VictorJuiz/thesis huggingface dataset:
To the Best of My Abilities and Understanding: Do Legal Credentials Affect Sentencing Decisions in South Carolina?
Keywords: Crime
Criminal Justice
Judicial Reform
Law and Economics
Legal Credentialing
Magistrate Court
Occupational Licensing
Race
South Carolina
Text
Thesis

This thesis considers the impact of legal credentialing on judicial sentencing behavior. In particular, the paper examines whether the lawyers appointed as judges to South Carolina’s Magistrate Court make different criminal sentencing decisions than the non-lawyers who serve as judges on the same court. To answer this question, the study uses a novel data set containing all publicly available criminal sentences handed down by the Magistrate Court in 2021 and cross-referenced the sentences with a roster of magistrate judges created through formal public information requests to the South Carolina Judicial System. The analysis also controls for other explanatory variables, including race and sex of the defendant, race and sex of the judge, and the crime committed. This paper finds that lawyer and non-lawyer magistrate judges impose similar sentences in nearly all respects. However, they differ along two important dimensions: racial bias (lawyer magistrate judges give Black defendants 22% greater fines than they give White defendants, while non-lawyer magistrate judges treat the groups similarly), and observance of a “plea premium” norm (lawyer magistrate judges give defendants who plead guilty 16% lower fines, while non-lawyer magistrate judges do not).
Tobacco cultivation and production in twentieth-century Palestine/Israel
Keywords: Humanities and Sciences.
History.

Tobacco cultivation began in Palestine in the late nineteenth century and lasted until the late 1980s. It existed under repeated monopolizations of the market: First by the Ottoman Regie company; then by the British American Tobacco Co. during the British Mandate; and finally, by Dubek Co. in Israel. Dubek's hold on cigarette production in Israel, cemented in 1971, marked the beginning of the end of Palestinian tobacco cultivation in Israel. Palestinian cigarette production, on the other hand, disappeared earlier, in 1966. That was the year in which the Arab Cigarette & Tombac Co., the only factory which survived the Nakba [Ar. Catastrophe] of 1948, finally closed. Tobacco was the main cash crop fellahin [peasants] could grow in the hilly hinterlands. Their family households, and sometimes entire villages, depended on it for an inflow of cash. Cash was desperately needed in a time of monetization and colonial development, which rarely benefitted the agrarian population. Colonial officers were prejudiced toward fellahin and deemed them incapable of adequately growing or marketing their tobacco. However, as I demonstrate using oral history interviews, and state and local archives in Israel, Turkey, and Britain, they were highly savvy about the methods of cultivation, the market, and the industry. Through the Arab Cigarette & Tombac Co., established in Nazareth in 1933 as a nationalist economic Palestinian venture, fellahin and Bedouin bought shares and took part in the entire production cycle of cigarettes. They also protested the three regimes' ban on the independent sale of tobacco, and while resisting the label "smuggling, " proceeded to sell their crop informally, risking fines and imprisonment. They continuously called for a free market, agricultural instruction, and export markets. As colonial policies deprived them of their lands and access to water and turned them into a minority in their country, the fellahin who remained and became citizens slowly gave up and pursued work outside agriculture. The history of tobacco in Palestine ultimately exemplifies some of the main processes through which Palestinians became depeasantized and excluded from the agrarian economy, culture, and landscape
Topics in econometrics and optimization
Keywords: Humanities and Sciences.
Economics.

This thesis investigates the interesting connections and interactions between some classic topics in econometrics and optimization. We show that insights and results from one area can help solve hard problems in the other, and that there is value in connecting seemingly unrelated disciplines and problems. We will focus on two important models of discrete choice: probit and logit models. Probit models are important tools for modeling discrete responses economics, finance, statistics, and epidemiology. However, the latent nature of probit coupled with identification constraints pose significant computational challenges for its estimation, especially when the dimension of the response variable is large. We propose a computationally efficient EM algorithm for estimating large multi-dimensional probit models. We use expectation propagation (EP) instead of simulation methods to approximate moments of the truncated multivariate normal (TMVN) in the E (expectation) step. In addition, we transform the constrained optimization problem in the M (maximization) step into a one-dimensional problem, which is solved directly using Newton's algorithm. Our method enables the analysis of choice data in the presence of many alternatives and features common in modern applications but has been difficult in practice with probit models. In the second part of the thesis, we show, for a broad class of choice and ranking models based on Luce's choice axiom, that the associated maximum likelihood estimation problem is equivalent to a classic matrix balancing problem with target row and column sums. We draw inspirations from these connections and resolve important open problems on the study of Sinkhorn's algorithm. We first prove the global linear convergence of Sinkhorn's algorithm for non-negative matrices whenever finite solutions to the balancing problem exist. We characterize this global rate of convergence in terms of the algebraic connectivity of the bipartite graph constructed from data. Next, we also derive the sharp asymptotic rate of linear convergence, which generalizes a classic result of Knight (2008). To our knowledge, these are the first quantitative linear convergence results for Sinkhorn's algorithm for general non-negative matrices and positive marginals.
Topics in statistical prediction and inference
Keywords: Humanities and Sciences.
Statistics.

