VictorJuiz/thesis huggingface dataset:
Social and environmental factors in the evolution of signalling
Keywords: parental care
social evolution
communication

Offspring across the animal kingdom have evolved a variety of vocalizations, postures and structural ornaments to help them solicit more food from their parents. These signals are typically assumed to convey information about offspring state that helps parents decide which offspring to feed, and the dominant framework holds that begging is a signal of need. However, species differ both in how they beg and how they respond to begging, and signalling theory has been unable to account for this variation. In this thesis, I use a combination of comparative, observational and theoretical data to explore how the degree of within-family conflict leads to differences in parent-offspring signalling systems both across and within bird species. I also explore the possibility that some signalling systems function differently because the information they transmit is not cryptic. Specifically: 1) I show that offspring signals of need are more likely to evolve in species living in predictable and/or unusually good environments, while signals or cues of quality are more likely to evolve in unpredictable and/or unusually poor environments, when conflict over food is greater because brood reduction is probable; 2) I show that the same patterns hold within-species, indicating that parents can facultatively adjust their responsiveness based on the risk of brood reduction; 3) I show that offspring signals of need are less honest when sibling competition is greater, due to offspring number and/or relatedness; 4) I argue, using a simple game, that signals of non-cryptic condition can be stable if signalling provides an efficiency benefit compared to parents’ using cues; and 5) I provide a potential example of such a signal in nature, gape size, and show that its honesty fluctuates based on local conditions.
Declarative nested data transformations at scale and biomedical applications
Keywords: nested data
unnesting
shredding
genomic analysis
distributed processing platforms
nested relational calculus

While large-scale distributed data processing platforms have become an attractive tar- get for query processing, these systems are problematic for applications that deal with nested collections. Programmers are forced either to perform non-trivial translations of collection programs or to employ automated flattening procedures, both of which lead to performance problems. These challenges only worsen for nested collections with skewed cardinalities, where both handcrafted rewriting and automated flattening are unable to enforce load balancing across partitions.
In this work, the TraNCE compilation framework is proposed that translates a program manipulating nested collections into a set of semantically equivalent shredded queries that can be efficiently evaluated. The framework employs a combination of query compilation techniques, an efficient data representation for nested collections, and automated skew-handling. Biomedical case studies are presented that outline research and clinical applications for the platform, including data integration support for building feature sets for classification. An extensive experimental evaluation is provided using both synthetic and real-world dataset from the biomedical domain. The evaluation shows that the system is capable of outperforming the common alternative, based on “flattening” complex data structures, and runs efficiently when alternative approaches are unable to perform at all.
Protein folding and structure prediction: biological and physical perspectives
Keywords: Protein folding
Mutual information
Cotranslation
Coevolution

Protein folding is poorly understood and distinct from protein structure prediction, encompassing the translation and folding process of proteins in vivo and in vitro. In this thesis, we examine the protein folding process from several perspectives. First, we implement models of the spatial constraints that the ribosome introduces in the protein folding process. We are unable to show that these constraints have a particular effect on the formation of protein structure. However, we identify and describe a method which improves protein structure prediction in SAINT2 by avoiding the disruptive effects of certain extension protocols. Then, we study the relationship between protein structure and the coevolutionary constraints under which the structure has evolved. We identify several relationships between the structure of the protein and the coevolutionary constraints that are found in the protein. We further compare contact prediction methods and show that different contact prediction methods identify different types of amino-acid contacts. Finally, we investigate obligate cotranslational protein folding in the E. coli proteome. We find a relationship between the behaviour of several folding analysis methods and the necessity of cotranslational folding for our proteins.
Mechanistic insights into the HPF1/PARP1 complex
Keywords: PARP inhibition
HPF1
PARP1
serine ADP-ribosylation

ADP-ribosylation is a vital post-translational modification associated with a large range of processes including DNA damage repair, transcription, replication, mitosis, inflammation and cell death. ADP-ribosylation arises through the covalent attachment of ADP-ribose from NAD+ onto target proteins. The modification strongly varies in size, ranging from a single ADP-ribose unit (MARylation) to large, branched chains (PARylation). In humans, one major family of ADP-ribosyltransferases constituting 17 members are poly-ADP-ribose polymerases (PARPs). Their most prominent member, the nuclear protein PARP1, alone is responsible for the overwhelming majority of ADP-ribosylation in cells. While PARP1 was originally described to modify target substrates on glutamate and aspartate sites, it has become apparent in recent years that the presence of another protein, histone PARylation factor 1 (HPF1), switches its preference to serine target sites. Following this discovery, serine ADP-ribosylation was shown to be the main type of ADP-ribosylation in response to DNA damage. However, how HPF1 engages with PARP1 and is able to change the nature of its catalytic activity has remained unclear. This thesis explores the interaction between HPF1 and PARP1 in human cells, the molecular basis of HPF1/PARP1-catalyzed serine ADP-ribosylation and the consequences of aberrations in both proteins. Crucially, colleagues and I describe the interaction sites on both proteins and show that HPF1 in cells is required for the catalytic completion of PARP1 through a glutamate "finger". Furthermore, we identify the main sites of PARP1 serine auto-modification and illustrate their significance for PARP inhibitor-resistant PARP1 auto-modification and the release of PARP1 from sites of DNA damage. Finally, we explore in human cells the phenotypic consequences of losing the main PARP1 serine auto-modification sites, the HPF1 catalytic glutamate finger or HPF1-PARP1 interaction. Altogether, these results draw a comprehensive picture of HPF1-PARP1 mechanistic functions and the wider implications of its disruption.
Charge mobility in π-conjugated polymer systems
Keywords: π-Conjugated Polymers
Charge Transfer
Quantum Mechanics

