Mixed spatial and nonspatial problems in location based services

With hundreds of millions of users reporting locations and embracing mobile technologies, Location Based Services (LBSs) are raising new challenges. In this dissertation, we address three emerging problems in location services, where geolocation data plays a central role. First, to handle the unprecedented growth of generated geolocation data, existing location services rely on geospatial database systems. However, their inability to leverage combined geographical and textual information in analytical queries (e.g. spatial similarity joins) remains an open problem. To address this, we introduce SpsJoin, a framework for computing spatial set-similarity joins. SpsJoin handles combined similarity queries that involve textual and spatial constraints simultaneously. LBSs use this system to tackle different types of problems, such as deduplication, geolocation enhancement and record linkage. We define the spatial set-similarity join problem in a general case and propose an algorithm for its efficient computation. Our solution utilizes parallel computing with MapReduce to handle scalability issues in large geospatial databases. Second, applications that use geolocation data are seldom concerned with ensuring the privacy of participating users. To motivate participation and address privacy concerns, we propose iSafe, a privacy preserving algorithm for computing safety snapshots of co-located mobile devices as well as geosocial network users. iSafe combines geolocation data extracted from crime datasets and geosocial networks such as Yelp. In order to enhance iSafe's ability to compute safety recommendations, even when crime information is incomplete or sparse, we need to identify relationships between Yelp venues and crime indices at their locations. To achieve this, we use SpsJoin on two datasets (Yelp venues and geolocated businesses) to find venues that have not been reviewed and to further compute the crime indices of their locations. Our results show a statistically significant dependence between location crime indices and Yelp features. Third, review centered LBSs (e.g., Yelp) are increasingly becoming targets of malicious campaigns that aim to bias the public image of represented businesses. Although Yelp actively attempts to detect and filter fraudulent reviews, our experiments showed that Yelp is still vulnerable. Fraudulent LBS information also impacts the ability of iSafe to provide correct safety values. We take steps toward addressing this problem by proposing SpiDeR, an algorithm that takes advantage of the richness of information available in Yelp to detect abnormal review patterns. We propose a fake venue detection solution that applies SpsJoin on Yelp and U.S. housing datasets. We validate the proposed solutions using ground truth data extracted by our experiments and reviews filtered by Yelp.

A heuristic algorithm to generate test program sequences for moving probe electronic test equipment

The electronics industry, is experiencing two trends one of which is the drive towards miniaturization of electronic products. The in-circuit testing predominantly used for continuity testing of printed circuit boards (PCB) can no longer meet the demands of smaller size circuits. This has lead to the development of moving probe testing equipment. Moving Probe Test opens up the opportunity to test PCBs where the test points are on a small pitch (distance between points). However, since the test uses probes that move sequentially to perform the test, the total test time is much greater than traditional in-circuit test. While significant effort has concentrated on the equipment design and development, little work has examined algorithms for efficient test sequencing. The test sequence has the greatest impact on total test time, which will determine the production cycle time of the product. Minimizing total test time is a NP-hard problem similar to the traveling salesman problem, except with two traveling salesmen that must coordinate their movements. The main goal of this thesis was to develop a heuristic algorithm to minimize the Flying Probe test time and evaluate the algorithm against a "Nearest Neighbor" algorithm. The algorithm was implemented with Visual Basic and MS Access database. The algorithm was evaluated with actual PCB test data taken from Industry. A statistical analysis with 95% C.C. was performed to test the hypothesis that the proposed algorithm finds a sequence which has a total test time less than the total test time found by the "Nearest Neighbor" approach. Findings demonstrated that the proposed heuristic algorithm reduces the total test time of the test and, therefore, production cycle time can be reduced through proper sequencing.

The French Canadian founder population : lessons and insights for genetic epidemiological research

