@article{AllenPfenningerParczyk, author = {Allen, Peter and Pfenninger, Vincent and Parczyk, Olaf}, title = {Resilience for tight Hamiltonicity}, series = {Combinatorial Theory}, journal = {Combinatorial Theory}, language = {en} } @article{BoettcherSguegliaSkokanetal., author = {B{\"o}ttcher, Julia and Sgueglia, Amedeo and Skokan, Jozef and Parczyk, Olaf}, title = {Triangles in randomly perturbed graphs}, series = {Combinatorics, Probability and Computing}, journal = {Combinatorics, Probability and Computing}, language = {en} } @article{VuHanSchettinoWeissetal., 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}, series = {Scientific Reports}, volume = {14}, journal = {Scientific Reports}, number = {11838}, doi = {https://doi.org/10.1038/s41598-024-62720-w}, 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} } @misc{HajarolasvadiBaum, author = {Hajarolasvadi, Noushin and Baum, Daniel}, title = {Data for Training the DeepOrientation Model: Simulated cryo-ET tomogram patches}, doi = {10.12752/9686}, abstract = {A major restriction to applying deep learning methods in cryo-electron tomography is the lack of annotated data. Many large learning-based models cannot be applied to these images due to the lack of adequate experimental ground truth. One appealing alternative solution to the time-consuming and expensive experimental data acquisition and annotation is the generation of simulated cryo-ET images. In this context, we exploit a public cryo-ET simulator called PolNet to generate three datasets of two macromolecular structures, namely the ribosomal complex 4v4r and Thermoplasma acidophilum 20S proteasome, 3j9i. We select these two specific particles to test whether our models work for macromolecular structures with and without rotational symmetry. The three datasets contain 50, 150, and 450 tomograms with a voxel size of 10 ̊A, respectively. Here, we publish patches of size 40 × 40 × 40 extracted from the medium-sized dataset with 26,703 samples of 4v4r and 40,671 samples of 3j9i. The original tomograms from which the samples were extracted are of size 500 × 500 × 250. Finally, it should be noted that the currently published test dataset is employed for reporting the results of our paper titled "DeepOrientation: Deep Orientation Estimation of Macromolecules in Cryo-electron tomography" paper.}, language = {en} } @article{SiqueiraRodriguesSchmidtIsraeletal., 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}, 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., 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}, series = {Computers \& Graphics}, journal = {Computers \& Graphics}, 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{WeimannTim, author = {Weimann, Kuba and Tim, Conrad}, title = {Federated Learning with Deep Neural Networks: A Privacy-Preserving Approach to Enhanced ECG Classification}, series = {IEEE Journal of Biomedical and Health Informatics}, journal = {IEEE Journal of Biomedical and Health Informatics}, doi = {10.1109/JBHI.2024.3427787}, language = {en} } @misc{Winkler, author = {Winkler, Alexander}, title = {URIs im GLAM-Bereich - was sie sind und wie man sie verwendet}, doi = {10.5281/zenodo.11608433}, pages = {7}, abstract = {Das Papier gibt Hinweise zu URIs (Uniform Resource Identifiers) im GLAM-Sektor.}, language = {de} } @article{DasSridharamurthyNatarajan, author = {Das, Somenath and Sridharamurthy, Raghavendra and Natarajan, Vijay}, title = {Time-varying Extremum Graphs}, series = {Computer Graphics Forum}, journal = {Computer Graphics Forum}, doi = {10.1111/cgf.15162}, pages = {e15162}, 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{DjurdjevacConradQuangVuNagel, author = {Djurdjevac Conrad, Natasa and Quang Vu, Nhu and Nagel, Soeren}, title = {Co-evolving networks for opinion and social dynamics in agent-based models}, series = {arxiv}, journal = {arxiv}, 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} } @inproceedings{SemlerWeiser, author = {Semler, Phillip and Weiser, Martin}, title = {Adaptive Gradient Enhanced Gaussian Process Surrogates for Inverse Problems}, series = {Proceedings of the MATH+ Thematic Einstein Semester on Mathematical Optimization for Machine Learning}, booktitle = {Proceedings of the MATH+ Thematic Einstein Semester on Mathematical Optimization for Machine Learning}, abstract = {Generating simulated training data needed for constructing sufficiently accurate surrogate models to be used for efficient optimization or parameter identification can incur a huge computational effort in the offline phase. We consider a fully adaptive greedy approach to the computational design of experiments problem using gradient-enhanced Gaussian process regression as surrogates. Designs are incrementally defined by solving an optimization problem for accuracy given a certain computational budget. We address not only the choice of evaluation points but also of required simulation accuracy, both of values and gradients of the forward model. Numerical results show a significant reduction of the computational effort compared to just position-adaptive and static designs as well as a clear benefit of including gradient information into the surrogate training.