@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}}, } @inproceedings{55634, author = {{Fourné, Marcel and Braga, Daniel De Almeida and Jancar, Jan and Sabt, Mohamed and Schwabe, Peter and Barthe, Gilles and Fouque, Pierre-Alain and Acar, Yasemin}}, 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 = {{{"These results must be false": A usability evaluation of constant-time analysis tools}}}, year = {{2024}}, } @inproceedings{55636, author = {{Huaman, Nicolas and Suray, Jacques and Klemmer, Jan H. and Fourné, Marcel and Amft, Sabrina and Trummová, Ivana and Acar, Yasemin 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 = {{{"You have to read 50 different RFCs that contradict each other": An Interview Study on the Experiences of Implementing Cryptographic Standards}}}, year = {{2024}}, } @inproceedings{55641, author = {{Panahi, Kabir and Robertson, Shawn and Acar, Yasemin and Bardas, Alexandru G. and Kohno, Tadayoshi and Simko, Lucy}}, 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 = {{{"But they have overlooked a few things in Afghanistan: " An Analysis of the Integration of Biometric Voter Verification in the 2019 Afghan Presidential Elections}}}, year = {{2024}}, } @inproceedings{55642, author = {{Ramulu, Harshini Sri and Schmitt, Helen and Wermke, Dominik and Acar, Yasemin}}, 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 = {{{Security and Privacy Software Creators’ Perspectives on Unintended Consequences}}}, year = {{2024}}, } @article{55667, abstract = {{This study investigates how 11- to 12-year-old students construct data-based decision trees using data cards for classification purposes. We examine the students' heuristics and reasoning during this process. The research is based on an eight-week teaching unit during which students labeled data, built decision trees, and assessed them using test data. They learned to manually construct decision trees to classify food items as recommendable or not. They utilized data cards with a heuristic that is a simplified form of a machine learning algorithm. We report on evidence that this topic is teachable to middle school students, along with insights for refining our teaching approach and broader implications for teaching machine learning at the school level.}}, author = {{Fleischer, Franz Yannik and Podworny, Susanne and Biehler, Rolf}}, issn = {{1570-1824}}, journal = {{Statistics Education Research Journal}}, number = {{1}}, publisher = {{International Association for Statistical Education}}, title = {{{Teaching and Learning to Construct Data-Based Decision Trees Using Data Cards as the First Introduction to Machine Learning in Middle School}}}, doi = {{10.52041/serj.v23i1.450}}, volume = {{23}}, year = {{2024}}, } @article{55751, abstract = {{Lateral leakage of TM modes in dielectric optical waveguides of rib/ridge or strip-loaded types can be fully suppressed, if the waveguide core is formed not through a strip that protrudes at one side (up) from the remaining lateral guiding slab, but through parallel strips on both sides (up and down), such that the resulting cross section becomes vertically symmetric. The fairly general arguments underlying the leakage suppression apply to TM modes of all orders simultaneously, and are independent of wavelength. These plus-shaped waveguides support strictly guided, non-leaky TM modes for, in principle, arbitrarily shallow etching.}}, author = {{Üstün, Necati and Farheen, Henna and Hammer, Manfred and Förstner, Jens}}, issn = {{0740-3224}}, journal = {{Journal of the Optical Society of America B}}, keywords = {{tet_topic_waveguide}}, number = {{9}}, pages = {{2077}}, publisher = {{Optica Publishing Group}}, title = {{{Symmetry-protected TM modes in rib-like, plus-shaped optical waveguides with shallow etching}}}, doi = {{10.1364/josab.528729}}, volume = {{41}}, year = {{2024}}, } @inbook{55756, author = {{Biehler, Rolf and Frischemeier, Daniel}}, booktitle = {{Inklusives Lehren und Lernen von Mathematik}}, isbn = {{9783658439637}}, publisher = {{Springer Fachmedien Wiesbaden}}, title = {{{Eine inklusive Lehr-Lernumgebung für die Leitidee „Daten und Zufall“ in der Primarstufe}}}, doi = {{10.1007/978-3-658-43964-4_13}}, year = {{2024}}, } @book{55758, author = {{Biehler, Rolf and Frischemeier, Daniel}}, publisher = {{Klett Kallmeyer}}, title = {{{Daten-Spürnasen auf Spurensuche: Datenanalyse in der Grundschule mit digitalen Werkzeugen}}}, year = {{2024}}, } @article{45972, author = {{Kovács, Balázs}}, journal = {{SIAM Journal on Scientific Computing}}, number = {{2}}, pages = {{A645----A669}}, title = {{{Numerical surgery for mean curvature flow of surfaces}}}, doi = {{10.1137/22M1531919}}, volume = {{46}}, year = {{2024}}, } @inproceedings{55365, author = {{Razavi, Kamran and Davari Fard, Shayan and Karlos, George and Nigade, Vinod and Mühlhäuser, Max and Wang, Lin}}, booktitle = {{Proceedings of the IEEE International Symposium on Computers and Communications (ISCC)}}, location = {{Paris, France}}, title = {{{NetNN: Neural Intrusion Detection System in Programmable Networks (Second Best Paper Award)}}}, year = {{2024}}, } @inproceedings{53095, author = {{Razavi, Kamran and Ghafouri, Saeid and Mühlhäuser, Max and Jamshidi, Pooyan and Wang, Lin}}, booktitle = {{Proceedings of the 4th Workshop on Machine Learning and Systems (EuroMLSys), colocated with EuroSys 2024}}, location = {{Athens, Greece}}, publisher = {{ACM}}, title = {{{Sponge: Inference Serving with Dynamic SLOs Using In-Place Vertical Scaling}}}, doi = {{10.1145/3642970.365583}}, year = {{2024}}, }