@article{GuptaHamannMueyesseretal.2023, author = {Gupta, Pranshu and Hamann, Fabian and M{\"u}yesser, Alp and Sgueglia, Amedeo and Parczyk, Olaf}, title = {A general approach to transversal versions of Dirac-type theorems}, volume = {55}, journal = {Bulletin of the London Mathematical Society}, number = {6}, pages = {2817 -- 2839}, year = {2023}, language = {en} } @article{BarrosCavalarMotaetal.2022, author = {Barros, Gabriel F. and Cavalar, Bruno P. and Mota, Guilherme O. and Parczyk, Olaf}, title = {Anti-Ramsey threshold of cycles for sparse graphs}, journal = {Discrete Applied Mathematics}, year = {2022}, language = {en} } @article{KohayakawaMotaSchnitzeretal.2023, author = {Kohayakawa, Yoshiharu and Mota, Guilherme O. and Schnitzer, Jakob and Parczyk, Olaf}, title = {Anti-Ramsey threshold of complete graphs for sparse graphs}, journal = {Discrete Mathematics}, year = {2023}, language = {en} } @article{HahnKlimrothParczykPerson2024, author = {Hahn-Klimroth, Max and Parczyk, Olaf and Person, Yury}, title = {Minimum degree conditions for containing an r-regular r-connected spanning subgraph}, volume = {118}, journal = {European Journal of Combinatorics}, doi = {10.1016/j.ejc.2024.103940}, pages = {103940}, year = {2024}, language = {en} } @article{MundingerPokuttaSpiegeletal.2024, author = {Mundinger, Konrad and Pokutta, Sebastian and Spiegel, Christoph and Zimmer, Max}, title = {Extending the Continuum of Six-Colorings}, volume = {34}, journal = {Geombinatorics Quarterly}, number = {1}, arxiv = {http://arxiv.org/abs/2404.05509}, pages = {20 -- 29}, year = {2024}, language = {en} } @article{GebhardHahnKlimrothPenschucketal.2022, author = {Gebhard, Oliver and Hahn-Klimroth, Max and Penschuck, Manuel and Rolvien, Maurice and Scarlett, Jonathan and Tan, Nelvin and Parczyk, Olaf}, title = {Near optimal sparsity-constrained group testing: improved bounds}, journal = {IEEE Transactions on Information Theory}, year = {2022}, language = {en} } @article{ParczykPokuttaSpiegeletal.2024, author = {Parczyk, Olaf and Pokutta, Sebastian and Spiegel, Christoph and Szab{\´o}, Tibor}, title = {New Ramsey multiplicity bounds and search heuristics}, journal = {Foundations of Computational Mathematics}, doi = {10.1007/s10208-024-09675-6}, year = {2024}, language = {en} } @article{AllenPfenningerParczyk2024, author = {Allen, Peter and Pfenninger, Vincent and Parczyk, Olaf}, title = {Resilience for tight Hamiltonicity}, volume = {4}, journal = {Combinatorial Theory}, number = {1}, doi = {10.5070/C64163846}, year = {2024}, language = {en} } @article{BoettcherSguegliaSkokanetal.2022, author = {B{\"o}ttcher, Julia and Sgueglia, Amedeo and Skokan, Jozef and Parczyk, Olaf}, title = {Triangles in randomly perturbed graphs}, journal = {Combinatorics, Probability and Computing}, year = {2022}, language = {en} } @article{VuHanSchettinoWeissetal.2024, author = {Vu-Han, Tu-Lan and Schettino, Rodrigo Bermudez and Weiß, Claudia and Perka, Carsten and Winkler, Tobias and Sunkara, Vikram and Pumberger, Matthias}, title = {An interpretable data-driven prediction model to anticipate scoliosis in spinal muscular atrophy in the era of (gene-) therapies}, volume = {14}, journal = {Scientific Reports}, number = {11838}, doi = {10.1038/s41598-024-62720-w}, year = {2024}, abstract = {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.}, language = {en} } @article{SiqueiraRodriguesSchmidtIsraeletal.2024, author = {Siqueira Rodrigues, Lucas and Schmidt, Timo Torsten and Israel, Johann Habakuk and Nyakatura, John and Zachow, Stefan and Kosch, Thomas}, title = {Comparing the Effects of Visual, Haptic, and Visuohaptic Encoding on Memory Retention of Digital Objects in Virtual Reality}, journal = {NordiCHI '24: Proceedings of the 13th Nordic Conference on Human-Computer Interaction}, arxiv = {http://arxiv.org/abs/2406.14139}, doi = {10.1145/3679318.3685349}, pages = {1 -- 13}, year = {2024}, abstract = {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.}, language = {en} } @article{VeldhuijzenVeltkampIkneetal.2024, author = {Veldhuijzen, Ben and Veltkamp, Remco C. and Ikne, Omar and Allaert, Benjamin and Wannous, Hazem and Emporio, Marco and Giachetti, Andrea and LaViola Jr, Joseph J. and He, Ruiwen and Benhabiles, Halim and Cabani, Adnane and Fleury, Anthony and Hammoudi, Karim and Gavalas, Konstantinos and Vlachos, Christoforos and Papanikolaou, Athanasios and Romanelis, Ioannis and Fotis, Vlassis and Arvanitis, Gerasimos and Moustakas, Konstantinos and Hanik, Martin and Nava-Yazdani, Esfandiar and von Tycowicz, Christoph}, title = {SHREC 2024: Recognition Of Dynamic Hand Motions Molding Clay}, volume = {123}, journal = {Computers \& Graphics}, doi = {10.1016/j.cag.2024.104012}, pages = {104012}, year = {2024}, abstract = {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.