It is recognised that nowadays, users interact with large amounts of data that exist in disparate forms, and are stored under different settings. Moreover, it is true that the amount of structured and un-structured data outside a single well organised data management system is expanding rapidly. To address the recent challenges of managing large amounts of potentially distributed data, the vision of a dataspace was introduced. This data management paradigm aims at reducing the complexity behind the challenges of integrating heterogeneous data sources. Recently, efforts by the Linked Data (LD) community gave rise to a Web of Data (WoD) that interweaves with the current Web of documents in a way that it is useful for data consumption by both humans and computational agents. On the WoD, datasets are structured under a common data model and published as Web resources following a simple set of guidelines that enables them to be linked with other pieces of data, as well as, to be annotated with useful meta data that help determine their semantics. The WoD is an evolving open ecosystem including specialist publishers as well as community efforts aiming at re-publishing isolated databases as LD on the WoD, and annotating them with meta data. The WoD raises new opportunities and challenges. However, currently it mostly relies on manual efforts for integrating the large amounts of heterogeneous data sources on the WoD. This dissertation makes the case that several techniques from the dataspaces research area (aiming at on-demand integration of data sources in a pay-as-you-go fashion) can support the integration of heterogeneous WoD sources. In so doing, this dissertation explores the opportunities and identifies the challenges of adapting existing pay-as-you-go data integration techniques in the context of LD. More specifically, this dissertation makes the following contributions: (1) a case-study for identifying the challenges when existing pay-as-you-go data integration techniques are applied in a setting where data sources are LD; (2) a methodology that deals with the 'schema-less' nature of LD sources by automatically inferring a conceptual structure from a given RDF graph thus enabling downstream tasks, such as the identification of matches and the derivation of mappings, which are, both, essential for the automatic bootstrapping of a dataspace; and (3) a well-defined, principled methodology that builds on a Bayesian inference technique for reasoning under uncertainty to improve pay-as-you-go integration. Although the developed methodology is generic in being able to reason with different hypothesis, its effectiveness has only been explored on reducing the uncertain decisions made by string-based matchers during the matching stage of a dataspace system.

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In quantum mechanics, measurements disturb the state of the system being measured. This disturbance is largest for complementary properties (e.g. position and momentum) and hence limits the precision with which such properties can be determined simultaneously. Often, this fact is conflated with Heisenberg's uncertainty principle, which refers to an uncertainty relation between complementary properties that is intrinsic to quantum states. In this thesis, the distinction between these two fundamental characteristics of quantum mechanics is made clear. At the intersection of the two are "joint measurements", which circumvent measurement disturbance to simultaneously determine complementary properties. They have applications in quantum metrology and enable a direct measurement of quantum states. The focus of this thesis is on the latter. The thesis is structured in the following way. The first chapter serves as an introduction to joint measurements. It surveys the seminal works in the field, doing so in a chronological manner to provide some historical context. The remainder of the thesis discusses two strategies to experimentally achieve joint measurements. The first strategy is to sequentially measure the complementary properties, making these measurements weak so that they do not disrupt each other. The second strategy is to first clone the system being measured, and then measure each complementary property on a separate clone. Both strategies are experimentally demonstrated on polarized photons, but can be readily extended to other systems.

The work described in this thesis stems from the idea that expert systems should be able to accurately and appropriately handle uncertain information. The traditional approaches to dealing with uncertainty are discussed and are shown to contain many inadequacies. The Dempster-Shafer, or D-S, theory of evidence is proposed as an appealing theoretical basis for representing uncertain knowledge and for performing inexact reasoning in expert systems. The D-S theory is reviewed in some detail; including its approaches to representing concepts, to representing belief, to combining belief and to performing inference. The D-S implementation approaches pursued by other researchers are described and critiqued. Attempts made early in the thesis research which failed to achieve the important goal of consistency with the D-S theory are also reviewed. Two approaches to implementing D-S theory in a completely consistent manner are discussed in detail. It is shown that the second of these systems, a frame network approach, has led to the development of a fully functional prototype expert system shell called FRO. In this system, concepts are represented using D-S frames of discernment, belief is represented using D-S belief functions, and inference is performed using stored relationships between frames of discernment (forming the frame network) and D-S belief combination rules. System control is accomplished using a discrete rule-based control component and uncertain input and output are performed through an interactive belief interface system called IBIS. Each of these features is reviewed. Finally, a simple but detailed example of an application of a frame network expert system is provided. The FRO system user's documentation is provided in the appendix. / Applied Science, Faculty of / Civil Engineering, Department of / Graduate

