TY - CHAP A1 - Riccardi, Gabor A1 - Lindner, Niels T1 - On The Minimum-Weight Forward (Weakly) Fundamental Cycle Basis Problem in Directed Graphs T2 - International Network Optimization Conference 2026 (INOC 2026) N2 - The cycle space of a directed graph is generated by a cycle basis, where, in general, cycles are allowed to have both forward and backward arcs. In a forward cycle, all arcs have to follow the given direction. We study the existence, structure, and computational complexity of minimum-weight forward cycle bases in directed graphs. We give a complete structural characterization of digraphs that admit weakly fundamental (and hence integral) forward cycle bases, showing that this holds if and only if every block is either strongly connected or a single arc. We further provide an easily verifiable characterization of when a strongly connected digraph admits a forward fundamental cycle basis, proving that such a basis exists if and only if the set of directed cycles has cardinality equal to the cycle rank; in this case, the basis is unique and computable in polynomial time, and nonexistence can likewise be certified efficiently. Lastly, we show that while minimum-weight forward fundamental cycle bases can be found in polynomial time whenever they exist, the minimum-weight forward weakly fundamental cycle basis problem is NP-hard via a polynomial-time reduction from the minimum-weight weakly fundamental cycle basis problem on digraphs with metric weights. Y1 - 2026 ER - TY - CHAP A1 - Schelten, Niklas A1 - Christgau, Steffen A1 - Hutzler, Merit A1 - Kreowsky, Philipp A1 - De Lucia, Marco A1 - Schnor, Bettina A1 - Signer, Hannes A1 - Spazier, Johannes A1 - Stabernack, Benno A1 - Yahdzhyiev, Serhii T1 - Using FPGA-based Network-Attached Accelerators for Energy-Efficient AI Training in HPC Datacenters T2 - 2026 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW) N2 - FPGA-based Network-Attached Accelerators offer a disaggregated alternative to closely-coupled FPGAs or GPUs, but their adoption is very limited due to missing hardware/software frameworks. However, the usability and energy-efficiency of FPGAs for HPC use-cases has been demonstrated previously. Yet, the support by HPC infrastructure for those devices is lacking behind other accelerators. This paper addresses this shortcoming and demonstrates a full-stack approach that seamlessly integrates Network-Attached Accelerators in HPC datacenters and allows flexible and scalable usage of FPGAs. The presented work includes the according framework, integration steps and a show-case application from the geoscience domain. We evaluate our approach by comparing the training of a Physics-Informed Neural Network on the NAA against two GPU implementations. The NAA reduces total energy consumption by about 50% and 17% compared to the Keras and CUDA implementation, respectively. These results demonstrate that FPGA-based NAAs can be successfully integrated into HPC environments and are a viable path toward more energy-efficient AI training. Y1 - 2026 ER - TY - CHAP A1 - Borndörfer, Ralf A1 - Jocas, Arturas A1 - Weiser, Martin T1 - An Eikonal Approach for Globally Optimal Free Flight Trajectories N2 - We present an eikonal-based approach that is capable of finding a continuous globally optimal trajectory for an aircraft in a stationary wind field. This minimizes emissions and fuel consumption. If the destination is close to a cut locus of the associated Hamilton-Jacobi-Bellman equation, small numerical discretization errors can lead to selecting a merely locally optimal trajectory and missing the globally optimal one. Based on finite element error estimates, we construct a trust region around the cut loci in order to guarantee uniqueness of trajectories for destinations sufficiently far from cut loci. Y1 - 2026 ER - TY - CHAP A1 - Sunkara, Vikram A1 - Rostami, Atefe A1 - von Tycowicz, Christoph A1 - Schütte, Christof T1 - Stop throwing away your Decoder; extract the learnt local coordinate system using Latent-XAI T2 - The 4th World Conference on Explainable Artificial Intelligence (XAI-2026) Y1 - 2026 ER - TY - CHAP A1 - Schelten, Niklas A1 - Christgau, Steffen A1 - Schulte, Anton A1 - Schnor, Bettina A1 - Signer, Hannes A1 - Stabernack, Benno T1 - A Flexible Open-Source Framework for FPGA-based Network-Attached Accelerators using SpinalHDL T2 - Architecture of Computing Systems - 39th International Conference, ARCS 2026, Mainz, Germany, March 24-26, 2026, Proceedings. N2 - Domain-specific accelerators are increasingly vital in heterogeneous computing systems, driven by the demand for higher computational capacity and especially energy efficiency. Network-attached FPGAs promise a scalable and flexible alternative to closely coupled FPGAs for integrating accelerators into computing environments. While the advantages of specialized hardware implementations are apparent, traditional hardware development and integration remain time-consuming and complex. We present an open-source framework which combines a hardware shell with supporting software libraries, which enables fast development and deployment of FPGA-based network-attached accelerators. In contrast to traditional approaches using VHDL or Verilog, we leverage generative programming with SpinalHDL, providing a flexible hardware description with multi-level abstractions. This work eases the integration of accelerators into existing network infrastructures and simplifies adaptation to different FPGAs, eliminating complex and lengthy top-level hardware descriptions. Y1 - 2026 ER - TY - CHAP A1 - Franke, Paula A1 - Hamacher, Kay A1 - Manns, Paul T1 - Minimizing and Maximizing the Shannon Entropy for Fixed Marginals T2 - Operations Research Proceedings 2025 N2 - The mutual information (MI) between two random variables is an important correlation measure in data analysis. The Shannon entropy of a joint probability distri- bution is the variable part under fixed marginals. We aim to minimize and maximize it to obtain the largest and smallest MI possible in this case, leading to a scaled MI ratio for better comparability. We present algorithmic approaches and optimal solutions for a set of problem instances based on data from molecular evolution. We show that this allows us to construct a sensible, systematic correction to raw MI values. Y1 - 2026 ER - TY - CHAP A1 - Sengupta, Agniva A1 - Kuş, Dilara A1 - Li, Jianning A1 - Zachow, Stefan T1 - Globally Optimal Pose from Orthographic Silhouettes N2 - We solve the problem of determining the pose of known shapes in R^3 from their unoccluded silhouettes. The pose is determined up to global optimality using a simple yet under-explored property of the area-of-silhouette: its continuity w.r.t trajectories in the rotation space. The proposed method utilises pre-computed silhouette-signatures, modelled as a response surface of the area-of-silhouettes. Querying this silhouette-signature response surface for pose estimation leads to a strong branching of the rotation search space, making resolution-guided candidate search feasible. Additionally, we utilise the aspect ratio of 2D ellipses fitted to projected silhouettes as an auxiliary global shape signature to accelerate the pose search. This combined strategy forms the first method to efficiently estimate globally optimal pose from just the silhouettes, without being guided by correspondences, for any shape, irrespective of its convexity and genus. We validate our method on synthetic and real examples, demonstrating significantly improved accuracy against comparable approaches. Y1 - 2026 ER - TY - CHAP A1 - Dinger, Patrick A1 - Gerber, Anja A1 - Gnyp, Anna A1 - von Hagel, Frank A1 - Hohmann, Georg A1 - Krause, Celia A1 - Wagner, Sarah A1 - Winkler, Alexander T1 - Sammlungsdaten als Forschungsdaten in den Digital Humanities T2 - Book of Abstracts - DHd 2026 Y1 - 2026 U6 - https://doi.org/10.5281/zenodo.18702753 SP - 69 EP - 74 ER - TY - CHAP A1 - Pedersen, Jaap A1 - Lindner, Niels A1 - Rehfeldt, Daniel A1 - Koch, Thorsten T1 - Comparing Branching Rules for the Quota Steiner Tree Problem with Interference T2 - Operations Research Proceedings 2025 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 whose cumulative profit meets or exceeds a given quota. The 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. Unlike classical STP variants, large QSTPI instances 