VictorJuiz/thesis huggingface dataset:
Mathematical Models and Control Algorithms for Traffic Automation

Transportation accounts for 28% of energy consumptionin the US, with 75% of that occurring on highways. Workers spent on aggregate over three million driver-years commuting to their jobs, contributing significantly to the nation-wide congestion. Based on 2012 estimates, US commuters experienced an average of 52 hours of delay in 2011, which amounts to $121 billion of fuel costs and opportunity costs due to delay annually. Estimates project that 4.2% of fuel will be wasted in congestion in 2050 with the adoption of autonomous vehicles. Mixed autonomy, the intermediate regime between a system with no adoption of autonomy and a system where autonomy is fully employed, is proposed as a potential solution to fuel consumption reduction and flow capacity improvement./nThe mixed autonomy is complicated to characterize and hard to tackle due to the degree of uncertainty in the system dynamics. The understanding of mixed autonomy is however crucial to designing automated and intelligent transportation systems, and to overhauling public policy with autonomous vehicles in the loop./nApproaches to mobile traffic control include machine learning methods, mathematical modeling and optimal control. The machine learning approaches achieve strong empirical results but in general lack optimality guarantees, theoretical studies of the convergence rate, out-of-sample performance guanrantees and interpretability. The mathematical approaches, on the other hand, are supported by highly developed mathematical theory, but suffer from a lack of attention to the theoretical characterization to the model solutions and the actuations function space. This is inevitable as mathematical methods trade model complexity for the tractability of analysis in the optimal control design./nThis thesis investigates mathematical models and control algorithms for traffic automation at multiple levels./nAt the vehicle level, this thesis demonstrates the lack of guarantee of well-posedness for the solution of the intelligent driver model (IDM) and closes the theoretical gap by proposing several modifications and rigorously showing the existence and uniqueness of their solutions./nAt the mixed autonomy vehicle control level, a systematic way to design the basis-based feedback controller is introduced, which is defined by the solution of a finite-dimensional constrained optimization problem. This thesis discusses theoretical characterization of the actuation function space. An optimal Legendre polynomial basis controller is trained with field data and has been evaluated with simulation data and compared with a range of other mobile traffic controllers./nAt the full origin-destination demand routing level, this thesis studies the repeated routing game problem on a parallel network with affine latency functions on each edge, transforming the dynamic traffic assignment problem into a control-theoretic problem. The impact of model parameters on the traffic performance is theoretically studied, andan algorithmic solution based on explicit model predictive control (MPC) is proposed.

Advisor: Bayen, Alexandre
Wilkening, Jon
Learning Beyond the Standard Model (of Data)

Classically, most machine learning (ML) methodology has made an innocuous modeling assumption: data drawn from both the training/test sets has been independently sampled from a pair of identical distributions with nice properties. Yet, in the situations modern ML methods must confront, deviations from this idealized setting are quickly becoming the norm–not the exception. In this thesis, we address the challenges arising in understanding the often unexpected phenomenology in these settings by developing theory in two areas of interest: transfer learning and robust learning. In particular, we focus on identifying what structural conditions/techniques are needed to permit sample-efficient learning in these new settings, in order to answer questions such as why pretraining is so effective and what the limits of learning are for extremely heavy-tailed distributions.

Advisor: Jordan, Michael
Aldolase-catalyzed synthesis of chiral organofluorines

Fluorine is a critically important element for the design of safe and effective drugs in the modern pharmaceutical industry. As the field of synthetic methodology for fluorination of organic compounds has flourished, biocatalysis has also been rapidly adopted as a means to achieve high reaction selectivity and process sustainability. However, the areas of biocatalysis and fluorine chemistry have rarely intersected directly, owing to the rarity of fluorine-processing enzymes in Nature. We envision that this methodology gap can be bridged by the action of carbon-carbon bond forming enzymes upon non-native fluorinated substrates, thus transforming simple organofluorine building blocks into more complex organofluorines. In this dissertation, we describe the application of enzymatic aldol addition to fluoro-aldol reactions that furnish value-added organofluorine products bearing fluorine stereocenters. Our development of a novel platform for biocatalytic organofluorine synthesis began with the discovery that Type II pyruvate aldolases of the HpcH family use fluoropyruvate as an alternative nucleophilic substrate. After studying the stereoselectivity, kinetics, and mechanism of the fluoropyruvate aldol reaction, the afforded products were converted to diversely functionalized organofluorines through downstream reactions. Next, the HpcH system was expanded beyond fluoropyruvate to generalized β-fluoro-α-ketoacids as nucleophiles. The investigation of these challenging substrates, aided by rational active site engineering, resulted in the first synthesis of tertiary fluorides via biocatalytic C-C bond formation. Usefulness of the HpcH platform was demonstrated with preparative syntheses of products relevant to drug fragments and bioactive natural products. Finally, we studied Type I aldolases with the unique ability to use simple aldehydes and ketones as nucleophiles. Their activity on fluorinated ketones may enable the efficient synthesis of fluorinated sugars, which are applicable to pharmaceuticals and chemical biology. Taken together, this dissertation delineates a selective and sustainable biocatalytic approach to access the unique properties conferred by fluorination, which may have broad implications for improved process design towards important organofluorines.

