VictorJuiz/thesis huggingface dataset:
Evolutionary rule learning in knowledge graphs with industry applications
Keywords: rule learning
knowledge graph
evolutionary algorithm

Artificial Intelligence harnessed from knowledge graphs and logical reasoning yields excellent interpretability as well as proficient reasoning and decision-making capabilities. Declarative rules, such as Prolog and Datalog, are prevalent formalisms for expressing expert knowledge and are employed in numerous systems due to their inherent explicability. This thesis introduces Evoda, a novel, evolutionary-based algorithm conceived for rule mining on large-scale knowledge graphs (KGs) and classification problems. While current methodologies examine the rule hypothesis space undertake exhaustive searches, subjected to heuristic and syntactic limitations on the rule language, thereby constraining the quality of the derived rules. In this research, the rule hypothesis space is broadened from conventional path rules to encompass general Datalog rules, with Evoda as a new metaheuristic genetic logic programming algorithm. Rule evaluation is extended from the restrictive closed world assumption (CWA) to a more practical open world assumption (OWA). Experimental results, assessed using various evaluation metrics on multiple real-world knowledge graphs, underscore Evoda's proficiency in mining superior quality rules, its enhanced precision in predictions within the enlarged rule space, and its significant strides in efficiency and scalability, handling up to 22 million facts within mere seconds. Additionally, we proposed and demonstrated that existential rules accelerate the learning rate through the inclusion of intermediate states. It surpasses several state-of-the-art rule mining and embedding models in KG completion tasks. From a machine learning standpoint, Evoda necessitates only a select few hyperparameters that display discernible patterns, thus obviating the need for hyperparameter tuning. Moreover, Evoda demonstrates exceptional performance on diverse classification datasets from academic to industry applications. In summary, this work accentuates the feasibility, effectiveness, and efficiency of the newly proposed evolutionary learning framework.
Modelling cell competition
Keywords: vertex-based model
epithelial tissue
programmed cell death
cell-based model

Competition in nature is traditionally conceptualised as a battle between individual organisms in the constant struggle for survival. The triumph of one organism results in the demise of the other. Cooperation on the other hand involves individuals acting in a mutually beneficial manner and is therefore often regarded as the opposite of competition. From the perspective of individual cells, multicellular life is perhaps the most extreme form of cooperation. However, even within multicellular organisms, cells compete for space and survival. Genetically damaged “loser” cells that grow at a slower rate than their neighbouring “winner” cells are eliminated from the body by apoptosis. Importantly, cell competition is context-dependent; loser cells are perfectly viable if the whole organism is composed of them. The demise of loser cells is triggered specifically by the presence of cells that are perceived to be more fit. Multiple triggers and pathways involved with cell competition have been discovered, but the mechanism by which cells measure and communicate their relative fitness remains elusive.
Current models of cell competition assert a priori winner or loser status to competing cell types. By their nature, such models cannot explain how the winner or loser identity is attained. In this thesis, we develop a modelling framework to study the emergence of winners and losers in cell competition. Specifically, we construct models of heterotypic populations where cell types can only vary in their model parameters. Using this approach, we show that i) variations in mechanical parameters are not sufficient for cell competition in a vertex-based model, and ii) winner or loser status in a competition system based on the exchange of death signals is determined by the emission rate of death signals and the tolerance to death signals. Finally, we make concrete suggestions for the experimental validation of our predictions.
Discourses of failure: Arabisation and nation-building in Morocco
Keywords: language politics
Arabisation
discourse
failure
nation-building
Morocco

