VictorJuiz/thesis huggingface dataset:
Musical Expression in German Neo-Baroque Organ Music: Case Studies of Selected Works by Hugo Distler and Ernst Pepping
Keywords: Baroque Musical Expression
Ernst Pepping
Hugo Distler
Musical Rhetoric
Neo-Baroque
Pipe Organ

The German neo-Baroque style of composition developed in the first half of the twentieth century. Within this style, composers used Baroque techniques and methods along with twentieth-century rhythms and harmonies. The style grew out of movements such as the Baroque revival and the Orgelbewegung, or Organ Reform Movement. In the organ world, a strong connection between organ builders and organists/composers created a specific neo-Baroque sound for the organ. Two composers, Hugo Distler (1908–1942), a professional organist, and Ernst Pepping (1901–1981), who casually played the organ, were among others in Germany to experiment in the neo-Baroque style. This thesis examines the lives of Distler and Pepping to see what may have influenced their style and the possible influences that may have contributed to their use of techniques of Baroque musical expression. Techniques such as musical rhetoric, affect, numerology, and word painting were often found in Baroque music as a way to artistically convey expression. This thesis presents evidence that these techniques may also be found in neo-Baroque music by Distler and Pepping and that they are used for similar expressive purposes. Specific case studies of the use of techniques of Baroque musical expression in Distler’s Partita “Wachet auf, ruft uns die Stimme” Op. 8, no. 2 (1935) and Pepping’s Toccata and Fugue “Mitten wir im Leben sind” (1941), and Distler’s Organ Sonata (Trio) Op. 18, no. 2 (1939) and Pepping’s Four Fugues in D, c, E♭, and f (1942) are explored. I also explore the difference between the use of techniques of Baroque expression in text-based works, using the first two scores, and in non-text-based works, using the second two scores. Finally, I conclude by showing how analyses such as those done in this thesis can be beneficial for the expression and understanding of the music by both performers and audiences.
Investigating Factors Underlying the Ability of Reward-Paired Cues to Function as Conditioned Reinforcers
Keywords: Alcohol
Conditioned reinforcement
Cues
Rats
Saccharin
Sucrose

Environmental stimuli paired with drugs-of-abuse can motivate drug use and increase the risk of relapse in abstinent individuals. These reward-paired stimuli can control behavior due in part to their ability to function as conditioned reinforcers (CRfs). In this thesis, I investigated factors underlying the ability of reward-paired stimuli to function as CRfs using the acquisition-of-a-new-response task. In Chapter 2, I examined the effects of reducing the reward value of saccharin on responding for a saccharin-paired CRf. Results showed that responding was not altered by saccharin devaluation. This raised the question of how drug-associated stimuli motivate drug use if they do so in a manner that is divorced from the value of the drug. One limitation of this chapter is that a non-drug reinforcer was used. Thus, in Chapter 3, I examined the effects of reducing the reward value of the drug reinforcer alcohol on responding for an alcohol-paired CRf. These studies found that responding was not altered by alcohol devaluation. Like drug-paired stimuli, stress increases vulnerability to drug use and relapse. In Chapter 4, I explored the effects of the pharmacological stressors, yohimbine and U50,488, on responding for an alcohol- or a sucrose-paired CRf. Yohimbine, but not U50,488, enhanced responding irrespective of the nature of the reward used. Some independent evidence also showed that yohimbine enhanced responding for sensory stimuli (e.g. light and tone) that had not been paired with reward. In Chapter 5, I revisted the question of whether the pairing between the stimulus and reward is important for the stimulus to function as CRf. This was done by examining the effects of manipulating the stimulus-reward association on responding. These studies found that the stimulus-reward association is important. However, the stimulus appeared to have intrinsic properties that facilitated responding. Taken together, these findings suggest that reward-paired stimuli acting as CRfs guide behavior in a manner that is independent of the value of the reward. They also substantiate a role for the intrinsic reinforcing properties of these stimuli in facilitating responding for apparent conditioned reinforcement.
Essays on Business Cycles, Banks, and Money
Keywords: bank credit supply shock
Bayesian estimation
business cycles
DSGE
money creation
VAR