Advisor: Chang, Michelle C Y
Manipulation and Perception Policies for Robot Mechanical Search

When operating in unstructured and semi-structured environments such as warehouses, homes, and retail centers, robots are frequently required to interactively search for and retrieve specific objects from cluttered bins, shelves, or tables, where the object may be partially or fully hidden behind other objects. The goal of this task, which we define as mechanical search, is to retrieve a target object in as few actions as possible. Robustly perceiving and manipulating objects is challenging in these scenarios due to the presence of sensor noise, occlusions, and unknown object properties. Because of these perception and manipulation challenges, learning end-to-end mechanical search policies from data is difficult. Instead, we break mechanical search policies into three modules, a perception module that creates an intermediate representation from the input observation, a set of low-level manipulation primitives, and a high-level action selection policy that iteratively chooses which low-level primitives to execute based on the output from the perception module. We explore progress made on manipulation primitives, such as pushing and grasping, segmentation of scenes with unknown objects and occupancy distribution predictions to infer likely locations of the target object. Additionally, we demonstrate that using simulated depth images or point clouds can allow rapid generation of large-scale training datasets for perception networks while allowing them to generalize to real-world objects and scenes. We show that integrating these components can result in an efficient mechanical search policy, improving the success rate by 15% and reducing the number of actions needed to extract the target object as compared to baseline policies in both bin and shelf environments across simulated and physical trials.

Advisor: Goldberg, Ken
High Resolution Optical Characterization of Graphene Functionalization

The past decades have witnessed the exponential growth of studies utilizing optical techniques to characterize graphene functionalization. The promise and potential application of graphene and related materials are substantially expanded through chemical functionalization. However, due to the fact that graphene is a single layer of carbon atoms, it is difficult to study the in situ dynamics of graphene chemistry. Moreover, the inertness of the graphene basal plane has notably limited its viable chemical modification pathways. This dissertation describes efforts by the author and colleagues to overcome these limitations. Part I of this dissertation demonstrates the direct optical visualization of in situ dynamics of graphene chemistry through interference reflection microscopy. Specifically, we uncover the unique dynamics of the redox reaction, diazonium reaction and solution-enclosing blister generation process of substrate-supported graphene at high spatiotemporal resolution. Part II of this dissertation reports facile approaches to the chemical modifications of the inert graphene basal plane under ambient conditions. Optical characterizations techniques including interference reflection microscopy, transmission microscopy, fluorescence microscopy, and Raman spectroscopy are utilized here to help establish the successful modifications of graphene through our facile approaches. These approaches include direct azidation and chlorination of the graphene basal plane through the electrochemical oxidation of an aqueous sodium azide and sodium chloride solution as well as a photocatalytic approach for the facile azidation and chemical patterning of graphene surface.

Advisor: Xu, Ke KX
Mixed Variational Bayesian Learning Methods and Inverse Prediction of Continuum Deformation Mappings

