Honey bees, Apis mellifera, play an important role in many ecosystems, pollinating a wide variety of native, agricultural, and exotic plants. The recent decline in the number of feral and managed honey bee colonies in North America, as well as the arrival of Africanized honey bees, have caused concern about adequate pollination for agricultural crops and natural plant communities. However, little is known about feral colonies, and the feral population is the source for Africanized honey bees as they spread and infiltrate managed populations. The goal of my dissertation was to examine the ecology of feral honey bee colonies, adding the spatial context necessary to understand the population ecology and patterns of resource use by feral honey bees on the Welder Wildlife Refuge. I defined the functional heterogeneity of feral honey bee habitat by identifying the suitability of different habitats for feral colonies based on the distribution and abundance of important resources (cavities, nectar, and pollen). I evaluated the distribution and abundance of feral colonies by examining nest site characteristics, population trends, and spatial and temporal patterns in cavity use. Lastly, I examined resource use by evaluating patterns in pollen collection and identifying where and when honey bees searched for resources. Overall, the Welder Wildlife Refuge provided excellent habitat for feral honey bees, supporting a high density of feral colonies. The dense live oak habitat was the best overall source for cavities, nectar, and pollen. Nectar and pollen were abundant throughout the year, with the exception of December and January, when a large number of honey bees searched for resources. Cavities did not appear to vary in their suitability for feral colonies based on measured structural and environmental attributes, since no cavity attributes were correlated with indices of cavity quality. However, the cavity quality indices varied between cavities, suggesting some cavities were more suitable for feral honey bees than others. Colonies were aggregated within the study area, probably due to the distribution of resources. The invasion of Africanized honey bees appeared to fragment the existing European population, with Africanized colonies aggregated in distribution and European colonies random in distribution.

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Given the overall increase in the availability of computational resources, and the importance of forecasting the future, it should come as no surprise that prediction is considered to be one of the most compelling and challenging problems for both academia and industry in the world of data analytics. But how is prediction done, what factors make it easier or harder to do, how accurate can we expect the results to be, and can we harness the available computational resources in meaningful ways? With efforts ranging from those designed to save lives in the moments before a near field tsunami to others attempting to predict the performance of Major League Baseball players, future generations need to have realistic expectations about prediction methods and analytics. This thesis takes a broad look at the problem, including motivation, methodology, accuracy, and infrastructure. In particular, a careful study involving experiments in regression, the prediction of continuous, numerical values, and classification, the assignment of a class to each sample, is provided. The results and conclusions of these experiments cover only the included data sets and the applied algorithms as implemented by the Python library. The evaluation includes accuracy and running time of different algorithms across several data sets to establish tradeoffs between the approaches, and determine the impact of variations in the size of the data sets involved. As scalability is a key characteristic required to meet the needs of future prediction problems, a discussion of some of the challenges associated with parallelization is included. / Graduate / 0984 / erickson@uvic.ca

This study aims to investigate the settlement patterns of Altinova in the Early Bronze Age and its reflection to social and cultural phenomena. Altinova, which is the most arable plain in Eastern Anatolia, is situated in the borders of Elazig province. The region in the Early Bronze Age was the conjunction and interaction area for two main cultural complexes in the Near East, which were Syro-Mesopotamia and Transcaucasia, with a strong local character. The effect of the foreign and local cultural interactions to the settlement patterns of Altinova in the Early Bronze Age and its reflection in the socio-economic structures have been discussed in the social perspective. In addition, the settlement distribution and its system were analyzed through the quantitative methods, that were gravity model, rank-size analysis, and nearest neighbor analysis. The results of these quantitative analyses with the archaeological data have been discussed in the social and theoretical context.

A k nearest neighbor query q retrieves k objects that lie closest to the query point q among a given set of objects P. With the availability of inexpensive location aware mobile devices, the continuous monitoring of such queries has gained lot of attention and many methods have been proposed for continuously monitoring the kNNs in highly dynamic environment. Multiple continuous queries require real-time results and both the objects and queries issue frequent location updates. Most popular spatial index, R-tree, is not suitable for continuous monitoring of these queries due to its inefficiency in handling frequent updates. Recently, the interest of database community has been shifting towards using grid-based index for continuous queries due to its simplicity and efficient update handling. For kNN queries, the order in which cells of the grid are accessed is very important. In this research, we present two efficient and effective grid access methods, CircularTrip and ArcTrip, that ensure that the number of cells visited for any continuous kNN query is minimum. Our extensive experimental study demonstrates that CircularTrip-based continuous kNN algorithm outperforms existing approaches in terms of both efficiency and space requirement. Moreover, we show that CircularTrip and ArcTrip can be used for many other variants of nearest neighbor queries like constrained nearest neighbor queries, farthest neighbor queries and (k + m)-NN queries. All the algorithms presented for these queries preserve the properties that they visit minimum number of cells for each query and the space requirement is low. Our proposed techniques are flexible and efficient and can be used to answer any query that is hybrid of above mentioned queries. For example, our algorithms can easily be used to efficiently monitor a (k + m) farthest neighbor query in a constrained region with the flexibility that the spatial conditions that constrain the region can be changed by the user at any time.

