Devido à progressiva importância atribuída à marca e, em especial, ao seu gerenciamento, diferentes teorias e modelos de compreensão, análise e prescrição foram e vêm sendo desenvolvidos, procurando se orientar abordagens e decisões estratégicas de negócio. Embora seja possível identificar algumas diferenças conceituais e/ou estruturais, dois aspectos, na prática, estão sempre presentes na análise de marcas e podem ser considerados decisivos na compreensão e gestão dessas: identidade e posicionamento de marca. Em relação a este segundo conceito, torna-se, ao longo do tempo, mais relevante, decisivo e crucial o processo de análise e avaliação da eficácia, adequação e relevância do posicionamento, em termos organizacionais, o entendimento da condição da marca e sua relação com o mercado, assim como modelos e procedimentos de avaliação para a decisão por sua eventual modificação ou adequação ao longo do tempo: o reposicionamento. Atualmente, o exame e a decisão de um reposicionamento de marcas está presente no cotidiano de profissionais de marketing e comunicação, porém não existe consenso ou uniformidade de abordagem deste tema, resultado, entre outras razões, da limitada literatura desenvolvida acerca do assunto. Este trabalho se propõe a abordar de forma estruturada conceitual e estrategicamente condições, desafios e riscos da adoção de uma estratégia de reposicionamento de marcas, utilizando argumentação baseada em literatura disponível, somada a intervenções exploratórias de exemplos reais, e propondo uma tipificação para as decisões e estratégias de reposicionamento, assim como orientações e alternativas para a gestão de marcas. A decisão por um reposicionamento de marca envolve desafios, investimentos e riscos, sendo que, na maioria das vezes, este processo se impõe ao gestor de marca. Com o objetivo de contribuir no exame e/ou avaliação do reposicionamento de marcas, este trabalho aborda as condições, as decisões e os impactos potenciais, oferecendo uma classificação e uma estrutura de análise orientadas para operação e gestão de estratégias de reposicionamento. / Different theories and comprehension, analysis and prescription models were and are being developed associated to progressive importance of brand management. Their objectives, in general, are to offer approached-based and strategic decisions for business. However, it is possible to identify some conceptual and/or structural differences among these papers, two aspects, in practice, are always present in brand’s analysis and may be recognized as decisive to understanding and administration brands: identity and positioning. This second concept is becoming more important and crucial as time goes by, playing a fundamental role in the brand efficacy evaluation, relevancy and compatibility of positioning and, particularly, the brand condition comprehension and its relationship with the market, as evaluation procedures and decision models for brand positioning change: the repositioning. Nowadays, brand repositioning analysis and decision are part of daily activities of marketing and communication professionals, but there is not approach consensus or uniformity to deal with this concept. As matter as fact, it is result – among other reasons – of limited developed literature about this issue. That thesis proposes itself to approach structurally, conceptually and strategically the conditions, challenges and risks to the adoption of brand repositioning strategy, using argumentation based on available literature added exploratory intervention in actual cases and proposing a taxonomy for repositioning strategies and decisions and brand management orientation and alternatives. Brand repositioning decision involves challenges, investments and risks. In general, this process in not a management decision, but an external imposition as a function of realized brand performance. With the objective to contribute to analysis and evaluation of brand repositioning, this research effort focus conditions, decisions and potential impacts, offering a structure and a classification oriented to repositioning strategies an operation management.

