Models for the re-use of learning scenarios

Full transcript of the chat at http://moodle.learningnetworks.org/course/view.php?id=34The aim of this paper is to contribute to increased reuse of pedagogical scenarios by teachers and trainers. We focus on the educational modelling languages framework, and propose a life cycle model for learning scenarios and describe the different aspects of a learning scenario through a second model. We also look at the functions that could be made available to users within new computer based environments.Used at UNFOLD Chat on December the 15th, at 16h CET, 200

Estimation of Dynamic Mixed Double Factors Model in High Dimensional Panel Data

The purpose of this article is to develop the dimension reduction techniques in panel data analysis when the number of individuals and indicators is large. We use Principal Component Analysis (PCA) method to represent large number of indicators by minority common factors in the factor models. We propose the Dynamic Mixed Double Factor Model (DMDFM for short) to re ect cross section and time series correlation with interactive factor structure. DMDFM not only reduce the dimension of indicators but also consider the time series and cross section mixed effect. Different from other models, mixed factor model have two styles of common factors. The regressors factors re flect common trend and reduce the dimension, error components factors re ect difference and weak correlation of individuals. The results of Monte Carlo simulation show that Generalized Method of Moments (GMM) estimators have good unbiasedness and consistency. Simulation also shows that the DMDFM can improve prediction power of the models effectively.Comment: 38 pages, 2 figure

187^{187}Re(\gamm,n) cross section close to and above the neutron threshold

The neutron capture cross section of the unstable nucleus $^{186}$Re is studied by investigating the inverse photodisintegration reaction $^{187}$Re($\gamma$,n). The special interest of the {\it s}-process branching point $^{186}$Re is related to the question of possible {\it s}-process contributions to the abundance of the {\it r}-process chronometer nucleus ^{187}$Re. We use the photoactivation technique to measure photodisintegration rates. Our experimental results are in good agreement with two different statistical model calculations. Although the cross sections predicted by both models for the inverse reaction$^{186}$Re(n,$\gamma$) is too low to remove the overproduction of$^{186}$Os; the two predicted neutron-capture cross sections differ by a factor of 2.4; this calls for future theoretical study.Comment: Phys. Rev. C, in pres

Intermittent turbulent dynamo at very low and high magnetic Prandtl numbers

Context: Direct numerical simulations have shown that the dynamo is efficient even at low Prandtl numbers, i.e., the critical magnetic Reynolds number Rm_c necessary for the dynamo to be efficient becomes smaller than the hydrodynamic Reynolds number Re when Re -> infinity. Aims: We test the conjecture (Iskakov et al. 2007) that Rm_c actually tends to a finite value when Re -> infinity, and we study the behavior of the dynamo growth factor \gamma\ at very low and high magnetic Prandtl numbers. Methods: We use local and nonlocal shell-models of magnetohydrodynamic (MHD) turbulence with parameters covering a much wider range of Reynolds numbers than direct numerical simulations, but of astrophysical relevance. Results: We confirm that Rm_c tends to a finite value when Re -> infinity. The limit for Rm -> infinity of the dynamo growth factor \gamma\ in the kinematic regime behaves like Re^\beta, and, similarly, the limit for Re -> infinity of \gamma\ behaves like Rm^{\beta'}, with \beta=\beta'=0.4. Conclusion: Comparison with a phenomenology based on an intermittent small-scale turbulent dynamo, together with the differences between the growth rates in the different local and nonlocal models, indicate a weak contribution of nonlocal terms to the dynamo effect.Comment: 5 pages, 6 figure

Generic object models and business process (re)design.

