VictorJuiz/thesis huggingface dataset:
Monitoring with limited information
Keywords: Business.

We consider a system with an evolving state that can be stopped at any time by a decision maker (DM), yielding a state-dependent reward. The DM does not observe the state except for a limited number of monitoring times, which he must choose, in conjunction with a suitable stopping policy, to maximize his reward. Dealing with this type of stopping problems, which arise in a variety of applications from healthcare to finance, often requires excessive amounts of data for calibration purposes, and prohibitive computational resources. To overcome these challenges, we propose a robust optimization approach, whereby adaptive uncertainty sets capture the information acquired through monitoring. We consider two versions of the problem -- static and dynamic -- depending on how the monitoring times are chosen. We show that, under certain conditions, the same worst-case reward is achievable under either static or dynamic monitoring. This allows recovering the optimal dynamic monitoring policy by resolving static versions of the problem. We discuss cases when the static problem becomes tractable, and highlight conditions when monitoring at equi-distant times is optimal. Lastly, we showcase our framework in the context of a healthcare problem (monitoring heart transplant patients for Cardiac Allograft Vasculopathy), where we design optimal monitoring policies that substantially improve over the status quo treatment recommendations.
Morphodynamic analysis and statistical synthesis of geomorphic data
Keywords: Energy Resources Engineering.

Many Earth-surface processes are studied using field, experimental, or numerical modeling data sets that, although realistic, only represent a small subset of possible outcomes observed in nature. Acquiring statistically significant quantities of data is often time- and/or cost intensive. Therefore it is not uncommon that a quantitative statement of statistical uncertainty is missing in predictions made from geomorphic data sets. To access such uncertainty for partially or unobserved states of the system, a stochastic model is needed that can mimic spatial as well as temporal variability. In this dissertation, we propose such a stochastic model. One component of the model explains long time-scale behavior using extreme value statistics that model large, but infrequent morphological changes; a second component describes short time-scale behavior by means of multiple-point geostatistics. We propose a Bayesian protocol to first falsify the stochastic model based on the limited time observations thereby improving physical plausibility. Then, we use approximate Bayesian computation to infer the stochastic model parameters from the limited (in time) geomorphic data. We apply our ideas to flume experiments of braided river channels evolving under steady water and sediment flux. Realizations of the stochastic model allow to obtain uncertainty estimates on the observed movement of channels in the flume and on the occurrence of extreme events unobserved in the actual experiment.
MotifTICC: Motif-Aware Toeplitz Inverse Covariance-Based Clustering in Time Series Data
Keywords:  Department of Computer Science
Time Series Data
Time Series Analytics
Motif Discovery
Computer Science
Thesis

Clustering and segmentation in multivariate time-series datasets is useful for understanding a system's behavior over time. In particular, the task of finding repeated heterogeneous patterns, or motifs, is helpful for interpreting large datasets and identifying interesting patterns. Performing hierarchical clustering is challenging because it involves simultaneously learning both a primary clustering layer and an overlying motif set. In this paper, we propose MotifTICC, an algorithm for discovering noisy motifs and hierarchically clustering time series data. MotifTICC both increases the robustness of the primary clustering layer and outputs a ranked list of motifs. We validate this algorithm on synthetic data and apply the method to an automobile dataset.
Moving worlds : maritime work and life on the social ocean
Keywords: Anthropology.

My doctoral dissertation is an anthropological study of the maritime world as a social and political world and of the people, such as seafarers and coastguards, who make and shape this world through their everyday work. With ninety percent of the world's goods traveling by sea, the maritime industry is central to our global society and economy. Yet, despite this importance, it has remained largely invisible in the social sciences' discussions of the global. Based on ethnographic research with maritime workers onboard ships and ashore, and with key maritime institutions of governance, my dissertation brings to light the invisible and hidden maritime worlds and life-worlds that make up the backbones of global trade. I show how this infrastructural maritime world of mobility is dependent on an ethnically stratified and unequally positioned labor force. Onboard contemporary cargo ships, colonial relationships of inequality are reproduced, even reinforced, as contracts and conditions of work are closely tied to seafarers' nationalities and the relative position of their countries in the global economy. By exploring social relations onboard ships, global maritime labor politics, and challenges of maritime governance, I show the enormous amount of work that is required for the global circulation of goods to appear as smooth and friction-free. This work involves not just logistical labor, but also the social labor of working with, and living alongside, people from different backgrounds who share the same limited and intimate physical space, but under vastly different conditions of work and in highly unequal positions of power.
Multimodal measurements of single-molecule conformational dynamics
Keywords: Chemical Engineering.

