Winchell Papers

Abstracts of Winchell Papers

Spontaneous thought and vulnerability to mood disorders : the dark side of the wandering mind

There is increasing interest in spontaneous thought, namely task-unrelated or rest-related mental activity. Spontaneous thought is an umbrella term for processes like mind-wandering, involuntary autobiographical memory, and daydreaming, with evidence elucidating adaptive and maladaptive consequences. In this theoretical framework, we propose that, apart from its positive functions, spontaneous thought is a precursor for cognitive vulnerability in individuals who are at risk for mood disorders. It is important that spontaneous thought mostly focuses on unattained goals and evaluates the discrepancy between current and desired status. In individuals who stably (i.e., trait negative affectivity) or transitorily (i.e., stress) experience negative emotions in reaction to goal-discrepancy, spontaneous thought fosters major cognitive vulnerabilities (e.g., rumination, hopelessness, low self-esteem, and cognitive reactivity), which, in turn, enhance depression. Furthermore, we also highlight preliminary links between spontaneous thought and bipolar disorder. The evidence for this framework is reviewed, and we discuss theoretical and clinical implications of our proposal

Internet performance modeling: the state of the art at the turn of the century

Seemingly overnight, the Internet has gone from an academic experiment to a worldwide information matrix. Along the way, computer scientists have come to realize that understanding the performance of the Internet is a remarkably challenging and subtle problem. This challenge is all the more important because of the increasingly significant role the Internet has come to play in society. To take stock of the field of Internet performance modeling, the authors organized a workshop at Schloß Dagstuhl. This paper summarizes the results of discussions, both plenary and in small groups, that took place during the four-day workshop. It identifies successes, points to areas where more work is needed, and poses “Grand Challenges” for the performance evaluation community with respect to the Internet

Bioinformatics

This book is divided into different research areas relevant in Bioinformatics such as biological networks, next generation sequencing, high performance computing, molecular modeling, structural bioinformatics, molecular modeling and intelligent data analysis. Each book section introduces the basic concepts and then explains its application to problems of great relevance, so both novice and expert readers can benefit from the information and research works presented here

Assortative Mating In Animals

Assortative mating occurs when there is a correlation (positive or negative) between male and female phenotypes or genotypes across mated pairs. To determine the typical strength and direction of assortative mating in animals, we carried out a meta-analysis of published measures of assortative mating for a variety of phenotypic and genotypic traits in a diverse set of animal taxa. We focused on the strength of assortment within populations, excluding reproductively isolated populations and species. We collected 1,116 published correlations between mated pairs from 254 species (360 unique species-trait combinations) in five phyla. The mean correlation between mates was 0.28, showing an overall tendency toward positive assortative mating within populations. Although 19% of the correlations were negative, simulations suggest that these could represent type I error and that negative assortative mating may be rare. We also find significant differences in the strength of assortment among major taxonomic groups and among trait categories. We discuss various possible reasons for the evolution of assortative mating and its implications for speciation.Integrative Biolog

The benefits of believing you can change: implicit malleability theories moderate the relationship between low self-esteem and negative outcomes

There are at least two ways to combat the negative effects of low self-esteem: directly improve people’s self-esteem, or to decouple the link between low self-esteem and negative outcomes (Hayes & Ciarrochi, 2015). Incremental theories are implicit beliefs that people’s attributes are malleable. In this thesis I argue that subscription to these beliefs may help combat the negative effects of low self-esteem. Incremental theories make individuals less likely to make trait attributions as a result of failure and so may prevent low-self-esteem from occurring in response to failure. Incremental theories may also make people less likely to treat negative self-evaluations as truths that permanently define them, thus decoupling the link between low self-esteem and negative outcomes. In study 1, I conducted a systematic review to examine the link between incremental theories and self-concept. I synthesize the results of 34 studies and found that incremental theories and self-esteem were modestly correlated (r = .16; 95% CI [0.11, 0.2]). In study 2 and 3, I examined the extent that perceptions of self-malleability moderated the link between self-esteem and negative outcomes. I surveyed 489 Australian female high school students (age: M = 14.7; SD = 1.5) and a representative sample of 7,884 adult Americans of both genders (age: M = 47.9; SD = 16; 52.5% female) respectively. Moderation analyses in both samples showed that the links between low self-esteem and negative outcomes (lower wellbeing and achievement) were weaker for those with stronger incremental theories. While in those with high self-esteem there was little difference in wellbeing and achievement regardless of the level of incremental theories; in those with low self-esteem strong incremental theories had substantially higher levels of wellbeing and achievement. People are likely to experience fluctuations in self-esteem due to success, failure, and social rejection. Incremental theories may help people respond to low self-esteem in more adaptive ways resulting in improved wellbeing and achievement

