@article{LamprechtWickert2014, author = {Lamprecht, Anna-Lena and Wickert, Alexander}, title = {The Course's SIB Libraries}, series = {Process Design for Natural Scientists: an agile model-driven approach}, journal = {Process Design for Natural Scientists: an agile model-driven approach}, number = {500}, editor = {Lambrecht, Anna-Lena and Margaria, Tiziana}, publisher = {Springer Verlag}, address = {Berlin}, isbn = {978-3-662-45005-5}, issn = {1865-0929}, pages = {30 -- 44}, year = {2014}, abstract = {This chapter gives a detailed description of the service framework underlying all the example projects that form the foundation of this book. It describes the different SIB libraries that we made available for the course "Process modeling in the natural sciences" to provide the functionality that was required for the envisaged applications. The students used these SIB libraries to realize their projects.}, language = {en} } @article{NaujokatNeubauerLamprechtetal.2014, author = {Naujokat, Stefan and Neubauer, Johannes and Lamprecht, Anna-Lena and Steffen, Bernhard and Joerges, Sven and Margaria, Tiziana}, title = {Simplicity-first model-based plug-in development}, series = {Software : practice \& experience}, volume = {44}, journal = {Software : practice \& experience}, number = {3}, publisher = {Wiley-Blackwell}, address = {Hoboken}, issn = {0038-0644}, doi = {10.1002/spe.2243}, pages = {277 -- 297}, year = {2014}, abstract = {In this article, we present our experience with over a decade of strict simplicity orientation in the development and evolution of plug-ins. The point of our approach is to enable our graphical modeling framework jABC to capture plug-in development in a domain-specific setting. The typically quite tedious and technical plug-in development is shifted this way from a programming task to the modeling level, where it can be mastered also by application experts without programming expertise. We show how the classical plug-in development profits from a systematic domain-specific API design and how the level of abstraction achieved this way can be further enhanced by defining adequate building blocks for high-level plug-in modeling. As the resulting plug-in models can be compiled and deployed automatically, our approach decomposes plug-in development into three phases where only the realization phase requires plug-in-specific effort. By using our modeling framework jABC, this effort boils down to graphical, tool-supported process modeling. Furthermore, we support the automatic completion of process sketches for executability. All this will be illustrated along the most recent plug-in-based evolution of the jABC framework, which witnessed quite some bootstrapping effects.}, language = {en} } @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} } @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} } @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} }