Conjugated polymers represent a class of materials which exhibit excellent potential as versatile and cheap semiconductors in microelectronic devices. Their use in OLED displays is already wide-spread, and increasingly efficient solar cells are reported continuously. A key theoretical challenge in π-conjugated polymer systems is the incomplete understanding of charge transport mechanisms, and how competing microscopic and macroscopic effects influence it. In this thesis, charge transport and charge mobility are modelled at various length scales and varying levels of computational accuracy to better understand these processes. The models are based on phenyl-containing polymeric systems, but are generic in their implementation and would be adaptable to a range of comparable systems.
A novel atomistic model of charge mobility in bulk poly(p-phenylene-vinylene) (PPV) is presented. The incorporated structural and energetic disorder results in Anderson localised and quasi-extended donor and acceptor states. Charge migration through the system is facilitated by a kinetic Monte Carlo algorithm, aided by transition rates of separate inter- and intra-chain events defined within the framework of Marcus theory. The effects of disorder, temperature, field strength bias are studied. The log µ ∝ √F field dependence of mobility, which has already been widely reported in literature, naturally emerges at high electric field values. Crucially, the relative importance of inter-chain and intra-chain is highlighted by this model – at low fields, charge transport occurs almost entirely intermolecularly, between highly localised states, while at high fields, intramolecular transitions with the aid of quasi-extended states become most important.
The nature of intramolecular charge dynamics is further examined using a mixed quantum-classical model of charge-polaron transport. The dynamics of the charge follows the time-dependent Schrödinger equation, while the rotational motion of monomers is modelled via the Langevin equation, encapsulating the effect of Brownian fluctuations in rotational displacements. The two are coupled using the Ehrenfest equations of motion. The emergence of two distinct temperature regimes are observed. At the lowest temperatures, the polaron `crawls' along the polymer chain stochastically, while remaining fully localised and in the lowest quasidiabatic state. As the temperature increases, an activated, Landau-Zener type of dynamics is observed, leading to hopping transport between diabatic states. This additional pathway provides a link to the bulk charge transport mechanism.
Synthesis of the Taxol core using asymmetric 1,4-additions
Keywords: asymmetric conjugate addition
Taxol core
trapping of zirconium enolate
1,4-addition

The topic of this thesis is the synthesis of Taxane derivatives using the asymmetric conjugate addition of alkenes. Taxol is an important anti-cancer drug and owing to its complex structure, remains a subject of fascination by chemists. The asymmetric conjugate addition of alkenes is a power transformation and holds promise in synthesising complex molecules such as Taxol. In Chapter 1, we prove that this is a feasible undertaking by synthesising the C-ring of the Taxol core containing the C8 stereocenter. A complex nucleophile containing a bromide and substituted alkene was successfully synthesised on decagram scale and underwent asymmetric conjugate addition to give this intermediate in 95% yield and 88% ee.
In Chapter 2, the trapping of zirconium enolates generated from asymmetric conjugate additions was investigated. Several electrophiles were screened and it was found that only formaldehyde or the Vilsmeier-Haack reagent would give rise to any trapped products. The Vilsmeier-Haack reagent, gave β-chloroaldehydes in modest yields (between 41-60%) and high enantioselectivities (up to 98% ee) for adducts containing quaternary stereocenters. The synthetic utility of these compounds was also demonstrated by synthesising derivatives (Grignard addition and Suzuki-Miyaura couplings).
In Chapter 3, we envisged using an unprecedented transannular electrocyclic reaction as a key step to simultaneously form the A and B rings. The precursor 10-membered ring was synthesised by cross metathesis of an intermediate prepared in chapter 2, with a geminal diboron butadiene followed by ring closing Suzuki coupling. Ultimately, we were unsuccessful in carrying out the desired electrocyclic reaction. We discuss possible reasons as to why this was the case.
In Chapter 4, the type-II intramolecular Diels–Alder reaction is reviewed in the context of Taxane syntheses and then applied to our model substrate. Using this reaction, we were able to achieve our original objective by synthesising the Taxol core in five steps from simple starting materials in 11% yield, 92% ee and 1:1 d.r. at C1.
Soul, seed, species: William Blake and the language of preformationist biology
Keywords: self-organisation
literature and science
epigenesis
organicism
original genius
preformation
William Blake

