VictorJuiz/thesis huggingface dataset:
Data-driven methods for modeling of extreme events [electronic resource]

The motivation of this thesis is to model the risk of extreme events. With rapidly increasing amounts of available data, the statistics on extreme events is more accurate. The developed methodology in this thesis work extends existing actuarial science and financial risk approaches. These approaches use a branch of statistics known as extreme value theory (EVT). One of the extensions is analyzing risk trends across years for each calendar month. This is a highly non-trivial problem because extreme events might not happen every year for a given month. A Bayesian maximum a posteriori (MAP) formulation is applied to tackle this problem. One of the main applications of this novel method is characterizing the risk trends of 100 year extreme high temperature events. We focus on these extreme weather events since their occurrences are immensely disruptive and damaging. The non-triviality of the problem is in distinguishing climate change from the immense variations in weather, both spatially and temporally. The data analyzed is from the continental U.S. from 1979 to 2015. We show that aggregation from multiple locations is valid in order to get more accurate statistics on 100 year extremes. There is a factor of 2.7 relative increase for 100-year extreme high temperature event risk that is found in the last 4 decades of the climate data. This is primarily caused by both a factor 2.3 relative increase in the number of and a 41\% increase in magnitude of extreme high temperature events over the last 37 years. Another method is also developed for building a non-parametric stochastic model of the distribution from large data sets. This is done with an modified version of quantile regression (QR). This new QR formulation also tackles two major issues with the original QR method itself. In the second main application example, we consider probabilistic forecasting of the loads in electrical power grid. A probabilistic forecast is given by our estimated QR model. The probabilistic evaluation of risk is important because load volatility is increasing with the on-going proliferation of the renewable generation. The most important risk factor is that the required amount of the electricity cannot be procured at the spot market, because the stand-by generation capacity is insufficient. An application to electric utility data shows significant improvement in load balancing risk compared to existing methods.

Keywords: Physics.
Advisor: Chu Steven
Gorinevskiĭ D. M. primary advisor. Thesis advisor
Boyd Stephen P. advisor. Thesis advisor
Data-driven verification [electronic resource]

Automatic software verification is an important but hard problem. Verifiers primarily rely on static analysis to reason about all possible program behaviors, where a purely static analysis makes inferences based solely on program text. However, since verification is so hard, to be successful it seems necessary to leverage all possible sources that can provide any useful information about the program. Hence, limiting a verifier to just the program text is unnecessarily restrictive. Our thesis is that verification can be aided and significantly improved by learning from data gathered from program executions. In particular, we focus on the problem of invariant inference and its applications in verification. Invariant inference is a core problem that every software verifier must address. The traditional verifiers infer invariants by analyzing program text alone. The main contribution of this thesis is an effective technique to infer loop invariants by combining static analysis and machine learning applied to program executions.

Advisor: Aiken Alexander primary advisor. Thesis advisor
Dill David L. advisor. Thesis advisor
Mitchell John C. advisor. Thesis advisor
Decision-making at scale [electronic resource]

Recent years have seen an increase in democratic innovations aimed at increasing the participation of the public in policy-making. This observation, coupled with the increasing prevalence of internet-based communication, points to a very real possibility of implementing participatory democracies on a mass-scale in which every individual is invited to contribute their ideas and opinions. This dissertation addresses two challenges to realizing such a vision: scalability and polarization. I first address the challenge of scaling up voting. In the classical theory of social choice, participants submit rankings over all the candidates. This is, unfortunately, not feasible when voting over a large number of contributed ideas. We give algorithms that elicit approximate winners for a large class of social choice functions while only requiring participants to make a small number of comparisons, significantly improving on previous lower bounds. We show that one can further improve these algorithms to preserve voter privacy, resist strategic manipulation, and account for streaming settings. We demonstrate these ideas in Finland's recent off-road traffic law reform. We also propose a model for scaling up aggregation more collaboratively, through sequences of small group deliberations. We consider whether such interactions can be used to find the wisdom of the crowd, defined here to be the median opinion, and show a result indicating the importance of having groups of at least three. Specifically, we show that a small number of triadic interactions are able to find a tight approximation of the generalized median, but that dyadic interactions satisfying natural axioms are unable to. Finally, we briefly describe our work addressing the challenge of polarization in society. Several empirical studies have shown that homophily, i.e. greater interaction between like-minded individuals, results in polarization. However, we show that well-known models of opinion formation based on repeated averaging can never be polarizing, even if individuals are arbitrarily homophilous. We generalize these models to account for a phenomenon well-known in social psychology as biased assimilation and show that a simple model of homophilous networks can result in either polarization, persistent disagreement or consensus depending on how biased individuals are. This model is then used to discuss methods for countering polarization.

