Bioculture Journal
Not a member yet
1309 research outputs found
Sort by
Institutional Accreditation: Making the Process More Efficient, Effective, and Meaningful to Colleges and Universities
No full textDoctor of Educational Leadership (EdD)Institutional accreditation is a voluntary, peer-review process that is overseen through the seven institutional accreditors governed by the U.S. Department of Education. The purpose of accreditation is to ensure institutional quality standards are being met by colleges and universities. The purpose of this study was to identify how the accreditation process could be improved with foci on efficiency, effectiveness, and more meaningful impact to the institutions. Drawing on Heifetz et al.'s (2009) theory of adaptive leadership, Kotter's (2012) accelerators and the integrated planning principles of Stephens (2017) and Immordino et al., (2016), this study employed grounded theory to discover the experiences, perceptions, and potential solutions to accreditation challenges. Within the region that is overseen by the Northwest Commission on Colleges and Universities (NWCCU), 23 institutional leaders including administrators and faculty from two- and four-year institutions from both the public and private sectors residing in four states were interviewed. The findings revealed several successes as well as challenges. In general, colleges using a more integrated rather than disparate-compliance approach to accreditation have found added success in all aspects of the process. The assessment of student learning remains a challenge at all levels due to a lack of clarity regarding how to design the evaluation of learning in a manner that prioritizes clear outcomes and meaningful planning. Findings from this study offer implications to support higher education personnel in integrated planning for greater alignment of resources and continuous improvement; the assessment of student learning and achievement; and institutional effectiveness
Learning and Decision-Making in Competitive and Uncertain Systems
No full textThesis (Ph.D.)--University of Washington, 2021As a result of the demonstrated potential for impact in traditional use cases, progressively more is being asked of machine learning methods. This evolution has lead to a renewed focus on learning and decision-making systems. In this domain, theoretical challenges relating to competition and uncertainty are emerging from the practical considerations that have motivated this paradigm shift. There is an increasing awareness that learning and decision-making algorithms will eventually need to be or already are being embedded into complex systems where game-theoretic considerations naturally arise owing to the presence of competing, self-interested entities. Moreover, it has become clear that the artificial introduction of competition in game-theoretic abstractions of machine learning problems can often be a convenient and effective modeling technique for many problems of interest. Consequently, tools from game theory are now critically needed to analyze coupled learning and decision-making algorithms for the purposes of characterizing the outcomes that can be expected from competitive interactions and computing meaningful solutions such as equilibria in machine learning problems. Meanwhile, the demands of learning and decision-making algorithms operating under uncertainty are both changing and becoming more challenging. This transformation includes a movement towards more general, yet structured feedback models and objectives that reflect the desire to enable downstream tasks and future inferences. To this end, important problems remain to be solved pertaining to designing theoretically sound sequential decision-making algorithms tailored to such tasks. This discussion motivates the research on learning and decision-making in competitive and uncertain systems presented in this thesis. Together, the contents of this thesis can be summarized by a pair of themes that form Parts I and II: game-theoretic methods for analyzing decision-making algorithms and solving machine learning problems, and machine learning methods for designing and analyzing sequential decision-making algorithms under uncertainty. The former theme is approached from a top-down perspective: general formulations of games and gradient-based learning algorithms are studied, theoretical characterizations are developed, and then the results are connected to specific problems of interest. In contrast, the latter theme is approached from a bottom-up perspective: models of practical sequential decision-making tasks are developed and then theoretically justified algorithms and solutions are constructed. While learning and optimization in games is a well-studied topic, the majority of past research has focused on highly structured settings. Part I of this thesis moves away from this practice and presents studies of nonconvex games on continuous strategy spaces and gradient-based learning algorithms within them. The intent of this research is to develop appropriate notions of game-theoretic equilibria, characterize and understand the behaviors of so-called `natural' learning dynamics, and establish methods for computing equilibria to solve machine learning problems formulated as games. Chapter 2 lays the foundation for Part I and is built upon thereafter. Based upon the idea of viewing the underlying interaction structure as a Stackelberg game, both a local Stackelberg equilibrium concept and a corresponding characterization in terms of gradient-based sufficient conditions called a differential Stackelberg equilibrium are presented. Learning dynamics emulating the natural game structure are then constructed and convergence