Recent advancements in data acquisition, storage, and computing power have enabled scientists from various scientific and engineering domains to simulate more complex and longer phenomena. Scientists are usually interested in understanding the behavior of a phenomenon in different conditions. To do so, they run multiple simulations with different configurations (i.e., parameter settings, boundary/initial conditions, or computational models), resulting in an ensemble dataset. An ensemble empowers scientists to quantify the uncertainty in the simulated phenomenon in terms of the variability between ensemble members, the parameter sensitivity and optimization, and the characteristics and outliers within the ensemble members, which could lead to valuable insight(s) about the simulated model. The size, complexity, and high dimensionality (e.g., simulation input and output parameters) of simulation ensembles pose a great challenge in their analysis and exploration. Ensemble visualization provides a convenient way to convey the main characteristics of the ensemble for enhanced understanding of the simulated model. The majority of the current ensemble visualization techniques are mainly focused on analyzing either the ensemble space or the parameter space. Most of the parameter space visualizations are not designed for high-dimensional data sets or did not show the intrinsic structures in the ensemble. Conversely, ensemble space has been visualized either as a comparative visualization of a limited number of ensemble members or as an aggregation of multiple ensemble members omitting potential details of the original ensemble. Thus, to unfold the full potential of simulation ensembles, we designed and developed an approach to the visual analysis of high-dimensional simulation ensembles that merges sensemaking, human expertise, and intuition with machine learning and statistics. In this work, we explore how semantic interaction and sensemaking could be used for building interactive and intelligent visual analysis tools for simulation ensembles. Specifically, we focus on the complex processes that derive meaningful insights from exploring and iteratively refining the analysis of high dimensional simulation ensembles when prior knowledge about ensemble features and correlations is limited or/and unavailable. We first developed GLEE (Graphically-Linked Ensemble Explorer), an exploratory visualization tool that enables scientists to analyze and explore correlations and relationships between non-spatial ensembles and their parameters. Then, we developed Spatial GLEE, an extension to GLEE that explores spatial data while simultaneously considering spatial characteristics (i.e., autocorrelation and spatial variability) and dimensionality of the ensemble. Finally, we developed Image-based GLEE to explore exascale simulation ensembles produced from in-situ visualization. We collaborated with domain experts to evaluate the effectiveness of GLEE using real-world case studies and experiments from different domains. The core contribution of this work is a visual approach that enables the exploration of parameter and ensemble spaces for 2D/3D high dimensional ensembles simultaneously, three interactive visualization tools that explore search, filter, and make sense of non-spatial, spatial, and image-based ensembles, and usage of real-world cases from different domains to demonstrate the effectiveness of the proposed approach. The aim of the proposed approach is to help scientists gain insights by answering questions or testing hypotheses about the different aspects of the simulated phenomenon or/and facilitate knowledge discovery of complex datasets. / Doctor of Philosophy / Scientists run simulations to understand complex phenomena and processes that are expensive, difficult, or even impossible to reproduce in the real world. Current advancements in high-performance computing have enabled scientists from various domains, such as climate, computational fluid dynamics, and aerodynamics to run more complex simulations than before. However, a single simulation run would not be enough to capture all features in a simulated phenomenon. Therefore, scientists run multiple simulations using perturbed input parameters, initial and boundary conditions, or different models resulting in what is known as an ensemble. An ensemble empowers scientists to understand the model's behavior by studying relationships between and among ensemble members, the optimal parameter settings, and the influence of input parameters on the simulation output, which could lead to useful knowledge and insights about the simulated phenomenon. To effectively analyze and explore simulation ensembles, visualization techniques play a significant role in facilitating knowledge discoveries through graphical representations. Ensemble visualization offers scientists a better way to understand the simulated model. Most of the current ensemble visualization techniques are designed to analyze or/and explore either the ensemble space or the parameter space. Therefore, we designed and developed a visual analysis approach for exploring and analyzing high-dimensional parameter and ensemble spaces simultaneously by integrating machine learning and statistics with sensemaking and human expertise. The contribution of this work is to explore how to use semantic interaction and sensemaking to explore and analyze high-dimensional simulation ensembles. To do so, we designed and developed a visual analysis approach that manifested in an exploratory visualization tool, GLEE (Graphically-Linked Ensemble Explorer), that allowed scientists to explore, search, filter, and make sense of high dimensional 2D/3D simulations ensemble. GLEE's visualization pipeline and interaction techniques used deep learning, feature extraction, spatial regression, and Semantic Interaction (SI) techniques to support the exploration of non-spatial, spatial, and image-based simulation ensembles. GLEE different visualization tools were evaluated with domain experts from different fields using real-world case studies and experiments.

