VictorJuiz/thesis huggingface dataset:
Development of the ionisation signal analysis and the thermometry system for the LUX-ZEPLIN direct dark matter detection experiment

There is substantiated astrophysical evidence for a substance known as Dark Matter within the universe, at both galactic and cosmological scales. Despite this evidence, the true nature of Dark Matter remains one of the most profound questions in modern physics. One viable candidate is the Weakly Interacting Massive Particle (WIMP), which could nicely explain the observed measurements. The LUX-ZEPLIN (LZ) detector is searching for WIMP interactions with xenon nuclei, through both scintillation and ionisation signals. It comprises a time-projection chamber filled with 7 tonnes of liquid xenon, situated at the Sanford Underground Research Facility in South Dakota.
The experiment operates at low temperatures (about 175
Kelvin). During the cool-down of the experiment from room temperature and to ensure stability during running, the temperature needs to be monitored closely to ensure the detector is not damaged and that high-quality data can be collected. The unique implementation of the thermometry system within LZ necessitates bespoke solutions to overcome challenges related to the radiopurity and cleanliness. This thesis examines how these problems are resolved through the careful material procurement and development of a successful calibration procedure.
The ionisation signal plays an important role in the reconstruction of event energies and in interaction type discrimination. However, detector-specific phenomena cause an inherent position dependence. The origins of this position dependence are examined, along with the development of a method to correct signals using dispersed calibration sources. A consideration of the detector contraction is made along with a calibration of the level sensors. Together the position dependence in the ionisation signal, along with these level sensors enables the detector to be precisely levelled.
This work concludes by examining techniques to improve the sensitivity of the experiment below nominal capabilities. Specifically, this includes an examination of realistic pulse shape characteristics to improve the energy threshold of the experiment. Crucially, this provides a series of techniques which can be utilised within LZ analyses.

Keywords: experiment
dark matter
LUX ZEPLIN
Advisor: Kraus, H
Learning based tracking and recognition with mmwave radar for smart environments

Ubiquitous sensing is a key enabler for smart environment applications, and mmWave radar is an ideal candidate due to its high usability, low cost and privacy preserving features. Tracking and recognition are two fundamental functions of mmWave radar sensing, but with conventional methods, their performance is often affected by many factors such as noise, limited resolution, and multi-path effects. In this thesis, we explore the use of novel deep learning techniques in radar signal processing chain for more accurate and robust tracking and recognition, to better support smart environment applications.
First we explore the problem of using radar point cloud for human tracking and identification. In the proposed system named 'mID', we first associate clusters between frames to track human targets, followed by using deep neural networks to take a sequence of voxelized point clusters as input for human identification, as well as using the latent features for intruder detection. In the following chapter we propose 'mBeats', a system to monitor user’s heart rate using deep learning with a robot mounted mmWave radar. 'mBeats' shows superior performance compared to conventional methods for heart rate prediction, and can also estimate the prediction uncertainty, which is very important for health-related applications. Next, we introduce using a deep learning based method for 3D tracking of an unmodified drone. The proposed technique can achieve a tracking accuracy 3 times better than conventional methods, with improved robustness and computational efficiency. In the last work, we use an end-to-end neural network, replacing the conventional Discrete Fourier Transform (DFT) based pre-processing with 'CubeLearn', which is our proposed learnable pre-processing module, for human gesture and activity recognition. 'CubeLearn' can adaptively extract the most informative features from raw data which best fit the target task, and shows consistently better performance than the conventional DFT pre-processing module with different models and on different tasks.
Through the research introduced in this thesis, we demonstrate that applying learning based techniques for mmWave radar data processing can significantly enhance tracking and recognition performance, and benefit downstream smart environment applications.