Com o crescente aumento no número de imagens geradas em mídias digitais surgiu a necessidade do desenvolvimento de novas técnicas de recuperação desses dados. Um critério de busca que pode ser utilizado na recuperação das imagens é o da dissimilaridade, no qual o usuário deseja recuperar as imagens semelhantes à uma imagem de consulta. Para a realização das consultas são empregados vetores de características extraídos das imagens e funções de distância para medir a dissimilaridade entre pares desses vetores. Infelizmente, a busca por conteúdo de imagens em consultas simples tende a gerar resultados que não correspondem ao interesse do usuário misturados aos resultados significativos encontrados, pois em geral há uma descontinuidade semântica entre as características extraídas automaticamente e a subjetividade da interpretação humana. Com o intuito de tratar esse problema, diversos métodos foram propostos para a diminuição da descontinuidade semântica. O foco principal desta tese é o desenvolvimento de métodos escaláveis para a redução da descontinuidade semântica em sistemas recuperação de imagens por conteúdo em tempo real. Nesta sentido, são apresentados: a formalização de consultas por similaridade que permitem a utilização de múltiplos centros de consulta em espaços métricos como base para métodos de realimentação de relevância; um método exato para otimização dessas consultas nesses espaços; e um modelo para tratamento da diversidade em consultas por similaridade e heurísticas para sua otimização / The increasing number of images captured in digital media fostered the developmet of new methods for the recovery of these images. Dissimilarity is a criteria that can be used for image retrieval, where the results are images that are similar to a given reference. The queries are based on feature vectors automatically extracted from the images and on distance functions to measure the dissimilarity between pair of vectors. Unfortunately, the search for images in simple queries may result in images that do not fulfill the user interest together with meaningful images, due to the semantic gap between the image features and to the subjectivity of the human interpretation. This problem leaded to the development of many methods to deal with the semantic gap. The focus of this thesis is the development of scalable methods aiming the semantic gap reduction in real time for content-based image retrieval systems. For this purpose, we present the formal definition of similarity queries based on multiple query centers in metric spaces to be used in relevance feedback methods, an exact method to optimize these queries and a model to deal with diversity in nearest neighbor queries including heuristics for its optimization

Essay I: Employment Security Agreements, which are elements of Swedish collective agreements, offer a unique opportunity to study very early job search counselling of displaced workers. These agreements provide individual job search assistance to workers who are dismissed due to redundancy, often as early as during the period of notice. Compared to traditional labor market policies, the assistance provided is earlier and more responsive to the needs of the individual worker. In this study, I investigate the effects of the individual counseling and job search assistance provided through the Employment Security Agreement for Swedish blue-collar workers on job finding and subsequent job quality. The empirical strategy is based on the rules of eligibility in a regression discontinuity framework. I estimate the effect for workers with short tenure, who are dismissed through mass-layoffs. My results do not suggest that the program has an effect on the probability of becoming unemployed, the duration of unemployment, or income. However, the results indicate that the program has a positive effect on the duration of the next job. Essay II: The well-known positive relationship between the unemployment benefit level and unemployment duration can be separated into two potential sources; a moral hazard effect, and a liquidity effect pertaining to the increased ability to smooth consumption. The latter is a socially optimal response due to credit and insurance market failures. These two effects are difficult to separate empirically, but the social optimality of an unemployment insurance policy can be evaluated by studying the effect of a non-distortionary lump-sum severance grant on unemployment durations. In this study, I evaluate the effects on unemployment duration and subsequent job quality of a lump-sum severance grant provided to displaced workers, by means of a Swedish collective agreement. I use a regression discontinuity design, based on the strict age requirement to be eligible for the grant. I find that the lump-sum grant has a positive effect on the probability of becoming unemployed and the length of the completed unemployment duration, but no effect on subsequent job quality. My analysis also indicates that spousal income is important for the consumption smoothing abilities of displaced workers, and that the grant may have a greater effect in times of more favorable labor market conditions. Essay III: Evidence from around the world suggest that individuals who are awarded disability benefits in some cases still have residual working capacity, while disability insurance systems typically involve strong disincentives for benefit recipients to work. Some countries have introduced policies to incentivize disability insurance recipients to use their residual working capacities on the labor market. One such policy is the continuous deduction program in Sweden, introduced in 2009. In this study, I investigate whether the financial incentives provided by this program induce disability insurance recipients to increase their labor supply or education level. Retroactively determined eligibility to the program with respect to time of benefit award provides a setting resembling a natural experiment, which could be used to estimate the effects of the program using a regression discontinuity design. However, a simultaneous regime change of disability insurance eligibility causes covariate differences between treated and controls, which I adjust for using a matching strategy. My results suggest that the financial incentives provided by the program have not had any effect on labor supply or educational attainment.

