VictorJuiz/thesis huggingface dataset:
A naturally mythopoieic creature: mythic sensibility and human participation in divine creativity
Keywords: Creation
Theology and Literature
Theology
John Milbank
Theology and Culture
Graham Ward
Terry Pratchett
Ursula K. Le Guin
Mythooeisis
J. R. R. Tolkien
Fantasy Fiction
Creativity

In this thesis, I argue that through mythopoiesis, human beings participate in God’s creativity. Certain stories are characterised by a mythic sensibility, an interpretive presence enabling the subject to encounter the world and the self as meaningful. The making of such stories is a characteristically human action distinct from but participating by analogy in God’s ongoing creative action. By approaching this creative action in terms of the Trinitarian return of God to God, mythopoiesis can be understood as a human participation in this return and so as a making toward God, always already theological. Because of the limitations on making imposed by human finitude and sin, mythic sensibilities reveal the world as meaningful in diverse and often contradictory ways. Because mythoi operate according to aesthetics, they do not conform to discursive reasoning, and in a Christian approach, the beauty of Christ becomes the criterion of all rival mythoi. The mythic sensibility revealed in Christ, manifesting as a mythos of unconditional peace, interrupts the violence of conflicting mythoi without succumbing to it. In this way, the mythos and mythopoiesis of Christ opens the redemption of all mythopoiesis by its assumption by God into the Trinitarian dynamic of creation.
Roles for NUCKS1 in cell cycle progression and the DNA damage response
Keywords: NUCKS1
mitogens
SKP2
S phase entry
G1/S transition
DNA damage

Regulated progression through the cell cycle is essential for healthy physiology, including for embryonic development and for repairing tissue. The transition from G1 to S phase is subject to particularly intricate regulation, since its overactivity can result in DNA damage and carcinogenesis. Although the G1/S transition has been studied extensively, gaps remain in our knowledge, and crucial questions remain unanswered. Mitogens, such as growth factors, stimulate S phase entry to promote proliferation, while DNA damage, such as ionising radiation, reduces S phase entry. Strikingly, new results show that it is a competition between mitogens and DNA damage that determines whether cells enter S phase, suggesting that cells deploy dedicated proteins to integrate the antagonistic mitogen and DNA damage information. Here, we identify NUCKS1 as one such integrator. NUCKS1 is a transcription factor implicated in cell cycle progression, DNA replication, and tumourigenesis. However, detailed mechanistic studies explaining how NUCKS1 controls these functions have not been performed. We show that NUCKS1 is required for S phase entry through transcriptional stimulation of SKP2, the F-box component of the SKP1-Cullin-F box (SCF) ubiquitin ligase complex, which is responsible for degradation of the CDK inhibitors, p21 and p27. During G1 progression, and following mitogenic stimulation, NUCKS1 is upregulated and recruited to chromatin, where it is required for SKP2’s transcriptional upregulation. Upon DNA damage, NUCKS1 and SKP2 are both downregulated, through p53-dependent transcriptional repression. Consistently, NUCKS1 levels are increased in p53-deficient cells, and this is rescued through expression of wildtype – but not mutant – p53. Finally, we show that cancer cells upregulate both NUCKS1 and SKP2, driven through increased copy number and loss of p53 activity. Altogether, this study identifies NUCKS1 as an important regulator of S phase entry, which integrates mitogenic and DNA damage signalling to control the S phase entry decision.
Spectral machine learning
Keywords: learning rate schedules
stochastic trace estimation
log determinants
neural networks
graph clustering
maximum entropy
random matrix theory
Hessian spectrum
graph similarity

In this thesis we develop a spectral approach to large kernel matrices, graphs and the Hessians of neural networks. This approach paves the way for efficient and improved inference schemes, novel algorithms and theoretical results. The first contribution of the thesis is the development of a novel Maximum Entropy algorithm applied to the problems of log determinant estimation (necessitated by Bayesian model selection), cluster counting and graph similarity. The method produces strong empirical results compared to existing established methods, such as the Lanczos algorithm. The second contribution is the development of a fielddependent, spiked random matrix theory for characterising the sub-sampling effects, due to mini-batching, on the loss surface of deep neural networks. The theory we develop predicts that, up to a threshold, non-adaptive-gradient methods should scale linearly with batch size and adaptive methods should scale with the squareroot of the batch size. We verify these results empirically. As final contributions we develop an open source software package for neural network curvature computations, show that spectral sharpness is not relevant for solution generalisation and discuss extensions to previous theoretical work. Based upon these theoretical contributions, we develop an adaptive algorithm (Gadam) which posts strong test set performance across a variety of image and text classification problems, regularly beating finely tuned non-adaptive methods.
Problems in parameterization, zero-estimates, point-counting, and o-minimality
Keywords: zeros of polynomials, rational functions, and other analytic functions
computational number theory
modular and automorphic functions
number theory
elliptic functions and integrals
class numbers, class groups, discriminants

