VictorJuiz/thesis huggingface dataset:
Microinstability in the pedestal
Keywords: Nuclear Fusion
Microstability
Turbulence

The pedestal is a region of increased equilibrium pressure gradients near the edge of high performance toroidal plasmas. While the MHD properties of this region have been fairly well-characterized, pedestal microinstabilities and turbulence remain rela- tively unexplored, particularly in steep gradient regions. In this thesis, we describe a new microinstability caused by the steep equilibrium temperature gradients and com- plex magnetic geometry. This instability has a critical temperature gradient that is much higher than core temperature gradients, and hence likely exists only in pedestals. Basic analytic arguments show that in the presence of magnetic shear and steep temperature gradients, this mode must be one of the fastest growing modes. In realistic magnetic equilibria that we study, it is the fastest growing mode at almost all scales comparable to ion and electron gyroradii. Therefore, it is a robust feature of pedestal microinstability. We also investigate nonlinear pedestal microturbulence. We find the turbulent satu- rated state to be inhomogeneous in the poloidal angle, in strong contrast to core micro- turbulence that is typically well-correlated for long distances along magnetic field lines. Turbulence is particularly strong in poloidal regions of (i) weaker local magnetic shear and (ii) shorter distances between flux surfaces. These two effects cause the perpendicular wavenumber of the turbulence to have a particularly strong poloidal angle dependence, which results in the turbulence being strongly damped by finite Larmor radius effects in certain poloidal regions, hence causing the turbulent poloidal inhomogeneity. While we find that the linear instabilities caused by magnetic drifts are the fastest growing modes, nonlinear simulations appear to reach a quasi-steady state insensitive to these drifts and dominated by the branch of electron temperature gradient turbulence caused by parallel streaming. The heat flux in this quasi-steady state is roughly constant in time, but some modes that appear to be driven by magnetic drifts have not yet saturated.
Influence of the input data on learning deep representations
Keywords: Computer Vision
Machine Learning
Deep Learning
Weakly-supervised Learning
Artificial Intelligence

This thesis studies, through the prism of image classification, the influence of the input data on learning deep representations. Indeed, data is abundant but also multifaceted: samples might come from a single or multiple domains, or they can also be more or less labelled. We show in this thesis that methods to learn deep network representations should be designed according to the type of input data at hand.
Our work covers different training data configurations. We start with the multiple visual domains setting. We tackle this challenge by modifying the network architecture with residual adapters, domain specific modules which lead to a high degree of parameter sharing across domains with better transfer learning performance than standard fine-tuning.
Second, we consider the settings where a part of the data is unlabelled. This thesis spans different levels of supervision but, in all cases, we promote the systematic use of self-supervision pre-training. First, we consider the semi-supervised setting with few labels per class for which we propose an alternating optimisation scheme to avoid overfitting the scarse labelled data. We then learn to discover new classes given some known classes by proposing a two-headed network which jointly learns to cluster the unlabelled data and to classify both the labelled and unlabelled samples. To this end, we propose a clustering loss which learns to connect pairs of samples based on their similarity at the feature level. We show that this loss, proposed for novel class discovery, can also be used to perform deep clustering by choosing a nearest neighbours based similarity.
From another perspective, we look at the influence of the data on the deep representations through the lens of saliency. To interpret which parts of the image contribute the most to the data representation, we introduce a unifying saliency framework based on the learning mechanism of convolutional neural networks. This framework can then be used to derive new or existing backpropagation based saliency methods.
Community mediation across ethnicities: building for conflict resolution in diverse societies 
Keywords: ethnicity
inter-ethnic conflict
community mediation
conflict resolution

The central purpose of this thesis is to explore how academics and dispute resolution centres discuss inter-ethnic dispute resolution. The study begins by reviewing the literature on this topic but quickly discerns that little research exists analysing the interactions between parties from different ethnic backgrounds (i.e. interethnic disputes). The study then turns its lens to look at how dispute resolution centres themselves are discussing these interactions—specifically, community mediation centres across both California and the United Kingdom. Community mediation centres are ideal subjects for the study of interethnic disputes because of their location in urban, multi-ethnic communities and their focus on interpersonal dynamics. As the focus narrows to community mediation, the information found on the websites of community mediation centres becomes highly valuable. This research collects data from the websites of 42 mediation centres across California and the United Kingdom—while some of these discuss ethnicity, they tend not to address inter-ethnic disputes. The final substantive chapter presents a more in-depth analysis of materials related to two community mediation centres, the San Francisco Community Boards in California and the Southwark Mediation Centre in the United Kingdom. Utilising data from social media, newsletters, training materials, and one exploratory interview, it examines the resources these two mediation centres provide to disputants in inter-ethnic conflicts.
Overall, this study suggests that, while there has been little emphasis on inter-ethnic disputes in scholarship and the websites of community mediation centres, these interactions are occurring and resources are available to navigate them. Future studies must continue to fill this missing gap in the literature as the study of inter-ethnic interactions grows in importance.
Development and application of computational methods to study DNA modifications
Keywords: TET-assisted pyridine borane sequencing
asTair
next-generation sequencing
variant calling
DNA modifications
modification calling
bisulfite-free
epigenetics
NGS
astair
SNP
method development
DNA
epigenomics
bioinformatics
cancer
genomics
CpG
TAPS
cfDNA
applied machine learning
DNA methylation

