VictorJuiz/thesis huggingface dataset:
An assemblage and material agency approach to the function of jades in a mortuary context: the case of Fu Hao's tomb in late Shang China
Keywords: assemblage
jade
Late Shang China
material agency

This research investigates the function of jades from “Fu Hao’s tomb”, a late Shang burial (c. 1200 BC) in central China. Contrary to previous typological approaches, this research argues that the function of jades should be examined in specific context, and the burial itself should be considered as a context of jade use in the past. It applies recently developed assemblage thinking and material agency in archaeological theories to study the role of jades in this context. By asking research questions — “who”, where, when, how, and why in terms of the participation of these jades in the burial process, this research regards the role of jades as participants in the creation of the past, instead of merely human affiliations. Through this new theoretical lens, this context of jade use is understood as an assemblage of both human and nonhuman actors, who/which gathered for the transformational rites of passage of the deceased. Through an examination of the material properties of jade, this research analyses possible ways in which jades could have taken part within the assemblage in relation to both human and nonhuman others. Based on their position inside the tomb, this research argues that while involved in multiple stages of the burial ceremony, jades were used in close association with the tomb occupant in this burial. They could have facilitated the transformation of the social status and social relations of the person, constituted composite objects as either their accessory or defining parts, and added to the whole assemblage by improving its status. This research aims to contribute to the contemporary discussion on the role of material things in the world in general.
Deep generative models for biology: represent, predict, design
Keywords: generative models
computational biology
protein modeling
deep learning
protein engineering

Deep generative models have revolutionized the field of artificial intelligence, fundamentally changing how we generate novel objects that imitate or extrapolate from training data, and transforming how we access and consume various types of information such as texts, images, speech, and computer programs. They have the potential to radically transform other scientific disciplines, ranging from mathematical problem solving, to supporting fast and accurate simulations in high-energy physics or enabling rapid weather forecasting. In computational biology, generative models hold immense promise for improving our understanding of complex biological processes, designing new drugs and therapies, and forecasting viral evolution during pandemics, among many other applications. Biological objects pose however unique challenges due to their inherent complexity, encompassing massive spaces, multiple complementary data modalities, and a unique interplay between highly structured and relatively unstructured components.
In this thesis, we develop several deep generative modeling frameworks that are motivated by key questions in computational biology. Given the interdisciplinary nature of this endeavor, we first provide a comprehensive background in generative modeling, uncertainty quantification, sequential decision making, as well as important concepts in biology and chemistry to facilitate a thorough understanding of our work. We then deep dive into the core of our contributions, which are structured around three chapters. The first chapter introduces methods for learning representations of biological sequences, laying the foundation for subsequent analyses. The second chapter illustrates how these representations can be leveraged to predict complex properties of biomolecules, focusing on three specific applications: protein fitness prediction, the effects of genetic variations on human disease risk and viral immune escape. Finally, the third chapter is dedicated to methods for designing novel biomolecules, including drug target identification, de novo molecular optimization, and protein engineering.
This thesis also makes several methodological contributions to broader machine learning challenges, such as uncertainty quantification in high-dimensional spaces or efficient transformer architectures, which hold potential value in other application domains. We conclude by summarizing our key findings, highlighting shortcomings of current approaches, proposing potential avenues for future research, and discussing emerging trends within the field.
Restitution for a failure of condition
Keywords: restitution
failure of condition
agreements

This thesis examines the justification for restitutionary awards for a failure of condition and considers when, given this justification, such awards should be made. It is argued that restitutionary awards for a failure of condition give effect to, but do not enforce, an agreement between the parties. As to when such awards should be made, it is argued that restitution for a failure of condition should generally be awarded where C and D agreed that D’s entitlement against C to a payment made by C to D, a performance of a service by C for D, or transfer of a right from C to D remained subject to a condition and that condition has failed. It is further demonstrated that where D’s entitlement against C to the transfer of a right was subject to a condition that has now failed, D may come under a restitutionary duty to transfer a right to C, which a court may specifically enforce.
B cells, T cells and sepsis: understanding the adaptive immune response
Keywords: TCR repertoire
BCR repertoire
immunology
sepsis

