VictorJuiz/thesis huggingface dataset:
Multi-resolution modelling of biomolecules

This work investigates how multi-resolution modelling can be applied to study biomolecules as a means to improve the efficiency of simulations. In practice, we resolve aspects of a model that are of greatest biological or physical interest in high detail, while describing the remainder more coarsely. We begin by taking a multi-resolution approach to solvent in simulations, replacing the forces a simple dimer model experiences in short-range and long-range heat baths with macroscopic Langevin and generalised Langevin models. The parameters for these stochastic models are determined analytically, or inferred from molecular dynamic simulation data via force and velocity correlation statistics for comparison. Additionally, the solvent of each monomer in the dimer are also modelled with different level of resolution, with these methodologies used to investigate the effect of a shared heat bath versus two uncoupled heat baths, one for each monomer. Attention is then turned to models of a target molecule, with a multi-resolution methodology for modelling F-actin filaments presented. It provides detailed microscopic information at the level of individual monomers at a lower computational cost by replacing the monomer-based model in parts of the simulated filament by a rod-based Cosserat description. In the monomer-based description G-actin is represented by ellipsoids bound at the surface in a double helical configuration to form F-actin. The rod-based model is coarser, in which F-actin is described using a Cosserat model. The multi-resolution methodology is illustrated using case studies, designed to test the properties of F-actin under stretching, bending, shearing and twisting.

Keywords: systems biology
Langevin dynamics
molecular dynamics
Brownian motion
mathematical biology
computational biology
multi-resolution modelling
Advisor: Erban, R
Developing Year 10 History students’ written causal arguments through a focus on their conceptual understanding and self-regulation

This project evolved from a desire to improve students’ written causal arguments, whilst also improving their metacognition and self-regulation. Baseline data, collected from student essays, a talk-through protocol, and a survey, confirmed initial hypotheses that students had a limited understanding of causation, found constructing causal arguments in History challenging, and possessed limited self-regulatory skills and techniques. An action research model was followed, with three teaching interventions interspersed with analysis of students’ work. The first phase used ‘link diagrams’ to cultivate an understanding of causation as ‘web-like’; the second used ‘circle diagrams’ to focus students’ attention on the need to reach decisions about the relative importance of different causes and provide them with a planning tool to promote self-regulation; and the third targeted the use of causation-specific language. Analysis of students’ work following each intervention and comparison with both the baseline data and the views of non-intervention class (used as a form of control group) indicated that the interventions had a positive impact, particularly on student understanding of causation. This was confirmed by a final round of data collection and analysis, using the same three sources as for the baseline. Whilst promising progress was made by most students in terms of their causal understanding and written arguments, and by some in terms of their self-regulation, further refinements to the three teaching interventions are needed in future years.

Keywords: MLT 2020
MLT
The Hadith of ʿĀʾišah’s Marital Age: a study in the evolution of early Islamic historical memory

This DPhil thesis explores the origins and development of one of the most famous traditions within the Islamic Hadith corpus: the hadith of ʿĀʾišah bt. ʾabī Bakr’s marriage to the Prophet at a young age. To this end, I surveyed all of the modern literature pertaining to the great debate over whether—or to what extent—we can date hadiths and their content, culminating in a defence of a specific—rigorous and systematic—version of the ʾisnād-cum-matn analysis. Thereafter, I collated every available version of every hadith pertaining to this topic and subjected them all to an ʾisnād-cum-matn analysis, which allowed me to reconstruct the underlying urtexts or redactions of various earlier tradents (mostly operating from the mid-to-late 8th Century CE), known as “common links”. I then subjected these common-link redactions to various form-critical, geographical, and historical-critical analyses, which produced a striking conclusion: all versions of the marital-age hadith likely derive a single archetype or ur-hadith. This ur-hadith appears to have been created and disseminated by the Madinan tradent Hišām b. ʿUrwah b. al-Zubayr (d. 146-147/763-765) after he moved to Iraq towards the end of his life, probably as a reaction to local proto-Šīʿī polemics against his great-aunt, ʿĀʾišah. Following on from this, I traced the spread and diversification of the hadith across the early Abbasid Caliphate, including the way in which some Hadith scholars reworked its content and/or replaced the original isnad with local and/or familial isnads, thereby naturalising it in their respective regions. Thereafter, I explored the reception of the hadith by the proto-Sunnī Hadith critics, who rejected or criticised some versions, but accepted others, seemingly without a thorough or systematic investigation of their provenance and transmission. Finally, I explored the broader implications of all of this, including the ways in which my findings variously confirm or disconfirm the conclusions and predictions of other scholars, concerning the authenticity of the marital-age hadith in particular and the historical development of Hadith in general. In short, this thesis tracks the provenance and development of a famous and widespread hadith, from its genesis in the sectarian milieu of mid-8th-Century Iraq, to its spread and diversification across the early Abbasid Caliphate, to its canonisation at the hands of the proto-Sunnī Hadith critics.

