@unpublished{65036, author = {{Cohen, Tal and Glöckner, Helge and Goffer, Gil and Lederle, Waltraud}}, title = {{{Compact invariant random subgroups}}}, year = {{2026}}, } @inproceedings{65101, abstract = {{Various methods to measure the dynamic behavior of particles require the calculation of autocorrelation functions. For this purpose, fast multi-tau correlators have been developed in dedicated hardware, in software, and on FPGAs. However, for methods such as X-ray Photon Correlation Spectroscopy (XPCS), which requires to calculate the autocorrelation function independently for hundreds of thousands to millions of pixels from high-resolution detectors, current approaches rely on offline processing after data acquisition. Moreover, the internal pipeline state of so many independent correlators is far too large to keep it on-chip. In this work, we propose a design approach on FPGAs, where pipeline contexts are stored in off-chip HBM memory. Each compute unit iteratively loads the state for a single pixel, processes a short time series for this pixel, and afterwards writes back the context in a dataflow pipeline. We have implemented the required compute kernels with Vitis HLS and analyze resulting designs on an Alveo U280 card. The design achieves the expected performance and for the first time provides sufficient throughput for current high-end detectors used in XPCS.}}, author = {{Tareen, Abdul Rehman and Plessl, Christian and Kenter, Tobias}}, booktitle = {{2025 International Conference on Field Programmable Technology (ICFPT)}}, publisher = {{IEEE}}, title = {{{Fast Multi-Tau Correlators on FPGA with Context Switching From and to High- Bandwidth Memory}}}, doi = {{10.1109/icfpt67023.2025.00027}}, year = {{2026}}, } @article{65099, author = {{Weber, Daniel and Schmies, Dominik and Lange, Jarren H. and Schenke, Maximilian and Wallscheid, Oliver}}, issn = {{2169-3536}}, journal = {{IEEE Access}}, pages = {{38517--38535}}, publisher = {{Institute of Electrical and Electronics Engineers (IEEE)}}, title = {{{Optimal Control of Voltage-Forming Grid Inverters by Model Predictive Control and Reinforcement Learning}}}, doi = {{10.1109/access.2026.3670948}}, volume = {{14}}, year = {{2026}}, } @article{65098, author = {{Weber, Daniel and Lange, Jarren and Wallscheid, Oliver}}, issn = {{2687-9735}}, journal = {{IEEE Journal of Emerging and Selected Topics in Industrial Electronics}}, pages = {{1--12}}, publisher = {{Institute of Electrical and Electronics Engineers (IEEE)}}, title = {{{Reinforcement Learning-Based Control of Voltage-Forming Grid Inverters With Arbitrary Loads}}}, doi = {{10.1109/jestie.2026.3654784}}, year = {{2026}}, } @inproceedings{65178, abstract = {{Large intermediate results can cause join queries to run unexpectedly long. This problem is particularly common for analytical queries, which aggregate data over many tables to produce a comparatively small final output, and queries on graph data, where intermediate results blow up quickly. Recent work inspired by Yannakakis’ algorithm approaches this by modifying the query engine to avoid materializing unnecessary tuples. However, this requires significant changes to the core of the system, which is not feasible in many situations such as cloud environments or proprietary systems. In this work, we propose a flexible approach for optimizing long-running join queries from the outside of the DBMS. Rewriting-based realizations of Yannakakis’ algorithm suffer from inherent overhead due to the creation of intermediate tables. Thus, we present an approach for detecting and targeting queries which would benefit from a Yannakakis-style optimization. We introduce a new benchmark combining 5 standard benchmarks and augmenting them with additional instances, which provides a sufficient size and diversity for a machine learning based solution. On PostgreSQL, DuckDB and SparkSQL, slowdowns on queries where the rewriting is counterproductive are mostly avoided, as opposed to a naïve application of the rewriting, and we observe significant improvements in end-to-end runtimes over standard query execution and unconditional rewriting.