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…

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

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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…

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.

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

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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…

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

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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

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

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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…

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

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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,…

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

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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.

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

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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.

Hidden Markov latent variable models with multivariate longitudinal data.

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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.

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...

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

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

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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.

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

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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.

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

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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.

Inverse Ising problem in continuous time: A latent variable approach

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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.

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

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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…

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.

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

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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.

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.

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

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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…

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...

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

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

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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.

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.

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

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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".

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

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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.

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

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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.

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

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...

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

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.

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…

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.

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.

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…

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.

Standard Errors of Estimated Latent Variable Scores with Estimated Structural Parameters

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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…

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

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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…

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

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

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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

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,

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

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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.

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

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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.

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

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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.

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

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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

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

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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.

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.

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…

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

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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…

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.

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

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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…

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

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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…

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

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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…

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.

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.

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

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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

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

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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.

Discriminative latent models for recognizing contextual group activities.

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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.

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

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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…

Latent Transition Analysis with a Mixture Item Response Theory Measurement Model

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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…

On the Latent Variable Interpretation in Sum-Product Networks.

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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.

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

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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.

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

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.

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…

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...

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:

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.

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.

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

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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.

Heteroscedastic Latent Trait Models for Dichotomous Data.

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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.

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

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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...

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

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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

Visualizing Confidence Bands for Semiparametrically Estimated Nonlinear Relations among Latent Variables

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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…

Labour market participants’ competitiveness assessment based on latent variables theory

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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.

Integrated Multiscale Latent Variable Regression and Application to Distillation Columns

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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.

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

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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

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

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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.

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

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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.

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

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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.

Investigation of Mediational Processes Using Parallel Process Latent Growth Curve Modeling

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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

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

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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.

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

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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.

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

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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.

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

Intra-individual variability as a predictor of learning

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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.

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...

Latent cluster analysis of ALS phenotypes identifies prognostically differing groups.

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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.

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

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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.

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…

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.

Predictive Variable Gain Iterative Learning Control for PMSM

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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.

Visual variability affects early verb learning.

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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.

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…

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...

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...

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.

Variables influencing medical student learning in the operating room.

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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.

Piecewise Linear-Linear Latent Growth Mixture Models with Unknown Knots

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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…

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

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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.

Conditional High-Order Boltzmann Machines for Supervised Relation Learning.

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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.

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

On Latent Growth Models for Composites and Their Constituents.

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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.

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...

An exploratory analysis of personality, attitudes, and study skills on the learning curve within a team-based learning environment.

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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.

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

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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.

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

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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

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

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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…

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

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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…

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

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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.

Multilevel Latent Class Analysis: Parametric and Nonparametric Models

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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…

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.

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

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

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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.

Repeatability and reproducibility of decisions by latent fingerprint examiners.

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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.

STATUS SOSIAL EKONOMI DAN FERTILITAS: A Latent Variable Approach

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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.

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

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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.

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

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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

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

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

Latent Class Analysis of Criminal Social Identity in a Prison Sample

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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.

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

The Latent Curriculum: Breaking Conceptual Barriers to Information Architecture

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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.

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)

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

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.

Evaluasi Human Machine Interface Menggunakan Kriteria Usability Pada Sistem E-learning Perguruan Tinggi

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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

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

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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.…

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....

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.

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.)

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…

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...

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

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...

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

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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.

Latent palmprint matching.

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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.

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

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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.

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

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

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.

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

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

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…

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.

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.

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.

Latent Inhibition in an Insect: The Role of Aminergic Signaling

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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…

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).

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.

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.

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

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....

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.

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.

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

A Particle Swarm Optimization Variant with an Inner Variable Learning Strategy

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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.

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

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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.

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

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.

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.

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

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.

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...

PET CT Identifies Reactivation Risk in Cynomolgus Macaques with Latent M. tuberculosis.

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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.

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…

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

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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.

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).

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.

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

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

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

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)

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.

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.

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.

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.

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

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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.

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.

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.

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.

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.

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

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...

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. �

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.

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....

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.

Age is associated with latent tuberculosis in nurses

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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.

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.

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.

Latent change models of adult cognition: are changes in processing speed and working memory associated with changes in episodic memory?

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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.

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…

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.

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 ...

A comparison of latent class, K-means, and K-median methods for clustering dichotomous data.

Science.gov (United States)

Brusco, Michael J; Shireman, Emilie; Steinley, Douglas

2017-09-01

The problem of partitioning a collection of objects based on their measurements on a set of dichotomous variables is a well-established problem in psychological research, with applications including clinical diagnosis, educational testing, cognitive categorization, and choice analysis. Latent class analysis and K-means clustering are popular methods for partitioning objects based on dichotomous measures in the psychological literature. The K-median clustering method has recently been touted as a potentially useful tool for psychological data and might be preferable to its close neighbor, K-means, when the variable measures are dichotomous. We conducted simulation-based comparisons of the latent class, K-means, and K-median approaches for partitioning dichotomous data. Although all 3 methods proved capable of recovering cluster structure, K-median clustering yielded the best average performance, followed closely by latent class analysis. We also report results for the 3 methods within the context of an application to transitive reasoning data, in which it was found that the 3 approaches can exhibit profound differences when applied to real data. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

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…

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.

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.

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.

Latent-Trait Latent-Class Analysis of Self-Disclosure in the Work Environment

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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…

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.

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

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

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

Using structural equation modeling to investigate relationships among ecological variables

Science.gov (United States)

Malaeb, Z.A.; Kevin, Summers J.; Pugesek, B.H.

2000-01-01

Structural equation modeling is an advanced multivariate statistical process with which a researcher can construct theoretical concepts, test their measurement reliability, hypothesize and test a theory about their relationships, take into account measurement errors, and consider both direct and indirect effects of variables on one another. Latent variables are theoretical concepts that unite phenomena under a single term, e.g., ecosystem health, environmental condition, and pollution (Bollen, 1989). Latent variables are not measured directly but can be expressed in terms of one or more directly measurable variables called indicators. For some researchers, defining, constructing, and examining the validity of latent variables may be the end task of itself. For others, testing hypothesized relationships of latent variables may be of interest. We analyzed the correlation matrix of eleven environmental variables from the U.S. Environmental Protection Agency's (USEPA) Environmental Monitoring and Assessment Program for Estuaries (EMAP-E) using methods of structural equation modeling. We hypothesized and tested a conceptual model to characterize the interdependencies between four latent variables-sediment contamination, natural variability, biodiversity, and growth potential. In particular, we were interested in measuring the direct, indirect, and total effects of sediment contamination and natural variability on biodiversity and growth potential. The model fit the data well and accounted for 81% of the variability in biodiversity and 69% of the variability in growth potential. It revealed a positive total effect of natural variability on growth potential that otherwise would have been judged negative had we not considered indirect effects. That is, natural variability had a negative direct effect on growth potential of magnitude -0.3251 and a positive indirect effect mediated through biodiversity of magnitude 0.4509, yielding a net positive total effect of 0

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

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.

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.

A Case Study on Sepsis Using PubMed and Deep Learning for Ontology Learning.

Science.gov (United States)

Arguello Casteleiro, Mercedes; Maseda Fernandez, Diego; Demetriou, George; Read, Warren; Fernandez Prieto, Maria Jesus; Des Diz, Julio; Nenadic, Goran; Keane, John; Stevens, Robert

2017-01-01

We investigate the application of distributional semantics models for facilitating unsupervised extraction of biomedical terms from unannotated corpora. Term extraction is used as the first step of an ontology learning process that aims to (semi-)automatic annotation of biomedical concepts and relations from more than 300K PubMed titles and abstracts. We experimented with both traditional distributional semantics methods such as Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA) as well as the neural language models CBOW and Skip-gram from Deep Learning. The evaluation conducted concentrates on sepsis, a major life-threatening condition, and shows that Deep Learning models outperform LSA and LDA with much higher precision.

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…

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

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.

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.

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.

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.

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…

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 ...

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.

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

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

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

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

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

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.

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.

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

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

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.

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…

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.

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

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…

Predicting language outcomes for children learning AAC: Child and environmental factors

Science.gov (United States)

Brady, Nancy C.; Thiemann-Bourque, Kathy; Fleming, Kandace; Matthews, Kris

2014-01-01

Purpose To investigate a model of language development for nonverbal preschool age children learning to communicate with AAC. Method Ninety-three preschool children with intellectual disabilities were assessed at Time 1, and 82 of these children were assessed one year later at Time 2. The outcome variable was the number of different words the children produced (with speech, sign or SGD). Children’s intrinsic predictor for language was modeled as a latent variable consisting of cognitive development, comprehension, play, and nonverbal communication complexity. Adult input at school and home, and amount of AAC instruction were proposed mediators of vocabulary acquisition. Results A confirmatory factor analysis revealed that measures converged as a coherent construct and an SEM model indicated that the intrinsic child predictor construct predicted different words children produced. The amount of input received at home but not at school was a significant mediator. Conclusions Our hypothesized model accurately reflected a latent construct of Intrinsic Symbolic Factor (ISF). Children who evidenced higher initial levels of ISF and more adult input at home produced more words one year later. Findings support the need to assess multiple child variables, and suggest interventions directed to the indicators of ISF and input. PMID:23785187

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.

Mobile Learning Via SMS Among Distance Learners: Does Learning Transfer Occur?

Directory of Open Access Journals (Sweden)

Aznarahayu Ramli

2010-07-01

Full Text Available The purpose of this study is to determine whether learners are willing to transfer learning in this mobile learning environment via SMS. The reason for this is to measure the effectiveness of the new method used in learning and education especially in distance education field. For this reason, students’ responses are gathered which looked at three factors namely learner characteristic, learning design and learning environment. The data are gathered through a survey research design with questionnaires using five-point likert scale. The questionnaires was administered for 105 distance education students from four courses including Bachelor of Science, Bachelor of Arts, Bachelor of Social Science and Bachelor of Management. The Rasch Model Analysis was used to measure these dimensions. Rasch Model is a one-parameter logistic model within item response theory (IRT whereby the amount of a given latent trait in a person and the amount of that same latent trait reflected in various items can be estimated independently yet still compared explicitly to one another. The result of the study showed that learning transfer occurred and being influenced most by learner’s characteristics especially in term of their motivation as well as their perceive utility/value of the SMS learning to their job and academic performances.

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...

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…

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

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.

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

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.

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.

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.

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

Poincaré Embeddings for Learning Hierarchical Representations

CERN Multimedia

CERN. Geneva

2018-01-01

Abstracts: Representation learning has become an invaluable approach for learning from symbolic data such as text and graphs. However, while complex symbolic datasets often exhibit a latent hierarchical structure, state-of-the-art methods typically do not account for this property. In this talk, I will discuss a new approach for learning hierarchical representations of symbolic data by embedding them into hyperbolic space -- or more precisely into an n-dimensional Poincaré ball. Due to the underlying hyperbolic geometry, this allows us to learn parsimonious representations of symbolic data by simultaneously capturing hierarchy and similarity. We introduce an efficient algorithm to learn the embeddings based on Riemannian optimization and show experimentally that Poincaré embeddings outperform Euclidean embeddings significantly on data with latent hierarchies, both in terms of representation capacity and in terms of generalization ability.      &...

Predicting Nurses' Turnover: The Aversive Effects of Decreased Identity, Poor Interpersonal Communication, and Learned Helplessness.

Science.gov (United States)

Moreland, Jennifer J; Ewoldsen, David R; Albert, Nancy M; Kosicki, Gerald M; Clayton, Margaret F

2015-01-01

Through a social identity theoretical lens, this study examines how nurses' identification with their working small group, unit, or floor, nursing role (e.g., staff ER nurse, nurse practitioner), and nursing profession relate to nurses' interaction involvement, willingness to confront conflict, feelings of learned helplessness, and tenure (employment turnover) intentions. A cross-sectional survey (N = 466) was conducted at a large, quaternary care hospital system. Structural equation modeling uncovered direct and indirect effects between the five primary variables. Findings demonstrate direct relationships between nurse identity (as a latent variable) and interaction involvement, willingness to confront conflict, and tenure intentions. Feelings of learned helplessness are attenuated by increased nurse identity through interaction involvement and willingness to confront conflict. In addition, willingness to confront conflict and learned helplessness mediate the relationship between interaction involvement and nurses' tenure intentions. Theoretical extensions include indirect links between nurse identity and learned helplessness via interaction involvement and willingness to confront conflict. Implications for interpersonal communication theory development, health communication, and the nursing profession are discussed.

