Task-Dependent Oscillatory Brain Activity During Oculomotor Delayed Response Task

This study used three Oculomotor Delayed Response (ODR) tasks to investigate the unique cognitive demands during the delay period. Changes in alpha power were used to index cognitive efforts during the delay period. Continuous EEGs from 25 healthy young adults (18-34 years) were recorded using dense electrode array. The data was analyzed by 6-cycle Morlet wavelet decompositions in the frequency range of 2-30 Hz to create time- frequency decompositions for four midline electrode sites. The 99% confidence intervals using the bootstrapped 20% trimmed mean of the 10 Hz frequency were used to examine the differences among conditions. Compared to two Memory conditions (Match and Non-Match), Control condition yielded significant differences in all frequencies over the entire trial period, suggesting a cognitive state difference. Compared to Match condition, the Non–Match condition had lower alpha activity during the delay period at each midline electrode site reflecting the higher cognitive effort required.

Real-Time Automatic Object Classification and Tracking using Genetic Programming and NVIDIA R CUDA TM

Genetic Programming (GP) is a widely used methodology for solving various computational problems. GP's problem solving ability is usually hindered by its long execution times. In this thesis, GP is applied toward real-time computer vision. In particular, object classification and tracking using a parallel GP system is discussed. First, a study of suitable GP languages for object classification is presented. Two main GP approaches for visual pattern classification, namely the block-classifiers and the pixel-classifiers, were studied. Results showed that the pixel-classifiers generally performed better. Using these results, a suitable language was selected for the real-time implementation. Synthetic video data was used in the experiments. The goal of the experiments was to evolve a unique classifier for each texture pattern that existed in the video. The experiments revealed that the system was capable of correctly tracking the textures in the video. The performance of the system was on-par with real-time requirements.

The Influence of Attentional Focus on the Self-Efficacy-Performance Relationship in a Continuous Running Task

The self-efficacy-performance relationship in continuous sport tasks has been shown to be significantly reciprocal yet unequal with stronger influences in the performance-to-self-efficacy pathway rather than self-efficacy-to-performance pathway (e.g., LaForge-MacKenzie & Sullivan, 2014b). Bandura (2012) suggested that sociocognitive variables may influence this relationship. Attention as a sociocognitve factor may bias the processing of performance and self-efficacy information (Bandura, 1982, 1997; Bandura & Jourden, 1991). As confidence and attention are important aspects of peak running performance (Brewer, Van Raalte, Linder, & VanRaalte, 1991), the primary purpose of the present study was to examine the self-efficacy-performance relationship under three conditions of attentional focus. The secondary purpose was to examine self-efficacy and performance as separate constructs under the same conditions of attention. Participants ran continuously for one kilometer in one of three randomly assigned attentional focus conditions: internal-focus (n = 51), external-focus (n = 50), and control (n = 49). Self-efficacy was assessed using a one-item measure every 200 meters. Path analyses examining the primary objective revealed significant self-efficacy-to-performance pathways in all conditions: external-focus (p < .05, βs ranging from -.17 to -.32), internal-focus (p < .05, βs ranging from -.26 to -.36), and control (p < .05, βs ranging from -.29 to -.42). Significant reciprocal relationships were absent in all conditions. ANOVAs examining the secondary objectives found significantly faster performance in the control condition at the start (F (2, 147) = 3.86, p < .05) and end of the task (F (2, 147) = 3.56, p < .05). Self-efficacy was significantly higher in the internal-focus condition at the end of the task (Self-Efficacy 4 (F (2, 147) = 3.21, p < .05) and Self-Efficacy 5 (F (2, 147) = 4.74, p < .05). In contrast to previous within-trial research (e.g., LaForge-MacKenzie & Sullivan, 2014b) self-efficacy-to-performance effects were more significant and robust than performance-to-self-efficacy effects. These results provided support for Bandura’s (2012) suggestion that sociocognitive factors may have the ability to alter the causal structure of the self-efficacy-performance relationship, proposing complexities in the self-efficacy-performance relationship (Sitzmann &Yeo, 2013). Results were discussed from both theoretical and applied perspectives.

