TY - JOUR A1 - Schwope, A.D. A1 - Marsh, T.R. A1 - Parsons, S.G. A1 - Vogel, J. A1 - Dhillon, V.S. T1 - Unveiling the white dwarf in the eclipsing polar HU Aquarii JF - Astronomy & Astrophysics Y1 - 2025 U6 - https://doi.org/10.1051/0004-6361/202557306 ER - TY - JOUR A1 - Hölter, Arne A1 - Lemke, Mathias A1 - Weinzierl, Stefan A1 - Stein, Lewin T1 - Non-Reflecting Characteristic Boundary Conditions for Adjoint Time-Domain Acoustic Simulations JF - Journal of Theoretical and Computational Acoustics N2 - Accurate acoustic simulations in the free field require non-reflective boundary conditions to suppress spurious reflections at the computational domain boundaries. Although several characteristic-based formulations for direct (forward) simulations have been proposed in recent decades, the adjoint formulations of such characteristic-based boundary conditions (CBCs) have received limited atten- tion in the literature and lack a comprehensive analysis. This paper presents the derivation and evaluation of adjoint CBCs complementing the existing direct CBCs. Both the forward and adjoint CBCs are applied to the (nonlinear) Euler equations and linear acoustic equations in time-domain simulations. In this manner, the CBCs are investigated and subsequently compared to assess their respective accuracy and consistency. The CBCs were implemented using both a single-point and a zonal approach, with the former optionally combined with a sponge layer. Both approaches yielded comparable results in direct and adjoint simulations, while the zonal CBC exhibited improved ac- curacy at lower frequencies. Across the evaluated frequency range of approximately 350 – 5600 Hz, spurious reflections were attenuated by up to −70dB in both forward and adjoint cases, demon- strating the effectiveness and consistency of the proposed boundary treatment. Y1 - 2025 ER - TY - GEN A1 - Lindner, Niels A1 - Mehl, Lukas A1 - Bartoszuk, Karolina A1 - Berendes, Sarah A1 - Zittel, Janina T1 - Demand Uncertainty in Energy Systems: Scenario Catalogs vs. Integrated Robust Optimization N2 - Designing efficient energy systems is indispensable for shaping a more sustainable society. This involves making infrastructure investment decisions that must be valid for a long-term time horizon. While energy system optimization models constitute a powerful technique to support planning decisions, they need to cope with inherent uncertainty. For example, predicting future demand on a scale of decades is not only an intricate challenge in itself, but small fluctuations in such a forecast might also largely impact the layout of a complex energy system. In this paper, we compare two methodologies of capturing demand uncertainty for linear-programming based energy system optimization models. On one hand, we generate and analyze catalogs of varying demand scenarios, where each individual scenario is considered independently, so that the optimization produces scenario-specific investment pathways. On the other hand, we make use of robust linear programming to meet the demand of all scenarios at once. Since including a multitude of scenarios increases the size and complexity of the optimization model, we will show how to use warm-starting approaches to accelerate the computation process, by exploiting the similar structure of the linear program across different demand inputs. This allows to integrate a meaningful number of demand scenarios with fully-fledged energy system models. We demonstrate the practical use of our methods in a case study of the Berlin-Brandenburg area in Germany, a region that contains both a metropolitan area and its rural surroundings. As a backbone, we use the open-source framework oemof to create a sector-coupled optimization model for planning an energy system with up to 100% reduction of greenhouse gas emissions. This model features a fine-grained temporal resolution of one hour for the full year 2050. We consider uncertainty in demand for electricity, hydrogen, natural gas, central, and decentral heat. Based on our computations, we analyze the trade-offs in terms of quality and computation time for scenario catalogs and the robust optimization approach. We further demonstrate that our procedure provides a valuable strategy for decision makers to gain insight on the robustness and sensitivity of solutions regarding demand variability. T3 - ZIB-Report - 25-19 Y1 - 2025 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-102404 SN - 1438-0064 ER - TY - JOUR A1 - Gorgulla, Christoph A1 - Garza, Alejandro J. A1 - Kapil, Venkat A1 - Fackeldey, Konstantin T1 - QUASAR: A Flexible QM-MM Method for Biomolecular Systems based on Restraining Spheres JF - Computer Physics Communications N2 - Quantum mechanical models of molecules theoretically offer unprecedented accuracy in predicting values associated with these systems, including the free energy of interaction between two molecules. However, high-accuracy quantum mechanical methods are computationally too expensive to be applied to larger systems, including most biomolecular systems such as proteins. To circumvent this challenge, the hybrid quantum mechanics/molecular mechanics (QM/MM) method was developed, allowing one to treat only the most important part of the system on the quantum mechanical level and the remaining part on the classical level. To date, QM/MM simulations for biomolecular systems have been carried out almost exclusively on the electronic structure level, neglecting nuclear quantum effects (NQEs). Yet NQEs can play a major role in biomolecular systems [1]. Here, we present i-QI, a QM/MM client for the path integral molecular dynamics (PIMD) software i-PI [2, 3, 4]. i-QI allows for carrying out QM/MM simulations simultaneously, allowing for the inclusion of electronic as well as nuclear quantum effects. i-QI implements a new QM/MM scheme based on constraining potentials called QUASAR, which allows handling diffusive systems, such as biomolecules solvated in water solvent. The QUASAR method is suitable in particular when the properties of interest are equilibrium properties, such as the free energy of binding. i-QI is freely available and open source, and we demonstrate it on a test system. KW - QM-MM Methods KW - Path Integral KW - biomolecular systems KW - CADD KW - Free Energy KW - Binding Y1 - 2025 U6 - https://doi.org/10.1016/j.cpc.2025.109949 SN - 0010-4655 VL - 320 ER - TY - GEN A1 - Weiser, Martin A1 - Hubig, Michael A1 - Shanmugam Subramaniam, Jayant T1 - Reconstructing Ambient Temperature Changes in Death Time Estimation with a Bayesian Double-Exponential Approach T2 - Zenodo N2 - Code and data for the reconstruction of ambient temperature drop in time of death estimation We provide Octave code and temperature measurement data for - empirircally estimating thermal sensor likelihood - estimating time and amplitude of a single sudden ambient temperature drop from temperature measurement data in two thermally different compartments. Y1 - 2025 U6 - https://doi.org/10.5281/zenodo.17702240 ER - TY - JOUR A1 - Kempke, Nils-Christian A1 - Rehfeldt, Daniel A1 - Koch, Thorsten T1 - A Massively Parallel Interior-Point-Method for Arrowhead Linear Programs JF - SIAM Journal on Scientific Computing Y1 - 2025 ER - TY - JOUR A1 - Navayazdani, Esfandiar T1 - Ridge Regression on Riemannian Manifolds for Time-Series Prediction JF - Information Geometry Y1 - 2026 ER - TY - JOUR A1 - Sengupta, Agniva A1 - Bartoli, Adrien T1 - Convex Solutions to SfT and NRSfM under Algebraic Deformation Models JF - IEEE Transactions on Pattern Analysis and Machine Intelligence N2 - We present nonlinear formulations to Shape-from-Template (SfT) and Non-Rigid Structure-from-Motion (NRSfM) faithfully exploiting the isometric, conformal and equiareal deformation models. Existing work uses relaxations such as inextensibility or requires knowing the optic flow field around the correspondences, an impractical assumption. In contrast, the proposed formulations only require point correspondences and resolve all ambiguities using the notions of maximal depth and maximal isometry heuristics. We propose solution methods using Semi-Definite Programming (SDP) for all formulations. We show that straightforward SDP models conflict with the usual maximal depth heuristic and propose an adapted opposite-depth parameterisation demonstrating a lesser relaxation gap. Experimental results on many real-world benchmark datasets demonstrate superior accuracy over existing methods. Y1 - 2025 U6 - https://doi.org/10.1109/TPAMI.2025.3635039 ER - TY - JOUR A1 - Clausecker, Robert A1 - Lemire, Daniel A1 - Schintke, Florian T1 - Faster Positional‐Population Counts for AVX2, AVX‐512, and ASIMD JF - Concurrency and Computation: Practice and Experience Y1 - 2025 U6 - https://doi.org/10.1002/cpe.70435 SN - 1532-0626 VL - 37 IS - 27-28 PB - Wiley ER - TY - JOUR A1 - Biernacka, Katarzyna A1 - Castro, Leyla Jael A1 - Fritzsch, Bernadette A1 - Grossmann, Yves Vincent A1 - Reidelbach, Marco A1 - Struck, Alexander A1 - Lanza, Giacomo A1 - Walter, David A1 - Ulusoy, Inga T1 - From Vision to Implementation: Current Practices and Challenges of Software Management Plans in Research JF - 2nd Conference on Research Data Infrastructure (CoRDI) Y1 - 2025 U6 - https://doi.org/10.5281/zenodo.16735302 ER - TY - JOUR A1 - Fischer, Kristina A1 - Gerber, Anja A1 - Koprucki, Thomas A1 - Noback, Andres A1 - Reidelbach, Marco A1 - Schrade, Torsten A1 - Thiery, Florian T1 - Windows on Data: Federating Research Data with FAIR Digital Objects and Linked Open Data JF - 2nd Conference on Research Data Infrastructure (CoRDI) Y1 - 2025 U6 - https://doi.org/10.5281/zenodo.16736222 ER - TY - JOUR A1 - Marco, Reidelbach A1 - Marcus, Weber T1 - From Documentation to Discovery - MaRDMO and the MaRDI Ecosystem JF - 2nd Conference on Research Data Infrastructure (CoRDI) Y1 - 2025 U6 - https://doi.org/10.5281/zenodo.16735889 ER - TY - JOUR A1 - Schembera, Björn A1 - Wübbeling, Frank A1 - Shehu, Aurela A1 - Biedinger, Christine A1 - Fiedler, Jochen A1 - Reidelbach, Marco A1 - Schmidt, Burkhard A1 - Ferrer, Eloi A1 - Koprucki, Thomas T1 - FAIR Representation of Mathematical Research Data: MathModDB and MathAlgoDB as Knowledge Graphs for Mathematical Models and Numerical Algorithms JF - 2nd Conference on Research Data Infrastructure (CoRDI) Y1 - 2025 U6 - https://doi.org/10.5281/zenodo.16735911 ER - TY - JOUR A1 - Marco, Reidelbach T1 - MaRDMO: Future Gateway to FAIR Mathematical Data JF - E-Science-Tage 2025: Research Data Management: Challenges in a Changing World N2 - Mathematical research data plays a crucial role across scientific disciplines, yet its documentation and dissemination remain challenging due to the lack of standardized research data management practices. The MaRDMO Plugin addresses these challenges by integrating mathematical models, algorithms, and interdisciplinary workflows into the established framework of the Research Data Management Organiser (RDMO). Built on FAIR principles, MaRDMO enables structured documentation and retrieval of mathematical research data through guided questionnaires. It connects to multiple knowledge graphs, including MathModDB, MathAlgoDB, and the MaRDI Portal. Users can document and search for models, algorithms, and workflows via dynamic selection interfaces that also leverage other sources such as Wikidata. The plugin facilitates the export to the individual MaRDI services, ensuring data quality through automated validation. By embedding mathematical research data management into the widely adopted RDMO platform, MaRDMO represents a significant step toward making mathematical research data more findable, accessible, and reusable. Y1 - 2025 U6 - https://doi.org/10.11588/heibooks.1652.c23913 SP - 40 EP - 53 ER - TY - CHAP A1 - Schembera, Björn A1 - Wübbeling, Frank A1 - Kleikamp, Hendrik A1 - Schmidt, Burkhard A1 - Shehu, Aurela A1 - Reidelbach, Marco A1 - Biedinger, Christine A1 - Fiedler, Jochen A1 - Koprucki, Thomas A1 - Iglezakis, Dorothea A1 - Göddeke, Dominik T1 - Towards a Knowledge Graph for Models and Algorithms in Applied Mathematics T2 - Metadata and Semantic Research. MTSR 2024 N2 - Mathematical models and algorithms are an essential part of mathematical research data, as they are epistemically grounding numerical data. To make this research data FAIR, we present how two previously distinct ontologies, MathAlgoDB for algorithms and MathModDB for models, were merged and extended into a living knowledge graph as the key outcome. This was achieved by connecting the ontologies through computational tasks that correspond to algorithmic tasks. Moreover, we show how models and algorithms can be enriched with subject-specific metadata, such as matrix symmetry or model linearity, essential for defining workflows and determining suitable algorithms. Additionally, we propose controlled vocabularies to be added, along with a new class that