Towards reliable data-based optimal and predictive control using extended DMD - Schaller, Manuel; Worthmann, Karl; Philipp, Friedrich; Peitz, Sebastian; Nüske, Feliks (IFAC-PapersOnLine)
We present an approach for guaranteed constraint satisfaction by means of data-based optimal control, where the model is unknown and has to be obtained from measurement data. To this end, we utilize the Koopman framework and an eDMD-based bilinear surrogate modeling approach for control systems to show an error bound on predicted observables, i.e., functions of the state. This result is then applied to the constraints of the optimal control problem to show that satisfaction of tightened constraints in the purely data-based surrogate model implies constraint satisfaction for the original system.

Phase-preserving methods to visualise ultrasonic fields with schlieren imaging - Hetkämper, Tim; Koch, Kevin; Claes, Leander; Henning, Bernd (tm - Technisches Messen)
Keywords: Schlierentechnik

Tailoring the directive nature of optical waveguide antennas - Farheen, Henna; Yan, Lok-Yee; Leuteritz, Till; Qiao, Siqi; Spreyer, Florian; Schlickriede, Christian; Quiring, Viktor; Eigner, Christof; Silberhorn, Christine; Zentgraf, Thomas; Linden, Stefan; Myroshnychenko, Viktor; et al. (Integrated Optics: Devices, Materials, and Technologies XXVII)
We demonstrate the numerical and experimental realization of optimized optical traveling-wave antennas made of low-loss dielectric materials. These antennas exhibit highly directive radiation patterns and our studies reveal that this nature comes from two dominant guided TE modes excited in the waveguide-like director of the antenna, in addition to the leaky modes. The optimized antennas possess a broadband nature and have a nearunity radiation efficiency at an operational wavelength of 780 nm. Compared to the previously studied plasmonic antennas for photon emission, our all-dielectric approach demonstrates a new class of highly directional, low-loss, and broadband optical antennas.
Keywords: tet_topic_opticalantenna

Optimized silicon antennas for optical phased arrays - Farheen, Henna; Strauch, Andreas; Scheytt, J. Christoph; Myroshnychenko, Viktor; Förstner, Jens (Integrated Optics: Devices, Materials, and Technologies XXVII)
We demonstrate a large-scale two dimensional silicon-based optical phased array (OPA) composed of nanoantennas with circular gratings that are balanced in power and aligned in phase, required for producing desired radiation patterns in the far-field. The OPAs are numerically optimized to have an upward efficiency of up to 90%, targeting radiation concentration mainly in the field of view. We envision that our OPAs have the ability of generating complex holographic images, rendering them an attractive candidate for a wide range of applications like LiDAR sensors, optical trapping, optogenetic stimulation and augmented-reality displays.
Keywords: tet_topic_opticalantenna

Possible points of blow-up in chemotaxis systems with spatially heterogeneous logistic source - Black, Tobias; Fuest, Mario; Lankeit, Johannes; Mizukami, Masaaki (Nonlinear Analysis: Real World Applications)
Keywords: Applied Mathematics; Computational Mathematics; General Economics; Econometrics and Finance; General Engineering; General Medicine; Analysis

Reziprokes Peer-Tutoring zur Förderung von Schüler*innen mit Schwierigkeiten beim Mathematiklernen. - Vitt, Vivian; Häsel-Weide, Uta (Mathematica Didactica, 46)

Inklusive Praktiken unterrichtsintegrierter Förderung im Mathematikunterricht. - Häsel-Weide, Uta; Nührenbörger, M. (Mathematica Didactica, 46)

The Lax Equation and Weak Regularity of Asymptotic Estimate Lie Groups - Hanusch, Maximilian (Annals of Global Analysis and Geometry)
Keywords: Lax equation; generalized Baker-Campbell-Dynkin-Hausdorff formula; regularity of Lie groups

On Cryptography Effects on Interconnect Reliability - Ghazal, Abdulkarim; Sadeghi-Kohan, Somayeh; Reimer, Jan Dennis; Hellebrand, Sybille
Keywords: WORKSHOP

Time-Optimal Model Predictive Control of Permanent Magnet Synchronous Motors Considering Current and Torque Constraints - Brosch, Anian; Wallscheid, Oliver; Böcker, Joachim (IEEE Transactions on Power Electronics)
Keywords: Electrical and Electronic Engineering

How to suppress radiative losses in high-contrast integrated Bragg gratings - Hammer, Manfred; Farheen, Henna; Förstner, Jens (Journal of the Optical Society of America B)
High-contrast slab waveguide Bragg gratings with 1D periodicity are investigated. For specific oblique excitation by semi-guided waves at sufficiently high angles of incidence, the idealized structures do not exhibit any radiative losses, such that reflectance and transmittance for the single port mode add strictly up to one. We consider a series of symmetric, fully and partly etched finite gratings, for parameters found in integrated silicon photonics. These can act as spectral filters with a reasonably flattop response. Apodization can lead to more box shaped reflectance and transmittance spectra. Together with a narrowband Fabry–Perot filter, these configurations are characterized by reflection bands, or transmittance peaks, with widths that span three orders of magnitude.
Keywords: tet_topic_waveguide

A multi-mode super-fano mechanism for enhanced third harmonic generation in silicon metasurfaces - Hähnel, David; Golla, Christian; Albert, Maximilian; Zentgraf, Thomas; Myroshnychenko, Viktor; Förstner, Jens; Meier, Cedrik (Light: Science & Applications)
We present strong enhancement of third harmonic generation in an amorphous silicon metasurface consisting of elliptical nano resonators. We show that this enhancement originates from a new type of multi-mode Fano mechanism. These ‘Super-Fano’ resonances are investigated numerically in great detail using full-wave simulations. The theoretically predicted behavior of the metasurface is experimentally verified by linear and nonlinear transmission spectroscopy. Moreover, quantitative nonlinear measurements are performed, in which an absolute conversion efficiency as high as ηmax ≈ 2.8 × 10−7 a peak power intensity of 1.2 GW cm−2 is found. Compared to an unpatterned silicon film of the same thickness amplification factors of up to ~900 are demonstrated. Our results pave the way to exploiting a strong Fano-type multi-mode coupling in metasurfaces for high THG in potential applications.
Keywords: tet_topic_meta

Online load balancing on uniform machines with limited migration - Maack, Marten (Operations Research Letters)
Keywords: Applied Mathematics; Industrial and Manufacturing Engineering; Management Science and Operations Research; Software

A Formal Comparison of Advanced Digital Signature Primitives - Schürmann, Patrick

The Complexity of Translationally Invariant Problems beyond Ground State Energies - Gharibian, Sevag; Watson, James; Bausch, Johannes (Proceedings of the 40th International Symposium on Theoretical Aspects of Computer Science (STACS))

Critically 3-frustrated signed graphs - Cappello, Chiara; Naserasr, Reza; Steffen, Eckhard; Wang, Zhouningxin (arXiv:2304.10243)
Extending the notion of maxcut, the study of the frustration index of signed graphs is one of the basic questions in the theory of signed graphs. Recently two of the authors initiated the study of critically frustrated signed graphs. That is a signed graph whose frustration index decreases with the removal of any edge. The main focus of this study is on critical signed graphs which are not edge-disjoint unions of critically frustrated signed graphs (namely non-decomposable signed graphs) and which are not built from other critically frustrated signed graphs by subdivision. We conjecture that for any given k there are only finitely many critically k-frustrated signed graphs of this kind. Providing support for this conjecture we show that there are only two of such critically 3-frustrated signed graphs where there is no pair of edge-disjoint negative cycles. Similarly, we show that there are exactly ten critically 3-frustrated signed planar graphs that are neither decomposable nor subdivisions of other critically frustrated signed graphs. We present a method for building non-decomposable critically frustrated signed graphs based on two given such signed graphs. We also show that the condition of being non-decomposable is necessary for our conjecture.

