Evolutionary synthesis of stochastic gene network models using feature-based search spaces

A feature-based fitness function is applied in a genetic programming system to synthesize stochastic gene regulatory network models whose behaviour is defined by a time course of protein expression levels. Typically, when targeting time series data, the fitness function is based on a sum-of-errors involving the values of the fluctuating signal. While this approach is successful in many instances, its performance can deteriorate in the presence of noise. This thesis explores a fitness measure determined from a set of statistical features characterizing the time series' sequence of values, rather than the actual values themselves. Through a series of experiments involving symbolic regression with added noise and gene regulatory network models based on the stochastic 'if-calculus, it is shown to successfully target oscillating and non-oscillating signals. This practical and versatile fitness function offers an alternate approach, worthy of consideration for use in algorithms that evaluate noisy or stochastic behaviour.

The convergence of psychopathy, self-rated vulnerability, and other-rated vulnerability

Previous research has found that victims of crime tend to exhibit asynchronous movement (e.g. Grayson & Stein, 1981), and the fact that victims display different body language suggests that they may be sending inadvertent signals to their own vulnerability (e.g. Murzynski & Degelman, 1996). Body language has also be en linked with s e l f identification as a victim (Wheeler et aI., 2009), and self-identification has be en found to act as a proxy for more severe victimization (Baumer, 2002) and greater fear of crime (Greenberg & Beach, 2004). The first prediction in the present study, then, was that self-perceived vulnerability would be correlated with body language, while number of previous victimizations mayor may not show the same relationship. Findings from the present study indicate that self-perceived vulnerability exhibits a positive correlation with the body language cues that approaches significance r (10) = .45,p =.07, one-tailed. Different types of victimization, however, were not significantly correlated with these cues. A second goal of the study was to examine the relationship between psychopathic traits and accuracy in judgments of vulnerability. Seventy male participants rated the vulnerability of 12 female targets filmed walking down a hallway who had provided selfratings of vulnerability. Individuals scoring higher on Factor 2 and total psychopathy were significantly less discrepant from target self-rat~ngs of vulnerability, r (64) = - .39,p < .001; r (64) = - .29,p >.01, respectively. The final purpose of this study was to determine which body language cues were mos t salient to raters when making judgments of vulnerability. Participants rated the apparent vulnerability of a target in 7 video clips portraying each body language cue in isolation and a natural walk. Results of repeated measures analyses indicate that the videos rated as most vulnerable to victimization were those displaying low energy and l a ck of synchrony, followed by wide stride, short stride, and stiffknees, while the video displaying ne ck stiffness did not receive significantly different ratings from the mode l ' s natural walk. Replication with a larger sample size is necessary to increase confidence in findings and implications.

In Search of Convergence: A Co-citation Analysis of Three Sport Management Journals

Responding to a series of articles in sport management literature calling for more diversity in terms of areas of interest or methods, this study warns against the danger of excessively fragmenting this field of research. The works of Kuhn (1962) and Pfeffer (1993) are taken as the basis of an argument that connects convergence with scientific strength. However, being aware of the large number of counterarguments directed at this line of reasoning, a new model of convergence, which focuses on clusters of research contributions with similar areas of interest, methods, and concepts, is proposed. The existence of these clusters is determined with the help of a bibliometric analysis of publications in three sport management journals. This examination determines that there are justified reasons to be concerned about the level of convergence in the field, pointing out to a reduced ability to create large clusters of contributions in similar areas of interest.

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.

Un exemple de convergence en études médiévales : le droit canonique

Le Code de droit canonique, que l’on appelle aussi droit ecclésiastique, constitue le corpus législatif de l’Église catholique. Depuis son ébauche au Moyen-Âge jusqu’à présent, on découvre, à travers son évolution, la richesse morale, théologique, historique et sociologique de son contenu, malgré l’apparente froideur de la lettre. C’est donc avec une attitude d’ouverture qu’il convient d’aborder le code, de façon à faire apparaître, au-delà de sa rigueur, sa profondeur et sa richesse. Les collections de la Bibliothèque des lettres et sciences humaines de l’Université de Montréal sont en mesure d’appuyer ce dévoilement.

Le modèle de convergence vu selon l'approche de Dan, Ben-David: une vérification empirique par rapport au 8e cycle du GATT; le cas de l'ALENA versus l'Union Européenne (période 1970-2000).

Rapport de recherche

Convergence, investissements directs étrangers et échanges commerciaux: le club de convergence pour les pays latino-américains.

Rapport de recherche

Étude économétrique de la convergence des pays du Franc CFA.

Rapport de recherche

La convergence des niveaux de revenus et l'Union européenne: Les pays de l'Union Européenne ont-ils plus tendance à converger que les autres pays du reste du monde?

Rapport de recherche

The Impact of International Trade and Domestic Savings on Convergence in China.

Rapport de recherche

Convergence des pays d'Afrique du Nord.

Rapport de recherche

Étude de faisabilité d'une union monétaire entre le Canada et les États-Unis: Évaluation de la convergence des déterminants du taux de change réel.

Rapport de recherche

Latent Variable Models for Stochastic Discount Factors.

Latent variable models in finance originate both from asset pricing theory and time series analysis. These two strands of literature appeal to two different concepts of latent structures, which are both useful to reduce the dimension of a statistical model specified for a multivariate time series of asset prices. In the CAPM or APT beta pricing models, the dimension reduction is cross-sectional in nature, while in time-series state-space models, dimension is reduced longitudinally by assuming conditional independence between consecutive returns, given a small number of state variables. In this paper, we use the concept of Stochastic Discount Factor (SDF) or pricing kernel as a unifying principle to integrate these two concepts of latent variables. Beta pricing relations amount to characterize the factors as a basis of a vectorial space for the SDF. The coefficients of the SDF with respect to the factors are specified as deterministic functions of some state variables which summarize their dynamics. In beta pricing models, it is often said that only the factorial risk is compensated since the remaining idiosyncratic risk is diversifiable. Implicitly, this argument can be interpreted as a conditional cross-sectional factor structure, that is, a conditional independence between contemporaneous returns of a large number of assets, given a small number of factors, like in standard Factor Analysis. We provide this unifying analysis in the context of conditional equilibrium beta pricing as well as asset pricing with stochastic volatility, stochastic interest rates and other state variables. We address the general issue of econometric specifications of dynamic asset pricing models, which cover the modern literature on conditionally heteroskedastic factor models as well as equilibrium-based asset pricing models with an intertemporal specification of preferences and market fundamentals. We interpret various instantaneous causality relationships between state variables and market fundamentals as leverage effects and discuss their central role relative to the validity of standard CAPM-like stock pricing and preference-free option pricing.