VictorJuiz/thesis huggingface dataset:
Theoretical approaches towards selective electrochemical nitrogen reduction

Anthropogenic climate change poses an extraordinary risk to all aspects of life on the planet in the coming decades. There is an urgent need to diminish the world's reliance on fossil fuels. However, transitioning industrial chemical processes to renewable energy inputs presents a unique challenge to net-zero emission targets. Many processes struggle with the intermittency of renewable energy sources, such as wind and solar, and even worse, several rely on fossil fuels as chemical inputs. Ammonia synthesis is one such chemical process. Ammonia plays a vital role as fertilizer in the world's global food supply chain. At 182 M tonnes of production volume per year, the Haber- Bosch process for synthesizing ammonia is the largest chemical process operating today. However, inherent drawbacks to the Haber-Bosch process make it unsuitable for a sustainable future. The process utilizes fossil-fuel derived hydrogen as an input, accounting for 1.6% of global carbon dioxide emissions. Additionally, the process's centralization and high capital costs make it challenging to deploy in the developing world, resulting in prohibitively high fertilizer costs in regions of the world with the most food scarcity. Developing alternative, sustainable forms of ammonia production is essential towards attaining global emissions goals and decentralizing agriculture. Electrochemical ammonia synthesis is an attractive alternative to the Haber-Bosch process. By removing the need for fossil-fuel derived hydrogen and replacing the prohibitively high pressures and temperatures with moderate reducing potentials, the electrochemical nitrogen reduction reaction (NRR) addresses many of the Haber-Bosch process's fundamental drawbacks. However, electrochemical ammonia synthesis is hindered by prohibitively low selectivity; hydrogen production is kinetically favored over ammonia production on every potential electrocatalyst currently known. In this thesis, we show how insights from electronic structure calculations and theoretical kinetic modeling can be used to (1) understand the activity and selectivity challenges in electrochemical NH3 synthesis and (2) propose and improve alternative ammonia synthesis systems. In particular, we examine the role that metal nitrides will play in achieving these goals by developing a theoretical understanding of their formation, activity, and stability. We also develop kinetic models for understanding non-aqueous NRR, with a focus on lithium-mediated nitrogen reduction. These theoretical frameworks are further improved through successful experimental collaborations. The resulting systems demonstrate a promising pathway towards selective and sustainable ammonia production

Keywords: Chemical Engineering.
Advisor: Cargnello Matteo
Noerskov Jens
Tarpeh William
Theoretical routes to advance oxygen electrocatalysis for energy conversion

Developing sustainable energy technologies is an essential step towards addressing climate change, which remains one of the greatest global challenges of our times. Electrochemistry offers a promising avenue to convert variable renewable energy (VRE), like solar and wind energy, into reliable carbon-neutral fuels, such as hydrogen gas. However, widespread adoption of electrochemical routes of sustainable energy conversion necessitates the development of active, stable, and selective catalysts that are economically viable. In this thesis, we explore various aspects of catalyst design from a theoretical perspective with a focus on electrochemical oxygen reduction (ORR) and evolution (OER). ORR is the rate-limiting reaction of hydrogen fuel cells, which convert hydrogen fuel and oxygen into water and electrical energy. OER limits the overall efficiency of the opposite process in water electrolyzers. To consider approaches to improve catalyst design for these and other electrochemical processes, we first apply density functional theory (DFT) to evaluate the promise of single atom catalysts to achieve high theoretical activity towards ORR and circumvent fundamental activity limitations through strategic design. We then turn our attention to stability considerations by implementing a pre-processing algorithm within the Pymatgen code to improve the efficiency of Pourbaix diagram construction for increasingly complex materials. We apply this algorithm to extract stability trends for a wide range of ternary oxides and build predictive models. By investigating the impacts of catalyst dissolution on activity for Ru-based pyrochlores for OER, we also combine activity and stability considerations to gain a more complete understanding of catalyst performance. Finally, we focus on kinetic modeling of electrochemical steps by studying generalizable trends in activation barriers for proton coupled electron transfers (PCET). We find that these barrier heights are largely governed by the identity of the proton acceptor, which can have profound impacts on simplifying microkinetic modeling and analyzing reaction pathways. Collectively, our findings shine light on thermodynamic activity, stability, and kinetic treatment of catalysts for oxygen electrochemistry

