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The purpose of this study was to record and analyze students' experiences with dropping out of high school within a social capital framework. Discussing the stories of high school dropouts provided valuable information related to the root causes of dropout behaviors in a social capital context. This information can be used to develop programs designed to increase social capital in schools, families, and communities, which can contribute to a decrease in dropout behaviors. This phenomenological study took a narratological research approach that focused on collecting the lived experiences of high school dropouts within a social capital framework. The context of this study was a high school in an urban school division in Southwestern Virginia. The participants were five high school dropouts who speak English as a primary language and were a part of the general education population at the high school. Triangulation of data sources included field notes, interviews with the participants, and archival documents. A three-iteration code mapping procedure was used for data analysis to provide an audit trail. Narrative descriptions of the life histories of each participant were written. The overarching themes resulting from the analysis across the narratives were that none of the students had relationships with members of their families or communities who had the capacity to assist them in their endeavors to complete school; students had the desire to complete school or obtain a GED, but did not have a relationship with any person outside of the school setting who was persistent with encouragement and knowledge; and the students had access to social capital, but did not understand how to use it effectively. Results and conclusions are included in two articles, one written for publication in Sociology of Education, and the other written for publication in Professional School Counseling. / Ph. D.

Abstract "Societal factors of racism and implicit bias are present in the United States' school systems and they result in inequities in achievement outcomes" (Green., 2016, p. 2). The objective of this qualitative study was to discover more about African American males raised by single mothers, and the perceived factors that helped them complete high school. The interview protocol for this study consisted of fourteen questions. Researching the perceived factors that contributed to the academic success of African American male students who came from single parent households may provide the insight needed to create strategies for schools, communities, and families to help African American males from single parent homes achieve high school completion. This study included a purposive sample of African American male alumni from a high school in Southwest Virginia as participants. The research question is, what perceived factors aided African American males from single parent homes completing high school and the role of their immediate family members, networks which may include teachers, counselors, administrators, coaches, mentorship organizations, places of worship, and after-school programs served as the research question. / Doctor of Education / General Audience Abstract The purpose of the study was to identify the factors African American males raised by single mothers attributed to them completing high school at a particular school in Southwest Virginia. Five African American males who completed high school from 2011 to 2021 were interviewed. From the interviews conducted, the researcher found seven themes. The themes were strong relationships with their mothers, relationships are important, mothers involved in schools, exposure to extracurricular activities and programs, extended family support, religion, and self-determination. School administrators, teachers, and school personnel could use the data from this study. Schools should consider incorporating activities to strengthen mother-son relationships, establish mentorship programs, and school personnel should consider organizing internships for the African American males who are being raised by single mothers.

Object-centric geometric perception aims at extracting the geometric attributes of 3D objects. These attributes include shape, pose, and motion of the target objects, which enable fine-grained object-level understanding for various tasks in graphics, computer vision, and robotics. With the growth of 3D geometry data and 3D deep learning methods, it becomes more and more likely to achieve such tasks directly using 3D input data. Among different 3D representations, a 3D point cloud is a simple, common, and memory-efficient representation that could be directly retrieved from multi-view images, depth scans, or LiDAR range images. Different challenges exist in achieving object-centric geometric perception, such as achieving a fine-grained geometric understanding of common articulated objects with multiple rigid parts, learning disentangled shape and pose representations with fewer labels, or tackling dynamic and sequential geometric input in an end-to-end fashion. Here we identify and solve these challenges from a 3D deep learning perspective by designing effective and generalizable 3D representations, architectures, and pipelines. We propose the first deep pose estimation for common articulated objects by designing a novel hierarchical invariant representation. To push the boundary of 6D pose estimation for common rigid objects, a simple yet effective self-supervised framework is designed to handle unlabeled partial segmented scans. We further contribute a novel 4D convolutional neural network called PointMotionNet to learn spatio-temporal features for 3D point cloud sequences. All these works advance the domain of object-centric geometric perception from a unique 3D deep learning perspective. / Doctor of Philosophy / 3D sensors these days are widely equipped on various mobile devices like a depth camera on iPhone, or laser LiDAR sensors on an autonomous driving vehicle. These 3D sensing techniques could help us get accurate measurements of the 3D world. For the field of machine intel- ligence, we also want to build intelligent system and algorithm to learn useful information and understand the 3D world better. We human beings have the incredible ability to sense and understand this 3D world through our visual or tactile system. For example, humans could infer the geometry structure and arrangement of furniture in a room without seeing the full room, we are able to track an 3D object no matter how its appearance, shape and scale changes, we could also predict the future motion of multiple objects based on sequential observation and complex reasoning. Here my work designs various frameworks to learn such 3D information from geometric data represented by a lot of 3D points, which achieves fine-grained geometric understanding of individual objects, and we can help machine tell the target objects' geometry, states, and dynamics. The work in this dissertation serves as building blocks towards a better understanding of this dynamic world.

