VictorJuiz/thesis huggingface dataset:
Cool: a postwar aesthetic and mode of being

This thesis examines cool and coolness, both as word and as mode of affective deportment. It treats cool as an historical and literary phenomenon in late twentieth-century Anglo-American culture, during the period between 1945 and the present day. From relatively obscure origins among the subcultures of nineteen-forties African-American inner-city communities, this word, and its cognate forms of disposition towards personal and political agency, diffused through society in the course of these six decades to become, by the turn of the 21st century, near ubiquitous: highly visible, highly recognizable, and closely bound up with certain kinds of social cachet and approval.
Through literary-critical analysis I aim to challenge and to deepen the accounts provided by the established literature on the topic, including the work of Lewis MacAdams (2001), Dick Pountain and David Robins (2000), Alan Liu (2004), and Ted Gioia (2009). The narrative emerging from these and other sources offers a unidirectional account of cool's passage through the culture, from fringe to mainstream to wide acceptance – a story that belies the more complex and fractured evolution to which literature can grant us access. An account of cool based in literature and literary analysis reveals how social and political forces, as well as individual authorial interpretations and creative misreadings, established a much more uneven interpretive terrain.

Advisor: Small, H
Ratcliffe, S
Electronic and optoelectronic devices based on 2-dimensional crystals

Two dimensional (2D) crystals including graphene and monolayer transition metal dichalcogenides (TMD) such as molybdenum disulphide (MoS2) and tungsten disulphide (WS2) have attracted widespread attention for both basic scientific interest and potential in device applications. In this thesis, we investigate the electronic and optoelectronic devices based on 2D crystals grown using chemical vapour deposition (CVD).
In our study on graphene transistors, we show that surface charge transfer between graphene and vertically stacked WS2 causes local modulation to doping levels of graphene, and can be further stimulated by photo-irradiation. We demonstrate the formation of an isotype junction in graphene, where two lateral sections of a graphene ribbon contain different dopant concentrations.
Ultrathin lateral photodetectors of 2 nm thickness are made by incorporating graphene as electrodes and WS2 as active semiconductor. We show that graphene is different to conventional metal electrodes due to the finite density of states from the Dirac cones of the valence and conduction bands, which enables modulation of photoresponsivity through electrostatic gating and light input control.
We demonstrate photodetectors with laterally spaced graphene electrodes and vertically stacked WS2/MoS2 as active light-adsorbing component, achieving photoresponsivity as high as 2340 A W-1. We observe in hybrid WS2/MoS2 photodetector massive enhancements in photogain compared to homobilayer and monolayer devices of MoS2 or WS2 due to efficient charge transfer between WS2 and MoS2 and long carrier lifetimes. Our results show that high performance photodetectors using all 2D materials can be realized using graphene electrodes and TMD heterostructures with type II band alignment.

Advisor: Warner, J
A conceptual framework for evaluating the security of household bank customers in the Kingdom of Saudi Arabia

This work evaluates the security of household banking in the Kingdom of Saudi Arabia by investigating the fit between the Saudi banking system and the practices of its household customers. A framework is developed to evaluate both technical and human factors, to help build a secure and effective banking system. Three building blocks contribute to the framework: the household context of use of bank systems in Saudi Arabia, banking technologies and processes, and Saudi banking policies and infrastructure. A qualitative study of the household context of use employs grounded theory to identify the banking practices of household customers and the factors influencing them. Data were collected in two phases: from 47 household customers, using telephone interviews, focus groups and the diary/interview method, then from 23 Saudi bankers interviewed by telephone. Each method was adjusted to accommodate the unique settings of the research context, such as being a Saudi female researcher collecting data from participants of both genders, the sensitivity of the topic and the intimate nature of the household context. This research contributes as a practical guide by describing each method used in detail, serving to inform and guide future work in a similar context. The second block in the framework is the security analysis of the banking technologies and processes used by participating household customers: online banking, telephone banking and bank cards. The two analytical approaches are STRIDE, which aims to identify objectives and vulnerabilities in bank technologies regarding specific security threats, and DREAD, which is used to quantify the risk from such threats. The third block is the Saudi banking policies and infrastructure that guide the use of the technologies issued by the Saudi Arabian Monetary Agency (SAMA) the leading authority to the Saudi banking sector, involving a deep analysis of the official policies and regulations related to the household context of use of bank technologies. The framework is applied to evaluate the security of household banking in two case studies: the use of new technologies such as mobile banking and the use of fingerprints as a new security mechanism in the authentication process of bank cards.

