@article{64877,
  author       = {{Taheri, Behnood and Kopylov, Denis and Hammer, Manfred and Meier, Torsten and Förstner, Jens and Sharapova, Polina R.}},
  journal      = {{arXiv}},
  title        = {{{Gain-induced spectral non-degeneracy in type-II parametric down-conversion}}},
  doi          = {{10.48550/ARXIV.2603.01656}},
  year         = {{2026}},
}

@inproceedings{64823,
  abstract     = {{Current legal frameworks enforce that Android developers accurately report the data their apps collect. However, large codebases can make this reporting challenging. This paper employs an empirical approach to understand developers' experience with Google Play Store's Data Safety Section (DSS) form.

We first survey 41 Android developers to understand how they categorize privacy-related data into DSS categories and how confident they feel when completing the DSS form. To gain a broader and more detailed view of the challenges developers encounter during the process, we complement the survey with an analysis of 172 online developer discussions, capturing the perspectives of 642 additional developers. Together, these two data sources represent insights from 683 developers.

Our findings reveal that developers often manually classify the privacy-related data their apps collect into the data categories defined by Google-or, in some cases, omit classification entirely-and rely heavily on existing online resources when completing the form. Moreover, developers are generally confident in recognizing the data their apps collect, yet they lack confidence in translating this knowledge into DSS-compliant disclosures. Key challenges include issues in identifying privacy-relevant data to complete the form, limited understanding of the form, and concerns about app rejection due to discrepancies with Google's privacy requirements.
These results underscore the need for clearer guidance and more accessible tooling to support developers in meeting privacy-aware reporting obligations. }},
  author       = {{Khedkar, Mugdha and Schlichtig, Michael and Soliman, Mohamed Aboubakr Mohamed and Bodden, Eric}},
  booktitle    = {{Proceedings of the IEEE/ACM 13th International Conference on Mobile Software Engineering and Systems (MOBILESoft '26). Association for Computing Machinery, New York, NY, USA, 65–68.}},
  keywords     = {{static analysis, data collection, data protection, privacy-aware reporting}},
  location     = {{Rio de Janeiro, Brazil}},
  title        = {{{Challenges in Android Data Disclosure: An Empirical Study.}}},
  year         = {{2026}},
}

@article{64821,
  author       = {{Khedkar, Mugdha and Schlichtig, Michael and Atakishiyev, Nihad and Bodden, Eric}},
  journal      = {{Automated Software Engineering }},
  number       = {{2}},
  publisher    = {{Springer US}},
  title        = {{{Between Law and Code: Challenges and Opportunities for Automating Privacy Assessments}}},
  doi          = {{10.1007/s10515-026-00601-4}},
  volume       = {{33}},
  year         = {{2026}},
}

@inproceedings{64909,
  author       = {{Khedkar, Mugdha and Schlichtig, Michael and Bodden, Eric}},
  booktitle    = {{IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER 2026)}},
  title        = {{{Source Code-Driven GDPR Documentation: Supporting RoPA with Assessor View}}},
  year         = {{2026}},
}

@article{64979,
  abstract     = {{We investigate homogeneous coupled cell systems with high-dimensional internal dynamics. In many studies on network dynamics, the analysis is restricted to networks with one-dimensional internal dynamics. Here, we show how symmetry explains the relation between dynamical behavior of systems with one-dimensional internal dynamics and with higher dimensional internal dynamics, when the underlying network topology is the same. Fundamental networks of homogeneous coupled cell systems (B. Rink, J. Sanders. Coupled Cell Networks and Their Hidden Symmetries. SIAM J. Math. Anal. 46.2 (2014)) can be expressed in terms of monoid representations, which uniquely decompose into indecomposable subrepresentations. In the high-dimensional internal dynamics case, these subrepresentations are isomorphic to multiple copies of those one computes in the one-dimensional internal dynamics case. This has interesting implications for possible center subspaces in bifurcation analysis. We describe the effect on steady state and Hopf bifurcations in l-parameter families of network vector fields. The main results in that regard are that (1) generic one-parameter steady state bifurcations are qualitatively independent of the dimension of the internal dynamics and that, (2) in order to observe all generic l-parameter bifurcations that may occur for internal dynamics of any dimension, the internal dynamics has to be at least l-dimensional for steady state bifurcations and 2l-dimensional for Hopf bifurcations. Furthermore, we illustrate how additional structure in the network can be exploited to obtain even greater understanding of bifurcation scenarios in the high-dimensional case beyond qualitative statements about the collective dynamics. One-parameter steady state bifurcations in feedforward networks exhibit an unusual amplification in the asymptotic growth rates of individual cells, when these are one-dimensional (S. von der Gracht, E. Nijholt, B. Rink. Amplified steady state bifurcations in feedforward networks. Nonlinearity 35.4 (2022)). As another main result, we prove that (3) the same cells exhibit this amplifying effect with the same growth rates when the internal dynamics is high-dimensional.}},
  author       = {{von der Gracht, Sören and Nijholt, Eddie and Rink, Bob}},
  issn         = {{0960-0779}},
  journal      = {{Chaos, Solitons & Fractals}},
  keywords     = {{Coupled cell systems, Network dynamics, Dimension reduction, Bifurcation theory, Symmetry, Monoid representation theory}},
  publisher    = {{Elsevier BV}},
  title        = {{{Homogeneous coupled cell systems with high-dimensional internal dynamics}}},
  doi          = {{10.1016/j.chaos.2026.118196}},
  volume       = {{208}},
  year         = {{2026}},
}

