TY - JOUR A1 - Ernst, Ariane A1 - Bankowski, Anastasia A1 - Jusyte, Meida A1 - Okunola, Toluwani A1 - Petrov, Tino A1 - Walter, Alexander A1 - Winkelmann, Stefanie T1 - Parameter Optimization for a Neurotransmission Recovery Model JF - Bulletin of Mathematical Biology N2 - We assess the empirical applicability of a simplified model for neurotransmitter release that incorporates maturation, fusion, and recovery of both release sites and vesicles. Model parameters are optimized by fitting the model to experimental data obtained from neuromuscular junction synapses of 3rd-instar Drosophila melanogaster larvae. In particular, the mean-squared error between the local extrema of the simulated total junction current and its experimental counterpart is minimized. We compare three estimation approaches, differing in the choice of optimized parameters and the fusion rate function. Despite the model’s minimalistic structure, it demonstrates a compelling ability to replicate experimental data, yielding plausible parameter estimates for five different animals. An additional identifiability analysis based on the profile likelihood reveals practical non-identifiabilities for several parameters, highlighting the need for additional constraints or data to improve estimation accuracy. Y1 - 2025 U6 - https://doi.org/10.1007/s11538-025-01486-2 VL - 87 PB - Bulletin of Mathematical Biology ER - TY - GEN A1 - Vu, Thi Huong A1 - Litzel, Ida A1 - Koch, Thorsten T1 - Similarity-based fuzzy clustering scientific articles: potentials and challenges from mathematical and computational perspectives N2 - Fuzzy clustering, which allows an article to belong to multiple clusters with soft membership degrees, plays a vital role in analyzing publication data. This problem can be formulated as a constrained optimization model, where the goal is to minimize the discrepancy between the similarity observed from data and the similarity derived from a predicted distribution. While this approach benefits from leveraging state-of-the-art optimization algorithms, tailoring them to work with real, massive databases like OpenAlex or Web of Science -- containing about 70 million articles and a billion citations -- poses significant challenges. We analyze potentials and challenges of the approach from both mathematical and computational perspectives. Among other things, second-order optimality conditions are established, providing new theoretical insights, and practical solution methods are proposed by exploiting the problem’s structure. Specifically, we accelerate the gradient projection method using GPU-based parallel computing to efficiently handle large-scale data. T3 - ZIB-Report - 25-09 KW - bibliometrics KW - fuzzy clustering KW - large-scale publication data KW - non-convex optimization KW - second-order optimality KW - gradient projection methods KW - Nesterov acceleration KW - GPU-based parallel computing Y1 - 2025 UR - https://opus4.kobv.de/opus4-zib/frontdoor/index/index/docId/10036 ER - TY - GEN A1 - Rusch, Beate A1 - Peters-Kottig, Wolfgang A1 - Ceynowa, Klaus A1 - Dinter, Joachim A1 - Dubberke, Ina A1 - Happel, Hans-Gerd A1 - Hilliger, Kirsten A1 - Kitaeva, Xenia A1 - Kleineberg, Michael A1 - Koch, Thorsten A1 - Mc Leod, Shirley A1 - Mutter, Moritz A1 - Müller, Anja A1 - Seeliger, Frank A1 - Segger, Elisabeth A1 - Stanek, Ursula A1 - Wiese, Robert A1 - Wrzesinski, Marcel T1 - KOBV Jahresbericht 2023-2024 T3 - KOBV-Jahresbericht - 2023-2024 Y1 - 2025 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-100440 SN - 0934-5892 VL - 2023-2024 CY - Berlin ER - TY - CHAP A1 - Yousefian, Maryam A1 - Donati, Luca A1 - Sikorski, Alexander A1 - Weber, Marcus A1 - Röblitz, Susanna T1 - Exploring Metastable Dynamics of Gene Regulatory Networks with ISOKANN T2 - Computational Methods in Systems Biology. CMSB 2025 N2 - Stochastic dynamical systems like gene regulatory networks (GRNs) often exhibit behavior characterized by metastable sets (representing cellular phenotypes), in which trajectories remain for long times, whereas switches between these sets in the phase space are rare events. One way to capture these rare events is to infer the system’s long-term behavior from the spectral characteristics (eigenvalues and eigenvectors) of its Koopman operator. For GRNs, the Koopman operator is