With the recent implementation of the Graduation and Completion Index (GCI), schools in the Commonwealth of Virginia are charged with graduating students within a four year time frame. The purpose of this study was to investigate the intervention strategies implemented by a school receiving the status of 'warned' for falling below the GCI benchmark in the school year 2011-2012 and subsequently receiving 'meets benchmark' status for the next two school years, 2012-2013 and 2013-2014. This study sought to determine what support and interventions this school provided for students who successfully graduated from high school within the designated four year time frame. A review of previous research and findings demonstrated the need for further research in this area. The intervention strategies implemented at a school that received 'warning' status yet currently maintains 'meets benchmark' status could benefit other schools and students. Some of the identified intervention strategies were: a) in-school detention was used as an intervention strategy; b) calling home and home visits were used as intervention strategies; c) the four by four block schedule was used as an intervention strategy d); attendance, retention, and discipline influenced a student's decision to stay in school; e) intervention strategies for students must be supported at the elementary, middle, and high school levels; and f) additional academic support (Saturday school) was an effective strategy for meeting the GCI benchmark. Implications were that school leaders should: a) consider creating a team that meets frequently to address the needs of at-risk students; b) identify ways to engage parents and the community in meeting the needs of students who are at risk of dropping out; c) consider alternatives to out of school suspension; and d) consider the four by four block scheduling model to support on time graduation rates for students. / Ed. D.

No / The aim of this study was to determine the number of OGDs (oesophago-gastro-duodenoscopies) trainees need to perform to acquire competency in terms of successful unassisted completion to the second part of the duodenum 95% of the time. Design: OGD data were retrieved from the trainee e-portfolio developed by the Joint Advisory Group on GI Endoscopy ( JAG) in the UK. All trainees were included unless they were known to have a baseline experience of >20 procedures or had submitted data for <20 procedures. The primary outcome measure was OGD completion, defined as passage of the endoscope to the second part of the duodenum without physical assistance. The number of OGDs required to achieve a 95% completion rate was calculated by the moving average method and learning curve cumulative summation (LC-Cusum) analysis. To determine which factors were independently associated with OGD completion, a mixed effects logistic regression model was constructed with OGD completion as the outcome variable. Results: Data were analysed for 1255 trainees over 288 centres, representing 243 555 OGDs. By moving average method, trainees attained a 95% completion rate at 187 procedures. By LC-Cusum analysis, after 200 procedures, >90% trainees had attained a 95% completion rate. Total number of OGDs performed, trainee age and experience in lower GI endoscopy were factors independently associated with OGD completion. Conclusions: There are limited published data on the OGD learning curve. This is the largest study to date analysing the learning curve for competency acquisition. The JAG competency requirement for 200 procedures appears appropriate