This thesis studies the relationship between business cycles, banks, and money. The first chapter focuses on the relationship between bank credit supply and economic volatility. I build a DSGE model with nominal and financial frictions. The novel aspect of the model is the banking sector which finances its lending activity with both pre-accumulated household savings and deposits that are created—ex nihilo—by the banking sector. I contribute to the literature by studying the relative importance of bank credit supply shocks in driving economic volatility. The model is estimated using US data. There are three main results of this chapter. First, I find that an unexpected contraction in the supply of bank credit leads to an economic recession. Second, bank credit supply shocks account for 55% of the volatility in investment and GDP. Third, the model can replicate two stylized facts previous models failed to capture: bank net worth is sticky and bank leverage is procyclical. In the second chapter I focus on the Canadian banking sector. The first half of the chapter provides a brief historical account of the development of the banking system in Canada and makes comparisons with the development of the US banking system. The second half of the chapter uses detailed bank-level balance sheet data of the Big Five Canadian banks to tease out six stylized facts of the Canadian banking sector. They are: first, it is dominated by five large banks; second, credit is deepening; third, net worth is ‘sticky’; fourth, leverage is procyclical; fifth, balance sheet risk is falling; and sixth, rising bank output is driven by liquid liabilities. In the third chapter I estimate a bank credit supply shock using both firm and bank qualitative survey data regarding lending/borrowing conditions in the Canadian economy. The survey data is purged of factors which also affect loan demand. Using a monetary VAR model, I find that an unexpected one-standard-deviation contraction of bank credit supply leads to a mild economic recession. Moreover, I find that the results are robust to a variety of specifications and a different identification strategy.
Optimisation Methods for Advanced Gamma Knife Radiosurgery Treatment
Keywords: Gamma Knife
healthcare
optimisation
radiosurgery

Stereotactic radiosurgery is a high-precision mode of radiotherapy that delivers high dose radiation to brain tumours in one or a few sessions. Recent developments in Gamma Knife radiosurgery machines (Elekta AB, Stockholm, Sweden) have increased treatment precision and functionality, allowing the exploration of new and promising treatment methods. This thesis works to bring two such methods towards clinical implementation. One method is dose painting, which leverages tumour heterogeneity to deliver variable prescribed doses to different parts of a tumour, potentially improving tumour control while reducing unnecessary dose to healthy tissue. A second method is dynamic treatment, which delivers radiation while moving the treatment isocentre, potentially reducing treatment time while improving dose conformity and homogeneity. For dose painting, we first developed a set of metrics to allow the assessment of dose painting radiosurgery plan quality, meeting both clinical radiosurgery needs while being calculated with non-uniform dose painting prescriptions. We then used treatment planning optimisation and automated isocentre placement to generate treatment plans for dose painting prescriptions, allowing us to examine the quality of dose painting Gamma Knife plans achievable through optimisation, quantify how dose painting prescription methods affect plan quality, and explore how automated isocentre placement affects plan quality. For dynamic treatment, we conducted physical experiments to validate the real-world feasibility of dynamic Gamma Knife treatment. The experiments allowed us to address assumptions made with previous computational studies (such as the validity of treating a continuous path as discretised points), investigate uncertainties in translating computed plans to actual dynamic treatment, and determine ideal treatment planning parameters (such as interval distance for the discretisation of movement paths, collimator change limitations, and minimum isocentre treatment times). Through our work, both dose painting and dynamic treatment are better investigated as potential treatment methods for the current Gamma Knife radiosurgery machines.
Supporting Aspirations in University Students
Keywords: Aspiration
Personal Projects Analysis
Virtue
Wisdom

Classical ideas of character education emphasize the importance of self-transformative aspiration to the successful cultivation of wisdom and virtue. Aspiration is however rarely discussed in contemporary models. Using the work of Callard (2018) on aspiration as a starting point, specifically her characterization of aspiration as a self-transformative project central to a university liberal arts education, two studies investigated aspiration in the personal projects of undergraduate students. The initial mixed-methods study conducted an online survey of 140 undergraduate students to solicit their personal projects, cognitive and affective appraisals of their projects, as well measures of potential aspirational outcomes. Canonical correlation analysis revealed two significant functions: an inverse relationship between Negativity and Positivity, and another inverse relationship between Challenge and Achievement. Environmental mastery and Self-Acceptance were more strongly associated with Achievement, while Autonomy and Purpose in Life were more strongly associated with Challenge. Following Study 1, a longitudinal mixed-methods analysis examined differences in students’ personal projects over the course of a university semester. Students were enrolled in either a Control course (an introductory cognitive science class; n = 42) or an Aspirational course (a course in the cognitive science of spirituality, covering topics such as the development of wisdom, self-transcendence, and mystical experience; n = 33; total N = 75). Both courses were delivered in the same semester and taught by the same instructor, with data being collected in September, October, November, and December. Coding of personal projects was broadened to include a category of change-related projects, and a series of measures assessing autonomous, controlled, intrinsic, and extrinsic motivations were added. Baseline bivariate correlations found that autonomous striving was most broadly associated with outcome measures. Multilevel modelling of change-related project prevalence and motivation variables found that both change-related project prevalence and autonomous striving declined month over month, while appraisals of projects as challenging and depressing increased. The aspirational course, however, showed recovery in autonomous striving in December. Overall, these results give some pause to Callard's characterization of university education as an inherently aspirational project, and raise concerns about the feasibility of teaching for aspiration within a university setting.
Computational Methods for Resilience Analysis in Interdependent Urban Infrastructure Systems
Keywords: Centrality measures
Component importance measures
Decision support systems
Reliability
Resilience
Urban planning

