VictorJuiz/thesis huggingface dataset:
Dataframe Systems: Theory, Architecture, and Implementation

Dataframes are a popular abstraction to represent, prepare, and analyze data. Despite the remarkable success of dataframe libraries in R and Python, dataframes face performance issues even on moderately large datasets. Moreover, there is significant ambiguity regarding dataframe semantics. In this thesis, we discuss the implications of signature dataframe features including flexible schemas, ordering, row/column equivalence, and data/meta-data fluidity, as well as the piecemeal, trial-and-error-based approach to interacting with dataframes. While most modern systems aim to scale dataframe workloads by changing properties of dataframes – or by adding new distributed systems knowledge requirements– we believe it is important to support scalable ops on dataframes without changing their semantics. This thesis takes a ground-up approach towards scaling dataframe systems,starting with a formal data model and algebra, and ending with a reference implementation. This implementation, Modin, has already accumulated a significant amount of community support: over 6,000 GitHub stars and over 1 million installs to date. This interest shows the need for systems that solve modern data science problems without changing semantics. Included in this thesis are several of our insights into how to build systems for data scientists and what data scientists prioritize. We believe these insights were instrumental in unlocking the interest and support from the community in our open source work.

Advisor: Joseph, Anthony D
Machine Learning for Deep Image Manipulation

Common types of image editing methods focus on low-level characteristics. In this thesis, I leverage machine learning to enable image editing that operates at a higher conceptual level. Fundamentally, the proposed methods aim to factor out the visual information that must be maintained in the editing process from the information that may be edited by incorporating the generic visual knowledge. As a result, the new methods can transform images in human-interpretable ways, such as turning one object into another, stylizing photographs into a specific artist's paintings, or adding sunset to a photo taken in daylight. We explore designing such methods in different settings with varying amounts of supervision: per-pixel labels, per-image labels, and no labels. First, using per-pixel supervision, I propose a new deep neural network architecture that can synthesize realistic images from scene layouts and optional target styles. Second, using per-image supervision, I explore the task of domain translation, where an input image of one class is transformed into another. Lastly, I design a framework that can still discover disentangled manipulation of structure and texture from a collection of unlabeled images. We present convincing visuals in a wide range of applications including interactive photo drawing tools, object transfiguration, domain gap reduction between virtual and real environment, and realistic manipulation of image textures.

Advisor: Efros, Alexei A
Speaking Truth to Power

\begin{abstract}Electricity is increasingly considered to be a human right. However, in much of the world, basic data about power outages and voltage quality is not available, making it difficult to ensure reliable service. Smart meters are difficult to deploy and slow to be adopted in lower-resourced areas, the same areas where frequent outages and voltage sags most impede economic growth./nIn this work, I present evidence that by aggregating simple measurements from networked sensors installed at outlets in households and businesses, we can detect both large and small power outages and power-quality issues, enabling a utility-independent, agile, high-resolution, and low-cost system well suited for deployment in under-instrumented areas./nI design, deploy, and operate a large network of sensors---called PowerWatch---at outlets in households and businesses across Accra, Ghana. I show that this deployment, when coupled with cloud-based analytics, matches utility-reported rates of high- and medium-voltage outages at a fraction of the cost. Further this methodology provides a good estimate of low-voltage outages, potentially filling a critical data gap present in most countries./nThe deployment methodology developed allows for longitudinal data to be gathered independently from the utility. Utility engineers are not required for sensor installation, and permission is not required as utility property is not impacted. I describe a number of novel measurement and analysis opportunities enabled by this independent deployment methodology, many of which are being piloted by \textit{n}Line, the company I co-founded to continue improving this work./nData from PowerWatch is being used by multiple governments and research institutions, including as a primary source for the Monitoring and Evaluation of the $315 Million USD MCC led Ghana Power Compact. \end{abstract}

Advisor: Dutta, Prabal
Brewer, Eric
Liberty and Justice for All? System-Justifying Ideologies, Sense of Belonging, and Academic Achievement Among Ethnic Minority Youth

