VictorJuiz/thesis huggingface dataset:
Methods for problems in generalized Ramsey theory
Keywords: Humanities and Sciences.
Mathematics.

Ramsey theory is the branch of combinatorics that studies the phenomenon of order arising within partitions of a large system. In this thesis, we address several problems involving more general Ramsey-type numbers in a wide range of settings, highlighting the diverse collection of methods and ideas used in making progress towards them. First, we study a recently introduced list-coloring version of the multicolor Ramsey numbers. We use a probabilistic argument based on the Lovász Local Lemma to prove an exponential lower bound on these quantities, then briefly discuss several other related Ramsey-type numbers. Next, we apply regularity methods to analyze another Ramsey number variant known as the blowup Ramsey numbers. We answer a question of Souza by showing an exponential upper bound with a more nuanced dependence on the graphs involved. Then, we investigate a new family of Ramsey-theoretic questions about the sizes of monochromatic components in an edge coloring of a graph, proving tight lower bounds in the 3- and 4-color cases of the problem, building up machinery in the process that aims to also be useful in the more general case. We also address the analogous problem for hypergraphs of higher uniformity, applying some tools from design theory to further understand this setting. Finally, we introduce a new variant of the polynomial method based on so-called "shift operators, " demonstrating its potential utility in additive combinatorics by using it to provide simple alternate proofs of the results of several very different polynomial methods
The MHC express : evolution of an immune-related gene family across the primates
Keywords: Humanities and Sciences.
Biology.

The adaptive immune system is highly specific to particular pathogens and "remembers" all past pathogen encounters, allowing an organism to fight those pathogens faster in the future. Central to this system is the Major Histocompatibility Complex (MHC), a large genomic locus whose "classical" gene products help T-cells distinguish which cells are normal and which are infected. With this key role in self-versus-non-self recognition, it is no surprise that the MHC is repeatedly identified in genome-wide association studies and is critical to matching donors for organ transplantation. The MHC genes constitute a large related family of genes with diverse functions; although they arise from a common ancestor, rapid evolution has made orthologous and paralogous relationships difficult to distinguish. In addition, individual genes have extreme allelic diversity and alleles are often shared between species (known as trans-species polymorphism), pointing to long-term balancing selection. These peculiarities make the MHC a fascinating yet difficult region to study from an evolutionary perspective. While many past researchers have investigated the evolution of the MHC region in the primates, the results are often outdated and scattered across hundreds of papers. In this dissertation, I revisit MHC evolution systematically with more data and modern methods, unifying and expanding upon the large existing body of work. Notably, I find new relationships between poorly-understood genes and pseudogenes across the region, long-term maintenance of alleles for over 31 million years in MHC-DQA1 and -DQB1, and rapid evolution in the peptide-binding regions of classical genes.
A microwell platform for high-throughput longitudinal phenotyping and selective retrieval of organoids
Keywords: Medicine.
Genetics.

Organoids are powerful experimental models for studying the ontogeny and progression of various diseases including cancer. Organoids are conventionally cultured in bulk using an extracellular matrix mimic. However, organoids cultured in bulk physically overlap, making it impossible to track the growth of individual organoids over time in high throughput. Moreover, local spatial variations in bulk matrix properties make it difficult to assess whether observed phenotypic heterogeneity between organoids results from intrinsic cell differences or differences in microenvironment. Here, we developed a microwell-based method that enables high-throughput quantification of image-based parameters for organoids grown from single cells, which can further be retrieved from their microwells for sequencing and molecular profiling. Coupled with a deep-learning image processing pipeline, we characterized phenotypic traits including growth rates, cellular movement, and apical-basal polarity in two CRISPR-engineered human gastric organoid models, identifying genomic changes associated with increased growth rate and changes in accessibility and expression correlated with apical-basal polarity
Migrating money : the social status repercussions of US-Mexico remittances
Keywords: Humanities and Sciences.
Sociology.