Dans de nombreux problèmes de classification, les données sont intrinsèquement incertaines. Les données d’apprentissage disponibles peuvent être imprécises, incomplètes, ou même peu fiables. En outre, des connaissances spécialisées partielles qui caractérisent le problème de classification peuvent également être disponibles. Ces différents types d’incertitude posent de grands défis pour la conception de classifieurs. La théorie des fonctions de croyance fournit un cadre rigoureux et élégant pour la représentation et la combinaison d’une grande variété d’informations incertaines. Dans cette thèse, nous utilisons cette théorie pour résoudre les problèmes de classification des données incertaines sur la base de deux approches courantes, à savoir, la méthode des k plus proches voisins (kNN) et la méthode à base de règles.Pour la méthode kNN, une préoccupation est que les données d’apprentissage imprécises dans les régions où les classes de chevauchent peuvent affecter ses performances de manière importante. Une méthode d’édition a été développée dans le cadre de la théorie des fonctions de croyance pour modéliser l’information imprécise apportée par les échantillons dans les régions qui se chevauchent. Une autre considération est que, parfois, seul un ensemble de données d’apprentissage incomplet est disponible, auquel cas les performances de la méthode kNN se dégradent considérablement. Motivé par ce problème, nous avons développé une méthode de fusion efficace pour combiner un ensemble de classifieurs kNN couplés utilisant des métriques couplées apprises localement. Pour la méthode à base de règles, afin d’améliorer sa performance dans les applications complexes, nous étendons la méthode traditionnelle dans le cadre des fonctions de croyance. Nous développons un système de classification fondé sur des règles de croyance pour traiter des informations incertains dans les problèmes de classification complexes. En outre, dans certaines applications, en plus de données d’apprentissage, des connaissances expertes peuvent également être disponibles. Nous avons donc développé un système de classification hybride fondé sur des règles de croyance permettant d’utiliser ces deux types d’information pour la classification. / In many classification problems, data are inherently uncertain. The available training data might be imprecise, incomplete, even unreliable. Besides, partial expert knowledge characterizing the classification problem may also be available. These different types of uncertainty bring great challenges to classifier design. The theory of belief functions provides a well-founded and elegant framework to represent and combine a large variety of uncertain information. In this thesis, we use this theory to address the uncertain data classification problems based on two popular approaches, i.e., the k-nearest neighbor rule (kNN) andrule-based classification systems. For the kNN rule, one concern is that the imprecise training data in class over lapping regions may greatly affect its performance. An evidential editing version of the kNNrule was developed based on the theory of belief functions in order to well model the imprecise information for those samples in over lapping regions. Another consideration is that, sometimes, only an incomplete training data set is available, in which case the ideal behaviors of the kNN rule degrade dramatically. Motivated by this problem, we designedan evidential fusion scheme for combining a group of pairwise kNN classifiers developed based on locally learned pairwise distance metrics.For rule-based classification systems, in order to improving their performance in complex applications, we extended the traditional fuzzy rule-based classification system in the framework of belief functions and develop a belief rule-based classification system to address uncertain information in complex classification problems. Further, considering that in some applications, apart from training data collected by sensors, partial expert knowledge can also be available, a hybrid belief rule-based classification system was developed to make use of these two types of information jointly for classification.

