based on the DES items. A marked proportion of the sample (42%) was assigned to the high dissociative class. In the further analyses, all sub-types of obsessive-compulsive symptoms significantly differed across latent classes. Regarding the relationships between obsessive-compulsive symptoms and dissociative symptomatology, low dissociation appeared to be a buffering factor dealing with obsessive-compulsive symptoms; whereas high dissociation appeared to be significantly associated with high levels of obsessive-compulsive symptoms. Conclusion It is concluded that the concept of dissociation can be best understood in a typological approach that dissociative symptomatology not only exacerbates obsessive-compulsive symptoms but also serves as an adaptive coping mechanism. PMID:28360635

  20. Cardiac strain findings in children with latent rheumatic heart disease detected by echocardiographic screening.

    Science.gov (United States)

    Beaton, Andrea; Richards, Hedda; Ploutz, Michelle; Gaur, Lasya; Aliku, Twalib; Lwabi, Peter; Ensing, Greg; Sable, Craig

    2017-08-01

    Identification of patients with latent rheumatic heart disease by echocardiography presents a unique opportunity to prevent disease progression. Myocardial strain is a more sensitive indicator of cardiac performance than traditional measures of systolic function. The objective of this study was to test the hypothesis that abnormalities in myocardial strain may be present in children with latent rheumatic heart disease. Standard echocardiography images with electrocardiogram gating were obtained from Ugandan children found to have latent rheumatic heart disease as well as control subjects. Traditional echocardiography measures of systolic function were obtained, and offline global longitudinal strain analysis was performed. Comparison between groups was performed using strain as a continuous (Mann-Whitney U-test) and categorical (cut-off 5th percentile for age) variable. Our study included 14 subjects with definite rheumatic heart disease, 13 with borderline rheumatic heart disease, and 112 control subjects. None of the subjects had abnormal left ventricular size or ejection fraction. Global longitudinal strain was lower than the 5th percentile in 44% of the subjects with any rheumatic heart disease (p=0.002 versus controls) and 57% of the subjects with definite rheumatic heart disease (p=0.03). The mean absolute strain values were significantly lower when comparing subjects with any rheumatic heart disease with controls (20.4±3.95 versus 22.4±4.35, p=0.025) and subjects with definite rheumatic heart disease with controls (19.9±4.25 versus 22.4±4.35, p=0.033). Global longitudinal strain is decreased in subjects with rheumatic heart disease in the absence of abnormal systolic function. Larger studies with longer-term follow-up are required to determine whether there is a role for strain to help better understand the pathophysiology of latent rheumatic heart disease.

  1. Detecting New Words from Chinese Text Using Latent Semi-CRF Models

    Science.gov (United States)

    Sun, Xiao; Huang, Degen; Ren, Fuji

    Chinese new words and their part-of-speech (POS) are particularly problematic in Chinese natural language processing. With the fast development of internet and information technology, it is impossible to get a complete system dictionary for Chinese natural language processing, as new words out of the basic system dictionary are always being created. A latent semi-CRF model, which combines the strengths of LDCRF (Latent-Dynamic Conditional Random Field) and semi-CRF, is proposed to detect the new words together with their POS synchronously regardless of the types of the new words from the Chinese text without being pre-segmented. Unlike the original semi-CRF, the LDCRF is applied to generate the candidate entities for training and testing the latent semi-CRF, which accelerates the training speed and decreases the computation cost. The complexity of the latent semi-CRF could be further adjusted by tuning the number of hidden variables in LDCRF and the number of the candidate entities from the Nbest outputs of the LDCRF. A new-words-generating framework is proposed for model training and testing, under which the definitions and distributions of the new words conform to the ones existing in real text. Specific features called “Global Fragment Information” for new word detection and POS tagging are adopted in the model training and testing. The experimental results show that the proposed method is capable of detecting even low frequency new words together with their POS tags. The proposed model is found to be performing competitively with the state-of-the-art models presented.

  2. Learning

    Directory of Open Access Journals (Sweden)

    Mohsen Laabidi

    2014-01-01

    Full Text Available Nowadays learning technologies transformed educational systems with impressive progress of Information and Communication Technologies (ICT. Furthermore, when these technologies are available, affordable and accessible, they represent more than a transformation for people with disabilities. They represent real opportunities with access to an inclusive education and help to overcome the obstacles they met in classical educational systems. In this paper, we will cover basic concepts of e-accessibility, universal design and assistive technologies, with a special focus on accessible e-learning systems. Then, we will present recent research works conducted in our research Laboratory LaTICE toward the development of an accessible online learning environment for persons with disabilities from the design and specification step to the implementation. We will present, in particular, the accessible version “MoodleAcc+” of the well known e-learning platform Moodle as well as new elaborated generic models and a range of tools for authoring and evaluating accessible educational content.

  3. A developmental study of latent absolute pitch memory.

    Science.gov (United States)

    Jakubowski, Kelly; Müllensiefen, Daniel; Stewart, Lauren

    2017-03-01

    The ability to recall the absolute pitch level of familiar music (latent absolute pitch memory) is widespread in adults, in contrast to the rare ability to label single pitches without a reference tone (overt absolute pitch memory). The present research investigated the developmental profile of latent absolute pitch (AP) memory and explored individual differences related to this ability. In two experiments, 288 children from 4 to12 years of age performed significantly above chance at recognizing the absolute pitch level of familiar melodies. No age-related improvement or decline, nor effects of musical training, gender, or familiarity with the stimuli were found in regard to latent AP task performance. These findings suggest that latent AP memory is a stable ability that is developed from as early as age 4 and persists into adulthood.

  4. Studies of Latent Acidity and Neutral Buffered Chloroaluminate Ionic Liquids

    National Research Council Canada - National Science Library

    Osteryoung, Robert

    2000-01-01

    Studies on ionic liquids composed of aluminum chloride and 1-ethyl-3-methylimidazolium chloride were carried out, with emphasis on understanding and explaining acidity and latent acidity in "neutral buffered" melts...