Monetary remittances, the money that immigrants send to their countries of origin, are incredibly important sources of economic support to the Global South. Perhaps due to their economic importance, there has been less work examining the social repercussions of this economic process especially in times of massive demographic change. The US-Mexico remittance corridor is the most consistent and abundant remittance relationship in the world. Contemporarily, it is experiencing dramatic economic and demographic shifts: continuing to break records in the amount of remittance sums that traverse it, the drop to a net zero Mexico to US migration, and finally the increasing feminization of remittance flows. This provides an opportunity to glimpse how changes in the remittance corridor could impact social factors like social status and emotional payoffs that are known to be negotiable via transactions. Using original qualitative and quantitative data sources I examine the question of how monetary remittances impact social status as well as how these impacts differ based on actor characteristics. Drawing on thematic analysis of 30 interviews with related remitters, receivers, and direct relatives in both the US and Mexico, I find that the remitter's gender and the receiver's age are very important to social payoffs. Specifically, I find that women remitters drew a lot of the affective parts of status from their role while experiencing a heavy economic burden. I also find that the cultural norm of deferred reciprocity, where care is cyclical between parents and children, drove which kinds of remittance transactions led to status and affective payoffs and which did not. Finally, I test these trends on a broader quota sample of N=379 Mexican remitters in the US and find that the most robust trend is that of rewarding and emphasizing remitting to young people—with remitters who sent money to the young consistently reporting significant status payoffs. Findings set the groundwork for future work on how remitting impacts how immigrant communities value others and draws attention to how important the receiver's characteristics are to determining status payoffs.
Migration and conflict in the Siyi Region, 1849-1949
Keywords: Humanities and Sciences.
History.

From the mid-nineteenth century to the Second World War, men from the "Four Counties" or Siyi region of South China -- lured by the prospect of fortune in gold fields and railway cuts and fleeing a devastating midcentury conflict in their home region -- formed the vast majority of Chinese migrants to North America. I argue that this diaspora, by virtue of the remittances that poured back into Siyi and the absence of so many men overseas, transformed the peasant household, enriched and empowered militarized lineages, challenged Qing authority, and intensified the internecine warfare that gripped the region. The flow of remittances remade the texture of peasant life, especially at the level of the household. As an unpredictable remittance economy enriched some and ruined others, long term male absence spurred the development of a market in children, through which households with men overseas (and unable to father natural-born heirs) used remittance money to purchase the sons of the impoverished for adoption. Meanwhile, Siyi's lineages battled one another in escalating conflicts that were simultaneously local and globalized, feuds made ever more lethal by the militias, arms, and munitions financed by overseas wealth. This cycle of bloodshed, endemic by the end of Qing rule, would continue until the consolidation of Communist power in the region.
Mind the gaps : an ethnography of the institutions shaping Ghana's technical labor market
Keywords: Engineering.
Mechanical Engineering.

This dissertation work represents an 18-month ethnography studying the experience of occupational mobility—a process by which individuals pursue and create jobs that may change their socioeconomic and professional status, career trajectory, and/or income—into technology-related fields. I pursued my fieldwork in a theoretically rich, institutionally unsupportive, and technology-emergent setting—with over 200 local labor market actors (i.e., employers, entrepreneurs, a start-up labor market intermediary, and job aspirants)—from 2019--2023 to observe over time how aspiring technical workers and entrepreneurs navigate the expectations of themselves, their culture, and their respective key stakeholders, in their pursuit of achieving occupational mobility and venture scale. Through my fieldwork, I show how aspirants enter into the technical labor market in some cases with "expectation gaps" and others with the burden of "negative social capital" that affect their decision-making processes, personal goals, and relative performance in the labor market.
Mobilizing Maidan: Civil Societies in Russia and Ukraine on the Eve of the Soviet Collapse
Keywords: Civil society
Ukraine
Perestroĭka
Glasnost
1985-1991
Russia
Text
Thesis

This thesis examines the differences between Ukrainian and Russian civil societies between 1985 and 1991. Contributing to a field of study on pre-collapse civil societies, this thesis attempts to explain why Ukraine and Russia took different paths after the Soviet Union’s dissolution. That is, why Ukraine saw two popular revolutions against unpopular, dictatorially-inclined leaders, and Russian civil society continues to live under Putin’s regime. Using KGB archives and contemporary social surveys, this study presents a comparison of each country’s civil society during Gorbachev’s reforms, finding two crucial differences. First, Russia’s civil society lacked a nationally evocative movement, impeding it from unification, whereas Ukraine’s developed rapidly after Chernobyl, unifying under anti-Soviet sentiment. Second, Russia’s status as the “ruler” resulted in the development of anti-Moscow attitudes among Ukrainians, but also inhibited the formation of its civil society. This thesis explains why and how these trends came to be in the Gorbachev era.
Modeling and interfacing with vine robots
Keywords: Engineering.
Mechanical Engineering.

