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"Shields, Michael D."
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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
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
Diagnostic accuracy of loop-mediated isothermal amplification as a near-patient test for meningococcal disease in children: an observational cohort study
Diagnosis of meningococcal disease relies on recognition of clinical signs and symptoms that are notoriously non-specific, variable, and often absent in the early stages of the disease. Loop-mediated isothermal amplification (LAMP) has previously been shown to be fast and effective for the molecular detection of meningococcal DNA in clinical specimens. We aimed to assess the diagnostic accuracy of meningococcal LAMP as a near-patient test in the emergency department.
For this observational cohort study of diagnostic accuracy, children aged 0–13 years presenting to the emergency department of the Royal Belfast Hospital for Sick Children (Belfast, UK) with suspected meningococcal disease were eligible for inclusion. Patients underwent a standard meningococcal pack of investigations testing for meningococcal disease. Respiratory (nasopharyngeal swab) and blood specimens were collected from patients and tested with near-patient meningococcal LAMP and the results were compared with those obtained by reference laboratory tests (culture and PCR of blood and cerebrospinal fluid).
Between Nov 1, 2009, and Jan 31, 2012, 161 eligible children presenting at the hospital underwent the meningococcal pack of investigations and were tested for meningococcal disease, of whom 148 consented and were enrolled in the study. Combined testing of respiratory and blood specimens with use of LAMP was accurate (sensitivity 89% [95% CI 72–96], specificity 100% [97–100], positive predictive value 100% [85–100]; negative predictive value 98% [93–99]) and diagnostically useful (positive likelihood ratio 213 [95% CI 13–infinity] and negative likelihood ratio 0·11 [0·04–0·32]). The median time required for near-patient testing from sample to result was 1 h 26 min (IQR 1 h 20 min–1 h 32 min).
Meningococcal LAMP is straightforward enough for use in any hospital with basic laboratory facilities, and near-patient testing with this method is both feasible and effective. By contrast with existing UK National Institute of Health and Care Excellence guidelines, we showed that molecular testing of non-invasive respiratory specimens from children is diagnostically accurate and clinically useful.
Health and Social Care Research and Development, Public Health Agency, Northern Ireland.
Journal Article
Active learning applied to automated physical systems increases the rate of discovery
Active machine learning is widely used in computational studies where repeated numerical simulations can be conducted on high performance computers without human intervention. But translation of these active learning methods to physical systems has proven more difficult and the accelerated pace of discoveries aided by these methods remains as yet unrealized. Through the presentation of a general active learning framework and its application to large-scale boundary layer wind tunnel experiments, we demonstrate that the active learning framework used so successfully in computational studies is directly applicable to the investigation of physical experimental systems and the corresponding improvements in the rate of discovery can be transformative. We specifically show that, for our wind tunnel experiments, we are able to achieve in approximately 300 experiments a learning objective that would be impossible using traditional methods.
Journal Article
Revealing the hidden structure of disordered materials by parameterizing their local structural manifold
Durable interest in developing a framework for the detailed structure of glassy materials has produced numerous structural descriptors that trade off between general applicability and interpretability. However, none approach the combination of simplicity and wide-ranging predictive power of the lattice-grain-defect framework for crystalline materials. Working from the hypothesis that the local atomic environments of a glassy material are constrained by enthalpy minimization to a low-dimensional manifold in atomic coordinate space, we develop a generalized distance function, the Gaussian Integral Inner Product (GIIP) distance, in connection with agglomerative clustering and diffusion maps, to parameterize that manifold. Applying this approach to a two-dimensional model crystal and a three-dimensional binary model metallic glass results in parameters interpretable as coordination number, composition, volumetric strain, and local symmetry. In particular, we show that a more slowly quenched glass has a higher degree of local tetrahedral symmetry at the expense of cyclic symmetry. While these descriptors require post-hoc interpretation, they minimize bias rooted in crystalline materials science and illuminate a range of structural trends that might otherwise be missed.
The structure of crystalline materials plays a central role in materials science, but the disordered structure of metallic glass is difficult to characterize and describe. Here, the authors use diffusion maps on atomistic data to obtain general structural descriptors tied to atomic positions.