MAAS: Hiding Trojans in Approximate Circuits - Ahmed, Qazi Arbab; Awais, Muhammad; Platzner, Marco (The 24th International Symposium on Quality Electronic Design (ISQED'23), San Francisco, Califorina USA)

Minimum Edge Cuts in Overlay Networks - Schweichhart, Jonas

Unifying Gathering Protocols for Swarms of Mobile Robots - Castenow, Jannik; Harbig, Jonas; Meyer auf der Heide, Friedhelm (Lecture Notes in Computer Science)

Algorithms for the genome median under a restricted measure of rearrangement - Silva, Helmuth O.M.; Rubert, Diego P.; Araujo, Eloi; Steffen, Eckhard; Doerr, Daniel; Martinez, Fábio V. (RAIRO - Operations Research)
Ancestral reconstruction is a classic task in comparative genomics. Here, we study the genome median problem, a related computational problem which, given a set of three or more genomes, asks to find a new genome that minimizes the sum of pairwise distances between it and the given genomes. The distance stands for the amount of evolution observed at the genome level, for which we determine the minimum number of rearrangement operations necessary to transform one genome into the other. For almost all rearrangement operations the median problem is NP-hard, with the exception of the breakpoint median that can be constructed efficiently for multichromosomal circular and mixed genomes. In this work, we study the median problem under a restricted rearrangement measure called c4-distance, which is closely related to the breakpoint and the DCJ distance. We identify tight bounds and decomposers of the c4-median and develop algorithms for its construction, one exact ILP-based and three combinatorial heuristics. Subsequently, we perform experiments on simulated data sets. Our results suggest that the c4-distance is useful for the study the genome median problem, from theoretical and practical perspectives.
Keywords: Management Science and Operations Research; Computer Science Applications; Theoretical Computer Science

Sets of r-graphs that color all r-graphs - Ma, Yulai; Mattiolo, Davide; Steffen, Eckhard; Wolf, Isaak Hieronymus (arXiv:2305.08619)

Model-driven Collaborative Design of Professional Education Programmes With Extended Online Whiteboards - Wolters, Dennis; Engels, Gregor (MODELSWARD'23)

Cooperative Android App Analysis - Pauck, Felix

Routing Schemes for Hybrid Communication Networks - Werthmann, Julian; Scheideler, Christian; Coy, Sam; Czumaj, Artur; Schneider, Philipp

Designing Business Reputation Ecosystems — A Method for Issuing and Trading Monetary Ratings on a Blockchain - Hemmrich, Simon; Bobolz, Jan; Beverungen, Daniel; Blömer, Johannes (ECIS 2023 Research Papers)
Market transactions are subject to information asymmetry about the delivered value proposition, causing transaction costs and adverse market effects among buyers and sellers. Information systems research has investigated how review systems can reduce information asymmetry in business-to-consumer markets. However, these systems cannot be readily applied to business-to-business markets, are vulnerable to manipulation, and suffer from conceptual weak spots since they use textual data or star ratings. Building on design science research, we conceptualize a new class of reputation systems based on monetary-based payments as quantitative ratings for each transaction stored on a blockchain. Using cryptography, we show that our system assures content confidentiality so that buyers can share and sell their ratings selectively, establishing a reputation ecosystem. Our prescriptive insights advance the design of reputation systems and offer new paths to understanding the antecedents, dynamics, and consequences to reduce information asymmetry in B2B transactions.

Improved determination of viscoelastic material parameters using a pulse-echo measurement setup - Dreiling, Dmitrij; Itner, Dominik; Hetkämper, Tim; Birk, Carolin; Gravenkamp, Hauke; Henning, Bernd (SMSI 2023 Conference)

Development and Evaluation of a Model-Based UI Prototyping Experimentation Approach - N., N.

Decompositions of Analytic 1-Manifolds - Hanusch, Maximilian (Indagationes Mathematicae.)
Keywords: Lie group actions and analytic 1-submanifolds

Two Sparsification Strategies for Accelerating Demand-Driven Pointer Analysis - Karakaya, Kadiray; Bodden, Eric (2023 IEEE Conference on Software Testing, Verification and Validation (ICST))

Privacy-Preserving Collection and Evaluation of Log Files - Koch, Angelina

Inverse procedure for measuring piezoelectric material parameters using a single multi-electrode sample - Claes, Leander; Feldmann, Nadine; Schulze, Veronika; Meihost, Lars; Kuhlmann, Henrik; Jurgelucks, Benjamin; Walther, Andrea; Henning, Bernd (Journal of Sensors and Sensor Systems)

Inverse procedure for the identification of piezoelectric material parameters supported by dense neural networks - Claes, Leander; Meihost, Lars; Jurgelucks, Benjamin

IngridKG: A FAIR Knowledge Graph of Graffiti - Sherif, Mohamed Ahmed; da Silva, Ana Alexandra Morim; Pestryakova, Svetlana; Ahmed, Abdullah Fathi; Niemann, Sven; Ngomo, Axel-Cyrille Ngonga (Scientific Data)
Abstract Graffiti is an urban phenomenon that is increasingly attracting the interest of the sciences. To the best of our knowledge, no suitable data corpora are available for systematic research until now. The Information System Graffiti in Germany project ( Ingrid ) closes this gap by dealing with graffiti image collections that have been made available to the project for public use. Within Ingrid , the graffiti images are collected, digitized and annotated. With this work, we aim to support the rapid access to a comprehensive data source on Ingrid targeted especially by researchers. In particular, we present Ingrid KG, an RDF knowledge graph of annotated graffiti, abides by the Linked Data and FAIR principles. We weekly update Ingrid KG by augmenting the new annotated graffiti to our knowledge graph. Our generation pipeline applies RDF data conversion, link discovery and data fusion approaches to the original data. The current version of Ingrid KG contains 460,640,154 triples and is linked to 3 other knowledge graphs by over 200,000 links. In our use case studies, we demonstrate the usefulness of our knowledge graph for different applications.
Keywords: Library and Information Sciences; Statistics; Probability and Uncertainty; Computer Science Applications; Education; Information Systems; Statistics and Probability