This thesis considers regularized block multi-convex optimization, where the feasible set and objective function are generally non-convex but convex in each block of variables. I review some of its interesting examples and propose a generalized block coordinate descent (BCD) method. The generalized BCD uses three different block-update schemes. Based on the property of one block subproblem, one can freely choose one of the three schemes to update the corresponding block of variables. Appropriate choices of block-update schemes can often speed up the algorithm and greatly save computing time. Under certain conditions, I show that any limit point satisfies the Nash equilibrium conditions. Furthermore, I establish its global convergence and estimate its asymptotic convergence rate by assuming a property based on the Kurdyka-{\L}ojasiewicz inequality. As a consequence, this thesis gives a global linear convergence result of cyclic block coordinate descent for strongly convex optimization. The proposed algorithms are adapted for factorizing nonnegative matrices and tensors, as well as completing them from their incomplete observations. The algorithms were tested on synthetic data, hyperspectral data, as well as image sets from the CBCL, ORL and Swimmer databases. Compared to the existing state-of-the-art algorithms, the proposed algorithms demonstrate superior performance in both speed and solution quality.

Θέμα της παρούσας εργασίας είναι η υλοποίηση και εξομοίωση δίκαιων αλγόριθμων χρονοπρογραμματισμού σε Grids και η σύγκρισή τους με κλασικούς αλγόριθμους χρονοπρογραμματισμού. Η βασική ιδέα πίσω από την τεχνολογία Grid και τις υπηρεσίες που παρέχει είναι η ενοποίηση υπολογιστικών και αποθηκευτικών πόρων και η συνολική θεώρηση τους από τους χρήστες. Με τον τρόπο αυτό γίνεται δυνατή η ανάπτυξη πολύπλοκων και απαιτητικών εφαρμογών, τόσο στον χώρο της επιστημονικής έρευνας, όσο και στα πλαίσια της παραγωγής εμπορικών λύσεων. Ένα τέτοιο σύστημα απαιτεί διαμοιρασμό των υπολογιστικών και άλλων πόρων καθώς και μεγάλες ταχύτητες σύνδεσης μεταξύ τους. Οι αλγόριθμοι χρονοπρογραμματισμού αναλαμβάνουν τον αποδοτικό διαμοιρασμό των πόρων ώστε να επιτυγχάνεται καλύτερη ποιότητα υπηρεσίας. Η αποτελεσματικότητα ενός αλγόριθμου χρονοπρογραμματισμού εξαρτάται από την συνάρτηση που θέλουμε να βελτιστοποιήσουμε, που με τη σειρά της εξαρτάται από τεχνο-οικονομικά κριτήρια. Στην προσπάθεια βελτιστοποίησης της εκάστοτε συνάρτησης ευνοούνται κάποιες προς εκτέλεση διεργασίες έναντι άλλων. Ένας δίκαιος αλγόριθμος χρονοπρογραμματισμού όμως θα πρέπει να συμπεριφέρεται με τον ίδιο τρόπο σε όλες τις διεργασίες ανεξαρτήτως των χαρακτηριστικών τους. Στην εργασία που θα παρουσιάσουμε, αναλύουμε δύο δίκαιους αλγόριθμους χρονοπρογραμματισμού, τον Fair Completion Time (Ordering) και τον Fair Completion Time Estimation (Assignment). Κατόπιν τους υλοποιούμε και τους εξομοιώνουμε με το GridSim Toolkit και συγκρίνουμε την απόδοση τους με κλασικούς αλγόριθμους χρονοπρογραμματισμού. / The subject of this thesis is the implementation and simulation of fair scheduling algorithms applied on Computational Grids and their comparison with the classic scheduling algorithms. The basic idea of the Grid Technology and the services it provides, is the unification of computational and storage resources. This way it is possible to serve sophisticated applications, in fields like scientific research and trading. A Grid Network demands the sharing of the computational and storage resources, and high bandwidth connections between them. Scheduling algorithms are responsible for the efficient assignment of tasks to resources for better quality of service. Evaluating the efficiency of a scheduling algorithm depends on a utility function that we seek to optimize which in turns depends on techno-economic criteria. As a result of trying to optimize the utility function, some tasks with specific characteristics are favoured against others. A fair scheduling algorithm however should treat all tasks in the same way regardless of their characteristics. In this thesis we study the Fair Completion Time Ordering Algorithm and suggest a new fair scheduling algorithm called Fair Completion Time Estimation Assignment Algorithm. We implement and simulate these algorithms using the GridSim Toolkit and compare them with the classic scheduling algorithms.