  5. Latent tuberculosis in nursing professionals of a Brazilian hospital

    Directory of Open Access Journals (Sweden)

    Valim Andréia

    2011-05-01

    Full Text Available Abstract Tuberculosis (TB is considered an occupational disease among health-care workers (HCWs. Direct contact with TB patients leads to an increased risk to become latently infected by Mycobacterium tuberculosis. The objective of this study is to estimate the prevalence of latent M. tuberculosis minfection among nursing professionals of a hospital in Rio Grande do Sul, Brazil, assessed by tuberculin skin test (TST. From November 2009 to May 2010, latent M. tuberculosis infection was assessed by TST in 55 nursing professionals. Epidemiological information was collected using a standardized questionnaire. A positive TST result (> or = 10 mm was observed in 47.3% of the HCWs tested. There was no significant difference in TST positivity when duration of employment or professional category (technician or nurse was evaluated. The results of this work reinforce the need for control measures to prevent latent M. tuberculosis infection among nursing professionals at the hospital where the study was conducted.

  6. A solar air collector with integrated latent heat thermal storage

    Directory of Open Access Journals (Sweden)

    Klimes Lubomir

    2012-04-01

    Full Text Available Simulations of the behaviour of a solar air collector with integrated latent heat thermal storage were performed. The model of the collector was created with the use of coupling between TRNSYS 17 and MATLAB. Latent heat storage (Phase Change Material - PCM was integrated with the solar absorber. The model of the latent heat storage absorber was created in MATLAB and the model of the solar air collector itself was created in TRNSYS with the use of TYPE 56. The model of the latent heat storage absorber allows specification of the PCM properties as well as other parameters. The simulated air collector was the front and back pass collector with the absorber in the middle of the air cavity. Two variants were considered for comparison; the light-weight absorber made of sheet metal and the heat-storage absorber with the PCM. Simulations were performed for the climatic conditions of the Czech Republic (using TMY weather data.

  7. Evaluasi Human Machine Interface Menggunakan Kriteria Usability Pada Sistem E-learning Perguruan Tinggi

    Directory of Open Access Journals (Sweden)

    Akhmad Qashlim

    2016-01-01

    Full Text Available Integration HMI with usability in user interface design process is a standart of the success of a website. The design process is done through the approach to the end user to find a problem solution of human machine interface phenomena. It can also generate the maximum level of satisfaction and success of implementation of the website. The purpose of this research is to evaluate HMI using usabilitycriteria to know the application of HMI concept in e-learning and provide proposals for improvements to the HMI. Questionnaire Data were processed using a descriptive analysis and methods of CFA to know the variables that are weakest and which indicators have an important role in shaping the research variables. Evaluation results indicate the application concept of HMI in the e-learning had been done but not the maximum. Data analysis of the results obtained that the main problem lies in the accessibility criteria in the meantime indicator latent variables from forming error prevention, learnability, memorability, visibility and accessibility of influential factor loading values indicated significantly (unidimensionalitas in shaping the criteria of latent variables in first-order CFA. The end result of this research is the proposal of improvement as a HMI solution in the form of principles and technicsuser interface design. This solution is focused on the development of standards for the quality of the interface in e-learning systems and not on the digital learning content presented on the e-learning system. Keywords: Descriptive analisis; Human machine interface; Usability; Confirmatory factor analisys; Elearning

  8. Chronic mild stress impairs latent inhibition and induces region-specific neural activation in CHL1-deficient mice, a mouse model of schizophrenia.

    Science.gov (United States)

    Buhusi, Mona; Obray, Daniel; Guercio, Bret; Bartlett, Mitchell J; Buhusi, Catalin V

    2017-08-30

    Schizophrenia is a neurodevelopmental disorder characterized by abnormal processing of information and attentional deficits. Schizophrenia has a high genetic component but is precipitated by environmental factors, as proposed by the 'two-hit' theory of schizophrenia. Here we compared latent inhibition as a measure of learning and attention, in CHL1-deficient mice, an animal model of schizophrenia, and their wild-type littermates, under no-stress and chronic mild stress conditions. All unstressed mice as well as the stressed wild-type mice showed latent inhibition. In contrast, CHL1-deficient mice did not show latent inhibition after exposure to chronic stress. Differences in neuronal activation (c-Fos-positive cell counts) were noted in brain regions associated with latent inhibition: Neuronal activation in the prelimbic/infralimbic cortices and the nucleus accumbens shell was affected solely by stress. Neuronal activation in basolateral amygdala and ventral hippocampus was affected independently by stress and genotype. Most importantly, neural activation in nucleus accumbens core was affected by the interaction between stress and genotype. These results provide strong support for a 'two-hit' (genes x environment) effect on latent inhibition in CHL1-deficient mice, and identify CHL1-deficient mice as a model of schizophrenia-like learning and attention impairments. Copyright © 2017 Elsevier B.V. All rights reserved.

  9. Intentional Learning Vs Incidental Learning

    OpenAIRE

    Shahbaz Ahmed

    2017-01-01

    This study is conducted to demonstrate the knowledge of intentional learning and incidental learning. Hypothesis of this experiment is intentional learning is better than incidental learning, participants were demonstrated and were asked to learn the 10 non sense syllables in a specific sequence from the colored cards in the end they were asked to recall the background color of each card instead of non-sense syllables. Independent variables of the experiment are the colored cards containing n...

  10. Reporting guidelines for diagnostic accuracy studies that use Bayesian latent class models (STARD-BLCM)

    DEFF Research Database (Denmark)

    Kostoulas, Polychronis; Nielsen, Søren S.; Branscum, Adam J.