Soft robots, constructed from inherently compliant materials such as fabric or silicone, have become increasingly popular in recent years. Their compliance enables them to navigate complex and cluttered environments to reach spaces that are inaccessible to traditional rigid robots. One category of soft robots, vine robots, can even grow to increase their length by orders of magnitudes. Because of this growth feature, vine robots have shown promise for applications ranging from debris inspection to surgical procedures. If we can increase our fundamental understanding of how these robots move through space and interact with their environments, we can further expand their applications and improve the performance of their current uses. To this end, my thesis investigates how these robots interact with humans, and how they respond to control inputs to navigate through space. First, I investigated the interaction of vine robots with humans by designing a wearable device that uses vine robot technology to deploy around a user's arm and provide haptic feedback to the wearer. This feedback is in the form of force cues that press on the user's arm in various locations. Next, I created new methods for fabricating vine robots and a model of fabrication to ensure they grow along a desired path. Finally, I created a dynamic model to simulate how these robots respond to force inputs to aid in the design of actuators to achieve desired vine robot behaviors.
Modeling human biology and complex diseases with iPSCs
Keywords: Medicine.

Human induced pluripotent stem cells (iPSCs) have been widely acknowledged in recent years as a model system for studying human disease and development. As part of my first project, we constructed a high-resolution regulatory atlas of the developing human fetal heart, defining important regulatory regions, such as enhancers, essential to cardiogenesis. Using a new deep learning model, we identified cell-type-specific de novo variants in predicted fetal human heart enhancers. We further used in vitro differentiated endothelial cells to validate the regulatory impact of a putative cell-type-specific enhancer predicted to harbor a deleterious congenital heart disorder (CHD) mutation. As part of my second project, we have created primate pluripotent stem cell-derived cardiomyocytes that mimic fetal cardiomyocytes in vitro to identify hundreds of ERV transcripts, including those regulated by TBX5. In particular, most of the significant RNAs found in primate fetal cardiomyocytes are located in BANCR, which is a long non-coding RNA (lncRNA) that is exclusively expressed from fetal cardiomyocytes. Based on functional studies, it has been demonstrated that BANCR promotes cardiomyocyte migration in vitro as well as ventricular enlargement in vivo. Our studies suggest that recently evolved TE loci like BANCR may serve as potent de novo regulatory elements capable of controlling developmental processes that can be studied with a pluripotent stem cell model
Modeling neural cellular signaling via an iPSC-derived co-culture platform
Keywords: Medicine.
Stem Cell Biology and Regenerative Medicine.

Much of modern therapeutic development is oriented towards modulating cell-cell communication, yet we do not have a comprehensive understanding of how neural cell types signal to each other and how this network of signals is dysregulated in disease states. Microglia, the dominant brain-resident immune cell, contribute significantly to this network but are highly sensitive to their environment and are thus difficult to profile in a standardized fashion using existing coculture models which are not optimized to support microglial homeostasis. Here, we present a simple coculture model of iPSC-derived neurons, astrocytes, and microglia in defined medium which is fully self-sustaining through endogenous signaling and stable in long-term culture past 54 days of coculture. All three cell types become functional as they mature, with microglia demonstrating phagocytic, surveillance, and immune cytokine production capabilities and settling into a carrying capacity of 5-15% of the culture. Orthologous scRNA-seq and snRNA-seq analysis show that it resembles primary human brain tissue, is distinct from human brain organoids, and its astrocytic and microglial states are distinct from diseased primary astrocytes and microglia. Inferring ligand-receptor relationships between cell types, we characterized the complete network of active autocrine and paracrine signaling pathways in the culture and found that astrocytes form the nexus of this network. These data indicate that standardized, self-sustaining iPSC-derived tricultures can be effectively combined with increasingly powerful computational methods to better measure how diseased human neural tissue deviates from homeostasis and to guide therapeutic development in neurological disease.
Modeling sequences with structured state spaces
Keywords: Engineering.