Sepsis is defined as life-threatening organ dysfunction resulting from a dysregulated host response to infection. Globally, sepsis is responsible for 11 million deaths each year and is the most common reason for admission to adult intensive care units (ICUs) in the UK. Despite its significant impact, there are currently no sepsis specific treatments targeting the host response. Sepsis is associated with high mortality, estimated at 30% for the initial episode, and around 15% of survivors die within the following year. Healthcare-associated or secondary infections and recurrent sepsis are common causes of long-term mortality, suggesting that persistent alterations in adaptive immunity contribute to the detrimental effects of sepsis. A greater understanding of underlying disease mechanisms is paramount for reducing the associated socio-economic burden.
To elucidate the adaptive immune response in sepsis, I have conducted the first comprehensive longitudinal study of the B and T cell receptor (B/TCR) repertoire using high-throughput multiplex PCR and next generation sequencing (NGS). Seventy-two patients with sepsis and twelve matched healthy controls were sampled at three time points, days one, three and five, post-ICU admission to understand the dynamic immune response. Patients were recruited as part of the UK Genomic Advances in Sepsis (GAinS) study.
As part of this thesis, I developed an optimised data dimensional reduction pipeline called repertoire (r) WGCNA which utilises weighted gene correlation network analysis. I demonstrate how rWGCNA can be used to identify a small number of modules which capture the combined effect of several correlated immune repertoire features (e.g. diversity and V gene usage). By applying conventional statistical analysis, I used these modules to characterise the alterations to the adaptive immune repertoire during sepsis, effectively distinguishing between health and disease. Additionally, I identified repertoire features with significant associations to clinical variables, Sepsis Response Signatures (SRS), and mortality, focusing on novel biology and generating hypotheses for future research.
Finally, by integrating matched bulk RNA sequencing data from whole blood, I identified genes and pathways associated with various aspects of the adaptive immune response, including clonal expansion and diversification. These findings provide a foundation for future research aimed at understanding the pathophysiology of sepsis.
Mathematical models of filtration
Keywords: homogenisation
differential equations
mathematics
size structured
filtration
simulation
multiscale model
mathematical
dynamical systems
asymptotic analysis
network model
industrial mathematics
network homogenisation
modelling
age structured
models

Filtration is the removal of particles from a fluid suspension by forcing it, either using a constant flow rate or pressure drop, through a porous material, which causes particles to become captured in the pores. Filtration using membranes is a well-studied industrial process with multiple important applications. Experimental observations of particle–pore interactions to gain insight into the filtration process are difficult and sometimes destructive. Some existing mathematical approaches either ignore important microscale information, such as the particle- and pore-size distribution or the connectivity of the pores, or are too computationally expensive for filter scale experiments. In this thesis we motivate, derive, solve, and discuss three novel models for particle filtration: (I) A size-structured model. In this work, the particle and pore sizes are treated as independent variables, which gives rise to a system of partial integro–differential equations within which the filter is treated as a macroscale continuum. We demonstrate that consideration of size structure allows for greater understanding of the filtration process; (II) A network model. Here, the filter is treated as a network composed of pores, which are modelled as edges, and junctions between pores, which are modelled as nodes. The connectivity of the network changes as particles deposit in pores. This gives rise to a system of ordinary algebraic–differential equations for variables defined at each pore and junction of the network. We show that this consideration of the structure of the microscale leads to physically intuitive behaviour of macroscale quantities, such as the flow rate and pressure; (III) A multiscale model. In this work, we extend the method of network homogenisation to apply to networks whose connectivity varies in time. We use this to derive a system of partial differential equations defined on the continuous macroscale geometry that is coupled, via its parameters, with variables that are defined on the discrete network that defines the microscale. We show that solutions of this multiscale model match those of the network model from (II) in a particular asymptotic limit, even though they are obtained at considerably reduced computational cost. These three models increase our understanding of the filtration process. The computational tractability of the models means that they are suitable candidates for filtration process optimisation tasks that involve repeated solution in future work.
The role played by ethics in the decision-making process of senior leaders: a study of health care during an extreme crisis
Keywords: ethics
senior leaders
decision-making
crisis

This dissertation researches ‘do senior leaders incorporate ethics into their decision-making during extreme crises and, if so, how?’ The research question arose from identifying a gap related to decision-making involving ethics in the ethics, leadership and crisis literature.
The research initially investigated whether and how ethical considerations were incorporated into the decision-making of 15 senior leaders. The interviewees included Australia’s health minister, a health department head, MDs of commercial organisations, and CEOs in national border force, child protection and emergency management.
Following the first findings, and consistent with grounded theory, the research proceeded to a 15-month case study at the second-largest public hospital in Australia, Royal Melbourne Hospital (RMH). The case study involved over 45 interviews with a total of 16 senior leaders, including senior nurses, clinicians and a social worker. This was the only non-medical research permitted at RMH during the pandemic. This privileged access enabled an in-depth analysis of senior leaders’ decision-making at key phases of the COVID-19 crisis.
The research finds that the senior leaders drew upon multiple forms of ethics and adapted their ethics-based practices while making decisions. They included ethics in their decision-making to lead change in the crisis. These key findings contribute to the literature and extend current knowledge as to how senior leaders engage in ethics-based decision-making.
The thesis also makes a practical contribution to the field of ethics-based decision-making by setting out an original decision-making framework that is intended to assist senior leaders to make ethics-based decisions in highly pressured situations as experienced in a crisis.
Mathematical foundations for a compositional account of the Bayesian brain
Keywords: Bayes' law
relative entropy
fibration
statistical games
neural circuits
polynomial functor
chain rule
predictive coding
dynamical systems
functorial semantics
Bayesian
applied category theory
compositionality
Bayesian brain
cybernetics
copy-composition
category theory
open systems
free energy