Effects of high versus low-quality preschool education : a longitudinal study in Mauritius

We report on a randomized controlled experiment in Mauritius by the Joint Child Health Project. This longitudinal study followed a cohort of children from different socio-economic backgrounds to examine educational outcomes among children in high and low-quality preschools. The findings show that quality of preschool education had no significant effect on children's overall educational attainment. However, academic performance of children in the experimental group was higher for children with poorly educated fathers, but lower for children with poorly educated mothers. Hence, the effects of high-quality preschool education worked in opposing directions—equalizing by compensating for the effect of father's level of education, and disequalizing by reinforcing the effect of mother's level of education

Spontaneous thought and vulnerability to mood disorders: The dark side of the wandering mind

There is increasing interest in spontaneous thought, namely task-unrelated or rest-related mental activity. Spontaneous thought is an umbrella term for processes like mindwandering, involuntary autobiographical memory, and daydreaming, with evidence elucidating adaptive and maladaptive consequences. In this theoretical framework, we propose that, apart from its positive functions, spontaneous thought is a precursor for cognitive vulnerability in individuals who are at-risk for mood disorders. Importantly, spontaneous thought mostly focuses on unattained goals and evaluates the discrepancy between current and desired status (Klinger, 1971, 2013a). In individuals who stably (i.e., trait negative affectivity) or transitorily (i.e., stress) experience negative emotions in reaction to goal-discrepancy, spontaneous thought fosters major cognitive vulnerabilities (e.g., rumination, hopelessness, low self-esteem, and cognitive reactivity) which, in turn, enhance depression. Furthermore, we also highlight preliminary links between spontaneous thought and bipolar disorder. The evidence for this framework is reviewed and we discuss theoretical and clinical implications of our proposal

A New Design of Multiple Classifier System and its Application to Classification of Time Series Data

To solve the challenging pattern classification problem, machine learning researchers have extensively studied Multiple Classifier Systems (MCSs). The motivations for combining classifiers are found in the literature from the statistical, computational and representational perspectives. Although the results of classifier combination does not always outperform the best individual classifier in the ensemble, empirical studies have demonstrated its superiority for various applications. A number of viable methods to design MCSs have been developed including bagging, adaboost, rotation forest, and random subspace. They have been successfully applied to solve various tasks. Currently, most of the research is being conducted on the behavior patterns of the base classifiers in the ensemble. However, a discussion from the learning point of view may provide insights into the robust design of MCSs. In this thesis, Generalized Exhaustive Search and Aggregation (GESA) method is developed for this objective. Robust performance is achieved using GESA by dynamically adjusting the trade-off between fitting the training data adequately and preventing the overfitting problem. Besides its learning algorithm, GESA is also distinguished from traditional designs by its architecture and level of decision-making. GESA generates a collection of ensembles and dynamically selects the most appropriate ensemble for decision-making at the local level. Although GESA provides a good improvement over traditional approaches, it is not very data-adaptive. A data- adaptive design of MCSs demands that the system can adaptively select representations and classifiers to generate effective decisions for aggregation. Another weakness of GESA is its high computation cost which prevents it from being scaled to large ensembles. Generalized Adaptive Ensemble Generation and Aggregation (GAEGA) is an extension of GESA to overcome these two difficulties. GAEGA employs a greedy algorithm to adaptively select the most effective representations and classifiers while excluding the noise ones as much as possible. Consequently, GAEGA can generate fewer ensembles and significantly reduce the computation cost. Bootstrapped Adaptive Ensemble Generation and Aggregation (BAEGA) is another extension of GESA, which is similar with GAEGA in the ensemble generation and decision aggregation. BAEGA adopts a different data manipulation strategy to improve the diversity of the generated ensembles and utilize the information in the data more effectively. As a specific application, the classification of time series data is chosen for the research reported in this thesis. This type of data contains dynamic information and proves to be more complex than others. Multiple Input Representation-Adaptive Ensemble Generation and Aggregation (MIR-AEGA) is derived from GAEGA for the classification of time series data. MIR-AEGA involves some novel representation methods that proved to be effective for time series data. All the proposed methods including GESA, GAEGA, MIR-AEGA, and BAEGA are tested on simulated and benchmark data sets from popular data repositories. The experimental results confirm that the newly developed methods are effective and efficient