TY - JOUR A1 - Gupta, Pranshu A1 - Hamann, Fabian A1 - Müyesser, Alp A1 - Sgueglia, Amedeo A1 - Parczyk, Olaf T1 - A general approach to transversal versions of Dirac-type theorems JF - Bulletin of the London Mathematical Society Y1 - 2023 VL - 55 IS - 6 SP - 2817 EP - 2839 ER - TY - JOUR A1 - Barros, Gabriel F. A1 - Cavalar, Bruno P. A1 - Mota, Guilherme O. A1 - Parczyk, Olaf T1 - Anti-Ramsey threshold of cycles for sparse graphs JF - Discrete Applied Mathematics Y1 - 2022 ER - TY - JOUR A1 - Kohayakawa, Yoshiharu A1 - Mota, Guilherme O. A1 - Schnitzer, Jakob A1 - Parczyk, Olaf T1 - Anti-Ramsey threshold of complete graphs for sparse graphs JF - Discrete Mathematics Y1 - 2023 ER - TY - JOUR A1 - Hahn-Klimroth, Max A1 - Parczyk, Olaf A1 - Person, Yury T1 - Minimum degree conditions for containing an r-regular r-connected spanning subgraph JF - European Journal of Combinatorics Y1 - 2024 U6 - https://doi.org/10.1016/j.ejc.2024.103940 VL - 118 SP - 103940 ER - TY - JOUR A1 - Mundinger, Konrad A1 - Pokutta, Sebastian A1 - Spiegel, Christoph A1 - Zimmer, Max T1 - Extending the Continuum of Six-Colorings JF - Geombinatorics Quarterly Y1 - 2024 VL - 34 IS - 1 SP - 20 EP - 29 ER - TY - JOUR A1 - Gebhard, Oliver A1 - Hahn-Klimroth, Max A1 - Penschuck, Manuel A1 - Rolvien, Maurice A1 - Scarlett, Jonathan A1 - Tan, Nelvin A1 - Parczyk, Olaf T1 - Near optimal sparsity-constrained group testing: improved bounds JF - IEEE Transactions on Information Theory Y1 - 2022 ER - TY - JOUR A1 - Parczyk, Olaf A1 - Pokutta, Sebastian A1 - Spiegel, Christoph A1 - Szabó, Tibor T1 - New Ramsey multiplicity bounds and search heuristics JF - Foundations of Computational Mathematics Y1 - 2024 U6 - https://doi.org/10.1007/s10208-024-09675-6 ER - TY - JOUR A1 - Allen, Peter A1 - Pfenninger, Vincent A1 - Parczyk, Olaf T1 - Resilience for tight Hamiltonicity JF - Combinatorial Theory Y1 - 2024 U6 - https://doi.org/10.5070/C64163846 VL - 4 IS - 1 ER - TY - JOUR A1 - Böttcher, Julia A1 - Sgueglia, Amedeo A1 - Skokan, Jozef A1 - Parczyk, Olaf T1 - Triangles in randomly perturbed graphs JF - Combinatorics, Probability and Computing Y1 - 2022 ER - TY - JOUR A1 - Vu-Han, Tu-Lan A1 - Schettino, Rodrigo Bermudez A1 - Weiß, Claudia A1 - Perka, Carsten A1 - Winkler, Tobias A1 - Sunkara, Vikram A1 - Pumberger, Matthias T1 - An interpretable data-driven prediction model to anticipate scoliosis in spinal muscular atrophy in the era of (gene-) therapies JF - Scientific Reports N2 - 5q-spinal muscular atrophy (SMA) is a neuromuscular disorder (NMD) that has become one of the first 5% treatable rare diseases. The efficacy of new SMA therapies is creating a dynamic SMA patient landscape, where disease progression and scoliosis development play a central role, however, remain difficult to anticipate. New approaches to anticipate disease progression and associated sequelae will be needed to continuously provide these patients the best standard of care. Here we developed an interpretable machine learning (ML) model that can function as an assistive tool in the anticipation of SMA-associated scoliosis based on disease progression markers. We collected longitudinal data from 86 genetically confirmed SMA patients. We selected six features routinely assessed over time to train a random forest classifier. The model achieved a mean accuracy of 0.77 (SD 0.2) and an average ROC AUC of 0.85 (SD 0.17). For class 1 ‘scoliosis’ the average precision was 0.84 (SD 0.11), recall 0.89 (SD 0.22), F1-score of 0.85 (SD 0.17), respectively. Our trained model could predict scoliosis using selected disease progression markers and was consistent with the radiological measurements. During post validation, the model could predict scoliosis in patients who were unseen during training. We also demonstrate that rare disease data sets can be wrangled to build predictive ML models. Interpretable ML models can function as assistive tools in a changing disease landscape and have the potential to democratize expertise that is otherwise clustered at specialized centers. Y1 - 2024 U6 - https://doi.org/10.1038/s41598-024-62720-w VL - 14 IS - 11838 ER - TY - JOUR A1 - Siqueira Rodrigues, Lucas A1 - Schmidt, Timo Torsten A1 - Israel, Johann Habakuk A1 - Nyakatura, John A1 - Zachow, Stefan A1 - Kosch, Thomas T1 - Comparing the Effects of Visual, Haptic, and Visuohaptic Encoding on Memory Retention of Digital Objects in Virtual Reality JF - NordiCHI '24: Proceedings of the 13th Nordic Conference on Human-Computer Interaction N2 - Although Virtual Reality (VR) has undoubtedly improved human interaction with 3D data, users still face difficulties retaining important details of complex digital objects in preparation for physical tasks. To address this issue, we evaluated the potential of visuohaptic integration to improve the memorability of virtual objects in immersive visualizations. In a user study (N=20), participants performed a delayed match-to-sample task where they memorized stimuli of visual, haptic, or visuohaptic encoding conditions. We assessed performance differences between the conditions through error rates and response time. We found that visuohaptic encoding significantly improved memorization accuracy compared to unimodal visual and haptic conditions. Our analysis indicates that integrating haptics into immersive visualizations enhances the memorability of digital objects. We discuss its implications for the optimal encoding design in VR applications that assist professionals who need to memorize and recall virtual objects in their daily work. Y1 - 2024 U6 - https://doi.org/10.1145/3679318.3685349 SP - 1 EP - 13 ER - TY - JOUR A1 - Veldhuijzen, Ben A1 - Veltkamp, Remco C. A1 - Ikne, Omar A1 - Allaert, Benjamin A1 - Wannous, Hazem A1 - Emporio, Marco A1 - Giachetti, Andrea A1 - LaViola Jr, Joseph J. A1 - He, Ruiwen A1 - Benhabiles, Halim A1 - Cabani, Adnane A1 - Fleury, Anthony A1 - Hammoudi, Karim A1 - Gavalas, Konstantinos A1 - Vlachos, Christoforos A1 - Papanikolaou, Athanasios A1 - Romanelis, Ioannis A1 - Fotis, Vlassis A1 - Arvanitis, Gerasimos A1 - Moustakas, Konstantinos A1 - Hanik, Martin A1 - Nava-Yazdani, Esfandiar A1 - von Tycowicz, Christoph T1 - SHREC 2024: Recognition Of Dynamic Hand Motions Molding Clay JF - Computers & Graphics N2 - Gesture recognition is a tool to enable novel interactions with different techniques and applications, like Mixed Reality and Virtual Reality environments. With all the recent advancements in gesture recognition from skeletal data, it is still unclear how well state-of- the-art techniques perform in a scenario using precise motions with two hands. This paper presents the results of the SHREC 2024 contest organized to evaluate methods for their recognition of highly similar hand motions using the skeletal spatial coordinate data of both hands. The task is the recognition of 7 motion classes given their spatial coordinates in a frame-by-frame motion. The skeletal data has been captured using a Vicon system and pre-processed into a coordinate system using Blender and Vicon Shogun Post. We created a small, novel dataset with a high variety of durations in frames. This paper shows the results of the contest, showing the techniques created by the 5 research groups on this challenging task and comparing them to our baseline method. Y1 - 2024 U6 - https://doi.org/10.1016/j.cag.2024.104012 VL - 123 SP - 104012 ER - TY - JOUR A1 - Weimann, Kuba A1 - Conrad, Tim T1 - Federated Learning with Deep Neural Networks: A Privacy-Preserving Approach to Enhanced ECG Classification JF - IEEE Journal of Biomedical and Health Informatics Y1 - 2024 U6 - https://doi.org/10.1109/JBHI.2024.3427787 VL - 28 IS - 11 ER - TY - JOUR A1 - Das, Somenath A1 - Sridharamurthy, Raghavendra A1 - Natarajan, Vijay T1 - Time-varying Extremum Graphs JF - Computer Graphics Forum N2 - We introduce time-varying extremum graph (TVEG), a topological structure to support visualization and analysis of a time- varying scalar field. The extremum graph is a substructure of the Morse-Smale complex. It captures the adjacency relationship between cells in the Morse decomposition of a scalar field. We define the TVEG as a time-varying extension of the extremum graph and demonstrate how it captures salient feature tracks within a dynamic scalar field. We formulate the construction of the TVEG as an optimization problem and describe an algorithm for computing the graph. We also demonstrate the capabilities of TVEG towards identification and exploration of topological events such as deletion, generation, split, and merge within a dynamic scalar field via comprehensive case studies including a viscous fingers and a 3D von Kármán vortex street dataset. Y1 - 2024 U6 - https://doi.org/10.1111/cgf.15162 VL - 43 IS - 6 SP - e15162 ER - TY - JOUR A1 - Djurdjevac Conrad, Natasa A1 - Quang Vu, Nhu A1 - Nagel, Soeren T1 - Co-evolving networks for opinion and social dynamics in agent-based models JF - Chaos: An Interdisciplinary Journal of Nonlinear Science N2 - The rise of digital social media has strengthened the coevolution of public opinions and social interactions, that shape social structures and collective outcomes in increasingly complex ways. Existing literature often explores this interplay as a one-directional influence, focusing on how opinions determine social ties within adaptive networks. However, this perspective overlooks the intrinsic dynamics driving social interactions, which can significantly influence how opinions form and evolve. In this work, we address this gap, by introducing the co-evolving opinion and social dynamics using stochastic agent-based models. Agents' mobility in a social space is governed by both their social and opinion similarity with others. Similarly, the dynamics of opinion formation is driven by the opinions of agents in their social vicinity. We analyze the underlying social and opinion interaction networks and explore the mechanisms influencing the appearance of emerging phenomena, like echo chambers and opinion consensus. To illustrate the model's potential for real-world analysis, we apply it to General Social Survey data on political identity and public opinion regarding governmental issues. Our findings highlight the model's strength in capturing the coevolution of social connections and individual opinions over time. Y1 - 2024 U6 - https://doi.org/10.1063/5.0226054 SN - 1054-1500 VL - 34 IS - 9 ER - TY - JOUR A1 - Fürst, Steffen A1 - Conrad, Tim A1 - Jaeger, Carlo A1 - Wolf, Sarah T1 - Vahana.jl - A framework (not only) for large-scale agent-based models JF - Proceedings of Social Simulation Conference 2024 (SSC24) Y1 - 2024 ER - TY - JOUR A1 - Alchikh, Maren A1 - Conrad, Tim A1 - Obermeier, Patrick A1 - Ma, Xiaolin A1 - Schweiger, Brunhilde A1 - Opota, Onya A1 - Rath, Barbara T1 - Disease Burden and Inpatient Management of Children with Acute Respiratory Viral Infections during the Pre-COVID Era in Germany: A Cost-of-Illness Study JF - Viruses N2 - Respiratory viral infections (RVIs) are common reasons for healthcare consultations. The inpatient management of RVIs consumes significant resources. From 2009 to 2014, we assessed the costs of RVI management in 4776 hospitalized children aged 0–18 years participating in a quality improvement program, where all ILI patients underwent virologic testing at the National Reference Centre followed by detailed recording of their clinical course. The direct (medical or non-medical) and indirect costs of inpatient management outside the ICU (‘non-ICU’) versus management requiring ICU care (‘ICU’) added up to EUR 2767.14 (non-ICU) vs. EUR 29,941.71 (ICU) for influenza, EUR 2713.14 (non-ICU) vs. EUR 16,951.06 (ICU) for RSV infections, and EUR 2767.33 (non-ICU) vs. EUR 14,394.02 (ICU) for human rhinovirus (hRV) infections, respectively. Non-ICU inpatient costs were similar for all eight RVIs studied: influenza, RSV, hRV, adenovirus (hAdV), metapneumovirus (hMPV), parainfluenza virus (hPIV), bocavirus (hBoV), and seasonal coronavirus (hCoV) infections. ICU costs for influenza, however, exceeded all other RVIs. At the time of the study, influenza was the only RVI with antiviral treatment options available for children, but only 9.8% of influenza patients (non-ICU) and 1.5% of ICU patients with influenza received antivirals; only 2.9% were vaccinated. Future studies should investigate the economic impact of treatment and prevention of influenza, COVID-19, and RSV