Research was conducted to determine whether there is a relationship between the theory of collective behavior and the selection of a corporate strategy. To conduct the first portion of the study, existing survey information was obtained from the National Association of Home Builders to investigate the existence of a heightened shared feeling of optimism within the home building industry. A test of the variance (ANOVA) suggests that during the housing boom the industry experienced peak levels of optimism consistent with the theory of collective behavior. Using secondary financial data, the research then investigated the possibility that builders moved to a growth strategy as a response to that heightened sense of optimism. A test of the variance (ANOVA) suggests that builders moved to a growth strategy as a response to the heightened optimism by substantially increasing their assets, debt and shares outstanding, as compared to previous years. Using the results of 150 surveys, the study investigated the theory that optimism lowers the effects of uncertainty. Hence, the research hypothesized that as optimism increases, uncertainty decreases, and as a result, strategic growth decisions become more likely. The research found strong correlations between respondents who were optimistic and those willing to make decisions that are consistent with growth strategies. The analysis found negative correlations with optimism and uncertainty. More specifically, the research concluded that as optimism increases, uncertainty decreases, and thus, home building managers are more likely to move to a growth strategy. In general, the research supports the hypothesis that collective behavior can have a significant impact on strategic decisions among managers. While previous research suggests that information is the variable that lowers levels of uncertainty, this research supports the possibility of an additional variable: optimism.

This dissertation describes two studies that empirically test instructional methods designed to promote learning, transfer, and curiosity in the context of real-world science classrooms. In the first study, I compared an inherently uncertain form of instruction to an inherently certain one, and in the second study, I compared different levels of uncertainty within the same inherently uncertain instruction type. The first study demonstrates that, compared to an inherently certain form of instruction (tell-then-practice), the inherently uncertain form of instruction (Invention) produced greater curiosity and transfer, which may reflect deeper learning. While this study showed promising results, it revealed additional questions, which were answered by the second study. Specifically, because there were differences other than uncertainty between conditions, I could not fully conclude that uncertainty is what caused group differences in curiosity or transfer. To confirm that it is, in fact, uncertainty influencing curiosity and transfer, the second study examined learning activities that were more similar to one another, but still differed in uncertainty. Specifically, I compared two Invention conditions, in which one group of students was given more information prior to invention than the other. This manipulation also showed that higher uncertainty led to greater curiosity and transfer. The research in this dissertation also examines how uncertainty influences affect and whether state-level curiosity influences learning and transfer. Further, it shows how curiosity changes over time and demonstrates a new way to behaviorally and qualitatively measure curiosity.

Ma thèse aboutit à un programme de recherche qui étudie l'investissement (et l'épargne) sous trois angles différents. Il renseigne sur 1) le comportement d'épargne des pays riches en pétrole, 2) la formation des prix et la dynamique de l'investissement sur le marché pétrolier, et 3) le rôle des rachats d'actions dans l'inhibition de l'effet positif d'une politique monétaire accommodante sur l'investissement au niveau des entreprises. Le point commun sous-jacent de ces trois axes de travail est la compréhension des facteurs qui influencent les décisions d'investissement au niveau de l'entreprise, de l'industrie ou du pays. Le premier chapitre de ma thèse, External Saving and Exhaustible Resource Extraction, aborde précisément la question de la gestion épuisable des ressources face à l'incertitude. En reliant le comportement d'extraction et d'économie dans un cadre théorique cohérent, ce chapitre contribue à deux veines de la littérature qui se sont développées séparément jusqu'à plus récemment. Le deuxième chapitre, L'effet retardateur du stockage sur l'investissement : Les données du secteur pétrolier américain continuent d'explorer le rôle de l'incertitude, mais cette fois-ci, elles analysent à la fois la dynamique des prix et celle des investissements lorsque les décisions d'investissement sont irréversibles. Le dernier chapitre de cette thèse, Rachat d'actions, politique monétaire et coût de la dette, porte sur une étude empirique des déterminants de l'investissement. Partant de la structure du capital des entreprises, cette partie de ma thèse porte sur le rôle des rachats dans le détournement de la dette à faible coût des investissements et de l'emploi. / My thesis culminates into a research program that studies investment (and saving) from three different perspectives. It informs on 1) the saving behaviour of oil-rich countries, on 2) price formation and investment dynamics in the oil market, and on 3) the role of share buybacks in muting the positive effect of accommodative monetary policy on firm-level investment. The underlying common thread among these three work streams is understanding factors that mediate the investment decisions at the firm, industry, or country level. The first chapter of my thesis, External Saving and Exhaustible Resource Extraction, addresses precisely the issue of exhaustible resource management in the face of uncertainty. In linking the extraction and saving behavior under a coherent theoretical framework, this chapter contributes to two veins of the literature that have developed separately until more recently. The second chapter, The Delaying Effect of Storage on Investment: Evidence from the US Oil Sector, continues to explore the role of uncertainty but this time analyses both price and investment dynamics when investment decisions are irreversible. The last chapter of this thesis, Share Buybacks, Monetary Policy and the Cost of Debt, turns it attention to an empirical investigation of the determinants of investment. Starting from the capital structure of firms, this part of my thesis focuses on the role of repurchases in diverting low-cost debt away from investment and employment.