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....

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.

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

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.

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.

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.

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...

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

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.

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…

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.

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.

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

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.

Interactive Algorithms for Unsupervised Machine Learning

Science.gov (United States)

2015-06-01

in Neural Information Processing Systems, 2013. 14 [3] Louigi Addario-Berry, Nicolas Broutin, Luc Devroye, and Gábor Lugosi. On combinato- rial...Myung Jin Choi, Vincent Y F Tan , Animashree Anandkumar, and Alan S Willsky. Learn- ing Latent Tree Graphical Models. Journal of Machine Learning

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

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

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

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.

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.

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

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

An information processing/associative learning account of behavioral disinhibition in externalizing psychopathology.

Science.gov (United States)

Endres, Michael J; Donkin, Chris; Finn, Peter R

2014-04-01

Externalizing psychopathology (EXT) is associated with low executive working memory (EWM) capacity and problems with inhibitory control and decision-making; however, the specific cognitive processes underlying these problems are not well known. This study used a linear ballistic accumulator computational model of go/no-go associative-incentive learning conducted with and without a working memory (WM) load to investigate these cognitive processes in 510 young adults varying in EXT (lifetime problems with substance use, conduct disorder, ADHD, adult antisocial behavior). High scores on an EXT factor were associated with low EWM capacity and higher scores on a latent variable reflecting the cognitive processes underlying disinhibited decision-making (more false alarms, faster evidence accumulation rates for false alarms [vFA], and lower scores on a Response Precision Index [RPI] measure of information processing efficiency). The WM load increased disinhibited decision-making, decisional uncertainty, and response caution for all subjects. Higher EWM capacity was associated with lower scores on the latent disinhibited decision-making variable (lower false alarms, lower vFAs and RPI scores) in both WM load conditions. EWM capacity partially mediated the association between EXT and disinhibited decision-making under no-WM load, and completely mediated this association under WM load. The results underline the role that EWM has in associative-incentive go/no-go learning and indicate that common to numerous types of EXT are impairments in the cognitive processes associated with the evidence accumulation-evaluation-decision process. PsycINFO Database Record (c) 2014 APA, all rights reserved.

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.

Predicting Learned Helplessness Based on Personality

Science.gov (United States)

Maadikhah, Elham; Erfani, Nasrollah

2014-01-01

Learned helplessness as a negative motivational state can latently underlie repeated failures and create negative feelings toward the education as well as depression in students and other members of a society. The purpose of this paper is to predict learned helplessness based on students' personality traits. The research is a predictive…

Binary classifiers and latent sequence models for emotion detection in suicide notes.

Science.gov (United States)

Cherry, Colin; Mohammad, Saif M; de Bruijn, Berry

2012-01-01

This paper describes the National Research Council of Canada's submission to the 2011 i2b2 NLP challenge on the detection of emotions in suicide notes. In this task, each sentence of a suicide note is annotated with zero or more emotions, making it a multi-label sentence classification task. We employ two distinct large-margin models capable of handling multiple labels. The first uses one classifier per emotion, and is built to simplify label balance issues and to allow extremely fast development. This approach is very effective, scoring an F-measure of 55.22 and placing fourth in the competition, making it the best system that does not use web-derived statistics or re-annotated training data. Second, we present a latent sequence model, which learns to segment the sentence into a number of emotion regions. This model is intended to gracefully handle sentences that convey multiple thoughts and emotions. Preliminary work with the latent sequence model shows promise, resulting in comparable performance using fewer features.

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.

[sup 31]P-magnetic resonance spectroscopy: Impaired energy metabolism in latent hyperthyroidism. [sup 31]Phosphor-Kernspinspektroskopie: Gestoerter Energiestoffwechsel bei latenter Hyperthyreose

Energy Technology Data Exchange (ETDEWEB)

Theissen, P. (Klinik und Poliklinik fuer Nuklearmedizin, Univ. Koeln (Germany)); Kaldewey, S. (Klinik und Poliklinik fuer Nuklearmedizin, Univ. Koeln (Germany)); Moka, D. (Klinik und Poliklinik fuer Nuklearmedizin, Univ. Koeln (Germany)); Bunke, J. (Philips Medizin Systeme, Hamburg (Germany)); Voth, E. (Klinik und Poliklinik fuer Nuklearmedizin, Univ. Koeln (Germany)); Schicha, H. (Klinik und Poliklinik fuer Nuklearmedizin, Univ. Koeln (Germany))

1993-06-01

[sup 31]Phosphorous magnetic resonance spectroscopy allows an in vivo examination of energy metabolism. The present study was designed to evaluate whether in patients with latent hyperthyroidism alterations of muscle energy metabolism could be found similar to those observed in patients with overt hyperthyroidism. In 10 patients with overt hyperthyroidism before therapy and 20 with latent hyperthyroidism (also without therapy) and in 24 healthy volunteers magnetic resonance spectroscopy of the calf muscle was performed within a 1.5-Tesla magnet. Muscle concentrations of phosphocreatine, inorganic phosphate, and ATP were quantified compared to an external standard solution of K[sub 2]HPO[sub 4]. In the patients with overt hyperthyroidism and with latent hyperthyroidism a significant decrease of phosphocreatine was found. Further, the ATP concentration in patients with latent and manifest hyperthyroidism tended towards lower values. There were no significant differences in the decrease of phosphocreatine and ATP between both patient groups. Therefore, this study for the first time shows that alterations of energy metabolism in latent hyperthyroidism can be measured and that they are similar to those observed in overt hyperthyroidism. (orig.)

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...

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

Latent trait cortisol (LTC) during pregnancy: Composition, continuity, change, and concomitants.

Science.gov (United States)

Giesbrecht, Gerald F; Bryce, Crystal I; Letourneau, Nicole; Granger, Douglas A

2015-12-01

Individual differences in the activity of the hypothalamic pituitary adrenal (HPA) axis are often operationalized using summary measures of cortisol that are taken to represent stable individual differences. Here we extend our understanding of a novel latent variable approach to latent trait cortisol (LTC) as a measure of trait-like HPA axis function during pregnancy. Pregnant women (n=380) prospectively collected 8 diurnal saliva samples (4 samples/day, 2 days) within each trimester. Saliva was assayed for cortisol. Confirmatory factor analyses were used to fit LTC models to early morning and daytime cortisol. For individual trimester data, only the daytime LTC models had adequate fit. These daytime LTC models were strongly correlated between trimesters and stable over pregnancy. Daytime LTC was unrelated to the cortisol awakening response and the daytime slope but strongly correlated with the area under the curve from ground. The findings support the validity of LTC as a measure of cortisol during pregnancy and suggest that it is not affected by pregnancy-related changes in HPA axis function. Copyright © 2015 Elsevier Ltd. All rights reserved.

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.

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.

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.

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.

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.

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.

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.

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...

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,

Analysis and Visualization of Relations in eLearning

Science.gov (United States)

Dráždilová, Pavla; Obadi, Gamila; Slaninová, Kateřina; Martinovič, Jan; Snášel, Václav

The popularity of eLearning systems is growing rapidly; this growth is enabled by the consecutive development in Internet and multimedia technologies. Web-based education became wide spread in the past few years. Various types of learning management systems facilitate development of Web-based courses. Users of these courses form social networks through the different activities performed by them. This chapter focuses on searching the latent social networks in eLearning systems data. These data consist of students activity records wherein latent ties among actors are embedded. The social network studied in this chapter is represented by groups of students who have similar contacts and interact in similar social circles. Different methods of data clustering analysis can be applied to these groups, and the findings show the existence of latent ties among the group members. The second part of this chapter focuses on social network visualization. Graphical representation of social network can describe its structure very efficiently. It can enable social network analysts to determine the network degree of connectivity. Analysts can easily determine individuals with a small or large amount of relationships as well as the amount of independent groups in a given network. When applied to the field of eLearning, data visualization simplifies the process of monitoring the study activities of individuals or groups, as well as the planning of educational curriculum, the evaluation of study processes, etc.

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,…

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.

Verification of High Resolution Soil Moisture and Latent Heat in Germany

Science.gov (United States)

Samaniego, L. E.; Warrach-Sagi, K.; Zink, M.; Wulfmeyer, V.

2012-12-01

Improving our understanding of soil-land-surface-atmosphere feedbacks is fundamental to make reliable predictions of water and energy fluxes on land systems influenced by anthropogenic activities. Estimating, for instance, which would be the likely consequences of changing climatic regimes on water availability and crop yield, requires of high resolution soil moisture. Modeling it at large-scales, however, is difficult and uncertain because of the interplay between state variables and fluxes and the significant parameter uncertainty of the predicting models. At larger scales, the sub-grid variability of the variables involved and the nonlinearity of the processes complicate the modeling exercise even further because parametrization schemes might be scale dependent. Two contrasting modeling paradigms (WRF/Noah-MP and mHM) were employed to quantify the effects of model and data complexity on soil moisture and latent heat over Germany. WRF/Noah-MP was forced ERA-interim on the boundaries of the rotated CORDEX-Grid (www.meteo.unican.es/wiki/cordexwrf) with a spatial resolution of 0.11o covering Europe during the period from 1989 to 2009. Land cover and soil texture were represented in WRF/Noah-MP with 1×1~km MODIS images and a single horizon, coarse resolution European-wide soil map with 16 soil texture classes, respectively. To ease comparison, the process-based hydrological model mHM was forced with daily precipitation and temperature fields generated by WRF during the same period. The spatial resolution of mHM was fixed at 4×4~km. The multiscale parameter regionalization technique (MPR, Samaniego et al. 2010) was embedded in mHM to be able to estimate effective model parameters using hyper-resolution input data (100×100~km) obtained from Corine land cover and detailed soil texture fields for various horizons comprising 72 soil texture classes for Germany, among other physiographical variables. mHM global parameters, in contrast with those of Noah-MP, were

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.

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.

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.

Learning Additional Languages as Hierarchical Probabilistic Inference: Insights From First Language Processing.

Science.gov (United States)

Pajak, Bozena; Fine, Alex B; Kleinschmidt, Dave F; Jaeger, T Florian

2016-12-01

We present a framework of second and additional language (L2/L n ) acquisition motivated by recent work on socio-indexical knowledge in first language (L1) processing. The distribution of linguistic categories covaries with socio-indexical variables (e.g., talker identity, gender, dialects). We summarize evidence that implicit probabilistic knowledge of this covariance is critical to L1 processing, and propose that L2/L n learning uses the same type of socio-indexical information to probabilistically infer latent hierarchical structure over previously learned and new languages. This structure guides the acquisition of new languages based on their inferred place within that hierarchy, and is itself continuously revised based on new input from any language. This proposal unifies L1 processing and L2/L n acquisition as probabilistic inference under uncertainty over socio-indexical structure. It also offers a new perspective on crosslinguistic influences during L2/L n learning, accommodating gradient and continued transfer (both negative and positive) from previously learned to novel languages, and vice versa.

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

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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.

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

A quantitative analysis on latent heat of an aqueous binary mixture.