A Pulsed Lidar System With Ultimate Quantum Range Accuracy - Kruse, Stephan; Serino, Laura; Folge, Patrick Fabian; Echeverria Oviedo, Dana; Bhattacharjee, Abhinandan; Stefszky, Michael; Scheytt, J. Christoph; Brecht, Benjamin; Silberhorn, Christine (IEEE Photonics Technology Letters)
Keywords: Electrical and Electronic Engineering; Atomic and Molecular Physics; and Optics; Electronic; Optical and Magnetic Materials

A parametrisation method for high-order phase reduction in coupled oscillator networks - von der Gracht, Sören; Nijholt, Eddie; Rink, Bob (arXiv:2306.03320)
We present a novel method for high-order phase reduction in networks of weakly coupled oscillators and, more generally, perturbations of reducible normally hyperbolic (quasi-)periodic tori. Our method works by computing an asymptotic expansion for an embedding of the perturbed invariant torus, as well as for the reduced phase dynamics in local coordinates. Both can be determined to arbitrary degrees of accuracy, and we show that the phase dynamics may directly be obtained in normal form. We apply the method to predict remote synchronisation in a chain of coupled Stuart-Landau oscillators.

Local Protocols for Contracting and Expanding Robot Formation Problems - Castenow, Jannik

Online Algorithms for Allocating Heterogeneous Resources - Knollmann, Till

Efficient Modeling and Tailoring of Nonlinear Wavefronts in Dielectric Metasurfaces - Hähnel, David; Förstner, Jens; Myroshnychenko, Viktor (ACS Photonics)
Dielectric metasurfaces provide a unique platform for efficient harmonic generation and optical wavefront manipulation at the nanoscale. Tailoring phase and amplitude of a nonlinearly generated wave with a high emission efficiency using resonance-based metasurfaces is a challenging task that often requires state-of-the-art numerical methods. Here, we propose a simple yet effective approach combining a sampling method with a Monte Carlo approach to design the third-harmonic wavefront generated by all-dielectric metasurfaces composed of elliptical silicon nanodisks. Using this approach, we theoretically demonstrate the full nonlinear 2π phase control with a uniform and highest possible amplitude in the considered parameter space, allowing us to design metasurfaces operating as third harmonic beam deflectors capable of steering light into a desired direction with high emission efficiency. The TH beam deflection with a record calculated average conversion efficiency of 1.2 × 10–1 W–2 is achieved. We anticipate that the proposed approach will be widely applied as alternative to commonly used optimization algorithms with higher complexity and implementation effort for the design of metasurfaces with other holographic functionalities.
Keywords: tet_topic_meta

Approximation Algorithms for Fair Range Clustering - Hotegni, Sedjro Salomon; Mahabadi, Sepideh; Vakilian, Ali (Proceedings of the 40th International Conference on Machine Learning, Honolulu, Hawaii, USA. PMLR 202, 2023.)
Keywords: Fair range clustering

We Really Need to Talk About Session Tickets: A Large-Scale Analysis of Cryptographic Dangers with TLS Session Tickets - Hebrok, Sven Niclas; Nachtigall, Simon; Maehren, Marcel; Erinola, Nurullah; Merget, Robert; Somorovsky, Juraj; Schwenk, Jörg (32nd USENIX Security Symposium)

Teaching Technologies: Continuous Interplay Between Educational and Industrial Advances - Jonas-Ahrend, Gabriela; Vernholz, Mats; Temmen, Katrin (Teacher Education in the Wake of Covid-19)
The field of teaching technologies is in constant interplay between educational and industrial advances. Since the beginning of the twenty-first century, digitalization and automatization have become increasingly important. In industrial and social life, we see similar fast-moving developments. These factors challenge education, specifically vocational education, greatly, and raise two very different, yet very much connected questions: how to prepare students for their vocational lives and how to prepare teachers to communicate the necessary competencies to their students? This chapter provides an overview of advances, challenges, and possible solutions, focusing on the three key fields of vocational education in Germany: Industry 4.0, Education 4.0, and innovative teacher education. Most importantly, however, the text examines the continuous interplay between and among these fields. The beginning of the chapter is dedicated to vocational teacher education, in accordance with industrial and educational advances. Specifying this, characteristics of Industry 4.0, as well as students' and teachers' perceptions of Industry 4.0, are discussed. This is followed by an introduction to the concept of so-called learning factories as a possible way of integrating aspects of Industry 4.0 in German vocational schools. The end of the chapter is dedicated to the required changes in educational settings today and in the future. Though Industry 4.0, Education 4.0, and innovative teacher education are each widely discussed in the current literature, the interplay of all three fields reveals a research gap. This chapter tries to close this gap and provide an important contribution to the research field.

Inklusiver Mathematikunterricht. Mathematiklernen in Vielfalt von Kompetenzen, Wegen und Lernsituationen - Häsel-Weide, Uta (Die Grundschulzeitschrift)

Addition und Subtraktion verstehen - Graf, Lara Marie; Wienhues, Inga; Häsel-Weide, Uta (Die Grundschulzeitschrift)

Data-Driven Product Management: A Practitioner-Driven Research Agenda - Grigoryan, Khoren; Fichtler, Timm; Schreiner, Nick; Rabe, Martin; Panzner, Melina; Kühn, Arno; Dumitrescu, Roman; Koldewey, Christian (Procedia CIRP 33)
The global megatrends of digitization and sustainability lead to new challenges for the design and management of technical products in industrial companies. Product management - as the bridge between market and company - has the task to absorb and combine the manifold requirements and make the right product-related decisions. In the process, product management is confronted with heterogeneous information, rapidly changing portfolio components, as well as increasing product, and organizational complexity. Combining and utilizing data from different sources, e.g., product usage data and social media data leads to promising potentials to improve the quality of product-related decisions. In this paper, we reinforce the need for data-driven product management as an interdisciplinary field of action. The state of data-driven product management in practice was analyzed by conducting workshops with six manufacturing companies and hosting a focus group meeting with experts from different industries. We investigate the expectations and derive requirements leading us to open research questions, a vision for data-driven product management, and a research agenda to shape future research efforts.
Keywords: Product Management; Data Analytics; Data-Driven Design; Product-related data; Lifecycle Data; Tool-support

Interview Study on Strategy Options for Platform Operation in B2B Markets - Özcan, Leon; Fichtler, Timm; Kasten, Benjamin; Koldewey, Christian; Dumitrescu, Roman
Keywords: Digital Platform; Platform Strategy; Strategic Management; Platform Life Cycle; Interview Study; Business Model; Business-to-Business; Two-sided Market; Multi-sided Market

