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[pt] A geometria diferencial descreve de maneira intuitiva os objetos suaves no espaço. Porém, com a evolução da modelagem geométrica por computador, essa ferramenta se tornou ao mesmo tempo necessária e difícil de se descrever no mundo discreto. A teoria de Morse ficou importante pela ligação que ela cria entre a topologia e a geometria diferenciais. Partindo de um ponto de vista mais combinatório, a teoria de Morse discreta de Forman liga de forma rigorosa os objetos discretos à topologia deles, abrindo essa teoria para estruturas discretas. Este trabalho propõe uma definição construtiva de funções de Morse geométricas no mundo discreto e do complexo de Morse-Smale correspondente, onde a geometria é definida como a amostragem de uma função suave nos vértices da estrutura discreta. Essa construção precisa de cálculos de homologia que se tornaram por si só uma melhoria significativa dos métodos existentes. A decomposição de Morse- Smale resultante pode ser eficientemente computada e usada para aplicações de cálculo da persistência, geração de grafos de Reeb, remoção de ruído e mais. . . / [en] Differential geometry provides an intuitive way of understanding smooth objects in the space. However, with the evolution of geometric modeling by computer, this tool became both necessary and difficult to transpose to the discrete setting. The power of Morse theory relies on the link it created between differential topology and geometry. Starting from a combinatorial point of view, Forman´s discrete Morse theory relates rigorously discrete objects to their topology, opening Morse theory to discrete structures. This work proposes a constructive definition of geometric discrete Morse functions and their corresponding discrete Morse-Smale complexes, where the geometry is defined as a smooth function sampled on the vertices of the discrete structure. This construction required some homology computations that turned out to be a significant improvement over existing methods by itself. The resulting Morse-Smale decomposition can then be efficiently computed, and used for applications to persistence computation, Reeb graph generation, noise removal. . .

[pt] A teoria de Morse relaciona a topologia de um espaço aos elementos críticos de uma função escalar definida nele. Isso vale tanto para a teoria clássica quanto para a versão discreta proposta por Forman em 1995. Essas teorias de Morse permitem caracterizar a topologia do espaço a partir de funções definidas nele, mas também permite estudar funções a partir de construções tipológicas derivadas dela, como por exemplo o complexo de Morse-Smale. Apesar da teoria de Morse discreta se aplicar para complexos celulares gerais de forma inteiramente combinatória, o que torna a teoria particularmente bem adaptada para o computador, as funções usadas na teoria não são amostragens de funções contínuas, mas casamentos especiais no grafo que codifica as adjacências no complexo celular, chamadas de casamentos de Morse. Quando usar essa teoria para estudar um espaço topológico, procura- se casamentos de Morse ótimos, i.e. com o menor número possível de elementos críticos, para obter uma informação topológica do complexo sem redundância. Na primeira parte desta tese, investiga-se a complexidade parametrizada de encontrar esses casamentos de Morse ótimos. Por um lado, prova-se que o problema ERASABILITY, um problema fortemente relacionado à encontrar casamentos de Morse ótimos, é W [P ]-completo. Por outro lado, um algoritmo é proposto para calcular casamentos de Morse ótimos em triangulações de 3-variedades, que é FPT no parâmetro do tree- width de seu grafo dual. Quando usar a teoria de Morse discreta para estudar uma função escalar definida no espaço, procura-se casamentos de Morse que capturam a informação geométrica dessa função. Na segunda parte é proposto uma construção de casamentos de Morse baseada em casamentos estáveis. As garantias teóricas sobre a relação desses casamentos com a geometria são elaboradas a partir de provas surpreendentemente simples que aproveitam da caracterização local do casamento estável. A construção e as suas garantias funcionam em qualquer dimensão. Finalmente, resultados mais fortes são obtidos quando a função for suave discreta, uma noção definida nesta tese. / [en] Morse theory relates the topology of a space to the critical elements of a scalar function defined on it. This applies in both the classical theory and a discrete version of it defined by Forman in 1995. Those Morse theories permit to characterize a topological space from functions defined on it, but also to study functions based on topological constructions it implies, such as the Morse-Smale complex. While discrete Morse theory applies on general cell complexes in an entirely combinatorial manner, which makes it suitable for computation, the functions it considers are not sampling of continuous functions, but special matchings in the graph encoding the cell complex adjacencies, called Morse matchings. When using this theory to study a topological space, one looks for optimal Morse matchings, i.e. one with the smallest number of critical elements, to get highly succinct topological information about the complex. The first part of this thesis investigates the parameterized complexity of finding such optimal Morse matching. On the one hand the Erasability problem, a closely related problem to finding optimal Morse matchings, is proven to be W[P]-complete. On the other hand, an algorithm is proposed for computing optimal Morse matchings on triangulations of 3-manifolds which is fixed parameter tractable in the tree-width of its dual graph. When using discrete Morse theory to study a scalar function defined on the space, one looks for a Morse matching that captures the geometric information of that function. The second part of this thesis introduces a construction of Morse matchings based on stable matchings. The theoretical guarantees about the relation of such matchings to the geometry are established through surprisingly simple proofs that benefits from the local characterization of the stable matching. The construction and its guarantees work in any dimension. Finally stronger results are obtained if the function is discrete smooth on the complex, a notion defined in this thesis.

