This diploma thesis analyzes current book market, its specific features, history, threats and opportunities. The practical part deals with research and analysis of customer behaviour and their shopping habits. The research is then used for market segmentation.

O objetivo deste trabalho é apresentar uma técnica automática baseada em morfologia matemática para medida de sinal em imagens de cDNA desenvolvida no BIOINFO,em parceria com o Instituto Ludwig de Pesquisa contra o Câncer. A tecnologia de lâminas de cDNA é um processo baseado em hibridização que possibilita observar a concentração relativa de mRNA de amostras de tecidos analisando a luminosidade de sinais fluorescentes ou radioativos. Hibridização é o processo bioquímico onde duas fitas de ácido nucleico com seqüências complementares se combinam. A técnica apresentada permite o cálculo da expressão gênica com alto grau de automação, podendo o usuário corrigir com facilidade eventuais erros de segmentação. O usuário interage com o programa apenas para selecionar as imagens e inserir os dados de geometria da lâmina. A estratégia de solução usada tem três fases: gradeamento dos blocos, gradeamento dos spots e segmentação dos spots. Todas as fases utilizam filtros morfológicos e as fases de gradeamento possuem um passo final de correção baseado nos dados de geometria da lâmina o que aumenta a robustez do processo, que funciona bem mesmo em imagens ruidosas. / The objective of this work is to present the automated technique for measuring signal from cDNA images developed in BIOINFO, associated with the Ludwig Institute for Cancer Research. Microarray technology is a hybridization based process that makes possible to quantify the relative abundance of mRNA in two tissue samples analysing the luminosity of fluorescent or radioactive signals. Hybridization is a biochemical process where a strand of nucleic acid matches up its counterpart. The developed technique permits the calculation of gene expression with a high level of automation. Besides that, the user can easily correct eventual segmentation mistakes. The user interacts with the program only to select the images and to set the slide geometry parameters. The solution strategy has three main steps: subarray griding, spots gridding and spots detection. All the steps use morphological filters, and the two gridding steps have a final correction substep based on the slide geometry, increasing the process robustness, that works well even in noisy images.

O estudo é uma sistematização do tema Segmentação de Clientes aplicada em bibliotecas. Seu objetivo é demonstrar como a Segmentação de Clientes contribui para uma gestão efetiva de Marketing na biblioteca auxiliando no desenvolvimento de produtos e ações; potencializando os seus resultados. Compõe-se de duas partes: a primeira, um levantamento bibliográfico buscando identificar as tipologias de Segmentação de Clientes existentes, tanto no Marketing quanto na biblioteca, além de cases internacionais com a aplicação da estratégia nesse ambiente; a segunda, uma demonstração de como essa estratégia, e os passos que a antecedem no processo de gestão de Marketing, pode contribuir para potencializar as ações e os produtos e serviços da biblioteca. Para essa demonstração foi utilizado como referência o modelo sistêmico de gestão de Marketing desenvolvido pelo Profº. Dr. Mitsuru Yanaze transpondo-o para uma situação hipotética de uma biblioteca especializada corporativa da área de Marketing. O exercício desenvolvido, além de contextualizar a estratégia de Segmentação no processo de gestão e indicar os benefícios conseguidos com a sua adoção, contribui para diferenciar Marketing e promoção, usados habitualmente como sinônimos. / The study is a systematization of the subject Client Segmentation used in libraries. Its aim is to demonstrate how the Client Segmentation contributes to an effective Marketing management in library, promoting the development of products, Marketing actions and leveraging their results. This is a two parts study: the first, a bibliographic review to identify different Segmentation categories either in Marketing as well as in libraries, there are also international cases with this segmentation strategy applied to libraries; the second part is to show how the strategy of Segmentation and the steps taken before its application can contribute to make the library products and its services to be successful. To demonstrate how this can be achieved, a management model, developed by Professor Mitsuru Yanaze, was used and transposed to simulate a management situation in a Marketing corporate library. This hypothetical situation contributed to contextualize Segmentation strategy in management process, which can help librarians to better understand its benefits. Furthermore, this study helped to explain that Marketing differs from promotion; usually mistaken as synonyms.