Le développement de novo de médicaments est un processus long et coûteux. De plus en plus, les développeurs de médicaments cherchent à mettre en oeuvre des stratégies rentables et à moindre risque pour le développement de produits pharmaceutiques. Le processus de trouver de nouveaux usages pour des médicaments existants en dehors de l'indication initiale pour laquelle ils ont été initialement approuvé est couramment désigné comme « repositionnement », « réorientation » ou « reprofilage ». Le développement de formulations différentes pour un même médicament pharmaceutique est communément désigné comme « reformulation » et le processus de trouver une autre utilisation thérapeutique d'un médicament déjà connu est dénommé « repositionnement ». Ces deux stratégies sont devenues un courant dominant dans le développement des médicaments. Les principaux objectifs de la recherche menée dans cette thèse sont de parvenir à proposer une nomenclature et la taxonomie solide et valable pour l'identification et la classification des stratégies de « repurposing » de médicaments ; évaluer les voies de régulation de stratégies de repositionnement et de reformulation, par types de stratégies et dans les 2 régions géographiques étudiées ; et déterminer les paramètres qui ont un impact sur la probabilité d'un résultat positif sur le prix, le remboursement et l'accès au marché vis-à-vis des conditions accordées pour le médicament original dans les deux régions géographiques dans l'étude / De novo drug development is a costly and lengthy process. As a result of such market forces, drug developers are increasingly striving to find cost effective and reduced-risk strategies for developing drug products and to protect existing products from competition, as well as to extend their patent protection time. The process of finding new uses for existing drugs outside the scope of the original indication for which they were initially approved is variously referred as repositioning, redirecting, repurposing, or reprofiling. The development of different formulations for a same pharmaceutical drug is commonly designated as “reformulation” and the process of finding a new therapeutic use for an already known drug is referred to as “repositioning”. Both strategies have become a mainstream in drug development. The main objectives of the research conducted in this thesis are to propose a robust and valid nomenclature and taxonomy for identification and classification of drug repurposing strategies, to evaluate which regulatory pathways and trends are taken by drug repositioning and reformulation, by repurposed types and within the Europe and the US and determine which parameters have the most and least impact on the probability of a successful outcome on pricing, reimbursement and market access in repurposing vis-à-vis the conditions granted for the original drug

The main goal of the master's thesis is to find reasons for and assess the results of recent repositioning of the brand Frisco conducted in 2013. In the theoretical part, main brand theories are analyzed together with a recent theory of so-called "Brand Archetypes". In the practical part, based on own consumer survey, market trends, brand history and company research, a brand SWOT analyses is conducted. As a result of this analysis, further recommendations for the brand heading are proposed.

