VictorJuiz/thesis huggingface dataset:
A Subdued Palette of Subversion: Artistic Expression, Creativity, and Family Coping Strategies in Soviet Latvia
Keywords: Arts-informed research
Epistolary studies
Intergenerational trauma
Life-history
Oppression
Soviet Latvia

This arts-informed life-history study reveals how intra- and interpersonal and intergenerational coping mechanisms were used to internalize Soviet rule in Latvian public and private lives and exposes how oppressions operate across generational time. Forty eight of the author's illustrations dispersed throughout the manuscript amplify the emotional content of the written word. The narrative is structured around family letters, memories, and pertinent conversations, which are organized in three chapters. Chapter 1 Subterfuge comprises the author's coming of age in Soviet Latvia and her journey towards attaining specific goals within the oppressive environment. Chapter 2 Subversion is a compilation of recollections of the author's family and immediate Latvian community. The chapter reveals how many Latvians, who cared deeply about their identity and the independence of their country, employed subverting and at times conflicting strategies in order to preserve their cultural identity, and artistic creativity during Soviet rule. Chapter 3 Longing is based on a collection of 110 letters written by the author's grandmother to her brother in the United States between 1947 and 1979. This epistolary correspondence of 32 years offers a rich trove of family history and cultural commentary. The open boundaries of thought in this project allow the audience to read between the lines and discover intimate micro-truths about a group of people who experienced Soviet times. The study questions the scholarly field that analyzes the Soviet Union in terms of binary thinking—the practice of dividing Soviet citizens into collaborators or dissidents, oppressors versus the oppressed, and defining space as private versus public. The project also explores the Soviet Latvian identity, including the causes of the disconnect and differences between the diaspora and the so-called "soviet" Latvians. Respect, tolerance, understanding, and appreciation between former Soviet Latvian and diaspora communities that the author intends to nurture, came forth in her firsthand as a result of deep reflection, listening, and learning during the creation of this project. This dissertation should prove valuable for scholars who study oppression and intergenerational trauma, as well as for educators and community workers who liaise with persons who have lived or are currently living in oppressive circumstances.
Vision, Fear, and Knowledge in Thucydides' History
Keywords: Affect
Emotion
Fear
Hope
Thucydides
Vision

In this dissertation I explore sense perception and emotional psychology in Thucydides’ History from several angles. My primary sensory focus throughout is the visual, while the principal emotion I examine is fear. Vision, as W. R. Connor noted, is “the privileged sense” in the History, just as fear is the text’s most pervasive and most frequently cited emotion. I show that vision is not isolated from the other senses, nor is fear from other seemingly discrete—or antithetical—emotions. Rather, in the historical world Thucydides fashions, multiple senses and emotions consistently exhibit interactive and even complementary relationships with one another. Furthermore, as I emphasize in every chapter, both the senses and the emotions depicted in Thucydides’ text are consistently entwined with cognitive processes, both in the narrative and in the recorded speeches. Each chapter focuses on passages of vividly depicted human sensory perception and psychology. The first half of the study seeks to use some of the historian’s battle narratives to draw certain metatextual conclusions about the work and our approach to it as readers. I argue that by looking closely at the sensory and psychological processes that Thucydides attributes to the agents of history, we are able to gain insights into the historian’s narrative self-positioning as well as into aspects of the intellectual and emotional utility he claims for his text. The second half of the study engages with both rhetorical and narrative passages in order to examine the visual, epistemological, and emotional dynamics at play in two particularly ideologically charged moments within the History. I argue that Thucydides uses these moments, in part, to demonstrate the limits of ideological structures, whose highly prescriptive modes of seeing, knowing, and feeling struggle to sustain themselves against the exigencies of the historical world he depicts.
Improving Hardware Design Reuse through Design-space Exploration
Keywords: Bayesian Optimization
Computer-Aided Design (CAD)
Electronic Design Automation (EDA)
Field-Programmable Gate Array (FPGA)
Gaussian Process
Kriging

