collaborators

5 papers

cs.LG2026

Why Ranking Anomaly Detection Algorithms Isn't as Reliable as You May Think

Simon Klüttermann, Jérôme Rutinowski, Frederik Polachowski +1

Anomaly detection is a safety-critical machine learning problem with applications ranging from fraud detection to network intrusion prevention and industrial monitoring. Despite th…

cs.DM2025

Why Districting Becomes NP-hard

Niklas Jost, Adolfo Escobedo, Alice Kirchheim

This paper investigates why and when the edge-based districting problem becomes computationally intractable. The overall problem is represented as an exact mathematical programming…

cs.CV2025

MR6D: Benchmarking 6D Pose Estimation for Mobile Robots

Anas Gouda, Shrutarv Awasthi, Christian Blesing +3

Existing 6D pose estimation datasets primarily focus on small household objects typically handled by robot arm manipulators, limiting their relevance to mobile robotics. Mobile pla…

cs.CV2025

Enhancing Long-Term Re-Identification Robustness Using Synthetic Data: A Comparative Analysis

Christian Pionzewski, Rebecca Rademacher, Jérôme Rutinowski +5

This contribution explores the impact of synthetic training data usage and the prediction of material wear and aging in the context of re-identification. Different experimental set…

cond-mat.mtrl-sci2025

Extremely asymmetric diffraction as a method of determining magneto-optical constants for X-rays near absorption edges

M A Andreeva, Yu. Repchenko, A G Smekhova +3

The spectral dependence of the Bragg peak position under conditions of extremely asymmetric diffraction has been analyzed in the kinematical and dynamical approximations of the dif…