VictorJuiz/thesis huggingface dataset:
Room to move : political accountability of "lawmakers" in the Kenya National Assembly, 1998-2019
Keywords: Political Science.

Over the past decade, a small minority of Kenyan legislators have invested heavily in their lawmaking duties, contributing to the transformation of the Kenya National Assembly from a rubber-stamp Parliament into a consequential actor in crafting the nation's laws. The rising assertiveness of the Kenya National Assembly is a welcome development, since strong independent legislatures are a fundamental building block of liberal democracy. However, Kenyan legislators -- as in many other emerging democracies across Africa, Latin America, Asia, the Middle East, and Eastern Europe -- face strong electoral incentives from voters to eschew their lawmaking responsibilities in favor of implementing local development projects, mediating local conflicts, and providing other forms of constituency service. Kenyan political parties magnify these electoral incentives by recruiting and rewarding candidates based on factors other than legislative ambition or past legislative accomplishments, such as a candidate's political connections, party loyalty, and ability to pay for the party nomination. So then why has a small minority of Kenyan legislators invested heavily in their lawmaking duties, while most of their parliamentary colleagues have not? In this dissertation, I address three sub-questions nested within this overarching question using a new dataset of 371,995 parliamentary speeches made by 959 legislators in the Kenya National Assembly between 1998 and 2019. First, in Chapter 2, I show that the electoral selection of "lawmaker" candidate types is predominantly responsible for variation in the lawmaking effort of Kenyan legislators, rather than electoral sanctioning pressures on legislators to invest in their lawmaking duties out of fear of electoral reprisal from voters or political parties. Second, in Chapter 3, I examine why these "lawmaker" candidate types are elected in some constituencies but not others, failing to find evidence that political violence and ethnic divisions -- two core features of Kenyan elections -- undermine the entry or selection of "lawmaker" candidates. Third, in Chapter 4, I show that the investments of Kenyan legislators in their lawmaking responsibilities do not come at the expense of constituency service, despite intense pressures from voters to prioritize constituency service above all else. Collectively, these findings imply that competitive multi-party elections in emerging democracies -- which tend to be plagued by political violence, ethnic divisions, and intense demands from voters for clientelism and constituency service -- are not incompatible with the emergence of strong independent legislatures
Rosario
Keywords: Mexico
Latinx
Film
Media
Art
Transnationality
Motherhood
Comparative Studies in Race and Ethnicity
Thesis

The film showcases a story centered around immigration, community, and family. It is a story that evolves ideas of transnationality, matrifocality, and transnational motherhood as the material conditions of the characters exist as a product of complex and changing systems of colonialism and culture. This history crafts the very existence of the story and the nature of the main relationship of the film. In Rosario and Alma’s specific experience of getting through this one day, a variety of histories emerge that tell a much longer story about the lives of the many other people both alike and unalike—a story certainly worth sharing and cherishing.
Round Up!
Keywords: Learning Design and Technology
LDT
GSE
Thesis

The medical residency is a unique time in the life of a resident (doctor-in-training). A resident’s role oscillates in a quantum state between “employee” and “learner” as they are tasked to simultaneously care for real patients during rounds while they learn in situ. Resident’s experiences during clinical rounds are the bedrock of their learning: As University of Wisconsin School of Medicine professor Dr. HB Slotnick writes, “both the experiences and the insights extracted from them become the corpus to which [residents] will refer when seeking to understand and resolve problems in the future” (Slotnick, 2001).
While the immersive, experiential element of rounding is a given, the “extraction of insights” is more complicated in practice. Residents are not tasked to reflect on their rounding experiences, nor are they provided tools/frameworks with which to do so (Nothnagle, Goldman, Quirk, & Reis, 2010). Residents struggle to identify their own strengths and weaknesses and are not incentivized to craft self-directed learning goals. Simultaneously, faculty attendings (training doctors) do not provide specific, targeted feedback to residents. These factors hinder residents’ potential for learning and growth during one of the most crucial chapters of their education as future doctors.
Round Up! seeks to challenge these phenomena by helping residents learn how to become self-reflective learners during their residency. Round Up! is an app-based intervention that provides tools to help residents reflect on their experiences, define learning goals, and seek targeted feedback from attendings. Ultimately, the objective is to support doctors not only as experts in their profession, but also, as learners with a mindset towards continuous growth.
A row-column addressed acoustic biometric scanner integrated with pulse-oximetry
Keywords: Electrical Engineering.

