@article{PieronekKleefeld2024,
  author    = {Pieronek, Lukas and Kleefeld, Andreas},
  title     = {On trajectories of complex-valued interior transmission eigenvalues},
  series = {Inverse problems and imaging : IPI},
  volume    = {18},
  journal   = {Inverse problems and imaging : IPI},
  number    = {2},
  publisher = {AIMS},
  address   = {Springfield, Mo},
  issn      = {1930-8337 (Print)},
  doi       = {10.3934/ipi.2023041},
  pages     = {480 -- 516},
  year      = {2024},
  abstract  = {This paper investigates the interior transmission problem for homogeneous media via eigenvalue trajectories parameterized by the magnitude of the refractive index. In the case that the scatterer is the unit disk, we prove that there is a one-to-one correspondence between complex-valued interior transmission eigenvalue trajectories and Dirichlet eigenvalues of the Laplacian which turn out to be exactly the trajectorial limit points as the refractive index tends to infinity. For general simply-connected scatterers in two or three dimensions, a corresponding relation is still open, but further theoretical results and numerical studies indicate a similar connection.},
  language  = {en}
}
@unpublished{GriegerMehrkanoonBialonski2024,
  author    = {Grieger, Niklas and Mehrkanoon, Siamak and Bialonski, Stephan},
  title     = {Preprint: Data-efficient sleep staging with synthetic time series pretraining},
  series = {arXiv},
  journal   = {arXiv},
  pages     = {10 Seiten},
  year      = {2024},
  abstract  = {Analyzing electroencephalographic (EEG) time series can be challenging, especially with deep neural networks, due to the large variability among human subjects and often small datasets. To address these challenges, various strategies, such as self-supervised learning, have been suggested, but they typically rely on extensive empirical datasets. Inspired by recent advances in computer vision, we propose a pretraining task termed "frequency pretraining" to pretrain a neural network for sleep staging by predicting the frequency content of randomly generated synthetic time series. Our experiments demonstrate that our method surpasses fully supervised learning in scenarios with limited data and few subjects, and matches its performance in regimes with many subjects. Furthermore, our results underline the relevance of frequency information for sleep stage scoring, while also demonstrating that deep neural networks utilize information beyond frequencies to enhance sleep staging performance, which is consistent with previous research. We anticipate that our approach will be advantageous across a broad spectrum of applications where EEG data is limited or derived from a small number of subjects, including the domain of brain-computer interfaces.},
  language  = {en}
}
@book{ElsaesserKlebinggatKuhnetal.2024,
  author    = {Elsaesser, Evelyn and Klebinggat, Michael and Kuhn, Wilfried and Michielsens, Constant and Pauels, Willibert and Popkes, Enno E. and Schneider, Elke and Laack, Walter van and Warven, Rinus van},
  title     = {Schnittstelle Tod - Ist die Menschheit zu retten ohne Vertrauen auf ein Danach},
  editor    = {Laack, Walter van},
  publisher = {van Laack Buchverlag},
  address   = {Aachen},
  isbn      = {978-3-936624-58-8},
  pages     = {156 Seiten},
  year      = {2024},
  language  = {de}
}
@book{StaatDigelTrzewiketal.2024,
  author    = {Staat, Manfred and Digel, Ilya and Trzewik, J{\"u}rgen and Sielemann, Stefanie and Erni, Daniel and Zylka, Waldemar},
  title     = {Symposium Proceedings; 4th YRA MedTech Symposium 2024 : February 1 / 2024 / FH Aachen},
  publisher = {Universit{\"a}t Duisburg-Essen},
  address   = {Duisburg},
  organization    = {MedTech Symposium},
  isbn      = {978-3-940402-65-3},
  doi       = {10.17185/duepublico/81475},
  pages     = {40 Seiten},
  year      = {2024},
  language  = {en}
}