VictorJuiz/thesis huggingface dataset:
Untangling Dynamics in Civil Conflict: Explaining Changes in Insurgent Targeting of Women

What explains variation in insurgent targeting of women within a conflict? What explains variation in insurgent targeting of women by insurgents of the same ideology between conflicts? This project examines the puzzle of inconsistency in rebel group targeting of women during insurgency operations. Using qualitative methods including comparative case selection, process tracing, and collecting novel data from original interviews I conducted, I find that targeting women can be a means of undermining and extracting concessions from the counterinsurgent, but that the efficacy of this strategy varies depending on the type of actor carrying out the counterinsurgency. Specifically, domestic counterinsurgents are more likely to be undermined by sex-selective targeting than are the other two types of actors I consider: foreign counterinsurgents or regional counterinsurgents. In addition, if targeting women is a means of undermining the counterinsurgent, I argue it operates through three mechanisms: (1) preventing information sharing between civilians and counterinsurgents; (2) emasculating male counterinsurgents for failing to protect "innocent" civilians; and (3) using violence against women as a bargaining tool to extract concessions from the counterinsurgent.

Advisor: Hyde, Susan D.
Identifying Freeway Locations Prone to High-risk Crashes

The crashes that cause fatalities and serious injuries typically occur at high speeds. Although the Federal Highway Administration (FHWA) asserts a need to identify the most dangerous locations on a roadway system, FHWA promotes a screening practice that fails to reveal these high-speed (high-risk) locations. In fact, the current practice obscures the most dangerous locations by conflating high-speed crashes with low-speed (low-risk) ones. The current practice supported by FHWA has two problems. Firstly, it analyzes crashes in uncongested and congested conditions together, without distinction. Secondly, it uses Vehicle Miles Traveled (VMT) as the measure of traffic exposure which cannot capture the vehicles’ extra time spent traveling in slow-moving congestion. These problems arise when the current practice is applied to freeways that see their fair share of congestion: the current practice tends to mistakenly identify locations with large numbers of low-speed (low-risk) crashes in congestion as if they were the most dangerous locations. The reason is that crash rates inside congestion are much higher than those occurring in uncongested traffic conditions./nTo remedy these problems with current practice, the present study analyzes crashes in a disaggregated fashion based upon the vehicle occupancies measured by the nearest freeway loop detector at the time of the crash. High-speed crashes in uncongested conditions are analyzed separately from low-speed crashes in congested conditions. Crash counts for both traffic conditions were normalized by Vehicle Hours Traveled (VHT), rather than the commonly used Vehicle Miles Traveled (VMT), to capture vehicles’ extra exposure inside congestion. The data used for this research came from a 6-month period in 2016 on a 10-mile stretch of the northbound Interstate 880 freeway in Alameda County, California. Crash records collected by state police and traffic data measured by the site's loop detectors were also used as inputs./nThe results show that by using the proposed method, one can better identifies the type of crashes, (uncongested and/or congested), that make some locations stand out as problematic. From there analysts can narrow down the set of these outliers to be the ones driven predominantly by uncongested crashes and are therefore the ones of the greatest safety concern. Additionally, one can rank uncongested outliers based on their magnitudes and focus on the highest ranked ones. For the reasons mentioned above, the proposed approach allows traffic agencies to focus their resources on these most dangerous locations and help improve the overall safety on the transportation system./nThe amount of data used for this study is limited. But the findings are very promising. Future research to advance the proposed method and to answer questions that are yet unresolved are discussed.

Advisor: Cassidy, Michael MC
The Pulse of Sleep: Sleep and Cardiometabolic Health

