VictorJuiz/thesis huggingface dataset:
Monetary policy uncertainty and its effects

This thesis consists of three chapters that study the effects of monetary policy uncertainty and deviations from rule-based policy. In the first chapter, I develop an extension of the standard New Keynesian model to monetary policy regime switching to study the impact of uncertainty around the future inflation target. First, I fully characterize how the responses of current inflation and output to inflation target uncertainty depend on the monetary policy rule. If monetary policy is passive, inflation may increase far beyond the anticipated increase in the inflation target, while a strong monetary response to expected inflation results in an immediate drop in the inflation rate. Next, I derive the optimal response of the central bank, which can be achieved by adjusting the current inflation target. A central bank unwilling to adjust the inflation target can optimally adjust other policy rule parameters and can often obtain comparatively similar welfare benefits. Finally, I examine the implications of a perfectly anticipated change in the inflation target and find it is likely to generate cyclical dynamics for inflation and output under a constant policy rule. An optimal time varying policy rule or uncertainty in the period of the inflation target change eliminates cyclical fluctuations and improves welfare. In the second chapter, I apply the New Keynesian model with regime switching of the policy rule to study other dimensions of monetary policy uncertainty. I find that expectations of a regime shift in the future affect current inflation stability in the direction of the expected policy shift, but paradoxically expectations of a shift to a sub-optimal regime may result in better outcomes under the current regime due to the asymmetric effect on output and inflation stability. Additionally, I find that the central bank's first best response is to completely eliminate the uncertainty and that regime switching can only be part of an optimal policy if eventually monetary policy converges to the optimal single regime policy. In the final chapter, I construct the differences between rule-based monetary policy for multiple interest rate rules and the actual interest rates for nine countries using real-time data available to policymakers at the time. I document that more rule-like policy is associated with greater economic stability. Additionally, I find evidence that the association between rule-like policy and greater economic stability is causal by examining the timing of the structural breaks and the changes in central bank policy, using vector autoregressions, and regression specifications relying on the temporal ordering of lagged deviations from the policy rules.

Keywords: Economics.
Advisor: Taylor John B. 
Auclert Adrien 
Klenow Peter J.
Monitoring bondline integrity via embedded piezoelectric sensors [electronic resource]

Monitoring the bondline integrity of adhesively bonded joints consists one of the most critical concerns in the design of aircraft structures up to date. Due to the lack of confidence on the integrity of the bondline both during fabrication and service, the industry standards and regulations require assembling the composites or even metallic structure using the inefficient "black-aluminum" approach, i.e. drill holes and use fasteners. Furthermore, state-of-the-art non-destructive evaluation (NDE) and structural health monitoring (SHM) approaches are not yet able to provide mature solutions on the issue of bondline integrity monitoring. Therefore, the objective of this work is the introduction and feasibility investigation of a novel bondline integrity monitoring method that is based on the use of piezoelectric sensors embedded inside adhesively bonded joints in order to provide an early detection of bondline degradation. The proposed approach incorporates: (i) micro-sensors embedded inside the adhesive layer leaving a minimal footprint on the material, (ii) numerical and analytical modeling of the electromechanical impedance (EMI) of the adhesive bondline, and (iii) EMI-based diagnostic algorithms for monitoring and assessing the bondline integrity. The literature review indicates that the key parameter to identify the kissing bond is the stiffness of the boundary layer of the bondline, a thin arbitrary layer between the adhesive and adherends. A simple theoretical model and a more comprehensive finite element model was developed to assess the impedance based method. The experimental validation and assessment of the proposed approach is achieved via the design and fabrication of prototype adhesively bonded lap-joints with embedded piezoelectric sensors and a series of mechanical tests under various static and dynamic (fatigue) loading conditions. The obtained results demonstrate the potential of the proposed approach in providing increased confidence on the use of adhesively bonded joints for aerospace structures. Based on the experimental validation, the detection algorithms is developed with the validation test to demonstrate the feasibility to predict the mechanical performance of the bonded joints using the impedance information from the embedded sensor. The optimal design for sensor is also studied with a preliminary exploration on monitoring the fabrication process of the bonded joints.

Keywords: Mechanical Engineering.
Advisor: Chang Fu-Kuo primary advisor. Thesis advisor
Cutkosky Mark R. advisor. Thesis advisor
Senesky Debbie advisor. Thesis advisor
Monitoring with limited information

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.

Keywords: Business.
Advisor: Iancu Dan 
Bayati Mohsen 
Trichakis Nikolaos
Morphodynamic analysis and statistical synthesis of geomorphic data

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.

