Cette thèse explore deux sujets liés dans le contexte de l'apprentissage profond : l'apprentissage incrémental et la génération des images. L'apprentissage incrémental étudie l'entrainement des modèles dont la fonction objective évolue avec le temps (exemple : Ajout de nouvelles catégories à une tâche de classification). La génération d'images cherche à apprendre une distribution d'images naturelles pour générer de nouvelles images ressemblant aux images de départ.L’apprentissage incrémental est un problème difficile dû au phénomène appelé l'oubli catastrophique : tout changement important de l’objectif au cours de l'entrainement provoque une grave dégradation des connaissances acquises précédemment. Nous présentons un cadre d'apprentissage permettant d'introduire de nouvelles classes dans un réseau de détection d'objets. Il est basé sur l’idée de la distillation du savoir pour lutter les effets de l’oubli catastrophique : une copie fixe du réseau évalue les anciens échantillons et sa sortie est réutilisée dans un objectif auxiliaire pour stabiliser l’apprentissage de nouvelles classes. Notre framework extrait ces échantillons d'anciennes classes à la volée à partir d'images entrantes, contrairement à d'autres solutions qui gardent un sous-ensemble d'échantillons en mémoire.Pour la génération d’images, nous nous appuyons sur le modèle du réseau adverse génératif (en anglais generative adversarial network ou GAN). Récemment, les GANs ont considérablement amélioré la qualité des images générées. Cependant, ils offrent une pauvre couverture de l'ensemble des données : alors que les échantillons individuels sont de grande qualité, certains modes de la distribution d'origine peuvent ne pas être capturés. De plus, contrairement à la mesure de vraisemblance couramment utilisée pour les modèles génératives, les méthodes existantes d'évaluation GAN sont axées sur la qualité de l'image et n'évaluent donc pas la qualité de la couverture du jeu de données. Nous présentons deux approches pour résoudre ces problèmes.La première approche évalue les GANs conditionnels à la classe en utilisant deux mesures complémentaires basées sur la classification d'image - GAN-train et GAN-test, qui approchent respectivement le rappel (diversité) et la précision (qualité d'image) des GANs. Nous évaluons plusieurs approches GANs récentes en fonction de ces deux mesures et démontrons une différence de performance importante. De plus, nous observons que la difficulté croissante du jeu de données, de CIFAR10 à ImageNet, indique une corrélation inverse avec la qualité des GANs, comme le montre clairement nos mesures.Inspirés par notre étude des modèles GANs, la seconde approche applique explicitement la couverture d'un jeux de données pendant la phase d'entrainement de GAN. Nous développons un modèle génératif combinant la qualité d'image GAN et l'architecture VAE dans l'espace latente engendré par un modèle basé sur le flux, Real-NVP. Cela nous permet d’évaluer une vraisemblance correcte et d’assouplir simultanément l’hypothèse d’indépendance dans l’espace RVB qui est courante pour les VAE. Nous obtenons le score Inception et la FID en concurrence avec les GANs à la pointe de la technologie, tout en maintenant une bonne vraisemblance pour cette classe de modèles. / This dissertation explores two related topics in the context of deep learning: incremental learning and image generation. Incremental learning studies training of models with the objective function evolving over time, eg, addition of new categories to a classification task. Image generation seeks to learn a distribution of natural images for generating new images resembling original ones.Incremental learning is a challenging problem due to the phenomenon called catastrophic forgetting: any significant change to the objective during training causes a severe degradation of previously learned knowledge. We present a learning framework to introduce new classes to an object detection network. It is based on the idea of knowledge distillation to counteract catastrophic forgetting effects: fixed copy of the network evaluates old samples and its output is reused in an auxiliary loss to stabilize learning of new classes. Our framework mines these samples of old classes on the fly from incoming images, in contrast to other solutions that keep a subset of samples in memory.On the second topic of image generation, we build on the Generative Adversarial Network (GAN) model. Recently, GANs significantly improved the quality of generated images. However, they suffer from poor coverage of the dataset: while individual samples have great quality, some modes of the original distribution may not be captured. In addition, existing GAN evaluation methods are focused on image quality, and thus do not evaluate how well the dataset is covered, in contrast to the likelihood measure commonly used for generative models. We present two approaches to address these problems.The first method evaluates class-conditional GANs using two complementary measures based on image classification - GAN-train and GAN-test, which approximate recall (diversity) and precision (quality of the image) of GANs respectively. We evaluate several recent GAN approaches based on these two measures, and demonstrate a clear difference in performance. Furthermore, we observe that the increasing difficulty of the dataset, from CIFAR10 over CIFAR100 to ImageNet, shows an inverse correlation with the quality of the GANs, as clearly evident from our measures.Inspired by our study of GAN models, we present a method to explicitly enforce dataset coverage during the GAN training phase. We develop a generative model that combines GAN image quality with VAE architecture in the feature space engendered by a flow-based model Real-NVP. This allows us to evaluate a valid likelihood and simultaneously relax the independence assumption in RGB space which is common for VAEs. We achieve Inception score and FID competitive with state-of-the-art GANs, while maintaining good likelihood for this class of models.

