VictorJuiz/thesis huggingface dataset:
Precision predictions for jet substructure

In this thesis we consider the phenomenology of Quantum Chromodynamics (QCD) in the context of precision predictions for the Large Hadron Collider (LHC). Our emphasis will be given to jets, the collimated sprays of hadrons present at high-energy colliders, their substructure, prediction and simulation.
To this end, we first introduce the fundamentals of QCD, resummation and jet substructure. We then introduce QCD parton shower event generators and the notion of their formal accuracy. We proceed to introduce two new algorithms for incorporating full-colour effects into parton showers and give particular emphasis to their testing and validation. Next, we introduce a novel algorithm for evaluating the (subjet) multiplicity at high-energy colliders called the Lund multiplicity. It is based on the Lund plane jet substructure technique and provides a simple procedure for evaluating the subjet multiplicity with an infra-red cutoff at a general collider. We first apply it to the case of e+e− collisions at the Large Electron Positron (LEP) collider. In this context we, for the first time, resum the average Lund and Cambridge multiplicities to next-to-next-to-double logarithmic accuracy and perform a phenomenological theory-to-data comparison with published LEP results. Finally, we extend the study to the context of high-energy jets at the LHC. Particularly, we resum their average Lund multiplicities to next-to-next-to-double logarithmic accuracy in Z+jet and inclusive jet events at the LHC. We then perform a phenomenological analysis for a realistic collider scenario and provide predictions ready for theory-to-data comparison. Alongside the phenomenological applications of this observable, the resummed average Lund multiplicities provide a necessary benchmark for testing the next generation of accurate parton showers.

Keywords: collider phenomenology
theoretical physics
Advisor: Salam, G
End-to-end learning, and audio-visual human-centric video understanding

The field of machine learning has seen tremendous progress in the last decade, largely due to the advent of deep neural networks. When trained on large-scale labelled datasets, these machine learning algorithms can learn powerful semantic representations directly from the input data, end-to-end. End-to-end learning requires the availability of three core components: useful input data, target outputs, and an objective function for measuring how well the model's predictions match the target outputs. In this thesis, we explore and overcome a series of challenges as related to assembling these three components in the sufficient format and scale for end-to-end learning.
The first key idea presented in this thesis is to learn representations by enabling end-to-end learning for tasks where such challenges exist. We first explore whether better representations can be learnt for the image retrieval task by directly optimising the evaluation metric, Average Precision. This is notoriously challenging task, because such rank-based metrics are non-differentiable. We introduce a simple objective function that optimises a smoothed approximation of Average Precision, termed Smooth-AP, and demonstrate the benefits of training end-to-end over prior approaches. Secondly, we explore whether a representation can be learnt end-to-end for the task of image editing, where target data does not exist in sufficient scale. We propose a self-supervised approach that simulates target data by augmenting off-the-shelf image data, giving remarkable benefits over prior work.
The second idea presented in this thesis is focused on how to use the rich multi-modal signals that are essential for human perceptual systems as input data for deep neural networks. More specifically, we explore the use of audio-visual input data for the human-centric video understanding task. Here, we first explore if highly optimised speaker verification representations can transfer to the domain of movies where humans intentionally disguise their voice. We do this by collecting an audio-visual dataset of humans speaking in movies. Second, given strong identity discriminating representations, we present two methods that harness the complementarity and redundancy between multi-modal signals in order to build robust perceptual systems for determining who is present in a scene. These methods include an automated pipeline for labelling people in unlabelled video archives, and an approach for clustering people by identity in videos.

