Interference Effects in Quantum Belief Networks

Probabilistic graphical models such as Bayesian Networks are one of the most powerful structures known by the Computer Science community for deriving probabilistic inferences. However, modern cognitive psychology has revealed that human decisions could not follow the rules of classical probability theory, because humans cannot process large amounts of data in order to make judgements. Consequently, the inferences performed are based on limited data coupled with several heuristics, leading to violations of the law of total probability. This means that probabilistic graphical models based on classical probability theory are too limited to fully simulate and explain various aspects of human decision making. Quantum probability theory was developed in order to accommodate the paradoxical findings that the classical theory could not explain. Recent findings in cognitive psychology revealed that quantum probability can fully describe human decisions in an elegant framework. Their findings suggest that, before taking a decision, human thoughts are seen as superposed waves that can interfere with each other, influencing the final decision. In this work, we propose a new Bayesian Network based on the psychological findings of cognitive scientists. We made experiments with two very well known Bayesian Networks from the literature. The results obtained revealed that the quantum like Bayesian Network can affect drastically the probabilistic inferences, specially when the levels of uncertainty of the network are very high (no pieces of evidence observed). When the levels of uncertainty are very low, then the proposed quantum like network collapses to its classical counterpart

Finding Academic Experts on a MultiSensor Approach using Shannon's Entropy

Expert finding is an information retrieval task concerned with the search for the most knowledgeable people, in some topic, with basis on documents describing peoples activities. The task involves taking a user query as input and returning a list of people sorted by their level of expertise regarding the user query. This paper introduces a novel approach for combining multiple estimators of expertise based on a multisensor data fusion framework together with the Dempster-Shafer theory of evidence and Shannon's entropy. More specifically, we defined three sensors which detect heterogeneous information derived from the textual contents, from the graph structure of the citation patterns for the community of experts, and from profile information about the academic experts. Given the evidences collected, each sensor may define different candidates as experts and consequently do not agree in a final ranking decision. To deal with these conflicts, we applied the Dempster-Shafer theory of evidence combined with Shannon's Entropy formula to fuse this information and come up with a more accurate and reliable final ranking list. Experiments made over two datasets of academic publications from the Computer Science domain attest for the adequacy of the proposed approach over the traditional state of the art approaches. We also made experiments against representative supervised state of the art algorithms. Results revealed that the proposed method achieved a similar performance when compared to these supervised techniques, confirming the capabilities of the proposed framework

Learning to Rank Academic Experts in the DBLP Dataset

Expert finding is an information retrieval task that is concerned with the search for the most knowledgeable people with respect to a specific topic, and the search is based on documents that describe people's activities. The task involves taking a user query as input and returning a list of people who are sorted by their level of expertise with respect to the user query. Despite recent interest in the area, the current state-of-the-art techniques lack in principled approaches for optimally combining different sources of evidence. This article proposes two frameworks for combining multiple estimators of expertise. These estimators are derived from textual contents, from graph-structure of the citation patterns for the community of experts, and from profile information about the experts. More specifically, this article explores the use of supervised learning to rank methods, as well as rank aggregation approaches, for combing all of the estimators of expertise. Several supervised learning algorithms, which are representative of the pointwise, pairwise and listwise approaches, were tested, and various state-of-the-art data fusion techniques were also explored for the rank aggregation framework. Experiments that were performed on a dataset of academic publications from the Computer Science domain attest the adequacy of the proposed approaches.Comment: Expert Systems, 2013. arXiv admin note: text overlap with arXiv:1302.041