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. Y1 - 2026 ER - TY - CHAP A1 - Villim, Ján A1 - Nguyen, Martin A1 - Bobik, Pavol A1 - Genci, Jan A1 - Gecášek, Daniel T1 - New Version of the COR Simulation Engine T2 - Proceedings of 39th International Cosmic Ray Conference — PoS(ICRC2025) N2 - The COR simulation engine is a tool devoted to evaluating cosmic ray trajectories in Earth's magnetosphere. It is part of the COR System, available at https://cor.crmodels.org, and it also functions as a standalone command line tool. The former tool was published in 2022. We report the new version of the tool status with improved performance, precision, new functions/features, and refactored code. Y1 - 2025 U6 - https://doi.org/https://doi.org/10.22323/1.501.1377 VL - 501 PB - Sissa Medialab CY - Trieste, Italy ER - TY - CHAP A1 - Koch, Thorsten A1 - Kempke, Nils-Christian A1 - Lindner, Niels A1 - Mehl, Lukas A1 - Wetzel, Manuel A1 - Zittel, Janina T1 - High-Performance Robust Energy System Planning with Storage: A Single-LP Approach T2 - Proceedings of URBSENSE 2026 - 1st International Workshop on URBan SENSEmaking and Intelligence for Safer Cities Y1 - 2026 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 - 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 - 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 - 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 - 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 - 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 - 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 - 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 - 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 - 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 - 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 - 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 - 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 - 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 - 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 - 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 - 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 - 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 - TY - CHAP A1 - Hendrych, Deborah A1 - Besançon, Mathieu A1 - Martínez-Rubio, David A1 - Pokutta, Sebastian T1 - Secant line search for Frank-Wolfe algorithms T2 - Proceedings of the 42nd International Conference on Machine Learning Y1 - 2025 UR - https://raw.githubusercontent.com/mlresearch/v267/main/assets/hendrych25a/hendrych25a.pdf VL - 267 SP - 23005 EP - 23029 ER - TY - CHAP A1 - Haase, Jennifer A1 - Hanel, Paul H. P. A1 - Pokutta, Sebastian T1 - S-DAT: a multilingual, GenAI-driven framework for automated divergent thinking assessment T2 - Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society (AIES) Y1 - 2025 VL - 8 SP - 1194 EP - 1205 ER - TY - CHAP A1 - Urbano, Alonso A1 - Romero, David W. T1 - Self-Supervised Detection of Perfect and Partial Input-Dependent Symmetries T2 - Proceedings of the Geometry-grounded Representation Learning and Generative Modeling Workshop (GRaM) at ICML 2024 Y1 - 2024 ER - TY - CHAP A1 - Löbel, Fabian A1 - Borndörfer, Ralf A1 - Weider, Steffen ED - Voigt, Guido ED - Fliedner, Malte ED - Haase, Knut ED - Brüggermann, Wolfgang ED - Hoberg, Kai ED - Meissner, Joern T1 - Non-linear Battery Behavior in Electric Vehicle Scheduling Problems T2 - Operations Research Proceedings 2023. OR 2023. N2 - The currently most popular approach to handle non-linear battery behavior for electric vehicle scheduling is to use a linear spline interpolation of the charge curve. We show that this can lead to approximate models that underestimate the charge duration and overestimate the state of charge, which is not desirable. While the error is of second order with respect to the interpolation step size, the associated mixed-integer linear programs do not scale well with the number of spline segments. It is therefore recommendable to use coarse interpolation grids adapted to the curvature of the charge curve, and to include sufficient safety margins to ensure solutions of approximate models remain feasible subjected to the exact charge curve. KW - Electric Vehicle Scheduling KW - Non-Linear Charging Y1 - 2025 SN - 9783031584046 U6 - https://doi.org/10.1007/978-3-031-58405-3_53 SN - 2731-040X SP - 415 EP - 421 PB - Springer Nature Switzerland CY - Cham ER - TY - CHAP A1 - Maignant, Elodie A1 - Conrad, Tim A1 - von Tycowicz, Christoph T1 - Tree inference with varifold distances T2 - Geometric Science of Information. GSI 2025 N2 - In this paper, we consider a tree inference problem motivated by the critical problem in single-cell genomics of reconstructing dynamic cellular processes from sequencing data. In particular, given a population of cells sampled from such a process, we are interested in the problem of ordering the cells according to their progression in the process. This is known as trajectory inference. If the process is differentiation, this amounts to reconstructing the corresponding differentiation tree. One way of doing this in practice is to estimate the shortest-path distance between nodes based on cell similarities observed in sequencing data. Recent sequencing techniques make it possible to measure two types of data: gene expression levels, and RNA velocity, a vector that predicts changes in gene expression. The data then consist of a discrete vector field on a (subset of a) Euclidean space of dimension equal to the number of genes under consideration. By integrating this velocity field, we trace the evolution of gene expression levels in each single cell from some initial stage to its current stage. Eventually, we assume that we have a faithful embedding of the differentiation tree in a Euclidean space, but which we only observe through the curves representing the paths from the root to the nodes. Using varifold distances between such curves, we define a similarity measure between nodes which we prove approximates the shortest-path distance in a tree that is isomorphic to the target tree. Y1 - 2025 U6 - https://doi.org/10.1007/978-3-032-03921-7_30 VL - 16034 ER - TY - CHAP A1 - Kunt, Tim A1 - Buchholz, Annika A1 - Khebouri, Imene A1 - Koch, Thorsten A1 - Litzel, Ida A1 - Vu, Thi Huong T1 - Mapping the Web of Science, a large-scale graph and text-based dataset with LLM embeddings T2 - Operations Research Proceedings 2025. OR 2025 N2 - Large text data sets, such as publications, websites, and other text-based media, inherit two distinct types of features: (1) the text itself, its information conveyed through semantics, and (2) its relationship to other texts through links, references, or shared attributes. While the latter can be described as a graph structure and can be handled by a range of established algorithms for classification and prediction, the former has recently gained new potential through the use of LLM embedding models. Demonstrating these possibilities and their practicability, we investigate the Web of Science dataset, containing ~56 million scientific publications through the lens of our proposed embedding method, revealing a self-structured landscape of texts. T3 - ZIB-Report - 25-11 Y1 - 2025 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-100646 SN - 1438-0064 ER - TY - CHAP A1 - Stokke, Jo Andersson A1 - Bergmann, Ronny A1 - Hanik, Martin A1 - von Tycowicz, Christoph T1 - p-Laplacians for Manifold-valued Hypergraphs T2 - Geometric Science of Information. GSI 2025 N2 - Hypergraphs extend traditional graphs by enabling the representation of N-ary relationships through higher-order edges. Akin to a common approach of deriving graph Laplacians, we define function spaces and corresponding symmetric products on the nodes and edges to derive hypergraph Laplacians. While this has been done before for Euclidean features, this work generalizes previous hypergraph Laplacian approaches to accommodate manifold-valued hypergraphs for many commonly encountered manifolds. Y1 - 2025 U6 - https://doi.org/10.1007/978-3-032-03924-8_17 VL - 16035 ER - TY - CHAP A1 - Lößer, Ansgar A1 - Schlecht, Max A1 - Schintke, Florian A1 - Witzke, Joel A1 - Weidlich, Matthias A1 - Scheuermann, Björn T1 - Fast Min-ϵ Segmented Regression using Constant-Time Segment Merging T2 - Proceedings of the 42nd International Conference on Machine Learning Y1 - 2025 UR - https://proceedings.mlr.press/v267/losser25a.html VL - 267 SP - 40312 EP - 40327 ER -