Infrastructure Federation Through Virtualized Delegation of Resources and Services

Infrastructure federation is becoming an increasingly important issue for modern Distributed Computing Infrastructures (DCIs): Dynamic elasticity of quasi-static Grid environments, incorporation of special-purpose resources into commoditized Cloud infrastructures, cross-community collaboration for increasingly diverging areas of modern e-Science, and Cloud Bursting pose major challenges on the technical level for many resource and middleware providers. Especially with respect to increasing costs of operating data centers, the intelligent yet automated and secure sharing of resources is a key factor for success. With the D-Grid Scheduler Interoperability (DGSI) project within the German D-Grid Initiative, we provide a strategic technology for the automatically negotiated, SLA-secured, dynamically provisioned federation of resources and services for Grid-and Cloud-type infrastructures. This goal is achieved by complementing current DCI schedulers with the ability to federate infrastructure for the temporary leasing of resources and rechanneling of workloads. In this work, we describe the overall architecture and SLA-secured negotiation protocols within DGSI and depict an advanced mechanism for resource delegation through means of dynamically provisioned, virtualized middleware. Through this methodology, we provide the technological foundation for intelligent capacity planning and workload management in a cross-infrastructure fashion.     

Optimization problems in contracted tensor networks

We discuss the calculus of variations in tensor representations with a special focus on tensor networks and apply it to functionals of practical interest. The survey provides all necessary ingredients for applying minimization methods in a general setting. The important cases of target functionals which are linear and quadratic with respect to the tensor product are discussed, and combinations of these functionals are presented in detail. As an example, we consider the representation rank compression in tensor networks. For the numerical treatment, we use the nonlinear block Gauss?CSeidel method. We demonstrate the rate of convergence in numerical tests.     

Automated Flaw Detection in Algebraic Specifications

In interactive theorem proving practice a significant amount of time is spent on unsuccessful proof attempts of wrong conjectures. An automatic method that reveals them by generating finite counter examples would offer an extremely valuable support for a proof engineer by saving his time and effort. In practice, such counter examples tend to be small, so usually there is no need to search for big instances. Most definitions of functions or predicates on infinite structures do not preserve the semantics if a transition to arbitrary finite substructures is made. We propose constraints which guarantee a correct axiomatization on finite structures and present an approach which uses the Alloy Analyzer to generate finite instances of theories in the theorem prover KIV. It is evaluated on the library of basic data types as well as on some challenging case studies in KIV. The technique is implemented using the Kodkod constraint solver which is a successor of Alloy.     

Method development for epoxy resin analysis

Epoxy resin based negative photo resists are showing interesting properties which are useful for a series of applications in electronic industries (Mark et al. in Encyclopedia of polymer science and engineering, Wiley, New York, 1986; Potter in Epoxide resins, Springer, New York, 1970; May and Tanka in Epoxy resin chemistry and technology, Marcel Dekker, New York, 1973; Bauer in Epoxy resin chemistry, vol 114, American Chemical Society, Washington, 1979; Hood in RAPRA Rev Rep 38:4, 1990). Especially in micro system technologies they promise a high potential. To adjust the properties for the particular application modification of the chemical composition and crosslinker system must be performed. An actual problem is the constancy in behaviour during the LIGA-process of the resist mixture. In this work analytical techniques are used to get a detailed insight into the chemical composition of a commercial available epoxy novolac resin. Methods like size exclusion chromatography (SEC), matrix assisted laser desorption-time of flight (MALDI-ToF) and fourier transform infrared spectroscopy (FTIR) offer information about the molecular weight and functionality. The results reveal a distribution in molecular weight and functionality. Due this the resin was chemically modified. After processing a dependency of molecular weight and therefore the amount of epoxy groups existent was obtained.     

Driving Automotive User Interface Research

Cars offer an interesting but challenging microcosm for pervasive computing research and, in particular, for interaction with pervasive computing systems. Increasingly, researchers are looking at interactive applications in the car and investigating human-car interaction from a computer science—rather than an ergonomics or mechanical engineering—perspective. This article reports on the International Conference on Automotive User Interfaces and Interactive Vehicular Applications, wherein participants shared presentations on topics such as aesthetics, user interaction and distraction, safety, and driver monitoring.     