Keywords: Isnad-Cum-Matn Analysis
Hadith
common link
Isnad
Aisha
Matn
Advisor: Melchert, C
Topics in probabilistic number theory

In this work we explore three distinct problems on the interface of number theory and random matrix theory. First, we compute the average of two shifted squares of the Riemann zeta on the critical line with shifts up to size T 6/5−ε. We give an explicit expression for such averages as well as the (2, 2)-moment of moment of the Riemann zeta. Our result confirms a conjecture of Chandee as well as a special case of Motohashi’s integral. Our approach relies on correlations of the divisor function. Next, we consider random symmetric matrices with independent entries distributed according to the Haar measure on Zp for odd primes p. We derive the distribution of the canonical form for such matrices with respect to Qp and Zp-equivalence using a simple combinatorial argument inspired by random matrix theory. Finally, jointly with Ofir Gorodetsky we prove that if X is a random matrix distributed according to the Haar measure on the finite general linear group GLn(Fq), then any non-trivial linear combination of Tr(X1), Tr(X2), . . . , Tr(Xk) has an asymptotically uniform distribution on Fq as n → ∞ as long as log k = o(n). Our method is inspired by a classical result of Montgomery and Vaughan on the cancellation in character sums over the integers under the Riemann Hypothesis.

Advisor: Keating, J
The economics of industrial cyberespionage

Industrial cyberespionage is a widespread phenomenon that costs the global economy up to $6 trillion annually. It influences how we invest in innovation, store data, and create laws, and ultimately the welfare of society. Yet, it has received limited attention from the economics community. This thesis contributes to the emerging interdisciplinary field of economics of information security, bringing an economic perspective to the matter and considering industrial cyberespionage from three different angles.
The first chapter examines a dynamic R&D race in which competitors can conduct cyberespionage against each other. We develop a framework that analyses the influence of cyberespionage on innovative incentives, companies' payoffs and the quality of the end product. We demonstrate that industrial espionage has an ambiguous influence on the overall investments exerted in the race and companies' expected payoffs and might even be beneficial for the quality of innovative end-products under certain circumstances.
The second chapter provides new empirical evidence that research-intensive industries are particularly susceptible to information leakage attacks. Based on Eurostat aggregated data on enterprises' innovative and digital activity, we construct a tailored data set that allows us to achieve robust statistical inference and study the relationship between information leakage attack rate, research-intensity, and companies' data reliance. The study uses multivariate fractional regression analysis to distinguish two industry-specific associations: high-tech manufacturing industries are prone to experience targeted attacks, while knowledge-intensive service companies are more likely to fall victim to opportunistic attacks.
The third chapter aims to understand efficient network formation and optimal defensive resource distribution in the presence of an intelligent attacker. We present a two-player dynamic framework in which the Defender and the Attacker compete in a network formation and defence game with heterogeneous vertices' values. Such a model allows for studying the trade-off between network efficiency and security. Contrary to the literature, we find that a centrally protected star network does not yield the maximum payoff for the defending side in most circumstances, even being the most secure network formation. Additionally, it reveals a new type of network that often arises in an equilibrium of the games with limited defensive resources---a maxi-core network.

Keywords: inventions
strategic network formation
panel data
conflict
firm strategy
innovation
network defence
cyber crime
cyber secuirty
Advisor: Taylor, G
Parakhonyak, A
Neural networks as data

In machine learning, neural networks are typically treated as models to be used for various tasks such as classification, regression or generative modeling. In this thesis, we take a different perspective and treat neural networks as data instead of models.
Indeed, in deep learning, data is often represented by arrays, such as a 2D grid of pixels for images. However, the underlying signal represented by these arrays is often continuous, such as the scene depicted in an image. A powerful continuous alternative to discrete arrays is then to represent such signals with an implicit neural representation (INR), a neural network trained to output the appropriate signal value for any input spatial location. An image for example, can be parameterized by a neural network mapping pixel locations to RGB values. In this thesis, we investigate the consequences and compelling properties of using such neural networks as data. We motivate and explore this approach through two main applications: compression, where we store data as neural networks, and generative modeling, where we train generative models directly on datasets of neural networks.
For data compression, we propose to store the quantized weights of an INR as a compressed code for a given signal (such as an image), instead of directly compressing the discretized signal. We show how this allows us to build a single neural codec that is seamlessly applicable to multiple data modalities, from images and audio to medical and climate data. For generative modeling, we propose to learn distributions of INRs, either implicitly by training on array data or explicitly by directly training models on datasets of INRs. We demonstrate how such an approach leads to compelling algorithms for a range of machine learning tasks, including 3D shape inference and generative modeling of neural radiance fields.