The objective of this study was to examine the correlation of system-justifying ideologies (O’Brien & Major, 2005)—a relatively new variable of interest in the context of education research—and four established psychosocial variables (grit, growth mindsets, ethnic identity, and other group orientation) with two outcome variables (academic achievement and sense of belonging) among underserved, ethnic minority youth with a particular interest in high achieving students among this group. The sample was comprised of 101 underserved Latinx and Black high school students. Using hierarchical regressions, I examined the contributions of psychosocial qualities to academic achievement and sense of belong. Next, I used qualitative analyses to better understand the nuances of how 34 participants perceived the fairness of society in relation to their motivation to perform in school. Academic achievement was negatively correlated with age and positively correlated with John Henryism, grit, growth mindset, and other group orientation (OGO). SJIs were not meaningful predictors of sense of belonging. Sense of belonging was positively correlated with grit. A positive prediction of GPA based on grit was found in Block 4 of the hierarchical regression for GPA. Three themes emerged from the qualitative analyses: (a) ambitious career aspirations, (b) systemic barriers that result in opportunity gaps and unequal access to the American Dream, and (c) skepticism of the American Dream and meritocracy narratives. The qualitative findings indicate that many high achieving ethnic minority youth are cautiously ambitious about pursuing their dreams despite their acknowledgement of the reality of systemic barriers that they will likely face./nKeywords: system-justifying ideologies, grit, growth mindset, ethnic identity, other group orientation, academic achievement, sense of belonging

Advisor: Worrell, Frank C
Spatiotemporal Signal Characteristics and Processing During Natural Vision

A current limitation in our understanding of the visual system is its function under natural viewing conditions, especially in the context of dynamic, human behavior.While there have been many advances in understanding the spatial response properties of visual neurons in relation to static stimuli such as natural images, understanding of the corresponding temporal properties has been limited by the lack of high-fidelity datasets that document the properties of the signal that reaches the human retina under natural conditions. In this thesis, I describe the design and construction of a mobile tracking device that can re-create the signal present on the retina of a human as they perform everyday tasks. This device is used to collect high fidelity video and tracking data from human subjects performing a set of tasks that sample the everyday human environment and behavioral repertoire. This new dataset makes it possible to characterize the spatiotemporal statistics of natural time-varying signals as they occur on the retina. Here I examine the spatio-temporal power spectrum, which is of interest as a natural scene statistic in part because it is the Fourier transform of the autocorrelation function. In the absence of ego motion (movement of head and body), the spatiotemporal power spectrum of the dynamic environment has similar power-law structure to that previously reported for Hollywood movies. Head and eye motion modulate the spatiotemporal signal, boosting mid- and high-range temporal frequencies, such that the visual input on the retina is nearly whitened. This can be beneficial for reducing signal redundancy and maximizing the use of available bandwidth in the optic nerve. The phase spectrum, which compliments the power spectrum, also carries relevant information about natural image statistics. Despite the strong perceptual signal carried by the classically defined global phase, I show that it has limited utility to differentiate natural images from noise. However, phase congruency, a locally-defined property of the phase, shows marked differences between the distributions of natural images and noise, as well as differences within separate categories of natural scenes. Finally, I explore the relationship between natural signals and the human visual system by optimizing a neural network to carry the most amount of information through a bottleneck inspired by the human optic nerve, while limiting the energy utilized by neural spikes. I show that a previously proposed model exhibits computational instabilities that hinder the use of autodifferentiation software in training this model, and I offer methods of addressing them. I also show that this model can be reformulated with a restructuring of the network, from a single layer model to an autoencoder framework, avoiding computational instabilities altogether. I conclude with a summary of contributions, as well as a discussion of future areas of exploration.

Advisor: Olshausen, Bruno A
Beyond Referentiality: New Language and Meaning in Spanish Avant-garde Novels of the Twentieth Century (and Beyond)

Beyond Referentiality: New Language and Meaning in Spanish Avant-garde Novels of the Twentieth Century (and Beyond) examines how language is treated as material (rather than medium) in Spanish experimental novels of the twentieth century. Chapter One centers on Cazador en el alba by Francisco Ayala and Víspera del gozo by Pedro Salinas, two key figures of the Spanish historical avant-garde. I demonstrate how these novelas líricas, using uniquely poetic language that embodies the rebellious artistic impulses of the period, rebuff narrative convention and thus compel a reconsideration of meaning. In the absence of plot, these richly sensorial works invite the reader to truly see language not as avenue to story, but as a carefully crafted art object that is itself inherently worthy of appreciation. Taking us into the future of Franco’s dictatorship, Chapter Two analyzes Volverás a Región by Juan Benet, an exemplar of the (revolutionary) nueva novela whose painstakingly dense language obscures narrative action even as it tackles the tragic (and long-lasting) destruction of the Spanish Civil War. I argue that Benet uses linguistic opacity—for him, the zone of the irrational—as an alternate way of comprehending the incomprehensible. In Chapter Three, I study Larva: Babel de una noche de San Juan by Julián Ríos, a product of the lighthearted Movida (and a newly democratic Spain) that nevertheless engages in its own intensely objectifying formal transformations. I show how narrative structure and language are (playfully) mutilated beyond recognition, leaving the reader to contend with an incredibly complex textual labyrinth in which signifiers do not straightforwardly (or reliably) signify. Here, as in the earlier instances, meaning is not a matter of interpretation, but instead attention, a shift that accentuates language’s (innately) material identity./nI will conclude my consideration of Spanish narrative objecthood by turning to the future, the (decidedly contemporary) concept of literary archaeology guiding me through a closing (and again, contemporary) counterpoint –the Proyecto Nocilla by Agustín Fernández Mallo. How do these twenty first century avant-garde novels compare (and contrast) with the earlier examples? What objectifying strategies do they share? What is new? And lastly, how do their particular formal innovations inform—and alter—my understanding of the previously discussed experimental works? In all cases, radical form makes narrative language uniquely dimensional. How does such dimensionality manifest differently in these different historical periods? These are the questions I will answer in order to demonstrate the shared, yet singular materiality that characterizes the language-focused novels of Francisco Ayala, Pedro Salinas, Juan Benet, Julián Ríos, and Agustín Fernández Mallo.