La population canadienne-française a une histoire démographique unique faisant d’elle une population d’intérêt pour l’épidémiologie et la génétique. Cette thèse vise à mettre en valeur les caractéristiques de la population québécoise qui peuvent être utilisées afin d’améliorer la conception et l’analyse d’études d’épidémiologie génétique. Dans un premier temps, nous profitons de la présence d’information généalogique détaillée concernant les Canadiens français pour estimer leur degré d’apparentement et le comparer au degré d’apparentement génétique. L’apparentement génétique calculé à partir du partage génétique identique par ascendance est corrélé à l’apparentement généalogique, ce qui démontre l'utilité de la détection des segments identiques par ascendance pour capturer l’apparentement complexe, impliquant entre autres de la consanguinité. Les conclusions de cette première étude pourront guider l'interprétation des résultats dans d’autres populations ne disposant pas d’information généalogique. Dans un deuxième temps, afin de tirer profit pleinement du potentiel des généalogies canadienne-françaises profondes, bien conservées et quasi complètes, nous présentons le package R GENLIB, développé pour étudier de grands ensembles de données généalogiques. Nous étudions également le partage identique par ascendance à l’aide de simulations et nous mettons en évidence le fait que la structure des populations régionales peut faciliter l'identification de fondateurs importants, qui auraient pu introduire des mutations pathologiques, ce qui ouvre la porte à la prévention et au dépistage de maladies héréditaires liées à certains fondateurs. Finalement, puisque nous savons que les Canadiens français ont accumulé des segments homozygotes, à cause de la présence de consanguinité lointaine, nous estimons la consanguinité chez les individus canadiens-français et nous étudions son impact sur plusieurs traits de santé. Nous montrons comment la dépression endogamique influence des traits complexes tels que la grandeur et des traits hématologiques. Nos résultats ne sont que quelques exemples de ce que nous pouvons apprendre de la population canadienne-française. Ils nous aideront à mieux comprendre les caractéristiques des autres populations de même qu’ils pourront aider la recherche en épidémiologie génétique au sein de la population canadienne-française.

The Role of Social Cognition and Person Perception in Economic Social Decision-Making

Social decision-making is often complex, requiring the decision-maker to make social inferences about another person in addition to engaging traditional decision-making processes. However, until recently, much research in neuroeconomics and behavioral economics has examined social decision-making while failing to take into account the importance of the social context and social cognitive processes that are engaged when viewing another person. Using social psychological theory to guide our hypotheses, four research studies investigate the role of social cognition and person perception in guiding economic decisions made in social contexts. The first study (Chapter 2) demonstrates that only specific types of social information engage brain regions implicated in social cognition and hinder learning in social contexts. Study 2 (Chapter 3) extends these findings and examines contexts in which this social information is used to generalize across contexts to form predictions about another person’s behavior. Study 3 (Chapter 4) demonstrates that under certain contexts these social cognitive processes may be withheld in order to more effectively complete the task at hand. Last, Study 4 (Chapter 5) examines how this knowledge of social cognitive processing can be used to change behavior in a prosocial group context. Taken together, these studies add to the growing body of literature examining decision-making in social contexts and highlight the importance of social cognitive processing in guiding these decisions. Although social cognitive processing typically facilitates social interactions, these processes may alter economic decision-making in social contexts.

A Framework to Support the Sharing and Reuse of Computable Phenotype Definitions Across Health Care Delivery and Clinical Research Applications.

INTRODUCTION: The ability to reproducibly identify clinically equivalent patient populations is critical to the vision of learning health care systems that implement and evaluate evidence-based treatments. The use of common or semantically equivalent phenotype definitions across research and health care use cases will support this aim. Currently, there is no single consolidated repository for computable phenotype definitions, making it difficult to find all definitions that already exist, and also hindering the sharing of definitions between user groups. METHOD: Drawing from our experience in an academic medical center that supports a number of multisite research projects and quality improvement studies, we articulate a framework that will support the sharing of phenotype definitions across research and health care use cases, and highlight gaps and areas that need attention and collaborative solutions. FRAMEWORK: An infrastructure for re-using computable phenotype definitions and sharing experience across health care delivery and clinical research applications includes: access to a collection of existing phenotype definitions, information to evaluate their appropriateness for particular applications, a knowledge base of implementation guidance, supporting tools that are user-friendly and intuitive, and a willingness to use them. NEXT STEPS: We encourage prospective researchers and health administrators to re-use existing EHR-based condition definitions where appropriate and share their results with others to support a national culture of learning health care. There are a number of federally funded resources to support these activities, and research sponsors should encourage their use.

