VictorJuiz/thesis huggingface dataset:
A data driven approach towards understanding deficits in olfactory and motor function and identifying applications of smartphone motor testing in prodromal and manifest Parkinson’s
Keywords: Outcome measures
Machine learning
REM sleep behaviour disorder
Smartphone
Parkinson's disease

Background:
Significant disease heterogeneity in Parkinson’s means that identifying ways of applying observations made at a group level, to specific individuals, remains challenging. Standardised clinical assessments, critical for capturing the scientific evidence that informs clinical practice, are rarely used outside of research due to the time required for their administration.
Methods:
Since 2010, the Oxford Discovery longitudinal cohort study has recruited over 1600 individuals with Parkinson’s, Rapid Eye Movement Sleep Behaviour disorder (RBD) (a condition associated with a high risk of developing Parkinson’s or another neurodegenerative disorder) and controls. Detailed clinical assessments, including clinical tests of olfactory and motor function and smartphone motor testing, were performed at 18-month intervals. Machine learning algorithms (chiefly random forests) were used to predict clinical scores and outcomes using clinical data or smartphone motor testing data alone.
Results:
The use of three Sniffin’ sticks (Anise, Liquorice, Banana) allowed the identification of individuals with a poor sense of smell (ordinarily requiring assessment with all 16 sticks) with excellent accuracy (area under the curve (AUCs) values of over 0.90 in development and validation cohorts). Summation of scores from 6 tasks of the Movement Disorders Society Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) motor examination: repetitive hand movements, finger tapping, pronation/supination, leg movements, constancy of rest tremor and bradykinesia, yielded an abbreviated score whose correlation coefficient with the total 18 task score was high at 0.91. Three approaches to using smartphone motor testing to quantify motor impairment were explored. Smartphone motor testing was also used to predict dopaminergic deficit in RBD with AUCs>0.73 and the new future onset of motor, cognitive and functional disability in Parkinson’s with AUCs>0.75.
Conclusion:
A data-driven approach can be used to promote understanding of existing clinical tests in prodromal and manifest Parkinson’s, and to drive their refinement. Smartphone motor testing can be used to quantify motor impairment and provide individual estimates of risk; however, both are likely to benefit from further evaluation within the context of clinical trials and routine clinical practice.
Computational strategies to overcome antibiotic resistance: Exploring novel tetramic acids as Undecaprenyl Pyrophosphate Synthase (UPPS) inhibitors
Keywords: antibiotics
drug design
undecaprenyl pyrophosphate synthase
tetramic acid
antimicrobial resistance

The rise of multiple antibiotic resistance is a critical obstacle to the design and development of novel antimicrobial agents. The historical paradigm of monotherapies (i.e., drugs acting on single biological targets) has been replaced in recent years by multi-targeting therapies (e.g., antibiotic combinations and hybrids) which act on two or more targets. The efficacy of this multi-targeting approach is increased if the targets belong to divergent metabolic pathways, thereby minimising the likelihood of simultaneous dual-target resistance. Thus, the development of novel antibiotic scaffolds capable of inhibiting multiple, diverse enzymes is critical to addressing antibiotic resistance. Computational techniques, molecular dynamics (MD) simulations in particular, play a fundamental role in structurally elucidating protein-drug binding interactions and in predicting their binding affinities, tasks which are essential to the antibiotic drug design process. In this thesis, the mode of inhibition of selected 3-carboxamide and 3-acyl tetramic acids (TAs), which exhibit dual inhibition of bacterial undecaprenyl pyrophosphate synthase (UPPS) and RNA polymerase (RNAP), is investigated within UPPS using computational methods. UPPS is an essential bacterial enzyme involved in peptidoglycan synthesis of the bacterial cell wall and is hence a prime antibiotic drug target. In addition, the TA inhibitors are not only novel but contain a single chemical scaffold which is multi-targeting, both of which represent desirable attributes of the latest antibiotic drugs. In the absence of an X-ray crystal structure of the UPPS-TA complex, this work elucidates the most probable binding site of the TAs in UPPS, employing rigid docking and MD-based free energy calculations. The area of antibiotic drug design may benefit greatly from such computational predictions since the structural basis for inhibition of UPPS by the TAs remains poorly understood.
Regulating administrative data sharing for research purposes in the United Kingdom
Keywords: data sharing regulation
administrative data
regulation
data sharing
regulatory space

