VictorJuiz/thesis huggingface dataset:
Parallel front propagation in medical image segmentation

Newly emerging medical image modalities, large high-dimensional images with resolution and quality improved over time, introduce new challenges for traditionally sequential segmentation procedures. We contribute to three aspects of medical image segmentation, and we provide a new, more reliable way to evaluate the results of segmentation.
The watershed transform is a popular image partitioning procedure from mathematical morphology, used in many applications of computer vision. Our first contribution is to automate an organ segmentation procedure. The algorithm is sequential and is based on the construction of an image partition forest—a watershed-based hierarchical partitioning of a 3D image—followed by the fast marching method for hypersurface front propagation.
Secondly, we parallelise the watershed procedure. Our algorithm constructs paths of steepest descent and reduces these paths into direct pointers to catchment basin minima in logarithmic time, also crucially incorporating successful resolution of plateaux. Three GPU implementation variants and their parameters are analysed through experiments on 2D and 3D images.
Thirdly, we propose the fast dashing parallel method for hypersurface front propagation in a partitioned image. This new parallel procedure solves the eikonal equation at voxels on partitioning region boundaries and ‘dashes’ with constant arrival time inside partitioning regions. Experiments on 2D and 3D data show that the GPU implementation of fast dashing executes faster than traditional eikonal solvers and produces qualitative results that fit better for medical image segmentation.
Our novel way to evaluate segmentation results relies on a new family of metrics, with hybrid characteristics. These metrics quantify the similarity or difference of segmented regions by considering their average overlap in fixed-size neighbourhoods of points on the boundaries of those regions. Our metrics are more sensitive to combinations of segmentation error types than other metrics in the existing literature. We compare the metric performance on collections of segmentation results sourced from carefully compiled 2D synthetic data and 3D medical images. We show that our metrics: (1) penalise errors successfully, especially those around region boundaries; (2) avoid overly inflated scores; and (3) score segmentation results over a wider range of values.

Advisor: Voiculescu, I
Data-driven algorithms for multi-agent optimization and games

Multi-agent optimization and game theory constitute fundamental frameworks for modelling a plethora of engineering problems. In some systems, agents cooperate to optimize a collective cost, thus giving rise to multi-agent optimization programs. In other cases, agents act as selfish entities wishing to minimize their own individual cost subject to local or global constraints. To model such behaviour, non-cooperative game theory is required. Finally, agents though selfish can opt towards the formation of coalitions such that they receive a higher payoff. Such systems are studied under the lenses of cooperative game theory. One of the most crucial challenges in such problems is finding solutions that are robust against the effects of uncertainty. Uncertainty represents our lack of knowledge about how the underlying system behaves and usually originates from unmodelled dynamics or external factors. Its significance is apparent in modern applications such as the electric vehicle charging problem, the optimal power flow problem with power generation from renewable energy sources, transportation and social networks. Following a data-driven approach, this thesis builds a theoretical framework for the provision of robustness certificates for solutions to multi-agent optimization problems and non-cooperative games with an arbitrary cost function and uncertainty in the constraints. The theoretical tools developed allow the provision of collective certificates for sets (and subsets) of solutions that do not necessarily need to be optimal. Focusing on the class of aggregative games, agents’ deviations from an equilibrium solution and uncertain constraints are considered. A data-driven equilibrium seeking algorithm is then proposed such that tunable collective guarantees can be provided for all possible feasible deviations from the equilibrium. Shifting our attention towards a particular class of optimization programs with uncertain cost, prevalent in practical applications, we provide probabilistic guarantees for the randomized optimizer that do not depend on the number of agents but only on the size of the decision vector of each agent. As such, the probabilistic guarantees provided are scalable as the number of agents increases. Finally, a data-driven theoretical framework is established for multi-agent cooperative games with uncertain value functions. Collective stability guarantees for the entire core set, a fundamental concept in cooperative game theory, are provided based on data from the value functions in a probably approximately correct learning fashion. The case where the core can be empty is considered and a methodology to accompany allocations in a relaxed core with stability certificates is proposed.

