Centrality Heuristics for Exact Model Counting

Model counting is the archetypical #P-complete problem consisting of determining the number of satisfying truth assignments of a given propositional formula. In this short paper, we empirically investigate the potential of employing graph centrality measures as a basis of search heuristics in the context of exact model counting. In particular, we integrate centrality-based heuristics into the search-based exact model counter sharpSAT. Our experiments show that employing centrality information significantly improves the empirical performance of sharpSAT, and also allows for simplifying the search heuristics compared to the current default heuristics of the model counter. In particular, we show that the VSIDS heuristic, which is an integral search heuristic employed in essentially all state-of-the-art conflict-driven clause learning Boolean satisfiability solvers, appears to be of very limited use in the context of model counting.Peer reviewe

Graphical Models and Symmetries : Loopy Belief Propagation Approaches

Whenever a person or an automated system has to reason in uncertain domains, probability theory is necessary. Probabilistic graphical models allow us to build statistical models that capture complex dependencies between random variables. Inference in these models, however, can easily become intractable. Typical ways to address this scaling issue are inference by approximate message-passing, stochastic gradients, and MapReduce, among others. Exploiting the symmetries of graphical models, however, has not yet been considered for scaling statistical machine learning applications. One instance of graphical models that are inherently symmetric are statistical relational models. These have recently gained attraction within the machine learning and AI communities and combine probability theory with first-order logic, thereby allowing for an efficient representation of structured relational domains. The provided formalisms to compactly represent complex real-world domains enable us to effectively describe large problem instances. Inference within and training of graphical models, however, have not been able to keep pace with the increased representational power. This thesis tackles two major aspects of graphical models and shows that both inference and training can indeed benefit from exploiting symmetries. It first deals with efficient inference exploiting symmetries in graphical models for various query types. We introduce lifted loopy belief propagation (lifted LBP), the first lifted parallel inference approach for relational as well as propositional graphical models. Lifted LBP can effectively speed up marginal inference, but cannot straightforwardly be applied to other types of queries. Thus we also demonstrate efficient lifted algorithms for MAP inference and higher order marginals, as well as the efficient handling of multiple inference tasks. Then we turn to the training of graphical models and introduce the first lifted online training for relational models. Our training procedure and the MapReduce lifting for loopy belief propagation combine lifting with the traditional statistical approaches to scaling, thereby bridging the gap between statistical relational learning and traditional statistical machine learning

