VictorJuiz/thesis huggingface dataset:
Le portrait dans le roman français au XVIII siècle

This study traces the history of the uses, transformations and repurposing of portraits in 17th- and 18th-century French fictional works. The first chapter analyses Fénelon's novel Télémaque (1699) and takes into consideration his original approach, which draws from ancient Greek sources and innovates at once 17th-century portraiture practices. The use of portraits as hermeneutic, proleptic and pedagogic tools in a text which will become one of the most read and studied works of the 18th-century paves the way for new applications of what had thus far been a sum of descriptive elements or a socialite practice at best. The second chapter brings to light the elements of continuity existing between 17th and 18th-century portraiture practices while analyzing the symbiotic relationship established between portraits and fictional memoirs. By considering the portrait as a technological tool I am able to uncover the complex and reciprocal transformations of novels and portraits, but also the latter's ludic, proleptic and pedagogic functions. The third chapter focuses on Jacques Cazotte's Le diable amoureux (1772) and explores this author's long and playful use of portraits across his works. The analysis of this novel reveals a major portraiture innovation of the late 18th century, one that transforms portraits into tools promoting obfuscation and suspense.

Keywords: French & Italian.
Advisor: Cohen Margaret
Bender John B.
Edelstein Dan
Power and the void : representations of sovereignty in unperformed plays of the Stalinist period

This dissertation explores the relationship between sovereign power, its subjects, and the theatrical form through four unpublished and unperformed (or barely performed) plays from the time of Stalin's reign. In analyzing these plays, I approach sovereign power as, above all, a performance. This framing allows for the consideration of the position of the sovereign as a role to be inhabited, just as an actor on the stage gives life to their character. In light of this approach, the dramatic form becomes an ideal medium through which to meditate on how power-performances function: how they succeed, their varied impacts on their subjects, and also what weaknesses they may possess. The four plays featured in this project present a variety of modes through which to understand sovereignty and subjecthood. In some, the fleeting nature of performance and theatrical presence is emphasized, and sovereigns are depicted as ghostly phantoms who cannot generate the physical substance necessary for their rule to endure. In others, the elusive, intangible nature of the sovereign and the structures that surround him are shown in a more menacing light, as subjects can be trapped and seized by this power without a chance to resist. Placed together, these plays present a complex depiction of how theater can illuminate the power dynamics and struggles between sovereigns and subjects. Additionally, these plays are not prominent in their authors' respect oeuvres; this project thus sheds light on lesser-studied but textually and dramatically rich works of the Stalin era.

Keywords: Slavic Languages and Literatures.
Advisor: Jakovljević Branislav
Ilchuk Yuliya
Safran Gabriella
Practical mechanisms for platforms in modern markets

The emergence of modern online marketplaces has certainly disrupted the way we trade goods and services. It has eliminated the need for intermediaries in numerous value chains and driven the growth and prosperity of the gig economy. Endowing (professional and nonprofessional) suppliers with the capability to directly participate in the market has allowed online platforms to flourish with ever-more diverse sets of listings. In the absence of other intermediaries, these platforms have taken an increasingly critical role in facilitating trades. Online platforms seek to create competitive markets where buyers can easily navigate through the wide sea of listings. Meanwhile, market mechanisms utilized in these marketplaces must be simple and transparent so that anyone can comfortably understand and trust them. Motivated by these challenges, I leverage techniques from game theory, optimization, algorithm design, and Bayesian statistics to better design practical market mechanisms for platforms in modern markets. In particular, I study how to optimally design assortment mechanisms---deciding which of the listings are showcased to buyers---for online marketplaces in practice. The first chapter of my dissertation looks at the mechanism design problem of finding an optimal pay-as-bid mechanism in which a platform chooses an assortment of suppliers in order to balance the trade-off between two objectives: providing enough variety to accommodate heterogeneous buyers, yet at low prices. The second chapter of my dissertation investigates the effect of quality uncertainty on the choice of ranking mechanisms in settings that mimic online consumer-to-consumer marketplaces

Advisor: Saban Daniela
Weintraub Gabriel
Johari Ramesh
Preconditioning techniques for sparse linear systems

