VictorJuiz/thesis huggingface dataset:
Motion tracking and modelling of the forefoot for clinical gait analysis
Keywords: Biomedical engineering
Forefoot
Gait analysis
Human motion analysis
Multi-segment foot model
Biomechanics
Engineering

Multi-segment foot models (MSFMs) are often used in clinical gait analysis (CGA) for the diagnosis and treatment planning for patients with foot deformities. Thus far, close to 40 MSFMs have been proposed, and each differs in terms of segmentation, marker placement protocol, tracking technology, and joint motion. By representing individual segments as a series of linked rigid body segments, the computation and interpretation of foot biomechanics can be greatly simplified. While this approach may be acceptable for lower-limb segments such as the thigh, shank, or hindfoot, the assumption that this is also valid for the forefoot has been met with criticism. Out of all the segments, there is least agreement on how the forefoot should be represented in an MSFM. In general, Whether or not the flexibility of the forefoot is clinically meaningful is controversial.
Therefore, the aims of this thesis were to investigate the most appropriate way of segmenting the forefoot based on a rigidity criteria and to develop a clinically relevant and practical forefoot model based on the recommended segmentation. It was hypothesised that minimising the violation of rigid body assumption in the proposed novel MSFMs will produce the most relevant forefoot representation that is both practical and useful for clinical gait analysis.
This study first validated the use of sigmaRBE to quantify MSFM segment rigidity through a Monte-Carlo based global sensitivity analysis, which showed that for a typical forefoot segment the sigmaRBE represented 86.7% of the deformation magnitude, with a linear relationship that can be predicted using a simple regression model. Experimental data were used to compute rigidity and track deformation of the Oxford Foot Model segments and the metatarsal markers (attached to the heads and bases of all metatarsals) over the full gait cycle. These data were used to determine appropriate segmentation and marker-placement strategies in the design of novel MSFMs.
Several novel forefoot models were proposed and tested through comparison of visual and descriptive statistics of kinematic output and repeatability analysis. A single novel forefoot model was selected based on practicality and kinematic advantages, and piloted in five individual clinical case studies. The research has contributed to the evidence that the forefoot should not be treated as a single segment, and a new forefoot model suitable for CGA has been proposed.
Not bringing things to a standstill: Plato's Theaetetus on how (not) to speak about a world in which knowledge is perception.
Keywords: Theaetetus
radical flux
Measure Doctrine
relativism
Protagoras
perception

This thesis is concerned with Theaetetus' definition of knowledge (‘Knowledge is perception') and Protagoras' Measure Doctrine (‘Man is the measure of all things') as discussed in Plato's Theaetetus (151e-187a). I shall argue that Protagoras' Measure Doctrine is best understood as a doctrine of infallibilism: all beliefs are true. I shall argue that Theaetetus' definition commits Theaetetus to perceptual infallibilism: all perceptual beliefs are true. Further, I shall argue that Protagoras' Secret Doctrine (152d-160c) underpins perceptual infallibilism by means of a process ontology: perceptual properties are processes involving objects and perceivers. I shall argue that this process ontology explains three key components of the Secret Doctrine: the Relationalist Thesis (‘Nothing is one thing just by itself'), the Flux Thesis (‘Nothing ever is, but things are always becoming') and the linguistic reforms (the rejection of ‘to be' in favour of ‘to become'). I shall provide a detailed analysis and criticism of the relativist interpretation, according to which the Measure Doctrine amounts to relativism about truth: each belief is true (not simpliciter, but) in relation to the believer. In particular, I shall argue that the refutation of the Measure Doctrine (170c-171c) targets infallibilism rather than relativism about truth. I shall argue that the refutation of the Radical Flux doctrine (‘Everything is always changing with every kind of change in every respect'; 179d-183c) hinges on the view that Radical Flux precludes universals from being instantiated. This view explains both the incompatibility between Radical Flux and Theaetetus' definition and the incoherence of Radical Flux. Further, I shall argue that neither Theaetetus nor Protagoras is committed to Radical Flux: the refutation targets proponents of Theaetetus' definition and of the Secret Doctrine who also endorse Radical Flux. Finally, I shall argue that the refutation of Theaetetus' definition (at 184b-187a) hinges on the view that perceiving does not—and knowing does—involve making true judgements.
Spin-ice physics and phase transitions in cadmium cyanide
Keywords: spin-ice
correlated disorder
negative thermal expansion