Image understanding is the task of interpreting images by effectively solving the individual tasks of object recognition and semantic image segmentation. An image understanding system must have the capacity to distinguish between similar looking image regions while being invariant in its response to regions that have been altered by the appearance-altering transformation. The fundamental challenge for any such system lies within this simultaneous requirement for both invariance and specificity. Many image understanding systems have been proposed that capture geometric properties such as shapes, textures, motion and 3D perspective projections using filtering, non-linear modulus, and pooling operations. Deep learning networks ignore these geometric considerations and compute descriptors having suitable invariance and stability to geometric transformations using (end-to-end) learned multi-layered network filters. These deep learning networks in recent years have come to dominate the previously separate fields of research in machine learning, computer vision, natural language understanding and speech recognition. Despite the success of these deep networks, there remains a fundamental lack of understanding in the design and optimization of these networks which makes it difficult to develop them. Also, training of these networks requires large labeled datasets which in numerous applications may not be available. In this dissertation, we propose the ScatterNet Hybrid Framework for Deep Learning that is inspired by the circuitry of the visual cortex. The framework uses a hand-crafted front-end, an unsupervised learning based middle-section, and a supervised back-end to rapidly learn hierarchical features from unlabelled data. Each layer in the proposed framework is automatically optimized to produce the desired computationally efficient architecture. The term `Hybrid' is coined because the framework uses both unsupervised as well as supervised learning. We propose two hand-crafted front-ends that can extract locally invariant features from the input signals. Next, two ScatterNet Hybrid Deep Learning (SHDL) networks (a generative and a deterministic) were introduced by combining the proposed front-ends with two unsupervised learning modules which learn hierarchical features. These hierarchical features were finally used by a supervised learning module to solve the task of either object recognition or semantic image segmentation. The proposed front-ends have also been shown to improve the performance and learning of current Deep Supervised Learning Networks (VGG, NIN, ResNet) with reduced computing overhead.

O estudo é uma sistematização do tema Segmentação de Clientes aplicada em bibliotecas. Seu objetivo é demonstrar como a Segmentação de Clientes contribui para uma gestão efetiva de Marketing na biblioteca auxiliando no desenvolvimento de produtos e ações; potencializando os seus resultados. Compõe-se de duas partes: a primeira, um levantamento bibliográfico buscando identificar as tipologias de Segmentação de Clientes existentes, tanto no Marketing quanto na biblioteca, além de cases internacionais com a aplicação da estratégia nesse ambiente; a segunda, uma demonstração de como essa estratégia, e os passos que a antecedem no processo de gestão de Marketing, pode contribuir para potencializar as ações e os produtos e serviços da biblioteca. Para essa demonstração foi utilizado como referência o modelo sistêmico de gestão de Marketing desenvolvido pelo Profº. Dr. Mitsuru Yanaze transpondo-o para uma situação hipotética de uma biblioteca especializada corporativa da área de Marketing. O exercício desenvolvido, além de contextualizar a estratégia de Segmentação no processo de gestão e indicar os benefícios conseguidos com a sua adoção, contribui para diferenciar Marketing e promoção, usados habitualmente como sinônimos. / The study is a systematization of the subject Client Segmentation used in libraries. Its aim is to demonstrate how the Client Segmentation contributes to an effective Marketing management in library, promoting the development of products, Marketing actions and leveraging their results. This is a two parts study: the first, a bibliographic review to identify different Segmentation categories either in Marketing as well as in libraries, there are also international cases with this segmentation strategy applied to libraries; the second part is to show how the strategy of Segmentation and the steps taken before its application can contribute to make the library products and its services to be successful. To demonstrate how this can be achieved, a management model, developed by Professor Mitsuru Yanaze, was used and transposed to simulate a management situation in a Marketing corporate library. This hypothetical situation contributed to contextualize Segmentation strategy in management process, which can help librarians to better understand its benefits. Furthermore, this study helped to explain that Marketing differs from promotion; usually mistaken as synonyms.