In this dissertation, I present a computational framework to predict the inverse deformation mapping. Under this framework, two algorithms have been developed. The first one is called the material deformation finding (MDF) algorithm with a specific application in additive manufacturing. This method is designed to quantify the permanent (non-zero strain) continuum/material deformation. Different from physical-based modeling, the method developed here is based on a data-driven statistics approach, which solves the problem without needing information about the physical deformation process. The proposed method relies only on the scanned material data from the thermal distorted configuration as well as the shape of the initial design configuration. In this work, the MDF algorithm was first validated by a 2D synthetic example. We then demonstrate that the proposed MDF method can accurately find the permanent thermal distortion of a complex 3D printed structural component, and hence identify the thermal compensation design configuration. The results obtained in this work indicate that one can use this data-driven statistics approach to significantly mitigate the thermal distortion of 3D printed products in additive manufacturing./nThe second algorithm developed is a mixed variational Bayesian learning finite element method (VBL-FEM), based on a Bayesian statistical continuum mechanics theory, in which elastic potential energy is used as a prior in a Bayesian regularization network, which can intelligently recover unknown continuum deformation mapping with only the information of the shapes of the deformed and undeformed continuum body without knowing actual boundary conditions, both traction as well as displacement boundary conditions and the precise material constitutive relation. Moreover, we also develop the related finite element formulation in a computational probabilistic mechanics framework. Using a data-driven likelihood function, we construct an entropic variational principle of continuum mechanics based on the maximum a posteriori (MAP) probability estimation and Bayesian regularization. By solving the probabilistic Galerkin variational problem, we also demonstrate in several examples that the proposed method is able to inversely predict continuum deformation mappings with strong discontinuity or fracture without knowing the external load conditions. This long-sought-after inverse problem solution has been a major challenge in structure failure forensic analysis in the past several decades, and the proposed method provides a machine intelligent solution for it.

Advisor: Li, Shaofan SL
Essays on Community-based Targeting in Developing Countries

How to target social program benefits to the poorest households in the developing world remains a key question for policymakers. This work examines community-based targeting (CBT), a targeting method in which local community members identify poor households to receive program benefits. The first chapter explores issues that arise in assessing the accuracy of community-based targeting exercises, especially for the purposes of comparison with more traditional proxy-based targeting methods. I propose the index of the marginal utility of expenditures (MUE) developed in Ligon (2020) as a potential welfare benchmark to use in such comparisons of accuracy. I show that unlike with accuracy comparisons using a standard consumption benchmark, community-based targeting may yield more accurate targeting outcomes than proxy-based methods under an MUE benchmark. The second chapter uses a set of lab-in-the-field experiments in Central Java, Indonesia to better understand the types of welfare information about others that community members have and use when making community-based targeting decisions. When asked to provide information on specific welfare benchmarks, community members appear to have little dynamic, current welfare information about others in their community. The results suggest that they instead rely on more static welfare attributes (such as asset ownership and demographic characteristics) when making targeting decisions. Finally, the third chapter investigates whether there are accuracy gains associated with joint community-based targeting procedures in which multiple community members make targeting decisions collaboratively, as opposed procedures where individuals make similar targeting decisions by themselves. Building on the lab-in-the field exercises from the previous chapter, I compare observations of the same individuals making joint and independent targeting decisions. There seem to be fairly negligible accuracy gains from convening a group targeting exercise as opposed to having the average community member make targeting decisions.

Advisor: Ligon, Ethan A
Exploring methods to identify individuals infected with hepatitis C virus in the United States: an application of ensemble learning with national survey data

Over 70 million people worldwide are living with chronic hepatitis C virus (HCV) infection. Untreated, HCV infection can progress to cirrhosis, advanced liver disease, and hepatocellular carcinoma. Our improved understanding of HCV transmission, coupled with significant advances in treatment have the potential to dramatically reduce the incidence and prevalence of HCV-related diseases. Based on these advances, the World Health Organization (WHO) has established a goal to eliminate HCV infection by 2030; however, a significant impediment to this goal is the lack of infection awareness, which perpetuates the spread of the virus. By improving the detection of HCV infection, we can connect patients to treatment to reduce its prevalence and curtail transmission to reduce future incidence of infection./nThis dissertation reviews the literature on known risk factors for HCV infection in the United States (US) and uses a large, contemporary, publicly available national dataset, the National Health and Nutrition Examination Survey (NHANES), to look for additional risk factors and to build an algorithm to identify individuals with a high probability of HCV infection. NHANES participants are randomly selected from the non-institutionalized and housed US population and screened for HCV RNA, regardless of insurance status or known risk factors, providing meaningful insights into the characteristics associated with HCV infection./nThe results of an umbrella review of circumstances associated with an increased prevalence of HCV infection in the US can be found in Chapter two. Risk factors were categorized as behavioral/lifestyle factors, risks associated with a medical condition, risks related to an occupation, or vulnerable populations. These findings can be used to improve outreach, education, and prevention programs, as many of the identified risk factors are present in marginalized groups that may not have access to regular healthcare or may be missed by existing HCV diagnosis and prevention efforts. Chapter three explores the use of ensemble learning methods to identify the features captured by NHANES that have the greatest impact on successful HCV infection prediction. NHANES data include HCV RNA measurements for all participants of the medical examination portion of the survey. With this information, the ensemble learning method Super Learner was used to identify complex patterns of characteristics associated with HCV infection and to identify the characteristics that had the greatest impact on successful HCV infection prediction in the US (ranked variable importance). Using a subset of the NHANES data that would likely be available and accurate in electronic medical records, Chapter 4 examines the development of an HCV prediction algorithm that could be used to prioritize candidates for HCV screening. Overall, these findings contribute to the national effort to increase HCV-infection detection and accelerate progress towards HCV elimination.

