VictorJuiz/thesis huggingface dataset:
Unpaid care work and social norms: Gender, generation and change in northern Uganda

There has been increasing interest in understanding care responsibilities in developing countries but there remains limited evidence on determinants of gendered participation in care. This thesis proposes social norms as an explanation for gender- and age-based differences in the time devoted to unpaid care work – including caring for people and domestic work. It investigates how social norms exert influence at different levels, why they are complied with and how they are contested and changing.
The research setting is northern Uganda, where researchers have observed socio-cultural changes resulting from a civil war, the rise of formal education, the increasing involvement of development agencies and new cultural and technological influences. I collected mixed-methods data from adults and children in this region between 2014 and 2019.
Building on theories of social norms, gender and childhood, this thesis argues that social norms dictate that females are supposed to do more unpaid care work than males, and boys more than men. I focus on three factors that shape whether individuals comply with, or deviate from, prevailing care-related norms. Individuals are more likely to deviate from these norms if (1) they are able to choose to deviate (agency); (2) they are aware of alternative options (awareness); and (3) if deviating is beneficial for them (advantage).
Some factors are reinforcing care-related norms in northern Uganda. However, shifts in agency, awareness and advantage suggest there is scope for change. It appears that care-related norms are changing subtly rather than radically. Whether these norms and the division of unpaid care work transform more fundamentally in the future will depend on the development of the region and remains to be explored in further research.

Keywords: Children's work
Domestic work
Division of labour
Northern Uganda
Gender
Gender norms
Social change
Time use
Social norms
Unpaid care work
Advisor: Gollin, D
Crivello, G
Who on earth wants global democracy – and why (not)? A theoretical and experimental study of international public opinion 

To what extent and why do people want the world to be governed democratically – or not? This is the central question of my dissertation. I create a novel theoretical framework starting with genetic and socio-environmental influences such as parents and peers, moving to proximate motives such as cosmopolitan values and interests, as well as conditioning factors like political knowledge and feasibility beliefs. These are connected to individual attitudes and actions on global democracy, which is conceptualized as a directly elected world parliament and government focused on transnational issues like global poverty and climate change. Furthermore, I theorize potential interrelationships between individual preferences for political parties and their global democracy attitudes and actions. In order to evaluate arising expectations, I conducted survey experiments on nationwide samples of citizens in Brazil, Germany, Japan, the United Kingdom, and the United States. My key findings are: First, once the proposals are considered, clear majorities of people in all five survey countries tend to support global democracy. Second, individual attitudes toward global democracy are significantly associated with the theorized factors of values and interests, especially the perceived necessity of global democracy to address problems like international peace and climate change, as well as the supposed importance of public participation in world politics. Third, while individual global democracy attitudes are affected by partisan cues in countries with long-established major parties, people’s global democracy attitudes tend to affect their voting intentions in more fluid multi-party systems – a finding with significant implications for domestic politics. My dissertation contributes to empirical and normative research on international organizations by providing the first social scientific study of world public opinion on global democracy. Moreover, my thesis bears important lessons for practitioners working on global governance, as the world struggles to address multiple transnational crises – from international mass migration to climate change.

Keywords: attitudes
partisan cues
world government
preferences
global democracy
reform
public opinion
cognitive mobilization
world parliament
global governance
partisan mobilization
international organizations
survey experiment
Advisor: Milewicz, K
Dialogue & empathy in middle school aged children - a configurative narrative review

The present study reports a configurative narrative review of studies pertaining to dialogue-based pedagogical practices and programmes with empathy in middle-school aged children. A total of 596 studies from three databases were found and, from pre-determined criteria, it was then decided that the data from nine eligible papers would be extracted, synthesised, and analysed. The data found in these papers varied greatly in design, methodological rigour, and the operationalisation of empathy as an outcome measure. Thematic extractions relating to the conceptualisations of empathy; an exploration of dialogic pedagogical practices adopted when seeking empathy as an outcome measure, and an examination of the evidence reported in these nine papers ensued. It was found that three key practices were adopted: facilitated dialogue, goal orientated dialogue and the implementation of small, collaborative work. Overall, the data revealed that there was conceptual and theoretical work associating dialogic practices and empathic arousal, behavioural change, and empathic enhancement. However, there was not enough robust empirical evidence to justify this linkage. Therefore, conclusions were drawn from the data to suggest further empirical, particularly quantitative, studies were required to better understand the relationship between dialogue and empathy. Subsequent to this finding, a description of a future mixed-methods study is outlined.

