TY - JOUR A1 - Göppel, Simon A1 - Frikel, Jürgen A1 - Haltmeier, Markus T1 - Regularization of Inverse Problems with Translation Invariant Frames JF - Proceedings of the 20th International Conference of Numerical Analysis and Applied Mathematics (ICNAAM-2022), 2022, Heraklion (Crete, Greece) N2 - In various fields of applications, inverse problems are characterized by their sensitivity to data perturbations which can cause severe reconstruction errors. Hence, regularization procedures are employed in order to ensure stability and reconstruction quality. To overcome limitations of classical approaches such as the filtered singular value decomposition (SVD), frame based diagonalization methods have been studied in the recent years, e.g., wavelet-vagulette (WVD) decomposition. While these methods can be well adapted to the problem at hand, it is well-known, that the lack of translation invariance in multiscale systems can cause specific artifacts in the recovered object. Thus, to overcome these drawbacks we use the translation invariant diagonal frame decomposition (TI-DFD) of linear operators. For illustration, we construct a TI-WVD for one-dimensional integration operator, and confirm our theoretical findings by numerical simulations. Y1 - 2023 PB - AIP Publishing ER - TY - INPR A1 - Murr, Florian A1 - Mauerer, Wolfgang T1 - McFSM: Near Turing-Complete Finite-State Based Programming N2 - Finite state machines (FSMs) are an appealing mechanism for simple practical computations: They lend themselves to very effcient and deterministic implementation, are easy to understand, and allow for formally proving many properties of interest. Unfortunately, their computational power is deemed insuffcient for many tasks, and their usefulness has been further hampered by the state space explosion problem and other issues when naïvely trying to scale them to sizes large enough for many real–life applications. This paper expounds on theory and implementation of multiple coupled fnite state machines (McFSMs), a novel mechanism that combines benefits of FSMs with near Turing-complete, practical computing power, and that was designed from the ground up to support static analysis and reasoning. We develop an elaborate category–theoretical foundation based on non–deterministic Mealy machines, which gives a suitable algebraic description for novel ways of blending di#erent computing models. Our experience is based on a domain specific language and an integrated development environment that can compile McFSM models to multiple target languages, applying it to use-cases based on industrial scenarios. We discuss properties and advantages of McFSMs, explain how the mechanism can interact with real–world systems and existing code without sacrificing provability, determinism or performance. We discuss how McFSMs can be used to replace and improve on commonly employed programming patterns, and show how their effcient handling of large state spaces enables them to be used as core building blocks for distributed, safety critical, and real–time systems of industrial complexity, which contributes to the longdesired goal of providing executable specifications. KW - Finite state machines KW - Mealy machines KW - automata KW - coupled machines KW - executable specifcation KW - category theory KW - generative approaches KW - formal models KW - static analysis Y1 - 2021 UR - https://www.lfdr.de/Publications/2021/MuMa21.pdf ER - TY - CHAP A1 - Mauerer, Wolfgang A1 - Klessinger, Stefan A1 - Scherzinger, Stefanie T1 - Beyond the badge: reproducibility engineering as a lifetime skill T2 - Proceedings 4th International Workshop on Software Engineering Education for the Next Generation SEENG 2022, 17 May 2022, Pittsburgh, PA, USA N2 - Ascertaining reproducibility of scientific experiments is receiving increased attention across disciplines. We argue that the necessary skills are important beyond pure scientific utility, and that they should be taught as part of software engineering (SWE) education. They serve a dual purpose: Apart from acquiring the coveted badges assigned to reproducible research, reproducibility engineering is a lifetime skill for a professional industrial career in computer science. SWE curricula seem an ideal fit for conveying such capabilities, yet they require some extensions, especially given that even at flagship conferences like ICSE, only slightly more than one-third of the technical papers (at the 2021 edition) receive recognition for artefact reusability. Knowledge and capabilities in setting up engineering environments that allow for reproducing artefacts and results over decades (a standard requirement in many traditional engineering disciplines), writing semi-literate commit messages that document crucial steps of a decision-making