@article{55276, author = {{Minelli, P. and Sourmelidis, A. and Technau, Marc}}, journal = {{Int. Math. Res. Not. IMRN}}, number = {{10}}, pages = {{8485–8502}}, title = {{{On restricted averages of Dedekind sums}}}, doi = {{10.1093/imrn/rnad283}}, volume = {{2024}}, year = {{2024}}, } @article{55278, author = {{Technau, Marc}}, journal = {{Proc. Amer. Math. Soc.}}, number = {{1}}, pages = {{63–69}}, title = {{{Remark on the Farey fraction spin chain}}}, doi = {{10.1090/proc/16520}}, volume = {{152}}, year = {{2024}}, } @inproceedings{55388, author = {{Adler, Enno and Böttcher, Stefan and Hartel, Rita}}, booktitle = {{2024 Data Compression Conference (DCC)}}, publisher = {{IEEE}}, title = {{{ITR: Grammar-based graph compression supporting fast triple queries}}}, doi = {{10.1109/dcc58796.2024.00062}}, year = {{2024}}, } @inproceedings{55338, abstract = {{Metaphorical language is a pivotal element inthe realm of political framing. Existing workfrom linguistics and the social sciences providescompelling evidence regarding the distinctivenessof conceptual framing for politicalideology perspectives. However, the nature andutilization of metaphors and the effect on audiencesof different political ideologies withinpolitical discourses are hardly explored. Toenable research in this direction, in this workwe create a dataset, originally based on newseditorials and labeled with their persuasive effectson liberals and conservatives and extend itwith annotations pertaining to metaphorical usageof language. To that end, first, we identifyall single metaphors and composite metaphors.Secondly, we provide annotations of the sourceand target domains for each metaphor. As aresult, our corpus consists of 300 news editorialsannotated with spans of texts containingmetaphors and the corresponding domains ofwhich these metaphors draw from. Our analysisshows that liberal readers are affected bymetaphors, whereas conservatives are resistantto them. Both ideologies are affected differentlybased on the metaphor source and targetcategory. For example, liberals are affected bymetaphors in the Darkness {&} Light (e.g., death)source domains, where as the source domain ofNature affects conservatives more significantly.}}, author = {{Sengupta, Meghdut and El Baff, Roxanne and Alshomary, Milad and Wachsmuth, Henning}}, booktitle = {{Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)}}, editor = {{Duh, Kevin and Gomez, Helena and Bethard, Steven}}, pages = {{3621–3631}}, publisher = {{Association for Computational Linguistics}}, title = {{{Analyzing the Use of Metaphors in News Editorials for Political Framing}}}, year = {{2024}}, } @inbook{55426, author = {{Gabriel, Stefan and Falkowski, Tommy and Graunke, Jannis and Dumitrescu, Roman and Murrenhoff, Anike and Kretzschmer, Veronika and ten Hompel, Michael }}, booktitle = {{Expertise des Forschungsbeirats Industrie 4.0}}, title = {{{Künstliche Intelligenz und industrielle Arbeit – Perspektiven und Gestaltungsoptionen}}}, year = {{2024}}, } @inbook{55422, author = {{Humpert, Lynn and Disselkamp, Jan-Philipp and Schierbaum, Anja and Zagatta, Kristin and Dumitrescu, Roman}}, booktitle = {{Expertise des Forschungsbeirats Industrie 4.0}}, title = {{{Engineering autonom wandelbarer Industrie 4.0-Systeme}}}, year = {{2024}}, } @inproceedings{55436, author = {{Stöhr, Bernd and Koldewey, Christian and Acar, Yasemin and Dumitrescu, Roman}}, booktitle = {{Proceedings of DRS}}, issn = {{2398-3132}}, publisher = {{Design Research Society}}, title = {{{Challenges for design and designers in interdisciplinary product development: A qualitative interview study in industry}}}, doi = {{10.21606/drs.2024.1001}}, year = {{2024}}, } @inproceedings{55453, author = {{Namujju, Lillian Donna and Mwammenywa, Ibrahim and Kagarura, Geoffrey Mark and Hilleringmann, Ulrich and Hehenkamp, Burkhard}}, booktitle = {{2024 IEEE 8th Energy Conference (ENERGYCON)}}, publisher = {{IEEE}}, title = {{{Smart Metering and Choice Architecture in Demand-Side Management: A Power Resource-Constrained Perspective}}}, doi = {{10.1109/energycon58629.2024.10488738}}, year = {{2024}}, } @inproceedings{55452, author = {{Namujju, Lillian Donna and Mwammenywa, Ibrahim and Kagarura, Geoffrey Mark and Hilleringmann, Ulrich and Hehenkamp, Burkhard}}, booktitle = {{2024 IEEE 8th Energy Conference (ENERGYCON)}}, publisher = {{IEEE}}, title = {{{Smart Metering and Choice Architecture in Demand-Side Management: A Power Resource-Constrained Perspective}}}, doi = {{10.1109/energycon58629.2024.10488738}}, year = {{2024}}, } @phdthesis{55455, author = {{Mwammenywa, Ibrahim Abdallah}}, title = {{{A Novel Autonomous and Real-time Load Monitoring and Control System in Microgrids based on Fuzzy Logic Control and LoRa Wireless Communication}}}, year = {{2024}}, } @inproceedings{53959, abstract = {{In light of the growing interest in type inference research for Python, both researchers and practitioners require a standardized process to assess the performance of various type inference techniques. This paper introduces TypeEvalPy, a comprehensive micro-benchmarking framework for evaluating type inference tools. TypeEvalPy contains 154 code snippets with 845 type annotations across 18 categories that target various Python features. The framework manages the execution of containerized tools, transforms inferred types into a standardized format, and produces meaningful metrics for assessment. Through our analysis, we compare the performance of six type inference tools, highlighting their strengths and limitations. Our findings provide a foundation for further research and optimization in the domain of Python type inference.}}, author = {{Shivarpatna Venkatesh, Ashwin Prasad and Sabu, Samkutty and Wang, Jiawei and Mir, Amir M. and Li, Li and Bodden, Eric}}, booktitle = {{Proceedings of the 2024 IEEE/ACM 46th International Conference on Software Engineering: Companion Proceedings}}, isbn = {{9798400705021}}, location = {{Lisbon, Portugal}}, pages = {{49--53}}, publisher = {{Association for Computing Machinery}}, title = {{{TypeEvalPy: A Micro-benchmarking Framework for Python Type Inference Tools}}}, doi = {{10.1145/3639478.3640033}}, year = {{2024}}, } @inproceedings{55516, author = {{Shivarpatna Venkatesh, Ashwin Prasad and Sabu, Samkutty and Mir, Amir M. and Reis, Sofia and Bodden, Eric}}, booktitle = {{Proceedings of the 2024 IEEE/ACM First International Conference on AI Foundation Models and Software Engineering}}, publisher = {{ACM}}, title = {{{The Emergence of Large Language Models in Static Analysis: A First Look through Micro-Benchmarks}}}, doi = {{10.1145/3650105.3652288}}, year = {{2024}}, } @inproceedings{55561, author = {{Jonas-Ahrend, Gabriela and Kapanadze, Marika and Mazzolini, Alexander and Joubran, Fadeel}}, booktitle = {{GDCP Jahrestagung Hamburg 2023}}, editor = {{van Vorst (Hrsg.), Helena}}, location = {{Hamburg}}, pages = {{570--572}}, title = {{{Ergebnisse einer Reviewstudie zur Evaluation von Physiklehrbüchern}}}, year = {{2024}}, } @inproceedings{55562, author = {{Jonas-Ahrend, Gabriela}}, location = {{Braga/Portugal}}, title = {{{Teacher shortage - characteristics, constraints, and challenges: The case of Germany}}}, year = {{2024}}, } @book{55193, author = {{Hoffmann, Max and Hilgert, Joachim and Weich, Tobias}}, isbn = {{9783662673560}}, publisher = {{Springer Berlin Heidelberg}}, title = {{{Ebene euklidische Geometrie. Algebraisierung, Axiomatisierung und Schnittstellen zur Schulmathematik}}}, doi = {{10.1007/978-3-662-67357-7}}, year = {{2024}}, } @article{46469, abstract = {{We show how to learn discrete field theories from observational data of fields on a space-time lattice. For this, we train a neural network model of a discrete Lagrangian density such that the discrete Euler--Lagrange equations are consistent