Neural Class Expression Synthesis - KOUAGOU, N'Dah Jean; Heindorf, Stefan; Demir, Caglar; Ngonga Ngomo, Axel-Cyrille (The Semantic Web - 20th Extended Semantic Web Conference (ESWC 2023))
Many applications require explainable node classification in knowledge graphs. Towards this end, a popular ``white-box'' approach is class expression learning: Given sets of positive and negative nodes, class expressions in description logics are learned that separate positive from negative nodes. Most existing approaches are search-based approaches generating many candidate class expressions and selecting the best one. However, they often take a long time to find suitable class expressions. In this paper, we cast class expression learning as a translation problem and propose a new family of class expression learning approaches which we dub neural class expression synthesizers. Training examples are ``translated'' into class expressions in a fashion akin to machine translation. Consequently, our synthesizers are not subject to the runtime limitations of search-based approaches. We study three instances of this novel family of approaches based on LSTMs, GRUs, and set transformers, respectively. An evaluation of our approach on four benchmark datasets suggests that it can effectively synthesize high-quality class expressions with respect to the input examples in approximately one second on average. Moreover, a comparison to state-of-the-art approaches suggests that we achieve better F-measures on large datasets. For reproducibility purposes, we provide our implementation as well as pretrained models in our public GitHub repository at https://github.com/dice-group/NeuralClassExpressionSynthesis
Keywords: Neural network; Concept learning; Description logics

Identifizierung quantifizierbarer Bewertungsinhalte und -kategorien mittels Text Mining - Kersting, Joschka
Reading between the lines has so far been reserved for humans. The present dissertation addresses this research gap using machine learning methods. Implicit expressions are not comprehensible by computers and cannot be localized in the text. However, many texts arise on interpersonal topics that, unlike commercial evaluation texts, often imply information only by means of longer phrases. Examples are the kindness and the attentiveness of a doctor, which are only paraphrased (“he didn’t even look me in the eye”). The analysis of such data, especially the identification and localization of implicit statements, is a research gap (1). This work uses so-called Aspect-based Sentiment Analysis as a method for this purpose. It remains open how the aspect categories to be extracted can be discovered and thematically delineated based on the data (2). Furthermore, it is not yet explored how a collection of tools should look like, with which implicit phrases can be identified and thus made explicit (3). Last, it is an open question how to correlate the identified phrases from the text data with other data, including the investigation of the relationship between quantitative scores (e.g., school grades) and the thematically related text (4). Based on these research gaps, the research question is posed as follows: Using text mining methods, how can implicit rating content be properly interpreted and thus made explicit before it is automatically categorized and quantified? The uniqueness of this dissertation is based on the automated recognition of implicit linguistic statements alongside explicit statements. These are identified in unstructured text data so that features expressed only in the text can later be compared across data sources, even though they were not included in rating categories such as stars or school grades. German-language physician ratings from websites in three countries serve as the sample domain. The solution approach consists of data creation, a pipeline for text processing and analyses based on this. In the data creation, aspect classes are identified and delineated across platforms and marked in text data. This results in six datasets with over 70,000 annotated sentences and detailed guidelines. The models that were created based on the training data extract and categorize the aspects. In addition, the sentiment polarity and the evaluation weight, i. e., the importance of each phrase, are determined. The models, which are combined in a pipeline, are used in a prototype in the form of a web application. The analyses built on the pipeline quantify the rating contents by linking the obtained information with further data, thus allowing new insights. As a result, a toolbox is provided to identify quantifiable rating content and categories using text mining for a sample domain. This is used to evaluate the approach, which in principle can also be adapted to any other domain.

Capabilities and Limitations of Local Strategies in Dynamic Networks - Götte, Thorsten; Knollmann, Till; Meyer auf der Heide, Friedhelm; Scheideler, Christian; Werthmann, Julian (On-The-Fly Computing -- Individualized IT-services in dynamic markets)

On-The-Fly Compute Centers II: Execution of Composed Services in Configurable Compute Centers - Karl, Holger; Maack, Marten; Meyer auf der Heide, Friedhelm; Pukrop, Simon; Redder, Adrian (On-The-Fly Computing -- Individualized IT-services in dynamic markets)

Practical Cryptograhic Techniques for Secure and Privacy-Preserving Customer Loyalty Systems - Blömer, Johannes; Bobolz, Jan; Eidens, Fabian; Jager, Tibor; Kramer, Paul (On-The-Fly Computing -- Individualized IT-services in dynamic markets)

Architectural Management of OTF Computing Markets - Gottschalk, Sebastian; Vorbohle, Christian; Kundisch, Dennis; Engels, Gregor; Wünderlich, Nacy V. (On-The-Fly Computing -- Individualized IT-services in dynamic markets)

Robustness and Security - Blömer, Johannes; Eidens, Fabian; Jager, Tibor; Niehues, David; Scheideler, Christian (On-The-Fly Computing -- Individualized IT-services in dynamic markets)

Dialogue-based Requirement Compensation and Style-adjusted Data-to-text Generation - Bäumer, Frederik Simon; Chen, Wei-Fan; Geierhos, Michaela; Kersting, Joschka; Wachsmuth, Henning (On-The-Fly Computing -- Individualized IT-services in dynamic markets)

Configuration and Evaluation - Hanselle, Jonas Manuel; Hüllermeier, Eyke; Mohr, Felix; Ngonga Ngomo, Axel-Cyrille; Sherif, Mohamed; Tornede, Alexander; Wever, Marcel Dominik (On-The-Fly Computing -- Individualized IT-services in dynamic markets)

The Market for Services: Incentives, Algorithms, Implementation - Haake, Claus-Jochen; Hehenkamp, Burkhard; Polevoy, Gleb (On-The-Fly Computing -- Individualized IT-services in dynamic markets)

Composition Analysis in Unknown Contexts - Wehrheim, Heike; Hüllermeier, Eyke; Becker, Steffen; Becker, Matthias; Richter, Cedric; Sharma, Arnab (On-The-Fly Computing -- Individualized IT-services in dynamic markets)

Reconfigurable Random Forest Implementation on FPGA - Raeisi Nafchi, Masood

Data Analytics for Predictive Maintenance of Time Series Data - Yadalam Murali Kumar, Nihal

The Optimal Depth of Variational Quantum Algorithms Is QCMA-Hard to Approximate - Bittel, Lennart; Gharibian, Sevag; Kliesch, Martin (Proceedings of the 38th Computational Complexity Conference (CCC))
Variational Quantum Algorithms (VQAs), such as the Quantum Approximate Optimization Algorithm (QAOA) of [Farhi, Goldstone, Gutmann, 2014], have seen intense study towards near-term applications on quantum hardware. A crucial parameter for VQAs is the depth of the variational ansatz used - the smaller the depth, the more amenable the ansatz is to near-term quantum hardware in that it gives the circuit a chance to be fully executed before the system decoheres. This potential for depth reduction has made VQAs a staple of Noisy Intermediate-Scale Quantum (NISQ)-era research. In this work, we show that approximating the optimal depth for a given VQA ansatz is intractable. Formally, we show that for any constant $ε>0$, it is QCMA-hard to approximate the optimal depth of a VQA ansatz within multiplicative factor $N^{1-ε}$, for $N$ denoting the encoding size of the VQA instance. (Here, Quantum Classical Merlin-Arthur (QCMA) is a quantum generalization of NP.) We then show that this hardness persists even in the "simpler" setting of QAOAs. To our knowledge, this yields the first natural QCMA-hard-to-approximate problems. To achieve these results, we bypass the need for a PCP theorem for QCMA by appealing to the disperser-based NP-hardness of approximation construction of [Umans, FOCS 1999].

