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Fast Inexact Bilevel Optimization for Analytical Deep Image Priors
Get PDFThe analytical deep image prior (ADP) introduced by Dittmer et al. (2020) establishes a link between deep image priors and classical regularization theory via bilevel optimization. While this is an elegant construction, it involves expensive computations if the lower-level problem is to be solved accurately. To overcome this issue, we propose to use adaptive inexact bilevel optimization to solve ADP problems. We discuss an extension of a recent inexact bilevel method called the method of adaptive inexact descent of Salehi et al.(2024) to an infinite-dimensional setting required by the ADP framework. In our numerical experiments we demonstrate that the computational speed-up achieved by adaptive inexact bilevel optimization allows one to use ADP on larger-scale problems than in the previous literature, e.g. in deblurring of 2D color images
Decoding the variable velocity of lower-limb stepping movements from EEG
Get PDFAccurate decoding of lower-limb movement from electroencephalography (EEG) is essential for developing brain-computer interface (BCI) controlled exoskeletons in neurorehabilitation. This study investigates 3D velocity decoding at three fibular anatomical markers during overground stepping in healthy participants (N=9), using two approaches: (1) linear regression (LR) and (2) a deep learning (DL) framework combining convolutional neural networks (CNNs) and long short-term memory (LSTM) units. Participants were divided into two groups: G1 (n=5) performed cued forward and self-paced backward steps; G2 (n=4) performed cued forward and backward steps. The DL model significantly outperformed LR, achieving highest decoding accuracy (DA) in the forward-backward direction at the fibular head (R = 0.63±0.06, M±SD). Topographical analysis identified dominant contributions from the sensorimotor cortex (coupled with frontal regions in G2) within the 8-40 Hz band. Functional connectivity (FC) analysis revealed significant differences: only G2 showed statistically significant FC (p<0.05), likely reflecting increased cognitive and sensorimotor demands under dual-cue conditions. In G2, FC occurred across delta (0-4 Hz), theta (4-8 Hz), alpha/mu (8-12 Hz), and low-beta (12-18 Hz) bands, linking motor areas associated with lower- and upper-limb control to other cortical regions, including the middle temporal gyrus (MTG), superior frontal gyrus (SFG), posterior cingulate cortex (PCC), superior parietal lobule (SPL), and supramarginal gyrus (SMG). These findings demonstrate that EEG-based 3D decoding of lower-limb kinematics is feasible during realistic locomotor tasks and suggest that cortical synchronization patterns vary with movement context. Our CNN-LSTM framework may support adaptive, intent-driven exoskeleton development for personalized neurorehabilitation.</p
Physico-Chemical Characterisation of Particulate Matter and Ash from Biomass Combustion in Rural Indian Kitchens
Get PDFIn developing countries, indoor air pollution in rural areas is often attributed to the use of solid biomass fuels for cooking. Such fuels generate particulate matter (PM), carbon monoxide (CO), carbon dioxide (CO2), polyaromatic hydrocarbons (PAHs), and volatile organic compounds (VOCs). PM created from biomass combustion is a pollutant particularly damaging to health. This rigorous study employed a personal sampling device and multi stage cascade impactor to collect airborne PM (including PM2.5) and deposited ash from 20 real-world kitchen microenvironments. A robust analysis of the PM was undertaken using a range of morphological, physical, and chemical techniques, the results of which were then compared to a controlled burn experiment. Results revealed that airborne PM was predominantly carbon (~85%), with the OC/EC ratio varying between 1.17 and 11.5. Particles were primarily spherical nanoparticles (50–100 nm) capable of deep penetration into the human respiratory tract (HRT). This is the first systematic characterisation of biomass cooking emissions in authentic rural kitchen settings, linking particle morphology, chemistry and toxicology at health-relevant scales. Toxic heavy metals like Cr, Pb, Cd, Zn, and Hg were detected in PM, while ash was dominated by crustal elements such as Ca, Mg and P. VOCs comprised benzene derivatives, esters, ethers, ketones, tetramethysilanes(TMS), and nitrogen-, phosphorus- and sulphur-containing compounds. This researchshowcases a unique collection technique that gathered particles indicative of their potential for penetration and deposition in the HRT. Impact stems from the close link between the physico-chemical properties of particle emissions and their environmental and epidemiological effects. By providing a critical evidence base for exposure modelling, risk assessment and clean cooking interventions, this study delivers internationally significant insights. Our methodological innovation, capturing respirable nanoparticles under real-world conditions, offers a transferable framework for indoor air quality research across low- and middle-income countries. The findings therefore advance both fundamental understanding of combustion-derived nanoparticle behaviour and practical knowledge to inform public health, environmental policy, and the UN Sustainable Development Goals
Dataset for "Effects of a combined energy restriction and vigorous-intensity exercise intervention on the human gut microbiome: A randomised controlled trial"