    2017-01-01

    of disease status (i.e., disease status is a latent variable). Statistical methods were introduced in this context by Hui and Walter and have been succesfully applied since then, with the majority of the work being carried out in a Bayesian framework. While STARD provides useful reporting guidelines...... for studies designed to estimate the accuracy of tests when disease status is known. The original STARD statement was initially published in seven journals, while an updated version — STARD2015 — has been recently released. More than 200 biomedical journals encourage its use in their instructions to authors...

  11. Latent-failure risk estimates for computer control

    Science.gov (United States)

    Dunn, William R.; Folsom, Rolfe A.; Green, Owen R.

    1991-01-01

    It is shown that critical computer controls employing unmonitored safety circuits are unsafe. Analysis supporting this result leads to two additional, important conclusions: (1) annual maintenance checks of safety circuit function do not, as widely believed, eliminate latent failure risk; (2) safety risk remains even if multiple, series-connected protection circuits are employed. Finally, it is shown analytically that latent failure risk is eliminated when continuous monitoring is employed.

  12. Fitting Latent Cluster Models for Networks with latentnet

    Directory of Open Access Journals (Sweden)

    Pavel N. Krivitsky

    2007-12-01

    Full Text Available latentnet is a package to fit and evaluate statistical latent position and cluster models for networks. Hoff, Raftery, and Handcock (2002 suggested an approach to modeling networks based on positing the existence of an latent space of characteristics of the actors. Relationships form as a function of distances between these characteristics as well as functions of observed dyadic level covariates. In latentnet social distances are represented in a Euclidean space. It also includes a variant of the extension of the latent position model to allow for clustering of the positions developed in Handcock, Raftery, and Tantrum (2007.The package implements Bayesian inference for the models based on an Markov chain Monte Carlo algorithm. It can also compute maximum likelihood estimates for the latent position model and a two-stage maximum likelihood method for the latent position cluster model. For latent position cluster models, the package provides a Bayesian way of assessing how many groups there are, and thus whether or not there is any clustering (since if the preferred number of groups is 1, there is little evidence for clustering. It also estimates which cluster each actor belongs to. These estimates are probabilistic, and provide the probability of each actor belonging to each cluster. It computes four types of point estimates for the coefficients and positions: maximum likelihood estimate, posterior mean, posterior mode and the estimator which minimizes Kullback-Leibler divergence from the posterior. You can assess the goodness-of-fit of the model via posterior predictive checks. It has a function to simulate networks from a latent position or latent position cluster model.

  13. Translating latent trends in food consumer behavior into new products

    OpenAIRE

    Gellynck, Xavier; Kühne, Bianka; Van Wezemael, Lynn; Verbeke, Wim

    2010-01-01

    For successful product development it is important to explore the latent changes in consumer behavior prior to the product development process. The identification of a latent trend before the manifestation moment can be achieved by trend analysis. Trend analysis delivers insights that explore the future in order to identify prospective consumers and new product ideas, but also includes a feeling for the currents in market and technology. Hence, the aim is to identify emerging weak signals in ...

  14. Cytokine profile in patients with early latent syphilis

    Directory of Open Access Journals (Sweden)

    Zakharov S.V.

    2018-03-01

    Full Text Available The purpose of this study was to study the change in the content of the most active cytokines (interleukins 6 and 10 during the formation of the immune response in patients with latent early syphilis, as well as to study the possible relationship between the concentrations of these cytokines and the duration of the disease. In 50 patients with early latent syphilis, the concentration of interleukins 6 and 10 in serum was studied. The serum level of interleukins was studied by the enzyme immunoassay. A statistically significant increase in the concentration of interleukin 6 in the blood of patients with latent syphilis and decrease in the interleukin 10 concentration in comparison with healthy people was established. At the same time, in patients with latent syphilis with term of infection for more than 1 year, interleukin 10 has been expressed, as compared with healthy people and, especially, with patients with syphilis with a duration of infection of up to 1 year. Along with this, a lower degree of increase in the concentration of interleukin 6 in patients with latent syphilis with a duration of infection over 1 year has been established, as compared with patients with latent syphilis with a term of infection up to 1 year, against the background of its increased concentration as compared with a group of healthy individuals.

  15. Latent fingerprints on different type of screen protective films

    Directory of Open Access Journals (Sweden)

    Yuttana Sudjaroen

    2016-07-01

    Full Text Available The purpose of this research was to study the quality of latent fingerprint on different types of screen protective films including screen protector, matte screen protector, anti-fingerprint clear screen protector and anti-fingerprint matte screen protector by using black powder method in developing latent fingerprints. The fingerprints were performed by 10 volunteers whose fingers (right index, right thumb, left index and left thumb were stubbing at different types of screen protective films and subsequently latent fingerprints were developed by brushing with black powder. Automated Fingerprint Identification System (AFIS counted the numbers of minutiae points from 320 latent fingerprints. Anti-fingerprint matte screen protective film produced the best quality of latent fingerprint with an average minutiae point 72.65, followed by matte screen protective film, clear screen protective film and anti-fingerprint clear screen protective film with an average minutiae point of 155.2, 135.0 and 72.65 respectively. The quality of latent fingerprints developed between a clear and a matte surface of screen protective films showed a significant difference (sig>0.05, whereas the coat and the non-coat with anti-fingerprint chemical revealed a non-significant difference (sig<0.05 in their number of minutiae points.

  16. A flexible latent class approach to estimating test-score reliability

    NARCIS (Netherlands)

    van der Palm, D.W.; van der Ark, L.A.; Sijtsma, K.

