VictorJuiz/thesis huggingface dataset:
Out of many, one : distilling the physics of behavior across scales for an animal without a brain
Keywords: Applied Physics.

The physics of behavior is a rapidly growing discipline which seeks simple descriptions of an animal's behavior in the language of low dimensional dynamical systems constructed from data driven approaches. Despite rapid progress, we still do not generally understand the rules which shape these emergent behavioral manifolds in the face of complicated neuro-construction --- even in the simplest of animals. In this work, we introduce a non-neuromuscular model system which is complex enough to teach us something new but also simple enough for us to understand deeply: Trichoplax Adhaerens. Through the study of this experimental model, we stand to learn more about how the animal kingdom builds successful animals from millions of specialized cells. Central to this work, we report five experimental discoveries: 1. a broad crossover regime between swimming and stalling in ciliary oscillators interacting with a surface via an adhesion energy which we call 'ciliary walking', 2. sub-second ciliary reorientations can self-organize into a collective phenomena which we call 'ciliary flocking', 3. the dominant behavioral manifold of ciliary flocking is shaped by a stable emergent coherent structure topologically classified as a +1 defect, 4. the top layer of the organism exhibits ultrafast cellular contractions which can reduce the cellular cross section by 50\% in a single second generating nonlinear contraction waves, and 5. the bottom tissue of the organism mixes different cell types to tune the material response introducing the concept of an 'epithelial alloy'. We complement these discoveries with a suite of models by construction at every scale of the problem from organelle to organism. Through a careful study of these frameworks of many-body dynamical systems driven out-of-equilibrium by distributed activity, we report an array of conceptual tools (excitable mechanics of spatio-temporal fields, active-elastic parametric resonance, and an inverse energy cascade) which enable these non-neuromuscular animals to perform agile locomotion across millions of cells without a central controller. We hope these results can inspire future approaches to technologies which exploit distributed agency such as swarm robotics and edge computing. Looking ahead, we suggest that the simple perception-actions cycles of this organism provide a promising opportunity to study the morphological computation embedded in this physical reservoir of high-dimensional, nonlinear tissue dynamics
Over-the-air statistical estimation
Keywords: Electrical Engineering.

The data fueling today's rise in machine learning is often generated by devices at the edge of a network, like sensors or mobile devices. To use all this data to train a common model, devices need to communicate something about their own data to a central server. But physical communication channels have limits, and these constraints are increasingly becoming the bottleneck in distributed and federated learning systems. Can we improve such learning algorithms by explicitly incorporating the physical communication layer into their design? We explore this question using a new framework that draws on wireless communication theory and statistical estimation. We propose "analog" estimation schemes that exploit the superposition inherent in multiple-access wireless networks, and analyze their performance. We then consider fundamental limits on how well "digital" schemes, which separate the communication and estimation stages, can possibly do. Comparing the two under several statistical models shows that the analog approach can yield drastic improvements in estimation error over the digital one. We also derive lower bounds for analog schemes that are within a logarithmic factor of our achievability results, and we present experimental results showing that these ideas can translate to performance gains in a federated machine learning context
Parasitic intimacies : life, love, and labor in post-socialist Central Asia
Keywords: Anthropology.

This dissertation considers how the lives and labors of a diverse and disparate set of Central Asian women might help us reimagine economic possibilities and socio-political futures in a time of precarious employment and diminished social security. Based on over two years of research in Kyrgyzstan and across its borders, it narrates the exploits and travails of entrepreneurial traders traveling to China and Turkey, rural migrants and undocumented women in the sex trade, and marginalized drug addicts as they seek care from service providers and public health programs. These diverse groups were once criminalized as "social parasites" in the Soviet Union. Today, despite the residues of stigma, their activities are the mainspring of livelihoods. By bringing together both economic and medical discourses, my dissertation analyzes how the figure of the "parasite"—those classes of people who are not viewed as productive members of society—carries gendered and racial connotations. It argues that so-called "unproductive" activities and relations of dependence are, in fact, forms of generative, relational work that shape local imaginaries of care, as well as transnational flows of people, goods, and wealth
Partisan polarization and international politics
Keywords: Political Science.