Keywords: Chemical Engineering.
Advisor: Jaramillo Thomas Francisco
Noerskov Jens
Reed Evan J.
Thermal and mechanical properties of molecularly-confirmed polymer/glass nanocomposites

Development of the next generation of high-performance polymer nanocomposites requires a detailed understanding of the underlying interfaces between constituents and their impact on the thermal and mechanical properties. In particular, the thermal conductivity and cohesive fracture resistance in nanocomposite materials is defined by the nature of interactions at the interface between the constituents. In my research, I have made advances in the performance of hyper-confined polymer nanocomposites using surface functionalization for fine-tuning the interfaces between molecularly confined polymers and the organosilicate glass matrix to improve the thermal and mechanical performance of these nanocomposites. Thermal reflectance and thin-film mechanics techniques were used to measure film thermal conductivity and mechanical properties of these nanocomposites. To correlate the improved thermal and mechanical properties, I used spectroscopic methods to quantify the chemical nature of the interface between functionalized porous organosilicate glass and organic polymers. A positive correlation is found between the extent of bonding at the interface as determined by infrared and X-ray photoelectron spectroscopies for multiple polymers and the resulting thermal and mechanical properties. Hyper-confined polyimide in porous organosilicate glass is a promising candidate for high temperature applications due to high fracture resistance and thermal stability. To characterize the nanocomposite performance, a thermal oxidative aging study was conducted and factors affecting the thermal stability of nanocomposite are analyzed. Using infra-red spectroscopy in addition to other techniques, I identified the key chemical and physical degradation pathways for the nanocomposite of polyimide and organosilicate glass at elevated temperatures. Surprisingly, the polyimide was found to be remarkably stable at > 300°C for over 200 hours in the nanocomposite, with the organosilicate glass being the limiting factor for thermal stability. Overall, I have developed an experimental framework combining chemical and mechanical methodologies to uncover the mechanisms governing the performance of hyper-confined polymer nanocomposites. These results will help further our understanding of the key structural and chemical factors governing the thermal and mechanical properties of nanocomposites

Keywords: Materials Science and Engineering.
Advisor: Dauskardt R. H. (Reinhold H.)
Appel Eric (Eric Andrew)
Qin Jian (Professor of Chemical Engineering)
A thesis of minimal degree : two

In this thesis, we study geometry and moduli spaces associated to low degree covers. There are two parts to this thesis. The first part comprises the bulk of the thesis and focuses on covers of degree 2. It is motivated by the Cohen-Lenstra heuristics in number theory. The second part contains three topics. The first relates to certain low degree covers of moduli spaces of elliptic curves, the second topic relates to heights on elliptic curves in characteristic 3, and the third topic relates to the Casnati-Ekedahl structure theorems for covers

Keywords: Mathematics.
Advisor: Vakil Ravi
Conrad Brian
Patel Anand
Three empirical essays on the historical development of cities