post vaccine introduction. Y1 - 2024 U6 - https://doi.org/10.3390/v16040507 VL - 16 IS - 4 ER - TY - JOUR A1 - Manley, Phillip A1 - Hammerschmidt, Martin A1 - Zschiedrich, Lin A1 - Jäger, Klaus A1 - Becker, Christiane A1 - Burger, Sven T1 - Modelling luminescent coupling in multi-junction solar cells: perovskite silicon tandem case study JF - Proc. SPIE Y1 - 2024 U6 - https://doi.org/10.1117/12.3023941 VL - 13014 SP - 1301404 ER - TY - JOUR A1 - Raharinirina, Nomenjanahary Alexia A1 - Sunkara, Vikram A1 - von Kleist, Max A1 - Fackeldey, Konstantin A1 - Weber, Marcus T1 - Multi-Input data ASsembly for joint Analysis (MIASA): A framework for the joint analysis of disjoint sets of variables JF - PLOS ONE Y1 - 2024 U6 - https://doi.org/10.1371/journal.pone.0302425 VL - 19 IS - 5 PB - Public Library of Science ER - TY - JOUR A1 - Pfeuffer, Julianus A1 - Bielow, Chris A1 - Wein, Samuel A1 - Jeong, Kyowon A1 - Netz, Eugen A1 - Walter, Axel A1 - Alka, Oliver A1 - Nilse, Lars A1 - Colaianni, Pasquale Domenico A1 - McCloskey, Douglas A1 - Kim, Jihyung A1 - Rosenberger, George A1 - Bichmann, Leon A1 - Walzer, Mathias A1 - Veit, Johannes A1 - Boudaud, Bertrand A1 - Bernt, Matthias A1 - Patikas, Nikolaos A1 - Pilz, Matteo A1 - Startek, Michał Piotr A1 - Kutuzova, Svetlana A1 - Heumos, Lukas A1 - Charkow, Joshua A1 - Sing, Justin Cyril A1 - Feroz, Ayesha A1 - Siraj, Arslan A1 - Weisser, Hendrik A1 - Dijkstra, Tjeerd M. H. A1 - Perez-Riverol, Yasset A1 - Röst, Hannes A1 - Kohlbacher, Oliver A1 - Sachsenberg, Timo T1 - OpenMS 3 enables reproducible analysis of large-scale mass spectrometry data JF - Nature Methods Y1 - 2024 U6 - https://doi.org/10.1038/s41592-024-02197-7 SN - 1548-7091 VL - 21 IS - 3 SP - 365 EP - 367 PB - Springer Science and Business Media LLC ER - TY - JOUR A1 - Ernst, Marco A1 - Asselineau, Charles-Alexis A1 - Tillmann, Peter A1 - Jäger, Klaus A1 - Becker, Christiane T1 - Modelling bifacial irradiance – Step-by-step comparison and validation of view factor and ray tracing models JF - Appl. Energy Y1 - 2024 U6 - https://doi.org/10.1016/j.apenergy.2024.123574 SN - 0306-2619 VL - 369 SP - 123574 PB - Elsevier BV ER - TY - JOUR A1 - Van Bulck, David A1 - Goossens, Dries A1 - Clarner, Jan-Patrick A1 - Dimitsas, Angelos A1 - Fonseca, Georg H. G. A1 - Lamas-Fernandez, Carlos A1 - Lester, Martin Mariusz A1 - Pedersen, Jaap A1 - Phillips, Antony E. A1 - Rosati, Roberto Maria T1 - Which algorithm to select in sports timetabling? JF - European Journal of Operational Research N2 - Any sports competition needs a timetable, specifying when and where teams meet each other. The recent International Timetabling Competition (ITC2021) on sports timetabling showed that, although it is possible to develop general algorithms, the performance of each algorithm varies considerably over the problem instances. This paper provides a problem type analysis for sports timetabling, resulting in powerful insights into the strengths and weaknesses of eight state-of-the-art algorithms. Based on machine learning techniques, we propose an algorithm selection system that predicts which algorithm is likely to perform best based on the type of competition and constraints being used (i.e., the problem type) in a given sports timetabling problem instance. Furthermore, we visualize how the problem type relates to algorithm performance, providing insights and possibilities to further enhance several algorithms. Finally, we assess the empirical hardness of the instances. Our results are based on large computational experiments involving about 50 years of CPU time on more than 500 newly generated problem instances. Y1 - 2024 U6 - https://doi.org/10.1016/j.ejor.2024.06.005 VL - 318 IS - 2 SP - 575 EP - 591 ER - TY - JOUR A1 - Paltra, Sydney A1 - Bostanci, Inan A1 - Nagel, Kai T1 - The effect of mobility reductions on infection growth is quadratic in many cases JF - Scientific Reports Y1 - 2024 U6 - https://doi.org/10.1038/s41598-024-64230-1 VL - 14 ER - TY - JOUR A1 - Conrad, Tim A1 - Ferrer, Eloi A1 - Mietchen, Daniel A1 - Pusch, Larissa A1 - Stegmuller, Johannes A1 - Schubotz, Moritz T1 - Making Mathematical Research Data FAIR: Pathways to Improved Data Sharing JF - Scientific Data Y1 - 2024 U6 - https://doi.org/10.1038/s41597-024-03480-0 VL - 11 ER - TY - JOUR A1 - Meduri, Dhruv A1 - Sharma, Mohit A1 - Natarajan, Vijay T1 - Jacobi Set Simplification for Tracking Topological Features in Time-Varying Scalar Fields JF - Vis Comput N2 - The Jacobi set of a bivariate scalar field is the set of points where the gradients of the two constituent scalar fields align with each other. It captures the regions of topological changes in the bivariate field. The Jacobi set is a bivariate analog of critical points, and may correspond to features of interest. In the specific case of time-varying fields and when one of the scalar fields is time, the Jacobi set corresponds to temporal tracks of critical points, and serves as a feature-tracking graph. The Jacobi set of a bivariate field or a time-varying scalar field is complex, resulting in cluttered visualizations that are difficult to analyze. This paper addresses the problem of Jacobi set simplification. Specifically, we use the time-varying scalar field scenario to introduce a method that computes a reduced Jacobi set. The method is based on a stability measure called robustness that was originally developed for vector fields and helps capture the structural stability of critical points. We also present a mathematical analysis for the method, and describe an implementation for 2D time-varying scalar fields. Applications to both synthetic and real-world datasets demonstrate the effectiveness of the method for tracking features. Y1 - 2024 U6 - https://doi.org/10.1007/s00371-024-03484-2 VL - 40 SP - 4843 EP - 4855 ER - TY - JOUR A1 - Singh, Upkar A1 - Vinayachandran, P. N. A1 - Natarajan, Vijay T1 - Advection-Based Tracking and Analysis of Salinity Movement in the Indian Ocean JF - Computers & Geosciences N2 - The Bay of Bengal (BoB) has maintained its salinity distribution over the years despite a continuous flow of fresh water entering it through rivers on the northern coast, which is capable of diluting the salinity. This can be attributed to the cyclic flow of high salinity water (>= 35 psu), coming from Arabian sea and entering BoB from the south, which moves northward and mixes with this fresh water. The movement of this high salinity water has been studied and analyzed in previous work (Singh et al., 2022). This paper extends the computational methods and analysis of salinity movement. Specifically, we introduce an advection based feature definition that represents the movement of high salinity water, and describe algorithms to track their evolution over time. This method allows us to trace the movement of high salinity water caused due to ocean currents. The method is validated via comparison with established observations on the flow of high salinity water in the BoB, including its entry from the Arabian Sea and its movement near Sri Lanka. Further, the visual analysis and tracking framework enables us to compare it with previous work and analyze the contribution of advection to salinity transport. Y1 - 2024 U6 - https://doi.org/10.1016/j.cageo.2023.105493 VL - 182 IS - 105493 SP - 1 EP - 10 ER - TY - JOUR A1 - Vohra, Sumit Kumar A1 - Herrera, Kristian A1 - Tavhelidse-Suck, Tinatini A1 - Knoblich, Simon A1 - Seleit, Ali A1 - Boulanger-Weill, Jonathan A1 - Chambule, Sydney A1 - Aspiras, Ariel A1 - Santoriello, Cristina A1 - Randlett, Owen A1 - Wittbrodt, Joachim A1 - Aulehla, Alexander A1 - Lichtman, Jeff W. A1 - Fishman, Mark A1 - Hege, Hans-Christian A1 - Baum, Daniel A1 - Engert, Florian A1 - Isoe, Yasuko T1 - Multi-species community platform for comparative neuroscience in teleost fish JF - bioRxiv N2 - Studying neural mechanisms in complementary model organisms from different ecological niches in the same animal class can leverage the comparative brain analysis at the cellular level. To advance such a direction, we developed a unified brain atlas platform and specialized tools that allowed us to quantitatively compare neural structures in two teleost larvae, medaka (Oryzias latipes) and zebrafish (Danio rerio). Leveraging this quantitative approach we found that most brain regions are similar but some subpopulations are unique in each species. Specifically, we confirmed the existence of a clear dorsal pallial region in the telencephalon in medaka lacking in zebrafish. Further, our approach allows for extraction of differentially expressed genes in both species, and for quantitative comparison of neural activity at cellular resolution. The web-based and interactive nature of this atlas platform will facilitate the teleost community’s research and its easy extensibility will encourage contributions to its continuous expansion. Y1 - 2024 U6 - https://doi.org/10.1101/2024.02.14.580400 ER - TY - JOUR A1 - Pedersen, Jaap A1 - Le, Thi Thai A1 - Koch, Thorsten A1 - Zittel, Janina T1 - Optimal discrete pipe sizing for tree-shaped CO2 networks JF - OR Spectrum N2 - For industries like the cement industry, switching to a carbon-neutral production process is impossible. They must rely on carbon capture, utilization and storage (CCUS) technologies to reduce their production processes’ inevitable carbon dioxide (CO2) emissions. For transporting continuously large amounts of CO2, utilizing a pipeline network is the most effective solution; however, building such a network is expensive. Therefore minimizing the cost of the pipelines to be built is extremely important to make the operation financially feasible. In this context, we investigate the problem of finding optimal pipeline diameters from a discrete set of diameters for a tree-shaped network transporting captured CO2 from multiple sources to a single sink. The general problem of optimizing arc capacities in potential-based fluid networks is already a challenging mixed-integer nonlinear optimization problem. The problem becomes even more complex when adding the highly sensitive nonlinear behavior of CO2 regarding temperature and pressure changes. We propose an iterative algorithm splitting the problem into two parts: a) the pipe-sizing problem under a fixed supply scenario and temperature distribution and b) the thermophysical modeling, including mixing effects, the Joule-Thomson effect, and heat exchange with the surrounding environment. We demonstrate the effectiveness of our approach by applying our algorithm to a real-world network planning problem for a CO2 network in Western Germany. Further, we show the robustness of the algorithm by solving a large artificially created set of network instances. Y1 - 2024 U6 - https://doi.org/10.1007/s00291-024-00773-z VL - 46 SP - 1163 EP - 1187 ER - TY - JOUR A1 - Euler, Ricardo A1 - Maristany de las Casas, Pedro T1 - Labeling methods for partially ordered paths JF - European Journal of Operational Research N2 - The landscape of applications and subroutines relying on shortest path computations continues to grow steadily. This growth is driven by the undeniable success of shortest path algorithms in theory and practice. It also introduces new challenges as the models and assessing the optimality of paths become more complicated. Hence, multiple recent publications in the field adapt existing labeling methods in an ad hoc fashion to their specific problem variant without considering the underlying general structure: they always deal with multi-criteria scenarios, and those criteria define different partial orders on the paths. In this paper, we introduce the partial order shortest path problem (POSP), a generalization of the multi-objective shortest path problem (MOSP) and in turn also of the classical shortest path problem. POSP captures the particular structure of many shortest path applications as special cases. In this generality, we study optimality conditions or the lack of them, depending on the objective functions’ properties. Our final contribution is a big lookup table summarizing our findings and providing the reader with an easy way to choose among the most recent multi-criteria shortest path algorithms depending on their problems’ weight structure. Examples range from time-dependent shortest path and bottleneck path problems to the electric vehicle shortest path problem with recharging and complex financial weight functions studied in the