VictorJuiz/thesis huggingface dataset:
Cognitive linguistics and the demonology of Luke’s gospel

This thesis is the first work to apply conceptual metaphor theory (e.g. Lakoff and Johnson 1980) and conceptual blending theory (e.g. Fauconnier and Turner 2002) to Luke’s conception and portrayal of demons, exorcisms, and Satan, with particular attention paid to Luke’s use of the Septuagint. By applying cognitive linguistics to biblical texts, I have been able to illuminate difficult passages and to develop new insights into the text, thus demonstrating the usefulness of cognitive linguistics as a methodology for examining biblical narrative as well as epistolary or discursive texts.
Chapters 1 and 2 respectively outline past scholarship on NT demonology (especially Lukan demonology) and the cognitive linguistic fields of conceptual metaphor theory and conceptual blending theory (particularly their increasing use in biblical studies). Chapter 3 contains a brief contextual overview of demons and Satan in the Greco-Roman world and in the Jewish scriptures, in preparation to discuss conceptual blending in Chapter 4, which focuses on Luke 4:1-13 and 4:33-37. Chapters 5 and 6 explore specific conceptual metaphors in the rest of the gospel (particularly the WARFARE metaphor), and their application to specific questions in Luke’s work, while Chapter 7 suggests future possible research.
Luke presents Satan as the leader of an enemy nation, and as such possession, exorcism, demons, evangelism, and other elements can all be conceptually mapped according to the WARFARE metaphor. Thus, applied to the particular fields of demonology and Satanology, conceptual metaphor theory can also help to deepen our understanding of Luke’s Christology, his presentation of the Gentile mission, and his understanding of the Septuagint and of Hellenistic culture. The topic of demonology cannot be ignored, and the field of cognitive linguistics provides a new, exciting lens through which to view it.

Keywords: Satan
Demonology
Septuagint Translation
Conceptual Blending Theory
Gospel of Luke
Lukan demonology
Cognitive Linguistics
Demons
Septuagint
Satanology
Conceptual Metaphor Theory
Advisor: Salvesen, A
Joynes, C
Physics-informed neural networks for data-efficient learning

The physical world around us is profoundly complex and for centuries we have sought to develop a deeper understanding of how it functions. Building models capable of forecasting the long term dynamics of multi-physics systems such as complex blood flow, chaotic oscillators and quantum mechanical systems thus continues to be a critical challenge within the sciences. While traditional and computational tools have dramatically improved to address parts of this open problem, they face numerous challenges, remain computationally resource intensive, and are susceptible to severe error accumulation. Now, modern machine learning techniques, augmented by a plethora of sensor data, are driving significant progress in this direction, helping us uncover sophisticated relationships from underlying physical processes. An emergent area within this domain is hybrid physics-informed machine learning where partial prior knowledge of the physical system is integrated into the machine learning pipeline to improve predictive performance and data-efficiency. In this thesis, we investigate how existing knowledge about the physical world can be used to improve and augment predictive performance of neural networks. First, we show that learning biases designed to preserve structure, connectivity and energy such as graphs, integrators and Hamiltonians can be effectively combined to learn the dynamics of complex many-body energy-conserving systems from sparse, noisy data. Secondly, by embedding a generalized formalism of port-Hamiltonians into neural networks, we accurately recover the dynamics of irreversible physical systems from data. Furthermore, we highlight how our models, by design, can discover the underlying force and damping terms from sparse data as well as reconstruct the Poincar\'e section of chaotic systems. Lastly, we show that physics-informed neural networks can be effectively exploited for efficient and accurate transfer learning - achieving orders of magnitude speed-up while maintaining high-fidelity on numerous well studied differential equations. Collectively, these innovations show case a new direction for scientific machine learning - one where existing knowledge is combined with machine learning methods. Many benefits naturally arise as a consequence of this including (1) accurate learning and long-term predictions (2) data-efficiency (3) reliability and (4) scalability. Such hybrid models are paramount to developing robust machine learning methods capable of modeling and forecasting complex multi-fidelity, multi-scale physical processes.