The continued demand for higher performance and more energy efficient systems has fueled interest in specialized hardware. However, the design of such systems has large non-recurring engineering costs. Thus, the reuse of digital hardware components, especially parameterizable hardware generators, has become a prominent method of continuing productive design of new systems. Challenges in dealing with an increasing amount of design reuse are determining how to select the correct set of generators to use for a design as well as how to parameterize them. The mapping of parameters to desired properties such as performance, area and power is not always clear. Further, obtaining the exact values for these properties involves long-running tool flows. The goals of the research in this thesis are to to improve design reuse by facilitating and speeding up this design space exploration task. To improve the speed of optimization of hardware designs, we study improvements to a model-based optimization method. Specifically, we examine how the underlying model used for optimization can be improved by prior design knowledge and information available in the FPGA design tool flow. First, we find that information about the relationships between parameters can be integrated into the model to reduce optimization time. Second, we demonstrate that informative and fast estimates are available in the High-Level Synthesis tool flow and incorporating these values leads to further improvements in optimization speed. Finally, we show how integrating hierarchical information about area, delay and latency can also reduce optimize time. Through these studies, we demonstrate how carefully adding design information into the model-based optimization process is an effective means of accelerating design-space exploration. In addition to improving optimization speed, we consider more briefly how to enumerate design spaces for systems of hardware generators. We find that a constraint-based interface for hardware generators is effective for design-space exploration in our case studies. Finally, we derive methods of bounding search heuristics used within the optimization scheme. As design reuse and complexity continues to increase, we hope that the methods proposed in this thesis will aid in the design of the next generation of digital systems.
Three Essays in Development and Cultural Economics
Keywords: Culture
Development
Entrepreneurship
Human Capital
Human Development
Natural Resources

This thesis collects three papers studying topics in development and cultural economics. In Chapter 1, I study the role of culture in driving entrepreneurship. I present robust evidence that cultural groups which emphasize obedience as a moral value produce fewer entrepreneurs. First, I document this relationship at the language group level, exploiting within-country variation in group-level values. Second, I show that these group-level values continue to predict entrepreneurship rates and related occupational choices among first- and second-generation immigrants to Canada. Third, I present evidence that obedience values form as a function of values received both from parents and from the wider community during childhood. In Chapter 2, I present joint work with Limin Fang and Tongtong Hao on the effects of China's One Child Policy on the education and early labour market outcomes of those born in its wake. We present evidence from a difference-in-difference design that individuals who grew up as only children as a result of the policy obtained significantly more education than their counterparts with siblings, while early labour market outcomes were less affected. We place our results in context of the large literature on quantity-quality tradeoffs and argue that they are consistent with existing theoretical models, which allow for non-linearity in the effect of child quantity on quality. In Chapter 3, I study the effect of natural resource abundance on human development in Sub-Saharan Africa between 1970 and 2013. I use cross-section and panel IV analysis to identify the long- and short-run impacts of resource wealth on human development, measured primarily by infant mortality. I find that natural resource revenue has had zero average impact on infant mortality rates in the short run and a potentially negative impact in the long run, despite increasing budget resources and reported health spending. I further find that while democratic transitions are generally followed by a drop in infant mortality, this is not true in resource rich nations with weak constraints on power. I conclude that the pass-through of natural resource wealth to broad-based development in Africa has been weak and that electoral competition without deeper political reform is unlikely to turn things around.
Computational Models for Mapping Transcriptome and Epigenome to Human Phenotypes
Keywords: Cancer
Epigenome
Pathway
Stem cell
Survival
Transcriptome

