VictorJuiz/thesis huggingface dataset:
Existential reflections : paintings of starry nights, 1888-1931
Keywords: Art and Art History.

We have long looked to the stars to understand our place in the universe. In the nineteenth century, the stars marked the shift from religious to natural-scientific worldview which was encapsulated by Nietzsche's declaration that God is dead (1882). The loss of an omnipotent creator precipitated a loss of meaning, setting the individual adrift in a chaotic and unintelligible universe. It was at this moment in time that European painters first made the starry night the main focus of their canvases. However, the pronounced painterly qualities and unusual compositional strategies of these landscapes endowed them with an expressiveness that clearly distinguished them from scientific illustrations. While the literature to date recognises the renewed interest in the stars in the nineteenth century, attributing it to astro-scientific advances, it has neither identified nor investigated the proliferation and striking development of the motif at the turn from the nineteenth to the twentieth century. I argue that the configuration of motif, technique, and composition in the starry night skies by, respectively, Vincent Van Gogh (1888), Edvard Munch (1893), and Paul Klee (1931) anticipates later analyses of death, angst, and the absurd by thinkers including Friedrich Nietzsche, Martin Heidegger, and Albert Camus. I draw on close visual analysis, artists' diaries and letters, philosophical texts as well as poetry to explore how painters not only represented a particular motif but also recreated processes of thought in a non-conceptual manner. By uncovering the affinity between paintings of the starry night and existentialist philosophy, my study offers a new interpretation of the modern, subjective landscape, emphasising lines of thinking over the expression of emotion. Moreover, I trace a second conversion undergone by the motif of the starry night at the turn of the twentieth century from natural-scientific to existential-nihilistic. The paintings by Van Gogh, Munch, and Klee may thus be seen to contend with the deficiencies and strangeness of human existence, tying them into the existentialist dialogue
Expanding access to allylic amines through C–H amination
Keywords:  Department of Biology
C–H amination
rhodium catalyst
allylic amines
therapeutics
Text
Thesis

The presence of nitrogen functional groups is ubiquitous in chemotherapies across a number of categories including antibiotics, anti-tumor agents, and anti-HIV drugs. C–H amination is a promising method for introducing amines and amine derivatives into small molecules. This process requires a metal catalyst to facilitate the conversion of traditionally unreactive C–H bonds into valuable C–N bonds. While competing methods for C–H amination utilize a range of metal catalysts, this research expands upon a class of dirhodium metal catalysts previously discovered in the Du Bois Lab. As we have shown, newly designed tris- and tetrahydroxypyridine dirhodium catalyst system greatly enhances the scope and application of C–H amination technology. Specifically, this work investigates how structural ligand differences affect reactivity behavior and reaction performance in an effort to optimize C–H amination reaction conditions for selective formation of allylic amines. Allylic C–H amination reactions are broadly divided between intramolecular and intermolecular C–H amination reactions. In intramolecular allylic systems, results indicate that these catalysts operate best in a partially oxidized state, reaching full conversion with selectivities far exceeding 20:1 ratios. Results collected from intermolecular reactions suggest that tris hydroxypyridine rhodium catalysts functionalize more efficiently than their tetrakis counterparts, reaching full conversion with a selectivity of 10:1 in favor of C–H amination. This research should impact the field of medicinal chemistry and allow for more direct access to many types of therapeutics to treat human disease.
Experimental characterization of genetic variants causing hypertrophic cardiomyopathy
Keywords: Genetics.

Hypertrophic cardiomyopathy (HCM) is a common inherited heart condition, affecting one in 500 people. It is most often caused by variants in the genes that encode the cardiac sarcomere, including the gene encoding beta cardiac myosin, MYH7. However, hundreds of such variants exist, and most are very rare. This presents an obstacle to variant pathogenicity assignment, and population genetic studies are limited in their utility because they will never capture every possible variant. In this dissertation, I discuss the diagnostic challenges presented by uncharacterized variation in disease genes like those associated with HCM. I then describe a combination of experimental approaches that provide insight into the pathogenicity of HCM-associated genetic variants. I performed high-throughput mutagenesis MYH7 in induced pluripotent stem cells (iPSCs) using two methods, the in vitro PCR-based strategy POPcode and the direct genomic mutagenesis method CRISPR-X, comparing efficacy and comprehensiveness. To study the impacts of changes to MYH7 copy number, which is altered by some mutagenesis strategies, I both knocked out a copy of MYH7 and inserted a copy of cDNA. I also developed two methods for pooled disease phenotyping of cells differentiated to iPSC-cardiomyocytes (iPSC-CMs). The first sorts cells by size; hypertrophy is the pathognomonic feature of HCM. The second utilizes a fluorescent reporter for a gene, NPPB, that is consistently up-regulated in HCM-affected cardiac tissue. I used both of these methods to sort mutagenized cells and identify variants that may cause cardiomyocyte hypertrophy. Part of the missing heritability of HCM may be explained by variation in non-sarcomeric genes. I also share a study where we identified a novel variant in the Wnt signaling regulator WTIP in a family with HCM, and characterize its phenotypic impacts in multiple models. Together, these strategies can be combined to generate functional phenotypic data across variants in MYH7 and other HCM-associated genes, providing us with a deeper biological understanding of the structure-function relationships of the variant proteins that cause HCM and informing patient diagnosis in the clinic
Experimental design and decision-making in marketplace platforms
Keywords: Management Science and Engineering.