Distributed Feature Selection in Large n and Large p Regression Problems

Fitting statistical models is computationally challenging when the sample size or the dimension of the dataset is huge. An attractive approach for down-scaling the problem size is to first partition the dataset into subsets and then fit using distributed algorithms. The dataset can be partitioned either horizontally (in the sample space) or vertically (in the feature space), and the challenge arise in defining an algorithm with low communication, theoretical guarantees and excellent practical performance in general settings. For sample space partitioning, I propose a MEdian Selection Subset AGgregation Estimator ({\em message}) algorithm for solving these issues. The algorithm applies feature selection in parallel for each subset using regularized regression or Bayesian variable selection method, calculates the `median' feature inclusion index, estimates coefficients for the selected features in parallel for each subset, and then averages these estimates. The algorithm is simple, involves very minimal communication, scales efficiently in sample size, and has theoretical guarantees. I provide extensive experiments to show excellent performance in feature selection, estimation, prediction, and computation time relative to usual competitors.

While sample space partitioning is useful in handling datasets with large sample size, feature space partitioning is more effective when the data dimension is high. Existing methods for partitioning features, however, are either vulnerable to high correlations or inefficient in reducing the model dimension. In the thesis, I propose a new embarrassingly parallel framework named {\em DECO} for distributed variable selection and parameter estimation. In {\em DECO}, variables are first partitioned and allocated to m distributed workers. The decorrelated subset data within each worker are then fitted via any algorithm designed for high-dimensional problems. We show that by incorporating the decorrelation step, DECO can achieve consistent variable selection and parameter estimation on each subset with (almost) no assumptions. In addition, the convergence rate is nearly minimax optimal for both sparse and weakly sparse models and does NOT depend on the partition number m. Extensive numerical experiments are provided to illustrate the performance of the new framework.

For datasets with both large sample sizes and high dimensionality, I propose a new "divided-and-conquer" framework {\em DEME} (DECO-message) by leveraging both the {\em DECO} and the {\em message} algorithm. The new framework first partitions the dataset in the sample space into row cubes using {\em message} and then partition the feature space of the cubes using {\em DECO}. This procedure is equivalent to partitioning the original data matrix into multiple small blocks, each with a feasible size that can be stored and fitted in a computer in parallel. The results are then synthezied via the {\em DECO} and {\em message} algorithm in a reverse order to produce the final output. The whole framework is extremely scalable.

The French Canadian founder population : lessons and insights for genetic epidemiological research

La population canadienne-française a une histoire démographique unique faisant d’elle une population d’intérêt pour l’épidémiologie et la génétique. Cette thèse vise à mettre en valeur les caractéristiques de la population québécoise qui peuvent être utilisées afin d’améliorer la conception et l’analyse d’études d’épidémiologie génétique. Dans un premier temps, nous profitons de la présence d’information généalogique détaillée concernant les Canadiens français pour estimer leur degré d’apparentement et le comparer au degré d’apparentement génétique. L’apparentement génétique calculé à partir du partage génétique identique par ascendance est corrélé à l’apparentement généalogique, ce qui démontre l'utilité de la détection des segments identiques par ascendance pour capturer l’apparentement complexe, impliquant entre autres de la consanguinité. Les conclusions de cette première étude pourront guider l'interprétation des résultats dans d’autres populations ne disposant pas d’information généalogique. Dans un deuxième temps, afin de tirer profit pleinement du potentiel des généalogies canadienne-françaises profondes, bien conservées et quasi complètes, nous présentons le package R GENLIB, développé pour étudier de grands ensembles de données généalogiques. Nous étudions également le partage identique par ascendance à l’aide de simulations et nous mettons en évidence le fait que la structure des populations régionales peut faciliter l'identification de fondateurs importants, qui auraient pu introduire des mutations pathologiques, ce qui ouvre la porte à la prévention et au dépistage de maladies héréditaires liées à certains fondateurs. Finalement, puisque nous savons que les Canadiens français ont accumulé des segments homozygotes, à cause de la présence de consanguinité lointaine, nous estimons la consanguinité chez les individus canadiens-français et nous étudions son impact sur plusieurs traits de santé. Nous montrons comment la dépression endogamique influence des traits complexes tels que la grandeur et des traits hématologiques. Nos résultats ne sont que quelques exemples de ce que nous pouvons apprendre de la population canadienne-française. Ils nous aideront à mieux comprendre les caractéristiques des autres populations de même qu’ils pourront aider la recherche en épidémiologie génétique au sein de la population canadienne-française.

n-Alkane content and compound-specific d13C values of soil samples, river samples, and marine surface samples