Fate and identity of eukaryotic cells are results of the cooperative function of biological features such as transcripts, cis-regulatory elements and transcription regulators. Recent advances in sequencing technologies helped the biomedical research community to study this cooperation and identify features associated with the identity of cell types, development of mature phenotypes from embryonic stem cells, development of malignancies like cancer, poor survival of cancer patients and resistance of tumours to drugs. Despite tremendous success in using these features in applications like the development of targeted therapies and identification of signatures of stemness, there is an undeniable gap for a better understanding of the healthy and malignant phenotypes. In this dissertation, I initially propose a transcriptomic feature extraction approach, Similarity Identification in Gene Expression (SIGN), as a new method for identifying patterns of transcriptomic expression within biological pathways. I found that these patterns can be used to predict the survival of breast cancer patients as well as their response to targeted therapies. SIGN can not only provide a highly accurate but highly interpretable scheme in predicting malignant phenotypes like aggressive tumours. Despite successful applications of transcriptomic profiles of cells in building predictive models of human phenotypes, more recently epigenomic profiles of cells were introduced as a complementary source of information for these task. Hence, going beyond the transcriptomic features, I propose another approach called CREAM (Clustering of genomic Regions Analysis Method) to extract DNA level features relying on cis-regulatory elements, histone modification as well as transcription factor binding loci. These features can provide complementary information to the transcriptomic features, regarding cell identities and their fate in physiological and disease developments. This work provides new approaches in extracting transcriptomic and epigenomic features in cell types and their association with cell type identity, the survival of cancer patients as well as the resistance of tumors to drugs.
The Role of the Costimulatory TNFR Family Member GITR in T Cell Immunity During an Acute Respiratory Infection
Keywords: GITR
Influenza
Signal 4
T cells
TNFR
Trm

T cells play a critical role in control of influenza virus, a major human pathogen. Glucocorticoid-induced TNFR-related Protein (GITR), a prosurvival member of the TNFR family, contributes to maximal CD8 T cell responses and protects mice from death upon severe influenza infection. In this thesis, I demonstrate that during influenza infection, GITR ligand (GITRL) is mainly expressed by a group of monocyte-derived tissue infiltrating cells known as inflammatory antigen presenting cells (InfAPC). I show that both GITR and GITRL are detected early in the mediastinal lymph node (mLN) and in the lung tissue when T cells start to accumulate after influenza infection. By detecting phospho-signals downstream of GITR, I show that InfAPC likely provide crucial signals through GITR on T cells in the mLN and afterwards in the lung. This signal is critical for the accumulation of lung effector CD4 and CD8 T cells as well as optimal formation of lung CD4 and CD8 tissue-resident memory T cells (Trm) after influenza infection. The signals that GITR delivers for effector T cell and Trm accumulation most likely occur at the effector phase of the response since GITRL expression is mainly confined to the first week of influenza infection. Thus, I investigated how GITR affects effector T cell subpopulations during influenza infection. Ly6C expression defines two effector CD4 T cell subsets while Ly6C and CX3CR1 expression defines three effector CD8 T cell subpopulations. In CD8 T cells, GITR affects the accumulation of all three subsets, with a predominant effect on the least differentiated subset. GITR also selectively upregulates CXCR6 on the less differentiated CX3CR1lo CD8 T cell subsets. GITR affects the accumulation of both CD4 T cell subsets, but selectively upregulates CD127 in the least differentiated CD4 T cell subpopulation which shows a preference for entry into the lung parenchyma. Together, this thesis demonstrates that during influenza infection GITR costimulation in the lung plays an important role in sustaining both highly differentiated effector T cells and Trm precursors, but with differential effects on effector T cell subpopulations. This signal is also critical for optimal formation of lung CD4 and CD8 Trm.
Increasing the Reach, Usage, and Impact of Scholarly Content
Keywords: Bibliometrics
Knowledge dissemination
Knowledge translation
Scholarly communication