Advisor: Reingold, Arthur L
Of Friends and Foes: A Corpus-Based Study of Conceptual Metaphor in the Discourse of Contemporary Right-Wing Populism in Germany

Political discourse is inherently abundant with metaphors, which speakers use to reframe complex issues to the public in more concrete, familiar, and approachable terms. As research has shown (Charteris-Black, 2004, 2005; Lakoff, 2002, 2004, 2010; Musolff, 2000), the different ways of metaphorically framing a subject can influence reasoning and decision-making. Thus, metaphors hold vast potential for manipulating the public by groups of authority. In the last twenty years, Europe has seen a rise in far-right parties entering government and securing seats in European Parliament. These parties have become powerful forces in Europe’s political landscape, garnering more voter support and polarizing public debate. This study explores the metaphorical expressions used by the German right-wing populist party, Alternative für Deutschland (AfD), to conceptualize relevant political issues before and after the 2017 federal election, in which they secured 12% of the seats, making them the third biggest party in the 19th German Bundestag. Building on two approaches, Conceptual Metaphor Theory (Lakoff & Johnson, 1980) and Critical Metaphor Analysis (Charteris-Black, 2004, 2005), this study not only identifies and analyzes the conceptual metaphors and source domains utilized in the AfD’s discourse on topics such as Europe and the state of the European Union, the Bundestag, and immigration, but also examines shifts in metaphor usage over time. Consequently, this work argues that an analysis of the metaphorical depictions illuminates the underlying ideological foundations of the party and provides insights to better understand the role of such rhetoric in persuasion and manipulation in the political sphere.

Advisor: Rauch, Irmengard
Shannon, Thomas
The complexity of non-convex and conic optimization problems in data science applications

Mathematical optimization is the cornerstone for the data-driven design and exploitation of various large-scale cyber-physical and information systems. The generic off-the-shelf tools for guaranteed global optimization used today often have a prohibitively high computational complexity on practical instances, preventing their convergence within a reasonable time. Instead, highly-scalable heuristics are utilized with no guarantee of global optimality of their result, which poses a problem in the analysis of complex safety-critical systems. Thus, specialized highly-scalable algorithms that come with a guarantee of their performance are required. These can be constructed by providing guarantees and limits of applicability for the well-performing heuristics. In this dissertation, we study how to leverage system-specific characteristics to analyze computational heuristics leading to guarantees of global optimality of their outputs. A prominent example of a large-scale safety-critical system that we consider throughout the first part of the dissertation is the sensor network within the measurement system of an electrical power grid. It is known that the associated generic problem of recovery of the state of a power system is computationally complex yet critical. The three main ways to reduce the complexity of computation associated with a sensor network are to increase the density of sensors, place them strategically, or enhance their security and accuracy to limit the set of possible errors and faults of measurement. All three of these paths are investigated within the first part of this dissertation. We provide numerical quantification to the computation complexity of inverse problems depending on the total number of measurements, the properties of the graph structure of the measurement network, and the signal-to-noise ratio measured as the ratio between the numbers of good and bad measurements. We address both the computational complexity and the matter of constructing a suitable, efficient algorithm in each of the three scenarios. Our results offer implications that should be considered at multiple stages throughout the life cycle of a system, starting from the design of the sensing mechanisms to ensure robustness and security of operation both in normal conditions and in situations of an emergency such as during a cyber-attack./nIn the second part of this dissertation, we present a pioneering work considering an automatic approach toward selecting the most suitable efficient heuristic algorithm for an optimization problem at hand. We focus on a machine learning technique that can be adopted for heuristic selection and investigate its properties and the guarantees of its performance from the perspective of mathematical optimization. We conclude that adopting this machine learning technique in its original form would not result in guaranteed optimal heuristic selection even in a benign scenario, propose modifications and outline future work toward the automatic selection of heuristics.

Advisor: Lavaei, Javad