VictorJuiz/thesis huggingface dataset:
Aristotle on the value of friends
Keywords: Friendship
Altruism
Aristotle
Egoism
Love

In this dissertation, I argue that Aristotle’s account of friendship is egoistic. Focusing on the Nicomachean Ethics, I begin with VIII.2. Here Aristotle claims that in all friendships, friends love only because of the lovable (φιλητόν), which divides into the useful, pleasant, and good. I argue that “because of (διὰ)” refers to at least the final cause and that “the lovable” refers to what appears to contribute one’s own happiness (εὐδαιμονία); therefore Aristotle claims that in all friendships, friends love only for the sake of their own happiness. This result may seem incompatible with some types of concern Aristotle principally attributes to his normative paradigm of complete friendship: wishing goods for the sake of the other and loving the other for himself. One might argue that these types of concern are altruistic, and so it cannot be the case that in all friendships, friends love only for the sake of their own happiness. I argue that these types of concern ultimately hinge on one’s own happiness. The object is the lovable (what appears to contribute to one’s own happiness), specifically the good instantiated by the other’s virtue; further, what a virtuous person takes as valuable about another’s virtue is how it facilitates her own virtuous activity, that is, her own happiness. From here I turn to Aristotle’s notion of “another self.” One popular interpretation of other selfhood defies the altruism/egoism divide. Here the essential feature of other selfhood is virtue, which allows for no prioritization among virtuous people; there is no prioritization of the other over oneself (as in altruism) nor of oneself over the other (as in egoism), since the relevant parties are equal in moral standing (they are virtuous). Assessing the instances of “another self” in the Nicomachean Ethics VIII.12, IX.4, and IX.9, I argue for an egoistic interpretation of other selfhood; the essential feature of other selfhood is involvement in one’s own actualization. That is, what makes other selves valuable is how they facilitate one’s own virtuous activity, one’s own happiness. Finally, I address the doctrine of self-love in the Nicomachean Ethics IX.8. Again, some interpreters derive non-prioritization from the text; Aristotle claims that all virtuous people identify with the understanding (νοῦς), so, the non-prioritization interpretation goes, there can be no prioritization among virtuous agents, as they are identical in the relevant way. I argue for an egoistic interpretation of IX.8; Aristotle endorses praiseworthy self-love, which involves maximizing the superlatively valuable fine (καλὸν) for oneself over others. Moreover, such self-prioritization occurs precisely by gratifying the understanding, that which was supposed to ground non-prioritization.
A conceptual framework for evaluating the security of household bank customers in the Kingdom of Saudi Arabia
Keywords: Bank system
HCI
Kingdom of Saudi Arabia
Security

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.
Representation of spatial transformations in deep neural networks
Keywords: Geometric Equivariance
Deep Learning
Convolutional Neural Networks
Image Representations
Equivalent Representations

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.
Inhibition of KDM4D and stabilisation of the PHF8 plant homeodomain’s transient structural states using antibodies
Keywords: Antibody-Inspired drug discovery
PHF8
Plant homeodomain
Protein dynamics
KDM4D

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.
IRF5 directs colonic inflammation and control of mononuclear phagocyte adaptation to the tissue environment
Keywords: Lamina Propria
Mononuclear Phagocyte
Macrophage
IRF5

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.
Synthesis studies towards daphlongeranine B
Keywords: daphlongeranine B
natural product synthesis

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.
Large-scale learning of discriminative image representations
Keywords: pattern recognition
computer vision
descriptor learning
image classification

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.
Producing animation: work, creativity, and aspirations in the Japanese animation industry
Keywords: anime
creativity
youth
animation
labour
Japan

This thesis examines shifting relations of labour, creativity, and political economy in the context of commercial animation production in contemporary Tokyo. Based on 12 months of fieldwork in the Japanese animation industry (2009-2010), the ethnography of the thesis is centred on young animation makers whose lives are fraught with persistent job insecurity and socio-economic precariousness. Contrary to celebratory narratives of the global success of anime, found in both Japanese media discourses and the literature of Japanese studies, these professional young workers live on the socio-economic fringe of mainstream Japanese society. Despite such instability, labour discourses in the animation industry are notable for their highly aspirational quality, which appears to be based on global liberal discourses of self-realisation through the pursuit of dreams in the labour market.
Commercial animation production in the Japanese industry entails a complex division of labour in which animation makers are, at the root, divided between managers and creators. This management-creative relation structures the primary context of commercial production. Thematically, the thesis engages mainly with three research literatures in anthropology: the anthropology of creativity, the anthropology of work, and Japanese ethnography. The analytical locus is built on the perspective of young entry-level managers, with whom I worked during fieldwork and who were on the lowest strata of the workplace hierarchy.
Through the detailed ethnography of animation production – one of Japan’s premier creative industries – the thesis examines creative processes of animation making within the terms of work and labour. In so doing, it engages critically with the social and economic structures of commercial animation production, and explores the lived dimension of labour on the production floor. Methodologically, this means combining the perspectives of political economy and phenomenology by situating the micro-processes of animation making firmly in the industry’s social and economic relations of production. I pay particular attention to the ways in which shifting social and cultural discourses of labour in Japan intersect with global liberal ideologies, such as creativity and self-realisation, in the context of commercial animation production. The major focus of the thesis is therefore to explicate what it is that makes these young animation makers, despite adverse conditions of labour, retain their aspirations to pursue the profession of animation making.
How can magnetoencephalography and magnetic resonance imaging improve our understanding of genetic susceptibility to Alzheimer's disease?
Keywords: Alzheimer's disease
biomarkers
neuroimaging

The Apolipoprotein E (APOE) ε4 allele is the best-established genetic risk factor for sporadic Alzheimer’s disease (AD) while the ε2 allele confers a reduced risk compared with the most common ε3 allele. Neuroimaging studies using magnetic resonance imaging (MRI) have shown that APOE genotype affects brain structure and function. The aims of the research presented in this DPhil thesis were twofold: 1) to investigate the effect of APOE genotype on brain function in healthy adults using magnetoencephalography (MEG), which is a direct measure of neuronal activity and 2) to explore interactions between the AD risk factors APOE ε4 allele, age and female gender and their effects on brain structure and function using resting-state functional MRI and diffusion tensor imaging. MEG revealed similar neuronal activity at rest for APOE ε2 and ε4 carriers, i.e. those with opposite AD risk, indicating a more general effect on the functional architecture of the brain that is not directly linked to AD risk. However, differences between APOE ε2 and ε4 carriers became apparent when reactivity to stimuli was explored using the excellent temporal resolution of MEG. APOE ε4 carriers showed faster sensory pro- cessing and APOE genotype effects were found for functional networks associated with attention. In the second part of this project, female APOE ε4 carriers showed overall significantly reduced functional connectivity between the hippocampus and precuneus and a significant age-related decrease in connectivity of these regions. Increased vulnerability of this connection might be one reason for increased AD risk and interventions targeting hippocampal connectivity might be especially effective in at-risk populations. The research presented in this DPhil thesis showed a complex pattern of APOE genotype effects on brain structure and function. While global APOE genotype effects on functional and structural connectivity do not follow patterns of AD risk, more specific measures of connectivity and task-related brain function could be of use in the development of preclinical markers for AD development.