VictorJuiz/thesis huggingface dataset:
Convective aggregation in idealised models: moving towards the real world

Great advances in our understanding of the climate have been made through utilising a hierarchy of models. In idealised models of the tropical climate, deep convection can spontaneously cluster together, a phenomenon termed convective self-aggregation. This clustering impacts the moisture and energy budgets and thus significant work has been undertaken to gain a more complete understanding of self-aggregation and its impacts on the climate. In particular, self-aggregation and convective organisation in the real world present quite differently. Thus, a key open question is how self-aggregation manifests in the real atmosphere. This thesis probes this question by incrementally increasing the complexity of a general circulation model in a radiative-convective equilibrium configuration to investigate the impact of an aerosol plume, a land continent, and a meridional sea surface temperature (SST) gradient on self-aggregation.
The diabatic heating from the aerosol plume generates a large-scale, overturning circulation, which forces convection to aggregate. However, despite the aggregation being forced, self-generated feedbacks between clouds and longwave radiative fluxes are essential for amplifying the aggregation. Thus, I postulate that self-aggregation feedbacks could contribute to real-world convective organisation, even when aggregation is initially driven by external forcing. This hypothesis is reinforced when investigating the impacts of land. A land-sea thermal contrast triggers a circulation which forces convection to aggregate, but as before, self-aggregation feedbacks are necessary for this to be maintained. Finally, I develop a novel budget for the variance in transient integrated frozen moist static energy. This is used to show that self-aggregation feedbacks also drive zonal organisation over a meridional SST gradient. This thesis consistently highlights that self-aggregation feedbacks could be contributing to real-world convective organisation, whilst also showing that the inclusion of more realistic processes can fundamentally alter the geometry, spatial and temporal scales of self-aggregation.

Keywords: tropical convection
convective aggregation
convective organisation
aerosols
idealised modelling
clouds
climate model hierarchy
Advisor: Stier, P
Dagan, G
Genomics and epidemiology of SARS-CoV-2 in Brazil

As of the 24th January 2021, it is estimated that the coronavirus disease 2019 (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has led to over 350 million reported cases and over 5.6 million deaths worldwide. Brazil has the third highest case count, over 24 million, and the second highest death count, over 623,000. In this thesis, I apply genomic and epidemiological approaches to describe and understand SARS-CoV-2 importation, transmission, spread, evolution and response during the first year of the COVID-19 pandemic in Brazil.
Chapter 2 provides and overview of the early importation, spread and response. I start by identifying the probable air routes for SARS-CoV-2 importation into Brazil. I also provide a description of the first SARS-CoV-2 cases reported in Latin America, followed by epidemiological estimates of the basic reproduction number for the most affected Brazilian states. This chapter ends with a description of the implementation and easing of non-pharmaceutical interventions (NPIs) in 72.3% of the Brazilian municipalities.
In Chapter 3, I couple genomic insights obtained from a novel representative dataset of 427 SARS-CoV-2 genomes from Brazil with human mobility data to describe SARSCoV-2 importation and genomic diversity, reconstruct SARS-CoV-2 nationwide spatial spread and investigate the impact of NPIs implemented in Brazil.
Chapter 4 covers the application of genomic epidemiology approaches to the identification and description of new SARS-CoV-2 variants of concern (VOCs). I describe the first two cases of the Alpha VOC in Brazil and provide a genomic characterization of the first cases of the Gamma VOC in Manaus, north Brazil.
Finally, I apply epidemiological and genomic approaches to uncover the dynamics of hospital-associated transmission in the largest hospital complex in Latin America. Chapter 5 shows evidence for SARS-CoV-2 within-hospital transmission to be higher in non-COVID-19 hospitals.

Keywords: genomics
epidemiology
Brazil
genomic epidemiology
spread
COVID-19
SARS-COV-2
virus
phylogenetics
Advisor: R Faria, N
Pybus, O
Augmented mapping and analysis of pancreatic fat by quantitative MRI

The incidence of pancreas pathology is rising rapidly. While early detection is critical, these are often "silent" conditions that only become symptomatic at a late stage, when they may already be untreatable. Incidental findings, where the target organ is near the pancreas, offer a way to detect pancreas pathology in time. Pancreatic ectopic fat is an early manifestation of disease, and evaluation using MRI is receiving increasing interest. However, it is challenged by the remarkable variability of the pancreas across individuals and its physiological complexity. Quantitative Magnetic Resonance Imaging (MRI) has shown potential for imaging biomarker development, including Proton Density Fat Fraction (PDFF) and T1, though some challenges remain.
The aim of the research from this thesis has been to produce an augmented representation of pancreatic disease state using quantitative MRI, focusing on practical implementation. First, regional quantification of pancreas imaging biomarkers, namely PDFF and T1, is explored through the development of a novel automated pancreas subsegmentation method that employs groupwise registration of UK Biobank data sets. The subsegmentation is validated with expert annotations using direct metrics (Dice overlap) and indirect metrics (PDFF quantification). Second, a novel PDFF reconstruction method is proposed based on chemical shift imaging and field inhomogeneity mapping. The method is robust to artefacts in one data set with challenging B0 field inhomogeneities. Third, semi-automated quantification of pancreas PDFF and T1 is demonstrated in a large population with Long COVID, enabling efficient processing. Finally, image-based quality control of PDFF is implemented through an extension of an error mapping technique previously applied to T1, and validated using simulations and in-vivo data sets. The output error maps are robust, intuitive, and can inform spatial disease heterogeneity and longitudinal change.