Science.gov (United States)

Han, Bumsoo; Choi, Jeung Hwan; Dantzig, Jonathan A; Bischof, John C

2006-02-01

The latent heat during phase change of water-NaCl binary mixture was measured using a differential scanning calorimeter, and the magnitude for two distinct phase change events, water/ice and eutectic phase change, were analyzed considering the phase change characteristics of a binary mixture. During the analysis, the latent heat associated with each event was calculated by normalizing the amount of each endothermic peak with only the amount of sample participating in each event estimated from the lever rule for the phase diagram. The resulting latent heat of each phase change measured is 303.7 +/- 2.5 J/g for water/ice phase change, and 233.0 +/- 1.6 J/g for eutectic phase change, respectively regardless of the initial concentration of mixture. Although the latent heats of water/ice phase change in water-NaCl mixtures are closely correlated, further study is warranted to investigate the reason for smaller latent heat of water/ice phase change than that in pure water (335 J/g). The analysis using the lever rule was extended to estimate the latent heat of dihydrate as 115 J/g with the measured eutectic and water/ice latent heat values. This new analysis based on the lever rule will be useful to estimate the latent heat of water-NaCl mixtures at various concentrations, and may become a framework for more general analysis of latent heat of various biological solutions.

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.

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 ...

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.

ABC of SV: Limited Information Likelihood Inference in Stochastic Volatility Jump-Diffusion Models

DEFF Research Database (Denmark)

Creel, Michael; Kristensen, Dennis

and latent variables. We show how the methods can incorporate intra-daily information to improve on the estimation and filtering. In particular, the availability of realized volatility measures help us in learning about parameters and latent states. The method is employed in the estimation of a flexible...

Effects of categorization method, regression type, and variable distribution on the inflation of Type-I error rate when categorizing a confounding variable.

Science.gov (United States)

Barnwell-Ménard, Jean-Louis; Li, Qing; Cohen, Alan A

2015-03-15

The loss of signal associated with categorizing a continuous variable is well known, and previous studies have demonstrated that this can lead to an inflation of Type-I error when the categorized variable is a confounder in a regression analysis estimating the effect of an exposure on an outcome. However, it is not known how the Type-I error may vary under different circumstances, including logistic versus linear regression, different distributions of the confounder, and different categorization methods. Here, we analytically quantified the effect of categorization and then performed a series of 9600 Monte Carlo simulations to estimate the Type-I error inflation associated with categorization of a confounder under different regression scenarios. We show that Type-I error is unacceptably high (>10% in most scenarios and often 100%). The only exception was when the variable categorized was a continuous mixture proxy for a genuinely dichotomous latent variable, where both the continuous proxy and the categorized variable are error-ridden proxies for the dichotomous latent variable. As expected, error inflation was also higher with larger sample size, fewer categories, and stronger associations between the confounder and the exposure or outcome. We provide online tools that can help researchers estimate the potential error inflation and understand how serious a problem this is. Copyright © 2014 John Wiley & Sons, Ltd.

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.

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.

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.

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.

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

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.

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

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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.

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

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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.

Latent-failure risk estimates for computer control

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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.

Exposing Latent Information in Folksonomies for Reasoning

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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

Learning effect and test-retest variability of pulsar perimetry.

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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.

Polytomous latent scales for the investigation of the ordering of items

NARCIS (Netherlands)

Ligtvoet, R.; van der Ark, L.A.; Bergsma, W. P.; Sijtsma, K.

2011-01-01

We propose three latent scales within the framework of nonparametric item response theory for polytomously scored items. Latent scales are models that imply an invariant item ordering, meaning that the order of the items is the same for each measurement value on the latent scale. This ordering

The relationship among self-determination, self-concept, and academic achievement for students with learning disabilities.

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Zheng, Chunmei; Gaumer Erickson, Amy; Kingston, Neal M; Noonan, Patricia M

2014-01-01

Research suggests that self-determination skills are positively correlated with factors that have been shown to improve academic achievement, but the direct relationship among self-determination, self-concept, and academic achievement is not fully understood. This study offers an empirical explanation of how self-determination and self-concept affect academic achievement for adolescents with learning disabilities after taking into consideration the covariates of gender, income, and urbanicity. In a nationally representative sample (N = 560), the proposed model closely fit the data, with all proposed path coefficients being statistically significant. The results indicated that there were significant correlations among the three latent variables (i.e., self-determination, self-concept, and academic achievement), with self-determination being a potential predictor of academic achievement for students with learning disabilities. © Hammill Institute on Disabilities 2012.

A probabilistic model for component-based shape synthesis

KAUST Repository

Kalogerakis, Evangelos; Chaudhuri, Siddhartha; Koller, Daphne; Koltun, Vladlen

2012-01-01

represents probabilistic relationships between properties of shape components, and relates them to learned underlying causes of structural variability within the domain. These causes are treated as latent variables, leading to a compact representation

The Latent Structure of Dictionaries.

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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.

Bayesian latent feature modeling for modeling bipartite networks with overlapping groups

DEFF Research Database (Denmark)

Jørgensen, Philip H.; Mørup, Morten; Schmidt, Mikkel Nørgaard

2016-01-01

Bi-partite networks are commonly modelled using latent class or latent feature models. Whereas the existing latent class models admit marginalization of parameters specifying the strength of interaction between groups, existing latent feature models do not admit analytical marginalization...... by the notion of community structure such that the edge density within groups is higher than between groups. Our model further assumes that entities can have different propensities of generating links in one of the modes. The proposed framework is contrasted on both synthetic and real bi-partite networks...... feature representations in bipartite networks provides a new framework for accounting for structure in bi-partite networks using binary latent feature representations providing interpretable representations that well characterize structure as quantified by link prediction....

Latent Growth and Dynamic Structural Equation Models.

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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.

A Disentangled Recognition and Nonlinear Dynamics Model for Unsupervised Learning

DEFF Research Database (Denmark)

Fraccaro, Marco; Kamronn, Simon Due; Paquet, Ulrich

2017-01-01

This paper takes a step towards temporal reasoning in a dynamically changing video, not in the pixel space that constitutes its frames, but in a latent space that describes the non-linear dynamics of the objects in its world. We introduce the Kalman variational auto-encoder, a framework...... for unsupervised learning of sequential data that disentangles two latent representations: an object’s representation, coming from a recognition model, and a latent state describing its dynamics. As a result, the evolution of the world can be imagined and missing data imputed, both without the need to generate...

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

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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

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)

Coupling of latent heat flux and the greenhouse effect by large-scale tropical/subtropical dynamics diagnosed in a set of observations and model simulations

Energy Technology Data Exchange (ETDEWEB)

Gershunov, A. [Climate Research Division, Scripps Institution of Oceanography, La Jolla, CA 92093-0224 (United States); Roca, R. [Laboratoire de Meteorologie Dynamique, Ecole Polytechnique, 91128 Palaiseau (France)

2004-03-01

Coupled variability of the greenhouse effect (GH) and latent heat flux (LHF) over the tropical - subtropical oceans is described, summarized and compared in observations and a coupled ocean-atmosphere general circulation model (CGCM). Coupled seasonal and interannual modes account for much of the total variability in both GH and LHF. In both observations and model, seasonal coupled variability is locally 180 out-of-phase throughout the tropics. Moisture is brought into convergent/convective regions from remote source areas located partly in the opposite, non-convective hemisphere. On interannual time scales, the tropical Pacific GH in the ENSO region of largest interannual variance is 180 out of phase with local LHF in observations but in phase in the model. A local source of moisture is thus present in the model on interannual time scales while in observations, moisture is mostly advected from remote source regions. The latent cooling and radiative heating of the surface as manifested in the interplay of LHF and GH is an important determinant of the current climate. Moreover, the hydrodynamic processes involved in the GH-LHF interplay determine in large part the climate response to external perturbations mainly through influencing the water vapor feedback but also through their intimate connection to the hydrological cycle. The diagnostic process proposed here can be performed on other CGCMs. Similarly, it should be repeated using a number of observational latent heat flux datasets to account for the variability in the different satellite retrievals. A realistic CGCM could be used to further study these coupled dynamics in natural and anthropogenically altered climate conditions. (orig.)

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.

Latent M. tuberculosis infection--pathogenesis, diagnosis, treatment and prevention strategies.

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Druszczyńska, Magdalena; Kowalewicz-Kulbat, Magdalena; Fol, Marek; Włodarczyk, Marcin; Rudnicka, Wiesława

2012-01-01

One third of the earths population is infected with Mycobacterium tuberculosis (Mtb), but only 5-10% of the infected individuals develop active tuberculosis (TB) over their lifetime. The remaining 90-95% stay healthy and are called latently infected individuals. They are the biggest reservoir of the tubercle bacilli and identifying the cases of latent TB is a part of the global plan of TB control. From the clinical point of view detection of latent TB infections (LTBI) in individuals with the highest active TB risk including cases of HIV infection, autoimmune inflammatory diseases or cancer, is a priority. This review summarizes the recent findings in the pathogenesis of latent TB, its diagnosis, treatment and prevention.

Modeling and impacts of the latent heat of phase change and specific heat for phase change materials

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Scoggin, J.; Khan, R. S.; Silva, H.; Gokirmak, A.

2018-05-01

We model the latent heats of crystallization and fusion in phase change materials with a unified latent heat of phase change, ensuring energy conservation by coupling the heat of phase change with amorphous and crystalline specific heats. We demonstrate the model with 2-D finite element simulations of Ge2Sb2Te5 and find that the heat of phase change increases local temperature up to 180 K in 300 nm × 300 nm structures during crystallization, significantly impacting grain distributions. We also show in electrothermal simulations of 45 nm confined and 10 nm mushroom cells that the higher amorphous specific heat predicted by this model increases nucleation probability at the end of reset operations. These nuclei can decrease set time, leading to variability, as demonstrated for the mushroom cell.

The Depression Anxiety Stress Scales (DASS): normative data and latent structure in a large non-clinical sample.

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Crawford, John R; Henry, Julie D

2003-06-01

To provide UK normative data for the Depression Anxiety and Stress Scale (DASS) and test its convergent, discriminant and construct validity. Cross-sectional, correlational and confirmatory factor analysis (CFA). The DASS was administered to a non-clinical sample, broadly representative of the general adult UK population (N = 1,771) in terms of demographic variables. Competing models of the latent structure of the DASS were derived from theoretical and empirical sources and evaluated using confirmatory factor analysis. Correlational analysis was used to determine the influence of demographic variables on DASS scores. The convergent and discriminant validity of the measure was examined through correlating the measure with two other measures of depression and anxiety (the HADS and the sAD), and a measure of positive and negative affectivity (the PANAS). The best fitting model (CFI =.93) of the latent structure of the DASS consisted of three correlated factors corresponding to the depression, anxiety and stress scales with correlated error permitted between items comprising the DASS subscales. Demographic variables had only very modest influences on DASS scores. The reliability of the DASS was excellent, and the measure possessed adequate convergent and discriminant validity Conclusions: The DASS is a reliable and valid measure of the constructs it was intended to assess. The utility of this measure for UK clinicians is enhanced by the provision of large sample normative data.

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

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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…

Environmental, morphological, and productive characterization of Sardinian goats and use of latent explanatory factors for population analysis.

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Vacca, G M; Paschino, P; Dettori, M L; Bergamaschi, M; Cipolat-Gotet, C; Bittante, G; Pazzola, M

2016-09-01

Dairy goat farming is practiced worldwide, within a range of different farming systems. Here we investigated the effects of environmental factors and morphology on milk traits of the Sardinian goat population. Sardinian goats are currently reared in Sardinia (Italy) in a low-input context, similar to many goat farming systems, especially in developing countries. Milk and morphological traits from 1,050 Sardinian goats from 42 farms were recorded. We observed a high variability regarding morphological traits, such as coat color, ear length and direction, horn presence, and udder shape. Such variability derived partly from the unplanned repeated crossbreeding of the native Sardinian goats with exotic breeds, especially Maltese goats. The farms located in the mountains were characterized by the traditional farming system and the lowest percentage of crossbred goats. Explanatory factors analysis was used to summarize the interrelated measured milk variables. The explanatory factor related to fat, protein, and energy content of milk (the "Quality" latent variable) explained about 30% of the variance of the whole data set of measured milk traits followed by the "Hygiene" (19%), "Production" (19%), and "Acidity" (11%) factors. The "Quality" and "Hygiene" factors were not affected by any of the farm classification items, whereas "Production" and "Acidity" were affected only by altitude and size of herds, respectively, indicating the adaptation of the local goat population to different environmental conditions. The use of latent explanatory factor analysis allowed us to clearly explain the large variability of milk traits, revealing that the Sardinian goat population cannot be divided into subpopulations based on milk attitude The factors, properly integrated with genetic data, may be useful tools in future selection programs.