Keywords: Dialogue
Empathy
Dialogic Teaching
Advisor: Nag, S
Modelling the effects of temporal variations of blood flow in tumours 

Solid tumours have highly irregular vasculatures that are constantly re-modelled, giving rise to spatio-temporal heterogeneity in the level of nutrients, metabolites, and drugs. Such variability requires cells to adapt, and is hypothesised to select for more aggressive cancer phenotypes. Risk spreading through spontaneous phenotypic variations is an ecological concept which is used to explain how species may survive in temporally varying environments. It allows individuals within a species to diversify their phenotypes ensuring that at least some of them can survive in the face of sudden environmental change. In this thesis, we aim to investigate whether cancer cells may adopt this strategy when dealing with rapidly changing levels of nutrient due to temporally-varying blood flow.
Accordingly, we develop and analyse a series of mathematical models of increasing biological complexity in order to investigate, in a systematic way, the impact that temporal variations in the nutrient supply might have on cancer cell populations. First, we present a mathematical model consisting of a system of non-local partial differential equations modelling the evolutionary dynamics of two competing phenotypically-structured populations in the presence of periodically oscillating nutrient levels. The two populations undergo heritable, spontaneous phenotypic variations at different rates. The phenotypic state of each individual is represented by a continuous variable, and the phenotypic landscape of the populations evolves in time due to variations in the nutrient level. We then extend the model by modelling nutrient dynamics explicitly and evaluate the effects of cellular feedback on the environment. Moreover, we develop corresponding individual-based models and study the differences that arise between continuum and discrete approaches at low population sizes.
Exploiting the analytical tractability of our models, coupled with numerical simulations, we identify environmental regimes that select the population with higher rate of spontaneous phenotypic variation. In particular, we expect environmental conditions where cells experience periods of starvation followed by re-oxygenation to promote phenotypic heterogeneity, which is consistent with experimental observations. This allows us to predict how certain interventions, for example, the vascular normalisation strategy that aims at stabilising the blood flow within tumours, could reduce phenotypic heterogeneity and drive the tumour to a treatable phenotype. Furthermore, we apply our discrete model to study adaptive strategies during the metastatic colonisation of distant organs.

Keywords: Cancer ecology and evolution
Adaptive strategies
Dynamic environments
Phenotype-structured populations
Advisor: Byrne, HM
Anderson, ARA
Maini, PK
Gatenby, RA
A laboratory investigation into the behaviour of sand at low confining stresses

The mechanical behaviour of sands at low confining stresses may have important implications for the design of offshore wind turbine (OWT) foundations. However, previous research investigating this behaviour is limited and conflicting, and cyclic testing campaigns have historically adopted load characteristics that may be unrepresentative of those sustained by OWT foundations. This thesis endeavours to fill the gap in the literature through creation of a new experimental database of soil element tests, that explores the constitutive response of sand at low stress levels subjected to monotonic and cyclic loading.
Results of the monotonic experimental campaign indicate that while the shear strength properties (ηmax and ϕ'max) tend to increase with decreasing effective confining stress, σ'c,0), this effect becomes increasingly minor for σ'c,0≤ 50 kN/m2. Furthermore, tests undertaken at lower confining stresses exhibit a comparatively stiffer response than those tests sheared at higher pressures. Results of the cyclic triaxial testing campaign exemplify the complexities of the response of sands subjected to cyclic loading. However, clear and systematic trends are found that can be robustly captured by the new cyclic modelling framework adopted in this study. Modern design methods for OWT foundations increasingly rely on finite element analysis (FEA); however, results depend critically on the constitutive models used, and the model parameters adopted in the numerical analyses.
This thesis reviews the state-of-the-art with regard to constitutive modelling frameworks developed for predicting sand behaviour. Evaluation of four sophisticated constitutive models found that it was not possible to determine a single set of calibrated model parameters that yield satisfactory model simulations for both drained and undrained load cases. Furthermore, only the general trends could be predicted when simulating sustained undrained cyclic loading. Further model development is therefore required if such models are to be relied upon for predicting the long-term behaviour of OWT foundations.

Keywords: Hyperplasticity
Triaxial
NorSand
SANISAND
Cyclic
HySand
sand
Low stress
Leighton Buzzard sand
Stress path
Advisor: Martin, C
Houlsby, G
Probing the dynamics of nanoparticles at oil-water interfaces using high-speed total-internal-reflection dark-field microscopy

