Infant attachment in the Czech Republic: Categorical and dimensional findings from a post-communist country

Infant attachment remains virtually unexplored in former Eastern Bloc countries. The dimensional approach to infant attachment, which could ease common obstacles in cross-cultural attachment research, necessitates more empirical support. This study explores infant attachment in the Czech Republic, a post-communist country with a unique family policy, using both the categorical and the dimensional models. It also compares the Czech infant attachment distribution to infant attachment distributions in other countries and compares infant attachment distributions in European countries to the Baltimore study sample. In the Strange Situation Procedure, forty-nine (74 %) out of sixty-six mother-infant dyads (35 boys, M = 13.8, SD = 0.9) received the B classification. Despite the generous family policy and cultural emphasis on close mother-infant relationships, the Czech distribution of insecure categories did not differ from the Baltimore study sample. Out of other post-communist countries, only the infant attachment distribution in former East Germany differed from the Czech and the Baltimore study samples due to a lower proportion of type B and a higher proportion of type A infants. There were also more type A infants in the Italian sample. Interactive behavior scales accurately predicted attachment categories in 91 % of cases. Contact-maintenance and proximity-seeking scales substantially improve the assessment of insecure resistant behavior. Our findings support the universality and normativity of attachment and the utility of the dimensional approach.     

Optimal scaling by alternating length-constrained nonnegative least squares, with application to distance-based analysis

An important feature of distance-based principal components analysis, is that the variables can be optimally transformed. For monotone spline transformation, a nonnegative least-squares problem with a length constraint has to be solved in each iteration. As an alternative algorithm to Lawson and Hanson (1974), we propose the Alternating Length-Constrained Non-Negative Least-Squares (ALC-NNLS) algorithm, which minimizes the nonnegative least-squares loss function over the parameters under a length constraint, by alternatingly minimizing over one parameter while keeping the others fixed. Several properties of the new algorithm are discussed. A Monte Carlo study is presented which shows that for most cases in distance-based principal components analysis, ALC-NNLS performs as good as the method of Lawson and Hanson or sometimes even better in terms of the quality of the solution. Supported by The Netherlands Organization for Scientific Research (NWO) by grant nr. 030-56403 for the “PIONEER” project “Subject Oriented Multivariate Analysis” to the third author. We would like to thank the anonymous referees for their valuable remarks that have improved the quality of this paper.     

A Classification Framework using a Diverse Intensified Strawberry Optimized Neural Network (DISON) for Clinical Decision-making

A novel classification framework for clinical decision making that uses an Extremely Randomized Tree (ERT) based feature selection and a Diverse Intensified Strawberry Optimized Neural network (DISON) is proposed. DISON is a Feed Forward Artificial Neural Network where the optimization of weights and bias is done using a two phase training strategy. Two algorithms namely Strawberry Plant Optimization (SPO) algorithm and Gradient-descent Back-propagation algorithm are used sequentially to identify the optimum weights and bias. The novel two phase training method and the stochastic duplicate-elimination strategy of SPO helps in addressing the issue of local optima associated with conventional neural networks. The relevant attributes are selected based on the feature importance values computed using an ERT classifier.Vertebral Column, Pima Indian diabetes (PID), Cleveland Heart disease (CHD) and Statlog Heart disease (SHD) datasets from the University of California Irvine machine learning repository are used for experimentation. The framework has achieved an accuracy of 87.17% for Vertebral Column, 90.92% for PID, 93.67% for CHD and 94.5% for SHD. The classifier performance has been compared with existing works and is found to be competitive in terms of accuracy, sensitivity and specificity. Wilcoxon test confirms the statistical superiority of the proposed method.     

On the Determination of Minimal Facets and Edges of a Polyhedral Set

In this paper we investigate the image of a polyhedral set under a linear map. Moreover, we present an algorithm for the determination of so-called minimal facets and certain minimal irredundant proper edges of a convex polyhedral set in ℝ³. © 1997 John Wiley & Sons, Ltd.     

Pareto simulated annealing—a metaheuristic technique for multiple-objective combinatorial optimization

This paper presents a multiple-objective metaheuristic procedure—Pareto simulated annealing. The goal of the procedure is to find in a relatively short time a good approximation of the set of efficient solutions of a multiple-objective combinatorial optimization problem. The procedure uses a sample, of so-called generating solutions. Each solution explores its neighbourhood in a way similar to that of classical simulated annealing. Weights of the objectives, used for their local aggregation, are tuned in each iteration in order to assure a tendency for approaching the efficient solutions set while maintaining a uniform distribution of the generating solutions over this set. A computational experiment shows that the method is a better tool for approximating the efficient set than some previous proposals. © 1998 John Wiley & Sons, Ltd.     