This dissertation explores several topics related to the historical development of cities. In the three chapters, I investigate 1) labor market consequences of urban displacement, 2) how land supply constraint impedes long-run urban growth, and 3) the phenomenon of urban mortality penalty in early modern Europe. The first chapter is titled "Does Slum Demolition Affect the Economic Outcomes of the Displaced? Evidence from Victorian England". I use a research design that compares displaced slum residents and comparable non-displaced ones living on the same street at baseline. I combine individual-level census data and detailed government archival records on slum demolition to show that displaced slum residents experienced labor market disruptions up to 10 years after eviction. The second chapter is titled "Does Land Use Restriction Impede Long-run Urban Development? Evidence from the Ejido System in Mexico". I find that a historical communal land system that outlawed the conversion of agricultural land for non-agricultural use impeded urban expansion in 20th century Mexico. Using a matched difference-in-difference design, I find cities that became surrounded by ejido land grew 10 percent slower in the second half of the 20th century. The third chapter is titled "Documenting the Urban Mortality Penalty in Early Modern Europe: Evidence from Migrants". I use a crowdsourced genealogy dataset that records the longevity and the migration history of millions of individuals in Europe. I find that, comparing between migrants born in the same area, those who moved to urban areas lived on average 1.6 years shorter than migrants who moved to rural areas

Keywords: Economics.
Advisor: Abramitzky Ran
Dupas Pascaline
Fetter Daniel Keath 
Morten Melanie
Tiny functions at large scale : new systems for interactive computing

Interactive computing has redefined the way that humans approach and solve complex problems. However, the slowdown of Moore's law, coupled with the massive increase in data volume and sophistication, has prevented some applications from running interactively on microcomputers. Tasks such as video processing, software compilation & testing, 3D rendering, simulations, and data analytics have turned into batch jobs running on large clusters, limiting our ability to tinker, iterate, and collaborate. Meanwhile, by offering an ocean of heterogeneous computing resources, cloud computing has provided us with a unique opportunity to bring interactivity to such applications. This dissertation presents my experiences in creating new systems and abstractions for large-scale interactive computing, where users can execute a wide range of resource-intensive tasks with low latency. My thesis is that commodity cloud platforms can be utilized as an accessible supercomputer-by-the-second for interactive execution of large jobs. By leveraging granular cloud services, users can burst to tens of thousands of parallel computations on demand for short periods of time. I will discuss my experience building such applications for massively burst-parallel video processing, 3D path tracing, software builds, and other tasks. First, I describe ExCamera, a system for low-latency video processing using thousands of tiny threads. ExCamera's core contribution is a video encoder intended for fine-grained parallelism that allows computation to be split into thousands of tiny tasks without harming compression efficiency. Next, I discuss R2E2, a highly scalable path tracer for 3D scenes with high complexity. R2E2's main contribution is an architecture for performing low-latency path-tracing of terabyte-scale scenes using serverless computing nodes in the cloud. This design allows R2E2 to leverage the unique strengths of hyper-elastic cloud platforms (e.g., availability of many CPUs/memory in aggregate) and mitigates their limitations (e.g., low per-node memory capacity and high latency inter-node communication). Finally, drawing on the experience of building burst-parallel applications like ExCamera, I describe gg, a framework designed to facilitate the implementation of burst-parallel algorithms on serverless platforms. gg specifies an intermediate representation that allows a diverse class of applications to be abstracted from the computing and storage platform, and leverage common services for dependency management, straggler mitigation, and scheduling. Using gg IR, the developers express their applications in terms of the relationships between code and data, while the framework carries the burden of efficiently executing them

Advisor: Winstein Keith
Fatahalian Kayvon
Zaharia Matei
To describe America, 1835-1941

When John James Audubon wanted to make life-like paintings of birds, he killed them. Assembled during the first decades of western expansion, The Birds of America (1838) catalogues herons and hawks and species extinct within a century of its publication, and as often participates in the precariousness it hopes to suspend. "To Describe America, 1835-1941" explores a long century of American description, and brings together writers and artists whose work becomes an urgent attempt to capture fleeting experience and fragile object-life amid environmental and social loss. My chapters move through time and across genres to reveal a diversity of descriptive practice, and its response to an evolving national identity and sense of place. I show how geology, the building of the Transcontinental Railroad, and the sublimated violence of the Civil War shape landscape writing in the 1860s; how the twinned media of portraits and telescope photography describe what we cannot see; how the pioneering flash photographers George Shiras and Jacob Riis document disappearing Midwestern wilderness and tenement life in New York City at the turn of the century; how novelists, filmmakers, photographers, and WPA writers and artists adapt the nineteenth century panorama to depict natural and socioeconomic disaster in Depression-era California; and how United States Signal Service Corps records and the George R. Stewart novel Storm (1941) register an ambivalent relationship between science and belief, individual and climate