Particles at fluid-fluid interfaces have a wide range of powerful uses in industry, technology and science. There is therefore a strong interest in exploring the dynamics of these systems to understand and gain more control in applications, as well as for fundamental investigations in condensed matter and statistical physics. Questions remain in particular about what governs adsorption dynamics and inter-particle interactions, and how mixtures of different-sized particles as well as particles and surfactants behave.
This thesis probes the dynamics of nanoparticles at oil-water interfaces using high-speed total-internal-reflection dark-field (TIR-DF) microscopy, comparing gold particles of diameters 20, 40 and 80 nm, and investigating the effect of an added surfactant (n-dodecyl-β-D-maltoside (DDM)). We built a TIR-DF microscope with a spatiotemporal resolution of up to 1 nm and 20 μs, and used home-written tracking and analysis software to characterise the particle motion. The diffusive motion within the interfacial plane is measured directly; out-of-plane motion is inferred from light scattering and in-plane motion.
The addition of DDM was found to qualitatively alter the interaction between the particles and the interface, with adsorptions becoming reversible and an inversion of the size trend in diffusivity (with larger particles diffusing faster than smaller ones, contrary to expectations). This, in combination with a paucity of adsorption events in larger particles, leads us to propose size-dependent barriers to adsorption to DDM interfaces. In addition, at DDM interfaces, many particles' diffusive behaviour changed over the course of a trajectory, indicating several different immersion states that are stable on the experimental timescale (~1 s).
This study shows that there remain qualitatively new dynamics of nanoparticles at oil-water interfaces to be explored. These dynamics can only be resolved at a high spatial resolution, and many of them only at high imaging speed, underlining the importance of novel imaging approaches probing the barriers of achievable spatiotemporal resolution.

Keywords: liquid-liquid interfaces
size-dependence of particle diffusion at interfaces
surfactant
oil-water interfaces
dark-field microscopy
total-internal-reflection microscopy
nanoparticle dynamics
single-particle tracking
high-speed optical microscopy
Advisor: Kukura, P
Fluid flow through vegetation

This thesis is concerned with the mechanical aspects of fluid-vegetation interaction in coastal flows and their resultant instabilities. Specifically, we consider the evolution of periodic waves as they propagate through vegetated domains, and the emergence of monami – the progressive, synchronous oscillation of submerged vegetation under sufficiently strong flows. We divide our analysis between rigid and flexible vegetative canopies. These new predictions and physical insights will be applicable in the analysis of a diverse range of industrial and environmental applications involving fluid-vegetation interactions.
In the case of rigid vegetation, using the method of multiple scales, we derive the evolution of small-amplitude waves propagating over a varying substrate fully covered with vegetation. In particular, we give time-averaged predictions for both the amplitude and the wavelength of the waves as functions of the distance of propagation. We then extend this analysis to include the situations of (i) combined current-wave flows and (ii) shallow-water waves through vegetation. For the case of combined current-wave flows, we demonstrate the manner in which the surface waves vary as a function of the current, and also how the current remains unaffected by the evolving wave. For shallow-water waves, we explore how cnoidal waves, nonlinear periodic solutions of the Korteweg-de Vries equation, evolve on horizontal substrates. For all of the flows considered above on rigid vegetation, their evolutions are only drag-dependent and hence independent of added mass and virtual buoyancy.
In the case of flexible vegetation, we propose a model where the plants are described by elastic cantilever beams. Our first analysis concerns the propagation of small-amplitude waves. Compared to the case of rigid vegetation, the fluid will load and deform each vegetative structure. This deformation, in turn, must affect the flow. Although the flow and the beam dynamics have to be determined simultaneously, we show that when the wavelength is sufficiently small, such quantities are asymptotically decoupled -- this allows us to first determine the local beam dynamics before evaluating the momentum loss in the macroscopic flow. In contrast to rigid vegetation, we find that added mass and virtual buoyancy play a role.
Finally, we focus on understanding the mechanisms and critical conditions for triggering monami. We treat the current as unidirectional and solve for the steady configurations of the flow and the deflected canopy. Our stability analysis predicts that monami is induced by shear along the top of the canopy. Meanwhile, monami can be suppressed if the canopy is sufficiently sparse or if there is sufficient inertia in the system.

Keywords: coastal engineering
hydrology
fluid mechanics
fluid-structure interaction
Advisor: Chapman, SJ
Trinh, P
Dimakopoulos, AS
Quantum correlations in space-time: foundations and applications

The absolute/relative debate on the nature of space and time is ongoing for thousands of years. Here we attempt to investigate space and time from the information theoretic point of view to understand spatial and temporal correlations under the relative assumption. Correlations, as a measure of relationship between two quantities, do not distinguish space and time in classical probability theory; quantum correlations in space are well-studied but temporal correlations are not well understood. The thesis investigates quantum correlations in space-time, by treating temporal correlations equally in form as spatial correlations and unifying quantum correlations in space and time. In particular, we follow the pseudo-density matrix formalism in which quantum states in spacetime are properly defined by correlations from measurements.
We first review classical correlations, quantum correlations in space and time, to motivate the pseudo-density matrix formalism in finite dimensions. Next we generalise the pseudo-density matrix formulation to continuous variables and general measurements. Specifically, we define Gaussian spacetime states by the first two statistical moments, and for general continuous variables spacetime states are defined via the Wigner function representation. We also define spacetime quantum states in position measurements and weak measurements for general measurement processes. Then we compare the pseudo-density matrix formalism with other spacetime formulations: indefinite causal structures, consistent histories, generalised non-local games, out-of-time-order correlation functions, and path integrals. We argue that in non-relativistic quantum mechanics, different spacetime formulations are closely related and almost equivalent via quantum correlations, except path integrals. Finally, we apply the pseudo-density matrix formulation to time crystals. By defining time crystals as long-range order in time, we analyse continuous and discrete time translation symmetry as well as discuss the existence of time crystals from an algebraic point of view. Finally, we summarise our work and provide the outlook for future directions.