Assessment of microbial community interactions using different tools

Dissertação de mestrado em BioinformaticsMicrobial communities participate in many biological processes, directly affecting its surrounding environment. Thus, the study of a community’s behaviour and interactions among its members can be very useful in the biotechnology, environmental and human health fields. Nevertheless, decoding the metabolic exchanges between microorganisms and community dynamics remains a challenge. Computational modelling methods have gained interest as a way to unravel the interactions and behaviour. GSM models allow the prediction of an organism’s response to changes in genetic and environmental conditions. Thus, the extension of such method to a community level can help decode a community’s phenotype. In this work, different GSM models and current bioinformatics tools were used to model the metabolism of different microbial communities. The different tools’ performances were compared to assess which is currently the best method to perform an analysis on a community level. Distinct case studies regarding microbial communities for which its interactions were already known, were selected. To assess the tools’ performances, each tools output was compared to what was expected in theory. COBRA Toolbox's methods proved to be useful to build a community structure from individual GSM models, while pFBA and SteadyCom’s simulation methods can predict exchange between the organisms and the environment. Additionally, Dynamic Flux Balance Analysis (dFBA) approaches, such as DFBAlab and DyMMM, can successfully simulate metabolite and biomass variation over time. Nevertheless, these methods are more limited as they require specific organism information, which is not always available. Several GSM models are available for use. Nonetheless, their quality control has to gain attention as the simulations’ results are directly affected by the individual models accuracy to represent an organism’s metabolism. Thus, community model builders should carefully chose a GSM model, or combination of models before performing simulations.Comunidades microbianas participam em inúmeros processos biológicos, afetando diretamente o ambiente que as engloba. Assim, o estudo do comportamento de uma comunidade e interações entre os seus membros pode ser muito útil nas áreas da biotecnologia, ambiente e saúde. No entanto, descodificar as trocas entre microrganismos e a dinâmica de comunidades continua um desafio. Métodos de modelação computacional têm ganho interesse como forma de desvendar tais interações e comportamento de comunidades. Modelos metabólicos à escala genómica permitem prever a resposta de um certo organismo a mudanças genéticas e ambientais. Assim, a extensão de tal método ao nível de comunidade pode ajudar a prever o fenótipo de uma certa comunidade. No presente trabalho, diferentes modelos metabólicos à escala genómica e ferramentas bioinformáticas foram utilizados para modelar o metabolismo de diferentes comunidades microbianas, comparando o desempenho destas ferramentas para avaliar qual o melhor método para análise ao nível da comunidade. Casos de estudo distintos, relativos a comunidades para as quais se conhecem as interações, foram selecionados. Por fim, para aferir o desempenho das ferramentas, os respetivos resultados foram comparados ao teoricamente esperado. Os métodos da ferramenta COBRA Toolbox provaram ser úteis para construir a estrutura da comunidade, usando modelos metabólicos à escala genómica dos organismos individuais. Quanto a métodos de simulação, pFBA e SteadyCom são úteis para prever trocas entre os organismos e o ambiente que os envolve. Para além disso, abordagens dFBA, como DFBAlab e DyMMM, podem simular a variação da concentração de metabolitos e biomassa ao longo do tempo. No entanto, estes métodos apresentam limitações por requererem informação específica ao organismo, que nem sempre se encontra disponível. Vários modelos metabólicos à escala genómica estão disponibilizados. No entanto, o controlo na qualidade destes tem que ganhar atenção, visto que os resultados das simulações são diretamente afetados pela sua precisão na representação do metabolismo de um organismo e consequentemente, da comunidade. Assim, para construir um modelo de comunidades, é necessária uma seleção cuidadosa dos modelos individuais a usar, antes de serem feitas simulações

Light wine. Technological and legal aspects of alcohol reduced wine

Mestrado em Viticultura e Enologia - Instituto Superior de Agronomia / Faculdade de Ciências. Universidade do PortoThe work investigates the technological and legal aspects of producing and commercializing alcohol reduced wine. For various reasons – related to health concerns, consumer fashions, and tax regimes among others – the global wine consumer market currently demands lower alcohol products. In response, industry and researchers have been working together to examine how to produce alcohol-reduced wines that maintain the technological features and organoleptic character of quality wine. As part of this effort, this work reviews the current state of the art in wine alcohol reduction technology, especially the stabilization of the wines during storage and their organoleptic quality. Through a series of cellar-based trials, the work shows that 50 mg/L of free SO2 are efficient to avoid microbial spoilage in wines containing 4% and 8% (v/v), respectively. Moreover, based on a series of sensorial taste panels, the work makes recommendations on how to improve the organoleptic quality of alcohol-reduced wines, especially with regard to acidity, bitterness and body. At a different level, the work examines the legal framework for alcohol-reduced wines. It argues that once the actually available technology allows the production of quality alcohol-reduced wines and consumers desire such products, current OIV and EU regulations defining wine as grape fermented beverage containing at least 8.5% (v/v) may need to be revised. It is recommended to create a new legal category for ‘light wines’ containing between 4% and 8,5% (v/v)