The Flemish Sports Compass: from sports orientation to elite performance prediction

The road from beginner to sports champion is a long and unpredictable one. Therefore, choosing a sport that fits their individual characteristics is essential for children to keep them involved in sports. The Flemish Sports Compass is a generic test battery designed to advise children in their sports choice. The test battery includes anthropometric, physical and motor performance measurements and it has the special quality that, in addition to talent detection and talent orientation, it also enhances various derivative test batteries for talent identification. The Flemish Sports Compass consists of field tests appliable in both elementary schools children and in Flemish elite sport schools. On the one hand it is possible to discriminate between different performance levels and on the other this test battery has te ability to detect sport-specific characteristics of an individual. First part of this doctoral dissertation consists of two introductory chapters. The first chapter provides an overview of definitions, theoretical talent models and practical talent systems. The second chapter explains the rationale and the design of the Flemish Sports Compass and displays the preliminary studies for designing the Flemish Sports Compass. In the second part of this dissertation, six original studies are reported. The first study highlights the potential of the Flemish Sports Compass for primary school children. In this study the differences between the sport specific profiles are less pronounced than in the second and the third study, which measured respectively the students of the Flemish elite sport schools and promising athletes of different sports federations. The first three studies indicated that the generic test battery can be deployed on beginners (talent detection) as well as elite athletes (talent identification). The fourth study, with increased specificity, indicates that the generic tests of the Flemish Sports Compass also are able to distinguish between medallists in international competitions and subelite volleyball players. The talent characteristics measured by the Flemish Sports Compass are not only good at predicting and identifying elite level, they also predict attrition in sport. In the fifth study, survival analysis was applied. Parallel to the methods used in medical science where examining the outcome of medication on the participants life expectations is the main goal, survival chances of athletes were calculated in our fifth study. The last study indicated the importance of predictive analytics of a generic test battery. It was shown that artificial neural networks reduce the risk of missing gifted athletes, when selecting the high potential athletes and how the cost of talent development can be reduced without losing talents. In the third part of this dissertation results are discussed and critical reflections and recommendations are given. The different studies provide opportunities to develop a specific talent system for a small country. Flanders’ disadvantage is, that it is hard to compete with giant nations such as China, Russia and the United States. However, the disadvantage of being small is an advantage at the same time. Smallness reduces the risk of missing one single talent. A coordinated approach is necessary, because implementing different talent programs in every single sports federation leads to fragmentation of the scarce resources. First steps have been made by starting up the Flemish sports compass project. Cooperation is the key for small countries. Talent detection in primary schools is the first step to be taken. The advantages are various and children learn to make choices, which is beneficial for their autonomy and competence. Children have different reasons for practicing sports. Some are interested in competition some are not and a few believe in their chances to win medals for their country. Whatever the underlying motivation, we assume that children choose their appropriate sport, although it is obvious that also the sport chooses the child, because the sport demands specific characteristics. This doctoral dissertation intends to to formulate a scientifically based proposal for the implementation of the Flemish Sports Compass. Undermentioned you find the detailed report

Evolution of Prosocial Behavior through Preferential Detachment and Its Implications for Morality.

The current project introduces a general theory and supporting models that offer a plausible explanation and viable mechanism for generating and perpetuating prosocial behavior. The proposed mechanism is preferential detachment and the theory proposed is that agents utilizing preferential detachment will sort themselves into social arrangements such that the agents who contribute a benefit to the members of their group also do better for themselves in the long run. Agents can do this with minimal information about their environment, the other agents, the future, and with minimal cognitive/computational ability. The conclusion is that self-organizing into groups that maintain prosocial behaviors may be simpler and more robust than previously thought. The primary contribution of this research is that a single, simple mechanism operating in different contexts generates the conceptually distinct prosocial behaviors achieved by other models, and in a manner that is more amenable to evolutionary explanations. It also bears importantly on explanations of the evolution of our moral experiences and their connection with prosociality.Ph.D.Political Science and PhilosophyUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/91448/1/bramson_1.pd

The rhythm that unites: an empirical investigation into synchrony, ritual, and hierarchy