}}, author = {{Böhm, Daniela and Gottlob, Georg and Lanzinger, Matthias and Longo, Davide Mario and Okulmus, Cem and Pichler, Reinhard and Selzer, Alexander}}, booktitle = {{Proceedings of the 28th International Workshop on Design, Optimization, Languages and Analytical Processing of Big Data (DOLAP 2026)}}, keywords = {{Join Queries, Acyclic Queries, Query Processing}}, title = {{{Selective Use of Yannakakis’ Algorithm for Consistent Performance Gains}}}, year = {{2026}}, } @article{57580, abstract = {{We investigate dispersive and Strichartz estimates for the Schrödinger equation involving the fractional Laplacian in real hyperbolic spaces and their discrete analogues, homogeneous trees. Due to the Knapp phenomenon, the Strichartz estimates on Euclidean spaces for the fractional Laplacian exhibit loss of derivatives. A similar phenomenon appears on real hyperbolic spaces. However, such a loss disappears on homogeneous trees, due to the triviality of the estimates for small times.}}, author = {{Palmirotta, Guendalina and Sire, Yannick and Anker, Jean-Philippe}}, journal = {{Journal of Differential Equations}}, keywords = {{Schrödinger equation, Fractional Laplacian, Dispersive estimates, Strichartz estimates, Real hyperbolic spaces, Homogeneous trees}}, publisher = {{Elsevier}}, title = {{{The Schrödinger equation with fractional Laplacian on hyperbolic spaces and homogeneous trees}}}, doi = {{10.1016/j.jde.2025.114065}}, year = {{2026}}, } @unpublished{65232, abstract = {{On finite regular graphs, we construct Patterson-Sullivan distributions associated with eigenfunctions of the discrete Laplace operator via their boundary values on the phase space. These distributions are closely related to Wigner distributions defined via a pseudo-differential calculus on graphs, which appear naturally in the study of quantum chaos. Using a pairing formula, we prove that Patterson-Sullivan distributions are also related to invariant Ruelle distributions arising from the transfer operator of the geodesic flow on the shift space. Both relationships provide discrete analogues of results for compact hyperbolic surfaces obtained by Anantharaman-Zelditch and by Guillarmou-Hilgert-Weich.}}, author = {{Arends, Christian and Palmirotta, Guendalina}}, booktitle = {{arXiv:2603.09779}}, pages = {{38}}, title = {{{Patterson-Sullivan distributions of finite regular graphs}}}, year = {{2026}}, } @article{65242, abstract = {{Abstract With the growing demand for lightweight solutions to reduce emissions, especially in the transportation, automotive and aerospace sectors, recyclable, continuous fiber-reinforced plastic composite laminates with a thermoplastic matrix are of rising interest. To achieve their maximum mechanical properties, the fiber-matrix adhesion (FMA) is critical. In this work, continuous fiber-reinforced thermoplastic laminates (CFRTPL) with a polypropylene (PP) matrix and twill woven glass fiber fabrics are produced by film stacking. The films used contain different amounts of maleic-anhydride-grafted PP (MA-g-PP) as a coupling agent to produce CFRTPL of different mechanical strengths. To analyze the FMA, the CFRTPL are subjected to Charpy-impact and tensile tests. Additionally, single fiber pull-out tests (SFPT) are conducted to further investigate the effect of MA-g-PP on the FMA. The results of the SFPT show an improvement in apparent interfacial shear strength (AIFSS) when the MA-g-PP content is increased, which can be attributed to an increase in FMA. However, the research shows that MA-g-PP has a low impact on the mechanical properties if the force is applied parallel to the warp and weft threads during tensile testing and the results of the Charpy-impact testing suffer from embrittlement of the matrix material. Subsequently, the results of this study are compared to three-point flexural tests conducted in a previous study. It can be concluded that tensile and impact tests are not suited to investigate FMA on a macroscopic scale, while SFPT and flexural tests provide a better alternative.