UWL Repository and open access for teaching and learning

This poster presents the benefits of open access for teachers and learners. It outlines: how open access publications may be integrated into teaching; the particular benefits of open access for undergraduate, masters and doctoral students undertaking research; and, the advantages for academics to ensure their publications are made available open access wherever possible. It also highlights the strategies taken by the UWL Library Repository Team to promote open access within the university, and how support may be sought if required

UWL Repository and open access for researchers

This poster presents the various benefits of making work open access via an institutional repository such as the UWL Repository. These benefits include: making work more accessible for researchers around the world; allowing NGOs, businesses, practitioners, policy makers and journalists the potential for free access to research; and, various academic advantages such as the possibility of increased citation rates and the potential for finding new collaborators for research. The poster also outlines the steps that have been made to improve repository discoverability, and presents statistics that show a sharp increase in repository usage over the past six months

Principled and Efficient Motif Finding for Structure Learning of Lifted Graphical Models

Structure learning is a core problem in AI central to the fields of neuro-symbolic AI and statistical relational learning. It consists in automatically learning a logical theory from data. The basis for structure learning is mining repeating patterns in the data, known as structural motifs. Finding these patterns reduces the exponential search space and therefore guides the learning of formulas. Despite the importance of motif learning, it is still not well understood. We present the first principled approach for mining structural motifs in lifted graphical models, languages that blend first-order logic with probabilistic models, which uses a stochastic process to measure the similarity of entities in the data. Our first contribution is an algorithm, which depends on two intuitive hyperparameters: one controlling the uncertainty in the entity similarity measure, and one controlling the softness of the resulting rules. Our second contribution is a preprocessing step where we perform hierarchical clustering on the data to reduce the search space to the most relevant data. Our third contribution is to introduce an O(n ln n) (in the size of the entities in the data) algorithm for clustering structurally-related data. We evaluate our approach using standard benchmarks and show that we outperform state-of-the-art structure learning approaches by up to 6% in terms of accuracy and up to 80% in terms of runtime.Comment: Submitted to AAAI23. 9 pages. Appendix include

Numerics with coordinate transforms for efficient Brownian dynamics simulations

Many stochastic processes in the physical and biological sciences can be modelled as Brownian dynamics with multiplicative noise. However, numerical integrators for these processes can lose accuracy or even fail to converge when the diffusion term is configuration-dependent. One remedy is to construct a transform to a constant-diffusion process and sample the transformed process instead. In this work, we explain how coordinate-based and time-rescaling-based transforms can be used either individually or in combination to map a general class of variable-diffusion Brownian motion processes into constant-diffusion ones. The transforms are invertible, thus allowing recovery of the original dynamics. We motivate our methodology using examples in one dimension before then considering multivariate diffusion processes. We illustrate the benefits of the transforms through numerical simulations, demonstrating how the right combination of integrator and transform can improve computational efficiency and the order of convergence to the invariant distribution. Notably, the transforms that we derive are applicable to a class of multibody, anisotropic Stokes-Einstein diffusion that has applications in biophysical modelling

“I thought I’m better off just trying to put this behind me” – a contemporary approach to understanding why women decide not to report sexual violence

Sexual offence disclosures are on the rise, thought to be the result of growing numbers of prosecutions brought against well-known public figures and mobilisation of movements such as #MeToo. Despite this, data continue to indicate that most victim-survivors will never report their abuse. This study aimed to explore why women continue to decide not to report sexual assault to the police. Secondary data were collated and analysed, pertaining to survivor accounts of sexual assault, posted in response to a prominent online video entitled ‘Women Tell Us Why They Didn’t Report Their Sexual Assault’. Thematic analysis revealed three main themes regarding why women chose not to report: (1) Lack of faith in the Criminal Justice System (encompassing two sub-themes, no evidence and traumatisation of reporting), (2) Self-blame, and (3) Knowing the perpetrator. Practical applications and reforms concerning empathic police responses and CJS improvements surrounding timeliness, case progression, and conviction rates are discussed

The genome sequence of the black arches, Lymantria monacha (Linnaeus, 1758)

We present a genome assembly from an individual male Lymantria monacha (the black arches; Arthropoda; Insecta; Lepidoptera; Erebidae). The genome sequence is 916 megabases in span. The majority of the assembly (99.99%) is scaffolded into 28 chromosomal pseudomolecules, with the Z sex chromosome assembled. The mitochondrial genome was also assembled, and is 15.6 kilobases in length

The genome sequence of the early thorn, Selenia dentaria (Fabricius, 1775)

We present a genome assembly from an individual male Selenia dentaria (the Early Thorn; Arthropoda; Insecta; Lepidoptera; Geometridae). The genome sequence is 1,030.8 megabases in span. Most of the assembly is scaffolded into 30 chromosomal pseudomolecules, including the Z sex chromosome. The mitochondrial genome has also been assembled and is 15.41 kilobases in length. Gene annotation of this assembly on Ensembl identified 21,390 protein coding genes

The genome sequence of the dusky thorn, Ennomos fuscantarius (Haworth, 1809)

We present a genome assembly from an individual male Ennomos fuscantarius (the Dusky Thorn; Arthropoda; Insecta; Lepidoptera; Geometridae). The genome sequence is 444.9 megabases in span. Most of the assembly is scaffolded into 31 chromosomal pseudomolecules, including the Z sex chromosome. The mitochondrial genome has also been assembled and is 15.49 kilobases in length. Gene annotation of this assembly on Ensembl identified 12,173 protein coding genes

The genome sequence of the August thorn, Ennomos quercinarius (Hufnagel, 1767)

We present a genome assembly from an individual male Ennomos quercinarius (the August Thorn; Arthropoda; Insecta; Lepidoptera; Geometridae). The genome sequence is 491.9 megabases in span. Most of the assembly is scaffolded into 31 chromosomal pseudomolecules, including the Z sex chromosome. The mitochondrial genome has also been assembled and is 15.68 kilobases in length. Gene annotation of this assembly on Ensembl identified 11,355 protein coding genes