In the era of big data with increasingly complex learning systems, reliable and valid statistical inference is crucial for extracting meaningful insights and ensuring the integrity of data analysis. For example, in error estimation and model evaluation, statistical inference provides principled methods to estimate and quantify uncertainty in prediction errors. In causal inference, randomized experiments and hypothesis testing are widely used to detect significant average treatment effect, etc. However, larger datasets and more complex settings can lead to misuse of existing inference tools in situations beyond their assumptions. This thesis explores the following challenges faced by modern statistical inference for the targets of prediction error and average treatment effect. For inference of prediction error, covariate shift may occur and time correlation could result in failure of CLT-based naive confidence intervals. For Inference of average treatment effect, "hunting" for significance can inflate type-I errors and false discovery rates in large-scale sequential experiments, and interference poses challenges to valid inference of exposure contrasts and optimal experimental design. In this thesis, we will examine particular settings with the above challenges and build upon existing literature to discuss principle-inspired methodologies to tackle them.
Topological and edge transport in two-dimensional electron systems
Keywords: Engineering.
Materials Science and Engineering.

Topological insulators host topologically-protected electronic states at their two-dimensional surface. This dissertation will use low-temperature electronic transport measurements to explore basic phenomenology and several applications of these topologically-protected surface states in topological insulators and magnetically-doped topological insulators. First, it reports on the quantum anomalous Hall effect, its applications in electrical resistance and current metrology, and the microscopic details of current flow. While topological surface states are robust against disorder, the topological insulators themselves are quite inhomogeneous and fragile, so a discussion of disorder and fabrication-induced damage to these materials is presented. Next, this dissertation discusses two more elaborate types of devices incorporating topological materials: microwave devices, aimed towards developing dissipationless, passive, nonreciprocal high-frequency circuit elements, and Josephson junctions, where topological weak links provide a platform for exploring Majorana physics. Finally, this dissertation reports on measurements of a disordered thin film of a new magnetic topological insulator, where universal conductance fluctuations with a dependence on the details of magnetic domain reconfiguration are identified.
Toward point-of-care molecular quantification : novel assays and detection strategies for biosensing in complex media
Keywords: Engineering.
Electrical Engineering.

Molecular quantification at the point-of-care offers a pathway toward personalized and accessible medicine. Rapid characterization of varying biomarker concentrations can enable timely intervention and preventative care. However, in order to function at the point-of-care, biosensors must perform rapidly and directly in complex biofluids such as blood. These biofluids create challenging environments, frequently causing biofouling, signal degradation, and nonspecific binding on the sensor surface. Overcoming these challenges requires innovation in both assay design and detection hardware in order to develop truly point-of-care devices. In this thesis, I first discuss three major objectives of point-of-care devices - (1) an actionable time-to-answer, (2) the ability to perform directly in patient samples, and (3) minimal patient inconvenience. Through this lens, I discuss the development of both custom hardware and novel fluorescent assays to enable rapid molecular quantification directly in complex media. I begin by presenting a custom fiber optic-based biosensor which leverages evanescent sensing to selectively interrogate the surface of the fiber probe, minimizing interference from the background biofluid. I then present two projects applying this biosensor to the detection of two classes of biomarkers. In the first project, I discuss the adaptation of existing aptamers, single stranded nucleic acid sequences that bind to target molecules, into fluorescently-labeled switches to enable continuous monitoring of small molecules. I then present the use of these aptamer switches in conjunction with our custom biosensor to continuously detect dopamine and cortisol in artificial cerebrospinal fluid and undiluted plasma, respectively. In the second project, I present the development of a generalizable fluorescent substrate architecture to detect enzymatic cleavage. This architecture has been coupled with our sensor to rapidly detect the activity of Factor Xa, an enzyme in the coagulation pathway, directly in whole human blood. Following this, I present a third project in which a triple-binding immunoassay is amplified by Hybridization Chain Reaction, a DNA self-assembly reaction, to establish a high signal to noise ratio assay. I discuss the potential optimization and integration of this amplified immunoassay with our fiber-based biosensor to enable rapid, low abundance protein detection at the point-of-care. Finally, I present a proof-of-concept work that makes use of 3D-printed scaffolding to pattern a hydrogel filter, allowing for the development of a point-of-care device that allows for filtration of target molecules out of complex media prior to optical detection. Together, these projects demonstrate a significant advance toward the ability to rapidly quantify biomarkers directly in complex media, providing a promising path toward more generalized point-of-care biosensing.
Toward the next generation of GPS signals : new codes and navigation security
Keywords: Engineering.
Electrical Engineering.

