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"Shields, Michael"
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Learning nonlinear operators in latent spaces for real-time predictions of complex dynamics in physical systems
Predicting complex dynamics in physical applications governed by partial differential equations in real-time is nearly impossible with traditional numerical simulations due to high computational cost. Neural operators offer a solution by approximating mappings between infinite-dimensional Banach spaces, yet their performance degrades with system size and complexity. We propose an approach for learning neural operators in latent spaces, facilitating real-time predictions for highly nonlinear and multiscale systems on high-dimensional domains. Our method utilizes the deep operator network architecture on a low-dimensional latent space to efficiently approximate underlying operators. Demonstrations on material fracture, fluid flow prediction, and climate modeling highlight superior prediction accuracy and computational efficiency compared to existing methods. Notably, our approach enables approximating large-scale atmospheric flows with millions of degrees, enhancing weather and climate forecasts. Here we show that the proposed approach enables real-time predictions that can facilitate decision-making for a wide range of applications in science and engineering.
Real-time prediction of dynamics for complex physical systems governed by partial differential equations is challenging and computationally expensive. The authors propose a framework for learning neural operators in latent spaces that allows real-time predictions of high-dimensional nonlinear systems.
Journal Article
Deep transfer operator learning for partial differential equations under conditional shift
Transfer learning enables the transfer of knowledge gained while learning to perform one task (source) to a related but different task (target), hence addressing the expense of data acquisition and labelling, potential computational power limitations and dataset distribution mismatches. We propose a new transfer learning framework for task-specific learning (functional regression in partial differential equations) under conditional shift based on the deep operator network (DeepONet). Task-specific operator learning is accomplished by fine-tuning task-specific layers of the target DeepONet using a hybrid loss function that allows for the matching of individual target samples while also preserving the global properties of the conditional distribution of the target data. Inspired by conditional embedding operator theory, we minimize the statistical distance between labelled target data and the surrogate prediction on unlabelled target data by embedding conditional distributions onto a reproducing kernel Hilbert space. We demonstrate the advantages of our approach for various transfer learning scenarios involving nonlinear partial differential equations under diverse conditions due to shifts in the geometric domain and model dynamics. Our transfer learning framework enables fast and efficient learning of heterogeneous tasks despite considerable differences between the source and target domains.
A promising area for deep learning is in modelling complex physical processes described by partial differential equations (PDEs), which is computationally expensive for conventional approaches. An operator learning approach called DeepONet was recently introduced to tackle PDE-related problems, and in new work, this approach is extended with transfer learning, which transfers knowledge obtained from learning to perform one task to a related but different task.
Journal Article
Relative Income, Happiness, and Utility: An Explanation for the Easterlin Paradox and Other Puzzles
The well-known Easterlin paradox points out that average happiness has remained constant over time despite sharp rises in GNP per head. At the same time, a micro literature has typically found positive correlations between individual income and individual measures of subjective well-being. This paper suggests that these two findings are consistent with the presence of relative income terms in the utility function. Income may be evaluated relative to others (social comparison) or to oneself in the past (habituation). We review the evidence on relative income from the subjective well-being literature. We also discuss the relation (or not) between happiness and utility, and discuss some nonhappiness research (behavioral, experimental, neurological) related to income comparisons. We last consider how relative income in the utility function can affect economic models of behavior in the domains of consumption, investment, economic growth, savings, taxation, labor supply, wages, and migration.