guarantees to differential Stackelberg equilibrium are proven. Chapter 3 follows along this path to study the role of timescale separation on the convergence of the canonical gradient descent-ascent learning dynamics in the subclass of nonconvex-nonconcave zero-sum games. The results characterize the timescales for which the dynamics both locally converge to differential Stackelberg equilibrium and locally avoid points lacking game-theoretic meaning. Finally, Chapter 4 considers zero-sum games in which the minimizing player faces a nonconvex objective and the maximizing player optimizes a Polyak-Lojasiewicz or strongly-concave objective. For this class of games, global convergence guarantees for gradient descent-ascent with timescale separation to only differential Stackelberg equilibrium are proven. Throughout Part I, the implications of the theoretical results for both competitive decision-making and methods for solving machine learning problems are discussed. Traditionally, the study of sequential decision-making under uncertainty in machine learning has focused on problems in which the evaluation criterion is directly linked to the immediate feedback. However, it has become clear that decision-making under uncertainty is often also pertinent to problems where the goal of the learner is instead to acquire information for the purpose of drawing inferences or fulfilling targets only partially linked to the immediate feedback. Part II of this thesis presents a pair of studies on well-motivated sequential decision-making problems with structured feedback models that fall under this theme. The intent of this research is to design sequential decision-making algorithms for solving practical problems that emerge in the real-world with desirable theoretical guarantees by exploiting structured feedback models. Chapter 5 commences Part II by formulating the task of ranking papers to reviewers in peer review bidding systems as a sequential decision-making problem. A model of this problem is developed that identifies a pair of misaligned objectives: ensuring that each paper obtains a sufficient number of bids to be matched adequately with qualified reviewers, and respecting the preferences of reviewers by showing them relevant papers early in the list. To balance the competing objectives, a sequential decision-making algorithm is constructed that exploits the objective structure and it is shown both theoretically and empirically to have a number of advantages over baselines currently used in practice.Chapter 6 then concludes Part II with an analysis of pure exploration transductive linear bandits, a problem that arises naturally in experimental design settings. A decision-maker in this problem sequentially samples measurement vectors from a given set and observes a noisy linear response with an unknown parameter vector. The goal is to infer with high confidence the item from a separate set of vectors that has the maximum inner product with the unknown parameter vector while taking a minimal number of measurements. The optimal achievable sample complexity for this problem is characterized and a near-optimal algorithm that exploits the information structure of the feedback model to enhance the sample efficiency is developed. Together, the contributions of this thesis take steps towards developing important theoretical foundations for learning and decision-making with competition and uncertainty
River Ice Measurements for Transportation Safety in Rural Communities
No full textThis project is relevant to the cold areas of Federal Region 10, where transportation routes occur on the frozen surfaces of lakes and rivers for three to four months each year Transportation safety on ice roads is a complex problem that involves people, vehicles, river ice, and weather conditions. While all these factors must be considered, this study focused on river ice measurements for ice road construction and transportation safety. Ice thickness measurements are critical in determining the bearing capacity of river ice cover and assessing the risk of breakthrough on ice roads. The project team collaborated with the city of Tanana, a rural Alaska community that builds a winter ice road across the Yukon River to connect to the state road system.US Department of Transportation
Pacific Northwest Transportation Consortium
University of Alaska Fairbank
Reimagining the Math Classroom through the Integration of Transformationally Play-Based Narrative Video Games
No full textThesis (Master's)--University of Washington, 2021Traditionally, many math classrooms focus on mathematical procedural knowledge and expect students to take that knowledge and know when and how to apply it to problem-solving situations. Consequently, this leads to a belief that being “good” at mathematics involves memorizing those specific procedures and arriving at the correct answer. Through a review of work written on two exemplar educational videogames, The Adventures of Jasper Woodbury and Quest Atlantis, I suggest a list of design commitments and features necessary for video games to support transforming classrooms to uplift all students as mathematically capable. I discuss what the role of the teacher may look like if such video games are integrated into the classroom and how these features may look to evolve to continue supporting all students. Finally, I address concerns around the design and implementation of transformationally play-based narrative-based video games and their place in the classroom as a tool to help transform what it means to do math
Determinants of immunization dropout among children under the age of two in Zambézia Province, Mozambique: A community-based participatory research study using Photovoice.
