VictorJuiz/thesis huggingface dataset:
Higher-order constrained Horn clauses for higher-order program verification

Higher-order constrained Horn clauses (HoCHC) are a fragment of higher-order logic modulo theories recently introduced by Cathcart Burn et al. (2018). This thesis explores the adequacy of HoCHC as a unifying framework for the algorithmic verification of higher-order programs, through links with existing approaches to program verification as well as a sound and refutationally complete solution method for HoCHC.
We establish a continuous interpretation for HoCHC in which the semantic domains are restricted to Scott-continuous functions. We show that the continuous HoCHC decision problem is equivalent to the original problem. This continuous interpretation serves as a foundation for a sound and refutation-complete resolution proof system - based on SLD-resolution - for solving unsatisfiable instances of the HoCHC problem over a semi-decidable background theory.
To address the relation between HoCHC and higher-order model checking, we introduce a coinductive version of HoCHC that is characterised by a greatest fixpoint construction. We define an encoding of higher-order recursion schemes into a HoCHC logic program, which can be used to reduce the open higher-order recursion scheme equivalence problem - and thus the lambdaY-calculus Böhm tree equivalence problem - to semi-decidability of coinductive HoCHC over Maher's complete and decidable background theory of trees (Maher, 1988; Djelloul et al., 2008).
Finally, we develop an axiomatic basis for relations as higher-order program invariants in the form of a Hoare logic. Our approach to Hoare logic for higher-order programs distinguishes itself from the literature through the combination of a higher-order assertion logic and a relatively complete proof system.

Keywords: resolution
higher-order model checking
higher-order recursion schemes
Horn clauses
higher-order program verification
Hoare logic
program verification
coinduction
higher-order constrained Horn clauses
higher-order logic
Advisor: Ong, L
Reputational concerns in online platform competition

This paper examines online platforms' incentives to protect their users' privacy, when users value the latter and the platforms' incentives arise through purely reputational concerns. The main question addressed is whether the introduction of a competing platform will increase effort incentives, relative to the monopoly case. When revenue is linear in market share and users' utilities are given by the Hotelling specification, I find that effort is reduced in the duopoly equilibrium. This is because in a duopoly setting, the expected increase in market share achieved through a high reputation is reduced. When the rewards-to-reputation function is sufficiently concave, the value of an increase in market share becomes larger for lower market shares and competition can increase effort incentives. I show that this "concavity" effect arises as the effect of platform popularity, when users' preferences favor one platform relative to the other and as the effect of decreasing marginal value of data, when users' data is used to estimate population parameters. Allowing platforms to respond to their posterior reputations through additional choice variables increases the convexity of the rewards-to-reputation mapping, thus worsening the impact of competition on effort incentives. I demonstrate this intuition by analyzing two-sided platform competition.

Keywords: competition
platforms
reputation
Advisor: Meyer, M
The reception history of Esther a genderqueer/non-binary reading of the ancient texts and in modern scholarship

The Book of Esther has been regarded by some critics as displaying unusual gender roles for a biblical text, particularly because there appear to be some gender role reversals or gender ambiguities occurring in the text. Modern reception of Esther can be broadly grouped into two categories: characters in the text are interpreted in a cisnormative manner, or some characters are interpreted as being subversive. Both groups of scholarship are problematic for non-binary/genderqueer readers in different ways, and this thesis aims to take a non-binary/genderqueer approach to Esther to assess if this text has the capacity to be read in a manner free from the gender binary.
It is the purpose of this thesis to work from the issues raised in contemporary reception and read the ancient versions of Esther that they often reference. The versions studied are the Hebrew Masoretic Text, the Septuagint, the Greek Alpha Text, Josephus’s Paraphrase of Esther in the ‘Judean Antiquities’ and Jerome’s Latin Vulgate. A reception-historical framework and a non-binary/genderqueer lens is used to assess characters’ capacity for non-binary/genderqueer reading in the ancient reception. I also discuss the similarities and differences between versions and their effect on how we can interpret characters’ behaviour.
Subsequently, this thesis highlights which characters in which texts can be read as non- binary/genderqueer and which cannot. Then, it is discussed which of the ancient receptions appear to have had the greatest impact on the binary interpretations that are prevalent today. It will be shown that the Masoretic Text has the greatest, consistent capacity for genderqueer/non-binary reading. Conversely, the Greek Additions do not allow for a genderqueer/non-binary interpretation. However, this material seems to have had the greatest impact on modern reception. I explore why this is by looking to the body of Esther scholarship and to cognitive bias theory.