Biometric technologies are widely used for identification and liveness detection in several sectors including consumer electronics, governmental, healthcare, and banking. Fingerprint technologies account for approximately one third of the total biometric technologies market share, and their popularity is attributed to their cost-effectiveness, security, and ability to be integrated with other biometric technologies. There are three main types of fingerprint sensors: optical, capacitive, and ultrasonic. Compared to optical and capacitive sensors, ultrasonic sensors provide higher security as they can acquire volumetric images of the finger and perform better in the presence of contaminants. In order to reduce the effect of acoustic diffraction and achieve the required imaging resolution, current ultrasonic fingerprint sensors operate at either high or low frequencies. At low frequencies, they require acoustic waveguides which makes fabrication complex and expensive. At high frequencies, performance is limited by high acoustic attenuation. Additionally, current ultrasonic sensors use an opaque material stack which restricts their ability to be integrated with optical based liveness detection devices such as pulse oximetry. In this work, we propose the design of a simple 3-layer sensor consisting of a piezoelectric material layer sandwiched between two orthogonal electrode layers. This leads to a simple and cost-effective fabrication. By exploring trade-offs between operation frequency, aperture size, and signal-to-noise ratio, the sensor can achieve the required imaging resolution while operating in a moderate frequency range ([50-500] MHz). This leads to relatively low acoustic attenuation. Also, given the minimal material stack, the sensor can be semi-transparent. This dissertation describes the structure and operation of the sensor, and two imaging schemes (with and without ultrasonic focusing) to achieve the required imaging resolution. It presents the design and characterization of a sensor implementation tailored for consumer electronics and other devices that use glass platens. The dissertation also discusses the design and testing of an electronic imaging system that demonstrates the ultrasonic imaging capabilities of the sensor and its operation with pulse oximetry. In addition, it highlights the different image processing techniques used to enhance the fingerprint images. Finally, the dissertation presents another sensor implementation tailored for financial smart cards applications, and presents its imaging results
Safe as Houses? The Impact of the Brexit Vote on London's Role as a Property Safe Haven
Keywords:  Department of Economics
Department of Economics
house prices
HPI
Brexit
synthetic control
safe haven
Thesis

London, like many global cities, has benefited from and been troubled by high levels of foreign investment in its housing market. On the one hand, its reputation as a ‘safe haven’ has helped drive the economy of the South East of the UK. However, on the other hand the increased property prices resulting from high overseas demand have exacerbated the affordability crisis in the capital. With the unexpected vote to leave the EU in June 2016, how has London’s role as a property safe haven changed? Using a synthetic control methodology, this paper estimates a counterfactual London house price index to show how house prices would have evolved in the absence of the Brexit vote. The results show that the vote to leave the EU reduced house prices across London, leaving them 22.7% lower by the third quarter of 2019 than under the counterfactual. Subsequent individual synthetic controls for sub-areas of London receiving high, medium, and low levels of foreign investment in housing also demonstrate a large negative hit to prices measured against their synthetic counterfactuals. However only the results for the low and medium investment groups are significant, which could indicate some persistence of the safe haven role in high investment London.
Safety-critical machine learning : development and testing
Keywords: Electrical Engineering.

As machine-learning systems begin deployment in safety-critical domains such as medical imaging and autonomous driving, model failure is increasingly costly. In such applications, it is dangerous to deploy models whose robustness and failure modes we do not understand or cannot certify. This thesis presents techniques to both train and test safety-critical machine-learning systems. In the first part of this work, we employ the lens of distributional robustness to develop safety-critical models. Instead of just performing well on a nominal training set, distributionally robust models are designed to perform well on uncertainty sets around the data-generating distribution. In regimes with limited, bounded uncertainty sets (e.g. adversarial perturbations to images), we train certifiably robust models with negligible losses in computational or statistical efficiency. In regimes with high uncertainty, we learn how to balance safety and model performance using synthetic data. We employ the latter technique in the domain of autonomous racing, demonstrating safe yet competitive autonomous racing algorithms on real 1/10th-scale vehicles. In the second part of the thesis, we frame testing safety-critical models through the lens of risk. In contrast to formal verification and other traditional software-testing techniques, we present a "risk-based framework, " where the goal is to calculate the probability of failure under a base distribution of environment behavior. For safety-critical algorithms, this probability is small and the resulting technical challenge is a rare-event simulation problem. We develop a novel, provably efficient rare-event simulation method that combines exploration, exploitation, and optimization techniques to efficiently find failure modes and estimate their rate of occurrence. We apply this technique as a tool for rapid sensitivity analysis and model comparison in a variety of applications, showcasing its usefulness in efficiently testing safety-critical autonomous systems
Salary History Bans and the Gender Wage Gap
Keywords:  Department of Economics
Department of Economics
salary history bans
wage gap
gender gap
Thesis