On Cloud Assisted, Restricted, and Reosurce Constrained Scheduling - Pukrop, Simon

Utilizing Redundancy in Distributed Heterogeneous Storage - Schneider, Fabian

Graph Neural Network-based Anomaly Detection in Smart Grid Energy Consumption - Ranade, Amruta

Highly accurate deep compressed facial recognition - Ali, Osama

Super-Gaussian decay of exponentials: A sufficient condition - Hinrichs, Benjamin; Janssen, Daan W.; Ziebell, Jobst (Journal of Mathematical Analysis and Applications)
Keywords: Applied Mathematics; Analysis

Virtual On-Demand Volunteer System Based on Delaunay Triangulation - Ashri, Nivedita

Temperedness of locally symmetric spaces: The product case - Weich, Tobias; Wolf, Lasse L. (arXiv:2304.09573)
Let $X=X_1× X_2$ be a product of two rank one symmetric spaces of non-compact type and $Γ$ a torsion-free discrete subgroup in $G_1× G_2$. We show that the spectrum of $Γ \backslash X$ is related to the asymptotic growth of $Γ$ in the two direction defined by the two factors. We obtain that $L^2(Γ \backslash G)$ is tempered for large class of $Γ$.

Finite Set Sensorless Control With Minimum a Priori Knowledge and Tuning Effort for Interior Permanent Magnet Synchronous Motors - Brosch, Anian; Tinazzi, Fabio; Wallscheid, Oliver; Zigliotto, Mauro; Böcker, Joachim (IEEE Transactions on Power Electronics)
Keywords: Electrical and Electronic Engineering

Multi-FPGA Designs and Scaling of HPC Challenge Benchmarks via MPI and Circuit-Switched Inter-FPGA Networks - Meyer, Marius; Kenter, Tobias; Plessl, Christian (ACM Transactions on Reconfigurable Technology and Systems)
While FPGA accelerator boards and their respective high-level design tools are maturing, there is still a lack of multi-FPGA applications, libraries, and not least, benchmarks and reference implementations towards sustained HPC usage of these devices. As in the early days of GPUs in HPC, for workloads that can reasonably be decoupled into loosely coupled working sets, multi-accelerator support can be achieved by using standard communication interfaces like MPI on the host side. However, for performance and productivity, some applications can profit from a tighter coupling of the accelerators. FPGAs offer unique opportunities here when extending the dataflow characteristics to their communication interfaces. In this work, we extend the HPCC FPGA benchmark suite by multi-FPGA support and three missing benchmarks that particularly characterize or stress inter-device communication: b_eff, PTRANS, and LINPACK. With all benchmarks implemented for current boards with Intel and Xilinx FPGAs, we established a baseline for multi-FPGA performance. Additionally, for the communication-centric benchmarks, we explored the potential of direct FPGA-to-FPGA communication with a circuit-switched inter-FPGA network that is currently only available for one of the boards. The evaluation with parallel execution on up to 26 FPGA boards makes use of one of the largest academic FPGA installations.
Keywords: General Computer Science

Steady-State Error Compensation for Reinforcement Learning-Based Control of Power Electronic Systems - Weber, Daniel; Schenke, Maximilian; Wallscheid, Oliver (IEEE Access)
Keywords: General Engineering; General Materials Science; General Computer Science; Electrical and Electronic Engineering

Safe Reinforcement Learning-Based Control in Power Electronic Systems - Weber, Daniel; Schenke, Maximilian; Wallscheid, Oliver (2023 International Conference on Future Energy Solutions (FES))

Improving the End-of-Line Test of Custom-Built Geared Motors using Clustering based on Neural Networks - N., N.

Neuro-Symbolic Class Expression Learning - Demir, Caglar; Ngonga Ngomo, Axel-Cyrille (International Joint Conference on Artificial Intelligence)

Pairwise Disjoint Perfect Matchings in r-Edge-Connected r-Regular Graphs - Ma, Yulai; Mattiolo, Davide; Steffen, Eckhard; Wolf, Isaak Hieronymus (SIAM Journal on Discrete Mathematics)
Keywords: General Mathematics

Computing and Compressing Electron Repulsion Integrals on FPGAs - Wu, Xin; Kenter, Tobias; Schade, Robert; Kühne, Thomas; Plessl, Christian (2023 IEEE 31st Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM))
The computation of electron repulsion integrals (ERIs) over Gaussian-type orbitals (GTOs) is a challenging problem in quantum-mechanics-based atomistic simulations. In practical simulations, several trillions of ERIs may have to be computed for every time step. In this work, we investigate FPGAs as accelerators for the ERI computation. We use template parameters, here within the Intel oneAPI tool flow, to create customized designs for 256 different ERI quartet classes, based on their orbitals. To maximize data reuse, all intermediates are buffered in FPGA on-chip memory with customized layout. The pre-calculation of intermediates also helps to overcome data dependencies caused by multi-dimensional recurrence relations. The involved loop structures are partially or even fully unrolled for high throughput of FPGA kernels. Furthermore, a lossy compression algorithm utilizing arbitrary bitwidth integers is integrated in the FPGA kernels. To our best knowledge, this is the first work on ERI computation on FPGAs that supports more than just the single most basic quartet class. Also, the integration of ERI computation and compression it a novelty that is not even covered by CPU or GPU libraries so far. Our evaluation shows that using 16-bit integer for the ERI compression, the fastest FPGA kernels exceed the performance of 10 GERIS ($10 × 10^9$ ERIs per second) on one Intel Stratix 10 GX 2800 FPGA, with maximum absolute errors around $10^{-7}$ - $10^{-5}$ Hartree. The measured throughput can be accurately explained by a performance model. The FPGA kernels deployed on 2 FPGAs outperform similar computations using the widely used libint reference on a two-socket server with 40 Xeon Gold 6148 CPU cores of the same process technology by factors up to 6.0x and on a new two-socket server with 128 EPYC 7713 CPU cores by up to 1.9x.