Feedback functions, optimization, and the relation of response rate to reinforcer rate

The present experiment arranged a series of inverted U-shaped feedback functions relating reinforcer rate to response rate to test whether responding was consistent with an optimization account or with a one-to-one relation of response rate to reinforcer rate such as linear system theory's rate equation or Herrnstein's hyperbola. Reinforcer rate was arranged according to a quadratic equation with a maximum at a unique response rate. The experiment consisted of two phases, during which 6 Long Evans rats lever pressed for food. In the first phase of the experiment, the rats responded on six fixed-interval-plus-quadratic-feedback schedules, and in the second phase the rats responded on three variable-interval-plus-quadratic-feedback schedules. Responding in both phases was inconsistent with a one-to-one relation of response rate to reinforcer rate. Instead, different response rates were obtained at equivalent reinforcer rates. Responding did vary directly with the vertex of the feedback function in both phases, a finding consistent with optimization of reinforcer rate. The present results suggest that the feedback function relating reinforcer rate to response rate imposed by a reinforcement schedule can be an important determinant of behavior. Furthermore, the present experiment illustrates the benefit of arranging feedback functions to investigate assumptions about the variables that control schedule performance.     

A global optimization approach for generating efficient points for multiobjective concave fractional programs

In this article, we present a global optimization approach for generating efficient points for multiobjective concave fractional programming problems. The main work of the approach involves solving an instance of a concave multiplicative fractional program (W̄). Problem (W̄) is a global optimization problem for which no known algorithms are available. Therefore, to render the approach practical, we develop and validate a branch and bound algorithm for globally solving problem (W̄). To illustrate the performance of the global optimization approach, we use it to generate efficient points for a sample multiobjective concave fractional program. Copyright © 2006 John Wiley & Sons, Ltd.     

生命进化动力之哲学思考

对生命进化的本质问题作了新的哲学思考,提出进化的本质是物种在生存的驱动下生存效率的优化过程,是原始细胞自主性与环境选择的双向过程。同时提出推动物种进化的动力在于生存资源稀缺性与物种自私性之间的矛盾和多细胞物种效率优化过程中自身的局部利益与整体利益之间的矛盾。     

Model specification search using a genetic algorithm with factor reordering for a simple structure factor analysis model

Abstract:  Many techniques for automated model specification search based on numerical indices have been proposed, but no single decisive method has yet been determined. In the present article, the performance and features of the model specification search method using a genetic algorithm (GA) were verified. A GA is a robust and simple metaheuristic algorithm with great searching power. While there has already been some research applying metaheuristics to the model fitting task, we focus here on the search for a simple structure factor analysis model and propose a customized algorithm for dealing with certain problems specific to that situation. First, implementation of model specification search using a GA with factor reordering for a simple structure factor analysis is proposed. Then, through a simulation study using generated data with a known true structure and through example analysis using real data, the effectiveness and applicability of the proposed method were demonstrated.     

The interaction of perfectionism, perceived weight status, and self-esteem to predict bulimic symptoms: the role of 'benign' perfectionism

The present study sought to replicate an interactive model of global perfectionism, perceived weight status, and self-esteem in predicting bulimic symptom development in a sample of young women [Bardone-Cone, et al. (2006). Predicting bulimic symptoms: An interactive model of self-efficacy, perfectionism, and perceived weight status. Behaviour Research and Therapy, 44, 27-42; Vohs, K. D., et al. (1999). Perfectionism, perceived weight status, and self-esteem interact to predict bulimic symptoms: A model of bulimic symptom development. Journal of Abnormal Psychology, 108, 695-700; Vohs, K. D., et al. (2001). Perfectionism, body dissatisfaction, and self-esteem: An interactive model of bulimic symptom development. Journal of Social and Clinical Psychology, 20, 476-497]. The aim was to investigate the role of 'problematic' and 'benign' perfectionism within this model, using data from 95 female university students over a 3-month period. Contrary to hypotheses, multivariate analyses revealed a significant three-way interaction only between 'benign' perfectionism, perceived weight status and self-esteem in predicting change in bulimic symptoms over a 3-month period. The predictive effect of the interaction between 'benign' perfectionism and perceived weight status on bulimic symptoms was strongest for women with high self-esteem, for whom feeling overweight and having perfectionistic attitudes preceded increased bulimic symptoms. These findings suggest that high self-esteem is insufficient to protect against the development of bulimic symptoms when both the perception of oneself as being overweight, and high levels of perfectionistic standards, are present. It would appear that the role of perfectionism within the context of disordered eating is complex.