}}, author = {{Moritzer, Elmar and Brandes, Philipp and Wittler, Maurice and Claes, Leander and Wippermann, Mareen and Haag, Markus and Gries, Thomas and Henning, Bernd}}, issn = {{0930-777X}}, journal = {{International Polymer Processing}}, publisher = {{Walter de Gruyter GmbH}}, title = {{{Fiber-matrix adhesion in glass fiber reinforced thermoplastic composite laminates and its effect on mechanical properties}}}, doi = {{10.1515/ipp-2025-0077}}, year = {{2026}}, } @unpublished{63530, abstract = {{The widespread deployment of 5G networks, together with the coexistence of 4G/LTE networks, provides mobile devices a diverse set of candidate cells to connect to. However, associating mobile devices to cells to maximize overall network performance, a.k.a. cell (re)selection, remains a key challenge for mobile operators. Today, cell (re)selection parameters are typically configured manually based on operator experience and rarely adapted to dynamic network conditions. In this work, we ask: Can an agent automatically learn and adapt cell (re)selection parameters to consistently improve network performance? We present a reinforcement learning (RL)-based framework called CellPilot that adaptively tunes cell (re)selection parameters by learning spatiotemporal patterns of mobile network dynamics. Our study with real-world data demonstrates that even a lightweight RL agent can outperform conventional heuristic reconfigurations by up to 167%, while generalizing effectively across different network scenarios. These results indicate that data-driven approaches can significantly improve cell (re)selection configurations and enhance mobile network performance.}}, author = {{Illian, Marvin and Khalili, Ramin and Rocha, Antonio A. de A. and Wang, Lin}}, booktitle = {{arXiv:2601.04083}}, title = {{{Cells on Autopilot: Adaptive Cell (Re)Selection via Reinforcement Learning}}}, year = {{2026}}, } @inproceedings{65249, author = {{Shaaban KabakiboKabakibo, Huzaifa and Trivedi, Animesh and Wang, Lin}}, booktitle = {{The 9th Annual Conference on Machine Learning and Systems (MLSys)}}, location = {{Bellevue, WA}}, title = {{{Breaking the Ice: Analyzing Cold Start Latency in vLLM}}}, year = {{2026}}, } @inproceedings{65250, author = {{Zohdi, Sepideh and Wang, Lin}}, booktitle = {{The 6th Workshop on Machine Learning and Systems (EuroMLSys)}}, location = {{Edinburg}}, title = {{{Before the First Token: Benchmarking Data Preprocessing in Vision-Language Models }}}, year = {{2026}}, } @inproceedings{65013, author = {{Illian, Marvin and Khalili, Ramin and A. de A. Rocha, Antonio and Wang, Lin}}, booktitle = {{2026 24th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt)}}, publisher = {{IFIP}}, title = {{{Cells on Autopilot: Adaptive Cell (Re)Selection via Reinforcement Learning}}}, year = {{2026}}, } @article{65253, author = {{Abdelwanis, Ali Hassan Ali and Haucke-Korber, Barnabas and Jakobeit, Darius and Kirchgässner, Wilhelm and Meyer, Marvin and Schenke, Maximilian and Vater, Hendrik and Wallscheid, Oliver and Weber, Daniel}}, issn = {{2577-3569}}, journal = {{Journal of Open Source Education}}, number = {{97}}, publisher = {{The Open Journal}}, title = {{{Reinforcement Learning: A Comprehensive Open-Source Course}}}, doi = {{10.21105/jose.00306}}, volume = {{9}}, year = {{2026}}, } @article{65440, abstract = {{Abstract We present a novel algorithm for quantization and subsequent hexahedral mesh generation from seamless volumetric maps. Quantization is the process of choosing integers that represent the numbers of hexahedral elements to be placed in each region of the volume, and transforming the