Keywords: gender
reception history
Old Testament
The Book of Esther
non-binary
genderqueer
Advisor: Gillingham, S
Monitoring and managing plant diversity in field margins in Southern England

Biodiversity is severely threatened by human activity, with one of the greatest threats coming from agriculture. To reduce the impacts of agriculture on biodiversity, one conservation strategy is to exclude strips of land with herbaceous vegetation from production, termed field margins. Conserving field margins provides a valuable habitat for plant and animal species, and is therefore predicted to boost biodiversity. However, despite being a widely implemented conservation strategy, regular monitoring of plant diversity in field margins is lacking, and the ecological impacts of field margin management are not well understood. This thesis aims to further our understanding of ecologically effective ways to monitor and manage field margin plant diversity, based on three years of fieldwork across four farms in Oxfordshire, England.
Based on this fieldwork, four main findings are presented. Firstly, I found that over two years of surveying only a limited amount of variation in plant community composition can be explained by margin-level characteristics. This suggests that margin-level management can only have a limited impact. Secondly, I observed little spatial variation in plant communities between different farms in the same county. This suggests that management recommendations for plant diversity in field margins can be generalised across farms within a region. Thirdly, I observed higher butterfly species richness in field margins with higher plant species richness and intermediate vegetation height. This suggests that management of plant height and diversity will have a positive effect on butterfly diversity. Finally, I found that assessing plant diversity using either transect or quadrat survey methods resulted in biases in the plant species recorded. This suggests that plant surveys in field margins should use both transects and quadrats, to reduce bias in plant species lists. Together, these findings provide ecological evidence to improve conservation efforts for farmland biodiversity, through monitoring and management of field margins.

Keywords: survey methods
hedgerows
plant diversity
butterflies
field margins
agriculture
Advisor: Harris, S
Colorectal cancer: the EMT transcription factor ZEB1, the interaction between myofibroblast and colorectal cancer cells and GMM analysis of cancer gene expression

Colorectal cancer is one of the most prevalent types of cancer. Many factors are associated to the poor prognosis of colorectal cancer including genetic mutations, epigenetic changes, myofibroblast recruitment, and metastasis. Metastasis is the major cause of death in colorectal cancer patients. The most common sites are liver and lymph node metastasis with approximately 20-30% of patients with liver metastasis. Most literature discuss epithelial to mesenchymal transition (EMT) as the molecular mechanism tumor cells undergo to increase motility and invasiveness, ultimately leading to cancer metastasis.
This thesis is consisted of two major sections: computational and experimental work. The first aim of the thesis is to demonstrate our Python-implemented pipeline GMMchi developed to identify bimodal patterns in a mixture of normal and non-normal components (Chapter 3). GMMchi is further applied onto multiple large datasets to demonstrate its capability in pattern-based analysis (Chapter 4). In addition, we built a GMMchi-based scRNA-seq postprocessing pipeline efficient in separating clusters in a population of similar cell types (Chapter 7).
In the second aim of this thesis, we studied colorectal cancer metastasis in the context of EMT-like colorectal cancer cell lines and myofibroblast recruitment via co-cultured system consisting of colorectal cancer and myofibroblast cell lines. We showed a novel epigenetic regulation of ZEB1, the gene encoding an EMT transcription factor highly associated with our CRC cell lines exhibiting EMT markers, via CpG island methylation (Chapter 5). The second part of this section focused on understanding PDGF-C as an important ligand responsible for myofibroblast and CRC cell line interaction (Chapter 6).
In this thesis, we identified a novel mechanism for ZEB1 promoter methylation, revalidated myofibroblast recruitment phenotype previously observed in co-culture assays as well as developed and demonstrated our suite of tools for researchers to carry out pattern-based analysis.

Keywords: Gaussian mixture modeling
colorectal cancer
myofibroblast
pattern based analysis
single cell sequencing
EMT
big data
Advisor: Bodmer, W
Leedham, S
Investigating the production of an inhibitor of plant B-galactosidase by the plant pathogen Pseudomonas syringae

Microbial pathogens are causative agents of various plant diseases. During infection, plants and bacteria participate in a dynamic interaction in the apoplast. Secreted hydrolases are prominent players in plant defence and are therefore targets of pathogen virulence factors. Using Nicotiana benthamiana and Pseudomonas syringae pv. tomato (Pst) DC3000 as a model system, we recently discovered a plant β-galactosidase (BGAL1) that facilitates the hydrolytic release of elicitor peptides from bacterial glycosylated flagellin to activate plant defences. The activity of BGAL1 is suppressed by an inhibitor secreted from PstDC3000 during infection. This thesis aims to determine the molecular identity of this inhibitor, characterise its biosynthetic and regulatory pathways and unravel biological roles and evolution of this inhibitor production. Here, I identify the bim biosynthetic gene cluster of the BGAL1 inhibitor, which is under the control of the conventional type III secretion system regulators HrpR/S/L and a putative novel virulence regulator HtpX. The bim cluster has been acquired via horizontal gene transfer and is widespread among P. syringae strains. A search for homologs of bim cluster genes also reveals different putative biosynthetic gene clusters of iminosugars in various bacteria. Analytical chemistry analyses suggest that the inhibitor is an iminosugar, an analogue of sugar with N replacing O in the ring, with a putative mass of 151 Da. The production of the inhibitor can also alter galactosylation of plant apoplastic glycoproteins and isolated inhibitor fractions can inhibit other glycosidases such as ⍺-galactosidase, ⍺-glucosidase and β-glucosidase, suggesting that multiple pathways in host plants could be targeted. This work has uncovered a novel small molecule inhibitor secreted by P. syringae during infection and highlights the role of glycans and glycosidases in plant-pathogen interactions that remains to be explored.