Keywords: proton density fat fraction (PDFF)
magnetic resonance imaging (MRI)
quality control (QC)
disease spatial heterogeneity
nonalcoholic fatty pancreas disease (NAFPD)
Advisor: Bulte, D
Brady, M
Understanding representation learning for deep reinforcement learning

Representation learning is essential to practical success of reinforcement learning. Through a state representation, an agent can describe its environment to efficiently explore the state space, generalize to new states and perform credit assignment from delayed feedback. These representations may be state abstractions, hand-engineered or fixed features or implied by a neural network. In this thesis, we investigate several desirable theoretical properties of state representations and, using this categorization, design novel principled RL algorithms aiming at learning these state representations at scale through deep learning.
First, we consider state abstractions induced by behavioral metrics and their generalization properties. We show that supporting the continuity of the value function is central to generalization in reinforcement learning. Together with this formalization, we provide an empirical evaluation comparing various metrics and demonstrating the importance of the choice of a neighborhood in RL algorithms.
Then, we draw on statistical learning theory to characterize what it means for arbitrary state features to generalize in RL. We introduce a new notion called effective dimension of a representation that drives the generalization to unseen states and demonstrate its usefulness for value-based deep reinforcement learning in Atari games.
The third contribution of this dissertation is a scalable algorithm to learn a state representation from a very large number of auxiliary tasks through deep learning. It is a stochastic gradient descent method to learn the principal components of a target matrix by means of a neural network from a handful of entries.
Finally, the last part presents our findings on how the state representation in reinforcement learning influences the quality of an agent’s predictions but is also shaped by these predictions. We provide a formal mathematical model for studying this phenomenon and show how these theoretical results can be leveraged to improve the quality of the learning process.

Keywords: generalization
auxiliary tasks
representation learning
deep reinforcement learning
Advisor: Bellemare, M
Teh, Y
Whiteson, S
Analysis of robust neural networks for control

The prevalence of neural networks in many application areas is expanding at an increasing rate, with the potential to provide huge benefits across numerous sectors. However, one of the greatest shortcomings of a trained neural network is its sensitivity to adversarial attacks. It is becoming clear that providing robust guarantees on systems that use neural networks is very important, especially in safety-critical applications. However, quantifying their safety and robustness properties has proven challenging due to the non-linearities of the activation functions inside the neural network.
This thesis addresses this problem from many different perspectives. Firstly, we investigate the sparsity that arises in a recently proposed semidefinite programming framework to verify a fully connected feed-forward neural network. We reformulate and exploit the sparsity in the optimisation problem, showing a significant speed-up in computation. In addition, we approach the problem using polynomial optimisation and show that by using the Positivstellensatz, bounds on the robustness guarantees can be tightened significantly over other popular methods. We then reformulate this approach to simultaneously exploit the sparsity in the problem, whilst improving the accuracy.
Neural networks have also seen a recent increased use in feedback control systems. This is primarily because they have the potential to improve the performance of these systems compared to traditional controllers, due to their ability to act as general function approximators. However, since feedback systems are usually subject to external perturbations and neural networks are sensitive to small changes, providing robustness guarantees has proven challenging.
In this thesis, we analyse non-linear systems that contain neural network controllers. We first address this problem by computing outer-approximations of the reachable sets using sparse polynomial optimisation. We then use a Sum of Squares programming framework to compute the stability of these systems. Both of these approaches provide better robustness guarantees over existing methods. Finally, we extend these approaches to neural network controllers with rational activation functions. We then propose a method to recover a stabilising controller from a Sum of Squares program and apply it to a modified rational neural network controller.

