VictorJuiz/thesis huggingface dataset:
The analysis, modelling, and acoustic control of fluidic devices

Zero-carbon flight represents arguably the biggest challenge of the green revolution. A variety of novel flight concepts have been proposed in response to emissions targets set by the International Air Transport Association (IATA). The common theme to enabling these innovative technologies is efficient, reliable, low-weight solutions to the control of airflow: from controlling aircraft aerodynamics to heat transfer in gas turbines. Active flow control represents a family of such solutions, and is based on the idea of harnessing the natural amplification provided by nonlinearities in fluid mechanics to bring about large-scale system change with only a small energy input. Fluidic devices are a highly-reliable means of controlling high-speed flows and have a small response time.
A analytical, dynamic model of an attached jet, the canonical form of all fluidic devices, is developed from first principles. The jet is excited acoustically, which is modelled as a time-varying component of additional entrainment. The core component of the model is a novel, unsteady jet curvature equation, which finally removes the need for the quasi-steady jet assumption. An accompanying experimental validation is performed, and a strategy for inverting system nonlinearities is presented. The model is linearised so that magnitude and phase frequency responses can be compared with the validation data. Good agreement is found for one roll-off in the data, and explanations for a second, unmodelled roll-off are given.
A closed-loop controller is designed and implemented for an acoustically-driven fluidic amplifier in which the flow is continuously modulated. The attached-jet model provides good agreement with experimentally-identified frequency responses, and a black-box identification is used for control purposes. The performance of the resulting LQG controller is evaluated in several experiments, and is shown either to match or outperform its open-loop counterpart in every respect. However, closed-loop bandwidth is limited by turbulent noise in the region of the system dynamics, and a means of overcoming this fundamental challenge is discussed.
The response of a jet shear layer to a modulated perturbation is explored from a signal processing perspective. The well-known shear layer demodulation property is identified specifically as an envelope detector because of its phase insensitivity. A novel input over-modulation technique is proposed that halves the required actuator bandwidth while suffering only a 2.7 dB reduction in response magnitude. It is further hypothesised that the shear layer response includes a sampler, and an analogy with the Nyquist sampling theorem is made. Early experiments appear to invalidate the analogy, although the experiments are somewhat inconclusive. If the Nyquist theorem does not hold, interesting implications are raised about the current paradigm of the response of shear layers to modulated perturbations.

Keywords: fluidic device
jet
control
Advisor: Bacic, M
Exploring interventions to reduce blood loss during and after caesarean delivery in Africa

Background
Women in Africa are fifty times more likely to die following caesarean delivery compared to high-income settings and blood loss during and after caesarean delivery is independently associated with mortality. We aimed to (i) identify interventions, and (ii) identify which interventions African clinicians and researchers recommend as feasible and effective to reduce blood loss during and after caesarean delivery.
Methods
(i) We conducted a systematic review and network meta-analysis to identify nonpharmacological interventions. We conducted a comprehensive search from database inception to 31 st December 2019, with no language restrictions. We included studies of women undergoing caesarean delivery, which assess nonpharmacological interventions, and report one of the study co-primary outcomes (blood loss [ml] or risk of post-partum haemorrhage [>1,000 ml]). We excluded retrospective studies, and study cohorts not representative of the general caesarean delivery population.
(ii) We conducted a Delphi Consensus study, including obstetrics and anaesthesia providers from across Africa. Rounds one to three were online surveys, and round four formed a live online discussion.
Findings
(i) We included 51 studies in the network-meta-analysis, forming 13 distinct intervention groups. Thirty studies were not included in the meta-analysis and were synthesised narratively. On random effects model analysis, only three interventions had clinically significant findings, of which one had statistical significance.
(ii) Twenty-eight interventions were considered highly effective and highly feasible. These interventions covered broad domains including community based; antenatal; perioperative; and health system strengthening interventions.
Interpretation
The published trials on the effect of non-pharmacological interventions on blood loss associated with caesarean delivery are inconclusive. There is a need for collaborative pragmatic trials to identify effective and generalisable interventions. The broad range of recommended interventions highlights the importance of a multifaceted systems strengthening approach to reduce morbidity and mortality associated with blood loss during and after caesarean delivery in Africa.

Keywords: caesarean
obstetrics
haemorrhage
surgery
blood loss
global surgery
Africa
Advisor: Lakhoo, K
Maswime, S
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

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.

Keywords: Outcome measures
Machine learning
REM sleep behaviour disorder
Smartphone
Parkinson's disease
Advisor: Hu, M
De Vos, M
Arora, S
Computational strategies to overcome antibiotic resistance: Exploring novel tetramic acids as Undecaprenyl Pyrophosphate Synthase (UPPS) inhibitors

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.

Keywords: antibiotics
drug design
undecaprenyl pyrophosphate synthase
tetramic acid
antimicrobial resistance
Advisor: Moloney, M
Regulating administrative data sharing for research purposes in the United Kingdom

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.

Keywords: data sharing regulation
administrative data
regulation
data sharing
regulatory space
Advisor: Kaye, J
Lange, B
How does polarisation spread on Twitter? A computational analysis of Matteo Salvini's Twitter rhetoric (2013-2019)

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.

Keywords: comparative politics
computational social science
polarisation
Advisor: Kosmidis, S
Formal verification of neural networks

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.

Keywords: robustness
verification
machine learning
neural networks
optimisation
Advisor: Kumar, P
Torr, P
Kohli, P
Gender, skills and infrastructure: constraints to private sector development in South Asia

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.

Keywords: labour market
South Asia
skills
promotion
private sector development
infrastructure
garment industry
intra-household bargaining
Bangladesh
India
gender
Advisor: Woodruff, C
Quinn, S
Unsupervised learning and continual learning in 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.

Keywords: generalization
continual learning
unsupervised learning
neural networks
Advisor: Vedaldi, A
Henriques, J
The influence of EU's constitutional and institutional peculiarities on the interpretation of abuse of dominance under Article 102 TFEU

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.

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
Advisor: Weatherill, S