L'objectif de cette thèse est d'étudier le comportement local d'une mesure de probabilité, notamment à l'aide d'un indice de régularité locale. Dans la première partie, nous établissons la normalité asymptotique de l'estimateur des kn plus proches voisins de la densité. Dans la deuxième, nous définissons un estimateur du mode sous des hypothèses affaiblies. Nous montrons que l'indice de régularité intervient dans ces deux problèmes. Enfin, nous construisons dans une troisième partie différents estimateurs pour l'indice de régularité à partir d'estimateurs de la fonction de répartition, dont nous réalisons une revue bibliographique. / The goal of this thesis is to study the local behavior of a probability measure, using a local regularity index. In the first part, we establish the asymptotic normality of the nearest neighbor density estimate. In the second, we define a mode estimator under weakened hypothesis. We show that the regularity index interferes in this two problems. Finally, we construct in a third part various estimators of the regularity index from estimators of the distribution function, which we achieve a review.

La principale contribution de cette thèse est le développement d'une méthode fiable, robuste, et rapide pour la prédiction des pré-miARNs. Deux objectifs avaient été assignés : efficacité et flexibilité. L'efficacité a été rendue possible au moyen d'un algorithme quadratique. La flexibilité repose sur deux aspects, la nature des données expérimentales et la position taxonomique de l'organisme (en particulier plantes ou animaux). Mirinho accepte en entrée des séquences de génomes complets mais aussi les très nombreuses séquences résultant d'un séquençage massif de type NGS de “RNAseq”. “L'universalité” taxonomique est obtenu par la possibilité de modifier les contraintes sur les tailles de la tige (double hélice) et de la boule terminale. Dans le cas de la prédiction des miARN de plantes la plus grande longueur de leur pré-miARN conduit à des méthodes d'extraction de la structure secondaire en tige-boule moins précises. Mirinho prend en compte ce problème lui permettant de fournir des structures secondaires de pré-miARN plus semblables à celles de miRBase que les autres méthodes disponibles. Mirinho a été utilisé dans le cadre de deux questions biologiques précises l'une concernant des RNAseq l'autre de l'ADN génomique. La première question a conduit au traitement et l'analyse des données RNAseq de Acyrthosiphon pisum, le puceron du pois. L'objectif était d'identifier les miARN qui sont différentiellement exprimés au cours des quatre stades de développement de cette espèce et sont donc des candidats à la régulation des gènes au cours du développement. Pour cette analyse, nous avons développé un pipeline, appelé MirinhoPipe. La deuxieme question a permis d'aborder les problèmes liés à la prévision et l'analyse des ARN non-codants (ARNnc) dans la bactérie Mycoplasma hyopneumoniae. Alvinho a été développé pour la prédiction de cibles des miRNA autour d'une segmentation d'une séquence numérique et de la détection de la conservation des séquences entre ncRNA utilisant un graphe k-partite. Nous avons finalement abordé un problème lié à la recherche de motifs conservés dans un ensemble de séquences et pouvant ainsi correspondre à des éléments fonctionnels / The main contribution of this thesis is the development of a reliable, robust, and much faster method for the prediction of pre-miRNAs. With this method, we aimed mainly at two goals: efficiency and flexibility. Efficiency was made possible by means of a quadratic algorithm. Flexibility relies on two aspects, the input type and the organism clade. Mirinho can receive as input both a genome sequence and small RNA sequencing (sRNA-seq) data of both animal and plant species. To change from one clade to another, it suffices to change the lengths of the stem-arms and of the terminal loop. Concerning the prediction of plant miRNAs, because their pre-miRNAs are longer, the methods for extracting the hairpin secondary structure are not as accurate as for shorter sequences. With Mirinho, we also addressed this problem, which enabled to provide pre-miRNA secondary structures more similar to the ones in miRBase than the other available methods. Mirinho served as the basis to two other issues we addressed. The first issue led to the treatment and analysis of sRNA-seq data of Acyrthosiphon pisum, the pea aphid. The goal was to identify the miRNAs that are expressed during the four developmental stages of this species, allowing further biological conclusions concerning the regulatory system of such an organism. For this analysis, we developed a whole pipeline, called MirinhoPipe, at the end of which Mirinho was aggregated. We then moved on to the second issue, that involved problems related to the prediction and analysis of non-coding RNAs (ncRNAs) in the bacterium Mycoplasma hyopneumoniae. A method, called Alvinho, was thus developed for the prediction of targets in this bacterium, together with a pipeline for the segmentation of a numerical sequence and detection of conservation among ncRNA sequences using a kpartite graph. We finally addressed a problem related to motifs, that is to patterns, that may be composed of one or more parts, that appear conserved in a set of sequences and may correspond to functional elements.