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Improving the Realism of Synthetic Cryogenic Electron Micrographs Using Generative Adversarial Networks
No full textThis thesis addresses the problem of synthetic-to-real image refinement applied to tilt series of cryogenic electron micrographs. It explores the possibility of improving the realism of synthesized micrographs using generative adversarial networks, which could help to improve the automatic segmentation of cellular structures based on deep learning methods. For image refinement, three image-to-image translation networks were used to transfer the appearance of real micrographs to synthetic micrographs while preserving their original content, including the location and shape of particles. The first model, called SimGAN, was unable to produce any meaningful refinement. Instead, the content of the synthetic micrographs was corrupted by the addition of extensive noise, making SimGAN unsuitable for the problem of this thesis. As a result, CycleGAN was introduced and its refinement of synthetic micrographs matches the appearance of real micrographs very well. However, structural changes in the position and shape of particles were observed after translation. To avoid this behavior, CUT was used as a third model on an exploratory basis but its performance was inferior to that of CycleGAN. In conclusion, CycleGAN proved to be the most promising image-to-image translation model for the images presented, although it does not solve the main problem of this thesis. In order to do so, further modifications, such as the addition of a structural constraint during translation, are required
A three-phase heuristic for the Fairness-Oriented Crew Rostering Problem
No full textThe Fairness-Oriented Crew Rostering Problem (FCRP) considers the joint optimization of attractiveness and fairness in cyclic crew rostering. Like many problems in scheduling and logistics, the combinatorial complexity of cyclic rostering causes exact methods to fail for large-scale practical instances. In case of the FCRP, this is accentuated by the additionally imposed fairness requirements. Hence, heuristic methods are necessary. We present a three-phase heuristic for the FCRP combining column generation techniques with variable-depth neighborhood search. The heuristic exploits different mathematical formulations to find feasible solutions and to search for improvements. We apply our methodology to practical instances from Netherlands Railways (NS), the main passenger railway operator in the Netherlands Our results show the three-phase heuristic finds good solutions for most instances and outperforms a state-of-the-art commercial solver
Mathematische Gesellschaft der DDR – die Vorgeschichte
No full textZusammenfassungDie Mathematische Gesellschaft der DDR (MGDDR) ist seit 1991 Geschichte. Die MGDDR spaltete sich 1962 auf Betreiben der Sozialistischen Einheitspartei (SED) der DDR von der Deutschen Mathematiker-Vereinigung (DMV) ab und beschloss 1990 den Zusammenschluss mit der DMV. Die Geschichte der MGDDR ist spannend, angefangen von der Vorgeschichte, ihren Aktivitäten in der DDR-Zeit bis zur Neupositionierung nach der Wende. Und die MGDDR hat ihre Spuren in der DMV hinterlassen. Etliche Ansätze und Aktivitäten der MGDDR wurden von der DMV fortgeführt und weiterentwickelt. Aus der Literatur sind bisher nur Darstellungen einzelner Aspekte der MGDDR bekannt. Dieser Artikel unternimmt den Versuch einer Gesamtdarstellung der Geschichte der MGDDR und soll die Ausführungen in [1] und [2] ergänzen und erweitern. Die MGDDR war ein Ergebnis des Kalten Krieges und – wesentlich stärker als bisher der Öffentlichkeit bekannt – ein Produkt der Wissenschaftspolitik von Partei und Staat der DDR. Der politische Einfluss wird in diesem Artikel durch Unterlagen belegt, die in verschiedenen Archiven gefunden wurden. Von besonderer Bedeutung bei der Recherche war das Archivgut der Stiftung Archiv der Parteien und Massenorganisationen der DDR im Bundesarchiv(SAPMO), wo seit 1990 die Unterlagen der SED, staatlicher Stellen und Massenorganisatioen der DDR systematisch aufbereitet und verfügbar gemacht worden sind. Die Mathematiker der DDR standen der Vereinnahmung durch Partei und Staat mehrheitlich kritisch gegenüber. Dies gilt insbesondere für die Zeit vor dem Mauerbau. Nach dem Mauerbau und den Einschränkungen des Reiseverkehrs war für die meisten Mathematiker der DDR die Gründung einer eigenen Fachgesellschaft alternativlos. Diese erfolgte am 8. Juni 1962. Nach dem Mauerbau war für die Mathematiker in der DDR wissenschaftlicher Austausch ohne Einbeziehung der MGDDR nahezu unmöglich. Für die Arbeit der MGDDR standen aber immer mathematische Ziele im Zentrum und die MGDDR war dabei – trotz einiger Behinderungen – durchaus erfolgreich. Nach der Wende positionierte sich die MGDDR neu und beschloss auf dem Mathematiker-Kongress der DDR im September 1990 den Zusammenschluss mit der DMV. Der vorliegende Artikel umfasst die Vorgeschichte der MGDDR bis zu deren Gründung am 08.06.1962. In zwei weiteren Publikationen soll die Arbeit der MGDDR bis zur Wende und dem Zusammenschluss mit der DMV dargestellt werden
Novel multi-objective affinity approach allows to identify pH-specific μ-opioid receptor agonists (Dataset)
