Submitted by Bruna Rodrigues (bruna92rodrigues@yahoo.com.br) on 2016-09-27T12:32:15Z No. of bitstreams: 1 TeseMAN.pdf: 2601601 bytes, checksum: 70c6b910d382e2015025a5c8ec5ddd14 (MD5) / Approved for entry into archive by Marina Freitas (marinapf@ufscar.br) on 2016-10-04T18:11:16Z (GMT) No. of bitstreams: 1 TeseMAN.pdf: 2601601 bytes, checksum: 70c6b910d382e2015025a5c8ec5ddd14 (MD5) / Approved for entry into archive by Marina Freitas (marinapf@ufscar.br) on 2016-10-04T18:11:29Z (GMT) No. of bitstreams: 1 TeseMAN.pdf: 2601601 bytes, checksum: 70c6b910d382e2015025a5c8ec5ddd14 (MD5) / Made available in DSpace on 2016-10-04T18:11:38Z (GMT). No. of bitstreams: 1 TeseMAN.pdf: 2601601 bytes, checksum: 70c6b910d382e2015025a5c8ec5ddd14 (MD5) Previous issue date: 2015-12-04 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / We present results of Ambrosseti-Prodi type to quasilinear problems involving the p-Laplace operator. We consider the scalar case and a a problem with systems of equations. In the scalar case, we work with the conditions of Neumann and Dirichlet. In the problem involving system, we consider the condition og Dirichlet. In order to get the results we use the theory of Leray-Schauder degree and a priori estimates. / Neste trabalho apresentamos resultados do tipo Ambrosseti-Prodi para problemas quasilineares envolvendo o aperador p-Laplaciano. Considerando o caso escalar eu um problema com sistemas de equações. Para os casos escalares, trabalhamos com a condições de Neumann e Dirichlet, já para o problema envolvendo sistema, consideramos a condição Dirichlet. Para obter mais resultados usamos a teoria do grau de Leray-Schauder e estimativas a priori.

Este trabalho tem como objetivo provar a existência de gráficos mínimos em domínios do plano Será garantido através do Método de Perron a existência de um gráfico mínimo num dommio limitado do plano. Será também estudado o comportamento dessa solução na fronteira do seu domínio através do conceito de função barreira Serão provados três teoremas que garantem a existcncia de soluções do problema de Dirichlct para as mínimas em don11nios não corwe:xos c não compactos do plano com condições especiais de fronteira, sendo estes discutidn no trabalho de J Ripoll and F. Tomi [RT]. / This work has as objective to prove some existence thcorems Jor minimal graphs over planar domains lt will be guaranteed employ the Perron method one existence of one minimars graph in arbitrarv bounded domain. lt will be too studied this solution at the boundary through the concept o f barrier fi.mction We obtain three cxistcncc theorems to Dirichlct 's problem for non convex and non compacts domains having special boundary data being that results are containcd in the J. Ripoll's and F. Tomi 's works [RT].

Este trabalho tem como objetivo provar a existência de gráficos mínimos em domínios do plano Será garantido através do Método de Perron a existência de um gráfico mínimo num dommio limitado do plano. Será também estudado o comportamento dessa solução na fronteira do seu domínio através do conceito de função barreira Serão provados três teoremas que garantem a existcncia de soluções do problema de Dirichlct para as mínimas em don11nios não corwe:xos c não compactos do plano com condições especiais de fronteira, sendo estes discutidn no trabalho de J Ripoll and F. Tomi [RT]. / This work has as objective to prove some existence thcorems Jor minimal graphs over planar domains lt will be guaranteed employ the Perron method one existence of one minimars graph in arbitrarv bounded domain. lt will be too studied this solution at the boundary through the concept o f barrier fi.mction We obtain three cxistcncc theorems to Dirichlct 's problem for non convex and non compacts domains having special boundary data being that results are containcd in the J. Ripoll's and F. Tomi 's works [RT].