This thesis concerns methods for reliability and resilience assessment of urban infrastructure systems. The demands made upon urban infrastructure systems are increasing due to population growth and the ongoing trend of urbanization. There is a corresponding need for tools that can aid urban planners in reasoning about the impacts of growth and planning interventions on legacy infrastructure. The first part of the thesis presents a method for constructing district-scale models from public datasets. Detailed information on infrastructure systems is typically not available to the public for a variety of reasons. Several sources of data are discussed, as well as synthetic data generation, virtual cities, and digital twins. The proposed method is a hybrid approach that uses street network and utility data as input. The second part introduces a new set of centrality measures that can be employed on either isolated or interconnected networks. Unlike existing measures, which were typically developed for social network analysis, the proposed measures are designed explicitly for use in flow networks. They can be combined with reliability estimation techniques to yield a set of composite measures that give information about fallback routes. A graph transformation technique is used to simplify computations on interconnected networks. The third part investigates methods for clustering components according to their functional role in resource delivery. Due to the lack of labeled datasets, only unsupervised methods are used. Infrastructure systems are represented as flow networks with dedicated backup components. Each network component has a rich set of features that includes structural node embeddings and multi-valued resilience measures.
Adaptive Regulation: Modelling Cerebellum-driven Processes
Keywords: averaging analysis
oculomotor system
optimal steady-state control
output regulation
switched systems
visuomotor adaptation

Understanding the function of the cerebellum remains an open problem of systems neuroscience. The thesis adopts a recent hypothesis that characterizes the cerebellum as an adaptive internal model, with the goal of rejecting disturbances acting on various regulated subsystems. The main challenge in utilizing regulator theory to explain cerebellar functions arises from its traditional development being primarily focused on engineering applications, which presents limitations in its application for brain modeling. The thesis aims to advance regulator theory and make it more suitable for systems neuroscience applications by addressing two main obstacles. The first obstacle regards the fact that traditional optimal control theory is concerned with optimizing transient behavior making it not suitable for capturing the brain’s optimization of its steady-state operation. To bridge this gap, we formulate the optimal steady-state regulation (OSSR) problem, which incorporates a cost on maintaining steady-state inputs and outputs of controllers contributing to regulation. We develop a fully adaptive control architecture to solve a specific instance of the OSSR problem involving two control modules: an inexpensive state feedback and a costly adaptive internal model. The design is used to realize a model of long-term adaptation of the brainstem neural integrator motor command in the oculomotorsystem, successfully recovering several experimental findings. The second obstacle addressed by the thesis regards intermittency of measurements which leads to a switched system with multiple equilibria. We extend the methods in the literature to provide the first stability results for discrete-time switched systems with multiple equilibria that do not impose any dwell-time constraint on the switching signal. These results are applied to show the stability ofa model of visuomotor adaptation under intermittent visual error measurement.
Addressing Challenges for Reliable Machine Learning Model Updates
Keywords: Machine Learning
Model Updates
Performance Stability