Keywords: machine learning
neural networks
deep learning
computer vision
multi-modality
Advisor: Zisserman, A
Measurement of the pion charge exchange differential cross section on Argon with the ProtoDUNE-SP detector

The Deep Underground Neutrino Experiment (DUNE) utilizes Liquid Argon Time Projection Chamber (LArTPC) technology in its pursuit of uncovering the origins of matter. As a crucial prototype for the DUNE far detector (FD) module, ProtoDUNE-SP at CERN Neutrino Platform Facility employs a hadron test beam with a momentum range of 0.3 to 7 GeV/c to study the detector response to particles that could be produced in the neutrino interactions at the DUNE FD. Furthermore, ProtoDUNE-SP is dedicated to testing event reconstruction and performing detector calibration under controlled conditions. Neutral pion reconstruction performance is an important benchmark of the calorimetric and tracking capability of LArTPC, and understanding neutral pion in LArTPC is crucial to characterize backgrounds to oscillation measurements and rare searches. To improve neutral pion reconstruction in LArTPC, a kinematic fitting algorithm is developed, achieving a 12% resolution in neutral pion energy in ProtoDUNE-SP. This enhancement enables the first measurements of the pion-argon charge exchange differential cross-section. By comparing these results with the current model, valuable constraints can be made to improve generator simulations in the future DUNE experiment.

Keywords: ProtoDUNE-SP
Advisor: Lu, X
Azfar, F
The cardiac L-type calcium current: existing models and methods of improvement

The heart plays a crucial role in the circulatory system, pumping blood to and from the body. The pumping action is due to the rhythmic contraction of the muscle cells of the heart (cardiomyocytes). This ‘beating’ is facilitated by ionic transport across the membrane of each cardiomyocyte due to electrophysiological activity. Mathematical models that describe this electrophysiology at the organ, tissue, and cellular level are critical for the prediction of irregular beating of the heart (arrhythmia). Arrhythmias can be initiated by unintended modulations of cardiac electrophysiology by pharmaceutical drugs. Therefore, in the past decade, drug regulatory bodies have implemented the use of cardiac models for cardiac safety assessment of novel drugs. Investigating the effects of the drug on individual ion channels of the heart is an important aspect of such cardiotoxicity assessment initiatives. The L-type calcium current (ICaL) is one such ionic current, key to cardiomyocyte contraction.
This thesis investigates if it is possible to select a model of ICaL that accurately reflects its biology. This was done by first gathering 73 ICaL models and identifying several choices available for individual components of the model. Next, these models were examined using identical experimental conditions thus revealing wide variability in their predictions. To understand this variability, novel experimental data was generated and a pipeline was set up to explore how data collected from a single cell using automated patch-clamp experiments can be used to build an ICaL model. Experimental recordings of ICaL were identified to be contaminated by rundown, which was shown, to be partly due to calcium-dependent inactivation (CDI) of ICaL. A mechanistic model was then used to establish experimental conditions at which CDI-induced rundown can be minimised. Rundown-corrected data was used to develop a preliminary model of ICaL characterising its voltage-dependence. Cell-specific calibrations of this model closely replicated the training data in addition to making predictions about other ICaL properties. Thus, this thesis sets up a method to develop an ICaL model representative of its underlying biology for a particular context of use.

Keywords: cardiac modelling
L-type calcium current
mathematical modelling
Advisor: Gavahan, D
Mirams, G
Clerx, M
Polonchuk, L
Wang, K
Experimental and analytical investigations into fretting figure

Fretting fatigue is a damage process that occurs in contacts subjected to oscillatory loading. It is complex and involves many processes such as wear and slip: predicting the life of fretting contacts analytically is challenging. One of the primary methods of achieving this, therefore, remains experimental investigation.
In this thesis, several pieces of research are presented that contribute towards the overall aim of improving methods available to design fretting fatigue experiments. The ultimate objectives are that future experiments better match prototypical contacts, and also allow experimental investigation of the fundamental causes of fretting fatigue.
The first paper examines whether the stress fields at the edge of a fretting contact has similarities with the stresses present at a crack tip, as has been suggested in earlier literature. It is shown that they do not, implying that it would not be appropriate to model fretting fatigue as a crack turning problem.
Two papers focus on the effect of moments in fretting fatigue experiments. It is shown that significant moments can occur due to the design of test apparatus. Then, a method of finding equivalence between normal and moment loadings is derived and applied to remove the effect of coupled moments.
A series of experiments using single crystal pads were performed, with the goal of investigating a previously identified trend in fretting fatigue life. This was motivated by an experimental study previously reported that indicated an effect but considered too few specimens to be regarded as conclusive. In the current work, it is shown that there is no evidence for orientation of the crystal structure affecting the life of the contact.
Several papers are then presented that cover the effect of load path on the behaviour of a fretting contact, in which it is demonstrated that it is possible to design experiments to investigate novel behaviours, such as slipping in only one direction. These allow for future investigation of the fundamental causes of fretting fatigue damage.
The work presented provides several methods of improving the design of fretting fatigue experiments. Also discussed are potential avenues for follow up work, such as the design of further load paths to investigate other properties of the fretting contact.