Advisor: Preston, G
van der Hoorn, R
Leveraging natural variation to study fitness costs and trade-offs in host immunity

During health and infection, hosts allocate energy to growth, maintenance, defense, and reproduction. Current theory in ecological immunology suggests that mounting an energetically-costly defense will lead to trade-offs against host fitness. In this thesis, we used lab-adapted and wild Caenorhabditis elegans as model hosts to directly test for fitness costs of immune-upregulation and pathogen damage, as well as trade-offs with reproduction, after transient exposure to an opportunistic pathogen, Staphylococcus aureus. Utilizing a spectrum of wild host genotypes allowed us to explore the breadth of genetic and functional variation for naturally-relevant alleles. We found that despite short-term delays in reproduction after S. aureus exposure, all host isolates recovered and had similar total brood sizes as nematodes fed only control bacterial food (Chapter 2). We subsequently examined the impact of pathogen exposure across several generations in population size assays. We found a genetic correlation across host isolates such that those with a relatively smaller population size after transient S. aureus exposure had relatively larger population sizes after exposure to a second opportunistic pathogen, Pseudomonas aeruginosa (Chapter 3). This finding suggests that C. elegans has ‘balanced immunity’ against these two distinct pathogen species, evidenced at the population level. Finally, we found no evidence of a beneficial pathogen-induced maternal effect but did identify that lab-adapted nematodes have the highest magnitude of cost from maternal pathogen exposure (Chapter 4) and this corresponds to the magnitude of the delay reported in Chapter 2. Surprisingly, our findings in genetically-diverse isolates identify trade-offs in pathogen-tailored defense strategies as more important drivers of population size than energy-based trade-offs from mounting an immune response or suffering pathogen damage. We discuss the potential of studying nematode-pathogen systems with more naturalistic conditions to address ecological immunology questions using naturally-derived isolates with ecologically-relevant parameters.

Advisor: King, K
Preston, G
Palladium-catalysed desulfinative cross-coupling reactions: Leveraging mechanistic insights  for reaction optimisation

In this thesis, the mechanism of the palladium-catalysed desulfinative cross-coupling of aryl bromides and (hetero)aryl sulfinate salts is investigated. The challenges that must be overcome to deliver improved, lower temperature versions of this process are discussed, and optimisation work based on these mechanistic insights is presented.
Chapter 1 is a literature review surveying the use of sulfinate salts in palladium-catalysed cross-coupling reactions. Particular emphasis is placed on the mechanism of these transformations and the structure of palladium sulfinate intermediates.
Chapter 2 describes the mechanistic studies of the palladium-catalysed desulfinative cross-coupling of 1-bromo-4-fluorobenzene and (hetero)aryl sulfinate salts. 2-Pyridyl and carbocyclic sulfinate substrates are compared, and different catalyst resting states and turnover limiting steps are established for the two classes of reagent. The role of the carbonate additive and the reduction of the palladium(II) precatalyst is also investigated.
Chapter 3 demonstrates the significance of the challenges associated with developing lower-temperature desulfinative cross-coupling reactions. A survey of a number of reaction parameters and their influence on the outcome of the reaction is presented.
Chapter 4 focuses on the use of the Xantphos ligand and the synthesis of Xantphos derivatives to promote desulfinative cross-coupling reactions at 100 °C.
Chapter 5 discusses the development of a desulfinamidative cross-coupling reaction using a primary aryl sulfinamide as the nucleophilic coupling partner. The feasibility of this process is demonstrated, and initial optimisation results are presented.
Chapter 6 discusses the conclusion of the research and the potential for future work.
Chapter 7 presents the experimental data.