Keywords: neural networks
chordal sparsity
sum of squares programming
neural feedback loops
Lyapunov stability
rational neural networks
Advisor: Papachristodoulou, A
Inductive logic programming as satisfiability modulo theories

At the intersection of machine learning, program synthesis and automated reasoning lies the field of Inductive Logic Programming (ILP). The aim of ILP is to automatically learn relational programs from input/output examples, typically through logic-based techniques. Inspired by Karl Popper’s falsification perspective on science, this dissertation sets out a new approach to ILP: Learning From Failures (LFF).
In science, starting from a huge set of a priori viable hypotheses, we select a hypothesis to test. This hypothesis typically gets falsified due to failing in some specific way. By examining the failure we learn that an entire space of related hypotheses is now ruled out. Having thus reduced our set of viable hypotheses, we subsequently select from just those that remain.
LFF applies this methodology to program induction, codifying it as a three-stage loop. The generate stage maintains a formula whose satisfying assignments correspond to the set of viable hypotheses. The test stage takes a satisfying assignment, interprets it as a logic program and tests it against training examples – imperfect fit is considered a failure. The constrain stage turns a failure into constraints to add to the generate stage’s formula, thereby eliminating a class of hypotheses which will fail for the same reason.
The thesis of this dissertation is three-fold. The first claim is that LFF frames the ILP problem as one of Satisfiability Modulo Theories (SMT). With the search for viable hypotheses handed-off to a SAT-solver, the test stage can be regarded as a theory solver collaborating with the SAT-solver, effectively making ILP’s notion of background knowledge into a (Horn) background theory.
The second claim is that LFF’s three-stage loop is an effective basis for falsification-based program induction. Chapter 4 develops the above correspondence into a feature-rich and flexible three-stage ILP system. Experimental evidence is provided for this system going beyond the state-of-the-art in ILP, e.g., by supporting large hypothesis spaces and large domains.
The third claim is that the LFF-as-SMT-perspective helps apply satisfiability solving techniques to ILP, in particular to reduce hypothesis space exploration. In Chapter 5, we show that SMT-based techniques for explaining conflicts have a natural analog in terms of explaining which parts of a hypothesis are responsible for its failure. In Chapter 6, we incorporate other theory solvers alongside the test stage to reason about the (satisfiability of) over-approximating properties of hypotheses. We show that both of these techniques can significantly reduce the number of iterations of the three-stage loop.

Keywords: constraint solving
inductive logic programming
satisfiability modulo theories
automated reasoning
program synthesis
machine learning
Advisor: Gibbons, J
Ong, L
Cropper, A
Asymmetric Suzuki-Miyaura couplings of acyclic allylic systems

The objective of this thesis is to investigate and develop Rh(I)-catalysed Suzuki-Miyaura cross-couplings of acyclic allylic species with arylboronic acids. While our group has achieved an asymmetric Rh-catalyzed Suzuki-Miyaura cross-coupling of cyclic racemic allylic halides and boronic acids, the catalyst-controlled stereoselective coupling of more complex acyclic unsymmetrical molecules has not been explored.
In this thesis, we present our efforts towards the development of a Rh-catalyzed enantioselective Suzuki-Miyaura cross-coupling with racemic acyclic substrates that have few or no symmetry elements. This approach represents an unprecedented advance that significantly broadens the applicability of this reaction.
We discuss the development of an enantioconvergent Suzuki-Miyaura cross-coupling of acyclic allylic systems in Chapter 2, while Chapter 3 explores the asymmetric formation of all-carbon quaternary stereocenters via the developed Suzuki-Miyaura cross-coupling of acyclic allylic systems. Additionally, Chapter 4 focuses on Suzuki-Miyaura couplings using terminal acyclic allylic systems and outlines a modular asymmetric synthesis of 3-aryl-α-tetralones via sequential double arylation.
Overall, the thesis addresses the challenges associated with synthesising acyclic C(sp3)-rich compounds using novel asymmetric Suzuki-Miyaura couplings, with the aim of developing more efficient methods for producing enantioenriched small molecules.

Keywords: asymmetric catalysis
Advisor: Fletcher, S
Genomic and image-based quantification of intra-tumoural heterogeneity

Cancer is a complex dynamic disease which can be explained by an evolutionary process of clonal expansion, genetic diversification, and clonal selection, leading to the presence of clonal heterogeneity in the tumour. Another key aspect of cancer heterogeneity is heterogeneity in the tumour microenvironment. This thesis addresses the challenge of investigating intra-tumour heterogeneity, and proposes statistics and machine learning based methods for exploring variability arising from both genomic and immunological differences, unleashing the potential for improved cancer subtyping.
Genomic sequencing allows us to dive deep into the clonal structure of each individual tumour, permitting the study of specific cell populations arising from distinct mutational processes. On the other hand, the study of whole slide tissue imaging allows us to investigate the tumour microenvironment by exploring the complex interaction between the developing tumour and the immune system.
In this work, I develop a methodology to obtain copy number calls from sequencing data in the more complex case where reads are only available for a set of targeted genes with no matched normal samples and define evolutionary-based clusters in colorectal samples from a large multi-centre consortium. The detected genomic events and resulting clusters are found to be significant for overall survival prediction across the different cohorts.
A second smaller set of colorectal samples is scanned using a novel multiplexed immunofluorescence platform. The analysis of these whole-slide antibody-labeled images involves the development of a graph-based multi-scale deep learning approach to combine single-cell with spatial context information. The model is able to extract features associated with distinct tumour stages. Moreover, the application of interpretability methods allows for the identification of regions with predictive importance. Finally, a third data modality is explored, namely, single-cell fluorescence in situ hybridization imaging, where graph neural networks are again utilised to combine single-cell morphological and genetic information in order to identify differences between non-progressed ductal carcinoma in situ samples and samples progressing to invasive breast cancer, opening up opportunities for early detection.