Single-molecule methods can monitor the dynamics of biological macromolecules in real time. Here we develop FluoRBT, which provides multimodal measurements of structural transitions in nucleic acids and nucleoprotein complexes by combining magnetic tweezers manipulation, infrared darkfield imaging, and single-molecule FRET. We demonstrate the capabilities of FluoRBT by observing structural transitions in DNA under mechanical strain through concurrent changes in FRET and angle or extension. We also apply FluoRBT to study the dynamics of DNA interrogation by the RNA-guided CRISPR nuclease Cas9. We identify a structural intermediate corresponding to DNA:RNA hybridization within the target "seed" region, and characterize how the interplay between sequence mismatches and DNA superhelicity modulates access to states competent for stable binding and cleavage. FluoRBT can further be used to dissect the mechanism of mechanochemical coupling in DNA gyrase by correlating progress through the supercoiling reaction with rich dynamics of the N-gate ATPase domain observed in single-molecule FRET assays. By combining mechanical measurements of global deformations in nucleic acids with FRET measurements of local conformational changes, FluoRBT enables multidimensional investigations of systems ranging from DNA structures to large molecular machines.
Multiscale modeling of brain mechanics
Keywords: Mechanical Engineering.

During our entire lifetime, we continuously rely on the brain to facilitate functioning of almost every part of our body. A proper development of the brain and its safe protection are, therefore, of utmost importance. Mechanics plays an important role in both of these aspects. Although evidence for the presence and significance of mechanics in the brain dates back several decades, a thorough understanding of its precise role has not yet been established. Computational modeling provides a powerful tool to investigate the role of mechanics and to probe causal relationships between mechanics and brain development or brain injury that are extremely difficult to obtain with experiments alone. We use and develop computational tools to investigate the role of mechanics in the brain at and across three spatial scales: the subcellular level of individual proteins, the cellular scale of the nerve cell and its axon, and the tissue scale of whole brain. We design a novel finite element framework that allows to assign a molecular mechanism to individual elements that represents the characteristic dynamic behavior of a particular protein at the subcellular level. By embedding these mechanisms into our computational model of the axon, we investigate their effects on emergent properties at the cellular level, such as axon stiffness, viscosity, and damage. Our results demonstrate mechanisms by which dynein and myosin motors, microtubule dynamics, and microtubule disassembly affect axon outgrowth. Our axon model further predicts critical thresholds for axon damage that are consistent with values reported in the literature. We use these results to develop a constitutive model for axon damage that can be embedded in a continuum model at the tissue level. Finally, we propose a continuum multifield model at the tissue level that accurately predicts the development of brain volume and structure during weeks 10-29 of gestation. We couple cell migration to mechanics and volume growth. Our model predicts a wide variety of complex folding patterns ranging from single creases to sinusoidal folding and provides additional insights into the role of mechanics during the complex process of cortical folding.
Multi-scale modeling of cardiac arrhythmias
Keywords: Mechanical Engineering.

In the United States, almost half a million people die each year as a result of cardiac arrhythmias. These diseases comprise a number of abnormal rhythms, where the electrical activity of the heart is disturbed. Despite decades of research, the mechanisms by which arrhythmias develop remain poorly explained. One of the main challenges is that even though arrhythmias appear at the organ level, they can be triggered by sub-cellular changes, creating an inherently multi-scale problem. Computational models can bridge across scales to provide mechanistic understanding of these conditions. This thesis elaborates on the development of these models and their applicability to different types of arrhythmias. We study how different components of these models affect their response and the consequences of clinical applications. Moreover, we develop a high resolution, multi-scale model of the heart. We model from the ionic channels in the cell, to the different distributions of cell populations, to the entire heart. This tool allow us to study, for example, the development of drug-induced arrhythmias, where if specific ionic channels are blocked, arrhythmias appear spontaneously. We also apply machine learning techniques to extract knowledge and alleviate the computational cost of these high resolution models. We anticipate the newly developed models and methodologies will help to elaborate mechanistic explanations of the development of arrhythmias and elucidate new treatments.
Multi-segment Well Modeling in a Sequential Implicit Coupling Framework
Keywords:  Department of Energy Resources Engineering
School of Earth Energy &amp; Environmental Sciences
Energy Resources Engineering
MultiSegment Well
Sequential Implicit Coupling
Thesis