based on the chemical master equation (CME), which provides a precise mathematical modeling framework for stochastic GRNs. Since the CME is typically analytically intractable, methods based on discretizing the CME operator have been developed. However, determining the number and location of metastable sets in the phase space as well as the transition rates between them remains computationally challenging, especially for large GRNs with many genes and interactions. A promising alternative method, called ISOKANN (invariant subspaces of Koopman operators with artificial neural networks) has been developed in the context of molecular dynamics. ISOKANN uses a combination of the power iteration and neural networks to learn the basis functions of an invariant subspace of the Koopman operator. In this paper, we extend the application of ISOKANN to the CME operator and apply it to two small GRNs: a genetic toggle switch model and a model for macrophage polarization. Our work opens a new field of application for the ISOKANN algorithm and demonstrates the potential of this algorithm for studying large GRNs. Y1 - 2026 U6 - https://doi.org/10.1007/978-3-032-01436-8_8 VL - 15959 SP - 126 EP - 149 ER - TY - JOUR A1 - Yousefian, Maryam A1 - Frank, Anne-Simone A1 - Weber, Marcus A1 - Röblitz, Susanna T1 - Efficient construction of Markov state models for stochastic gene regulatory networks by domain decomposition JF - BMC Bioinformatics N2 - The dynamics of many gene regulatory networks (GRNs) is characterized by the occurrence of metastable phenotypes and stochastic phenotype switches. The chemical master equation (CME) is the most accurate description to model such stochastic dynamics, whereby the long-time dynamics of the system is encoded in the spectral properties of the CME operator. Markov State Models (MSMs) provide a general framework for analyzing and visualizing stochastic multistability and state transitions based on these spectral properties. Until now, however, this approach is either limited to low-dimensional systems or requires the use of high-performance computing facilities, thus limiting its usability. Y1 - 2025 U6 - https://doi.org/10.1186/s12859-025-06174-5 VL - 26 IS - 147 ER - TY - GEN A1 - Vu, Thi Huong A1 - Koch, Thorsten T1 - Clustering scientific publications: lessons learned through experiments with a real citation network N2 - Clustering scientific publications helps uncover research structures within bibliographic databases. Graph-based methods such as spectral, Louvain, and Leiden clustering are commonly used due to their ability to model citation networks. However, their effectiveness can diminish when applied to real-world data. This study evaluates these clustering algorithms on a citation graph of about 700,000 articles and 4.6 million citations from the Web of Science. The results show that while scalable methods like Louvain and Leiden perform efficiently, their default settings often yield poor partitioning. Meaningful outcomes require careful parameter tuning, especially for large networks with uneven structures, including a dense core and loosely connected papers. These findings highlight practical lessons about the challenges of large-scale data, method selection and tuning based on specific structures of bibliometric clustering tasks. T3 - ZIB-Report - 25-05 KW - graph clustering KW - citation networks KW - Web of Science KW - bibliometric analysis KW - unsupervised learning Y1 - 2025 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-100418 ER - TY - THES A1 - el Bahnasawy, Henry T1 - Konzeption und Entwicklung einer Plattform zur geographisch verteilten Bereitstellung von latenzkritischen Anwendungen Y1 - 2025 ER - TY - JOUR A1 - Vu, Thi Huong A1 - Litzel, Ida A1 - Koch, Thorsten T1 - Similarity-based fuzzy clustering scientific articles: potentials and challenges from mathematical and computational perspectives JF - Journal of Nonlinear and Variational Analysis N2 - Fuzzy clustering, which allows an article to belong to multiple clusters with soft membership degrees, plays a vital role in analyzing publication data. This problem can be formulated as a constrained optimization model, where the goal is to minimize the discrepancy between the similarity observed from data and the similarity derived from a predicted