This thesis studies the disavowal by Morocco of Arabisation, one of the main nation-building policies it adopted after independence in 1956. Replacing French with Standard Arabic in the public space, Arabisation was a largely consensual—or at least not contested—policy associated with decolonisation and endogenous modernity. It has nevertheless grown increasingly controversial as claims of its ‘failure’ issued from across the political spectrum, culminating in the Ministry of Education’s renouncing it in 2019 and reverting to French in the teaching of scientific subjects. The repudiation of Arabisation runs counter to the predominant understanding of the policy as an instrument legitimating the state and the monarchy. The thesis constitutes a political history of the discourse on the ‘failure’ of Arabisation, tracing its trajectory from a marginal to a dominant position in the Moroccan public sphere and exploring the implications thereof for nation-building. Relying on extensive archival research and interviews, it investigates discourses on language from the late nineteenth century until 2019 in four fields of the Moroccan public sphere—political, social/cultural, expert/academic, and artistic. It shows that nationalists conceived of Arabisation as a renaissance and modernity project by concomitantly rejecting the substance of the colonial paradigm of language and reproducing its structural basis, thereby framing early Moroccan nation-building in civilisational terms. In the 1960s, the Ministry of Education and the monarchy voiced early doubts over the state’s capacity to implement Arabisation under the financial and technical constraints of post-independence. The thesis contends that the association of Arabisation with Islamisation, carried out in the 1970s and 1980s under the leadership of King Hassan II, not only transformed the content of the policy but also its support base. Islamists became the main defenders of the policy while the political left progressively dismissed it. In the 1990s and 2000s, new discourses on Moroccan national culture and identity emerged among challengers to Arabisation—Amazigh (Berber) and Darija (Moroccan Arabic vernacular) advocates. They placed diversity at the centre of ‘Moroccanness’ and advanced the notion of a multicultural ‘Moroccan civilisation,’ thus challenging previously predominant discourses on Arabness. The thesis argues that the regime seized on this emerging nation-building language in the context of the 2011 Uprisings, promoting the discourse on the ‘failure’ of Arabisation so as to counter the electoral success of Islamists and nurture the idea of a multicultural ‘Moroccan exception’ that set Morocco apart from the ‘Arab’ Spring countries.
Computational ethology for primate sociality: a novel paradigm for computer-vision-based analysis of animal behaviour
Keywords: social networks
computer vision
deep learning
primate sociality
chimpanzee
animal behaviour
computational ethology

Research in the biological and wildlife sciences is increasingly reliant on video data for measuring animal behaviour, however large-scale analysis is often limited by the time and resources it takes to process video archives. Computer vision holds serious potential to unlock these datasets to analyse behaviour at an unprecedented level of scale, depth and reliability, however thus far a framework for processing and analysing behaviour from large-scale video datasets is lacking. This thesis attempts to solve this problem by developing the theory and methods for capturing long-term sociality of animal populations from longitudinal video archives, laying the foundations for an emerging field; computational ethology of animals in the wild. It makes several key contributions by a) establishing the first unified longitudinal video dataset of wild chimpanzee stone tool use across a 30 year period, and building a framework for collaborative research using cloud-technology b) developing a set of computational tools to allow for processing of large volumes of video data for automated individual identification and behaviour recognition c) applying these automated methods to validate use for social network analysis and d) measuring the social dynamics and behaviour of a group of wild chimpanzees living in the forest of Bossou, Guinea, West Africa.
In Chapter 1 I introduce the theoretical and historical context for the thesis, and outline the novel methodological framework for using computer vision to measure animal social behaviour in video. In Chapter 2 I introduce the methodology for processing and managing a longitudinal video archive, and future directions for a new framework for collaborative research workflows in the wildlife sciences using cloud technology. In Chapter 3 I lay the foundations of this framework for analysing behaviour and unlocking video datasets, using deep learning and face recognition. In Chapter 4 I evaluate the robustness of the method for modelling long-term sociality and social networks at Bossou and test whether life history variables predict individual-level sociality patterns. In Chapter 5 I introduce the final component to this framework for measuring long-term animal behaviour, through audiovisual behavioural recognition of chimpanzee nut-cracking. In my final chapter (6) I discuss the main contributions, limitations and future directions for research. Overall this thesis integrates a diverse range of interdisciplinary methods and concepts from primatology, ethology, engineering, and computer vision, to build the foundations for further exploration of cognition, ecology and evolution in wild animals using automated methods.
The transformation of an agricultural economy : forms of rural settlement in Epirus from the second century BCE to the fourth century CE
Keywords: rescue archaeology
rural settlement
Roman Empire
ancient agriculture
Roman Greece
ancient economy
Epirus

This thesis investigates the transformation of the agricultural economy of Epirus in Roman times, approximately from its conquest in 168/7 BCE and its annexation in the province of Macedonia in 146 BCE, until the 4th century CE. It does so through study of diverse forms of rural settlement in the Epirote countryside, using untapped rescue archaeology to distinguish between different farms, to refine their chronologies, and to assess their agricultural productions. In so doing, the nature of the villa and its temporal spread in this area of Greece is also thoroughly examined and clarified. The development of Epirus has in the past often been closely linked to narratives painted by Roman authors of its allegedly devastating destruction and subsequent agricultural exploitation by members of the Roman aristocracy, who called themselves "Synepirotae". Many rural sites have been interpreted in this light and called "villas", leading scholars to postulate that the villa spread widely in Epirus during the early Roman period. In contrast to this theory, it is argued that, while a few exceptional villas were constructed early on, they did not become popular until the middle and later Imperial periods; instead, in early Roman times dispersed villages and fortified farms, many of which had been inhabited before the Roman period, were more numerous. Consequently, the villa was not always the typical way to manage agricultural resources of substantial scale in this part of the Roman world. The thesis also combines rural rescue archaeology with material culture from cities and with written evidence, to reconstruct a new view of societal and economic change in Epirus during the Roman period. It is shown how gradual changes in the agricultural economy reflected changes in local societies, which were also measured in pace, and it is highlighted that the individuals making up those societies played a more active role in the ways that Epirus became incorporated into the Roman Empire than has previously been thought.
Solving optimisation problems on near-term quantum computers
Keywords: hybrid quantum classical algorithms
variational quantum algorithms
quantum simulation
quantum algorithms
quantum machine learning
quantum computing