Les entrepôts de la logistique, secteur charnière entre production et consommation, ne produisent pas une marchandise en particulier, mais un flux de marchandises. Partant du constat que le secteur, en forte croissance ces dernières années, a été le plus souvent étudié par les sciences sociales de manière globale, l’objectif de la thèse est d’analyser de manière intensive deux entrepôts logistiques appartenant à la même entreprise dans deux contextes nationaux et régionaux différents, le nord-est de l’Italie et la région parisienne. Plusieurs interrogations traversent cette enquête. Comment les entreprises multinationales de logistique s’adaptent-elles au contexte juridique, économique et social dans lequel elles opèrent et comment participent-elles à le transformer ? Quels sont les personnels mobilisés ? Quelles sont leurs conditions et pratiques de travail ? L’analyse porte donc sur l’organisation du travail, sur les effets des politiques de recrutement et de gestion sur les expériences sociales des ouvriers migrants et locaux, sur les interactions entre salariés et entre groupes, ainsi que sur les pratiques de résistances formelles et informelles. La recherche s’appuie sur une enquête ethnographique dans les deux entrepôts, ainsi que sur une série d’entretiens biographiques. La thèse se situe au croisement d’une sociologie du travail et d’une sociologie des organisations, d’une sociologie des mobilisations collectives et de la segmentation du marché de l’emploi, d’une sociologie des classes populaires, et plus secondairement d’une sociologie des espaces urbains. / Zipper between production and consumption, the logistics warehouses, rather than a specific good, product a flow of commodities. The sector has been growing fastly in the last years, and it is usually studied from a global perspective within social sciences. The aim of this thesis is to elaborate an in depth analysis on two warehouses of the same multinational logistics company within two distinct national and regional contexts: the Italian North-East and the Paris metropolis. The thesis is structured around many research questions: How do the logistics multinational companies adapt to the juridical, economic, and social context where they operate? How do they contribute to its transformation? Which recruitment policies do they apply? What are the practices and the working conditions? The analysis is then focused on the organization of the labour process, on the specific effects that the recruitment policies and the labour-force management produce on the social experiences of local and migrant workers, on the interactions and relationships between groups within the workplace, as well as on the formal and informal resistance practices played by the workers. The research is based on an ethnographic enquiry inside the two warehouses and on a series of biographic interviews. The thesis is at the crossroad between different sociological approaches: sociology of work and organizations, collective mobilizations and the segmentation of labour market, popular classes and, to a lesser extent, sociology of urban spaces.

Deep Learning is dominant in the field of computer vision, thanks to its high performance. This high performance is driven by large annotated datasets and proper evaluation benchmarks. However, two important areas in computer vision, depth-based hand segmentation, and local features, respectively lack a large well-annotated dataset and a benchmark protocol that properly demonstrates its practical performance. Therefore, in this thesis, we focus on these two problems. For hand segmentation, we create a novel systematic way to easily create automatic semantic segmentation annotations for large datasets. We achieved this with the help of traditional computer vision techniques and minimal hardware setup of one RGB-D camera and two distinctly colored skin-tight gloves. Our method allows easy creation of large-scale datasets with high annotation quality. For local features, we create a new modern benchmark, that reveals their different aspects. Specifically wide-baseline stereo matching and Multi-View Stereo (MVS), of keypoints in a more practical setup, namely Structure-from-Motion (SfM). We believe that through our new benchmark, we will be able to spur research on learned local features to a more practical direction. In this respect, the benchmark developed for the thesis will be used to host a challenge on local features. / Graduate