public transportation community. Our results hold for general digraphs and, therefore, surpass previous generalizations that were limited to acyclic graphs. Y1 - 2024 U6 - https://doi.org/10.1016/j.ejor.2024.05.002 VL - 318 IS - 1 SP - 19 EP - 30 PB - Elsevier BV ET - 1 ER - TY - JOUR A1 - Diederichs, Katja A1 - Krause, Celia A1 - Lemaire, Marina A1 - Reidelbach, Marco A1 - Windeck, Jürgen T1 - A Vision for Data Management Plans in the NFDI JF - ZENODO N2 - At present, data management plans (DMPs) are still often perceived as mere documents for funding agencies providing clarity on how research data will be handled during a funded project, but are not usually actively involved in the processes. However, they contain a great deal of information that can be shared automatically to facilitate active research data management (RDM) by providing metadata to research infrastructures and supporting communication between all involved stakeholders. This position paper brings together a number of ideas developed and collected during interdisciplinary workshops of the Data Management Planning Working Group (infra-dmp), which is part of the section Common Infrastructures of the National Research Data Infrastructure (NFDI) in Germany. We present our vision of a possible future role of DMPs, templates, and tools in the upcoming NFDI service architecture. Y1 - 2024 U6 - https://doi.org/10.5281/zenodo.10570653 ER - TY - JOUR A1 - Schembera, Björn A1 - Wübbeling, Frank A1 - Koprucki, Thomas A1 - Biedinger, Christine A1 - Reidelbach, Marco A1 - Schmidt, Burkhard A1 - Göddeke, Dominik A1 - Fiedler, Jochen T1 - Building Ontologies and Knowledge Graphs for Mathematics and its Applications JF - Proceedings of the Conference on Research Data Infrastructure N2 - Ontologies and knowledge graphs for mathematical algorithms and models are presented, that have been developed by the Mathematical Research Data Initiative. This enables FAIR data handling in mathematics and the applied disciplines. Moreover, challenges of harmonization during the ontology development are discussed. Y1 - 2023 U6 - https://doi.org/10.52825/cordi.v1i.255 SN - 2941-296X VL - 1 PB - TIB Open Publishing ER - TY - JOUR A1 - Reidelbach, Marco A1 - Ferrer, Eloi A1 - Weber, Marcus T1 - MaRDMO Plugin BT - Document and Retrieve Workflows Using the MaRDI Portal JF - Proceedings of the Conference on Research Data Infrastructure N2 - MaRDMO, a plugin for the Research Data Management Organiser, was developed in the Mathematical Research Data Initiative to document interdisciplinary workflows using a standardised scheme. Interdisciplinary workflows recorded this way are published directly on the MaRDI portal. In addition, central information is integrated into the MaRDI knowledge graph. Next to the documentation, MaRDMO offers the possibility to retrieve existing interdisciplinary workflows from the MaRDI Knowledge Graph to allow the reproduction of the initial work and to provide scientists with new researchimpulses. Thus, MaRDMO creates a community-driven knowledge loop that could help to overcome the replication crisis. Y1 - 2023 U6 - https://doi.org/10.52825/cordi.v1i.254 SN - 2941-296X VL - 1 PB - TIB Open Publishing ER - TY - JOUR A1 - Benner, Peter A1 - Danabalan, Renita A1 - Gödekke, Dominik A1 - Kastner, Lars A1 - Krause, Tabea A1 - Mietchen, Daniel A1 - Reidelbach, Marco A1 - Schembera, Björn A1 - Schubotz, Moritz A1 - Sinn, Rainer A1 - Tabelow, Karsten T1 - Research Data Management Planning in Mathematics N2 - Research data are crucial in mathematics and all scientific disciplines, as they form the foundation for empirical evidence, by enabling the validation and reproducibility of scientific findings. Mathematical research data (MathRD) have become vast and complex, and their interdisciplinary potential and abstract nature make them ubiquitous in various scientific fields. The volume of data and the velocity of its creation are rapidly increasing due to advancements in data science and computing power. This complexity extends to other disciplines, resulting in diverse research data and computational models. Thus, proper handling of research data is crucial both within mathematics and for its manifold connections and exchange with other disciplines. The National Research Data Infrastructure (NFDI), funded by the federal and state governments of Germany, consists of discipline-oriented consortia, including the Mathematical Research Data Initiative (MaRDI). MaRDI has been established to develop services, guidelines and outreach measures for all aspects of MathRD, and thus support the mathematical research community. Research data management (RDM) should be an integral component of every scientific project, and is becoming a mandatory component of grants with funding bodies such as the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation). At the core of RDM are the FAIR (Findable, Accessible, Interoperable, and Reusable) principles. This document aims to guide mathematicians and researchers from related disciplines who create RDM plans. It highlights the benefits and opportunities of RDM in mathematics and interdisciplinary studies, showcases examples of diverse MathRD, and suggests technical solutions that meet the requirements of funding agencies with specific examples. The document is regularly updated to reflect the latest developments within the mathematical community represented by MaRDI. Y1 - 2023 U6 - https://doi.org/10.5281/zenodo.10018245 ER - TY - JOUR A1 - Sikorski, Alexander A1 - Heida, Martin T1 - Voronoi Graph - Improved raycasting and integration schemes for high dimensional Voronoi diagrams N2 - The computation of Voronoi Diagrams, or their dual Delauney triangulations is difficult in high dimensions. In a recent publication Polianskii and Pokorny propose an iterative randomized algorithm facilitating the approximation of Voronoi tesselations in high dimensions. In this paper, we provide an improved vertex search method that is not only exact but even faster than the bisection method that was previously recommended. Building on this we also provide a depth-first graph-traversal algorithm which allows us to compute the entire Voronoi diagram. This enables us to compare the outcomes with those of classical algorithms like qHull, which we either match or marginally beat in terms of computation time. We furthermore show how the raycasting algorithm naturally lends to a Monte Carlo approximation for the volume and boundary integrals of the Voronoi cells, both of which are of importance for finite Volume methods. We compare the Monte-Carlo methods to the exact polygonal integration, as well as a hybrid approximation scheme. Y1 - 2024 ER - TY - JOUR A1 - Schaible, Jonas A1 - Nouri, Bijan A1 - Höpken, Lars A1 - Kotzab, Tim A1 - Loevenich, Matthias A1 - Blum, Niklas A1 - Hammer, Annette A1 - Stührenberg, Jonas A1 - Jäger, Klaus A1 - Becker, Christiane A1 - Wilbert, Stefan T1 - Application of nowcasting to reduce the impact of irradiance ramps on PV power plants JF - EPJ Photovolt. Y1 - 2024 U6 - https://doi.org/10.1051/epjpv/2024009 VL - 15 SP - 15 ER - TY - JOUR A1 - Krüger, Jan A1 - Manley, Phillip A1 - Bergmann, Detlef A1 - Köning, Rainer A1 - Bodermann, Bernd A1 - Eder, Christian A1 - Heinrich, Andreas A1 - Schneider, Philipp-Immanuel A1 - Hammerschmidt, Martin A1 - Zschiedrich, Lin A1 - Manske, Eberhard T1 - Introduction and application of a new approach for model-based optical bidirectional measurements JF - Meas. Sci. Technol. Y1 - 2024 U6 - https://doi.org/10.1088/1361-6501/ad4b53 VL - 35 SP - 085014 ER - TY - JOUR A1 - Klus, Stefan A1 - Djurdjevac Conrad, Natasa T1 - Dynamical systems and complex networks: A Koopman operator perspective JF - Journal of Physics: Complexity N2 - The Koopman operator has entered and transformed many research areas over the last years. Although the underlying concept–representing highly nonlinear dynamical systems by infinite-dimensional linear operators–has been known for a long time, the availability of large data sets and efficient machine learning algorithms for estimating the Koopman operator from data make this framework extremely powerful and popular. Koopman operator theory allows us to gain insights into the characteristic global properties of a system without requiring detailed mathematical models. We will show how these methods can also be used to analyze complex networks and highlight relationships between Koopman operators and graph Laplacians. Y1 - 2024 U6 - https://doi.org/10.1088/2632-072X/ad9e60 VL - 5 IS - 4 SP - 041001 PB - IOP Publishing ER - TY - JOUR A1 - Zhang, Wei A1 - Schütte, Christof T1 - On finding optimal collective variables for complex systems by minimizing the deviation between effective and full dynamics JF - Multiscale Modeling and Simulation N2 - This paper is concerned with collective variables, or reaction coordinates, that map a discrete-in-time Markov process X_n in R^d to a (much) smaller dimension k≪d. We define the effective dynamics under a given collective variable map ξ as the best Markovian representation of X_n under ξ. The novelty of the paper is that it gives strict criteria for selecting optimal collective variables via the properties of the effective dynamics. In particular, we show that the transition density of the effective dynamics of the optimal collective variable solves a relative entropy minimization problem from certain family of densities to the transition density of X_n. We also show that many transfer operator-based data-driven numerical approaches essentially learn quantities of the effective dynamics. Furthermore, we obtain various error estimates for the effective dynamics in approximating dominant timescales / eigenvalues and transition rates of the original process X_n and how optimal collective variables minimize these errors. Our results contribute to the development of theoretical tools for the understanding of complex dynamical systems, e.g. molecular kinetics, on large timescales. These results shed light on the relations among existing data-driven numerical approaches for identifying good collective variables, and they also motivate the development of new methods. Y1 - 2025 U6 - https://doi.org/10.1137/24M1658917 VL - 23 IS - 2 SP - 924 EP - 958 ER - TY - JOUR A1 - Erban, Radek A1 - Winkelmann, Stefanie T1 - Multi-grid reaction-diffusion master equation: applications to morphogen gradient modelling JF - Bulletin of Mathematical Biology N2 - The multi-grid reaction-diffusion master equation (mgRDME) provides a generalization of stochastic compartment-based reaction-diffusion modelling described by the standard reaction-diffusion master equation (RDME). By enabling different resolutions on lattices for biochemical species with different diffusion constants, the mgRDME approach improves both accuracy and efficiency of compartment-based reaction-diffusion simulations. The mgRDME framework is examined through its application to morphogen gradient formation in stochastic reaction-diffusion scenarios, using both an analytically tractable first-order reaction network and a model with a second-order reaction. The results obtained by the mgRDME modelling are compared with the standard RDME model and with the (more detailed) particle-based Brownian dynamics simulations. The dependence of error and numerical cost on the compartment sizes is defined and investigated through a multi-objective optimization problem. Y1 - 2025 U6 - https://doi.org/10.1007/s11538-024-01377-y VL - 87 SP - 6 ER - TY - JOUR A1 - Müller, Gabriel A1 - Martínez-Lahuerta, Victor J. A1 - Sekulic, Ivan A1 - Burger, Sven A1 - Schneider, Philipp-Immanuel A1 - Gaaloul, Naceur T1 - Bayesian optimization for state engineering of quantum gases JF - Quantum Sci. Technol. Y1 - 2025 U6 - https://doi.org/10.1088/2058-9565/ad9050 VL - 10 SP - 015033 ER - TY - JOUR A1 - Pedersen, Jaap A1 - Weinand, Jann Michael A1 - Syranidou, Chloi A1 - Rehfeldt, Daniel T1 - An efficient solver for large-scale onshore wind farm siting including cable routing JF - European Journal of Operational Research N2 - Existing planning approaches for onshore wind farm siting and grid integration often do not meet minimum cost solutions or social and environmental considerations. In this paper, we develop an exact approach for the integrated layout and cable routing problem of onshore wind farm planning using the Quota Steiner tree problem. Applying a