Advisor: Saum-Pascual, Alexandra
Dougherty, Dru
Biogeochemical response to hurricanes and droughts: indicators of climate change impacts on tropical forest ecosystems

The effects of climate change are causing widespread changes in forest disturbanceregimes, with significant implications for ecosystem carbon (C) and nutrient cycles in wet tropical forests. These humid ecosystems are faced with multiple types of natural disturbances, including hurricanes and drought, both of which are projected to increase in frequency due to climate change. Understanding future trajectories of wet tropical forests exposed to novel disturbance regimes requires the study of belowground biogeochemical responses to disturbance at multiple temporal and spatial scales. In this dissertation, I focused on studying the responses of soil biogeochemistry to hurricanes and drought in a wet tropical forest in Puerto Rico. In the first chapter, I present the soil biogeochemical responses to a long-term (10 yr) ecosystem manipulation experiment called the Canopy Trimming Experiment, which revealed the significant impact of hurricane-induced debris deposition on soil biogeochemistry throughout the soil profile. In the second chapter, I make use of an ecosystem biogeochemical model (DayCent) to study the long-term (decades to centuries) effects of changes in the hurricane disturbance regime on soil biogeochemistry, demonstrating the significance of changes in live biomass and soil C pools due to increases in hurricane frequency for ecosystem-scale C fluxes. In the third chapter, I present the results of a throughfall exclusion experiment that demonstrated the rapid and significant effects of drought on soil microclimate and biogeochemical cycling, even within the context of a Category 4 hurricane that had a major impact on the site. This unexpected event gave me the opportunity to study the interaction between these two major disturbances in the field, revealing important insights for better understanding the consequences of changing disturbance regimes for soil biogeochemical cycling and the implications for wet tropical forests.

Advisor: Silver, Whendee L
Statistical Complexity and Regret in Linear Control

The field of linear control has seen broad application in fields as diverse as robotics, aviation, and power-grid maintenance. Accordingly, decades of research have been dedicated to learning to control systems when the dynamics are not known a priori, but must be inferred from data. However, much of the classical work considered asymptotic regimes, which shed little light on precisely how much data are required to attain desired control performance in the presence of statistical noise./nIn this thesis, we apply modern advances in statistical and online learning theory to enrich our understanding of the statistical complexity of data-driven control. In the first half of the thesis, we study estimation of system parameters, known as system identification. We show that the stability of the system - long thought to characterize the sample complexity of estimation - is often inversely proportional to the number of trajectories required to recovery the system parameters to specified tolerance./nWe then turn to adaptive control: the problem in which a learning agent interacts with an unknown dynamical system in hopes of attaining control performance comparable to if she had foreknowledge of the system dynamics. Adopting the conventions of modern online learning theory, we study the problem from the perspective of regret, or cumulative performance relative to an idealized benchmark. Building on the system identification results, we present upper and lower bounds that demonstrate that a remarkably simple strategy enjoys optimal regret for the adaptive control of the linear quadratic regulator. We then propose a unified framework - based on online convex optimization - which enjoys similar regret guarantees in the considerably more general setting of nonstochastic control, accommodating partial observation, time-varying costs, and adversarially-chosen disturbances. Surprisingly, we find that the best-attainable regret (with respect to a suitable benchmark of stabilizing LTI control policies) exhibits the same scaling in the problem horizon as the optimal regret for the online LQR problem.