Keywords: Bank system
HCI
Kingdom of Saudi Arabia
Security
Advisor: Flechais, I
Jirotka, M
Representation of spatial transformations in deep neural networks

This thesis addresses the problem of investigating the properties and abilities of a variety of computer vision representations with respect to spatial geometric transformations. Our approach is to employ machine learning methods for finding the behaviour of existing image representations empirically and to apply deep learning to new computer vision tasks where the underlying spatial information is of importance. The results help to further the understanding of modern computer vision representations, such as convolutional neural networks (CNNs) in image classification and object detection and to enable their application to new domains such as local feature detection.
Because our theoretical understanding of CNNs remains limited, we investigate two key mathematical properties of representations: equivariance (how transformations of the input image are encoded) and equivalence (how two representations, for example two different parameterizations, layers or architectures share the same visual information). A number of methods to establish these properties empirically are proposed. These methods reveal interesting aspects of their structure, including clarifying at which layers in a CNN geometric invariances are achieved and how various CNN architectures differ. We identify several predictors of geometric and architectural compatibility. Direct applications to structured-output regression are demonstrated as well.
Local covariant feature detection has been difficult to approach with machine learning techniques. We propose the first fully general formulation for learning local covariant feature detectors which casts detection as a regression problem, enabling the use of powerful regressors such as deep neural networks. The derived covariance constraint can be used to automatically learn which visual structures provide stable anchors for local feature detection. We support these ideas theoretically, and show that existing detectors can be derived in this framework. Additionally, in cooperation with Imperial College London, we introduce a novel large-scale dataset for evaluation of local detectors and descriptors. It is suitable for training and testing modern local features, together with strictly defined evaluation protocols for descriptors in several tasks such as matching, retrieval and verification.
The importance of pixel-wise image geometry for object detection is unknown as the best results used to be obtained with combination of CNNs with cues from image segmentation. We propose a detector which uses constant region proposals and, while it approximates objects poorly, we show that a bounding box regressor using intermediate convolutional features can recover sufficiently accurate bounding boxes, demonstrating that the required geometric information is contained in the CNN itself. Combined with other improvements, we obtain an excellent and fast detector that processes an image only with the CNN.

Keywords: Geometric Equivariance
Deep Learning
Convolutional Neural Networks
Image Representations
Equivalent Representations
Advisor: Vedaldi, A
Inhibition of KDM4D and stabilisation of the PHF8 plant homeodomain’s transient structural states using antibodies

Though antibodies as therapeutics are limited to extracellular targets, their repertoire of molecular interactions has particular relevance to the many intracellular cellular proteins for which small molecule screening has reached impasse. For such proteins there is little recourse to theory, since molecular recognition is, in practical terms, still not well understood. Here I apply antibody discovery to the lysine demthylases KDM4D and PHF8, two proteins difficult to inhibit selectively due to the similarity of their binding pockets to those of the larger family. With a selective, picomolar affinity antibody, dependent on residues distal to the KDM4D active site, I present what is likely the first example of allosteric inhibition of a KDM4 lysine demethylase, demonstrating that there is opportunity outside active sites oversubscribed with pan inhibitors. Antibody discovery for PHF8, however, was plagued by a familiar problem: antibodies that bound when their antigen was immobilised directly to a surface, but barely bound at all when it was free in solution. The common explanation is that the partial denaturation that accompanies immobilisation reveals epitopes unavailable in solution, but examining the problem in detail for the Plant Homeodomain of PHF8 revealed a connection to its rarely sampled conformations. The prominence these antibodies in the immune responses to PHF8, and to some extent KDM4D, motivates two hypotheses on their origin: either the states are very immunogenic or there is a connection between states of irreversible damage and those sampled reversibly, but rarely, by a protein in solution.