This thesis discusses how Blake transformed biological images of birth and regeneration into complex symbols of spiritual, political, and artistic rebirth. Situating Blake against key developments in the eighteenth-century life sciences, it finds Blake resisting evolutionary materialism and its rationalist preference for material progress and self-preservation over the spiritual values of redemption, self-sacrifice, and renewal. Blake juxtaposes two biological paradigms— preformation and epigenesis—throughout his corpus. The former emphasises the continuity and heredity of form, while the latter emphasises self-organisation and plasticity. Working against recent criticism, this thesis aligns Blake with preformationist thinkers such as Charles Bonnet and Johann Kaspar Lavater. Insisting on the importance of reading Blake's organic symbolism in the context of his unorthodox Christian views, it argues that Blake saw epigenesis as a materialist conception of life masquerading as a vitalist one, and saw preformation as a model more sympathetic to his spiritual conception of vitality.
Examining works across Blake's corpus, chapter 1 argues that preformation science offered Blake potent symbols with which to address the soteriological and eschatological nuances of regeneration. Chapter 2 discusses the political dimensions of Blakean regeneration. Reading the Urizen Books and The Four Zoas against Blake's neglected The French Revolution, it contests assumptions in recent Blake criticism that the poet found images of freedom in biological self-organisation. Engaging with eighteenth century materialist neurophysiology, chapter 3 argues that The Four Zoas registers a deep dissatisfaction with the autopoietic nature of life and language. Chapter 4 examines Blake’s engagement with the organicist cult of original genius, arguing that Blake uses preformationist symbolism in Milton to emphasise prophetic tradition over individual originality. Finally, discussing engravings such as the Laocoön reproduction alongside Blake’s pronouncements against the classics, chapter 5 argues that Blake uses preformationist language to undermine neoclassical values of formal autonomy and cultural autochthony.
Managing online rumour proportions during online protests
Keywords: Empirical stud- ies in collaborative and social computing
Social media
Human computer interaction (HCI)

Protests are a key mechanism for collective action and are increasingly reliant on on- line communication. In addition, large, online protests are also events with tremendous amounts of ambiguity. Those on the ground are the first to know of new developments, but it is difficult to verify information in a timely manner. Furthermore, little experimental research has been done to understand how design factors online affect the information that activists have access to. This preregistered study investigates this gap by implementing an experimental analysis of rumour and misinformation sharing during a mock-protest demonstration. Through a Bayesian, “rumour proportions” framework, this study observes how adding credibility indicators (i.e., warnings) to misleading content affects the belief in, and rate at which participants share, rumours. Additional exploratory analyses examine the role of post attributes in sharing and the possibility of an “implied truth effect.” The results show that the treatment made participants share in greater alignment with what they perceived as accurate, but did not alter those perceptions themselves. Thus, those with biased accuracy estimates continued to share misleading information. By developing a custom experimental environment, moving beyond general misinformation stimuli and focusing on a specific application of credibility indicators, this study calls into question the notion that individuals spread misleading information simply because their attention is focused on factors other than accuracy.
Discovering knowledge abstractions for sample efficient embodied transfer learning
Keywords: deep learning
embodied learning
continual learning
artificial intelligence
robotics
reinforcement learning
modularity and composition
transfer learning
machine learning
domain adaptation
knowledge abstractions
skills

This thesis concerns sample-efficient embodied machine learning. Machine learning success in sequential decision problems has been limited to domains with a narrow range of goals, requiring orders more experience than humans. Additionally, they lack the ability to generalise to new related goals. In contrast, humans are continual learners. Given their embodiment and computational constraints, humans are forced to reuse knowledge (compressed abstractions of repeated structures present across their lifetime) to tackle novel scenarios in as sample-efficient and safe manner as possible. In robotics, similar traits are desired, given they are also embodied learners. Taking inspiration from humans, the central claim of this thesis is that knowledge abstractions acquired from prior experience can be used to design domain-independent sample-efficient algorithms that improve generalisation across modular domains. We refer to modular domains as Markov decision processes (MDPs) whose optimal policies can be obtained when reasoning and acting occurs over compressed abstractions shared across them. The challenge is how to discover these abstractions with minimal supervision and sample-efficiently. Additionally, for embodied machine learning it is important the approach supports continuous, potentially unbounded, state-action spaces. Adhering to these constraints, we first develop novel self- (Chapter 3) and weakly-supervised (Chapter 4) knowledge abstraction, domain adaptation, methods for zero-shot generalisation to unseen domains. We demonstrate their potential on robotic applications including sim2real transfer (Chapter 3) and generalisation using a human-robot command interface (Chapter 4). We continue by developing novel unsupervised knowledge abstraction, transfer learning, methods for sample-efficient adaptation to unseen domains (Chapters 5 and 6). We highlight their relevance in robotics and continual learning. We introduce a hierarchical KL-regularised RL approach based on novel theory behind the transferability-expressivity trade-off of abstractions (Chapter 5) and develop the first, to our knowledge, bottleneck-options approach adhering to the aforementioned embodied machine learning constraints (Chapter 6).
Membership and exile: Inclusion in the state and the political rights of refugees 
Keywords: citizenship
membership
enfranchisement
political theory
asylum
refugee