novel transformation on a known directed cut formulation, reduction techniques, and heuristics, we design an exact solver that makes large problem instances solvable and outperforms generic MIP solvers. In selected regions of Germany, the trade-offs between minimizing costs and landscape impact of onshore wind farm siting are investigated. Although our case studies show large trade-offs between the objective criteria of cost and landscape impact, small burdens on one criterion can significantly improve the other criteria. In addition, we demonstrate that contrary to many approaches for exclusive turbine siting, grid integration must be simultaneously optimized to avoid excessive costs or landscape impacts in the course of a wind farm project. Our novel problem formulation and the developed solver can assist planners in decision-making and help optimize wind farms in large regions in the future. Y1 - 2024 U6 - https://doi.org/10.1016/j.ejor.2024.04.026 VL - 317 IS - 2 SP - 616 EP - 630 ER - TY - JOUR A1 - Rodrigues Pelá, Ronaldo A1 - Draxl, Claudia T1 - Speeding up all-electron real-time TDDFT demonstrated by the exciting package JF - Comput. Phys. Commun. Y1 - 2024 U6 - https://doi.org/10.1016/j.cpc.2024.109292 VL - 304 SP - 109292 ER - TY - JOUR A1 - Trepte, Philipp A1 - Secker, Christopher A1 - Olivet, Julien A1 - Blavier, Jeremy A1 - Kostova, Simona A1 - Maseko, Sibusiso B A1 - Minia, Igor A1 - Silva Ramos, Eduardo A1 - Cassonnet, Patricia A1 - Golusik, Sabrina A1 - Zenkner, Martina A1 - Beetz, Stephanie A1 - Liebich, Mara J A1 - Scharek, Nadine A1 - Schütz, Anja A1 - Sperling, Marcel A1 - Lisurek, Michael A1 - Wang, Yang A1 - Spirohn, Kerstin A1 - Hao, Tong A1 - Calderwood, Michael A A1 - Hill, David E A1 - Landthaler, Markus A1 - Choi, Soon Gang A1 - Twizere, Jean-Claude A1 - Vidal, Marc A1 - Wanker, Erich E T1 - AI-guided pipeline for protein–protein interaction drug discovery identifies a SARS-CoV-2 inhibitor JF - Molecular Systems Biology N2 - Protein–protein interactions (PPIs) offer great opportunities to expand the druggable proteome and therapeutically tackle various diseases, but remain challenging targets for drug discovery. Here, we provide a comprehensive pipeline that combines experimental and computational tools to identify and validate PPI targets and perform early-stage drug discovery. We have developed a machine learning approach that prioritizes interactions by analyzing quantitative data from binary PPI assays or AlphaFold-Multimer predictions. Using the quantitative assay LuTHy together with our machine learning algorithm, we identified high-confidence interactions among SARS-CoV-2 proteins for which we predicted three-dimensional structures using AlphaFold-Multimer. We employed VirtualFlow to target the contact interface of the NSP10-NSP16 SARS-CoV-2 methyltransferase complex by ultra-large virtual drug screening. Thereby, we identified a compound that binds to NSP10 and inhibits its interaction with NSP16, while also disrupting the methyltransferase activity of the complex, and SARS-CoV-2 replication. Overall, this pipeline will help to prioritize PPI targets to accelerate the discovery of early-stage drug candidates targeting protein complexes and pathways. KW - Applied Mathematics KW - Computational Theory and Mathematics KW - General Agricultural and Biological Sciences KW - General Immunology and Microbiology KW - General Biochemistry, Genetics and Molecular Biology KW - Information Systems Y1 - 2024 U6 - https://doi.org/https://doi.org/10.1038/s44320-024-00019-8 SN - 1744-4292 VL - 20 IS - 4 SP - 428 EP - 457 PB - Springer Science and Business Media LLC ER - TY - JOUR A1 - Prause, Felix A1 - Borndörfer, Ralf T1 - An Iterative Refinement Approach for the Rolling Stock Rotation Problem with Predictive Maintenance N2 - The rolling stock rotation problem with predictive maintenance (RSRP-PdM) involves the assignment of trips to a fleet of vehicles with integrated maintenance scheduling based on the predicted failure probability of the vehicles. These probabilities are determined by the health states of the vehicles, which are considered to be random variables distributed by a parameterized family of probability distribution functions. During the operation of the trips, the corresponding parameters get updated. In this article, we present a dual solution approach for RSRP-PdM and generalize a linear programming based lower bound for this problem to families of probability distribution functions with more than one parameter. For this purpose, we define a rounding function that allows for a consistent underestimation of the parameters and model the problem by a state-expanded event-graph in which the possible states are restricted to a discrete set. This induces a flow problem that is solved by an integer linear program. We show that the iterative refinement of the underlying discretization leads to solutions that converge from below to an optimal solution of the original instance. Thus, the linear relaxation of the considered integer linear program results in a lower bound for RSRP-PdM. Finally, we report on the results of computational experiments conducted on a library of test instances. Y1 - 2024 ER - TY - JOUR A1 - Winkler, Alexander T1 - Berliner Kulturerbe digital: Ein quantitativer Blick auf die Metadatenqualität JF - EVA BERLIN 2023. Elektronische Medien & Kunst, Kultur und Historie. 27. Berliner Veranstaltung der internationalen EVA-Serie (29. November 2023 – 01. Dezember 2023) N2 - Die Metadatenqualität bestimmt wesentlich den Nutzen und Wert von Kulturerbedaten. ‚Gute‘ Metadaten erhöhen die Auffindbarkeit, Interoperabilität und Nutzbarkeit von Daten signifikant. Mit Blick auf Retrieval bzw. Discovery, Vernetzung im Kontext von Linked Open Data und wissenschaftliches Data Mining hängt die Qualität dabei wesentlich von der Verwendung von maschinenlesbaren kontrollierten Vokabularen ab. Diese wird in der vorliegenden Arbeit quantitativ untersucht. Als Datengrundlage dienen die in der Deutschen Digitalen Bibliothek aggregierten Metadaten aus Berliner Museen (ca. 1,2 Millionen Metadatenobjekte im LIDO-Format) Y1 - 2023 SP - 101 EP - 108 ER - TY - JOUR A1 - Sherratt, Katharine A1 - Srivastava, Ajitesh A1 - Ainslie, Kylie A1 - Singh, David E. A1 - Cublier, Aymar A1 - Marinescu, Maria Cristina A1 - Carretero, Jesus A1 - Garcia, Alberto Cascajo A1 - Franco, Nicolas A1 - Willem, Lander A1 - Abrams, Steven A1 - Faes, Christel A1 - Beutels, Philippe A1 - Hens, Niel A1 - Müller, Sebastian A1 - Charlton, Billy A1 - Ewert, Ricardo A1 - Paltra, Sydney A1 - Rakow, Christian A1 - Rehmann, Jakob A1 - Conrad, Tim A1 - Schütte, Christof A1 - Nagel, Kai A1 - Abbott, Sam A1 - Grah, Rok A1 - Niehus, Rene A1 - Prasse, Bastian A1 - Sandmann, Frank A1 - Funk, Sebastian T1 - Characterising information gains and losses when collecting multiple epidemic model outputs JF - Epidemics N2 - Collaborative comparisons and combinations of epidemic models are used as policy-relevant evidence during epidemic outbreaks. In the process of collecting multiple model projections, such collaborations may gain or lose relevant information. Typically, modellers contribute a probabilistic summary at each time-step. We compared this to directly collecting simulated trajectories. We aimed to explore information on key epidemic quantities; ensemble uncertainty; and performance against data, investigating potential to continuously gain information from a single cross-sectional collection of model results. Methods We compared July 2022 projections from the European COVID-19 Scenario Modelling Hub. Five modelling teams projected incidence in Belgium, the Netherlands, and Spain. We compared projections by incidence, peaks, and cumulative totals. We created a probabilistic ensemble drawn from all trajectories, and compared to ensembles from a median across each model’s quantiles, or a linear opinion pool. We measured the predictive accuracy of individual trajectories against observations, using this in a weighted ensemble. We repeated this sequentially against increasing weeks of observed data. We evaluated these ensembles to reflect performance with varying observed data. Results. By collecting modelled trajectories, we showed policy-relevant epidemic characteristics. Trajectories contained a right-skewed distribution well represented by an ensemble of trajectories or a linear opinion pool, but not models’ quantile intervals. Ensembles weighted by performance typically retained the range of plausible incidence over time, and in some cases narrowed this by excluding some epidemic shapes. Conclusions. We observed several information gains from collecting modelled trajectories rather than quantile distributions, including potential for continuously updated information from a single model collection. The value of information gains and losses may vary with each collaborative effort’s aims, depending on the needs of projection users. Understanding the differing information potential of methods to collect model projections can support the accuracy, sustainability, and communication of collaborative infectious disease modelling efforts. Data availability All code and data available on Github: https://github.com/covid19-forecast-hub-europe/aggregation-info-loss KW - Virology KW - Infectious Diseases KW - Public Health, Environmental and Occupational Health KW - Microbiology KW - Parasitology KW - Epidemiology Y1 - 2024 U6 - https://doi.org/10.1016/j.epidem.2024.100765 SN - 1755-4365 VL - 47 PB - Elsevier BV ER - TY - JOUR A1 - Betz, Fridtjof A1 - Hammerschmidt, Martin A1 - Zschiedrich, Lin A1 - Burger, Sven A1 - Binkowski, Felix T1 - Efficient rational approximation of optical response functions with the AAA algorithm JF - Laser Photonics Rev. Y1 - 2024 U6 - https://doi.org/10.1002/lpor.202400584 VL - 18 SP - 2400584 ER - TY - JOUR A1 - Schintke, Florian A1 - Belhajjame, Khalid A1 - De Mecquenem, Ninon A1 - Frantz, David A1 - Guarino, Vanessa Emanuela A1 - Hilbrich, Marcus A1 - Lehmann, Fabian A1 - Missier, Paolo A1 - Sattler, Rebecca A1 - Sparka, Jan Arne A1 - Speckhard, Daniel T. A1 - Stolte, Hermann A1 - Vu, Anh Duc A1 - Leser, Ulf T1 - Validity constraints for data analysis workflows JF - Future Generation Computer Systems Y1 - 2024 U6 - https://doi.org/10.1016/j.future.2024.03.037 VL - 157 SP - 82 EP - 97 ER - TY - JOUR A1 - Betz, Fridtjof A1 - Binkowski, Felix A1 - Kuen, Lilli A1 - Burger, Sven T1 - Version 2 — RPExpand: Software for Riesz projection expansion of resonance phenomena JF - SoftwareX Y1 - 2024 U6 - https://doi.org/10.1016/j.softx.2024.101694 VL - 26 SP - 101694 ER - TY - JOUR A1 - Mukherjee, Deshabrato A1 - Kertész, Krisztián A1 - Zolnai, Zsolt A1 - Kovács, Zoltán A1 - Deák, András A1 - Pálinkás, András A1 - Osváth, Zoltán A1 - Olasz, Dániel A1 - Romanenko, Alekszej A1 - Fried, Miklós A1 - Burger, Sven A1 - Sáfrán, György A1 - Petrik, Peter T1 - Optimized Sensing on Gold Nanoparticles Created by Graded-Layer Magnetron Sputtering and Annealing JF - Sens. Actuators B Chem. Y1 - 2025 U6 - https://doi.org/10.1016/j.snb.2024.136875 VL - 425 SP - 136875 ER - TY - JOUR A1 - Kofler, Andreas A1 - Wald, Christian A1 - Kolbitsch, Christoph A1 - von Tycowicz, Christoph A1 - Ambellan, Felix T1 - Joint Reconstruction and Segmentation in Undersampled 3D Knee MRI combining Shape Knowledge and Deep Learning JF - Physics in Medicine and Biology N2 - Task-adapted image reconstruction methods using end-to-end trainable neural networks (NNs) have been proposed to optimize reconstruction for subsequent processing tasks, such as segmentation. However, their training typically requires considerable hardware resources and thus, only relatively simple building blocks, e.g. U-Nets, are typically used, which, albeit powerful, do not integrate model-specific knowledge. In this work, we extend an end-to-end trainable task-adapted image reconstruction method for a clinically realistic reconstruction and segmentation problem of bone and cartilage in 3D knee MRI by incorporating statistical shape models (SSMs). The SSMs model the prior information and help to regularize the segmentation maps as a final post-processing step. We compare the proposed method to a state-of-the-art (SOTA) simultaneous multitask learning approach for image reconstruction and segmentation (MTL) and to a complex SSMs-informed segmentation pipeline (SIS). Our experiments show that the combination of joint