Synchrony, or rhythmic bodily unison activities such as drumming or cadence marching, has attracted growing scholarly interest. Among laboratory subjects, synchrony elicits prosocial responses, including altruism and empathy. In light of such findings, researchers in social psychology and the bio-cultural study of religion have suggested that synchrony played a role in humanity’s evolutionary history by engendering collectivistic commitments and social cohesion. These models propose that synchrony enhances cohesion by making people feel united. However, such models overlook the importance of differentiated social relations, such as hierarchies. This dissertation builds on this insight by drawing on neuroscience, coordination dynamics, social psychology, anthropology, and ritual studies to generate a complex model of synchrony, ritual, and social hierarchy, which is then tested in an experimental study. In the hypothesized model, shared motor unison suppresses the brain’s ability to distinguish cognitively between self-caused and exogenous motor acts, resulting in subjective self-other overlap. During synchrony each participant is dynamically entrained to a group mean rhythm; this “immanent authority” prevents any one participant from unilaterally dictating the rhythm, flattening relative hierarchy. As a ritualized behavior, synchrony therefore paradigmatically evokes shared ideals of equality and unity. However, when lab participants were assigned to either a synchrony or asynchrony manipulation and given a collaborative task requiring complex coordination, synchrony predicted a marginally lower degree of collaboration and significantly lower interpersonal satisfaction. These findings imply that unity and equality can undercut group cohesion if the collective agenda is a shared goal that requires interpersonal coordination. My results emphasize that, despite the inevitable tensions associated with social hierarchy, complementary roles and hierarchy are vital for certain aspects of social cohesion. Ritual and convention institute social boundaries that can be adroitly negotiated, even as egalitarian effervescence such as communitas (in the sense of Victor Turner) facilitates social unity and inspires affective commitments. These findings corroborate theories in ritual studies and sociology that caution both against excessive emphasis on inner emotive states (such as empathy) and against excessively rigid conventions or roles. An organic balance between unity and functional differentiation is vital for genuinely robust, long-term social cohesion.2018-06-21T00:00:00

The total assessment profile, volume 1

A methodology is described for the evaluation of societal impacts associated with the implementation of a new technology. Theoretical foundations for the methodology, called the total assessment profile, are established from both the economic and social science perspectives. The procedure provides for accountability of nonquantifiable factors and measures through the use of a comparative value matrix by assessing the impacts of the technology on the value system of the society

Studies in modern competitive fencing

Monografia jest zbiorem prac polskich autorów realizujących projekty badawcze dotyczące szermierki. Praca podzielona na jest na trzy części o różnej tematyce. Pierwsza stanowi historyczne wprowadzenie do tematu, druga prezentuje psychologiczne aspekty szermierki, w trzeciej przedstawiono przegląd prac naukowych (badawczych) z zakresu teorii sportu i antropologii. Autorzy są cenionymi specjalistami z zakresu nauk o kulturze fizycznej z szczególnym uwzględnieniem szermierki

Hierarchical information representation and efficient classification of gene expression microarray data