Breaking the exascale barrier for the electronic structure problem in ab-initio molecular dynamics - Schade, Robert; Kenter, Tobias; Elgabarty, Hossam; Lass, Michael; Kühne, Thomas; Plessl, Christian (The International Journal of High Performance Computing Applications)
The non-orthogonal local submatrix method applied to electronic structure–based molecular dynamics simulations is shown to exceed 1.1 EFLOP/s in FP16/FP32-mixed floating-point arithmetic when using 4400 NVIDIA A100 GPUs of the Perlmutter system. This is enabled by a modification of the original method that pushes the sustained fraction of the peak performance to about 80%. Example calculations are performed for SARS-CoV-2 spike proteins with up to 83 million atoms.
Keywords: Hardware and Architecture; Theoretical Computer Science; Software

Advanced Algorithm Selection with Machine Learning: Handling Large Algorithm Sets, Learning From Censored Data, and Simplyfing Meta Level Decisions - Tornede, Alexander

Generation of 100 GHz Periodic Nyquist Pulses using Cascaded Mach-Zehnder Modulators in a Silicon Electronic-Photonic Platform - Kress, Christian; Schwabe, Tobias; Silberhorn, Christine; Scheytt, J. Christoph (Conference on Lasers and Electro-Optics (CLEO) 2023)
A frequency-flexible Nyquist pulse synthesizer is presented with optical pulse bandwidths up to fopt=100 GHz and repetition rates equal to fopt/9, fabricated in an electronic-photonic co-integrated platform utilizing linear on-chip drivers.

Learning discrete Lagrangians for variational PDEs from data and detection of travelling waves - Offen, Christian; Ober-Blöbaum, Sina (Geometric Science of Information)
The article shows how to learn models of dynamical systems from data which are governed by an unknown variational PDE. Rather than employing reduction techniques, we learn a discrete field theory governed by a discrete Lagrangian density $L_d$ that is modelled as a neural network. Careful regularisation of the loss function for training $L_d$ is necessary to obtain a field theory that is suitable for numerical computations: we derive a regularisation term which optimises the solvability of the discrete Euler--Lagrange equations. Secondly, we develop a method to find solutions to machine learned discrete field theories which constitute travelling waves of the underlying continuous PDE.
Keywords: System identification; discrete Lagrangians; travelling waves

Variational Learning of Euler–Lagrange Dynamics from Data - Ober-Blöbaum, Sina; Offen, Christian (Journal of Computational and Applied Mathematics)
The principle of least action is one of the most fundamental physical principle. It says that among all possible motions connecting two points in a phase space, the system will exhibit those motions which extremise an action functional. Many qualitative features of dynamical systems, such as the presence of conservation laws and energy balance equations, are related to the existence of an action functional. Incorporating variational structure into learning algorithms for dynamical systems is, therefore, crucial in order to make sure that the learned model shares important features with the exact physical system. In this paper we show how to incorporate variational principles into trajectory predictions of learned dynamical systems. The novelty of this work is that (1) our technique relies only on discrete position data of observed trajectories. Velocities or conjugate momenta do not need to be observed or approximated and no prior knowledge about the form of the variational principle is assumed. Instead, they are recovered using backward error analysis. (2) Moreover, our technique compensates discretisation errors when trajectories are computed from the learned system. This is important when moderate to large step-sizes are used and high accuracy is required. For this, we introduce and rigorously analyse the concept of inverse modified Lagrangians by developing an inverse version of variational backward error analysis. (3) Finally, we introduce a method to perform system identification from position observations only, based on variational backward error analysis.
Keywords: Lagrangian learning; variational backward error analysis; modified Lagrangian; variational integrators; physics informed learning

Backward error analysis for conjugate symplectic methods - McLachlan, Robert; Offen, Christian (Journal of Geometric Mechanics)
The numerical solution of an ordinary differential equation can be interpreted as the exact solution of a nearby modified equation. Investigating the behaviour of numerical solutions by analysing the modified equation is known as backward error analysis. If the original and modified equation share structural properties, then the exact and approximate solution share geometric features such as the existence of conserved quantities. Conjugate symplectic methods preserve a modified symplectic form and a modified Hamiltonian when applied to a Hamiltonian system. We show how a blended version of variational and symplectic techniques can be used to compute modified symplectic and Hamiltonian structures. In contrast to other approaches, our backward error analysis method does not rely on an ansatz but computes the structures systematically, provided that a variational formulation of the method is known. The technique is illustrated on the example of symmetric linear multistep methods with matrix coefficients.
Keywords: variational integrators; backward error analysis; Euler--Lagrange equations; multistep methods; conjugate symplectic methods

Hamiltonian Neural Networks with Automatic Symmetry Detection - Dierkes, Eva; Offen, Christian; Ober-Blöbaum, Sina; Flaßkamp, Kathrin (Chaos)
Recently, Hamiltonian neural networks (HNN) have been introduced to incorporate prior physical knowledge when learning the dynamical equations of Hamiltonian systems. Hereby, the symplectic system structure is preserved despite the data-driven modeling approach. However, preserving symmetries requires additional attention. In this research, we enhance the HNN with a Lie algebra framework to detect and embed symmetries in the neural network. This approach allows to simultaneously learn the symmetry group action and the total energy of the system. As illustrating examples, a pendulum on a cart and a two-body problem from astrodynamics are considered.

Testinstrumente und Testdatenanalyse zur Verarbeitung von Unsicherheiten in Logikblöcken hochintegrierter Schaltungen - Sprenger, Alexander
Ever increasing demands on the performance of microchips are leading to ever more complex semiconductor technologies with ever shrinking feature sizes. Complex applications with high demands on safety and reliability, such as autonomous driving, are simultaneously driving the requirements for test and diagnosis of VLSI circuits. Throughout the life cycle of a microchip, uncertainties occur that affect its timing behavior. For example, weak circuit structures, aging effects, or process variations can lead to a change in the timing behavior of the circuit. While these uncertainties do not necessarily lead to a change of the functional behavior, they can lead to a reliability problem. With modular and hybrid compaction two test instruments are presented in this work that can be used for X-tolerant test response compaction in the built-in Faster-than-At-Speed Test (FAST) which is used to detect uncertainties in VLSI circuits. One challenge for test response compaction during FAST is the high and varying X-rate at the outputs of the circuit under test. By dividing the circuit outputs into test groups and separately compacting these test groups using stochastic compactors, the modular compaction is able to handle these high and varying X-rates. To deal with uncertainties on logic interconnects, a method for distinguishing crosstalk and process variation is presented. In current semiconductor technologies, the number of parasitic coupling capacitances between logic interconnects is growing. These coupling capacitances can lead to crosstalk, which causes increased current flow in the logic interconnects, which in turn can lead to increased electromigration. In the presented method, delay maps describing the timing behavior of the circuit outputs at different operating points are used to train artificial neural networks which classify the tested circuits into fault-free and faulty.
Keywords: Testantwortkompaktierung; Prozessvariation; Silicon Lifecycle Management

Explainable Integration of Knowledge Graphs Using Large Language Models - Ahmed, Abdullah Fathi Ahmed; Firmansyah, Asep Fajar; Sherif, Mohamed; Moussallem, Diego; Ngonga Ngomo, Axel-Cyrille (Natural Language Processing and Information Systems)
Linked knowledge graphs build the backbone of many data-driven applications such as search engines, conversational agents and e-commerce solutions. Declarative link discovery frameworks use complex link specifications to express the conditions under which a link between two resources can be deemed to exist. However, understanding such complex link specifications is a challenging task for non-expert users of link discovery frameworks. In this paper, we address this drawback by devising NMV-LS, a language model-based verbalization approach for translating complex link specifications into natural language. NMV-LS relies on the results of rule-based link specification verbalization to apply continuous training on T5, a large language model based on the Transformerarchitecture. We evaluated NMV-LS on English and German datasets using well-known machine translation metrics such as BLUE, METEOR, ChrF++ and TER. Our results suggest that our approach achieves a verbalization performance close to that of humans and outperforms state of the art approaches. Our source code and datasets are publicly available at https://github.com/dice-group/NMV-LS.