differentiates base quantities from specific use case quantities. We illustrate the capabilities of the developed knowledge graph using two detailed examples from different application areas of applied mathematics, having already integrated over 250 research assets into the knowledge graph. Y1 - 2025 SN - 9783031819735 U6 - https://doi.org/10.1007/978-3-031-81974-2_8 SN - 1865-0929 VL - 2331 SP - 95 EP - 109 PB - Springer Nature Switzerland CY - Cham ER - TY - JOUR A1 - Sekulic, Ivan A1 - Schaible, Jonas A1 - Müller, Gabriel A1 - Plock, Matthias A1 - Burger, Sven A1 - Martínez-Lahuerta, Víctor José A1 - Gaaloul, Naceur A1 - Schneider, Philipp-Immanuel T1 - Physics-informed Bayesian optimization of expensive-to-evaluate black-box functions JF - Mach. Learn. Sci. Technol. N2 - Abstract Bayesian optimization with Gaussian process surrogates is a popular approach for optimizing expensive-to-evaluate functions in terms of time, energy, or computational resources. Typically, a Gaussian process models a scalar objective derived from observed data. However, in many real-world applications, the objective is a combination of multiple outputs from physical experiments or simulations. Converting these multidimensional observations into a single scalar can lead to information loss, slowing convergence and yielding suboptimal results. To address this, we propose to use multi-output Gaussian processes to learn the full vector of observations directly, before mapping them to the scalar objective via an inexpensive analytical function. This physics-informed approach retains more information from the underlying physical processes, improving surrogate model accuracy. As a result, the approach accelerates optimization and produces better final designs compared to standard implementations. Y1 - 2025 U6 - https://doi.org/10.1088/2632-2153/ae1f5f VL - 6 SP - 040503 PB - IOP Publishing ER - TY - CHAP A1 - Schummel, Philip A1 - Teichmann, Malte A1 - Gonnermann-Müller, Jana T1 - Specifying ten roles of using chatgpt in secondary education: a teacher´s perspective T2 - Proceedings of the European Conference on Information Systems N2 - ChatGPT can benefit education; however, challenges arise that demand competent and responsible usage. Competence development needs to start in schools, with the teacher being the gatekeeper for integrating it into the teaching process. Existing research suggests that schoolteachers lack specific guidance on integrating ChatGPT into teaching, as current discussions remain high-level and omit specific use cases throughout the entire teaching process. To guide an informed usage, this paper presents the results of a systematic literature review, analyzing 38 selected papers on investigating ChatGPT in schools. This selection is based on rigorously defined criteria, adhering to the PRISMA guidelines, initially screening 1075 papers. The central value of this paper is to aggregate research into ten roles for secondary teachers on using ChatGPT in schools, presenting corresponding use cases throughout the teaching process. Future research validates the ten roles, conducting interviews with teachers to present an exhaustive framework for integrating ChatGPT in schoolteacher practice. Y1 - 2025 UR - https://aisel.aisnet.org/ecis2025/education/education/3/ ER - TY - CHAP A1 - Kera, Hiroshi A1 - Pelleriti, Nico A1 - Ishihara, Yuki A1 - Zimmer, Max A1 - Pokutta, Sebastian T1 - Computational Algebra with Attention: Transformer Oracles for Border Basis Algorithms T2 - Proceedings of the Conference on Neural Information Processing Systems Y1 - 2025 ER - TY - CHAP A1 - Gonnermann-Müller, Jana A1 - Sahling, Kristina A1 - Haase, Jennifer T1 - Let's Be Realistic: AI-Recommender Use in a Complex Management Setting T2 - CHI EA '25: Proceedings of the Extended Abstracts of the CHI Conference on Human Factors in Computing Systems Y1 - 2025 U6 - https://doi.org/10.1145/3706599.3720131 ER - TY - JOUR A1 - Leins, Nicolas A1 - Gonnermann-Müller, Jana A1 - Teichmann, Malte T1 - Comparing head-mounted and handheld augmented reality for guided assembly JF - Journal on Multimodal User Interfaces Y1 - 2024 ER - TY - JOUR A1 - Gonnermann-Müller, Jana A1 - Krüger, Jule M. T1 - Unlocking Augmented Reality Learning Design Based on Evidence From Empirical Cognitive Load Studies—A Systematic Literature Review JF - Journal of Computer Assisted Learning Y1 - 2024 ER - TY - CHAP A1 - Głuch, Grzegorz A1 - Turan, Berkant A1 - Nagarajan, Sai Ganesh A1 - Pokutta, Sebastian T1 - The Good, the Bad and the Ugly: Meta-Analysis of Watermarks, Transferable Attacks and Adversarial Defenses T2 - Proceedings of the Conference on Neural Information Processing Systems Y1 - 2025 ER - TY - JOUR A1 - Hoen, Alexander A1 - Kamp, Dominik A1 - Gleixner, Ambros T1 - MIP-DD: Delta Debugging for Mixed-Integer Programming Solvers JF - INFORMS Journal on Computing Y1 - 2025 U6 - https://doi.org/10.1287/ijoc.2024.0844 ER - TY - CHAP A1 - Gonnermann-Müller, Jana A1 - Leins, Nicolas A1 - Gronau, Norbert A1 - Kosch, Thomas T1 - Value by design: Reducing Cognitive Load by Using Visual Guidance in Augmented RealityAn Eye-Tracking Study T2 - Proceedings of the International Conference on Information Systems Y1 - 2024 ER - TY - CHAP A1 - Gonnermann-Müller, Jana A1 - Brandenburger, Bonny A1 - Vladova, Gergana A1 - Gronau, Norbert T1 - To What Extent Can Individualisation in Terms of Different Types of Mode Improve Learning Outcomes and Learner Satisfaction? A Pre-study T2 - Proceedings of the Hawaii International Conference on System Sciences Y1 - 2023 ER - TY - JOUR A1 - Hendrych, Deborah A1 - Troppens, Hannah A1 - Besançon, Mathieu A1 - Pokutta, Sebastian T1 - Convex mixed-integer optimization with Frank-Wolfe methods JF - Mathematical Programming Computation Y1 - 2025 U6 - https://doi.org/10.1007/s12532-025-00288-w VL - 17 SP - 731 EP - 757 ER - TY - CHAP A1 - Kumano, Soichiro A1 - Kera, Hiroshi A1 - Yamasaki, Toshihiko T1 - Wide Two-Layer Networks can Learn from Adversarial Perturbations T2 - Proceedings of the Conference on Neural Information Processing Systems Y1 - 2024 ER - TY - CHAP A1 - Fukuda, Takuma A1 - Kera, Hiroshi A1 - Kawamoto, Kazuhiko T1 - Adapter Merging with Centroid Prototype Mapping for Scalable Class-Incremental Learning T2 - 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Y1 - 2025 U6 - https://doi.org/10.1109/CVPR52734.2025.00460 SP - 4884 EP - 4893 ER - TY - CHAP A1 - Kera, Hiroshi A1 - Ishihara, Yuki A1 - Kambe, Yuta A1 - Vaccon, Tristan A1 - Yokoyama, Kazuhiro T1 - Learning to Compute Gr\"obner Bases T2 - Proceedings of the Conference on Neural Information Processing Systems Y1 - 2024 ER - TY - GEN A1 - Ebert, Patricia A1 - Schlechte, Thomas A1 - Schwartz, Stephan T1 - Scheduling for German Road Inspectors N2 - For the yearly over 500,000 vehicle inspections of the German Federal Logistics and Mobility Office (BALM), crew rosters must be scheduled to efficiently achieve Germany's road inspection control targets. For that, we present a model to solve the respective duty scheduling and crew rostering problem in order to obtain duty rosters that comply with numerous legal regulations while maximizing the 'control success' to achieve the control targets. We formulate the Template Assignment Problem, which can be modelled as a large scale mixed-integer linear program. Here, feasible combinations of control topics are assigned to the duties using a hypergraph approach. The model is used in production by BALM, and we prove its effectiveness on a number of real-world instances. T3 - ZIB-Report - 25-13 KW - scheduling KW - rostering KW - mixed-integer linear program KW - crew scheduling KW - hypergraph Y1 - 2025 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-101999 SN - 1438-0064 ER - TY - GEN A1 - Betz, Fridtjof A1 - Binkowski, Felix A1 - Fischbach, Jan David A1 - Feldman, Nick A1 - Zschiedrich, Lin A1 - Rockstuhl, Carsten A1 - Koenderink, A. Femius A1 - Burger, Sven T1 - Uncovering Hidden Resonances in Non-Hermitian Systems with Scattering Thresholds (Laser Photonics Rev. 19(21)/2025) T2 - Laser Photonics Rev. Y1 - 2025 U6 - https://doi.org/10.1002/lpor.70534 VL - 19 IS - 21 SP - e70534 ER - TY - CHAP A1 - Manogue, Kevin A1 - Schang, Tomasz A1 - Kuş, Dilara A1 - Müller, Jonas A1 - Zachow, Stefan A1 - Sengupta, Agniva T1 - Generalizing Shape-from-Template to Topological Changes T2 - Smart Tools and Applications in Graphics - Eurographics Italian Chapter Conference N2 - Reconstructing the surfaces of deformable objects from correspondences between a 3D template and a 2D image is well studied under Shape-from-Template (SfT) methods; however, existing approaches break down when topological changes accompany the deformation. We propose a principled extension of SfT that enables reconstruction in the presence of such changes. Our approach is initialized with a classical SfT solution and iteratively adapts the template by partitioning its spatial domain so as to minimize an energy functional that jointly encodes physical plausibility and reprojection consistency. We demonstrate that the method robustly captures a wide range of practically relevant topological events including tears and cuts on bounded 2D surfaces, thereby establishing the first general framework for topological-change-aware SfT. Experiments on both synthetic and real data confirm that our approach consistently outperforms baseline methods. Y1 - 2025 SN - 978-3-03868-296-7 U6 - https://doi.org/10.2312/stag.20251322 PB - The Eurographics Association ER - TY - GEN A1 - Sekulic, Ivan A1 - Schaible, Jonas A1 - Müller, Gabriel A1 - Plock, Matthias A1 - Burger, Sven A1 - Martinez-Lahuerta, Victor J. A1 - Gaaloul, Naceur A1 - Schneider, Philipp-Immanuel T1 - Data publication for Physics-informed Bayesian optimization of expensive-to-evaluate black-box functions T2 - Zenodo Y1 - 2025 U6 - https://doi.org/10.5281/zenodo.16751507 ER - TY - THES A1 - Plock, Matthias T1 - On Methods for Bayesian Optimization of Least Squares Problems and Optimization of Nanophotonic Devices Y1 - 2025 U6 - https://doi.org/10.17169/refubium-48179 ER - TY - CHAP A1 - Zschiedrich, Lin A1 - Betz, Fridtjof A1 - Binkowski, Felix A1 - Kuen, Lilli A1 - Hammerschmidt, Martin A1 - Burger, Sven T1 - Efficient Photonic Component Analysis via AAA Rational Approximation T2 - 2025 International Conference on Numerical Simulation of Optoelectronic Devices (NUSOD) Y1 - 2025 U6 - https://doi.org/10.1109/NUSOD64393.2025.11199710 SP - 115 EP - 116 ER - TY - GEN A1 - Zakiyeva, Nazgul A1 - Petkovic, Milena T1 - Large-scale functional network time series model solved with mathematical programming approach T2 - Econometrics and Statistics N2 - A functional network autoregressive model is proposed for studying large-scale network time series observed at high temporal resolution. The model incorporates high-dimensional curves to capture both serial and cross-sectional dependence in large-scale network functional time series. Estimation of the model is approached using a Mixed Integer Optimization method. Simulation studies confirm the consistency of parameter and adjacency matrix estimation. The method is applied to data from a real-life natural gas supply network. Compared to alternative prediction models, the proposed model delivers more accurate day-ahead hourly out-of-sample forecasts of the gas inflows and outflows at most gas nodes. T3 - ZIB-Report - 25-17 Y1 - 2025 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-101936 ER - TY - JOUR A1 - Liu, Zichen A1 - Zhang, Wei A1 - Li, Tiejun T1 - Improving the Euclidean Diffusion Generation of Manifold Data by Mitigating Score Function Singularity JF - NeurIPS 2025 N2 - Euclidean diffusion models have achieved remarkable success in generative modeling across diverse domains, and they have been extended to manifold case in recent advances. Instead of explicitly utilizing the structure of special manifolds as studied in previous works, we investigate direct sampling of the Euclidean diffusion models for general manifold-constrained data in this paper. We reveal the multiscale singularity of the score function in the embedded space of manifold, which hinders the accuracy of diffusion-generated samples. We then present an elaborate theoretical analysis of the singularity structure of the score function by separating it along the tangential and normal directions of the manifold. To mitigate the singularity and improve the sampling accuracy, we propose two novel methods: (1) Niso-DM, which introduces non-isotropic noise along the normal direction to reduce scale discrepancies, and (2) Tango-DM, which trains only the tangential component of the score function using a tangential-only loss function. Numerical experiments demonstrate that our methods achieve superior performance on distributions over various manifolds with complex geometries. Y1 - 2025 ER - TY - GEN A1 - Shinano, Yuji A1 - Vigerske, Stefan T1 - Smoothie: Mixing the strongest MIP solvers to solve hard MIP instances on supercomputers - Phase I development N2 - Mixed-Integer Linear Programming (MIP) is applicable to such a wide range of real-world decision problems that the competition for the best code to solve such problems has lead to tremendous progress over the last decades. While current solvers can solve some of the problems that seemed completely out-of-reach just 10 years ago, there are always relevant MIP problems that currently cannot be solved. With the Smoothie solver we intend to solve extremely hard MIP problems by building on the many years that went into the development of several state-of-the-art MIP solvers and by utilizing some of the largest computing resources available. The high-level task parallelization framework UG (Ubiquity Generator) is used and extended by Smoothie to build a solver that uses large-scale parallelization to distribute the solution of a single MIP on a shared- or distributed-memory computing infrastructure, thereby employing several established MIP solvers simultaneously. For the first development phase, which is the topic of this report, both FICO Xpress and Gurobi are used in concurrent mode on a single machine, while information on incumbent solutions and explored branch-and-bound subtrees is exchanged. A dynamic restarting mechanism ensures that solver configurations are selected that promise most suitable for the MIP to be solved. We report on initial findings using this early version of Smoothie on unsolved problems from MIPLIB 2017. T3 - ZIB-Report - 25-18 KW - MIPLIB, MIP, UG, Parallelization Y1 - 2025 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-101906 SN - 1438-0064 ER - TY - JOUR A1 - Zakiyeva, Nazgul A1 - Petkovic, Milena T1 - Large-scale functional network time series model solved with mathematical programming approach JF - Econometrics and Statistics Y1 - 2025 U6 - https://doi.org/10.1016/j.ecosta.2025.10.001 SN - 2452-3062 PB - Elsevier BV ER - TY - CHAP A1 - Bortoletto, Enrico A1 - Lindner, Niels ED - Voigt, Guido ED - Fliedner, Malte ED - Haase, Knut ED - Brüggemann, Wolfgang ED - Hoberg, Kai ED - Meissner, Jörn T1 - Scaling and Rounding Periodic Event Scheduling Instances to Different Period Times T2 - Operations Research Proceedings 2023 N2 - The Periodic Event Scheduling Problem (PESP) is a notoriously hard combinatorial optimization problem, essential for the design of periodic timetables in public transportation. The coefficients of the integer variables in the standard mixed integer linear programming formulations of PESP are the period time, e.g., 60 for a horizon of one hour with a resolution of one minute. In many application scenarios, lines with different frequencies have to be scheduled, leading to period times with many divisors. It then seems natural to consider derived instances, where the period time is a divisor of the original one, thereby smaller, and bounds are scaled and rounded accordingly. To this end, we identify two rounding schemes: wide and tight. We then discuss the approximation performance of both strategies, in theory and practice. Y1 - 2025 U6 - https://doi.org/10.1007/978-3-031-58405-3_51 SP - 397 EP - 402 ER - TY - CHAP A1 - Lindner, Niels A1 - Liebchen, Christian ED - Voigt, Guido ED - Fliedner, Malte ED - Haase, Knut ED - Brüggemann, Wolfgang ED - Hoberg, Kai ED - Meissner, Jörn T1 - Incremental Heuristics for Periodic Timetabling T2 - Operations Research Proceedings 2023 N2 - We present incremental heuristics for the Periodic Event Scheduling Problem (PESP), the standard mathematical tool to optimize periodic timetables in public transport. The core of our method is to solve successively larger subinstances making use of previously found solutions. Introducing the technical notion of free stratifications, we formulate a general scheme for incremental heuristics for PESP. More practically, we use line and station information to create heuristics that add lines or stations one by one, and we evaluate these heuristics on instances of the benchmarking library PESPlib. This approach is indeed viable, and leads to new incumbent solutions for six PESPlib instances. Y1 - 2025 U6 - https://doi.org/10.1007/978-3-031-58405-3_59 SP - 461 EP - 467 PB - Springer Nature Switzerland CY - Cham ER - TY - JOUR A1 - Krishnadas, Anirudh A1 - Moshi, Maryam A1 - Quintana, Ramon Alain Miranda A1 - Fournier, Rene T1 - Geometric Features and a Neural Network Classifier for Detecting Melting-Like Transitions in Clusters. JF - AIP Publications - The Journal of Chemical Physics N2 - Melting-like transitions in clusters are normally identified by a peak in the heat capacity curve C(T ) at T = Tc. Computing C(T ) requires costly simulations with millions of steps. We discuss four easily calculated functions of temperature that help detect and characterize melting-like transitions. The first, f1 (or WU ), is the width of the potential energy distribution which shows an abrupt increase near Tc. The other three are statistics of the ordered set of N(N − 1)/2 interatomic distances rij: (i) f2 is a measure of dissimilarity to the lowest energy configuration, or global minimum; (ii) f3 is the effective number of rij’s found in a small interval centered around (r1 + r2)/2 where r1, r2 are the positions of the first two peaks in the pair distribution function; and (iii) f4 is a measure of non-uniformity in the distribution of the ri j’s. Numerical tests with empirical potentials that model three types of bonding (van der Waals, covalent, and metallic) show that f1, f2, f3, and f4 produce estimates for the middle of the melting region in general agreement with Tc. An Artificial Neural Network (ANN) classifier that takes, as inputs, f2, f3, and many variants of f4, is used to calculate the solid fraction FS(T) and find the solid-liquid coexistence region between freezing and melting temperatures, [Tf , Tm]. Inflection points in f3(T ) and FS(T ) are very sensitive indicators of phase transitions. Estimates of Tc obtained from them converge one to three orders of magnitude faster, in simulation time, than those obtained with C(T ). Y1 - 2025 ER - TY - JOUR A1 - Krishnadas, Anirudh A1 - Charron, Nicolas A1 - Fournier, Rene T1 - Melting Transitions in Small Aluminum Clusters Simulated with Energies Approaching DFT Accuracy. JF - ACS Publications- Journal of Chemical Theory and Computation N2 - We describe a computational framework for modelling melting-like transitions in atomic clusters that combines first-principles energy calculations, global optimization, and machine-learned interatomic potentials. A diverse set of configurations is generated by global optimization, and energies are calculated by Density Functional Theory. The energies are fitted to an accuracy of 10 meV/atom or better with an Allegro E(3)-equivariant neural network potential. The model allows efficient parallel tempering Monte Carlo simulations with near DFT-level accuracy. This methodology was validated by simulating Na_20 and comparing it to earlier experimental and computational results. We used it to study melting-like transitions in Al_n+ clusters (n=9 to 16), and Al_n and Al_n^- (n=12, 13,14). The simulated heat capacity of these clusters, in particular Al_16+, are in qualitative agreement with experiments. The melting point of Al_n+ clusters with n=11-16 are well above the bulk melting point (934 K). The closed-shell Al_13- species has an exceptionally high melting point, close to 2100 K. Y1 - 2025 ER - TY - JOUR A1 - Krishnadas, Anirudh A1 - Sinha, Nidhi A1 - Kirchner, Tom A1 - Antony, Bobby T1 - Calculation of electron-impact ionization of various benzene derivatives JF - IOP Publications- Physica Scripta N2 - Calculations of the electron-impact ionization cross-section of pyrene, anthracene, benzoyl chloride, benzophenone, and phthalonitrile are reported over a wide energy range. A comparison of theoretical models, viz. spherical complex optical potential (SCOP), pixel counting method (PCM), and the binary encounter Bethe (BEB) model is carried out. SCOP calculations provide the inelastic cross-section, and the ionization cross-section is extracted from it. This result is modified with PCM, a model previously applied to ion collisions which takes geometric screening corrections into account. The BEB model is used as an independent approach to calculate the ionization cross-section. It is demonstrated that all model results are in reasonable agreement with each other. Comparison is also made with other theoretical data where available. Y1 - 2024 U6 - https://doi.org/10.1088/1402-4896/ad640a VL - 99 IS - 9 SP - 095403 ER - TY - CHAP A1 - Löbel, Fabian A1 - Lindner, Niels ED - Sauer, Jonas ED - Schmidt, Marie T1 - A Geometric Approach to Integrated Periodic Timetabling and Passenger Routing T2 - 25th Symposium on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems (ATMOS 2025) N2 - We offer a geometric perspective on the problem of integrated periodic timetabling and passenger routing in public transport. Inside the space of periodic tensions, we single out those regions, where the same set of paths provides shortest passenger routes. This results in a polyhedral subdivision, which we combine with the known decomposition by polytropes. On each maximal region of the common refinement, the integrated problem is solvable in polynomial time. We transform these insights into a new geometry-driven primal heuristic, integrated tropical neighborhood search (ITNS). Computationally, we compare implementations of ITNS and the integrated (restricted) modulo network simplex algorithm on the TimPassLib benchmark set, and contribute better solutions in terms of total travel time for all but one of the twenty-five instances for which a proven optimal solution is not yet known. KW - Periodic Timetabling KW - Passenger Routing KW - Polyhedral Complexes Y1 - 2025 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0030-drops-247580 SN - 978-3-95977-404-8 SN - 2190-6807 VL - 137 SP - 2:1 EP - 2:19 PB - Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik CY - Dagstuhl, Germany ER - TY - CHAP A1 - Ghannam, Mohammed A1 - Gleixner, Ambros T1 - Hybrid Genetic Search for Dynamic Vehicle Routing with Time Windows T2 - Operations Research Proceedings 2023. OR 2023 Y1 - 2025 U6 - https://doi.org/10.1007/978-3-031-58405-3_35 ER - TY - JOUR A1 - Okafornta, Chukwuebuka William A1 - Farhadifar, Reza A1 - Fabig, Gunar A1 - Wu, Hai-Yin A1 - Köckert, Maria A1 - Vogel, Martin A1 - Baum, Daniel A1 - Haase, Robert A1 - Shelley, Michael J. A1 - Needleman, Daniel J. A1 - Müller-Reichert, Thomas T1 - Cell size reduction scales spindle elongation but not chromosome segregation in C. elegans JF - bioRxiv N2 - How embryos adapt their internal cellular machinery to reductions in cell size during development remains a fundamental question in cell biology. Here, we use high-resolution lattice light-sheet fluorescence microscopy and automated image analysis to quantify lineage-resolved mitotic spindle and chromosome segregation dynamics from the 2– to 64–cell stages in Caenorhabditis elegans embryos. While spindle length scales with cell size across both wild-type and size-perturbed embryos, chromosome segregation dynamics remain largely invariant, suggesting that distinct mechanisms govern these mitotic processes. Combining femtosecond laser ablation with large-scale electron tomography, we find that central spindle microtubules mediate chromosome segregation dynamics and remain uncoupled from cell size across all stages of early development. In contrast, spindle elongation is driven by cortically anchored motor proteins and astral microtubules, rendering it sensitive to cell size. Incorporating these experimental results into an extended stoichiometric model for both the spindle and chromosomes, we find that allowing only cell size and microtubule catastrophe rates to vary reproduces elongation dynamics across development. The same model also accounts for centrosome separation and pronuclear positioning in the one-cell C. elegans embryo, spindle-length scaling across nematode species spanning ~100 million years of divergence, and spindle rotation in human cells. Thus, a unified stoichiometric framework provides a predictive, mechanistic account of spindle and nuclear dynamics across scales and species. Y1 - 2025 U6 - https://doi.org/10.1101/2025.10.13.681585 ER - TY - JOUR A1 - Riedmüller, Stephanie A1 - Lindner, Niels T1 - Column Generation for Periodic Timetabling N2 - Periodic timetabling for public transportation networks is typically modelled as a Periodic Event Scheduling Problem (PESP). Solving instances of the benchmark library PESPlib to optimality continues to pose a challenge. As a further approach towards this goal, we remodel the problem by a time discretization of the underlying graph and consider arc-based as well as path-based integer