Keywords: quantum correlations
space-time
Advisor: Vedral, V
Nested multilevel Monte Carlo methods and a modified Euler-Maruyama scheme  utilising approximate Gaussian random variables suitable for vectorised hardware and low-precisions

We present a modified Euler-Maruyama scheme using approximate random variables, produced by the inverse transform method, using cheap approximations to the inverse Gaussian cumulative distribution function. We analyse the error for two approximations: a piecewise constant approximation on equally spaced intervals, and a piecewise linear approximation using geometric intervals dense at the singularities. High speed implementations faster than Intel's MKL are provided, suitable for modern vector hardware. The error between the approximations from the exact and modified Euler-Maruyama schemes is bounded by the error from the approximate random variables.
We incorporate this scheme into a multilevel Monte Carlo framework producing a nested scheme, and show that the discretisation error couples to the random variables' approximation error. The result directly extends to Lipschitz and differentiable payoff functions. For Lipschitz and non-differentiable payoff functions simulated using a time step delta, there is a transition from a variance decay of O(delta) to order(delta1/2) as the discretisation error becomes dominant. These variance bounds are demonstrated numerically for geometric Brownian motion and a variety of payoff functions of varying smoothness.
For approximate random variables computed in low-precision, a model for the accumulated rounding error is developed and assessed. Half-precision is viable for a range of coarse path simulations, and can be extended further by incorporating a Kahan compensated summation. We empirically demonstrate these ideas are transferable to the Milstein scheme, and the more difficult Cox-Ingersoll-Ross process and its non-central chi-squared distribution. We estimate that under the Black-Scholes model, options can be priced using path simulations with approximate Gaussian random variables, obtaining a five-times or more speed improvement without losing accuracy.

Keywords: Approximations
Monte Carlo
Random Numbers
Computing
Analysis
Advisor: Giles, M
Goodyer, C
Continual learning for efficient machine learning

Deep learning has enjoyed tremendous success over the last decade, but the training of practically useful deep models remains highly inefficient both in terms of the number of weight updates and training samples. To address one aspect of these issues, this thesis studies the continual learning setting whereby a model utilizes a sequence of tasks leveraging previous knowledge to learn new tasks quickly. The main challenge in continual learning is to keep the model from catastrophically forgetting the previous information when updating it for a new task.
Towards this, firstly this thesis proposes a continual learning algorithm that preserves previous knowledge by regularizing the KL-divergence between the conditional likelihoods of two successive tasks. It is shown that this regularization imposes a quadratic penalty on the network weights based on the curvature at the minimum of the previous task.
Second, this thesis presents a more efficient continual learning algorithm, utilizing an episodic memory of past tasks as a constraint such that the loss of episodic memory does not increase when a weight update is made for a new task. It is shown that using episodic memory to constrain the objective is more effective than regularizing the network parameters. Furthermore, to increase the speed of learning of new tasks, the use of compositional task descriptors using a joint embedding model is proposed that greatly improves the forward transfer.
The episodic memory-based continual learning objective is then simplified by directly using the memory in the loss function. Despite its tendency to memorize the data present in the tiny episodic memory, the resulting algorithm is shown to generalize better than the one where memory is used as a constraint. An analysis is proposed that attributes this sur- prising generalization to the regularization effect brought by the data of new tasks.
This algorithm is then used to learn continually from synthetic and real data. For this, a method is proposed that generates synthetic data points for each task by optimizing the forgetting loss in hindsight on the replay buffer. A nested optimization objective for continual learning is devised that effectively utilizes these synthetic points to reduce forgetting in memory-based continual learning methods.
Finally, this thesis presents a continual learning algorithm that learns different tasks in nonoverlapping feature subspaces. It is shown that minimizing the overlap by keeping the subspaces of different tasks orthogonal to each other reduces the interference between the representations of these tasks.

Keywords: Fast Adaptation
Catastrophic Forgetting
Machine Learning
Multitask Learning
Continual Learning
Lifelong Learning
Advisor: Dokania, P
Torr, P