Today, billions of users rely on GPS on a daily basis. Woven into the fabric of modern civilization, GPS underlies nearly every aspect of society's functions, including our transportation, agriculture, financial transactions, energy grid, and communication systems. Yet, despite the critical importance of GPS, its civilian signals currently (1) are open and unencrypted, meaning that an attacker can generate counterfeit GPS signals and spoof a user's position or timing solution, and (2) use codes which were designed before the recent advancements in computing. However, in 2024, the NTS-3 satellite will be launched to test new signal capabilities for the future generations of GPS, including a new digital watermark called Chimera to periodically authenticate one's received GPS signal. Furthermore, starting launches in 2026, the GPS IIIF satellites will be the first to have a fully-digital, reprogrammable payload, allowing for new opportunities to re-explore the design of the GPS signal, including their underlying codes. In this dissertation, we seek to leverage these upcoming opportunities in order to advance the next-generation signal capabilities. In particular, we develop strategies to utilize Chimera and additional self-contained sensors onboard a vehicle, such as an inertial measurement unit or wheel encoder, in order to perform continuous spoofing detection and secure, attack-resilient navigation. For our continuous spoofing detector, we use stochastic reachability analysis to conservatively model the error distributions from the self-contained sensor and GPS measurements, in order to provably satisfy a user-defined false alarm guarantee. To perform continuous, attack-resilient navigation, we further leverage the self-contained sensors and Chimera to determine how much to rely on the received GPS measurements, in order to strategically improve real-time navigation performance while mitigating any induced errors during an experienced attack. With the forthcoming ability to reprogram the GPS signals, this dissertation further proposes a new framework to design the GPS codes, leveraging stochastic optimization methods as well as present-day computational tools. To design the GPS codes within the discrete, exponentially large space of binary code sets, we utilize a natural evolution strategy to optimize a smooth probability distribution over the code space, allowing us to evaluate a gradient estimate and leverage state-of-the-art first-order optimization algorithms for code design. We demonstrate that the framework proposed in this dissertation designs codes which have lower self- and inter-signal interference, through reduced mean-squared correlation sidelobes, than competitive sets of Gold and Weil codes, which are code families used by GPS today.
Towards a novel lesioning method to causally study neuronal population dynamics underlying motor control
Keywords: Engineering.
Electrical Engineering.

Systems neuroscience currently lacks both a mechanistic understanding of how neuronal activity drives behavior and high-spatiotemporal-resolution insight into how the brain responds when neurons are lost due to disease or injury. While there is some knowledge of the correlation between loss of neurons and changes in functional behavior, to date, there is a limited understanding of the changes in neuronal activity in response to loss of neurons and subsequent recovery, which also limits the ability to develop a causal explanation of the relationship between neuronal activity and behavior. This dissertation takes a step towards addressing these outstanding questions by introducing a novel causal investigation method, neuroelectrophysiology-compatible electrolytic lesioning, which allows for both direct inactivation of neuronal activity and the collection of electrophysiology on physiologically relevant timescales for adaptation and recovery. This method was used successfully to create several lesions in two awake-behaving animals, removing neurons from cortex to affect both neuronal activity and behavioral performance on a skilled reaching task. These minor, heterogeneous behavioral effects last for days to weeks before the animal recovers back to baseline behavioral performance. In order to understand the changes taking place in the neuronal activity after lesioning and also as the animal behaviorally recovers, this dissertation explores analyses at two levels of supervision. A supervised offline decoding analysis uses changes in decoding performance to uncover aspects of neuronal population activity that remain or are changed after lesioning. An unsupervised analysis develops two information theoretic metrics that can be calculated on high-dimensional data -- one measure of complexity and one of efficiency at the action potential level -- and evaluates their effectiveness as biomarkers of both loss of neurons and behavioral deficit. Ultimately, results from these studies may be applied to develop assistive brain-computer interfaces that are robust to neuronal loss or to advance rehabilitation methods for diseases that lead to neuronal loss, including stroke and Alzheimer's disease.
Towards AI-driven networks : hardware and software for data-plane ML
Keywords: Engineering.
Electrical Engineering.