Advisor: Markham, A
Trigoni, A
The design of Montaigne's Essais

Dessein is, in the sixteenth century, a polysemic term whose meanings cover the entire spectrum of the creative process: from an originating idea, project or intention, to a material plan or visual design. This thesis reads Montaigne’s Essais through the lens of this spectrum of meanings, showing that attending to the uses of, and approaches to, design in the Essais reveals the ways in which Montaigne conceived of his writing project as the product of a mode of experimentation and mediation between thought and world. One of the contentions of this thesis is that much like the practice of drawing that was central to the Renaissance artistic process, Montaigne’s writing retains a marked indeterminacy, possessing an unresolved, ‘sketch-like’ quality. Like Renaissance artists, who in the century preceding the publication of the Essais had become increasingly interested in the process of artistic creation (a change reflected in the growing importance and value of drawing), Montaigne is also fascinated by the process of translating an internal notion or idea into its public manifestation. This fascination animates the Essais, and influences Montaigne’s position on questions of authorial intention, posthumous reputation, and his relationship to both real and imagined readers, including Montaigne himself. Making use of a hybrid methodology that brings together word history, art theory, image analysis, close reading and design theory, this thesis uncovers what Montaigne has to say about design; how the notion of design functions in the Essais; and how Montaigne’s relationship to designed objects sheds light on what we might call a ‘Montaignian aesthetics’. Understanding Montaigne’s uses of, and attitudes to, design ultimately allows us to reimagine the kind of project constituted by the Essais.

Keywords: design
Montaigne
disegno
dessein
Chemogenetic silencing of sensory neuron populations to uncover their role in touch and pain 

The ability of humans to experience touch and pain sensation is critical for our survival. The experience of pain is typically a protective mechanism, and ensures we avoid dangerous environmental cues. However, following incidences of genetic mutations, injury or disease of the somatosensory nervous system, protective pain can become pathological. Indeed, neuropathic pain affects seven to ten percent of the general population, and our current treatment strategies show poor efficacy and tolerability.
Currently, mechanisms surrounding neuropathic pain involve both peripheral and central sensitisation. However, there is an incomplete understanding of the functional roles of different sensory neuron populations, the sensory modalities they encode, and how they change under neuropathic conditions. Therefore, it is unclear which primary afferent subpopulations contribute to the induction and maintenance of neuropathic pain and which should be therapeutically targeted. This is largely due to the fact that peripheral sensory neurons within dorsal root ganglia (DRG) are highly heterogeneous and are thought to contain up to 17 different subpopulations.
A key aim of this work is to ascribe roles to primary afferent subpopulations in naïve rodents and those that have undergone traumatic nerve injury. Therefore, I have developed a novel chemogenetic toolbox to facilitate selective, reversible and repeatable neuronal silencing of defined sensory neuron populations. I have employed AAV9-GluClv2.0 to broadly silence all DRG neurons and observed functional recovery from neuropathic pain in vivo. Subsequently, I used AAV9-GluCl.CreON to target two populations of sensory neurons: the Nav1.8 positive (largely nociceptive) population, and the tyrosine hydroxylase positive C-low threshold mechanoreceptor (C-LTMR) population. Silencing each of these populations resulted in population-specific sensory phenotypes, with subtle or no changes in neuropathic mechanical hypersensitivity. Finally, using a molecule to man approach, the role of the voltage-gated sodium channel Nav1.7 was investigated in C-LTMRs and pleasant touch sensation. In humans and mice either genetic or pharmacological disruption of Nav1.7 resulted in deficits in C-LTMR function. This work challenges the current dogma that therapeutically targeting Nav1.7 will only effect the nociceptive system. Together, this work illustrates the need to better understand the roles of different primary afferent subpopulations and provides chemogenetic tools to achieve this. Ultimately, this work will facilitate the development of therapeutics which are better targeted towards the appropriate sensory neuron populations.

Keywords: Chemogenetics
Touch
Pain
Neuroscience
Nav1.7
Advisor: Bennett, D
The active brain: relationships between physical activity, fitness, cognitive performance and brain structure in young adolescents