Two of the top killers of the human race are cardiovascular disease and type 2 diabetes. These diseases, which have shared etiology, collectively form the domain of cardiometabolic health. Multiple functions of cardiometabolic health are perturbed by impaired sleep, and conversely, restored by increased sleep quantity and/or improved sleep quality. That is, impairments in sleep are closely related to impairments in cardiometabolic health. This leads to the proposition that specific features of sleep act as biomarkers for specific features of cardiometabolic health. This thesis aims to determine the impact of sleep on three major domains of cardiometabolic health: 1) atherosclerosis (arterial plaque accumulation), 2) dyslipidemia (unhealthy blood lipids), and 3) hyperglycemia (high blood sugar). Accordingly, three key findings emerge that comprise the three chapters of this report. 1) The quality of human sleep, specifically the degree of fragmentation, raises inflammatory-related white blood cells, thereby conferring an increased risk for atherosclerosis. This was true of sleep fragmentation assessed across a week or a single night, which predicted increasingly higher CAC scores through a mediating association with increased neutrophils. 2) Unique features of an individual’s sleep (duration, efficiency, timing, regularity) predict aspects of triglyceride metabolism under fasting (homeostatic) and post-food intake (allostatic) conditions. First, later sleep timing predicts higher fasting triglycerides, emphasizing a unique relation with triglyceride homeostasis (not allostasis). Second, this homeostatic link is mediated by increased systemic inflammation (GlycA levels). Third, and conversely, declining sleep efficiency negatively impacts post-food lipid clearance, reflecting allostatic regulation. Fourth, these effects are replicated in a second independent cohort. 3) The coupling of NREM sleep spindles and slow oscillations the night prior is associated with improved next-day peripheral glucose control. Further, this sleep-associated glucose pathway may influence glycemic status through altered insulin sensitivity, rather than through altered pancreatic beta cell function. Moreover, these associations are replicated in an independent dataset of over 1900 adults. Of therapeutic significance, the coupling between slow oscillations and spindles was the most significant sleep predictor of next-day fasting glucose, even more so than traditional sleep markers, relevant to the possibility of an EEG index of hyperglycemia. Collectively, beyond these scientific insights, these results support a sleep–cardiometabolic homeostasis framework, such that unique features of sleep act as biomarkers for unique aspects of cardiometabolic health. Importantly, these findings help reinform public health sleep-related guidelines to reduce the mortality and economic burden of cardiometabolic disease globally.

Advisor: Walker, Matthew P
Efficient Resource Management for Machine Learning

The increasing computational demands of Machine Learning (ML) models, coupled with a slowdown in hardware advancements, have led to a significant compute supply-demand gap. This gap is evident in the rising costs and limited availability of resources needed for training complex ML models like GPT-4. These challenges hinder the progress and accessibility of ML./nThis dissertation aims to bridge the compute supply-demand gap by improving resource efficiency of ML. We introduce Ekya, Cilantro, and ESCHER, three new systems and methods for improving resource efficiency at different layer in the ML stack. Ekya, at the ML application layer, implements a Thief Scheduling algorithm and a Microprofiler to intelligently redistribute resources between inference and retraining tasks, thereby making continuous learning four times more resource-efficient. Cilantro, in the cluster management layer, utilizes online learning to develop dynamic resource-performance models, enabling performance-aware resource allocation in multi-tenant environments. At the orchestration layer, ESCHER introduces ephemeral resources, allowing ML applications to specify custom scheduling requirements without overhauling the underlying cluster manager. This unique approach provides applications with the flexibility to adapt to evolving needs while maintaining simplicity in system design. Together, these systems represent a comprehensive approach to mitigating the compute supply-demand gap, contributing sustainable and efficient resource management techniques.

Advisor: Stoica, Ion
Experimental Considerations and Theoretical Treatment of Time Resolved Polarization Spectroscopy

The decay dynamics in molecular systems are important to consider for a wide number of phenomena and have implications across many fields of study. One common set of methods used to study these dynamics involve measurements of the time evolution of the excited state enhancement/suppression of nonlinear spectroscopic signals. Here we develop the novel technique of ultrafast transient polarization spectroscopy which allows us to measure decay dynamics in molecular systems using a polarization sensitive technique based on the optical Kerr effect. This is a three pulse technique where an exciting-pump pulse brings a portion of a sample into an electronic excited state and a pair of probing pulses measure changes in the sample's intensity dependent refractive index as a function of time delay. We then apply this technique to the study of neat liquid nitrobenzene where we measure oscillations in the dephasing time that we associate with oscillatory wave packet motion on the $S_1$ excited state. We then develop a set of theoretical methods that can be applied to study these polarization sensitive signals starting from a foundation in quantum chemistry. We also introduce a formalism for the projected density matrix which allows for an implicit transformation of lab frame signals into a molecular frame representation given a polarization sensitive measurement. These methods are then used to simulate the electronic response from a number of polarization sensitive signals with applications discussed.

Advisor: Saykally, Richard J
Slaughter, Daniel S
Algorithm Design for Safe and Efficient Societal-Scale Navigation