programming formulations. For the path-based case, we use cycles on the graph expansion of the operational lines as variables and, therefore, include more of the problem inherent structure into the model. A consequence is the validity of several known inequalities and a lower bound on the LP-relaxation, that is the best known to date. As an extension we integrate passenger routing into the new model. The proposed models have an advantage in the linear programming relaxation, on the one hand, but have an increased problem size, on the other hand. We define the corresponding pricing problems for the use of column generation to handle the size. Both models are practically tested on different problem instances. Y1 - 2025 ER - TY - JOUR A1 - Gonnermann-Müller, Jana A1 - Haase, Jennifer A1 - Fackeldey, Konstantin A1 - Pokutta, Sebastian T1 - FACET: Teacher-Centred LLM-Based Multi-Agent Systems-Towards Personalized Educational Worksheets N2 - The increasing heterogeneity of student populations poses significant challenges for teachers, particularly in mathematics education, where cognitive, motivational, and emotional differences strongly influence learning outcomes. While AI-driven personalization tools have emerged, most remain performance-focused, offering limited support for teachers and neglecting broader pedagogical needs. This paper presents the FACET framework, a teacher-facing, large language model (LLM)-based multi-agent system designed to generate individualized classroom materials that integrate both cognitive and motivational dimensions of learner profiles. The framework comprises three specialized agents: (1) learner agents that simulate diverse profiles incorporating topic proficiency and intrinsic motivation, (2) a teacher agent that adapts instructional content according to didactical principles, and (3) an evaluator agent that provides automated quality assurance. We tested the system using authentic grade 8 mathematics curriculum content and evaluated its feasibility through a) automated agent-based assessment of output quality and b) exploratory feedback from K-12 in-service teachers. Results from ten internal evaluations highlighted high stability and alignment between generated materials and learner profiles, and teacher feedback particularly highlighted structure and suitability of tasks. The findings demonstrate the potential of multi-agent LLM architectures to provide scalable, context-aware personalization in heterogeneous classroom settings, and outline directions for extending the framework to richer learner profiles and real-world classroom trials. Y1 - 2025 ER - TY - GEN ED - Fackeldey, Konstantin ED - Kannan, Aswin ED - Pokutta, Sebastian ED - Sharma, Kartikey ED - Walter, Daniel ED - Walter, Andrea ED - Weiser, Martin T1 - Mathematical Optimization for Machine Learning N2 - Mathematical optimization and machine learning are closely related. This proceedings volume of the Thematic Einstein Semester 2023 of the Berlin Mathematics Research Center MATH+ collects recent progress on their interplay in topics such as discrete optimization, nonlinear programming, optimal control, first-order methods, multilevel optimization, machine learning in optimization, physics-informed learning, and fairness in machine learning. Y1 - 2025 SN - 9783111376776 U6 - https://doi.org/10.1515/9783111376776 PB - De Gruyter ER - TY - JOUR A1 - Andrés Arcones, Daniel A1 - Weiser, Martin A1 - Koutsourelakis, Phaedon-Stelios A1 - Unger, Jörg F. T1 - Embedded Model Form Uncertainty Quantification with Measurement Noise for Bayesian Model Calibration JF - Data-Centric Engineering N2 - A key factor in ensuring the accuracy of computer simulations that model physical systems is the proper calibration of their parameters based on real-world observations or experimental data. Inevitably, uncertainties arise, and Bayesian methods provide a robust framework for quantifying and propagating these uncertainties to model predictions. Nevertheless, Bayesian methods paired with inexact models usually produce predictions unable to represent the observed datapoints. Additionally, the quantified uncertainties of these overconfident models cannot be propagated to other Quantities of Interest (QoIs) reliably. A promising solution involves embedding a model inadequacy term in the inference parameters, allowing the quantified model form uncertainty to influence non-observed QoIs. This paper introduces a more interpretable framework for embedding the model inadequacy compared to existing methods. To overcome the limitations of current approaches, we adapt the existing likelihood models to properly account for noise in the measurements and propose two new formulations designed to address their shortcomings. Moreover, we evaluate the performance of this inadequacy-embedding approach in the presence of discrepancies between measurements and model predictions, including noise and outliers. Particular attention is given to how the uncertainty associated with the model inadequacy term propagates to the QoIs, enabling a more comprehensive statistical analysis of prediction’s reliability. Finally, the proposed approach is applied to estimate the uncertainty in the predicted heat flux from a transient thermal simulation using temperature bservations. Y1 - 2026 U6 - https://doi.org/10.1017/dce.2025.10035 VL - 7 ER - TY - JOUR A1 - Agarwal, A. A1 - Greve, Nico A1 - Reinefeld, Alexander A1 - Schintke, Florian A1 - et al., T1 - Performance of the prototype Silicon Tracking System of the CBM experiment tested with heavy-ion beams at SIS18 JF - Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment Y1 - 2025 U6 - https://doi.org/10.1016/j.nima.2025.171059 SN - 0168-9002 N1 - This paper has 343 authors. For the complete list of authors use the DOI, please. VL - 1082 SP - 171059 PB - Elsevier BV ER - TY - JOUR A1 - Xie, Kunpeng A1 - Gruber, Lennart Johannes A1 - Crampen, Martin A1 - Li, Yao A1 - Ferreira, André A1 - Tappeiner, Elias A1 - Gillot, Maxime A1 - Schepers, Jan A1 - Xu, Jiangchang A1 - Pankert, Tobias A1 - Beyer, Michel A1 - Shahamiri, Negar A1 - ten Brink, Reinier A1 - Dot, Gauthier A1 - Weschke, Charlotte A1 - van Nistelrooij, Niels A1 - Verhelst, Pieter-Jan A1 - Guo, Yan A1 - Xu, Zhibin A1 - Bienzeisler, Jonas A1 - Rashad, Ashkan A1 - Flügge, Tabea A1 - Cotton, Ross A1 - Vinayahalingam, Shankeeth A1 - Ilesan, Robert A1 - Raith, Stefan A1 - Madsen, Dennis A1 - Seibold, Constantin A1 - Xi, Tong A1 - Bergé, Stefaan A1 - Nebelung, Sven A1 - Kodym, Oldřich A1 - Sundqvist, Osku A1 - Thieringer, Florian A1 - Lamecker, Hans A1 - Coppens, Antoine A1 - Potrusil, Thomas A1 - Kraeima, Joep A1 - Witjes, Max A1 - Wu, Guomin A1 - Chen, Xiaojun A1 - Lambrechts, Adriaan A1 - Cevidanes, Lucia H Soares A1 - Zachow, Stefan A1 - Hermans, Alexander A1 - Truhn, Daniel A1 - Alves, Victor A1 - Egger, Jan A1 - Röhrig, Rainer A1 - Hölzle, Frank A1 - Puladi, Behrus T1 - Beyond Benchmarks: Towards Robust Artificial Intelligence Bone Segmentation in Socio-Technical Systems JF - Expert Systems With Applications N2 - Despite the advances in automated medical image segmentation, AI models still underperform in various clinical settings, challenging real-world integration. In this multicenter evaluation, we analyzed 20 state-of-the-art mandibular segmentation models across 19,218 segmentations of 1,000 clinically resampled CT/CBCT scans. We show that segmentation accuracy varies by up to 25% depending on socio-technical factors such as voxel size, bone orientation, and patient conditions such as osteosynthesis or pathology. Higher sharpness, isotropic smaller voxels, and neutral orientation significantly improved results, while metallic osteosynthesis and anatomical complexity led to significant degradation. Our findings challenge the common view of AI models as “plug-and-play” tools and suggest evidence-based optimization recommendations for both clinicians and developers. This will in turn boost the integration of AI segmentation tools in routine healthcare. Y1 - 2025 UR - https://www.medrxiv.org/content/10.1101/2025.06.11.25329022v1 U6 - https://doi.org/10.1016/j.eswa.2025.130031 VL - 299 IS - Part D ER - TY - THES A1 - Amiranashvili, Tamaz T1 - Universal and Expressive Statistical Shape Models for Anatomical Structures N2 - Form and function of anatomical structures are intimately linked. Pathological changes in form can be associated with the loss of function. For example, diseases often cause characteristic shape changes, making shape a sensitive structural biomarker for medical diagnosis. If the link between form and function is causal, correcting a pathological shape can even restore the healthy function of an organ. Accurate shape reconstruction is then crucial for effective, patient-specific treatment planning. This demonstrates the importance of shape in clinical interventions and its potential to improve overall patient outcomes. Statistical shape models are computational methods that capture shape variations in a given population and enable precise shape analysis and generation. We focus on two key properties of a good statistical shape model. First, it should be easy to construct, and second, it should accurately represent the underlying shape distribution. Established existing approaches can only be constructed from surfaces with pre-defined dense correspondence. Such correspondence is tedious to obtain, can introduce undesired biases, and prevents training on partial or sparse observations. While correspondence-free methods exist, they struggle to accurately capture shape distributions with intricate details and large variations. In this thesis, we develop shape models that simplify training and improve accuracy over state-of-the-art. To achieve these goals, we build on approximately diffeomorphic neural deformations and implicit neural representations. First, our proposed methods are trainable on correspondence-free surfaces and even partial segmentations with large slice distances. This makes them universal since they can be trained on heterogeneous data, enabling scalability to large datasets and avoiding potential biases of pre-defined correspondence. Second, our methods are highly expressive, accurately capturing intricate shape details in complex distributions. We evaluate effectiveness of our models on multiple anatomical structures, outperforming established baselines in both generative and discriminative settings. Y1 - 2025 UR - https://mediatum.ub.tum.de/?id=1776778 UR - https://nbn-resolving.org/urn:nbn:de:bvb:91-diss-20250717-1776778-0-4 ER - TY - THES A1 - Özmen, Mahmut T1 - Medical Image registration with B-Spline Transformation N2 - Medical image registration is an important task in various applications such as temporal analysis of medical images from a single modality (where structural or anatomical changes over time are studied), multi-modal image fusion (in which data acquired from different imaging devices or protocols is fused to help with diagnosis and treatment planning), or atlas matching (where uses normal anatomical variability). Establishing functional and/or spatial anatomical correspondences between different image acquisitions is the goal of image registration. As rigid alignment might be insufficient to describe variations of images in certain applications where deformations play a role, we should rely on non-rigid image registration. Non-rigid registration methods are capable of aligning images where correspondence cannot be achieved without localized deformations and can therefore better accommodate anatomical, physiological, and pathological variability between patients. Elastix image registration software is employed in this thesis to conduct our experiments. In this thesis, we use interpolation-based B-spline transformation models that parameterize a Free-Form Deformation Field. B-splines interpolate the dense deformation field from a given set of control points. B-spline transformation model assumes the smoothness and continuity of the deformation field on the image. However, when aligning two images that contain sliding contact, B-spline transformation leads to an inaccurate registration result, and the deformation field is not always smooth or the deformation field is always discontinuous, but not necessarily everywhere. When the knee is bent, the sliding contact occurs at the knee cartilage and around the capsular tissues. Different types of discontinuities in the overall deformation field may occur when sliding contact occurs. Imposing smoothness and continuity in the registration will generate artifacts in the consequent deformations, as well as any quantities derived from it. To handle the artifacts in the resulting deformations, one can use a regularizer or implement the sliding motion in the transformation model. Hence, in this thesis, we compare the B-spline registration, B-spline with Transform Rigidity Penalty, B-spline