distribution. While this approach benefits from leveraging state-of-the-art optimization algorithms, tailoring them to work with real, massive databases like OpenAlex or Web of Science - containing about 70 million articles and a billion citations - poses significant challenges. We analyze potentials and challenges of the approach from both mathematical and computational perspectives. Among other things, second-order optimality conditions are established, providing new theoretical insights, and practical solution methods are proposed by exploiting the structure of the problem. Specifically, we accelerate the gradient projection method using GPU-based parallel computing to efficiently handle large-scale data. Y1 - 2026 U6 - https://doi.org/https://doi.org/10.23952/jnva.10.2026.2.8 VL - 10 IS - 2 SP - 381 EP - 401 PB - Biemdas ER - TY - CHAP A1 - Ribera Borrell, Enric A1 - Richter, Lorenz A1 - Schütte, Christof T1 - Reinforcement Learning with Random Time Horizons T2 - Proceedings of the 42nd International Conference on Machine Learning Y1 - 2025 UR - https://raw.githubusercontent.com/mlresearch/v267/main/assets/borrell25a/borrell25a.pdf VL - 267 SP - 5101 EP - 5123 ER - TY - JOUR A1 - Läuter, Matthias A1 - Kramer, Tobias T1 - Rotation dynamics and torque efficiency of cometary nuclei JF - Astronomy & Astrophysics N2 - The dynamics of a rigid cometary nucleus is described by the evolutions of its center-of-mass and of its rotation state. Solar irradiation that reaches the surface of a cometary nucleus causes the sublimation of volatiles that form the coma around the nucleus. The sublimation process transfers linear momentum and rotational angular momentum from the nucleus to the surrounding space, and thus affects the dynamics via nongravitational forces and nongravitational torques. With the exception of close approaches to planets, these torques exert the dominant influence on the rotation states of cometary nuclei. The Rosetta mission 2014-2016 accompanying comet 67P/Churyumov-Gerasimenko provides the longest continuous observational data to track its rotation state. In particular, the data set encompasses the direction of the angular velocity, denoted by ω, and the angular frequency |ω|over a time period of approximately 700 days. The observed change of the rotation state is not explained by a low heat conductivity thermophysical model in combination with a homogeneous surface ice coverage of comet 67P. Spatially and/or temporally varying weights for effective active fraction with respect to a prescribed set of surface regions provide a potential solution to this problem. Here, we present a methodology for classifying the surface based on vectorial efficiency of the torque. On any cometary surface without geometric symmetry, the methodology highlights the decomposition into eight characteristic regions that encode the signs of torque efficiency with respect to all vector components. This decomposition is divided into two subsets of four regions each of which is located in one of both hemispheric regions. We analyze in detail rotation states close to lowest energy and different thermophysical models, and we discuss how the uncertainties of observations affect the model parameters. We study the occurrence of these regions for an oblate ellipsoid, a nearprolate ellipsoid, a bilobed shape, and a shape model analogous to that of comet 67P. The sensitivity analysis for comet 67P indicates that the observations constrain only one of the eight weights uniquely. The other directions are poorly constrained and show the limitation of the rotational data to determine the regional activity on comet 67P. Y1 - 2025 U6 - https://doi.org/10.1051/0004-6361/202553845 VL - 699 SP - A75 ER - TY - GEN A1 - Riedmüller, Stephanie A1 - Buchholz, Annika A1 - Zittel, Janina T1 - Enhancing Multi-Energy Modeling: The Role of Mixed-Integer Optimization Decisions N2 - The goal to decarbonize the energy sector has led to increased research in modeling and optimizing multi-energy systems. One of the most promising and popular techniques for modeling and solving (multi-)energy optimization problems is (multi-objective) mixed-integer programming, valued for its ability to represent the complexities of integrated energy systems. While the literature often focuses on deriving mathematical