This dissertation reports some first steps towards a compositional account of active inference and the Bayesian brain. Specifically, we use the tools of contemporary applied category theory to supply functorial semantics for approximate inference. To do so, we define on the 'syntactic' side the new notion of Bayesian lens and show that Bayesian updating composes according to the compositional lens pattern. Using Bayesian lenses, and inspired by compositional game theory, we define fibrations of statistical games and classify various problems of statistical inference as corresponding sections: the chain rule of the relative entropy is formalized as a strict section, while maximum likelihood estimation and the free energy give lax sections. In the process, we introduce a new notion of 'copy-composition'.
On the 'semantic' side, we present a new formalization of general open dynamical systems (particularly: deterministic, stochastic, and random; and discrete- and continuous-time) as certain coalgebras of polynomial functors, which we show collect into monoidal opindexed categories (or, alternatively, into algebras for multicategories of generalized polynomial functors). We use these opindexed categories to define monoidal bicategories of 'cilia': dynamical systems which control lenses, and which supply the target for our functorial semantics. Accordingly, we construct functors which explain the bidirectional compositional structure of predictive coding neural circuits under the free energy principle, thereby giving a formal mathematical underpinning to the bidirectionality observed in the cortex. Along the way, we explain how to compose rate-coded neural circuits using an algebra for a multicategory of linear circuit diagrams, showing subsequently that this is subsumed by lenses and polynomial functors.
Because category theory is unfamiliar to many computational neuroscientists and cognitive scientists, we have made a particular effort to give clear, detailed, and approachable expositions of all the category-theoretic structures and results of which we make use. We hope that this dissertation will prove helpful in establishing a new "well-typed'' science of life and mind, and in facilitating interdisciplinary communication.
Re-formulating the public-private divide in light of the emergence of data dependent utilities under article 6 of the GDPR

Keywords: GDPR Art 6
Public Sphere
Data Protection
Data Dependent Utilities
Res Publica
Public-Private Divide

This thesis argues that the public sphere should not be confined to states and that data dependent utilities (DDUs) belong in the public sphere. DDUs are essential utilities for social and political engagement which are inherently and fully dependent on processing personal data. In this respect, the thesis challenges the traditional state-centric public-private divide (namely the consideration of the public as the governmental sphere and the private as the non-governmental sphere) in legal frameworks, specifically under article 6 of the General Data Protection Regulation (GDPR) which lays down the lawfulness grounds for personal data processing and is thus the foundation of each personal data processing activity. It argues that this state-centric approach does not respond to the versatile powers of DDUs and instead exacerbates concerns by considering DDUs as private entities and allowing them to arbitrarily apply article 6 in detriment to individuals’ autonomy and democratic values.
In this regard, relying on the res publica example from the Roman Republic era, this thesis offers a novel formula on the constituents of the public sphere which are referred as public features and formulated as: (i) public matters, (ii) public utilities, and (iii) power, as a barrier before the governance and the use of these utilities. The thesis then claims that DDUs share these features, hence they belong in the public sphere; and it argues that once we place them in the public sphere we could only then address the externalities that land both on the individuals and society at large. It then offers a normative account on how the grounds in article 6 of the GDPR – specifically consent, necessity of a performance of a contract, legitimate interest, and public interest grounds should be applied to DDUs considering their public features. As such, the thesis aims to transform these currently toothless lawfulness grounds into functioning ones upon adopting a DDU-targeted approach, thus diminishing DDUs’ exploitative business model.
Causal discovery with ancestral graphs
Keywords: causal discovery
graphical model
maximal ancestral graphs