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

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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

Longitudinal Effects of Student-Perceived Classroom Support on Motivation - A Latent Change Model.

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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.

Religiousness and Levels of Hazardous Alcohol Use: A Latent Profile Analysis.

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Jankowski, Peter J; Hardy, Sam A; Zamboanga, Byron L; Ham, Lindsay S; Schwartz, Seth J; Kim, Su Yeong; Forthun, Larry F; Bersamin, Melina M; Donovan, Roxanne A; Whitbourne, Susan Krauss; Hurley, Eric A; Cano, Miguel Ángel

2015-10-01

Prior person-centered research has consistently identified a subgroup of highly religious participants that uses significantly less alcohol when compared to the other subgroups. The construct of religious motivation is absent from existing examinations of the nuanced combinations of religiousness dimensions within persons, and alcohol expectancy valuations have yet to be included as outcome variables. Variable-centered approaches have found religious motivation and alcohol expectancy valuations to play a protective role against individuals' hazardous alcohol use. The current study examined latent religiousness profiles and hazardous alcohol use in a large, multisite sample of ethnically diverse college students. The sample consisted of 7412 college students aged 18-25 (M age = 19.77, SD age = 1.61; 75% female; 61% European American). Three latent profiles were derived from measures of religious involvement, salience, and religious motivations: Quest-Intrinsic Religiousness (highest levels of salience, involvement, and quest and intrinsic motivations; lowest level of extrinsic motivation), Moderate Religiousness (intermediate levels of salience, involvement, and motivations) and Extrinsic Religiousness (lowest levels of salience, involvement, and quest and intrinsic motivations; highest level of extrinsic motivation). The Quest-Intrinsic Religiousness profile scored significantly lower on hazardous alcohol use, positive expectancy outcomes, positive expectancy valuations, and negative expectancy valuations, and significantly higher on negative expectancy outcomes, compared to the other two profiles. The Extrinsic and Moderate Religiousness profiles did not differ significantly on positive expectancy outcomes, negative expectancy outcomes, negative expectancy valuations, or hazardous alcohol use. The results advance existing research by demonstrating that the protective influence of religiousness on college students' hazardous alcohol use may involve high levels on

Towards an HIV-1 cure: measuring the latent reservoir

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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

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

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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…

Spectral Learning for Supervised Topic Models.

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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.

Multilevel Latent Class Analysis for Large-Scale Educational Assessment Data: Exploring the Relation between the Curriculum and Students' Mathematical Strategies

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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.…

Cardiac strain findings in children with latent rheumatic heart disease detected by echocardiographic screening.

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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.

Ridge Width Correlations between Inked Prints and Powdered Latent Fingerprints.

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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.

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.

Insights into the latent multinomial model through mark-resight data on female grizzly bears with cubs-of-the-year

Science.gov (United States)

Higgs, Megan D.; Link, William; White, Gary C.; Haroldson, Mark A.; Bjornlie, Daniel D.

2013-01-01

Mark-resight designs for estimation of population abundance are common and attractive to researchers. However, inference from such designs is very limited when faced with sparse data, either from a low number of marked animals, a low probability of detection, or both. In the Greater Yellowstone Ecosystem, yearly mark-resight data are collected for female grizzly bears with cubs-of-the-year (FCOY), and inference suffers from both limitations. To overcome difficulties due to sparseness, we assume homogeneity in sighting probabilities over 16 years of bi-annual aerial surveys. We model counts of marked and unmarked animals as multinomial random variables, using the capture frequencies of marked animals for inference about the latent multinomial frequencies for unmarked animals. We discuss undesirable behavior of the commonly used discrete uniform prior distribution on the population size parameter and provide OpenBUGS code for fitting such models. The application provides valuable insights into subtleties of implementing Bayesian inference for latent multinomial models. We tie the discussion to our application, though the insights are broadly useful for applications of the latent multinomial model.

Latent tuberculosis in nursing professionals of a Brazilian hospital

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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.

Men and women are from Earth: examining the latent structure of gender.

Science.gov (United States)

Carothers, Bobbi J; Reis, Harry T

2013-02-01

Taxometric methods enable determination of whether the latent structure of a construct is dimensional or taxonic (nonarbitrary categories). Although sex as a biological category is taxonic, psychological gender differences have not been examined in this way. The taxometric methods of mean above minus below a cut, maximum eigenvalue, and latent mode were used to investigate whether gender is taxonic or dimensional. Behavioral measures of stereotyped hobbies and physiological characteristics (physical strength, anthropometric measurements) were examined for validation purposes, and were taxonic by sex. Psychological indicators included sexuality and mating (sexual attitudes and behaviors, mate selectivity, sociosexual orientation), interpersonal orientation (empathy, relational-interdependent self-construal), gender-related dispositions (masculinity, femininity, care orientation, unmitigated communion, fear of success, science inclination, Big Five personality), and intimacy (intimacy prototypes and stages, social provisions, intimacy with best friend). Constructs were with few exceptions dimensional, speaking to Spence's (1993) gender identity theory. Average differences between men and women are not under dispute, but the dimensionality of gender indicates that these differences are inappropriate for diagnosing gender-typical psychological variables on the basis of sex. (c) 2013 APA, all rights reserved.

Primiparous women's preferences for care during a prolonged latent phase of labour.

Science.gov (United States)

Ängeby, Karin; Wilde-Larsson, Bodil; Hildingsson, Ingegerd; Sandin-Bojö, Ann-Kristin

2015-10-01

To investigate primiparous women's preferences for care during a prolonged latent phase of labour. A qualitative study based on focus groups and individual interviews and analysed with inductive content analysis. Sixteen primiparous women with a prolonged latent phase of labour >18 hours were interviewed in five focus groups (n = 11) or individually (n = 5). One main category emerged "Beyond normality - a need of individual adapted guidance in order to understand and manage an extended latent phase of labour" which covers the women's preferences during the prolonged latent phase. Five categories were generated from the data: "A welcoming manner and not being rejected", "Individually adapted care", "Important information which prepares for reality and coping", "Participation and need for feedback" and "Staying nearby the labour ward or being admitted for midwifery support". Women with a prolonged latent phase of labour sought to use their own resources, but their needs for professional support increased as time passed. A welcoming attitude from an available midwife during the latent phase created a feeling of security, and personally adapted care was perceived positively. Women with a prolonged latent phase of labour preferred woman-centred care. Midwives play an important role in supporting these women. Women's need for midwifery-support increases as the time spent in latent phase increases. Copyright © 2015 Elsevier B.V. All rights reserved.

Tratamento da tuberculose de infecção latente: As recomendações actuais Latent tuberculosis infection treatment: Current recommendations

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R Duarte

2010-09-01

Full Text Available O diagnóstico e tratamento da infecção latente por Mycobacterium tuberculosis reduz significativamente o risco de desenvolvimento de tuberculose activa e a transmissão da doença na comunidade. O rastreio da tuberculose infecção latente deve passar pela exclusão de doença activa (inquérito de sintomas e radiografia pulmonar e avaliação da resposta imunológica ao M. tuberculosis através dos testes actualmente ao dispor, como o teste tuberculínico e os testes IGRA (interferon-gamma release assay. A escolha do esquema de tratamento deve ter em linha de conta a eficácia, a adesão e os efeitos colaterais associados ao mesmo Este documento actualiza as recomendações sobre tratamento da tuberculose infecção latente. São apresentadas indicações sobre quem deve ser rastreado e revistos os esquemas de tratamento.Diagnosis and treatment of latent infection with Mycobacterium tuberculosis (LTBI, significantly reduces the risk of developing active tuberculosis and the transmission of the disease in the community. LTBI screening must pass by the exclusion of active disease (symptoms enquiry and chest radiography and assessment of immune response to Mycobacterium tuberculosis testing with the tests currently available - tuberculin skin test and interferon-gamma release assay (IGRA. The choice of treatment must take into account the efficacy and side effects associated with the same. This document provides updated recommendations on latent tuberculosis infection treatment. Topics covered include whom to test for TB and reviewed LTBI treatment regimens.

Calculating latent frequencies of systems with local damping

International Nuclear Information System (INIS)

Kolonits, Ferenc

2005-01-01

Modal analysis of damped systems often cannot proceed with common real-eigenvalue techniques. The system of equilibrium equations leads to a matrix with elements being quadratic functions of a parameter λ. The values of that which make the matrix singular are the latent roots, while the solutions of the associated homogenous equation are the latent vectors. They are the (generally complex) characteristic frequencies and the mode shapes of the system, respectively. Although the theory is well developed, the numerical application is open to refinements yet. A reduction to better-known real-domain subtasks deserves attention. With a theorem of Popper and Gaspar, a n x n λ-matrix problem can be cut in two: into n-size asymmetric real matrices having as eigenvalues the n lower and n upper latent roots, ranked by absolute value. This approach may be of use for systems with high number of degrees of freedom while damped by a relatively few concentrated devices. It might fit also an earthquake analysis, where the lower portion of eigenvalues is customarily what counts. The dampers appear in the splitting algorithm as restricted-size modifications, ready for use by the Sherman-Morrison-Woodbury identity. The task is re-traced this way to a more usual real-asymmetric eigenproblem. A requirement of convergence is that the lower and upper n-set of latent values must be distinct. With odd-number degrees of freedom and neither over-damped, i.e. all latent roots being complex, this condition is surely violated. For such cases, a supplemental algorithm is proposed

Discrimination learning with variable stimulus 'salience'

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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.

Learning slow features for behavior analysis

NARCIS (Netherlands)

Zafeiriou, Lazaros; Nicolaou, Mihalis A.; Zafeiriou, Stefanos; Nikitids, Symeon; Pantic, Maja

2013-01-01

A recently introduced latent feature learning technique for time varying dynamic phenomena analysis is the socalled Slow Feature Analysis (SFA). SFA is a deterministic component analysis technique for multi-dimensional sequences that by minimizing the variance of the first order time derivative

Latent topic discovery of clinical concepts from hospital discharge summaries of a heterogeneous patient cohort.

Science.gov (United States)

Lehman, Li-Wei; Long, William; Saeed, Mohammed; Mark, Roger

2014-01-01

Patients in critical care often exhibit complex disease patterns. A fundamental challenge in clinical research is to identify clinical features that may be characteristic of adverse patient outcomes. In this work, we propose a data-driven approach for phenotype discovery of patients in critical care. We used Hierarchical Dirichlet Process (HDP) as a non-parametric topic modeling technique to automatically discover the latent "topic" structure of diseases, symptoms, and findings documented in hospital discharge summaries. We show that the latent topic structure can be used to reveal phenotypic patterns of diseases and symptoms shared across subgroups of a patient cohort, and may contain prognostic value in stratifying patients' post hospital discharge mortality risks. Using discharge summaries of a large patient cohort from the MIMIC II database, we evaluate the clinical utility of the discovered topic structure in identifying patients who are at high risk of mortality within one year post hospital discharge. We demonstrate that the learned topic structure has statistically significant associations with mortality post hospital discharge, and may provide valuable insights in defining new feature sets for predicting patient outcomes.

Uncovering a latent multinomial: Analysis of mark-recapture data with misidentification

Science.gov (United States)

Link, W.A.; Yoshizaki, J.; Bailey, L.L.; Pollock, K.H.

