VictorJuiz/thesis huggingface dataset:
Black Box Exposures: Enriching Public Engagement with Human-Data Relations Through Intermedial Performance Strategies
Keywords: data
informatic
performance
praxis
theatre

Through a combination of this artistic and scholarly research rooted in praxis, this dissertation demonstrates how the medium of theatre provides an accessible, alternative laboratory space wherein the changing relations between humans, data and informatics may be examined. This study begins by examining several topics: the diverse array of analogies and metaphors that currently characterize data out in the world; popular notions surrounding data as a material for ‘telling stories’ about events, objects or people; and growing communities of practice that prioritize intimate and hand-made forms of engagement with data as material for creative expression. In the process, the metaphor of a black box is established as an evocative means of conjoinment between theatre studies, critical data studies and information design, while identifying three salient cross-disciplinary themes—velocities, assemblages, and representativeness. These three themes provide a useful framework for examining several recent theatrical productions wherein aspects of data production, processing and representation are juxtaposed with lived human experiences. These themes are further elaborated through two praxis-based experiments designed and run by the author: a collection of intermedial performance prototypes and an experimental university course. Drawing upon methods and materials developed by the Quantified Self Movement and the Data Humanism Movement, both experiments emphasized strategies of creative resistence in which the activities of data production, processing and representation are explored through forms of enactment. The resulting contribution is a provisional model for both artistic and pedagogical praxis in which these activities are translated into ‘stages of informatic enactment.’ Infused with the methods, practices and sensibilities of the Quantified Self and Data Humanism movements, this model encourages intimate and bespoke forms of creative engagement with data in a performing arts context, but with a greater emphasis on exploring the underlying systems and processes that produce it.
Palliative Care in Patients with Noncancer Illness
Keywords: End-of-Life Care
Health Services Research
Palliative Care

The evidence base for palliative care is heavily skewed toward patients with cancer, despite the fact that there are twice as many patients with palliative care needs and noncancer illness. This thesis seeks to establish the evidence for clinical practice and policy development for palliative care programs to improve end-of-life care. The first study was a systematic review and meta-analysis of randomized clinical trials of patients with primarily noncancer illness. We found that receipt of palliative care, compared with usual care, was significantly associated with less acute healthcare use and modestly lower symptom burden, and no significant difference in quality of life. The second study measured the association between newly initiated palliative care in the last 6 months of life, healthcare use and location of death in a cohort of adults dying from noncancer illness; and compared these associations with those who die from cancer. We found that among those dying of chronic organ failure, palliative care was associated with a reduction in the rate of emergency department use, hospitalizations and ICU admissions. Palliative care was associated with increased rates of emergency department use and hospitalization in patients dying of dementia, which differed depending upon whether they lived in the community or in a nursing home. In our third study, we measured the association between physician rates of referral to palliative care and location of death in hospitalized adults with serious illness, which include patients dying of cancer and noncancer illness. We found that patients who were cared for by physicians with higher rates of referral to palliative care were less likely to die in hospital and more likely to die at home. Standardizing referral to palliative care may help reduce physician-level variation in referral as a barrier to access. Collectively, these thesis findings highlight the potential benefits of palliative care in patients with select noncancer illness and identify further knowledge gaps for other common noncancer illnesses. Scaling existing palliative care to increase access through sustained investment in physician training and current models of collaborative palliative care may improve end-of-life care, which have significant implications for health policy.
Inductive Transfer Learning for Incremental Modeling and Optimization of Cloud Systems Performance
Keywords: Incremental modeling
Performance modeling
Transfer learning