seamless map into an integer‐grid map matching that choice, inducing a hexahedral mesh. Previous work computes such quantizations under the restriction of a fixed predetermined singularity graph. Our novel approach allows for implicit modification and, in particular, simplification of the map's singularity structure wherever that benefits the chosen objective, such as matching target hexahedron sizes as closely as possible. It comes with two novel ingredients: A feature‐focused distortion measure guiding the quantization, and constraints ensuring map injectivity and structure preservation of geometric and topological features, both without relying on a fixed singularity structure. We demonstrate the benefit of the added flexibility offered by this approach: it allows for the generation of hexahedral meshes that more accurately match a desired resolution globally, as well as of meshes exhibiting a simpler block structure.}}, author = {{Brückler, Hendrik and Campen, Marcel}}, issn = {{0167-7055}}, journal = {{Computer Graphics Forum}}, publisher = {{Wiley}}, title = {{{Volume Quantization with Flexible Singularities for Hexahedral Meshing}}}, doi = {{10.1111/cgf.70349}}, year = {{2026}}, } @techreport{65426, abstract = {{In diesem Forschungsprojekt wurde ein Messverfahren zur Bestimmung akustischer Materialparameter von Polymeren im Ultraschallfrequenzbereich entwickelt. Das Verfahrens sollte, die üblichen standardisierten Prüfmethoden erweitern, die bislang primär im quasistatischen oder niederfrequenten Bereich eingesetzt wurden. Im Gegensatz zu bestehenden Verfahren wie dem Zeitstandversuch oder der Dynamisch Mechanischen Analyse (DMA) nach [DIN6721] sollte die neue Methode eine nicht-invasive Charakterisierung der (visko-)elastischen Materialparameter im Frequenzbereich von 0,75 MHz bis 2,5 MHz ermöglichen. Das entwickelte Ultraschallmesssystem arbeitet nach dem Puls Echo-Prinzip und kann eine räumlich segmentierte, ringförmige Anregung erzeugen. Die Bestimmung der frequenzabhängigen Materialparameter geschieht hierbei über ein inverses Verfahren. Die Ergebnisse des Projekts zeigen, dass die Segmentierung der Anregung, die Geometrie der Probe sowie das Puls-Echo-Messprinzip die Messergebnisse sowie die Sensitivität gegenüber Scherparametern wesentlich beeinflussen. Im Rahmen des Projektes wurde auch eine statistische Auswertung des Optimierungsverfahrens hinsichtlich transversal-isotroper Materialsymmetrie mit Rayleigh-Dämpfung durchgeführt. Die Ergebnisse zeigen, dass das entwickelte Verfahren gute Konvergenzeigenschaften aufweist und sich durch verbesserte Robustheit auszeichnet.}}, author = {{Dreiling, Dmitrij and Itner, Dominik and Birk, Carolin and Gravenkamp, Hauke and Henning, Bernd}}, keywords = {{Materialcharakterisierung, Polymer, Inverses Problem, Ultraschall, Optimierung}}, pages = {{12}}, publisher = {{Hannover : Technische Informationsbibliothek}}, title = {{{Vollständige Bestimmung der akustischen Materialparameter von Polymeren II}}}, doi = {{https://doi.org/10.34657/33602}}, year = {{2026}}, } @inproceedings{61922, abstract = {{We present an extremely simple polynomial-space exponential-time $(1-\varepsilon)$-approximation algorithm for MAX-k-SAT that is (slightly) faster than the previous known polynomial-space $(1-\varepsilon)$-approximation algorithms by Hirsch (Discrete Applied Mathematics, 2003) and Escoffier, Paschos and Tourniaire (Theoretical Computer Science, 2014). Our algorithm repeatedly samples an assignment uniformly at random until finding an assignment that satisfies a large enough fraction of clauses. Surprisingly, we can show the efficiency of this simpler approach by proving that in any instance of MAX-k-SAT (or more generally any instance of MAXCSP), an exponential number of assignments satisfy a fraction of clauses close to the optimal value.