IngridKG: A FAIR Knowledge Graph of Graffiti - Sherif, Mohamed; Morim da Silva, Ana Alexandra; Pestryakova, Svetlana; Ahmed, Abdullah Fathi Ahmed; Niemann, Sven; Ngonga Ngomo, Axel-Cyrille
Graffiti is an urban phenomenon that is increasingly attracting the interest of the sciences. To the best of our knowledge, no suitable data corpora are available for systematic research until now. The Information System Graffiti in Germany project (Ingrid) closes this gap by dealing with graffiti image collections that have been made available to the project for public use. Within Ingrid, the graffiti images are collected, digitized and annotated. With this work, we aim to support the rapid access to a comprehensive data source on Ingrid targeted especially by researchers. In particular, we present IngridKG, an RDF knowledge graph of annotated graffiti, abides by the Linked Data and FAIR principles. We weekly update IngridKG by augmenting the new annotated graffiti to our knowledge graph. Our generation pipeline applies RDF data conversion, link discovery and data fusion approaches to the original data. The current version of IngridKG contains 460,640,154 triples and is linked to 3 other knowledge graphs by over 200,000 links. In our use case studies, we demonstrate the usefulness of our knowledge graph for different applications.

IndQNER: Named Entity Recognition Benchmark Dataset from the Indonesian Translation of the Quran - Gusmita, Ria Hari; Firmansyah, Asep Fajar; Moussallem, Diego; Ngonga Ngomo, Axel-Cyrille (Natural Language Processing and Information Systems)
Indonesian is classified as underrepresented in the Natural Language Processing (NLP) field, despite being the tenth most spoken language in the world with 198 million speakers. The paucity of datasets is recognized as the main reason for the slow advancements in NLP research for underrepresented languages. Significant attempts were made in 2020 to address this drawback for Indonesian. The Indonesian Natural Language Understanding (IndoNLU) benchmark was introduced alongside IndoBERT pre-trained language model. The second benchmark, Indonesian Language Evaluation Montage (IndoLEM), was presented in the same year. These benchmarks support several tasks, including Named Entity Recognition (NER). However, all NER datasets are in the public domain and do not contain domain-specific datasets. To alleviate this drawback, we introduce IndQNER, a manually annotated NER benchmark dataset in the religious domain that adheres to a meticulously designed annotation guideline. Since Indonesia has the world’s largest Muslim population, we build the dataset from the Indonesian translation of the Quran. The dataset includes 2475 named entities representing 18 different classes. To assess the annotation quality of IndQNER, we perform experiments with BiLSTM and CRF-based NER, as well as IndoBERT fine-tuning. The results reveal that the first model outperforms the second model achieving 0.98 F1 points. This outcome indicates that IndQNER may be an acceptable evaluation metric for Indonesian NER tasks in the aforementioned domain, widening the research’s domain range.

Partial observations, coarse graining and equivariance in Koopman operator theory for large-scale dynamical systems - Peitz, Sebastian; Harder, Hans; Nüske, Feliks; Philipp, Friedrich; Schaller, Manuel; Worthmann, Karl (arXiv:2307.15325)
The Koopman operator has become an essential tool for data-driven analysis, prediction and control of complex systems, the main reason being the enormous potential of identifying linear function space representations of nonlinear dynamics from measurements. Until now, the situation where for large-scale systems, we (i) only have access to partial observations (i.e., measurements, as is very common for experimental data) or (ii) deliberately perform coarse graining (for efficiency reasons) has not been treated to its full extent. In this paper, we address the pitfall associated with this situation, that the classical EDMD algorithm does not automatically provide a Koopman operator approximation for the underlying system if we do not carefully select the number of observables. Moreover, we show that symmetries in the system dynamics can be carried over to the Koopman operator, which allows us to massively increase the model efficiency. We also briefly draw a connection to domain decomposition techniques for partial differential equations and present numerical evidence using the Kuramoto--Sivashinsky equation.

Calibration of Large Coherent MIMO Radar Arrays: Channel Imbalances and 3D Antenna Positions - Greiff, Christian; Mateos-Núñez, David; Simoni, Renato; González-Huici, Maria; Kruse, Stephan; Scheytt, J. Christoph; Kolk, Karl; Höller, Christian; Kurz, Heiko Gustav; Meinecke, Marc-Michael; Gisder, Thomas (2023 24th International Radar Symposium (IRS))
One of the main challenges for next generation automotive radars is the improvement of angular resolution to a sub-degree level. In this context, wide aperture automotive radars of 1m length or more and resolution close to 0.1° in azimuth and 0.5° in elevation could be beneficial. To enable coherent processing of arrays with such large aperture, prior (i.e offline) and online calibration are necessary: channel imbalances (gains and phases) and three dimensional coordinates of transmit and receive elements need to be determined. We propose a calibration strategy based on alternating steps between the two subtasks of i) channel imbalance estimation with ‘known’ array positions, by applying a singular value decomposition to the resulting tensor calculus problem; and ii) antenna position estimation with ’known’ channel imbalances, by numerically maximizing the Bayesian posterior probability; in both cases operating on range/Doppler snapshots of disjoint targets (with potentially unknown locations). Simulation studies based on the parameters of a MIMO 8x6 linear sparse array show promising results as long as the initial position errors do not exceed half a wavelength (2mm), beyond which we observe strong effects of ambiguity. Experimental results with real measurements show that after calibration in laboratory conditions, our MIMO 8x6 demonstrator with 50cm aperture is able to resolve two targets at the same range with angular separation at least as close as 0.4°.

Finite-data error bounds for Koopman-based prediction and control - Nüske, Feliks; Peitz, Sebastian; Philipp, Friedrich; Schaller, Manuel; Worthmann, Karl (Journal of Nonlinear Science)
The Koopman operator has become an essential tool for data-driven approximation of dynamical (control) systems in recent years, e.g., via extended dynamic mode decomposition. Despite its popularity, convergence results and, in particular, error bounds are still quite scarce. In this paper, we derive probabilistic bounds for the approximation error and the prediction error depending on the number of training data points; for both ordinary and stochastic differential equations. Moreover, we extend our analysis to nonlinear control-affine systems using either ergodic trajectories or i.i.d. samples. Here, we exploit the linearity of the Koopman generator to obtain a bilinear system and, thus, circumvent the curse of dimensionality since we do not autonomize the system by augmenting the state by the control inputs. To the best of our knowledge, this is the first finite-data error analysis in the stochastic and/or control setting. Finally, we demonstrate the effectiveness of the proposed approach by comparing it with state-of-the-art techniques showing its superiority whenever state and control are coupled.