with Distance Rigidity Penalty, and the sliding motion with B-splines. B-spline with Transform Rigidity Penalty (TRP) and B-spline with Distance Rigidity Penalty (DPRP) models use a regularizer to handle the sliding motion. However, the sliding Motion with B-splines model handles sliding motion in the transformation model itself. The chosen registration parameters and registration algorithm were assessed first using three synthetic datasets. In a second validation, Knee MRI images that belong to two different patient datasets are registered on a temporal sequence of each patient. Registration accuracy was assessed based on the corresponding segmented bones and neighboring soft tissue. Compared to B-spline approach, the B-spline with TRP, B-spline with DPRP, and the sliding motion with B-splines gave deformation fields that represented more plausible image correspondences, while giving rise to comparable registration accuracy. In one dataset, the B-spline with TRP and B-spline with DPRP models reflected the deformation better than other approaches. However, sliding motion with B-splines model outperformed all other approaches in the other dataset. Y1 - 2022 ER - TY - THES A1 - Kasravi, Javad T1 - Completion of Missing Parts in Medical Images Using Generative Adversarial Networks and Transformers N2 - Image inpainting (image completion) is a technique to remove undesirable elements or fill missing (repair damaged) sections in an image [1]. In medical images, inpainting can be applied for various applications. Issues like image artifacts (e.g., metal artifacts in CT and MRI) can introduce alterations to medical images. In simpler terms, these artifacts may introduce unwanted elements or anomalies into the medical images. These unwanted elements can be replaced with appropriate intensity in the images. In the context of defect reconstruction, which involves repairing skull defects, medical images are taken. To design an implant for a defective skull, image inpainting can be applied to an image containing the defective skull. This process generates an image with skull tissue, which can then be utilized for implant design. Additionally, in situations involving a restricted field of view, such as when only accessing the lower part of the full-body MRI, predicting the missing information (e.g., the upper part of the full-body MRI) can be beneficial for subsequent tasks, such as extracting bone structures for 3D shape modeling studies. Another application of inpainting is observed during actual stereo-fluoroscopic X-ray measurements. In these measurements, the calibration grid, which is beneficial for correcting distortions, introduces unwanted black dots into the image content. These black dots need to be removed from the image and replaced with the appropriate intensity. This thesis extends an existing architecture named Mask-aware Transformer (MAT) to fill in missing information in medical scans. The proposed method utilizes Generative Adversarial Networks and a Mask-aware Swin Transformer. Various adjustments, including changes in data type, model architecture, and adaptation for multinode training, have been implemented to address missing information in three different datasets. In one of our datasets, where black dots disrupt the image content, we applied our method to fill in the missing information. The results indicate that our approach outperforms other model-driven methods. In the second dataset, we employed the German National Cohort full-body MRI dataset to train a network for predicting the upper part of the full-body MRI. The outcomes indicate that the generated 2D slices exhibit realism and perform well in both pixel-wise and perceptual metrics. However, when these 2D slices are stacked into a 3D volume, inconsistencies between different slices become apparent. Furthermore, when we compare our 2D-aware image inpainting method to 3D inpainting algorithms used in the Brain Tumor Segmentation 2023 challenge, the results confirm the findings of the earlier experiment. The 2D slices produced are realistic, but there is inconsistency between them. In summary, theMAT demonstrates its ability to capture global dependencies by utilizing an adapted Swin Transformer for X-ray images, inherently 2D images. The results for 2D slices from a 3D MRI volume are also reasonable because the network is trained specifically on 2D slices. However, due to the lack of awareness regarding this architecture in the third dimension, inconsistencies between slices are observed. KW - Image Inpainting KW - Generative Adversarial Networks KW - Swin Transformer KW - Mask-Tware Transformer Y1 - 2024 ER - TY - THES A1 - Mengüllüoglu, Ecegül T1 - Analysis of the NORA Anaphylaxis Database: Towards Identifying Severity Factors using Machine Learning N2 - Anaphylaxis is a severe allergic reaction, which occurs instantaneously with potential life-threatening symptoms by affecting different organ systems. The reaction could be triggered by an exposure to various types of allergens including foods, drugs, insects, venom and others. The European Anaphylaxis Registry (NORA) is a large database gathered to collect information on anaphylaxis cases through the allergy centers in European and other countries. This questionnaire-based survey data was designed in a way to provide information on patients’ demographic characteristics, eliciting allergens, symptoms, reaction severity, emergency treatment and long-term management of the disease and aims to improve the knowledge on reactions and provide treatment methods to affected patients. Unfortunately, the data is partly incomplete. However, incomplete data hinders some algorithms to work properly and may lead to misleading analysis results. Another issue regarding the dataset is, it contains a large number of variables, which introduces complex linear or/and non-linear relationships to the data and makes it difficult to reveal those relationships, which makes analyzing and driving conclusions from the data a challenging task. The problems mentioned above, leads us to the questions asked in this thesis. The fact that the data is incomplete raises the main question, which is whether and how it is possible to impute the missing data and how this affects subsequent steps, such as classification. The size and the complexity of the data raises the second question: Are there certain relationships within variables that can be used to predict the severity of the disease? In case there is, are there any potential augmentation factors, also called cofactors in the dataset that may increase the severity of a reaction to an allergen that would normally not occur when the cofactor does not exist? As explained, the goal of this thesis is to find answers to the questions indicated above. For that reason, initially, we searched for a suitable method for data imputation and compared performances of two different tree-based multiple imputation methods on the dataset. After obtaining multiple complete datasets, we tried to predict the severity of anaphylaxis from the dataset to determine hidden risk factors behind anaphylaxis using high power gradient boosting methods. It is concluded that, multiple imputation with chained equations (MICE) based on classification and regression trees can be useful for imputing NORA dataset. However, we could not detect enough associations between study variables and anaphylaxis severity in the data, to predict anaphylaxis severity from the dataset. Y1 - 2022 ER - TY - THES A1 - Lukanek, Sandra T1 - 3D Reconstruction of the Mandible from Panoramic Dental X-Ray Projections N2 - Panoramic radiography is a popular imaging technique in dental medicine. It omprehensively represents the entire jaw, including the mandible, maxilla, and teeth in a compact two-dimensional (2D) image. It offers several advantages over three-dimensional (3D) imaging, such as Cone Beam Computed Tomography (CBCT), including cost-effectiveness and reduced radiation exposure. However, its major drawback lies in its ambiguities in spatial distribution, leading to inaccuracies in linear measurements due to the dependency on the parameters used for generating the image. In this thesis, we propose a methodology for reconstructing a 3D anatomy of the mandible from a given 2D panoramic X-ray image. Our approach involves extracting information from the 2D scan, considering the projection geometry, and aiming to achieve a realistic spatial distribution. The methodology builds upon an existing intensity-based 2D-3D registration algorithm, which is modified to accommodate the unique geometric parameters of panoramic radiography. By employing a virtual X-ray setup, we generate a panoramic X-ray from a Statistical Shape and Intensity Model (SSIM) derived from 25 tetrahedral meshes. The intensity information for each mesh is extracted from CBCT scans representing jaw anatomies. To reconstruct the true mandible, the model is iteratively optimized by comparing the simulated X-ray with the original panoramic X-ray. We evaluate and establish the setup using synthetically generated panoramic X-rays from multiple mandibles obtained from CBCT scans. After analyzing the feasibility of this method, we conclude that 2D-3D registration for panoramic X-rays presents a challenging task, as the ambiguity of the 2D projection involved in panoramic X-ray imaging can result in varied 3D shape representations. Y1 - 2024 ER - TY - JOUR A1 - Sato, Mako A1 - Koch, Thorsten A1 - Le, Thi Thai T1 - Influence of frictional drag on Kelvin-Helmholtz instability in viscous fluids JF - Physics of Fluids N2 - The Kelvin-Helmholtz instability arises at the interface between two fluid layers with a tangential velocity discontinuity, significantly impacting the safety of systems in industrial applications. Despite extensive studies, many theoretical analyses neglect viscosity and frictional drag due to the complexity of solving the dispersion equation. However, including these effects is crucial, as viscosity alters the growth rate of the instability, while frictional drag influences energy dissipation and momentum transfer. In this study, these effects are incorporated into both finite and infinite flows. The assumption of a potential flow combined with a dissipation method is employed to derive the dispersion equation, providing a more tractable approach than direct calculation methods. The results indicate that, in the case of infinite flows, the frictional drag suppresses the growth of the instability for long waves (small wavenumber k) but enhances it for short waves (large k). In contrast, the surface tension dominates, while the frictional drag only slightly affects the growth rate in the case of finite flows bounded by solid walls. Y1 - 2026 U6 - https://doi.org/10.1063/5.0306177 VL - 38 IS - 1 PB - AIP Publishing CY - Physics of Fluids ER - TY - JOUR A1 - Hu, Chenhao A1 - Tutika, Ravi A1 - Deng, Zhifei A1 - Jia, Zian A1 - Chen, Liuni A1 - Chen, Hongshun A1 - Geng, Yang A1 - Xiao, Xianghui A1 - Shevchenko, Pavel D. A1 - Pierre, Christoph A1 - Weaver, James C. A1 - Baum, Daniel A1 - Bartlett, Michael D. A1 - Li, Ling T1 - Mineralized sclerites in the gorgonian coral Leptogorgia chilensis as a natural jamming system JF - PNAS Y1 - 2025 U6 - https://doi.org/10.1073/pnas.2504541122 VL - 122 IS - 44 ER - TY - CHAP A1 - Jäger, Klaus A1 - Mandal, Jyotirmoy A1 - Rand, Barry P. A1 - Meggers, Forrest A1 - Becker, Christiane T1 - Thermal Management of Solar Modules with Infrared-Antireflective Coatings T2 - Advanced Photonics Congress Y1 - 2025 U6 - https://doi.org/10.1364/SOLITH.2025.SM4E.2 VL - SOLITH SP - SM4E.2 ER - TY - JOUR A1 - Zittel, Janina A1 - Petkovic, Milena A1 - Einck, Mathias T1 - Forecasting Hourly Gas Flows JF - Foresight - The International Journal of Applied Forecasting Y1 - 2025 UR - https://econpapers.repec.org/RePEc:for:ijafaa:y:2025:i:79:p:40-44 VL - 79 SP - 40 EP - 44 ER - TY - JOUR A1 - Kießling, David A1 - Leyffer, Sven A1 - Vanaret, Charlie T1 - A Unified Funnel Restoration SQP Algorithm JF - Mathematical Programming B N2 - We consider nonlinearly constrained optimization problems and discuss a generic double-loop framework consisting of basic algorithmic ingredients that unifies a broad range of nonlinear optimization solvers. This framework has been implemented in the open-source solver Uno, a Swiss Army knife-like C++ optimization framework that unifies many nonlinearly constrained nonconvex optimization solvers. We illustrate the framework with a sequential quadratic programming (SQP) algorithm that maintains an acceptable upper bound on the constraint violation, called a funnel, that is monotonically decreased to control the feasibility of the iterates. Infeasible quadratic subproblems are handled by a feasibility restoration strategy. Globalization is controlled by a line search or a trust-region method. We prove global convergence of