Cada vez mais equipamentos eletrônicos digitais têm sido fabricados utilizando um sistema operacional embarcado. Por razões de custo, estes sistemas operacionais são implementados sobre um hardware com os requisitos mínimos para atender as necessidades da aplicação. Este trabalho apresenta um estudo sobre a viabilidade de implementação de suporte a multitarefa sobre a arquitetura FemtoJava, um microcontrolador monotarefa dedicado a sistemas embarcados. Para tanto, o suporte de hardware necessário é adicionado à arquitetura. Também são implementados dois escalonadores de tarefas diretamente em bytecodes Java, visando à otimização de área e o compromisso com desempenho e consumo de energia. Modificações no ambiente de desenvolvimento e uma ferramenta de relocação de endereços são propostas, objetivando a utilização dos escalonadores de tarefas implementados junto ao fluxo de desenvolvimento existente. Por fim, uma análise é realizada sobre o impacto que a capacidade de multitarefa produz no sistema em termos de desempenho, consumo de área e energia. / Most digital electronic equipments are produced using an embedded operating system. Due to economic reasons, these operating systems are implemented on hardware with minimal requirements to support the application needs. This work will present a viability study to implement multitask support on the FemtoJava architecture, a monotask microcontroller dedicated to embedded applications. The support to multitask involves the addition of specific hardware mechanisms to the architecture. Two different scheduling policies are then directly implemented using Java bytecodes, aiming area optimization as well as a good performance/energy-consumption trade-off. Some modifications in the development environment and a code relocation tool were introduced, in order to enable the use of the schedulers in the existing design tool flow. Finally, an analysis is performed to evaluate the impact that the multitask support produces in the system with respect to the final performance, area and energy consumption.

Cada vez mais equipamentos eletrônicos digitais têm sido fabricados utilizando um sistema operacional embarcado. Por razões de custo, estes sistemas operacionais são implementados sobre um hardware com os requisitos mínimos para atender as necessidades da aplicação. Este trabalho apresenta um estudo sobre a viabilidade de implementação de suporte a multitarefa sobre a arquitetura FemtoJava, um microcontrolador monotarefa dedicado a sistemas embarcados. Para tanto, o suporte de hardware necessário é adicionado à arquitetura. Também são implementados dois escalonadores de tarefas diretamente em bytecodes Java, visando à otimização de área e o compromisso com desempenho e consumo de energia. Modificações no ambiente de desenvolvimento e uma ferramenta de relocação de endereços são propostas, objetivando a utilização dos escalonadores de tarefas implementados junto ao fluxo de desenvolvimento existente. Por fim, uma análise é realizada sobre o impacto que a capacidade de multitarefa produz no sistema em termos de desempenho, consumo de área e energia. / Most digital electronic equipments are produced using an embedded operating system. Due to economic reasons, these operating systems are implemented on hardware with minimal requirements to support the application needs. This work will present a viability study to implement multitask support on the FemtoJava architecture, a monotask microcontroller dedicated to embedded applications. The support to multitask involves the addition of specific hardware mechanisms to the architecture. Two different scheduling policies are then directly implemented using Java bytecodes, aiming area optimization as well as a good performance/energy-consumption trade-off. Some modifications in the development environment and a code relocation tool were introduced, in order to enable the use of the schedulers in the existing design tool flow. Finally, an analysis is performed to evaluate the impact that the multitask support produces in the system with respect to the final performance, area and energy consumption.

Clinical intelligence about a patient's risk of future adverse health events can support clinical decision making in personalized and preventive care. Healthcare predictive analytics using electronic health records offers a promising direction to address the challenging tasks of risk profiling. Patients with chronic diseases often face risks of not just one, but an array of adverse health events. However, existing risk models typically focus on one specific event and do not predict multiple outcomes. To attain enhanced risk profiling, we adopt the design science paradigm and propose a principled approach called Bayesian multitask learning (BMTL). Considering the model development for an event as a single task, our BMTL approach is to coordinate a set of baseline models-one for each event-and communicate training information across the models. The BMTL approach allows healthcare providers to achieve multifaceted risk profiling and model an arbitrary number of events simultaneously. Our experimental evaluations demonstrate that the BMTL approach attains an improved predictive performance when compared with the alternatives that model multiple events separately. We also find that, in most cases, the BMTL approach significantly outperforms existing multitask learning techniques. More importantly, our analysis shows that the BMTL approach can create significant potential impacts on clinical practice in reducing the failures and delays in preventive interventions. We discuss several implications of this study for health IT, big data and predictive analytics, and design science research.