The epigenetic modifications of DNA shape cell fate in development, differentiation, and disease. The existing gold standard sequencing technologies for epigenetic DNA modifications are based on sodium bisulfite, which is a harsh chemical treatment resulting in DNA degradation. A novel bisulfite-free and base-resolution sequencing method, TET Assisted Pyridine-borane Sequencing (TAPS) was developed to detect the most abundant DNA modifications. In comparison to bisulfite sequencing (BS), TAPS relies on mild reactions for the detection of modified bases. From a bioinformatics perspective, sodium bisulfite substantially reduces information content, complicating data processing and the detection of genetic variation. In fact, most existing modification calling tools for bisulfite-treated data do not distinguish between modifications and genetic variants, which results in false positives. A computational tool, asTair, was created to process DNA modification sequencing data. It was designed primarily for handling TAPS sequencing output, but also contains functions that are useful for bisulfite sequencing data analyses. It was shown that TAPS has more even coverage than BS while having a comparable conversion rate over CpGs, and is applicable to low input samples. A Deep Neural Network (DNN) model that detects single-nucleotide variants in TAPS- and BS-converted sequencing data was created to enable sensitive modification and variant calling. The algorithm showed precision and recall above 0.9 for classifying variants, modifications and reference positions. The model outperformed available variant callers for whole-genome sequencing and BS data. Applying such a model on real datasets could improve the accuracy of identifying real DNA modifications masked by genetic variation and errors, as around a sixth of all SNPs could be misclassified as modifications.
The molecular basis of Plasmodium falciparum merozoite invasion inhibition via anti-PfCyRPA antibodies
Keywords: Antibody
Malaria
CyRPA
Synergy
Vaccine

Plasmodium falciparum, the pathogen responsible for the deadliest form of malaria, continues to exact an immense human toll, disproportionality shouldered by countries in sub-Saharan Africa. Significant progress has been made this century in reducing global malaria mortality and morbidity, but these gains remain fragile. The development of a malaria vaccine that protects against clinical disease would be an invaluable addition to the malaria control toolkit, but has remained elusive to date. As a result, the search for new, more potent vaccine antigens continues. Emerging from these efforts was the identification of the PfRH5:PfCyRPA:PfRipr complex (RCR), which is essential for merozoite invasion into the host erythrocyte. Here, a new panel of anti-PfCyRPA antibodies are evaluated for their ability to block merozoite invasion, including two new mAbs that are the most potently inhibitory anti-PfCyRPA mAbs described to-date. Structural studies revealed a single critical face of PfCyRPA that elicits neutralizing antibodies, where all four of the newly described inhibitory mAbs bound, opening the door for further studies on synthetic PfCyRPA immunogens that can focus the immune response on this vulnerable region of PfCyRPA. Finally, studies of synergy and antagonism between pairs of anti-PfCyRPA mAbs revealed that non-competing inhibitory anti-PfCyRPA mAbs potently synergize with each other. A novel mechanism of synergy to explain this observation was described, whereby lateral interactions between adjacent antibody Fab arms stabilize and enhance antibody affinity for PfCyRPA. Together, these data can inform the design of the next generation of malaria therapeutics.
Variance reduction techniques for chemical reaction network simulation
Keywords: stochastic simulation
variance reduction methods
quasi-Monte Carlo
chemical reaction networks
Monte Carlo methods