end-to-end training and SSMs to further regularize the segmentation maps obtained by MTL highly improves the results, especially in terms of mean and maximal surface errors. In particular, we achieve the segmentation quality of SIS and, at the same time, a substantial model reduction that yields a five-fold decimation in model parameters and a computational speedup of an order of magnitude. Remarkably, even for undersampling factors of up to R=8, the obtained segmentation maps are of comparable quality to those obtained by SIS from ground-truth images. Y1 - 2024 U6 - https://doi.org/10.1088/1361-6560/ad3797 VL - 69 IS - 9 ER - TY - JOUR A1 - Prause, Felix A1 - Borndörfer, Ralf A1 - Grimm, Boris A1 - Tesch, Alexander T1 - Approximating rolling stock rotations with integrated predictive maintenance JF - Journal of Rail Transport Planning & Management N2 - We study the solution of the rolling stock rotation problem with predictive maintenance (RSRP-PdM) by an iterative refinement approach that is based on a state-expanded event-graph. In this graph, the states are parameters of a failure distribution, and paths correspond to vehicle rotations with associated health state approximations. An optimal set of paths including maintenance can be computed by solving an integer linear program. Afterwards, the graph is refined and the procedure repeated. An associated linear program gives rise to a lower bound that can be used to determine the solution quality. Computational results for six instances derived from real-world timetables of a German railway company are presented. The results show the effectiveness of the approach and the quality of the solutions. Y1 - 2024 U6 - https://doi.org/10.1016/j.jrtpm.2024.100434 VL - 30 SP - 100434 ER - TY - JOUR A1 - Sikorski, Alexander A1 - Niknejad, Amir A1 - Weber, Marcus A1 - Donati, Luca T1 - Tensor-SqRA: Modeling the transition rates of interacting molecular systems in terms of potential energies JF - Journal of Chemical Physics N2 - Estimating the rate of rare conformational changes in molecular systems is one of the goals of molecular dynamics simulations. In the past few decades, a lot of progress has been done in data-based approaches toward this problem. In contrast, model-based methods, such as the Square Root Approximation (SqRA), directly derive these quantities from the potential energy functions. In this article, we demonstrate how the SqRA formalism naturally blends with the tensor structure obtained by coupling multiple systems, resulting in the tensor-based Square Root Approximation (tSqRA). It enables efficient treatment of high-dimensional systems using the SqRA and provides an algebraic expression of the impact of coupling energies between molecular subsystems. Based on the tSqRA, we also develop the projected rate estimation, a hybrid data-model-based algorithm that efficiently estimates the slowest rates for coupled systems. In addition, we investigate the possibility of integrating low-rank approximations within this framework to maximize the potential of the tSqRA. Y1 - 2024 U6 - https://doi.org/10.1063/5.0187792 VL - 160 SP - 104112 ER - TY - JOUR A1 - Tjusila, Gennesaret A1 - Besançon, Mathieu A1 - Turner, Mark A1 - Koch, Thorsten T1 - How Many Clues To Give? A Bilevel Formulation For The Minimum Sudoku Clue Problem JF - Operations Research Letters N2 - It has been shown that any 9 by 9 Sudoku puzzle must contain at least 17 clues to have a unique solution. This paper investigates the more specific question: given a particular completed Sudoku grid, what is the minimum number of clues in any puzzle whose unique solution is the given grid? We call this problem the Minimum Sudoku Clue Problem (MSCP). We formulate MSCP as a binary bilevel linear program, present a class of globally valid inequalities, and provide a computational study on 50 MSCP instances of 9 by 9 Sudoku grids. Using a general bilevel solver, we solve 95% of instances to optimality, and show that the solution process benefits from the addition of a moderate amount of inequalities. Finally, we extend the proposed model to other combinatorial problems in which uniqueness of the solution is of interest. Y1 - 2024 U6 - https://doi.org/10.1016/j.orl.2024.107105 VL - 54 SP - 107105 ER - TY - JOUR A1 - Breugem, Thomas A1 - Schlechte, Thomas A1 - Schulz, Christof A1 - Borndörfer, Ralf T1 - A three-phase heuristic for the Fairness-Oriented Crew Rostering Problem JF - Computers & Operations Research N2 - The Fairness-Oriented Crew Rostering Problem (FCRP) considers the joint optimization of attractiveness and fairness in cyclic crew rostering. Like many problems in scheduling and logistics, the combinatorial complexity of cyclic rostering causes exact methods to fail for large-scale practical instances. In case of the FCRP, this is accentuated by the additionally imposed fairness requirements. Hence, heuristic methods are necessary. We present a three-phase heuristic for the FCRP combining column generation techniques with variable-depth neighborhood search. The heuristic exploits different mathematical formulations to find feasible solutions and to search for improvements. We apply our methodology to practical instances from Netherlands Railways (NS), the main passenger railway operator in the Netherlands Our results show the three-phase heuristic finds good solutions for most instances and outperforms a state-of-the-art commercial solver. Y1 - 2023 U6 - https://doi.org/10.1016/j.cor.2023.106186 VL - 154 SP - 106186 ER - TY - JOUR A1 - Binkowski, Felix A1 - Kullig, Julius A1 - Betz, Fridtjof A1 - Zschiedrich, Lin A1 - Walther, Andrea A1 - Wiersig, Jan A1 - Burger, Sven T1 - Computing eigenfrequency sensitivities near exceptional points JF - Phys. Rev. Research Y1 - 2024 U6 - https://doi.org/10.1103/PhysRevResearch.6.023148 VL - 6 SP - 023148 ER - TY - JOUR A1 - Kreimeier, Timo A1 - Pokutta, Sebastian A1 - Walther, Andrea A1 - Woodstock, Zev T1 - On a Frank-Wolfe approach for abs-smooth functions JF - Optimization Methods and Software Y1 - U6 - https://doi.org/10.1080/10556788.2023.2296985 ER - TY - JOUR A1 - Hunkenschröder, Christoph A1 - Pokutta, Sebastian A1 - Weismantel, Robert T1 - Optimizing a low-dimensional convex function over a high-dimensional cube JF - SIAM Journal on Optimization Y1 - 2022 ER - TY - JOUR A1 - Designolle, Sébastien A1 - Vértesi, Tamás A1 - Pokutta, Sebastian T1 - Symmetric multipartite Bell inequalities via Frank-Wolfe algorithms JF - Physics Review A N2 - In multipartite Bell scenarios, we study the nonlocality robustness of the Greenberger-Horne-Zeilinger (GHZ) state. When each party performs planar measurements forming a regular polygon, we exploit the symmetry of the resulting correlation tensor to drastically accelerate the computation of (i) a Bell inequality via Frank-Wolfe algorithms and (ii) the corresponding local bound. The Bell inequalities obtained are facets of the symmetrized local polytope and they give the best-known upper bounds on the nonlocality robustness of the GHZ state for three to ten parties. Moreover, for four measurements per party, we generalize our facets and hence show, for any number of parties, an improvement on Mermin's inequality in terms of noise robustness. We also compute the detection efficiency of our inequalities and show that some give rise to the activation of nonlocality in star networks, a property that was only shown with an infinite number of measurements. Y1 - 2024 U6 - https://doi.org/10.1103/PhysRevA.109.022205 VL - 109 IS - 2 ER - TY - JOUR A1 - Eifler, Leon A1 - Gleixner, Ambros T1 - Safe and verified Gomory mixed integer cuts in a rational MIP framework JF - SIAM Journal on Optimization N2 - This paper is concerned with the exact solution of mixed-integer programs (MIPs) over the rational numbers, i.e., without any roundoff errors and error tolerances. Here, one computational bottleneck that should be avoided whenever possible is to employ large-scale symbolic computations. Instead it is often possible to use safe directed rounding methods, e.g., to generate provably correct dual bounds. In this work, we continue to leverage this paradigm and extend an exact branch-and-bound framework by separation routines for safe cutting planes, based on the approach first introduced by Cook, Dash, Fukasawa, and Goycoolea in 2009 [INFORMS J. Comput., 21 (2009), pp. 641–649]. Constraints are aggregated safely using approximate dual multipliers from an LP solve, followed by mixed-integer rounding to generate provably valid, although slightly weaker inequalities. We generalize this approach to problem data that is not representable in floating-point arithmetic, add routines for controlling the encoding length of the resulting cutting planes, and show how these cutting planes can be verified according to the VIPR certificate standard. Furthermore, we analyze the performance impact of these cutting planes in the context of an exact MIP framework, showing that we can solve 21.5% more instances to exact optimality and reduce solving times by 26.8% on the MIPLIB 2017 benchmark test set. Y1 - 2024 U6 - https://doi.org/10.1137/23M156046X VL - 34 IS - 1 ER - TY - JOUR A1 - Plock, Matthias A1 - Binkowski, Felix A1 - Zschiedrich, Lin A1 - Schneider, Phillip-Immanuel A1 - Burger, Sven T1 - Fabrication uncertainty guided design optimization of a photonic crystal cavity by using Gaussian processes JF - J. Opt. Soc. Am. B Y1 - 2024 U6 - https://doi.org/10.1364/JOSAB.505767 VL - 41 SP - 850 ER - TY - JOUR A1 - Zhao, Yue A1 - Zhang, Wei A1 - Li, Tiejun T1 - EPR-Net: Constructing non-equilibrium potential landscape via a variational force projection formulation JF - National Science Review N2 - We present EPR-Net, a novel and effective deep learning approach that tackles a crucial challenge in biophysics: constructing potential landscapes for high-dimensional non-equilibrium steady-state (NESS) systems. EPR-Net leverages a nice mathematical fact that the desired negative potential gradient is simply the orthogonal projection of the driving force of the underlying dynamics in a weighted inner-product space. Remarkably, our loss function has an intimate connection with the steady entropy production rate (EPR), enabling simultaneous landscape construction and EPR estimation. We introduce an enhanced learning strategy for systems with small noise, and extend our framework to include dimensionality reduction and state-dependent diffusion coefficient case in a unified fashion. Comparative evaluations on benchmark problems demonstrate the superior accuracy, effectiveness, and robustness of EPR-Net compared to existing methods. We apply our approach to challenging biophysical problems, such as an 8D limit cycle and a 52D multi-stability problem, which provide accurate solutions and interesting insights on constructed landscapes. With its versatility and power, EPR-Net offers a promising solution for diverse landscape construction problems in biophysics. Y1 - 2024 U6 - https://doi.org/10.1093/nsr/nwae052 VL - 11 IS - 7 ER - TY - JOUR A1 - Euler, Ricardo A1 - Lindner, Niels A1 - Borndörfer, Ralf T1 - Price optimal routing in public transportation JF - EURO Journal on Transportation and Logistics KW - Management Science and Operations Research KW - Transportation KW - Modeling and Simulation Y1 - 2024 U6 - https://doi.org/10.1016/j.ejtl.2024.100128 SN - 2192-4376 VL - 13 SP - 1 EP - 15 PB - Elsevier BV ER - TY - JOUR A1 - Hennings, Felix A1 - Hoppmann-Baum, Kai A1 - Zittel, Janina T1 - Optimizing transient gas network control for challenging real-world instances using MIP-based heuristics JF - Open Journal of Mathematical Optimization Y1 - 2024 U6 - https://doi.org/10.5802/ojmo.29 VL - 5 ER - TY - JOUR A1 - Hanik, Martin A1 - Navayazdani, Esfandiar A1 - von Tycowicz, Christoph T1 - De Casteljau's Algorithm in Geometric Data Analysis: Theory and Application JF - Computer Aided Geometric Design N2 - For decades, de Casteljau's algorithm has been used as a fundamental building block in curve and surface design and has found a wide range of applications in fields such as scientific computing, and discrete geometry to name but a few. With increasing interest in nonlinear data science, its constructive approach has been shown to provide a principled way to generalize parametric smooth curves to manifolds. These curves have found remarkable new applications in the analysis of parameter-dependent, geometric