Online platforms often rely on experiments to aid decision-making. When considering a new change, they test the intervention on a subset of the users before deciding whether to launch platform-wide. However, in the setting of marketplace platforms, prior work shows that treatment effect estimates can be biased. Users in a market interact with each other, which violates the Stable Unit Treatment Value Assumption (SUTVA), creates biased estimates, and may impact the resulting decisions made from these experiments. We develop models to capture market dynamics and investigate the effect of interference on different designs and estimators. In particular, we are able to highlight and formalize the relationship between the magnitude of the treatment effect bias in commonly run experiments and the level of supply and demand imbalance in the market. Building on these insights, we propose a novel class of experimental designs and estimators using two-sided randomization (TSR), as a method to reduce bias. In addition, we show that the commonly used standard error estimates are also biased in these marketplace settings. We analyze the impact of the statistical biases on the resulting decisions based on the experiment, show that both forms of biases interact to negatively impact decision-making, and propose practical methods to mitigate such biases
Experiments in learning and transfer of inquiry strategies using short instructional videos
Keywords: Education.

Science simulations enable students to conduct rapid experimentation. Many students conduct sub-optimal experiments. Consequently, they may not discover the underlying scientific regularities. The current research examined whether simple instructional videos can help students learn more effective inquiry strategies such that they will spontaneously use them in a new situation. Five experiments with 591 eighth-graders in Colombia compared the effectiveness of five instructional conditions and a Control condition. Instructional conditions varied in the number of pedagogical features hypothesized to be helpful. At the simplest level -- Show Effective -- an instructional video simply showed an effective inquiry strategy for a simulation after the students had already tried the simulation themselves. At the most complex level -- Expanded -- students were shown the effective strategy along with an explanation. They were also shown a common ineffective (default) strategy and an explanation of why this strategy is an alluring default. Importantly, the Expanded instruction also told students that the effective strategy is more cognitively demanding in the short run but better than the tempting default strategy in the long run. Several additional conditions partied the relative effect of the different elements added to the Expanded instruction. Students engaged in simulations as a pre-test and a transfer task roughly two weeks later. We analyzed changes in their inquiry patterns because of the intervening instructional videos. The experiments were conducted under the highly variable school conditions brought about by COVID-19, as well as sampling across very different types of schools. No experiment yielded definitive results. However, when aggregating across the studies, the Expanded instructional model led to significantly greater transfer of the effective strategy while reducing the default strategy. This work provides initial evidence that adding a cue to resist the lure of a poor strategy plus recognizing the effort of the effective strategy improves transfer over simply explaining the effective strategy and alerting students to a common ineffective strategy
Exploring Associative Memory Performance of Confocal Cavity QED Network
Keywords: Physics
associative memory
cavity
mean-field
Text
Thesis

Driven-dissipative systems have emerged as featured models of study between the domains of physics, mathematics, and computer science, notably atomic molecular and optical (AMO) physics and quantum information. The ability of these systems to interact with their external environment heralds applications in quantum computing, quantum communication, metrology, and sensing via phenomena such as natural error correction. We explore a candidate experimental framework for achieving driven-dissipative computation in the form of an associative memory, a neural network capable of pattern storage and recollection. This platform is realized by a confocal cavity QED apparatus, where the modes of this optical cavity behave as synapses, mediating interactions between a network of spinful bosonic ensembles which function as neurons. In this thesis we investigate the capabilities of this multimode cavity system to perform memory retrieval where the intracavity spin relaxation dynamics are modeled according to a mean-field treatment. We confirm (from previous works by Marsh et. all) that the system minimizes energy according to deterministic steepest descent dynamics instead of Glauber dynamics, in concert with previous results which do not utilize a mean field approach. Furthermore, these dynamics were predicted to occur at faster timescales than the previous non mean-field treatment. We also explore the efficiency of memory recollection over expanded ranges of input errors when the system is subjected to auxiliary longitudinal fields. We observed that these longitudinal fields performed state preparation effectively, and provided a slight improvement in memory recall contingent upon the appropriate tuning.
Exploring structure-function relationships in knee cartilage and meniscus using medical imaging and soft tissue biomechanics techniques
Keywords: Mechanical Engineering.