Object-Oriented Genetic Programming for the Automatic Inference of Graph Models for Complex Networks

Complex networks are systems of entities that are interconnected through meaningful relationships. The result of the relations between entities forms a structure that has a statistical complexity that is not formed by random chance. In the study of complex networks, many graph models have been proposed to model the behaviours observed. However, constructing graph models manually is tedious and problematic. Many of the models proposed in the literature have been cited as having inaccuracies with respect to the complex networks they represent. However, recently, an approach that automates the inference of graph models was proposed by Bailey [10] The proposed methodology employs genetic programming (GP) to produce graph models that approximate various properties of an exemplary graph of a targeted complex network. However, there is a great deal already known about complex networks, in general, and often specific knowledge is held about the network being modelled. The knowledge, albeit incomplete, is important in constructing a graph model. However it is difficult to incorporate such knowledge using existing GP techniques. Thus, this thesis proposes a novel GP system which can incorporate incomplete expert knowledge that assists in the evolution of a graph model. Inspired by existing graph models, an abstract graph model was developed to serve as an embryo for inferring graph models of some complex networks. The GP system and abstract model were used to reproduce well-known graph models. The results indicated that the system was able to evolve models that produced networks that had structural similarities to the networks generated by the respective target models.

The Impact of Personal Trainer’s Leadership Style on Self-Presentational Concerns, Enjoyment, Task Self-Efficacy and Intention to Exercise in an Introductory Weight Training Orientation

The purpose of the present study was to examine two leadership styles of personal trainers (bland versus enriched) to evaluate their effects on exercise-related outcomes. Participants were 103 university women with no previous experience weight training. Participants were randomly assigned to one of the two leadership style conditions. They completed primary measures prior to being introduced to the personal trainer. Next, participants completed an introductory weight training session, followed by post-manipulation measures. The leadership styles were successfully manipulated. Participants in the enriched leadership style condition reported significantly higher levels of enjoyment and intention to exercise. Participants in the bland leadership style condition reported significantly higher levels of social anxiety; no differences were found for task self-efficacy, self-presentational efficacy, social physique anxiety, or handgrip performance between groups. Thus, an enriched leadership style of personal trainers can increase positive psychological outcomes.

Inverse Illumination Design with Genetic Programming

Interior illumination is a complex problem involving numerous interacting factors. This research applies genetic programming towards problems in illumination design. The Radiance system is used for performing accurate illumination simulations. Radiance accounts for a number of important environmental factors, which we exploit during fitness evaluation. Illumination requirements include local illumination intensity from natural and artificial sources, colour, and uniformity. Evolved solutions incorporate design elements such as artificial lights, room materials, windows, and glass properties. A number of case studies are examined, including many-objective problems involving up to 7 illumination requirements, the design of a decorative wall of lights, and the creation of a stained-glass window for a large public space. Our results show the technical and creative possibilities of applying genetic programming to illumination design.

Disease-Gene Association Using Genetic Programming

As a result of mutation in genes, which is a simple change in our DNA, we will have undesirable phenotypes which are known as genetic diseases or disorders. These small changes, which happen frequently, can have extreme results. Understanding and identifying these changes and associating these mutated genes with genetic diseases can play an important role in our health, by making us able to find better diagnosis and therapeutic strategies for these genetic diseases. As a result of years of experiments, there is a vast amount of data regarding human genome and different genetic diseases that they still need to be processed properly to extract useful information. This work is an effort to analyze some useful datasets and to apply different techniques to associate genes with genetic diseases. Two genetic diseases were studied here: Parkinson’s disease and breast cancer. Using genetic programming, we analyzed the complex network around known disease genes of the aforementioned diseases, and based on that we generated a ranking for genes, based on their relevance to these diseases. In order to generate these rankings, centrality measures of all nodes in the complex network surrounding the known disease genes of the given genetic disease were calculated. Using genetic programming, all the nodes were assigned scores based on the similarity of their centrality measures to those of the known disease genes. Obtained results showed that this method is successful at finding these patterns in centrality measures and the highly ranked genes are worthy as good candidate disease genes for being studied. Using standard benchmark tests, we tested our approach against ENDEAVOUR and CIPHER - two well known disease gene ranking frameworks - and we obtained comparable results.

