VictorJuiz/thesis huggingface dataset:
Causal inference methods for supporting, understanding, and improving decision-making
Keywords: counterfactual reasoning
causal inference
machine learning
healthcare
treatment effects estimation

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.
Legibility, ambiguity, and the patterning of Arabic script: Epigraphic ceramics of the early Islamic world
Keywords: pseudo-Kufic
Arabic
early Islamic art
ceramics
epigraphic

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.
Syncytiotrophoblast extracellular vesicles:their role in gestational diabetes mellitus
Keywords: Dipeptidyl Peptidase 4
Placenta
Insulin Receptor
Syncytiotrophoblast Extracellular Vesicles
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.
Spatial and contextual long-term and working memory in ageing and disease
Keywords: spatial memory
associative memory
long-term memory
working memory
limbic encephalitis
retro-cueing
navigation

Initially thought to be two independent memory systems, working memory (WM) and long-term memory (LTM) both present associative features controlled by the medial temporal lobe (MTL). Despite their similarities, the two memory systems have mostly been explored and compared using distinct methods and measures. Considering their differences and their correspondences, this thesis focuses on spatial associative WM and LTM performance in healthy ageing and in the presence of MTL impairment. The studies presented investigate the mechanisms of the two memory systems, their interplay, and the modulatory relationship between them and attention, using analogous tasks and equivalent behavioural measures. Specifically, Chapter 2 explores if retro-cueing benefits, previously observed in WM, extend to LTM retrieval, and whether this could represent a means of ameliorating age-related LTM impairment. Next, in Chapter 3, I compare the performance of young and older healthy adults in a spatial memory task on two devices – a personal computer and an immersive VR device – to test whether old adults benefit from an increased level of immersion and determine the optimal methods of measuring spatial memory. In the second study, I use a navigation-based memory task measuring WM and LTM, to investigate their patterns of use and interplay that might change with ageing. Lastly, Chapter 4 relies on the same task and aims to analyse patterns of performance modulated by different levels of MTL impairment in old healthy adults and individuals with limbic encephalitis. The results propose that WM and LTM show age-related associative deficits and that their interplay is preserved in ageing, despite old adults’ increased forgetting. Additionally, they suggest that WM and LTM may rely on different retro-cueing mechanisms. Lastly, the most noteworthy outcomes are the tasks developed to measure spatial associative memory of different timespans in equivalent ways, using analogous steps and stimuli, which can become great tools in the research of WM and LTM retrieval processes.
Stravinsky and his sketches : the composing of Agon and other serial works of the 1950s
Keywords: Stravinsky
nineteenth fifties
sketches
1950s
nineteenth-fifties
composing
Agon

This thesis takes as its starting point the unpublished source materials--musical sketches, manuscripts and archival material--that relate to Stravinsky’s stylistic transition in the 1950s, during which serial principles were gradually incorporated into his musical language. Drawn mainly from the Stravinsky Nachlass at the Paul Sacher Stiftung in Basel, and available for research there only since 1986, these previously unexplored sources offer an unusual and flexible approach to Stravinsky’s early serial works.
After a brief consideration of the basic aims of musical sketch studies and the relatively recent appearance of this type of inquiry in Stravinsky research, the rich and complex sketches for the ballet Agon (1953-7) are the principle concern of the thesis. Of all the source materials for Stravinsky’s works of the 1950s, these are perhaps the most extensive in their coverage of his working methods, most intricately developed, and most illustrative of wider issues in his compositional technique. Throughout the thesis, analytical observations are prompted almost exclusively by information contained within the sketches. The procedures and preoccupations reflected in these materials are related to those in other 1950s sketch collections, particularly Canticum Sacrum, allowing Stravinsky’s working methods to be assessed not only in evolutionary terms, but also in terms of recurring compositional premises, or procedural constancies.
Evaluated in conjunction with corroborative evidence in the archival material of the Nachlass, these sources offer an unrivalled opportunity for a re-examination of statements made by Stravinsky on his own compositional method, and of certain interpretations of Agon in secondary sources. Systems of classification of source materials are developed in the thesis, and an Appendix gives detailed listings of the contents and physical characteristics of the Agon and Canticum Sacrum sketchleaves in the Stravinsky Nachlass.
Symposion and philosophy
Keywords: symposium
Classical Greek
drinking party
philosophy