This dissertation explores the relationship between partisan polarization and international politics. The project asks two questions. The primary question is: How does polarization affect the way democracies behave in international politics? Relative to non-democracies, democracies are generally better at keeping foreign policy consistent despite regular leadership turnover (stability advantage), credibly signaling information to adversaries (credibility advantage), and maintaining their international commitments (reliability advantage). I argue that extreme polarization undermines these "democratic advantages" by eroding the vertical and horizontal constraints on political power that confer them. I build evidence for this argument by leveraging new cross-national data on foreign policy positions of executives in 55 democratic countries from 1945-2015. I then focus on how polarization affects contemporary American foreign policy. Drawing on three original survey experiments, analyses of descriptive data on the behavior of political officials, and a series of elite interviews conducted in Washington, D.C., I show that growing polarization makes the United States a less credible adversary and less reliable ally. The secondary question this dissertation asks is: How does a state's security environment affect domestic polarization? A common explanation for the increasing polarization in contemporary American foreign policy is the absence of external threat. Using three methods—computational text analysis of congressional speeches, examination of historic public opinion polls, and a survey experiment about threats from a rival foreign power—I find that the external threat hypothesis has limited ability to explain either polarization in US foreign policy or affective polarization among the American public. Instead, responses to external threats reflect the domestic political environment in which they are introduced. Overall, this dissertation project emphasizes that partisan polarization is an important and under-explored source of variation in democratic foreign policymaking. I argue that we have little evidence that international threats have major impacts on domestic polarization. However, polarization over foreign affairs has important—and largely negative—consequences for the stability, credibility, and reliability of democratic states
Past and future changes in river temperatures and nitrogen export
Keywords: Environmental Earth System Science.

Climate change poses a growing threat to freshwater ecosystems and water quality. This dissertation examines the relationship between changes in climate and two key water quality parameters: the transport of nitrogen within rivers, referred to as nitrogen loading, and river temperature. Chapter 2 examines how changes in air temperature and precipitation influence historical trends in U.S. nitrogen loading. I find that annual precipitation, extreme springtime precipitation, and springtime temperature are key drivers of trends in historical loading. The Appendix addresses time scales for recovery of Gulf of Mexico nitrogen loading, challenging an assertion made in a previous study that achieving nitrogen reduction goals will inevitably take decades. Chapter 3 quantifies how river temperature extremes and variability have changed in recent decades. I find that the majority of rivers have already experienced warming of hot extremes and a significant change in variability. Chapter 4 explores the sensitivity of river heatwaves— periods of persistent, extreme river temperature—to future climate change using a neural network and global climate models. I find that river heatwaves are projected to increase significantly in frequency, duration, and severity in response to climate change, including a tripling of heatwave days by 2050 under a high carbon emissions scenario. Overall, this dissertation reveals that substantial changes in nitrogen loading and river temperatures are already underway in rivers across the U.S. Increases in river temperature extremes and ensuing ecological and socioeconomic impacts may also become critically severe in the coming decades
Past present : the literature of human rights in postdictatorial Brazil, Argentina and Chile
Keywords: Iberian and Latin American Cultures.

From an interdisciplinary perspective situated across memory studies, law, and literature, I examine what I define as the literature of human rights in contemporary Latin America: legal and literary narratives with a truth-seeking function vis-à-vis the past that have the potential to impact the present. I analyze narratives from postdictatorial Brazil, Chile, and Argentina, namely truth and reconciliation commission reports and the following fictional narratives: Julián Fuks's A resistência (2016), Alia Trabucco Zerán's La resta (2014), and Selva Almada's Chicas muertas (2015). In each chapter, I interrogate the relevance of concepts such as "truth" and "trust" to memory studies and jurisprudence, as well as their impact on the development of public awareness and discussion—the necessary preconditions for democracy and political change. Considering legal and literary narratives to be part of the activity frameworks that drive social and political change, I explore literature's relevance to human rights and its potential to uncover and examine issues, subjects, and geographic regions that legal systems might exclude
Patterns in fungal community composition and decomposition
Keywords: Biology.

