A path-integral approach to Bayesian inference for inverse problems using the semiclassical approximation

We demonstrate how path integrals often used in problems of theoretical physics can be adapted to provide a machinery for performing Bayesian inference in function spaces. Such inference comes about naturally in the study of inverse problems of recovering continuous (infinite dimensional) coefficient functions from ordinary or partial differential equations (ODE, PDE), a problem which is typically ill-posed. Regularization of these problems using $L^2$ function spaces (Tikhonov regularization) is equivalent to Bayesian probabilistic inference, using a Gaussian prior. The Bayesian interpretation of inverse problem regularization is useful since it allows one to quantify and characterize error and degree of precision in the solution of inverse problems, as well as examine assumptions made in solving the problem -- namely whether the subjective choice of regularization is compatible with prior knowledge. Using path-integral formalism, Bayesian inference can be explored through various perturbative techniques, such as the semiclassical approximation, which we use in this manuscript. Perturbative path-integral approaches, while offering alternatives to computational approaches like Markov-Chain-Monte-Carlo (MCMC), also provide natural starting points for MCMC methods that can be used to refine approximations. In this manuscript, we illustrate a path-integral formulation for inverse problems and demonstrate it on an inverse problem in membrane biophysics as well as inverse problems in potential theories involving the Poisson equation.Comment: Fixed some spelling errors and the author affiliations. This is the version accepted for publication by J Stat Phy

Jubilee mugs:the monarchy and the Sex Pistols

With rare exceptions sociologists have traditionally had little to say about the British monarchy. In the exceptional cases of the Durkheimian functionalism of Shills and Young (1953), the left humanism of Birnbaum (1955), or the archaic state/backward nation thesis of Nairn (1988), the British nation has been conceived as a homogenous mass. The brief episode of the Sex Pistols' Jubilee year song 'God Save the Queen' exposed some of the divisions within the national 'mass', forcing a re-ordering of the balance between detachment and belonging to the Royal idea. I argue that the song acted as a kind of 'breaching experiment'. Its wilful provocation of Royalist sentiment revealed the level of sanction available to the media-industrial complex to enforce compliance to British self-images of loyal and devoted national communicants

Understanding the assumptions underlying Mendelian randomization

With the rapidly increasing availability of large genetic data sets in recent years, Mendelian Randomization (MR) has quickly gained popularity as a novel secondary analysis method. Leveraging genetic variants as instrumental variables, MR can be used to estimate the causal effects of one phenotype on another even when experimental research is not feasible, and therefore has the potential to be highly informative. It is dependent on strong assumptions however, often producing biased results if these are not met. It is therefore imperative that these assumptions are well-understood by researchers aiming to use MR, in order to evaluate their validity in the context of their analyses and data. The aim of this perspective is therefore to further elucidate these assumptions and the role they play in MR, as well as how different kinds of data can be used to further support them

Multi-Fidelity Data-Driven Design and Analysis of Reactor and Tube Simulations

The development of new manufacturing techniques such as 3D printing have enabled the creation of previously infeasible chemical reactor designs. Systematically optimizing the highly parameterized geometries involved in these new classes of reactor is vital to ensure enhanced mixing characteristics and feasible manufacturability. Here we present a framework to rapidly solve this nonlinear, computationally expensive, and derivative-free problem, enabling the fast prototype of novel reactor parameterizations. We take advantage of Gaussian processes to adaptively learn a multi-fidelity model of reactor simulations across a number of different continuous mesh fidelities. The search space of reactor geometries is explored through an amalgam of different, potentially lower, fidelity simulations which are chosen for evaluation based on weighted acquisition function, trading off information gain with cost of simulation. Within our framework we derive a novel criteria for monitoring the progress and dictating the termination of multi-fidelity Bayesian optimization, ensuring a high fidelity solution is returned before experimental budget is exhausted. The class of reactor we investigate are helical-tube reactors under pulsed-flow conditions, which have demonstrated outstanding mixing characteristics, have the potential to be highly parameterized, and are easily manufactured using 3D printing. To validate our results, we 3D print and experimentally validate the optimal reactor geometry, confirming its mixing performance. In doing so we demonstrate our design framework to be extensible to a broad variety of expensive simulation-based optimization problems, supporting the design of the next generation of highly parameterized chemical reactors.Comment: 22 Pages with Appendi

Machine Learning-Assisted Discovery of Novel Reactor Designs via CFD-Coupled Multi-fidelity Bayesian Optimisation

Additive manufacturing has enabled the production of more advanced reactor geometries, resulting in the potential for significantly larger and more complex design spaces. Identifying and optimising promising configurations within broader design spaces presents a significant challenge for existing human-centric design approaches. As such, existing parameterisations of coiled-tube reactor geometries are low-dimensional with expensive optimisation limiting more complex solutions. Given algorithmic improvements and the onset of additive manufacturing, we propose two novel coiled-tube parameterisations enabling the variation of cross-section and coil path, resulting in a series of high dimensional, complex optimisation problems. To ensure tractable, non-local optimisation where gradients are not available, we apply multi-fidelity Bayesian optimisation. Our approach characterises multiple continuous fidelities and is coupled with parameterised meshing and simulation, enabling lower quality, but faster simulations to be exploited throughout optimisation. Through maximising the plug-flow performance, we identify key characteristics of optimal reactor designs, and extrapolate these to produce two novel geometries that we 3D print and experimentally validate. By demonstrating the design, optimisation, and manufacture of highly parameterised reactors, we seek to establish a framework for the next-generation of reactors, demonstrating that intelligent design coupled with new manufacturing processes can significantly improve the performance and sustainability of future chemical processes.Comment: 11 pages, 8 figure