Recent efforts to address the gender wage gap have led state and local legislators to enact policies limiting an employer’s access to a prospective employee’s salary history. These salary history bans are intended to remedy existing wage inequality by preventing employers from differentiating between candidates with lower historic wages and those without, thus necessitating more equitable wages for future employees. Opponents of these policies argue that they place an undue burden on employers with limited effect on the wage gap. This study investigates the efficacy of salary history bans by analyzing their impact on the wage gap for employees of the State of New Jersey and the Government of New York City. I find conflicting effects, with salary history bans exacerbating the wage gap for new hires in New Jersey but improving it in New York City. The increased wage gap in New Jersey, however, appears to be the result of an effect predating the implementation of the policy. Statewide analysis complicates the outcome, identifying a reduction in the wage gap in New Jersey’s private sector but finding no significant change in the public sector or in New York City at all. Overall, the results suggest that it is unlikely that salary history bans have any large effect on the wage gap, positive or negative.
Salmonella exploits the intestinal microbiota to gain a competitive advantage in the mammalian gut
Keywords: Microbiology and Immunology.

Foodborne bacterial pathogens cause approximately 3.6 million infections in the United States every year, with nontyphoidal Salmonella being the top pathogen responsible for most hospitalizations. While numerous studies have highlighted the importance of the intestinal microbiota in blocking these bacteria from colonizing the intestinal tract (known as 'colonization resistance'), little is known about what mechanisms pathogens use to overcome this resistance or how individual members of the microbiota might be inadvertently promoting such infections. This dissertation focuses on understanding how Salmonella enterica serovar Typhimurium interacts with the intestinal microbiota in order to colonize the mammalian gastrointestinal tract. In Chapter 1, I provide a general overview of what is known about Salmonella Typhimurium's biology and its interactions with the intestinal microbiota. As the gut is limited in nutrients and physical space, Salmonella is known to use various virulence strategies in order to disrupt the stability of the microbiota and create its own niche. In Chapter 2, I introduce the Type Six Secretion System (T6SS), a specialized secretion system utilized by certain Gram (-) bacteria for anti-prokaryotic and anti- eukaryotic purposes. While some bacteria switch their metabolism to utilize secondary nutrients to avoid metabolic competition in the gut, others opt to take a more direct approach and directly kill their competitors by secreting effectors via specific secretion systems such as the T6SS. In Chapter 3, I describe how Salmonella's anti-prokaryotic use of its T6SS impacts colonization in the gut. Finally, in Chapters 4 and 5, I probe how factors such as host- and microbial- derived metabolism impact pathogen establishment in the gut, and describe how providing mice a cholesterol-rich diet leads to Salmonella expansion in vivo. While Salmonella does not metabolize cholesterol itself, certain microbiota commensals can, resulting in a metabolic cross-feeding situation that appears to be spurring the pathogen's growth. Taken together, this dissertation highlights how Salmonella uses physical competition and metabolic exploitation in order to outcompete the intestinal microbiota, and describes potential mechanisms that could be therapeutically targeted in order to prevent future infections
Same Story, Different Incentives: Evaluating the Impact of Racial and Ethnic Minorities on Medicare ACO Performance
Keywords: Accountable care organizations
Medicare
racial and ethnic health disparities
health expenditures
health outcomes
alternative payment models
Thesis

Health care expenditures in the United States have risen dramatically over the past twenty years, representing almost one in every five U.S. dollars spent in 2018. This has prompted the development of new governmental ways to pay for health care, one of the largest efforts being accountable care organizations (ACOs) within Medicare. ACOs have been successful in reducing spending while maintaining or improving the quality of health care overall. However, there exists some evidence that ACOs with large proportions of racial and ethnic minorities lag behind in performance standards. The effect of racial and ethnic minority populations on ACO spending and health outcomes is not fully understood. This thesis compares ACO spending and health outcomes to traditional Medicare fee-for-service (FFS) data at varying proportions of minority patients in the beneficiary population. Ultimately, this thesis found that relative to FFS, more racially and ethnically diverse ACOs have lower total and per-capita spending, more emergency department visits, and unchanged readmission rates and overall mortality. This work concludes that ACOs preserve existing disparities in health expenditures and outcomes that minority patients have experienced historically. Future research should focus on understanding the exact mechanism of these dynamics as new payment models transform the U.S. healthcare system.
Sampling methodology for intractable counting problems
Keywords: Statistics.