Fungi play important roles in terrestrial ecosystems. Here, I perform four investigations (chapters two, three, four, and five) into variation in fungal communities and examine how this variation affects processes controlled by fungi. In chapter two, I use a natural dispersal gradient to evaluate whether a mechanism proposed to support diversity in plant communities whereby the dispersal ability of a species trades off with its competitive strength is applicable to communities of root-symbiotic fungi. I determine that such a tradeoff exists and maintains fungal diversity in my study system. In chapter three, I compare the effects of wildfire on soil fungal communities across two different ecosystem types, finding that ecosystems which are adapted to regular fire exhibit less change in their fungal communities when burned than other ecosystems. In chapter four, I combine modelling and meta-analysis to determine the effects of competition between different types of fungi on litter decomposition. I find that fungal competition has context-specific effects, with decreases in decomposition rate occurring only where recalcitrant litter chemistry allows root-symbiotic fungi to prevail against free-living decomposer fungi. In chapter five, I show that communities of wood-decay fungi have multiple qualitatively different links with wood decomposition rate, the relative strength of which are subject to the spatial scale at which systems are examined
People and pedagogies : children's accounts of loving and mattering that shape their wellbeing in school
Keywords: Education.

Over the last few decades, there has been a growing interest and advocacy of children's wellbeing around the world. To better understand and address the wellbeing of children in schools this study focuses on the perspectives of 121 students in six urban classrooms from two elementary schools serving mostly Brown and Black families from low-income communities. Findings revealed that the children in this study generally perceived school to be positive experiences according to survey responses, especially as they related to questions about school and life satisfaction, relationships, and health. Aspects of "Loving, " the quality of children's social relationship (Allardt, 1976; Konu & Rimpelä, 2002), appeared to be a particularly important aspect of their wellbeing with relationship-related items being the only survey category that produced strong correlation coefficients with children's overall satisfaction with life and with school. Further analysis of qualitative data, such as classroom observations and children's captured images and words, further supported and complexified the important role that people and school pedagogies had on their wellbeing, especially as they related to children's ability to exist as their full selves as well as to participate in community (e.g., "communal constructions") with their peers and with grown-ups in school. This study hopes to contribute to the larger discourse of children's wellbeing, school practices, and broadly to other disciplines concerned with improving children's lives, such as children's health, social work, and juvenile justice reform. It concludes by arguing that if grown-ups truly seek to promote children's wellbeing, they need to rethink what it means to love children and to advocate for children's mattering for their full intersectional identities, including their childhood
Performing intersectional identities : four Jewish women poets in the 20th century
Keywords: Comparative Literature.

This dissertation considers how poetry can be a mode of performing identity, particularly for those on the margins. I argue that because it is not only composed of but also always draws our attention back to language itself, poetry is a kind of performative utterance that constitutes that which it names. Through a consideration of works by four poets positioned at the intersection of multiple margins, as women and Jews, some of them queer, some immigrants, I demonstrate the particular strategies that poetry offers for reappropriating tactics of oppression in the performance of a self. The first chapter focuses on Gertrude Stein's "surface poetics" through which Stein reappropriates modes of objectification while resisting a penetrative/interpretive gaze, drawing our attention to the surface of her poems, a surface on which, I argue, her performance of a lesbian Jewish identity unfolds. In the second chapter, I examine anti-grammaticality in the works of the first "native" modern Hebrew poet, Esther Raab, and show how she deploys Hebrew's highly gendered grammar to enact a female-Jewish identity in a language and tradition that has historically erased women's voices and experiences. A third chapter on Zelda Schneerson explores how the poet reappropriates Hebrew's capacity for agentless and impersonal constructions in the enactment of her identity as a religious woman poet in a literary milieu dominated by secular men. My final chapter on Adrienne Rich focuses on her revaluation of relationality as a poetic and political strategy through which the poet reimagines personal and collective identities, revealing the ties that bind us to one another
Performing, recording, and producing immersive music in virtual acoustics
Keywords: Music.

The systems that allow us to experience the auditory world in three spatial dimensions are extraordinarily complex. Humans possess a remarkable ability for identifying the distance, location, and size of a sound source with high accuracy, and these spatial auditory cues fundamentally shape the way we interact with the world. Moreover, spaces impart their own distinctive coloration to sounds occurring within their walls. This thesis explores how spatial cues affect they way we create, produce, and listen to music. We take two approaches: the first uses virtual acoustics to model and simulate the acoustics of specific spaces. We show approaches for using real-time signal processing to perform concerts, produce recordings, rehearse, and study aspects of performance practice in the virtual acoustics of spaces the performers do not physically inhabit. We demonstrate our methods using acoustic measurements of two spaces: the Hagia Sophia in Istanbul and the Chiesa di Sant'Aniceto in Rome. The second topic of this thesis is signal decorrelation, a class of algorithm used to create copies of an audio signal with a controlled amount of correlation. We show several allpass filter-based decorrelation techniques and describe methods for evaluating their performance. Overall, this thesis is about understanding how architecture, music composition, and performance practice are intertwined
Perovskite solar cells : out of the lab and into a panel
Keywords: Applied Physics.