In the field of computational biology, microarryas are used to measure the activity of thousands of genes at once and create a global picture of cellular function. Microarrays allow scientists to analyze expression of many genes in a single experiment quickly and eficiently. Even if microarrays are a consolidated research technology nowadays and the trends in high-throughput data analysis are shifting towards new technologies like Next Generation Sequencing (NGS), an optimum method for sample classification has not been found yet. Microarray classification is a complicated task, not only due to the high dimensionality of the feature set, but also to an apparent lack of data structure. This characteristic limits the applicability of processing techniques, such as wavelet filtering or other filtering techniques that take advantage of known structural relation. On the other hand, it is well known that genes are not expressed independently from other each other: genes have a high interdependence related to the involved regulating biological process. This thesis aims to improve the current state of the art in microarray classification and to contribute to understand how signal processing techniques can be developed and applied to analyze microarray data. The goal of building a classification framework needs an exploratory work in which algorithms are constantly tried and adapted to the analyzed data. The developed algorithms and classification frameworks in this thesis tackle the problem with two essential building blocks. The first one deals with the lack of a priori structure by inferring a data-driven structure with unsupervised hierarchical clustering tools. The second key element is a proper feature selection tool to produce a precise classifier as an output and to reduce the overfitting risk. The main focus in this thesis is the binary data classification, field in which we obtained relevant improvements to the state of the art. The first key element is the data-driven structure, obtained by modifying hierarchical clustering algorithms derived from the Treelets algorithm from the literature. Several alternatives to the original reference algorithm have been tested, changing either the similarity metric to merge the feature or the way two feature are merged. Moreover, the possibility to include external sources of information from publicly available biological knowledge and ontologies to improve the structure generation has been studied too. About the feature selection, two alternative approaches have been studied: the first one is a modification of the IFFS algorithm as a wrapper feature selection, while the second approach involved an ensemble learning focus. To obtain good results, the IFFS algorithm has been adapted to the data characteristics by introducing new elements to the selection process like a reliability measure and a scoring system to better select the best feature at each iteration. The second feature selection approach is based on Ensemble learning, taking advantage of the microarryas feature abundance to implement a different selection scheme. New algorithms have been studied in this field, improving state of the art algorithms to the microarray data characteristic of small sample and high feature numbers. In addition to the binary classification problem, the multiclass case has been addressed too. A new algorithm combining multiple binary classifiers has been evaluated, exploiting the redundancy offered by multiple classifiers to obtain better predictions. All the studied algorithm throughout this thesis have been evaluated using high quality publicly available data, following established testing protocols from the literature to offer a proper benchmarking with the state of the art. Whenever possible, multiple Monte Carlo simulations have been performed to increase the robustness of the obtained results.En el campo de la biología computacional, los microarrays son utilizados para medir la actividad de miles de genes a la vez y producir una representación global de la función celular. Los microarrays permiten analizar la expresión de muchos genes en un solo experimento, rápidamente y eficazmente. Aunque los microarrays sean una tecnología de investigación consolidada hoy en día y la tendencia es en utilizar nuevas tecnologías como Next Generation Sequencing (NGS), aun no se ha encontrado un método óptimo para la clasificación de muestras. La clasificación de muestras de microarray es una tarea complicada, debido al alto número de variables y a la falta de estructura entre los datos. Esta característica impide la aplicación de técnicas de procesado que se basan en relaciones estructurales, como el filtrado con wavelet u otras técnicas de filltrado. Por otro lado, los genes no se expresen independientemente unos de otros: los genes están inter-relacionados según el proceso biológico que les regula. El objetivo de esta tesis es mejorar el estado del arte en la clasi cación de microarrays y contribuir a entender cómo se pueden diseñar y aplicar técnicas de procesado de señal para analizar microarrays. El objetivo de construir un algoritmo de clasi cación, necesita un estudio de comprobaciones y adaptaciones de algoritmos existentes a los datos analizados. Los algoritmo desarrollados en esta tesis encaran el problema con dos bloques esenciales. El primero ataca la falta de estructura, derivando un árbol binario usando herramientas de clustering no supervisado. El segundo elemento fundamental para obtener clasificadores precisos reduciendo el riesgo de overfitting es un elemento de selección de variables. La principal tarea en esta tesis es la clasificación de datos binarios en la cual hemos obtenido mejoras relevantes al estado del arte. El primer paso es la generación de una estructura, para eso se ha utilizado el algoritmo Treelets disponible en la literatura. Múltiples alternativas a este algoritmo original han sido propuestas y evaluadas, cambiando las métricas de similitud o las reglas de fusión durante el proceso. Además, se ha estudiado la posibilidad de usar fuentes de información externas, como ontologías de información biológica, para mejorar la inferencia de la estructura. Se han estudiado dos enfoques diferentes para la selección de variables: el primero es una modificación del algoritmo IFFS y el segundo utiliza un esquema de aprendizaje con “ensembles”. El algoritmo IFFS ha sido adaptado a las características de microarrays para obtener mejores resultados, añadiendo elementos como la medida de fiabilidad y un sistema de evaluación para seleccionar la mejor variable en cada iteración. El método que utiliza “ensembles” aprovecha la abundancia de features de los microarrays para implementar una selección diferente. En este campo se han estudiado diferentes algoritmos, mejorando alternativas ya existentes al escaso número de muestras y al alto número de variables, típicos de los microarrays. El problema de clasificación con más de dos clases ha sido también tratado al estudiar un nuevo algoritmo que combina múltiples clasificadores binarios. El algoritmo propuesto aprovecha la redundancia ofrecida por múltiples clasificadores para obtener predicciones más fiables. Todos los algoritmos propuestos en esta tesis han sido evaluados con datos públicos y de alta calidad, siguiendo protocolos establecidos en la literatura para poder ofrecer una comparación fiable con el estado del arte. Cuando ha sido posible, se han aplicado simulaciones Monte Carlo para mejorar la robustez de los resultados