Many problems in statistics involve distributions which are difficult to sample from, or combinatorial objects which are difficult to enumerate. These challenges are typically resolved through approximate sampling or counting algorithms. However, the focus of the literature thus far on approximation algorithms has been on general results. This thesis examines situations for which these general bounds are extremely loose. First, the problem of sampling perfect matchings of bipartite graphs is considered. While Markov chains on the space of perfect matchings have been shown to mix in polynomial time, restricting to certain subclasses of bipartite graphs allows for massive improvements to the runtime bounds. Furthermore, a sequential importance sampling algorithm, which has an asymptotically exponential runtime, is shown to outperform MCMC algorithms for a large range of graph sizes. The second part of this thesis concerns the problem of independence testing against a uniform alternative in a two-way table. A novel algorithm for carrying out the conditional volume test is proposed and is shown to be consistent and computationally more efficient than existing estimators
A scalable all-digital 64x48 pixel flash LiDaR image sensor
Keywords: Electrical Engineering.

In recent decades, there has been an increasing interest in development of depth-sensing systems with a wide range of applications including autonomous driving cars, computer vision, motion-detection and object recognition. One class of such depth-sensors is known as Time-of-Flight (ToF) LiDaR, which estimates the distance of an object by illuminating it with a laser beam, and measuring the time of flight of the reﬂected light. There are two methods with which LiDaR systems generate a full 3D image of a scene. Most systems used today are Scanning LiDaRs. These devices illuminate a scene sequentially and mostly use mechanical motion to move the beam. The Flash architecture removes the need for beam motion by illuminating an entire scene at once. This thesis explores the design space for a Flash LiDaR detector. Starting with the Poisson process, we derive the probability of photon detections as a function of time. The distribution, which includes the contributions of background and laser light sources, can best be described as a mixture exponential distribution that is used to build multi-exposure histograms under different conditions. Using the aforementioned procedure, we developed a simulator to analyze measurement uncertainty as a function of parameters such as photon flux, SNR, number of exposures, and object distance. An exponential weighting function which scales the histogram is then shown to improve recovery of distant objects. Finally, we provide an overview of the Flash LiDaR ICs and the issues associated with such designs comprising collisions, detection-rate, and timing accuracy. As photon detections are inherently asynchronous events, we use a classical circuit technique, self-resetting logic, to eliminate collisions and reliably increase the detection rates. We also use array-partitioning techniques to scale the design to large-scale arrays while maintaining high-performance and reliability. The Time-to-Digital Converter (TDC) circuits which timestamp photon arrivals use digitally-assisted free-running oscillators to decrease complexity, precluding the need to distribute high-frequency clocks or replica biasing. As a result, the design uses a single low-frequency external clock-source (< 500MHz) which simplifies clock routing and reduces clock-distribution timing uncertainty
Scattering models for the analysis and design of quantum hardware
Keywords: Electrical Engineering.

Recent years have seen the unprecedented ability to control individual quantum systems in a variety of experimental platforms (atomic systems, superconducting systems, solid-state systems). This has opened up the possibility of designing quantum hardware for technological applications in computing, communication and metrology. From a hardware simulation and design perspective, it has become important to develop mathematical models to accurately describe the building blocks of such quantum hardware, as well as numerical and analytical tools for simulating these models. In this thesis, I will present theoretical and computational studies aimed towards understanding the physics and design of quantum hardware. I will present theoretical results on the computation of scattering matrices for several classes of quantum open system models - 1. Time-dependent Markovian models, 2. Non-Markovian models with two-particle repulsions and 3. High-dimensional bath models. I will also describe applications of these theoretical results to understanding the physics of experimental systems that are technologically interesting for non-classical light generation and manipulation (broadband linear optical devices, multi-emitter cavity QED systems), spectral shaping of photon wave-packets (modulated quantum systems) as well as quantum memories (bound states in non-Markovian waveguide QED). Finally, I will demonstrate automation of quantum device design using these scattering models as the simulation backend
School District Boundaries and Segregation: Evidence from Secessions
Keywords:  Department of Economics
Department of Economics
Education Economics
Public Economics
Segregation
Secession
School Districts
Thesis

Using a novel dataset of 175 unique instances since 1985 of United States metropolitan areas seeking to secede and form new, independent school districts, this paper examines the causal effect of a school district secession on segregation within the originally defined district boundaries. Synthetic controls are used to compute counterfactual estimations of how segregation would have progressed in that district absent a secession. This paper finds that secession causes significant increases in income segregation. Additionally, secession produces significant increases in racial segregation in districts that are less racially homogenous at the beginning of the period analyzed in this paper. I verify these results with a battery of sensitivity tests, assessing the stability of the estimates to changes in the specification of the control pool used by the synthetic control. As a further test, I find no significant increase in segregation districts that attempted and failed to secede.