VictorJuiz/thesis huggingface dataset:
Mg(II) heterodinuclear catalysts for the synthesis of oxygenated polymers
Keywords: polymers
carbon dioxide
catalysis

The thesis describes the use of heterodinuclear Mg(II)M(II) complexes (M = transition metals) as catalysts for the ring-opening copolymerisation (ROCOP) of epoxides and anhydrides, and epoxides and CO2.
Chapter 1 provides an introduction to ROCOP, outlining the mechanism and catalyst developments, with a particular focus on epoxides and CO2. Routes towards the construction of ROCOP-derived block copolymers, and the concept of switch catalysis are also described.
Chapter 2 details the ROCOP of norbornene anhydride (NA) and cyclohexene oxide (CHO) for a series of heterodinuclear Mg(II)M(II) complexes, were M = first row transition metals. A representative rate law is proposed on the basis of kinetic experiments for the most active complex, Mg(II)Co(II). Subsequently, monomer insertion selectivity for ROCOP of tricyclic anhydride (TCA), cyclohexene oxide (CHO), and CO2 is determined for complexes Zn(II)Zn(II), Mg(II)Mg(II), Mg(II)Zn(II) and Mg(II)Co(II). It is found that Zn(II)Zn(II) selectively forms poly(TCA-alt-CHO), Mg(II)Zn(II) forms poly((TCA-alt-CHO)-ran-cyclohexene carbonate), and Mg(II)Mg(II) and Mg(II)Co(II) selectively form poly(cyclohexene carbonate) (PCHC).
Chapter 3 describes the synthesis of multi-block copolymers, up to heptablock, of PCHC and PE in one-pot by changing the headspace between CO2 and N2 gas at 1 bar, using the Mg(II)Co(II) catalyst. Switch catalysis is also used to make a pentablock polymer, the final block afforded by the ring-opening polymerisation of ε-caprolactone (CL).
Chapter 4 details the synthesis of and characterisation of a Mg(II)Co(II) complex with organometallic co-ligands. It is demonstrated that when using this complex for the ROCOP of CO2 and epoxides, in addition with bifunctional protic chain transfer agents, high molar mass polycarbonates with monodisperse distributions are yielded. Mechanical data is obtained for a high molar mass poly(vinyl-cyclohexene carbonate) (PvCHC).
Chapter 5 details the synthesis and characterisation of Ni(II) phenoxyiminato complexes, and their application in ethylene polymerisation under various conditions. Three different strategies towards Ni(II) alkoxide complexes are attempted, as they can operate as initiators for either ROP or ROCOP processes.
Chapter 6 summarises the key findings of the thesis and suggests future research which could follow the work described.
Chapter 7 provides experimental details for Chapters 2–5.
Appendix contains supplementary figures and data for Chapters 2–5.
Anything but quiet: essays on the metaphysics of normativity
Keywords: Tim Scanlon
Relaxed Realism
John Skorupski
Derek Parfit
Meta-Ethical Quietism
Normative Realism
Non-Naturalist Normative Realism
Truthmaking
Meta-Metaphysics
Meta-Ethics
Non-Realist Cognitivism
Ontological Pluralism
Meinongianism
Irrealist Cognitivism

This thesis concerns the meta-metaphysics of morality and of normativity more generally. Normative metaphysics often proceeds without much engagement with meta-metaphysics. My first aim is to rectify this neglect and encourage its broader correction. I do this by offering a sympathetic yet critical discussion of the so-called ‘quietist’ or ‘relaxed’ normative realism of Derek Parfit, John Skorupski, and Tim Scanlon. This discussion illustrates how attention to meta-metaphysics can demystify such views and explain their attractive features, the difficulties they face, and what meta-ethicists and meta-metaphysicians stand to gain from engagement with one another. My second aim is to explore two questions my discussion raises: is the moral error theory epistemically possible and, if so, what follows? And is there a way to generate a view in the spirit of ‘quietist’ views that appeals to assumptions likely held by their opponents?
Chapter 1 sympathetically develops Parfit’s non-realist cognitivism by appeal to truthmaker theory and reveals a troubling dilemma. Chapter 2 develops a response to this dilemma that suggests that non-realist cognitivism can be plausibly developed into what I call normative trivialism. Chapter 3 concerns Skorupski’s irrealist cognitivism and Meinongian meta-ethical theories in general, where I argue that we have good reason to pay attention to such views, dispel some misunderstandings of them, and argue that Skorupski’s irrealist cognitivism faces strong objections. Chapter 4 develops Scanlon’s novel theory and the background meta-metaphysics it relies upon, arguing that whilst my development can respond to common objections to Scanlon’s theory, it highlights novel challenges. Chapter 5 offers an argument from the epistemic possibility of the moral error theory against each of the aforementioned views, as well as any form of moral non-cognitivism and moral naturalism. Chapter 6 argues that if three widely held views are true, then the naturalism/non-naturalism debate is settled by ethical debate and concerned meta-ethicists should do normative ethics instead. I conclude whilst noting that, properly understood, ‘meta-ethical quietism’ is anything but quiet.
An investigation of transition metal and oxygen redox in lithium-rich cathode materials
Keywords: HERFD
lithium ion battery
XES
lithium-rich
spectroscopy
XANES
lithium ion