No full textVirtual Screening Dataset for the paper "Novel multi-objective affinity approach allows to identify pH-specific μ-opioid receptor agonists" by Secker et al. (https://doi.org/10.1186/s13321-023-00746-4
Ontologies for Models and Algorithms in Applied Mathematics and Related Disciplines
No full textIn applied mathematics and related disciplines, the modeling-simulation-optimization workflow is a prominent scheme, with mathematical models and numerical algorithms playing a crucial role. For these types of mathematical research data, the Mathematical Research Data Initiative has developed, merged and implemented ontologies and knowledge graphs. This contributes to making mathematical research data FAIR by introducing semantic technology and documenting the mathematical foundations accordingly. Using the concrete example of microfracture analysis of porous media, it is shown how the knowledge of the underlying mathematical model and the corresponding numerical algorithms for its solution can be represented by the ontologies
A Discrete-Continuous Algorithm for Globally Optimal Free Flight Trajectory Optimization
No full textThis thesis introduces the novel hybrid algorithm DisCOptER for globally optimal flight planning. DisCOptER (Discrete-Continuous Optimization for Enhanced Resolution) com- bines discrete and continuous optimization in a two-stage approach to find optimal trajectories up to arbitrary precision in finite time. In the discrete phase, a directed auxiliary graph is created in order to define a set of candidate paths that densely covers the relevant part of the trajectory space. Then, Yen’s algorithm is employed to identify a set of promising candidate paths. These are used as starting points for the subsequent stage in which they are refined with a locally convergent optimal control method. The correctness, accuracy, and complexity of DisCOptER are intricately linked to the choice of the switch-over point, defined by the discretization coarseness. Only a sufficiently dense graph enables the algorithm to find a path within the convex domain surrounding the global minimizer. Initialized with such a path, the second stage rapidly converges to the optimum. Conversely, an excessively dense graph poses the risk of overly costly and redundant computations. The determination of the optimal switch-over point necessitates a profound understanding of the local behavior of the problem, the approximation properties of the graph, and the convergence characteristics of the employed optimal control method. These topics are explored extensively in this thesis. Crucially, the density of the auxiliary graph is solely dependent on the en- vironmental conditions, yet independent of the desired solution accuracy. As a consequence, the algorithm inherits the superior asymptotic convergence properties of the optimal control stage. The practical implications of this computational efficiency are demonstrated in realistic environments, where the DisCOptER algorithm consistently delivers highly accurate globally optimal trajectories with exceptional computational efficiency. This notable improvement upon existing approaches underscores the algorithm’s significance. Beyond its technical prowess, the DisCOptER algorithm stands as a valuable tool contributing to the reduction of costs and the overall enhancement of flight operations efficiency
Research-Data Management Planning in the German Mathematical Community
No full textIn this paper we discuss the notion of research data for the field of mathematics and report on the status quo of research-data management and planning. A number of decentralized approaches are presented and compared to needs and challenges faced in three use cases from different mathematical subdisciplines. We highlight the importance of tailoring research-data management plans to mathematicians’ research processes and discuss their usage all along the data life cycle
Parallel Exchange of Randomized SubGraphs for Optimization of Network Alignment: PERSONA
No full textThe aim of Network Alignment in Protein-Protein Interaction Networks is discovering functionally similar regions between compared organisms. One major compromise for solving a network alignment problem is the trade-off among multiple similarity objectives while applying an alignment strategy. An alignment may lose its biological relevance while favoring certain objectives upon others due to the actual relevance of unfavored objectives. One possible solution for solving this issue may be blending the stronger aspects of various alignment strategies until achieving mature solutions. This study proposes a parallel approach called PERSONA that allows aligners to share their partial solutions continuously while they progress. All these aligners pursue their particular heuristics as part of a particle swarm that searches for multi-objective solutions of the same alignment problem in a reactive actor environment.
The actors use the stronger portion of a solution as a subgraph that they receive from leading or other actors and send their own stronger subgraphs back upon evaluation of those partial solutions. Moreover, the individual heuristics of each actor takes randomized parameter values at each cycle of parallel execution so that the problem search space can thoroughly be investigated. The results achieved with PERSONA are remarkably optimized and balanced for both topological and node similarity objectives