O Color Mixture é uma técnica para segmentação de imagens coloridas, que cria uma \"Retina Artificial\" baseada na mistura de cores, e faz a quantização da imagem projetando todas as cores em 256 planos no cubo RGB. Em seguida, atravessa todos esses planos com um classificador Gaussiano, visando à segmentação da imagem. Porém, a abordagem atual possui algumas limitações. O classificador atual resolve exclusivamente problemas binários. Inspirado nesta \"Retina Artificial\" do Color Mixture, esta tese define uma nova \"Retina Artificial\", propondo a substituição do classificador atual por uma rede neural artificial para cada um dos 256 planos, com o objetivo de melhorar o desempenho atual e estender sua aplicação para problemas multiclasse e multiescala. Para esta nova abordagem é dado o nome de Neural Color Mixture. Para a validação da proposta foram realizadas análises estatísticas em duas áreas de aplicação. Primeiramente para a segmentação de pele humana, tendo sido comparado seus resultados com oito métodos conhecidos, utilizando quatro conjuntos de dados de tamanhos diferentes. A acurácia de segmentação da abordagem proposta nesta tese superou a de todos os métodos comparados. A segunda avaliação prática do modelo proposto foi realizada com imagens de satélite devido à vasta aplicabilidade em áreas urbanas e rurais. Para isto, foi criado e disponibilizado um banco de imagens, extraídas do Google Earth, de dez regiões diferentes do planeta, com quatro escalas de zoom (500 m, 1000 m, 1500 m e 2000 m), e que continham pelo menos quatro classes de interesse: árvore, solo, rua e água. Foram executados quatro experimentos, sendo comparados com dois métodos, e novamente a proposta foi superior. Conclui-se que a nova proposta pode ser utilizada para problemas de segmentação de imagens coloridas multiclasse e multiescala. E que possivelmente permite estender o seu uso para qualquer aplicação, pois envolve uma fase de treinamento, em que se adapta ao problema. / The Color Mixture is a technique for color images segmentation, which creates an \"Artificial Retina\" based on the color mixture, and quantizes the image by projecting all the colors in 256 plans into the RGB cube. Then, it traverses all those plans with a Gaussian classifier, aiming to reach the image segmentation. However, the current approach has some limitations. The current classifier solves exclusively binary problems. Inspired by this \"Artificial Retina\" of the Color Mixture, we defined a new \"Artificial Retina\", as well as we proposed the replacement of the current classifier by an artificial neural network for each of the 256 plans, with the goal of improving current performance and extending your application to multiclass and multiscale issues. We called this new approach \"Neural Color Mixture\". To validate the proposal, we analyzed it statistically in two areas of application. Firstly for the human skin segmentation, its results were compared with eight known methods using four datasets of different sizes. The segmentation accuracy of the our proposal in this thesis surpassed all the methods compared. The second practical evaluation of the our proposal was carried out with satellite images due to the wide applicability in urban and rural areas. In order to do this, we created and made available a database of satellite images, extracted from Google Earth, from ten different regions of the planet, with four zoom scales (500 m, 1000 m, 1500 m and 2000 m), which contained at least four classes of interest: tree, soil, street and water. We compared our proposal with a neural network of the multilayer type (ANN-MLP) and an Support Vector Machine (SVM). Four experiments were performed, compared to two methods, and again the proposal was superior. We concluded that our proposal can be used for multiclass and multiscale color image segmentation problems, and that it possibly allows to extend its use to any application, as it involves a training phase, in which our methodology adapts itself to any kind of problem.

The purpose of this research is to show how Performance Solutions Group, LLC can effectively use segmentation marketing both in their current market and in expansion. The goal is to find a solution and suggest changes that should be made to the marketing team at Performance Solutions Group. This research was completed by looking at how segmentation marketing is used in broad industries currently and investigating how Performance Solutions Group can use it in their company. This case study shows that segmentation marketing is an effective way for Performance Solutions Group to market its services.

abstract: 中国证券市场一直存在着双重上市公司A、H股价差异现象，这一“同股同权不同价”的现象，长期以来都是国内外学者热议的课题之一。 本文在系统性整理前人研究成果基础上，首先对造成A、H股价差效应的内在逻辑进行了系统梳理，提炼出影响双重上市公司A、H股价格差异的9个潜在因素：信息不对称、需求差异、流动性差异、投机性差异、风险差异、公司治理结构、利率差异、市场强弱差异、汇率预期。其次，本文为各潜在影响因素构建了新的代理变量，建立面板数据模型，从全市场和行业两大视角做了实证分析，验证了影响双重上市公司A、H股价格差异的可能因素，且实证结果均通过了平稳性检验。实证结果显示：全市场视角下，仅公司治理结构和市场强弱差异对A、H价格差异的影响不显著。行业视角下，对于金融行业的双重上市公司而言，影响其A、H股价格差异的因素包括：需求差异、流动性差异、风险差异、市场强弱差异、利率差异；信息不对称、投机性差异、公司治理结构、汇率预期不具有显著影响。而对于非金融行业的双重上市公司而言，影响其A、H股价格差异的因素包括：信息不对称、需求差异、流动性差异、风险差异、投机性差异、市场强弱差异、利率差异、汇率预期；公司治理结构则不是显著的影响因素。 本文在实证分析所得结论的基础上，考虑到当前A、H股市场的现状，提出了加强资本市场双向开放、大力发展以基金为代表的机构投资者、坚定推行股票发行注册制改革、推动金融创新、丰富投资工具等建议。这一研究结果对于推动我国资本市场进一步完善，具有重要的理论与现实意义。 / Dissertation/Thesis / Doctoral Dissertation Business Administration 2019