No full textThis CSV file contains the raw data for all the anthropological, physiological, and biochemical parameters for each human participant, pre and post intervention or control reported in the associated article in the Journal of Physiology
Dataset for "The Catalytic Enantioselective [1,2]-Wittig Rearrangement Cascade of Allylic Ethers"
No full textThis data set includes output files from the quantum chemical calculations run with Gaussian16 (Revision C.01) that support our computational mechanistic study of the enantioselective [1,2]-Wittig rearrangement of allylic ethers. It also contains three sets of in situ reaction monitoring data (collected by University of St Andrews contributors) and a Python script that fits the rate constants of a first-order kinetics model to the experimental data
Comic Relief:Sport for Change Research
Get PDFThis report presents the findings of a piece of independent research which sought to examine Comic Relief’s ‘Sport for Change’ funding approach. It charts the operationalisation and impact of this approach which, since 2002, has invested £80 million into projects in the UK and internationally. During that time over 500 projects have been funded which have used a variety of sports, including skateboarding, surfing, football, martial arts, yoga and boxing, to promote an array of social issues such as: education, employment, mental health, gender equality, social inclusion
Tetramethylrhodamine self-quenching is a probe of conformational change on the scale of 15 - 25 Å
Get PDFTetramethylrhodamine (TMR) is a fluorescent dye whose self-quenching has been used as a probe of multiple biological phenomena. We determine the distance-dependence of self-quenching and place bounds on the timescale of TMR dissociation. Our results validate fluorescence self-quenching as an alternative to FRET and enable future assays to be designed with confidence.</p
Can a novel computer vision-based framework detect head-on-head impacts during a rugby league tackle?
Get PDFBackground: Head-on-head impacts are a risk factor for concussion, which is a concern for sports. Computer vision frameworks may provide an automated process to identify head-on-head impacts, although this has not been applied or evaluated in rugby. Methods: This study developed and evaluated a novel computer vision framework to automatically classify head-on-head and non-head-on-head impacts. Tackle events from professional rugby league matches were coded as either head-on-head or non-head-on-head impacts. These included non-televised standard-definition and televised high-definition video clips to train (n=341) and test (n=670) the framework. A computer vision framework consisting of two deep learning networks, an object detection algorithm and three-dimensional Convolutional Neural Networks, was employed and compared with the analyst-coded criterion. Sensitivity, specificity and positive predictive value were reported. Results: The overall performance evaluation of the framework to classify head-on-head impacts against manual coding had a sensitivity, specificity and positive predictive value (95% CIs) of 68% (58% to 78%), 84% (78% to 88%) and 0.61 (0.54 to 0.69) in standard-definition clips, and 65% (55% to 75%), 84% (79% to 89%) and 0.61 (0.53 to 0.68) in high-definition clips. Conclusion: The study introduces a novel computer vision framework for head-on-head impact detection. Governing bodies may also use the framework in real time, or for retrospective analysis of historical videos, to establish head-on-head rates and evaluate prevention strategies. Future work should explore the application of the framework to other head-contact mechanisms and also the utility in real time to identify potential events for clinical assessment.</p
Tetramethylrhodamine self-quenching is a probe of conformational change on the scale of 15 - 25 Å
Get PDFTetramethylrhodamine (TMR) is a fluorescent dye whose self-quenching has been used as a probe of multiple biological phenomena. We determine the distance-dependence of self-quenching and place bounds on the timescale of TMR dissociation. Our results validate fluorescence self-quenching as an alternative to FRET and enable future assays to be designed with confidence.</p
A Theoretical Framework for Explaining Reinforcement Learning with Shapley Values
Get PDFReinforcement learning agents can achieve super-human performance in complex decision-making tasks, but their behaviour is often difficult to understand and explain. This lack of explanation limits deployment, especially in safety-critical settings where understanding and trust are essential. We identify three core explanatory targets that together provide a comprehensive view of reinforcement learning agents: behaviour, outcomes, and predictions. We develop a unified theoretical framework for explaining these three elements of reinforcement learning agents through the influence of individual features that the agent observes in its environment. We derive feature influences by using Shapley values, which collectively and uniquely satisfy a set of well-motivated axioms for fair and consistent credit assignment. The proposed approach, Shapley Values for Explaining Reinforcement Learning (SVERL), provides a single theoretical framework to comprehensively and meaningfully explain reinforcement learning agents. It yields explanations with precise semantics that are not only interpretable but also mathematically justified, enabling us to identify and correct conceptual issues in prior explanations. Through illustrative examples, we show how SVERL produces useful, intuitive explanations of agent behaviour, outcomes, and predictions, which are not apparent from observing agent behaviour alone
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