@unpublished{65017,
  abstract     = {{Static Application Security Testing (SAST) tools play a vital role in modern software development by automatically detecting potential vulnerabilities in source code. However, their effectiveness is often limited by a high rate of false positives, which wastes developer's effort and undermines trust in automated analysis. This work presents a Graph Convolutional Network (GCN) model designed to predict SAST reports as true and false positive. The model leverages Code Property Graphs (CPGs) constructed from static analysis results to capture both, structural and semantic relationships within code. Trained on the CamBenchCAP dataset, the model achieved an accuracy of 100% on the test set using an 80/20 train-test split. Evaluation on the CryptoAPI-Bench benchmark further demonstrated the model's practical applicability, reaching an overall accuracy of up to 96.6%. A detailed qualitative inspection revealed that many cases marked as misclassifications corresponded to genuine security weaknesses, indicating that the model effectively reflects conservative, security-aware reasoning. Identified limitations include incomplete control-flow representation due to missing interprocedural connections. Future work will focus on integrating call graphs, applying graph explainability techniques, and extending training data across multiple SAST tools to improve generalization and interpretability.}},
  author       = {{Ohlmer, Tom and Schlichtig, Michael and Bodden, Eric}},
  booktitle    = {{arXiv:2603.10558}},
  title        = {{{FP-Predictor - False Positive Prediction for Static Analysis Reports}}},
  year         = {{2026}},
}

@article{64176,
  author       = {{Deutschen, Katrin and Neufeld, Inga and Häsel-Weide, Uta}},
  journal      = {{Grundschule aktuell}},
  number       = {{173}},
  pages        = {{10--11}},
  title        = {{{Lernbegleitung produktiv gestalten. Mathematische Verstehensprozesse von Kindern anregen.}}},
  year         = {{2026}},
}

@inbook{65026,
  author       = {{Neufeld, Inga and Häsel-Weide, Uta}},
  booktitle    = {{Handlungsorientierung in der Ausbildung von Lehrkräften und pädagogischen Fachkräften. Konzeptionen und Forschungsperspektiven}},
  editor       = {{Vogelsang, C. and Grotegurt, L. and Bruns, J. and Riese, J. and Fechner, S.}},
  pages        = {{119--134}},
  title        = {{{ Lernbegleitung in der mathematischen Förderung. Praktiken von (angehenden) Lehrkräften bei der Förderung arithmetischer Basiskompetenzen}}},
  doi          = {{10.31244/9783818851057 }},
  year         = {{2026}},
}

@inproceedings{65030,
  author       = {{Amaral, Luis and Schlichtig, Michael and Emanuel, Wagner and Almeida, Joilton and Ferreira, Carine and Kempf, Jérôme and Bonifácio, Rodrigo and Bodden, Eric and Peotta, Laerte and Pinto, Gustavo and Ribeiro, Márcio}},
  booktitle    = {{2026 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)}},
  title        = {{{From Legacy Designs to Vulnerability Fixes: Understanding SAST Adoption in Non-Technological Companies}}},
  year         = {{2026}},
}

@inproceedings{63754,
  abstract     = {{Data spaces are receiving an emerging interest in Information Systems Research and industry practice. They are central to many European research initiatives and shape the data economy in Industry 4.0. Generally, they aim to create secure environments for cross-organizational data management and sharing. Currently, there is considerable interest in developing new data spaces in Industry 4.0, also accelerated through regulatory changes. However, key questions about what precisely characterizes a data space in Industry 4.0 remain unresolved. Against this backdrop, we build a taxonomy of data spaces in the Industry 4.0 context. We identified nine distinctive dimensions and 40 corresponding characteristics among the 19 data spaces analyzed. The taxonomy enables clearer classification and nomenclature of data spaces in this context. This short paper will ignite planned further research on data spaces in Industry 4.0 and contribute to a conceptualization of a taxonomic theory for interested researchers.}},
  author       = {{Werth, Oliver and Koldewey, Christian and Uslar, Mathias and Zerbin, Julian}},
  booktitle    = {{Lecture Notes in Business Information Processing}},
  isbn         = {{9783032145178}},
  issn         = {{1865-1348}},
  keywords     = {{Industry 4.0, Taxonomy, Data spaces, Characterization}},
  location     = {{Stuttgart, Germany}},
  publisher    = {{Springer Nature Switzerland}},
  title        = {{{What Characterizes Data Spaces in Industry 4.0? Towards a Better Understanding}}},
  doi          = {{10.1007/978-3-032-14518-5_3}},
  year         = {{2026}},
}

@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     = {{<jats:title>Abstract</jats:title>
                  <jats:p>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.</jats:p>}},
  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}},
}