2010-01-01

Natural tags based on DNA fingerprints or natural features of animals are now becoming very widely used in wildlife population biology. However, classic capture-recapture models do not allow for misidentification of animals which is a potentially very serious problem with natural tags. Statistical analysis of misidentification processes is extremely difficult using traditional likelihood methods but is easily handled using Bayesian methods. We present a general framework for Bayesian analysis of categorical data arising from a latent multinomial distribution. Although our work is motivated by a specific model for misidentification in closed population capture-recapture analyses, with crucial assumptions which may not always be appropriate, the methods we develop extend naturally to a variety of other models with similar structure. Suppose that observed frequencies f are a known linear transformation f = A???x of a latent multinomial variable x with cell probability vector ?? = ??(??). Given that full conditional distributions [?? | x] can be sampled, implementation of Gibbs sampling requires only that we can sample from the full conditional distribution [x | f, ??], which is made possible by knowledge of the null space of A???. We illustrate the approach using two data sets with individual misidentification, one simulated, the other summarizing recapture data for salamanders based on natural marks. ?? 2009, The International Biometric Society.

Current management options for latent tuberculosis: a review

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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

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.

Harry Potter and the sorcerer's scope: latent scope biases in explanatory reasoning.

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Khemlani, Sangeet S; Sussman, Abigail B; Oppenheimer, Daniel M

2011-04-01

What makes a good explanation? We examine the function of latent scope, i.e., the number of unobserved phenomena that an explanation can account for. We show that individuals prefer narrow latent scope explanations-those that account for fewer unobserved effects-to broader explanations. In Experiments 1a-d, participants found narrow latent scope explanations to be both more satisfying and more likely. In Experiment 2 we directly manipulated base rate information and again found a preference for narrow latent scope explanations. Participants in Experiment 3 evaluated more natural explanations of unexpected observations, and again displayed a bias for narrow latent scope explanations. We conclude by considering what this novel bias tells us about how humans evaluate explanations and engage in causal reasoning.

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

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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.

Latent Cognitive Phenotypes in De Novo Parkinson's Disease: A Person-Centered Approach.

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LaBelle, Denise R; Walsh, Ryan R; Banks, Sarah J

2017-08-01

Cognitive impairment is an important aspect of Parkinson's disease (PD), but there is considerable heterogeneity in its presentation. This investigation aims to identify and characterize latent cognitive phenotypes in early PD. Latent class analysis, a data-driven, person-centered, cluster analysis was performed on cognitive data from the Parkinson's Progressive Markers Initiative baseline visit. This analytic method facilitates identification of naturally occurring endophenotypes. Resulting classes were compared across biomarker, symptom, and demographic data. Six cognitive phenotypes were identified. Three demonstrated consistent performance across indicators, representing poor ("Weak-Overall"), average ("Typical-Overall"), and strong ("Strong-Overall") cognition. The remaining classes demonstrated unique patterns of cognition, characterized by "Strong-Memory," "Weak-Visuospatial," and "Amnestic" profiles. The Amnestic class evidenced greater tremor severity and anosmia, but was unassociated with biomarkers linked with Alzheimer's disease. The Weak-Overall class was older and reported more non-motor features associated with cognitive decline, including anxiety, depression, autonomic dysfunction, anosmia, and REM sleep behaviors. The Strong-Overall class was younger, more female, and reported less dysautonomia and anosmia. Classes were unrelated to disease duration, functional independence, or available biomarkers. Latent cognitive phenotypes with focal patterns of impairment were observed in recently diagnosed individuals with PD. Cognitive profiles were found to be independent of traditional biomarkers and motoric indices of disease progression. Only globally impaired class was associated with previously reported indicators of cognitive decline, suggesting this group may drive the effects reported in studies using variable-based analysis. Longitudinal and neuroanatomical characterization of classes will yield further insight into the evolution of cognitive

A Latent Class Approach to Estimating Test-Score Reliability

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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…

Tuberculosis and latent tuberculosis infection among healthcare workers in Kisumu, Kenya.

Science.gov (United States)

Agaya, Janet; Nnadi, Chimeremma D; Odhiambo, Joseph; Obonyo, Charles; Obiero, Vincent; Lipke, Virginia; Okeyo, Elisha; Cain, Kevin; Oeltmann, John E

2015-12-01

To assess prevalence and occupational risk factors of latent TB infection and history of TB disease ascribed to work in a healthcare setting in western Kenya. We conducted a cross-sectional survey among healthcare workers in western Kenya in 2013. They were recruited from dispensaries, health centres and hospitals that offer both TB and HIV services. School workers from the health facilities' catchment communities were randomly selected to serve as the community comparison group. Latent TB infection was diagnosed by tuberculin skin testing. HIV status of participants was assessed. Using a logistic regression model, we determined the adjusted odds of latent TB infection among healthcare workers compared to school workers; and among healthcare workers only, we assessed work-related risk factors for latent TB infection. We enrolled 1005 healthcare workers and 411 school workers. Approximately 60% of both groups were female. A total of 22% of 958 healthcare workers and 12% of 392 school workers tested HIV positive. Prevalence of self-reported history of TB disease was 7.4% among healthcare workers and 3.6% among school workers. Prevalence of latent TB infection was 60% among healthcare workers and 48% among school workers. Adjusted odds of latent TB infection were 1.5 times higher among healthcare workers than school workers (95% confidence interval 1.2-2.0). Healthcare workers at all three facility types had similar prevalence of latent TB infection (P = 0.72), but increasing years of employment was associated with increased odds of LTBI (P Kenya which offer TB and HIV services are at increased risk of latent TB infection, and the risk is similar across facility types. Implementation of WHO-recommended TB infection control measures are urgently needed in health facilities to protect healthcare workers. © 2015 John Wiley & Sons Ltd.

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...

Child involvement, alliance, and therapist flexibility: process variables in cognitive-behavioural therapy for anxiety disorders in childhood.

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Hudson, Jennifer L; Kendall, Philip C; Chu, Brian C; Gosch, Elizabeth; Martin, Erin; Taylor, Alan; Knight, Ashleigh

2014-01-01

This study examined the relations between treatment process variables and child anxiety outcomes. Independent raters watched/listened to taped therapy sessions of 151 anxiety-disordered (6-14 yr-old; M = 10.71) children (43% boys) and assessed process variables (child alliance, therapist alliance, child involvement, therapist flexibility and therapist functionality) within a manual-based cognitive-behavioural treatment. Latent growth modelling examined three latent variables (intercept, slope, and quadratic) for each process variable. Child age, gender, family income and ethnicity were examined as potential antecedents. Outcome was analyzed using factorially derived clinician, mother, father, child and teacher scores from questionnaire and structured diagnostic interviews at pretreatment, posttreatment and 12-month follow-up. Latent growth models demonstrated a concave quadratic curve for child involvement and therapist flexibility over time. A predominantly linear, downward slope was observed for alliance, and functional flexibility remained consistent over time. Increased alliance, child involvement and therapist flexibility showed some albeit inconsistent, associations with positive treatment outcome. Findings support the notion that maintaining the initial high level of alliance or involvement is important for clinical improvement. There is some support that progressively increasing alliance/involvement also positively impacts on treatment outcome. These findings were not consistent across outcome measurement points or reporters. Copyright © 2013 Elsevier Ltd. All rights reserved.

Verbal learning changes in older adults across 18 months.

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Zimprich, Daniel; Rast, Philippe

2009-07-01

The major aim of this study was to investigate individual changes in verbal learning across a period of 18 months. Individual differences in verbal learning have largely been neglected in the last years and, even more so, individual differences in change in verbal learning. The sample for this study comes from the Zurich Longitudinal Study on Cognitive Aging (ZULU; Zimprich et al., 2008a) and comprised 336 older adults in the age range of 65-80 years at first measurement occasion. In order to address change in verbal learning we used a latent change model of structured latent growth curves to account for the non-linearity of the verbal learning data. The individual learning trajectories were captured by a hyperbolic function which yielded three psychologically distinct parameters: initial performance, learning rate, and asymptotic performance. We found that average performance increased with respect to initial performance, but not in learning rate or in asymptotic performance. Further, variances and covariances remained stable across both measurement occasions, indicating that the amount of individual differences in the three parameters remained stable, as did the relationships among them. Moreover, older adults differed reliably in their amount of change in initial performance and asymptotic performance. Eventually, changes in asymptotic performance and learning rate were strongly negatively correlated. It thus appears as if change in verbal learning in old age is a constrained process: an increase in total learning capacity implies that it takes longer to learn. Together, these results point to the significance of individual differences in change of verbal learning in the elderly.

State-Space Inference and Learning with Gaussian Processes

OpenAIRE

Turner, R; Deisenroth, MP; Rasmussen, CE

2010-01-01

18.10.13 KB. Ok to add author version to spiral, authors hold copyright. State-space inference and learning with Gaussian processes (GPs) is an unsolved problem. We propose a new, general methodology for inference and learning in nonlinear state-space models that are described probabilistically by non-parametric GP models. We apply the expectation maximization algorithm to iterate between inference in the latent state-space and learning the parameters of the underlying GP dynamics model. C...

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…

Competing risk model for reduction in life expectancy from radiogenic latent cancer

International Nuclear Information System (INIS)

Davis, H.T.

1978-01-01

Because of the large number of persons who could potentially receive low doses of radiation as a result of a nuclear reactor accident, the number of fatalities from latent cancers is generally larger than the early, or prompt, fatalities. For this reason the latent cancer fatality risk of reactor accidents is perceived as being more important than the early fatality risk. In addition, there exists the temptation to add the latent cancer fatality risk to the early fatality risk for the purpose of comparing reactor accident risks to other risks that society is exposed to, such as automobile accidents, airplane accidents, hurricanes, etc. However, the impact on the individual, and society as a whole, due to latent cancer fatalities is significantly different from the impact produced by early fatalities. Early fatalities generally result in appreciable life shortening for the affected individual while latent cancer fatalities generally result in very limited life shortening. A mathematical model was developed to express the reduction in life expectancy due to latent radiogenic cancer as a function of dose received

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

The selection of a mode of urban transportation: Integrating psychological variables to discrete choice models

International Nuclear Information System (INIS)

Cordoba Maquilon, Jorge E; Gonzalez Calderon, Carlos A; Posada Henao, John J

2011-01-01

A study using revealed preference surveys and psychological tests was conducted. Key psychological variables of behavior involved in the choice of transportation mode in a population sample of the Metropolitan Area of the Valle de Aburra were detected. The experiment used the random utility theory for discrete choice models and reasoned action in order to assess beliefs. This was used as a tool for analysis of the psychological variables using the sixteen personality factor questionnaire (16PF test). In addition to the revealed preference surveys, two other surveys were carried out: one with socio-economic characteristics and the other with latent indicators. This methodology allows for an integration of discrete choice models and latent variables. The integration makes the model operational and quantifies the unobservable psychological variables. The most relevant result obtained was that anxiety affects the choice of urban transportation mode and shows that physiological alterations, as well as problems in perception and beliefs, can affect the decision-making process.

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.

Skin Resistivity Value of Upper Trapezius Latent Trigger Points

Directory of Open Access Journals (Sweden)

Elżbieta Skorupska

2015-01-01

Full Text Available Introduction. The skin resistivity (SkR measurement is commonly recommended for acupoints measurement, but for trigger points (TrPs only one study is available. The purpose of the study was to evaluate SkR for latent TrPs compared to non-TrPs and the surrounding tissue. Material and Methods. Forty-two healthy volunteers with unilateral latent upper trapezius TrPs (12 men, 30 women aged 21–23 (mean age: 22.1 ± 0.6 y participated in the study. Keithley electrometer 610B was used for measuring SkR (Ag/AgCl self-adhesive, disposable ground electrode: 30 mm diameter. SkR was measured for latent TrPs and compared to opposite non-TrPs sites and the surrounding tissue. Results. The SkR decrease of TrPs-positive sites as compared to TrPs-negative sites and the surrounding tissue was confirmed. However, no statistically significant difference in the SkR value occurred when all data were analyzed. The same was confirmed after gender division and for TrPs-positive subjects examined for referred pain and twitch response presence. Conclusion. SkR reactive changes at latent TrPs are possible but the results were not consistent with the previous study. Thus, caution in applying SkR to latent TrPs isolation is recommended and its clinical use should not be encouraged yet. Further studies, especially on active TrPs, are yet required.