No full textThesis (Master's)--University of Washington, 2021Introduction: Immunizations are highly impactful, cost-effective public health interventions. However, there remain significant gaps and inequities in complete vaccination coverage. In Zambézia Province, Mozambique, 37.9% of children under-2 years start but do not complete the basic vaccination schedule. We aimed to describe caregivers’ experiences with the immunization process and identify determinants of vaccine dropout in two districts in Zambézia. Methods: Following a community-based participatory research approach, we used Photovoice and semi-structured in-depth interviews (IDIs) to explore vaccination experiences for ten and 22 caregivers of children aged 25-34 months who were fully-vaccinated and partially-vaccinated, respectively. We also collected data from 12 health workers via SMS exchanges and IDIs. The Increasing Vaccination Model informed the analysis, which focused on describing facilitators and barriers to vaccination. Themes were generated through identifying patterns between vaccination determinants, comparing caregivers’ experiences, disaggregated by vaccination status. Health worker data added depth to the themes and illuminated where caregiver and health worker perspectives did and did not align. Results: Four main patterns of barriers leading to vaccination dropout emerged: 1) social norms and lack of family support place the immunization-seeking burden largely on mothers, 2) perceived poor quality of health services, including vaccine stockouts, reduces caregivers’ trust in health services, 3) concern about side-effects, exacerbated by vaccine “accumulation” when catch-up doses are needed, leads to vaccine hesitancy, and 4) caregivers feel hesitant to seek and advocate for vaccination due to power imbalances between them and health workers. Vaccination dropout occurred after encountering multiple barriers that simultaneously influenced the vaccination process. Caregivers who completed vaccination noted specific strategies, including accompaniment to the health facility by their husbands or assistance with caring for other children while they were gone, that enabled them to overcome barriers and complete vaccination. Conclusion: Barriers to immunization are multi-factorial and require strengthening health systems to overcome, including improving logistics to avert vaccine stockouts and building health worker capacity while emphasizing empathic communication with caregivers. Improving the reliability of routine immunization outreach services could address access challenges and improve immunization uptake, particularly for caregivers located far from health facilities and those who lack family support
A Joint Model Provisioning and Request Dispatch Solution for Mobile Inference Serving at the Edge
No full textThesis (Master's)--University of Washington, 2021With the advancement of machine learning (ML), a growing number of mobile clients rely onML inference for making time-sensitive and safety-critical decisions. Therefore, the demand
for high-quality and low-latency inference services at the network edge has become the key to
the modern intelligent society. This thesis proposes a novel solution that jointly provisions
inference models and dispatches inference requests for reducing the latency of mobile inference serving on edge nodes. Unlike existing solutions that either direct inference requests
to the nearest edge node or balance the workload between edge nodes, the solution we propose provisions each edge node with the optimal type and the number of inference serving
instances under a holistic consideration of networking, computing, and memory resources.
Mobile clients can thus utilize ML inference services on edge nodes that offer minimal inference serving latency.
In this work, we implement the proposed solution using TensorFlow Serving and Kubernetes on a cluster of edge nodes, including Nvidia Jetson Nano and Jetson Xavier. We
further demonstrate the proposed solution’s effectiveness in reducing the overall inference
latency under various system parameters and practical system settings through simulation
and testbed experiments, respectively
Culturally Responsive Teaching: Music Educators' Beliefs
No full textThesis (Master's)--University of Washington, 2021As music educators seek to offer equitable education for racially, culturally, and ethnically minoritized students, they may turn to the pedagogical framework of Culturally Responsive Teaching (CRT) for guidance. Instruction in CRT can occur through professional development (PD), in-service training designed to influence the beliefs, knowledge, and practice of teachers to improve instructional outcomes. This emerging grounded theory study sought to understand teachers’ beliefs regarding CRT and offer suggestions for more effective PD by examining the following research questions: What are music educators’ beliefs regarding CRT; what do music teachers believe about how these practices impact students? What role do music educators believe that PD plays in shaping their own beliefs and practices? An exploration of ten music educators’ beliefs occurred through analysis of data collected in semi-structured interviews. The development of a model ensued to reflect the dynamic interactions between belief, knowledge, practice, and situational motivation, followed by recommendations for site-specific PD. Suggestions for further research are offered for assessing the effectiveness of such PD as well as for exploring the impact of PD and teacher practice on students themselves
Life’s Simple 7 in relation to supraventricular and ventricular arrhythmias on extended ambulatory cardiac monitoring: The Multi-Ethnic Study of Atherosclerosis
No full textThesis (Master's)--University of Washington, 2021Background: The Life’s Simple 7 (LS7) metric consists of seven health behaviors and measures that are known risk factors for cardiovascular disease. Relatively little is known about the association of LS7 score with cardiac arrhythmias.Methods: In the setting of the Multi-Ethnic Study of Atherosclerosis (MESA), we studied LS7 score, assessed at the 2010-2102 study visit, in relation to cardiac arrhythmias assessed by Zio Patch ambulatory electrocardiographic monitoring in 2016-2018. In participants free of clinically recognized cardiovascular disease and AF, we examined the association of total LS7 score with atrial fibrillation, supraventricular arrhythmias, and ventricular arrhythmias using logistic regression and linear regression.
Results: Among 1329 participants in the analysis, the mean (SD) age was 67(8) years and 48% were men. More favorable total LS7 score was associated with fewer PVCs per hour (ratio of geometric means for the upper quartile vs. the lower quartile 0.52 [0.34-0.81]). After adjustment for sociodemographic characteristics, the association was attenuated (0.66 [0.43, 1.01]). Among the LS7 components, only body mass index (BMI) was associated with ventricular ectopy. In an adjusted model, compared with participants with poor body mass index (BMI), those with intermediate BMI had a 30% fewer PVCs/hour (ratio of geometric means 0.70 [0.50- 0.96]). We did not detect associations of total LS7 score with atrial arrhythmias.