In this thesis we consider problems of effectivity for zero-bounds, point-counting, computation, and class numbers.
Using the tameness (in the sense of o-minimality and Khovanskii) of the modular function (j-function) and Weierstrass elliptic functions on certain complex arcs we prove new zero-bounds for some polynomials involving these functions. Using the method of mild parameterizations, we prove a new result on the number of algebraic points on a curve involving the j-function, and give a new proof of the six exponentials theorem.
We develop new algorithms to invert the j-function without using the elliptic curve representation of a j-invariant, to determine the torsion subgroup of rational elliptic curves in the manner of Doud, and to test an elliptic curve for complex multiplication. All these algorithms either improve on previous methods, or are of equivalent optimal complexity. We develop a new algorithm to find numbers satisfying a set of modular conditions, which is significantly faster than the fastest previous method, with which we find some new Cunningham chains of primes.
Finally we consider Gauss' conjecture that there are no negative discriminants with one class of binary quadratic forms in each genus which have at least 32 genera. We apply our algorithm to find numbers satisfying modular conditions to prove a new general lower bound for such discriminants, adapt the methods of Watkins in his determination of discriminants with class number at most 100 to prove a large lower bound for discriminants indivisible by 2, 3 or 5, and adapt Baker's method for class numbers one and two to prove that such a discriminant cannot have a particularly large prime factor.
An evidence-based approach to assess the use of opioids
Keywords: essential medicines
Prevent future deaths
primary health care
opiates
narcotics
over-the-counter
opioids
harms
codeine
preventable deaths
pharmacoepidemiology
evidence-based medicine
drug safety
coroners' reports
avoidable harms
epidemiology

Background: Since the 1990s, the prescribing of opioids has increased in England and most high-income countries. However, insufficient datasets used in previous research may have underrepresented the extent of the problem.
Aims: To assess global and national use of opioids, drivers of suboptimal use, and consider strategies to prevent opioid-related deaths.
Methods: I conducted two cross-sectional analyses and two retrospective observational studies, using several datasets, including controlled opioid consumption statistics from the International Narcotic Control Board, the Global Essential Medicines Database of 137 national essential medicines lists, sales of over-the-counter products containing codeine, and the Preventable Deaths Database. I performed a synthesis of pharmacological resources and a systematic review of observational studies to explore factors associated with high-dose opioids.
Results: 687 million people (10% of the global population) consumed 89% of the world’s controlled opioids in 2015-17. Countries included a median of 6 opioids (IQR: 5-9; range: 0-19) in national essential medicines lists; morphine (95%), fentanyl (83%), and codeine (69%) were the most common. 31.5 billion units of codeine, costing £2.55 billion, were sold over-the-counter across 31 countries in 2013-19, with 3% and 54% increases respectively. In the UK, 4.75 billion dosage units of codeine in combination with 1711 tonnes of paracetamol and 96 tonnes of ibuprofen, costing £638 million, were purchased over-the-counter in 2013-19. I identified 233 opioid drugs and created the Oxford Catalogue of Opioids (https://www.catalogueofopioids.net/). Five factors were associated with high-dose opioids, including co-prescription of benzodiazepines, depression, Emergency Department visits, unemployment, and being male. There were 176 opioid-related Prevention of Future Deaths reports in 2013-19, with a 262% increase. I created the Preventable Deaths Tracker (https://preventabledeathstracker.net/) and launched the Coroners’ Concerns to Prevent Harms series in BMJ Evidence-Based Medicine.
Conclusions: There are persistent disparities and variations in the use of and access to prescribed and non-prescribed opioids. While opioids can be overused or underused, and can cause fatal and non-fatal harms, they still have a place in modern medicine in the right patient, at the right dosage regimen, and for the right reasons. I have outlined future research to serve patient safety, reduce avoidable harms, and improve evidence-based decision-making in clinical practice and policy.
Conditional source-term estimation for diesel combustion
Keywords: Computational Fluid Dynamics
Combustion Modelling
Conditional Source-term Estimation