There is increasing interest in the role of materials with correlated structural disorder in mimicking the exotic properties exploited in electronic condensed matter phases. Spin ices, for example, are important class of frustrated magnets in which the spin configuration obeys the `two-in two-out' ice rules that govern proton order in water-ice. Such ice rules are implicated in a wide range of functional responses in different materials, from heavy-fermion behaviour to superconductivity. Ice-rules-obeying states are characterised by an absence of long range order, leading to a manifold of degenerate ground states, extensive residual entropy and exotic excitations; in spin-ices, these excitations correspond to topological defects in the form of effective magnetic monopoles. The main barrier to experimentation and application of these topological defects is the extremely low temperatures (~ 1 K) at which the spin-ice correlations develop. This thesis explores the structural ice analogue cadmium cyanide, Cd(CN)2, a cubic negative thermal expansion (NTE) material, in which the orientation of cyanide ions maps onto the spin orientation in spin-ices. Cd(CN)2 is shown here to display spin-ice physics at room temperature.
Chapter 3 outlines the extreme sensitivity of Cd(CN)2 to X-ray irradiation, including unit-cell contraction, and the selection or suppression of particular phase transitions. These results explain earlier discrepancies regarding the phase behaviour and negative thermal expansion in Cd(CN)2 and motivated the development of a synthesis of isotopically-enriched 114Cd(CN)2 suitable for neutron scattering studies. The synthesis of Cd(CN)2 directly from Cd metal forms the basis of Chapter 4. The structure of previously-unreported intermediate phase, Cd(NH3)2[Cd(CN)4], is characterised, as well as its thermomechanical response. This represents the first variable-temperature study of this type of framework (PtS), shown to display anisotropic NTE and negative linear compressibility.
The main result of this thesis is to demonstrate that by replacing the magnetic dipole in spin-ice with the electric dipole of the cyanide ion, cadmium cyanide displays spin-ice behaviour at temperatures nearly sixty times higher that in its magnetic analogue. I present the results of neutron scattering measurements on 114Cd(CN)2 along with density functional theory calculations, Monte Carlo simulations, total scattering and nuclear magnetic resonance measurements. Having confirmed the importance of spin-ice physics, chapter 6 explores the pressure and temperature dependence of the ice-like correlations. At least two phase transitions are identied at low pressure, kinetic trapping is observed and the extreme compressibility shown to be a function of cyanide reorientation and ice-like coordination. It is hoped that increase in energy scale, identified here, will enable further exploration that may shed further light on the physics of the spin and water-ices themselves.
Integrating chromatin and development: LDB1 regulates genome architecture and gene expression in motor neuron differentiation
Keywords: Isl1
Lhx3
Motor Neuron
LIM Domain
LDB1
SSDP1

Dynamic changes in chromatin architecture underpin gene expression in development. The chromatin integrator protein LDB1 is a key regulatory node in the organization of developmentally-specific gene expression programs, particularly in motor neurons (MNs) of the vertebrate spinal cord. By coordinating transcription factor binding, chromatin remodeling, and 3D genome organization, LDB1 is an essential chromatin integrator protein in the development of MNs. In this thesis I characterise the deletion of LDB1 in an in vitro MN differentiation system in the mouse. By measuring transcription factor (TF) binding, protein association, gene expression, and 3D-genome organization, I specify the genetic elements that LDB1 regulates. I find that LDB1 regulates binding of the promiscuous TFs Isl1 and Lhx3 at a subset of critical MN genes, specified particularly in cooperation with the LDB1 binding partner SSDP1, where it drives open chromatin, H3K27ac deposition, gene expression, and likely enhancer function. Thus, LDB1 integrates multiple levels of specificity to regulate dozens of genes and enhancers that are critical to the expression of MN genes. I propose that this is a key feature of spatial genome regulation and is mediated by LDB1 and the interaction with its binding partner SSDP1. I identify a minimal set of MN-specific genes regulated by LDB1, as well as a set of elements that regulate subtle features of 3D genome organization and transcription. Together, I establish LDB1 as a critical regulator of 3D genome organization, transcription factor assembly, and gene expression, making LDB1 and genes downstream of its effects into promising targets for improved cellular reprogramming strategies.
The practicalities of scaling Bayesian neural networks to real-world applications
Keywords: Markov chain Monte Carlo
Bayesian Neural Networks
Variational Inference

In this work, I will focus on ways in which we can build machine learning models that appropriately account for uncertainty, whether with computationally cheap estimates or with more expensive and reliable ones. In particular, I will explore how we can model distributions with Bayesian neural networks and how we can manipulate them depending on the task. The two main techniques for performing inference in Bayesian neural networks are variational inference and Markov chain Monte Carlo. I will look into the advantages and disadvantages of both methods and apply them to real-world problems. The emphasis is on how to achieve calibrated uncertainty estimates without compromising scalability. One contribution of this work is to offer a new method for implementing Bayesian neural networks within the framework of Bayesian decision theory, where Bayesian decision theory is important in all decision-making applications. A further contribution is developing sampling techniques that provide more reliable uncertainties, especially over data that lie outside the training distribution. Finally I also introduce a method for using Bayesian neural networks in an astrophysical application where it is vital that uncertainties are calibrated appropriately for the task.
Genealogy estimation for thousands of samples
Keywords: Natural Selection
Population Genetics
Genealogies

