942 resultados para sequential exploitation


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Sequential pattern mining is an important subject in data mining with broad applications in many different areas. However, previous sequential mining algorithms mostly aimed to calculate the number of occurrences (the support) without regard to the degree of importance of different data items. In this paper, we propose to explore the search space of subsequences with normalized weights. We are not only interested in the number of occurrences of the sequences (supports of sequences), but also concerned about importance of sequences (weights). When generating subsequence candidates we use both the support and the weight of the candidates while maintaining the downward closure property of these patterns which allows to accelerate the process of candidate generation.

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Heterogeneous datasets arise naturally in most applications due to the use of a variety of sensors and measuring platforms. Such datasets can be heterogeneous in terms of the error characteristics and sensor models. Treating such data is most naturally accomplished using a Bayesian or model-based geostatistical approach; however, such methods generally scale rather badly with the size of dataset, and require computationally expensive Monte Carlo based inference. Recently within the machine learning and spatial statistics communities many papers have explored the potential of reduced rank representations of the covariance matrix, often referred to as projected or fixed rank approaches. In such methods the covariance function of the posterior process is represented by a reduced rank approximation which is chosen such that there is minimal information loss. In this paper a sequential Bayesian framework for inference in such projected processes is presented. The observations are considered one at a time which avoids the need for high dimensional integrals typically required in a Bayesian approach. A C++ library, gptk, which is part of the INTAMAP web service, is introduced which implements projected, sequential estimation and adds several novel features. In particular the library includes the ability to use a generic observation operator, or sensor model, to permit data fusion. It is also possible to cope with a range of observation error characteristics, including non-Gaussian observation errors. Inference for the covariance parameters is explored, including the impact of the projected process approximation on likelihood profiles. We illustrate the projected sequential method in application to synthetic and real datasets. Limitations and extensions are discussed. © 2010 Elsevier Ltd.

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DUE TO COPYRIGHT RESTRICTIONS ONLY AVAILABLE FOR CONSULTATION AT ASTON UNIVERSITY LIBRARY AND INFORMATION SERVICES WITH PRIOR ARRANGEMENT

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This paper addresses a problem with an argument in Kranich, Perea, and Peters (2005) supporting their definition of the Weak Sequential Core and their characterization result. We also provide the remedy, a modification of the definition, to rescue the characterization.

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This paper uses self-efficacy to predict the success of women in introductory physics. We show how sequential logistic regression demonstrates the predictive ability of self-efficacy, and reveals variations with type of physics course. Also discussed are the sources of self-efficacy that have the largest impact on predictive ability.

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Acknowledgments Y.Y. acknowledges the financial support from “973” Program (2012CB721006) and National Natural Science Foundation of China (31570033). R.E., K.K., H.D., and M.J. acknowledge the financial support of the Leverhulme Trust-Royal Society Africa Award (AA090088).

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The authors would like to express their gratitude to organizations and people that supported this research. Piotr Omenzetter’s work within the Lloyd’s Register Foundation Centre for Safety and Reliability Engineering at the University of Aberdeen is supported by Lloyd’s Register Foundation. The Foundation helps to protect life and property by supporting engineering-related education, public engagement and the application of research. Ben Ryder of Aurecon and Graeme Cummings of HEB Construction assisted in obtaining access to the bridge and information for modelling. Luke Williams and Graham Bougen, undergraduate research students, assisted with testing.

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Thèse numérisée par la Direction des bibliothèques de l'Université de Montréal.

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The advances in three related areas of state-space modeling, sequential Bayesian learning, and decision analysis are addressed, with the statistical challenges of scalability and associated dynamic sparsity. The key theme that ties the three areas is Bayesian model emulation: solving challenging analysis/computational problems using creative model emulators. This idea defines theoretical and applied advances in non-linear, non-Gaussian state-space modeling, dynamic sparsity, decision analysis and statistical computation, across linked contexts of multivariate time series and dynamic networks studies. Examples and applications in financial time series and portfolio analysis, macroeconomics and internet studies from computational advertising demonstrate the utility of the core methodological innovations.

Chapter 1 summarizes the three areas/problems and the key idea of emulating in those areas. Chapter 2 discusses the sequential analysis of latent threshold models with use of emulating models that allows for analytical filtering to enhance the efficiency of posterior sampling. Chapter 3 examines the emulator model in decision analysis, or the synthetic model, that is equivalent to the loss function in the original minimization problem, and shows its performance in the context of sequential portfolio optimization. Chapter 4 describes the method for modeling the steaming data of counts observed on a large network that relies on emulating the whole, dependent network model by independent, conjugate sub-models customized to each set of flow. Chapter 5 reviews those advances and makes the concluding remarks.

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This paper focuses on James March’s 1991 article on ‘Exploration and Exploitation in Organizational Learning’, which is now the seventh most highly cited paper in management and organisation studies. March’s paper is based on a computer program that simulates the collective and individual learning of a group of fifty individuals. The largely forgotten story that this paper re-calls is the real-life experiment that March, in large part, designed and conducted when he was the new ‘boy Dean’ of the School of Social Sciences in the University of California at Irvine between 1964 and 1969. Taken together, both stories illuminate important moments in the history of organisation studies. The comparison suggests that March’s model, which was probably the first simulation of an organisation learning, also worked to constitute rather than model the phenomenon.