Keywords: Antibody-Inspired drug discovery
PHF8
Plant homeodomain
Protein dynamics
KDM4D
Advisor: Schofield, C
Brennan, P
Lawson, A
Visual-inertial odometry, mapping and re-localization through learning

Precise pose information is a fundamental prerequisite for numerous applications in robotics, AI and mobile computing. Monocular cameras are the ideal sensor for this purpose - they are cheap, lightweight and ubiquitous. As such, monocular visual localization is widely regarded as a cornerstone requirement of machine perception. However, a large gap still exists between the performance that these applications require and that which is achievable through existing monocular perception algorithms.
In this thesis we directly tackle the issue of robust egocentric visual localization and mapping through a data-centric approach. As a first major contribution we propose novel learnt models for visual odometry which form the basis of the ego-motion estimates used in later chapters. The proposed approaches are less fragile and much more robust than existing approaches. We present experimental evidence that these approaches can not only approach the accuracy of standard methods but in many cases also show major improvements in computational and memory efficiency.
To cope with the drift inherent to the odometry methods, we then introduce a novel learnt spatio-temporal model for performing global relocalization updates. The proposed approach allows one to efficiently infer the global location of an image stream at the fraction of the time of traditional feature-based approaches with minimal loss in localization accuracy.
Finally, we present a novel SLAM system integrating our learnt priors for creating 3D maps from monocular image sequences. The approach is designed to harness multiple input sources, including prior depth and ego-motion estimates and incorporates both loop-closure and relocalization updates. The approach, based on the well-established standard visual-inertial structure-from-motion process, allows us to perform accurate posterior inference of camera poses and scene structure to significantly boost the reconstruction robustness and fidelity.
Through our qualitative and quantitative experimentation on a wide range of datasets, we conclude that the proposed methods can bring accurate visual localization to a wide class of consumer devices and robotic platforms.

Advisor: Markham, A
Trigoni, A
IRF5 directs colonic inflammation and control of mononuclear phagocyte adaptation to the tissue environment

Macrophages are leukocytes of the innate immune system that display great phenotypic plasticity to mediate diverse functions. The ontogeny of tissue resident macrophages has been debated in recent decades. It is now recognised that tissue macrophages can be replenished from embryonically-derived precursors, and/or monocyte intermediates in a tissue specific manner.
Interferon Regulatory Factor 5 (IRF5) is a transcription factor that promotes a pro-inflammatory phenotype in macrophages in vitro and in vivo. Indeed, IRF5 contributes to the pathogenesis of experimental inflammatory arthritis, lupus, and obesity via recruitment and activation of effector cells.
Research described here as part of this thesis, involves the profiling of the intestinal Mononuclear Phagocyte system to investigate the role of IRF5 in the development of monocyte-derived macrophages in the Colonic Lamina Propria (cLP) which are exclusively replenished by adult Ly6Chi monocytes. Using Mixed Bone Marrow Chimaeras (MBMCs) we showed that in shared environment Wild-Type (WT) cLP macrophages dominated IRF5-deficient (Irf5-/-) cLP macrophages in both steady state and inflammation. The development of in vitro bone marrow derived macrophages, and the reconstitution of the haematopoietic compartment in bone marrow of MBMCs were not significantly affected by IRF5 deficiency. IRF5 promoted the accumulation of WT monocytes in the cLP of MBMCs in a process possibly dependent on the CCL2/CCR2 axis. Furthermore, IRF5 expression committed Ly6Chi monocytes to a pro-inflammatory macrophage fate in the inflamed cLP, characterised by protein expression of the cytokines IL1β, and TNFα, and the expression of Ccl4 and Ccl8 transcripts, whilst loss of IRF5 favoured accumulation of CD11b+ IRF4-dependent Dendritic Cells. Of significance, IRF5 expression might have prevented further differentiation of inflammatory macrophages into tissue-resident macrophages, thus supporting an inflammatory state.
Irf5-/- mice were protected from Helicobacter hepaticus + αIL10R colitis. Intriguingly, protection from colitis may also be conferred by the presence of Irf5-/- haematopoietic cells, evidenced by WT:Irf5-/- MBMCs . Modulation of IRF5 activity may therefore be a viable therapeutic strategy. RNA sequencing identified that C1q, Cd81, and Ccl8 were upregulated in WT macrophages from MBMC, which may prove therapeutic targets.

Keywords: Lamina Propria
Mononuclear Phagocyte
Macrophage
IRF5
Advisor: Udalova, I
Powrie, F
A study of prosody in modern English translations of Molière’s plays