Redes neurais convolucionais para deteção de landmarks gástricas

Gastric cancer is the fifth most incident cancer in the world and, when diagnosed at an advanced stage, its survival rate is only 5%-25%, providing that it is essential that the cancer is detected at an early stage. However, physicians specialized in this diagnosis have difficulties in detecting early lesions during a diagnostic examination, esophagogastroduodenoscopy (EGD). Early lesions on the walls of the digestive system are imperceptible and confounded with the stomach mucosa, being difficult to detect. On the other hand, physicians run the risk of not covering all areas of the stomach during diagnosis, especially areas that may have lesions. The introduction of artificial intelligence into this diagnostic method may help to detect gastric cancer at an earlier stage. The implementation of a system capable of monitoring all areas of the digestive system during EGD would be a solution to prevent the diagnosis of gastric cancer in advanced states. This work focuses on the study of upper gastrointestinal (GI) landmarks monitoring, which are anatomical areas of the digestive system more conducive to the appearance of lesions and that allow better control of the missed areas during EGD exam. The use of convolutional neural networks (CNNs) in GI landmarks monitoring has been a great target of study by the scientific community, with such networks having a good capacity to extract features that better characterize EGD images. The aim of this work consisted in testing new automatic algorithms, specifically CNN-based systems able to detect upper GI landmarks to avoid the presence of blind spots during EGD to increase the quality of endoscopic exams. In contrast with related works in the literature, in this work we used upper GI landmarks images closer to real-world environments. In particular, images for each anatomical landmark class include both examples affected by pathologies and healthy tissue. We tested some pre-trained architectures as the ResNet-50, DenseNet-121, and VGG-16. For each pre-trained architecture, we tested different learning approaches, including the use of class weights (CW), the use of batch normalization and dropout layers, and the use of data augmentation to train the network. The CW ResNet-50 achieved an accuracy of 71.79% and a Mathews Correlation Coefficient (MCC) of 65.06%. In current state-of-art studies, only supervised learning approaches were used to classify EGD images. On the other hand, in our work, we tested the use of unsupervised learning to increase classification performance. In particular, convolutional autoencoder architectures to extract representative features from unlabeled GI images and concatenated their outputs withs with the CW ResNet-50 architecture. We achieved an accuracy of 72.45% and an MCC of 65.08%.O cancro gástrico é o quinto cancro mais incidente no mundo e quando diagnosticado numa fase avançada a taxa de sobrevivência é de apenas 5%-25%. Assim, é essencial que este cancro seja detetado numa fase precoce. No entanto, os médicos especializados neste diagnóstico nem sempre são capazes de uma boa performance de deteção durante o exame de diagnóstico, a esofagogastroduodenoscopia (EGD). As lesões precoces nas paredes do sistema digestivo são quase impercetíveis e confundíveis com a mucosa do estômago, sendo difíceis de detetar. Por outro lado, os médicos correm o risco de não cobrirem todas as áreas do estômago durante o diagnóstico, podendo estas áreas ter lesões. A introdução da inteligência artificial neste método de diagnóstico poderá ajudar a detetar o cancro gástrico numa fase mais precoce. A implementação de um sistema capaz de fazer a monitorização de todas as áreas do sistema digestivo durante a EGD seria uma solução de forma a prevenir o diagnóstico de cancro gástrico em estados avançados. Este trabalho tem como foco o estudo da monitorização de landmarks gastrointestinais (GI) superiores, que são zonas anatómicas do sistema digestivo mais propícias ao surgimento de lesões e que permitem fazer um melhor controlo das áreas esquecidas durante a EGD. O uso de redes neurais convolucionais (CNNs) na monitorização de landmarks GI tem sido grande alvo de estudo pela comunidade científica, por serem redes com uma boa capacidade de extração features que melhor caraterizam as imagens da EGD. O objetivo deste trabalho consistiu em testar novos algoritmos automáticos baseados em CNNs capazes de detetar landmarks GI superiores para evitar a presença áreas não cobertas durante a EGD, aumentando a qualidade deste exame. Este trabalho difere de outros estudos porque foram usadas classes de landmarks GI superiores mais próximas do ambiente real da EGD. Dentro de cada classe incluímos imagens com patologias e de tecido saudável da respetiva zona anatómica, ao contrário dos demais estudos. Nos estudos apresentados no estado de arte apenas foram consideradas classes de landmarks com tecido saudável em tarefas de deteção de landmarks GI. Testámos algumas arquiteturas pré-treinadas como a ResNet-50, a DenseNet-121 e a VGG-16. Para cada arquitetura pré-treinada, testámos algumas variáveis: o uso de class weights (CW), o uso das camadas batch normalization e dropout, e o uso de data augmentation. A arquitetura CW ResNet-50 atingiu uma accuracy de 71,79% e um coeficiente de correlação de Mathews (MCC) de 65,06%. Nos estudos apresentados no estado de arte, apenas foram estudados sistemas de supervised learning para classificação de imagens EGD enquanto, que no nosso trabalho, foram também testados sistemas de unsupervised learning para aumentar o desempenho da classificação. Em particular, arquiteturas autoencoder convolucionais para extração de features de imagens GI sem labels. Assim, concatenámos os outputs das arquiteturas autoencoder convolucionais com a arquitetura CW ResNet-50 e alcançamos uma accuracy de 72,45% e um MCC de 65,08%.Mestrado em Engenharia Biomédic

Impact of the feeding eycle upon the neuronal membrane properties of rat hippocampal neurones : the involvement of voltage-gated sodium and calcium currents and the maintenance of plasma membrane organization