formulations and parameter settings, less attention is given to critical post-formulation decisions. Modeling multi-energy systems as mixed-integer linear optimization programs demands decisions across multiple degrees of freedom. Key steps include reducing a real-world multi energy network into an abstract topology, defining variables, formulating the relevant (in-)equalities to represent technical requirements, setting (multiple) objectives, and integrating these elements into a mixed-integer program (MIP). However, with these elements fixed, the specific transformation of the abstract topology into a graph structure and the construction of the MIP remain non-uniquely. These choices can significantly impact user-friendliness, problem size, and computational efficiency, thus affecting the feasibility and efficiency of modeling efforts. In this work, we identify and analyze the additional degrees of freedom and describe two distinct approaches to address them. The approaches are compared regarding mathematical equivalence, suitability for solution algorithms, and clarity of the underlying topology. A case study on a realistic subarea of Berlin’s district heating network involving tri-objective optimization for a unit commitment problem demonstrates the practical significance of these decisions. By highlighting these critical yet often overlooked aspects, our work equips energy system modelers with insights to improve computational efficiency, scalability, and interpretability in their optimization efforts, ultimately enhancing the practicality and effectiveness of multi-energy system models. T3 - ZIB-Report - 25-08 Y1 - 2025 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-100329 SN - 1438-0064 ER - TY - JOUR A1 - Khatri, Vinod A1 - Boback, Nico A1 - Abdelwahab, Hassan A1 - Niemeyer, Daniela A1 - Palmer, Tahlia M. A1 - Sahoo, Anil Kumar A1 - Kerkhoff, Yannic A1 - Ludwig, Kai A1 - Balci, Dilara A1 - Trimpert, Jakob A1 - Haag, Rainer A1 - Povolotsky, Tatyana L. A1 - Netz, Roland R. A1 - Drosten, Christian A1 - Lauster, Daniel C. A1 - Bhatia, Sumati T1 - Polysialosides outperform sulfated analogs for the inhibition of SARS-CoV-2 JF - Small N2 - Both polysialosides and polysulfates are known to interact with the receptor binding domain (RBD) of the SARS-CoV-2 spike protein. However, a comprehensive site by site analysis of their binding affinities and potential synergistic antiviral effects have not been performed. Here, we report on the synthesis of polysialosides with nanomolar binding affinities to spike proteins of SARS-CoV-2 in solution using microscale thermophoresis (MST). The dendritic polyglycerol based polysialosides dPG500(SA)0.55 and dPG500(SA)0.25, with a dissociation constant Kd of 4.78 nM and 10.85 nM, respectively, bind ~500 times stronger than the high density polysulfated analog dPG500(OSO3Na)0.55, to intact SARS-CoV-2 virus particles or isolated spike protein. In fact, the presence of sulfate groups in a heteromultivalent compound dPG500(SA)0.20(OSO3Na)0.20 weakens the binding to spike proteins. A polycarboxylated analog does not bind to SARS-CoV-2, ruling out that the interaction of polysialoside is simply driven by electrostatic interactions. Furthermore, we found potent nanomolar binding of dPG500(SA)0.55 to SARS-CoV-2 variant B.1.617 (Delta) and B.1.1.529 (Omicron) RBD. Using explicit-solvent all-atom molecular dynamics (MD) simulations and docking studies, we obtain atomistic details on the interaction of different functional groups with the SARS-CoV-2 RBD and their binding affinities. Our data support the conclusion that sialosides interact stronger with RBD than sulfates. Notably, our most affine binder dPG500(SA)0.55 inhibits SARS-CoV-2 (WT, D614G) replication up to 98.6% at low nanomolar concentrations. Y1 - 2025 UR - https://doi.org/10.26434/chemrxiv-2024-8b0gb U6 - https://doi.org/10.1002/smll.202500719 VL - 21 IS - 34 ER - TY - JOUR A1 - Fricke, Mackenzie S. A1 - Frometa, Magalee R. A1 - Kerkhoff, Yannic A1 - Bernhard, Samuel P. A1 - Tahir, Ramat S. A1 - Quaas, Elisa A1 - Totten, William H. A1 - Haag, Rainer A1 - Achazi, Katharina A1 - Cloninger, Mary J. T1 - The toxicity, uptake, and impact on galectin-3 mediated apoptosis of lactose functionalized PAMAM dendrimers JF - Materials Advances N2 - Poly(amidoamine) (PAMAM) dendrimers functionalized with ligands that are designed to interact with biological receptors are important macromolecules for the elucidation and mediation of biological recognition processes. Specifically, carbohydrate functionalized dendrimers are useful synthetic multivalent systems for the study of multivalent protein–carbohydrate interactions. For example, lactose functionalized glycodendrimers can be used to discern the function of galectins, galactoside-binding proteins that are often over-expressed during cancer progression. In order to effectively interpret cancer cellular assays using glycodendrimers, however, their properties in the presence of cells must first be assessed. Macromolecules that are taken up by cells would be expected to have access to many different cell signaling pathways and modes of action that solely extracellular macromolecules cannot utilize. In addition, macromolecules that display cellular toxicity could not be used as drug delivery vehicles. Here, we report fundamental studies of cellular toxicity, viability, and uptake with four generations of lactose functionalized PAMAM dendrimers. In all cases, the dendrimers are readily taken up by the cells but do not display any significant cellular toxicity. The glycodendrimers also increase cellular apoptosis, suggesting that they may abrogate the antiapoptotic protections afforded by galectin-3 to cancer cells. The results reported here indicate that appropriately functionalized PAMAM dendrimers can be used as nontoxic tools for the study and mediation of both extra and intracellular cancer processes. Y1 - 2025 U6 - https://doi.org/10.1039/D4MA00782D VL - 6 SP - 3171 EP - 3184 ER - TY - CHAP 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 T2 - Proceedings of the 38th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems 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. Y1 - 2025 ER - TY - JOUR A1 - Chen, Ying A1 - Koch, Thorsten A1 - Peng, Hanqui A1 - Zhang, Hongrui T1 - Benchmarking of Quantum and Classical Computing in Large-Scale Dynamic Portfolio Optimization Under Market Frictions N2 - Quantum computing is poised to transform the financial industry, yet its advantages over traditional methods have not been evidenced. As this technology rapidly evolves, benchmarking is essential to fairly evaluate and compare different computational strategies. This study presents a challenging yet solvable problem of large-scale dynamic portfolio optimization under realistic market conditions with frictions. We frame this issue as a Quadratic Unconstrained Binary Optimization (QUBO) problem, compatible with digital computing and ready for quantum computing, to establish a reliable benchmark. By applying the latest solvers to real data, we release benchmarks that help verify true advancements in dynamic trading strategies, either quantum or digital computing, ensuring that reported improvements in portfolio optimization are based on robust, transparent, and comparable metrics. Y1 - 2025 ER - TY - JOUR A1 - Hawthorne, Felipe A1 - Raulino, Paulo R. E. A1 - Rodrigues Pelá, Ronaldo A1 - Woellner, Cristiano F. T1 - Efficient and Accurate Machine Learning Interatomic Potential for Graphene: Capturing Stress–Strain and Vibrational Properties JF - The Journal of Physical Chemistry C Y1 - 2025 U6 - https://doi.org/10.1021/acs.jpcc.5c03470 VL - 129 SP - 16319 EP - 16326 ER - TY - JOUR A1 - Rossi, Thomas A1 - Qiao, Lu A1 - Dykstra, Conner P. A1 - Rodrigues Pelá, Ronaldo A1 - Gnewkow, Richard A1 - Wallick, Rachel F. A1 - Burke, John H. A1 - Nicholas, Erin A1 - March, Anne-Marie A1 - Doumy, Gilles A1 - Buchholz, D. Bruce A1 - Deparis, Christiane A1 - Zuñiga-Pérez, Jesus A1 - Weise, Michael A1 - Ellmer, Klaus A1 - Fondell, Mattis A1 - Draxl, Claudia A1 - van der Veen, Renske T1 - Dynamic control of X-ray core-exciton resonances by Coulomb screening in photoexcited semiconductors JF - Communications Materials Y1 - 2025 U6 - https://doi.org/10.1038/s43246-025-00909-w VL - 6 SP - 191 ER - TY - CHAP A1 - Riedmüller, Stephanie A1 - Buchholz, Annika A1 - Zittel, Janina T1 - Enhancing Multi-Energy Modeling: The Role of Mixed-Integer Optimization Decisions T2 - The 38th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems – ECOS 2025 N2 - The goal to decarbonize the energy