My thesis discusses prosody, in the narrow sense of verse, meter and rhyme, used by modern English translators to revitalise Molière’s comedies in order to make them successful on the modern English-speaking stage. The thesis first discusses the problems and paradoxes of verse translations, and questions the aesthetic equivalence between the alexandrine and blank verse, rhyming couplets and the English dodecasyllable. It points to different cultural associations, and the different reception they have enjoyed on stage. My thesis moves away from the usual criticisms of English verse forms and considers instances where the English and French verse systems can achieve similar dramatic effects and can be more efficient than prose in contributing to the workings of comedy. The reason behind the choice of self-conscious rhyming couplets is to enhance comic effects. Thanks to the use of witty rhymes, translators transform Molière’s situational humour into verbal humour, particularly appealing to English audiences. This style serves both Molière, by introducing his comedies to the commercial stage, and also the translators, who gain more visibility. The thesis then analyses a number of strategies used to advertise the presence of rhymes, by way of a methodical computer-aided quantitative analysis and more traditional stylistic analyses, pointing to striking differences in the poetics of rhyme between Molière and his modern translators. The link between verse form and aesthetics is reasserted in the last chapter of the thesis through the analysis of how particular prosodic systems can have a significant impact on the aesthetics and the dramaturgy of the play. Five case studies of non-standard prosodic systems are examined as well as the case of verse adaptations from prose plays.

Advisor: Hawcroft, M
Synthesis studies towards daphlongeranine B

This thesis describes the development of a synthetic route towards daphlongeranine B, an alkaloid isolated from the fruits of Daphniphyllum longeracemosum, by utilising an intramolecular Michael addition to form its unique tricyclic core.
Chapter 1 gives a general introduction to the family of Daphniphyllum alkaloids together with some recent examples, from the literature, illustrating some synthetic attempts towards structurally similar alkaloids. This chapter also features our retrosynthetic analysis of daphlongeranine B.
Chapter 2 details the synthesis of the model spirocyclic enone 72 which was the vital building block needed to investigate the key intramolecular Michael addition. This key reaction was then successfully validated and access to the unique tricyclic core 64 of daphlongeranine B was made possible.
Chapter 3 expands the scope of the key intramolecular Michael addition step. This chapter first describes a synthetic route to the β-substituted spirocyclic enone 112 and subsequently validates the key intramolecular Michael addition step to give the tricyclic core 138 of daphlongeranine B.
Chapter 4 details a synthetic route towards the spirocyclic fragment 141 by utilising a Baker's yeast reduction and a tandem addition/cyclisation reaction.

Keywords: daphlongeranine B
natural product synthesis
Advisor: Dixon, DJ
Large-scale learning of discriminative image representations

This thesis addresses the problem of designing discriminative image representations for a variety of computer vision tasks. Our approach is to employ large-scale machine learning to obtain novel representations and improve the existing ones. This allows us to propose descriptors for a variety of applications, such as local feature matching, image retrieval, image classification, and face verification. Our image and region descriptors are discriminative, compact, and achieve state-of-the-art results on challenging benchmarks.
Local region descriptors play an important role in image matching and retrieval applications. We train the descriptors using a convex learning framework, which learns the configuration of spatial pooling regions, as well as a discriminative linear projection onto a lower-dimensional subspace. The convexity of the corresponding optimisation problems is achieved by using convex, sparsity-inducing regularisers: the L1 norm and the nuclear (trace) norm. We then extend the descriptor learning framework to the setting, where learning is performed from large image collections, for which the ground-truth feature matches are not available. To tackle this problem, we use the latent variables formulation, which allows us to avoid prefixing correct and incorrect matches based on heuristics.
Image recognition systems strongly rely on discriminative image representations to achieve high accuracy. We propose several improvements for the Fisher vector and VLAD image descriptors, showing that better image classification performance can be achieved by using appropriate normalisation and local feature transformation. We then turn to the face image domain, where image descriptors, based on handcrafted facial landmarks, are currently widely employed. Our approach is different: we densely compute local features over face images, and then encode them using the Fisher vector. The latter is then projected onto a learnt low-dimensional subspace, yielding a compact and discriminative face image representation. We also introduce a deep image representation, termed the Fisher network, which can be seen as a hybrid between shallow representations (which it generalises) and deep neural networks. The Fisher network is based on stacking Fisher encodings, which is feasible due to the supervised dimensionality reduction, injected between encodings.
Finally, we address the problem of fast medical image search, where we are interested in designing a system, which can be instantly queried by an arbitrary Region of Interest (ROI). To facilitate that, we present a medical image repository representation, based on the pre-computed non-rigid transformations between selected images (exemplars) and all other images. This allows for a fast retrieval of the query ROI, since only a fixed number of registrations to the exemplars should be computed to establish the ROI correspondences in all repository images.

Keywords: pattern recognition
computer vision
descriptor learning
image classification
Advisor: Zisserman, A
Criminisi, A