}}, author = {{Buhrman, Harry and Gharibian, Sevag and Landau, Zeph and Gall, François Le and Schuch, Norbert and Tamaki, Suguru}}, booktitle = {{SIAM Symposium on Simplicity in Algorithms (SOSA)}}, pages = {{247--253}}, title = {{{A Simpler Exponential-Time Approximation Algorithm for MAX-k-SAT}}}, year = {{2026}}, } @proceedings{64797, editor = {{Birk, Lisa and Loth, Gerrit and Jotzo, Luca and Binder, Karin and Frischemeier, Daniel}}, location = {{Münster}}, publisher = {{International Association for Statistics Education}}, title = {{{14th IASE Satellite Conference "Statistics and Data Science Education in STEAM"}}}, doi = {{10.52041/iase25.158}}, year = {{2026}}, } @inproceedings{63918, abstract = {{Many real-world datasets, such as citation networks, social networks, and molecular structures, are naturally represented as heterogeneous graphs, where nodes belong to different types and have additional features. For example, in a citation network, nodes representing "Paper" or "Author" may include attributes like keywords or affiliations. A critical machine learning task on these graphs is node classification, which is useful for applications such as fake news detection, corporate risk assessment, and molecular property prediction. Although Heterogeneous Graph Neural Networks (HGNNs) perform well in these contexts, their predictions remain opaque. Existing post-hoc explanation methods lack support for actual node features beyond one-hot encoding of node type and often fail to generate realistic, faithful explanations. To address these gaps, we propose DiGNNExplainer, a model-level explanation approach that synthesizes heterogeneous graphs with realistic node features via discrete denoising diffusion. In particular, we generate realistic discrete features (e.g., bag-of-words features) using diffusion models within a discrete space, whereas previous approaches are limited to continuous spaces. We evaluate our approach on multiple datasets and show that DiGNNExplainer produces explanations that are realistic and faithful to the model's decision-making, outperforming state-of-the-art methods.}}, author = {{Das, Pallabee and Heindorf, Stefan}}, booktitle = {{Proceedings of the ACM Web Conference 2026 (WWW ’26)}}, location = {{Dubai, United Arab Emirates}}, publisher = {{ACM}}, title = {{{Discrete Diffusion-Based Model-Level Explanation of Heterogeneous GNNs with Node Features}}}, year = {{2026}}, } @inproceedings{65489, author = {{Okulmus, Cem and Ahmetaj, Shqiponja and Boneva, Iovka and Hidders, Jan and Jakubowski, Maxime and Labra Gayo, José Emilio and Martens, Wim and Mogavero, Fabio and Murlak, Filip and Savković, Ognjen and Šimkus, Mantas and Tomaszuk, Dominik }}, booktitle = {{Proceedings of the 23rd International Conference on Principles of Knowledge Representation and Reasoning (KR 2026)}}, location = {{Lisbon, Portugal}}, title = {{{Common Foundations for Recursive Shape Languages}}}, year = {{2026}}, } @article{63135, abstract = {{We propose a definition of Coxeter-Dynkin algebras of canonical type generalising the definition as a path algebra of a quiver. Moreover, we construct two tilting objects over the squid algebra - one via generalised APR-tilting and one via one-point-extensions and reflection functors - and identify their endomorphism algebras with the Coxeter-Dynkin algebra. This shows that our definition gives another representative in the derived equivalence class of the squid algebra, and hence of the corresponding canonical algebra. Finally, we have a closer look at the Grothendieck group and the Euler form which illustrates the connection to Saito's classification of marked extended affine root systems. On the other hand, this enables us to prove that in the domestic case Coxeter-Dynkin algebras are of finite representation type.}}, author = {{Perniok, Daniel}}, journal = {{Journal of Pure and Applied Algebra}}, number = {{5}}, title = {{{Coxeter-Dynkin algebras of canonical type}}}, doi = {{10.1016/j.jpaa.2026.108250}}, volume = {{230}}, year = {{2026}}, }