the trust-region funnel SQP method, building on known results from filter methods. We implement the algorithm in Uno, and we provide extensive test results for the trust-region line-search funnel SQP on small CUTEst instances. Y1 - 2025 U6 - https://doi.org/10.1007/s10107-025-02284-3 ER - TY - JOUR A1 - Bartoli, Adrien A1 - Sengupta, Agniva T1 - Camera Pose in SfT and NRSfM under Isometric and Weaker Deformation Models JF - Computer Vision and Image Understanding N2 - Camera pose is a very natural concept in 3D vision in the rigid setting. It is however much more difficult to work with in deformable settings. Consequently, numerous deformable reconstruction methods simply ignore camera pose. We analyse the concept of pose in deformable settings and prove that it is unconstrained with the existing formulations, properly justifying the existing pose-less methods reconstructing structure only. We explain this result intuitively by the impossibility to define an intrinsic coordinate frame to a general deforming object. The proposed analysis uses the isometric deformation model and extends to the weaker models including conformality and equiareality. We propose a novel prior to rescue camera pose estimation in deformable settings, which attributes the deforming object’s dominant rigid-body motion to the camera. We show that adding this prior to any existing formulation fully constrains camera pose and leads to elegant two-step solution methods, involving deformable structure reconstruction using a base method in the first step, and absolute orientation or Procrustes analysis in the second step. We derive the proposed approach for the template-based and template-less settings, respectively implemented using Shape-from-Template (SfT) and Non-Rigid Structure-from-Motion (NRSfM) as base methods, and validate them experimentally, showing that the computed pose is qualitatively and quantitatively plausible. Y1 - 2025 U6 - https://doi.org/10.1016/j.cviu.2025.104488 VL - 261 ER - TY - GEN A1 - Dörffel, Tom A1 - Mikula, Natalia A1 - Schielicke, Lisa A1 - Kiszler, Theresa A1 - Faranda, Davide A1 - Debrulle, Bérengère A1 - Vercauteren, Nikki T1 - Characterizing inertial and diabatic energy transfers in tropical cyclones: Data N2 - The multiscale organization of tropical cyclones (TCs) is investigated by means of three-dimensional data produced by the atmospheric model CM1. We provide a sample dataset in NetCDF format covering the TC evolution from incipient to mature under the influence of externally imposed wind shear. This dataset serves as a testbed for applying energy-tranfer analyses based on the Duchon-Robert index as well as diabatic transfer based on an asymptotic theory on TCs. Y1 - 2025 U6 - https://doi.org/10.12752/10135 ER - TY - CHAP A1 - Schneider, Philipp-Immanuel A1 - Sekulic, Ivan A1 - Plock, Matthias A1 - Hammerschmidt, Martin A1 - Rodt, Sven A1 - Reitzenstein, Stephan A1 - Burger, Sven T1 - Physics-informed Bayesian optimization of nanophotonic devices T2 - Advanced Photonics Congress Y1 - 2025 U6 - https://doi.org/10.1364/IPRSN.2025.ITu1A.2 VL - IPRSN SP - ITu1A.2 ER - TY - JOUR A1 - Reuss, Joana A1 - Macdonald, Jan A1 - Becker, Simon A1 - Schultka, Konrad A1 - Richter, Lorenz A1 - Körner, Marco T1 - Meta-learning For Few-Shot Time Series Crop Type Classification: A Benchmark On The EuroCropsML Dataset N2 - Spatial imbalances in crop type data pose significant challenges for accurate classification in remote sensing applications. Algorithms aiming at transferring knowledge from data-rich to data-scarce tasks have thus surged in popularity. However, despite their effectiveness in previous evaluations, their performance in challenging real-world applications is unclear and needs to be evaluated. This study benchmarks transfer learning and several meta-learning algorithms, including (First-Order) Model-Agnostic Meta-Learning ((FO)-MAML), Almost No Inner Loop (ANIL), and Task-Informed Meta-Learning (TIML), on the real-world EuroCropsML time series dataset, which combines farmer-reported crop data with Sentinel-2 satellite observations from Estonia, Latvia, and Portugal. Our findings indicate that MAML-based meta-learning algorithms achieve slightly higher accuracy compared to simpler transfer learning methods when applied to crop type classification tasks in Estonia after pre-training on data from Latvia. However, this improvement comes at the cost of increased computational demands and training time. Moreover, we find that the transfer of knowledge between geographically disparate regions, such as Estonia and Portugal, poses significant challenges to all investigated algorithms. These insights underscore the trade-offs between accuracy and computational resource requirements in selecting machine learning methods for real-world crop type classification tasks and highlight the difficulties of transferring knowledge between different regions of the Earth. To facilitate future research in this domain, we present the first comprehensive benchmark for evaluating transfer and meta-learning methods for crop type classification under real-world conditions. The corresponding code is publicly available at this https URL. Y1 - 2025 ER - TY - CHAP A1 - Chen, Junhua A1 - Richter, Lorenz A1 - Berner, Julius A1 - Blessing, Denis A1 - Neumann, Gerhard A1 - Anandkumar, Anima T1 - Sequential Controlled Langevin Diffusions T2 - 13th International Conference on Learning Representations (ICLR 2025) N2 - An effective approach for sampling from unnormalized densities is based on the idea of gradually transporting samples from an easy prior to the complicated target distribution. Two popular methods are (1) Sequential Monte Carlo (SMC), where the transport is performed through successive annealed densities via prescribed Markov chains and resampling steps, and (2) recently developed diffusion-based sampling methods, where a learned dynamical transport is used. Despite the common goal, both approaches have different, often complementary, advantages and drawbacks. The resampling steps in SMC allow focusing on promising regions of the space, often leading to robust performance. While the algorithm enjoys asymptotic guarantees, the lack of flexible, learnable transitions can lead to slow convergence. On the other hand, diffusion-based samplers are learned and can potentially better adapt themselves to the target at hand, yet often suffer from training instabilities. In this work, we present a principled framework for combining SMC with diffusion-based samplers by viewing both methods in continuous time and considering measures on path space. This culminates in the new Sequential Controlled Langevin Diffusion (SCLD) sampling method, which is able to utilize the benefits of both methods and reaches improved performance on multiple benchmark problems, in many cases using only 10% of the training budget of previous diffusion-based samplers. Y1 - 2025 UR - https://openreview.net/pdf?id=dImD2sgy86 ER - TY - JOUR A1 - Berner, Julius A1 - Richter, Lorenz A1 - Sendera, Marcin A1 - Rector-Brooks, Jarrid A1 - Malkin, Nikolay T1 - From discrete-time policies to continuous-time diffusion samplers: Asymptotic equivalences and faster training N2 - We study the problem of training neural stochastic differential equations, or diffusion models, to sample from a Boltzmann distribution without access to target samples. Existing methods for training such models enforce time-reversal of the generative and noising processes, using either differentiable simulation or off-policy reinforcement learning (RL). We prove equivalences between families of objectives in the limit of infinitesimal discretization steps, linking entropic RL methods (GFlowNets) with continuous-time objects (partial differential equations and path space measures). We further show that an appropriate choice of coarse time discretization during training allows greatly improved sample efficiency and the use of time-local objectives, achieving competitive performance on standard sampling benchmarks with reduced computational cost. Y1 - 2025 ER - TY - CHAP A1 - Blessing, Denis A1 - Berner, Julius A1 - Richter, Lorenz A1 - Neumann, Gerhard T1 - Underdamped Diffusion Bridges with Applications to Sampling T2 - 13th International Conference on Learning Representations (ICLR 2025) N2 - We provide a general framework for learning diffusion bridges that transport prior to target distributions. It includes existing diffusion models for generative modeling, but also underdamped versions with degenerate diffusion matrices, where the noise only acts in certain dimensions. Extending previous findings, our framework allows to rigorously show that score matching in the underdamped case is indeed equivalent to maximizing a lower bound on the likelihood. Motivated by superior convergence properties and compatibility with sophisticated numerical integration schemes of underdamped stochastic processes, we propose \emph{underdamped diffusion bridges}, where a general density evolution is learned rather than prescribed by a fixed noising process. We apply our method to the challenging task of sampling from unnormalized densities without access to samples from the target distribution. Across a diverse range of sampling problems, our approach demonstrates state-of-the-art performance, notably outperforming alternative methods, while requiring significantly fewer discretization steps and no hyperparameter tuning. Y1 - 2025 UR - https://openreview.net/attachment?id=Q1QTxFm0Is&name=pdf ER - TY - JOUR A1 - Blessing, Denis A1 - Berner, Julius A1 - Richter, Lorenz A1 - Domingo-Enrich, Carles A1 - Du, Yuanqi A1 - Vahdat, Arash A1 - Neumann, Gerhard T1 - Trust Region Constrained Measure Transport in Path Space for Stochastic Optimal Control and Inference N2 - Solving stochastic optimal control problems with quadratic control costs can be viewed as approximating a target path space measure, e.g. via gradient-based optimization. In practice, however, this optimization is challenging in particular if the target measure differs substantially from the prior. In this work, we therefore approach the problem by iteratively solving constrained problems incorporating trust regions that aim for approaching the target measure gradually in a systematic way. It turns out that this trust region based strategy can be understood as a geometric annealing from the prior to the target measure, where, however, the incorporated trust regions lead to a principled and educated way of choosing the time steps in the annealing path. We demonstrate in multiple optimal control applications that our novel method can improve performance significantly, including tasks in diffusion-based sampling, transition path sampling, and fine-tuning of diffusion models. Y1 - 2025 ER - TY - JOUR A1 - Reuss, Joana A1 - Macdonald, Jan A1 - Becker, Simon A1 - Richter, Lorenz A1 - Körner, Marco T1 - The EuroCropsML time series benchmark dataset for few-shot crop type classification in Europe JF - Scientific Data N2 - We introduce EuroCropsML, an analysis-ready remote sensing dataset based on the open-source EuroCrops collection, for machine learning (ML) benchmarking of time series crop type classification in Europe. It is the first time-resolved remote sensing dataset designed to benchmark transnational few-shot crop type classification algorithms that supports advancements in algorithmic development and research comparability. It comprises 706683 multi-class labeled data points across 176 crop classes. Each data point features a time series of per-parcel median pixel values extracted from Sentinel-2 L1C data and precise geospatial coordinates. EuroCropsML is publicly available on Zenodo. Y1 - 2025 U6 - https://doi.org/10.1038/s41597-025-04952-7 VL - 12 ER - TY - JOUR A1 - Eigen, Lennart A1 - Ladenburger, Pius A1 - Brence, Blaž A1 - Shubitidze, Ani A1 - Baum, Daniel A1 - Hildebrandt, Thomas A1 - Brecht, Michael T1 - Elephant trunk tip musculature reflects species differences in grasping behavior JF - Communications Biology N2 - Elephants use their trunks, muscular hydrostats, to perform a plethora of tasks. Trunk tip morphology as well as grasping behavior differ between elephant species. While African savanna elephants (Loxodonta africana) use their dorsal and ventral finger for pinching movements, Asian elephants (Elephas maximus) prefer to wrap around objects with their one dorsal finger and ventral bulb trunk tip lip. Moreover, E. maximus can flip their ventral bulb backwards to clamp objects behind the trunk tip. Whether trunk tip musculature differs between elephant species and muscle architecture is reflected by preferred grasping behavior is, however, not clear. In this study, we performed dense muscle fascicle reconstruction of three L. africana and three E. maximus hemi-trunk tips using a combination of manual and automated segmentation of high-resolution microfocus