abstract: Deep learning is a sub-field of machine learning in which models are developed to imitate the workings of the human brain in processing data and creating patterns for decision making. This dissertation is focused on developing deep learning models for medical imaging analysis of different modalities for different tasks including detection, segmentation and classification. Imaging modalities including digital mammography (DM), magnetic resonance imaging (MRI), positron emission tomography (PET) and computed tomography (CT) are studied in the dissertation for various medical applications. The first phase of the research is to develop a novel shallow-deep convolutional neural network (SD-CNN) model for improved breast cancer diagnosis. This model takes one type of medical image as input and synthesizes different modalities for additional feature sources; both original image and synthetic image are used for feature generation. This proposed architecture is validated in the application of breast cancer diagnosis and proved to be outperforming the competing models. Motivated by the success from the first phase, the second phase focuses on improving medical imaging synthesis performance with advanced deep learning architecture. A new architecture named deep residual inception encoder-decoder network (RIED-Net) is proposed. RIED-Net has the advantages of preserving pixel-level information and cross-modality feature transferring. The applicability of RIED-Net is validated in breast cancer diagnosis and Alzheimer’s disease (AD) staging. Recognizing medical imaging research often has multiples inter-related tasks, namely, detection, segmentation and classification, my third phase of the research is to develop a multi-task deep learning model. Specifically, a feature transfer enabled multi-task deep learning model (FT-MTL-Net) is proposed to transfer high-resolution features from segmentation task to low-resolution feature-based classification task. The application of FT-MTL-Net on breast cancer detection, segmentation and classification using DM images is studied. As a continuing effort on exploring the transfer learning in deep models for medical application, the last phase is to develop a deep learning model for both feature transfer and knowledge from pre-training age prediction task to new domain of Mild cognitive impairment (MCI) to AD conversion prediction task. It is validated in the application of predicting MCI patients’ conversion to AD with 3D MRI images. / Dissertation/Thesis / Doctoral Dissertation Industrial Engineering 2019

Impact investing is an important theme in the financial industry because it seeks to achieve both financial returns and positive social and environmental impacts. The purpose of this study is to investigate the decision-making processes of impact investing firms in Europe and Brazil, focusing on asset allocation and the complexities of achieving dual returns. The study was based on multitask contract theory and followed a qualitative methodology, including interviews with key personnel from impact investing firms. Organizations can use these findings to improve decision-making, enhance business operations, and manage risks effectively.

L’analyse syntaxique consiste à prédire la représentation syntaxique de phrases en langue naturelle sous la forme d’arbres syntaxiques. Cette tâche pose des problèmes particuliers pour les langues non-configurationnelles ou qui ont une morphologie flexionnelle plus riche que celle de l’anglais. En particulier, ces langues manifestent une dispersion lexicale problématique, des variations d’ordre des mots plus fréquentes et nécessitent de prendre en compte la structure interne des mots-formes pour permettre une analyse syntaxique de qualité satisfaisante. Dans cette thèse, nous nous plaçons dans le cadre de l’analyse syntaxique robuste en constituants par transitions. Dans un premier temps, nous étudions comment intégrer l’analyse morphologique à l’analyse syntaxique, à l’aide d’une architecture de réseaux de neurones basée sur l’apprentissage multitâches. Dans un second temps, nous proposons un système de transitions qui permet de prédire des structures générées par des grammaires légèrement sensibles au contexte telles que les LCFRS. Enfin, nous étudions la question de la lexicalisation de l’analyse syntaxique. Les analyseurs syntaxiques en constituants lexicalisés font l’hypothèse que les constituants s’organisent autour d’une tête lexicale et que la modélisation des relations bilexicales est cruciale pour désambiguïser. Nous proposons un système de transition non lexicalisé pour l’analyse en constituants discontinus et un modèle de scorage basé sur les frontières de constituants et montrons que ce système, plus simple que des systèmes lexicalisés, obtient de meilleurs résultats que ces derniers. / Syntactic parsing consists in assigning syntactic trees to sentences in natural language. Syntactic parsing of non-configurational languages, or languages with a rich inflectional morphology, raises specific problems. These languages suffer more from lexical data sparsity and exhibit word order variation phenomena more frequently. For these languages, exploiting information about the internal structure of word forms is crucial for accurate parsing. This dissertation investigates transition-based methods for robust discontinuous constituency parsing. First of all, we propose a multitask learning neural architecture that performs joint parsing and morphological analysis. Then, we introduce a new transition system that is able to predict discontinuous constituency trees, i.e.\ syntactic structures that can be seen as derivations of mildly context-sensitive grammars, such as LCFRS. Finally, we investigate the question of lexicalization in syntactic parsing. Some syntactic parsers are based on the hypothesis that constituent are organized around a lexical head and that modelling bilexical dependencies is essential to solve ambiguities. We introduce an unlexicalized transition system for discontinuous constituency parsing and a scoring model based on constituent boundaries. The resulting parser is simpler than lexicalized parser and achieves better results in both discontinuous and projective constituency parsing.

