Reduced-basis output bound methods for parabolic problems

In this paper, we extend reduced-basis output bound methods developed earlier for elliptic problems, to problems described by ‘parameterized parabolic’ partial differential equations. The essential new ingredient and the novelty of this paper consist in the presence of time in the formulation and solution of the problem. First, without assuming a time discretization, a reduced-basis procedure is presented to ‘efficiently’ compute accurate approximations to the solution of the parabolic problem and ‘relevant’ outputs of interest. In addition, we develop an error estimation procedure to ‘a posteriori validate’ the accuracy of our output predictions. Second, using the discontinuous Galerkin method for the temporal discretization, the reduced-basis method and the output bound procedure are analysed for the semi-discrete case. In both cases the reduced-basis is constructed by taking ‘snapshots’ of the solution both in time and in the parameters: in that sense the method is close to Proper Orthogonal Decomposition (POD)

Reduced-basis Output Bound Methods for Parametrised Partial Differential Equations

An efficient and reliable method for the prediction of outputs of interest of partial differential equations with affine parameter dependence is presented. To achieve efficiency we employ the reduced-basis method: a weighted residual Galerkin-type method, where the solution is projected onto low-dimensional spaces with certain problem-specific approximation properties. Reliability is obtained by a posteriori error estimation methods - relaxations of the standard error-residual equation that provide inexpensive but sharp and rigorous bounds for the error in outputs of interest. Special affine parameter dependence of the differential operator is exploited to develop a two-stage off-line/on-line blackbox computational procedure. In the on-line stage, for every new parameter value, we calculate the output of interest and an associated error bound. The computational complexity of the on-line stage of the procedure scales only with the dimension of the reduced-basis space and the parametric complexity of the partial differential operator; the method is thus ideally suited for the repeated and rapid evaluations required in the context of parameter estimation, design, optimization, and real-time control. The theory and corroborating numerical results are presented for: symmetric coercive problems (e.g. problems in conduction heat transfer), parabolic problems (e.g. unsteady heat transfer), noncoercive problems (e.g. the reduced-wave, or Helmholtz, equation), the Stokes problem (e.g flow of highly viscous fluids), and certain nonlinear equations (e.g. eigenvalue problems)

The NOPTILUS project: Autonomous multi-AUV navigation for exploration of unknown environments

Current multi-AUV systems are far from being capable of fully autonomously taking over real-life complex situation-awareness operations. As such operations require advanced reasoning and decision-making abilities, current designs have to heavily rely on human operators. The involvement of humans, however, is by no means a guarantee of performance; humans can easily be over-whelmed by the information overload, fatigue can act detrimentally to their performance, properly coordinating vehicles actions is hard, and continuous operation is all but impossible. Within the European funded project NOPTILUS we take the view that an effective fully-autonomous multi-AUV concept/system, is capable of overcoming these shortcomings, by replacing human-operated operations by a fully autonomous one. In this paper, we present a new approach that is able to efficiently and fully-autonomously navigate a team of AUVs when deployed in exploration of unknown static and dynamic environments towards providing accurate static/dynamic maps of the environment. Additionally to achieving to efficiently and fully-autonomously navigate the AUV team, the proposed approach possesses certain advantages such as its extremely computational simplicity and scalability, and the fact that it can very straightforwardly embed and type of physical or other constraints and limitations (e.g., obstacle avoidance, nonlinear sensor noise models, localization fading environments, etc)

An Event-driven SOA-based Platform for Energy-efficiency Applications in Buildings

The topic of optimization of building operation is attracting significant interest in the community: monitoring of relevant Key Performance Indicators can help enhance state awareness and understanding; fault detection and identification can help identify irregular and ineffective operational modes; and, advanced control design techniques can yield effective/optimized operation with regards to energy performance and thermal comfort. Despite significant effort on development of algorithmic and methodological approaches to address these problems, the inherent complexity associated with practical demonstrations, has precluded testing and implementation of such approaches in realistic contexts. Within this paper, an event-driven Service-Oriented Architecture platform, is developed to address this gap and help facilitate the provision of advanced energyefficiency and energy-management services in buildings. The use of the Industry Foundation Classes provides a standardized data-model for describing the building and its components, while the use of Model View definitions is employed to define the exchange requirements for proper software component interoperability. Data collection and homogenization from the building is addressed through an abstraction layer, capable of hiding many of the intricacies and providing a clean interface for the development of building services. An exemplary application of the proposed architecture in a real office building in Greece is presented

