The dissertation seeks to meld the two dominant competing theories of party identifi?cation in the US context: the expressive view, where Party ID is seen as a long standing psychological attachment to a political party; and the instrumental view, where Party ID is subject to reevaluation. Using ANES panel data, the paper examines both expressive and instrumental elements of partisanship. In keeping with past research, it finds strong evidence for the expressive understanding of Party ID; partisan groupings tend to be highly stable. However, the strength of identifications varies considerably over time, with perceptions of candidates, presidential approval, policy preferences, and ideological orientations driving these changes. These results are in keeping with an instrumental conceptualization of partisan identities.

The thesis analyses the customer behavior of Kiwi.com, a global online retail company for booking flights and proposes marketing interventions to increase the conversion rates in various customer segments. The effectiveness of new behavioral interventions is tested against current marketing efforts using experimental A/B methods. Additionally, areas for further improvements are explored and a design of future product features and marketing behavioral interventions is proposed.

In this thesis, we describe an approach that won a psychiatric symptom severity prediction challenge. The challenge was to correctly predict the severity of psychiatric symptoms on a 4-point scale. Our winning submission uses a novel stacked machine learning architecture in which (i) a base data ingestion/cleaning step was followed by the (ii) derivation of a base set of features defined using text analytics, after which (iii) association rule learning was used in a novel way to generate new features, followed by a (iv) feature selection step to eliminate irrelevant features, followed by a (v) classifier training algorithm in which a total of 22 classifiers including new classifier variants of AdaBoost and RandomForest were trained on seven different data views, and (vi) finally an ensemble learning step, in which ensembles of best learners were used to improve on the accuracy of individual learners. All of this was tested via standard 10-fold cross-validation on training data provided by the N-GRID challenge organizers, of which the three best ensembles were selected for submission to N-GRID's blind testing. The best of our submitted solutions garnered an overall final score of 0.863 according to the organizer's measure. All 3 of our submissions placed within the top 10 out of the 65 total submissions. The challenge constituted Track 2 of the 2016 Centers of Excellence in Genomic Science (CEGS) Neuropsychiatric Genome-Scale and RDOC Individualized Domains (N-GRID) Shared Task in Clinical Natural Language Processing.

Le satellite spatial Kepler a détecté des oscillations de type solaire parmi plusieurs centaines d'étoiles, permettant la détermination de leurs propriétés physiques à l'aide de l’astérosismologie. Les modèles d'évolution stellaire et les lois d'échelle employés pour déterminer les paramètres tels que la masse, le rayon et l'âge nécessitent toutefois une calibration adaptée. Dans ce contexte, l'utilisation des systèmes binaires présentant des oscillations de type solaires pour les deux étoiles semble particulièrement appropriée. Au cours de cette thèse, nous avons procédé à un travail de détection de ces binaires sismiques parmi les données de Kepler ainsi qu'au développement des outils nécessaires à leur analyse. Bien que la découverte d'une nouvelle binaire sismique semblait très peu probable, nous avons pu rapporter pour la toute première fois la détection d'oscillations de type solaire associées aux deux étoiles les plus brillantes d'un système triple, à savoir HD 188753. À partir de la modélisation, nous avons déterminé des âges semblables pour les deux étoiles détectées en astérosismologie, comme attendu en raison de leur origine commune. Par ailleurs, nous avons entrepris la première analyse orbitale de ce système hiérarchique dans le but d'obtenir une estimation directe des masses et de la parallaxe. Finalement, l'exemple de HD 188753 illustre notre capacité à détecter et à modéliser chacune des étoiles d'un système binaire ou multiple tout en réalisant l'analyse orbitale de celui-ci. Les différents outils développés au cours de cette thèse seront intensivement utilisés dans le cadre des futures missions TESS et PLATO. / The Kepler space telescope detected solar-like oscillations in several hundreds of stars, providing a way to determine their physical properties using asteroseismology. However, the stellar evolutionary models and scaling relations employed to determine parameters such as the mass, the radius and the age require a proper calibration. In this context, the use of seismic binaries showing solar-like oscillations in both stars is especially suitable. During this thesis, we have worked towards the detection of such seismic binaries from the Kepler database and developed the necessary tools to study them. Although the discovery of a new seismic binary was very unlikely, we were able to report for the first time the detection of solar-like oscillations in the two brightest stars of a triple stellar system, namely HD 188753. Using stellar modelling, we found compatible ages for the two stars derived from asteroseismology, as expected from their common origin. In addition, we performed the first orbital analysis of this hierarchical system in order to derive a direct estimate of masses and parallax. Finally, the example of HD 188753 shows our capability to detect and model each of the stars of a binary or multiple system and to perform the orbital analysis of this one. The various tools developed during this thesis will be extensively used in the context of the future missions TESS and PLATO.

