Cluster formation in a time-varying multi-agent system

We introduce time-varying parameters in a multi-agent clustering model and we derive necessary and sufficient conditions for the occurrence of clustering behavior with respect to a given cluster structure. For periodically varying parameters the clustering conditions may be formulated in a similar way as for the time-invariant model. The results require the individual weights assigned to the agents to be constant. For time-varying weights we illustrate with an example that the obtained results can no longer be applied.     

A modern workflow for force-field development – Bridging quantum mechanics and atomistic computational models

In this article we present our recent efforts in designing a comprehensive consistent scientific workflow, nicknamed Wolf₂ Pack, for force-field optimization in the field of computational chemistry. Atomistic force fields represent a multiscale bridge that connects high-resolution quantum mechanics knowledge to coarser molecular mechanics-based models. Force-field optimization has so far been a time-consuming and error-prone process, and is a topic where the use of a scientific workflow can provide obvious great benefits. As a case study we generate a gas-phase force field for methanol using Wolf₂ Pack, with special attention given toward deriving partial atomic charges.     

An experiment on the role of graphical elements in architecture visualization

The evolution and maintenance of large-scale software systems requires first an understanding of its architecture before delving into lower-level details. Tools facilitating the architecture comprehension tasks by visualization provide different sets of configurable, graphical elements to present information to their users. We conducted a controlled experiment that exemplifies the critical role of such graphical elements when aiming at understanding the architecture. In our setting, a different configuration of graphical elements had significant influence on program comprehension tasks. In particular, a 63% gain in effectiveness in architectural analysis tasks was achieved simply by changing the configuration of the graphical elements of the same tool. Based on the results, we claim that significant effort should be spent on the configuration of architecture visualization tools and that configurability should be a requirement for such tools.

Evolution strategies with cumulative step length adaptation on the noisy parabolic ridge

This paper presents an analysis of the performance of the (μ/μ,λ)-ES with isotropically distributed mutations and cumulative step length adaptation on the noisy parabolic ridge. Several forms of dependency of the noise strength on the distance from the ridge axis are considered. Closed form expressions are derived that describe the mutation strength and the progress rate of the strategy in high-dimensional search spaces. It is seen that as for the sphere model, larger levels of noise present lead to cumulative step length adaptation generating increasingly inadequate mutation strengths, and that the problem can be ameliorated to some degree by working with larger populations.     

Die Mifare-Attacke

Zusammenfassung  Eine Kryptoanalyse des verbreiteten Mifare-Chips, der in ca. 85% aller kontaktlosen Chipkarten zum Einsatz kommt, wurde erstmalig auf dem CCC-Jahreskongress am 28.12.2007 vorgestellt. Inzwischen wurden weitere Details bekannt — und der Angriff erheblich verbessert. Mitte April 2008 stellten Forscher der Radboud Universiteit Nijmegen einen Kurzfilm in You-Tube ein, in dem sie den erfolgreichen Angriff auf eine Campus-Zugangskarte zeigen. Dirk Fox Gesch?ftsführer der Secorvo Security Consulting GmbH und Herausgeber der DuD.     

Design optimization of an enzymatic assay in an electrokinetically-driven microfluidic device

Microfluidic systems are increasingly popular for rapid and cheap determinations of enzyme assays and other biochemical analysis. In this study reduced order models (ROM) were developed for the optimization of enzymatic assays performed in a microchip. The model enzyme assay used was β-galactosidase (β-Gal) that catalyzes the conversion of Resorufin β-d-galactopyranoside (RBG) to a fluorescent product as previously reported by Hadd et al. (Anal Chem 69(17): 3407–3412, 1997). The assay was implemented in a microfluidic device as a continuous flow system controlled electrokinetically and with a fluorescence detection device. The results from ROM agreed well with both computational fluid dynamic (CFD) simulations and experimental values. While the CFD model allowed for assessment of local transport phenomena, the CPU time was significantly reduced by the ROM approach. The operational parameters of the assay were optimized using the validated ROM to significantly reduce the amount of reagents consumed and the total biochip assay time. After optimization the analysis time would be reduced from 20 to 5.25 min which would also resulted in 50% reduction in reagent consumption.     

Autonomic information flow improves prognostic impact of task force HRV monitoring

Heart rate variability (HRV) represents the cardiovascular control mediated by the autonomic nervous system and other mechanisms. In the established task force HRV monitoring different cardiovascular control mechanisms can approximately be identified at typical frequencies of heart rate oscillations by power spectral analysis. HRV measures assessing complex and fractal behavior partly improved clinical risk stratification. However, their relationship to (patho-)physiology is not sufficiently explored. Objective of the present work is the introduction of complexity measures of different physiologically relevant time scales. This is achieved by a new concept of the autonomic information flow (AIF) analysis which was designed according to task force HRV. First applications show that different time scales of AIF improve the risk stratification of patients with multiple organ dysfunction syndrome and cardiac arrest patients in comparison to standard HRV. Each group's significant time scales correspond to their respective pathomechanisms.     

Meetings and meeting modeling in smart environments

In this paper we survey our research on smart meeting rooms and its relevance for augmented reality meeting support and virtual reality generation of meetings in real time or off-line. The research reported here forms part of the European 5th and 6th framework programme projects multi-modal meeting manager (M4) and augmented multi-party interaction (AMI). Both projects aim at building a smart meeting environment that is able to collect multimodal captures of the activities and discussions in a meeting room, with the aim to use this information as input to tools that allow real-time support, browsing, retrieval and summarization of meetings. Our aim is to research (semantic) representations of what takes place during meetings in order to allow generation, e.g. in virtual reality, of meeting activities (discussions, presentations, voting, etc.). Being able to do so also allows us to look at tools that provide support during a meeting and at tools that allow those not able to be physically present during a meeting to take part in a virtual way. This may lead to situations where the differences between real meeting participants, human-controlled virtual participants and (semi-) autonomous virtual participants disappear.

Optimum tracking with evolution strategies

Evolutionary algorithms are frequently applied to dynamic optimization problems in which the objective varies with time. It is desirable to gain an improved understanding of the influence of different genetic operators and of the parameters of a strategy on its tracking performance. An approach that has proven useful in the past is to mathematically analyze the strategy's behavior in simple, idealized environments. The present paper investigates the performance of a multiparent evolution strategy that employs cumulative step length adaptation for an optimization task in which the target moves linearly with uniform speed. Scaling laws that quite accurately describe the behavior of the strategy and that greatly contribute to its understanding are derived. It is shown that in contrast to previously obtained results for a randomly moving target, cumulative step length adaptation fails to achieve optimal step lengths if the target moves in a linear fashion. Implications for the choice of population size parameters are discussed.     

Machine learning for vessel trajectories using compression,alignments and domain knowledge

In this paper we present a machine learning framework to analyze moving object trajectories from maritime vessels. Within this framework we perform the tasks of clustering, classification and outlier detection with vessel trajectory data. First, we apply a piecewise linear segmentation method to the trajectories to compress them. We adapt an existing technique to better retain stop and move information and show the better performance of our method with experimental results. Second, we use a similarity based approach to perform the clustering, classification and outlier detection tasks using kernel methods. We present experiments that investigate different alignment kernels and the effect of piecewise linear segmentation in the three different tasks. The experimental results show that compression does not negatively impact task performance and greatly reduces computation time for the alignment kernels. Finally, the alignment kernels allow for easy integration of geographical domain knowledge. In experiments we show that this added domain knowledge enhances performance in the clustering and classification tasks.