VictorJuiz/thesis huggingface dataset:
Learning through university-business collaboration: case studies from the University of Oxford
Keywords: university-business collaboration
leadership in collaborations
management in collaborations
inter-organisational learning
collaborative relationships
research exploitation
partnership
collaboration
university research
organisational learning
collaborative learning
knowledge transfer
learning outcomes
social power
learning
trust
knowledge exchange
research and development exploitation
motivation
university business partnerships

In the UK, like other advanced economies, university-business collaborations in research and development (R&D) activities have an increasingly important role to play in driving innovation and providing wider benefits to the economy and society. The future development of university approaches and the policy framework to support further expansion of these activities needs to be informed by a greater understanding of the processes involved. However, given that previous research into the internal dynamics of university-business collaborations is limited, this study adopts a learning perspective to investigate how interactions within such collaborations influence their outcomes, drawing on a diverse sample of case studies from the University of Oxford.
The findings demonstrate how the dynamic and highly interactive nature of collaborative learning requires positive and agile relationships to be maintained throughout, particularly when faced with challenges or with opportunities to exploit unforeseen learning. At their core, effective collaborative relationships are shown to rest on the building of partner motivation and trust and through the appropriate use of social power bases. In practice these are mediated by collaborative structures and communication that in turn are shaped by approaches to leadership and management. The resulting learning outcomes are shown to be far wider than are commonly captured, despite systemic constraints on some aspects of academic engagement. Following this analysis, recommendations are made for the development of university approaches with renewed emphasis on collaborative learning.
Health facility data to describe the epidemiology of malaria in sub-Saharan Africa
Keywords: Test positivity rate
Immunity
Malaria
Age-profile
Parasite rate
Rapid diagnostic test
Systematic Review
Spatial-temporal
Hotspots
Heterogeneity
Malaria Atlas Project
Plasmodium falciparum

The current understanding of malaria burden in Africa relies on modelled estimates based on incomplete and outdated data. The WHO defines malaria disease surveillance as a central pillar of its Global Technical Strategy. The thesis examines the precision of existing models to predict malaria burden changes and interrogation of malaria metrics from health facility and community surveys on the Kenyan coast.
Geostatistical, epidemiological models provide approximations of malaria disease burden but are rarely tested against empirical data. A systematic review included 93 health facility sites across Africa with a minimum of 5 complete years of temporal data. There was a broad congruence in the matched changes at 70 sites, but significant discordance at 23 sites which showed stagnated or upward trends.
Data on the pathway from infection to death, necessary to parametrise models of morbidity and mortality risk are rare. Prospective 12-month surveillance at six health facilities, the county hospital and repeat surveys among 36 matched communities was used to describe the epidemiology of malaria in all age groups. Despite conditions of declining transmission intensity, immunity to disease and the fatal consequences of infection continue to be acquired in early childhood, faster than anti-parasitic immunity and without evidence of emerging burdens of severe malaria or mortality among young-older non-pregnant adults.
The value of routinely collected data from health facilities to define malaria risk was explored at 36 health facility-community pairs. There was a direct non-linear polynomial relationship between passively detected fever test-positivity and traditional community-based parasite prevalence. Information obtained through routine testing of febrile patients for malaria was able to identify spatial and temporal heterogeneities of malaria risk.
Routine data offers important insights into local malaria transmission patterns, disease burdens and changing patterns of disease. An increased effort is required to replace malaria burden models with empirical data.
Machine learning for acoustic mosquito detection
Keywords: Decision theory
Bayesian neural networks
Deep learning
Convolutional neural networks
Weak labelling
Wavelets
Audio detection
Acoustic detection

Mosquitoes are responsible for over one billion cases of disease and over one million deaths each year. The data produced during mosquito surveillance are needed to identify emerging insecticide resistance, facilitate effective and evidence-led insecticide intervention programmes as well as model current and future vector-borne disease transmission. Traditional mosquito survey methods are time-consuming, expensive, and spatially limited. Consequently, many mosquito distribution models that map the range of these insects rely on small quantities of poorly spatially distributed occurrence data. There is therefore an urgent need to develop new mosquito survey methods that can provide real-time species-specific occurrence and abundant data without human risk. Here we consider an acoustic detection paradigm, in which the distinctive buzz of mosquito flight is used as a characteristic signature for detection and subsequent species identification. We show it is possible to achieve high classification accuracy even in data-scarce scenarios, using a combination of deep learning and wavelet encoding. Additionally, we develop and deploy a smartphone app that allows mosquito detection at scale and can discriminate between species with high accuracy. We garner low-resolution labelling for parts of our data via crowdsourcing, supplementing the high-resolution labels obtained from experts and publicly release a baseline model and dataset.
The technical materials that underpin this thesis detail development of machine learning approaches for detecting and identifying events in data, with the primary focus of finding mosquito flight tones in acoustic time series. Solutions specific to such audio detection are developed, tested and applied to field-gathered data. Although the research is specific to one focal application domain, the approaches developed are generic and were motivated by canonical problems of low signal-to-noise ratio, sparse data, multiple resolution labelling, class imbalance, and decision bias. They thus apply to a far wider set of detection problems, in audio time series and beyond.
Surface wave-induced Eulerian-mean flows in the open ocean
Keywords: Geophysical fluid dynamics
Wave mean-flow interaction
Surface gravity waves
Eulerian-mean flows
Wave-induced transport