Tese de doutoramento, Bioquímica (Biofísica Molecular), Universidade de Lisboa, Faculdade de Ciências, 2018Feeding behaviour and energy balance is regulated by the central nervous system, through a concerted endeavour of different brain areas. The hippocampus, historically regarded as a substrate for learning and memory processes, has also been implicated in such energy regulation. In recent years, researchers have established that hippocampal neurones form a memory of a meal and act to delay meal initiation during the postprandial period. However, more experiments are needed to identify the processes involved in such control. The present thesis starts to fill this gap, by identifying possible neuronal mechanisms by which the hippocampus processes satiety and meal termination. By assessing the functioning of ion currents/channels and the lipid composition and organization of the plasma membrane throughout the feeding cycle, this study furnishes a global perspective of the effect of post-prandial and fasting conditions upon intrinsic neuronal plasma membrane (PM) properties. The involvement of ion channels of rat hippocampal CA1 neurones in a feeding cycle context has already been studied. Indeed, the feeding cycle was found to impact the excitability of these neurones by modulating the activity of voltage-gated potassium currents. This finding has urged further investigation to evaluate the broadness of the effect of feeding cycle on the activity of other ion channels. Hence, it was critical to address the involvement of a) voltage-gated sodium (Na+) currents/channels, given their importance in the initiation and propagation of action potentials, and b) voltage-gated calcium (Ca2+) currents/channels, as they mediate the influx of this ubiquitous second messenger, with wide-ranging physiological roles, into the interior of the neurones. The influence of feeding cycle on the biophysics of Na+ and Ca2+ channels was undertaken in neurones acutely isolated from the CA1 subfield of the rat hippocampus. Two classes of neurones were used: those obtained from animals that fasted overnight (‘fasted neurones’) and those from animals that, after such period, were fed (‘fed neurones’). Voltage-gated Na+ currents were recorded by applying electrophysiological voltage clamp techniques - namely whole-cell (WC) and excised inside-out patches. Fed neurones, in comparison to fasted neurones, showed increased mean maximum macroscopic Na+ current density (1.5 ± 0.12mA.cm-2 vs. 1±0.10mA.cm-2) and a greater single-channel conductance (16.7 ± 0.76pS vs. 12.6 ± 1.30pS). Furthermore, the larger amplitude of the ‘window current’ obtained in fed neurones, derived from hyperpolarized activation curves and depolarized steady-state of inactivation curves (h∞), indicates a greater Na+ channel availability to respond to activation. Such variation is supported by a higher concentration of Nav1.2 isoform at the plasma membrane-enriched fractions of hippocampus of fed animals. Overall, the results indicate a variation in the biophysics and expression of voltage gated Na+ channels of rat hippocampal CA1 neurones, pointing out that feeding cycle changes the neuronal excitability. Voltage-gated Ca2+ currents were analysed with whole-cell recordings. It was observed heterogeneity in whole-cell Ca2+ currents, here sorted into three categories – ‘A’, ‘B’, and ‘C’ currents. The differential distribution of these currents between fed and fasted neurones determined significant alterations on the inactivation properties of Ca2+ currents. The increased values of the time-constant of inactivation - τh -, observed upon feeding, can be ascribed to a conspicuous slowly-inactivating current mainly assigned to fed neurones (current ‘A’), as oppose to the fastest kinetics of inactivation, solely seen in fasted neurones (current ‘C’). Furthermore, in fed neurones, a depolarizing shift of the most depolarized component (Vh2) of the voltage-dependence of h∞ was observed, which indicates that fasted neurones inactivate at more negative membrane potentials. Altogether, these observations point to a facilitated entry of Ca2+ into the soma of fed neurones, which, ultimately, potentiates the Ca2+-dependent intracellular events. The observed influence of feeding cycle on the biophysical and molecular expression of voltage-gated Na+ and Ca2+ channels did not have repercussions on the lipid environment of the PM. The plasma membrane-enriched fractions of rat hippocampus were labeled with molecular probes: 1,6-diphenyl 1,3,5-hexatriene (DPH) and trans-parinaric acid (t-PnA). By assessing the fluorescence properties of these probes, it was possible to study the molecular organization and lateral heterogeneity (in the membrane plane) of the lipid domains. Specifically, two types of fluorescence spectroscopy measurements were used, either in steady state (anisotropy measurements) and time-resolved domains (fluorescence intensity decay). The molecular biophysics analysis indicated that the order and rigidity of the acyl chains of the phospholipids constituents of the PM is not altered during the feeding cycle. Furthermore, the proportion of the different lipid domains at the surface of the neuronal PM is identical between conditions, which clearly indicates that the lateral heterogeneity of such domains is similar throughout the feeding cycle. This observation must be interpreted at a hydrophobic core level, where the t-Pna and DPH preferentially locate within the PM. The lipid content of the plasma membrane of rat hippocampus also did not endure any variation during the feeding cycle. The ratios calculated for the total lipid, phospholipid and cholesterol content were identical between the membranes of fed and fasted animals. The results concerning the molecular biophysics and biochemical characterization of the lipids imbedded in the neuronal plasma membrane indicate that neurones must have a shield mechanism to preserve their functional viability, regardless of the peripheral metabolic state. In summary, the greater levels of neuronal excitability and the promotion of Ca2+ entry into the neurones upon feeding may imply a subsequent increase on neuronal synaptic performance. A positive relationship between feeding and higher levels of synaptic plasticity-related phenomena (formation and consolidation of memories) is suggested, which could help to explain the role of hippocampus on the regulation of energy intake, mainly due to its role on meal-related episodic memories. This work gives new insights into the function of hippocampus on energy homeostasis, by adding new elements to the equation, namely, voltage-gated Na+ and Ca2+ channels.Fundação para a Ciência e a Tecnologia (FCT