Southwestern Africa's coastal marine mudbelt, a prominent Holocene sediment package, provides a valuable archive for reconstructing terrestrial palaeoclimates on the adjacent continent. While the origin of terrestrial inorganic material has been intensively studied, the sources of terrigenous organic material deposited in the mudbelt are yet unclear. In this study, plant wax derived n-alkanes and their compound-specific d13C in soils, flood deposits and suspension loads from regional fluvial systems and marine sediments are analysed to characterize the origin of terrestrial organic material in the southwest African mudbelt. Soils from different biomes in the catchments of the Orange River and small west coast rivers show on average distinct n-alkane distributions and compound-specific d13C values reflecting biome-specific vegetation types, most notably the winter rainfall associated Fynbos Biome of the southwestern Cape. In the fluvial sediment samples from the Orange River, changes in the n-alkane distributions and compound-specific d13C compositions reveal an overprint by local vegetation along the river's course. The smaller west coast rivers show distinct signals, reflecting their small catchment areas and particular vegetation communities. Marine surface sediments spanning a transect from the northern mudbelt (29°S) to St. Helena Bay (33°S) reveal subtle, but spatially coherent, changes in n-alkane distributions and compound-specific d13C, indicating the influence of Orange River sediments in the northern mudbelt, the increasing importance of terrigenous input from the adjacent western coastal biomes in the central mudbelt, and contributions from the Fynbos Biome to the southern mudbelt. These findings indicate the different sources of terrestrial organic material deposited in the mudbelt, and highlight the potential the mudbelt has to preserve evidence of environmental change from the adjacent continent.

Expertise Development in Volleyball: the Role of Early Sport Activities and Players’ Age and Height

The purpose of this study was to analyse the developmental pathway of skilled and less skilled volleyball players by focusing on the quantity and type of sporting activities, as well as their age and height in comparison to peers in those experiences. Retrospective interviews were conducted to provide a longitudinal and detailed account of sport involvement of 30 skilled and 30 less skilled volleyball players (15 male and 15 female players per group) throughout different developmental stages (stage 1: 8-12 years; stage 2: 13-16 years; stage 3: 17-20 years). Results indicated that the developmental pathway of these volleyball players (i.e. skilled and less skilled) was characterized by an early diversified sport involvement with a greater participation in sport activities during stages 1 and 2. However, skilled players specialized later in volleyball (between age 14 and 15) and performed more hours of volleyball at stage 3 (from 17 years of age onwards). Also, skilled players (male and female) were younger in both the diversified sport activities and volleyball at the later stages of development (i.e. stages 2 and 3), and skilled female players were taller than peers in those activities in the early stages of development (i.e. stages 1 and 2). The present findings suggest early diversification as a feasible pathway to reach expertise in volleyball and highlight the importance of practicing with older peers once specialization in the main sport has occurred. The findings highlight the need for coaches and sport programs to consider different stimuli existing within the training environment (i.e. characteristics of athletes, such as age and height) that influence the quality of practice and contribute to players’ expertise development.

'A Kind of Logic, A Kind of Dominant Logic': Navigating Colonialism, Honoring Black Mobility, and Thinking on Moving Through

My thesis thinks through the ways Newtonian logics require linear mobility in order to produce narratives of progress. I argue that this linear mobility, and the resulting logics, potentially erases the chaotic and non-linear motions that are required to navigate a colonial landscape. I suggest that these non-linear movements produce important critiques of the seeming stasis of colonial constructs and highlight the ways these logics must appear neutral and scientific in an attempt to conceal the constant and complex adjustments these frameworks require. In order to make room for these complex motions, I develop a quantum intervention. Specifically, I use quantum physics as a metaphor to think through the significance of black life, the double-consciousness ofland, and the intricate motions of sound. In order to put forth this intervention, I look at news coverage of Hurricane Katrina, Du Bois’s characterization of land in Souls of Black Folks, and the aural mobilities of blackness articulated in an academic discussion and interview about post- humanism.