Knee cartilage and meniscus have important roles in supporting loading in the knee, and their functional ability to support these loads is in large part due to their structure. Degeneration of these tissues through the process of aging, traumatic injury, and diseases like osteoarthritis can drastically change the structure and function of knee cartilage and meniscus. Because the structure and function of cartilage and meniscus are inherently linked, changes in one impact the other. To evaluate the effects of changes in functional loading of cartilage on its structure, non-invasive medical imaging techniques were used to analyze pre- and post-run scans to look for post-exercise changes in the quantitative magnetic resonance imaging parameter T2 relaxation time to better design future studies monitoring cartilage health. To evaluate the effects of changes in cartilage and meniscus structure on their function, mechanical testing techniques were used to examine the specific use case of the impact of imaging contrast agent solution exposure on meniscus and cartilage structure to develop a road map for designing safer contrast agent solutions. Finally, a combination of function and structural measurements were leveraged to characterize material properties of previously understudied pediatric meniscus specimens. Insights from this line of investigation will inform future studies focused on developing treatments and therapies targeted specifically for pediatric meniscus tissue. The work presented in this thesis advances our scientific understanding of the structure-function relationship of knee cartilage and meniscus and will provide a foundation for research supporting the development of treatments, interventions, and therapies for injury and disease in the knee for people of all ages
Exploring Vietnamese American Identity and Political Affiliation: An Auto-Ethography on Experiences of Loss and Belonging
Keywords: Vietnamese
Vietnamese Americans
Vietnam War (1961-1975)
asian american
Text
Thesis

In this work, I, a second generation Vietnamese American, conduct an auto-ethnography of myself, my mother, and my grandfather on their political beliefs to gather qualitative data on shifting Vietnamese American political attitudes between generations. Through a series of interviews, I aim to determine to what extent Vietnamese American ethnic background has on individual political beliefs, if at all. Ultimately, I have found that it is not ethnic and racial identity that uniformly influence political affiliation; rather, it is the range of experiences in immigration and assimilation that affect said beliefs. I divide my findings into three core themes: the refugee experience, the Americanization experience, and the racial consciousness experience.
Extreme precipitation and flooding in a changing climate : new quantitative approaches and process understanding
Keywords: Earth System Science.

Extreme climate events like severe precipitation, extreme heat, and drought, have large, negative impacts on society and ecosystems, but are challenging to model and predict due to their complexity. Thus, there remain many unresolved questions about the characteristics of extreme events and their impacts in a changing climate. This dissertation develops new methodological approaches to understand how and why the risks of climate extremes are changing, focusing specifically on two of the most widespread hazards: extreme precipitation and flooding. In Chapter 1, I quantify the impact of long-term changes in precipitation on the cost of flooding in the United States. This research finds that historical changes in precipitation have contributed to around 36% of U.S. flood damages between 1988-2017, providing the first empirical evidence that historical climate changes have already increased the cost of flooding in the U.S. I also analyze precipitation changes simulated by an ensemble of global climate models, finding that pattern of historical change is consistent with human-caused climate change, and that further global warmer will lead to additional increases in extreme precipitation. In Chapter 2, I analyze the changes in flood risk that result from warmer conditions that cause a shift from winter snow to winter rain. Using causal inference regression techniques to analyze data from over 400 watersheds in the western U.S., I show that this shift from snow to rain leads to non-linear increases in flood size due to larger rain-driven floods compared to snowmelt. However, this analysis also indicates some heterogeneity in this response. I find that (i) larger changes in flood risk occur for watersheds with higher average precipitation and (ii) the coldest watersheds may temporarily see decreases in flood risk with initial warming. In Chapter 3, I demonstrate a new application of explainable deep learning to identify the processes that have led to increases in extreme precipitation. Using the U.S. Midwest region as a case study, I find that large-scale atmospheric circulation conditions associated with extreme precipitation have become more frequent over the past 20 years, at a rate of around one additional day per year. These changes in the frequency of atmospheric circulation conditions have co-occurred with increases in atmospheric moisture flux to the Midwest region. As a result, these atmospheric circulation conditions are more likely to result in extreme precipitation now than in the past. Combined, this dissertation (i) provides new empirical evidence quantifying the impacts of climate change, which can inform policy decisions and climate adaptation, and (ii) provides new understanding into the processes that shape extreme precipitation and flooding risks in a warmer climate. Further, this dissertation demonstrates new quantitative approaches to analyze climate hazards and climate impacts that can be extended broadly to study other hazards and regions
F4graph : an API for camera scheduling and heterogeneous image processing on mobile devices
Keywords: Electrical Engineering.