This diploma thesis is focused on long term assets financing with focus on most commonly available forms of financing through leasing and bank loan. Objective of this diploma thesis is decision about choosing more profitable form of long term assets financing through various methods of decision making.

New Insights on the Uncertainties in Finite-Fault Earthquake Source Inversion Hoby Njara Tendrisoa Razafindrakoto Earthquake source inversion is a non-linear problem that leads to non-unique solutions. The aim of this dissertation is to understand the uncertainty and reliability in earthquake source inversion, as well as to quantify variability in earthquake rupture models. The source inversion is performed using a Bayesian inference. This technique augments optimization approaches through its ability to image the entire solution space which is consistent with the data and prior information. In this study, the uncertainty related to the choice of source-time function and crustal structure is investigated. Three predefined analytical source-time functions are analyzed; isosceles triangle, Yoffe with acceleration time of 0.1 and 0.3 s. The use of the isosceles triangle as source-time function is found to bias the finite-fault source inversion results. It accelerates the rupture to propagate faster compared to that of the Yoffe function. Moreover, it generates an artificial linear correlation between parameters that does not exist for the Yoffe source-time functions. The effect of inadequate knowledge of Earth’s crustal structure in earthquake rupture models is subsequently investigated. The results show that one-dimensional structure variability leads to parameters resolution changes, with a broadening of the posterior 5 PDFs and shifts in the peak location. These changes in the PDFs of kinematic parameters are associated with the blurring effect of using incorrect Earth structure. As an application to real earthquake, finite-fault source models for the 2009 L’Aquila earthquake are examined using one- and three-dimensional crustal structures. One- dimensional structure is found to degrade the data fitting. However, there is no significant effect on the rupture parameters aside from differences in the spatial slip extension. Stable features are maintained for both structures. In the last part of this work, a multidimensional scaling method is presented to compare and classify earthquake slip distributions. A similarity scale to rank them are thus formulated. Dissimilarities among slip models (from various parameterizations) are computed using two different distance metrics, normalized squared and gray-scale metrics. Multidimensional scaling is then used to visualize the differences among the models. The analyzes are done for 2 case studies; one based on artificial scenarios with a known answer and another one based on the published rupture models of the 2011 Tohoku earthquake.

In this dissertation, I examine how buyers and sellers interact in the Massachusetts funeral market. I utilize theories in economic sociology and ritual studies to explain how these interactions coalesce into a functioning market. To do so, I draw on semi-structured interviews with funeral consumers and funeral directors in Massachusetts. Standard economic theories would predict that funeral consumers weigh the costs and benefits associated with each choice they face before purchasing those products that best maximize their individual utility. Economic sociologists respond by pointing out that economic actors face uncertainty, a state in which they cannot assess the costs and benefits of their many options. Instead, consumers rely on 'social devices' - such as social norms and rules - to guide their behavior; however, they are 'intentionally rational' in that they seek to maximize their utility. Rather than thinking of consumers as rational utility maximizers or as uncertain, intentionally-rational actors, I argue that the majority of funeral consumers' purchases are unreflexive and thus cannot be thought of as choices at all. When consumers do make choices, they do not seek to maximize their utility, but instead purchase goods and services that perform what Viviana Zelizer labels relational work. Such purchases serve to define, maintain, or change social relationships. The ways that consumers approach their purchases shape the ways that sellers compete with one another. Because most consumers return to the same funeral home again and again without considering alternative providers and because consumers are socially required to purchase those goods and services necessary to complete the funeral ritual, sellers cannot draw in new customers by lowering prices or by developing new products. With these avenues closed off, sellers must compete by building social networks in their communities; however, they must work to define their network connections in specific ways. Customers must see their involvement in the community as motivated by a desire to contribute to civic life rather than a desire to generate business. Ultimately, then, sellers also perform relational work, and their relational work serves as the main competitive mechanism in funeral markets.