VictorJuiz/thesis huggingface dataset:
Data driven and uncertainty aware physical modeling [electronic resource]
Keywords: Mechanical Engineering.

As computing capabilities grow and the amount of experimental and numerical data increases, computational strategies can be designed to automatically test and assess different modeling assumptions. We introduce a general data-driven statistical framework that bridges the gap between (numerical or laboratory) experimentation, physical modeling and uncertainty quantification. The framework enables the study of uncertainties and bias in physical models estimated from data. We differentiate between two types of modeling uncertainties and bias, the first one due to physical errors in the models and the second one due to noise introduced by the data-acquisition process. We also present different procedures to build models under different noise assumptions and propose a metric to quantify the quality of the data-driven estimations. The framework is tested in the context of combustion science and chemical kinetics and it is driven by empirical data and simple chemistry models. Why reaction rates? A combination of a rigorous application of the statistical framework as well as recently measured kinetic rates data will allow us to propose new modeling strategies for chemical reaction rates, their associated uncertainties, and how these uncertainties propagate into relevant combustion problems. This thesis also shows that the current state of the art of reporting kinetic uncertainties relevant for predictive problems in combustion sciences is incomplete and only focuses on describing the experimental variability. We propose a technique to report uncertainties in a useful manner for scientists interested in studying the predictive capabilities of their numerical simulations where chemical reaction rates are input parameters. Applications include hydrogen chemistry, explosion limits and initial mixture compositions uncertainties in gaseous mixtures. To represent as closely as possible actual experiments in our models, we will review the process of inferring reaction rates from shock tubes devices. Shock tubes are one of the most popular devices used to measure kinetic rates. We will closely examine the uncertainties of measurements inside a shock tube: 1- due to the presence of non-ideal phenomena in the real device (departures from the ideal operation sequence), 2- incomplete knowledge (unknown parameters needed to model the operation of shock tubes) and 3- sensor uncertainties. This framework can be extended to complex predictive problems relevant to turbulence, turbulent combustion and safety related applications (e.g. nuclear waste treatment, detonations etc.) - and to more complicated reaction rates and larger chemical mechanisms when both raw experimental signals and processed reaction rates become more accessible.
Data-driven cooperative control for wind farm power maximization [electronic resource]
Keywords: Civil and Environmental Engineering.

Among the various renewable energy sources, wind power has proven effective for large-scale energy production. To increase wind power production, it is essential not only to increase the number of wind farms but also to operate them efficiently. Conventionally, for a given wind condition, each individual wind turbine maximizes its own power production without taking into consideration the conditions of other wind turbines. Under this greedy control strategy, the wake formed by the upstream wind turbine, resulting in reduced wind speed and increased turbulence intensity inside the wake, would affect and lower the power production of downstream wind turbines. This thesis investigates a cooperative wind farm control approach to optimally coordinate the control actions (i.e., the operational conditions) of the wind turbines. The optimally coordinated control actions minimize the wake interference among the wind turbines and would therefore increase the total wind farm power production. To determine the optimum coordinated control actions, two methods are discussed in this thesis. First, the optimum coordinated control actions for wind turbines are determined using an analytical approach by employing mathematical optimization. In this approach, the total wind farm power is expressed as a function of the control actions of all the wind turbines. The wind farm power function is then maximized using sequential quadratic programming to determine the optimum coordinated control actions for the wind turbines. The effectiveness of the cooperative control strategy is studied using an example wind farm site and available wind data. For the second approach, the optimum coordinated control actions of the wind turbines are derived using the input (control actions of wind turbines) and output (wind farm power) data of a target wind farm. For real-time, data-driven wind farm control, an optimization algorithm should be able to improve target wind farm power production by executing as few trial actions as possible using the wind farm power monitoring data. To achieve this goal, a Bayesian Ascent (BA) algorithm is developed by incorporating into the Bayesian Optimization framework a trust region strategy that regulates the search domain. Numerical simulations using the wind farm power function show that the BA algorithm can be as effective as the analytical approach. Wind tunnel experiments with scaled wind turbines are conducted to further demonstrate the effectiveness of the data-driven BA algorithm for real-time control. Experimental results show that the BA algorithm can achieve a monotonic increase in the total wind farm power production using a small number of trial actions and demonstrate the potential of the BA algorithm for the real-time wind farm control problem.
Data-driven methods for modeling of extreme events [electronic resource]
Keywords: Physics.

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.
Decision-making at scale [electronic resource]
Keywords: Electrical Engineering.

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.
Decoder algorithm design for high-performance and robust neural prostheses [electronic resource]
Keywords: Electrical Engineering.

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.
Deep exploration via randomized value functions [electronic resource]
Keywords: Management Science and Engineering.

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.
Democratic justice in global affairs [electronic resource]
Keywords: Political Science.

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.
Demography and population health [electronic resource] : views from above and below
Keywords: Biology.

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.
Demonstration of the hollow channel plasma wakefield accelerator [electronic resource]
Keywords: Physics.

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.
Density functional theory and machine learning methods for dielectric materials discovery [electronic resource]
Keywords: Materials Science and Engineering.

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.