Scholarly communication is facing an information overload crisis. There are now over 8,000 new papers published daily. Aside from the difficulty readers face in keeping up with the literature, the deluge of papers is making it less likely for any given paper to be read, used, and ultimately cited. The growing information overload problem has created a pressing need for evidence-based strategies to disseminate scholarly content. This thesis aims to evaluate whether a novel content distribution channel, TrendMD, could be used to augment the reach, usage, and impact of published peer-reviewed content. We conducted four experimental studies. The first study was a 6-week randomized controlled trial where we found that TrendMD conferred an 87% visibility advantage relative to control articles, as measured by a statistically significant increase in pageviews. The second study, which was a 3-week crossover trial, found that TrendMD yielded a statistically significant 30% and 49% weekly visibility advantage; the higher the intensity of article promotion in TrendMD, the more significant the increase in pageviews. For the third study, we conducted a 4-week randomized controlled trial and found that TrendMD conferred a statistically significant 77% and 95% increase in Mendeley saves and pageviews, respectively, relative to control. These results were particularly significant since replicated evidence indicates that Mendeley saves are a robust predictor of citations. Accordingly, in our fourth study, we conducted a 12-month randomized controlled trial examining to what extent TrendMD may increase citations of scholarly content. At six months, we found that TrendMD yielded a 21% and 55% statistically significant increase in mean citations and Mendeley saves, respectively, relative to control. At 12-months, we found that TrendMD had an even larger, statistically significant effect on citations; articles randomized to TrendMD showed a 50% increase in citations relative to control. Taken together, our results suggest that the promotion of scholarly articles in TrendMD is an effective way to increase the reach, usage, and impact of those articles. The data presented in this thesis address a pressing unmet need for evidence-based strategies that can be used by scholarly content producers to disseminate published research.
Amplified Detection of microRNA with a Novel Fluorescent Oligonucleotide Probe: microRNA:Argonaute Cleavage (miRACle) Beacons
Keywords: microRNA
Molecular Beacon
Probe
Sensor

This thesis concerns the development and exploration of a new class of fluorogenic microRNA sensors which exploit cellular processes to enable new capabilities. These sensors consist of a fluorophore and matching quencher, tethered to opposite ends of an oligonucleotide strand. Previously, miRNA sensors were simple adaptations from DNA-targeting sensors and relied on simple hybridization to achieve their specificity. MicroRNAs, however, are genetic sequences that are only one component of the multi-protein complex called the RNA-induced Silencing Complex (RISC), a major regulatory nexus for gene expression present in eukaryotic cells. This new class of sensor binds to a specific microRNA within RISC and undergoes enzymatic processing through the endonuclease Argonaute-2 to permanently activate fluorescence. This microRNA:Argonaute Cleavage (miRACle) beacon was investigated in a recombinant system and validated in solution with immunoprecipitated RISC. Fluorescence upon miRACle beacon activation increased 13-fold above hybridization and showed single nucleotide specificity. After validating the concept, the project split into two avenues of research. The first avenue was evaluating if the system could discern between frame-shifted miRNAs, termed isomiRs. To investigate this, common nuclease resistance modifications were shifted along the oligonucleotide strand to assess their impact on assay performance. Modifications within the catalytic site abrogated activity, demonstrating that miRACle beacons may detect frame shifted isomiRs correctly. The second avenue for miRACle beacons focused on bringing the assay into cells and resisting the processes that give false positive signals. Established strategies to prevent non-specific nuclear accumulation or nuclease degradation were screened to determine compatible modifications to the assay. A porphyrin dye, pyropheophorbide-a, could exclude the beacons from the nucleus in vitro, however no tested strategy definitively showed miRNA specific activation and stability to non-specific nucleases in vitro
Investigation of Hydroxide Ion Conducting Polymer Electrolytes for Solid Supercapacitor Applications
Keywords: Alkaline electrolyte
Electrochemical capacitors
Hydroxide ion
Polymer electrolytes
Solid state

