Computational Methods in the Study of Political Behavior

In this thesis, I explore how individual-level actions contribute to aggregate political outcomes. In each chapter, I aim to understand an observed political behavior using data or methodologies previously unused in their contexts. The subject matter ranges from protest activity and vote choice to theoretical opinion models and re-examining how socioeconomic class is understood in quantitative work. In the first two chapters I employ novel datasets to understand phenomena where popular theories differ from empirical observations. In Chapter 1 I examine protest behavior, which is not the equilibrium prediction of models of collective action. I investigate what aspects of published language can predict protest participation and how these change leading up to and following protests. Specifically, I collect and, using natural language processing methods, analyze 4 million tweets of individuals who participated in the Black Lives Matter protests during the summer of 2020. Using geographical and temporal variation to isolate results, I find evidence that interest in the subject, measured as percentage of online time discussing the matter, is correlated with protest behavior. However, I also find that collective identity, measured through pronoun use, does not have a strong relationship with protest behavior. Next, in Chapter 2, I use a survey---which I helped to develop and field---to understand the 2020 midterm elections' surprising results. While most accepted models of midterm elections predicted massive Democratic losses (averaging around 40 seats in the House), these predictions were not met. In fact, the Democratic party did well---they did not lose a single state legislature, expanded some majorities, and lost only 9 seats in the House of Representatives. Testing various models of midterm elections, I show that the 2020 midterms were issue-based elections, where views on abortion had a large impact on vote choice. In the second half of the thesis I focus on methodologies. Specifically, in Chapter 3, I expanded on mathematical models of consensus building to better mimic reality. Bounded confidence models have historically been used to explain convergence of opinions. In this chapter I add a repulsive element, modeling the inclination to differentiate oneself from someone who otherwise has similar beliefs. With this added component, convergence is no longer assumed. I explore both analytical and simulated numerical results to understand the dynamics of opinions in this new context. Finally, in Chapter 4, I introduce a method for operationalizing socioeconomic class as a latent variable in regression models. While there has been a plethora of research which shows that class affects opinions, views, and actions, the definition of class is nebulous. I argue that this is a result of the nature of class, which is context dependent. Therefore, rather than explicitly determining class, I present using class within a mixture model framework. This allows for the exact definition of class to change within the context being analyzed and enables researchers to use class within their work. Following the theoretical arguments, I present the efficacy of the approach using the American National Election Studies survey from 2020 to show how class differs when related to views of the U.S. Immigration and Customs Enforcement agency and the Black Lives Matter movement.</p

Preference-Based Learning for Exoskeleton Gait Optimization

This paper presents a personalized gait optimization framework for lower-body exoskeletons. Rather than optimizing numerical objectives such as the mechanical cost of transport, our approach directly learns from user preferences, e.g., for comfort. Building upon work in preference-based interactive learning, we present the CoSpar algorithm. CoSpar prompts the user to give pairwise preferences between trials and suggest improvements; as exoskeleton walking is a non-intuitive behavior, users can provide preferences more easily and reliably than numerical feedback. We show that CoSpar performs competitively in simulation and demonstrate a prototype implementation of CoSpar on a lower-body exoskeleton to optimize human walking trajectory features. In the experiments, CoSpar consistently found user-preferred parameters of the exoskeleton’s walking gait, which suggests that it is a promising starting point for adapting and personalizing exoskeletons (or other assistive devices) to individual users

Stabilization of Exoskeletons through Active Ankle Compensation

This paper presents an active stabilization method for a fully actuated lower-limb exoskeleton. The method was tested on the exoskeleton ATALANTE, which was designed and built by the French start-up company Wandercraft. The main objective of this paper is to present a practical method of realizing more robust walking on hardware through active ankle compensation. The nominal gait was generated through the hybrid zero dynamic framework. The ankles are individually controlled to establish three main directives; (1) keeping the non-stance foot parallel to the ground, (2) maintaining rigid contact between the stance foot and the ground, and (3) closing the loop on pelvis orientation to achieve better tracking. Each individual component of this method was demonstrated separately to show each component's contribution to stability. The results showed that the ankle controller was able to experimentally maintain static balance in the sagittal plane while the exoskeleton was balanced on one leg, even when disturbed. The entire ankle controller was then also demonstrated on crutch-less dynamic walking. During testing, an anatomically correct manikin was placed in the exoskeleton, in lieu of a paraplegic patient. The pitch of the pelvis of the exoskeleton-manikin system was shown to track the gait trajectory better when ankle compensation was used. Overall, active ankle compensation was demonstrated experimentally to improve balance in the sagittal plane of the exoskeleton manikin system and points to an improved practical approach for stable walking

Preference-Based Learning for Exoskeleton Gait Optimization

This paper presents a personalized gait optimization framework for lower-body exoskeletons. Rather than optimizing numerical objectives such as the mechanical cost of transport, our approach directly learns from user preferences, e.g., for comfort. Building upon work in preference-based interactive learning, we present the CoSpar algorithm. CoSpar prompts the user to give pairwise preferences between trials and suggest improvements; as exoskeleton walking is a non-intuitive behavior, users can provide preferences more easily and reliably than numerical feedback. We show that CoSpar performs competitively in simulation and demonstrate a prototype implementation of CoSpar on a lower-body exoskeleton to optimize human walking trajectory features. In the experiments, CoSpar consistently found user-preferred parameters of the exoskeleton’s walking gait, which suggests that it is a promising starting point for adapting and personalizing exoskeletons (or other assistive devices) to individual users

Tumor Angiogenesis Phenotyping by Nanoparticle-facilitated Magnetic Resonance and Near-infrared Fluorescence Molecular Imaging

AbstractOne of the challenges of tailored antiangiogenic therapy is the ability to adequately monitor the angiogenic activity of a malignancy in response to treatment. The αvβ3 integrin, highly overexpressed on newly formed tumor vessels, has been successfully used as a target for Arg-Gly-Asp (RGD)-functionalized nanoparticle contrast agents. In the present study, an RGD-functionalized nanocarrier was used to image ongoing angiogenesis in two different xenograft tumor models with varying intensities of angiogenesis (LS174T > EW7). To that end, iron oxide nanocrystals were included in the core of the nanoparticles to provide contrast for T2*-weighted magnetic resonance imaging (MRI), whereas the fluorophore Cy7 was attached to the surface to enable near-infrared fluorescence (NIRF) imaging. The mouse tumor models were used to test the potential of the nanoparticle probe in combination with dual modality imaging for in vivo detection of tumor angiogenesis. Pre-contrast and post-contrast images (4 hours) were acquired at a 9.4-T MRI system and revealed significant differences in the nanoparticle accumulation patterns between the two tumor models. In the case of the highly vascularized LS174T tumors, the accumulation was more confined to the periphery of the tumors, where angiogenesis is predominantly occurring. NIRF imaging revealed significant differences in accumulation kinetics between the models. In conclusion, this technology can serve as an in vivo biomarker for antiangiogenesis treatment and angiogenesis phenotyping