Keywords: digital twins
artificial intelligence
physics
machine learning
surrogates
Advisor: Stephen, R
The use of human genetics in drug target identification and validation

The process of developing novel medicines is complex, expensive, time-consuming and fraught with failure. A major contributor to the high failure rate of drug development is the poor ability of existing preclinical models to predict drug efficacy and safety in humans. Human genetics enables an emerging set of investigations with utility in drug target identification and validation, akin to a “natural randomised trial”. In this thesis, I applied these methods to examine the efficacy and safety of several drug targets. Using variants in the gene encoding angiotensinogen, I found that inhibition of this novel anti-hypertensive drug target is likely to safely reduce the risk of cardiovascular outcomes, with vascular effects that are comparable to those arising from other targets in the renin-angiotensin pathway, and blood pressure lowering more broadly. Next, I applied human genetics to examine several therapeutic questions relating to coronavirus disease 2019 (COVID-19). I found that genetic proxies for renin-angiotensin system modulating therapies show no strong evidence of association with COVID-19, suggesting that these therapies are unlikely to produce substantial benefit or harm in the context of COVID-19. I then investigated the use of human genetic data for potential repurposing of immunomodulatory therapies for the treatment of COVID-19. This work was the first to show that genetic proxies for interleukin-6 receptor (IL-6R) inhibition associate with a lower risk of severe COVID-19. Meta-analysis of IL-6R inhibitor clinical trial data showed these medicines reduced the risk of COVID-19 progression and mortality, therefore recapitulating the findings from the genetic analysis, and illustrating the value of applying human genetics to guide drug repurposing in the context of an emerging pandemic disease. I perform a further set of analysis to show that COVID-19 GWAS data can be leveraged to prioritise a further set of immunomodulatory drug targets. I next analysed clinical trial data of a sclerostin inhibitor (romosozumab) approved for the treatment of osteoporosis, and found evidence suggesting that this drug may increase cardiovascular risk. Genetic variants in the gene encoding sclerosting (SOST) were also associated with a higher risk of cardiovascular events, suggesting that this adverse effect is likely real and target-mediated. Finally, I performed a genome-wide association meta-analysis of erectile dysfunction, which led to the discovery of the first robust risk locus, and a potential drug target, for this common condition. The work presented in this thesis showcases the utility of human genetics in guiding drug target identification and validation, and its potential for ameliorating the high failure rate of drug development.

Keywords: drug development
human genetics
Advisor: Lindgren, C
Holmes, M
The twentieth-century English university and its exclusions: literature and the politics of expanding higher education

This thesis, situated at the intersection of literary scholarship, intellectual and social history, and critical university studies, explores the richly heterogeneous, and largely overlooked, body of responses by a number of major literary writers to the mass expansion of higher education during the ‘long twentieth century.’ The discussions trace how literary writers problematised the dominant cultural narratives about the extension of access to English higher education and responded to the ideological tensions between democratic and emerging meritocratic principles as they played out in educational reforms. I examine how their works bring to light the rigid hierarchisation of universities in the English system and the concerning correlations between this hierarchy and the reproduction of wider social inequalities. What was, initially, understood as a problem mostly of class has been rewritten over and over again as a problem, also, of gender, of race, of sexual orientation, and of ableist biases. Chapters 1-5 shed light on those aspects of the writings of E. M. Forster, D. H. Lawrence, Virginia Woolf, Evelyn Waugh, and W. H. Auden that evidence significant, and sometimes surprising, investments in the issues surrounding under-representation, underachievement, and maladjustment of certain groups of students. A final chapter considers educational ‘access’ from a present-day vantage point by turning to Zadie Smith’s university narratives, which deepen our appreciation of the prescience of earlier writers who anticipated some of the controversies that would beset the contemporary ‘neoliberal’ and ‘meritocratic’ university. This thesis, in short, demonstrates how longstanding dissatisfactions with the universities’ manifold forms of exclusivity have been; how idealism in this area keeps getting abraded by socioeconomic and political realities; and the diverse range of ways in which modern literary writers have articulated and finessed their criticisms of institutional entrenchments of intersectional disadvantage.

Keywords: critical university studies
twentieth-century English Literature
universities and colleges in literature
modernism
Advisor: Small, H
Post-wildfire morel mushroom harvesting and the production of post-disaster opportunity in settler-colonial Canada