Accurate well modeling becomes essential in the reservoir simulation because more and more complex well operations are employed in the petroleum industry. The prevailed standard well model is proved to be computationally efficient, while it lacks the ability to capture the multiphase flow details within the wellbore. MultiSegment well (MSWell) model was then proposed to approximate the actual physical shape of the well and perform accurate simulation within the wellbore. However, the increasing complexity of the MSwell model also introduced additional difficulties in solving the coupled MSWell and reservoir flow problem. Several nonlinear solver and linear solver techniques were proposed to accelerate the convergence of the MSWell problem, while there isn't a systematic discussion about the convergence and the accuracy of the various proposed techniques. In this work, we discuss the convergence and the accuracy for different coupling strategies for coupled MSWell and reservoir flow problem. First, we introduce the numerical implementation of the MultiSegment well (MSWell) in an in-house simulation Automatic-Differentiation General Purpose Research Simulator (AD-GPRS) under the General Implicit Coupling Framework (GENIC). A consistent normalization technique for MSWell equations is discussed and carefully clarified. In addition, the component-based CFL number and the phase-based CFL number for MSWell equations are introduced, which are coherent with the CFL number defined in the reservoir block. After the clear illustration of the model details, we investigate three different coupling strategies for couple MSWell and reservoir flow problem, which are fully implicit method, sequential fully implicit method and fully implicit method with local facility solver. Numerical examples are constructed to validate the convergence and the accuracy of these three coupling strategies. Then a general discussion about the convergence and accuracy based on CFL number is conducted regarding different time step and grid size (segment length). The numerical results and the theoretical analysis show that the relative nonlinearity between the MSWell equations and the reservoir flow equations is the primary factor affecting the global convergence rate, while the time step will significantly influence the simulation accuracy of the wellbore flow. Thus, we proposed a possible solution to achieve both high accuracy for wellbore flow and global computational efficiency.
Nanomaterial design via ALD : new methods and applications in catalysis
Keywords: Chemistry.

Producing chemicals and liquid fuels from renewable carbon monoxide and hydrogen (syngas) is a key route towards an environmentally sustainable economy. Alcohols beyond methanol are key targets for fuel and chemical production; however despite almost a century of research, there are no industrially appropriate catalysts for the direct formation of these compounds. New methods are needed to synthesize catalysts for higher alcohol production from syngas. Atomic layer deposition (ALD) is one such synthetic strategy. In this thesis, a new method for ALD nanoparticle synthesis was developed that can be used to prepare catalysts. Using oxygen gas as the co-reactant, area-selective ALD of metal oxides by metalorganic precursors that typically reactive strong oxidants to proceed was achieved selectively on noble metal surfaces by exploiting the catalytic dissociation of oxygen molecules at the metal surface. To explore metal alloy catalysts for syngas conversion, CoZn and PtCo catalysts were synthesized through a combination of traditional wet synthesis and ALD. During synthesis, the number ALD cycles was tuned to optimize catalytic performance with precise control. These catalysts had greatly improved selectivity for the alcohols during syngas conversion. By performing in-situ measurements of CoZn at high pressure, it was discovered that the presence of Zn leads to Co2C formation during catalyst operation. The resulting Co2C was found to be critical in alcohol formation on these catalysts. Beyond CO, CO2 is an emerging feedstock for renewable chemical production. New CoGa catalysts were synthesized for CO2 hydrogenation. The Co:Ga ratio was found to play a critical role in the selectivity, with increasing Co content favoring methane production and increasing Ga content favoring dimethyl ether production.
Nanophotonics for enhanced solar efficiency, electrostatic screening, and polarization control
Keywords: Electrical Engineering.

Nanophotonics offers a variety of applications including renewable energy, telecommunication and electronics. This thesis explores optical and electrical aspects of nanophotonic devices in three different areas: enhancing the efficiency of solar cells, controlling electrostatic screening using metamaterials, and manipulating polarization with dielectric chiral metamaterials. As the first area, a combined optical and electrical analysis is developed for nanophotonic solar cells. The analysis is applied to two types nanophotonic structures: nanostructured films and nanowires. Such a combined analysis approach is critical for nanophotonic solar cells as they typically have large absorption enhancement but more recombination losses. This combined analysis determines the current and voltage characteristics simultaneously and computes an upper bound on the efficiency while incorporating the fundamental losses due to Auger and surface recombinations. Moreover, an analytical model is introduced, which simplifies the analysis and is used in place of electrical simulations. The second area investigates electrostatic screening in metamaterials. Electrostatic behaviors of metamaterials have not been studied before, and in order to demonstrate different kinds of electrostatic responses, two classes of metamaterials (cubic arrays and wire media) are studied in the electrostatic limit. While cubic arrays are described by a local dielectric function and have a relative permittivity constant like dielectrics do, wire media are described by a nonlocal dielectric function that gives rise to an exponentially decaying electric potential like in bulk metals. The properties of electrostatic screening in both classes of metamaterials are determined by the geometrical parameters of metamaterials, which shows that metamaterials offer geometrical control of electrostatic screening and, therefore, the Coulomb interaction. In the third area, dielectric helices are shown to exhibit strong circular dichroism and to have polarization stop gaps for light propagating perpendicular to helices. Band structures and reflectance spectra are used to illustrate such effects. Perturbation theory is applied to give a quantitative reasoning of how the effects emerge. These effects enable a practical application as a chiral mirror, which completely reflects normally incident light of one circular polarization while preserving the handedness of polarization.
Nanoscale membrane structure : from lateral organization to proton permeability
Keywords: Chemistry.