1 Comparison of Judgment by Default under Czech and Spanish Law Abstract This diploma thesis addresses Czech and Spanish legislation with respect to judgement by default and the subsequent compassion thereof. First chapter focuses on the defendant's default under Czech law, more specifically on the concept of the defendant's default during court proceedings, conditions that must be met in order to deliver a judgement by default, impermissibility of delivering a judgement by default, excusable grounds of default as well as the remedies that can be relied upon against such judgement. Second chapter follows with a description of Spanish legislation on the defendant's default during court proceedings. It deals with the concept of the defendant's default during court proceedings, conditions under which it is possible to issue a declaration of defendant's default, consequences associated with the defendant's default during court proceedings, delivering court documents to the defendant and to application for annulment of the final decision on the matter of the defendant in default and other remedies available to the defendant under Spanish law. The final chapter of the thesis outlines important differences which the author came across whilst studying each legislation. The author attempts to draw her own critical...

The use of machine learning algorithms to enhance and facilitate medical diagnosis and analysis is a promising and an important area, which could improve the workload of clinicians’ substantially. In order for machine learning algorithms to learn a certain task, large amount of data needs to be available. Data sets for medical image analysis are rarely public due to restrictions concerning the sharing of patient data. The production of synthetic images could act as an anonymization tool to enable the distribution of medical images and facilitate the training of machine learning algorithms, which could be used in practice. This thesis investigates the use of Generative Adversarial Networks (GAN) for synthesis of new thoracic computer tomography (CT) images, with no connection to real patients. It also examines the usefulness of the images by comparing the quantitative performance of a segmentation network trained with the synthetic images with the quantitative performance of the same segmentation network trained with real thoracic CT images. The synthetic thoracic CT images were generated using CycleGAN for image-to-image translation between label map ground truth images and thoracic CT images. The synthetic images were evaluated using different set-ups of synthetic and real images for training the segmentation network. All set-ups were evaluated according to sensitivity, accuracy, Dice and F2-score and compared to the same parameters evaluated from a segmentation network trained with 344 real images. The thesis shows that it was possible to generate synthetic thoracic CT images using GAN. However, it was not possible to achieve an equal quantitative performance of a segmentation network trained with synthetic data compared to a segmentation network trained with the same amount of real images in the scope of this thesis. It was possible to achieve equal quantitative performance of a segmentation network, as a segmentation network trained on real images, by training it with a combination of real and synthetic images, where a majority of the images were synthetic images and a minority were real images. By using a combination of 59 real images and 590 synthetic images, equal performance as a segmentation network trained with 344 real images was achieved regarding sensitivity, Dice and F2-score. Equal quantitative performance of a segmentation network could thus be achieved by using fewer real images together with an abundance of synthetic images, created at close to no cost, indicating a usefulness of synthetically generated images.