data. This article provides a survey of the recent theoretical developments in this exciting area as well as its applications in fields such as geometric morphometrics and longitudinal data analysis in medicine, archaeology, and meteorology. Y1 - 2024 U6 - https://doi.org/10.1016/j.cagd.2024.102288 VL - 110 SP - 102288 ER - TY - JOUR A1 - Navayazdani, Esfandiar T1 - On Geodesics in the Spaces of Constrained Curves JF - Journal of Differential Geometry and its Applications N2 - In this work, we study the geodesics of the space of certain geometrically and physically motivated subspaces of the space of immersed curves endowed with a first order Sobolev metric. This includes elastic curves and also an extension of some results on planar concentric circles to surfaces. The work focuses on intrinsic and constructive approaches. Y1 - 2024 U6 - https://doi.org/https://doi.org/10.1016/j.difgeo.2024.102209 VL - 97 SP - 102209 ER - TY - JOUR A1 - Amiranashvili, Tamaz A1 - Lüdke, David A1 - Li, Hongwei Bran A1 - Zachow, Stefan A1 - Menze, Bjoern T1 - Learning continuous shape priors from sparse data with neural implicit functions JF - Medical Image Analysis N2 - Statistical shape models are an essential tool for various tasks in medical image analysis, including shape generation, reconstruction and classification. Shape models are learned from a population of example shapes, which are typically obtained through segmentation of volumetric medical images. In clinical practice, highly anisotropic volumetric scans with large slice distances are prevalent, e.g., to reduce radiation exposure in CT or image acquisition time in MR imaging. For existing shape modeling approaches, the resolution of the emerging model is limited to the resolution of the training shapes. Therefore, any missing information between slices prohibits existing methods from learning a high-resolution shape prior. We propose a novel shape modeling approach that can be trained on sparse, binary segmentation masks with large slice distances. This is achieved through employing continuous shape representations based on neural implicit functions. After training, our model can reconstruct shapes from various sparse inputs at high target resolutions beyond the resolution of individual training examples. We successfully reconstruct high-resolution shapes from as few as three orthogonal slices. Furthermore, our shape model allows us to embed various sparse segmentation masks into a common, low-dimensional latent space — independent of the acquisition direction, resolution, spacing, and field of view. We show that the emerging latent representation discriminates between healthy and pathological shapes, even when provided with sparse segmentation masks. Lastly, we qualitatively demonstrate that the emerging latent space is smooth and captures characteristic modes of shape variation. We evaluate our shape model on two anatomical structures: the lumbar vertebra and the distal femur, both from publicly available datasets. Y1 - 2024 U6 - https://doi.org/10.1016/j.media.2024.103099 VL - 94 SP - 103099 ER - TY - JOUR A1 - Gaskin, Thomas A1 - Conrad, Tim A1 - Pavliotis, Grigorios A. A1 - Schütte, Christof T1 - Neural parameter calibration and uncertainty quantification for epidemic forecasting JF - PLOS ONE N2 - The recent COVID-19 pandemic has thrown the importance of accurately forecasting contagion dynamics and learning infection parameters into sharp focus. At the same time, effective policy-making requires knowledge of the uncertainty on such predictions, in order, for instance, to be able to ready hospitals and intensive care units for a worst-case scenario without needlessly wasting resources. In this work, we apply a novel and powerful computational method to the problem of learning probability densities on contagion parameters and providing uncertainty quantification for pandemic projections. Using a neural network, we calibrate an ODE model to data of the spread of COVID-19 in Berlin in 2020, achieving both a significantly more accurate calibration and prediction than Markov-Chain Monte Carlo (MCMC)-based sampling schemes. The uncertainties on our predictions provide meaningful confidence intervals e.g. on infection figures and hospitalisation rates, while training and running the neural scheme takes minutes where MCMC takes hours. We show convergence of our method to the true posterior on a simplified SIR model of epidemics, and also demonstrate our method's learning capabilities on a reduced dataset, where a complex model is learned from a small number of compartments for which data is available. Y1 - 2024 U6 - https://doi.org/10.1371/journal.pone.0306704 VL - 19 IS - 10 ER - TY - JOUR A1 - Donati, Luca A1 - Schütte, Christof A1 - Weber, Marcus T1 - The Kramers turnover in terms of a macro-state projection on phase space JF - Molecular Physics N2 - We have investigated how Langevin dynamics is affected by the friction coefficient using the novel algorithm ISOKANN, which combines the transfer operator approach with modern machine learning techniques. ISOKANN describes the dynamics in terms of an invariant subspace projection of the Koopman operator defined in the entire state space, avoiding approximations due to dimensionality reduction and discretization. Our results are consistent with the Kramers turnover and show that in the low and moderate friction regimes, metastable macro-states and transition rates are defined in phase space, not only in position space. Y1 - 2025 U6 - https://doi.org/10.1080/00268976.2024.2356748 VL - 123 IS - 7-8: Giovanni Ciccotti: A Renaissance Physicist SP - e2356748 PB - Taylor & Francis ER - TY - JOUR A1 - Kuen, Lilli A1 - Löffler, Lorenz A1 - Tsarapkin, Aleksei A1 - Zschiedrich, Lin A1 - Feichtner, Thorsten A1 - Burger, Sven A1 - Höflich, Katja T1 - Chiral and directional optical emission from a dipole source coupled to a helical plasmonic antenna JF - Appl. Phys. Lett. Y1 - 2024 U6 - https://doi.org/10.1063/5.0201748 VL - 124 SP - 231102 ER - TY - JOUR A1 - Grimm, Boris A1 - Hoogervorst, Rowan A1 - Borndörfer, Ralf T1 - A Comparison of Two Models for Rolling Stock Scheduling JF - Transportation Science N2 - A major step in the planning process of passenger railway operators is the assignment of rolling stock, that is, train units, to the trips of the timetable. A wide variety of mathematical optimization models have been proposed to support this task, which we discuss and argue to be justified in order to deal with operational differences between railway operators, and hence different planning requirements, in the best possible way. Our investigation focuses on two commonly used models, the composition model and the hypergraph model, that were developed for Netherlands Railways (NS) and DB Fernverkehr AG (DB), respectively. We compare these models in two distinct problem settings, an NS setting and DB-light setting and consider different model variants to tune the models to these settings. We prove that in both of these settings, the linear programming bounds of the two models are equally strong as long as a number of reasonable assumptions are met. However, through a numerical evaluation on NS and DB-light instances, we show that the numerical performance of the models strongly depends on the instances. Although the composition model is the most compact and fastest model for the NS instances, an adjusted version of this model grows quickly for the DB-light instances and is then outperformed by the considered hypergraph model variants. Moreover, we show that a depot-extended version of the hypergraph model is able to combine strengths of both models and show good performance on both the NS and DB-light instances. Y1 - 2025 U6 - https://doi.org/10.1287/trsc.2024.0505 VL - 59 IS - 5 ER - TY - JOUR A1 - Combettes, P. L. A1 - Woodstock, Zev T1 - a variational inequality model for the construction of signals from inconsistent nonlinear equations JF - SIAM Journal on Imaging Sciences Y1 - 2022 ER - TY - JOUR A1 - Wilken, St. Elmo A1 - Besançon, Mathieu A1 - Kratochvíl, Miroslav A1 - Kuate, Chilperic Armel Foko A1 - Trefois, Christophe A1 - Gu, Wei A1 - Ebenhöh, Oliver T1 - Interrogating the effect of enzyme kinetics on metabolism using differentiable constraint-based models JF - Metabolic Engineering Y1 - 2022 ER - TY - JOUR A1 - Bolusani, Suresh A1 - Ralphs, Ted K. T1 - a framework for generalized Benders' decomposition and its applications to multilevel optimization JF - Mathematical Programming Y1 - 2022 ER - TY - JOUR A1 - Chmiela, Antonia A1 - Muñoz, Gonzalo A1 - Serrano, Felipe T1 - On the implementation and strengthening of intersection cuts for QCQPs JF - Mathematical Programming B Y1 - 2023 U6 - https://doi.org/10.1007/s10107-022-01808-5 VL - 197 SP - 549 EP - 586 ER - TY - JOUR A1 - Pokutta, Sebastian T1 - The Frank-Wolfe algorithm: a short introduction JF - Jahresbericht der Deutschen Mathematiker-Vereinigung Y1 - 2024 U6 - https://doi.org/10.1365/s13291-023-00275-x VL - 126 SP - 3 EP - 35 ER - TY - JOUR A1 - Deza, Antoine A1 - Pokutta, Sebastian A1 - Pournin, Lionel T1 - The complexity of geometric scaling JF - Operations Research Letters Y1 - 2024 U6 - https://doi.org/10.1016/j.orl.2023.11.010 VL - 52 SP - 107057 ER - TY - JOUR A1 - Besançon, Mathieu A1 - Garcia, Joaquim Dias A1 - Legat, Benoît A1 - Sharma, Akshay T1 - Flexible Differentiable Optimization via Model Transformations JF - INFORMS Journal on Computing N2 - We introduce DiffOpt.jl, a Julia library to differentiate through the solution of optimization problems with respect to arbitrary parameters present in the objective and/or constraints. The library builds upon MathOptInterface, thus leveraging the rich ecosystem of solvers and composing well with modeling languages like JuMP. DiffOpt offers both forward and reverse differentiation modes, enabling multiple use cases from hyperparameter optimization to backpropagation and sensitivity analysis, bridging constrained optimization with end-to-end differentiable programming. DiffOpt is built on two known rules for differentiating quadratic programming and conic programming standard forms. However, thanks to its ability to differentiate through model transformations, the user is not limited to these forms and can differentiate with respect to the parameters of any model that can be reformulated into these standard forms. This notably includes programs mixing affine conic constraints and convex quadratic constraints or objective function. Y1 - 2023 U6 - https://doi.org/10.1287/ijoc.2022.0283 VL - 36 IS - 2 SP - 456 EP - 478 ER - TY - JOUR A1 - Designolle, Sébastien A1 - Iommazzo, Gabriele A1 - Besançon, Mathieu A1 - Knebel, Sebastian A1 - Gelß, Patrick A1 - Pokutta, Sebastian T1 - Improved local models and new Bell inequalities via Frank-Wolfe algorithms JF - Physical Review Research N2 - In Bell scenarios with two outcomes per party, we algorithmically consider the two sides of the membership problem for the local polytope: Constructing local models and deriving separating hyperplanes, that is, Bell inequalities. We take advantage of the recent developments in so-called Frank-Wolfe algorithms to significantly increase the convergence rate of existing methods. First, we study the threshold value for the nonlocality of two-qubit Werner states under projective measurements. Here, we improve on both the upper and lower bounds present in the literature. Importantly, our bounds are entirely analytical; moreover, they yield refined bounds on the value of the Grothendieck constant of order three: 1.4367⩽KG(3)⩽1.4546. Second, we demonstrate the efficiency of our approach in multipartite Bell scenarios, and present local models for all projective measurements with visibilities noticeably higher than the entanglement threshold. We make our entire code accessible as a julia library called BellPolytopes.jl. Y1 - 2023 U6 - https://doi.org/10.1103/PhysRevResearch.5.043059 VL - 5 SP - 043059 ER - TY - JOUR A1 - Eifler, Leon A1 - Gleixner, Ambros T1 - A computational status update for exact rational mixed integer programming JF - Mathematical Programming N2 - The last milestone achievement for the roundoff-error-free solution of general mixed integer programs over the rational numbers was a hybrid-precision branch-and-bound algorithm published by Cook, Koch, Steffy, and Wolter in 2013. We describe a substantial revision and extension of this framework that