Keywords: uncertain coalitional games
multi-agent optimization
multi-agent games
statistical learning
data-driven algorithms
scenario approach
The role of TIRR in DNA damage and repair

The integrity of our DNA genetic code is constantly under threat from a variety of different endogenous and exogenous sources. Failure to accurately repair damage to our DNA can lead to the development of diseases such as cancer. In order to maintain genomic stability, cells possess a network of highly regulated cellular processes which act together to repair DNA, known as the DNA damage response. Many different factors contribute to the repair of DNA lesions, including different proteins and RNA species. A central player in the repair of DNA double strand breaks, a particularly toxic type of DNA damage, is a protein known as 53BP1. The recruitment of 53BP1 to double strand breaks is a highly regulated process, and 53BP1 is constitutively bound by the inhibitor protein TIRR. When a double strand break occurs, TIRR and 53BP1 dissociate, allowing 53BP1 to engage chromatin at the break and function in repair. In Chapter 3 of this thesis, we explore the mechanism of this dissociation, identifying a role for RNA which is transcribed at double strand break itself. We also further characterise the role of TIRR in the wider DNA damage response, firstly in Chapter 4, where we explore the function of TIRR RNA binding in the regulation of RNA processing, and in Chapter 5, where we perform a proximity labelling experiment to identify novel interactors of TIRR. Taken together, this thesis characterises the function of the DNA repair factor TIRR, both as a 53BP1 negative regulator, and as an RNA-binding protein.

Keywords: DNA Damage
53BP1
TIRR
DSB Repair
Advisor: Gullerova, M
Scalable learning for expanding robot vision

Vision is hard, but the rewards for cracking the problems that stand between us and robust robot vision more than make up for the arduous journey. This work aims to guide the reader through some ideas and concepts that try to make robot vision better under difficult conditions. We tackle the shortcomings of robot vision from three different directions: domain adaptation, image de-noising and finally training data generation. However, the boundaries between these categories are fluid, allowing for complementary operation.
Throughout this work, we successfully demonstrate domain adaptation for localisation across any weather and appearance using generative adversarial networks (GANs), and subsequently extend this to an unsupervised setting that generalises to more than one task. Similarly, we show that convolutional neural networks are a suitable choice for image de-noising - in the context of de-raining and inverse perspective mapping - and present data and ground-truth collection strategies that make this possible. Finally, we tackle the scarcity of training data via data generation. We propose an improvement to cycle-consistency GANs, and a very simple method for collecting images affected by adherent droplets complete with a clear ground-truth. Scene synthesis and randomization techniques are used for improved semantic segmentation models, finishing with an investigation of a collision mitigation system that leverages data created using a simulator.
The techniques described lead to significant gains under difficult conditions such as low illumination, large changes in appearance (domain shift) and noisy data, as well as allowing machine learning models to be trained using data that would normally be very hard, expensive or impossible to obtain.

Keywords: Domain shift in computer vision
Image synthesis and transformation for extending existing datasets to cover new environmental conditions
Domain adaptation for robotic perception
Synthetic data for robotics vision
Advisor: Newman, P
Optical and electronic studies of new materials for perovskite multijunction photovoltaics