Network management is an increasingly difficult task for researchers and industry alike. Networks are growing rapidly in both scale and complexity. They now have to cater to a bigger application set and a larger user base than ever before while adhering to more and more stringent performance requirements. With so many challenges to running a network, operators must move beyond the era of hand-tuned algorithms and instead, adopt more automated approaches, i.e. AI-driven networks. In the search for more versatile tools in networks, many researchers have looked to machine learning (ML) as a vehicle for data-driven, adaptive mechanisms in networking systems. However, a number of pragmatic issues have plagued such development. Can we run ML in the packet path? Must operators build each new ML model by hand? How can we incorporate new data? In this dissertation, we show the construction of integral components required to build AI-driven networks. We first describe the design of Taurus, a platform to enable data-plane ML to run in the packet path of the network with a per-packet granularity, at line-rate. Furthermore, we demonstrate that the hardware for Taurus adds minimal overhead, less than 4% chip area and less than 3% power in our prototype. Next, we discuss Homunculus, a compiler stack for data-plane ML platforms (like Taurus) that allows for the automatic generation of resource and performance compliant ML models which outperform hand-tuned models by up to 16.9% in our tests. Finally, we show how these tools can be assembled to enable an adaptive ML loop in a network. Online labelling of raw data in the network can feed Homunculus, enabling the network to build new ML models from its own packet data. These models can then deploy learned policies in Taurus, setting the groundwork for upcoming AI-driven networks
Towards cost-effective and trustworthy healthcare machine learning
Keywords: Engineering.
Electrical Engineering.

Machine learning (ML) has made exciting progress across many healthcare tasks, such as chest X-Ray interpretation and seizure detection from electroencephalograms (EEGs). However, while ML models exhibit impressive overall performance, we identify two critical roadblocks to model development and deployment: (1) reliance on massive training datasets created via manual expert labeling, resulting in high annotation costs, and (2) reliance on non-generalizable features, resulting in unexpected poor performance on subgroups of patients ("hidden stratification"). As a result, healthcare ML is currently costly to develop and can be untrustworthy to deploy. To address the first roadblock, we develop new forms of cost-effective supervised training of ML models with cross-modal data programming (XMDP). In particular, healthcare data are often accompanied by other data modalities that contain task-related information for which it is feasible to create labeling functions at low cost, such as clinical reports, workflow notes, or (in the future) gaze data (i.e., eye-tracking data). We develop labeling functions that map the auxiliary data modalities (either text or gaze) to labels using only a small amount of manual labels. Once we develop these labeling functions, we are then able to scale labeled training sets without additional annotation costs. To address the second roadblock, we propose to improve robustness of ML models to hidden stratification by increasing task specificity in two exemplar medical tasks, pneumothorax detection in medical imaging and seizure detection in electroencephalogram (EEG) time series data. For medical imaging, as opposed to training a binary classification model for detecting pneumothorax (low task specificity), we first train an image segmentation model to localize pneumothorax (higher task specificity) and use the segmentation output to derive the binary prediction. For detection of seizure on EEG, we increase task specificity by training a model to classify additional attributes in the EEG, such as artifacts. We find that increasing task specificity significantly reduces reliance on non-generalizable features and improves performance among clinically meaningful subgroups, which decreases performance gaps among subgroups and results in more trustworthy models. In summary, our work investigated new forms of model supervision that are less costly than existing approaches, and uncovered strong connections between task specificity and model robustness. While our experiments focus on chest X-ray classification and EEG seizure detection, our proposed methods are applicable to a wider range of healthcare applications.