LocoMouse: a novel system for studying the role of cerebellum in gait coordination

Smooth and efficient walking requires the coordination of movement across different parts of the body. The cerebellum plays an important role in this process, yet the specific neural circuit mechanisms of whole-body coordination are poorly understood. Although sophisticated genetic tools exist to manipulate the cerebellar circuit in mice, analyses of mouse gait have typically been limited to gross performance measures and lack detail about precision and timing of limb movements. In this project, I developed an automated, high-throughput, markerless 3D tracking system (LocoMouse) for quantifying locomotion in freely walking mice. Using LocoMouse, I showed that locomotor parameters for individual limbs vary systematically with mouse walking speed and body size. In visibly ataxic Purkinje cell degeneration (pcd) and reeler mice, I found that 3D limb trajectories and, especially, interlimb and whole-body coordination are specifically impaired. Our findings suggest a failure to predict the consequences of movement across joints, limbs, and body. These experiments were essential to establish a quantitative framework for whole-body locomotor coordination in mice (Machado, Darmohray et al. eLife 2015). The LocoMouse system was then combined with optogenetic tools to ask how different output regions of the cerebellum differentially contribute to locomotor coordination. I expressed ChR2 in Purkinje cells and stimulated their terminals in the medial, interposed, and lateral cerebellar nuclei of freely walking mice. Here, I identified locomotor parameters that were specifically related to the manipulation of each nucleus. Acute disruption of neural activity in medial and interposed nuclei immediately perturbed ongoing locomotion. In contrast, similar manipulation of Purkinje cell inputs to the lateral nucleus had no observable effect on ongoing locomotor behavior. These results are broadly consistent with previous anatomical and lesion studies suggesting a medial-to-lateral functional organization of cerebellar outputs. Taken together, these experiments isolated impairments in interlimb and whole-body coordination in mice with cerebellar manipulations. In contrast, spinal cord mutant mice revealed impairments at the intralimb level with no alteration in the interlimb coordination. I characterized distinct motor deficits associated with manipulations in different brain regions and identified and quantified core features of cerebellar ataxia in mice. These experiments establish the LocoMouse system, combined with genetic manipulations, as a powerful system to dissect cerebellar circuit mechanisms of coordinated locomotion

Poverty measurement : a theoretical contribution and application to Portugal 2007