The increasing variety of data mining tools offers a large palette of types and representation formats for predictive models. Managing the models then becomes a big challenge, as well as reusing the models and keeping the consistency of model and data repositories. Sustainable access and quality assessment of these models become limited to researchers. The approach for the Data and Model Governance (DMG) makes easier to process and support complex solutions. In this thesis, contributions are proposed towards ensembles of models with a focus on model representation, comparison and usage. Predictive Toxicology was chosen as an application field to demonstrate the proposed approach to represent predictive models linked to data for DMG. Further analysing methods such as predictive models comparison and predictive models combination for reusing the models from a collection of models were studied. Thus in this thesis, an original structure of the pool of models was proposed to represent predictive toxicology models called Predictive Toxicology Markup Language (PTML). PTML offers a representation scheme for predictive toxicology data and models generated by data mining tools. In this research, the proposed representation offers possibilities to compare models and select the relevant models based on different performance measures using proposed similarity measuring techniques. The relevant models were selected using a proposed cost function which is a composite of performance measures such as Accuracy (Acc), False Negative Rate (FNR) and False Positive Rate (FPR). The cost function will ensure that only quality models be selected as the candidate models for an ensemble. The proposed algorithm for optimisation and combination of Acc, FNR and FPR of ensemble models using double fault measure as the diversity measure improves Acc between 0.01 to 0.30 for all toxicology data sets compared to other ensemble methods such as Bagging, Stacking, Bayes and Boosting. The highest improvements for Acc were for data sets Bee (0.30), Oral Quail (0.13) and Daphnia (0.10). A small improvement (of about 0.01) in Acc was achieved for Dietary Quail and Trout. Important results by combining all the three performance measures are also related to reducing the distance between FNR and FPR for Bee, Daphnia, Oral Quail and Trout data sets for about 0.17 to 0.28. For Dietary Quail data set the improvement was about 0.01 though, but this data set is well known as a difficult learning exercise. For five UCI data sets tested, similar results were achieved with Acc improvement between 0.10 to 0.11, closing more the gaps between FNR and FPR. As a conclusion, the results show that by combining performance measures (Acc, FNR and FPR), as proposed within this thesis, the Acc increased and the distance between FNR and FPR decreased.

No description available.

No description available.

No description available.

Note: Sheet music available upon request.

Savoir si un ensemble est de capacité nulle ou connaître sa dimension capacitaire est une question importante. De nombreux articles (tels que [3], [5], [6]) ont élucidé la question dans le cas de certains ensembles de Cantor. Les K-sets sont des ensembles de R. En particulier, les ensembles de Cantor les plus réguliers, pour lesquels on connaît une condition simple de capacité nulle, sont des K-sets. Ce mémoire a pour but de montrer l'efficacité d'une méthode dans le cadre des ensembles de Cantor et ses limites dans le cadre des K-sets. Il est principalement inspiré de l'article [8].

Les problèmes logiciels sont frustrants et diminuent l’expérience utilisateur. Par exemple, la fuite de données bancaires, la publication de vidéos ou de photos compromettantes peuvent affecter gravement une vie. Comment éviter de telles situations ? Utiliser des tests est une bonne stratégie, mais certains bogues persistent. Une autre solution est d’utiliser des méthodes plus mathématiques, aussi appelées méthodes formelles. Parmi celles-ci se trouve la sémantique dénotationnelle. Elle met la sémantique extraite de vos logiciels préférés en correspondance avec des objets mathématiques. Sur ceux-ci, des propriétés peuvent être vérifiées. Par exemple, il est possible de déterminer, sous certaines conditions, si votre logiciel donnera une réponse. Pour répondre à ce besoin, il est nécessaire de s’intéresser à des théories mathématiques suffisamment riches. Parmi les candidates se trouvent le sujet de ce mémoire : la théorie des domaines. Elle offre des objets permettant de modéliser formellement les données et les instructions à l’aide de relations d’ordre. Cet écrit présente les concepts fondamentaux tout en se voulant simple à lire et didactique. Il offre aussi une base solide pour des lectures plus poussées et contient tout le matériel nécessaire à sa lecture, notamment les preuves des énoncés présentés. / Bugs in programs are definitively annoying and have a negative impact on the user experience. For example, leaks of bank data or leaks of compromising videos or photos have a serious effect on someone’s life. How can we prevent these situations from happening? We can do tests, but many bugs may persist. Another way is to use mathematics, namely formal methods. Among them, there is denotational semantics. It links the semantics of your favorite program to mathematical objects. On the latter, we can verify properties, e.g., absence of bugs. Hence, we need a rich theory in which we can express the denotational semantics of programs. Domain Theory is a good candidate and is the main subject of this master thesis. It provides mathematical objects for data and instructions based on order relations. This thesis presents fundamental concepts in a simple and pedagogical way. It is a solid basis for advanced readings as well as containing all the needed knowledge for its reading, notably proofs for all presented statements.