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Multitask Learning (MTL) was conceived as an approach to improve thegeneralization ability of machine learning models. When applied to neu-ral networks, multitask models take advantage of sharing resources forreducing the total inference time, memory footprint and model size. Wepropose MTL as a way to speed up deep learning models for applicationsin which multiple tasks need to be solved simultaneously, which is par-ticularly useful in embedded, real-time systems such as the ones foundin autonomous cars or UAVs.In order to study this approach, we apply MTL to a Computer Vi-sion problem in which both Object Detection and Semantic Segmenta-tion tasks are solved based on the Single Shot Multibox Detector andFully Convolutional Networks with skip connections respectively, usinga ResNet-50 as the base network. We train multitask models for twodifferent datasets, Pascal VOC, which is used to validate the decisionsmade, and a combination of datasets with aerial view images capturedfrom UAVs.Finally, we analyse the challenges that appear during the process of train-ing multitask networks and try to overcome them. However, these hinderthe capacity of our multitask models to reach the performance of the bestsingle-task models trained without the limitations imposed by applyingMTL. Nevertheless, multitask networks benefit from sharing resourcesand are 1.6x faster, lighter and use less memory compared to deployingthe single-task models in parallel, which turns essential when runningthem on a Jetson TX1 SoC as the parallel approach does not fit intomemory. We conclude that MTL has the potential to give superior per-formance as far as the object detection and semantic segmentation tasksare concerned in exchange of a more complex training process that re-quires overcoming challenges not present in the training of single-taskmodels.

Les effets indésirables médicamenteux (EIM) ont des répercussions considérables tant sur la santé que sur l'économie. De 1,9% à 2,3% des patients hospitalisés en sont victimes, et leur coût a récemment été estimé aux alentours de 400 millions d'euros pour la seule Allemagne. De plus, les EIM sont fréquemment la cause du retrait d'un médicament du marché, conduisant à des pertes pour l'industrie pharmaceutique se chiffrant parfois en millions d'euros.De multiples études suggèrent que des facteurs génétiques jouent un rôle non négligeable dans la réponse des patients à leur traitement. Cette réponse comprend non seulement les effets thérapeutiques attendus, mais aussi les effets secondaires potentiels. C'est un phénomène complexe, et nous nous tournons vers l'apprentissage statistique pour proposer de nouveaux outils permettant de mieux le comprendre.Nous étudions différents problèmes liés à la prédiction de la réponse d'un patient à son traitement à partir de son profil génétique. Pour ce faire, nous nous plaçons dans le cadre de l'apprentissage statistique multitâche, qui consiste à combiner les données disponibles pour plusieurs problèmes liés afin de les résoudre simultanément.Nous proposons un nouveau modèle linéaire de prédiction multitâche qui s'appuie sur des descripteurs des tâches pour sélectionner les variables pertinentes et améliorer les prédictions obtenues par les algorithmes de l'état de l'art. Enfin, nous étudions comment améliorer la stabilité des variables sélectionnées, afin d'obtenir des modèles interprétables. / Adverse drug reaction (ADR) is a serious concern that has important health and economical repercussions. Between 1.9%-2.3% of the hospitalized patients suffer from ADR, and the annual cost of ADR have been estimated to be of 400 million euros in Germany alone. Furthermore, ADRs can cause the withdrawal of a drug from the market, which can cause up to millions of dollars of losses to the pharmaceutical industry.Multiple studies suggest that genetic factors may play a role in the response of the patients to their treatment. This covers not only the response in terms of the intended main effect, but also % according toin terms of potential side effects. The complexity of predicting drug response suggests that machine learning could bring new tools and techniques for understanding ADR.In this doctoral thesis, we study different problems related to drug response prediction, based on the genetic characteristics of patients.We frame them through multitask machine learning frameworks, which combine all data available for related problems in order to solve them at the same time.We propose a novel model for multitask linear prediction that uses task descriptors to select relevant features and make predictions with better performance as state-of-the-art algorithms. Finally, we study strategies for increasing the stability of the selected features, in order to improve interpretability for biological applications.