The present work has a double purpose: to redefine the notion of the ‘philosophical symposion’, formulated by Wilamowitz, from a larger perspective on the relation between symposion and philosophy; and to study the origins of this phenomenon in Plato’s conception of the symposion, especially as illustrated in the Laws. Section i is dedicated to the reconstruction of the philosophical symposion: it analyses the role played by the symposion in the main Hellenistic schools at a relatively early stage after Plato - roughly down to the cessation of scholarchs. The most specific features of the notion which I propose to define result from the fact that philosophers started to take a theoretical approach to the symposion; Chapter I considers this issue. Chapter 2 deals with the philosophical symposion in the literary sense: it presents some of the problems raised by the very fragmentary evidence on ‘Symposia’ written after Plato in the Academy, Peripatos, Stoa and Garden. Chapter 3 examines the scanty evidence for an early adoption of sympotic practices by philosophers, at school or otherwise. Section 11 is devoted to Plato’s conception of the symposion. The Athenian’s ideas about reforming this institution are analysed in Chapters 4 and 5: each chapter focuses on a paragraph containing a different project. Chapter 6 considers, in the end, a few cases of earlier Platonic imagery, which is relevant to the formation of the theory in the Laws and allows us to integrate it in a broader perspective on Plato. By adopting this order, the present work attempts to present Plato’s conception as a part of a much wider phenomenon, generated by Plato himself.
The phoenix of power: the reconstruction of political authority following revolutionary regime change
Keywords: Political Authority
Arab Spring
Political transitions
Legitimacy
Regime change
Revolutions
Transitional Administrations

Periods following revolutionary regime change are often governed by Transitional Administrations (TAs). Such bodies are seldom elected or even selected in any formal process. As such the political authority they enjoy is a priori deeply puzzling. This thesis seeks to understand the factors which affect the likelihood that a TA will successfully be able to establish its authority. It closely examines two cases of political transition in Libya and Tunisia, as well as the broader universe of TAs. The research concludes that the composition of the TA, in particular its relative inclusivity of the principal socio-political components of the society, greatly impacts the prospects that the TA will establish its authority. However, the research also finds that the inclusivity of the TA is conditioned by other factors, including historical socio-institutional legacies, pre-existing social ties between elites, chance contingencies leading to path-dependent outcomes, and the agency of leaders. As such the thesis proposes a novel socio-institutional explanation of authority- building in political transitions.
Large scale methods for kernels, causal inference and survival modelling
Keywords: Kernel methods
Causal inference
Survival analysis
Interpretability

Machine Learning is widely applied in industrial applications such as product design and recommendations, sales and churn prediction among many others. This thesis proposes scalable methods geared towards industrial application where we address problems related to dimensionality, uncertainty quantification and inference.
First, we inferring features from images of products that are pertinent to high sales. We propose an additional regularization objective inspired by kernel two-sample testing to separate high-sellers from low-sellers in latent space for generative models. Experiments demonstrate that we can achieve linear separation in latent space and consequently infer important features using lasso methods.
Next, we develop a non-parametric test for causal association. We propose the backdoor-Hilbert Schmidt Independence Criterion with importance weights that encode the interventional distribution. Experiments demonstrate that the proposed test is calibrated and has good power.
We then propose Kernel Fried Tensor (KFT), which is a tensor factorization model augmented with kernelized side information, designed to handle high-dimensional forecasting. To improve the utility KFT, we derive a variational inference scheme to provide uncertainty quantification around our estimates. Experiments demonstrate competitive forecasting results and calibrated uncertainty quantification.
Further, we consider right-censored data where existing survival models do not have a tractable likelihood and evaluate on non-proper-scores. We propose survival monotonic neural networks (SuMo-net) to train and evaluate on the right-censored likelihood, a proper score. Experiments demonstrate that SuMo-net achieves improved likelihood scores with lower inference times.
As many of our proposed methods rely on kernel matrix vector multiplication (KMVM), they are bottlenecked by the O(n2) memory and complexity. We propose Faster Fast and Free Memory Method (F3M) and achieve O(n) memory and complexity for tall (n 109) and skinny data (D ≤ 7).
In summary, the methods proposed in this thesis provide a collection of scalable methods for various use cases often encountered in industrial settings and contributes both through novel methodology and statistical characterization of the tasks, which hopefully inspires further research in the machine learning community.
Compositionality and functorial invariants in machine learning
Keywords: Category Theory
Machine Learning

