VictorJuiz/thesis huggingface dataset:
Rapidly migrating zebrafish keratocytes and their interactions with the physical environment

Skin wound healing provides essential protection to multicellular organisms. To successfully heal, skin cells need to migrate to cover the wound bed. For mammals this migration occurs during many days and is hidden beneath the scab; therefore, we leverage the larval zebrafish to image their rapid wound healing (duration of < 30 minutes). Zebrafish basal epidermal cells, or keratocytes, migrate towards wounds using the actin cytoskeleton. Keratocyte migration can additionally be studied in isolated cell culture conditions, allowing experimental separation of the many physical factors present at wound sites. Here, we dissect the complex interplay between migrating skin cells and their environment using both isolated keratocytes in various micro-engineered environments and in vivo keratocytes in the skin of larval zebrafish. First, we investigated the speed of isolated keratocytes on nano-topographical substrate features, which have relevance to biomaterial design. Nanopillars caused slower cell speed, in association with actin accumulation on the pillars. Aberrant actin polymerization may effectively increase cell-substrate adhesion, leading to the observed rear retraction defect. This study demonstrates that keratocytes adjust their molecular- and cell-scale behaviors in response to non-molecular changes in the environment. We next examined isolated keratocytes in multiple two-dimensionally confining environments, which mimic the flat, thin shape of the zebrafish epidermis. Confined isolated cells recapitulate the shape of their in vivo counterparts but migrate more slowly than unconfined cells. We observed that cell speed reduction is associated with cell shrinkage following both confinement and hypertonic shock, potentially due to cytoplasmic crowding. We speculate that this volume-speed relationship may promote rapid migration following injury in freshwater, which causes cell swelling. Finally, we returned to in vivo wound healing to explore the physical implications of freshwater injury. We discovered a novel fluid force during late stages of wound healing, which causes fracture events between adjacent cells. Keratocytes respond by intaking large volumes of excess fluid. Overall, these studies highlight the need to consider molecularly simple contributors, including geometry and water, because of their surprising effects on molecular networks, cell behavior, and ultimately healing efficacy

Advisor: Dunn Alexander Robert
Wang Bo (Artificial intelligence scientist)
Zuchero J.
Rate-based Carbon Pricing Mechanism Design and Macroeconomic Impacts

This paper studies the market design and macroeconomic aspects of carbon pricing. Based on a firm-level intertemporal decision model, this paper argues that an auction should be adopted as the allocation method for emissions allowances. It further examines the dynamics of allowances demand through four auction-based allocation formats. Using panel data from 11 member countries in the European Union Emissions Trading System (EU ETS) between 2005 and 2021, this paper performs regression analyses that show a moderately positive impact of carbon pricing on the overall price level of an economy.

Keywords: Carbon pricing
Auctions
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Article
Thesis
Advisor: Taylor John
Recognizing Humanity: Classifying Certain Discriminatory Practices Against Disabled People as Crimes Against Humanity

Brickelle Bro’s capstone project centered on examining the political, social, and historical conditions of people with disabilities and the lack of knowledge surrounding disability rights in the international human rights community. She notes how lack of knowledge and empathy has led this community to face stigma, discrimination, abuse, exploitation, and even violence during intense periods of conflict, underlining how cultural beliefs, institutional practices, and government regimes all contribute to this unfortunate treatment. As people with disabilities across various countries and governments continue to be victims of violence, such as abandonment, mutilation, and even death, Brickelle argues that in order to promote the rights of people with disabilities, the international order must recognize these as human rights violations and crimes against humanity. Through three case studies ranging from the Holocaust, USSR institutionalization, and South Sudan imprisonment laws, Brickelle shows how people with disabilities cannot be protected until harmful institutional and cultural practices are changed.

Keywords: Disability Rights
Crimes against humanity
World politics
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Thesis
Recuerdos: Queer Central American Identity Formation in/through Photography

What does it mean to be a Queer Central American? This photo exhibition is an elevation of my family’s personal photography as part of a transnational, intergenerational journey in/through Queerness. Being Central American is already fraught with colonial and imperial trauma; US imperialism and the dispossession of land from Campesinos, nation-states that employ a culture of fear and silencing to further their political projects, and a culture that centers religiosity with oftentimes anti-Queer/Trans sentiment/social structures. Put into conversation with my own photography of Queer Central Americans, I ask us to complicate our understandings of Diaspora, Family, and what it looks like to be Queer Central American today by applying Queer framework of orientation and using photography as a mode of analysis.