Keywords: implicit neural representations
generative models
neural compression
neural networks
Advisor: Teh, Y
Doucet, A
Learning ecologies of lifelong learners: development and outcomes

Advancements in information technologies have opened up access to learning resources that support lifelong learning. The learning ecologies paradigm has been advocated to capture the nuances of learning as afforded by these advances and across contexts. However, studies adopting this paradigm to subtleties of lifelong learning are limited. This study proposes and evaluates a detailed conceptual framework and an associated data collection methodology to explore lifelong learners' learning ecologies. The hallmark of the framework is the consideration of the multiple topical ecologies making up the overall lifelong learner’s learning ecology. The thesis uses a mixed method approach to follow 24 lifelong learners, enrolled in an online course, based in Egypt, over an 8-week period, to understand the development and outcomes of their learning ecologies.
The study found that lifelong learners, while interested in a myriad of topics, activate, downscale, and hibernate topics as needed over time. Topics are relatively homogeneous in their ecological makeup, especially when compared with the overall ecologies. Moving between topics, lifelong learners showcase an ecological signature that reflects both their innate preferences and the wider context within which they live and learn.
The study’s framework operationalises three outcome variables to study the effects of the variations in learning ecologies: (a) amount of learning activity, (b) progress along the individual topics studied, and (c) satisfaction with the support provided by both the topical and overall ecologies. The study found satisfaction to be very subjective and individually driven. Diversifying the learning resources has been identified as the strategy most associated with higher levels of satisfaction. Satisfaction with the overall ecology is not associated with total amount of learning activity. And satisfaction with the respective topical ecologies is not associated with the progress along those topics. Increasing the number of resources, as opposed to diversifying their types, has been associated with both higher levels of activity and more progress. Multiple other factors have been significantly associated with level of activity and progress respectively.

Keywords: event-contingent recording
lifelong learning
learning ecologies
educational technology
Advisor: Hogan, B
Winters, N
Better conditioning on context for natural language processing

Learning distributed representations of natural language has become a common practice for Natural Language Processing (NLP). Non-contextual embeddings map each token in the vocabulary to a low-dimensional real-valued vector. Although these representations perform competitively on word-level tasks, e.g. measuring word similarities, they are context-independent and fail to distinguish the semantics of words in different contexts. This gives rise to contextual embeddings, where each token is associated with a representation that is a function of the entire input sequence. Contextual embeddings are shown to better capture complex characteristics of word use and model polysemous words.
In this thesis, we follow up the line of research on contextual embeddings and explore better methods of conditioning on context for natural language processing. Specifically, we study incorporating contextual information for Natural Language Generation (NLG). We explore three different sources of context including local context of each input sequence (Chapter 3), global context from training data (Chapter 4) and external context from the Web (Chapter 5). In Chapter 3, we utilize the local context of each input sequence to better model word polysemy. We introduce a technique called translation-counterfactual word replacement, which leverages both local context and alignment for augmenting machine translation data. In Chapter 4, we retrieve relevant global context about a particular entity from training data. A memory-augmented approach is presented to condition an autoregressive language model on global context for more coherent and logical generation. In Chapter 5, we pre-train a noisy channel model with external context (e.g. Reddit posts from the Web) for task-oriented dialogue and show that the noisy channel model decodes better responses compared to direct decoding. Finally, Chapter 6 summarizes our findings and directions for future work.

Keywords: deep learning
contextual embeddings
language model
natural language processing
Advisor: Blunsom, P
Kusner, M
Clinical phenotypes in heart failure defined by cardiac magnetic resonance feature tracking and parametric mapping

In this thesis I applied cardiovascular magnetic resonance (CMR) feature tracking (FT) and parametric mapping, along with more general CMR and echocardiography methods and other specific methods (e.g. biochemistry, endomyocardial biopsy) to characterize various types of cardiac remodelling associated with heart failure (HF) or conditions that create cardiac dysfunction potentially associated to a preclinical phenotype of HF (patients with elevated left ventricular filling pressures, treated phenylketonuria and convalescent patients recovered from COVID-19).
My results indicate the important and specific role of FT CMR in quantification of functional deficit associated with HF. A multilayer systolic peak strain comparison individuates subendocardium as a particularly sensitive region of the myocardium for quantification of myocardial strain. I showed that Endo-Epi circumferential strain gradients, in particular, are progressively blunted in HFmrEF (Heart Failure with midrange Ejection Fraction) and HFrEF (Heart Failure with reduced Ejection Fraction) but remain at physiological levels in HFpEF (Heart Failure with preserved Ejection Fraction). I further showed the feasibility and importance of this technique in assessing phasic emptying fractions and strains of the left atrium and proposed CMR criteria for the diagnosis of a newly described clinical entity, atrial failure. Following this line, I characterized what elements of LV remodelling related to atrial failure or phasic dysfunction. I further applied FT and derived mathematical models for systolic and diastolic hemodynamic forces approximation to characterize CMR surrogates of catheter-measured LV end-diastolic pressure. Here, I characterized for the first time the cardiac phenotype of adult patients with phenylketonuria, who demonstrate reduced myocardial mass and structural modifications related to their persistent metabolic abnormalities and showed the importance of life-long phenylalanine control in preventing adverse cardiac remodelling. I compared the cardiac manifestations of COVID-19 with the ones observed in non-SARS-CoV2 viral myocarditis and point to a slower functional recovery in patients with more severe disease presentation and intense hospital care.