}, language = {en} } @article{FuerstConradJaegeretal., 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}, series = {Proceedings of Social Simulation Conference 2024 (SSC24)}, journal = {Proceedings of Social Simulation Conference 2024 (SSC24)}, language = {en} } @article{AlchikhConradObermeieretal., 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}, series = {Viruses}, volume = {16}, journal = {Viruses}, number = {4}, doi = {10.3390/v16040507}, 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} } @inproceedings{BeckerBlaesiBurgeretal., author = {Becker, Christiane and Bl{\"a}si, Benedikt and Burger, Sven and Ehrler, Bruno and Gordon, Ivan and Isabella, Olindo and Jaeger, Klaus and Jost, Marko and Manley, Phillip and Mu{\~n}oz, Delfina and Paetzold, Ulrich W. and Sai, Hitoshi and Yablonovitch, Eli}, title = {Optical challenges for terawatt-scale photovoltaics}, series = {Proc. SPIE}, volume = {PC13014}, booktitle = {Proc. SPIE}, publisher = {SPIE}, doi = {10.1117/12.3016415}, pages = {PC1301401}, language = {en} } @inproceedings{SchaibleYooWinartoetal., author = {Schaible, Jonas and Yoo, Danbi and Winarto, Hanifa and Miaskiewicz, Alexandra and Albrecht, Steve and Wessels, Andreas and Bl{\"a}si, Benedikt and Burger, Sven and Jaeger, Klaus and Becker, Christiane}, title = {On aesthetical appearance of colored perovskite solar modules}, series = {Proc. SPIE}, volume = {PC13014}, booktitle = {Proc. SPIE}, publisher = {SPIE}, doi = {10.1117/12.3017427}, pages = {PC1301406}, language = {en} } @misc{VanaretLeyffer, author = {Vanaret, Charlie and Leyffer, Sven}, title = {Unifying nonlinearly constrained nonconvex optimization}, abstract = {Derivative-based iterative methods for nonlinearly constrained non-convex optimization usually share common algorithmic components, such as strategies for computing a descent direction and mechanisms that promote global convergence. Based on this observation, we introduce an abstract framework based on four common ingredients that describes most derivative-based iterative methods and unifies their workflows. We then present Uno, a modular C++ solver that implements our abstract framework and allows the automatic generation of various strategy combinations with no programming effort from the user. Uno is meant to (1) organize mathematical optimization strategies into a coherent hierarchy; (2) offer a wide range of efficient and robust methods that can be compared for a given instance; (3) enable researchers to experiment with novel optimization strategies; and (4) reduce the cost of development and maintenance of multiple optimization solvers. Uno's software design allows user to compose new customized solvers for emerging optimization areas such as robust optimization or optimization problems with complementarity constraints, while building on reliable nonlinear optimization techniques. We demonstrate that Uno is highly competitive against state-of-the-art solvers filterSQP, IPOPT, SNOPT, MINOS, LANCELOT, LOQO, and CONOPT on a subset of 429 small problems from the CUTEst collection. Uno is available as open-source software under the MIT license at https://github.com/cvanaret/Uno .}, language = {en} } @masterthesis{Bena, type = {Bachelor Thesis}, author = {Bena, Noor-Cella}, title = {Finding Rules and Patterns in Egyptological Research Objects using Boolean Polynomial Rings}, pages = {35}, language = {en} } @article{ManleyHammerschmidtZschiedrichetal., 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}, series = {Proc. SPIE}, volume = {13014}, journal = {Proc. SPIE}, doi = {10.1117/12.3023941}, pages = {1301404}, language = {en} } @article{RaharinirinaSunkaravonKleistetal., 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}, series = {PLOS ONE}, volume = {19}, journal = {PLOS ONE}, number = {5}, publisher = {Public Library of Science}, doi = {10.1371/journal.pone.0302425}, pages = {26}, language = {en} } @misc{SekuliĉSchneiderHammerschmidtetal., author = {Sekuliĉ, Ivan and Schneider, Philipp-Immanuel and Hammerschmidt, Martin and Burger, Sven}, title = {Machine-learning driven design of metasurfaces: learn the physics and not the objective function}, series = {Proc. SPIE}, volume = {PC13017}, journal = {Proc. SPIE}, doi = {10.1117/12.3022119}, pages = {PC130170X}, language = {en} }