Keywords: Central Americans
US-Central Americans
Latinx
Queer theory
Queer phenomenology
Queer Latinx
Decoloniality
Photographs
visual studies
queer photography
queer central americans
latinx photography
Diaspora
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Thesis
A recurrent neural network proxy for production optimization with nonlinear constraints under geological uncertainty

In well control optimization problems, the goal is to determine the time-varying well settings that maximize an appropriate objective function, which is often specified to be the net present value (NPV) of the operation. Derivative-free optimization algorithms have been widely used in this setting because of their flexibility and ease of implementation. Because these approaches require many simulation runs, proxy models have been developed to predict objective function values for a set of inputs such as time-varying well bottom-hole pressures (BHPs). However, when nonlinear output constraints such as maximum well/field water production rate or minimum well/field oil rate are specified, the problem is more challenging because well rates as a function of time are required for constraint evaluations. In this work, we first introduce a recurrent neural network (RNN)-based proxy model to predict oil and water production and injection rates, as a function of time, for specified time-varying well BHP schedules. This proxy model is incorporated into a derivative-free (particle swarm optimization) production optimization procedure and tested for cases that include nonlinear output constraints. Next, we extend the RNN-based proxy to predict well responses over multiple realizations. This is achieved by incorporating a convolutional neural network (CNN) with the RNN. This enables us to perform robust optimization over an ensemble of geomodels. The proxy is then incorporated into a closed-loop reservoir management (CLRM) workflow, in which we successively apply history matching followed by production optimization to maximize expected NPV. Lastly, we extend the CNN-RNN proxy to estimate production and injection rates over multiple realizations of embedded discrete fracture models (EDFMs). This EDFM-CNN-RNN proxy is used as a function evaluator in robust production optimization problems

Keywords: Energy Resources Engineering.
Advisor: Durlofsky Louis
Horne Roland N.
Volkov Oleg
Redirected reaching in virtual reality : modeling, control & applications

Virtual reality (VR) systems are inherently limited by their inability to replicate physical reality. Even as technology advances, VR experiences will always be subject to the constraints of the user's hardware setup and external environment. However, the immersive nature of VR systems allows them to create convincing illusions that take advantage of our bias towards believing what we see. Reach redirection is one such illusory technique that influences where users believe their hand is in space. This is done by gradually offsetting the virtual representation of the user's hand during reach. Researchers have used this to alter the perceived properties of real-world objects and enable more physically ergonomic layouts of virtual environments. While there has been considerable research studying the usefulness and perceptibility of redirection, very little focus has been placed on how it works from a sensorimotor perspective. In this thesis, I apply a sensorimotor lens to the study of redirected interactions -- particularly through computational modeling -- to enable more robust and diverse redirection techniques that better handle the complexities of real-world interactions. First, I illustrate how modeling movement duration improves interactions with dynamic encountered-type haptic devices during redirected reaching. Next, I introduce a more adaptable, user-aware approach to redirection using real-time model predictive control. Finally, I present a stochastic sensorimotor simulation of redirected reaching and demonstrate how it can be used to gain insights about the effects of visual attention. Throughout this thesis, I highlight how incorporating sensorimotor control principles can improve the study of redirected reaching and further extend users' experience in VR beyond the physical limitations of reality

Keywords: Mechanical Engineering.
Advisor: Follmer Sean
Kennedy Monroe
Okamura Allison
Reduced-order models of transport phenomena

Reduced-order models (ROMs) have been developed to obtain "cheap" yet accurate surrogates of high-fidelity simulations, which remain a challenging and often unfeasible task due to the nonlinear nature of coupled transport phenomena and the heterogeneity of ambient environments. The goal is to alleviate the expensive computational costs, while simultaneously capturing the underlying dynamic features. This dissertation addresses several challenges in construction of conventional ROMs for flow and transport problems, and introduces a physics-aware dynamic mode decomposition (DMD) framework to ameliorate the shortcomings of conventional ROMs. This framework supplements DMD, a data-driven tool that uses best linear approximations to construct efficient ROMs for complex systems, with physics-aware ingredients. The resulting ROMs are capable of capturing key features of the underlying dynamics with higher-order accuracy than conventional pure data-driven methods. They do so at a small fraction of the computational time of the iteration-based methods, which explains its rapid adoption by engineers in a plethora of applications

Keywords: Energy Resources Engineering.
Advisor: Tartakovsky Daniel
Durlofsky Louis
Tchelepi Hamdi
Re-examining dominance as an antecedent of social rank attainment