Hybrid perovskite semiconductors have recently emerged as ideal candidates for multijunction photovoltaics due to their tunable bandgap. This thesis examines some key issues limiting the development of such next-generation photovoltaic technology.
A key problem is that mixed-halide devices fail to deliver the expected increase in quasi-fermi level splitting when the bromide fraction is increased. Here, spectroscopy along with detailed balance calculations is used to quantify the voltage loss resulting from halide segregation. Our results indicate that, contrary to popular belief, the primary loss mechanism in wide-bandgap cells is high trap state densities in the bulk and at the interfaces. We thus identify that focussing on maximising the initial radiative efficiency of the mixed halide films and devices is more important than attempting to suppress phase segregation.
The thesis also investigates loss mechanisms in wide-bandgap double perovskite Cs2AgBiBr6, which has shown promising stability and optoelectronic properties. Experiment and modelling are combined to reveal that short electron diffusion length underpins the poor short-circuit current seen in such devices. Our findings point to an important materials issue that must be attended to with priority to bring Cs2AgBiBr6 solar cells on track towards maturity.
Finally, optical modelling is used to analyse what is arguably the largest optical loss mechanism in all-perovskite tandems today: a large refractive index mismatch at the recombination layer. This loss is quantified, and the thesis demonstrates that replacing ITO with an intermediate refractive index material, such as Nb doped TiO2, can significantly enhance light incoupling into the low bandgap cell, boosting the PCE by upto 1% absolute. Interestingly, index matching also has the unintended consequence of making cell performance less sensitive to thickness variation during manufacturing.

Keywords: spectroscopy
perovskite
solar
multijunction
wide bandgap
tandem
photovoltaic
physics
semiconductors
renewables
modelling
Advisor: Snaith, H
National heritage in private hands: the political and cultural status of country houses in Britain, 1950-2000

This study examines the role of privately owned country houses in political and cultural discourse in Britain between 1950 and 2000. It expands existing scholarship on how and why policy affecting country houses has developed, and contextualises these developments broadly within cultural and political changes of the late-twentieth century.
The thesis situates privately owned country houses within the broader history of the rise of the ‘heritage industry’ and heritage preservation policy in Britain. It utilises the Historic House Association’s (HHA’s) archives, and the personal archives of some of its key figures, which have not been studied by researchers before. This approach contributes unprecedented detail to the study of built heritage preservation policy changes, and particularly to our understanding of how aristocrats have influenced modern politics via the heritage sector.
Existing accounts of the country house’s “survival” in twentieth-century Britain are predominantly cultural. This perspective in isolation obscures critical dynamics within governments, and between governments and lobbyists. Relationships between country house lobbyists and Treasury and Inland Revenue officials, which long outlasted individual governments, were essential in the development of crucial taxation exemptions for heritage. The conditional exemption of heritage properties from Capital Transfer Tax in 1975/76, under Wilson’s government, set a critical precedent for the special treatment of historic houses under taxation, which was expanded during the 1980s. This became the foundation of the cross-party political principle that country houses are best maintained in private hands, although Wilson’s cabinet had not truly resolved their objections to granting country house owners financial privileges. Governments’ subsequent expansion of public aid for privately owned houses and collections was rooted in this political principle. During the 1980s and 1990s, the importance of “saving” country houses and their collections was taken for granted by both the public and governments—even under the allegedly heritage-averse New Labour.

Keywords: country houses
historic houses
arts funding
inheritance tax
heritage industry
property taxation
capital transfer tax
modern British history
Advisor: Whyte, W
Cox, O
Robinson, E
Narratives of love: emotional morality in five Mamluk-era treatises on love

