TY - JOUR A1 - Allen, Peter A1 - Pfenninger, Vincent A1 - Parczyk, Olaf T1 - Resilience for tight Hamiltonicity JF - Combinatorial Theory Y1 - 2024 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/https://doi.org/10.1038/s41598-024-62720-w VL - 14 IS - 11838 ER - TY - GEN A1 - Hajarolasvadi, Noushin A1 - Baum, Daniel T1 - Data for Training the DeepOrientation Model: Simulated cryo-ET tomogram patches N2 - 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. Y1 - 2024 U6 - https://doi.org/10.12752/9686 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 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 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 ER - TY - JOUR A1 - Weimann, Kuba A1 - Tim, Conrad 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 ER - TY - GEN A1 - Winkler, Alexander T1 - URIs im GLAM-Bereich – was sie sind und wie man sie verwendet N2 - Das Papier gibt Hinweise zu URIs (Uniform Resource Identifiers) im GLAM-Sektor. Y1 - 2024 U6 - https://doi.org/10.5281/zenodo.11608433 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 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 - arxiv 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 ER - TY - CHAP A1 - Semler, Phillip A1 - Weiser, Martin T1 - Adaptive Gradient Enhanced Gaussian Process Surrogates for Inverse Problems T2 - Proceedings of the MATH+ Thematic Einstein Semester on Mathematical Optimization for Machine Learning N2 - 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. Y1 - 2024 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 - CHAP A1 - Becker, Christiane A1 - Bläsi, Benedikt A1 - Burger, Sven A1 - Ehrler, Bruno A1 - Gordon, Ivan A1 - Isabella, Olindo A1 - Jaeger, Klaus A1 - Jost, Marko A1 - Manley, Phillip A1 - Muñoz, Delfina A1 - Paetzold, Ulrich W. A1 - Sai, Hitoshi A1 - Yablonovitch, Eli T1 - Optical challenges for terawatt-scale photovoltaics T2 - Proc. SPIE Y1 - 2024 U6 - https://doi.org/10.1117/12.3016415 VL - PC13014 SP - PC1301401 PB - SPIE ER - TY - CHAP A1 - Schaible, Jonas A1 - Yoo, Danbi A1 - Winarto, Hanifa A1 - Miaskiewicz, Alexandra A1 - Albrecht, Steve A1 - Wessels, Andreas A1 - Bläsi, Benedikt A1 - Burger, Sven A1 - Jaeger, Klaus A1 - Becker, Christiane T1 - On aesthetical appearance of colored perovskite solar modules T2 - Proc. SPIE Y1 - 2024 U6 - https://doi.org/10.1117/12.3017427 VL - PC13014 SP - PC1301406 PB - SPIE ER - TY - GEN A1 - Vanaret, Charlie A1 - Leyffer, Sven T1 - Unifying nonlinearly constrained nonconvex optimization N2 - 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 . Y1 - 2024 ER - TY - THES A1 - Bena, Noor-Cella T1 - Finding Rules and Patterns in Egyptological Research Objects using Boolean Polynomial Rings Y1 - 2023 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 - GEN A1 - Sekuliĉ, Ivan A1 - Schneider, Philipp-Immanuel A1 - Hammerschmidt, Martin A1 - Burger, Sven T1 - Machine-learning driven design of metasurfaces: learn the physics and not the objective function T2 - Proc. SPIE Y1 - 2024 U6 - https://doi.org/10.1117/12.3022119 VL - PC13017 SP - PC130170X ER -