This thesis examines Lagrangian-mean flows induced by surface gravity waves, with particular emphasis on the displacements associated with these flows. Stokes drift, the small Lagrangian-mean velocity accompanying the orbital motion of particles beneath surface waves, has been well-studied, and is proven to play a role in ocean transport. However, Eulerian-mean flows induced by slow variation of the wave field (divergence-driven), viscous effects, or geophysical effects such as density stratification and planetary rotation, have received less attention. It is the aim of this thesis to study these wave-induced Eulerian-mean flows, which must be superimposed on the Stokes drift to obtain the correct Lagrangian-mean (or mass transport) velocity induced by surface gravity waves. In Chapter 2, the effects of wave directional spread and density stratification on the mean flow induced by a surface gravity wavepacket in deep water are studied, and the corresponding displacements are derived. Chapter 3 examines ‘Ekman–Stokes’ dynamics driven by the Stokes drift and Coriolis force within the turbulent upper ocean boundary layer, and uses buoy data to explore the impact these dynamics have on the drift of a floating particle. Finally, in Chapter 4 I combine the insights of the two previous chapters to develop an analytical model capable of describing both divergence-driven and geophysically-driven Eulerian-mean flows induced by surface waves.
Exploring the effect of polyfluorene copolymerisation for improved material design
Keywords: triplets
charge transport
copolymers
polyfluorene

The discovery of electroluminescence from conjugated polymers was followed by tremendous academic and commercial interest in the field of polymer optoelectronics. Conjugated polymer materials have been shown to possess beneficial qualities for device fabrication such as low-cost, large area manufacturing and the potential for realizing flexible applications. Polyfluorenes, in particular, are a class of conjugated polymers which have been realized in a number of applications such as organic light emitting diodes, solar cells and field-effect transistors. The widespread commercialization of such products critically depends on improving device efficiencies. This can be accomplished by both advancing device engineering and improving material design. For the realization of the latter, fundamental understanding of the interplay between material chemistry and physics is required. This motivated the work in this thesis which is concerned with the structure-property relationship in polyfluorene containing copolymers. The well studied polyfluorene poly(9,9-dioctylfluorene) (PFO) was selected as the model polymer due to its complex phase morphology which allowed to additionally investigate the effect of film microstructure on the photophysics of the copolymers. The optoelectronic properties of PFO can be varied by the copolymerisation of suitable comonomers giving valuable insight into the relationship between chemical structure and photophysical properties that can guide future material design for device applications.
In this work three different comonomers have been studied. Firstly, the hole mobility of four fluorene-arylamine copolymers has been investigated. It is shown that transport in these copolymers is controlled over a large range by the copolymerisation of different amounts of a lower ionisation potential transporting moiety. Next, the effect of increasing backbone rigidity on hole carrier transport is studied via the copolymerisation of fluorene and phenoxazine. The resulting copolymer has a high glass transition temperature suggesting a rigid polymer chain. Yet, its hole mobility is lower than for the chemically similar hole transporting material poly(9,9-dioctylfluorene-alt-N-(4-butylphenyl)-diphenylamine) (TFB) which possesses a greater number of torsions. Finally, the copolymerisation of PFO with a cyclometalated platinum complex has been investigated. The heavy platinum atom increases spin-orbit coupling leading to the radiative decay of the triplet state. It is shown that at low temperatures kinetic frustration of the triplet state is lifted in the planarised ß phase of the copolymer demonstrating that triplets can diffuse more effectively along highly conjugated polymer chains than along materials with short conjugation segments.
Anthropogenic climate change and extreme weather events: implications for Bangladesh
Keywords: Climate change
Extreme weather events
Bangladesh
South Asia
Attribution