The dynamic nature of data and user expectations requires machine learning models to be updated throughout deployment for maximal performance. Updating models presents a set of challenges that must be handled carefully to ensure that performance improves.The non-convex nature of neural network training makes it challenging to update a model without introducing new errors relative to the original model, regardless if accuracy increases overall. Flipping previously correct predictions to incorrect is known as predictive churn and decreases user trust. I expand the understanding of how churn happens by explaining it through the lens of incompatible parameter updates. I use this analysis to motivate the need for a churn reduction method which does not suffer from a stability-plasticity tradeoff. Finally, I propose a solution for reducing predictive churn called accumulated model combination (AMC) that achieves state-of-the-art churn reduction performance. In some cases, the quality of newly gathered data may be lower than the initial training data. This leads to model updates decreasing performance; i.e., deterioration. To understand how deterioration occurs, I use model-dependent noise (MDN) where samples of a particular difficulty are corrupted. I show that depending on the sample difficulty targeted (easy, intermediate, hard), deterioration occurs mainly through two mechanisms: forgetting or learning prevention. Given that hard samples are more likely to be corrupted than easy samples, I demonstrate the potential societal implications as difficulty-based subgroups can coincide with demographic subgroups. I also provide various insights which explain why continual learning is particularly susceptible to deterioration. Lastly, I introduce the feedback loop problem which occurs in settings where data is confounded by model predictions. I investigate the role that clinician trust plays in limiting feedback loops, and also show the feasibility of detecting this phenomenon. I then present a variety of design choices which need to be adjusted to account for feedback loops in order to limit their effect. As part of a first-of-its-kind study on machine learning deployment in healthcare, I investigate to what extent clinician impressions are influenced by an early warning system, and how these predictions subsequently change behaviour and outcomes.
A Framework for Automated, Unsupervised Control Synthesis in Noiseless Subspaces of Open Quantum Systems
Keywords: decoherence-free subspaces
noiseless subspaces
noiseless subsystems
open quantum systems
quantum control

In this thesis, we develop an automated framework for open-loop quantum control design, called the Generalized Control of Noiseless Subspaces (GCNS) framework, that targets a broad class of open quantum systems subject to the Born-Markov approximation. The framework is automated, highly generalized, computationally efficient, and extensible. It rests on an eleven-point Formal Framework Scope that specifies the model classes, control standards, information preservation standards, and control input classes it supports. It additionally rests on a nine-point set of Software Implementation Guidelines (SIG) that specifies how any software implementation of the framework should ideally perform with respect to efficiency, automaticity, and scalability. In constructing the framework, we develop novel theorems for controllability in Quantum Noiseless Subsystems, novel algorithms for the efficient, automated detection and parameterization of subsystem codes, and novel algorithms for the systematic selection and synthesis of control Hamiltonians from large candidate sets of feasible controls. Additionally, we develop the theory of Transition-Effort-based Control Input Synthesis: a versatile approach to generating control input signals, subject to a wide potential range of constraints, for the purpose of noiselessly effecting quantum operations on encoded quantum states. The thesis provides a comprehensive examination of a MATLAB- and Java-based implementation of the framework called the QBox library. The examination covers supporting algorithms critical for meeting the efficiency and extensibility guidelines in the SIG. It additionally covers all major aspects of the software library's anatomy, its application programming interface, and its suite of supplementary tools assisting control design. To validate the GCNS framework, its principal workflow is applied to two open quantum system models selected from the quantum information processing literature, based on trapped ions and superconducting transmon qutrits. The models and workflow parameters are selected to demonstrate a broad cross-section of the algorithms, subroutines, and software tools developed in the thesis. The examples further establish the exceptional scope and configurability of the framework, highlighting its importance to rapid design iteration in the noisy intermediate-scale quantum computing era.
The Meaning of Equality
Keywords: Early Modern
Equality
Feminism
Political Theory

The subject of this dissertation is the foundations of human equality. It investigates what exactly we mean when we claim we are one another’s equals. Why do we believe that no one is naturally the subject of another? While many assume that we are equals because we are all rational beings, my project defends an alternative conception of equality. We are all equals because we are equally dangerous. This dissertation turns to the Early Modern period to uncover forgotten ways of articulating what we mean by natural equality. Contrary to common assumptions that equality must be based on our shared rational capacities, this dissertation finds that the early modern period offers multiple ways of articulating our belief in equality, which were not predicated on the similarity of our rational minds. I identify three different theories of equality, each of which is associated with a different school of thought that emerged in the Early Modern period: skepticism, empiricism, and materialism. The skepticist conception of equality suggests that we are equal because of our inability to judge the relative value of our differences. Empiricists’ theories of equality hold that we are all born blank slates, and hence we cannot know who is superior a priori. Materialist theories on equality base equality on our bodies, and particularly our capacities to kill. This project is by no means unique in turning to this period to think about equality, however, it is distinctive in placing the ‘woman question’ at the center of this inquiry. This dissertation considers, not just canonical articulations of equality, but how these ideas were taken up and transformed by women writers to defend the equality of the sexes. Each chapter stages a dialogue between canonical male thinkers—Montaigne, Hobbes, and Locke and their female interlocutors—Marie de Gournay, Margaret Cavendish, and Judith Drake. By turning to women writers, my project spurs a reevaluation of the apparently ‘brutal’ materialist theory of equality. When considering the debate through a feminist lens, I conclude that focusing on dangers equals pose, rather than their shared rational capacities, might have more emancipatory potential than previously imagined.