Graphical models serve as a visual representation that captures the underlying conditional independence relationships within distributions, employing either directed or undirected graphs. In this thesis, we explore maximal ancestral graphs (MAGs), which is an extension to the conventionaldirected acyclic graphs (DAGs). While DAGs excel in illustrating causal relationships, they fail to capture all the conditional independences on the margin in the absence of latent confounders and selection bias. MAGs provide a more comprehensive depiction of complex dependencies by encompassing both direct causal connections and indirect influences stemming from latent variables and selection bias.
The scalability and accuracy of MAG learning algorithms have been some problems due to the complexity of the space of Markov equivalence classes (MECs) of MAGs and instability of scoring criteria. We first use the concept of heads, tails and parametrizing sets to characterize Markov equivalent MAGs. Then we study imsets of MAGs to address the above issues.
The framework of imsets (Studeny, 2006) is an algebraic approach to represent conditional independences. Given the remarkable success of standard imsets within DAGs, where they efficiently represent MECs and offer reliable scoring criteria, we endeavor to extend this framework to MAGs. Through an exploration of 0-1 imsets defined by parametrizing sets, we show under which conditions does this extended `standard imset' of MAGs define the correct model. Consequently, we refine the ordered local Markov property of MAGs (Richardson, 2003), demonstrating that the newly proposed refined Markov property can be constructed in polynomial time if we bound maximal head size.
Finally, we apply the above results to develop novel score-based learning algorithms for MAGs. To efficiently traverse between MECs of MAGs, we identify some important graphical features within MAGs whose independence models are subsets of others. Leveraging the imsets derived from the refined Markov property, we establish a consistent scoring criterion, offering an alternative to BIC by relying solely on estimates of entropy over subsets of variables. Empirical experiments show promising results when compared to state-of-the-art algorithms.
Ancient Latin epics in Girolamo Vida's Christiad
Keywords: Neo-Latin
Latin epics
Christian epic
Renaissance
Literary Imitation
Classical Reception

The topic of this thesis is the influence of ancient Latin epics on the Christiad, a Neo-Latin epic poem published in 1535 in Cremona and written by an Italian poet and Catholic bishop, Girolamo Vida (circa 1485-1566).
While the impact of Virgil’s Aeneid on Vida’s poem has been studied, it is still in need of further investigation. Likewise, recent scholarship has detected many loci similes between the Christiad on the one hand, and Lucretius’ De Rerum Natura, Ovid’s Metamorphoses, Lucan’s Bellum Civile, Valerius Flaccus’ Argonautica, Silius Italicus’ Punica and Statius’ Thebaid, on the other, without systematically studying the nature of such resemblances.
This thesis argues that many of these loci similes, including borrowings from Virgil, affect the meaning of the Christiad. Vida’s technique covers a wide range of nuances of literary imitation on a hypothetical spectrum that has at its poles, respectively, the inert refashioning of the words of a previous author and the in-depth engagement with the source on an ideological level. Virgil is undoubtedly his favourite model both qualitatively and quantitatively. However, Vida regarded Lucretius and the post-Virgilian epicists of the first century AD as valuable, though problematic, poets and interlocutors.
The first part of the thesis contextualises the Christiad by analysing the role of ancient Latin epics and literary imitation in Italy in the fifteenth and sixteenth Centuries (chapter 1), Vida’s imitative theory and practice in his De Arte Poetica (1527) (chapter 2), and the historical, ideological and intertextual framework of the Christiad (chapter 3). The second part focuses on four major themes, namely knowledge (chapter 4), marvels and miracles (chapter 5), Good and Evil (chapter 6), and Succession (chapter 7), to illustrate the actual presence of ancient epic models in the poem.
Building a Corpus of Oral Testimonies of the Holocaust: a case study in corpus linguistics and oral history
Keywords: tagged corpus
digital scholarship
oral history
digital humanities
Holocaust studies
corpus linguistics
digital archive

This dissertation will give an overview of what goes into creating a digital cross- linguistic corpus. Using a case study, I will describe the elements, methodology, and possible research outcomes of building a digital corpus. The case study con- sists of a collection of oral testimonies of Holocaust survivors that have been tran- scribed into a collection of English, Polish, Russian, Czech, and Hungarian texts. Building on existing literature, this dissertation will describe how corpus linguis- tics can make research data more accessible to researchers of many different areas of scholarship. Having given an overview of how a corpus is created and digi- tally distributed, including the range of tools and platforms available for analysing corpora, I will explore two areas of possible research within corpus linguistics: geo-location using named entity recognition programmes, and syntactic analysis of hedging used in oral histories thanks to part of speech tagging. Through build- ing a corpus of Holocaust Oral Testimonies, I hope to demonstrate how digital methods of corpus linguistics can help further interdisciplinary research within the digital humanities.