As the provision of more and more public services becomes digitalised, administrative data research, i.e. statistical research on datasets collected by the central and local government throughout its operation, has received increased attention from policy-makers over the last few decades. In the UK, the government has embarked on a series of policy and legal initiatives, as well as financial investments, to widen access to public-sector datasets for research use. Yet, empirical studies identify the existence of ‘legal’ and ‘cultural’ barriers to ‘sharing’ administrative data for research purposes, raising questions about the forces that regulate data access decisions by public-sector organisations responsible for managing the datasets. This thesis examines the regulation of research data access decisions in the UK public sector through a ‘regulatory space analysis’ lens by asking: what are the ‘rules of the game’ that filter access to public-sector administrative data for research purposes in the UK? How and by which actors have these rules been developed over the last four decades?
To address these questions, the thesis employs an in-depth, qualitative research design which involved semi-structured interviews (with 42 participants) and documentary data about the regulation of research data sharing decisions by specific organisations in the UK public sector. The thesis shows that research data access has been governed by rules which are incrementally established and revised via small-scale interactions, often between low-level technical staff, rather than momentous legal and policy decisions made by high-level officials or bodies. Through these interactions, the ‘political’ is artfully concealed behind the ‘technical’ and the ‘commonplace’. Well-established political views, reflected in unwritten, conventional normative assumptions about the ownership of administrative data and the relevance of administrative data research for policy-making, effectively shape the ‘rules of the game’ that govern data access in practice.
How does polarisation spread on Twitter? A computational analysis of Matteo Salvini's Twitter rhetoric (2013-2019)
Keywords: comparative politics
computational social science
polarisation

Recent studies show that polarised (or, negative emotionally-charged) social media posts lead to the public’s negative attitudes towards democratic institutions and politicians. However, thus far, scholars are yet to definitively grasp how and under what conditions such polarising rhetoric spreads (i.e., it is replicated or augmented) between politicians and the public. This thesis contributes to this gap in the literature by investigating Matteo Salvini’s rhetoric on Twitter, as well as his respondents’ replies. In particular, this research provides a com- putational analysis of the levels of polarised language in both Salvini’s tweets and replies to his tweets, between 2013 and 2019. With a combination of cutting-edge web-scraping methods, machine learning and time-series analyses, I study over 10 million Twitter data- points. First, I find that polarised language spreads bi-directionally from Salvini’s tweets to his respondents, and vice-versa. Second, Salvini’s followers’ replies not only act as an initial trigger to activate polarisation, but also sustain the use of polarised language in both Salvini and respondents across-time. Third, political content matters. Political replies are more likely to be polarised than non-political replies. Moreover, political replies trigger greater polarisation in tweets in the short-term. Instead, political tweets are more likely to display lower levels of polarisation than non-political tweets, and do not have triggering effects on replies’ polarisation in the short-term. These results may shed light on how polarisation affects online behaviour in both politicians and the public. Ultimately, this thesis provides a diverse theoretical and methodological toolkit to further investigate polarisation on Twitter and beyond.
Formal verification of neural networks
Keywords: robustness
verification
machine learning
neural networks
optimisation

Machine learning models and in particular Deep Neural Networks are being deployed in an ever increasing number of applications, making it crucial for methods capable of verifying their behaviour to be developed. In order to go beyond treating them as black boxes, we study the feasibility of verifying that certain properties on the mapping that the models encode should always hold and propose algorithms to perform this verification. We advocate the developments of these methods through the lens of optimisation.
By reformulating the verification of properties as optimisation problems over Neural Networks, we introduce a unified framework to reason about algorithms and identify the necessary components of verification systems. An attempt at verifying a property either results in a formal proof that the property holds or in concrete examples of cases where it is violated.
Current state of the art in verification technology of machine learning artifacts is constrained by both the restricted applicability of existing techniques and by their limited scalability. With the aim of making it more feasible to prove statements over widely used models, we present general methods that increase the diversity of verifiable properties and architectures amenable to verification. In order to get closer to the scale of models that power industrial applications, we refine heuristics used during the proving process and introduce algorithmic improvements to the optimisation algorithms underlying the methods, leading to order of magnitude speedups. Benchmarks empirically validate our runtime improvement claims, showing our contribution to addressing the problem of limited scalability of formal methods in the context of Neural Networks.
Gender, skills and infrastructure: constraints to private sector development in South Asia
Keywords: labour market
South Asia
skills
promotion
private sector development
infrastructure
garment industry
intra-household bargaining
Bangladesh
India
gender