Parkinson’s Disease (PD) is the fastest growing neurodegenerative disease, currently affecting two to three percent of the population over 65. Studying functional connectivity (FC) in PD patients may provide new insights into how the disease alters brain organization in different subjects. We explored persistent homology (PH) as a method for studying FC based on the functional magnetic resonance imaging (fMRI) recordings of 63 subjects, of which 56 were diagnosed with PD.  We used PH to translate each set of fMRI recordings into a stable rank. Stable ranks are homological invariants that are amenable for statistical analysis. The pipeline has multiple parameters, and we explored the effect of these parameters on the shape of the stable ranks. Moreover, we fitted functions to reduce the stable ranks to points in two or three dimensions. We clustered the stable ranks based on the fitted parameter values and based on the integral distance between them. For some of the parameter combinations, not all clusters were located in the space covered by controls. These clusters correspond to patients with a topologically distinct connectivity structure, which may be clinically relevant. However, we found no relation between the clusters and the medication status or cognitive ability of the patients. It should be noted that this study was an exploration of applying persistent homology to PD data, and that statistical testing was not performed. Consequently, the presented results should be considered with care. Furthermore, we did not explore the full parameter space, as time was limited and the data set was small. In a follow-up study, a measurable desired outcome of the pipeline should be defined and the data set should be expanded to allow for optimizing over the full parameter space. / Parkinsons sjukdom är den snabbast växande neurodegenerativa sjukdomen och drabbar för närvarande två till tre procent av befolkningen över 65 år. Att studera funktionell konnektivitet (FC) hos patienter med Parkinson kan ge nya insikter om hur sjukdomen förändrar hjärnans uppsättning i olika områden. Vi använde oss av persistent homologi (PH) som en metod för att studera FC baserat på inspelningar av funktionell magnetresonanstomografi (fMRI) av 63 försökspersoner varav 56 hade diagnosen PD. Vi använde oss av persistent homologi (PH) som en metod för att studera FC baserat på inspelningar av funktionell magnetresonanstomografi (fMRI) av 63 försökspersoner varav 56 hade diagnosen PD. Vi använde PH för att översätta varje uppsättning fMRI-prov vardera till en stable rank. Stable ranks är homologiska invarianter som är lämpliga för statistisk analys. Pipelinen har flera parametrar och vi undersökte effekten av dessa parametrar på formen av dessa stable ranks. Vi anpassade funktioner för att reducera alla stable ranks till punkter i två eller tre dimensioner. Vi grupperade alla stable ranks utifrån de anpassade parametervärdena och utifrån det integrala avståndet mellan dem. För vissa parameterkombinationer kunde inte alla kluster inom det område som täcks av kontrollerna bli funna. Dessa kluster motsvarar patienter med en topologiskt distinkt konnektivitetsstruktur, vilket kan vara kliniskt relevant. Vi fann dock inget samband mellan klustren och patienternas läkemedelsstatus eller kognitiva förmåga.  Det bör noteras att den här studien var en undersökning på tillämpningen av persistent homologi på PD-data och att statistiska tester inte utfördes. Följaktligen bör de presenterade resultaten betraktas med försiktighet. Dessutom undersökte vi inte hela parameterutrymmet eftersom tiden var begränsad och datamängden liten. I en uppföljningsstudie bör man definiera ett mätbart önskat resultat av pipelinen och datamängden bör utökas för att möjliggöra optimering av hela parameterutrymmet.

<p>The only datasets used in my thesis work are from TUDatasets, <a href="https://chrsmrrs.github.io/datasets/">TUDataset | TUD Benchmark datasets (chrsmrrs.github.io)</a>, a collection of public benchmark datasets for graph classification and regression.</p><p><br></p>

No description available.

Variational Auto-Encoders (VAEs) are one of the most famous deep generative models. After showing that standard VAEs may not preserve the topology, that is the shape of the data, between the input and the latent space, we tried to modify them so that the topology is preserved. This would help in particular for performing interpolations in the latent space. Our main contribution is two folds. Firstly, we propose successfully the InvMap-VAE which is a simple way to turn any dimensionality reduction technique, given its embedding, into a generative model within a VAE framework providing an inverse mapping, with all the advantages that this implies. Secondly, we propose the Witness Simplicial VAE as an extension of the Simplicial Auto-Encoder to the variational setup using a Witness Complex for computing a simplicial regularization. The Witness Simplicial VAE is independent of any dimensionality reduction technique and seems to better preserve the persistent Betti numbers of a data set than a standard VAE, although it would still need some further improvements. Finally, the two first chapters of this master thesis can also be used as an introduction to Topological Data Analysis, General Topology and Computational Topology (or Algorithmic Topology), for any machine learning student, engineer or researcher interested in these areas with no background in topology. / Variations autokodare (VAE) är en av de mest kända djupa generativa modellerna. Efter att ha visat att standard VAE inte nödvändigtvis bevarar topologiska egenskaper, det vill säga formen på datan, mellan inmatningsdatan och det latenta rummet, försökte vi modifiera den så att topologin är bevarad. Det här skulle i synnerhet underlätta när man genomför interpolering i det latenta rummet. Denna avhandling består av två centrala bidrag. I första hand så utvecklar vi InvMap-VAE, som är en enkel metod att omvandla vilken metod inom dimensionalitetsreducering, givet dess inbäddning, till en generativ modell inom VAE ramverket, vilket ger en invers avbildning och dess tillhörande fördelar. För det andra så presenterar vi Witness Simplicial VAE som en förlängning av en Simplicial Auto-Encoder till dess variationella variant genom att använda ett vittneskomplex för att beräkna en simpliciel regularisering. Witness Simplicial VAE är oberoende av dimensionalitets reducerings teknik och verkar bättre bevara Betti-nummer av ett dataset än en vanlig VAE, även om det finns utrymme för förbättring. Slutligen så kan de första två kapitlena av detta examensarbete också användas som en introduktion till Topologisk Data Analys, Allmän Topologi och Beräkningstopologi (eller Algoritmisk Topologi) till vilken maskininlärnings student, ingenjör eller forskare som är intresserad av dessa ämnesområden men saknar bakgrund i topologi.