Keywords: Electrical Engineering.
Advisor: Goel Ashish primary advisor. Thesis advisor
Johari Ramesh
Saberi Amin advisor. Thesis advisor
Decoder algorithm design for high-performance and robust neural prostheses [electronic resource]

Millions of people suffer from motor-related neurological injury or disease, which in some cases is so severe that even the ability to communicate is lost. For people with lost motor function, including paralysis and amyotrophic lateral sclerosis, neural prostheses are an emerging technology that has the potential to increase quality of life and enable greater communication with the world. Neural prostheses record neural signals from the brain and, through a decoder algorithm, translates these neural signals into control signals for actuating machines such as a computer cursor or a prosthetic arm. There are three important areas of development for neural prostheses to be clinically viable. Specifically, these systems must be (1) high-performance, (2) robust to perturbations and noise, and (3) usable for decades. My dissertation is focused on the design of decoder algorithms that achieve state-of-the-art performance in all three of these areas. These decoder algorithms rely on techniques and intuition from machine learning, statistical signal processing, and neuroscience. Together, these advances in decode algorithm design increase the clinical viability of neural prostheses.

Keywords: Electrical Engineering.
Advisor: Shenoy Krishna V. (Krishna Vaughn) primary advisor. Thesis advisor
Goldsmith Andrea
Henderson Jaimie (Jaimie M.)
Deep exploration via randomized value functions [electronic resource]

The "Big Data" revolution is spawning systems designed to make decisions from data. Statistics and machine learning has made great strides in prediction and estimation from any fixed dataset. However, if you want to learn to take actions where your choices can affect both the underlying system and the data you observe, you need reinforcement learning. Reinforcement learning builds upon learning from datasets, but also addresses the issues of partial feedback and long term consequences. In a reinforcement learning problem the decisions you make may affect the data you get, and even alter the underlying system for future timesteps. Statistically efficient reinforcement learning requires "deep exploration" or the ability to plan to learn. Previous approaches to deep exploration have not been computationally tractable beyond small scale problems. For this reason, most practical implementations use statistically inefficient methods for exploration such as epsilon-greedy dithering, which can lead to exponentially slower learning. In this dissertation we present an alternative approach to deep exploration through the use of randomized value functions. Our work is inspired by the Thompson sampling heuristic for multi-armed bandits which suggests, at a high level, to "randomly select a policy according to the probability that it is optimal". We provide insight into why this algorithm can be simultaneously more statistically efficient and more computationally efficient than existing approaches. We leverage these insights to establish several state of the art theoretical results and performance guarantees. Importantly, and unlike previous approaches to deep exploration, this approach also scales gracefully to complex domains with generalization. We complement our analysis with extensive empirical experiments; these include several didactic examples as well as a recommendation system, Tetris, and Atari 2600 games.

Keywords: Management Science and Engineering.
Advisor: Van Roy Benjamin primary advisor. Thesis advisor
Duchi John advisor. Thesis advisor
Johari Ramesh
Kochenderfer Mykel J.
Deep learning and probabilistic methods for robotic perception from streaming data [electronic resource]

Many robots today are confined to operate in relatively simple, controlled environments. One reason for this is that current methods for processing visual data tend to break down when faced with occlusions, viewpoint changes, poor lighting, and other challenging but common situations that occur when robots are placed in the real world. I will show that we can train robots to handle these challenges by modeling the causes behind visual appearance changes. If we model how the world changes over time, we can be robust to the types of transitions that objects often undergo. I show how we can use this idea to improve performance on four different tasks: segmentation, tracking, velocity estimation, and object recognition. Many of the methods in this dissertation are demonstrated in the context of autonomous driving, although they are generally applicable to other robotic applications for dynamic environments. By modeling the causes of appearance variations over time, we can make our methods more robust to a variety of challenging situations that commonly occur in the real world.

Advisor: Savarese Silvio primary advisor. Thesis advisor
Ng Andrew Y.
Thrun Sebastian
Defining trafficking programs of human plasmablasts responding to tissue-specific immune challenge [electronic resource]

Antibody secreting cells are generated in regional lymphoid tissues and traffic as plasmablasts (PB) via lymph and blood to target sites for local immunity. The PB, a transiently activated population of B cells, are present in low numbers in the peripheral blood of healthy individuals. After acute infection or in setting of active immune responses, however, the frequency of blood PB increases dramatically, peaking 7 to 9 days after immunization or the onset of symptom. PB localized to target tissues undergo further maturation into antibody-secreting plasma cells. For this tissue-specific migration, the homing signature is imprinted on activated lymphocytes during their development by local environmental signals, but the trafficking mechanisms of PB remain poorly characterized. Here, we used multi-parameter flow cytometry to define trafficking programs (TPs, combinations of adhesion molecules and chemoattractant receptors) of PB, and their imprinting in patients in response to localized infection or immune insults. Using single cell-based analysis, we show that TPs enriched after infection or autoimmune inflammation of mucosae correlate with sites of immune response or symptoms, with different TPs imprinted during small intestinal, colon, throat and upper respiratory immune challenge. PB induced after intramuscular or intradermal influenza vaccination, including flu-specific antibody secreting cells, display TPs characterized by the lack of mucosal homing receptors. PB isolated from healthy donors display diverse mucosa-associated TPs, consistent with homeostatic immune activity. Identification of TP signatures of PB may facilitate non-invasive monitoring of organ-specific immune responses.