Identifying the types of algorithms and applications that can be solved on today's quantum computers is one of the fundamental goals of modern quantum algorithms research. Underpinning this effort is the potential leap in human technology should it be shown that quantum computers can perform useful classically-intractable calculations in the absence of quantum error correction.
In this thesis we study the new paradigm of variational quantum algorithms (VQAs), designed specifically to solve optimisation problems on near-term hardware. We develop several novel algorithms spanning a range of application areas and provide estimates of the physical resources required to match classical methods. Thereafter, we attempt to run these algorithms on current quantum computers, probing their capabilities in the process. In this endeavour, we also develop our own algorithmic solution to device noise in the form of a new approach to approximate quantum circuit recompilation.
We begin with studying the feasibility of simulating the time evolution of quantum systems on current quantum computers, an important problem due to its classical hardness. We build an algorithm specifically focused on the electron-phonon Hamiltonian and subsequently realise the first demonstration of obtaining its dynamics on real quantum hardware. Subsequently, we test whether a similar result is possible for time-evolution based VQAs. For this we construct a near-term quantum computing approach to dynamical mean-field theory and find that whilst not possible currently, such an algorithm could be realistically evaluated on the next generation of quantum computers in the immediate future.
We then show how quantum circuits can be used as machine learning models and apply them to self-supervised learning, one of the most demanding tasks in deep learning. Through our experiments we observe a numerical advantage for the learning of visual representations using small-scale quantum neural networks over equivalently structured classical networks, a first step on the ladder towards general quantum advantage. Through this, we also highlight the potential of near-term quantum computing in problems with only empirically established classical complexity.
Multimodal characterisation of the infant response to retinopathy of prematurity screening and treatment
Keywords: retinopathy of prematurity
pain
physiology
heart rate variability
neonate
nociception
ophthalmology
electroencephalography

Retinopathy of prematurity (ROP) is a condition which affects premature infants and is a cause of childhood blindness. Screening is performed repeatedly during the preterm period, using binocular indirect ophthalmoscopy (BIO), to identify disease at a treatable stage; unfortunately screening and treatment are considered to be painful and stressful for infants. Pain and stress during the preterm period can lead to negative consequences for infant development. Reduction of pain and stress during ROP procedures has therefore been attempted using pharmacological and non-pharmacological strategies. However, it is challenging to evaluate the effectiveness of such interventions due to limitations in the accurate measurement of infant pain and stress.
In this thesis, novel approaches to quantifying infant pain and stress evoked by ROP procedures are presented. Infant brain activity was characterised using quantitative electroencephalography (EEG) analysis to test the hypothesis that ROP screening evokes noxious-related changes in infant brain activity. The results of this study suggest BIO ROP screening evokes a significant increase in higher frequency brain activity (12 - 30 Hz), and that increase in relative beta power may be a measure of nociception in preterm infants.
Infant cardiac autonomic reactivity was characterised using heart rate variability (HRV) analysis to test the hypothesis that ROP screening evokes stress-related autonomic changes. The results of this study suggest BIO ROP screening evokes significant reduction in HRV measures of parasympathetic nervous system activity, indicating a physiological stress response in preterm infants.
An approach to characterising the infant response to non-contact ultra-widefield photography (Optos screening) and ROP treatment was also demonstrated. Recruitment of subjects was curtailed by the outbreak of COVID-19, therefore the investigations are presented as an example of approaches which could be performed in a larger sample size.
In summary, the research described in this thesis aims to contribute to understanding of the infant experience of ROP procedures; to characterise changes in noxious-related brain activity and stress-related cardiac reactivity evoked by BIO ROP screening, and to use these measures to investigate the infant response to an alternative screening method and to ROP treatments. Improved understanding of the infant experience of ROP screening and treatment may allow clinicians to better identify and treat infant pain and stress during essential clinical procedures.
In silico methods for the prediction of drug-induced cardiotoxicity
Keywords: machine learning
pathway analysis
safety pharmacology
cardiac safety
action potential models
simulations