Quantum learning of classical stochastic processes: The completely positive realization problem

International Nuclear Information System (INIS)

Monràs, Alex; Winter, Andreas

2016-01-01

Among several tasks in Machine Learning, a specially important one is the problem of inferring the latent variables of a system and their causal relations with the observed behavior. A paradigmatic instance of this is the task of inferring the hidden Markov model underlying a given stochastic process. This is known as the positive realization problem (PRP), [L. Benvenuti and L. Farina, IEEE Trans. Autom. Control 49(5), 651–664 (2004)] and constitutes a central problem in machine learning. The PRP and its solutions have far-reaching consequences in many areas of systems and control theory, and is nowadays an important piece in the broad field of positive systems theory. We consider the scenario where the latent variables are quantum (i.e., quantum states of a finite-dimensional system) and the system dynamics is constrained only by physical transformations on the quantum system. The observable dynamics is then described by a quantum instrument, and the task is to determine which quantum instrument — if any — yields the process at hand by iterative application. We take as a starting point the theory of quasi-realizations, whence a description of the dynamics of the process is given in terms of linear maps on state vectors and probabilities are given by linear functionals on the state vectors. This description, despite its remarkable resemblance with the hidden Markov model, or the iterated quantum instrument, is however devoid of any stochastic or quantum mechanical interpretation, as said maps fail to satisfy any positivity conditions. The completely positive realization problem then consists in determining whether an equivalent quantum mechanical description of the same process exists. We generalize some key results of stochastic realization theory, and show that the problem has deep connections with operator systems theory, giving possible insight to the lifting problem in quotient operator systems. Our results have potential applications in quantum machine

Quantum learning of classical stochastic processes: The completely positive realization problem

Science.gov (United States)

Monràs, Alex; Winter, Andreas

2016-01-01

Among several tasks in Machine Learning, a specially important one is the problem of inferring the latent variables of a system and their causal relations with the observed behavior. A paradigmatic instance of this is the task of inferring the hidden Markov model underlying a given stochastic process. This is known as the positive realization problem (PRP), [L. Benvenuti and L. Farina, IEEE Trans. Autom. Control 49(5), 651-664 (2004)] and constitutes a central problem in machine learning. The PRP and its solutions have far-reaching consequences in many areas of systems and control theory, and is nowadays an important piece in the broad field of positive systems theory. We consider the scenario where the latent variables are quantum (i.e., quantum states of a finite-dimensional system) and the system dynamics is constrained only by physical transformations on the quantum system. The observable dynamics is then described by a quantum instrument, and the task is to determine which quantum instrument — if any — yields the process at hand by iterative application. We take as a starting point the theory of quasi-realizations, whence a description of the dynamics of the process is given in terms of linear maps on state vectors and probabilities are given by linear functionals on the state vectors. This description, despite its remarkable resemblance with the hidden Markov model, or the iterated quantum instrument, is however devoid of any stochastic or quantum mechanical interpretation, as said maps fail to satisfy any positivity conditions. The completely positive realization problem then consists in determining whether an equivalent quantum mechanical description of the same process exists. We generalize some key results of stochastic realization theory, and show that the problem has deep connections with operator systems theory, giving possible insight to the lifting problem in quotient operator systems. Our results have potential applications in quantum machine

Quantum learning of classical stochastic processes: The completely positive realization problem

Energy Technology Data Exchange (ETDEWEB)

Monràs, Alex [Física Teòrica: Informació i Fenòmens Quàntics, Universitat Autònoma de Barcelona, 08193 Bellaterra (Barcelona) (Spain); Centre for Quantum Technologies, National University of Singapore, 3 Science Drive 2, Singapore 117543 (Singapore); Winter, Andreas [Física Teòrica: Informació i Fenòmens Quàntics, Universitat Autònoma de Barcelona, 08193 Bellaterra (Barcelona) (Spain); Centre for Quantum Technologies, National University of Singapore, 3 Science Drive 2, Singapore 117543 (Singapore); ICREA—Institució Catalana de Recerca i Estudis Avançats, Pg. Lluis Companys, 23, 08010 Barcelona (Spain)

2016-01-15

Among several tasks in Machine Learning, a specially important one is the problem of inferring the latent variables of a system and their causal relations with the observed behavior. A paradigmatic instance of this is the task of inferring the hidden Markov model underlying a given stochastic process. This is known as the positive realization problem (PRP), [L. Benvenuti and L. Farina, IEEE Trans. Autom. Control 49(5), 651–664 (2004)] and constitutes a central problem in machine learning. The PRP and its solutions have far-reaching consequences in many areas of systems and control theory, and is nowadays an important piece in the broad field of positive systems theory. We consider the scenario where the latent variables are quantum (i.e., quantum states of a finite-dimensional system) and the system dynamics is constrained only by physical transformations on the quantum system. The observable dynamics is then described by a quantum instrument, and the task is to determine which quantum instrument — if any — yields the process at hand by iterative application. We take as a starting point the theory of quasi-realizations, whence a description of the dynamics of the process is given in terms of linear maps on state vectors and probabilities are given by linear functionals on the state vectors. This description, despite its remarkable resemblance with the hidden Markov model, or the iterated quantum instrument, is however devoid of any stochastic or quantum mechanical interpretation, as said maps fail to satisfy any positivity conditions. The completely positive realization problem then consists in determining whether an equivalent quantum mechanical description of the same process exists. We generalize some key results of stochastic realization theory, and show that the problem has deep connections with operator systems theory, giving possible insight to the lifting problem in quotient operator systems. Our results have potential applications in quantum machine

31P-magnetic resonance spectroscopy: Impaired energy metabolism in latent hyperthyroidism

International Nuclear Information System (INIS)

Theissen, P.; Kaldewey, S.; Moka, D.; Bunke, J.; Voth, E.; Schicha, H.

1993-01-01

31 Phosphorous magnetic resonance spectroscopy allows an in vivo examination of energy metabolism. The present study was designed to evaluate whether in patients with latent hyperthyroidism alterations of muscle energy metabolism could be found similar to those observed in patients with overt hyperthyroidism. In 10 patients with overt hyperthyroidism before therapy and 20 with latent hyperthyroidism (also without therapy) and in 24 healthy volunteers magnetic resonance spectroscopy of the calf muscle was performed within a 1.5-Tesla magnet. Muscle concentrations of phosphocreatine, inorganic phosphate, and ATP were quantified compared to an external standard solution of K 2 HPO 4 . In the patients with overt hyperthyroidism and with latent hyperthyroidism a significant decrease of phosphocreatine was found. Further, the ATP concentration in patients with latent and manifest hyperthyroidism tended towards lower values. There were no significant differences in the decrease of phosphocreatine and ATP between both patient groups. Therefore, this study for the first time shows that alterations of energy metabolism in latent hyperthyroidism can be measured and that they are similar to those observed in overt hyperthyroidism. (orig.) [de

Latent class factor and cluster models, bi-plots and tri-plots and related graphical displays

NARCIS (Netherlands)

Magidson, J.; Vermunt, J.K.

2001-01-01

We propose an alternative method of conducting exploratory latent class analysis that utilizes latent class factor models, and compare it to the more traditional approach based on latent class cluster models. We show that when formulated in terms of R mutually independent, dichotomous latent

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...

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…

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.

Learning and memory

Directory of Open Access Journals (Sweden)

P. A. J. Ryke

1989-03-01

Full Text Available 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 learning, learning-set learning and instinct. The term memory must include at least two separate processes. It must involve, on the one hand, that of learning something and on the other, at some later date, recalling that thing. What lies between the learning and (he remembering must be some permanent record — a memory trace — within the brain. Memory exists in at least two forms: memory for very recent events (short-term which is relatively labile and easily disruptable; and long-term memory, which is much more stable. Not everything that gets into short-term memory becomes fixed in the long-term store; a filtering mechanism selects things that might be important and discards the rest.

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

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

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.

A personality-based latent class typology of outpatients with major depressive disorder: association with symptomatology, prescription pattern and social function.

Science.gov (United States)

Hori, Hiroaki; Teraishi, Toshiya; Nagashima, Anna; Koga, Norie; Ota, Miho; Hattori, Kotaro; Kim, Yoshiharu; Higuchi, Teruhiko; Kunugi, Hiroshi

2017-08-01

While major depressive disorder (MDD) is considered to be a heterogeneous disorder, the nature of the heterogeneity remains unclear. Studies have attempted to classify patients with MDD using latent variable techniques, yet the empirical approaches to symptom-based subtyping of MDD have not provided conclusive evidence. Here we aimed to identify homogeneous classes of MDD based on personality traits, using a latent profile analysis. We studied 238 outpatients with DSM-IV MDD recruited from our specialized depression outpatient clinic and assessed their dimensional personality traits with the Temperament and Character Inventory. Latent profile analysis was conducted with 7 dimensions of the Temperament and Character Inventory as indicators. Relationships of the identified classes with symptomatology, prescription pattern, and social function were then examined. The latent profile analysis indicated that a 3-class solution best fit the data. Of the sample, 46.2% was classified into a "neurotic" group characterized by high harm avoidance and low self-directedness; 30.3% into an "adaptive" group characterized by high self-directedness and cooperativeness; and 23.5% into a "socially-detached" group characterized by low reward dependence and cooperativeness and high self-transcendence. The 2 maladaptive groups, namely neurotic and socially-detached groups, demonstrated unique patterns of symptom expression, different classes of psychotropic medication use, and lower social functioning. Generalizability of the findings was limited since our patients were recruited from the specialized depression outpatient clinic. Our personality-based latent profile analysis identified clinically meaningful 3 MDD groups that were markedly different in their personality profiles associated with distinct symptomatology and functioning. Copyright © 2017 Elsevier B.V. All rights reserved.

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.

Teachers' Engagement in Professional Learning: Exploring Motivational Profiles

NARCIS (Netherlands)

Jansen in de Wal, Joost; Den Brok, Perry; Hooijer, Janneke; Martens, Rob; Van den Beemt, Antoine

2017-01-01

This study investigated to what extent secondary school teachers are motivated to work on their professional learning. To this end, profiles ofmotivational dimensions fromself-determination theorywere explored in a sample of 2360 teachers by means of latent profile analysis. The motivational

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

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

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...

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)

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.

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…

Friendship networks of inner-city adults: a latent class analysis and multi-level regression of supporter types and the association of supporter latent class membership with supporter and recipient drug use.

Science.gov (United States)

Bohnert, Amy S B; German, Danielle; Knowlton, Amy R; Latkin, Carl A

2010-03-01

Social support is a multi-dimensional construct that is important to drug use cessation. The present study identified types of supportive friends among the social network members in a community-based sample and examined the relationship of supporter-type classes with supporter, recipient, and supporter-recipient relationship characteristics. We hypothesized that the most supportive network members and their support recipients would be less likely to be current heroin/cocaine users. Participants (n=1453) were recruited from low-income neighborhoods with a high prevalence of drug use. Participants identified their friends via a network inventory, and all nominated friends were included in a latent class analysis and grouped based on their probability of providing seven types of support. These latent classes were included as the dependent variable in a multi-level regression of supporter drug use, recipient drug use, and other characteristics. The best-fitting latent class model identified five support patterns: friends who provided Little/No Support, Low/Moderate Support, High Support, Socialization Support, and Financial Support. In bivariate models, friends in the High, Low/Moderate, and Financial Support were less likely to use heroin or cocaine and had less conflict with and were more trusted by the support recipient than friends in the Low/No Support class. Individuals with supporters in those same support classes compared to the Low/No Support class were less likely to use heroin or cocaine, or to be homeless or female. Multivariable models suggested similar trends. Those with current heroin/cocaine use were less likely to provide or receive comprehensive support from friends. Published by Elsevier Ireland Ltd.