Journal Article
What causes prescribing errors in children? Scoping review
Objectives(1) Systematically assemble, analyse and synthesise published evidence on causes of prescribing error in children. (2) Present results to a multidisciplinary group of paediatric prescribing stakeholders to validate findings and establish how causative factors lead to errors in practice.DesignScoping review using Arksey and O’Malley’s framework, including stakeholder consultation; qualitative evidence synthesis.MethodsWe followed the six scoping review stages. (1) Research question—the research question was ‘What is known about causes of prescribing error in children?’ (2) Search strategy—we searched MEDLINE, EMBASE, CINAHL (from inception to February 2018), grey literature and reference lists of included studies. (3) Article selection—all published evidence contributing information on the causes of prescribing error in children was eligible for inclusion. We included review articles as secondary evidence to broaden understanding. (4) Charting data—results were collated in a custom data charting form. (5) Reporting results—we summarised article characteristics, extracted causal evidence and thematically synthesised findings. (6) Stakeholder consultation—results were presented to a multidisciplinary focus group of six prescribing stakeholders to establish validity, relevance and mechanisms by which causes lead to errors in practice.Results68 articles were included. We identified six main causes of prescribing errors: children’s fundamental differences led to individualised dosing and calculations; off-licence prescribing; medication formulations; communication with children; and experience working with children. Primary evidence clarifying causes was lacking.ConclusionsSpecific factors complicate prescribing for children and increase risk of errors. Primary research is needed to confirm and elaborate these causes of error. In the meantime, this review uses existing evidence to make provisional paediatric-specific recommendations for policy, practice and education.
Journal Article
Budgeting, Psychological Contracts, and Budgetary Misreporting
This study examines the effect of psychological contract breach on budgetary misreporting. Psychological contracts are mental models or schemas that govern how employees understand their exchange relationships with their employers. Psychological contract breach leads to feelings of violation and can occur even when employees’ economic contracts are fulfilled. We study the effects of psychological contract breach on three common types of employee participation in budgeting that differ in the degree of employees’ influence over their approved budgets. These include affirmative budgeting (full influence), consultative budgeting (moderate influence), and authoritative budgeting (low influence). When organizations communicate that employees will be involved in budgeting, employees develop psychological contracts of affirmative budgeting. If employees subsequently experience authoritative or consultative budgeting, their psychological contracts are breached. Employees who experience psychological contract breach seek redress through budgetary misreporting. Experimental results indicate that psychological contract breach partially mediates the relation between budgeting type and budgetary misreporting. Results also indicate asymmetry in the effects of psychological contract breach versus repair. Effects of breach on budgetary misreporting persist even after the breach no longer occurs.
This paper was accepted by Shivaram Rajgopal, accounting.
The online appendix is available at
https://doi.org/10.1287/mnsc.2018.3067
.
Journal Article
EGFR Interacts with the Fusion Protein of Respiratory Syncytial Virus Strain 2-20 and Mediates Infection and Mucin Expression
Respiratory syncytial virus (RSV) is the major cause of viral lower respiratory tract illness in children. In contrast to the RSV prototypic strain A2, clinical isolate RSV 2-20 induces airway mucin expression in mice, a clinically relevant phenotype dependent on the fusion (F) protein of the RSV strain. Epidermal growth factor receptor (EGFR) plays a role in airway mucin expression in other systems; therefore, we hypothesized that the RSV 2-20 F protein stimulates EGFR signaling. Infection of cells with chimeric strains RSV A2-2-20F and A2-2-20GF or over-expression of 2-20 F protein resulted in greater phosphorylation of EGFR than infection with RSV A2 or over-expression of A2 F, respectively. Chemical inhibition of EGFR signaling or knockdown of EGFR resulted in diminished infectivity of RSV A2-2-20F but not RSV A2. Over-expression of EGFR enhanced the fusion activity of 2-20 F protein in trans. EGFR co-immunoprecipitated most efficiently with RSV F proteins derived from \"mucogenic\" strains. RSV 2-20 F and EGFR co-localized in H292 cells, and A2-2-20GF-induced MUC5AC expression was ablated by EGFR inhibitors in these cells. Treatment of BALB/c mice with the EGFR inhibitor erlotinib significantly reduced the amount of RSV A2-2-20F-induced airway mucin expression. Our results demonstrate that RSV F interacts with EGFR in a strain-specific manner, EGFR is a co-factor for infection, and EGFR plays a role in RSV-induced mucin expression, suggesting EGFR is a potential target for RSV disease.
Journal Article