Unexpected adverse reactions, especially unsafe cardiac effects, are a major concern of pharmaceutical companies that can prompt them to both discontinue drugs currently in development and withdraw drugs already on the market. Therefore, the safety assessment is a key stage of both the drug development process and the current regulatory framework of clinical trials. Given the importance of unforeseen acute electrophysiological effects in precipitating potentially lethal arrhythmias, the current preclinical testing stages of drug development are largely focused on their detection. However, a substantial number of drugs also affect cardiac function on many other levels, including contractility, mitochondria function and cell signalling. A number of in vitro, in vivo and in silico approaches capable of detecting different types of possible cardiovascular side effects have been proposed recently. Among those, human-based computational methods hold a great potential to increase the productivity of drug discovery pipelines, drive a more rational drug design and replace costly animal experiments that have limited translational ability for humans.
Therefore, the goal of this thesis is to propose a computational approach to predict drug-induced cardiotoxicity. A multi-label machine learning classification approach is used to simultaneously predict multiple forms of clinical cardiac side effects and take into account relationships between those forms of toxicity. In the last part of this thesis, the effects of trafficking impairment, as one of the cardiotoxicity mechanisms, are then investigated using simulations of action potential models.
On the representation of visions in Coptic hagiographical texts
Keywords: Visions
Miracles
Christianity
Martyrs
Hagiography
Egypt
Coptic
Cult of saints

My thesis examines the functions of visions of heavenly beings in Coptic hagiography and their relationship to the cult of saints in Egypt. I use six saints’ hagiographical traditions as case studies: Kollouthos, Menas, Phoibammon of Preht, Merkourios, Viktor, son of Romanos, and James the Persian. My source base includes all the Coptic hagiographical texts in these saints’ traditions which contain visions experienced by them prior to their deaths and/or visions of them appearing posthumously to laypeople. I am thus able to understand the functions of visions through the writers’ choices in presenting them, and through that how they were perceived and utilised within the cult of saints.
I achieve two aims. Firstly, I address the lack of attention that Coptic hagiography has received in both Late Antique and Byzantine Studies and Egyptology, showing that it contributes significantly to our understanding of the cult of saints. Secondly, I focus on visions because, where supernatural phenomena within hagiography have been studied, there has been an overwhelming emphasis on posthumous miracles, meaning that the functions of other phenomena have gone unexplored.
The thesis begins with a close examination of my source base and the issues inherent in the creation, transmission, and historicity of hagiographies in Chapter 1. In Chapter 2, I contextualise my source base using archaeological evidence of the cult of saints, demonstrating in detail what we can learn from these texts and how to negotiate the issues with them. Then, in Chapters 3 and 4, I perform narratological analysis of the visions experienced by martyr saints and by laypeople, comparing them to illustrate that saints were portrayed as idealised Christians and that laypeople were instructed through visions how to participate in the cult in order to be allowed access to the divine.
Deep probabilistic methods for improved radar sensor modelling and pose estimation
Keywords: simulation
machine learning
robotics
sensor modelling
odometry
radar

Radar’s ability to sense under adverse conditions and at far-range makes it a valuable alternative to vision and lidar for mobile robotic applications. However, its complex, scene-dependent sensing process and significant noise artefacts makes working with radar challenging. Moving past classical rule-based approaches, which have dominated the literature to date, this thesis investigates deep and data-driven solutions across a range of tasks in robotics.
Firstly, a deep approach is developed for mapping raw sensor measurements to a grid-map of occupancy probabilities, outperforming classical filtering approaches by a significant margin. A distribution over the occupancy state is captured, additionally allowing uncertainty in predictions to be identified and managed. The approach is trained entirely using partial labels generated automatically from lidar, without requiring manual labelling.
Next, a deep model is proposed for generating stochastic radar measurements from simulated elevation maps. The model is trained by learning the forward and backward processes side-by-side, using a combination of adversarial and cyclical consistency constraints in combination with a partial alignment loss, using labels generated in lidar. By faithfully replicating the radar sensing process, new models can be trained for down-stream tasks, using labels that are readily available in simulation. In this case, segmentation models trained on simulated radar measurements, when deployed in the real world, are shown to approach the performance of a model trained entirely on real-world measurements.
Finally, the potential of deep approaches applied to the radar odometry task are explored. A learnt feature space is combined with a classical correlative scan matching procedure and optimised for pose prediction, allowing the proposed method to outperform the previous state-of-the-art by a significant margin. Through a probabilistic consideration the uncertainty in the pose is also successfully characterised. Building upon this success, properties of the Fourier Transform are then utilised to separate the search for translation and angle. It is shown that this decoupled search results in a significant boost to run-time performance, allowing the approach to run in real-time on CPUs and embedded devices, whilst remaining competitive with other radar odometry methods proposed in the literature.