The compression ignition engine will continue to be a significant factor in the transportation sector due to its unmatched efficiency and robustness. As emissions legislation becomes more stringent, pressure on the automotive industry will continue to increase as both government and consumers demand cleaner technologies. The detailed understanding of in-cylinder physics is paramount to improving efficiency and reducing engine-out emissions. Consequently, numerical analysis is playing a much more pivotal role in the engine design phase with the requirement to predict complex combustion events and pollutant formation driving the need for ever better models. In this investigation, current numerical tools are used to elucidate the effects of spray targetting and piston bowl geometry on combustion evolution and pollutant formation. Closed cycle computational fluid dynamics simulations are performed on a sector mesh at various load points using the 3 Zones Extended Coherent Flame Model coupled with adaptive mesh refinement. The computational fluid dynamics model is validated experimentally at the baseline conditions at each test point after-which, parametric sweeps of bowl geometry, exhaust gas recirculation rate and nozzle tip protrusion are conducted. Results indicate that appropriately pairing fuel injection strategy and piston geometry is essential in reducing engine-out emissions.
In addition, a novel chemical source term closure based on Conditional Moment Closure (CMC) is developed to simulate diesel combustion. Conditional Source-term Estimation (CSE) uses the conditional averages in evaluating the mean chemical source term. However, unlike Conditional Moment Closure where transport equations are solved for the conditional averages. CSE approximates the conditional averages through inversion of an integral. Previous studies have shown CSE is capable of accurately simulating non-premixed flames of light hydrocarbon fuels. In this study, CSE is extended to simulate spray flame combustion by coupling the CSE combustion model with Flamelet Generated Manifolds chemistry reduction methodology. The CSE-FGM model is applied to the Engine Combustion Network n-Dodecane Spray.A -- in a Large Eddy Simulation turbulence modelling framework. The model is successfully able to predict ignition delay and flame lift-off length with good agreement to experimental measurements. Additionally, the CSE-FGM model is validated further within a RANS framework to predict ignition delay and flame lift-off length over a wide range of ambient temperature and oxygen concentration conditions. The CSE-FGM model is successfully able to predict experimental trends and sensitivity with respect to ambient conditions.
On natural convection in a large civil gas turbine
Keywords: natural convection
gas turbine
heat transfer

As a large civil gas turbine is cooling following operation, natural convective flows develop, which can lead to numerous issues. The ability to predict and understand this transient shutdown cooling cycle is becoming increasingly more important. However, this has proven to be extremely difficult owing to the complex nature of the flow physics and its dependency on a large number of parameters.
Two sophisticated experimental facilities that simulate the natural convective flow field during shutdown cooling of a large civil gas turbine, have been designed and commissioned. This has allowed for the influence of numerous features to be investigated, which has helped to increase understanding of these flow fields. Furthermore, a robust thermocouple probe has been developed that allows for accurate measurements in a natural convective boundary layer, along with a novel method of determining heat flux fluctuations.
An extensive experimental study has shown that the overall heat transfer in a non-isothermal concentric annulus is adequately captured by isothermal correlations when the area-averaged temperature difference is used, as the local heat flux is dominated by the local radial temperature gradient. This finding was validated up to a diameter ratio of 1:5, which is applicable to the majority of the main gas path.
The interaction between the induced draft in the main gas path (due to natural convection) and obstructions in this area has been proven to be critical in defining the severity of thermal rotor bow - the thermal gradient that develops across the rotor drum during shutdown. Two viable solutions to help alleviate rotor bow have been developed: jet impingement cooling at the top of the annulus, and circumferentially non-uniform emissivity distributions.
Single-cell imaging assay for rapid antimicrobial resistance testing
Keywords: AMR
fluorescence in-situ hybridisation
FISH
antimicrobial resistance
fluorescence imaging

Antimicrobial resistance (AMR) is arguably one of the biggest healthcare challenges in the new century, the constant rise of which presents extra threat, projecting to cause 10 million deaths and billions of dollars in cost by 2050 if no action is taken. Drug-resistant pathogens have no border and can travel to any corner of the world, regardless of wealth or distance. One of the biggest challenges in addressing this rise of AMR is the low speed of current diagnostic methods, forcing physicians to prescribe inappropriate or unnecessary antibiotics before a diagnosis is reached, and such misuse inevitably exacerbates the rise of AMR. In this project, a proof-of-principle imaging assay was developed to significantly accelerate the procedure to reach a correct diagnosis. This assay used fluorescence in situ hybridisation (FISH) to detect RNA targets that led to the identification of bacterial speciation and the bacteria’s resistance to certain antibiotics. It was validated with laboratory and clinical isolates of E. coli with promising results. This project paved the way to a multiplex assay that would generate robust and reliable results within one hour.
Attention mechanisms for fine-grained biomedical image analysis
Keywords: Deep learning
Biomedical image analysis
Attention mechanisms