Due to the cost of sampling system performance, it is expensive to obtain performance characteristics of a complex computer system in different configurations. As an alternative, in this dissertation, we propose to reuse existing partial and full performance models and explicitly model the effects of configuration variations, with a goal of substantially reducing the time and effort required to perform performance reasoning and optimization on cloud-based systems, applications and services. We introduce Model Mapping, a novel inductive transfer learning technique for incremental performance modeling of highly configurable systems. Model Mapping captures many explicit and latent types of dynamic system evolution, including configuration changes, scaling and hardware upgrades, by deriving and modeling these kinds of incremental transformations between system and/or application instances, over time. Modeling these transformations allows us to build accurate models for new configuration instances with just a few samples.We experimentally test our method on a variety of system performance modeling and optimization scenarios, using a carefully designed experimental testbed and realistic benchmarks, to obtain insight on the method's applicability in real-world cloud computing environments. Among other examples, we show how our method can be used to quickly derive an accurate resource allocation split that optimizes a given overall performance goal for co-hosted applications in a virtualized environment. Compared to using conventional direct and incremental modeling techniques, our method achieves higher accuracy by up to an order of magnitude when the sampling budget is extremely limited, in particular when samples are limited to between 0% to 5% of an exhaustive sampling budget.
Photocatalytic Production of Dihydroxyacetone from Glycerol Using Non-aqueous Solvents
Keywords: Adsorption
Catalysis
Non-aqueous
Photocatalysis
Photoreactor
Reactor Design

This thesis discusses the opportunities of using photocatalysis to valorize glycerol, a biodiesel waste product, to dihydroxyacetone (DHA), a high-value pharmaceutical. Finding environmentally friendly processes that use less energy input and do not create harmful emissions is of increasing importance. Similarly, ever-increasing waste streams need to be reduced and repurposed. Photocatalysis using solar light as an energy input and performed using waste streams, is an ideal method to combat both of these problems. Typically photocatalysis is performed using water as a solvent because it is both cheap and environmentally friendly. However, this leaves a large non-water space mostly unexplored. This space is investigated in two ways, the adsorption that the catalyst and substrate display, by changing solvent, as well as the change in light distribution caused by the addition of an inactive, immiscible co-solvent. The photocatalytic production of DHA from glycerol in acetonitrile on TiO2 was investigated. After 300 min, with 1 g/L catalyst loading and 4 mM initial glycerol concentration, glycerol conversion and DHA selectivity were 96.8% and 17.8% in acetonitrile compared to 36.1% and 14.7% in water. The half-life of glycerol decreased by a factor of 6.2, from 467 min to 75 min, by changing the solvent from water to acetonitrile. Experiments using biodiesel-derived crude glycerol verified the utility of the proposed process for the photocatalytic production of DHA from crude glycerol. A mechanism is proposed to explain the higher selectivity towards DHA over GAD in this process. The first biphasic photoreactor was investigated and utilized for the conversion of glycerol to GAD and DHA using water and ethyl acetate as dispersed (active) and continuous (inactive) phases, respectively. Compared to a monophasic photoreactor containing only water and identical amounts of glycerol and photocatalyst, the biphasic reactor containing 90 vol % ethyl acetate increased the DHA yield by a factor of 2.9 (from 4.5% to 13%) and the concentration of DHA by approximately 14 times (from 0.08 mM to 1.1 mM) after 240 min. Additionally, conversion of crude glycerol extracted using a 90:10 (vol/vol) ethyl acetate-water mixture showed a similar DHA conversion and yield to that of pure glycerol.
Automating disease diagnosis and cause-of-death classification from medical narratives using event extraction and temporal ordering
Keywords: automated diagnosis
cause of death
clinical narratives
natural language processing
temporal ordering
text classification

The digitization of health records has provided a wealth of data that can be used for machine learning tasks such as automated diagnosis of illness or cause of death. However, crucial contextual information such as time and duration have often been omitted from such models. Although the words and phrases describing time can be modeled with semantic representations such as word embeddings, these representations often fail to capture the temporal value of such time phrases. The goal of this work is to present models for identifying symptoms and time information in the text of different types of health documents (while explicitly modeling temporal value), determining the chronological order of the extracted symptoms, and classifying the documents according to diagnosis or cause-of-death. Since the style and content of health text documents can be widely variable, these models are evaluated on three different types of health records: clinical notes (formal written health records), verbal autopsies (informal written records of deaths that occurred outside of health facilities), and transcripts of clinical conversations (verbal dialogues between medical staff, patients, and caregivers). We conduct end-to-end disease classification using neural network models trained on free-text narratives. The system includes supervised learning models for event and time extraction, listwise temporal ordering of events, and classification. We find that classification models that include event timeline representations perform better than methods that use only the raw text of the narrative. Furthermore, we present several models and metrics for listwise temporal ordering, including the ability to model simultaneous events. These models can be applied to different types of medical narratives, and can improve the performance of automated classifiers which can be used in real-world contexts to improve cause-of-death coding efficiency and clinical decision support.
Investigating Secondary School Teachers’ Understandings of and Approaches to STEM Integration
Keywords: Curriculum Integration
PBL
Problem-based Learning
STEM