Substantial recent progress in machine learning has been driven by advances in sequence models, which form the backbone of deep learning models that have achieved widespread success across scientific applications. However, existing methods require extensive specialization to different tasks, modalities, and capabilities; suffer from computational efficiency bottlenecks; and have difficulty modeling more complex sequential data, such as when long dependencies are involved. As such, it remains of fundamental importance to continue to develop principled and practical methods for modeling general sequences. This thesis develops a new approach to deep sequence modeling using state space models, a flexible method that is theoretically grounded, computationally efficient, and achieves strong results across a wide variety of data modalities and applications. First, we introduce a class of models with numerous representations and properties that generalize the strengths of standard deep sequence models such as recurrent neural networks and convolutional neural networks. However, we show that computing these models can be challenging, and develop new classes of structured state spaces that are very fast on modern hardware, both when scaling to long sequences and in other settings such as autoregressive inference. Finally, we present a novel mathematical framework for incrementally modeling continuous signals, which can be combined with state space models to endow them with principled state representations and improve their ability to model long-range dependencies. Together, this new class of methods provides effective and versatile building blocks for machine learning models, especially towards addressing general sequential data at scale
Modeling the effects of surface waves on the atmospheric boundary layer
Keywords: Engineering.

The interaction between wind and water waves is an important process that has broad influences on coastal and offshore phenomena across many scales. A thorough understanding of wind-wave interactions is critical for accurate marine weather predictions, climate modeling and offshore engineering applications. Most previous modeling studies used idealized wind and wave conditions. To study wind-wave interactions in real-world scenarios, it is crucial to employ a model that can simulate realistic meteorological and wave conditions. The Weather Research and Forecasting (WRF) model is widely used for numerical weather predictions and capable of large-eddy simulations (LES). While WRF can incorporate realistic meteorological conditions into the simulations, it can only simulate the atmospheric boundary layer (ABL) over stationary terrain. In this dissertation work, we implemented a moving bottom boundary condition into WRF-LES to enable wind-wave coupling. We validated the code using idealized test cases with analytical solutions for laminar flow with and without viscosity. For turbulent flows, we compared results from WRF over different monochromatic moving waves and achieved satisfactory agreement with the literature. Next, we extended the ability of WRF to simulate the ABL over broadband waves generated from empirical wave spectra, and studied the effects of wave propagating direction and the width of spreading on wind turbulence. We found that the wave propagating direction has significant influences on the pressure drag and the wave growth rate, while the wave spreading width plays a smaller role. In the future, more realistic conditions such as temperature stratification and moisture can be added to WRF to further investigate the influence of water surface waves on the ABL.
Modeling virtual environments 3D assets from visual inputs
Keywords: Engineering.
Electrical Engineering.

Modeling the physical world is crucial for many applications that significantly impact society, includ- ing robotics, autonomous vehicles, virtual and augmented reality (AR/VR). Research works com- monly leverage 3D virtual environments as a proxy for real environments to perform experiments. By using virtual environments, experiments like robotic simulations, navigation, and interactions can be performed safely and at a lower cost compared to real-world environments. 3D geometric and material assets are the building blocks of virtual environments. However the process of creating 3D assets from scratch is mostly manual, time-consuming, and requires significant expertise. This challenge motivates the development of automated methods for 3D assets generation. We introduce a Computer Vision framework for generating 3D assets from visual inputs that include images and 3D point clouds from real scenes. Visual inputs from a scene are parsed into individual objects, structures, and materials which can be processed into 3D assets. This framework involves four key tasks: 3D semantic segmentation, shape completion, shape modeling, and material prediction. We introduce representations, algorithms, machine learning models, and datasets to address the chal- lenges associated with each of these tasks. We introduces new models for 3D semantic segmentation and shape completion. It also introduces a graph representation and search algorithm that facilitates generating multiple variations of a given shape. Last, it contributes a large scale dataset of procedu- ral materials along with a neural network model that generates procedural materials from images. The results of this thesis is a set of methods addressing the aforementioned tasks and supporting the creation of realistic virtual environments at scale