The Kemeny Constant for Finite Homogeneous Ergodic Markov Chains

A quantity known as the Kemeny constant, which is used to measure the expected number of links that a surfer on the World Wide Web, located on a random web page, needs to follow before reaching his/her desired location, coincides with the more well known notion of the expected time to mixing, i.e., to reaching stationarity of an ergodic Markov chain. In this paper we present a new formula for the Kemeny constant and we develop several perturbation results for the constant, including conditions under which it is a convex function. Finally, for chains whose transition matrix has a certain directed graph structure we show that the Kemeny constant is dependent only on the common length of the cycles and the total number of vertices and not on the specific transition probabilities of the chain.     

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.     

The use of ‘exploratory learning’ for supporting immersive learning in virtual environments

User interfaces are becoming more intuitive following the requirements of the individual learner and reinforcing the drive towards more personalised learning and greater learner autonomy. There are clearly a new set of challenges emerging for teaching practitioners that will have implications upon not just what is learned but importantly upon lesson planning. This paper explores these changes to teaching through a consideration of an exploratory learning model which allows practitioners to rethink how they teach in 3D and immersive spaces where learning sequences and experiences are choreographed to support peer interactions and exchanges. The ELM extends from Kolb’s experiential learning model to adapt the use of 3D applications, and provides examples from research and development projects to exemplify how the model works in practice. Teaching in these contexts provides less emphasis upon curriculum and more emphasis upon sequencing learning experiences, meta-reflection, peer assessment and group work.     

Distributed top-<Emphasis Type="Italic">k</Emphasis> aggregation queries at large

Top-k query processing is a fundamental building block for efficient ranking in a large number of applications. Efficiency is a central issue, especially for distributed settings, when the data is spread across different nodes in a network. This paper introduces novel optimization methods for top-k aggregation queries in such distributed environments. The optimizations can be applied to all algorithms that fall into the frameworks of the prior TPUT and KLEE methods. The optimizations address three degrees of freedom: 1) hierarchically grouping input lists into top-k operator trees and optimizing the tree structure, 2) computing data-adaptive scan depths for different input sources, and 3) data-adaptive sampling of a small subset of input sources in scenarios with hundreds or thousands of query-relevant network nodes. All optimizations are based on a statistical cost model that utilizes local synopses, e.g., in the form of histograms, efficiently computed convolutions, and estimators based on order statistics. The paper presents comprehensive experiments, with three different real-life datasets and using the ns-2 network simulator for a packet-level simulation of a large Internet-style network.     

TimeLapseAnalyzer: multi-target analysis for live-cell imaging and time-lapse microscopy

The direct observation of cells over time using time-lapse microscopy can provide deep insights into many important biological processes. Reliable analyses of motility, proliferation, invasive potential or mortality of cells are essential to many studies involving live cell imaging and can aid in biomarker discovery and diagnostic decisions. Given the vast amount of image- and time-series data produced by modern microscopes, automated analysis is a key feature to capitalize the potential of time-lapse imaging devices. To provide fast and reproducible analyses of multiple aspects of cell behaviour, we developed TimeLapseAnalyzer. Apart from general purpose image enhancements and segmentation procedures, this extensible, self-contained, modular cross-platform package provides dedicated modalities for fast and reliable analysis of multi-target cell tracking, scratch wound healing analysis, cell counting and tube formation analysis in high throughput screening of live-cell experiments. TimeLapseAnalyzer is freely available (MATLAB, Open Source) at http://www.informatik.uni-ulm.de/ni/mitarbeiter/HKestler/tla.     

The cognitive legacy of norm simulation

The comprehension of norms in complex social systems is one of the most active fields of research in agent-based modelling. This is faced with the challenge to comprehend the recursive interaction between inter- and intra-agent processes. In this article, a comparative analysis of selected cases of normative agent architectures will be given based on a review of theories of norms in the social sciences. This allows to identify the prerequisites for a representation of the cognitive processes of norm recognition. As yet, there is no unequivocal concept for the design of normative agents. Different approaches are compared along the line of different theoretical accounts.     

Intentions Determine the Effect of Invisible Metacontrast-Masked Primes: Evidence for Top-Down Contingencies in a Peripheral Cuing Task.

In 5 experiments, the authors tested whether the processing of nonconscious spatial stimulus information depends on a prior intention. This test was conducted with the metacontrast dissociation paradigm. Experiment 1 demonstrated that masked primes that could not be discriminated above chance level affected responses to the visible stimuli that masked them. Experiments 2 and 3 showed that this effect was abolished when the task instruction was changed in such a way that the primes ceased to be task relevant. Experiments 4 and 5 demonstrated that a prime's effect depended on whether it was associated with the same response as the target or with an opposite response. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Ion-velocity dependence of high-density electronic excitation effects in oxide superconductors

Thin films of EuBa₂Cu₃O_y oxide superconductor have been irradiated with high energy heavy ions (80 MeV I, 125 MeV Br, 1.1 GeV Mo and 3.5 GeV Xe) having same electronic stopping power, S_e, in order to investigate the ion-velocity dependence of the electronic excitation effects under the constant electronic energy deposition. Although S_e is constant, a strong reduction in the irradiation effect on lattice parameter with increasing ion-velocity is observed in the low ion-velocity region around E  1 MeV/nucleon, while the ion-velocity dependence is hardly observed in the high ion-velocity region of E > 10 MeV/nucleon. If the observed velocity-dependence is assumed to be due to the change in the fraction of S_e contributing to defect creation, the fraction in the low velocity region (E  0.6 MeV/nucleon) is estimated to be about two times larger than that in the high velocity region (E > 10 MeV/nucleon).     

A novel methodology to study shape and surface tension of drops in Electric Fields

A novel methodology is introduced that can be used to study the behavior of conducting drops in electrostatic fields, when gravity effects are negligible. This methodology, called Axisymmetric Drop Shape Analysis — Electric Field (ADSA-EF), generates numerical drop profiles in the electrostatic field, for a given surface tension. Then, it calculates the true value of the surface tension by matching the theoretical profiles with the shape of the experimental drops, with the surface tension as an adjustable parameter. ADSA-EF can be employed for simulating drop shapes in the electric field, detecting the effect of an electric field on liquid surface tensions, and measuring surface tensions in microgravity, where current drop-shape techniques are not applicable. The predicted drop shapes in the electric field were compared with experimental images, indicating good agreement. Preliminary experiments according to ADSA-EF methodology suggested that the surface tension of water increases by about one percent in the electric field.