A key and fundamental concept that captures our shared genetic history is the genealogy, which traces the genetic relationships of present-day individuals to their most-recent common ancestors. Knowledge of the genealogy would, in principle, capture all evolutionary forces that modified the genetic material ancestral to our DNA, and would hence simplify - and enhance - many inference problems about past demography and evolution. Despite their importance, estimation of genealogies has remained unsolved even for moderately sized data sets, with existing methods unable to handle sample sizes beyond a few hundred samples, yet modern data sets often exceed tens of thousands of samples.
In this thesis, I present a method, Relate, that estimates such genealogies for thousands of samples. I demonstrate on a variety of population genetic applications that Relate-based inferences improve in accuracy, resolution, or statistical power on state-of-the-art alternatives. I then reconstruct the genealogy of 2478 humans from 26 populations. I infer historical population sizes and population split times with higher resolution than previously possible and identify highly diverged lineages, reflecting Neanderthal and Denisovan introgression in non-Africans, and unknown events in Africans. I report regions that show evidence of being under strong positive selection that were previously unreported and identify multi-allelic traits likely to be under selection. I additionally apply Relate to 50 wild mice sampled in France, India, and Taiwan and demonstrate that the estimated genealogies contain rich information about their demographic history, mutation rate trends consistent with GC biased gene conversion, as well as strong indications of selective sweeps in each population.
The economics of rhino poaching
Keywords: Environmental Economics
Resource Economics
Elephant Poaching
Rhino Poaching
Conservation Policy
Poaching

This thesis analyses the economics of rhino and elephant conservation in light of the recent upsurge in poaching in Africa. Each chapter focuses on a particular component of the new poaching crisis.
Chapter One examines the development of market power by poachers and the implications for how owners should use patrols to defend their rhino. A dynamic model is developed and simulated. The owner is the Stackelberg leader, investing in anti-poaching patrols to protect rhino from poaching cartels who strategically compete in a closed-loop Nash Equilibrium ‘game’. Understanding the industrial organisation of cartels is shown to be of paramount importance in designing optimal anti-poaching policy because, perversely, patrol spending works to increase strategic behaviour between cartels.
Chapter Two considers whether establishing a legalised trade in rhino horn could benefit conservation. A dynamic model is formulated and simulated. It is found that, even if the institutional arrangements are robust enough to support legalised trade, the economics suggest trade is not guaranteed to improve the conservation prospects of the species. In the absence of private sector speculation, a legalised trade could be used to sustainably conserve a rhino population when extinction would otherwise occur. However, with the introduction of speculation, speculative attack on the remaining stockpile is highly profitable, leading to extinction of the species.
Chapter Three determines the consequences of increasingly fragmented conservation policy. A new panel of elephant census data for 1,262 reserves across 37 countries in Africa is used to examine the effect of varying anti-poaching policy across borders. An international border is found to result in an elephant population up to 2.5 times smaller than a reserve without a border. In addition, evidence that poachers arbitrage across reserves is found. The policy consequence is clear: as populations become increasingly fragmented, transfrontier conservation policy becomes more important.
Cyber supply chain risks in cloud computing - the effect of transparency on the risk assessment of SaaS applications
Keywords: Supplier Assessment
risk assessment
Quantitative risk assessment
systems thinking
Monte Carlo
cloud computing
Delphi