process and that are tightly coupled with code, or sustainably taming dynamic, quickly changing software dependencies, to name a few: They all contribute to solving the scientific reproducibility crisis, and enable software engineers to build sustainable, long-term maintainable, software-intensive, industrial systems. We propose to teach these skills at the undergraduate level, on par with traditional SWE topics. KW - reproducibility engineering KW - teaching software engineering Y1 - 2022 SN - 9781450393362 U6 - https://doi.org/10.1145/3528231.3528359 N1 - Preprint unter: https://doi.org/10.48550/arXiv.2203.05283 SP - 1 EP - 4 PB - ACM CY - New York, NY, USA ER - TY - CHAP A1 - Winker, Tobias A1 - Groppe, Sven A1 - Uotila, Valter Johan Edvard A1 - Yan, Zhengtong A1 - Lu, Jiaheng A1 - Maja, Franz A1 - Mauerer, Wolfgang T1 - Quantum Machine Learning: Foundation, New Techniques, and Opportunities for Database Research T2 - SIGMOD '23, proceedings of the 2023 International Conference on Management of Data: June 18-23, 2023, Seattle, WA, USA N2 - In the last few years, the field of quantum computing has experienced remarkable progress. The prototypes of quantum computers already exist and have been made available to users through cloud services (e.g., IBM Q experience, Google quantum AI, or Xanadu quantum cloud). While fault-tolerant and large-scale quantum computers are not available yet (and may not be for a long time, if ever), the potential of this new technology is undeniable. Quantum algorithms havethe proven ability to either outperform classical approaches for several tasks, or are impossible to be efficiently simulated by classical means under reasonable complexity-theoretic assumptions. Even imperfect current-day technology is speculated to exhibit computational advantages over classical systems. Recent research is using quantum computers to solve machine learning tasks. Meanwhile, the database community already successfully applied various machine learning algorithms for data management tasks, so combining the fields seems to be a promising endeavour. However, quantum machine learning is a new research field for most database researchers. In this tutorial, we provide a fundamental introduction to quantum computing and quantum machine learning and show the potential benefits and applications for database research. In addition, we demonstrate how to apply quantum machine learning to the optimization of join order problem for databases. Y1 - 2023 U6 - https://doi.org/10.1145/3555041.3589404 PB - ACM CY - New York ER - TY - CHAP A1 - Dochow, C. A1 - Herpers, F. A1 - Raptis, Georgios ED - Kipker, Dennis-Kenji T1 - Gesundheit und Soziales T2 - Cybersecurity Y1 - 2023 SN - 978-3-406-79263-2 SP - 663 EP - 760 PB - C.H.Beck CY - München ET - 2. Aufl. ER - TY - GEN ED - Frommeld, Debora ED - Gerhards, Helene ED - Weber, Karsten T1 - Gesellschaften in der Krise : Praktiken, Diskurse und Wissensregime in Zeiten von Corona N2 - Das Virus SARS-CoV-2 und die dadurch ausgelöste Coronapandemie haben die Gesellschaft in einen Krisenmodus versetzt: Die Coronapandemie hat tiefgreifenden Einfluss auf den Alltag von Subjekten in allen Lebenslagen genommen, gesellschaftliche Bedingungen verändert und institutionelle Veränderungen angestoßen. Ob das Tragen eines Mund-Nasen-Schutzes, die virtuelle Kommunikation als neuer Standard in Arbeitsumgebungen oder Regelungen zu Impfungen und öffentlichem Gesundheitsschutz – gesellschaftliche Praktiken und Diskurse haben sich verändert sowie Wissensregime etabliert, die einer genaueren multidisziplinären Analyse würdig sind. Der Band versammelt Beiträge zu Bereichen, die von einem krisenbedingten Wandel betroffen sind: Alter, Bildung, Emotion, Freiheit, Geschlecht, Gesundheit, Digitalisierung, Körper, Medizin und Versorgung sowie Sorgebeziehungen. Y1 - 2023 SN - 9783658391287 SN - 978-3-658-39129-4 PB - Springer Fachmedien CY - Wiesbaden ER - TY - JOUR A1 - Jain, Brijnesh A1 - Froese, Vincent A1 - Schultz, David T1 - An average-compress algorithm for the sample mean problem under dynamic time warping JF - Journal of Global Optimization N2 - Computing a sample mean of time series under dynamic time warping is NP-hard. Consequently, there is an ongoing research effort to devise efficient heuristics. The majority of heuristics have been developed for the constrained sample mean problem that assumes a solution of predefined length. In contrast, research on the unconstrained sample mean problem is underdeveloped. In this article, we propose a generic average-compress (AC) algorithm to address the unconstrained problem. The algorithm alternates between averaging (A-step) and compression (C-step). The A-step takes an initial guess as input and returns an approximation of a sample mean. Then the