Advisor: Jones Gavin Roger
Nemerov Alexander
Richardson Judith
Ruttenburg Nancy
Topics in convex optimization for optimal control and reinforcement learning

Optimal control describes the problem of finding a control to minimize an objective function for a dynamical system over a period of time. It has been applied in a wide variety of applications in robotics, signal processing, and machine learning over several decades. On the other hand, reinforcement learning (RL) assumes the uncertain model of transition dynamics and has achieved great empirical success in recent years including simulated game scenarios and robotics. This thesis focuses on topics in both optimal control and reinforcement learning

Keywords: Electrical Engineering.
Advisor: Boyd Stephen P. 
Lall Sanjay
Pilanci Mert
Topics in exact asymptotics for high-dimensional regression

Exact asymptotic theory refers to a collection of techniques for precisely characterizing the distribution of high-dimensional regression estimators. Examples of the estimators it characterizes include the Lasso, ridge regression, the elastic net, and SLOPE, among others. The theory requires strong assumptions---Gaussian and sometimes independent covariates---and is usually developed for one restricted class of estimators at a time. This thesis expands the scope of exact asymptotic theory by providing generalizations to symmetric but possibly non-separable penalties with independent covariates and to correlated covariates in the context of the Lasso. Further, it provides novel exact asymptotic characterizations of the joint distribution of two regression estimators computed on the same data. It applies these developments and existing theory to important statistical problems, including optimal penalty design, adaptive estimation, inference with the debiased Lasso, hyperparameter tuning, and consistent estimation of low-dimensional parameters when high-dimensional nuisance parameters cannot be estimated well

Keywords: Statistics.
Advisor: Montanari Andrea
Candès Emmanuel J. (Emmanuel Jean)
Donoho David Leigh
Topics in optimization and learning

Optimization algorithms that learn from data have been around for a long time. Nevertheless, the ever-changing nature of computational resources, the development of modern theoretical tools, and the availability of new kinds of data keep us wondering what is possible in this field. This work gives us a fresh look at three classical problems: point-set registration, multi-resolution analysis, and tensor factorization. First, we explore how the least unsquared loss ensures exact recovery of the optimal rotation between two point clouds under gross corruption. We also show a phase transition of the probability of exact recovery when the least unsquared loss is optimized over convex sets containing the special orthogonal group. Second, we present a neural network architecture inspired in the non-standard wavelet form, called BCR-net. The BCR-net uses significantly fewer parameters than standard neural networks and shows promising behavior compressing non-linear integral operators. Lastly, we discuss the implications of defining a factorization-preserving algebra to evaluate functions over high-dimensional tensors. We focus on what makes tensors in tensor ring form challenging to work with, and we give insights on how to overcome these challenges

Advisor: Ying Lexing
Darve Eric
Gerritsen Margot (Margot G.)
Toward Few-Shot Response Generation for Conversational Agents

One of the major impediments to the deployment of task-oriented response generation models in real-world scenarios is a lack of labeled in-domain training data. In this thesis, we explore a multi-task learning approach in which we pre-train a model with unlabeled dialogues, and then fine-tuned in a few-shot setting to generate factually accurate and natural agent responses.
We formulate our primary task as utterance paraphrasing, where a template-based agent utterance in the context of the preceding dialogue is converted into a more natural-sounding one. We adapt unlabeled dialogues as pre-training data for this task by using automatically generated paraphrases of the last agent utterance as templates to predict the utterance in the context of the preceding dialogue turns. Since the real templates lack the contextual information embedded in the paraphrases, we introduce an auxiliary task, next sentence prediction, where the last turn of a dialogue is masked in the input and used as the predicted gold output.
Evaluation across several conversational datasets shows that our pre-training strategy improves the quality of responses in an in-domain context, indicating that leveraging unlabeled datasets can be an effective method for improving the quality of responses.