Keywords: contact mechanics
solid mechanics
experimental techniques
fretting fatigue
Advisor: Hills, D
Blades, L
Topics in arithmetic and determinacy

This thesis is about Arithmetical Determinacy. Loosely, this is the problem of whether every question in arithmetic has a determinate answer. In this work I discuss how to exactly understand the concept of determinacy, I criticise arguments for and against the claim that arithmetic is determinate, and examine how questions about determinacy may be applied to other debates in the philosophy of mathematics.
Chapter 1 isolates different ways of understanding the problem of arithmetical determinacy. Chapter 2 turns to mathematical structuralism and explains how popular computability constraints thought to determine the reference of our arithmetical vocabulary are actually unsuccessful in securing determinacy. Chapter 3 criticises an interesting idea for securing determinacy via our experience with supertasks. Chapter 4 explores the phenomenon of mutually inconsistent satisfaction classes and motivates a new account of determinacy in terms of sentences possessing non-classical truth-values. Chapter 5 defends strict finitism, framing some objections against the view in terms of the concept of arithmetical determinacy.

Advisor: Hamkins, J
Paseau, A
Analysis of cellular heterogeneity in breast cancer by single cell sequencing

Breast cancer is a complex heterogenous 3D ecosystem. The heterogenous composition of breast cancer determines disease progression and treatment responses. Triple receptor negative breast cancer (TNBC) is a distinct subtype with poor clinical outcomes. Deconvolution of spatially-regulated transcriptomic and microenvironmental drivers unique to TNBC offers the potential to reveal new therapeutic vulnerabilities.
Single cell RNA sequencing (scRNA-seq) and spatial transcriptomic technologies were applied to three treatment naive patient-derived breast cancer samples. New spatial transcriptomic and scRNA-seq experimental pipelines were established. The new technologies were successfully applied to clinical grade biopsy samples. Cellular heterogeneity within the epithelial and non-epithelial compartment was identified across the three samples. The heterogeneity identified is consistent with the published literature.
Knowledge in the theoretical underpinnings for scRNA-seq analysis along with the skills required for data analysis in a small patient cohort were acquired during the DPhil. The application of algebraic topology, manifold learning and graph theory in evaluating and interpreting scRNA-seq has been studied.
The computational tools available for integrating spatial transcriptomics and scRNA-seq data were critically appraised. Future perspectives on approachesfor multimodal integration were explored.

Keywords: manifold theory
spatial transcriptomics
single cell sequencing
breast cancer
machine learning
Advisor: Harris, A
Buffa, F
Casual and trustworthy machine learning: methods and applications

This work focuses on the intersection of machine learning and causal inference and the way in which the two fields can enhance each other by sharing ideas: utilizing machine learning techniques for the computation of causal quantities, the use of ideas from causal inference for invariant predictions under unseen treatment regimes, and the exploration of topics in trustworthy machine learning, including interpretability and fairness, with a causal lens. In each one of the presented works, we grappled with the strength of assumptions needed to utilize causal inference techniques and relax portions of them when possible.
In Chapter 1, we introduce the motivation behind the works and the challenges that sparked this plan of study. Chapter 2 provides a foundation on basic topics in causal machine learning and trustworthy machine learning. In Chapter 3, we introduce a causal effect estimation method under partial causal graph knowledge. In Chapter 4, we look at causal effect estimation in complex data settings, such as images, text, and gene expression networks, and propose an invariant estimation approach utilizing crude interventions. In Chapter 5, we provide a causal perspective on explainable machine learning, unifying existing works and providing a sound and complete algorithm involving the concepts of sufficiency and necessity. Finally, in Chapters 6 and 7, we introduce methods and investigations in fair machine learning.