C-step reduces the length of the approximate solution. The compressed approximation serves as initial guess of the A-step in the next iteration. The purpose of the C-step is to direct the algorithm to more promising solutions of shorter length. The proposed algorithm is generic in the sense that any averaging and any compression method can be used. Experimental results show that the AC algorithm substantially outperforms current state-of-the-art algorithms for time series averaging. KW - Time series averaging KW - Fréchet function KW - Heuristic KW - Nonconvex optimization Y1 - 2023 U6 - https://doi.org/10.1007/s10898-023-01294-9 PB - Springer ER - TY - CHAP A1 - Thelen, Simon A1 - Volbert, Klaus A1 - Nunes, Danilo Weber T1 - A Survey on Algorithmic Problems in Wireless Systems T2 - Proceedings of the 12th International Conference on Sensor Networks (SENSORNETS), Vol 1, Feb 23, 2023 - Feb 24, 2023, Lisbon, Portugal N2 - Considering the ongoing growth of Wireless Sensor Networks (WSNs) and the challenges they pose due to their hardware limitations as well as the intrinsic complexity of their interactions, specialized algorithms have the potential to help solving these challenges. We present a survey on recent developments regarding algorithmic problems which have applications in wireless systems and WSNs in particular. Focusing on the intersection between WSNs and algorithms, we give an overview of recent results inside this intersection, concerning topics such as routing, interference minimization, latency reduction, localization among others. Progress on solving these problems could be potentially beneficial for the industry as a whole by increasing network throughput, reducing latency or making systems more energy-efficient. We summarize and structure these recent developments and list interesting open problems to be investigated in future works. KW - Algorithms KW - WSNs KW - Survey KW - Network Construction KW - Routing KW - Interference KW - Localization KW - Charging KW - Latency Y1 - 2023 SN - 978-989-758-635-4 U6 - https://doi.org/10.5220/0011791200003399 SP - 101 EP - 111 PB - SCITEPRESS - Science and Technology Publications ER - TY - CHAP A1 - Dünnweber, Jan A1 - Stadler, Timo A1 - Weikl, Sandra A1 - Schäfer, Andreas A1 - Georg, Peter A1 - Wein, Simon T1 - Bus Route Classification for Rural Areas using Graph Convolutional Networks T2 - Proceedings of the 22th International Conference on Artificial Intelligence and Soft Computing, Zakopane, Poland N2 - In this paper, we present a new approach to determine the estimated time of arrival (ETA) for bus routes using (Deep) Graph Convolutional Networks (DGCNs). In addition we use the same DGCN to detect detours within a route. In our application, a classification of routes and their underlying graph structure is performed using Graph Learning. Our model leads to a fast prediction and avoids solving the vehicle routing problem (VRP) through expensive computations. Moreover, we describe how to predict travel time for all routes using the same DGCN Model. This method makes it possible not to use a more computationally intensive approximation algorithm when determining long travel times with many intermediate stops, but to use our network for an early estimate of the quality of a route. Long travel times, in our case result from the use of a call-bus system, which must distribute many passengers among several vehicles and can take them to places without a regular stop. For a case study, the rural town of Roding in Bavaria is used. Our training data for this area results from an approximation algorithm that we implemented to optimize routes, and to generate an archive of routes of varying quality simultaneously. Y1 - 2023 ER - TY - GEN ED - George, Wolfgang Michael ED - Weber, Karsten T1 - Wie werden wir in Zukunft sterben? Szenarien zu Sterben, Tod und Trauer im Jahr 2045 N2 - Wie werden wir die Themen Sterben, Tod und Trauer im Jahr 2045 auf individueller, gesellschaftlicher und institutioneller Ebene behandeln? Wie werden sich Bedürfnisse und Erwartungen entwickeln? Wird der Trend zur Individualisierung und Vereinzelung fortgeschrieben? Werden immer mehr Menschen in Pflegeeinrichtungen sterben? Wie werden sich erweiterte technologische Möglichkeiten und Digitalisierung auswirken? Experten*innen aus unterschiedlichen Handlungsfeldern und wissenschaftlichen Disziplinen entwickeln Szenarien, wie Sterben, Tod und Trauer in der Zukunft gesellschaftlich undversorgungs-systematisch organisiert werden. Zudem machen sie "wünschenswerte" Perspektiven sichtbar und zeigen auf, was zu tun ist, damit diese Ausblicke Realität werden können. Y1 - 2023 SN - 978-3-8379-3232-4 SN - 978-3-8379-6105-8 U6 - https://doi.org/10.30820/9783837961058 PB - Psychosozial-Verlag CY - Gießen ER -