    2014-01-01

    The latent class reliability coefficient (LCRC) is improved by using the divisive latent class model instead of the unrestricted latent class model. This results in the divisive latent class reliability coefficient (DLCRC), which unlike LCRC avoids making subjective decisions about the best solution

  17. Application of a latent variables model for the medical images analysis; Aplicacion de un modelo de variables latentes para el analisis de imagenes medicas

    Energy Technology Data Exchange (ETDEWEB)

    Campos S, Y.; Ruiz C, S. [Centro de Investigacion en Matematica, A.C. Jalisco s/n, Col. Valenciana, Guanajuato (Mexico)

    2008-07-01

    In recent years the technological advance has allowed the significant advance in diverse research fields, the medicine has not been exempt of this technology and the use of this technology has allowed a significant advance in the equipment that are used to obtain medical images. The quantity of information that is generated with this equipment has grown in exponential form and it is a difficult task to carry out a quantitative analysis of the data also the manipulation of big quantities of information makes the medical images analysis a complicated task. It is in fact this complexity what motivates this work where one of the main objectives is the analysis of techniques that allow to work with the complexity of the data generated with medical equipment. Likewise, it is wanted to illustrate an application of the peaceful uses of the nuclear energy to treat a medical problem where the diagnostic it depends essentially on the current medical equipment to give an appropriate treatment to the patients. (Author)

  18. Adolescent substance use behavior and suicidal behavior for boys and girls: a cross-sectional study by latent analysis approach

    OpenAIRE

    Wang, Peng-Wei; Yen, Cheng-Fang

    2017-01-01

    Background Adolescent suicidal behavior may consist of different symptoms, including suicidal ideation, suicidal planning and suicidal attempts. Adolescent substance use behavior may contribute to adolescent suicidal behavior. However, research on the relationships between specific substance use and individual suicidal behavior is insufficient, as adolescents may not use only one substance or develop only one facet of suicidal behavior. Latent variables permit us to describe the relationships...

  19. Regulating approaches to learning: Testing learning strategy convergences across a year at university.

    Science.gov (United States)

    Fryer, Luke K; Vermunt, Jan D

    2018-03-01

    Contemporary models of student learning within higher education are often inclusive of processing and regulation strategies. Considerable research has examined their use over time and their (person-centred) convergence. The longitudinal stability/variability of learning strategy use, however, is poorly understood, but essential to supporting student learning across university experiences. Develop and test a person-centred longitudinal model of learning strategies across the first-year university experience. Japanese university students (n = 933) completed surveys (deep and surface approaches to learning; self, external, and lack of regulation) at the beginning and end of their first year. Following invariance and cross-sectional tests, latent profile transition analysis (LPTA) was undertaken. Initial difference testing supported small but significant differences for self-/external regulation. Fit indices supported a four-group model, consistent across both measurement points. These subgroups were labelled Low Quality (low deep approaches and self-regulation), Low Quantity (low strategy use generally), Average (moderate strategy use), and High Quantity (intense use of all strategies) strategies. The stability of these groups ranged from stable to variable: Average (93% stayers), Low Quality (90% stayers), High Quantity (72% stayers), and Low Quantity (40% stayers). The three largest transitions presented joint shifts in processing/regulation strategy preference across the year, from adaptive to maladaptive and vice versa. Person-centred longitudinal findings presented patterns of learning transitions that different students experience during their first year at university. Stability/variability of students' strategy use was linked to the nature of initial subgroup membership. Findings also indicated strong connections between processing and regulation strategy changes across first-year university experiences. Implications for theory and practice are discussed.

  20. Children's Learning

    Science.gov (United States)

    Siegler, Robert S.

    2005-01-01

    A new field of children's learning is emerging. This new field differs from the old in recognizing that children's learning includes active as well as passive mechanisms and qualitative as well as quantitative changes. Children's learning involves substantial variability of representations and strategies within individual children as well as…

  1. Do recognizable lifetime eating disorder phenotypes naturally occur in a culturally asian population? A combined latent profile and taxometric approach.

    Science.gov (United States)

    Thomas, Jennifer J; Eddy, Kamryn T; Ruscio, John; Ng, King Lam; Casale, Kristen E; Becker, Anne E; Lee, Sing

    2015-05-01

    We examined whether empirically derived eating disorder (ED) categories in Hong Kong Chinese patients (N = 454) would be consistent with recognizable lifetime ED phenotypes derived from latent structure models of European and American samples. We performed latent profile analysis (LPA) using indicator variables from data collected during routine assessment, and then applied taxometric analysis to determine whether latent classes were qualitatively versus quantitatively distinct. Latent profile analysis identified four classes: (i) binge/purge (47%); (ii) non-fat-phobic low-weight (34%); (iii) fat-phobic low-weight (12%); and (iv) overweight disordered eating (6%). Taxometric analysis identified qualitative (categorical) distinctions between the binge/purge and non-fat-phobic low-weight classes, and also between the fat-phobic and non-fat-phobic low-weight classes. Distinctions between the fat-phobic low-weight and binge/purge classes were indeterminate. Empirically derived categories in Hong Kong showed recognizable correspondence with recognizable lifetime ED phenotypes. Although taxometric findings support two distinct classes of low weight EDs, LPA findings also support heterogeneity among non-fat-phobic individuals. Copyright © 2015 John Wiley & Sons, Ltd and Eating Disorders Association.

  2. Latent Virus Reactivation: From Space to Earth

    Science.gov (United States)

    Mehta, Satish K.; Cohrs, Randall J.; Gilden, Donald H.; Tyring, Stephen K.; Castro, Victoria A.; Ott, C. Mark; Pierson, Duane L.