This thesis studies firm development and labour markets in South Asia from three different perspectives – gender, skills and infrastructure. Chapter 1 investigates how female career advancement in the workplace impacts on bargaining in the household. It exploits the design of a promotion programme for women in Bangladesh’s garment industry, a sector where women continue to be underrepresented even in low-level management positions. The results suggest that women’s promotions lead to higher bargaining power at home not just for the promoted women, but also for the female subordinates that they supervise. Chapter 2 – written with Christopher Woodruff – turns to analyse the effects of the same promotion programme on factory outcomes. It reports on the importance of selecting managers based on their initial characteristics as compared to training candidates in the same skills. The evidence suggests that formal diagnostics lead factories to choose candidates with higher initial skills, which are predictive of official promotions and performance. Training results in higher promotion rates but has only marginal effects on performance. Soft skills, such as confidence and leadership, are particularly relevant for the success of the new female managers. Lastly, Chapter 3 documents heterogeneous short-term effects of infrastructure improvements on firms in India. It finds large and significant negative impacts on incumbent manufacturing firms close to improved highways, which are driven by initially less profitable, younger and smaller firms. This appears to provide a snapshot of the process of reallocation of resources towards more successful firms when areas become better connected.
Unsupervised learning and continual learning in neural networks
Keywords: generalization
continual learning
unsupervised learning
neural networks

For decades research has pursued the ambitious goal of designing computer models that learn to solve problems as effectively as humans can. Artificial neural networks -- generic, optimizable models that were originally inspired by biological neurons in the brain -- appear to provide a promising answer. However, a significant limitation of current models is that they tend to only be reliably proficient at tasks and datasets that they were explicitly trained on. If more than one task or dataset is being trained on, samples need to be appropriately mixed and balanced so that training on successive batches does not induce forgetting of knowledge learned in previous batches, which is an impediment to continual learning. Furthermore, associations need to be made explicit via paired input-target samples for the trained network to achieve its best performance on desired tasks; when the network is trained in an unsupervised manner without explicit targets, in an effort to reduce the cost of data collection, knowledge learned by the network transfers to desired tasks significantly worse compared to supervised training with explicit associations.
Each of these problems relates to the fundamental issue of generalization, which is the ability to perform well despite novelty. In chapter 2, we discuss conditions under which good generalization can be expected to arise, including small model size and similarity between training and test data in supervised, unsupervised and continual learning contexts. Chapter 3 proposes a method for predicting when a model does not generalize to a test sample, deriving generalization bounds that quantify predictive reliability using both model size and similarity with training data. Chapter 4 presents a clustering method that learns how to approximately separate data between semantic concepts with an unsupervised objective that does not use manual labels. Chapter 5 contains a method for performing the task of object localization without specialized training data, by repurposing saliency maps. Chapter 6 contains a continual learning method where the model is forced to reconsider previously held knowledge concurrent with new knowledge, and chapter 7 uses a dynamic architecture to suppress interference from new learning episodes on old knowledge.
Without solutions to these generalization problems, neural networks cannot learn effectively in real time from naturally sequential and un-annotated real world data, which limits their deployment options. Generalization is therefore a problem with immense practical implications, as well as being interesting theoretically and from the perspective of biologically-inspired learning.
The influence of EU's constitutional and institutional peculiarities on the interpretation of abuse of dominance under Article 102 TFEU
Keywords: EU case law
Article 102 TFEU
integrating economic analysis into legal thinking
EU constitutional foundations
EU institutional structure
economic analysis
abuse of dominance
internal market
socio-political values and general principles of EU law in the analysis of Article 102

This thesis examines the implications of EU’s constitutional and institutional peculiarities for the interpretation of abuse of dominance under Article 102 TFEU. The purpose is to shed light on the limitations to the interpretation of Article 102, which stem from EU’s constitutional and institutional framework. These limitations impact on both the substance and the form of the designed legal tests for Article 102 and dictate the proper role of economics in interpreting the concept of abuse of dominance. In constitutional terms, the thesis finds that the EU Treaties mandate a multi-goal and holistic approach to Article 102, meaning that there are multiple objectives and values external to market competition that are relevant to the interpretation of Article 102. The discussion of the relevant case law documents that the designed legal rules successfully reconcile the market-oriented objectives of Article 102 with policies of social nature, thereby allowing the provision to operate according to the constitutional role imposed on it by the Treaties. From an institutional perspective, the thesis maps out the complex institutional framework for the decentralised enforcement of Article 102. This unveils the mismatch between the complicated institutional setting and the necessity of ensuring effective enforcement and uniform interpretation of Article 102. The thesis argues that in this constitutional and institutional context the employed tests must remain clear so as to permit Article 102 to effectively and consistently pursue its objectives across the EU. In this regard, any economic statement or theory must be compatible with both the constitutional foundations of Article 102 and the institutional setting within which it must be enforced. More economic analysis may thus be injected into the interpretation of abuse of dominance only if it can be translated into clear and workable legal rules that are compatible with EU’s constitutional foundations and institutional realities.
Spectroscopy and modelling of catalyst nanoparticles using STEM-EELS
Keywords: spectroscopy
platinum group metals
modelling
EELS
cobalt
oxygen
DFT