Cathode materials remain one of the main limiting factors for increasing the specific energy of lithium ion batteries. The reversible storage of charge on lattice oxygen, called oxygen redox (O redox), has been identified as a promising route for addressing the problem in layered and disordered rocksalt materials. Currently, several interrelated challenges, including slow kinetics, voltage and capacity fade, as well as structural changes, remain, that are investigated in this thesis.
A dedicated operando cell, capable of applying significant stack pressure, was built to investigate layered lithium-rich materials. It is used to study Li1.2Ni0.13Co0.13Mn0.54O2 and Li1.2Ni0.2Mn0.6O2 using high energy-resolution fluorescence-detected X-ray absorption near edge structure (HERFD-XANES), as well as X-ray emission spectroscopy (XES). The complex interplay between bulk oxygen and transition-metal redox is analysed, and it is demonstrated that Ni is fully oxidised to Ni4+ in one compound but not the other.
The structure of the disordered rocksalt Li2MnO2F is solved using a combination of neutron pair distribution function (PDF) and neutron diffraction, simultaneously fit using a Reverse Monte Carlo (RMC) approach. The capacity fade for the same material is investigated and found to be initially based on a large charge-transfer resistance, associated with the slow O redox kinetics on discharge.
Patterned communication between synthetic tissues and bacterial cells
Keywords: synthetic tissues

Droplet networks (synthetic tissues) comprised of aqueous droplets interfaced by bilayers show great promise for the replacement and augmentation of native tissues. However, this requires droplet networks to interact with biological cells in a spatially precise manner. Here, we report the construction of 3D-printed synthetic tissues that locally control population gene expression in a layer of bacterial cells by the patterned release of a chemical inducer. Precise micron-sized areas of gene expression are achieved by controlling the rate of release of the inducer at a threshold concentration to match its subsequent uptake and consumption by the surrounding cells. Our system provides a basis for the construction of droplet networks capable of interacting with cells at a spatially precise level and serves as the groundwork for the use of synthetic tissues in medical applications requiring local control of native tissues.
Death, demons, and the medium of the message: exploring the exegesis of 1 Samuel 28:3-25 in the early church 

Keywords: Antiochene
Harrowing of Hell
Reception history
Origen of Alexandria
1 Samuel 28
Alexandrian
Demons
Witch
Saul
Samuel
Tertullian
Eustathius of Antioch
Descent to Hades
Early Church
Justin Martyr
Exegesis
Endor
Medium
Parting of the ways
Augustine

This thesis will consider the reception history of 1 Samuel 28 in the early Church by analysing citations of the passage in detail, from the earliest Christian reference (Justin Martyr) to Augustine. Before approaching these sources, the study will set the context by considering Christian exegesis more broadly, and how Jewish writers understood 1 Samuel 28. The next chapter will consider those early Christian references to 1 Samuel 28 which believe that it was the real Samuel that was raised (namely Justin Martyr, Eusebius of Caesarea, Apollinaris of Laodicea, Diodore of Tarsus, Zeno of Verona, Ambrose of Milan, and Sulpicius Severus). The following chapter will consider those who argue that it was a demon in disguise that appeared (namely Tertullian, Basil of Caesarea, Gregory of Nyssa, Ambrosiaster, and Jerome). Having considered both views, we shall then observe these differing readings in conflict by focusing on Origen’s interpretation of the passage and Eustathius of Antioch’s rebuttal of Origen’s reading. The thesis will conclude with a detailed study of Augustine’s exegesis of 1 Samuel 28, mapping how his understanding of the passage changes over time. Throughout this study we shall bring to light the particular history of the exegesis of this passage, and the important ideas, themes, and features, which typically appear as early Christians discuss it, from matters relating to Christ’s descent to Hades, to the regular references to Luke 16:19-31 and 2 Corinthians 11:14-15. In the process, this thesis will contribute to a wider understanding of the authors under consideration, examining their theological convictions on themes such as death, demonology, and how they approached Scripture.
Self-supervised learning of structural representations of visual objects
Keywords: unsupervised
computer vision
keypoints
3D reconstruction
self-supervised