tomography (microCT) scans. We distinguish three types of muscle fascicles: longitudinal (bending and shortening), radial (elongating) and transversal muscle fascicles (elongating). We found that trunk tips of L. africana consist to one third of longitudinal and two thirds radial/transversal muscle fascicles, likely aiding in their grasping behavior, while E. maximus trunk tips consist to two thirds of longitudinal and one third radial/transversal muscle fascicles, which is advantageous for their wrapping and backward clamping behavior. Y1 - 2025 U6 - https://doi.org/10.1038/s42003-025-08998-6 VL - 8 ER - TY - JOUR A1 - Brence, Blaž A1 - Wandelt, Laura R. A1 - Walter, Sophie A1 - Sigrist, Stephan J. A1 - Petzoldt, Astrid G. A1 - Baum, Daniel T1 - Semi-automatic 3D-quantification of in-vivo synapse formation JF - ResearchSquare N2 - Background: Synapses, as specialised cell-cell contacts, allow for a faithful and controlled signal transmission between a neuron and a target cell. Presynapses, the sites of neurotransmitter release, form de novo throughout the development of an organism. Although this process is fundamental to the development and function of synaptic circuits, how developing neurons control number and distribution of individual synapses remains poorly understood. In-vivo imaging analysis of synapse formation at the neuromuscular junction of anaesthetised Drosophila third instar larvae allows for spatial and temporal resolution of the underlying molecular processes. However, high-throughput, comprehensive analysis are hampered by the manual and time-consuming imaging analysis methods applied hitherto. Here, we focus on the early presynaptic formation steps, that is, the presynaptic seeding, initiated by the formation of transient Liprin-a/SYD1 seeding sites, either stabilised or disintegrated over a time span of 30-90 min. Results: To investigate the dynamics of the Liprin-a/SYD1 seeding sites, we developed an automated analysis pipeline for 3D confocal images from in-vivo imaging at distinct time points to analyse fluorescently labelled presynaptic protein dynamics during early synapse formation. The workflow is realised in the data analysis software Amira, utilising the hierarchical watershed algorithm, and was designed for automatic processing with an option for manual proofreading. Compared to the previous 2D manual quantification, this automated approach provides a higher sensitivity in single Liprin-a seeding site detection in low-intensity areas and in regions of dense seeding sites.In addition, it substantially reduces the work time. To account for possible errors occurring in the automated processing, we implemented an additional proofreading step allowing for a manual correction of Liprin-a seeding site segmentation and assignment, thus greatly improving the analysis while only marginally increasing work time by 10% to a total work time reduction of 80% compared to the 2D manual analysis paradigm. Conclusion: The process of synaptogenesis underlies the general principles of locomotion, learning and memory formation. The developed fast and accurate semi-automated 3D workflow provides a substantial progress in the analysis of this molecular process and its application can be easily extended to other dynamic in-vivo research approaches across species. Y1 - 2025 U6 - https://doi.org/10.21203/rs.3.rs-6073150/v1 ER - TY - JOUR A1 - Brence, Blaž A1 - Brummer, Josephine A1 - Dercksen, Vincent J. A1 - Özel, Mehmet Neset A1 - Kulkarni, Abhishkek A1 - Wolterhoff, Neele A1 - Prohaska, Steffen A1 - Hiesinger, Peter Robin A1 - Baum, Daniel T1 - Semi-automatic Geometrical Reconstruction and Analysis of Filopodia Dynamics in 4D Two-Photon Microscopy Images JF - bioRxiv N2 - Background: Filopodia are thin and dynamic membrane protrusions that play a crucial role in cell migration, axon guidance, and other processes where cells explore and interact with their surroundings. Historically, filopodial dynamics have been studied in great detail in 2D in cultured cells, and more recently in 3D culture as well as living brains. However, there is a lack of efficient tools to trace and track filopodia in 4D images of complex brain cells. Results: To address this issue, we have developed a semi-automatic workflow for tracing filopodia in 3D images and tracking the traced filopodia over time. The workflow was developed based on high-resolution data of photoreceptor axon terminals in the in vivo context of normal Drosophila brain development, but devised to be applicable to filopodia in any system, including at different temporal and spatial scales. In contrast to the pre-existing methods, our workflow relies solely on the original intensity images without the requirement for segmentation or complex preprocessing. The workflow was realized in C++ within the Amira software system and consists of two main parts, dataset pre-processing, and geometrical filopodia reconstruction, where each of the two parts comprises multiple steps. In this paper, we provide an extensive workflow description and demonstrate its versatility for two different axo-dendritic morphologies, R7 and Dm8 cells. Finally, we provide an analysis of the time requirements for user input and data processing. Conclusion: To facilitate simple application within Amira or other frameworks, we share the source code, which is available athttps://github.com/zibamira/filopodia-tool. Y1 - 2025 U6 - https://doi.org/10.1101/2025.05.20.654789 ER - TY - CHAP A1 - Brence, Blaž A1 - Fuchs, Joachim A1 - Hiesinger, Peter Robin A1 - Baum, Daniel ED - Garrison, Laura ED - Krueger, Robert T1 - Fully automated quantification of synaptic locations in multi-channel Drosophila photoreceptor microscopy data T2 - Eurographics Workshop on Visual Computing for Biology and Medicine N2 - The workload posed by image analysis remains a major bottleneck for advances across the life sciences. To address this challenge, we have developed a fully automated workflow for processing complex 3D multi-channel microscopy images. Specifically, our workflow addresses the analysis of photoreceptor synapses in confocal images of the Drosophila melanogaster optic lobe. The workflow consists of multiple stages, combining traditional and machine learning–based approaches for image analysis and visual computing. It performs segmentation of brain regions, photoreceptor instance identification, and precise localization of synapses. The key novelty of the workflow is an automatic alignment of synapses into a cylindrical reference coordinate system, enabling comparative synaptic analysis across photoreceptors. To demonstrate the workflow’s applicability, preliminary biological results and their interpretation based on 50 images are presented. While the workflow is still being improved further, here, we showcase its capacity for efficient and objective data processing for high-throughput neurobiological analyses. Y1 - 2025 U6 - https://doi.org/10.2312/vcbm.20251254 ER - TY - JOUR A1 - Trepczynski, Adam A1 - Kneifel, Paul A1 - Heyland, Mark A1 - Leskovar, Marko A1 - Moewis, Philippe A1 - Damm, Philipp A1 - Taylor, William R. A1 - Zachow, Stefan A1 - Duda, Georg N. T1 - Impact of the external knee flexion moment on patello-femoral loading derived from in vivo loads and kinematics JF - Frontiers in Bioengineering and Biotechnology N2 - Introduction: Anterior knee pain and other patello-femoral (PF) complications frequently limit the success of total knee arthroplasty as the final treatment of end stage osteoarthritis. However, knowledge about the in-vivo loading conditions at the PF joint remains limited, as no direct measurements are available. We hypothesised that the external knee flexion moment (EFM) is highly predictive of the PF contact forces during activities with substantial flexion of the loaded knee. Materials and methods: Six patients (65–80 years, 67–101 kg) with total knee arthroplasty (TKA) performed two activities of daily living: sit-stand-sit and squat. Tibio-femoral (TF) contact forces were measured in vivo using instrumented tibial components, while synchronously internal TF and PF kinematics were captured with mobile fluoroscopy. The measurements were used to compute PF contact forces using patient specific musculoskeletal models. The relationship between the EFM and the PF contact force was quantified using linear regression. Results: Mean peak TF contact forces of 1.97–3.24 times body weight (BW) were found while peak PF forces reached 1.75 to 3.29 times body weight (BW). The peak EFM ranged from 3.2 to 5.9 %BW times body height, and was a good predictor of the PF contact force (R2 = 0.95 and 0.88 for sit-stand-sit and squat, respectively). Discussion: The novel combination of in vivo TF contact forces and internal patellar kinematics enabled a reliable assessment of PF contact forces. The results of the regression analysis suggest that PF forces can be estimated based solely on the EFM from quantitative gait analysis. Our study also demonstrates the relevance of PF contact forces, which reach magnitudes similar to TF forces during activities of daily living. Y1 - 2024 U6 - https://doi.org/10.3389/fbioe.2024.1473951 SN - 2296-4185 VL - 12/2024 PB - Frontiers Media SA ER - TY - GEN A1 - Pedersen, Jaap A1 - Lindner, Niels A1 - Rehfeldt, Daniel A1 - Koch, Thorsten T1 - Comparing Branching Rules for the Quota Steiner Tree Problem with Interference N2 - Branching decisions play a crucial role in branch-and-bound algorithms for solving combinatorial optimization problems. In this paper, we investigate several branching rules applied to the Quota Steiner Tree Problem with Interference (QSTPI). The Quota Steiner Tree Problem (QSTP) generalizes the classical Steiner Tree Problem (STP) in graphs by seeking a minimum-cost tree that connects a subset of profit-associated vertices to meet a given quota. The extended version, QSTPI, introduces interference among vertices: Selecting certain vertices simultaneously reduces their individual contributions to the overall profit. This problem arises, for example, in positioning and connecting wind turbines, where turbines possibly shadow other turbines, reducing their energy yield. While exact solvers for standard STP-related problems often rely heavily on reduction techniques and cutting-plane methods – rarely generating large branch-and-bound trees – experiments reveal that large instances of QSTPI require significantly more branching to compute provably optimal solutions. In contrast to branching on variables, we utilize the combinatorial structure of the QSTPI by branching on the graph’s vertices. We adapt classical and problem-specific branching rules and present a comprehensive computational study comparing the effectiveness of these branching strategies. T3 - ZIB-Report - 25-16 Y1 - 2025 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-101250 SN - 1438-0064 ER - TY - GEN A1 - Mehl, Lukas A1 - Lindner, Niels A1 - Bartoszuk, Karolina A1 - Zittel, Janina T1 - Prototypical warm-starts for demand-robust LP-based energy system optimization N2 - The expressiveness of energy system optimization models (ESOMs) depends on a multitude of exogenous parameters. For example, sound estimates of the future energy demand are essential to enable qualified decisions on long-term investments. However, the enormous demand fluctuations even on a fine-grained scale diminish the computational performance of large-scale ESOMs. We therefore propose a clustering-and-decomposition method for linear programming based ESOMs that first identifies and solves prototypical demand scenarios with the dual simplex algorithm, and then composes dual optimal prototype bases to a warm-start basis for the full model. We evaluate the feasibility and computational efficiency our approach on a real-world case study, using a sector-coupled ESOM with hourly resolution for the Berlin-Brandenburg area in Germany, based on the oemof framework. T3 - ZIB-Report - 25-15 Y1 - 2025 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-101242 SN - 1438-0064 ER - TY - GEN A1 - Lindner, Niels A1 - Liebchen, Christian T1 - A two-stage model for periodic timetabling with fixed line activities N2 - The timetable is a central pillar of any public transportation system. Constructing and optimizing periodic timetables in terms of passenger comfort and operational efficiency leads to NP-hard optimization problems that are also computationally challenging in applications. The Periodic Event Scheduling Problem (PESP) as standard mathematical tool benefits from its succinct formulation and rich combinatorial structure, but suffers from poor linear programming relaxations and weak dual bounds. These difficulties persist in a reduced version, where driving and dwelling activities of the lines are assumed to be fixed. In this case, fixing the initial departure time of each line fully determines the timetable, and for