This thesis explores processes of opportunism and socio-political change following so-called “natural” disasters through a multi-sited case study of the post-wildfire morel mushroom (Morchella sp.) harvest in Western Canada. Morels are edible fungi which fruit en masse the first spring season following large wildfires in western North America. This work follows harvesters who picked morels after the 2016 Horse River wildfire on Treaty 8 territory (near Fort McMurray, Alberta), and the 2018 Shovel Lake wildfire on Nadleh Whut’en, Stellat’en, and Nak’azdli Whut’en Territories (British Columbia).
Thinking with and beyond the concepts of disaster capitalism and disaster colonialism, this thesis extends analyses of post-disaster change from considerations of states and large corporations to smaller-scale actors. As such, I consider the roles of hobbyist local harvesters, precarious pieceworkers in the wild mushroom industry, Indigenous Guardians of the Land, and forest ecologies more broadly. I demonstrate that while disaster capitalism and disaster colonialism are pervasive in the post-disaster landscape, they are not inevitable. Instead, I argue that post-disaster opportunity is emergent, contingent, and includes possibilities for reworking, resistance, and resurgence.
In this work, I argue that settler-colonial aims to subsume nature produce the ecological conditions which make the commercial mushroom harvest possible. This industry, in turn, disproportionately benefits settler harvesters over Indigenous Nations and forest ecologies. I also demonstrate that the materiality of wildfire memories affects different groups’ capacities to harvest mushrooms, influence others, and define the ethical standards of the harvest. Finally, I examine how settler claims to post-disaster opportunity on Indigenous lands¬ are connected to broader affective “settler common sense” and “white possessive” claims to adventure, freedom and commerce. Together, these findings demonstrate how the concurrent and often contradicting post-disaster opportunism demonstrated by small-scale actors relate to broader politics about natural disasters, environmental politics, resource extraction, and Indigenous governance within Canada and in other settler-colonial contexts.

Keywords: wildfire
disaster capitalism
affective geography
human geography
decolonization
indigenous peoples
Canada
settler colonialism
affect
more-than-human
disaster
environmental geography
Western Canada
Advisor: Ernwein, M
Vasudevan, A
Grossman, Z
The American monsoon system: variability and teleconnections

This thesis investigates the representation of the variability and teleconnections of the American Monsoon System in the state-of-the-art general circulation models. The models exhibit several biases including an overestimation of the strength of the East Pacific Intertropical Convergence Zone (ITCZ) and the position of the Atlantic ITCZ. The representation of the seasonality and magnitude of monsoon precipitation in the North American and Central American monsoons has improved compared to previous generations of the models, however, the spatial distribution of precipitation in the South American Monsoon is still poorly represented due to biases in the equatorial Atlantic sea-surface temperatures (SSTs)s. These simulations reasonably represent the seasonal cycle of precipitation in southern Mexico and Central America, where a bimodal signal known as the Mid-summer drought (MSD) has been important for agriculture in the region for centuries. A wavelet transform method is developed to diagnose the timings of the bimodal seasonal cycle and results illustrate that the method can diagnose monsoon timings in any monsoon region for any precipitation time series, including climate model output. Using this method, several theories that explain the existence and timing of the MSD signal in southern Mexico and Central America are evaluated using reanalysis and climate models. These results suggest that the MSD can be most consistently explained through the effect of seasonally varying moisture transport driven by the low-level flow in the Caribbean Sea. The influence of the quasi-biennial oscillation (QBO) teleconnections is explored in several climate model simulations, and the results suggest that the QBO could affect ocean-atmosphere phenomena. More frequent positive phases of El Niño-Southern Oscillation and the Indian Ocean Dipole, and a weaker Walker circulation are found during QBOW compared to QBOE in the models. Atmosphere-only and coupled ocean-atmosphere experiments are performed where the zonal winds in the equatorial stratosphere are specified, by relaxing them towards reanalysis data. The representation of the QBO is improved in these experiments, particularly in the lower stratosphere. However, the surface impacts of the QBO in the experiments with the relaxation result in a weaker tropical response to the QBO phase compared to the free-running models. These results imply that the relaxation has disrupted processes and/or feedbacks that are important forthe surface impact of the QBO. The results of these experiments highlight the importance of SST feedbacks and the interaction of tropical waves with the zonal flow in the stratosphere for the interaction of the QBO on tropical convection.

Keywords: precipitation
onset
decay
stratospheric-tropospheric coupling
quasi-biennial oscillation
climate model
bimodal regime
Advisor: Gray, L
Osprey, S
The contours of free will scepticism