Dominant actors—individuals who engage in aggressive behaviors aimed at controlling others—are reliably deferred to, propelling them to positions of status and influence in groups, organizations, and society more broadly. This dissertation considers the potential precarity of dominance as a rank-ascending strategy by considering novel pathways through which dominance succeeds, and fails, to incite submission in others. While the majority of research on dominance and social rank attainment adopts a dyadic level of analysis—that is, it considers a single dominant actor and a single potential submitter—this dissertation widens the lens to consider the entire group context in which dominance attempts often take place. Chapter 1 provides a review of the dominance literature in order to identify sources of conflicting accounts of dominance's effectiveness and highlights potentially critical yet largely untested moderators of the dominance-submission relationship. Chapter 2 highlights the crucial role that others' deference towards dominant actors plays in individuals' own decisions to defer to dominant actors. Finally, Chapter 3 shows that individuals think others respect dominant actors more than they themselves do, and perceptions of others' respect inform individuals' own deference decisions, even after accounting for individuals' own first-order respect for, and fear of, dominant actors. Collectively, this research suggests that dominance is not by default a reliable "path to the top, " but instead is contingent upon a host of moderating factors that have gone largely overlooked in research on dominance's effectiveness, particularly those at the group level

Keywords: Business.
Advisor: Gruenfeld Deborah H.
Miller Dale T.
Sorensen Jesper B.
Regulating data privacy in the age of surveillance capitalism : the making of the European general data protection regulation and the California Consumer Privacy Act

Drawing from original interviews with the lawmakers, archival materials, internal emails, and other primary and secondary sources, this dissertation presents a comprehensive, comparative case study of the legislative histories of the European General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA). It explores the role of institutions, business interest groups and civil society, as well of individual policy entrepreneurs in the policymaking process and explains their ability to pass the world's two strictest privacy laws despite significant push-back from powerful tech giants and other business interests. The dissertation identifies three common factors in the GDPR and CCPA policymaking process. First, both laws were influenced by the presence of foreign elements. In the GDPR case, American tech giants were direct participants through their extensive lobbying in the European institutions. Their lobbying, perceived as aggressive, nontransparent and self-serving, backfired and allowed privacy advocates to frame the need for the GDPR to protect Europeans from their public-private surveillance. Conversely, for the subsequently enacted CCPA, the GDPR served both as a sample for legislative transplant and also as a case in point that comprehensive data privacy laws are possible and will not "break the Internet." Given that many California-based tech giants had to comply with the stricter GDPR standards, the CCPA has also arguably received less resistance than it would have without the GDPR. Second, both the GDPR and CCPA proponents benefited from exogenous events, major scandals related to the collection and use of personal information. For the GDPR process, that scandal was Edward Snowden's revelations about a massive US surveillance program based on the data collected by US-based tech companies. For the CCPA, the Cambridge Analytica scandal revealed that personal data from millions of Facebook profiles was used without consent for political advertising and amplified the concerns about implications of individual data privacy violations on democratic process. Both scandals made the arguments against a data privacy law politically unpopular. Third, this dissertation highlights the increasingly important role of underdog policy entrepreneurs, individuals outside of traditional lawmaking institutions or organized interest groups in promoting policy change

Keywords: Law JSD.
Advisor: Lemley Mark A.
Keller Daphne
MacCoun Robert J.
Regulation of beta-catenin degradation by the adenomatous polyposis coli protein

The Wnt/Beta-catenin pathway controls the differentiation and growth of tissues by regulating the stability of the transcription factor Beta-catenin. In the absence of a Wnt growth factor, Beta-catenin is destroyed in a multiprotein complex that contains the protein adenomatous polyposis coli (APC). Over two-thirds of colorectal cancers bear mutations in APC, which leads to inappropriate stabilization of Beta-catenin. Despite its importance, the precise role of APC in Beta-catenin destruction has not been defined. Here I present studies showing the identification of cyclin-dependent kinases as critical for APC's interactions with the Beta-catenin destruction complex. Phosphorylated APC binds directly to the ubiquitin E3 ligase Beta-TrCP and facilitates ubiquitination of Beta-catenin, which is required for its destruction. These findings establish a new link between the cell division cycle and the Wnt growth factor pathway

Keywords: Molecular and Cellular Physiology.
Advisor: Weis William I.
Ferrell James Ellsworth
Jackson Peter K. (Peter Kent)
Kopito Ron Rieger
Reindustrialized socialism : expanding steel cities and second world interconnections

Utilizing interlinked urban histories of three key industrial cities -- Kryvyi Rih (Soviet Ukraine), Dąbrowa Górnicza (Poland), and Staryi Oskol (Russian Soviet Republic), this dissertation argues that a particular, Socialist Bloc approach to planning and development from the mid-1960s to the mid-1980s wrought momentous transformation of production networks, social structures, urban space, and individual lives. Challenging the normalization of a painful, parallel transition to 'post-industrial society' which has helped reinforce oversimplified diagnoses of political and economic 'stagnation' under state socialism, this study explores the impetus for and impact of a conscious recommitment to heavy industry. East European leaders embraced perceived opportunities based on access to natural resources, increased integration into systems of regional and global exchange, and an extended social contract with the urban working class. The uneven results of this vision were inscribed in the cityscape, environment, and external economic relationships of these three iron and steel industry hubs, leaving evidence crucial to understanding state socialism and its legacies. Ground-level inquiry at each of these three sites also reveals their interconnection as nodes of a shared economic, professional, and social geography. Expanded extraction of iron ore at Kryvyi Rih was a key condition for the USSR's Polish partners to build a gargantuan new plant in Dąbrowa Górnicza. This and other Eastern Bloc demands for steel industry inputs justified development of a new mining and enrichment hub around Staryi Oskol. Looking beyond narrowly national frameworks common in post-socialist successor states, this study points to infrastructures of interdependence constructed alongside these cities. Whether trade networks, a specially-built rail line, or strategies for coping with supply shortfalls, these were practices and physical structures that -- in some cases -- both conditioned and outlasted the collapse of the system that created them