Conclusion: In this longitudinal study of individuals free of clinically-recognized cardiovascular disease, there was little evidence of association of total LS7 score with cardiac arrhythmias. However, there was a suggestion that more favorable LS7 score was associated with fewer PVCs and specifically, that more favorable BMI was associated with fewer PVCs
Decolonization and Databases: Examining Collections Management Systems and Decolonizing Practices
No full textThesis (Master's)--University of Washington, 2021Decolonizing museum collections continues to be an important topic in the museum field, but limited research has been done on the efficacy of databases in terms of enhancing decolonizing practices. As such, the purpose of this study was to examine decolonizing practices in collections management databases in museums with Indigenous collections. Those selected for study included privileging of the following practices: incorporating Indigenous knowledge (perspective, language, and protocols), accepting Indigenous authority, and providing Indigenous peoples access to information and objects in museum collections. The first method in this phenomenological study used semi-structured interviews with seven collections specialists about their experiences with collections management databases and decolonizing practices in six institutions in Canada, the United Kingdom, and the United States. The second used document analysis of three institutions’ collections policies and decolonizing initiatives. Findings suggest all museums had collaborated with source communities about Indigenous knowledge entered into the databases, but the extent of capabilities and utilization of decolonizing practices in collections management systems was inconsistent. None of those interviewed had discussions with Indigenous communities on the choice of the current collections management database, though a majority were in the process of seeking new collections management systems to replace those that had been in use for ten or more years in the museum. Access to the database was also inconsistent, and particularly dependent on system features. Limitations of this study included the impact of the COVID-19 pandemic on scheduling interviews, the time needed to complete the research, and the final sample size potentially not being representative of all museums
Learning Topological Structures and Vector Fields on Manifolds with (Higher-order) Discrete Laplacians
No full textThesis (Ph.D.)--University of Washington, 2021Unsupervised learning algorithms, which extract geometric information without labels, are pivotal in analyzing high-dimensional observational data in complex physical and social systems. Prior accomplishments of scientific discoveries with these methods include applying (i) non-linear dimensionality reduction, also called manifold learning (ML), algorithms in revealing hidden structures of quantum chemistry and astronomy datasets. Additionally, (ii) clustering analysis techniques are critical for categorizing stages in cellular differentiation or analyzing community structures in social networks. Finally, (iii) topological data analysis (TDA) methods are essential for investigating the cyclic/periodic structures in the neuroscientific, galactic, and human action systems. Despite these early successes in the scientific community, the vast majority of the unsupervised learning methodologies are highly unexplored. For instance, how can we learn from a dataset equipped with temporal information? On the other hand, how can we tell/test whether the obtained topological structures are signals instead of noises or algorithmic defects? Lastly, can we extend the current unsupervised learning framework to deal with the higher-order (e.g., triplet-wise) relations? If so, what potentials does it open up? In this thesis, we will answer some of these questions under the lens of differential geometry, topology, and machine learning. This thesis centers around the estimators for the Laplace-Beltrami operator of a manifold and its higher-order counterparts (called the -Laplacian). In particular, we are interested in the spectral (i.e., the eigenvalues and the eigenvectors) properties of these estimators. First, we analyze a known deficiency in the outputs of the standard embedding algorithms when the aspect ratio of the manifold is large. This deficiency, called the Independent Eigencoordinate Search (IES) problem, arises due to the functional dependencies in the eigenfunctions of . We address the IES problem by proposing a bicriterial algorithm that has a low computational overhead and has an analyzable asymptotic limit. Second, the discrete Helmholtzian (a first-order extension of the graph Laplacian to the edge space) is introduced to enrich the manifold learning methodology. We provide a theoretical analysis of the large sample limit of and show its connection to the manifold Helmholtzian (1-Laplacian) , an operator that acts on vector fields on the manifold. The proposed Helmholtzian estimator made it possible to distill higher-order topological structures, such as the first homology vector space encoding the cyclic information. Third, we explore the possibility of utilizing the vector field basis defined from the eigenflows of ; specifically, we study the extensions of the learning algorithms that are based on to vector fields smoothing, vector field interpolation, and inferring underlying vector fields from sparsely observed trajectories. Lastly, we study the decomposition of the -th homology vector space (null space of the -Laplacian ) of the sparsely connected manifolds; under this condition, we show that the homology embedding can be roughly factorized. Our analysis is conducted by viewing the connected sum (gluing) of manifolds as a perturbation to the matrix . We exemplify the efficacy of the proposed framework by applying it to the {\em shortest homologous loop detection} problem, a problem known to be NP-hard in general. We support our claims in each section with an extensive set of experiments on synthetic manifolds along with real datasets from chemistry, biology, medical imaging, and astronomy