We present two novel methods, SSAI and HyKKT, for sparse linear systems. The methods differ in that SSAI is meant to be an out of the box solver that is robust on many different types of Hermitian positive definite (HPD) linear systems and has a variant that can be used to solve general systems (including rectangular ones), but is not necessarily the best for a given linear system. This is due to the fact that SSAI in an effort to be general, ignores domain knowledge of the problem. In contrast, HyKKT is a specialized solver for very sparse symmetric indefinite linear systems with a Karush-Kuhn-Tucker (KKT) structure. These two methods represent the main parts of the thesis. Chapter 2 introduces SSAI, a method for solving a Hermitian positive definite linear system Ax=b, where A is an explicit sparse matrix (real or complex). A sparse approximate right inverse is computed and replaced by its symmetrization M, which is used as a left-right preconditioner in a modified version of the preconditioned conjugate-gradient method (PCG), where M is modified occasionally, if necessary, to make it more positive definite. Before symmetrization, M is formed column by column and can therefore be computed in parallel with no communication except at the beginning and end. PCG requires only matrix-vector multiplications with A and M (not solving a linear system with M), and so too can be carried out in parallel. We compare it with incomplete Cholesky factorization (the gold standard for PCG) and with a direct Cholesky factorization and solve on sparse matrices from various applications and show it is robust. For least-squares problems, we implement an analogous form of preconditioned Conjugate Gradient Least-Squares (PCGLS) and show it is also robust. The contributions of the work in Chapter 2 are summarized in Section 2.2. Chapter 3 introduces HyKKT, a solution strategy for the large indefinite linear systems arising in interior methods for nonlinear optimization. The method is suitable for implementation on hardware accelerators such as graphical processing units (GPUs). The current gold standard for sparse indefinite systems is the LBL factorization, where L is a lower triangular matrix and B is 1X1 or 2X2 block diagonal. However, this requires pivoting, which substantially increases communication cost and degrades performance on GPUs. Our approach solves a large indefinite system by solving multiple smaller positive definite systems, using an iterative solver on the Schur complement and an inner direct solve (via Cholesky factorization) within each iteration. Cholesky is stable without pivoting, thereby reducing communication and allowing reuse of the symbolic factorization. We demonstrate the practicality of our approach on large optimal power flow problems and show that it can efficiently utilize GPUs and outperform LBL factorization of the full system. The contributions of the work in Chapter 3 are summarized in Section 3.3.

Advisor: Darve Eric
Saunders Michael A.
Wootters Mary
Ying Lexing
Predicting the Spatial Distribution of Snow Water Equivalent using Transfer Learning

Globally, water resources are under increasing stress that risks both water availability and quality. Hydrologic and watershed models have been used to forecast both water flow and quality. Furthermore, these models serve as decision making tools for sustainable water management. These models are subject to high uncertainties that can be reduced by accurate estimates of high-resolution spatial distribution of Snow Water Equivalent (SWE). The Airborne Snow Observatory generates Lidar maps that quantify SWE at a 50 m resolution. Although these maps provide detailed information, their infrequent temporal availability limits their use for modeling. For instance, in the Rocky Mountains of Colorado, only 12 maps are present in total for the period covering 2016 to 2019. In this work, we develop a neural network (NN) framework to model this temporally sparse Lidar data, which enables the estimation of SWE at unmapped time points in the Rocky Mountains of Colorado. To achieve this, we first incorporate meteorological and topographic data that describe the physical processes governing SWE. This data is used to extract seven predictor variables: accumulated snow, accumulated precipitation, mean seasonal temperature, the sum of positive degree days, elevation, slope, and aspect. Next, we tackle data scarcity by using transfer learning to integrate information from 80 Lidar maps in the Sierra Nevada mountains in California. An explanatory factor analysis is conducted to justify and reinforce the use of transfer learning. Results show that our NN framework, using the seven predictor variables, models SWE with a mean R2 of 0.44 prior to transfer learning. After applying transfer learning, we improve the mean R2 results to 0.56. Overall, transfer learning increases model predictability while decreasing both scatter and bias.

Keywords: Mital, Utkarsh
Snow water equivalent
Transfer learning (Machine learning)
Deep learning (Machine learning)
Factor analysis
Text
Capstone
Report
Thesis
Student project report
Advisor: Tartakovsky Daniel
A predictive simulation framework for buoyancy-driven natural ventilation in office buildings

Natural ventilation and cooling can significantly reduce building energy consumption, but inherent variability in boundary and operating conditions makes optimal and robust design of natural ventilation a challenging task. The goal of this study is to enable robust design and operation of natural ventilation systems by developing an efficient simulation framework that can predict the building's thermal response under buoyancy-driven natural ventilation. A simplified building thermal model that solves for the time-evolution of the volume-averaged indoor air temperature with uncertain quantification (UQ) can provide an intuition of the likely effects of design choices under a variety of operating conditions in the early building design stage. Subsequently, computational fluid dynamics (CFD) simulations can be employed to fine-tune the building design at the design development stage, and the CFD results can be used to reduce the uncertainty in the building thermal model coefficients that determine the flow and heat transfer rates. The predictive capability of the resulting multi-fidelity framework was validated with a carefully designed full-scale experiment performed in an operational atrium building. A CFD-based experimental design ensured optimal temperature sensor placement under the full range of expected operating conditions to ensure an accurate characterization of the volume-averaged indoor air temperature, as well as of the spatial variability in the temperature field. The prediction of thermal performance matches well to the experiments with discrepancies lower than 0.38 °C for all the approaches, and the final widths of the 95% confidence interval are reduced to less than 0.18 °C, indicating promising predictive capabilities for buoyancy-driven natural ventilation at all stages of designs