Multi-Objective Optimization for Sparse Deep Neural Network Training - Hotegni, Sedjro Salomon; Peitz, Sebastian; Berkemeier, Manuel Bastian (arXiv:2308.12243)
Different conflicting optimization criteria arise naturally in various Deep Learning scenarios. These can address different main tasks (i.e., in the setting of Multi-Task Learning), but also main and secondary tasks such as loss minimization versus sparsity. The usual approach is a simple weighting of the criteria, which formally only works in the convex setting. In this paper, we present a Multi-Objective Optimization algorithm using a modified Weighted Chebyshev scalarization for training Deep Neural Networks (DNNs) with respect to several tasks. By employing this scalarization technique, the algorithm can identify all optimal solutions of the original problem while reducing its complexity to a sequence of single-objective problems. The simplified problems are then solved using an Augmented Lagrangian method, enabling the use of popular optimization techniques such as Adam and Stochastic Gradient Descent, while efficaciously handling constraints. Our work aims to address the (economical and also ecological) sustainability issue of DNN models, with a particular focus on Deep Multi-Task models, which are typically designed with a very large number of weights to perform equally well on multiple tasks. Through experiments conducted on two Machine Learning datasets, we demonstrate the possibility of adaptively sparsifying the model during training without significantly impacting its performance, if we are willing to apply task-specific adaptations to the network weights. Code is available at https://github.com/salomonhotegni/MDMTN.

Efficient time stepping for numerical integration using reinforcement learning - Dellnitz, Michael; Hüllermeier, Eyke; Lücke, Marvin; Ober-Blöbaum, Sina; Offen, Christian; Peitz, Sebastian; Pfannschmidt, Karlson (SIAM Journal on Scientific Computing)
Many problems in science and engineering require an efficient numerical approximation of integrals or solutions to differential equations. For systems with rapidly changing dynamics, an equidistant discretization is often inadvisable as it results in prohibitively large errors or computational effort. To this end, adaptive schemes, such as solvers based on Runge–Kutta pairs, have been developed which adapt the step size based on local error estimations at each step. While the classical schemes apply very generally and are highly efficient on regular systems, they can behave suboptimally when an inefficient step rejection mechanism is triggered by structurally complex systems such as chaotic systems. To overcome these issues, we propose a method to tailor numerical schemes to the problem class at hand. This is achieved by combining simple, classical quadrature rules or ODE solvers with data-driven time-stepping controllers. Compared with learning solution operators to ODEs directly, it generalizes better to unseen initial data as our approach employs classical numerical schemes as base methods. At the same time it can make use of identified structures of a problem class and, therefore, outperforms state-of-the-art adaptive schemes. Several examples demonstrate superior efficiency. Source code is available at https://github.com/lueckem/quadrature-ML.

Low Power Streaming of Sensor Data Using Gray Code-Based Approximate Communication - Sadeghi-Kohan, Somayeh; Hellebrand, Sybille; Wunderlich, Hans-Joachim (2023 53rd Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W))

Second grader´s understanding of doubling and halfing in various representations - Schwerin, Imke; Häsel-Weide, Uta (International Symposium in Elementary Mathematics Teaching. Proceedings: New Directions in Elementary Mathematics Education)

Lehre in Zeiten von Digitalisierung und Inklusion - Beispiele aus drei Fächern - Schmidt, Rebekka; Tenberge, Claudia; Häsel-Weide, Uta (Aktive Teilhabe fördern – ICM und Student Engagement in der Hochschullehre)

Mit dem Leben Schritt halten - Eine Analyse des Wearable-Dispositivs - Schloots, Franziska Margarete
„Lerne deinen Körper besser kennen“, „Das Beste für deine Gesundheit“ und „Ihre Transformation beginnt jetzt“ - mit Versprechen wie diesen vermitteln die Produkttexte von Wearables wie Fitnesstracker und Smartwatches ein ganz bestimmtes Bild ihrer vorgesehenen Nutzer*innen und deren Nutzung. Verbunden mit den kleinen, am Handgelenk getragenen Geräten sind Fragen nach Erkenntnisgewinn und Kontrollverlust, Selbstoptimierung und Quantifizierungslogiken, Eigenverantwortung und Fremdsteuerung. Die vorliegende Arbeit widmet sich diesem komplexen Spannungsfeld und verfolgt dabei einen multiperspektivischen Ansatz: im Rahmen einer Dispositivanalyse werden die einzelnen Elemente des Wearable-Dispositivs als eigenständige, empirisch zu untersuchende Analysegegenstände betrachtet, um so das Zusammenwirken und die komplexe Beziehung von Diskursen, Gegenständen, Nutzung, Subjekten und Gesellschaft zu erforschen. Ein besonderes Erkenntnisinteresse liegt dabei auf dem Wissen, was sich über Wearables etabliert hat und sich in den Alltagspraktiken der Nutzer*innen widerspiegelt sowie bei der Frage nach den möglichen Funktionen und Auswirkungen des Wearable-Dispositivs.
Keywords: Selbstvermessung; Dispositivanalyse; Gesundheitsdiskurs; Quantifizierungsgesellschaft; Wearables; Selbstoptimierung

ElectricGrid.jl - A Julia-based modeling and simulationtool for power electronics-driven electric energy grids - Wallscheid, Oliver; Peitz, Sebastian; Stenner, Jan; Weber, Daniel; Boshoff, Septimus; Meyer, Marvin; Chidananda, Vikas; Schweins, Oliver (Journal of Open Source Software)
Keywords: General Earth and Planetary Sciences; General Environmental Science

Paradigmenwechsel vom klassischen zum datengetriebenen Problemlösen im Informatikunterricht - Höper, Lukas; Schulte, Carsten (MNU journal)

On the impossibility of surviving (iterated) deletion of weakly dominated strategies in rational MPC - Blömer, Johannes; Bobolz, Jan; Bröcher, Henrik

A Generic Construction of an Anonymous Reputation System and Instantiations from Lattices - Blömer, Johannes; Bobolz, Jan; Porzenheim, Laurens Alexander

Gewerblich-technische Lehrkräftebildung in Deutschland – Analyse der Einflüsse auf das akademische Selbstkonzept von Lehramtsstudierenden technischer (beruflicher) Fachrichtungen - Vernholz, Mats; Temmen, Katrin

Minimizing Eye Movements and Distractions in Head-Mounted Augmented Reality through Eye-Gaze Adaptiveness - Wecker, Daniel; Yigitbas, Enes (Proceedings of the ACM Symposium on Spatial User Interaction (SUI 2023))