each pair of lines, the resulting (weighted) transfer durations can be expressed in terms of a piecewise linear non-convex function in terms of the difference of the initial times. When the number of activities between two lines is bounded, this function can be computed in polynomial time. By inserting precomputed piecewise linear functions into a mixed-integer program with the initial departure times as variables, we introduce an equivalent formulation for reduced PESP instances. The model bears analogies with quadratic semi-assignment approaches and offers alternative ways to compute primal and dual bounds. We evaluate the computational behavior of our approach on realistic benchmarking instances. T3 - ZIB-Report - 25-14 Y1 - 2025 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-101234 SN - 1438-0064 ER - TY - JOUR A1 - Rong, Guoyang A1 - Chen, Ying A1 - Ma, Feicheng A1 - Koch, Thorsten T1 - Exploring Interdisciplinary Research Trends through Critical Years for Interdisciplinary Citation JF - Journal of Informetrics Y1 - 2025 U6 - https://doi.org/10.1016/j.joi.2025.101726 VL - 19 IS - 4 ER - TY - JOUR A1 - Shinano, Yuji A1 - Fujii, Koichi T1 - Introduction to Quadratic Programming Solvers JF - Journal of The Society of Instrument and Control Engineers Y1 - 2025 U6 - https://doi.org/10.11499/sicejl.64.532 VL - 64 IS - 8 SP - 532 EP - 540 ER - TY - CHAP A1 - Hammerschmidt, Martin A1 - Plock, Matthias A1 - Burger, Sven A1 - Truong, Vinh A1 - Soltwisch, Victor A1 - Schneider, Philipp-Immanuel T1 - Machine learning approach for full Bayesian parameter reconstruction T2 - Proc. SPIE Y1 - 2025 U6 - https://doi.org/10.1117/12.3062268 VL - 13568 SP - 1356806 ER - TY - CHAP A1 - Sekulic, Ivan A1 - Schneider, Philipp-Immanuel A1 - Hammerschmidt, Martin A1 - Schaible, Jonas A1 - Burger, Sven T1 - Physics informed Bayesian optimization for inverse design of diffractive optical elements T2 - Proc. SPIE Y1 - 2025 U6 - https://doi.org/10.1117/12.3064372 VL - PC13573 SP - PC135730R ER - TY - JOUR A1 - Bauer, Wolfgang A1 - Galtung, Noa A1 - Geserick, Peter A1 - Friedrich, Katharina A1 - Weber, Marcus A1 - Somasundaram, Rajan A1 - Diehl-Wiesenecker, Eva A1 - Kappert, Kai T1 - Pentraxin-3, MyD88, GLP-1, and PD-L1: Performance assessment and composite algorithmic analysis for sepsis identification JF - Journal of Infection N2 - This study examines nine emerging biomarkers as possible indicators for diagnosing sepsis in emergency department patient. Y1 - 2025 U6 - https://doi.org/10.1016/j.jinf.2025.106599 VL - 91 IS - 3 SP - 106599 ER - TY - JOUR A1 - Kourov, Mikhail N. A1 - Samoilova, Anna E. A1 - Straube, Arthur T1 - Dynamics of a chain of interacting magnetic particles in a one-dimensional periodic energy landscape JF - Bull. Russ. Acad. Sci. Phys. N2 - We explore the dynamics of a one-dimensional chain of paramagnetic colloidal particles in a periodic potential. The model accounts for a constant external force, along with magnetic dipolar attraction and hard-core repulsive interactions between particles. Numerical simulations reveal the emergence of a traveling kink – a chain defect propagating along the chain. We show that the kink emerges beyond a critical force threshold and identify parameter regimes corresponding to distinct dynamic modes such as a pinned kink, a running kink, a cluster kink, and chain drift. Y1 - 2025 U6 - https://doi.org/10.1134/S1062873825711742 SN - 1062-8738 VL - 89 IS - 7 SP - 1086 EP - 1092 ER - TY - JOUR A1 - Straube, Arthur A1 - Olicón Méndez, Guillermo A1 - Winkelmann, Stefanie A1 - Höfling, Felix A1 - Engel, Maximilian T1 - Unfolding the geometric structure and multiple timescales of the urea-urease pH oscillator N2 - We study a two-variable dynamical system modeling pH oscillations in the urea–urease reaction within giant lipid vesicles – a problem that intrinsically contains multiple, well-separated timescales. Building on an existing, deterministic formulation via ordinary differential equations, we resolve different orders of magnitude within a small parameter and analyze the system's limit cycle behavior using geometric singular perturbation theory (GSPT). By introducing two different coordinate scalings – each valid in a distinct region of the phase space – we resolve the local dynamics near critical fold points, using the extension of GSPT through such singular points due to Krupa and Szmolyan. This framework enables a geometric decomposition of the periodic orbits into slow and fast segments and yields closed-form estimates for the period of oscillation. In particular, we link the existence of such oscillations to an underlying biochemical asymmetry, namely, the differential transport across the vesicle membrane. Y1 - 2025 ER - TY - CHAP A1 - Riedmüller, Stephanie A1 - Zittel, Janina A1 - Koch, Thorsten T1 - Warm-starting Strategies in Scalarization Methods for Multi-Objective Optimization T2 - Operations Research Proceedings 2025 N2 - We explore how warm-starting strategies can be integrated into scalarization-based approaches for multi-objective optimization in (mixed) integer linear programming. Scalarization methods remain widely used classical techniques to compute Pareto-optimal solutions in applied settings. They are favored due to their algorithmic simplicity and broad applicability across continuous and integer programs with an arbitrary number of objectives. While warm-starting has been applied in this context before, a systematic methodology and analysis remain lacking. We address this gap by providing a theoretical characterization of warm-starting within scalarization methods, focusing on the sequencing of subproblems. However, optimizing the order of subproblems to maximize warm-start efficiency may conflict with alternative criteria, such as early identification of infeasible regions. We quantify these trade-offs through an extensive computational study. Y1 - 2026 ER - TY - GEN A1 - Riedmüller, Stephanie A1 - Zittel, Janina A1 - Koch, Thorsten T1 - Warm-starting Strategies in Scalarization Methods for Multi-Objective Optimization N2 - We explore how warm-starting strategies can be integrated into scalarization-based approaches for multi-objective optimization in (mixed) integer linear programming. Scalarization methods remain widely used classical techniques to compute Pareto-optimal solutions in applied settings. They are favored due to their algorithmic simplicity and broad applicability across continuous and integer programs with an arbitrary number of objectives. While warm-starting has been applied in this context before, a systematic methodology and analysis remain lacking. We address this gap by providing a theoretical characterization of warm-starting within scalarization methods, focusing on the sequencing of subproblems. However, optimizing the order of subproblems to maximize warm-start efficiency may conflict with alternative criteria, such as early identification of infeasible regions. We quantify these trade-offs through an extensive computational study. T3 - ZIB-Report - 25-12 Y1 - 2025 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-101073 SN - 1438-0064 ER - TY - GEN A1 - Binkowski, Felix A1 - Koulas-Simos, Aris A1 - Betz, Fridtjof A1 - Plock, Matthias A1 - Sekulic, Ivan A1 - Manley, Phillip A1 - Hammerschmidt, Martin A1 - Schneider, Philipp-Immanuel A1 - Zschiedrich, Lin A1 - Munkhbat, Battulga A1 - Reitzenstein, Stephan A1 - Burger, Sven T1 - Source code and simulation results: High Purcell enhancement in all-TMDC nanobeam resonator designs with active monolayers for nanolasers T2 - Zenodo Y1 - 2025 U6 - https://doi.org/10.5281/zenodo.16533803 ER - TY - JOUR A1 - Binkowski, Felix A1 - Koulas-Simos, Aris A1 - Betz, Fridtjof A1 - Plock, Matthias A1 - Sekulic, Ivan A1 - Manley, Phillip A1 - Hammerschmidt, Martin A1 - Schneider, Philipp-Immanuel A1 - Zschiedrich, Lin A1 - Munkhbat, Battulga A1 - Reitzenstein, Stephan A1 - Burger, Sven T1 - High Purcell enhancement in all-TMDC nanobeam resonator designs with active monolayers for nanolasers JF - Phys. Rev. B Y1 - 2025 U6 - https://doi.org/10.1103/nxh9-dhvx VL - 112 SP - 235410 ER - TY - JOUR A1 - Weimann, Kuba A1 - Conrad, Tim T1 - Self-supervised pre-training with joint-embedding predictive architecture boosts ECG classification performance JF - Computers in Biology and Medicine N2 - Accurate diagnosis of heart arrhythmias requires the interpretation of electrocardiograms (ECG), which capture the electrical activity of the heart. Automating this process through machine learning is challenging due to the need for large annotated datasets, which are difficult and costly to collect. To address this issue, transfer learning is often employed, where models are pre-trained on large datasets and fine-tuned for specific ECG classification tasks with limited labeled data. Self-supervised learning has become a widely adopted pre-training method, enabling models to learn meaningful representations from unlabeled datasets. In this work, we explore the joint-embedding predictive architecture (JEPA) for self-supervised learning from ECG data. Unlike invariance-based methods, JEPA does not rely on hand-crafted data augmentations, and unlike generative methods, it predicts latent features rather than reconstructing input data. We create a large unsupervised pre-training dataset by combining ten public ECG databases, amounting to over one million records. We pre-train Vision Transformers using JEPA on this dataset and fine-tune them on various PTB-XL benchmarks. Our results show that JEPA outperforms existing invariance-based and generative approaches, achieving an AUC of 0.945 on the PTB-XL all statements task. JEPA consistently learns the highest quality representations, as demonstrated in frozen evaluations, and proves advantageous for pre-training even in the absence of additional data. Y1 - 2025 U6 - https://doi.org/10.1016/j.compbiomed.2025.110809 SN - 0010-4825 VL - 196 PB - Elsevier BV ER - TY - CHAP A1 - Fayad, Ibrahim A1 - Zimmer, Max A1 - Schwartz, Martin A1 - Ciais, Philippe A1 - Gieseke, Fabian A1 - Belouze, Gabriel A1 - Brood, Sarah A1 - De Truchis, Aurelien A1 - d'Aspremont, Alexandre T1 - DUNIA: Pixel-Sized Embeddings via Cross-Modal Alignment for Earth Observation Applications T2 - Proceedings of the 42nd International Conference on Machine Learning Y1 - 2025 UR - https://raw.githubusercontent.com/mlresearch/v267/main/assets/fayad25a/fayad25a.pdf VL - 267 SP - 16375 EP - 16406 ER - TY - CHAP A1 - Pauls, Jan A1 - Zimmer, Max A1 - Turan, Berkant A1 - Saatchi, Sassan A1 - Ciais, Philippe A1 - Pokutta, Sebastian A1 - Gieseke, Fabian T1 - Capturing Temporal Dynamics in Large-Scale Canopy Tree Height Estimation T2 - Proceedings of the 42nd International Conference on Machine Learning Y1 - 2025 UR - https://raw.githubusercontent.com/mlresearch/v267/main/assets/pauls25a/pauls25a.pdf VL - 267 SP - 48422 EP - 48438 ER - TY - JOUR A1 - Klus, Stefan A1 - Gelß, Patrick T1 - Continuous optimization methods for the graph isomorphism problem JF - Information and Inference: A Journal of the IMA Y1 - 2025 U6 - https://doi.org/10.1093/imaiai/iaaf011 VL - 14 IS - 2 ER - TY - CHAP A1 - Mundinger, Konrad A1 - Zimmer, Max A1 - Kiem, Aldo A1 - Spiegel, Christoph A1 - Pokutta, Sebastian T1 - Neural Discovery in Mathematics: Do Machines Dream of Colored Planes? T2 - Proceedings of the 42nd International Conference on Machine Learning Y1 - 2025 UR - https://raw.githubusercontent.com/mlresearch/v267/main/assets/mundinger25a/mundinger25a.pdf VL - 267 SP - 45236 EP - 45255 ER - TY - CHAP A1 - Pelleriti, Nico A1 - Zimmer, Max A1 - Wirth, Elias A1 - Pokutta, Sebastian T1 - Approximating Latent Manifolds in Neural Networks via Vanishing Ideals T2 - Proceedings of the 42nd International Conference on Machine Learning Y1 - 2025 UR - https://raw.githubusercontent.com/mlresearch/v267/main/assets/pelleriti25a/pelleriti25a.pdf VL - 267 SP - 48734 EP - 48761 ER - TY - CHAP A1 - Roux, Christophe A1 - Martínez-Rubio, David A1 - Pokutta, Sebastian T1 - Implicit Riemannian optimism with applications to min-max problems T2 - Proceedings of the 42nd International Conference on Machine Learning Y1 - 2025 UR - https://raw.githubusercontent.com/mlresearch/v267/main/assets/roux25a/roux25a.pdf VL - 267 SP - 52139 EP - 52172 ER - TY - CHAP A1 - Turan, Berkant A1 - Asadulla, Suhrab A1 - Steinmann, David A1 - Stammer, Wolfgang A1 - Pokutta, Sebastian T1 - Neural Concept Verifier: Scaling Prover-Verifier Games via Concept Encodings T2 - Proceedings of the ICML Workshop on Actionable Interpretability Y1 - 2025 ER - TY - CHAP A1 - Xu, Liding A1 - Mexi, Gioni A1 - Bestuzheva, Ksenia T1 - Sparsity-driven Aggregation of Mixed Integer Programs T2 - 23rd International Symposium on Experimental Algorithms (SEA 2025) Y1 - 2025 U6 - https://doi.org/10.4230/LIPIcs.SEA.2025.27 VL - 338 SP - 27:1 EP - 27:15 CY - Schloss Dagstuhl – Leibniz-Zentrum für Informatik ER -