Membranes composed of lipids and proteins are universal features of living organisms. These layers that are only a few nanometers thick differentiate between life and death for cells. Despite the importance of biological membranes in compartmentalizing cellular space and separating the cell from its surroundings, many aspects of membrane structure and functional organization remain a mystery. The plasma membrane of mammalian cells contains thousands of different membrane proteins and thousands of different lipid species, and the absolute and relative amounts of these components are tightly regulated. Given this dizzying molecular complexity, how these molecules are functionally organized to optimize biochemical processes that occur at the membrane remains poorly understood. In addition to this complexity, non-mammalian cells contain structurally exotic lipids, whose physical properties and functions in their membranes remain unexplored. This thesis describes my work on elucidating the structure and biophysics of lipid membranes and the functional consequences of membrane structure. I have applied biophysical characterization techniques, imaging mass spectrometry, and chemical synthesis to address these questions. I take a reductionist approach and utilize model membranes and single cell analysis to unravel the complexities of biological systems. I will first describe the development of new methods for using nanoscale secondary ion mass spectrometry (NanoSIMS) to measure the distance between isotopically labeled molecules in lipid bilayers. I take advantage of a process called atomic recombination, in which atoms in sample from different molecules rearrange to form secondary ions when bombarded by a high energy ion beam. This process depends on the distance between the molecules and can therefore be used to measure the distance between the molecules. After benchmarking this new method, I use it to show that there are nanoscale lipid clusters in bilayers that had previously been observed indirectly or not at all. I then describe recent results from the structurally unique ladderane lipids, which are exclusively found in bacteria that perform anaerobic ammonium oxidation (anammox). The biological role of these lipids and biophysics of membranes containing them are unknown due to a lack of pure lipids. We approach this problem by synthesizing the natural lipids and unnatural analogs and performing structure-function studies. These experiments reveal an anomalously low proton permeability for ladderane bilayers, suggesting a role for ladderane lipids in preventing the breakdown of transmembrane proton gradients. We further explore the structure-function relationships between lipid molecular structure and resulting lipid bilayer structure.
Narratives of detachment and literary transculturation : Catalan exiles in Mexico
Keywords: Iberian and Latin American Cultures.

In 1939, due to Franco's victory in the Spanish Civil War and the fall of the Second Spanish Republic, hundreds of thousands of citizens had to emigrate from the country and find refuge in different parts of the world. Among them, a group of writers went into exile in Mexico, after being incarcereted in inhumane concentration camps in France. This dissertation is a study of narrative works written by Agustí Bartra (1908-1982), Viçens Riera Llorca (1903-1991), Avel·lí Artís-Gener (Tísner) (1912-2000), Pere Calders (1912-1994), and Lluís Ferran de Pol (1911-1995), inspired by their decades-long Mexican exile. In my analysis, I demonstrate that just as the authors crossed the national border, the literature they wrote in exile crosses multiple boundaries, including those of language, identity, and literary traditions. Through their writings, the Catalan authors did the opposite of what the Franco regime sought to do with them: instead of persecuting and annihilating difference, they embraced it and let it live.
Nartshell: A Time Travel Journey for Creativity in Art
Keywords: Creativity
creative confidence
art education
Thesis

Creative skills and creative confidence in problem-solving efforts are demanded in all fields, from art, literature to STEM fields. Research shows that art education is strongly connected with creative skills and creative confidence development, but oftentimes students have limited access to creative art resources and personal attention in art curriculum at school (Davies, 2000; White, 2018). Learning opportunities outside of classrooms like drawing apps claim to foster creativity in children. However, most of these apps only focus on teaching artistic techniques, rather than utilizing learning mechanics to reinforce the creative aspect of art. Without quality learning experiences in or outside of school, students in underserved communities are left behind in developing their creativity and confidence.
Nartshell is a browser-based digital game that offers students a safe space to take creative challenges with adaptive difficulty and personalized feedback. Nartshell is a virtual world where students can travel around to learn from game characters that are based on famous artists (e.g., Andy Warhol). Students explore art challenges in order to unlock art tools and cities on the virtual map. They can use the art tools later to create their own paintings. Preliminary tests indicated that learners performed more creatively in art challenges when receiving art-tool rewards, and felt more positive about their accomplishments when receiving constructive feedback. The game can be accessed by any student for free on a website. In future, the game will be introduced to students by collaborating with schools, daycare centers, afterschool programs and local public libraries in underserved communities.