Keywords: causal inference
machine learning
trustworthy ML
trustworthy AI
artificial intelligence
robustness
fairness
AI
interpretability
ML
computer science
Advisor: Kanade, V
Aqueous electrochemical CO2 reduction on copper-based catalysts in flow cell system

Carbon dioxide emission has exacerbated over the years ever since the usage of fossil fuels in human society. Development of electrochemistry can enable a green conversion of CO2 into valuable chemicals such as methane and ethylene under mild conditions. Electrocatalysts and electrolysers are likely to be the keys to achieve the process more efficiently to fulfil the zero-emission goal.
Under such context, this thesis explores various copper-based materials targeting at hydrocarbons. Modifications on the surface and interface of the Cu catalysts are made, and in-depth catalytical performance is studied to illustrate its response to potentials and current. A Cu/MgO supported catalyst was found to be selective to methane up to 50%. Relations between the selectivity and structure are obtained through surface characterisations and spectroscopies. In-situ characterisations were also conducted to understand the structural evolution during the electrochemical reactions.
Meanwhile, influence from the electrolytes was demonstrated on a model Cu GDE. A sharp contrast on the products distribution was shown with the alkaline solution favouring ethylene and the neutral one methane. A Pd-doped Cu catalyst was then developed to alleviate the need of high pH environment essential for ethylene production, and a comparable efficiency was achieved under neutral electrolyte. Comprehensive studies on the electrodes were carried including EIS and PTR-TOF-MS which revealed that C-C coupling is not the rate-determine step of CO2-to-C2+ conversion. Introducing an exotic element to copper was, therefore, proved to be an effective strategy to alter the preference to the adsorption of carbon intermediates.

Keywords: gas diffusion electrode
copper
electrolysis
CO2 conversion
Advisor: Tsang, S
The evolution and role of Natural Killer (NK) cell subsets during acute HIV-1 infection and emerging SARS-CoV-2 infection

Natural killer (NK) cells are innate lymphocytes with potent antiviral capacities and are capable of mediating early protection against viral infections. In this thesis, I investigated NK cell responses in acute human immunodeficiency virus type 1 (HIV-1) infection and the newly emerged severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. In the first part of this thesis, I performed an in-depth analysis of NK cell responses, including their phenotypic characteristics, antibody-dependent cellular cytotoxicity (ADCC) functions, and their relationship with T-cell activation, in a well-characterised cohort of patients acutely infected with different HIV-1 subtypes. This analysis demonstrated that subtype A infection is associated with a preferential increase in adaptive NK cells, characterised by an enhanced metabolic profile and a superior capacity for ADCC. The increased frequencies of adaptive NK cells were associated with lower HIV-1 viral load and higher CD4+ T-cell counts, suggesting that adaptive NK cells could confer better HIV-1 control. In the second part of this thesis, I performed a comprehensive assessment of the NK cell repertoire, including their adaptive properties and their potential for Fc-dependent activation, in people living with HIV-1 (PLWH) following SARS-CoV-2 infection and/or vaccination. I evaluated NK cell responses in relation to the magnitude and breadth of T-cell and antibody responses to assess the interplay between NK cells and virus-specific T-cells. This analysis shows that adaptive NK cells contribute to vaccine-mediated responses in PLWH, so could be promoted to potentiate vaccine efficiency. Overall, the findings of this thesis contribute significantly to our understanding of the early innate and adaptive immune responses that occur during acute viral infections, their sustained responses after the viral resolution, and their role in promoting viral clearance following vaccination.