Advisor: Butcher Eugene primary advisor. Thesis advisor
Habtezion Aida advisor. Thesis advisor
Robinson William (William Hewitt) advisor. Thesis advisor
Wu Joy
Democratic justice in global affairs [electronic resource]

This dissertation presents a reformulation of deliberative democracy as a theory of procedural justice, with the goal of applying democratic principles to the global context. It seeks to address the question: what does deliberative democracy require if we don't assume either a bounded demos or state institutions? Global decision-making already exists and is consequential; in additional, a host of global challenges may require expanding existing global institutions. Therefore, we need a theory that provides procedural standards in the global context. Democracy seems a fitting contender, but recent democratic theory has evolved, for the most part, against the background assumptions of a bounded community (that acts as a demos) and state institutions. Therefore, democratic theory needs revising before it can be applied in the global context. To address that, I present a novel theory of democratic justice. I argue that decision-making processes are a distinct matter of justice that I call procedural justice. Furthermore, I argue that deliberative democracy is best seen as a theory of procedural justice and not as a theory of legitimacy or justified authority. I do argue that democracy is relevant to global affairs by reformulating the concept of global public reason: the set of political values and principles for assessing political action that can be globally share. I argue that global public reason must rely on foundational democratic ideas such as the conceptions of global citizens as free and equal persons and of global society as a system of fair cooperation. Global public reason is a repository of values and principles that global citizens can expect others to accept. One such principle is the all-affected principle (AAP). I consider the boundary problem of democratic theory and offer an answer on the basis of a pluralistic interpretation of the AAP. I reformulate the AAP to overcome the charges of vagueness, circularity and indeterminacy that plague the formulation common in the existing literature. The AAP has to be interpreted pluralistically—people who are affected differently have different participation rights in decision-making processes. In the conclusion I apply my criteria to global decision-making structures. I argue that the processes of global norm formation as well as the decision-making structures of international organizations could be democratized by fostering and maintaining new avenues of participation and deliberation for affected people whose voices are missing from the current global conversation. Though many NGOs, activists, journalists and bureaucrats are already working to expand a global conversation on matters of global concern, there is more work to be done in order to create transnational communities of common concerns and global fora for deliberation.

Keywords: Political Science.
Advisor: Cohen Joshua
Reich Rob primary advisor. Thesis advisor
Holloway David
McQueen Alison
Demography and population health [electronic resource] : views from above and below

The study of populations can be seen as taking on two forms: a top-down perspective and a bottomup perspective. The former has been the traditional perspective of demographers, working with aggregate population data to understand trends and age-patterns in birth, death, and migration. In a sense, this is viewing people as particles that ebb and flow. The latter has typically been the perspective used by epidemiologists and sociologists, who have delved into the particulars of individuals that keep us from actually being particles. These perspectives each have strengths. For the top-down view, the advantage has been in wellspecified dynamic models, enabling population forecasting. For the bottom-up view, it has been in identifying risks for particular events, such as the identification of hypertension, high cholesterol, and tobacco use as raising risk of cardiovascular disease. They also have costs, in richness of detail and in reliance upon statistical, rather than more clearly mechanistic, models respectively. The subsequent chapters explore how these two perspectives inform our understanding of populations. The first three chapters consider the top-down view. In chapter one, I consider how the the state-of-the-art model for mortality forecasting - the Lee-Carter Model - has performed since its development and how this relates (or does not) to the model's assumptions. In the second chapter, I study the male-female gap in life expectancy and how causes of death contributed differentially over specific ages to the narrowing sex difference in life span. In the third, I break apart trends in life expectancy and in life span variance - a measure of life span inequality - by cause of death and reveal how the two measures have been driven by different causes. Finally, I consider the bottom-up view. Using a variety of machine learning methods, widely used in biomedical research, I explore how they do and do not inform our understanding of the social determinants of health.