Advisor: Gorle Catherine
Fischer Martin
Kitanidis P. K. (Peter K.)
Pre-finetuning methods for domain and task adaptation, with applications to discourse and translation

A recently adopted standard technique for training models for NLP applications is to pretrain a model on a large generic dataset and then finetune the model on the target domain training data. However, the pretraining task often differs greatly from the target task. Target tasks can use data from different domains, incorporate custom labels, and require modifications to base models, such as adding parameters. Performance on downstream tasks can be improved by bridging the gap between the pretraining and finetuning phases. An intermediate phase, called pre-finetuning, can be used to adapt to the target domain prior to finetuning. Introducing an intermediate phase incorporates some domain knowledge into the model, resulting in better performance after finetuning. We study applications to discourse and translation, both of which are settings where high quality in-domain labels are expensive to acquire. We present a theoretical framework for language model adaptation and three applications of pre-finetuning, each an exemplar of pre-finetuning focused on the data, model and annotations respectively: (1) data selection for machine translation, (2) sentence-level objectives for discourse performance of language models and (3) weakly-supervised discourse relation recognition. We present empirical results on data selection for neural machine translation and show that pre-finetuning on a subset of pretraining data that is most similar to the target domain improves the performance of the final model. However, we show that while trivially selecting the most similar data improves performance, the optimal setting requires finding data that complements the target domain data rather than mirroring it. Then, we present Conpono, a novel objective introduced during pre-finetuning. The inter-sentence objective models discourse coherence and the distance between sentences. We show that by pre-finetuning a pretrained language model with Conpono, the model improves on the previous state-of-the art on discourse representation evaluation benchmarks. Lastly, we introduce DiscoMtB, a method for discourse representation learning to discover discourse structure that acts as pseudo-labels in a text corpus. The weakly supervised discourse relations are used to both create new pre-finetuning training data for sentence relation classification tasks and to augment text generation models as an interpretable knob for controlling generation to introduce more diversity to the generation space while maintaining discourse coherence in the generated text. This dissertation presents the benefits of a three-phase training process and presents three applications, which bridge the gap between pretraining and finetuning during pre-funetuning by adapting the data, model architecture and labels, respectively

Advisor: Jurafsky Dan
Hashimoto Tatsunori
Liang Percy
Preliminary Insights on TRAPPIST-1's Planetesimal Disk Architecture from N-Body Simulations

One of the driving forces of modern astronomy is to detect and image habitable Earth-sized exoplanets to better understand the requirements for life and, by extension, our place in the universe. Recently, the TRAPPIST-1 system has become the focus of intense study because three of its seven Earth-sized terrestrial planets lie within the habitable zone of an ultra-cool red dwarf star, where water — if present at the surface — could be liquid. The orbital periods of all seven planets in the TRAPPIST-1 system are observed to be in small-integer ratios, with all seven planets linked via three-body resonances. Through N-body simulations, we use the long-term stability of the system and survivability of the resonance chain to constrain the feasible architecture of a theoretical planetesimal belt, in turn improving our understanding of potential volatile delivery to the planets within the habitable zone. Here we use the open-source REBOUND package to first improve on previous dynamical studies of the TRAPPIST-1 system with refined orbital parameters, and then introduce a custom collision model that can extract relative impact velocities at times of collision. We then show that planetesimal impactors preferentially target the outermost planet h, and most collisions occur in the first tens of thousands of Earth years of integration time. Our results imply that a theoretical planetesimal belt would preferentially affect the outermost planet on short timescales and initiate migration of the outer planets, potentially disrupting system stability. These N-body simulations contribute to long-running conversations regarding volatile transport via planetesimal impactors and the habitability of the TRAPPIST-1 system.