In last years Hidden Markov Models (HMMs) have received significant attention in the task off-line handwritten text recognition (HTR). As in automatic speech recognition (ASR), HMMs are used to model the probability of an observation sequence, given its corresponding text transcription. However, in contrast to what happens in ASR, in HTR there is no standard set of local features being used by most of the proposed systems. In this thesis we propose the use of raw binary pixels as features, in conjunction with models that deal more directly with the binary data. In particular, we propose the use of Bernoulli HMMs (BHMMs), that is, conventional HMMs in which Gaussian (mixture) distributions have been replaced by Bernoulli (mixture) probability functions. The objective is twofold: on the one hand, this allows us to better modeling the binary nature of text images (foreground/background) using BHMMs. On the other hand, this guarantees that no discriminative information is filtered out during feature extraction (most HTR available datasets can be easily binarized without a relevant loss of information). In this thesis, all the HMM theory required to develop a HMM based HTR toolkit is reviewed and adapted to the case of BHMMs. Specifically, we begin by defining a simple classifier based on BHMMs with Bernoulli probability functions at the states, and we end with an embedded Bernoulli mixture HMM recognizer for continuous HTR. Regarding the binary features, we propose a simple binary feature extraction process without significant loss of information. All input images are scaled and binarized, in order to easily reinterpret them as sequences of binary feature vectors. Two extensions are proposed to this basic feature extraction method: the use of a sliding window in order to better capture the context, and a repositioning method in order to better deal with vertical distortions. Competitive results were obtained when BHMMs and proposed methods were applied to well-known HTR databases. In particular, we ranked first at the Arabic Handwriting Recognition Competition organized during the 12th International Conference on Frontiers in Handwriting Recognition (ICFHR 2010), and at the Arabic Recognition Competition: Multi-font Multi-size Digitally Represented Text organized during the 11th International Conference on Document Analysis and Recognition (ICDAR 2011). In the last part of this thesis we propose a method for training BHMM classifiers using In last years Hidden Markov Models (HMMs) have received significant attention in the task off-line handwritten text recognition (HTR). As in automatic speech recognition (ASR), HMMs are used to model the probability of an observation sequence, given its corresponding text transcription. However, in contrast to what happens in ASR, in HTR there is no standard set of local features being used by most of the proposed systems. In this thesis we propose the use of raw binary pixels as features, in conjunction with models that deal more directly with the binary data. In particular, we propose the use of Bernoulli HMMs (BHMMs), that is, conventional HMMs in which Gaussian (mixture) distributions have been replaced by Bernoulli (mixture) probability functions. The objective is twofold: on the one hand, this allows us to better modeling the binary nature of text images (foreground/background) using BHMMs. On the other hand, this guarantees that no discriminative information is filtered out during feature extraction (most HTR available datasets can be easily binarized without a relevant loss of information). In this thesis, all the HMM theory required to develop a HMM based HTR toolkit is reviewed and adapted to the case of BHMMs. Specifically, we begin by defining a simple classifier based on BHMMs with Bernoulli probability functions at the states, and we end with an embedded Bernoulli mixture HMM recognizer for continuous HTR. Regarding the binary features, we propose a simple binary feature extraction process without significant loss of information. All input images are scaled and binarized, in order to easily reinterpret them as sequences of binary feature vectors. Two extensions are proposed to this basic feature extraction method: the use of a sliding window in order to better capture the context, and a repositioning method in order to better deal with vertical distortions. Competitive results were obtained when BHMMs and proposed methods were applied to well-known HTR databases. In particular, we ranked first at the Arabic Handwriting Recognition Competition organized during the 12th International Conference on Frontiers in Handwriting Recognition (ICFHR 2010), and at the Arabic Recognition Competition: Multi-font Multi-size Digitally Represented Text organized during the 11th International Conference on Document Analysis and Recognition (ICDAR 2011). In the last part of this thesis we propose a method for training BHMM classifiers using In last years Hidden Markov Models (HMMs) have received significant attention in the task off-line handwritten text recognition (HTR). As in automatic speech recognition (ASR), HMMs are used to model the probability of an observation sequence, given its corresponding text transcription. However, in contrast to what happens in ASR, in HTR there is no standard set of local features being used by most of the proposed systems. In this thesis we propose the use of raw binary pixels as features, in conjunction with models that deal more directly with the binary data. In particular, we propose the use of Bernoulli HMMs (BHMMs), that is, conventional HMMs in which Gaussian (mixture) distributions have been replaced by Bernoulli (mixture) probability functions. The objective is twofold: on the one hand, this allows us to better modeling the binary nature of text images (foreground/background) using BHMMs. On the other hand, this guarantees that no discriminative information is filtered out during feature extraction (most HTR available datasets can be easily binarized without a relevant loss of information). In this thesis, all the HMM theory required to develop a HMM based HTR toolkit is reviewed and adapted to the case of BHMMs. Specifically, we begin by defining a simple classifier based on BHMMs with Bernoulli probability functions at the states, and we end with an embedded Bernoulli mixture HMM recognizer for continuous HTR. Regarding the binary features, we propose a simple binary feature extraction process without significant loss of information. All input images are scaled and binarized, in order to easily reinterpret them as sequences of binary feature vectors. Two extensions are proposed to this basic feature extraction method: the use of a sliding window in order to better capture the context, and a repositioning method in order to better deal with vertical distortions. Competitive results were obtained when BHMMs and proposed methods were applied to well-known HTR databases. In particular, we ranked first at the Arabic Handwriting Recognition Competition organized during the 12th International Conference on Frontiers in Handwriting Recognition (ICFHR 2010), and at the Arabic Recognition Competition: Multi-font Multi-size Digitally Represented Text organized during the 11th International Conference on Document Analysis and Recognition (ICDAR 2011). In the last part of this thesis we propose a method for training BHMM classifiers using discriminative training criteria, instead of the conventionalMaximum Likelihood Estimation (MLE). Specifically, we propose a log-linear classifier for binary data based on the BHMM classifier. Parameter estimation of this model can be carried out using discriminative training criteria for log-linear models. In particular, we show the formulae for several MMI based criteria. Finally, we prove the equivalence between both classifiers, hence, discriminative training of a BHMM classifier can be carried out by obtaining its equivalent log-linear classifier. Reported results show that discriminative BHMMs clearly outperform conventional generative BHMMs. / Giménez Pastor, A. (2014). Bernoulli HMMs for Handwritten Text Recognition [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/37978 / TESIS