Growing clean energy demands have been incentivizing the development of next-generation energy storage technologies such as solid supercapacitors that are safe, compact, low cost, and capable of high-throughput reel-to-reel processing. While many advanced solid supercapacitors are demonstrated using hydroxide (OH-) ion conducting polymer electrolytes, the fundamental understanding of the performance of the electrolyte and its role in solid-state devices requires substantial elucidation. In this thesis, alkaline polymer electrolytes (APEs) for solid supercapacitors were developed based on tetraethylammonium hydroxide (TEAOH) and polyacrylamide (PAM) that exhibit (i) high ionic conductivity, (ii) long-term environmental stability, and (iii) good accessibility with electrode materials. An in-depth understanding of OH- ion conduction in polymer matrix and the interactions between the ionic conductor, polymer matrix, and various organic and inorganic additives has been established. The synergistic pairing of PAM and TEAOH enhanced the OH- ion conduction and prolonged its storage-life. Investigations of the dielectric properties of the TEAOH-PAM APE revealed the influence of H2O:TEAOH crystal hydrates on the ionic conductivity of the polymer electrolyte. Evaluations at elevated temperatures showed that the OH- ion conduction in the polymer electrolyte involves an interplay of segmental motion and OH- ion hopping. Correlative spectroscopic and electrochemical studies are used to determine the effects of crosslinking and the addition of inorganic filler additives on TEAOH-PAM. It was discovered that the presence and size of SiO¬2 alters the crystallization dynamics. Solid supercapacitors using TEAOH-PAM were fabricated in 3D sandwiched and 2D interdigitated configurations. The APE shows excellent compatibility with multiwalled carbon nanotubes as well as ultra-high surface area activated carbon electrodes, resulting in capacitances of ca. 10 F g-1 and ca. 240 F g-1, respectively. TEAOH-PAM was also matched with vertically-oriented graphene nanosheet (VOGN) electrodes as a 2D interdigitated device to replace traditional electrolytic capacitors for high frequency filtering applications. The combination of the highly electronically conductive VOGN electrodes and the highly ionically conductive, thermally tolerant TEAOH-PAM APE enabled a solid supercapacitor with ultra-high rate capacitive performance up to 120 Hz and outstanding thermal resiliency up to 120 °C.
Optimal Planning with Learned Neural Network Transition Models
Keywords: Automated Planning
Deep Neural Networks
Optimization

For many real-world automated planning problems, it is difficult to obtain a transition model that governs state evolution with complex dynamics. Fortunately, the availability of sensors allows the collection of large quantities of data which can be input to efficient machine learning algorithms to accurately approximate the unknown transition model as a neural network. The central thesis of this dissertation is that automated planning problems with unknown transition models can be efficiently learned from data using neural networks, and the resulting learned planning problems can be efficiently compiled and solved as mathematical optimization problems. In this dissertation, we formally define the deterministic factored planning problem and learn its unknown state transition function with two distinct neural network structures, namely: (i) densely-connected neural networks with Rectified Linear Units (ReLUs), and (ii) Binarized Neural Networks (BNNs). For these learned neural network structures, we provide efficient compilations based on Mixed-Integer Linear Programming (MILP), and Binary Linear Programming (BLP) and Weighted Partial Maximum Satisfiability (WP-MaxSAT), respectively. For the MILP model, we investigate different preprocessing methodologies based on reachability, network sparsification and potential functions to speed up the experimental runtime performance of the underlying MILP solver. In order to find potential functions that support efficient planning with learned neural networks, we formalize the problem of finding potential functions for learned neural networks as a Bilevel Programming model, and solve it efficiently using a finite-time constraint generation algorithm. For the WP-MaxSAT model, we introduce a novel neuron activation compilation that is asymptotically the most compact encoding in the literature that maintains generalized arc-consistency property through unit propagation -- an important property that facilitates efficiency in WP-MaxSAT solvers. For all compilations presented, we experimentally demonstrate their performance over multiple learned planning domains and instances. Overall, we present the first theoretical and experimental results for planning optimally with respect to learned neural networks.