Improving the structure MCMC sampler for Bayesian networks by introducing a new edge reversal move

Applications of Bayesian networks in systems biology are computationally demanding due to the large number of model parameters. Conventional MCMC schemes based on proposal moves in structure space tend to be too slow in mixing and convergence, and have recently been superseded by proposal moves in the space of node orders. A disadvantage of the latter approach is the intrinsic inability to specify the prior probability on network structures explicitly. The relative paucity of different experimental conditions in contemporary systems biology implies a strong influence of the prior probability on the posterior probability and, hence, the outcome of inference. Consequently, the paradigm of performing MCMC proposal moves in order rather than structure space is not entirely satisfactory. In the present article, we propose a new and more extensive edge reversal move in the original structure space, and we show that this significantly improves the convergence of the classical structure MCMC scheme.     

Result Merging Technique for Answering XPath Query over XSLT Transformed Data

Caching stores the results of previously answered queries in order to answer succeeding queries faster by reusing these results. We propose two different approaches for using caches of XSLT transformed XML data in order to answer queries. The first approach checks whether or not a current query Q can be directly answered from the result of a previously answered query Qi stored in the cache. The new query is otherwise submitted to the source over the network, the answer of the query is determined, transmitted back to the client, and stored in the cache. The second approach determines only the intersection Q−Qi and integrates the result of Q−-Qi into the previous results in the cache, which requires applying a numbering scheme for the output of the XSLT stylesheet. We show by experimental results that the second approach can significantly speed up the answering time in comparison to the first approach, but is not significantly slower in few worst cases than the second approach.     

Investigation of Ablation Efficiency of Stainless Steel Using Pulsed Lasers in Burst Mode

Due to its high corrosion resistance and mechanical properties, stainless steel is commonly used in various industrial applications. Although different types of stainless steel are similar in their chemical composition, they can differ significantly in their thermal diffusivity. This property is relevant in the ability of a material to conduct heat and thus, in laser processing. In this frame, this study compares the ablation efficiency and characteristics of polished stainless steel samples of the alloys AISI 304, AISI 420, and AISI 316Ti. They are irradiated with single ultrashort pulses having pulse durations between 250 fs and 10 ps as well as using GHz burst modii. The goal is to investigate the differences in both the ablation threshold and the ablation rate to improve the ablation efficiency. The results show that shorter pulse durations lead to a more efficient ablation process. On the other hand, GHz bursts are found to be, in general, less efficient. In addition, there is a significant difference in the surface morphology depending on the process parameters. The differences in the thermal diffusivity do not significantly influence the ablation threshold fluence but surface morphology and the ablation rate.     

Nested multigrid methods for time-periodic,parabolic optimal control problems

We present a nested multigrid method to optimize time-periodic, parabolic, partial differential equations (PDE). We consider a quadratic tracking objective with a linear parabolic PDE constraint. The first order optimality conditions, given by a coupled system of boundary value problems can be rewritten as an Fredholm integral equation of the second kind, which is solved by a multigrid of the second kind. The evaluation of the integral operator consists of solving sequentially a boundary value problem for respectively the state and the adjoints. Both problems are solved efficiently by a time-periodic space-time multigrid method.     

Ensemble classification based on generalized additive models

Generalized additive models (GAMs) are a generalization of generalized linear models (GLMs) and constitute a powerful technique which has successfully proven its ability to capture nonlinear relationships between explanatory variables and a response variable in many domains. In this paper, GAMs are proposed as base classifiers for ensemble learning. Three alternative ensemble strategies for binary classification using GAMs as base classifiers are proposed: (i) GAMbag based on Bagging, (ii) GAMrsm based on the Random Subspace Method (RSM), and (iii) GAMens as a combination of both. In an experimental validation performed on 12 data sets from the UCI repository, the proposed algorithms are benchmarked to a single GAM and to decision tree based ensemble classifiers (i.e. RSM, Bagging, Random Forest, and the recently proposed Rotation Forest). From the results a number of conclusions can be drawn. Firstly, the use of an ensemble of GAMs instead of a single GAM always leads to improved prediction performance. Secondly, GAMrsm and GAMens perform comparably, while both versions outperform GAMbag. Finally, the value of using GAMs as base classifiers in an ensemble instead of standard decision trees is demonstrated. GAMbag demonstrates performance comparable to ordinary Bagging. Moreover, GAMrsm and GAMens outperform RSM and Bagging, while these two GAM ensemble variations perform comparably to Random Forest and Rotation Forest. Sensitivity analyses are included for the number of member classifiers in the ensemble, the number of variables included in a random feature subspace and the number of degrees of freedom for GAM spline estimation.     