sector has led to increased research in modeling and optimizing multi-energy systems. One of the most promising and popular techniques for modeling and solving (multi-)energy optimization problems is (multi-objective) mixed-integer programming, valued for its ability to represent the complexities of integrated energy systems. While the literature often focuses on deriving mathematical formulations and parameter settings, less attention is given to critical post-formulation decisions. Modeling multi-energy systems as mixed-integer linear optimization programs demands decisions across multiple degrees of freedom. Key steps include reducing a real-world multi energy network into an abstract topology, defining variables, formulating the relevant (in-)equalities to represent technical requirements, setting (multiple) objectives, and integrating these elements into a mixed-integer program (MIP). However, with these elements fixed, the specific transformation of the abstract topology into a graph structure and the construction of the MIP remain non-uniquely. These choices can significantly impact user-friendliness, problem size, and computational efficiency, thus affecting the feasibility and efficiency of modeling efforts. In this work, we identify and analyze the additional degrees of freedom and describe two distinct approaches to address them. The approaches are compared regarding mathematical equivalence, suitability for solution algorithms, and clarity of the underlying topology. A case study on a realistic subarea of Berlin’s district heating network involving tri-objective optimization for a unit commitment problem demonstrates the practical significance of these decisions. By highlighting these critical yet often overlooked aspects, our work equips energy system modelers with insights to improve computational efficiency, scalability, and interpretability in their optimization efforts, ultimately enhancing the practicality and effectiveness of multi-energy system models. Y1 - 2025 ER - TY - JOUR A1 - Liu, Zichen A1 - Zhang, Wei A1 - Schütte, Christof A1 - Li, Tiejun T1 - Riemannian denoising diffusion probabilistic models JF - Communications in Mathematical Sciences N2 - We propose Riemannian Denoising Diffusion Probabilistic Models (RDDPMs) for learning distributions on submanifolds of Euclidean space that are level sets of functions, including most of the manifolds relevant to applications. Existing methods for generative modeling on manifolds rely on substantial geometric information such as geodesic curves or eigenfunctions of the Laplace-Beltrami operator and, as a result, they are limited to manifolds where such information is available. In contrast, our method, built on a projection scheme, can be applied to more general manifolds, as it only requires being able to evaluate the value and the first order derivatives of the function that defines the submanifold. We provide a theoretical analysis of our method in the continuous-time limit, which elucidates the connection between our RDDPMs and score-based generative models on manifolds. The capability of our method is demonstrated on datasets from previous studies and on new datasets sampled from two high-dimensional manifolds, i.e. SO(10) and the configuration space of molecular system alanine dipeptide with fixed dihedral angle. Y1 - 2025 ER - TY - JOUR A1 - Sengupta, Agniva A1 - Zachow, Stefan T1 - Shape-from-Template with Generalised Camera JF - Image and Vision Computing N2 - This article presents a new method for non-rigidly registering a 3D shape to 2D keypoints observed by a constellation of multiple cameras. Non-rigid registration of a 3D shape to observed 2D keypoints, i.e., Shape-from-Template (SfT), has been widely studied using single images, but SfT with information from multiple-cameras jointly opens new directions for extending the scope of known use-cases such as 3D shape registration in medical imaging and registration from hand-held cameras, to name a few. We represent such multi-camera setup with the generalised camera model; therefore any collection of perspective or orthographic cameras observing any deforming object can be registered. We propose multiple approaches for such SfT: the first approach where the corresponded keypoints lie on a direction vector from a known 