Keywords: TRAPPIST, N-body, exoplanet, habitability
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Thesis
Advisor: Hilley George
Lissauer Jack
Preschool-Aged Children Can Infer What Speakers Know Based on How They Influence Others

How do we know what others know? Prior work has examined how children use evidence about isolated agents, like their perceptual access and actions, to infer what they know. However, humans are rarely fully isolated; instead, we are often surrounded by others whom we interact with, influence, and are influenced by. In these contexts, we can use a speaker’s communication and the way it causes a listener to behave to infer what that speaker knows - even if we do not know the specific content of what was communicated. The present studies investigated how preschool-aged children use two pieces of evidence about listeners to reason about what speakers know: changes in the outcomes of a listener’s actions following communication (Study 1) and changes in a listener’s actions themselves following communication (Study 2). In both studies, children observed two scenarios where a listener failed to activate a toy before succeeding. In Study 1, children observed a speaker produce nonsense language towards a listener after they failed but before they succeeded to activate a toy, as well as another speaker who spoke to a listener prior to initial failure. In Study 2, children observed a speaker communicate with a listener before a distinct change in action, followed by success, as well as another speaker who communicated with a listener resulting in no distinct change in action, followed by success. When asked which speaker knows how to make the toy work, 5 year-olds chose the speaker who appeared to cause the listener to succeed (Study 1) or change their action (Study 2). These results suggest that preschool-aged children are sensitive to the way speakers influence others via communication and can use evidence of that influence to infer what speakers know. More broadly, these studies highlight children’s ability to reason about the knowledge of one agent (a speaker) based primarily on evidence about another agent (a listener).

Keywords: Cognitive Development
Social Cognition
Theory of Mind
Child Development
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Thesis
Advisor: Gweon Hyowon
Chuey Aaron
Prestige, Preference, and Preservation: Language Policies for Nahuatl and Guarani, from Empire to Nation

The Spanish colonization of the Americas has ultimately created a situation where no indigenous language is favored. In this thesis, I provide a comparative history of the language policies governing two indigenous languages, Nahuatl and Guarani, from the colonial period to the present. These languages were chosen because of their historic and demographic significance; Nahuatl was the language of the Aztec Empire and Guarani still remains the majority language of Paraguay. While much has been written about both languages, their histories have not been compared. My research was purely bibliographical. I consulted primary and secondary sources in English, Spanish, Nahuatl and Guarani; the thesis is a synthesis of these various histories, historical documents, and laws. My research has shown that Nahuatl and Guarani were subjected to similar policies, even after the independence of Mexico and Paraguay. In fact, Guarani’s policies were even less tolerant than Mexico’s at certain points in history. However, Nahuatl and Guarani’s policies were not always intolerant. Bans on both languages were lifted, relaxed, and overlooked by those in power many times when these languages were useful in gaining popular appeal. Even in times of official intolerance, there were impracticalities in enforcing such policies, depending on the situation of each country. Therefore, policy alone cannot account for the current disparity between each language’s vitality. Understanding the history of each language can allow us to identify the key actions for reversing language shift.

Keywords: Language policy
Mexico
Paraguay
Indigenous peoples > Languages
Nahuatl language
Guarani language
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Thesis
Advisor: Solano-Flores Guillermo
Principled approaches to modern machine learning challenges

Learning based models have been successfully deployed in various sciences and engineering applications. Despite the empirical successes, there is little understanding about the workings and the failure modes of these models. In this thesis, we focus on the following questions: a) when and why do these models generalize beyond the set of training data, and b) what conclusions can one reliably make when the data available is insufficient to learn accurate models. In the first part, we focus on provable generalization guarantees. We take the first steps towards understanding why deep neural networks generalize to unseen data despite having a larger number of parameters than the available data points to learn from. We characterize the ``simple" behaviour of the functions learned by neural networks in certain settings, which in turn can be shown to generalize well on unseen data. We next design algorithms for provably learning decision trees inspired from the top-down heuristics commonly used in practice. The key idea is to use a small subset of features to determine the splitting criterion at each node of the decision tree, instead of a single one as is currently used by practitioners. Our proposed algorithm is both sample and time efficient, and allows us to provide generalization guarantees for decision functions which are well represented by small decision trees. In the second part, we focus on what conclusions can we make when do not have sufficient labeled data. Learning methods, especially deep neural networks, require large amounts of labeled training data to learn meaningful predictive models. In many domains, such labeled data comes as a result of performing extensive experiments, thus restricting the amount of data one can receive. Given such limited data, one is often interested in knowing what useful conclusions can we reliably make when the data is insufficient to learn any reliable predictor. For example, if we are given the test points in advance, is it easier to predict the labels of those given test points rather than learning the entire model? In this part of the thesis, we show that given a particular test point along with a batch of unlabeled data set, one needs much fewer active label queries (independent of the complexity of the function class) to make a good prediction on this test point as compared to learning the complete function for several hypothesis classes

Advisor: Charikar Moses
Valiant Gregory
Tan Li-Yang