@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} }