In recent decades stochastic models have become an indispensable tool when analysing quantitative biological data, which are often subject to noise, both from intrinsic and extrinsic sources. Though such models generate richer, and perhaps more realistic, behaviour than their counterpart deterministic models, they are also inherently more difficult to study. Since exact solutions describing the dynamics of stochastic models are scarce, one is often forced to resort to simulation of these models using Monte Carlo methods, which are generally easy to implement. However, the output of such computational methods is itself subject to statistical error, and consequently it is often computationally demanding to get numerical predictions from stochastic models to a satisfactory degree of accuracy.
In this thesis we therefore consider methods that improve the efficiency of stochastic simulation algorithms, specifically in the context of well-mixed chemical reaction network models. We primarily achieve this by utilising variance reduction techniques to reduce statistical error in the outputs of simulation algorithms without incurring extra computational costs. In particular, as our key contribution, we study three different ideas leading to more efficient simulation approaches.
Firstly, we derive an efficient implementation of the uniformisation technique for continuous-time Markov chain models. Using stratified sampling we can leverage the extra structure in a uniformised chemical reaction network to subsequently produce a more efficient simulation method. In addition we show how to use the uniformisation technique to reduce statistical error via what is effectively a low-pass filter.
Secondly, we study the synthesis of quasi-Monte Carlo methodology with simulation methods for chemical reaction network models. Whilst such methods can significantly improve efficiency, we show that, due to the problem structure of chemical reaction network models, care must be taken in the specific implementation choices, both regarding the quasi-Monte Carlo techniques and simulation methods.
Lastly, we provide a significantly improved method for generating unit-rate Poisson processes via Poisson bridge constructions in combination with antithetic sampling. This method is subsequently used to improve the efficiency of standard simulation algorithms for chemical reaction networks.
The ecology of reproductive tract microbiota of a polygynandrous host, the red junglefowl (Gallus gallus)
Keywords: red junglefowl
reproductive microbiota
reproductive bacteria
reproductive tract microbiome
seminal microbiome
sexual selection
cloacal microbiome

In a world dominated by bacteria, animals harbour microorganisms both within and on their bodies. Host-associated bacteria, or microbiota, can play a key role in host health, metabolism, immune functioning, and even behaviour. These microbiota are not stable over time—novel bacteria can be acquired from the surrounding environment and exchanged between multiple hosts. In the current literature, hypotheses have emerged that suggest the potential for host social behaviour to influence its gut microbiota, while the gut bacteria may also affect host social decision making. The focus of host microbiota research has been on bacterial communities found in host guts, but a similar interdependence may occur between host sexual behaviour and its reproductive microbiota. Studying the role of bacteria in the context of host reproduction may aid in understanding previously unexplored avenues in host evolutionary ecology. For example, reproductive bacteria may partake in host mating behaviour, play a role in sexual selection and conflict, and determine host socio-sexual network structure. Surprisingly little, however, is known about the bacterial communities in the reproductive tracts of non-human hosts. To fill this gap in knowledge, my thesis investigated the ecology of reproductive tract microbiota of the polygynandrous red junglefowl (Gallus gallus). By using state-of-the-art technologies developed in the field of microbial ecology, I carried out a thorough survey of bacteria associated with reproductive tracts. First, I show that both females and males harbour bacteria across the continuum of their internal reproductive tracts, and that these assemblages are distinct from the gut microbiota and spatially-structured in females, while more homogenous in males. I then explore temporal variation in fowl ejaculate microbiota, and show that while ejaculate-associated bacteria differ between young and old males, ejaculates have a consistent bacterial composition over the course of breeding season and across a sequence of matings. Finally, I focus on the association between fowl mating interactions and cloacal bacteria, and find some evidence consistent with the idea that socio-sexual interactions during experimental trials may be associated with variation in cloacal microbiota. Throughout the thesis, I report considerable intra- and inter-individual variation, highlighting the importance of including phenotypic variation in host microbiota studies. Combined, the findings of my thesis show the potential importance of studying reproductive microbiota, and encourage further interdisciplinary work to understand the role of reproductive bacteria in host evolution and ecology.
Educating awareness: Using variation theory in task design
Keywords: MTED 2020
MTED

Researchers claim that implementing highly demanding cognitive tasks helps students improve their mathematical thinking, which is critical for a deep understanding of mathematics (Stein et al., 1996; Charalambous, 2010). In this action research, I investigate how participation in a professional development program on variation theory increases teachers’ awareness about the cognitive demands of the tasks they use in class. This study’s data were collected from various sources: classroom observations, semi-structured interviews, field notes from meetings, and classroom artefacts. The main finding is that teachers source their tasks from the published curriculum materials and do not implement higher cognitive demand tasks than those in the textbooks or past exam questions. This research takes an original approach by using variation theory to increase teachers’ awareness of cognitive demands and improve their subject matter and pedagogical knowledge.
Statistical Inference in fMRI using Random Field Theory and resampling methods
Keywords: random field theory
resampling
multiple testing