}, language = {en} } @article{WeimannConrad2024, author = {Weimann, Kuba and Conrad, Tim}, title = {Federated Learning with Deep Neural Networks: A Privacy-Preserving Approach to Enhanced ECG Classification}, volume = {28}, journal = {IEEE Journal of Biomedical and Health Informatics}, number = {11}, doi = {10.1109/JBHI.2024.3427787}, year = {2024}, language = {en} } @article{DasSridharamurthyNatarajan2024, author = {Das, Somenath and Sridharamurthy, Raghavendra and Natarajan, Vijay}, title = {Time-varying Extremum Graphs}, volume = {43}, journal = {Computer Graphics Forum}, number = {6}, doi = {10.1111/cgf.15162}, pages = {e15162}, year = {2024}, abstract = {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 K{\´a}rm{\´a}n vortex street dataset.}, language = {en} } @article{DjurdjevacConradQuangVuNagel2024, author = {Djurdjevac Conrad, Natasa and Quang Vu, Nhu and Nagel, Soeren}, title = {Co-evolving networks for opinion and social dynamics in agent-based models}, volume = {34}, journal = {Chaos: An Interdisciplinary Journal of Nonlinear Science}, number = {9}, issn = {1054-1500}, arxiv = {http://arxiv.org/abs/2407.00145}, doi = {10.1063/5.0226054}, year = {2024}, abstract = {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.}, language = {en} } @article{FuerstConradJaegeretal.2024, author = {F{\"u}rst, Steffen and Conrad, Tim and Jaeger, Carlo and Wolf, Sarah}, title = {Vahana.jl - A framework (not only) for large-scale agent-based models}, journal = {Proceedings of Social Simulation Conference 2024 (SSC24)}, year = {2024}, language = {en} } @article{AlchikhConradObermeieretal.2024, author = {Alchikh, Maren and Conrad, Tim and Obermeier, Patrick and Ma, Xiaolin and Schweiger, Brunhilde and Opota, Onya and Rath, Barbara}, title = {Disease Burden and Inpatient Management of Children with Acute Respiratory Viral Infections during the Pre-COVID Era in Germany: A Cost-of-Illness Study}, volume = {16}, journal = {Viruses}, number = {4}, doi = {10.3390/v16040507}, year = {2024}, abstract = {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.}, language = {en} } @article{ManleyHammerschmidtZschiedrichetal.2024, author = {Manley, Phillip and Hammerschmidt, Martin and Zschiedrich, Lin and J{\"a}ger, Klaus and Becker, Christiane and Burger, Sven}, title = {Modelling luminescent coupling in multi-junction solar cells: perovskite silicon tandem case study}, volume = {13014}, journal = {Proc. SPIE}, arxiv = {http://arxiv.org/abs/2409.15987}, doi = {10.1117/12.3023941}, pages = {1301404}, year = {2024}, language = {en} } @article{RaharinirinaSunkaravonKleistetal.2024, author = {Raharinirina, Nomenjanahary Alexia and Sunkara, Vikram and von Kleist, Max and Fackeldey, Konstantin and Weber, Marcus}, title = {Multi-Input data ASsembly for joint Analysis (MIASA): A framework for the joint analysis of disjoint sets of variables}, volume = {19}, journal = {PLOS ONE}, number = {5}, publisher = {Public Library of Science}, doi = {10.1371/journal.pone.0302425}, pages = {26}, year = {2024}, language = {en} } @article{PfeufferBielowWeinetal.2024, author = {Pfeuffer, Julianus and Bielow, Chris and Wein, Samuel and Jeong, Kyowon and Netz, Eugen and Walter, Axel and Alka, Oliver and Nilse, Lars and Colaianni, Pasquale Domenico and McCloskey, Douglas and Kim, Jihyung and Rosenberger, George and Bichmann, Leon and Walzer, Mathias and Veit, Johannes and Boudaud, Bertrand and Bernt, Matthias and Patikas, Nikolaos and Pilz, Matteo and Startek, Michał Piotr and Kutuzova, Svetlana and Heumos, Lukas and Charkow, Joshua and Sing, Justin Cyril and Feroz, Ayesha and Siraj, Arslan and Weisser, Hendrik and Dijkstra, Tjeerd M. H. and Perez-Riverol, Yasset and R{\"o}st, Hannes and Kohlbacher, Oliver and Sachsenberg, Timo}, title = {OpenMS 3 enables reproducible analysis of large-scale mass spectrometry data}, volume = {21}, journal = {Nature Methods}, number = {3}, publisher = {Springer Science and Business Media LLC}, issn = {1548-7091}, doi = {10.1038/s41592-024-02197-7}, pages = {365 -- 367}, year = {2024}, language = {en} }