Coralline algae in a changing world: integrating ecological, physiological, and geochemical responses to global change

Coralline algae are globally distributed benthic primary producers that secrete calcium carbonate skeletons. In the context of ocean acidification, they have received much recent attention due to the potential vulnerability of their high-Mg calcite skeletons and their many important ecological roles. Herein, we summarize what is known about coralline algal ecology and physiology, providing context to understand their responses to global climate change. We review the impacts of these changes, including ocean acidification, rising temperatures, and pollution, on coralline algal growth and calcification. We also assess the ongoing use of coralline algae as marine climate proxies via calibration of skeletal morphology and geochemistry to environmental conditions. Finally, we indicate critical gaps in our understanding of coralline algal calcification and physiology and highlight key areas for future research. These include analytical areas that recently have become more accessible, such as resolving phylogenetic relationships at all taxonomic ranks, elucidating the genes regulating algal photosynthesis and calcification, and calibrating skeletal geochemical metrics, as well as research directions that are broadly applicable to global change ecology, such as the importance of community-scale and long-term experiments in stress response.

Coralline algae (Rhodophyta) in a changing world: integrating ecological, physiological, and geochemical responses to global change

Coralline algae are globally distributed benthic primary producers that secrete calcium carbonate skeletons. In the context of ocean acidification, they have received much recent attention due to the potential vulnerability of their high-Mg calcite skeletons and their many important ecological roles. Herein, we summarize what is known about coralline algal ecology and physiology, providing context to understand their responses to global climate change. We review the impacts of these changes, including ocean acidification, rising temperatures, and pollution, on coralline algal growth and calcification. We also assess the ongoing use of coralline algae as marine climate proxies via calibration of skeletal morphology and geochemistry to environmental conditions. Finally, we indicate critical gaps in our understanding of coralline algal calcification and physiology and highlight key areas for future research. These include analytical areas that recently have become more accessible, such as resolving phylogenetic relationships at all taxonomic ranks, elucidating the genes regulating algal photosynthesis and calcification, and calibrating skeletal geochemical metrics, as well as research directions that are broadly applicable to global change ecology, such as the importance of community-scale and long-term experiments in stress response.

STPA-SafeSec: Safety and Security Analysis for Cyber-Physical Systems

Cyber-physical systems tightly integrate physical processes and information and communication technologies. As today’s critical infrastructures, e.g., the power grid or water distribution networks, are complex cyber-physical systems, ensuring their safety and security becomes of paramount importance. Traditional safety analysis methods, such as HAZOP, are ill-suited to assess these systems. Furthermore, cybersecurity vulnerabilities are often not considered critical, because their effects on the physical processes are not fully understood. In this work, we present STPA-SafeSec, a novel analysis methodology for both safety and security. Its results show the dependencies between cybersecurity vulnerabilities and system safety. Using this information, the most effective mitigation strategies to ensure safety and security of the system can be readily identified. We apply STPA-SafeSec to a use case in the power grid domain, and highlight its benefits.

Murine Models of Diastolic Dysfunction and Heart Failure With Preserved Ejection Fraction

Left ventricular diastolic dysfunction leads to heart failure with preserved ejection fraction, an increasingly prevalent condition largely driven by modern day lifestyle risk factors. As heart failure with preserved ejection fraction accounts for almost one-half of all patients with heart failure, appropriate nonhuman animal models are required to improve our understanding of the pathophysiology of this syndrome and to provide a platform for preclinical investigation of potential therapies. Hypertension, obesity, and diabetes are major risk factors for diastolic dysfunction and heart failure with preserved ejection fraction. This review focuses on murine models reflecting this disease continuum driven by the aforementioned common risk factors. We describe various models of diastolic dysfunction and highlight models of heart failure with preserved ejection fraction reported in the literature. Strengths and weaknesses of the different models are discussed to provide an aid to translational scientists when selecting an appropriate model. We also bring attention to the fact that heart failure with preserved ejection fraction is difficult to diagnose in animal models and that, therefore, there is a paucity of well described animal models of this increasingly important condition.

Who needs evidence? Radical Feminism, the Christian Right and sex work research in Northern Ireland

This chapter describes my experiences of conducting research on commercial sex in Belfast, Northern Ireland which was conducted as part of a larger British Academy – Leverhulme Trust funded study that examined the policing and legal regulation of commercial sex in Belfast (Northern Ireland) along with three other cities: Manchester (England), Berlin (Germany) and Prague (Czech Republic). This study provided the first empirical analysis of commercial sex in the jurisdiction and was instrumental in shedding light on prevalence rates for those involved in the industry as well as providing demographic information on the age, nationality and sexual orientation of sex workers along with the sector worked in, whether on-street or off-street. In the chapter I consider my role as a researcher and highlight some of the difficulties that I experienced conducting what was seen as controversial research in the politically, socially and culturally conservative context of Northern Ireland.