Reaction-diffusion equations have been used to study various phenomena across different fields. These equations can be posed on the whole real line, or on a subinterval, depending on the situation being studied. For finite intervals, we also impose diverse boundary conditions on the system. In the present thesis, we solely focus on the bistable reaction-diffusion equation while working on a bounded interval of the form $[0,L]$ ($L>0$). Furthermore, we consider both mixed and no-flux boundary conditions, where we extend the former to Dirichlet boundary conditions once our analysis of that system is complete. We first use phase-plane analysis to set up our initial investigation of both systems. This gives us an integral describing the transit time of orbits within the phase-plane. This allows us to determine the bifurcation diagram of both systems. We then transform the integral to ease numerical calculations. Finally, we determine the stability of the steady states of each system.

The use of a finite mixture of normal distributions in model-based clustering allows to capture non-Gaussian data clusters. However, identifying the clusters from the normal components is challenging and in general either achieved by imposing constraints on the model or by using post-processing procedures. Within the Bayesian framework we propose a different approach based on sparse finite mixtures to achieve identifiability. We specify a hierarchical prior where the hyperparameters are carefully selected such that they are reflective of the cluster structure aimed at. In addition, this prior allows to estimate the model using standard MCMC sampling methods. In combination with a post-processing approach which resolves the label switching issue and results in an identified model, our approach allows to simultaneously (1) determine the number of clusters, (2) flexibly approximate the cluster distributions in a semi-parametric way using finite mixtures of normals and (3) identify cluster-specific parameters and classify observations. The proposed approach is illustrated in two simulation studies and on benchmark data sets.

Finding the most relevant candidate for a position represents an ubiquitous challenge for organisations. It can also be arduous for a candidate to explain on a concise resume what they have experience with. Due to the fact that the candidate usually has to select which experience to expose and filter out some of them, they might not be detected by the person carrying out the search, whereas they were indeed having the desired experience. In the field of software engineering, developing one's experience usually leaves traces behind: the code one produced. This project explores approaches to tackle the screening challenges with an automated way of extracting experience directly from code by defining common lexical patterns in code for different experience fields, using topic modeling. Two different techniques were compared. On one hand, Latent Dirichlet Allocation (LDA) is a generative statistical model which has proven to yield good results in topic modeling. On the other hand Non-Negative Matrix Factorization (NMF) is simply a singular value decomposition of a matrix representing the code corpus as word counts per piece of code.The code gathered consisted of 30 random repositories from all the collaborators of the open-source Ruby-on-Rails project on GitHub, which was then applied common natural language processing transformation steps. The results of both techniques were compared using respectively perplexity for LDA, reconstruction error for NMF and topic coherence for both. The two first represent how well the data could be represented by the topics produced while the later estimates the hanging and fitting together of the elements of a topic, and can depict human understandability and interpretability. Given that we did not have any similar work to benchmark with, the performance of the values obtained is hard to assess scientifically. However, the method seems promising as we would have been rather confident in assigning labels to 10 of the topics generated. The results imply that one could probably use natural language processing methods directly on code production in order to extend the detected fields of experience of a developer, with a finer granularity than traditional resumes and with fields definition evolving dynamically with the technology.

A topic modeling method, smoothed Latent Dirichlet Allocation (LDA) is applied on a text corpus data of classical Swedish prose fiction. The thesis consists of two parts. In the first part, a smoothed LDA model is applied to the corpus, investigating how changes in hyperparameter values affect the topics in terms of distribution of words within topics and topics within novels. In the second part, two smoothed LDA models are applied to a reduced corpus, only consisting of adjectives. The generated topics are examined to see if they are more likely to occur in a text of a particular author and if the model could be used for identification of writing style. With this new approach, the ability of the smoothed LDA model as a writing style identifier is explored. While the texts analyzed in this thesis is unusally long - as they are not seg- mented prose fiction - the effect of the hyperparameters on model performance was found to be similar to those found in previous research. For the adjectives corpus, the models did succeed in generating topics with a higher probability of occurring in novels by the same author. The smoothed LDA was shown to be a good model for identification of writing style. Keywords: Topic modeling, Smoothed Latent Dirichlet Allocation, Gibbs sam- pling, MCMC, Bayesian statistics, Swedish prose fiction.