Made available in DSpace on 2016-04-27T16:57:33Z (GMT). No. of bitstreams: 1 Maria Teresa Merino Ruz Mastroianni.pdf: 2350266 bytes, checksum: f691786370f9c1b61d80e442274a76ce (MD5) Previous issue date: 2014-10-30 / Problem Solving represents an important line of research in Mathematics Education, expressing the posture of researchers and teachers willing to review the methodologies of the teaching-learning process of the school mathematics, looking for better results inside the classrooms and outside them. With the increase of the complex systems in today's world, the type of abilities needed for problem solving for the success beyond school have changed. However, there are still few researches about the development of concepts through problem solving, mainly problems which deal with the early years, in which the multitask teachers teach mathematics. These teachers have dealt with a variety of approaches, as a result of their historical paths and experiences. The present research, with a qualitative approach aims at investigating which conceptions a group of multitask female teachers from Elementary School in a private school from São Paulo have about the issue Problem Solving, being able to comprehend how they can influence their practice. The theoretical framework of the present investigation is based on the ideas of Guy Brousseau, specifically those related to the theory of didactic situations (TDS) and to the concept of didactic contract. As a subsidiary stream, a survey about the researches on the theme Problem Solving in Mathematics Education was done. Such theoretical movements tried to provide some basis to answer the leading questions, elaborated around the conceptions of the individuals on problem solving in mathematics classes, related to possible effects of the didactic contract which occur in the practice of these female teachers and the positioning of the pedagogical strategies employed, from the perspective of TDS. In order to answer the questions raised, a questionnaire has been used; its answers have made it possible to analyze the conceptions of six female teachers related to the theme. These teachers have had their classes observed, so that a comparison has been made between discourse and practice. The results show that the individuals of this research comprehend the importance of its critical role in the classes and value the mathematical thought of the students, although they still have some difficulty in organizing an antagonistic milieu , capable of provoking imbalance and searches for knowledge to be built through an investigative process, which indicates that, in practice, the individuals not always distinguish exercises from mathematical problems. Yet, some effects of the didactic contract over the relationships among teachers, students and the mathematical knowledge have been identified / A Resolução de Problemas representa uma importante vertente de pesquisa em Educação Matemática, expressando a postura de pesquisadores e professores dispostos a reverem as metodologias do processo de ensino-aprendizagem da matemática escolar, buscando melhores resultados nas salas de aula e fora delas. Com o aumento dos sistemas complexos no mundo atual, os tipos de habilidades necessárias na resolução de problemas para o sucesso além da escola, mudaram. Porém, ainda há poucas pesquisas sobre o desenvolvimento de conceitos por meio da resolução de problemas, principalmente que tratem dos anos iniciais, nos quais professores polivalentes ensinam matemática. Esses professores transitam por diversas abordagens, resultado de um percurso histórico e de suas vivências. A presente pesquisa, de abordagem qualitativa, tem por objetivo investigar quais concepções têm um grupo de professoras polivalentes do Ensino Fundamental de uma escola da rede particular de São Paulo sobre o tema Resolução de Problemas, compreendendo de que maneira exercem influência em sua prática. O quadro teórico da presente investigação recorre às ideias de Guy Brousseau, especificamente aquelas relativas à teoria das situações didáticas (TSD) e ao conceito de contrato didático. De forma subsidiária, um levantamento acerca das pesquisas realizadas sobre o tema Resolução de Problemas em Educação Matemática foi feito. Tais movimentos teóricos procuraram fornecer base para responder às questões norteadoras, elaboradas em torno das concepções dos sujeitos acerca da resolução de problemas em aulas de Matemática, dos eventuais efeitos do contrato didático que ocorrem na prática destas professoras e do posicionamento das estratégias pedagógicas empregadas, sob a ótica da TSD. Para responder às perguntas levantadas, empregou-se um questionário cujas respostas permitissem analisar as concepções de seis professoras relativas ao tema. A observação das aulas destas mesmas professoras também foi realizada, de modo a permitir um confronto entre discurso e prática. Os resultados apontam que os sujeitos desta pesquisa compreendem a importância de seu papel problematizador nas aulas e valorizam o pensamento matemático dos alunos, contudo ainda têm certa dificuldade em organizar um milieu antagonista, capaz de provocar desequilíbrios e buscas pelo conhecimento a construir por meio de um processo investigativo, o que indica que, na prática, nem sempre os sujeitos diferenciam exercícios de problemas matemáticos. Identificou-se, ainda, alguns efeitos do contrato didático nas relações entre os professores, os alunos e o saber matemático