Keywords: human immunodeficiency virus type 1 (HIV-1)
Advisor: Rowland-Jones, S
Peppa, D
Sanders, E
Esbjörnsson, J
Modelling the carding of recycled carbon fibre

Carbon fibre is a very versatile material with applications in many different industries from medical, electronics, marine, to space. The demand for carbon fibre composites is increasing yearly and therefore the amount of waste carbon fibre is rising. This waste can be recovered and turned into a material that can be used in industry. The waste carbon fibre is converted into a “nonwoven” form using a carding process. This is carried out in a carding machine, with the aim of producing a homogeneous web of aligned fibres.
A continuum model is derived for a web of carbon fibre in the carding machine. Properties such as the density and order of fibres vary through the machine. This work gives insight into the key parameters in producing a web of aligned fibres, and is the first step in making the process more efficient. The combing effect of carding teeth is then modelled at the microscale level, with focus on the force required to both comb and break different arrangements of fibres.
The breaking up of “tows” of fibres in the carding machine is also studied, by focusing on microscale and probabilistic models. The tows are modelled in 2D as two joined elasticas that are `peeled' apart by a circular tooth. The results depend on properties such as the force and radius of the tooth, and the bending stiffness of the tows. To model the breakup of a distribution of tows, a master equation is derived for the change in tow properties in the region between two rollers. The distribution of tow size and length is studied as the material moves through several of these regions. Transition probabilities are proposed that describe tow splitting, tow breakage, and transfer between carding rollers.
Finally, an example is presented where manufactured data is used to fit the probability parameters. It is then discussed how these models could be used to improve the carding process in the future. This work was conducted at the University of Oxford, in collaboration with Gen 2 Carbon.

Keywords: recycling
carding
carbon fibre
Advisor: Ockendon, H
Howell, P
Riaz, M
Alexandre Brongniart (1770-1847): Kinship, natural history, and the invention of ceramic science

In this thesis, I examine the life of Alexandre Brongniart, whose career in natural history paralleled a forty-seven-year directorship of the Sèvres Porcelain Manufactory. Critically, I show that Brongniart created a coherent science for “the arts of the Earth,” for which he established the term “la céramique.” Brongniart placed the study of the ceramic arts firmly within the context of natural history as well as that of expansive, geological time. As a case study, Brongniart’s lifetime highlights the transitions in the study of natural history that took place between the Enlightenment and the mid-nineteenth century. Brongniart was born into a family centred in artisanal and noble networks, where Enlightenment views were often admired and upheld. Shared kinships, guild ties, and social networks strengthened families and expanded financial interests, while marriages frequently established or enlarged alliances. Surrounded by prominent savants, artists, and artisans, Brongniart inherited significant advantage, but he also actively shaped his own education and career. Brongniart’s early education took place amid the chemical revolution – learning chemistry from Lavoisier himself. By his early career, Brongniart had begun the kinds of analysis and categorization that were instrumental in establishing scientific disciplines such as biology, palaeontology, geology, and mineralogy out of the study of natural history. By the end of the eighteenth century, all-encompassing, cosmological “systems” fell into disfavour, as a focus on facts, experimentation, analysis, measurement, and observation became indispensable to the practice of science. Both Brongniart, and his famous associate and friend, Georges Cuvier, sought to record observations with restricted emphasis on theory, and both considered that precise nomenclature was a key component of the practice of natural history and science. My discussion traces Brongniart’s successful and unsuccessful contributions to expanding scientific disciplines and to scientific language, including words such as Jurassic, Jovienne, Saturnienne, and, perhaps most importantly, "la céramique".

Keywords: art history
Earth history
stratigraphy
palaeontology
Jardin des plantes
artisanal networks
porcelain
Sèvres
Paris
craft
industry
history of science
ceramics
history of time
deep time
natural science
mineralogy
chemistry
geology
Muséum d'Histoire naturelle
biography
mining
scientific networks
Advisor: Iliffe, R