3D point in space, the second approach where the corresponded keypoints lie on a direction vector from an unknown 3D point in space but with known orientation w.r.t some local reference frame, and a third approach where, apart from correspondences, the silhouette of the imaged object is also known. Together, these form the first set of solutions to the SfT problem with generalised cameras. The key idea behind SfT with generalised camera is the improved reconstruction accuracy from estimating deformed shape while utilising the additional information from the mutual constraints between multiple views of a deformed object. The correspondence-based approaches are solved with convex programming while the silhouette-based approach is an iterative refinement of the results from the convex solutions. We demonstrate the accuracy of our proposed methods on many synthetic and real data. Y1 - 2025 U6 - https://doi.org/10.1016/j.imavis.2025.105579 VL - 162 ER - TY - JOUR A1 - Zhou, Lei A1 - Chen, Ying A1 - Peng, Hanqiu A1 - Koch, Thorsten T1 - Is innovation slowing down? Insights from the AIMS framework of patent values JF - Expert Systems with Applications N2 - Amidst the unprecedented expansion of scientific and technological knowledge over the past century, concerns persist regarding a slowdown in innovation. To address this, we introduce the AIMS framework, which categorizes patents into four types—Aurora, Invisible, Mirage, and Success—based on their respective inherent scientific values and market-recognized economic values. Utilizing USPTO patent and citation data from 1976 to 2022, our analysis reveals an increasing volume of patent issuances but a concerning dilution in scientific quality starting in the 2000s. This trend is primarily attributed to the rise of low scientific value patents—categorized as Mirage and Invisible—and a modest decline in high-impact scientific patents—categorized as Success and Aurora. Meanwhile, the economic value of patents has risen, especially noted with the growth in Mirage patents since the 2010s, indicating a shift towards strategies that prioritize market-driven patenting. This study highlights the evolving nature of patents from mere indicators of scientific innovation to strategic tools for market dominance, providing an alternative understanding of patent value and its implications for firms’ strategic decisions over patent issuance across different sectors. Y1 - 2025 U6 - https://doi.org/10.1016/j.eswa.2025.127355 VL - 280 SP - 127355 ER - TY - JOUR A1 - Bostanci, Inan A1 - Conrad, Tim T1 - Integrating Agent-Based and Compartmental Models for Infectious Disease Modeling: A Novel Hybrid Approach JF - Journal of Artificial Societies and Social Simulation N2 - This study investigates the spatial integration of agent-based models (ABMs) and compartmental models for infectious disease modeling, presenting a novel hybrid approach and examining its implications. ABMs offer detailed insights by simulating interactions and decisions among individuals but are computationally expensive for large populations. Compartmental models capture population-level dynamics more efficiently but lack granular detail. We developed a hybrid model that aims to balance the granularity of ABMs with the computational efficiency of compartmental models, offering a more nuanced understanding of disease spread in diverse scenarios, including large populations. This model spatially couples discrete and continuous populations by integrating an ordinary differential equation model with a spatially explicit ABM. Our key objectives were to systematically assess the consistency of disease dynamics and the computational efficiency across various configurations. For this, we evaluated two experimental scenarios and varied the influence of each sub-model via spatial distribution. In the first, the ABM component modeled a homogeneous population; in the second, it simulated a heterogeneous population with landscape-driven movement. Results show that the hybrid model can significantly reduce computational costs but is sensitive to between-model differences, highlighting the importance of model equivalence in hybrid approaches. The code is available at: git.zib.de/ibostanc/hybrid_abm_ode. Y1 - 2025 U6 - https://doi.org/10.18564/jasss.5567 VL - 28 IS - 1 ER -