Mobile device photography is in the midst of a transformation, with a proliferation of new hardware for both image capture and processing. However, the camera APIs for mobile operating systems rarely expose the full capabilities of the hardware, because these depend on low-level code or platform-specific details. This work introduces F4graph, an application model and software interface which opens up many more capabilities of the imaging hardware to userspace applications. Within F4graph, captures on multiple image sensors or other hardware can be linked together to produce complex sequences of actions using a straightforward constraint-based scheduling model. The system provides strong guarantees by checking as many constraints as possible before committing actions to hardware and by only breaking constraints at well-defined points. Additionally, F4graph allows the application to define the processing pipeline along with the capture sequence so image data can be streamed directly to a reconfigurable image processor or other hardware. This includes the ability to stream continuously, to process multiple images from a stream, and to create feedback cycles for metering and similar applications. This thesis also describes how F4graph can be implemented in a real system, using a Xilinx Zynq Ultrascale board as a prototyping platform. Our demonstration system uses the Zynq's FPGA fabric as a reconfigurable image processing accelerator and its real-time core to orchestrate timing-critical events while the application processor cores run a normal multitasking Linux OS. Using the F4graph API on our prototype, userspace applications are able to schedule and execute actions with a temporal resolution under 20 microseconds. The complexity of this heterogeneous hardware system is managed by a build system which generates the entire hardware/software stack from the application code and a short human-readable system description file. This makes it possible to develop complete applications that use precise capture sequencing and hardware-accelerated image processing without the need to write platform-specific or real-time code
Far from home : understanding pleiotropic effects of adaptive mutations in non-home environments
Keywords: Biology.

Understanding how organisms adapt to their environment and determining the fitness consequences of such adaptation is a major goal of evolutionary biology. It is unknown how mutations that are adaptive in one environment fare in a new environment. The fitness consequences of mutations in many different environments is what we define as pleiotropy. In this thesis, I shed light on adaptive mutations in the environment they evolved in (or "home" environment) and their effects in novel environments ("non-home" environments). First, I characterize components of the morphological adaptation of the dynamic pyrenoid in the algae, Chlamydomonas reinhardtii in both low carbon concentrations ("home environment") and high carbon concentrations ("non-home environment"). Second, I leverage barcode lineage tracking to analyze individual adaptive mutations from both haploid and diploid evolved populations and estimate their fitness effects in both their home and non-home environments. Lastly, I identified adaptive mutations and constructed homozygous and heterozygous mutant strains to gain insight into the roles of dominance and pleiotropy in adaptation. I find that environment is not the sole driver of pleiotropy, but ploidy, dominance, and target genes contribute to fitness in other environments. Together, this study of adaptive mutations reveals a diversity of forces driving pleiotropy that are hidden and cannot be revealed by the mutation's fitness in its home environment alone
Fed Transparency: A Sentiment Analysis Approach
Keywords: Transparency
Sentiment analysis
United States. Federal Open Market Committee
Monetary policy
Text
Thesis

This paper studies the transparency of the Federal Open Market Committee (FOMC) in terms of its communication of sentiment. I use automated text analysis methods to produce topic and sentiment labels for all FOMC communications and documents, including meeting transcripts, meeting minutes, speeches, statements, press conferences, and staff briefings. I define transparency as the extent to which the sentiment of communications can predict meeting transcripts sentiment. I establish three key results. First, the sentiment of communications is consistently inflated relative to transcript sentiment. This is highly consistent with a model in which the FOMC receives asymmetric payoffs to market misinterpretation of communications. Second, despite sentiment inflation, communications can be used to very accurately predict transcript sentiment, with pseudo-R2 around 0.8 since 2000. Models using only communications sentiment can also predict future policy changes, with classification accuracy of 71%. Third, the FOMC's 1993 decision to publish its meeting transcripts sharply increased the transparency of the meeting minutes and led committee members to more closely adhere to the views of their staff, but at a potential cost of greater conformity among members.
Female Economic Empowerment: A Rights-Based Approach
Keywords: Economic policy
Women's rights
Labor and Gender
Text
Thesis

Annika Mulaney’s capstone project examined the economic barriers to labor participation among women in India, and how this in turn affects women’s labor supply, gender inequalities in the workforce, and policies that promote women’s participation and equal rights in the workforce. Through her capstone project, Annika also analyzed how a human rights framework can be applied to economic policy to better assess quality and distribution of growth, especially in developing countries. She hopes this project will also contribute to a necessary conversation about patriarchal values, gender roles in various communities, and women’s economic empowerment.