    2010-01-01

    Reactivation of latent viruses is a recognized consequence of decreased immunity. More recently viral reactivation has been identified as an important in vivo indicator of clinically relevant immune changes. Viral reactivation can be determined quickly and easily by the presence of virus in saliva and other body fluids. Real-time polymerase chain reaction (PCR) is a highly sensitive and specific molecular method to detect the presence of specific viral DNA. Studies in astronauts demonstrated that herpes simplex virus type 1(HSV-1), Epstein-Barr Virus (EBV), cytomegalovirus (CMV), and varicella zoster virus (VZV) reactivate at rates above normal during and after spaceflight in response to moderately decreased T-cell immunity. This technology was expanded to patients on Earth beginning with human immune deficiency virus (HIV) immuno-compromised patients. The HIV patients shed EBV in saliva at rates 9-fold higher than observed in astronauts demonstrating that the level of EBV shedding reflects the severity of impaired immunity. Whereas EBV reactivation is not expected to produce serious effects in astronauts on missions of 6 months or less, VZV reactivation in astronauts could produce shingles. Reactivation of live, infectious VZV in astronauts with no symptoms was demonstrated in astronauts during and after spaceflight. We applied our technology to study VZV-induced shingles in patients. In a study of 54 shingles patients, we showed salivary VZV was present in every patient on the day antiviral (acyclovir) treatment was initiated. Pain and skin lesions decreased with antiviral treatment. Corresponding decreases in levels of VZV were also observed and accompanied recovery. Although the level of VZV in shingles patients before the treatment was generally higher than those found in astronauts, lower range of VZV numbers in shingles patients overlapped with astronaut s levels. This suggests a potential risk of shingles to astronauts resulting from reactivation of VZV. In

  3. Variable training does not lead to better motor learning compared to repetitive training in children with DCD when exposed to video games

    NARCIS (Netherlands)

    Bonney, E.; Jelsma, Lemke; Ferguson, F; Smits-Engelsman, B.C.M.

    Background Little is known about the influence of practice schedules on motor learning and skills transfer in children with and without developmental coordination disorder (DCD). Understanding how practice schedules affect motor learning is necessary for motor skills development and rehabilitation.

  4. Multilevel Latent Class Analysis for Large-Scale Educational Assessment Data: Exploring the Relation between the Curriculum and Students' Mathematical Strategies

    Science.gov (United States)

    Fagginger Auer, Marije F.; Hickendorff, Marian; Van Putten, Cornelis M.; Béguin, Anton A.; Heiser, Willem J.

    2016-01-01

    A first application of multilevel latent class analysis (MLCA) to educational large-scale assessment data is demonstrated. This statistical technique addresses several of the challenges that assessment data offers. Importantly, MLCA allows modeling of the often ignored teacher effects and of the joint influence of teacher and student variables.…

  5. Spectral Learning for Supervised Topic Models.

    Science.gov (United States)

    Ren, Yong; Wang, Yining; Zhu, Jun

    2018-03-01

    Supervised topic models simultaneously model the latent topic structure of large collections of documents and a response variable associated with each document. Existing inference methods are based on variational approximation or Monte Carlo sampling, which often suffers from the local minimum defect. Spectral methods have been applied to learn unsupervised topic models, such as latent Dirichlet allocation (LDA), with provable guarantees. This paper investigates the possibility of applying spectral methods to recover the parameters of supervised LDA (sLDA). We first present a two-stage spectral method, which recovers the parameters of LDA followed by a power update method to recover the regression model parameters. Then, we further present a single-phase spectral algorithm to jointly recover the topic distribution matrix as well as the regression weights. Our spectral algorithms are provably correct and computationally efficient. We prove a sample complexity bound for each algorithm and subsequently derive a sufficient condition for the identifiability of sLDA. Thorough experiments on synthetic and real-world datasets verify the theory and demonstrate the practical effectiveness of the spectral algorithms. In fact, our results on a large-scale review rating dataset demonstrate that our single-phase spectral algorithm alone gets comparable or even better performance than state-of-the-art methods, while previous work on spectral methods has rarely reported such promising performance.

  6. Comparing the Measured and Latent Dark Triad: Are Three Measures Better than One?

    Directory of Open Access Journals (Sweden)

    Peter K. Jonason

    2011-10-01

    Full Text Available Could measurement level be a factor worth considering when studying the Dark Triad (i.e., narcissism, psychopathy, and Machiavellianism? In two studies (N  = 465, we compared the relative fit of two Dark Triad models: one that treats the three measures as separate-yet-related personality traits and another that treats the measures as tapping a single, latent construct. Mid-level personality traits, such as mate-retention strategies (Study 1 were best explained by a three-measure model, whereas the higher-order trait of sociosexuality (Study 2, were best explained by a single, latent-factor model. When considering mid-level measurement in personality, the three traits may provide independent effects for interpersonal relationships, whereas at the higher-order level, the three traits may function as a single entity relating to other higher-order traits. We suggest one should consider level of measurement between the predictor and criterion variables to better predict correlations among variables such as the Dark Triad. DOI: 10.2458/azu_jmmss.v2i1.12363

  7. Latent transition analysis of pre-service teachers' efficacy in mathematics and science

    Science.gov (United States)

    Ward, Elizabeth Kennedy

    This study modeled changes in pre-service teacher efficacy in mathematics and science over the course of the final year of teacher preparation using latent transition analysis (LTA), a longitudinal form of analysis that builds on two modeling traditions (latent class analysis (LCA) and auto-regressive modeling). Data were collected using the STEBI-B, MTEBI-r, and the ABNTMS instruments. The findings suggest that LTA is a viable technique for use in teacher efficacy research. Teacher efficacy is modeled as a construct with two dimensions: personal teaching efficacy (PTE) and outcome expectancy (OE). Findings suggest that the mathematics and science teaching efficacy (PTE) of pre-service teachers is a multi-class phenomena. The analyses revealed a four-class model of PTE at the beginning and end of the final year of teacher training. Results indicate that when pre-service teachers transition between classes, they tend to move from a lower efficacy class into a higher efficacy class. In addition, the findings suggest that time-varying variables (attitudes and beliefs) and time-invariant variables (previous coursework, previous experiences, and teacher perceptions) are statistically significant predictors of efficacy class membership. Further, analyses suggest that the measures used to assess outcome expectancy are not suitable for LCA and LTA procedures.