Journal Article
Mobile direct observation of therapy (MDOT) - A rapid systematic review and pilot study in children with asthma
We describe, for the first time, the use of a mobile device platform for remote direct observation of inhaler use and technique. The research programme commenced with a rapid systematic review of mobile device (or videophone) use for direct observation of therapy (MDOT). Ten studies (mainly pilots) were identified involving patients with tuberculosis, sickle cell disease and Alzheimer's disease. New studies are ongoing (ClinicalTrials.gov website) in TB, stroke, sickle cell disease, HIV and opioid dependence. Having identified no prior use of MDOT in inhaler monitoring, we implemented a feasibility study in 12 healthy volunteer children (2-12 years; 8 females and 4 males) over a period of 14 days, with twice daily video upload of their 'dummy' inhaler use. Two children uploaded 100% of the requested videos, with only one child having an inhaler upload rate of <75%. The quality of uploaded videos was generally good (only 1.7% of unacceptable quality for evaluation). The final aspect of the research was a pilot study using MDOT (6 weeks) in 22 children with difficult to treat asthma. Healthcare professionals evaluated inhaler technique using uploaded videos and provided telephone instruction on improving inhaler use. The main outcomes were assessed at week 12 post initiation of MDOT. By week 5, all children still engaging in MDOT (n = 18) were judged to have effective inhaler technique. Spirometry values did not vary to a significantly significant degree between baseline and 12 weeks (P>0.05), however, mean fraction of exhaled nitric oxide (FeNO) values normalised (mean 38.7 to 19.3ppm) and mean Asthma Control Test values improved (13.1 to mean 17.8). Feedback from participants was positive. Overall the findings open up a new paradigm in device independent (can be used for any type of inhaler device) monitoring, providing a platform for evaluating / improving inhaler use at home.
Journal Article
VICTIMISATION, WELL-BEING AND COMPENSATION: USING PANEL DATA TO ESTIMATE THE COSTS OF VIOLENT CRIME
The costs of violent crime victimisation are often left to a tribunal, judge or jury to determine, which can lead to considerable subjectivity and variation. Using panel data, this article provides compensation estimates that help reduce the subjectivity of awards by providing a benchmark for the compensation required to offset direct and intangible costs. Individual-area fixed-effects models of well-being that allow for adaptation and the endogeneity of income suggest that, on average, A$88,000 is required to compensate a violent crime victim, with the amount being greater for females (A$102,000) than males (A$79,000).
Journal Article
Medication Adherence in Children with Asthma
Asthma is the most common chronic disease in childhood. If untreated, asthma can lead to debilitating daily symptoms which affect quality of life, but more importantly can lead to fatal asthma attacks which unfortunately still occur globally. The most effective treatment strategy for controlling asthma is for the patient to follow a personalised asthma action plan (PAAP) which will invariably include regular use of an inhaled corticosteroid. To examine medication adherence in children with asthma, we collated recent evidence from systematic reviews in this area to address the following 5 key questions; What is adherence? Is there evidence that children are not adhering to preventer medication? Why is adherence poor and what are the barriers to adherence? Does good adherence improve outcomes in asthma? And lastly, how can treatment adherence be improved?
Journal Article
Life Satisfaction Dynamics with Quarterly Life Event Data
Using life satisfaction responses from Australian panel data we examine the questions of when and to what extent individuals are affected by major positive and negative life events, including changes in financial situation, marital status, death of a close relative, and being the victim of crime. The key advantage of our data is that we are able to identify these events on a quarterly basis rather than on the yearly basis used by previous studies. We find evidence that life events are not randomly distributed, that individuals anticipate major events to a large extent, and that they fully adapt to many events within 12 months. The estimates can be used to calculate monetary values needed to compensate individuals for life events. Using a new valuation methodology that incorporates these dynamic factors produces considerably smaller compensation valuations than those calculated using the standard approach.
Journal Article
Review of the Safety, Efficacy and Tolerability of Palivizumab in the Prevention of Severe Respiratory Syncytial Virus (RSV) Disease
Respiratory Syncytial Virus (RSV) is a major global cause of childhood morbidity and mortality. Palivizumab, a monoclonal antibody that provides passive immunity against RSV, is currently licensed for prophylactic use in specific \"high-risk\" populations, including congenital heart disease, bronchopulmonary dysplasia and prematurity. Available research suggests palivizumab use in these high-risk populations can lead to a reduction in RSV-related hospitalization. However, palivizumab has not been demonstrated to reduce mortality, adverse events or length of hospital stay related to RSV. In this article, we review the management of RSV, indications for palivizumab prophylaxis, the safety, cost-effectiveness and efficacy of this preventative medication, and emerging therapeutics that could revolutionize future prevention of this significant pathogen. Keywords: RSV, palivizumab, efficacy, safety, future directions
Journal Article