Governmental, scientific and public health communities have become increasingly interested in the potential of physical activity to promote neurocognitive health across the lifespan. While research in adults has advanced rapidly in the past 20 years, much less is known about the effects of a physically active lifestyle on cognitive and brain health in young people, especially adolescents. Using magnetic resonance imaging (MRI) and cognitive data from the Fit to Study cluster randomised controlled trial, this thesis therefore examined relationships between neurocognitive health and markers of physical activity and cardiorespiratory fitness in this age group.
A cross-sectional study involving baseline Fit to Study data showed that higher levels of physical activity and fitness are associated with superior performance on domain-specific cognitive assessments (working memory, cognitive flexibility, inhibitory control, visual memory and processing speed) in a large sample of young British adolescents (n = 16,017), but that relationships are small and explain less than 10% of the variance in cognitive outcomes. It also showed that relationships may be stronger in females compared to males and that a physically active lifestyle is linked with more favourable cognitive outcomes. In a sub-sample of adolescents (n = 61), however, no significant relationships between markers of a physically active lifestyle and volumes or multi-modal MRI markers of tissue (micro)structure in the anterior hippocampus were observed.
An intervention study explored whether increasing the amount of vigorous intensity physical activity during school physical education (PE) lessons for one academic year could improve cardiorespiratory fitness and induce neurocognitive changes in the Fit to Study sample (n = 93 schools). The intervention, designed with PE teachers to be scalable and feasible, did not significantly improve fitness or cognitive performance. In addition, no robust intervention-related changes in anterior hippocampal MRI metrics were observed. These findings should, however, be interpreted with caution given low implementation fidelity and high drop-out.
This thesis supports the hypothesis that higher levels of physical activity and cardiorespiratory fitness are associated with superior cognitive performance in young adolescents. However, no physical-activity induced effects on neurocognitive health were observed. Further investigation of whether and how physical activity may promote positive brain and cognitive changes in adolescents is needed.

Keywords: Cognition
Adolescence
Brain
Physical activity
Advisor: Johansen-Berg, H
Sexton, C
Geometry and representation learning in deep generative models

Deep generative models have de facto emerged as state of the art when it comes to density estimation and sampling high-dimensional and multi-modal data. They combine the abstract yet practical mathematical description of probabilistic modelling with the flexibility and scalability brought by neural networks. They have been successfully applied to a wide spectrum of problems ranging from computer vision and natural language, to the realms of physical sciences. Probabilistic modelling, and in particular Bayesian statistics, have long enabled scientists and practitioners to include prior knowledge that they may have about the data into models. Building well-specified models is beneficial as it leads to improved generalisation capacity, data efficiency and interpretability of the model. In contrast, principled methods to encode such inductive biases in deep generative models are still under development.
This thesis presents three pieces of work aimed at addressing this problem. First, we propose a principled approach to introduce inductive biases in variational auto-encoders. Taking a Bayesian perspective, we show that encoding a desired structure into the prior distribution, and applying a proper regularisation, can lead to the desired decomposition in the learnt encodings. We demonstrate this approach on a variety of computer vision datasets and successfully learn representations with sparsity, clustering, and even intricate hierarchical dependency relationships. Next, we introduce an extension of variational auto-encoders to model data with underlying hierarchical structure. As hyperbolic spaces are perfectly suited to embed tree-like data, in contrast to Euclidean geometry, we endow the latent space with hyperbolic geometry. We do so by deriving the necessary methods to work with two main Gaussian generalisations and geometry-aware architectures for the encoder and decoder networks. Finally, we leverage the formalism of Riemannian geometry to define flexible distributions for data that are assumed to live on a given manifold. We do so by extending continuous normalising flows and parametrising manifold-valued diffeomorphisms as solutions of ordinary differential equations.

Keywords: machine learning
deep learning
variational inference
manifolds
geometry
probabilistic modelling
Advisor: Teh, YW
Developing a risk score for early detection of bowel cancer using routine blood test data

Introduction
Bowel cancer is the fourth most common type of cancer in the UK. Approximately 55% of patients are diagnosed with a late-stage tumour (Stage 3 and 4), where likelihood of survival is reduced: five-year survival is 93% at Stage 1 vs. 10% at stage 4. Early detection is crucial. The full blood count (FBC) blood test is commonly performed in primary care. It may help but currently there is limited evidence on how to utilise this test for early detection.
Methods
I performed four research objectives. I reviewed relevant research studies in the literature to learn about the relevance of the FBC and eliminate some key uncertainties in existing approaches for early detection. I performed a data quality and validation assessment of electronic health record (EHR) patient data. I then performed longitudinal analyses to identify patterns in the FBC over time, which could help identify presence of bowel cancer. I subsequently incorporated my findings into a dynamic prediction model that could facilitate earlier detection and improve survival rates.
Results
My systematic review identified many relevant FBC parameters for bowel cancer detection, such as haemoglobin, mean corpuscular volume, and platelets, and that all existing FBC-based prediction models are flawed. Using primary care data from the Clinical Practice Research Datalink, I identified relevant trajectories in many FBC parameters in patients with bowel cancer, not observed in patients without. Relevant differences in FBC levels occurred for years before diagnosis, suggesting the FBC could facilitate earlier detection. I incorporated the trajectories in haemoglobin, mean corpuscular volume, and platelets into multivariate joint models of longitudinal and time-to-event data to identify two-year risk of diagnosis. My joint models perform well based on my initial testing.
Discussion
My joint models use routinely available data and would not require additional work for healthcare workers or patients. These models perform well based on my testing, but further testing is required before use in general practice. My models could facilitate earlier detection and improve survival rates.