Keywords:  Program in Symbolic Systems
stanford
symbolic systems
response generation
conversational agent
virtual assistant
natural language processing
task oriented dialogue
Thesis
Advisor: Lam Monica
Potts Christopher
Towards collaborative development of lineage-agnostic cells

Present methods for engineering living organisms tend to rely on "atheoretical" tinker-test or "brute-force" design via large-scale automation. To fully enable a "biology age", we must understand cells -- the fundamental unit of life -- well enough to build them. The two existing cell-building approaches are based on (i) minimizing genomes, and (ii) ab initio synthesis of protocells. Minimization of natural genomes benefits from the viability of existing cells but has failed to resolve the functions of all the genes necessary for life. Conversely, bottom-up cell building efforts start from known components but have yet to produce viable cells. Here, I report methods for building synthetic cells to broadly enable the cell building community. First, I combine the strengths of existing cell building approaches by evaluating the potential of minicells -- cell-like "containers" devoid of genetic material -- as a natural chassis for instantiating fully-understood synthetic genomes. I demonstrate that minicells can be extracted at high purity from a growing culture of progenitors. Next, I show that batches of purified minicells are capable of heterologous gene expression. Finally, I quantify the absolute expression capacity of individual minicells and find that about 13% of gene-expressing minicells should have the capacity to express an entire genome. Thus, minicells should be capable of "booting-up" ab initio genomes sufficient to enable a framework supporting distributed design of lineage-agnostic genomes. Second, as a significant step towards genome design, I determine the degree to which the PURE (protein synthesis using recombinant elements) cell-free system is qualitatively capable of self-regeneration. Specifically, I measure the extent to which single-enzyme depletions of PURE result in significant decreases in protein expression. For those single-enzyme depletions that exhibit substantial decreases in expression, I demonstrate that PURE-produced versions of each enzyme can be used to recover PURE activity, signifying successful self-regeneration. The culmination of my work with PURE is a formalized framework undergirding collaborative development of lineage-agnostic cells

Keywords: Electrical Engineering.
Advisor: Endy Andrew D. 
Horowitz Mark (Mark Alan)
Soh H. Tom
Towards comprehensive action understanding in videos

An enormous amount of videos are created, spread, and watched daily. In the ocean of videos, the actions and activities of humans are often the pivots. We desire machines to understand human actions in videos as this is essential to various applications, including but not limited to healthcare, security system, and human-robot interactions. For these applications to be realized, action understanding must go beyond simply answering "what is the action", but more comprehensive. An intelligent agent should be able to know "who/where is the actor", "what/where is the object", "what interaction is happening between the actor and the object", "when does an action start and end", and more. Achieving comprehensive action understanding is non-trivial since the need for data and labels combinatorially increases when trying to solve multiple problems, not to mention that video data and labels are expensive to collect, store, and consume. Therefore, to obtain comprehensive action understanding, we not only need to perform multiple tasks but also have to ensure data efficiency. In this dissertation, we discuss three questions to realize data-efficient and comprehensive action understanding. How to reduce the need for data and labels? How to perform multiple tasks without combinatorial growth of data? How to solve new problems efficiently with some other problems solved? For the first question, our works on few-shot video classification and semi-supervised temporal action proposals introduce video-specific techniques and strategies for learning with less supervision. For the second question, we demonstrate how to avoid enumerating all combinations of categories from subtasks by knowledge disentanglement in a study on actor-action segmentation. For the third question, we propose constructing compositional representation from human-object relationships in videos, and such representation leads to better generalizability in action recognition models

Keywords: Electrical Engineering.
Advisor: Li Fei Fei
Niebles Duque Juan Carlos
Guibas Leonidas J.
Savarese Silvio
Yeung Serena