In modern urban centers, traffic networks increasingly experience higher densities of both human-operated and self-driving vehicles. Unfortunately, high traffic loads can increase the likelihood of accidents and gridlock at individual intersections and roads, and produce societal-scale externalities in the form of pollution and excessive commute times. To address these issues, modern navigation and transportation technologies increasingly leverage machine learning-enabled components to safely and efficiently guide commuters towards their desired destinations. Examples range from learning-enabled perception and motion planning algorithms deployed on self-driving vehicles, to recommendation systems for route planning in large-scale transportation networks. Unfortunately, learning-based algorithms can exhibit unexpected and alarming behavior when deployed in real-world environments. For instance, computer vision modules in self-driving vehicles frequently fail to correctly identify traffic signs and predict pedestrian motion, with fatal consequences. Meanwhile, route recommendation platforms can induce high congestion levels, by directing self-interested travelers to overcrowd paths of least perceived latency. These phenomena highlight that, despite their promise, modern ML algorithms for localized and societal-scale navigation remain unable to operate robustly in real-world traffic scenarios./nTo overcome these challenges, this thesis draws from tools in control theory, estimation theory, numerical optimization, game theory, and mechanism design to design algorithms that ensure safe and efficient navigation in modern transportation systems. In particular, the thesis consists of the following parts, each of which targets a different facet of decision making in traffic navigation: (I) A unified optimization-based state estimation algorithm for autonomous agents; (II) Game-theoretic motion planners that characterize multi-agent interactions in local traffic scenarios; (III) A dynamic tolling scheme and learning updates for large traffic networks. We conclude by describing promising avenues of future work.

Advisor: Sastry, Shankar
Chemical Investigations into Pyrrole-Containing Secondary Metabolites Isolated From Curvularia Sp. and Bipolaris Maydis

Pyrrole-containing alkaloids have long captured the attention of the synthetic chemistrycommunity due to their intriguing and reactive chemical architectures; such is the case of curvulamine and associated polypyrrole natural products. Curvulamine, curindolizine, and the related bipolamines share a unique compact pentacyclic skeleton flanked by two electron-rich pyrroles and up to seven stereocenters. Synthetic access to these complex alkaloids has previously been limited to curvulamine and bipolamine I. In this work, we aim to chronicle the results of our synthetic investigations into this family of unique alkaloids. In the first chapter, we provide an overview into their isolation and antibiotic properties, and discuss our initial synthesis of curvulamine which frames our studies in subsequent chapters. The second chapter describes the total synthesis of curindolizine and the substrate scope of a microwave-assisted method to access pyrroloazepinones, bicyclic heterocycles used in the synthesis of curvulamine and curindolizine. Finally, in the last chapter we describe the synthetic strategies used to access bipolamine C, D, G, and I and describe the unexpected reactivity of some pyrrole-containing intermediates along this journey.

Advisor: Maimone, Thomas J
Partisan Polarization of Trust in Societal Institutions: Causes and Effects

Partisan polarization spreads through more and more of American society over time. Republicans and Democrats held different opinions toward partisan-valenced institutions like the presidency and a few economic institutions (e.g. big business) in the 1970s, but today almost all societal institutions – whether governmental or ostensibly apolitical – are understood by many Americans as liberal or conservative, made up mostly of one partisan group or the other. As a result many partisans trust only institutions they believe are made up of people who are similar to them ideologically.This dissertation uses attempts to answer these questions: When did this polarization of trust emerge in each institution? Why did this polarization occur? What effect does it have on regular Americans? Chapter 1 documents trust in institutions using 50 years of existing public opinion data and original public opinion measurement. Chapter 2 uses campaign donation data and machine learning to establish a possible cause of polarization of trust: the real composition of institutions. Chapter 3 demonstrates one reason this polarization is important: if it is provided, Americans will use partisan information about doctors to make decisions about health care. There is already evidence of individual-level partisan differences in trust in doctors, as well as aggregate-level evidence of differential outcomes between Republicans and Democrats.

Advisor: Brady, Henry E
Lenz, Gabriel
Aspects of S-Duality

Classically, the ground states of $\mathcal{N}=4$ Super Yang-Mills Theory (SYM) on $\mathbb{R}\times S^3/\Gamma$ where $\Gamma$ is one of the ADE subgroup of $SU(2)$ are flat Wilson lines winding around the ADE singularity. S-duality acts on this finite-dimensional ground state Hilbert space and its action is the same as the $S$ operator in a certain dual Chern-Simons theory on $T^2$. The dual Chern-Simons theory arises out of the only long-range interaction in a string/M theory construction by considering a stack of D3(M5) branes on ADE singularity. This SYM/Chern-Simons duality is verified by matching the ground state Hilbert spaces of both theories and by comparing the S-duality operators of both theories./nTo one-loop order, the SYM ground state degeneracy is exact. A detailed computation using the superconformal index shows that each classical SYM ground state acquires the same supersymmetric Casimir energy. S-duality maps the SYM ground state Wilson lines to ground state t' Hooft lines taking values in the Langlands dual group. The number of t' Hooft lines are shown to agree with that of the Wilson lines. In addition, the t' Hooft lines have the same supersymmetric Casimir energy as the corresponding Wilson lines. These two facts provide a ground state test of S-duality./nThe SYM/Chern-Simons duality has an important extension to the class S theory obtained from compactifying M5 branes on a Riemann surface $\mathcal{R}$. The ground states of class S theory on $\mathbb{R}\times S^3/\Gamma$ are dual to the states of the dual Chern-Simons theory on $\mathcal{R}$. In particular, we uncover a surprising result that there is only one unique ground state for the conformal $\mathcal{N}=2$ $SU(2)$ four-flavor theory on $\mathbb{R}\times S^3/\Gamma$. Finally, we apply the SYM/Chern-Simons duality to a non-Lagrangian class S theory and find that its ground states obey the fusion rule of the current algebra of the dual Chern-Simons theory.