This multiple-case study explores the integration experiences of four secondary school science and mathematics teachers. The study draws upon both semi-structured interviews and classroom observations to illustrate each of the participants strategies to STEM integration and to understand the benefits and challenges of each of their approaches. A number of common themes emerged from the individual findings and are consolidated to form the overall results.The results indicate that Project-based learning (PBL) represents the most common approach to STEM integration. The benefits associated with PBL include increases in student engagement and motivation, as well as improvements in achievement that are related to group work and personalization of learning. Challenges to implementation include the need for time for collaboration and professional development, the need for administrative support, and access to resources. Teacher STEM pedagogical content knowledge was identified as a useful construct in assessing teacher needs and the ability to develop authentic and open-ended projects that allow for the personalization of learning. The eight major findings are: (1) project-based learning forms the foundation of STEM integration; (2) teachers notice an improvement in achievement for those students enrolled in STEM programs; (3) project-based learning allows for ongoing feedback loops that enhance teacher tracking and assessment of student progress; (4) field trips and partnerships represent a powerful tool in complementing PBL approaches to STEM integration; (5) STEM integration through a PBL approach requires additional time for teachers; (6) Administrator support for a STEM program is key to its implementation; (7) STEM integrative pedagogical knowledge is regarded as a useful construct in describing the unique skills that are required by teachers who wish to implement STEM integration; and (8) teachers with a high degree of STEM pedagogical content knowledge are better equipped to develop, implement, and adapt projects to suit student needs and interests. STEM integration through a project-based learning approach represents a compelling strategy that offers multiple benefits to student engagement and learning. It also represents a challenging instructional strategy as it requires the coordinated efforts of school administrators and teachers for effective implementation.
Assembling Solidarity: Proletarian Arts and Internationalism in East Asia
Keywords: Birth Control Politics
East Asia (China
Japan
Korea
Taiwan)
Esperanto
International Solidarity
May Day
Proletarian Literature

This dissertation examines the theories and practices of proletarian international solidarity during the 1920s and 30s in East Asia, found in various writings and activities. Locating its arguments in concurrent scientific studies, linguistic theory, and literary and art criticism, this dissertation argues that such theoretico-practical manifestations of solidarity did not merely follow a party and union allegiance based on unidirectional and monolithic forms of organization, but were assembled in constant-changing and multidirectional configurations. Through practice in and experience of solidarity emerging in encounters, exchanges, and events, intellectuals, writers, visual artists, activists, workers, and farmers produced networks of international solidarity of unprecedented scale in East Asia. They understood the literary and artistic production that accompanied these transnational networks not so much supplementary to its realities but rather coeval in constructing realities beneficial to forging relations of solidarity. Grappling with hierarchal divisions inherent in capitalism, proletarian writers aimed to portray strategies for how to evade such divisive mechanisms installed by imperial and nation-state apparatuses and constructed alternative narratives of social relations and organizations of life. Chapter 1 introduces how proletarian cultural movements across East Asia prepared and organized May Day, the international day of the proletariat, to illuminate how they experimented with various artistic and literary techniques to assemble numerous proletarian struggles into cohesive relations of solidarity. Chapter 2 examines Esperanto as the aspired proletarian language and how proletarian cultural movements reconfigured language as a mode of resistance. They facilitated exchange among Esperantists, created learning materials for proletarians and sympathizers, and wrote literature in Esperanto as attempts to create mutual comprehensible narratives beyond imperial languages. Chapter 3 probes anti-natalist narratives in relation to international debates on birth control politics among proletarian movements, in which writers grappled with the tensions of how to assemble a gendered proletarian solidarity. Chapter 4 analyses the cooperation among East Asian POW’s, soldiers, workers, and activists agitating against war and militarism in proletarian literature that reveals how international solidarity was essential in resisting Japanese imperialism and fascism, presenting a transnational history of proletarian antiwar literature in East Asia.
Empirical Models of Heuristic Search in AI Planning and Neural Sequence Decoding
Keywords: AI Planning
Heuristic Search
Neural Sequence Decoding