Keywords: Energy Resources Engineering.
Advisor: Caers Jef 
Mukerji Tapan
Scheidt Celine
MotifTICC: Motif-Aware Toeplitz Inverse Covariance-Based Clustering in Time Series Data

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.

Keywords:  Department of Computer Science
Time Series Data
Time Series Analytics
Motif Discovery
Computer Science
Thesis
Advisor: Leskovec Jure
Moveable types : shaping the text in early modern England

Following an era that suffused literary works with physical meaning—through the handwriting, thread, pigment, and skin that characterize late medieval manuscripts—how did early modern writers and printers reimagine readers' engagement with their books? New critical attention to early print has shown that typography, ornament, and mise-en-page create visible aesthetics that recall the expressiveness of late medieval manuscripts. My research uncovers writers and printers engaging in a more ambitious practice by inviting nonlinear, recursive, and even tactile reading practices that invest meaning in the interactions between reader and text. The project demonstrates how "dimensional texts"—so named because they present themselves as spaces to be explored through discontinuous reading—challenge a standard narrative in which the wide dissemination of print diminishes the aura of the literary object. It revisits printed works from Anne Vaughan Locke to William Shakespeare to recover a set of practices that deliberately interpolate literary forms and material formats to invite both serial and nonserial reading. This interpolation produces an effect that I term the "kinetic metaphor, " in which disorderly reading makes palpable motifs of temporality, communion, hypnosis, or desire that the work might otherwise merely describe. In chapters on multiform texts, lyric sequences, and prose fictions, this dissertation crafts a theoretical framework that connects how writers and printers use the material text to influence reading practices. The prevalence of dimensional texts in the early modern canon indicates how print forces into view an underlying tension in literary history, in which texts may dictate meaning or invite readers to become active agents in its production. This study intervenes in this discussion by emphasizing the equal pressures that literary form and material format place upon readers, illuminating in turn how printed works negotiate the visibility and invisibility of the page.

Advisor: Greene Roland
Orgel Stephen 
Lupic Ivan 
Treharne Elaine
Moving worlds : maritime work and life on the social ocean

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.

Keywords: Anthropology.
Advisor: Malkki Liisa H. (Liisa Helena) 
Yanagisako Sylvia Junko
Thiranagama Sharika
Multimodal measurements of single-molecule conformational dynamics

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.

Keywords: Chemical Engineering.
Advisor: Bryant Zev David 
Spakowitz Andrew James 
Khosla Chaitan
De la Zerda Adam
The multi-scalar drivers of resident invasive species control action on the island of Hawai'i

Invasive species are a key driver of global environmental change and can threaten ecosystems, economies, and human health and well-being. To achieve landscape-scale reductions in the cover and abundance of invasive species, management efforts often must be expanded beyond conservation reserves to private lands. To manage invasive species across private lands, conservation organizations and government agencies must motivate landowners to control these species individually on their property and collectively with their neighbors across property boundaries. This dissertation examines when, why, and how landowners can be motivated to engage in such individual and collective efforts to control invasive species in the Puna District of Hawai'i. I use remote sensing data, spatial analysis, cross-sectional surveys, interviews, and a field experiment to examine the multi-scalar factors influencing landowner invasive species control action and invasive species distributions on private lands. My findings suggest that policies and outreach approaches could potentially enhance resident individual and collective action for invasive species control by: 1) facilitating management efforts on absentee lands, 2) providing subsidies and assistance to low-income landowners and renters, 3) informing landowners about the risk posed by invasive species and how to effectively control these species, and 4) building perceptions of community norms and reciprocity regarding invasive species control through interventions based in social psychology theory. My dissertation highlights the need for integrating social and ecological data and theory to manage invasion across private lands.

Advisor: Ardoin Nicole M. (Nicole Michele) 
Asner Gregory P. 
Vitousek Peter Morrison 
Willer Robert Bartley
Multiscale modeling of brain mechanics

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.

Keywords: Mechanical Engineering.
Advisor: Kuhl Ellen
Cai Wei
Pinsky Peter M. (Peter Michael)
Multi-scale modeling of cardiac arrhythmias

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.

Keywords: Mechanical Engineering.
Advisor: Kuhl Ellen
Narayan Sanjiv M.
Pinsky Peter M. (Peter Michael)
Multi-segment Well Modeling in a Sequential Implicit Coupling Framework

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.

Keywords:  Department of Energy Resources Engineering
School of Earth Energy &amp; Environmental Sciences
Energy Resources Engineering
MultiSegment Well
Sequential Implicit Coupling
Thesis
Advisor: Tchelepi Hamdi