integrates symbolic presolving, features an exact repair step for solutions from primal heuristics, employs a faster rational LP solver based on LP iterative refinement, and is able to produce independently verifiable certificates of optimality. We study the significantly improved performance and give insights into the computational behavior of the new algorithmic components. On the MIPLIB 2017 benchmark set, we observe an average speedup of 10.7x over the original framework and 2.9 times as many instances solved within a time limit of two hours. Y1 - 2023 U6 - https://doi.org/10.1007/s10107-021-01749-5 VL - 197 SP - 793 EP - 812 ER - TY - JOUR A1 - Liberti, Leo A1 - Iommazzo, Gabriele A1 - Lavor, Carlile A1 - Maculan, Nelson T1 - Cycle-based formulations in distance geometry JF - Open Journal of Mathematical Optimization N2 - The distance geometry problem asks to find a realization of a given simple edge-weighted graph in a Euclidean space of given dimension , where the edges are realized as straight segments of lengths equal (or as close as possible) to the edge weights. The problem is often modelled as a mathematical programming formulation involving decision variables that determine the position of the vertices in the given Euclidean space. Solution algorithms are generally constructed using local or global nonlinear optimization techniques. We present a new modelling technique for this problem where, instead of deciding vertex positions, the formulations decide the length of the segments representing the edges in each cycle in the graph, projected in every dimension. We propose an exact formulation and a relaxation based on a Eulerian cycle. We then compare computational results from protein conformation instances obtained with stochastic global optimization techniques on the new cycle-based formulation and on the existing edge-based formulation. While edge-based formulations take less time to reach termination, cycle-based formulations are generally better on solution quality measures. Y1 - 2023 U6 - https://doi.org/10.5802/ojmo.18 VL - 4 ER - TY - JOUR A1 - Braun, Gábor A1 - Guzmán, Cristóbal A1 - Pokutta, Sebastian T1 - Corrections to “Lower Bounds on the Oracle Complexity of Nonsmooth Convex Optimization via Information Theory” JF - IEEE Transactions on Information Theory N2 - This note closes a gap in the proof of Theorem VI.3 from the article “Lower Bounds on the Oracle Complexity of Nonsmooth Convex Optimization via Information Theory” (2017). Y1 - 2024 U6 - https://doi.org/10.1109/TIT.2024.3357200 VL - 70 IS - 7 SP - 5408 EP - 5409 ER - TY - JOUR A1 - Bestuzheva, Ksenia A1 - Besançon, Mathieu A1 - Chen, Wei-Kun A1 - Chmiela, Antonia A1 - Donkiewicz, Tim A1 - Doornmalen, Jasper A1 - Eifler, Leon A1 - Gaul, Oliver A1 - Gamrath, Gerald A1 - Gleixner, Ambros A1 - Gottwald, Leona A1 - Graczyk, Christoph A1 - Halbig, Katrin A1 - Hoen, Alexander A1 - Hojny, Christopher A1 - Hulst, Rolf A1 - Koch, Thorsten A1 - Lübbecke, Marco A1 - Maher, Stephen J. A1 - Matter, Frederic A1 - Mühmer, Erik A1 - Müller, Benjamin A1 - Pfetsch, Marc A1 - Rehfeldt, Daniel A1 - Schlein, Steffan A1 - Schlösser, Franziska A1 - Serrano, Felipe A1 - Shinano, Yuji A1 - Sofranac, Boro A1 - Turner, Mark A1 - Vigerske, Stefan A1 - Wegscheider, Fabian A1 - Wellner, Philipp A1 - Weninger, Dieter A1 - Witzig, Jakob T1 - Enabling research through the SCIP optimization suite 8.0 JF - ACM Transactions on Mathematical Software N2 - The SCIP Optimization Suite provides a collection of software packages for mathematical optimization centered around the constraint integer programming framework SCIP. The focus of this article is on the role of the SCIP Optimization Suite in supporting research. SCIP’s main design principles are discussed, followed by a presentation of the latest performance improvements and developments in version 8.0, which serve both as examples of SCIP’s application as a research tool and as a platform for further developments. Furthermore, this article gives an overview of interfaces to other programming and modeling languages, new features that expand the possibilities for user interaction with the framework, and the latest developments in several extensions built upon SCIP. Y1 - 2023 U6 - https://doi.org/10.1145/3585516 VL - 49 IS - 2 SP - 1 EP - 21 ER - TY - JOUR A1 - Hanik, Martin A1 - Steidl, Gabriele A1 - von Tycowicz, Christoph T1 - Manifold GCN: Diffusion-based Convolutional Neural Network for Manifold-valued Graphs N2 - We propose two graph neural network layers for graphs with features in a Riemannian manifold. First, based on a manifold-valued graph diffusion equation, we construct a diffusion layer that can be applied to an arbitrary number of nodes and graph connectivity patterns. Second, we model a tangent multilayer perceptron by transferring ideas from the vector neuron framework to our general setting. Both layers are equivariant with respect to node permutations and isometries of the feature manifold. These properties have been shown to lead to a beneficial inductive bias in many deep learning tasks. Numerical examples on synthetic data as well as on triangle meshes of the right hippocampus to classify Alzheimer's disease demonstrate the very good performance of our layers. Y1 - 2024 ER - TY - JOUR A1 - Trepte, Philipp A1 - Secker, Christopher A1 - Kostova, Simona A1 - Maseko, Sibusiso B. A1 - Gang Choi, Soon A1 - Blavier, Jeremy A1 - Minia, Igor A1 - Silva Ramos, Eduardo A1 - Cassonnet, Patricia A1 - Golusik, Sabrina A1 - Zenkner, Martina A1 - Beetz, Stephanie A1 - Liebich, Mara J. A1 - Scharek, Nadine A1 - Schütz, Anja A1 - Sperling, Marcel A1 - Lisurek, Michael A1 - Wang, Yang A1 - Spirohn, Kerstin A1 - Hao, Tong A1 - Calderwood, Michael A. A1 - Hill, David E. A1 - Landthaler, Markus A1 - Olivet, Julien A1 - Twizere, Jean-Claude A1 - Vidal, Marc A1 - Wanker, Erich E. T1 - AI-guided pipeline for protein-protein interaction drug discovery identifies a SARS-CoV-2 inhibitor JF - bioRxiv Y1 - 2023 U6 - https://doi.org/10.1101/2023.06.14.544560 ER - TY - JOUR A1 - Bürger, Johanna A1 - Hayne, Paul A1 - Gundlach, Bastian A1 - Läuter, Matthias A1 - Kramer, Tobias A1 - Blum, Jürgen T1 - A Microphysical Thermal Model for the Lunar Regolith: Investigating the Latitudinal Dependence of Regolith Properties JF - Journal of Geophysical Research: Planets N2 - The microphysical structure of the lunar regolith provides information on the geologic history of the Moon. We used remote sensing measurements of thermal emission and a thermophysical model to determine the microphysical properties of the lunar regolith. We expand upon previous investigations by developing a microphysical thermal model, which more directly simulates regolith properties, such as grain size and volume filling factor. The modeled temperatures are matched with surface temperatures measured by the Diviner Lunar Radiometer Experiment on board the Lunar Reconnaissance Orbiter. The maria and highlands are investigated separately and characterized in the model by a difference in albedo and grain density. We find similar regolith temperatures for both terrains, which can be well described by similar volume filling factor profiles and mean grain sizes obtained from returned Apollo samples. We also investigate a significantly lower thermal conductivity for highlands, which formally also gives a very good solution, but in a parameter range that is well outside the Apollo data. We then study the latitudinal dependence of regolith properties up to ±80° latitude. When assuming constant regolith properties, we find that a variation of the solar incidence-dependent albedo can reduce the initially observed latitudinal gradient between model and Diviner measurements significantly. A better match between measurements and model can be achieved by a variation in intrinsic regolith properties with a decrease in bulk density with increasing latitude. We find that a variation in grain size alone cannot explain the Diviner measurements at higher latitudes. Y1 - 2024 U6 - https://doi.org/10.1029/2023JE008152 VL - 129 IS - 3 ER - TY - JOUR A1 - Majewski, Maciej A1 - Pérez, Adrià A1 - Thölke, Philipp A1 - Doerr, Stefan A1 - Charron, Nicholas A1 - Giorgino, Toni A1 - Husic, Brooke A1 - Clementi, Cecilia A1 - Noé, Frank A1 - De Fabritiis, Gianni T1 - Machine learning coarse-grained potentials of protein thermodynamics JF - Nature Communications N2 - A generalized understanding of protein dynamics is an unsolved scientific problem, the solution of which is critical to the interpretation of the structure-function relationships that govern essential biological processes. Here, we approach this problem by constructing coarse-grained molecular potentials based on artificial neural networks and grounded in statistical mechanics. For training, we build a unique dataset of unbiased all-atom molecular dynamics simulations of approximately 9 ms for twelve different proteins with multiple secondary structure arrangements. The coarse-grained models are capable of accelerating the dynamics by more than three orders of magnitude while preserving the thermodynamics of the systems. Coarse-grained simulations identify relevant structural states in the ensemble with comparable energetics to the all-atom systems. Furthermore, we show that a single coarse-grained potential can integrate all twelve proteins and can capture experimental structural features of mutated proteins. These results indicate that machine learning coarse-grained potentials could provide a feasible approach to simulate and understand protein dynamics. Y1 - 2023 U6 - https://doi.org/10.1038/s41467-023-41343-1 VL - 14 ER - TY - JOUR A1 - Durumeric, Aleksander A1 - Charron, Nicholas A1 - Templeton, Clark A1 - Musil, Félix A1 - Bonneau, Klara A1 - Pasos-Trejo, Aldo A1 - Chen, Yaoyi A1 - Kelkar, Atharva A1 - Noé, Frank A1 - Clementi, Cecilia T1 - Machine learned coarse-grained protein force-fields: Are we there yet? JF - Current Opinion in Structural Biology N2 - The successful recent application of machine learning methods to scientific problems includes the learning of flexible and accurate atomic-level force-fields for materials and biomolecules from quantum chemical data. In parallel, the machine learning of force-fields at coarser resolutions is rapidly gaining relevance as an efficient way to represent the higher-body interactions needed in coarse-grained force-fields to compensate for the omitted degrees of freedom. Coarse-grained models are important for the study of systems at time and length scales exceeding those of atomistic simulations. However, the development of transferable coarse-grained models via machine learning still presents significant challenges. Here, we discuss recent developments in this field and current efforts to address the remaining challenges. Y1 - 2023 U6 - https://doi.org/10.1016/j.sbi.2023.102533 VL - 79 ER - TY - JOUR A1 - Krämer, Andreas A1 - Durumeric, Aleksander A1 - Charron, Nicholas A1 - Chen, Yaoyi A1 - Clementi, Cecilia A1 - Noé, Frank T1 - Statistically optimal force aggregation for coarse-graining molecular dynamics JF - The Journal of Physical Chemistry Letters N2 - Machine-learned coarse-grained (CG) models have the potential for simulating large molecular complexes beyond what is possible with atomistic molecular dynamics. However, training accurate CG models remains a challenge. A widely used methodology for learning bottom-up CG force fields maps forces from all-atom molecular dynamics to the CG representation and matches them with a CG force field on average. We show that there is flexibility in how to map all-atom forces to the CG representation and that the most commonly used mapping methods are statistically inefficient and potentially even incorrect in the presence of constraints in the all-atom simulation. We define an optimization statement for force mappings and demonstrate that substantially improved CG force fields can be learned from the same simulation data when using optimized force maps. The method is demonstrated on the miniproteins chignolin and tryptophan cage and published as open-source code. Y1 - 2023 U6 - https://doi.org/10.1021/acs.jpclett.3c00444 VL - 14 IS - 17 SP - 3970 EP - 3979 ER - TY - JOUR A1 - Koyanagi, Gregory K. A1 - Lambo, Ricardo Lewis A1 - Ragyanszki, Anita A1 - Fournier, Rene A. A1 - Horbatsch, Marko W. A1 - Hessels, Eric A. T1 - Accurate calculation of the interaction of a barium