Feature Selection and Classification Using Age Layered Population Structure Genetic Programming

The curse of dimensionality is a major problem in the fields of machine learning, data mining and knowledge discovery. Exhaustive search for the most optimal subset of relevant features from a high dimensional dataset is NP hard. Sub–optimal population based stochastic algorithms such as GP and GA are good choices for searching through large search spaces, and are usually more feasible than exhaustive and deterministic search algorithms. On the other hand, population based stochastic algorithms often suffer from premature convergence on mediocre sub–optimal solutions. The Age Layered Population Structure (ALPS) is a novel metaheuristic for overcoming the problem of premature convergence in evolutionary algorithms, and for improving search in the fitness landscape. The ALPS paradigm uses an age–measure to control breeding and competition between individuals in the population. This thesis uses a modification of the ALPS GP strategy called Feature Selection ALPS (FSALPS) for feature subset selection and classification of varied supervised learning tasks. FSALPS uses a novel frequency count system to rank features in the GP population based on evolved feature frequencies. The ranked features are translated into probabilities, which are used to control evolutionary processes such as terminal–symbol selection for the construction of GP trees/sub-trees. The FSALPS metaheuristic continuously refines the feature subset selection process whiles simultaneously evolving efficient classifiers through a non–converging evolutionary process that favors selection of features with high discrimination of class labels. We investigated and compared the performance of canonical GP, ALPS and FSALPS on high–dimensional benchmark classification datasets, including a hyperspectral image. Using Tukey’s HSD ANOVA test at a 95% confidence interval, ALPS and FSALPS dominated canonical GP in evolving smaller but efficient trees with less bloat expressions. FSALPS significantly outperformed canonical GP and ALPS and some reported feature selection strategies in related literature on dimensionality reduction.

Feature Selection and Classification Using Age Layered Population Structure Genetic Programming

The curse of dimensionality is a major problem in the fields of machine learning, data mining and knowledge discovery. Exhaustive search for the most optimal subset of relevant features from a high dimensional dataset is NP hard. Sub–optimal population based stochastic algorithms such as GP and GA are good choices for searching through large search spaces, and are usually more feasible than exhaustive and determinis- tic search algorithms. On the other hand, population based stochastic algorithms often suffer from premature convergence on mediocre sub–optimal solutions. The Age Layered Population Structure (ALPS) is a novel meta–heuristic for overcoming the problem of premature convergence in evolutionary algorithms, and for improving search in the fitness landscape. The ALPS paradigm uses an age–measure to control breeding and competition between individuals in the population. This thesis uses a modification of the ALPS GP strategy called Feature Selection ALPS (FSALPS) for feature subset selection and classification of varied supervised learning tasks. FSALPS uses a novel frequency count system to rank features in the GP population based on evolved feature frequencies. The ranked features are translated into probabilities, which are used to control evolutionary processes such as terminal–symbol selection for the construction of GP trees/sub-trees. The FSALPS meta–heuristic continuously refines the feature subset selection process whiles simultaneously evolving efficient classifiers through a non–converging evolutionary process that favors selection of features with high discrimination of class labels. We investigated and compared the performance of canonical GP, ALPS and FSALPS on high–dimensional benchmark classification datasets, including a hyperspectral image. Using Tukey’s HSD ANOVA test at a 95% confidence interval, ALPS and FSALPS dominated canonical GP in evolving smaller but efficient trees with less bloat expressions. FSALPS significantly outperformed canonical GP and ALPS and some reported feature selection strategies in related literature on dimensionality reduction.

Examining peer-controlled KR schedules during the learning of a movement-timing task as a function of task experience.