Keywords: palladium-catalysed cross-coupling reactions
reaction optimisation
mechanistic investigation
Advisor: Willis, MC
Deep learning for alternative splicing

Mutations that affect RNA splicing can have severe phenotypic consequences, and contribute to rare and sporadic human disease. Whole-genome sequencing promises to improve diagnosis, but it is often difficult to identify mutations that disrupt splicing, except when they affect canonical donor and acceptor sites. In particular, cryptic mutations that cause novel splice junctions to appear deep within introns of genes are very hard to identify. Accurate computational models are therefore crucial for effective diagnosis.
The availability of large amounts of expression data across a range of genomes and cell types, and the development of neural network technologies that can learn features directly from data, together provide an opportunity for developing computational models that predict splicing activity directly from genome sequence.
In this work, I first develop and validate a methodology to improve the training of neural networks operating on sequence data using the formalism of equivariant maps. We demonstrated increased representational stability for networks constructed using these techniques and subsequently used this approach to discover and quantify novel binding sites for PRDM9.
Next, I develop models to predict splice site location and exon inclusion ratios simultaneously using modifications to the dilated convolutional network and show state-of-the-art performance at this task. I apply these models to mutation databases and find that the model can make interpretable predictions about the consequences of deep intronic mutations, explaining 27% of pathogenic cryptic splice variants.

Advisor: Lunter, G
Machine learning for functional connectomics in Caenorhabditis elegans

Santiago Ram n y Cajal first traced the microscopic intricacies of individual neurons in the late 19th century. His work revolutionised neuroscience, and earned him the moniker “the father of modern neuroscience.” Cajal was a major contributor to the newly established neuron doctrine, outlining that even the most complex organism behaviours are simply the result of the interactions between networks of small, discrete units, called neurons. Since then, neuroscience research has measured, codified, and modelled these neural interactions in progressively more detail.
However, these neural models are rarely applied at whole-brain scales, and are even less frequently used as mechanistic models of neural activity in which Bayesian inference is performed. This offers an exciting and truly ground-breaking opportunity. Application of Bayesian inference to whole-connectome neural models would allow previously unobservable physiological states and parameter values to be inferred from readily observable data at an unprecedented scale. It would also allow interpretable neural models to be refined, tested and even learned directly from observed data without manual input. Finally, tuned whole-brain simulators promise in silico neuroscience experiments, where hypotheses can be investigated without the need for biological specimens. Such experiments are not hindered by the logistical requirements of obtaining measurements in vivo, and can be easily, cheaply and reliably reproduced.
In this thesis we develop tools to perform this estimation. Specifically, we study performing this estimation in the nematode roundworm Caenorhabditis elegans, the only organism for which the entire connectome has been fully mapped. At its core, the work we present is the first attempt to infer, at whole-connectome scale, neural states and parameters from limited observations. We introduce the machine learning and computational neuroscience tools required to make this tractable, and explore extensions arising directly from this work. We conclude by suggesting further extensions of this work that are within reach, and discussing more wide-reaching ideas and implications of this work.

Keywords: Caenorhabditis elegans
Neuroscience
C. elegans
Connectomics
Machine Learning
Advisor: Wood, F
Numerical schemes for stochastic hybrid control problems in finance

In this thesis, we propose a class of numerical schemes for weakly coupled systems of Hamilton-Jacobi-Bellman quasi-variational inequalities (HJBQVIs) arising from stochastic hybrid control problems of regime-switching models with both continuous and impulse controls. By penalizing the difference between the solution and the obstacles, we reduce the HJBQVIs into a sequence of HJB equations, whose solutions converge monotonically to those of the HJBQVIs. We show that the penalty scheme is first-order accurate for HJBQVIs with discrete state spaces, half-order accurate for HJBQVIs with Lipschitz coefficients, and first-order accurate for equations with more regular coefficients. We also demonstrate the convergence of monotone discretizations of the penalized equations, and establish that policy iteration applied to the discrete equation is monotonically convergent with an arbitrary initial guess in an infinite dimensional setting. Numerical examples for infinite-horizon optimal switching problems are presented to illustrate that the penalty scheme along with a continuation procedure in the penalty parameter is significantly more efficient than the conventional direct control scheme for solving HJBQVIs.
We further propose an efficient neural network-based policy iteration algorithm for solving semilinear Hamilton-Jacobi-Bellman-Isaacs (HJBI) boundary value problems, which not only can be combined with the penalty approach to solve high-dimensional stochastic hybrid control problems, but is also applicable to high-dimensional stochastic games of diffusion processes with controlled drift. The algorithm exploits policy iteration to reduce the semilinear problem into a sequence of linear Dirichlet problems, which are subsequently approximated by a multilayer feedforward neural network ansatz. We establish that the numerical solutions converge globally and further demonstrate that this convergence is superlinear, by interpreting the algorithm as an inexact Newton iteration for the HJBI equation. Moreover, we construct the optimal feedback controls from the numerical value functions and deduce convergence. Numerical experiments on the stochastic Zermelo navigation problem are presented to illustrate the theoretical results and to demonstrate the effectiveness of the method.

Keywords: error estimate
Hamilton-Jacobi-Bellman quasi-variational inequalities
impulse control
penalty method
neural network
policy iteration
Advisor: Reisinger, C