While the cloud model has many economic and functional advantages, the increased external interactions of cloud applications have expanded the complexity of its architectures and reshaped its supply chain. Due to the variety of parties involved in cloud service delivery and the high degree of supplier autonomy, assessing cloud risks has become a challenge. Also, the widespread application of traditional frameworks to cloud risk assessment has several shortcomings, including the subjectivity of risk evaluation and inability to measure cyber risk in complex systems.
Recognising that recent work on cloud risk assessment has focussed on cloud consumer risks, we sought to address the cloud service provider (CSP) risk assessment challenge. This research began with an in-depth assessment of the literature in cloud risk assessment and supply chain transparency. We conducted surveys and semi-structured interviews to validate the transparency gap and establish its link with qualitative risk assessment methods. The results of the studies substantiated the need for more rigour in cloud risk assessments and provided evidence on how this can be improved with supply chain transparency.
To address this gap, we proposed the Cyber Supply Chain Cloud Risk Assessment (CSCCRA) model; a quantitative and supply chain-inclusive model targeted at Software-as-a-Service (SaaS) CSPs. The model is made up of three main components, two of which are novel inclusions to cloud risk assessment, i.e. supply chain mapping and supplier security assessment. The CSCCRA model reflects the systems thinking approach, enabling CSPs to visualise information flow through the supply chain, assess supplier security posture, document assumptions regarding the risk factors, and appraise security controls.
In evaluating the CSCCRA model, a three-step approach was adopted. First, the developed model was evaluated by the author and members of the academic community to ensure that it met our initial criteria. Second, the model was face-validated by cloud and risk experts within the industry. Third, we conducted three real-world case studies, using the model to assess the risks of SaaS providers. The result of these evaluations confirmed the usefulness and applicability of the model for assessing cloud provider risks. Also, the case study results and subsequent development of the CSCCRA web application showed that a structured and systematic application of the proposed model within a SaaS organisation was capable of yielding objective and defensible results. The model demonstrated its utility by assisting stakeholders to quantify cloud risks, while also promoting cost-effective risk mitigation and optimal risk prioritisation.
Overall, these results advance knowledge both for research and in practice, taking us one step further into improving cloud risk assessment.
The coarse geometry of group splittings
Keywords: Geometric group theory
Quasi-isometric rigidity
Splittings
Right-angled Artin groups

This thesis addresses Gromov’s program of studying the geometry of finitely generated groups up to quasi-isometry. In particular, we investigate when we can characterise group splittings geometrically, and how this can be used to determine whether groups are quasi-isometric.
In Chapters 2 and 3 we develop several coarse topological techniques, building on work of Kapovich–Kleiner [KK05], that we expect to be of interest in their own right. We use these techniques to show that if G is a group of type FPn+1 that is coarsely 3-separated by an essentially embedded coarse PDn space W, then G splits over a subgroup H that is at finite Hausdorff distance from W. This can used to deduce that splittings of the form G = A *H B, where G is of type FPn+1 and H is a coarse PDn group such that both |CommA(H) : H| and |CommB(H) : H| are greater than two, are invariant under quasi-isometry. These results are contained in [Mar18b].
In Chapter 4 we extend the methods of Chapter 3 to understand the 2-separating case. Building on work of Scott–Swarup [SS03], we describe a regular neighbourhood of essentially embedded coarse PDn spaces.
In Chapter 5 we show that the JSJ tree of cylinders over abelian subgroups of a RAAG is a quasi-isometry invariant. This provides a necessary condition for two RAAGs to be quasi-isometric. We then restrict to the class of RAAGs whose JSJ trees of cylinders over infinite cyclic subgroups have abelian rigid vertex stabilizers. We show that two such RAAGs are quasi-isometric if and only if they have equivalent JSJ trees of cylinders. The latter result is a special case of results we prove in [Mar18a].
Computerising diagnosis: minds, medicine, and machines in twentieth-century America
Keywords: artificial intelligence
diagnosis
health informatics
epistemology
disease
biomedical computing
history of medicine
computing
bias

In the mid-twentieth century, physicians, engineers, mathematicians, philosophers, and others started to think about the ways in which a new technology might be applied to an old problem. These interdisciplinary teams designed computer programmes that aimed to automate one of the most fundamental tasks of medicine—diagnosis. This dissertation offers a history of computerising diagnosis through the prism of three such efforts: the Medical Data Screen, a computerised method of capturing the health of ‘the total patient’; HEME, a computer programme created to aid in the diagnosis of haematologic diseases; and MYCIN, an early expert system developed to assist in the diagnosis and treatment of microbial infections. Drawing on these case studies, this project examines early efforts to computerise medical diagnosis and decision making. It explores how these efforts built upon, interacted with, and produced certain professional tensions, disease constructions, personal identities, cultural ideals, economic interests, and material practices.
This dissertation supports a number of conclusions. First, it demonstrates that efforts to computerise medical diagnosis and decision making raised philosophical and moral questions that lie at the heart of medical practice. As physicians and engineers worked to computerise diagnosis, they were forced to reckon with fundamental epistemological and ontological questions—questions about the nature of clinical reasoning and the definitions of disease. In creating computerised diagnostic systems, early developers articulated new ideas about how physicians think and how disease categories are defined. Second, this dissertation argues that the introduction of computers into medical diagnosis was both the cause and the effect of new ways of collecting, storing, and using medical information. Third, this dissertation highlights the complexities inherent to medicine and medical diagnosis. Repeatedly early developers of computerised diagnostic systems ran up against unanticipated epistemological, social, and moral challenges.