Organic-Inorganic Metal Halide Perovskites are an increasingly promising next-generation solar absorber technology on pace to take the solar industry by storm. Although efficiency is the oft quoted figure of merit for solar cell technologies, their energy yield is a potentially more relevant metric when comparing different solar technologies as energy yield considers local weather conditions for a desired installation location. Energy yield calculations are used to compare the performance of various perovskite modules under a variety of weather conditions. As perovskites progress towards commercialization, problems that are not easily explored in typical laboratory conditions become more important. When a single solar cell in a panel is shaded, the illuminated cells that are connected in series with it can place a large reverse bias on the shaded cell to attempt to force current through it. While this situation can allow the panel to continue to produce power, it can cause significant problems for the shaded cell. Specifically, in the case of perovskite solar modules, a combination of Joule heating and irreversible electrochemical reactions will degrade the cell. We explore how many solar cells can be protected by one bypass diode in both single junction and multijunction perovskite modules. We also discuss how one might integrate bypass diodes into modules that are made by connecting singulated cells with wires versus those that are monolithically integrated. We show that an advantage of perovskite-silicon tandems is that the silicon substantially increases the breakdown voltage and thereby the number of cells that can be protected by one bypass diode. It is well known that stability is a major concern that must be addressed before perovskites enter the solar market. Accelerated aging is commonly used to evaluate the viability of any new technology on a reasonable time scale by probing various degradation mechanisms. Thermal acceleration factors for perovskites are explored as well as their relationship with the residual stress in the thin film absorber
Phaneron
Keywords: Music.

Included in this thesis are the score materials and documentation for Phaneron (2019), an intermedia piece for violin, electronics, projection, and lighting, created for and in collaboration with Marco Fusi and Michiko Theurer. This project was a means to explore the topic of Hyperrealism through musical composition, resulting in a deeper study of its surrounding theory, aesthetics, and philosophy. My understanding of this term, and also the impetus for this project, was informed by the writings of Jean Baudrillard in his 1981 treatise, "Simulacra and Simulation." This provided a structural basis for the piece while also prompting a philosophical-aesthetic question: how can the real and artificial become blurred, and, furthermore, what are the perceptual effects of these blurrings? In Phaneron, I endeavored to create a piece in which stagecraft, rhetorical techniques, and the incorporation of different media were employed to cause confusion between the real and fake, and to explore nuances within this space
Pharmacogenomics at scale : population analysis and machine learning applications in pharmacogenomics
Keywords: Biomedical Informatics.

Pharmacogenomics promises the ability to provide personalized therapeutic guidance for patients based on their genetics. Understanding how genetic variation leads to heterogeneity in drug response could dramatically increase patient outcomes by increasing efficacy and minimizing adverse drug reactions. Pharmacogenomics research has historically suffered from small sample sizes, which limits our understanding of global allele frequencies, drug-gene associations, and the identification and functional assessment of rare variants. In recent years, biobanks containing phenotype-linked genetic data for hundreds of thousands of participants have become available, presenting an unprecedented opportunity for population-scale analysis of the effect of genetics on drug response. Simultaneously, the capabilities of deep learning algorithms have advanced significantly, enabling powerful predictions about properties of DNA sequence data. This dissertation illustrates how biobanks can be used to study pharmacogenetics and how deep neural networks can be used to predict metabolic function of haplotypes in pharmacogenes. I present results from the largest pharmacogenetic study to date, analyzing pharmacogenetic allele and phenotype frequencies and a cohort of 500,000 individuals. I demonstrate how these data can be used to discover drug-gene associations and drug-gene-response associations through genome-wide and candidate gene studies by integrating clinical records and prescription data. Finally, I present a novel deep learning approach to predicting haplotype function in an important drug-metabolizing enzyme, CYP2D6