Learners can be provided with feedback in the form of knowledge of results (KR), under self-controlled and peer-controlled schedules. Recently, McRae, Hansen, and Patterson (2015), identified that inexperienced peers can provide KR that can facilitate motor skill acquisition. However, it is currently unknown whether previous task experience differentially impacts how peers present learners with KR and whether this KR impacts motor skill acquisition. In the present study, participants were randomly assigned to become inexperienced peer facilitators, learners with an inexperienced peer, learners with self-control who later became experienced peers, learners with an experienced peer, or learners in a control group. During acquisition learners completed a serial-timing task with a goal of 2500ms and returned approximately twenty four hours later for a delayed retention, time transfer, and pattern transfer test. We predicted that during the delayed tests, learners with self-control would outperform all other groups. Furthermore, we predicted that learners who received KR from experienced peers would outperform learners who received KR from inexperienced peers. However, our results indicated that participants who received peer-controlled and self-controlled KR schedules learned the task in an equivalent manner. Thus, our results are novel as they identify that inexperienced peers can provide KR that is as effective as KR provided by experienced peers and KR requested under self-controlled conditions.

Implantation des futures sur un système distribué par passage de messages

Ce mémoire présente une implantation de la création paresseuse de tâches desti- née à des systèmes multiprocesseurs à mémoire distribuée. Elle offre un sous-ensemble des fonctionnalités du Message-Passing Interface et permet de paralléliser certains problèmes qui se partitionnent difficilement de manière statique grâce à un système de partitionnement dynamique et de balancement de charge. Pour ce faire, il se base sur le langage Multilisp, un dialecte de Scheme orienté vers le traitement parallèle, et implante sur ce dernier une interface semblable à MPI permettant le calcul distribué multipro- cessus. Ce système offre un langage beaucoup plus riche et expressif que le C et réduit considérablement le travail nécessaire au programmeur pour pouvoir développer des programmes équivalents à ceux en MPI. Enfin, le partitionnement dynamique permet de concevoir des programmes qui seraient très complexes à réaliser sur MPI. Des tests ont été effectués sur un système local à 16 processeurs et une grappe à 16 processeurs et il offre de bonnes accélérations en comparaison à des programmes séquentiels équiva- lents ainsi que des performances acceptables par rapport à MPI. Ce mémoire démontre que l’usage des futures comme technique de partitionnement dynamique est faisable sur des multiprocesseurs à mémoire distribuée.

Observational learning of motor skills : Looking for optimal models

L’observation d’un modèle pratiquant une habileté motrice promeut l’apprentissage de l’habileté en question. Toutefois, peu de chercheurs se sont attardés à étudier les caractéristiques d’un bon modèle et à mettre en évidence les conditions d’observation pouvant optimiser l’apprentissage. Dans les trois études composant cette thèse, nous avons examiné les effets du niveau d’habileté du modèle, de la latéralité du modèle, du point de vue auquel l’observateur est placé, et du mode de présentation de l’information sur l’apprentissage d’une tâche de timing séquentielle composée de quatre segments. Dans la première expérience de la première étude, les participants observaient soit un novice, soit un expert, soit un novice et un expert. Les résultats des tests de rétention et de transfert ont révélé que l’observation d’un novice était moins bénéfique pour l’apprentissage que le fait d’observer un expert ou une combinaison des deux (condition mixte). Par ailleurs, il semblerait que l’observation combinée de modèles novice et expert induise un mouvement plus stable et une meilleure généralisation du timing relatif imposé comparativement aux deux autres conditions. Dans la seconde expérience, nous voulions déterminer si un certain type de performance chez un novice (très variable, avec ou sans amélioration de la performance) dans l’observation d’une condition mixte amenait un meilleur apprentissage de la tâche. Aucune différence significative n’a été observée entre les différents types de modèle novices employés dans l’observation de la condition mixte. Ces résultats suggèrent qu’une observation mixte fournit une représentation précise de ce qu’il faut faire (modèle expert) et que l’apprentissage est d’autant plus amélioré lorsque l’apprenant peut contraster cela avec la performance de modèles ayant moins de succès. Dans notre seconde étude, des participants droitiers devaient observer un modèle à la première ou à la troisième personne. L’observation d’un modèle utilisant la même main préférentielle que soi induit un meilleur apprentissage de la tâche que l’observation d’un modèle dont la dominance latérale est opposée à la sienne, et ce, quel que soit l’angle d’observation. Ce résultat suggère que le réseau d’observation de l’action (AON) est plus sensible à la latéralité du modèle qu’à l’angle de vue de l’observateur. Ainsi, le réseau d’observation de l’action semble lié à des régions sensorimotrices du cerveau qui simulent la programmation motrice comme si le mouvement observé était réalisé par sa propre main dominante. Pour finir, dans la troisième étude, nous nous sommes intéressés à déterminer si le mode de présentation (en direct ou en vidéo) influait sur l’apprentissage par observation et si cet effet est modulé par le point de vue de l’observateur (première ou troisième personne). Pour cela, les participants observaient soit un modèle en direct soit une présentation vidéo du modèle et ceci avec une vue soit à la première soit à la troisième personne. Nos résultats ont révélé que l’observation ne diffère pas significativement selon le type de présentation utilisée ou le point de vue auquel l’observateur est placé. Ces résultats sont contraires aux prédictions découlant des études d’imagerie cérébrale ayant montré une activation plus importante du cortex sensorimoteur lors d’une observation en direct comparée à une observation vidéo et de la première personne comparée à la troisième personne. Dans l’ensemble, nos résultats indiquent que le niveau d’habileté du modèle et sa latéralité sont des déterminants importants de l’apprentissage par observation alors que le point de vue de l’observateur et le moyen de présentation n’ont pas d’effets significatifs sur l’apprentissage d’une tâche motrice. De plus, nos résultats suggèrent que la plus grande activation du réseau d’observation de l’action révélée par les études en imagerie mentale durant l’observation d’une action n’induit pas nécessairement un meilleur apprentissage de la tâche.