  8. Ridge Width Correlations between Inked Prints and Powdered Latent Fingerprints.

    Science.gov (United States)

    De Alcaraz-Fossoul, Josep; Barrot-Feixat, Carme; Zapico, Sara C; Mancenido, Michelle; Broatch, Jennifer; Roberts, Katherine A; Carreras-Marin, Clara; Tasker, Jack

    2017-10-03

    A methodology to estimate the time of latent fingerprint deposition would be of great value to law enforcement and courts. It has been observed that ridge topography changes as latent prints age, including the widths of ridges that could be measured as a function of time. Crime suspects are commonly identified using fingerprint databases that contain reference inked tenprints (flat and rolled impressions). These can be of interest in aging studies as they provide baseline information relating to the original (nonaged) ridges' widths. In practice, the age of latent fingerprints could be estimated following a comparison process between the evidentiary aged print and the corresponding reference inked print. The present article explores possible correlations between inked and fresh latent fingerprints deposited on different substrates and visualized with TiO 2 . The results indicate that the ridge width of flat inked prints is most similar to fresh latent fingerprints , and these should be used as the comparison standard for future aging studies. © 2017 American Academy of Forensic Sciences.

  9. Study of noninvasive detection of latent fingerprints using UV laser

    Science.gov (United States)

    Li, Hong-xia; Cao, Jing; Niu, Jie-qing; Huang, Yun-gang; Mao, Lin-jie; Chen, Jing-rong

    2011-06-01

    Latent fingerprints present a considerable challenge in forensics, and noninvasive procedure that captures a digital image of the latent fingerprints is significant in the field of criminal investigation. The capability of photography technologies using 266nm UV Nd:YAG solid state laser as excitation light source to provide detailed images of unprocessed latent fingerprints is demonstrated. Unprocessed latent fingerprints were developed on various non-absorbent and absorbing substrates. According to the special absorption, reflection, scattering and fluorescence characterization of the various residues in fingerprints (fatty acid ester, protein, and carbosylic acid salts etc) to the UV light to weaken or eliminate the background disturbance and increase the brightness contrast of fingerprints with the background, and using 266nm UV laser as excitation light source, fresh and old latent fingerprints on the surface of four types of non-absorbent objects as magazine cover, glass, back of cellphone, wood desktop paintwork and two types of absorbing objects as manila envelope, notebook paper were noninvasive detected and appeared through reflection photography and fluorescence photography technologies, and the results meet the fingerprint identification requirements in forensic science.

  10. Latent class analysis derived subgroups of low back pain patients - do they have prognostic capacity?

    Science.gov (United States)

    Molgaard Nielsen, Anne; Hestbaek, Lise; Vach, Werner; Kent, Peter; Kongsted, Alice

    2017-08-09

    Heterogeneity in patients with low back pain is well recognised and different approaches to subgrouping have been proposed. One statistical technique that is increasingly being used is Latent Class Analysis as it performs subgrouping based on pattern recognition with high accuracy. Previously, we developed two novel suggestions for subgrouping patients with low back pain based on Latent Class Analysis of patient baseline characteristics (patient history and physical examination), which resulted in 7 subgroups when using a single-stage analysis, and 9 subgroups when using a two-stage approach. However, their prognostic capacity was unexplored. This study (i) determined whether the subgrouping approaches were associated with the future outcomes of pain intensity, pain frequency and disability, (ii) assessed whether one of these two approaches was more strongly or more consistently associated with these outcomes, and (iii) assessed the performance of the novel subgroupings as compared to the following variables: two existing subgrouping tools (STarT Back Tool and Quebec Task Force classification), four baseline characteristics and a group of previously identified domain-specific patient categorisations (collectively, the 'comparator variables'). This was a longitudinal cohort study of 928 patients consulting for low back pain in primary care. The associations between each subgroup approach and outcomes at 2 weeks, 3 and 12 months, and with weekly SMS responses were tested in linear regression models, and their prognostic capacity (variance explained) was compared to that of the comparator variables listed above. The two previously identified subgroupings were similarly associated with all outcomes. The prognostic capacity of both subgroupings was better than that of the comparator variables, except for participants' recovery beliefs and the domain-specific categorisations, but was still limited. The explained variance ranged from 4.3%-6.9% for pain intensity and

  11. High Resolution Mapping of Soil Properties Using Remote Sensing Variables in South-Western Burkina Faso: A Comparison of Machine Learning and Multiple Linear Regression Models.

    Science.gov (United States)

    Forkuor, Gerald; Hounkpatin, Ozias K L; Welp, Gerhard; Thiel, Michael

    2017-01-01

    Accurate and detailed spatial soil information is essential for environmental modelling, risk assessment and decision making. The use of Remote Sensing data as secondary sources of information in digital soil mapping has been found to be cost effective and less time consuming compared to traditional soil mapping approaches. But the potentials of Remote Sensing data in improving knowledge of local scale soil information in West Africa have not been fully explored. This study investigated the use of high spatial resolution satellite data (RapidEye and Landsat), terrain/climatic data and laboratory analysed soil samples to map the spatial distribution of six soil properties-sand, silt, clay, cation exchange capacity (CEC), soil organic carbon (SOC) and nitrogen-in a 580 km2 agricultural watershed in south-western Burkina Faso. Four statistical prediction models-multiple linear regression (MLR), random forest regression (RFR), support vector machine (SVM), stochastic gradient boosting (SGB)-were tested and compared. Internal validation was conducted by cross validation while the predictions were validated against an independent set of soil samples considering the modelling area and an extrapolation area. Model performance statistics revealed that the machine learning techniques performed marginally better than the MLR, with the RFR providing in most cases the highest accuracy. The inability of MLR to handle non-linear relationships between dependent and independent variables was found to be a limitation in accurately predicting soil properties at unsampled locations. Satellite data acquired during ploughing or early crop development stages (e.g. May, June) were found to be the most important spectral predictors while elevation, temperature and precipitation came up as prominent terrain/climatic variables in predicting soil properties. The results further showed that shortwave infrared and near infrared channels of Landsat8 as well as soil specific indices of redness

  12. High Resolution Mapping of Soil Properties Using Remote Sensing Variables in South-Western Burkina Faso: A Comparison of Machine Learning and Multiple Linear Regression Models.