monofluoride molecule with an argon atom: A step towards using matrix isolation of BaF for determining the electron electric dipole moment JF - Journal of Molecular Spectroscopy Y1 - 2023 U6 - https://doi.org/10.1016/j.jms.2023.111736 VL - 391 SP - 111736 ER - TY - JOUR A1 - Lambo, Ricardo Lewis A1 - Koyanagi, Gregory K. A1 - Ragyanszki, Anita A1 - Horbatsch, Marko A1 - Fournier, Rene A1 - Hessels, Eric A. T1 - Calculation of the local environment of a barium monofluoride molecule in an argon matrix: a step towards using matrix-isolated BaF for determining the electron electric dipole moment JF - Molecular Physics Y1 - 2023 U6 - https://doi.org/10.1080/00268976.2023.2198044 VL - 121 IS - 6 ER - TY - JOUR A1 - Steudle, Gesine A1 - Winkelmann, Stefanie A1 - Fürst, Steffen A1 - Wolf, Sarah T1 - Understanding Memory Mechanisms in Socio-Technical Systems: the Case of an Agent-based Mobility Model JF - Advances in Complex Systems N2 - This paper explores memory mechanisms in complex socio-technical systems, using a mobility demand model as an example case. We simplified a large-scale agent-based mobility model into a Markov process and discover that the mobility decision process is non-Markovian. This is due to its dependence on the system’s history, including social structure and local infrastructure, which evolve based on prior mobility decisions. To make the process Markovian, we extend the state space by incorporating two history-dependent components. Although our model is a very much reduced version of the original one, it remains too complex for the application of usual analytic methods. Instead, we employ simulations to examine the functionalities of the two history-dependent components. We think that the structure of the analyzed stochastic process is exemplary for many socio-technical, -economic, -ecological systems. Additionally, it exhibits analogies with the framework of extended evolution, which has previously been used to study cultural evolution. Y1 - 2024 U6 - https://doi.org/10.1142/S0219525924400034 VL - 27 ER - TY - JOUR A1 - Anton, Oliver A1 - Henderson, Victoria A. A1 - Da Ros, Elisa A1 - Sekulic, Ivan A1 - Burger, Sven A1 - Schneider, Philipp-Immanuel A1 - Krutzik, Markus T1 - Review and experimental benchmarking of machine learning algorithms for efficient optimization of cold atom experiments JF - Mach. Learn. Sci. Technol. Y1 - 2024 U6 - https://doi.org/10.1088/2632-2153/ad3cb6 VL - 5 SP - 025022 ER - TY - JOUR A1 - Nagel, Soeren A1 - Heitzig, Jobst A1 - Schoell, Eckehard T1 - Macroscopic Stochastic Model for Economic Cycle Dynamics JF - Physical Review Letters N2 - We present a stochastic dynamic model which can explain economic cycles. We show that the macroscopic description yields a complex dynamical landscape consisting of multiple stable fixed points, each corresponding to a split of the population into a large low and a small high income group. The stochastic fluctuations induce switching between the resulting metastable states and excitation oscillations just below a deterministic bifurcation. The shocks are caused by the decisions of a few agents who have a disproportionate influence over the macroscopic state of the economy due to the unequal distribution of wealth among the population. The fluctuations have a long-term effect on the growth of economic output and lead to business cycle oscillations exhibiting coherence resonance, where the correlation time is controlled by the population size which is inversely proportional to the noise intensity. Y1 - 2025 U6 - https://doi.org/https://doi.org/10.1103/PhysRevLett.134.047402 VL - 134 IS - 4 SP - 1 EP - 6 PB - American Physical Society ER - TY - JOUR A1 - Vanaret, Charlie T1 - Interval constraint programming for globally solving catalog-based categorical optimization JF - Journal of Global Optimization N2 - In this article, we propose an interval constraint programming method for globally solving catalog-based categorical optimization problems. It supports catalogs of arbitrary size and properties of arbitrary dimension, and does not require any modeling effort from the user. A novel catalog-based contractor (or filtering operator) guarantees consistency between the categorical properties and the existing catalog items. This results in an intuitive and generic approach that is exact, rigorous (robust to roundoff errors) and can be easily implemented in an off-the-shelf interval-based continuous solver that interleaves branching and constraint propagation. We demonstrate the validity of the approach on a numerical problem in which a categorical variable is described by a two-dimensional property space. A Julia prototype is available as open-source software under the MIT license. Y1 - 2024 U6 - https://doi.org/10.1007/s10898-023-01362-0 VL - 89 SP - 457 EP - 476 ER - TY - JOUR A1 - Cereceda-López, Eric A1 - Ostinato, Mattia A1 - Ortiz-Ambriz, Antonio A1 - Straube, Arthur A1 - Palassini, Matteo A1 - Tierno, Pietro T1 - Excluded volume induces buckling in optically driven colloidal rings JF - Phys. Rev. Research N2 - In our combined experimental, theoretical and numerical work, we study the out of equilibrium deformations in a shrinking ring of optically trapped, interacting colloidal particles. Steerable optical tweezers are used to confine dielectric microparticles along a circle of discrete harmonic potential wells, and to reduce the ring radius at a controlled quench speed. We show that excluded-volume interactions are enough to induce particle sliding from their equilibrium positions and nonequilibrium zigzag roughening of the colloidal structure. Our work unveils the underlying mechanism of interfacial deformation in radially driven microscopic discrete rings. Y1 - 2024 U6 - https://doi.org/10.1103/PhysRevResearch.6.L012044 VL - 6 SP - L012044 ER - TY - JOUR A1 - Ghysbrecht, Simon A1 - Donati, Luca A1 - Keller, Bettina G. T1 - Accuracy of reaction coordinate based rate theories for modelling chemical reactions: insights from the thermal isomerization in retinal JF - Journal of Computational Chemistry N2 - Modern potential energy surfaces have shifted attention to molecular simulations of chemical reactions. While various methods can estimate rate constants for conformational transitions in molecular dynamics simulations, their applicability to studying chemical reactions remains uncertain due to the high and sharp energy barriers and complex reaction coordinates involved. This study focuses on the thermal cis-trans isomerization in retinal, employing molecular simulations and comparing rate constant estimates based on one-dimensional rate theories with those based on sampling transitions and grid-based models for low-dimensional collective variable spaces. Even though each individual method to estimate the rate passes its quality tests, the rate constant estimates exhibit disparities of up to four orders of magnitude. Rate constant estimates based on one-dimensional reaction coordinates prove challenging to converge, even if the reaction coordinate is optimized. However, consistent estimates of the rate constant are achieved by sampling transitions and by multi-dimensional grid-based models. Y1 - 2024 U6 - https://doi.org/10.1002/jcc.27529 VL - 46 IS - 1 SP - e27529 ER - TY - JOUR A1 - Zakiyeva, Nazgul A1 - Petkovic, Milena T1 - High-dimensional high-frequency time series prediction with a mixed integer optimisation method JF - Operations Research Proceedings 2023. OR 2023 N2 - We study a functional autoregressive model for high-frequency time series. We approach the estimation of the proposed model using a Mixed Integer Optimisation method. The proposed model captures serial dependence in the functional time series by including high-dimensional curves. We illustrate our methodology on large-scale natural gas network data. Our model provides more accurate day-ahead hourly out-of-sample forecast of the gas in and out-flows compared to alternative prediction models. Y1 - 2025 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-93068 SN - 1438-0064 SP - 423 EP - 429 ER - TY - JOUR A1 - Schimunek, Johannes A1 - Seidl, Philipp A1 - Elez, Katarina A1 - Hempel, Tim A1 - Le, Tuan A1 - Noé, Frank A1 - Olsson, Simon A1 - Raich, Lluís A1 - Winter, Robin A1 - Gokcan, Hatice A1 - Gusev, Filipp A1 - Gutkin, Evgeny M. A1 - Isayev, Olexandr A1 - Kurnikova, Maria G. A1 - Narangoda, Chamali H. A1 - Zubatyuk, Roman A1 - Bosko, Ivan P. A1 - Furs, Konstantin V. A1 - Karpenko, Anna D. A1 - Kornoushenko, Yury V. A1 - Shuldau, Mikita A1 - Yushkevich, Artsemi A1 - Benabderrahmane, Mohammed B. A1 - Bousquet-Melou, Patrick A1 - Bureau, Ronan A1 - Charton, Beatrice A1 - Cirou, Bertrand C. A1 - Gil, Gérard A1 - Allen, William J. A1 - Sirimulla, Suman A1 - Watowich, Stanley A1 - Antonopoulos, Nick A1 - Epitropakis, Nikolaos A1 - Krasoulis, Agamemnon A1 - Itsikalis, Vassilis A1 - Theodorakis, Stavros A1 - Kozlovskii, Igor A1 - Maliutin, Anton A1 - Medvedev, Alexander A1 - Popov, Petr A1 - Zaretckii, Mark A1 - Eghbal-Zadeh, Hamid A1 - Halmich, Christina A1 - Hochreiter, Sepp A1 - Mayr, Andreas A1 - Ruch, Peter A1 - Widrich, Michael A1 - Berenger, Francois A1 - Kumar, Ashutosh A1 - Yamanishi, Yoshihiro A1 - Zhang, Kam Y. J. A1 - Bengio, Emmanuel A1 - Bengio, Yoshua A1 - Jain, Moksh J. A1 - Korablyov, Maksym A1 - Liu, Cheng-Hao A1 - Marcou, Gilles A1 - Glaab, Enrico A1 - Barnsley, Kelly A1 - Iyengar, Suhasini M. A1 - Ondrechen, Mary Jo A1 - Haupt, V. Joachim A1 - Kaiser, Florian A1 - Schroeder, Michael A1 - Pugliese, Luisa A1 - Albani, Simone A1 - Athanasiou, Christina A1 - Beccari, Andrea A1 - Carloni, Paolo A1 - D’Arrigo, Giulia A1 - Gianquinto, Eleonora A1 - Goßen, Jonas A1 - Hanke, Anton A1 - Joseph, Benjamin P. A1 - Kokh, Daria B. A1 - Kovachka, Sandra A1 - Manelfi, Candida A1 - Mukherjee, Goutam A1 - Muñiz-Chicharro, Abraham A1 - Musiani, Francesco A1 - Nunes-Alves, Ariane A1 - Paiardi, Giulia A1 - Rossetti, Giulia A1 - Sadiq, S. Kashif A1 - Spyrakis, Francesca A1 - Talarico, Carmine A1 - Tsengenes, Alexandros A1 - Wade, Rebecca C. A1 - Copeland, Conner A1 - Gaiser, Jeremiah A1 - Olson, Daniel R. A1 - Roy, Amitava A1 - Venkatraman, Vishwesh A1 - Wheeler, Travis J. A1 - Arthanari, Haribabu A1 - Blaschitz, Klara A1 - Cespugli, Marco A1 - Durmaz, Vedat A1 - Fackeldey, Konstantin A1 - Fischer, Patrick D. A1 - Gorgulla, Christoph A1 - Gruber, Christian A1 - Gruber, Karl A1 - Hetmann, Michael A1 - Kinney, Jamie E. A1 - Padmanabha Das, Krishna M. A1 - Pandita, Shreya A1 - Singh, Amit A1 - Steinkellner, Georg A1 - Tesseyre, Guilhem A1 - Wagner, Gerhard A1 - Wang, Zi-Fu A1 - Yust, Ryan J. A1 - Druzhilovskiy, Dmitry S. A1 - Filimonov, Dmitry A. A1 - Pogodin, Pavel V. A1 - Poroikov, Vladimir A1 - Rudik, Anastassia V. A1 - Stolbov, Leonid A. A1 - Veselovsky, Alexander V. A1 - De Rosa, Maria A1 - De Simone, Giada A1 - Gulotta, Maria R. A1 - Lombino, Jessica A1 - Mekni, Nedra A1 - Perricone, Ugo A1 - Casini, Arturo A1 - Embree, Amanda A1 - Gordon, D. Benjamin A1 - Lei, David A1 - Pratt, Katelin A1 - Voigt, Christopher A. A1 - Chen, Kuang-Yu A1 - Jacob, Yves A1 - Krischuns, Tim A1 - Lafaye, Pierre A1 - Zettor, Agnès A1 - Rodríguez, M. Luis A1 - White, Kris M. A1 - Fearon, Daren A1 - Von Delft, Frank A1 - Walsh, Martin A. A1 - Horvath, Dragos A1 - Brooks III, Charles L. A1 - Falsafi, Babak A1 - Ford, Bryan A1 - García-Sastre, Adolfo A1 - Yup Lee, Sang A1 - Naffakh, Nadia A1 - Varnek, Alexandre A1 - Klambauer, Günter A1 - Hermans, Thomas M. T1 - A community effort in SARS-CoV-2 drug discovery JF - Molecular Informatics KW - COVID-19 KW - drug discovery KW - machine learning KW - SARS-CoV-2 Y1 - 2023 U6 - https://doi.org/https://doi.org/10.1002/minf.202300262 VL - 43 IS - 1 SP - e202300262 ER - TY - JOUR A1 - Gorgulla, Christoph A1 - Nigam, AkshatKumar A1 - Koop, Matt A1 - Selim Çınaroğlu, Süleyman A1 - Secker, Christopher A1 - Haddadnia, Mohammad A1 - Kumar, Abhishek A1 - Malets, Yehor A1 - Hasson, Alexander A1 - Li, Minkai A1 - Tang, Ming A1 - Levin-Konigsberg, Roni A1 - Radchenko, Dmitry A1 - Kumar, Aditya A1 - Gehev, Minko A1 - Aquilanti, Pierre-Yves A1 - Gabb, Henry A1 - Alhossary, Amr A1 - Wagner, Gerhard A1 - Aspuru-Guzik, Alán A1 - Moroz, Yurii S. A1 - Fackeldey, Konstantin A1 - Arthanari, Haribabu T1 - VirtualFlow 2.0 - The Next Generation Drug Discovery Platform Enabling Adaptive Screens of 69 Billion Molecules JF - bioRxiv KW - preprint Y1 - 2023 U6 - https://doi.org/10.1101/2023.04.25.537981 ER -