This thesis provides a set of tools for analysing random images with a specific focus on applications in functional Magnetic Resonance Imaging (fMRI). To do so we employ Random Field Theory (RFT), a set of theoretical parametric results that can be used to analyse multidimensional random processes (known as random fields), and resampling methods, which draw samples from the data (with or without replacement).
We extend the voxelwise inference framework of Worsley et al so that it provides accurate control of the familywise error rate in neuroimaging. We drop the standard RFT assumptions of high smoothness and stationarity and develop a quick parametric framework that provides powerful and valid inference even when the underlying data is non-Gaussian. We validate this using a massive resting state analysis, involving brain imaging data from 7000 subjects from the UK Biobank.
We further use RFT techniques to derive an asymptotic distribution for the extent of a cluster above a threshold u in a non-stationary Gaussian random field as u → ∞. To do so we define the notion of horizontal-window (HW) conditioning and take advantage of recent advances (Cheng and Schwartzman (2015a)) on the HW-distribution of the height of a peak in a non-stationary Gaussian random field. Our results extend those of Nosko (1969) in which the asymptotic cluster size distribution is derived under the assumption of stationarity.
In order to infer upon random fields whose mean is non-zero we derive asymptotic confidence regions for the location of a peak of the true signal given multiple realizations of random fields. These results are valid under non-stationarity and are derived using the theory of extremum estimators. Under the assumption of stationarity we improve upon these asymptotic results using a Monte Carlo approach that provides confidence regions for peaks of the mean which have better coverage in the finite sample.
A second quantity of interest when considering fields whose mean is non-zero is the height of the true signal at the location of a peak in the observed random field. These peaks are typically subject to the winner's curse, which causes inflated effect sizes at peak locations (Vul et al., 2009). We develop a resampling based procedure that obtains low bias estimates of the true signal at the location of the peak. We validate this using task data from over 8000 subjects from the UK Biobank, setting aside 4000 subjects to compute a ground truth, and dividing the remaining subjects into small samples on which to test the results.
Terahertz spectroscopy of charge-carrier transport in metal-halide perovskites and narrow-gap semiconductors
Keywords: Metal-halide perovskites
Charge-carrier mobility
Terahertz spectroscopy
Narrow-gap semiconductors
Non-parabolicity

Organic-inorganic metal-halide perovskites and narrow-gap semiconductors have been studied extensively over the last few decades. Their peculiar optoelectronic properties make them ideal candidates for future photovoltaic applications and ultrafast electronic devices. Specifically, knowledge of the charge-carrier transport and electronic band structure is key to understanding the fundamental principles that underpin their optoelectronic behaviors, and hence to improve the performance of devices based on them.
Terahertz spectroscopy provides an ideal pathway to probe the optoelectronic properties of these semiconductor materials not only because of its non-contact nature which avoids damaging the sample and removes the complications arising from metallic contacts, but also due to its ability to measure the charge-carrier dynamics on an ultrafast (sub-picosecond) timescale. The versatility of terahertz spectroscopy allows a large phase space of optoelectronic parameters to be probed, such as charge-carrier mobility, carrier diffusion length, and photoconductivity.
A comparative study on the prototypical metal-halide perovskite MAPbI3 as both single crystals and polycrystalline films suggests that Frohlich scattering is the dominant mechanism limiting charge-carrier mobility, which arises from the electron-phonon coupling effect. Further investigations of the phonon modes in MAPbI3 show a phonon-hardening effect under photo-excitation, which is attributed to the increase in dielectric screening due to photo-induced electron doping. In addition, grain-boundary scattering is shown to further reduce mobility in polycrystalline films at cryogenic temperature. By taking into account the effect of photon reabsorption, the wide range of values for charge recombination parameters and charge diffusion lengths reported previously is reconciled. The benign grain boundaries observed in polycrystalline films offer great promise for future high-performance devices.
Time-resolved magneto-terahertz spectroscopy is utilized to investigate the cyclotron effective mass of a narrow-gap semiconductor InSb. A significant reduction of effective mass observed in the first 50ps after photoexcitation indicates a fast hot-electron cooling process. Meanwhile, the increase in effective mass with increasing carrier density agrees with the non-parabolic conduction band predicted by the ab initio calculations of the quasiparticle band structure based on the Kane model.
This thesis, which combines a thorough experimental study utilizing terahertz spectroscopy with a complementary theoretical study based on first-principles calculations, helps to uncover a complete picture of the fundamental optoelectronic properties of metal-halide perovskites and narrow-gap semiconductors.