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Cell-to-cell signals of the Diffusible Signal Factor (DSF) family are cis-2-unsaturated fatty acids of differing chain length and branching pattern. DSF signalling has been described in diverse bacteria to include plant and human pathogens where it acts to regulate functions such as biofilm formation, antibiotic tolerance and the production of virulence factors. DSF family signals can also participate in interspecies signalling with other bacteria and interkingdom signaling such as with the yeast Candida albicans. Interference with DSF signalling may afford new opportunities for the control of bacterial disease. Such strategies will depend in part on detailed knowledge of the molecular mechanisms underlying the processes of signal synthesis, perception and turnover. Here, I review both recent progress in understanding DSF signalling at the molecular level and prospects for translating this knowledge into approaches for disease control.

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Le but de cette thèse est d’explorer le potentiel sismique des étoiles naines blanches pulsantes, et en particulier celles à atmosphères riches en hydrogène, les étoiles ZZ Ceti. La technique d’astérosismologie exploite l’information contenue dans les modes normaux de vibration qui peuvent être excités lors de phases particulières de l’évolution d’une étoile. Ces modes modulent le flux émergent de l’étoile pulsante et se manifestent principalement en termes de variations lumineuses multi-périodiques. L’astérosismologie consiste donc à examiner la luminosité d’étoiles pulsantes en fonction du temps, afin d’en extraire les périodes, les amplitudes apparentes, ainsi que les phases relatives des modes de pulsation détectés, en utilisant des méthodes standards de traitement de signal, telles que des techniques de Fourier. L’étape suivante consiste à comparer les périodes de pulsation observées avec des périodes générées par un modèle stellaire en cherchant l’accord optimal avec un modèle physique reconstituant le plus fidèlement possible l’étoile pulsante. Afin d’assurer une recherche optimale dans l’espace des paramètres, il est nécessaire d’avoir de bons modèles physiques, un algorithme d’optimisation de comparaison de périodes efficace, et une puissance de calcul considérable. Les périodes des modes de pulsation de modèles stellaires de naines blanches peuvent être généralement calculées de manière précise et fiable sur la base de la théorie linéaire des pulsations stellaires dans sa version adiabatique. Afin de définir dans son ensemble un modèle statique de naine blanche propre à l’analyse astérosismologique, il est nécessaire de spécifier la gravité de surface, la température effective, ainsi que différents paramètres décrivant la disposition en couche de l’enveloppe. En utilisant parallèlement les informations obtenues de manière indépendante (température effective et gravité de surface) par la méthode spectroscopique, il devient possible de vérifier la validité de la solution obtenue et de restreindre de manière remarquable l’espace des paramètres. L’exercice astérosismologique, s’il est réussi, mène donc à la détermination précise des paramètres de la structure globale de l’étoile pulsante et fournit de l’information unique sur sa structure interne et l’état de sa phase évolutive. On présente dans cette thèse l’analyse complète réussie, de l’extraction des fréquences à la solution sismique, de quatre étoiles naines blanches pulsantes. Il a été possible de déterminer les paramètres structuraux de ces étoiles et de les comparer remarquablement à toutes les contraintes indépendantes disponibles dans la littérature, mais aussi d’inférer sur la dynamique interne et de reconstruire le profil de rotation interne. Dans un premier temps, on analyse le duo d’étoiles ZZ Ceti, GD 165 et Ross 548, afin de comprendre les différences entre leurs propriétés de pulsation, malgré le fait qu’elles soient des étoiles similaires en tout point, spectroscopiquement parlant. L’analyse sismique révèle des structures internes différentes, et dévoile la sensibilité de certains modes de pulsation à la composition interne du noyau de l’étoile. Afin de palier à cette sensibilité, nouvellement découverte, et de rivaliser avec les données de qualité exceptionnelle que nous fournissent les missions spatiales Kepler et Kepler2, on développe une nouvelle paramétrisation des profils chimiques dans le coeur, et on valide la robustesse de notre technique et de nos modèles par de nombreux tests. Avec en main la nouvelle paramétrisation du noyau, on décroche enfin le ”Saint Graal” de l’astérosismologie, en étant capable de reproduire pour la première fois les périodes observées à la précision des observations, dans le cas de l’étude sismique des étoiles KIC 08626021 et de GD 1212.

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Thèse numérisée par la Direction des bibliothèques de l'Université de Montréal.

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Phosphorus is an essential nutrient for life. In the ocean, phosphorus burial regulates marine primary production**1, 2. Phosphorus is removed from the ocean by sedimentation of organic matter, and the subsequent conversion of organic phosphorus to phosphate minerals such as apatite, and ultimately phosphorite deposits**3, 4. Bacteria are thought to mediate these processes**5, but the mechanism of sequestration has remained unclear. Here, we present results from laboratory incubations in which we labelled organic-rich sediments from the Benguela upwelling system, Namibia, with a 33P-radiotracer, and tracked the fate of the phosphorus. We show that under both anoxic and oxic conditions, large sulphide-oxidizing bacteria accumulate 33P in their cells, and catalyse the nearly instantaneous conversion of phosphate to apatite. Apatite formation was greatest under anoxic conditions. Nutrient analyses of Namibian upwelling waters and sediments suggest that the rate of phosphate-to-apatite conversion beneath anoxic bottom waters exceeds the rate of phosphorus release during organic matter mineralization in the upper sediment layers. We suggest that bacterial apatite formation is a significant phosphorus sink under anoxic bottom-water conditions. Expanding oxygen minimum zones are projected in simulations of future climate change**6, potentially increasing sequestration of marine phosphate, and restricting marine productivity.