Neue Unabhängigkeit für den betrieblichen Datenschutzbeauftragten?

Der betriebliche Datenschutzbeauftragte dient der Selbstkontrolle der verantwortlichen Stelle. Wirksam ist solch eine Selbstkontrolle aber nur dann, wenn der Datenschutzbeauftragte auch Unabh?gigkeit genie?t. Welche Voraussetzungen hierfür erfüllt sein müssen, erl?utert der folgende Beitrag.     

A two-layer architecture for economically optimal process control and operation

A two-layer architecture for dynamic real-time optimization (or nonlinear modelpredictive control (NMPC) with an economic objective) is presented, where the solution of the dynamic optimization problem is computed on two time-scales. On the upper layer, a rigorous optimization problem is solved with an economic objective function at a slow time-scale, which captures slow trends in process uncertainties. On the lower layer, a fast neighboring-extremal controller is tracking the trajectory in order to deal with fast disturbances acting on the process. Compared to a single-layer architecture, the two-layer architecture is able to address control systems with complex models leading to high computational load, since the rigorous optimization problem can be solved at a slower rate than the process sampling time. Furthermore, solving a new rigorous optimization problem is not necessary at each sampling time if the process has rather slow dynamics compared to the disturbance dynamics. The two-layer control strategy is illustrated with a simulated case study of an industrial polymerization process.     

The double-edged effects of explanation prompts

Explanation prompts usually foster conceptual understanding. However, it has been claimed within cognitive load theory that prompts can take cognitive load to the upper limit when learning complex contents. Under such circumstances, prompts focusing the learners’ attention on specific aspects (e.g., conceptual aspects such as elaborations on domain principles) might have some costs: Other important aspects (e.g., procedural aspects such as how to calculate) cannot be processed deeply. Thus, we expected that conceptually-oriented explanation prompts would foster the detailedness of explanations, the number of elaborations on domain principles, and conceptual knowledge. In addition, we tested the influence of such prompts on the number of calculations performed during learning and procedural knowledge. We conducted an experiment in which we employed conceptually-oriented explanation prompts in a complex e-learning module on tax law. Tax law university students (N = 40) worked on this e-learning module under two conditions: (a) conceptually-oriented explanation prompts, (b) no prompts. The prompts led to double-edged effects: positive effects on the detailedness of explanations and on the number of elaborations on domain principles, as well as on conceptual knowledge and simultaneously negative effects on the number of calculations performed during learning as well as on procedural knowledge.     

To Score or Not to Score? Tripling Insights for Participatory Design

For evaluating visual-analytics tools, many studies confine to scoring user insights into data. For participatory design of those tools, we propose a three-level methodology to make more out of users' insights. The Relational Insight Organizer (RIO) helps to understand how insights emerge and build on one each other.     

New global optimization algorithms for model-based clustering

The Expectation-Maximization (EM) algorithm is a very popular optimization tool for mixture problems and in particular for model-based clustering problems. However, while the algorithm is convenient to implement and numerically very stable, it only produces local solutions. Thus, it may not achieve the globally optimal solution in problems that have a large number of local optima. This paper introduces several new algorithms designed to produce global solutions in model-based clustering. The building blocks for these algorithms are methods from the operations research literature, namely the Cross-Entropy (CE) method and Model Reference Adaptive Search (MRAS). One problem with applying these methods directly is the efficient simulation of positive definite covariance matrices. We propose several new solutions to this problem. One solution is to apply the principles of Expectation-Maximization updating, which leads to two new algorithms, CE-EM and MRAS-EM. We also propose two additional algorithms, CE-CD and MRAS-CD, which rely on the Cholesky decomposition. We conduct numerical experiments of varying complexity to evaluate the effectiveness of the proposed algorithms in comparison to classical EM. We find that although a single run of the new algorithms is slower than a single run of EM, all have the potential for producing significantly better solutions. We also find that although repeat application of EM may achieve similar results, our algorithms provide automated, data-driven decision rules which may significantly reduce the burden of searching for the global optimum.