Evaluation of mitochondrial function in a model of developmental programming of hypertension associated with transient neonatal oxygen exposure

UNE EXPOSITION NÉONATALE À L’OXYGÈNE MÈNE À DES MODIFICATIONS DE LA FONCTION MITOCHONDRIALE CHEZ LE RAT ADULTE Introduction: L’exposition à l’oxygène (O2) des ratons nouveau-nés a des conséquences à l’âge adulte dont une hypertension artérielle (HTA), une dysfonction vasculaire, une néphropénie et des indices de stress oxydant. En considérant que les reins sont encore en développement actif lors des premiers jours après la naissance chez les rats, jouent un rôle clé dans le développement de l’hypertension et qu’une dysfonction mitochondriale est associé à une augmentation du stress oxydant, nous postulons que les conditions délétères néonatales peuvent avoir un impact significatif au niveau rénal sur la modulation de l’expression de protéines clés du fonctionnement mitochondrial et une production mitochondriale excessive d’espèces réactives de l’ O2. Méthodes: Des ratons Sprague-Dawley sont exposés à 80% d’O2 (H) ou 21% O2 (Ctrl) du 3e au 10e jr de vie. En considérant que plusieurs organes des rats sont encore en développement actif à la naissance, ces rongeurs sont un modèle reconnu pour étudier les complications d’une hyperoxie néonatale, comme celles liées à une naissance prématurée chez l’homme. À 4 et à 16 semaines, les reins sont prélevés et les mitochondries sont extraites suivant une méthode d’extraction standard, avec un tampon contenant du sucrose 0.32 M et différentes centrifugations. L’expression des protéines mitochondriales a été mesurée par Western blot, tandis que la production d’ H202 et les activités des enzymes clés du cycle de Krebs ont été évaluées par spectrophotométrie. Les résultats sont exprimés par la moyenne ± SD. Résultats: Les rats mâles H de 16 semaines (n=6) présentent une activité de citrate synthase (considéré standard interne de l’expression protéique et de l’abondance mitochondriales) augmentée (12.4 ± 8.4 vs 4.1 ± 0.5 μmole/mL/min), une diminution de l’activité d’aconitase (enzyme sensible au redox mitochondrial) (0.11 ± 0.05 vs 0.20 ± 0.04 μmoles/min/mg mitochondrie), ainsi qu’une augmentation dans la production de H202 (7.0 ± 1.3 vs 5.4 ± 0.8 ρmoles/mg protéines mitochondriales) comparativement au groupe Ctrl (n=6 mâles et 4 femelles). Le groupe H (vs Ctrl) présente également une diminution dans l’expression de peroxiredoxin-3 (Prx3) (H 0.61±0.06 vs. Ctrl 0.78±0.02 unité relative, -23%; p<0.05), une protéine impliquée dans l’élimination d’ H202, de l’expression du cytochrome C oxidase (Complexe IV) (H 1.02±0.04 vs. Ctrl 1.20±0.02 unité relative, -15%; p<0.05), une protéine de la chaine de respiration mitochondriale, tandis que l’expression de la protéine de découplage (uncoupling protein)-2 (UCP2), impliquée dans la dispersion du gradient proton, est significativement augmentée (H 1.05±0.02 vs. Ctrl 0.90±0.03 unité relative, +17%; p<0.05). Les femelles H (n=6) (vs Ctrl, n=6) de 16 semaines démontrent une augmentation significative de l’activité de l’aconitase (0.33±0.03 vs 0.17±0.02 μmoles/min/mg mitochondrie), de l’expression de l’ATP synthase sous unité β (H 0.73±0.02 vs. Ctrl 0.59±0.02 unité relative, +25%; p<0.05) et de l’expression de MnSOD (H 0.89±0.02 vs. Ctrl 0.74±0.03 unité relative, +20%; p<0.05) (superoxide dismutase mitochondriale, important antioxidant), tandis que l’expression de Prx3 est significativement réduite (H 1.1±0.07 vs. Ctrl 0.85±0.01 unité relative, -24%; p<0.05). À 4 semaines, les mâles H (vs Ctrl) présentent une augmentation significative de l’expression de Prx3 (H 0.72±0.03 vs. Ctrl 0.56±0.04 unité relative, +31%; p<0.05) et les femelles présentent une augmentation significative de l’expression d’UCP2 (H 1.22±0.05 vs. Ctrl 1.03±0.04 unité relative, +18%; p<0.05) et de l’expression de MnSOD (H 1.36±0.01 vs. 1.19±0.06 unité relative, +14%; p<0.05). Conclusions: Une exposition néonatale à l’O2 chez le rat adulte mène à des indices de dysfonction mitochondriale dans les reins adultes, associée à une augmentation dans la production d’espèces réactives de l’oxygène, suggérant que ces modifications mitochondriales pourraient jouer un rôle dans l’hypertension artérielle et d’un stress oxydant, et par conséquent, être un facteur possible dans la progression vers des maladies cardiovasculaires. Mots-clés: Mitochondries, Reins, Hypertension, Oxygène, Stress Oxydant, Programmation

Lagrangian-informed mixed integer programming reformulations

La programmation linéaire en nombres entiers est une approche robuste qui permet de résoudre rapidement de grandes instances de problèmes d'optimisation discrète. Toutefois, les problèmes gagnent constamment en complexité et imposent parfois de fortes limites sur le temps de calcul. Il devient alors nécessaire de développer des méthodes spécialisées afin de résoudre approximativement ces problèmes, tout en calculant des bornes sur leurs valeurs optimales afin de prouver la qualité des solutions obtenues. Nous proposons d'explorer une approche de reformulation en nombres entiers guidée par la relaxation lagrangienne. Après l'identification d'une forte relaxation lagrangienne, un processus systématique permet d'obtenir une seconde formulation en nombres entiers. Cette reformulation, plus compacte que celle de Dantzig et Wolfe, comporte exactement les mêmes solutions entières que la formulation initiale, mais en améliore la borne linéaire: elle devient égale à la borne lagrangienne. L'approche de reformulation permet d'unifier et de généraliser des formulations et des méthodes de borne connues. De plus, elle offre une manière simple d'obtenir des reformulations de moins grandes tailles en contrepartie de bornes plus faibles. Ces reformulations demeurent de grandes tailles. C'est pourquoi nous décrivons aussi des méthodes spécialisées pour en résoudre les relaxations linéaires. Finalement, nous appliquons l'approche de reformulation à deux problèmes de localisation. Cela nous mène à de nouvelles formulations pour ces problèmes; certaines sont de très grandes tailles, mais nos méthodes de résolution spécialisées les rendent pratiques.