Probabilistic Event Attribution (PEA) quantifies the change in risk of extreme weather events due to external drivers of climate change. The regional climate model (RCM) of the weather@home system has been successfully used for this purpose. So far, most PEA based studies have focused on extreme events of mid-latitudes and developed countries, while the events occurring in tropical regions and developing countries have gained little attention. This thesis evaluates the performance of the RCM of weather@home over the complex tropical monsoon climate region of South Asia, with particular focus on Bangladesh. In a combined process-based and performance-based evaluation process, first the model’s response to observed variability of sea surface temperatures (SSTs) are assessed. Second, the RCM’s skill to simulate the extreme events in Bangladesh is evaluated against observations. The results suggest that the RCM performs well, capturing both, influence of observed natural SST variations and extreme weather events in Bangladesh. The thesis uses large ensembles of weather@home RCM simulations for two studies. PEA is first applied to attribute contributions of past and future greenhouse gas (GHG) emissions to extreme events under current climate conditions and future warmer worlds. In accordance with the targets set out in the Paris Agreement, 1.5 and 2.0 degrees warming scenarios are investigated. Results indicate that the risk of seasonal extreme rainfall events across Bangladesh will increase with additional global warming. Anthropogenic aerosols are found to be a significant influencing factor along with GHG concentrations and large-scale temperature in determining the climate change impacts. The RCM based results are found to be consistent with the findings based on simulations from four other Atmosphere Only Global Climate Models (AGCMs) under the Half a degree Additional warming, Prognosis and Projected Impacts (HAPPI) project. PEA is secondly applied for an extreme pre-monsoon rainfall event of 2017 that occurred over north-east Bangladesh and caused flash flood and severe crop damage. According to this analysis, anthropogenic climate change is assessed to have increased the risk of such event occurring by more than a factor of 2. Findings of this thesis show that the RCM of weather@home system may provide a sufficiently reliable tool for attributing human impacts on the likelihood of extreme events over the complex tropical monsoon climate region of South Asia in general and Bangladesh in particular. Since anthropogenic climate change is found to be of crucial importance with regard to extreme weather events, the conclusions of this thesis will facilitate further attribution studies over the region and help to provide guidance for local policy makers as far as climate change adaption is concerned.
Nature after Romanticism. Literary explorations of the natural world in the works of Heine, Lenau, and Droste-Hülshoff
Keywords: Romanticism
Science and Literature
Nikolaus Lenau
Nineteenth Century
Annette von Droste-Hülshoff
Nature
Heinrich Heine
Representations of Nature

During the Biedermeier period, literary representations of nature came to incorporate perspectives from the increasingly dominant natural sciences alongside the aesthetic conventions concerning the depiction of nature inherited from the late eighteenth century.
In a comparative study, the following thesis investigates how three of the period’s most prolific writers, Heinrich Heine, Nikolaus Lenau, and Annette von Droste-Hülshoff, explored the natural world through a variety of literary forms and modes of expression. Although the concept of nature took on different functions within the literary oeuvre of these three writers, we are able to see similarities emerge in the way they forged intertextual connections with their Romantic predecessors. The period typical focus on historicity brought forth a literary style that was rich in allusions to an aesthetic tradition of representing nature, which was now being recontextualised. Thus, literature and nature alike appeared historically layered, heterogeneous, and even disharmonious, creating a contrast to the holistic concepts of nature and the Kunstideal prevalent at the turn of the century.
Through a close-reading of literary works that dealt explicitly with the relationship to nature, my analysis has retraced intertextual relationships as well as references to contemporary scientific, philosophical, theological, and political discourses. By investigating how Heine’s early prose showed nature and society as intertwined, how Lenau’s poetry radically questioned the possibility to have an aesthetic experience of nature, and Droste-Hülshoff juxtaposed scientific, aesthetic, and theological concepts of nature within her writing, we can see the multi-facetted character of literary concepts of nature in the 1820s to 1840s emerge.
By taking into consideration the embeddedness of these popular works of literature in their contemporary social context, the following thesis also seeks to understand how a changing relationship between nature and art reflects the new role of literature in a bourgeois Wissensgesellschaft.
Studies towards the total synthesis of heteroclitin F and taiwanschirin A, B, C, and D
Keywords: total synthesis
natural product