Heuristic search algorithms are widely used in both AI planning and the decoding of sequences from deep neural networks. In recent years, several lines of work have highlighted different factors that impact the empirical performance of heuristic search algorithms. However, a principled empirical understanding of the search behavior of these heuristic search algorithms has yet to be developed. Empirical models, such as the phase transition and the heavy-tailed behavior, have been central to the development of empirical understanding of combinatorial search problems such as constraint satisfaction problems (CSP) and satisfiability (SAT). In this dissertation, we investigate the use of empirical models to explain the behavior of heuristic search algorithms in AI planning and neural sequence decoding and support the development of more efficient search algorithms. In AI planning, we develop empirical models for problem difficulty of greedy best first (GBFS), the most commonly used algorithm for satisficing planning. First, we establish the existence of a phase transition in the solubility of planning problems and investigate its implications to problem difficulty. Then, we demonstrate the heavy-tailed behavior of GBFS and provide a deeper understanding of the connection between constrainedness and local minima. Informed by our analysis, we develop a novel variant of GBFS that outperforms the baseline. In neural sequence decoding, we develop empirical models for the performance of beam search, the ubiquitous algorithm for decoding deep sequence models. First, we investigate the empirical problem of performance degradation in beam search. We present an explanatory model based on search discrepancies that generalizes previous observations on the behavior of beam search. Building on our analysis, we present two heuristic techniques that eliminate the problem. Next, we study goal-oriented sequence decoding and show that, similar to GBFS, we observe heavy-tailed behavior. We present a novel variant of goal-oriented beam search that exploits our insights and outperforms the baseline. Our work shows the importance of empirical models in the study and development of heuristic search algorithms and demonstrates that empirical models developed for CSPs and SAT can be adapted to AI planning and neural sequence decoding.
Mental Health and Academic Performance in Postsecondary Education: Sociodemographic Risk Factors and Links to Childhood Adversity
Keywords: Childhood Adversity
Learning
Marginalization
Mental Health

A significant body of literature has demonstrated that marginalized sociodemographic groups are subject to increased frequency and severity of stress, deemed ‘minority stress,’ associated with discrimination, harassment, systemic barriers, and oppression (Meyer, 2003 2015). Furthermore, early childhood adversity has been shown to be linked to increased stress reactivity and reduced mental health outcomes in early adulthood and across lifespan development (Fergusson Lynskey, 1996; Lupien, McEwen, Gunnar, Heim, 2009; Shonkoff et al., 2012). Experiences of stress in academic settings, while performance-enhancing at low and moderate levels, has been shown to be detrimental to performance and learning at chronic or severe levels (Hartley, 2011; Keogh, Bond, Flaxman, 2006; Liston, McEwen, Casey, Posner, 2012b). This dissertation leverages an ecological systems theoretical framework to identify differences in stress, wellness and learning among marginalized sociodemographic postsecondary students in Canada, and individuals who have experienced adverse childhood experiences. In study one, National College Health Assessment (2016) data from 43,780 participants across 42 Canadian postsecondary education institutions were analyzed. Findings showed that students who identified with marginalized identities were significantly more likely to experience poor wellness during their postsecondary studies, and that wellness served as a mediator between identifying with a marginalized sociodemographic group and reduced academic performance. In study two, links between stress and academic performance during transition into university were examined through a survey administered at a large, research-intensive university in Southwestern Ontario. This research demonstrated that stress was negatively related to sense of belonging and academic performance during first-year university, and that sense of belonging partially mediated the negative relationship between stress and academic performance. Furthermore, students who experienced chaotic family environments and childhood adversity were shown to have increased levels of stress and reduced academic performance during their transition into university. Collectively, this research underscores the critical role that wellness plays in student learning and establishes imperatives for postsecondary institutions to engage in efforts to enhance equity, inclusion, and transitional supports that create wellness-promoting educational contexts supporting the learning of all students, with particular benefit to communities who are vulnerable to systemic barriers and stress.