The purpose of the study was to determine how South African mathematics teachers were integrating computers into their classrooms. The study was a response to the low achievement scores in mathematics as attained by grade nine learners in the 2011 Trends in International Mathematics and Science Study (TIMSS). TIMSS 2011 assessed Grade four and eight learners. However, South Africa as well as Botswana and Honduras opted to administer the Grade eight assessment to their Grade nine learners instead. South Africa’s Grade nine learners achieved an average score of 352 (35.2%) out of a possible 1 000 points. This quantitative secondary data analysis study utilised data collected from mathematics teachers from 298 schools in South Africa. The dataset was analysed using descriptive analysis that included percentages as well as the Pearson two-way Chi-square tabulations. The major finding of the study is that 73. 9% of South African mathematics teachers are still not integrating computers into mathematics education. Results showed that teachers are mostly using computers for preparation (35.5%) and administration (65.3%) purposes. Even though 45.5% of the teachers reported that they feel comfortable using computers, others feel that they are still in need of technical support. Moreover, the findings showed that 64.8% of the teachers do not attend professional development programmes that focus on the integration of Information Technology (IT) into mathematics. / Dissertation (MEd)--University of Pretoria, 2017. / Science, Mathematics and Technology Education / MEd / Unrestricted

of the Ph.D. Thesis Netrdová, P.: Current trends in quantitative analysis of geographical data: potentialities and limitations of spatial analysis The thesis is a contribution to the discussion about the potentialities of the quantitative approach in geography. It follows the current trends in quantitative analysis of geographical data, specifically spatial analysis, particularly from the perspective of changes in the concept and character of applied methods and their possible contribution in geographical research. Due to the research focus of the author, the entire work is focused primarily on the issue of using quantitative methods in terms of social geography. Attention is focused particularly on statistically spatial analyses, which are the most widely used techniques in social geography, with a wide range of possible applications. One of the goals of this work is to bring the current development in quantitative geography closer to the Czech academic community, and thus contribute to the increased awareness of the potentialities of the application of quantitative methods and spatial analyses in geographical research. Methodological problems in the analysis of spatial data, theoretical changes in the concept of quantitative analysis and also newly emerging quantitative methods have not so far...

In the thesis we study the decay of B0 meson to D∗ s and ρ mesons. The thesis explains the methods and approaches to data analysis in so-called B-factories, simmilar to the KEKB accelerator. The aim of this thesis is to calculate the branching fraction of this decay to further improve the previously measured branching ratio at BaBar experiment with additional data gained from an ex- periment with higher integrated luminosity. This thesis's prospect is not only broadening our knowledge of branching fractions of B0 meson decays, but also it is a starting point for further analysis with the goal of broadening our knowl- edge of CP symmetry violation in the Standard Model by measuring angles of the Unitary Triangle. 1

Die Fähigkeit zur Analyse großer Datenmengen sowie das extrahieren wichtiger Erkenntnisse daraus, sind in der modernen Unternehmenswelt ein entscheidender Wettbewerbsvorteil geworden. Umso wichtiger ist es, dabei vor allem nachvollziehbar, reproduzierbar und effizient vorzugehen. Der Beitrag stellt mit dem Instrument der skriptbasierten Datenanalyse eine Möglichkeit vor, um diesen Anforderungen gerecht zu werden.

archives@tulane.edu / The current system for evaluating prostate cancer architecture is the Gleason Grade system, which divides the morphology of cancer into five distinct architectural patterns, labeled numerically in increasing levels of cancer aggressiveness and generates a score by summing the labels of the two most dominant patterns. The Gleason score is currently the most powerful prognostic predictor of patient outcomes; however, it suffers from problems in reproducibility and consistency due to the high intra-observer and inter-observer variability among pathologists. In addition, the Gleason system lacks the granularity to address potentially prognostic architectural features beyond Gleason patterns. We look towards persistent homology, a tool from topological data analysis, to provide a means of evaluating prostate cancer glandular architecture. The objective of this work is to demonstrate the capacity of persistent homology to capture architectural features independently of Gleason patterns in a representation suitable for unsupervised and supervised machine learning. Specifically, using persistent homology, we compute topological representations of purely graded prostate cancer histopathology images of Gleason patterns and show that persistent homology is capable of clustering prostate cancer histology into architectural groups through discrete representations of persistent homology in both two-dimensional and three-dimensional histopathology. We then demonstrate the performance of persistent homology based features in common machine learning classifiers, indicating that persistent homology can both separate unique architectures in prostate cancer, but is also predictive of prostate cancer aggressiveness. Our results indicate the ability of persistent homology to cluster into unique groups with dominant architectural patterns consistent with the continuum of Gleason patterns. In addition, of particular interest, is the sensitivity of persistent homology to identify specific sub-architectural groups within single Gleason patterns, suggesting that persistent homology could represent a robust quantification method for prostate cancer architecture with higher granularity than the existing semi-quantitative measures. This work develops a framework for segregating prostate cancer aggressiveness by architectural subtype using topological representations, in a supervised machine learning setting, and lays the groundwork for augmenting traditional approaches with topological features for improved diagnosis and prognosis. / 1 / Peter Lawson

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