@phdthesis{Schnjakin2014, author = {Schnjakin, Maxim}, title = {Cloud-RAID}, pages = {137}, year = {2014}, language = {de} } @phdthesis{Gericke2014, author = {Gericke, Lutz}, title = {Tele-Board - Supporting and analyzing creative collaboration in synchronous and asynchronous scenario}, pages = {186}, year = {2014}, language = {en} } @article{SchickBojahrHerzogetal.2014, author = {Schick, Daniel and Bojahr, Andre and Herzog, Marc and Shayduk, Roman and von Korff Schmising, Clemens and Bargheer, Matias}, title = {Udkm1Dsim-A simulation toolkit for 1D ultrafast dynamics in condensed matter}, series = {Computer physics communications : an international journal devoted to computational physics and computer programs in physics}, volume = {185}, journal = {Computer physics communications : an international journal devoted to computational physics and computer programs in physics}, number = {2}, publisher = {Elsevier}, address = {Amsterdam}, issn = {0010-4655}, doi = {10.1016/j.cpc.2013.10.009}, pages = {651 -- 660}, year = {2014}, abstract = {The UDKM1DSIM toolbox is a collection of MATLAB (MathWorks Inc.) classes and routines to simulate the structural dynamics and the according X-ray diffraction response in one-dimensional crystalline sample structures upon an arbitrary time-dependent external stimulus, e.g. an ultrashort laser pulse. The toolbox provides the capabilities to define arbitrary layered structures on the atomic level including a rich database of corresponding element-specific physical properties. The excitation of ultrafast dynamics is represented by an N-temperature model which is commonly applied for ultrafast optical excitations. Structural dynamics due to thermal stress are calculated by a linear-chain model of masses and springs. The resulting X-ray diffraction response is computed by dynamical X-ray theory. The UDKM1DSIM toolbox is highly modular and allows for introducing user-defined results at any step in the simulation procedure. Program summary Program title: udkm1Dsim Catalogue identifier: AERH_v1_0 Program summary URL: http://cpc.cs.qub.ac.uk/summaries/AERH_v1_0.html Licensing provisions: BSD No. of lines in distributed program, including test data, etc.: 130221 No. of bytes in distributed program, including test data, etc.: 2746036 Distribution format: tar.gz Programming language: Matlab (MathWorks Inc.). Computer: PC/Workstation. Operating system: Running Matlab installation required (tested on MS Win XP -7, Ubuntu Linux 11.04-13.04). Has the code been vectorized or parallelized?: Parallelization for dynamical XRD computations. Number of processors used: 1-12 for Matlab Parallel Computing Toolbox; 1 - infinity for Matlab Distributed Computing Toolbox External routines: Optional: Matlab Parallel Computing Toolbox, Matlab Distributed Computing Toolbox Required (included in the package): mtimesx Fast Matrix Multiply for Matlab by James Tursa, xml io tools by Jaroslaw Tuszynski, textprogressbar by Paul Proteus Nature of problem: Simulate the lattice dynamics of 1D crystalline sample structures due to an ultrafast excitation including thermal transport and compute the corresponding transient X-ray diffraction pattern. Solution method: Restrictions: The program is restricted to 1D sample structures and is further limited to longitudinal acoustic phonon modes and symmetrical X-ray diffraction geometries. Unusual features: The program is highly modular and allows the inclusion of user-defined inputs at any time of the simulation procedure. Running time: The running time is highly dependent on the number of unit cells in the sample structure and other simulation parameters such as time span or angular grid for X-ray diffraction computations. However, the example files are computed in approx. 1-5 min each on a 8 Core Processor with 16 GB RAM available.}, language = {en} } @phdthesis{Lindauer2014, author = {Lindauer, T. Marius}, title = {Algorithm selection, scheduling and configuration of Boolean constraint solvers}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-71260}, school = {Universit{\"a}t Potsdam}, pages = {ii, 130}, year = {2014}, abstract = {Boolean constraint solving technology has made tremendous progress over the last decade, leading to industrial-strength solvers, for example, in the areas of answer set programming (ASP), the constraint satisfaction problem (CSP), propositional satisfiability (SAT) and satisfiability of quantified Boolean formulas (QBF). However, in all these areas, there exist multiple solving strategies that work well on different applications; no strategy dominates all other strategies. Therefore, no individual solver shows robust state-of-the-art performance in all kinds of applications. Additionally, the question arises how to choose a well-performing solving strategy for a given application; this is a challenging question even for solver and