    Directory of Open Access Journals (Sweden)

    Gerald Forkuor

    Full Text Available Accurate and detailed spatial soil information is essential for environmental modelling, risk assessment and decision making. The use of Remote Sensing data as secondary sources of information in digital soil mapping has been found to be cost effective and less time consuming compared to traditional soil mapping approaches. But the potentials of Remote Sensing data in improving knowledge of local scale soil information in West Africa have not been fully explored. This study investigated the use of high spatial resolution satellite data (RapidEye and Landsat, terrain/climatic data and laboratory analysed soil samples to map the spatial distribution of six soil properties-sand, silt, clay, cation exchange capacity (CEC, soil organic carbon (SOC and nitrogen-in a 580 km2 agricultural watershed in south-western Burkina Faso. Four statistical prediction models-multiple linear regression (MLR, random forest regression (RFR, support vector machine (SVM, stochastic gradient boosting (SGB-were tested and compared. Internal validation was conducted by cross validation while the predictions were validated against an independent set of soil samples considering the modelling area and an extrapolation area. Model performance statistics revealed that the machine learning techniques performed marginally better than the MLR, with the RFR providing in most cases the highest accuracy. The inability of MLR to handle non-linear relationships between dependent and independent variables was found to be a limitation in accurately predicting soil properties at unsampled locations. Satellite data acquired during ploughing or early crop development stages (e.g. May, June were found to be the most important spectral predictors while elevation, temperature and precipitation came up as prominent terrain/climatic variables in predicting soil properties. The results further showed that shortwave infrared and near infrared channels of Landsat8 as well as soil specific indices

  13. Latent Tuberculosis in Pregnancy: A Systematic Review.

    Directory of Open Access Journals (Sweden)

    Isabelle Malhamé

    Full Text Available In countries with low tuberculosis (TB incidence, immigrants from higher incidence countries represent the major pool of individuals with latent TB infection (LTBI. The antenatal period represents an opportunity for immigrant women to access the medical system, and hence for potential screening and treatment of LTBI. However, such screening and treatment during pregnancy remains controversial.In order to further understand the prevalence, natural history, screening and management of LTBI in pregnancy, we conducted a systematic literature review addressing the screening and treatment of LTBI, in pregnant women without known HIV infection.A systematic review of 4 databases (Embase, Embase Classic, Medline, Cochrane Library covering articles published from January 1st 1980 to April 30th 2014. Articles in English, French or Spanish with relevant information on prevalence, natural history, screening tools, screening strategies and treatment of LTBI during pregnancy were eligible for inclusion. Articles were excluded if (1 Full text was not available (2 they were case series or case studies (3 they focused exclusively on prevalence, diagnosis and treatment of active TB (4 the study population was exclusively HIV-infected.Of 4,193 titles initially identified, 208 abstracts were eligible for review. Of these, 30 articles qualified for full text review and 22 were retained: 3 cohort studies, 2 case-control studies, and 17 cross-sectional studies. In the USA, the estimated prevalence of LTBI ranged from 14 to 48% in women tested, and tuberculin skin test (TST positivity was associated with ethnicity. One study suggested that incidence of active TB was significantly increased during the 180 days postpartum (Incidence rate ratio, 1.95 (95% CI 1.24-3.07. There was a high level of adherence with both skin testing (between 90-100% and chest radiography (93-100%.. In three studies from low incidence settings, concordance between TST and an interferon

  14. Latent class analysis of diagnostic science assessment data using Bayesian networks

    Science.gov (United States)

    Steedle, Jeffrey Thomas

    2008-10-01

    Diagnostic science assessments seek to draw inferences about student understanding by eliciting evidence about the mental models that underlie students' reasoning about physical systems. Measurement techniques for analyzing data from such assessments embody one of two contrasting assessment programs: learning progressions and facet-based assessments. Learning progressions assume that students have coherent theories that they apply systematically across different problem contexts. In contrast, the facet approach makes no such assumption, so students should not be expected to reason systematically across different problem contexts. A systematic comparison of these two approaches is of great practical value to assessment programs such as the National Assessment of Educational Progress as they seek to incorporate small clusters of related items in their tests for the purpose of measuring depth of understanding. This dissertation describes an investigation comparing learning progression and facet models. Data comprised student responses to small clusters of multiple-choice diagnostic science items focusing on narrow aspects of understanding of Newtonian mechanics. Latent class analysis was employed using Bayesian networks in order to model the relationship between students' science understanding and item responses. Separate models reflecting the assumptions of the learning progression and facet approaches were fit to the data. The technical qualities of inferences about student understanding resulting from the two models were compared in order to determine if either modeling approach was more appropriate. Specifically, models were compared on model-data fit, diagnostic reliability, diagnostic certainty, and predictive accuracy. In addition, the effects of test length were evaluated for both models in order to inform the number of items required to obtain adequately reliable latent class diagnoses. Lastly, changes in student understanding over time were studied with a

  15. The latent effect of inertia in the modal choice

    DEFF Research Database (Denmark)

    Cherchi, Elisabetta; Meloni, Italo; Ortúzar, Juan de Dios

    2014-01-01

    The existence of habit (leading to inertia) in the choice process has been approached in the literature in a number of ways. In transport, inertia has been studied mainly using “long panel” data, or mixed revealed and stated preference data. In these studies inertia links the choice made in two...... approaches. We assume that inertia is revealed by past behaviour and affects also the initial condition, but we recognise that past behaviour is only an indicator of habitual behaviour, the true process behind the formation of habitual behaviour being latent. We estimate a hybrid choice model using a set...... of revealed and stated mode choice preferences collected in Cagliari (Italy). We found a significant latent inertia in the revealed preference data, indicating that inertia affects the initial conditions. The latent inertia is revealed by the frequency of past behaviour but the effect of trip frequency...