Keywords: joint modelling
full blood count
prediction model
primary care
bowel cancer
early detection
Advisor: Birks, J
Holt, T
Patnick, J
Watkinson, P
Becoming a farmer in contemporary Japan

This thesis explores the rise of pro-rural migration and the possibilities of agricultural modes of livelihood in Japan. The question driving this research was why, despite the growing trend of new entry in agriculture since the mid-2010s, half of new farmers from a non-farming background fail to secure sufficient income to live on from their activity five years into farm management. Based on twelve months fieldwork in eastern Nagano prefecture and Kyoto city, and on participant observation conducted for eight months in an agricultural cooperative, this study investigates the four key hurdles faced by agricultural new entrants in the establishment of a farming business: the acquisition of land and housing, farming know-how, capital, and market outlets. New farmers look with fresh eyes at agricultural issues, and their experiences provide a vantage point over the institutions shaping rural and agricultural life. This thesis documents the mounting problem of land and house abandonment in regional Japan, showing how norms and institutions shape the bundle of rights surrounding property and how this reflects on rural communities’ revitalisation. This study also challenges the widespread view in the English literature explaining Japanese agriculture’s decline as being largely due to institutional inertia and entrenched vested interests. Ethnographic material documents the larger constellation of forces driving the reproduction of Japan’s agrarian structure, while also providing a more comprehensive account of agricultural cooperatives and JA group. To avoid reinforcing Japan’s exceptionalism, this study contextualises Japanese agriculture within a broader agri-food framework. Agricultural policy, farming practices, and fresh food distribution are compared with EU countries and Italy in particular, shedding new light on processes of agrarian change and different configurations of food provisioning in capitalist economies. The analysis of fresh food production and distribution, with special attention given to issues of asymmetric market power and price formation in food supply chains, contributes to debates in the anthropology of markets and agrarian political economy.

Keywords: Pro-rural migration
Japanese agriculture
Regional Japan
Advisor: Goodman, R
Whittaker, H
Integrating art, landscape, and archaeology: Investigations of prehistoric wall paintings at Çatalhöyük

The wall paintings from Çatalhöyük, Turkey, form one of the largest sources of complex art on domestic buildings from the Neolithic period and provide a complete context within which to examine the relationships between art and artists’ materials. To date, materials and paintings have been studied separately: this thesis integrates these aspects for the first time. These links become apparent through a comprehensive analysis of the seventy-seven decorated walls and fifty-five samples of mineral colourants over the occupation of the site from 7,400-6,000 BC.
A typological and spatial analysis of wall paintings is conducted using a new database of painted images. These data are then synthesised with the use of colourants for an examination of the connection between the art and its materials. To see how colour use changes over time, the occurrences of pigments are tracked in multiple contexts including paintings and burials. The life cycle of pigments is examined starting with the broader landscape in which minerals are sourced.
The research design includes X-ray fluorescence, X-ray diffraction, and Fourier transform infrared spectroscopy. Results identify standard information about primary minerals of pigments, but go further to characterise secondary minerals, to better understand sourcing and processing. These more detailed findings identify distinctions within pigment groups, specifically the iron oxide and copper pigments, and increase our knowledge of connections between the pigments and the landscape.
This thesis explores innovation in artists’ materials and art in prehistory, and charts developments as they occur in images and materials over time, giving rise to a reinterpretation of the paintings. Evidence supports a more robust re-reading of several paintings from the site, especially the ‘Volcano and City Plan’ mural. This illustrates the knowledgeable engagement of peoples at Çatalhöyük with the exploration of their landscape, architecture, materials, and experience, culminating in the creation of extraordinary paintings.

Keywords: James Mellaart
Çatalhöyük excavation
artist's pigments
Prehistoric murals
murals
Prehistoric art
scientific analysis of artist's materials
Çatalhöyük
scientific analysis of paintings
Neolithic Art
artist's materials
wall art
Advisor: Pollard, MA
Gosden, C