This thesis examines five treatises on love from fourteenth-century Cairo and Damascus, all of which have been categorised by Giffen as pertaining to the “love theory” tradition. Fourteenth-century Cairo and Damascus, by this time the two centres of knowledge production in Arabic, is the only context in which we see multiple treatises being written in this tradition on the subject of love by authors who knew each other, read each other’s near-contemporaneous works and responded directly to each other in their own writing, making this a dynamic and intertextual corpus of materials. Moreover, that five of the same kind of treatise are written in the century, more so than at any other time, suggests that this kind of treatise, and the questions these treatises explore, had some currency in the period under investigation. These treatises are Shihāb al-Dīn Maḥmūd al-Ḥalabī’s (d.725/1325) Manāzil al-aḥbāb wa-manāzih al-albāb, the third chapter of the first section of the second book of Shihāb al-Dīn ibn ʿAbd al-Wahhāb al-Nuwayrī’s (d.733/1333) Nihāyat al-arab fī funūn al-adab, Mughulṭāy ibn Qilīj’s (d.762/1361) al-Wāḍiḥ al-mubīn fī man ustushhida min al-muḥibbīn, Ibn Qayyim al-Jawziyyah’s (d.751/1350) Rawḍat al-muḥibbīn wa-nuzhat al-mushtāqīn, and Ibn Abī Ḥajalah’s (d.776/1375) Dīwān al-ṣabābah.
The contribution of this thesis to the emerging field of the history of emotions in pre-modern Arabic literature is an analysis of the compilers’ approach to moral questions that derive from the experience of same-sex love, drawing from religious, medical, literary, historiographical and legal sources of knowledge. In particular, there is considerable focus on the psychology of same-sex love and desire and on the compilers’ depiction of the psychological and emotional conflict that derives from the condemnation of same-sex sexual intercourse in the individual’s experience of love. The thesis is broken into four chapters, which each survey different aspects of the compilers’ approach to moral questions. Starting with an examination of the proscription of same-sex intercourse, the thesis then examines the ways in which that prohibition has ramifications on the life of the moral subject, the idealised reader of the compilation; the thesis examines in turn the morality of gazing upon phenomenal beauty, chaste same-sex relationships and the ways in which women’s same-sex desire could be controlled by men. The thesis demonstrates that same-sex lovers are faced with very specific demands and expectations in their navigation of their experience of love. By zoning in on the particular contour of moral questions that arise within the same-sex relationship, we begin to see how the compilers understand love to be inflected by its personal circumstances.

Keywords: Islam
Sexuality
Arabic Literature
Love
Emotions
Morality
Advisor: Bray, J
Causal inference methods for supporting, understanding, and improving decision-making

Causality and the ability to reason about cause-and-effect relationships are central to decision-making. This thesis contributes to the area of causal inference by proposing new machine learning methods that can be used for supporting, understanding, and improving decision-making, with a focus on the healthcare setting.
Firstly, we introduce several causal inference tools for supporting decision-making by estimating the causal effects of interventions (treatments) from observational data, such as electronic health records. We begin by addressing the under-explored problem of estimating counterfactual outcomes for continuous-valued interventions and propose a method based on generative adversarial networks that achieves state-of-the-art performance and that can help us choose both the correct treatment and dosage for each patient. Then, we shift our attention to the temporal setting where we develop a sequence-to-sequence model that uses domain adversarial training to handle time-dependent confounding and that can help us determine the best sequence of treatments for each patient. Moreover, we introduce the first method that can handle the presence of multi-cause hidden confounders in temporal data. By taking advantage of the dependencies in the treatment assignments over time, our method learns latent variables that can be used as substitutes for the hidden confounders.
Secondly, we integrate counterfactual reasoning into batch inverse reinforcement learning to develop a method for better understanding the decision-making behaviour of experts by modelling their reward functions in terms of preferences over `what-if' (counterfactual) outcomes. We show that this allows us to obtain an interpretable parameterization of the experts' decision-making process and subsequently uncover the trade-offs and preferences associated with their actions.
Thirdly, we improve the robustness of decision-making by proposing a new model for batch imitation learning that incorporates causal structure into the learnt imitation policy. By ensuring that the imitation policy only depends on the causal parents of the actions, we learn a decision-making guideline that is robust to spurious correlations and that generalizes well to new environments.
Overall, this thesis introduces methodological advances in machine learning capable of reasoning about cause-and-effect relationships that can enable us to improve delivery of personalized care for patients, support clinical decision-making and build a more transparent account of clinical practice. We provide a discussion highlighting the challenges of incorporating such methods into practice and include suggestions for future work in this direction.