  16. Current management options for latent tuberculosis: a review

    Directory of Open Access Journals (Sweden)

    Norton BL

    2012-11-01

    Full Text Available Brianna L Norton, David P HollandDepartment of Medicine, Division of Infectious Diseases, Duke University Medical Center, Durham, NC, USAAbstract: Tuberculosis remains the world’s second leading infectious cause of death, with nearly one-third of the global population latently infected. Treatment of latent tuberculosis infection is a mainstay of tuberculosis-control efforts in low-to medium-incidence countries. Isoniazid monotherapy has been the standard of care for decades, but its utility is impaired by poor completion rates. However, new, shorter-course regimens using rifamycins improve completion rates and are cost-saving compared with standard isoniazid monotherapy. We review the currently available therapies for latent tuberculosis infection and their toxicities and include a brief economic comparison of the different regimens.Keywords: isoniazid, rifampin, rifapentine, tuberculin skin test, interferon-gamma release assay

  17. Effects of latent toxoplasmosis on autoimmune thyroid diseases in pregnancy.

    Science.gov (United States)

    Kaňková, Šárka; Procházková, Lucie; Flegr, Jaroslav; Calda, Pavel; Springer, Drahomíra; Potluková, Eliška

    2014-01-01

    Toxoplasmosis, one of the most common zoonotic diseases worldwide, can induce various hormonal and behavioural alterations in infected hosts, and its most common form, latent toxoplasmosis, influences the course of pregnancy. Autoimmune thyroid diseases (AITD) belong to the well-defined risk factors for adverse pregnancy outcomes. The aim of this study was to investigate whether there is a link between latent toxoplasmosis and maternal AITD in pregnancy. Cross-sectional study in 1248 consecutive pregnant women in the 9-12th gestational weeks. Serum thyroid-stimulating hormone (TSH), thyroperoxidase antibodies (TPOAb), and free thyroxine (FT4) were assessed by chemiluminescence; the Toxoplasma status was detected by the complement fixation test (CFT) and anti-Toxoplasma IgG enzyme-linked immunosorbent assay (ELISA). Overall, 22.5% of the women were positive for latent toxoplasmosis and 14.7% were screened positive for AITD. Women with latent toxoplasmosis had more often highly elevated TPOAb than the Toxoplasma-negative ones (p = 0.004), and latent toxoplasmosis was associated with decrease in serum TSH levels (p = 0.049). Moreover, we found a positive correlation between FT4 and the index of positivity for anti-Toxoplasma IgG antibodies (p = 0.033), which was even stronger in the TPOAb-positive Toxoplasma-positive women, (p = 0.014), as well as a positive correlation between FT4 and log2 CFT (p = 0.009). Latent toxoplasmosis was associated with a mild increase in thyroid hormone production in pregnancy. The observed Toxoplasma-associated changes in the parameters of AITD are mild and do not seem to be clinically relevant; however, they could provide new clues to the complex pathogenesis of autoimmune thyroid diseases.

  18. A coarse to fine minutiae-based latent palmprint matching.

    Science.gov (United States)

    Liu, Eryun; Jain, Anil K; Tian, Jie

    2013-10-01

    With the availability of live-scan palmprint technology, high resolution palmprint recognition has started to receive significant attention in forensics and law enforcement. In forensic applications, latent palmprints provide critical evidence as it is estimated that about 30 percent of the latents recovered at crime scenes are those of palms. Most of the available high-resolution palmprint matching algorithms essentially follow the minutiae-based fingerprint matching strategy. Considering the large number of minutiae (about 1,000 minutiae in a full palmprint compared to about 100 minutiae in a rolled fingerprint) and large area of foreground region in full palmprints, novel strategies need to be developed for efficient and robust latent palmprint matching. In this paper, a coarse to fine matching strategy based on minutiae clustering and minutiae match propagation is designed specifically for palmprint matching. To deal with the large number of minutiae, a local feature-based minutiae clustering algorithm is designed to cluster minutiae into several groups such that minutiae belonging to the same group have similar local characteristics. The coarse matching is then performed within each cluster to establish initial minutiae correspondences between two palmprints. Starting with each initial correspondence, a minutiae match propagation algorithm searches for mated minutiae in the full palmprint. The proposed palmprint matching algorithm has been evaluated on a latent-to-full palmprint database consisting of 446 latents and 12,489 background full prints. The matching results show a rank-1 identification accuracy of 79.4 percent, which is significantly higher than the 60.8 percent identification accuracy of a state-of-the-art latent palmprint matching algorithm on the same latent database. The average computation time of our algorithm for a single latent-to-full match is about 141 ms for genuine match and 50 ms for impostor match, on a Windows XP desktop system with 2

  19. Rapid detection of drug metabolites in latent fingermarks.

    Science.gov (United States)

    Hazarika, Pompi; Jickells, Sue M; Russell, David A

    2009-01-01

    Magnetic particles functionalised with anti-cotinine antibody have been used to image latent fingermarks through the detection of the cotinine antigen in the sweat deposited within the fingerprints of smokers. The antibody-magnetic particle conjugates are readily applied to latent fingerprints while excess reagents are removed through the use of a magnetic wand. The results have shown that drug metabolites, such as cotinine, can be detected and used to image the fingermark to establish the identity of an individual within 15 minutes.

  20. Chromatin Structure of Epstein-Barr Virus Latent Episomes.

    Science.gov (United States)

    Lieberman, Paul M

    2015-01-01

    EBV latent infection is characterized by a highly restricted pattern of viral gene expression. EBV can establish latent infections in multiple different tissue types with remarkable variation and plasticity in viral transcription and replication. During latency, the viral genome persists as a multi-copy episome, a non-integrated-closed circular DNA with nucleosome structure similar to cellular chromosomes. Chromatin assembly and histone modifications contribute to the regulation of viral gene expression, DNA replication, and episome persistence during latency. This review focuses on how EBV latency is regulated by chromatin and its associated processes.