Software maintenance and the understanding of where in the source code features are implemented are two strongly coupled tasks that make up a large portion of the effort spent on developing applications. The concept of feature location investigated in this thesis can serve as a supporting factor in those tasks as it facilitates the automation of otherwise manual searches for source code artifacts. Challenges in this subject area include the aggregation and composition of a training corpus from historical codebase data for models as well as the integration and optimization of qualified topic modeling techniques. Building up on previous research, this thesis provides a comparison of two different techniques and introduces a toolkit that can be used to reproduce and extend on the results discussed. Specifically, in this thesis a changeset-based approach to feature location is pursued and applied to a large open-source Java project. The project is used to optimize and evaluate the performance of Latent Dirichlet Allocation models and Pachinko Allocation models, as well as to compare the accuracy of the two models with each other. As discussed at the end of the thesis, the results do not indicate a clear favorite between the models. Instead, the outcome of the comparison depends on the metric and viewpoint from which it is assessed.

The purpose of the thesis is i) to discover the latent topics of SDG13 and their coverage in news media ii) to investigate the share of OA and Non-OA articles and reviews in each topic iii) to compare the share of different OA types (Green, Gold, Hybrid and Bronze) in each topic. It imposes a heuristic perspective and explorative approach in reviewing the three concepts open access, altmetrics and climate action (SDG13). Data is collected from SciVal, Unpaywall, Altmetric.com and Scopus rendering a dataset of 70,206 articles and reviews published between 2014-2018. The documents retrieved are analyzed with descriptive statistics and topic modeling using Sklearn’s package for LDA(Latent Dirichlet Allocation) in Python. The findings show an altmetric advantage for OA in the case of news media and SDG13 which fluctuates over topics. News media is shown to focus on subjects with “visible” effects in concordance with previous research on media coverage. Examples of this were topics concerning emissions of greenhouse gases and melting glaciers. Gold OA is the most common type being mentioned in news outlets. It also generates the highest number of news mentions while the average sum of news mentions was highest for documents published as Bronze. Moreover, the thesis is largely driven by methods used and most notably the programming language Python. As such it outlines future paths for research into the three concepts reviewed as well as methods used for topic modeling and programming.

abstract: The problem of multiple object tracking seeks to jointly estimate the time-varying cardinality and trajectory of each object. There are numerous challenges that are encountered in tracking multiple objects including a time-varying number of measurements, under varying constraints, and environmental conditions. In this thesis, the proposed statistical methods integrate the use of physical-based models with Bayesian nonparametric methods to address the main challenges in a tracking problem. In particular, Bayesian nonparametric methods are exploited to efficiently and robustly infer object identity and learn time-dependent cardinality; together with Bayesian inference methods, they are also used to associate measurements to objects and estimate the trajectory of objects. These methods differ from the current methods to the core as the existing methods are mainly based on random finite set theory. The first contribution proposes dependent nonparametric models such as the dependent Dirichlet process and the dependent Pitman-Yor process to capture the inherent time-dependency in the problem at hand. These processes are used as priors for object state distributions to learn dependent information between previous and current time steps. Markov chain Monte Carlo sampling methods exploit the learned information to sample from posterior distributions and update the estimated object parameters. The second contribution proposes a novel, robust, and fast nonparametric approach based on a diffusion process over infinite random trees to infer information on object cardinality and trajectory. This method follows the hierarchy induced by objects entering and leaving a scene and the time-dependency between unknown object parameters. Markov chain Monte Carlo sampling methods integrate the prior distributions over the infinite random trees with time-dependent diffusion processes to update object states. The third contribution develops the use of hierarchical models to form a prior for statistically dependent measurements in a single object tracking setup. Dependency among the sensor measurements provides extra information which is incorporated to achieve the optimal tracking performance. The hierarchical Dirichlet process as a prior provides the required flexibility to do inference. Bayesian tracker is integrated with the hierarchical Dirichlet process prior to accurately estimate the object trajectory. The fourth contribution proposes an approach to model both the multiple dependent objects and multiple dependent measurements. This approach integrates the dependent Dirichlet process modeling over the dependent object with the hierarchical Dirichlet process modeling of the measurements to fully capture the dependency among both object and measurements. Bayesian nonparametric models can successfully associate each measurement to the corresponding object and exploit dependency among them to more accurately infer the trajectory of objects. Markov chain Monte Carlo methods amalgamate the dependent Dirichlet process with the hierarchical Dirichlet process to infer the object identity and object cardinality. Simulations are exploited to demonstrate the improvement in multiple object tracking performance when compared to approaches that are developed based on random finite set theory. / Dissertation/Thesis / Doctoral Dissertation Electrical Engineering 2019