This thesis concerns the oxygen reduction reaction (ORR) in the proton exchange membrane fuel cell (PEMFC) and aims to understand the interaction of oxygen with platinum-cobalt (PtCo) catalyst nanoparticles. The techniques employed are aberrated scanning transmission electron microscopy (STEM), electron energy loss spectroscopy (EELS) and modelling through density functional theory (DFT). The comparison of certain features in the experimental and simulated EEL spectra allows for selective optimisation in catalyst design. It is imperative to optimise the design of Pt-based alloys because Pt catalysts are costly. There is also an added advantage of manifold increase in catalytic activity with PtCo alloys in particular.
In order to study the oxygen interaction, the focal point of study becomes the oxygen coverage on the surface of the PtCo alloys. This requires detailed characterisation at the nanoscale and as such, optical techniques are not ideal. Using HAADF STEM means high spatial resolution and Z-contrast, which is crucial to investigating light and heavy elements often seen in catalyst materials. However, due to the Z-dependence, elements as light as oxygen may not readily appear in the HAADF image of catalysts and therefore EELS becomes an important tool due to its large range of elemental sensitivity to chemical composition.
The most logical starting point to investigate the oxygen coverage on the PtCo alloys is to find a link between the oxidation state of the metals and the oxygen binding in the alloys. The next step is to correlate detailed features in the oxygen and metals EEL spectra and explain them through modelling. DFT modelling is not enough to fully explain these features and hence atomic multiplet theory (AMT) is another tool used to corroborate experiment, particularly in the calculation of Co spectra.
A methodology for the extraction of oxidation states using the white line ratio in the cobalt EEL signal (Co L2,3-edge) is developed. Specifically, the PtCo nanoparticles are separated into core and shell regions in order to highlight possible differences in the binding of oxygen on the surface compared to rest of the nanoparticle. In addition, the influence of size on the oxygen binding is also examined. The errors associated with this method as well as potential difficulties in its application are discussed. The experimental results are tied to observed features in the calculated Co and oxygen spectra from various environments using AMT and DFT.
Combining spectroscopy and modelling, it is shown that the understanding of the oxygen interaction with the PtCo alloys only increases efficiency of catalyst design. This advantage acts as a stepping stone for other studies with such nanoscale specimens like nanoparticles in the catalyst research field.
Russian theatre in the age of High Putinism: politics and aesthetics
Keywords: Capitalist Realism
Actionist theatre
postdramatic theatre
performance
contemporary theatre
High Putinism
new documentarism
documentary theatre
Russian theatre
Censorship

Setting out from the premise that legislation is performative, this study initially explores the relationship between state cultural policy, censorship, and theatre production. The case is made that following the so-called ‘conservative-authoritarian turn’ in Russia in 2012, there has been a gradual return to an interventionist cultural policy qualitatively similar in some respects to that of Soviet-era Socialist Realism. I identify and describe the period in question – which is characterised by intensified censorship in its numerous guises, a new highly prescriptive cultural policy, and arbitrary repressions of nonconformist elements in society – as the period of High Putinism.
Having established the environment in which contemporary theatre in Russia operated during the second decade of the twenty-first century, surveys of the field are combined with close analysis of a dozen productions from the period. Collectively, these works all demonstrate in various ways what Jacques Rancière calls ‘dissensus’, which is to say they resist, circumvent, and transgress the norms of the ideological consensus set out by the state cultural policy. What they develop instead are alternative ways of thinking, seeing and doing that look beyond the horizons of the status quo. They achieve this through a variety of means, including aesthetic experimentation, audience participation, digital technology, contemporary dance and music, as well as radical postdramatic staging devices – in short, a hybridization of genres, styles, disciplines, and media. The resulting thesis is a cross-section of a vivid contemporary theatre scene existing largely on the margins of cultural life. From this position of marginality, a diverse set of emancipatory performance practices emerges.