This thesis argues for a new normative understanding of refugeehood built around the concept of membership. I argue that, as a matter of legitimacy, all individuals require membership in a political community that is able to protect their basic rights. In our current way of organising the world, this means membership in the state. Refugees, I contend, are those who lack such membership and thus lack the effective guarantee of protection and security. Therefore, as a matter of legitimacy, refugees require new membership elsewhere. The first section of the thesis builds this new normative standard. Here I argue that the concept of membership already underpins many discussions in the political theory of refuge. I, therefore, aim to draw out and define the conception of membership that is most useful for thinking through refugeehood and exclusion from the state. I call this membership as effective citizenship which depends on three conditions: formal citizenship, the capacity of the state, and individual social standing. I then show that lacking membership in this sense is the central predicament of refugeehood, arguing that we should expand the definition of refugeehood beyond those who suffer persecution. In chapter four, I fully outline the membership view of refugeehood. The second section applies this view to three different spaces: the state of asylum, the state of origin, and the refugee camp. Here I focus specifically on refugees’ claims to political inclusion. By conceptualising refugeehood as a lack of membership in the state, I show that refugees are entitled to new citizenship elsewhere and substantive political inclusion under conditions of voluntary return. I also show that a focus on membership gives us another reason to reject refugee camps as legitimate forms of refugee protection. I conclude by considering whether it is possible to imagine membership beyond the state.
Machine learning applications of controlled differential equations to streamed data
Keywords: sepsis
neural cdes
medical time series
signatures
rough paths
time series
neural odes

The amalgamation of rough path theory and machine learning for sequential data has been a topic of increasing interest over the last ten or so years. The unity of these two subject areas is a natural one: rough path theory provides us with the language to describe the solutions to differential equations driven by multidimensional (and potentially highly irregular) signals, and machine learning provides the tools to learn such solutions from data.
The central aim of rough path theory is to provide a general mathematical framework for answering questions about the effect a stream of data can have on a system. One common example of such data is a time series, pervasive in all areas of life (and will be the type of streams we consider most frequently in this thesis); as such, framing problems in the language of rough paths provides us with models that have genuine utility in the real-world.
The aim of this thesis is to provide an accessible introduction to the field of rough path theory for application in machine learning, and then to provide an account of recent and effective contributions that further connect the two fields.
Topics covered in this thesis include: neural controlled differential equations (neural CDEs) -- extensions of neural ordinary differential equation that can incorporate changes in external data processes; neural rough differential equations (neural RDEs) -- a rough path extension to neural CDEs that leads to benefits for long or high-frequency time series; the generalised signature method -- a collection of feature extraction techniques for multivariate time series; and finally real-world applications of the signature method on sepsis and stress detection.
Advanced analysis methods for Imaging Atmospheric Cherenkov Telescope data with SSTCAM and VERITAS
Keywords: VERITAS
Imaging Atmospheric Cherenkov Telescope
CTA
Deep Learning

Imaging Atmospheric Cherenkov Telescope arrays allow us to probe the gamma-ray sky from tens of GeV up to hundreds of TeV. They operate by stereoscopically imaging the Cherenkov light generated when an astrophysical gamma-ray interacts with Earth's atmosphere. In order to reject charged cosmic ray events, and to reconstruct the direction and energy of the incident gamma-ray, machine learning methods are used in combination with parametric descriptions of the detected images. One potential means of improving performance for the next-generation Cherenkov Telescope Array (CTA) is to apply new deep learning methods in place of these parametric techniques. In this thesis, we explore the complexity of deploying deep learning methods, first considering the application of high precision timing data, and then testing such methods' performance on real observations from the current generation VERITAS array. Finally, we explore improvements to the modelling of Night Sky Background observed by Cherenkov instruments, that can be used to both inform the design of the Small Sized Telescope Camera (SSTCAM) for CTA, as well as potentially augment simulated data for deep-learning-based event classification.