Keepers of the Metaphorical Gate: The Role of Journal Editors

Academic journals are central to a discipline’s professionalism and are the principal means of communication. The purpose of this symposium is explore the nature academic journals, their purposes and what they reveal about the field from the perspective of nine editors whose primary mission is to cover the broad field of adult education

The Metallicity and Dust Content of HVC 287.5+22.5+240: Evidence for a Magellanic Clouds Origin

We estimate the abundances of S and Fe in the high velocity cloud HVC 287.5+22.5+240, which has a velocity of +240 km/s with respect to the local standard of rest and is in the Galactic direction l~287, b~23. The measurements are based on UV absorption lines of these elements in the Hubble Space Telescope spectrum of NGC 3783, a background Seyfert galaxy, as well as new H I 21-cm interferometric data taken with the Australia Telescope. We find S/H=0.25+/-0.07 and Fe/H=0.033+/-0.006 solar, with S/Fe=7.6+/-2.2 times the solar ratio. The S/H value provides an accurate measure of the chemical enrichment level in the HVC, while the super-solar S/Fe ratio clearly indicates the presence of dust, which depletes the gas-phase abundance of Fe. The metallicity and depletion information obtained here, coupled with the velocity and the position of the HVC in the sky, strongly suggest that the HVC originated from the Magellanic Clouds. It is likely (though not necessary) that the same process(es) that generated the Magellanic Stream is also responsible for HVC 287.5+22.5+240.Comment: AASTEX, 3 postscript figures, AJ, 1998, Jan issu

An Operational Model of Iceberg Drift

A new iceberg drift, deterioration and calving model has been under development at the Canadian Ice Service (CIS). The model includes several new features including the utilization of detailed environmental forcing input, and a robust implicit numerical solution method. In particular, the vertical distribution of water current is incorporated in calculations of water drag force on the iceberg keel. The model is also the first to include treatment of calving, prediction of calved ice piece size distribution and deterioration, as well as the drift of calved pieces. This paper gives a description of the drift model formulation, and verification tests that include comparisons of model predictions with field observations. Additionally, the paper presents the outcome of a parametric study aimed at examining the sensitivity of iceberg drift to input parameters and environmental forcing. Tests examined the role of water and air drag coefficients, water current, wind waves and the waterline length of the iceberg. A number of scenarios of input water current, and wind drag force were also considered. The results determine the impact of the input parameters and variables on predicted iceberg tracks.NRC publication: Ye

Flow of Ice through Long Converging Channels

This paper examines some issues of the flow of ice in long and converging channels which may affect navigation. The emphasis is on pressure distributions and the role of ice properties and tidal currents. The present work employs an ice dynamics model that is based on a viscous plastic constitutive model with an elliptical yield envelope, the thickness redistribution model of Savage (2007), and a Particle-In-Cell (PIC) advection approach. Pressure distributions are obtained for an idealized geometry and uniform wind forcing. The results indicate that zones of relatively low pressures develop along the centre of the channel. Increasing the shear strength of the ice cover leads to somewhat lower pressures within such zones along the centre of the channel. That reduction of pressure is caused by the increased transfer of wind forces to land boundaries. Tides are shown to generally decrease pressures. The tidal currents also increase the overall drift, although drift slows and even reverses direction during parts of the tidal cycle.NRC publication: Ye

The effect of adult Early Warning Systems education on nurses' knowledge, confidence and clinical performance: A systematic review

Aims: This review aims to determine the effect of adult Early Warning Systems education on nurses’ knowledge, confidence and clinical performance. Background: Early Warning Systems support timely identification of clinical deterioration and prevention of avoidable deaths. Several educational programmes have been designed to help nurses recognize and manage deteriorating patients. Little is known as to the effectiveness of these programmes. Design: Systematic review. Data sources: Academic Search Complete, CINAHL, MEDLINE, PsycINFO, PsycARTICLES, Psychology and Behavioral Science Collection, SocINDEX and the UK & Ireland Reference Centre, EMBASE, the Turning Research Into Practice database, the Cochrane Central Register of Controlled Trials (CENTRAL) and Grey Literature sources were searched between October and November 2015. Review methods: This is a quantitative systematic review using Cochrane methods. Studies published between January 2011 - November 2015 in English were sought. The risk of bias, level of evidence and the quality of evidence per outcome were assessed. Results: Eleven articles with 10 studies were included. Nine studies addressed clinical performance, four addressed knowledge and two addressed confidence. Knowledge, vital signs recording and Early Warning Score calculation were improved in the short term. Two interventions had no effect on nurses’ response to clinical deterioration and use of communication tools. Conclusion: This review highlights the importance of measuring outcomes using standardized tools and valid and reliable instruments. Using longitudinal designs, researchers are encouraged to investigate the effect of Early Warning Systems educational programmes. These can include interactive e-learning, on-site interdisciplinary Early Warning Scoring systems training sessions and simulated scenarios