This thesis explores how a computer can learn the structure of visual objects in the absence of strong supervision using self-supervised learning. We demonstrate that we can learn structural representations of objects using an autoencoding framework with reconstruction as the key learning signal. We do this by engineering bottlenecks that disentangle object structure from other factors of variation. Moreover, we design the bottlenecks to represent the object structure in the form of 2D and 3D object landmarks or 3D mesh. Specifically, we develop a method that automatically discovers 2D object landmarks without any annotations using a conditional autoencoder with 2D keypoint bottleneck that disentangles pose, represented as 2D keypoints, and appearance. Despite the ability of self-supervised learning methods to learn stable object landmarks, the automatically discovered landmarks are not aligned with landmarks that would be annotated by human annotators. To address this, we present a method that can inject an unpaired empirical prior into a conditional autoencoder by introducing a novel landmark autoencoding that can leverage powerful image discriminators used in adversarial learning. A by-product of these conditional autoencoding methods is that the generation can be interactively controlled by manipulating the keypoints in the bottleneck. We leverage this feature in a novel method for interactive 3D shape deformation. The method is trained in a self-supervised way to use automatically discovered 3D landmarks to align pairs of 3D shapes. In the test time, the method allows the user to interactively deform the object shape via the discovered 3D object landmarks. Finally, we present a method that uses a photo-geometric autoencoder to recover 3D shape of an object category without any 3D annotations. It uses videos for training and learns to disentangle an image input into a rigid pose, texture and deformable shape model.
Impact of pre-training on background knowledge and societal bias
Keywords: knowledge base completion
commonsense reasoning
natural language processing
bias detection
pre-training
fairness in AI

With appropriate pre-training on unstructured text, larger and more accurate neural network models can be trained. Unfortunately, unstructured pre-training data may contain undesired societal biases, which a model may mimic and amplify. This thesis focuses on both improving unsupervised pre-training and developing diagnostics of obtained pre-trained models for potential undesired behaviour.

Pre-training and diagnostics are done on two tasks: coreference resolution and knowledge base completion. For both of them, a novel task-specific method for unsupervised pre-training is introduced. Then, the obtained models are analysed for potential undesired behaviour by evaluating them on relevant datasets, focusing on gender bias in particular.

Two novel pre-training datasets for coreference resolution are introduced, MaskedWiki and WikiCREM. By fine-tuning on these datasets, state-of-the-art performance on multiple benchmarks is achieved, including on the Winograd Schema Challenge, a commonsense reasoning benchmark that requires a lot of background knowledge. The obtained pre-trained models are then evaluated on the Gap benchmark. On this benchmark, potentially problematic patterns in the test set are demonstrated. To remove these undesired patterns, a novel test sample weighting method and a proof of its correctness are introduced.

A method for pre-training in knowledge base completion is introduced, the first of its kind, significantly improving the results on multiple smaller datasets. The obtained models outperform much larger and highly trained models, which are trained on more general language-modelling tasks. To better understand the behaviour of the obtained models for knowledge base completion, the first diagnostic dataset for pre-trained knowledge base completion models is introduced, demonstrating how stereotypes in the pre-training data can affect the predictions of a model on the target knowledge base.

The future developments of both task-specific pre-training and bias detection are discussed, motivating future research directions in the field.
Elucidating the effects of DNA modification on transcription
Keywords: epigenetics
TAPS
DNA methylation
PRO-Cap
HEK293
RNA-Seq
5hmC
PRO-Seq
transcription
hydroxymethylation
TAPS-Beta
5mC

DNA modifications play important roles in genome function of vertebrates. 5-methylcytosine (mC) has been extensively studied over the last 50 years as the most common covalent DNA modification, present at roughly 70% of CpGs in the human genome. At promoters it is mostly associated with silencing of genes. An oxidative product of mC, 5-hydroxymethylcytosine (hmC), was found to be common in brain cells in 2007, and studies have attempted to elucidate the function of this stable modification ever since. Although gene silencing through promoter-proximal DNA methylation has been extensively described, the correlation between abundance of mC and hmC in gene bodies and expression is less well understood.

In this study we investigate the relationship between DNA modifications, active transcription, and gene expression. Using a system of global mC and hmC perturbation, we contrast the effects of mC loss (mC to C) and hmC gain (mC to hmC) on different transcription stages: initiation (sense and antisense), pausing, and elongation. The induced changes in transcription are modest but proportional to the changes in DNA modifications, and observations are consistent across multiple analyses.

mC loss is more disruptive to actively transcribing RNA polymerase (RNAP) occupancy and gene expression compared to hmC gain. The effects of hmC gain are modest, but both mC loss and hmC gain result in upregulated initiation (both canonical and spurious), elongation, pause-release, and gene expression. Overall we observe quantitative differences between loss of mC and gain oh hmC suggesting that hmC may have a role beyond intermediate of demethylation.
All-optical associative learning element
Keywords: Integrated optics
optical computing