This paper explores the capacities of generic object-relationship models in the context of business process modeling and business process re-engineering. The presentation is based on a framework for strategic business function typology. It is shown how generic models can be developed for each kind of business function within the typology. Business process re-engineering can be represented by transformations of business models, corresponding to shifts within the typology framework. Although the results of the paper are presented by means of one particular dialect of the object-relationship approach, the results remain valid for all object oriented approaches that make use of objects and relationships. This paper contributes to the further formalisation of business process modeling.Models; Model; Processes;

The Impact of Rate-of-Return Regulation on Electricity Generation from Renewable Energy

Traditional electric utility companies face a trade-off between building generation facilities that utilize renewable energy (RE) and non-renewable energy (non-RE). The firm’s input decision to build capacity for either source depends on several constraining factors, including input prices, policies that promote or discourage RE use, and the type of regulation faced by the firm. This paper models the utility company’s decision between RE and non-RE capital types. From the model, two main results are derived. First, rate-of-return (ROR) regulation decreases the investment in RE capital relative to the unregulated firm. These findings suggest restructuring electricity generation markets, which removes the ROR on generating assets, can increase the relative use of RE. Second, the renewable portfolio standard (RPS) increases the investment in capital and labor that requires RE as a source of electricity, as expected. The model shows that the impact of an RPS depends on the amount of ROR regulation.renewable portfolio standard, renewable energy, rate-of-return regulation

Re-engineering a nanodosimetry Monte Carlo code into Geant4: software design and first results

A set of physics models for nanodosimetry simulation is being re-engineered for use in Geant4-based simulations. This extension of Geant4 capabilities is part of a larger scale R&D project for multi-scale simulation involving adaptable, co-working condensed and discrete transport schemes. The project in progress reengineers the physics modeling capabilities associated with an existing FORTRAN track-structure code for nanodosimetry into a software design suitable to collaborate with an object oriented simulation kernel. The first experience and results of the ongoing re-engineering process are presented.Comment: 4 pages, 2 figures and images, to appear in proceedings of the Nuclear Science Symposium and Medical Imaging Conference 2009, Orland

Dropout Inference in Bayesian Neural Networks with Alpha-divergences

To obtain uncertainty estimates with real-world Bayesian deep learning models, practical inference approximations are needed. Dropout variational inference (VI) for example has been used for machine vision and medical applications, but VI can severely underestimates model uncertainty. Alpha-divergences are alternative divergences to VI's KL objective, which are able to avoid VI's uncertainty underestimation. But these are hard to use in practice: existing techniques can only use Gaussian approximating distributions, and require existing models to be changed radically, thus are of limited use for practitioners. We propose a re-parametrisation of the alpha-divergence objectives, deriving a simple inference technique which, together with dropout, can be easily implemented with existing models by simply changing the loss of the model. We demonstrate improved uncertainty estimates and accuracy compared to VI in dropout networks. We study our model's epistemic uncertainty far away from the data using adversarial images, showing that these can be distinguished from non-adversarial images by examining our model's uncertainty

Semantic web service architecture for simulation model reuse

COTS simulation packages (CSPs) have proved popular in an industrial setting with a number of software vendors. In contrast, options for re-using existing models seem more limited. Re-use of simulation component models by collaborating organizations is restricted by the same semantic issues however that restrict the inter-organization use of web services. The current representations of web components are predominantly syntactic in nature lacking the fundamental semantic underpinning required to support discovery on the emerging semantic web. Semantic models, in the form of ontology, utilized by web service discovery and deployment architecture provide one approach to support simulation model reuse. Semantic interoperation is achieved through the use of simulation component ontology to identify required components at varying levels of granularity (including both abstract and specialized components). Selected simulation components are loaded into a CSP, modified according to the requirements of the new model and executed. The paper presents the development of ontology, connector software and web service discovery architecture in order to understand how such ontology are created, maintained and subsequently used for simulation model reuse. The ontology is extracted from health service simulation - comprising hospitals and the National Blood Service. The ontology engineering framework and discovery architecture provide a novel approach to inter- organization simulation, uncovering domain semantics and adopting a less intrusive interface between participants. Although specific to CSPs the work has wider implications for the simulation community