Thesis advisor: Shep Melnick / Most of the literature on the separation of powers in the war on terror vastly overstates the power of the presidency and pays little attention to the respective roles of Congress, the courts, and the bureaucracy in prosecuting that conflict. Scholars – especially those in the legal academy – have consistently failed to appreciate the ways in which the president has been, and continues to be, checked and constrained by a variety of forces. In my dissertation, I engage in highly detailed case studies of U.S. law and policy with respect to detention and military commissions in the war on terror. I pay special attention to the complex interactions that occurred within and between our governing institutions in these policy areas. There are two central arguments that come out of my research and run through my case studies. First, the political scientist Robert Kagan’s work on “adversarial legalism” is no longer simply applicable to the domestic policy realm. The proliferation of legal rules and extensive litigation has increasingly come to characterize foreign affairs as well, with important consequences for how the U.S. implements its national security policies and fights its armed conflicts. In short, adversarial legalism has gone to war. Second, loose talk about the “unitary” nature of the executive branch is misleading. The executive branch is a sprawling bureaucracy made up of diverse actors with different perspectives, preferences, and norms, and that bureaucracy has interacted with Congress and the courts in surprising ways to constrain the presidency in the war on terror. / Thesis (PhD) — Boston College, 2016. / Submitted to: Boston College. Graduate School of Arts and Sciences. / Discipline: Political Science.

O Código de Processo Penal brasileiro foi alterado em 2008 em decorrência da promulgação de algumas Leis Ordinárias. Uma delas (nº 11.690) prescreveu a modificação do artigo 155, a fim de regulamentar a aceitação de provas não repetíveis (e outras produzidas durante as investigações) para o convencimento do julgador. No entanto, como o legislador não atribuiu significado às provas não repetíveis, tampouco teceu esclarecimentos a respeito do modo como tais provas seriam compatibilizadas com o princípio constitucional do contraditório, há muitas incertezas sobre a disposição, que tem sido objeto de discussão pela comunidade jurídica. O silêncio do legislador impediu o desenvolvimento de uma regulação eficiente sobre o assunto. Com o objetivo de contribuir para as atuais discussões, propomos uma análise comparativa da doutrina sobre provas não repetíveis utilizada na Itália, país que serviu de inspiração à criação da norma brasileira. Por meio deste estudo, pretendemos: (i) clarificar o conceito de provas não repetíveis; (ii) analisar a interação do conceito de provas não repetíveis com outras provas produzidas durante as investigações; (iii) alcançar a compreensão do tratamento normativo e doutrinário das provas não repetíveis nos processos penais brasileiro e italiano; e (iv) refletir, à luz da das regras estabelecidas na Constituição Brasileira, se a regulamentação italiana sobre as provas não repetíveis teria aplicação no processo penal brasileiro. Depois de realizadas tais aferições, refletiremos sobre a necessidade de reformulação do artigo 155 que, se confirmada, nos levará à porposição de um novo texto normativo. / The Brazilian Criminal Procedure Code was altered in 2008 as a result of the adoption of some Ordinary Laws. One of them (nº. 11.690) prescribed amendments in article 155, which from then on stipulates the acceptance of non-repeatable evidence (as well as other types of evidence produced during investigations), as means of conviction. Nevertheless, as the legislator neither provided a definition of non-repeatable evidence nor instructed how this evidence should be treated in regards to the adversarial system of justice guaranteed by the Brazilian Constitution, there is a lot of uncertainty on the juridical community concerning this provision. The silence of the legislator deterred the development of an efficient regulation on the matter. Aiming to contribute to the current discussions, this work is focused on the comparative analysis of the doctrine of nonrepeatable evidence as applied in Italy, cradle of this idea. This study intends to: (i) clarify the concept of non-repeatable evidence; (ii) scrutinize the interaction of the concept of non-repeatable evidence with the further evidences produced during investigation; (iii) comprehend, in light of the Italian doctrine and the rules set forth in the Brazilian Constitution, the scope of application of the non-repeatable evidence; and (iv) analyze, bearing in mind the rules contained in the Brazilian Constitution, whether the system of non-repeatable evidence prescribed in Italy could also be applied in the Brazilian Criminal Procedure. After all these considerations are made, the crux of this work will be on whether article 155 should be rephrased and, if affirmative, how the new article should be worded.

Email spam has steadily grown and has become a major problem for users, email service providers, and many other organizations. Many adversarial methods have been proposed to combat spam and various studies have been made on the evolution of email spam, by finding evolution patterns and trends based on historical spam data and by incorporating spam filters. In this thesis, we try to understand the evolution of email spam and how we can build better classifiers that will remain effective against adaptive adversaries like spammers. We compare various methods for analyzing the evolution of spam emails by incorporating spam filters along with a spam dataset. We explore the trends based on the weights of the features learned by the classifiers and the accuracies of the classifiers trained and tested in different settings. We also evaluate the effectiveness of the classifier trained in adversarial settings on synthetic data.