Keywords: heart failure
phenylketonuria
cardiac magnetic resonance
cardiac remodeling
feature tracking
cardiomyopathy
parametric mapping
Advisor: Gilbert, R
Kelle, S
Data quality in health research: the development of methods to improve the assessment of temporal data quality in electronic health records

Background: Electronic health records (EHR) are increasingly used in medical research, but the prevalence of temporal artefacts that may bias study findings is not widely understood or reported. Furthermore, methods aimed at efficient and transparent assessment of temporal data quality in EHR datasets are unfortunately lacking.
Methods: 7959 time series representing different measures of data quality were generated from eight different EHR data extracts covering activity between 1986-2019 at a large UK hospital group. These time series were visually inspected and annotated via a citizen-science crowd-sourcing platform, and consensus labels for the locations of all change points (i.e. places where the distribution of data values changed suddenly and unpredictably) were constructed using density-based clustering with noise. The crowd-sourced consensus labels were validated against labels produced by an experienced data scientist, and a diverse range of automated change point detection methods were assessed for accuracy against these consensus labels using a novel approximation to a binary classifier. Lastly, an R package was developed to facilitate assessment of temporal data quality in EHR datasets.
Results: Over 2000 volunteers participated in the citizen-science project, performing 341,800 visual inspections of the time series. A total of 4477 distinct change points were identified across the eight data extracts, covering almost every year of data and virtually all data fields. Compared to expert labels, accuracy of crowd-sourced consensus labels identifying the locations of individual change points had high sensitivity 80.4% (95% CI 77.1, 83.3), specificity 99.8% (99.7, 99.8), positive predictive value (PPV) 84.5% (81.4, 87.2) and negative predictive value (NPV) 99.7% (99.6, 99.7). Automated change point detection methods failed to detect the crowd-sourced change points accurately, with maximum sensitivity 36.9% (35.2, 38.8), specificity 100% (100, 100), PPV 51.6% (49.4, 53.8), and NPV 99.9% (99.9, 99.9).
Conclusions: This large study of real-world EHR found temporal artefacts occurred with very high frequency, which could impact findings from analyses using these data. Crowd-sourced labels of change points compared favourably to expert labels, but currently-available automated methods performed poorly at identifying such artefacts when compared to human visual inspection. To improve reproducibility and transparency of studies using EHRs, thorough visual assessment of temporal data quality should be conducted and reported, which can be assisted by tools such as the new daiquiri R package developed as part of this thesis.

Keywords: temporal
change point detection
data quality
time series
electronic medical records
EHR
electronic health records
initial data analysis
citizen science
EMR
Advisor: Walker, A
Peto, T
Landray, M
Lacey, B
Optimization of Georgian competition framework on the basis of the EU experience

This thesis examines how to transform the Georgian competition framework into its most effective version while keeping in mind the real-life legal, economic and institutional constraints posed by broader national and international settings (‘optimization’). It deploys the EU competition framework as a blueprint due to recently intensified trade relations between the two jurisdictions and the resulting resemblance of their competition frameworks. The thesis builds on an existing scholarship regarding legal transplants and the jurisdiction-specific nature of the competition law. It aims to offer a set of lessons for optimizing the Georgian competition framework by utilizing the EU experience, considering the inherent jurisdictional differences. To achieve its aim, the thesis examines three dimensions of the Georgian competition framework – policy, law and enforcement – in a comparative context by analysing historical developments, the existing legislative landscape and institutional dynamics among primary actors responsible for the framework implementation. It finds partial incoherence among current policy objectives and the infeasibility of their simultaneous implementation. The legislative level has recently benefited from comprehensive amendments. However, sub-optimally configured ex-ante (concentration) control jurisdiction complicates realizing these benefits in practice. Similarly, an initially limited capability of case handling has conditioned administrative self-restraint in ex-post (antitrust) assessments, while legislative changes might unintentionally solidify this trend. The thesis elaborates on all these shortcomings and offers ways of overcoming them based on the analysis of selected EU experiences.

Keywords: EU
Georgia
competition law and policy
legal transplants
Free Trade Agreements
Advisor: Weatherill, S