@article{vonKleistSchuetteZhang2018, author = {von Kleist, Max and Sch{\"u}tte, Christof and Zhang, Wei}, title = {Statistical analysis of the first passage path ensemble of jump processes}, volume = {170}, journal = {Journal of Statistical Physics}, doi = {10.1007/s10955-017-1949-x}, pages = {809 -- 843}, year = {2018}, abstract = {The transition mechanism of jump processes between two different subsets in state space reveals important dynamical information of the processes and therefore has attracted considerable attention in the past years. In this paper, we study the first passage path ensemble of both discrete-time and continuous-time jump processes on a finite state space. The main approach is to divide each first passage path into nonreactive and reactive segments and to study them separately. The analysis can be applied to jump processes which are non-ergodic, as well as continuous-time jump processes where the waiting time distributions are non-exponential. In the particular case that the jump processes are both Markovian and ergodic, our analysis elucidates the relations between the study of the first passage paths and the study of the transition paths in transition path theory. We provide algorithms to numerically compute statistics of the first passage path ensemble. The computational complexity of these algorithms scales with the complexity of solving a linear system, for which efficient methods are available. Several examples demonstrate the wide applicability of the derived results across research areas.}, language = {en} } @article{SchmittTitschackBaum2025, author = {Schmitt, Kira and Titschack, J{\"u}rgen and Baum, Daniel}, title = {CoDA: Interactive Segmentation and Morphological Analysis of Dendroid Structures Exemplified on Stony Cold-Water Corals}, journal = {IEEE Transactions on Visualization and Computer Graphics}, doi = {10.1109/TVCG.2026.3656066}, year = {2025}, language = {en} } @article{NgokinghaTchoutoMehlKhomeijaniFarahanietal.2026, author = {Ngokingha Tchouto, Mireille and Mehl, Julia and Khomeijani Farahani, Saeed and Baum, Daniel and Duda, Georg}, title = {Novel image registration approach for combining 2D Osterix and collagen bundles images with 3D µCT}, volume = {10}, journal = {Journal of Bone and Mineral Research}, number = {3}, doi = {10.1093/jbmrpl/ziag009}, year = {2026}, language = {en} } @inproceedings{SenguptaKuşLietal.2026, author = {Sengupta, Agniva and Ku{\c{s}}, Dilara and Li, Jianning and Zachow, Stefan}, title = {Globally Optimal Pose from Orthographic Silhouettes}, year = {2026}, abstract = {We solve the problem of determining the pose of known shapes in R^3 from their unoccluded silhouettes. The pose is determined up to global optimality using a simple yet under-explored property of the area-of-silhouette: its continuity w.r.t trajectories in the rotation space. The proposed method utilises pre-computed silhouette-signatures, modelled as a response surface of the area-of-silhouettes. Querying this silhouette-signature response surface for pose estimation leads to a strong branching of the rotation search space, making resolution-guided candidate search feasible. Additionally, we utilise the aspect ratio of 2D ellipses fitted to projected silhouettes as an auxiliary global shape signature to accelerate the pose search. This combined strategy forms the first method to efficiently estimate globally optimal pose from just the silhouettes, without being guided by correspondences, for any shape, irrespective of its convexity and genus. We validate our method on synthetic and real examples, demonstrating significantly improved accuracy against comparable approaches.}, language = {en} } @inproceedings{SunkaraRostamivonTycowiczetal.2026, author = {Sunkara, Vikram and Rostami, Atefe and von Tycowicz, Christoph and Sch{\"u}tte, Christof}, title = {Stop throwing away your Decoder; extract the learnt local coordinate system using Latent-XAI}, booktitle = {The 4th World Conference on Explainable Artificial Intelligence (XAI-2026)}, year = {2026}, language = {en} } @inproceedings{StokkeBergmannHaniketal.2025, author = {Stokke, Jo Andersson and Bergmann, Ronny and Hanik, Martin and von Tycowicz, Christoph}, title = {p-Laplacians for Manifold-valued Hypergraphs}, volume = {16035}, booktitle = {Geometric Science of Information. GSI 2025}, arxiv = {http://arxiv.org/abs/2507.10335}, doi = {10.1007/978-3-032-03924-8_17}, year = {2025}, abstract = {Hypergraphs extend traditional graphs by enabling the representation of N-ary relationships through higher-order edges. Akin to a common approach of deriving graph Laplacians, we define function spaces and corresponding symmetric products on the nodes and edges to derive hypergraph Laplacians. While this has been done before for Euclidean features, this work generalizes previous hypergraph Laplacian approaches to accommodate manifold-valued hypergraphs for many commonly encountered manifolds.}, language = {en} } @article{Navayazdani2026, author = {Navayazdani, Esfandiar}, title = {Ridge Regression on Riemannian Manifolds for Time-Series Prediction}, journal = {Information Geometry}, arxiv = {http://arxiv.org/abs/2411.18339}, year = {2026}, language = {en} }