This Thesis surveys the landscape of Data Augmentation for image datasets. Completing this survey inspired further study into a method of generative modeling known as Generative Adversarial Networks (GANs). A survey on GANs was conducted to understood recent developments and the problems related to training them. Following this survey, four experiments were proposed to test the application of GANs for data augmentation and to contribute to the quality improvement in GAN-generated data. Experimental results demonstrate the effectiveness of GAN-generated data as a pre-training metric. The other experiments discuss important characteristics of GAN models such as the refining of prior information, transferring generative models from large datasets to small data, and automating the design of Deep Neural Networks within the context of the GAN framework. This Thesis will provide readers with a complete introduction to Data Augmentation and Generative Adversarial Networks, as well as insights into the future of these techniques. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2019. / FAU Electronic Theses and Dissertations Collection

Recent discoveries uncovered flaws in machine learning algorithms such as deep neural networks. Deep neural networks seem vulnerable to small amounts of non-random noise, created by exploiting the input to output mapping of the network. Applying this noise to an input image drastically decreases classication performance. Such image is referred to as an adversarial example. The purpose of this thesis is to examine how known regularization/robustness methods perform on adversarial examples. The robustness methods: dropout, low-pass filtering, denoising autoencoder, adversarial training and committees have been implemented, combined and tested. For the well-known benchmark, the MNIST (Mixed National Institute of Standards and Technology) dataset, the best combination of robustness methods has been found. Emerged from the results of the experiments, ensemble of models trained on adversarial examples is considered to be the best approach for MNIST. Harmfulness of the adversarial noise and some robustness experiments are demonstrated on CIFAR10 (The Canadian Institute for Advanced Research) dataset as well. Apart from robustness tests, the thesis describes experiments with human classification performance on noisy images and the comparison with performance of deep neural network.

Environment maps are widely used in several computer graphics fields, such as realistic architectural rendering or computer games as sources of the light in the scene. Obtaining these maps is not easy, since they have to have both a high- dynamic range as well as a high resolution. As a result, they are expensive to make and the supply is limited. Deep neural networks are a widely unexplored research area and have been successfully used for generating complex and realistic images like human portraits. Neural networks perform well at predicting data from complex models, which are easily observable, such as photos of the real world. This thesis explores the idea of generating physically plausible environment maps by utilizing deep neural networks known as generative adversarial networks. Since a skydome dataset is not publicly available, we develop a scalable capture process with both low-end and high-end hardware. We implement a pipeline to process the captured data before feeding it to a network and extend an already existing network architecture to generate HDR environment maps. We then run a series of experiments to determine the quality of the results and uncover the directions of possible further research.

The transformative process of personal growth following suffering and challenges, or posttraumatic growth (PTG), is limited in persons with acquired disability. The dimensions of acquired disability, as outlined by the World Health Organization, include impairments in body functions, body structures, and growth restrictions in activities and participation. The 5 domains of PTG include personal strength, new possibilities, relating to other people, appreciation of life, and spiritual change. Using discriminant function analysis, the purpose of this quantitative study was to identify a discriminant analysis of the dimensions of acquired disability on the domains of posttraumatic growth. The first research question focused on investigating the number of statistically significant uncorrelated linear combinations. The second research question reviewed the multivariate profile (or profiles if there is more than one statistically significant function) of the Posttraumatic Growth Inventory domains that discriminant the dimensions of acquired disability. A cross-sectional survey design was used to gather data from 161 individuals with acquired disability who were over 18 years of age and were at least 1 year postdiagnosis. Participants were invited to participate using a Facebook page and targeted advertising, as well as personal invitations to online support groups advocating for persons with acquired disability. This study and analysis only found 1 significant pairwise connection between impairment in body structure and growth, activity, and participation with the PTG domain of personal strength. Results may be used to guide the planning and implementation of aftercare programs for individuals diagnosed with an acquired disability to help promote PTG.