Keywords: counterfactual reasoning
causal inference
machine learning
healthcare
treatment effects estimation
Advisor: van der Schaar, M
Prisacariu, V
Legibility, ambiguity, and the patterning of Arabic script: Epigraphic ceramics of the early Islamic world

Throughout the ninth to eleventh centuries, in nearly every region of the Islamic world, Arabic script became ubiquitous amongst glazed ceramics and firmly established in the decorative language of potters. This thesis examines the trajectory and development of Arabic script throughout the period on such wares. The origins of glazed script can be traced to vessels of the Yellow Glaze Family in the ninth-century Eastern Mediterranean, many of which followed established epigraphic modes of other media, particularly metalwork and glassware. The practices and traditions first exhibited in the region subsequently spread throughout the entirety of the Islamic world over the ensuing centuries, developing into disparate local variations, and, in many instances, distinctly ceramic typologies. The opaque white glazed wares of southern Mesopotamia established standard epigraphic formulae and styles, many of which had little precedent in other media. The inscribed slip-painted ceramics of the eastern Iranian world developed into a robust assortment of illegible epigraphic patterns and legible Arabic aphorisms, exhibiting a diverse array of epigraphic types produced by local potters. The polychrome and lustre types of Egypt spread across North Africa into the repertoire of potters on the Iberian Peninsula, disseminating epigraphic ceramic practices throughout the Islamic West. Across the entire breadth of the Islamic world, both legible and illegible inscriptions were created by potters and desired by buyers. Epigraphic configurations more akin to pattern than legible text were widespread, suggesting that the appearance of script was of foremost importance. Though precise epigraphic preferences differed from region to region, they collectively reveal the growing fashion to create and own epigraphic materials, the aestheticisation of Arabic writing, as well as the increasing appreciation of Arabic script amongst the non-courtly class.

Keywords: pseudo-Kufic
Arabic
early Islamic art
ceramics
epigraphic
Advisor: George, A
Syncytiotrophoblast extracellular vesicles:their role in gestational diabetes mellitus

Gestational diabetes mellitus (GDM) is the most common metabolic complication of pregnancy. It affects more than 17 million women each year and causes adverse consequences for both mother and baby. Previous research has observed that the placenta plays an important role in the pathogenesis of GDM, since delivery of the placenta results in the immediate reversal of diabetic symptoms. The aim of this thesis is to investigate the nature of the placenta’s role in GDM, building on the knowledge that the organ releases extracellular vesicles (EVs) into the maternal circulation from six weeks gestation until delivery. EVs carry a variety of ligands, including both proteins and RNA species, between the cells. As such, they have an important role in cell-cell communication in both physiological and pathophysiological conditions, and may provide novel biomarkers of disease. We hypothesized that placental EVs might carry factors impacting insulin availability, a hallmark of pregnancy altered during the establishment of GDM. We identified two novel biomarkers (dipeptidyl peptidase 4 (DPPIV) and insulin receptor (IR)) on placental EVs. DPPIV is a glycoprotein that rapidly degrades incretin hormones, which are known to increase insulin secretion, and thus regulate glucose homeostasis. DPPIV inhibitors are established as therapeutic agents for type 2 diabetes mellitus, and we confirmed that they can be used to inhibit placental DPPIV-EV activity. IR is a key regulator of glucose uptake by skeletal muscles and adipose cells. We showed that placental IR-EVs can act as decoy receptors capable of neutralising maternal insulin. We demonstrated that GDM patients exhibited significantly higher levels of circulating placental DPPIV-EVs and IR-EVs. The final aim of this project was to characterise the protein cargo of placental EVs from GDM pregnancy using mass spectrometry. We identified 56 proteins as differentially expressed between GDM and normal pregnancy. In conclusion, this DPhil project shows that STB-EVs carry biologically active molecules that have the potential to regulate maternal insulin levels in GDM and could represent biologically plausible biomarkers for the disease.

Keywords: Dipeptidyl Peptidase 4
Placenta
Insulin Receptor
Syncytiotrophoblast Extracellular Vesicles
Gestational Diabetes Mellitus
Advisor: Vatish, M
Zhang, W
Coward, K