Advisor: Jordan, Michael I
Recht, Benjamin
Understanding Radiation Physics and Chemitry of Extreme Ultraviolet Resists

Extreme Ultraviolet Lithography (EUVL) has been introduced to meet the need for smaller feature sizes. EUVL imaging chemistry is revolutionary because of ionizing radiation is involved. Novel physical and chemical processes has made material development more akin to shooting in the dark. In this work, the author delineates the material challenge of EUV photo-sensitization at the molecular level, presents investigations in elucidating sensitization mechanisms and explores new possibilities. The author argues that chemical activation, which connects EUV photon absorption and generation of reactive species, is one of the main driver of resist performance. Three electron spectroscopy techniques are introduced and characterized to interrogate the electron cascade subsequent to photo-ionization. These experiments mainly answer questions about how reactive radical cations and slow electrons are generated. For example, the size of this cascade was found to be between 1 to 4 nanometers in various materials. Once the radical cations are cooled and the slow electrons have attached to electron acceptors, existing computational chemistry techniques are used to understand the subsequent generation of active species. Specifically, photo acid generators (PAG) and a prototypical organo-tin oxo cluster are studied in detail. This study revealed that the said organo-tin oxo cluster reacts to both ionization and electron attachment with similar chemical outcomes, thus partially explaining its superior performance. Manipulating dose by modifying interface dipole is also investigated. Finally, consolidating what he has learned along the way, the author discusses possibilities in EUV material engineering.

Advisor: Neaton, Jeffrey B
The melanocortin 4 receptor functions at the neuronal primary cilium to control long-term energy homeostasis

The melanocortin 4 Receptor (MC4R) is a central component of the leptin-melanocortin pathway in the hypothalamus. MC4R plays a critical role in the long-term regulation of energy homeostasis and mutations in MC4R are the most common cause of monogenic obesity. However, the precise molecular and cellular mechanisms underlying the maintenance of energy balance within MC4R expressing neurons are unknown. The expression of MC4R in neurons from the Paraventricular Nucleus of the hypothalamus (PVN) has been shown to be both necessary and sufficient to regulate food intake and body weight, and we discovered that MC4R localizes at the primary cilium of those neurons. Primary cilia are unique microtubule-based organelles that protrude from the cell membrane to sense and relay extracellular signals. In Chapter one, we first mapped the expression of MC4R in the brain, to then determine the extent of MC4R localization at the primary cilia in some of the identified regions. We discovered that, in the PVN, MC4R localization at the primary cilium is dynamic and depends on age and physiological status relayed by MC4R ligands levels. Together, these results shed light on the complex regulation of MC4R bioavailability at the primary cilium. In Chapter two, we found that cilia were required specifically on MC4R-expressing neurons for the control of energy homeostasis. Moreover, these cilia were critical for pharmacological activators of MC4R to exert an anorexigenic effect. Using a combination of mouse genetic approaches, we demonstrated that MC4R signals via cilia of PVN neurons to control food intake and body weight. In Chapter three, we demonstrate that the Melanocortin Receptor Associated Protein 2 (MRAP2) is critical for the ciliary localization and weight-regulating function of MC4R. These findings demonstrate that targeting of MC4R to neuronal primary cilia is essential for the control of long-term energy homeostasis and suggests that genetic disruption of MC4R ciliary localization may frequently underlie inherited forms of obesity.

Advisor: Stahl, Andreas
Vaisse, Christian
Co-design ofAlgorithms, Hardware, and Scheduling for Deep Learning Applications

For decades, ever-increasing computing power has been a driving force behind many technology revolutions, including the recent advances in artificial intelligence. However, due to the slowing of integrated circuit process scaling, for system architects to continue to satisfy the ever-growing compute appetite of today's applications, they must now resort to employing heterogeneous systems with specialized accelerators./nBuilding these accelerator systems, though, is extremely expensive and time-consuming. First, the development cycle for hardware is notoriously long, making it difficult to keep up with the rapid progress in algorithms. Meanwhile, existing compilers are incapable of navigating the intractable mapping space exposed by the novel accelerator architectures. Lastly, algorithms are often designed without hardware efficiency as a key metric, and therefore, pose extra challenges in designing efficient hardware./nThis thesis tackles the significant challenges in jointly designing and optimizing algorithms, scheduling, and hardware designs for acceleration. We aim to advance the state-of-the-art through a three-pronged approach: the development of methodologies and tools that automatically generate accelerator systems from high-level abstractions, shortening the hardware development cycle; the adaptation of machine learning and other optimization techniques to improve accelerator design and compilation flows; and the co-design of algorithms and accelerators to exploit more optimization opportunities./nThe target application domain of this thesis is deep learning which has achieved unprecedented success in a wide range of tasks such as computer vision, neural language processing, etc. As intelligent devices prevail, deep learning is foreseeably becoming a major computation demand in our everyday life. Therefore, by performing end-to-end system optimization with hardware acceleration, the dissertation aims to unleash the ubiquitous adoption of cutting-edge deep learning algorithms to transform various aspects of life.

Advisor: Wawrzynek, John