How to Train Deep Variational Autoencoders and Probabilistic Ladder Networks

DEFF Research Database (Denmark)

Sønderby, Casper Kaae; Raiko, Tapani; Maaløe, Lars

2016-01-01

Variational autoencoders are a powerful framework for unsupervised learning. However, previous work has been restricted to shallow models with one or two layers of fully factorized stochastic latent variables, limiting the flexibility of the latent representation. We propose three advances...... of batch normalization. Using these improvements we show state-of-the-art log-likelihood results for generative modeling on several benchmark datasets....

Reliability measures in item response theory: manifest versus latent correlation functions.

Science.gov (United States)

Milanzi, Elasma; Molenberghs, Geert; Alonso, Ariel; Verbeke, Geert; De Boeck, Paul

2015-02-01

For item response theory (IRT) models, which belong to the class of generalized linear or non-linear mixed models, reliability at the scale of observed scores (i.e., manifest correlation) is more difficult to calculate than latent correlation based reliability, but usually of greater scientific interest. This is not least because it cannot be calculated explicitly when the logit link is used in conjunction with normal random effects. As such, approximations such as Fisher's information coefficient, Cronbach's α, or the latent correlation are calculated, allegedly because it is easy to do so. Cronbach's α has well-known and serious drawbacks, Fisher's information is not meaningful under certain circumstances, and there is an important but often overlooked difference between latent and manifest correlations. Here, manifest correlation refers to correlation between observed scores, while latent correlation refers to correlation between scores at the latent (e.g., logit or probit) scale. Thus, using one in place of the other can lead to erroneous conclusions. Taylor series based reliability measures, which are based on manifest correlation functions, are derived and a careful comparison of reliability measures based on latent correlations, Fisher's information, and exact reliability is carried out. The latent correlations are virtually always considerably higher than their manifest counterparts, Fisher's information measure shows no coherent behaviour (it is even negative in some cases), while the newly introduced Taylor series based approximations reflect the exact reliability very closely. Comparisons among the various types of correlations, for various IRT models, are made using algebraic expressions, Monte Carlo simulations, and data analysis. Given the light computational burden and the performance of Taylor series based reliability measures, their use is recommended. © 2014 The British Psychological Society.

Longitudinal Physical Activity Patterns Among Older Adults: A Latent Transition Analysis.

Science.gov (United States)

Mooney, Stephen J; Joshi, Spruha; Cerdá, Magdalena; Kennedy, Gary J; Beard, John R; Rundle, Andrew G

2018-05-14

Most epidemiologic studies of physical activity measure either total energy expenditure or engagement in a single activity type, such as walking. These approaches may gloss over important nuances in activity patterns. We performed a latent transition analysis to identify patterns of activity types as well as neighborhood and individual determinants of changes in those activity patterns over two years in a cohort of 2,023 older adult residents of New York City, NY, surveyed between 2011 and 2013. We identified seven latent classes: 1) Mostly Inactive, 2) Walking, 3) Exercise, 4) Household Activities and Walking, 5) Household Activities and Exercise, 6) Gardening and Household Activities, and 7) Gardening, Household Activities, and Exercise. The majority of subjects retained the same activity patterns between waves (54% unchanged between waves 1 and 2, 66% unchanged between waves 2 and 3).Most latent class transitions were between classes distinguished only by one form of activity, and only neighborhood unemployment was consistently associated with changing between activity latent classes. Future latent transition analyses of physical activity would benefit from larger cohorts and longer follow-up periods to assess predictors of and long-term impacts of changes in activity patterns.

Personal and Financial Risk Typologies Among Women Who Engage in Sex Work in Mongolia: A Latent Class Analysis.

Science.gov (United States)

Offringa, Reid; Tsai, Laura Cordisco; Aira, Toivgoo; Riedel, Marion; Witte, Susan S

2017-08-01

Women engaged in sex work bear a disproportionate burden of HIV infection worldwide, particularly in low- to middle-income countries. Stakeholders interested in promoting prevention and treatment programs are challenged to efficiently and effectively target heterogeneous groups of women. This problem is particularly difficult because it is nearly impossible to know how those groups are composed a priori. Although grouping based on individual variables (e.g., age or place of solicitation) can describe a sample of women engaged in sex work, selecting these variables requires a strong intuitive understanding of the population. Furthermore, this approach is difficult to quantify and has the potential to reinforce preconceived notions, rather than generate new information. We aimed to investigate groupings of women engaged in sex work. The data were collected from a sample of 204 women who were referred to an HIV prevention intervention in Ulaanbaatar, Mongolia. Latent class analysis was used to create subgroups of women engaged in sex work, based on personal and financial risk factors. This analysis found three latent classes, representing unique response pattern profiles of personal and financial risk. The current study approached typology research in a novel, more empirical way and provided a description of different subgroups, which may respond differently to HIV risk interventions.

A Taxonomy of Latent Structure Assumptions for Probability Matrix Decomposition Models.

Science.gov (United States)

Meulders, Michel; De Boeck, Paul; Van Mechelen, Iven

2003-01-01

Proposed a taxonomy of latent structure assumptions for probability matrix decomposition (PMD) that includes the original PMD model and a three-way extension of the multiple classification latent class model. Simulation study results show the usefulness of the taxonomy. (SLD)

Rapid visualization of latent fingermarks using gold seed-mediated enhancement

Directory of Open Access Journals (Sweden)

Chia-Hao Su

2016-11-01

Full Text Available Abstract Background Fingermarks are one of the most important and useful forms of physical evidence in forensic investigations. However, latent fingermarks are not directly visible, but can be visualized due to the presence of other residues (such as inorganic salts, proteins, polypeptides, enzymes and human metabolites which can be detected or recognized through various strategies. Convenient and rapid techniques are still needed to provide obvious contrast between the background and the fingermark ridges and to then visualize latent fingermark with a high degree of selectivity and sensitivity. Results In this work, lysozyme-binding aptamer-conjugated Au nanoparticles (NPs are used to recognize and target lysozyme in the fingermark ridges, and Au+-complex solution is used as a growth agent to reduce Au+ from Au+ to Au0 on the surface of the Au NPs. Distinct fingermark patterns were visualized on a range of professional forensic within 3 min; the resulting images could be observed by the naked eye without background interference. The entire processes from fingermark collection to visualization only entails two steps and can be completed in less than 10 min. The proposed method provides cost and time savings over current fingermark visualization methods. Conclusions We report a simple, inexpensive, and fast method for the rapid visualization of latent fingermarks on the non-porous substrates using Au seed-mediated enhancement. Au seed-mediated enhancement is used to achieve the rapid visualization of latent fingermarks on non-porous substrates by the naked eye without the use of expensive or sophisticated instruments. The proposed approach offers faster detection and visualization of latent fingermarks than existing methods. The proposed method is expected to increase detection efficiency for latent fingermarks and reduce time requirements and costs for forensic investigations.

An instance theory of associative learning.

Science.gov (United States)

Jamieson, Randall K; Crump, Matthew J C; Hannah, Samuel D

2012-03-01

We present and test an instance model of associative learning. The model, Minerva-AL, treats associative learning as cued recall. Memory preserves the events of individual trials in separate traces. A probe presented to memory contacts all traces in parallel and retrieves a weighted sum of the traces, a structure called the echo. Learning of a cue-outcome relationship is measured by the cue's ability to retrieve a target outcome. The theory predicts a number of associative learning phenomena, including acquisition, extinction, reacquisition, conditioned inhibition, external inhibition, latent inhibition, discrimination, generalization, blocking, overshadowing, overexpectation, superconditioning, recovery from blocking, recovery from overshadowing, recovery from overexpectation, backward blocking, backward conditioned inhibition, and second-order retrospective revaluation. We argue that associative learning is consistent with an instance-based approach to learning and memory.

Interferon-gamma release assay for the diagnosis of latent tuberculosis infection: A latent-class analysis.

Directory of Open Access Journals (Sweden)

Tan N Doan

Full Text Available Accurate diagnosis and subsequent treatment of latent tuberculosis infection (LTBI is essential for TB elimination. However, the absence of a gold standard test for diagnosing LTBI makes assessment of the true prevalence of LTBI and the accuracy of diagnostic tests challenging. Bayesian latent class models can be used to make inferences about disease prevalence and the sensitivity and specificity of diagnostic tests using data on the concordance between tests. We performed the largest meta-analysis to date aiming to evaluate the performance of tuberculin skin test (TST and interferon-gamma release assays (IGRAs for LTBI diagnosis in various patient populations using Bayesian latent class modelling.Systematic search of PubMeb, Embase and African Index Medicus was conducted without date and language restrictions on September 11, 2017 to identify studies that compared the performance of TST and IGRAs for LTBI diagnosis. Two IGRA methods were considered: QuantiFERON-TB Gold In Tube (QFT-GIT and T-SPOT.TB. Studies were included if they reported 2x2 agreement data between TST and QFT-GIT or T-SPOT.TB. A Bayesian latent class model was developed to estimate the sensitivity and specificity of TST and IGRAs in various populations, including immune-competent adults, immune-compromised adults and children. A TST cut-off value of 10 mm was used for immune-competent subjects and 5 mm for immune-compromised individuals.A total of 157 studies were included in the analysis. In immune-competent adults, the sensitivity of TST and QFT-GIT were estimated to be 84% (95% credible interval [CrI] 82-85% and 52% (50-53%, respectively. The specificity of QFT-GIT was 97% (96-97% in non-BCG-vaccinated and 93% (92-94% in BCG-vaccinated immune-competent adults. The estimated figures for TST were 100% (99-100% and 79% (76-82%, respectively. T-SPOT.TB has comparable specificity (97% for both tests and better sensitivity (68% versus 52% than QFT-GIT in immune-competent adults

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

Latent Toxoplasma gondii infection leads to improved action control.

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Stock, Ann-Kathrin; Heintschel von Heinegg, Evelyn; Köhling, Hedda-Luise; Beste, Christian

2014-03-01

The parasite Toxoplasma gondii has been found to manipulate the behavior of its secondary hosts to increase its own dissemination which is commonly believed to be to the detriment of the host (manipulation hypothesis). The manipulation correlates with an up-regulation of dopaminergic neurotransmission. In humans, different pathologies have been associated with T. gondii infections but most latently infected humans do not seem to display overt impairments. Since a dopamine plus does not necessarily bear exclusively negative consequences in humans, we investigated potential positive consequences of latent toxoplasmosis (and the presumed boosting of dopaminergic neurotransmission) on human cognition and behavior. For this purpose, we focused on action cascading which has been shown to be modulated by dopamine. Based on behavioral and neurophysiological (EEG) data obtained by means of a stop-change paradigm, we were able to demonstrate that healthy young humans can actually benefit from latent T. gondii infection as regards their performance in this task (as indicated by faster response times and a smaller P3 component). The data shows that a latent infection which is assumed to affect the dopaminergic system can lead to paradoxical improvements of cognitive control processes in humans. Copyright © 2013 Elsevier Inc. All rights reserved.

Chromatin Structure of Epstein-Barr Virus Latent Episomes.

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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.

A joint latent class model for classifying severely hemorrhaging trauma patients.

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Rahbar, Mohammad H; Ning, Jing; Choi, Sangbum; Piao, Jin; Hong, Chuan; Huang, Hanwen; Del Junco, Deborah J; Fox, Erin E; Rahbar, Elaheh; Holcomb, John B

2015-10-24

In trauma research, "massive transfusion" (MT), historically defined as receiving ≥10 units of red blood cells (RBCs) within 24 h of admission, has been routinely used as a "gold standard" for quantifying bleeding severity. Due to early in-hospital mortality, however, MT is subject to survivor bias and thus a poorly defined criterion to classify bleeding trauma patients. Using the data from a retrospective trauma transfusion study, we applied a latent-class (LC) mixture model to identify severely hemorrhaging (SH) patients. Based on the joint distribution of cumulative units of RBCs and binary survival outcome at 24 h of admission, we applied an expectation-maximization (EM) algorithm to obtain model parameters. Estimated posterior probabilities were used for patients' classification and compared with the MT rule. To evaluate predictive performance of the LC-based classification, we examined the role of six clinical variables as predictors using two separate logistic regression models. Out of 471 trauma patients, 211 (45 %) were MT, while our latent SH classifier identified only 127 (27 %) of patients as SH. The agreement between the two classification methods was 73 %. A non-ignorable portion of patients (17 out of 68, 25 %) who died within 24 h were not classified as MT but the SH group included 62 patients (91 %) who died during the same period. Our comparison of the predictive models based on MT and SH revealed significant differences between the coefficients of potential predictors of patients who may be in need of activation of the massive transfusion protocol. The traditional MT classification does not adequately reflect transfusion practices and outcomes during the trauma reception and initial resuscitation phase. Although we have demonstrated that joint latent class modeling could be used to correct for potential bias caused by misclassification of severely bleeding patients, improvement in this approach could be made in the presence of time to event

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

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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.