Advisor: Rittscher, J
Holmes, C
Wedge, D
Visually guided pursuit in Harris's hawks

Prey pursuit in birds is a complex goal-oriented behaviour mechanised via active control of the visual system. This active gaze control enables the bird to track their targets. In this thesis, I investigate the pursuit behaviour of Harris’s hawks (Parabuteo unicinctus), which are dependent on visual cues to catch their prey. I build upon existing research on the mathematical rules that describe pursuit in raptors and to describe the gaze strategy of Harris’s hawks for the first time. To do this, I develop and validate a highly accurate biologger, which combines post processed GPS data with an inertial measurement unit (IMU) to record the head movements of hawks. The accuracy of the GPS solution validated to be < 0.02m marks a significant improvement over currently used systems in animal behaviour research. Using this logger, I capture flight data of Harris’s hawks in aerial pursuit of a smoothly manoeuvring target. I fit several known guidance laws to the experimental flight data and identify the best fitting model. I investigate the head movements of hawks during flight and identify a gaze strategy that describes their behaviour. I then use this in combination with guidance modelling to hypothesise how sensory outputs might be used to mechanise guidance in Harris’s hawks and discuss the implications of this for animal behaviour and for improvements to target tracking in aerial robotics.

Keywords: GPS biologger
GPS
gaze strategy
aerial pursuit
pursuit
bird flight
biologging
GPS tracking
flight
visually guided pursuit
guidance
GNSS
Advisor: Taylor, G
Gods, heroes, saints, and friends: ethical exemplarism and practical paideia in Gregory Nazianzen and his peers

In this thesis I analyse the uses of ethical exemplarism in Gregory of Nazianzus and several of his contemporaries: his friends and Cappadocian compatriots, Basil of Caesarea and Gregory of Nyssa, and Emperor Julian the Apostate. I begin by surveying a selection of their writings and those of other authors active in the fourth-century culture wars, analysing the frequency of exempla (incidents featuring a moral exemplar) found therein, as well as the cultural and historical origins of those exempla (Chapter 1). Besides filling a lacuna noted by Demoen (1996), this chapter indicates our authors’ favoured periods of reference and suggests new avenues of analysis. Next, I connect educational practices known from literary sources and recent papyrological finds to the ways in which Nazianzen and others employed exemplars as models of particular virtues and vices (Chapter 2). There I also present a selective analytical catalogue of the gods and mythical heroes of Hellenic culture as found in Nazianzen, Basil, Nyssen, and Julian (Chapter 2). I then turn to the uses they made of a number of secular historical exemplars, selected in part on the basis of appearances in Plutarch’s Lives and Valerius Maximus’ Facta et dicta (Chapter 3). I next apply the same method to prominent figures from Scripture, here starting from the virtue-exemplar associations in Hebrews 11 and 1 Clement (Chapter 4). Chapters 2–4 together comprise the most thorough catalogue and analysis to date of heroes and villains in Julian and the Cappadocians. Finally, I assess Nazianzen’s presentation of exemplars to reconstruct his vision of how aspiring ethical agents are meant to apply their knowledge of models of virtue and vice in practice, and his thoughts on how ethical exemplarism can go wrong (Chapter 5). Against the backdrop of recent work on exemplars in Roman history (Langlands [2018], Roller [2018]) and contemporary metaethics (Zagzebski [2017]), this last chapter provides a much-needed perspective from the late ancient Hellenic world as well as a new framework for understanding the experience of ethical exemplarism more generally. Taken as a whole, this dissertation illuminates the workings of the moral imagination during a period of crisis in Late Antiquity, provides a novel framework for a developing branch of historical and contemporary ethics, and demonstrates the utility of exemplar analysis as a method for understanding cultural priorities and cultural change.

Keywords: progymnasmata
Panegyrici Latini
Late Antiquity
virtue education
Synesius of Cyrene
mimesis
Basil of Caesarea
philosophy of education
exemplars
ethical exemplarism
Classical reception
history of education
imitation
moral imagination
Julian the Apostate
Lucifer of Cagliari
paideia
Gregory of Nyssa
role models
moral formation
Athanasius of Alexandria
Classics
Patristics
Themistius
Gregory of Nazianzus
Hilary of Poitiers
classical education
Advisor: McLynn, N