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Listeriosis During Pregnancy: Maternal and Neonatal Consequences—A Case Report

Linas Rovas,1,2 Arturas Razbadauskas,1 Gabriele Slauzgalvyte2 1Klaipeda University, Klaipeda, Lithuania; 2Department of Obstetrics and Gynecology, Klaipeda University Hospital, Klaipeda, LithuaniaCorrespondence: Linas Rovas, Klaipeda University Hospital, Klaipeda University, H. Manto g. 84, Klaipeda, 92294, Lithuania, Tel +37069843875, Email [email protected]: Listeriosis is a rare but extremely dangerous infection for both mother and fetus. This pathogen can spread in humans’ bodies by consumption of contaminated food. The main high-risk groups of people for being infected are immunosuppressed and especially pregnant women. We present a case of materno-neonatal listeriosis illustrating that empiric antimicrobial therapy of chorioamnionitis during labor and neonate postpartum can also cover listeriosis which was not diagnosed prior to obtaining cultures.Keywords: listeriosis, chorioamnionitis, pregnancy, neonatal infection, antimicrobial treatmen

Blackbox reduced-basis output bound methods for shape optimisation

We present a two-stage off-line/on-line blackbox reduced-basis output bound method for the prediction of outputs of coercive partial differential equations with affine parameter dependence. The computational complexity of the on-line stage of the procedure scales only with the dimension of the reduced-basis space and the parametric complexity of the partial differential operator. The method is both efficient and certain: thanks to rigorous a posteriori error bounds, we may retain only the minimal number of modes necessary to achieve the prescribed accuracy in the output of interest. The technique is particularly appropriate for applications such as design and optimization, in which repeated and rapid evaluation of the output is required. Reduced-basis methods [ASB78, Nag79, NP80] — projection onto low-order approximation spaces comprising solutions of the problem of interest at selected points in the parameter/design space — are efficient techniques for the prediction of linear functional outputs. These methods enjoy an optimality property which ensures rapid convergence even in high-dimensional parameter spaces; good accuracy is obtained even for very few modes (basis functions), and thus the computational cost is typically very small. It is often the case that the parameter enters affinely in the differential operator. This allows us to separate the computational steps into two stages:(i) the off-line stage, in which the reduced-basis space is constructed; and (ii) the on-line/real time stage, in which for each new parameter value the reduced-basis approximation for the output of interest is calculated. The on-line stage is “blackbox” in the sense that there is no longer any reference to the original problem formulation:the computational complexity of this stage scales only with the dimension of the reduced-basis space and the parametric complexity of the partial differential operator. Although a priori theory [FR83, Por85] suggests the optimality of the reducedbasis space approximation, for a particular choice of the reduced-basis space the error in the output of interest is typically not known, and hence the minimal number of basis functions required to satisfy the desired error tolerance can not be ascertained. As a result, either too many or too few basis functions are retained; the former results in computational inefficiency, the latter in uncertainty and unacceptably inaccurate predictions. In this paper we develop blackbox a posteriori methods that address these shortcomings. We consider here equilibrium solutions of coercive problems within the context of shape optimization; see also [MPR00] for treatment of noncoercive equilibrium problems and [MMO+00] for symmetric eigenvalue problems

Reliable real-time solution of parametrized partial differential equations: Reduced-basis output bound methods

We present a technique for the rapid and reliable prediction of linear-functional outputs of elliptic (and parabolic) partial differential equations with affine parameter dependence. The essential components are (i) (provably) rapidly convergent global reduced-basis approximations—Galerkin projection onto a space WN spanned by solutions of the governing partial differential equation at N selected points in parameter space; (ii) a posteriori error estimation—relaxations of the error-residual equation that provide inexpensive yet sharp and rigorous bounds for the error in the outputs of interest; and (iii) off-line/ on-line computational procedures methods which decouple the generation and projection stages of the approximation process. The operation count for the on-line stage in which, given a new parameter value, we calculate the output of interest and associated error bound, depends only on N (typically very small) and the parametric complexity of the problem; the method is thus ideally suited for the repeated and rapid evaluations required in the context of parameter estimation, design, optimization, and real-time control