The objective of this thesis is to show that studying the underlying compositional and functorial structure in machine learning systems allows us to better understand them. In order to do this, we explore category theoretic formulations of many subareas of machine learning, including optimization, probability, unsupervised learning, and supervised learning.
We begin with an investigation of how various optimization algorithms behave when we replace the gradient with a generic category theoretic structure. We prove that the key properties of these algorithms hold under very relaxed assumptions, and demonstrate this result through numerical experiments. We also explore a category theoretic perspective on dynamical systems that enables us to build powerful optimizers from the composition of simple operations.
Next, we take a category theoretic perspective on the relationship between probabilistic modeling and gradient based optimization. We use this perspective to study how maximum likelihood estimation preserves certain key structures in the transformation from a statistical model to a supervised learning algorithm.
Next, we take a functorial perspective on unsupervised learning. We develop taxonomies of unsupervised learning algorithms based on the category theoretic properties of their functorial representations, and demonstrate that these taxonomies are predictive of algorithm behavior. We use this perspective to derive a host of new unsupervised learning algorithms for clustering and manifold learning, and demonstrate that these new algorithms can outperform commonly used alternatives on real world data. We also use these tools to prove new results on the behavior and limitations of popular unsupervised learning algorithms, including refinement bounds and stability in the face of noise.
Finally, we turn to supervised learning and demonstrate that many of the most common problems in data science and machine learning can be expressed as Kan extensions. We use this perspective to derive novel classification and supervised clustering algorithms. We also explore the performance of these algorithms on real data.
Machine learning approaches to extract higher-order features from non-contrast computerised tomography images enables stratification of diseases
Keywords: image processing
abdominal aortic aneurysms
machine learning
computerized tomography
image segmentation
generative adversarial networks

Medical Imaging, which allows for the non-invasive assessment of biological tissues, is a rapidly growing health care service that has evolved from a diagnostic tool to a platform for personalized precision medicine. Computerized Tomography (CT), which is a commonly obtained imaging study worldwide, utilizes X-ray radiation to differentiate tissues based on density differences. More complex imaging studies are obtained based on the clinical question to supplement the CT. Image overutilization, which is the acquisition of medical images that have minimal impact on patient care and increasing global inequities due to the cost/availability of medical imaging services are significant problems that need to be addressed.
One possible solution would be to maximize the amount of clinically relevant information extracted from routine CT images using machine learning approaches. Recent developments in machine and deep learning have provided a powerful set of tools for the automated and complex analysis of medical images. Leveraging these techniques, it is possible to develop algorithms capable of learning from human annotations or paired images. These methods can also be applied to large datasets to minimise human input and to enable the rapid scale up of analyses.
In this thesis, I hypothesize that Non-Contrast CT images contain higher-order information to differentiate tissue anatomy or pathology without the need of intravenous contrast agents or radioactive tracers. I further hypothesized that such higher-order information enables stratification of disease progression without the need of additional imaging studies. I focus on two pathologies, abdominal aortic aneurysms (AAA) and Head and Neck Squamous Cell Carcinoma (HNSCC) as demonstration of feasibility. The methods described in this thesis can be applied to other pathologies and will be poised to disrupt clinical pathways in the future.
Muon sites and muon states in model fluorides and magnets
Keywords: decoherence
nuclear magnetism
muon
fluoride
magnets
quantum
DFT
inorganic
entanglement

The F–μ–F state occurs when a spin polarised muon implanted in a fluoride crystal becomes entangled with its nearest neighbour fluorine nuclei, resulting in a beating pattern observed in the muon decay asymmetry. Ever since these states were discovered in the 1980s, they have never been fully understood, as fitting the data always required a phenomenological relaxation function to account for the observed decay of the oscillations.
This thesis shows how this relaxation can be understood by considering it as a decohering process, achieved by taking into account the effect of all the nuclear spins beyond the nearest-neighbours. Starting with the simple cubic compounds CaF2 and NaF, techniques are developed which allows one to calculate the way in which the quantum information of the muon gets irreversibly lost into the crystal. This thesis then expands on this to discuss the compounds PbF2 and YF3, which have a more complicated crystal structure, and in the former compound it is possible to identify Frenkel defects with μSR for the first time.
This thesis then shows how it is possible to apply these techniques to magnets: starting with KPF6 and KBF4, which contain the [PF6]− and [BF4]− molecular ions which are often found in molecular magnets, it shows how the location of the muon site in the compound considerably changes the observed muon polarisation, determining the muon stopping site by carefully analysing the muon’s environment. Finally, the antiferromagnetic material KAgF3 is analysed with these methods, showing how a combination of DFT+μ, dipole field calculations and the techniques developed here can be used to determine the size of the electronic moments, confirm magnetic structures and narrow down the muon site. This final chapter also shows how the nearest-neighbour fluorine nuclear moment can affect the muon polarisation in magnets in a fascinating way.