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.

A Review of the Latent and Manifest Benefits (LAMB) Scale

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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…

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...

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

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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…

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

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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

Teachers' engagement in professional learning : exploring motivational profiles

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Jansen in de Wal, J.; Brok, den P.J.; Hooijer, J.G.; Martens, R.L.; Beemt, van den A.A.J.

2014-01-01

This study investigated to what extent secondary school teachers are motivated to work on their professional learning. To this end, profiles of motivational dimensions from self-determination theory were explored in a sample of 2360 teachers by means of latent profile analysis. The motivational

Probabilistic Latent Semantic Analyses (PLSA in Bibliometric Analysis for Technology Forecasting

Directory of Open Access Journals (Sweden)

Wang Zan

2007-03-01

Full Text Available Due to the availability of internet-based abstract services and patent databases, bibliometric analysis has become one of key technology forecasting approaches. Recently, latent semantic analysis (LSA has been applied to improve the accuracy in document clustering. In this paper, a new LSA method, probabilistic latent semantic analysis (PLSA which uses probabilistic methods and algebra to search latent space in the corpus is further applied in document clustering. The results show that PLSA is more accurate than LSA and the improved iteration method proposed by authors can simplify the computing process and improve the computing efficiency

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

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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…

Latent class models in financial data analysis

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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.

Undergraduate Nurse Variables that Predict Academic Achievement and Clinical Competence in Nursing

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Blackman, Ian; Hall, Margaret; Darmawan, I Gusti Ngurah.

2007-01-01

A hypothetical model was formulated to explore factors that influenced academic and clinical achievement for undergraduate nursing students. Sixteen latent variables were considered including the students' background, gender, type of first language, age, their previous successes with their undergraduate nursing studies and status given for…

Global, broad, or specific cognitive differences? Using a MIMIC model to examine differences in CHC abilities in children with learning disabilities.

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Niileksela, Christopher R; Reynolds, Matthew R

2014-01-01

This study was designed to better understand the relations between learning disabilities and different levels of latent cognitive abilities, including general intelligence (g), broad cognitive abilities, and specific abilities based on the Cattell-Horn-Carroll theory of intelligence (CHC theory). Data from the Differential Ability Scales-Second Edition (DAS-II) were used to create a multiple-indicator multiple cause model to examine the latent mean differences in cognitive abilities between children with and without learning disabilities in reading (LD reading), math (LD math), and reading and writing(LD reading and writing). Statistically significant differences were found in the g factor between the norm group and the LD groups. After controlling for differences in g, the LD reading and LD reading and writing groups showed relatively lower latent processing speed, and the LD math group showed relatively higher latent comprehension-knowledge. There were also some differences in some specific cognitive abilities, including lower scores in spatial relations and numerical facility for the LD math group, and lower scores in visual memory for the LD reading and writing group. These specific mean differences were above and beyond any differences in the latent cognitive factor means.

Characterization of a Latent Virus-Like Infection of Symbiotic Zooxanthellae▿

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Lohr, Jayme; Munn, Colin B.; Wilson, William H.

2007-01-01

A latent virus-like agent, which we designated zooxanthella filamentous virus 1 (ZFV1), was isolated from Symbiodinium sp. strain CCMP 2465 and characterized. Transmission electron microscopy and analytical flow cytometry revealed the presence of a new group of distinctive filamentous virus-like particles after exposure of the zooxanthellae to UV light. Examination of thin sections of the zooxanthellae revealed the formation and proliferation of filamentous virus-like particles in the UV-induced cells. Assessment of Symbiodinium sp. cultures was used here as a model to show the effects of UV irradiance and induction of potential latent viruses. The unique host-virus system described here provides insight into the role of latent infections in zooxanthellae through environmentally regulated viral induction mechanisms. PMID:17351090

Characterization of a latent virus-like infection of symbiotic zooxanthellae.

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Lohr, Jayme; Munn, Colin B; Wilson, William H

2007-05-01

A latent virus-like agent, which we designated zooxanthella filamentous virus 1 (ZFV1), was isolated from Symbiodinium sp. strain CCMP 2465 and characterized. Transmission electron microscopy and analytical flow cytometry revealed the presence of a new group of distinctive filamentous virus-like particles after exposure of the zooxanthellae to UV light. Examination of thin sections of the zooxanthellae revealed the formation and proliferation of filamentous virus-like particles in the UV-induced cells. Assessment of Symbiodinium sp. cultures was used here as a model to show the effects of UV irradiance and induction of potential latent viruses. The unique host-virus system described here provides insight into the role of latent infections in zooxanthellae through environmentally regulated viral induction mechanisms.

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...

Maltreatment histories of foster youth exiting out-of-home care through emancipation: a latent class analysis.

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Havlicek, Judy

2014-01-01

Little is known about maltreatment among foster youth transitioning to adulthood. Multiple entries into out-of-home care and unsuccessful attempts at reunification may nevertheless reflect extended exposure to chronic maltreatment and multiple types of victimization. This study used administrative data from the Illinois Department of Children and Family Services to identify all unduplicated allegations of maltreatment in a cohort of 801 foster youth transitioning to adulthood in the state of Illinois. A latent variable modeling approach generated profiles of maltreatment based on substantiated and unsubstantiated reports of maltreatment taken from state administrative data. Four indicators of maltreatment were included in the latent class analysis: multiple types of maltreatment, predominant type of maltreatment, chronicity, and number of different perpetrators. The analysis identified four subpopulations of foster youth in relation to maltreatment. Study findings highlight the heterogeneity of maltreatment in the lives of foster youth transitioning to adulthood and draw attention to a need to raise awareness among service providers to screen for chronic maltreatment and multiple types of victimization. © The Author(s) 2014.

Using standard serology blood tests to diagnose latent syphilis

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G. L. Katunin

2016-01-01

Full Text Available Goal. To conduct a comparative assessment of the results of regulated serological tests obtained as a result of blood tests in patients suffering from latent syphilis. Materials and methods. The authors examined 187 patient medical records with newly diagnosed latent syphilis in FGBU GNTsDK (State Research Center for Dermatology, Venereology and Cosmetology, Health Ministry of the Russian Federation, in 2006-2015. The results of patient blood tests were analyzed with the use of non-treponemal (microprecipitation test/RPR and treponemal (passive hemagglutination test, immune-enzyme assay (IgA, IgM, IgG, IFabs, immunofluorescence test and Treponema pallidum immobilization test serology tests. Results. According to the results of blood tests of latent syphilis patients, the largest number of positive results was obtained as a result of treponemal serology tests such as immune-enzyme assay (100%, passive hemagglutination test (100% and IFabs (100%. The greatest number of negative results was observed in non-treponemal (microprecipitation test/RPR serology tests: in 136 (72.7% patients; evidently positive results (4+ test results were obtained in 8 (4.3% patients only. According to the results of a comparative analysis of blood tests in patients suffering from latent syphilis obtained with the use of treponemal serology tests, the greatest number of evidently positive results (4+ was noted for the passive hemagglutination test (67.9%. Negative treponemal test results were obtained with the use of the immunofluorescence test and Treponema pallidum immobilization test (21.9% and 11.8% of cases, respectively. Moreover, weakly positive results prevailed for the immunofluorescence test: in 65 (34.7% patients. Conclusion. These data confirm that the following treponemal tests belong to the most reliable ones for revealing patients suffering from latent syphilis: immune-enzyme assay, passive hemagglutination test and IFabs.

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...

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

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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…

Learning to Estimate Dynamical State with Probabilistic Population Codes.

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Joseph G Makin

2015-11-01

Full Text Available Tracking moving objects, including one's own body, is a fundamental ability of higher organisms, playing a central role in many perceptual and motor tasks. While it is unknown how the brain learns to follow and predict the dynamics of objects, it is known that this process of state estimation can be learned purely from the statistics of noisy observations. When the dynamics are simply linear with additive Gaussian noise, the optimal solution is the well known Kalman filter (KF, the parameters of which can be learned via latent-variable density estimation (the EM algorithm. The brain does not, however, directly manipulate matrices and vectors, but instead appears to represent probability distributions with the firing rates of population of neurons, "probabilistic population codes." We show that a recurrent neural network-a modified form of an exponential family harmonium (EFH-that takes a linear probabilistic population code as input can learn, without supervision, to estimate the state of a linear dynamical system. After observing a series of population responses (spike counts to the position of a moving object, the network learns to represent the velocity of the object and forms nearly optimal predictions about the position at the next time-step. This result builds on our previous work showing that a similar network can learn to perform multisensory integration and coordinate transformations for static stimuli. The receptive fields of the trained network also make qualitative predictions about the developing and learning brain: tuning gradually emerges for higher-order dynamical states not explicitly present in the inputs, appearing as delayed tuning for the lower-order states.

Automated LSA Assessment of Summaries in Distance Education: Some Variables to Be Considered

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Jorge-Botana, Guillermo; Luzón, José M.; Gómez-Veiga, Isabel; Martín-Cordero, Jesús I.

2015-01-01

A latent semantic analysis-based automated summary assessment is described; this automated system is applied to a real learning from text task in a Distance Education context. We comment on the use of automated content, plagiarism, text coherence measures, and word weights average and their impact on predicting human judges summary scoring. A…

Perbandingan Hasil Deteksi Plagiarisme Dokumen dengan Metode Jaro-Winkler Distance dan Metode Latent Semantic Analysis

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Tinaliah Tinaliah

2018-01-01

Full Text Available Various methods are applied in the application of plagiarism detection to help check the similarity of a document. Jaro-Winkler Distance can measure the distance between two strings. However, this method basically depends on the position of the word. Latent Semantic Analysis emphasizes the words contained in the document regardless of its linguistic character. This study compares the results of plagiarism detection using the Jaro-Winkler Distance and the Latent Semantic Analysis method. From comparing results of  Jaro-Winkler Distance method and Latent Semantic Analysis method, Jaro-Winkler Distance method is better than Latent Semantic Analysis method if using the same test data. Jaro-Winkler Distance method will give plagiarism result 100% and Latent Semantic Analysis method will give plagiarism result 97,14%. Beragam metode diterapkan dalam aplikasi deteksi plagiarisme untuk membantu mengecek tingkat kesamaan sebuah dokumen. Metode Jaro-Winkler Distance dapat mengukur kesamaan antara dua buah string dan sangat bergantung pada urutan atau posisi kata. Latent Semantic Analysis mementingkan kata-kata yang terkandung di dalam dokumen tanpa memperhatikan karakter linguistiknya. Penelitian ini melakukan perbandingan hasil deteksi plagiarisme dengan menggunakan metode Jaro-Winkler Distance dan metode Latent Semantic Analysis. Hasil pendeteksian plagiarisme dokumen menggunakan metode Jaro-Winkler Distance memberikan hasil yang lebih baik daripada metode Latent Semantic Analysis, yaitu jika data yang dibandingkan sama persis maka akan menghasilkan nilai plagiat sebesar 100%, sedangkan metode Latent Semantic Analysis menghasilkan nilai plagiat sebesar 97,14%.

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

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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…

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

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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.

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

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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.

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