Information and communication technologies in the life of university freshmen: An analysis of change

The passage from secondary school to university puts students in an environment with different expectations. Not only the expectations towards learning might change, but also towards ICT competences and computer use. The purpose of this article is to find out whether freshmen, after 6 months at the university, changed their self-perception of ICT competences and computer use in comparison with their behaviour at secondary school, and what factors can explain the self-perception of ICT competences and computer use in secondary school, in the university and their possible change. Based on a panel research among 714 freshmen of a large university, this article answers the following questions: (1) What is the self-perception of ICT competences among freshmen and is there a change in this self-perception 6 months after entering the university? (2) How often and for what purpose do freshmen use a computer and is there a change in the frequency of the use of a computer? (3) What factors might influence this attitude, behaviour, and possible change? In function of the basic components of Unified Theory of Acceptance and Use of Technology (Venkatesh, Morris, Davis, & Davis, 2003) hypotheses was developed and tested to answer these questions. Students who consider the computer to be a useful instrument, have control over the computer, possess a certain level of Internet competence, and are at ease with computers are more likely to have the skills needed to maintain a computer, to develop a web site, and to use basic ICT skills. The predictors have little influence on Internet usage. The same predictors contribute modestly to the explanation of the different frequencies of computer use, and a few of the predictors explain parts of change in ICT skills and frequency of computer use.     

Automated bidding in computational markets: an application in market-based allocation of computing services

Autonomous agents are widely applied to automate interactions in robotics, e.g. for selling and purchasing goods on eBay, and in financial markets, e.g. in the form of quote machines and algorithmic traders. Current research investigates efficient economic mechanisms that fully automate the provisioning and usage processes of Grid-based services. On the one hand, consumers want to allocate resources on demand for their various applications, e.g. data sharing, stream processing, email, business applications and simulations. On the other hand, providers of Grid services want to govern business policies to meet their utilization and profit goals. The above-mentioned processes are not manually manageable, however, because decisions need to be taken within milliseconds. Therefore, such processes have to be automated to minimize human interactions. Hence, market mechanisms and strategic behavior play important roles when it comes to achieving automated and efficient allocation of Grid services. The paper begins by presenting a framework for automated bidding, providing a methodology for the design and implementation of configurable bidding strategies. Second, it presents a novel bidding strategy based on a reinforcement learning technique. This strategy is designed to automate the bid generation processes of consumers and providers in various market mechanisms. Third, the behavior and convergence of the strategy is evaluated in a centralized Continuous Double Auction and a decentralized on-line machine scheduling mechanism against selected benchmark bidding strategies. Fourth, we define a bidding language for communicating consumer and provider preferences to the market as well as report back the match of the market-based allocation process.     

Miniaturized ceramic differential scanning calorimeter with integrated oven and crucible in LTCC technology

A miniaturized ceramic differential scanning calorimeter (MC-DSC) with integrated oven and crucible is presented. Despite its small size of only 11 mm × 39 mm × 1.5 mm, all functions of a conventional DSC apparatus are integrated in this novel device - including the oven. The MC-DSC is fully manufactured in thick-film and green glass ceramic tape-based low temperature co-fired ceramics (LTCC) technology. Therefore, production costs are considered to be low. Initial results using indium as a sample material show a good dynamic performance of the MC-DSC. Full width at half maximum of the melting peak is 2.4 °C (sample mass approx. 11 mg, heating rate approx. 50 °C/min). Repeatability of the indium melting point is within ±0.02 °C. The melting peak area increases linearly with the sample mass up to at least 26 mg. Simulations of a strongly simplified finite element model of the MC-DSC are in a good agreement with measurement results allowing a model-based prediction of its basic characteristics.