The amount of biomedical literature has been increasing rapidly during the last decade. Text mining techniques can harness this large-scale data, shed light onto complex drug mechanisms, and extract relation information that can support computational polypharmacology. In this work, we introduce CASSANDRA, a fully corpus-based and unsupervised algorithm which uses the MEDLINE indexed titles and abstracts to infer drug gene associations and assist drug repositioning. CASSANDRA measures the Pointwise Mutual Information (PMI) between biomedical terms derived from Gene Ontology (GO) and Medical Subject Headings (MeSH). Based on the PMI scores, drug and gene profiles are generated and candidate drug gene associations are inferred when computing the relatedness of their profiles. Results show that an Area Under the Curve (AUC) of up to 0.88 can be achieved. The algorithm can successfully identify direct drug gene associations with high precision and prioritize them over indirect drug gene associations. Validation shows that the statistically derived profiles from literature perform as good as (and at times better than) the manually curated profiles. In addition, we examine CASSANDRA’s potential towards drug repositioning. For all FDA-approved drugs repositioned over the last 5 years, we generate profiles from publications before 2009 and show that the new indications rank high in these profiles. In summary, co-occurrence based profiles derived from the biomedical literature can accurately predict drug gene associations and provide insights onto potential repositioning cases.

京都大学 / 新制・課程博士 / 博士(医学) / 甲第24879号 / 医博第5013号 / 新制||医||1068(附属図書館) / 京都大学大学院医学研究科医学専攻 / (主査)教授 後藤 慎平, 教授 渡邊 直樹, 教授 平井 豊博 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

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In supervised learning, it is usually assumed that true labels are readily available from a single annotator or source. However, recent advances in corroborative technology have given rise to situations where the true label of the target is unknown. In such problems, multiple sources or annotators are often available that provide noisy labels of the targets. In these multi-annotator problems, building a classifier in the traditional single-annotator manner, without regard for the annotator properties may not be effective in general. In recent years, how to make the best use of the labeling information provided by multiple annotators to approximate the hidden true concept has drawn the attention of researchers in machine learning and data mining. In our previous work, a probabilistic method (i.e., MAP-ML algorithm) of iteratively evaluating the different annotators and giving an estimate of the hidden true labels is developed. However, the method assumes the error rate of each annotator is consistent across all the input data. This is an impractical assumption in many cases since annotator knowledge can fluctuate considerably depending on the groups of input instances. In this dissertation, one of our proposed methods, GMM-MAPML algorithm, follows MAP-ML but relaxes the data-independent assumption, i.e., we assume an annotator may not be consistently accurate across the entire feature space. GMM-MAPML uses a Gaussian mixture model (GMM) and Bayesian information criterion (BIC) to find the fittest model to approximate the distribution of the instances. Then the maximum a posterior (MAP) estimation of the hidden true labels and the maximum-likelihood (ML) estimation of quality of multiple annotators at each Gaussian component are provided alternately. Recent studies show that it is not the case that employing more annotators regardless of their expertise will result in improved highest aggregating performance. In this dissertation, we also propose a novel algorithm to integrate multiple annotators by Aggregating Experts and Filtering Novices, which we call AEFN. AEFN iteratively evaluates annotators, filters the low-quality annotators, and re-estimates the labels based only on information obtained from the good annotators. The noisy annotations we integrate are from any combination of human and previously existing machine-based classifiers, and thus AEFN can be applied to many real-world problems. Emotional speech classification, CASP9 protein disorder prediction, and biomedical text annotation experiments show a significant performance improvement of the proposed methods (i.e., GMM-MAPML and AEFN) as compared to the majority voting baseline and the previous data-independent MAP-ML method. Recent experiments include predicting novel drug indications (i.e., drug repositioning) for both approved drugs and new molecules by integrating multiple chemical, biological or phenotypic data sources. / Computer and Information Science

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