Free will sceptics claim that we lack free will, i.e. the command or control of our conduct that is required for moral responsibility. There are different conceptions of free will: it is sometimes understood as having the ability to choose between real options or alternatives; and sometimes as being the original or true source of our own conduct. Whether conceived in the first or in the second way, free will is subject to strong sceptical arguments. However, free will sceptics face a difficulty: doubting the existence of free will might itself be problematic, whether free will exists or not. Three main considerations are offered in support of this claim. Firstly, it is said that free will sceptics confuse contexts: even if they are right to doubt the existence of free will in the philosophy classroom, they are wrong to do so in other contexts, such as day-to-day interactions or a court of law. Secondly, it is said that free will scepticism is incompatible with inescapable and indispensable human practices such as practical deliberation or moral criticism and evaluation. For, to deliberate about what to do, we must presume that we have true options or alternatives. And to judge that some human practices are wrong, we need to assume the existence of moral responsibility. Thirdly, free will scepticism is sometimes said to be self-defeating, on the grounds that it cannot be rational to believe free will scepticism, because free will is required for rationality. This thesis critically assesses these arguments. None takes free will scepticism down, but all are instructive about how not to be a sceptic. They trace the ‘contours’ of free will scepticism by discrediting some conceptions of free will and some arguments for free will scepticism.

Keywords: moral responsibility
self-defeat
excuses
free will
knowledge of responsibility
free will scepticism
deliberation
coercion
contextualism
manipulation
Advisor: Hyman, J
Hills, A
Essays on foreign direct investment and international trade

This thesis consists of three standalone chapters split between two main topics that speak to the effects of using unilateral, national policies in place of pursuing international cooperation. The first topic focuses on the recent move towards increased trade barriers and their effects on labour demand, and the second addresses the utilisation of tax policy to attract internationally mobile capital. Chapters 1 and 2 cover the first topic, using high frequency data on the near universe of job adverts posted online in the United Kingdom and United States to investigate the relationship between trade barriers and labour demand. Chapter 1 specifically studies the impact of the Brexit referendum on labour demand, with the emphasis placed on potential future trade barriers between the UK and European Union. The main results show that commuting zones that were more exposed to potential barriers on professional services exports saw a differential decline in online job adverts in the period after the referendum, particularly after the middle of 2018, and in months when there was heightened uncertainty about trade policy. No such effect is found for commuting zones than were more exposed to future tariff barriers of goods. Chapter 2 investigates the impact of the 2018 tariff increases on US labour demand, in this case focusing on the actual tariffs that were applied during this period rather than expectations about the future. The analysis finds that commuting zones that were more exposed to tariffs on imported inputs, as well as those more exposed to retaliatory tariffs, saw a differential decline in online job adverts in 2018. No equivalent positive effect was found for tariffs on imports competing with US production. Chapter 3 relates to the second topic, analysing the impact of corporate tax rates and tax holidays on inward greenfield Foreign Direct Investment (FDI) using a new global dataset compiled by the World Bank. Higher corporate tax rates are shown to have a negative impact on FDI attraction, but there is no robust evidence that tax holidays are effective.

Keywords: labour markets
international trade
Advisor: Javorcik, B
Bond, S
Deep vision for indoor understanding and localisation

The challenge considered in this thesis is that of domesticating deep learning-based vision. Firstly, we advance the use of deep learning for indoor architectural comprehension. Secondly, we explore a taming of deep neural networks for low-cost inference such that they can be used in real-time or on constrained hardware.
Room or building architecture forms the foundational level of geometry that will be visible within an indoor space, and remains static. Therefore, better understanding of architectural structure enables reasoning over a long period of time without re-training or re-mapping. We introduce Planar R-CNN which can perform unconstrained room layout estimation. Planar R-CNN detects instances of architectural planes, as well as regresses the 3D plane equation for each detection. Planar R-CNN moves room layout estimation beyond strict constraints and can infer arbitrary planar geometries. Following this, we explore visual localisation through architectural reasoning. We introduce LaLaLoc, which learns a latent representation of layout shared between rendered structure and panorama images, enabling cross-modal alignment without explicit layout prediction. LaLaLoc is able to localise across furniture configurations and in previously unvisited scenes given only a floor plan structure as a prior. LaLaLoc++ extends this by unifying representations of layout structure between 2D floor plans and images.
Neural network based methods can often be too slow to meet the requirements of a mobile robot, or too large to run on its hardware. We consider low-cost network design, and in particular group convolution as a design tool. Grouped convolution has become increasingly common across the spectrum of speed and accuracy trade-offs architecture design. However, the implications of grouped convolutional layers have remained relatively unexplored. We introduce Mobile R-CNN which, through group convolutional design, performs highly accurate real-time instance segmentation. Finally, we formalise this design of grouped convolutional architectures with the GroSS representation of group convolution blocks, which ultimately enables thorough exploration of the design space where it may not have been feasible before.

Keywords: scene understanding
group convolution
localisation
room layout estimation
object detection
neural architecture search
Advisor: Prisacariu, VA