Keywords: History.
Advisor: Weiner Amir
Holloway David
Naimark Norman M.
Reinforcement learning for adaptive sampling in X-ray applications

We propose adaptive sampling algorithms for automating image sampling in scientific x-ray applications. In these applications, we query measurements from various functions of an image in order to estimate it. Since collecting samples is expensive both in terms of time, human resources, and the cost of operating machinery, our goal is to produce autonomous, adaptive sampling methods that attempt to optimize some tradeoff between cost and quality of image estimation, based on information gained from previous measurements. In order to accomplish this, we propose a general methodology that uses reinforcement learning to train autonomous, image-sampling policies that optimize our objective

Advisor: Brunskill Emma
Kochenderfer Mykel J.
Ahmadipouranari Nima
Wetzstein Gordon
Relating large-scale population activity to movement using machine learning

Neural representations in cognitive tasks are typically considered in terms of abstract cognitive performance, yet it is known that cognitive and emotional states are associated with typical movements, e.g., pacing when nervous. This makes the assignment of neural representations to movement, internal state or other factors difficult. To better understand this problem, We developed and applied a series of methods to directly relate neural activity to high-speed video of mice performing a short-term memory task. Our methods span from interpretable but less expressive tracking of manually defined animal features, through projection of videos to low-dimensional embedding vectors with convolutional auto-encoders, to expressive but less interpretable end-to-end learning by neural networks. We dissect motor-related and sensory-related neurons, by measuring the time offsets between the embedding and neural activity. We map the brain region and location of motor or sensory time offsets, and demonstrate this map is consistent with previous findings in the literature regarding sensory vs. motor processing. Further, we revisit the long suggested but relatively unexplored question of micro-movements directly associated with specific task contingencies. We demonstrate that we can directly predict single-trial animal choices from video embedding vectors. Based on this prediction, we analyze preparatory micro-movements, and divide neurons into neurons whose activity more closely follows micro-movements (motor-type) and neurons whose activity is more agnostically linked to trial type. This suggests an initial direction in separating the internal-state information and motor-movement information. We also introduce other computational methods for analyzing the connection between videos and neural activities, including canonical correlation analysis (CCA), session non-specific auto-encoder, DeepLabCut marker corrector, and soft GLM-HMM. Apart from the mice short-term memory dataset, we analyzed a drosophila walking dataset and developed inferred dynamics models to transform the joint angles into a simpler, low-dimensional representation, with potential application in understanding how ventral nerve cord controls walking. These methods are important steps in linking neural representations to movement

Keywords: Applied Physics.
Advisor: Druckmann Shaul
Ganguli Surya
Haroush Keren
Reliability and stability in statistical and machine learning problems

The increasing application of statistical machine learning techniques has substantial promise in diverse fields such as medicine, policy-making, and finance. Hence, very complex machine learning models are being built for use in automated decision-making protocols. However, with the growing sophistication of these models, they are often impossible to analyze statistically, and hence, the predictions made by such models often come with no guarantees. Furthermore, we often have data from heterogeneous sources, shifting distributions, or only have access to noisy or weakly supervised data (e.g., it may be expensive to obtain properly labeled data for a classification task). With such additional challenges, how can we trust the results we achieve when we apply machine-learned methods particularly in critical areas? Further, a shift in data generating distributions makes it challenging for statistical knowledge to generalize well. For instance, the performance of predictive models may drastically deteriorate when deployed on a new test set or a statistical finding from an experiment may not replicate in future experiments. Is it possible to identify situations where models or statistical findings are sensitive to changes in the data generating distributions? Can we suggest methods to transfer statistical knowledge more accurately in such situations? To address the above challenges, this dissertation focuses on developing methods that leverage conformal inference, distributional robustness, and causal inference. The efficacy of these methods are further demonstrated via extensive experiments including applications to real-world machine learning and statistical problems

Keywords: Statistics.
Advisor: Duchi John
Candès Emmanuel J. (Emmanuel Jean)
Donoho David Leigh
Rothenhäusler Dominik