Keywords: Biology.
Advisor: Tuljapurkar Shripad
Cullen Mark R.
Feldman Marcus W. advisor. Thesis advisor
Rehkopf David advisor. Thesis advisor
Demonstration of the hollow channel plasma wakefield accelerator [electronic resource]

A plasma wakefield accelerator is a device that converts the energy of a relativistic particle beam into a large-amplitude wave in a plasma. The plasma wave, or wakefield, supports an enormous electric field that is used to accelerate a trailing particle beam. The plasma wakefield accelerator can therefore be used as a transformer, transferring energy from a high-charge, low-energy particle beam into a high-energy, low-charge particle beam. This technique may lead to a new generation of ultra-compact, high-energy particle accelerators. The past decade has seen enormous progress in the field of plasma wakefield acceleration with experimental demonstrations of the acceleration of electron beams by several gigaelectron-volts. The acceleration of positron beams in plasma is more challenging, but also necessary for the creation of a high-energy electron-positron collider. Part of the challenge is that the plasma responds asymmetrically to electrons and positrons, leading to increased disruption of the positron beam. One solution to this problem, first proposed over twenty years ago, is to use a hollow channel plasma which symmetrizes the response of the plasma to beams of positive and negative charge, making it possible to accelerate positrons in plasma without disruption. In this thesis, we describe the theory relevant to our experiment and derive new results when needed. We discuss the development and implementation of special optical devices used to create long plasma channels. We demonstrate for the first time the generation of meter-scale plasma channels and the acceleration of positron beams therein.

Keywords: Physics.
Advisor: Raubenheimer Tor O.
Chao Alexander Wu
Hogan Mark J. (Scientist)
Peskin Michael Edward
Ruth Ronald D. advisor. Thesis advisor
Density functional theory and machine learning methods for dielectric materials discovery [electronic resource]

Dielectrics are an important class of materials that are ubiquitous in modern electronic applications. Even though their properties are important for the performance of devices, the number of compounds with known dielectric constant is on the order of a few hundred. In this thesis, Density Functional Perturbation Theory (DFPT) is used to screen for the dielectric constant and refractive index of materials in a fast and computationally efficient way. By benchmarking against experiments, it is found that DFPT can in general predict the experimental dielectric constant with a deviation of less than 25%. Despite some discrepancies between DFPT results and reported experimental values, the high-throughput methodology is found to be useful in identifying interesting compounds by ranking. This is demonstrated by the high Spearman correlation factor (0.92). It is also demonstrated that DFPT provides a good estimate for the refractive index of a compound without calculating the frequency dependence of the dielectric matrix (MARD=5.7%). By applying the screening methodology, the largest publicly available dielectric tensors database to date is obtained containing 1,056 compounds. Finally, a machine learning model that is able to predict the electronic dielectric constant is developed as an alternative screening method. The average prediction error came out as less than 18\% for the dielectric constant and less than 9% for the refractive index. Given the high Spearman correlation factor (0.89), such a model can be used as an even faster materials screening method.

Keywords: Materials Science and Engineering.
Advisor: Persson Kristin A.
Prinz F. B. primary advisor. Thesis advisor
McIntyre Paul Cameron advisor. Thesis advisor
Reed Evan J. advisor. Thesis advisor
Design and characterization of novel optical sensors for monitoring membrane tension in vivo

Cells must monitor and respond to chemical and physical changes in their environments. For example, swelling or shrinking in response to osmotic changes presents both chemical and physical stimuli. Mechanisms of sensing and responding to these stimuli fall into two broad categories: signal transducers and safety valves. Bacteria utilize the safety valve mechanism, as exemplified by the protein MscS (mechanosensitive channel of small conductance). A family of MscS-like (MSL) proteins has been identified in plants, including Arabidopsis thaliana. Several MSLs have been previously characterized, for example, MSL9 and MSL10 have been shown to respond to membrane stretching. By constructing fluorescent biosensors based on these MSLs, we aimed to design a sensor which reports on changes in membrane tension. Several such chimeric proteins have been constructed based on MSL9 and MSL10, and their response to hyperosmotic osmotic stress has been characterized in Saccharomyces cerevisiae. Surprisingly, we discovered that sensor expression in yeast leads to aggregation, and closer inspection of the aggregates reveals that the majority of the signal is derived from these aggregates. These sensor aggregates could be used to detect osmotic changes in yeast and other systems, however, these aggregates do not report on membrane tension at the cell surface. Future work with these sensors includes reducing aggregation in yeast systems as well as improving surface targeting. Applications include using these novel sensors for in vivo visualizations of membrane stress in physiological conditions, such as plant roots encountering a barrier, or the effect of membrane stress on growth and development.

Keywords:  Department of Biology
Biology
molecular biology
membrane tension
mechanosensitive channel
osmotic shock
fluorescence
FRET
biosensor
Saccharomyces cerevisiae
Arabidopsis thaliana
Thesis
Advisor: Frommer Wolf
Feng Liang