This thesis initially describes model studies developed towards key structural fragments of the taiwanschirin family of natural products. Through the use of these models, an original approach was conceived to enable the installation of the (Z)–enone as a single diastereoisomer. This novel sequence has been successfully applied to a wide substrate scope including esters, amides, ketones and thioesters. Furthermore, these studies led to the assembly of the 8–membered ring in high yield, and we were delighted to observe the formation of the desired alkene as a single (exo)–diastereoisomer in 81% yield. Extensive investigations are described into the incorporation of the pyruvate functional group in model compounds. This group proved to be particularly challenging, however a breakthrough was obtained and this approach enabled the assembly of a model compound that incorporated both the pyruvate group and the (Z)–enone. With a method developed for the incorporation of the three contiguous stereocentres, in addition to the pyruvate functional group, presented herein is a novel approach towards the first total synthesis of the taiwanschirin family. Finally, an approach to the heterocyclic core was developed and the planned synthesis successfully completed up to step 16 inclusive of an anticipated 22 steps. To the best of our knowledge, this is the first work to investigate the total synthesis of this family of highly structurally complex natural products, and represents a significant advancement in this field of research. The taiwanschirins represent an extremely challenging and biologically significant family of targets in total synthesis.
Automating inference for non–standard models
Keywords: Non–Standard Statistical Models
Probabilistic Programming
Bayesian Inference

Probabilistic models enable us to infer the underlying relationships within data and make decisions based on this information. Certain models are more commonly used not because they are more appropriate to imitate a particular system, but because they are simple enough to analyze given the current computational resources and available inference algorithms. However, there are a broad range of non-standard models that we could use to describe real-world tasks better; they may have complex dependency structures, variables following non-common distributions, and/or a stochastic number of variables. But we typically avoid using them as these features substantially complicate the inference procedure such that many conventional inference algorithms would fail when dealing with these models.
To alleviate the computational concerns of data scientists or domain experts and free them to develop non-standard, complex models as needed, more sophisticated inference methods need to be more easily accessible. Additionally, because it is usually difﬁcult to extract helpful information of non-standard models ahead of inference, like where the modes might be, such inference techniques should be expected to work reasonably well without too much such information. In an ideal world, domain experts would be able to specify any model of interest, and no longer need to worry about the technical details of inference algorithms or how to implement them. Moreover, these algorithms are provided as generic engines and can be used to reason the models in an automated manner. Achieving so is the ambitious, long-term goal of the emerging ﬁeld called probabilistic programming.
The aim of this thesis is to make inroads to this ultimate goal: we develop and automate advanced inference methods that are applicable for a broad range of non-standard probabilistic models. In particular, we introduce a novel class of adaptive inference algorithms which can be implemented in a black-box manner. They are especially useful for the models with multiple, separated modes where many off-the-shelf options struggle. Furthermore, we investigate both a restricted class of probabilistic programming systems (PPSs) that impose strong constraints on the model class to ensure inference efﬁciency, and universal PPSs which are designed around the long-term goal and aim to support any possible model but substantially complicate the inference procedures. For the former, we propose a principled way to extend them to incorporate a broader range of models and inference engines that are not supported before. For the latter, we develop a general framework to handle one class of the most challenging models where the support of a model can be stochastic.
Essays in private equity
Keywords: Private Equity
Leveraged Buyouts
Incentives

This dissertation presents three empirical analyses on private equity (PE) investments. The first chapter, The accuracy of net asset values in private equity: Evidence from the secondary market, examines pricing in the secondary market for PE fund stakes. The average transaction occurs at a discount, which is commonly attributed to compensation for liquidity provision. As a quarter of bids come in at a par or a premium, this cannot be the full explanation. I document that cross-sectional variation in discounts predicts future fund returns. The second chapter explores The dynamics of pay-for-performance sensitivity in private equity funds arising from carried interest. As carried interest is conditional on beating a hurdle rate, pay-for-performance sensitivity is increasing in fund performance, and increasingly so as the fund matures. I document that within-fund variation in pay-for-performance sensitivity is positively related to growth in portfolio companies acquired in buyouts. The effect is stronger when the importance of the direct component is high relative to the indirect component of pay for performance. To rule out confounding factors, I utilize public market movements to generate exogenous variation in the measure. My findings provide evidence of the importance of incentives for value creation. The third chapter, Managerial ownership and operational improvements in buyouts, investigates the relevance of CEO ownership stakes in companies acquired in buyouts. I find that post-buyout CEO equity ownership stakes are positively associated with profitability improvements. Consistent with an effect of incentives, the results are stronger when the ownership stake increases, and are driven entirely by the subsample in which the CEO is retained. Taken together, these three studies contribute to the growing body of work on private equity investments and the impact of private equity ownership on companies acquired in buyouts. They open the door to further theoretical and empirical research in the field.