with the given training data. We, thus, obtain a structure-preserving machine learning architecture. Lagrangian densities are not uniquely defined by the solutions of a field theory. We introduce a technique to derive regularisers for the training process which optimise numerical regularity of the discrete field theory. Minimisation of the regularisers guarantees that close to the training data the discrete field theory behaves robust and efficient when used in numerical simulations. Further, we show how to identify structurally simple solutions of the underlying continuous field theory such as travelling waves. This is possible even when travelling waves are not present in the training data. This is compared to data-driven model order reduction based approaches, which struggle to identify suitable latent spaces containing structurally simple solutions when these are not present in the training data. Ideas are demonstrated on examples based on the wave equation and the Schrödinger equation. }}, author = {{Offen, Christian and Ober-Blöbaum, Sina}}, issn = {{1054-1500}}, journal = {{Chaos}}, number = {{1}}, publisher = {{AIP Publishing}}, title = {{{Learning of discrete models of variational PDEs from data}}}, doi = {{10.1063/5.0172287}}, volume = {{34}}, year = {{2024}}, } @unpublished{55159, abstract = {{We introduce a method based on Gaussian process regression to identify discrete variational principles from observed solutions of a field theory. The method is based on the data-based identification of a discrete Lagrangian density. It is a geometric machine learning technique in the sense that the variational structure of the true field theory is reflected in the data-driven model by design. We provide a rigorous convergence statement of the method. The proof circumvents challenges posed by the ambiguity of discrete Lagrangian densities in the inverse problem of variational calculus. Moreover, our method can be used to quantify model uncertainty in the equations of motions and any linear observable of the discrete field theory. This is illustrated on the example of the discrete wave equation and Schrödinger equation. The article constitutes an extension of our previous article arXiv:2404.19626 for the data-driven identification of (discrete) Lagrangians for variational dynamics from an ode setting to the setting of discrete pdes.}}, author = {{Offen, Christian}}, keywords = {{System identification, inverse problem of variational calculus, Gaussian process, Lagrangian learning, physics informed machine learning, geometry aware learning}}, pages = {{28}}, title = {{{Machine learning of discrete field theories with guaranteed convergence and uncertainty quantification}}}, year = {{2024}}, } @misc{55302, author = {{Claes, Leander and Wippermann, Mareen}}, title = {{{Analysis of guided acoustic waves in periodically structured plates}}}, year = {{2024}}, } @inproceedings{55633, author = {{Höltervennhoff, Sandra and Wöhler, Noah and Möhle, Arne and Oltrogge, Marten and Acar, Yasemin and Wiese, Oliver and Fahl, Sascha}}, booktitle = {{33rd USENIX Security Symposium, USENIX Security 2024, Philadelphia, PA, USA, August 14-16, 2024}}, editor = {{Balzarotti, Davide and Xu, Wenyuan}}, publisher = {{USENIX Association}}, title = {{{A Mixed-Methods Study on User Experiences and Challenges of Recovery Codes for an End-to-End Encrypted Service}}}, year = {{2024}}, } @inproceedings{55632, author = {{Fischer, Konstantin and Trummová, Ivana and Gajland, Phillip and Acar, Yasemin and Fahl, Sascha and Sasse, M. Angela}}, booktitle = {{33rd USENIX Security Symposium, USENIX Security 2024, Philadelphia, PA, USA, August 14-16, 2024}}, editor = {{Balzarotti, Davide and Xu, Wenyuan}}, publisher = {{USENIX Association}}, title = {{{The Challenges of Bringing Cryptography from Research Papers to Products: Results from an Interview Study with Experts}}}, year = {{2024}}, }