Doutoramento em Economia.Neste trabalho apresenta-se uma história do pensamento económico na medição de pobreza - desde o que pode ser considerado o inicio da economia da pobreza ate a era de redescoberta da pobreza durante a década de 1960 - bern como uma revisão da literatura sobre as principais ferramentas de medição da pobreza apresentadas pela Ciência Económica. E ainda apresentado, no âmbito da abordagem multidimensional, uma proposta de um índice de medição de pobreza, inovadora quanto a ponderação dos diferentes atributos considerados como elementos de privação. 0 propósito do indice proposto e o de medir a pobreza na sua multidimensionalidade, sendo que cada dimensão de privação e ponderada no indice de acordo com a Hierarquia de Necessidades de Maslow. Esta forma de ponderação faz com que o indice proposto seja diferente dos ja existentes pelo facto de se incorporarem elementos de uma teoria psicológica consolidada na sua estrutura. Por fim, o indice apresentado e aplicado atraves de dados do European Union Statistics on Income and Living Conditions (EU-SILC) para Portugal em 2007 e comparado com dois outros metodos multidimensionais de medi9ao da pobreza.This work presents a history of the economic thought on poverty measurement - from what can be considered the beginning of the Poverty Economics until the "Rediscovering Poverty" era during the 1960s - as well as a review of the literature on the main poverty measurement tools presented by the Economic Science. We also present, having the multidimensional approach as background, a proposal for a poverty measurement index, somehow innovative regarding the weighting of different attributes considered as elements of deprivation. The aim of the proposed index is to measure poverty as a multidimensional phenomenon, where each dimension of deprivation is weighted in the index according to the Maslow's Hierarchy of Needs. This way of weighting makes the proposed index different from the existing indices given that it incorporates elements of a consolidated psychological theory in its structure. Finally, the index is applied using the European Union Statistics on Income and Living Conditions (EU-SILC) microdata for Portugal in 2007, and compared with two other methods of measuring multidimensional poverty

Equity research - Pirelli & C. S.P.A.

Mestrado em FinançasO objetivo do relatório é a elaboração de uma recomendação de investimento na Pirelli & C. S.p.A. e portanto irá ser incluído neste relatório uma avaliação detalhada da empresa. O motivo da seleção da empresa a estudar deveu-se unicamente à unidade curricular de Equity Research, leccionada no semestre anterior à elaboração deste projecto, na qual estudei a indústria em que a Pirelli opera tendo despertado o meu interesse para aprender um pouco mais sobre a esta empresa, uma das maiores a operar na Indústria de Pneus e Borracha mundialmente. O preço calculado para a acção da Pirelli foi obtido ao aplicar o modelo dos Fluxos de Caixa Descontados (DCF), como avaliação absoluta, e confirmado pela avaliação relativa obtida através do método dos múltiplos comparáveis. A recomendação deste relatório é de Compra, tendo sido calculado um preço de €5.51 para o final do ano de 2019, o que representa uma valorização de 17% face ao preço da acção à data do relatório. Neste relatório é também analisado o risco do investimento na Pirelli e foi mantida a recomendação de Compra, mesmo considerando os potenciais riscos que a empresa enfrenta ao ser depende do sector automóvel e do preço das matérias-primas.The purpose of the report is to assemble an investment proposition for Pirelli & C. S.p.A. Therefore a detailed valuation of the company will be included in this report. The reason behind the selection of the company to study was solely due to the Equity Research course attended in the previous semester. In the course I had the opportunity to study the industry where Pirelli operates and my interested in learning a little more about this company, one of the largest companies in the world to operate in the Tyre and Rubber Industry, increased. The price calculated for Pirelli's share was obtained by applying the Discounted Cash Flow (DCF) model as a type of an absolute valuation and it was confirmed by the relative valuation obtained using the comparable multiples method. At the end the recommendation of this report is to buy the stock as a price of €5.51 is expected for the year-end 2019, which represents a 17% appreciation from the share price at the date of the report. This report also analyzes the risk of investing in Pirelli and the buy recommendation did preserve, even considering the potential risks that the company faces in being dependent on the automotive sector and the price of the raw materials.info:eu-repo/semantics/publishedVersio