This thesis examines the idea that Pavlovian associative learning may be a building block in neural networks. With emphasis on hardware acceleration, on-chip optical associative learning device is conceived as an alternative to artificial neural networks (ANNs) hardware for high-speed applications. It is well known that ‘conventional’ ANNs, particularly in the form of modern deep neural networks (DNNs), are usually carried out using the backpropagation method. In the method, network weights are updated such that the net output better resembles the desired output after each forward-backward iteration, until convergence is met. A distinct approach is presented using associative learning as the basis of AI learning process. In formulating the architecture, associative learning is linked with supervised learning, based on their common goal of associating certain inputs with ‘correct’ outputs. The intuition leads to a reductionist framework to the problem, facilitating the transition from a single (or monadic) Pavlovian single Input-Teacher association to any arbitrary n Input-Teacher associations.
The hardware associative learning network is realised using silicon-based on-chip optical waveguides. Optical implementation of the learning network enables simultaneous parallel calculations using wavelength division multiplexing (WDM), which increases the capacity of AI information processing. This is adopted in the work described in this thesis. Monolithic integration of waveguide components, through which optical signals are channeled, augurs well for its adoption as artificial intelligence (AI) hardware accelerator. Machine learning using associative learning hardware network is demonstrated, with the network solving pattern and image recognition tasks. The patterns are randomised optical signals binary pattern, while the images are the discretised 72x72 patterns in the form of image pixels. Computational density of 118 TOPS/mm² is demonstrated, limited only by the available setup in the laboratory. The density is estimated to reach approximately 2.5× 104 TOPS/mm², with 50 GHz modulation using 16 wavelengths. In solving the image recognition task, the hardware network is shown to be able to create a predictive model capable of generalising a set of patterns, instead of recognising only one particular pattern.
The optical associative learning device paves the way for further realisations of photonic intelligent systems that can infer both symbolically and numerically via associations. Further improvements in other relevant metrics (for example, volume and learning energy) are anticipated on different material platform and/or with other optimisation methods. These will be reserved for a future time when they shall have been more completely worked out. The device realisation potentially opens up new avenues of research in machine learning architectures and algorithms.
Using ultrasound to enhance targeted radiotherapy
Keywords: radiation
microparticles
radioembolic
HIFU
intraoperative therapy
ultrasound
cavitation
glioblastoma
radiotherapy
GBM
cavitation nuclei
acoustic radiation
cancer
cloud cavitation
SIRT
adjuvant therapy
microbubbles
medical device
tissue phantom
high-speed imaging

The use of radiation as a selective modality for the treatment of cancer and various other diseases is a fundamental principle in modern medicine. However, despite decades of research and development, selective internal radiation therapy (SIRT), the arterial delivery of radioactive particles for the treatment of hepatic tumours, remains a poorly utilised technology outside of the liver. A major challenge for SIRT is the current lack of control over the distribution of particles in the affected tissue. Achieving a uniform distribution throughout the tumour mass is critical for ensuring a therapeutically relevant dose of radiation and hence successful treatment. Cavitation of microbubbles has been successfully employed to facilitate the extravasation of various nanoparticles and therapeutic agents in tissue. It is hypothesised that ultrasound induced cavitation has the potential to move larger micron sized particles used within SIRT, to increase the distribution of the therapeutic microspheres. The primary goal of this doctoral thesis is to investigate the potential use of ultrasound-induced cavitation to enhance SIRT by facilitating transport of radioactive microspheres in tissue.
A secondary goal of the thesis is to explore its use in the treatment of other types of cancer, in particular Glioblastoma Multiforme; a particularly grave cancer of high incidence. First, a tissue phantom and experimental protocol are designed to enable observation of the transport of microspheres induced by ultrasound exposure using high resolution X-ray computed tomography (µCT) imaging. Proof of concept results are obtained to confirm the feasibility of the approach. An analysis framework and image processing software are then employed to enable quantification of microsphere transport in three dimensions and the corresponding radiation exposure that could be achieved. A detailed investigation of the parameter space is then made using a statistical Design-of-Experiments methodology to identify the conditions under which a suitable microsphere distribution can be achieved; peak negative pressure correlated non-linearly with post focal projection depth and could be used to make predictions with 88.4% confidence for 95% of the observations used in the model. Finally, ultra-high speed imaging (0.1-2Mfps) is used to explore the mechanisms by which cavitation promotes microsphere transport; producing novel brightfield images of microbubble cloud activity and mass transport of dense glass microspheres. Overall, the results presented in the thesis demonstrate the feasibility of using ultrasound induced cavitation to enhance SIRT within target tissues not readily treated by an arterial route. Further work to understand the impact of tissue heterogeneity and the design of a bespoke ultrasound transducer will be required to enable clinical translation.