Advisor: Ganor, Ori
Automated Domain Decomposition for Multi-GPU Monte Carlo Electron-Photon Radiation Transport

A Convolutional Neural Net (CNN) was trained to determine the optimal decomposition ofa voxel domain for Monte Carlo electron-photon radiation transport. The training database was developed by collecting photon flux and surface current tally data for a simple shielding problem and a simplified human phantom brain model. The voxel matrix inputs were mapped to the tally outputs by the CNN, allowing the CNN to accurately predict tally results from material and source data. The predicted flux was then used to determine the shape and size of the subdomains, and to calculate the size of the ghost zones. The intent for the CNN is to reduce the amount of trial and error that is often necessary to decompose domains manually by a user. Poor domain decomposition can lead to longer run times. Furthermore, this work is meant to serve as a proof of concept for more complex geometry types and applications. The CNN predicted decomposition performed at best 1.6x than other decomposition schemes for the shielding problem and 1.3x for the human phantom problem. This process was done to demonstrate the power of intelligently choosing subdomain boundaries rather than using arbitrarily chosen boundaries.

Advisor: Scarlat, Raluca
Targeting Ferroptosis as a Novel Therapeutic Strategy for Combating Cancer

Cancer is a leading cause of death worldwide and has propensity for rapid mutation,which constantly challenges our ability to treat aggressive forms. There is an unmet need for next generation cancer medicine capable of overcoming chemotherapeutic resistance in cancer. Ferroptosis is a form of regulated cell death that is caused by the iron dependent peroxidation of lipids1,2. The glutathione-dependent lipid hydroperoxidase glutathione peroxidase 4 (GPX4) prevents ferroptosis by converting lipid hydroperoxides into non-toxic lipid alcohols3,4. Ferroptosis has been implicated in the cell death that underlies several degenerative conditions2, and induction of ferroptosis by inhibition of GPX4 has emerged as a therapeutic strategy to trigger cancer cell death5. However, sensitivity to GPX4 inhibitors varies greatly across cancer cell lines6, suggesting that additional factors govern resistance to ferroptosis. Here in this dissertation, we employ a synthetic lethal CRISPR/Cas9 screen, and we identify ferroptosis suppressor protein 1 (FSP1) (previously known as apoptosis-inducing factor mitochondrial 2 (AIFM2)) as a potent ferroptosis resistance factor. Our data indicate that myristoylation recruits FSP1 to the plasma membrane where it functions as an oxidoreductase that reduces coenzyme Q10 (CoQ), generating a lipophilic radical-trapping antioxidant that halts the propagation of lipid peroxides. We further find that FSP1 expression positively correlates with ferroptosis resistance across hundreds of cancer cell lines, and that FSP1 mediates resistance to ferroptosis in lung cancer cells in culture and in mouse tumor xenografts. Thus, our data identify FSP1 as a key component of a non-mitochondrial CoQ antioxidant system that acts in parallel to the canonical glutathione-based GPX4 pathway. These findings define a new ferroptosis suppression pathway and indicate that pharmacological inhibition of FSP1 may provide an effective strategy to sensitize cancer cells to ferroptosis-inducing chemotherapeutics. In addition, we screened more than 120,000 compounds using an in vitro FSP1 activity assay and identified 168 compounds that directly inhibit FSP1 oxidoreductase activity. We further characterized 19 FSP1 inhibitors, belonging to 7 structurally distinct groups, and find that these compounds exhibit synthetic lethality with loss of GPX4 in H460 lung cancer cells. The most potent of these FSP1 inhibitors, ferroptosis sensitizer 1 (FSEN1),/nacts specifically through inhibition of FSP1 to sensitize cancer cells of different tissueorigins to ferroptosis induced by multiple compounds targeting the GSH-GPX4 pathway. Together, our results define several structurally unique FSP1 inhibitors that sensitize cancer cells to ferroptosis, providing tools for the study of FSP1 biology and enabling further exploration of the potential of FSP1 as a therapeutic target.

Advisor: Olzmann, James