Em diversos domínios, além das informações sobre os objetos ou entidades que os compõem, existem, também, informaçõoes a respeito das relações entre esses objetos. Alguns desses domínios são, por exemplo, as redes de co-autoria, e as páginas Web. Nesse sentido, é natural procurar por técnicas de classificação que levem em conta estas informações. Dentre essas técnicas estão as denominadas classificação baseada em grafos, que visam classificar os exemplos levando em conta as relações existentes entre eles. Este trabalho aborda o desenvolvimento de métodos para melhorar o desempenho de classificadores baseados em grafos utilizando estratégias de ensembles. Um classificador ensemble considera um conjunto de classificadores cujas predições individuais são combinadas de alguma forma. Este classificador normalmente apresenta um melhor desempenho do que seus classificadores individualmente. Assim, foram desenvolvidas três técnicas: a primeira para dados originalmente no formato proposicional e transformados para formato relacional baseado em grafo e a segunda e terceira para dados originalmente já no formato de grafo. A primeira técnica, inspirada no algoritmo de boosting, originou o algoritmo KNN Adaptativo Baseado em Grafos (A-KNN). A segunda ténica, inspirada no algoritmo de Bagging originou trê abordagens de Bagging Baseado em Grafos (BG). Finalmente, a terceira técnica, inspirada no algoritmo de Cross-Validated Committees, originou o Cross-Validated Committees Baseado em Grafos (CVCG). Os experimentos foram realizados em 38 conjuntos de dados, sendo 22 conjuntos proposicionais e 16 conjuntos no formato relacional. Na avaliação foi utilizado o esquema de 10-fold stratified cross-validation e para determinar diferenças estatísticas entre classificadores foi utilizado o método proposto por Demsar (2006). Em relação aos resultados, as três técnicas melhoraram ou mantiveram o desempenho dos classificadores bases. Concluindo, ensembles aplicados em classificadores baseados em grafos apresentam bons resultados no desempenho destes / In many fields, besides information about the objects or entities that compose them, there is also information about the relationships between objects. Some of these fields are, for example, co-authorship networks and Web pages. Therefore, it is natural to search for classification techniques that take into account this information. Among these techniques are the so-called graphbased classification, which seek to classify examples taking into account the relationships between them. This paper presents the development of methods to improve the performance of graph-based classifiers by using strategies of ensembles. An ensemble classifier considers a set of classifiers whose individual predictions are combined in some way. This combined classifier usually performs better than its individual classifiers. Three techniques have been developed: the first applied for originally propositional data transformed to relational format based on graphs and the second and the third applied for data originally in graph format. The first technique, inspired by the boosting algorithm originated the Adaptive Graph-Based K-Nearest Neighbor (A-KNN). The second technique, inspired by the bagging algorithm led to three approaches of Graph-Based Bagging (BG). Finally the third technique, inspired by the Cross- Validated Committees algorithm led to the Graph-Based Cross-Validated Committees (CVCG). The experiments were performed on 38 data sets, 22 datasets in propositional format and 16 in relational format. Evaluation was performed using the scheme of 10-fold stratified cross-validation and to determine statistical differences between the classifiers it was used the method proposed by Demsar (2006). Regarding the results, these three techniques improved or at least maintain the performance of the base classifiers. In conclusion, ensembles applied to graph-based classifiers have good results in the performance of them

Nesta tese de doutorado apresentamos um estudo sobre o comportamento de diversos sistemas irrevers?veis, caracterizados pela existencia de estados absorventes, atraves de abordagens distintas. Utilizamos aproximacoes de campo medio dinamico, simulacoes numericas usuais, mudanca de ensemble e expanso em serie. Alem disso, mostramos numa parte deste trabalho que a abordagem proposta para o estudo de sistemas irrevers?veis no ensemble em que o numero de part?culas e constante tambem pode ser estendida para sistemas em equil´?brio termodinamico, descrito pela distribuicao de probabilidades de Gibbs. Finalmente mostramos problemas em aberto para trabalhos futuros. / In this PHD thesis, we have presented a study about several nonequilibrium systems with absorbing states by means of different approaches, such as mean-field analysis, usual numerical simulations, analysis in another ensemble and perturbative series expansions. In a specific part of this thesis, we have shown that the approach proposed here for describing nonequilibrium systems in the constant particle number ensemble can also be used to caracterize equilibrium systems, described by Gibbs probability distribution. Finally, we have shown open problems for future researchs.