Problem: Recently, deep convolutional neural networks have greatly improved our ability to develop robust image analysis methods, especially those tailored to biomedical scenarios. Despite important advances in computer vision, it is usually challenging to achieve the same success on specific biomedical datasets due to certain specific challenges, such as limited size datasets, expensive annotations, high-resolution information, multiple discernible biomedical objects, and lack of interpretability. Therefore, it is still in high demand to investigate automatic image analysis methods with high interpretability and accuracy to transform high-resolution biomedical image data into meaningful quantitative information with weak annotations and limited availability of training data.
Methodology: Considering the challenges of biomedical datasets and the corresponding technical limitations, this thesis aims to incorporate global context-aware information to enable automated image analysis systems to provide reliable diagnostics in clinical applications. The thesis first introduces a hybrid method equipped with U-Net and a global probabilistic model for the segmentation of dense cell populations. This model jointly exploits shape priors and global cellular interactions to compensate for the limitations of standard deep networks. Next, this thesis focuses on attention-based, interpretable fine-grained classification methods for biomedical images that is capable of jointly capturing local high-resolution detail and global background information.
Outcomes: In this thesis, a number of fine-grained feature learning methods were proposed for high-resolution biomedical images. The original contributions are as follows: (1) A global probabilistic model for dense cell nucleus segmentation. (2) A gated attention mechanism for simultaneous localisation of multiple discriminative instances. (3) A multi-task learning scheme for fine-grained feature learning. (4) Two new clinical datasets for biomedical image classification.
Does young children’s free play predict social competence in preschool?
Keywords: early years
play
social competence

It is well documented that both play and social competence serve an important role in promoting the development of young children. The relationship between play and social competence is well established. However, methodological approaches in research to date has been limited to observational data combined with parent reports or teacher reports, or questionnaire reports exclusively. Therefore, the current study aimed to investigate the relationship between play and social competence employing a multimethod and multisource approach. Measures of play ability were obtained from observations and teachers. Measures of social competence were obtained from parents and teachers. Results revealed that despite being somewhat related to teacher-reported play, observed play did not predict social competence. Results also showed that teacher-reported play did predict social competence. It was concluded that there was evidence supporting the predictive relationship between play and social competence. However, further research would be required to investigate the predictability of observed play.
The Palestinian transnational student movement 1948-1982: a study on popular organization and transnational mobilization
Keywords: Palestinians
Student Movements
Middle East
Postcolonialism

This thesis addresses the question of how non-state actors impact regional and international systems dominated by states. It does so by analysing the Palestinian transnational student movement between 1948 and 1982. It argues that in different phases of Palestinian history, Palestinian student movements played a crucial role in reorganising their scattered society transnationally, establishing and sustaining mechanisms and practices of popular mobilation and internationalising their struggle. In doing so, they contributed to the emergence of the Palestinian movement as a recognised actor able to impact regional and international systems dominated by states. Building on post-colonial approaches to IR and particularly on Gramsci´s theory of organicitá, this study defines the transnational Palestinian student movement as an anti-colonial organic vanguard of the Palestinian people. Students were a vanguard as they led Palestinian political reorganisation, creating unions often recognised as representative bodies of their people in the context of exile, statelessness and absence of national institutions. This role of “engine” was made possible by the “organic relation” between students and the people: students were an expression of their own society and voiced its anti-colonial ambitions. The study analyses Palestinian student mobilisation in Cairo, Beirut and Amman in the aftermath of the Nakba (catastrophe) in 1948. It explores the emergence of the General Union of Palestinian Students (GUPS) as the first transnational popular organisation able to voice Palestinian demands at the international level. It analyses GUPS role in the national movement before and after the establishment of the PLO. Methodologically this study draws on archival research and particularly oral history, relying on 76 interviews with former members and leaders of the Palestinians student movement. The thesis contributes to IR theories by providing an exhaustive study of the role of non-state actors in state-centered systems such as the Middle East, analysing the specific role of the people.