Convergence Analysis of the Ensemble Kalman Filter for Inverse Problems: the Noisy Case

We present an analysis of the ensemble Kalman filter for inverse problems based on the continuous time limit of the algorithm. The analysis of the dynamical behaviour of the ensemble allows to establish well-posedness and convergence results for a fixed ensemble size. We will build on the results presented in [Schillings, Stuart 2017] and generalise them to the case of noisy observational data, in particular the influence of the noise on the convergence will be investigated, both theoretically and numerically

Problems Encountered During the Radiological Remediation of Old Buildings

With several military base closures resulting in property transfer to public use and the decommissioning of many legacy waste facilities, the opportunity for remediation of older buildings is increasing. Along with these projects, come several problems that could give the potential remediator some surprises. During the preconstruction and planning phases of the original construction activities, several generations of drawings were most likely produced for approval and permit submittal. Over the years, buildings may undergo several renovations with or without the full characterization or remediation that should be done when radioactive materials are used on a site. New walls or floors may be built over the original construction materials. Contamination in and around the building may have resulted from processes that were accepted at the time or from inadvertent activities that may have been covered up, including accidental spills. Many buildings contain hidden rooms or accesses that over time became useless and have been closed up or over, these areas may not be very obvious. When characterizing a building the effluents of the building are usually forgotten, sewer lines are important areas to investigate. All these items could cause a remediator to overlook a potentially highly contaminated area. With more of these facilities being turned over for public use, correctly characterizing these buildings will become a more common problem

Towards a Lagrange-Newton approach for PDE constrained shape optimization

The novel Riemannian view on shape optimization developed in [Schulz, FoCM, 2014] is extended to a Lagrange-Newton approach for PDE constrained shape optimization problems. The extension is based on optimization on Riemannian vector space bundles and exemplified for a simple numerical example.Comment: 16 pages, 4 figures, 1 tabl

Managing end-user participation for the adoption of digital livestock technologies: expectations, performance, relationships, and support

Purpose: End-user participation is often encouraged to promote the uptake of Digital Livestock Technologies (DLTs). However, managing participation during DLT development can be challenging. We explore how participation decisions can impact end-users’ engagement and attitudes towards the process, before suggesting strategies for improved management of the participation process. Methodology: We explored the experiences of end-users (e.g. farmers and farm assessors) and other stakeholders (e.g. developers, researchers, industry) involved in the development and testing of DLTs on UK farms, using semi-structured, in-depth interviews (N = 31). Findings: Participation can help develop technologies that better align with users’ needs, promote learning, and encourage feelings of ownership. However, participation can be a double-edged sword. Inadequate levels of involvement, management of stakeholder relationships and expectations, and available support can negatively impact end-users’ engagement and attitudes. Practical implications: Our study highlights the importance of understanding how management decisions during the participatory development of DLTs can influence the engagement and attitudes of end-users towards the process. Theoretical implications: The study contributes to the participation literature in agriculture and demonstrates the importance of using a critical lens to avoid making normative assumptions that participation necessarily promotes uptake in a linear, uncomplicated fashion. Originality/Value: Participation is seen as key for technology adoption. However, the potential downsides of participation have received less attention in relation to the engagement of end-users in the process

Complexity Analysis of Accelerated MCMC Methods for Bayesian Inversion

We study Bayesian inversion for a model elliptic PDE with unknown diffusion coefficient. We provide complexity analyses of several Markov Chain-Monte Carlo (MCMC) methods for the efficient numerical evaluation of expectations under the Bayesian posterior distribution, given data $\delta$. Particular attention is given to bounds on the overall work required to achieve a prescribed error level $\varepsilon$. Specifically, we first bound the computational complexity of "plain" MCMC, based on combining MCMC sampling with linear complexity multilevel solvers for elliptic PDE. Our (new) work versus accuracy bounds show that the complexity of this approach can be quite prohibitive. Two strategies for reducing the computational complexity are then proposed and analyzed: first, a sparse, parametric and deterministic generalized polynomial chaos (gpc) "surrogate" representation of the forward response map of the PDE over the entire parameter space, and, second, a novel Multi-Level Markov Chain Monte Carlo (MLMCMC) strategy which utilizes sampling from a multilevel discretization of the posterior and of the forward PDE. For both of these strategies we derive asymptotic bounds on work versus accuracy, and hence asymptotic bounds on the computational complexity of the algorithms. In particular we provide sufficient conditions on the regularity of the unknown coefficients of the PDE, and on the approximation methods used, in order for the accelerations of MCMC resulting from these strategies to lead to complexity reductions over "plain" MCMC algorithms for Bayesian inversion of PDEs.

Videos and podcasts as potential approaches for knowledge exchange with farmers: testing their potential role in ELM

Digital extension methods have received renewed attention with the onset of the COVID-19 pandemic. Based on our empirical research, if videos and podcasts are to be used to deliver information and advice to farmers about Environmental Land Management, the following key messages should guide their design and delivery: • Farmers tend to prefer information and advice delivered face-to-face, preferably by trusted sources, such as peers or known advisers. • Digital extension methods, such as videos and podcasts, as well as live interactive events, have been used more by farmers since the COVID-19 pandemic. They can be an effective form of information delivery. • Benefits of digital events have included reducing the time and resources needed to access in-person events, as well as increasing national and international knowledge exchange. • Videos and podcasts should seek to recreate some of the hallmarks of trusted, in-person advice delivery – i.e. delivered by trusted individuals and with ‘live’ or other forms of interactivity delivered through monitored comments sections. • Videos should use appropriate language for the viewer, be concise, filmed with high-quality visuals and sound, and show how to do something in practice. • Podcasts may be longer, describing something in detail, and should also use appropriate language and have good sound quality. • Both videos and podcasts should be clearly indexed and accessible with viewers/listeners knowing where to go to find them. • Barriers of poor rural connectivity and lack of digital skills need to be overcome. Digital extension should only be one method of information delivery otherwise those who do not use videos and podcasts may be further marginalised

Does osteoporosis predispose falls? a study on obstacle avoidance and balance confidence

Contains fulltext : 96832.pdf (publisher's version ) (Open Access)BACKGROUND: Osteoporosis is associated with changes in balance and physical performance and has psychosocial consequences which increase the risk of falling. Most falls occur during walking; therefore an efficient obstacle avoidance performance might contribute to a reduction in fall risk. Since it was shown that persons with osteoporosis are unstable during obstacle crossing it was hypothesized that they more frequently hit obstacles, specifically under challenging conditions. METHODS: Obstacle avoidance performance was measured on a treadmill and compared between persons with osteoporosis (n = 85) and the comparison group (n = 99). The obstacle was released at different available response times (ART) to create different levels of difficulty by increasing time pressure. Furthermore, balance confidence, measured with the short ABC-questionnaire, was compared between the groups. RESULTS: No differences were found between the groups in success rates on the obstacle avoidance task (p = 0.173). Furthermore, the persons with osteoporosis had similar levels of balance confidence as the comparison group (p = 0.091). The level of balance confidence was not associated with the performance on the obstacle avoidance task (p = 0.145). CONCLUSION: Obstacle avoidance abilities were not impaired in persons with osteoporosis and they did not experience less balance confidence than the comparison group. These findings imply that persons with osteoporosis do not have an additional risk of falling because of poorer obstacle avoidance abilities

Attentive Learning of Sequential Handwriting Movements: A Neural Network Model

Defense Advanced research Projects Agency and the Office of Naval Research (N00014-95-1-0409, N00014-92-J-1309); National Science Foundation (IRI-97-20333); National Institutes of Health (I-R29-DC02952-01)