domain experts. One way to address this issue is the use of portfolio solvers, that is, a set of different solvers or solver configurations. We present three new automatic portfolio methods: (i) automatic construction of parallel portfolio solvers (ACPP) via algorithm configuration,(ii) solving the \$NP\$-hard problem of finding effective algorithm schedules with Answer Set Programming (aspeed), and (iii) a flexible algorithm selection framework (claspfolio2) allowing for fair comparison of different selection approaches. All three methods show improved performance and robustness in comparison to individual solvers on heterogeneous instance sets from many different applications. Since parallel solvers are important to effectively solve hard problems on parallel computation systems (e.g., multi-core processors), we extend all three approaches to be effectively applicable in parallel settings. We conducted extensive experimental studies different instance sets from ASP, CSP, MAXSAT, Operation Research (OR), SAT and QBF that indicate an improvement in the state-of-the-art solving heterogeneous instance sets. Last but not least, from our experimental studies, we deduce practical advice regarding the question when to apply which of our methods.}, language = {en} } @article{HoosLindauerSchaub2014, author = {Hoos, Holger and Lindauer, Marius and Schaub, Torsten}, title = {claspfolio 2}, series = {Theory and practice of logic programming}, volume = {14}, journal = {Theory and practice of logic programming}, publisher = {Cambridge Univ. Press}, address = {New York}, issn = {1471-0684}, doi = {10.1017/S1471068414000210}, pages = {569 -- 585}, year = {2014}, abstract = {Building on the award-winning, portfolio-based ASP solver claspfolio, we present claspfolio 2, a modular and open solver architecture that integrates several different portfolio-based algorithm selection approaches and techniques. The claspfolio 2 solver framework supports various feature generators, solver selection approaches, solver portfolios, as well as solver-schedule-based pre-solving techniques. The default configuration of claspfolio 2 relies on a light-weight version of the ASP solver clasp to generate static and dynamic instance features. The flexible open design of claspfolio 2 is a distinguishing factor even beyond ASP. As such, it provides a unique framework for comparing and combining existing portfolio-based algorithm selection approaches and techniques in a single, unified framework. Taking advantage of this, we conducted an extensive experimental study to assess the impact of different feature sets, selection approaches and base solver portfolios. In addition to gaining substantial insights into the utility of the various approaches and techniques, we identified a default configuration of claspfolio 2 that achieves substantial performance gains not only over clasp's default configuration and the earlier version of claspfolio, but also over manually tuned configurations of clasp.}, language = {en} } @phdthesis{Videla2014, author = {Videla, Santiago}, title = {Reasoning on the response of logical signaling networks with answer set programming}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-71890}, school = {Universit{\"a}t Potsdam}, year = {2014}, abstract = {Deciphering the functioning of biological networks is one of the central tasks in systems biology. In particular, signal transduction networks are crucial for the understanding of the cellular response to external and internal perturbations. Importantly, in order to cope with the complexity of these networks, mathematical and computational modeling is required. We propose a computational modeling framework in order to achieve more robust discoveries in the context of logical signaling networks. More precisely, we focus on modeling the response of logical signaling networks by means of automated reasoning using Answer Set Programming (ASP). ASP provides a declarative language for modeling various knowledge representation and reasoning problems. Moreover, available ASP solvers provide several reasoning modes for assessing the multitude of answer sets. Therefore, leveraging its rich modeling language and its highly efficient solving capacities, we use ASP to address three challenging problems in the context of logical signaling networks: learning of (Boolean) logical networks, experimental design, and identification of intervention strategies. Overall, the contribution of this thesis is three-fold. Firstly, we introduce a mathematical framework for characterizing and reasoning on the response of logical signaling networks. Secondly, we contribute to a growing list of successful applications of ASP in systems biology. Thirdly, we present a software providing a complete pipeline for automated reasoning on the response of logical signaling networks.}, language = {en} }