@inproceedings{BurgethKleefeldZhangetal.2022,
  author    = {Burgeth, Bernhard and Kleefeld, Andreas and Zhang, Eugene and Zhang, Yue},
  title     = {Towards Topological Analysis of Non-symmetric Tensor Fields via Complexification},
  series = {Discrete Geometry and Mathematical Morphology},
  booktitle = {Discrete Geometry and Mathematical Morphology},
  editor    = {Baudrier, {\´E}tienne and Naegel, Beno{\^i}t and Kr{\"a}henb{\"u}hl, Adrien and Tajine, Mohamed},
  publisher = {Springer},
  address   = {Cham},
  isbn      = {978-3-031-19897-7},
  doi       = {10.1007/978-3-031-19897-7_5},
  pages     = {48 -- 59},
  year      = {2022},
  abstract  = {Fields of asymmetric tensors play an important role in many applications such as medical imaging (diffusion tensor magnetic resonance imaging), physics, and civil engineering (for example Cauchy-Green-deformation tensor, strain tensor with local rotations, etc.). However, such asymmetric tensors are usually symmetrized and then further processed. Using this procedure results in a loss of information. A new method for the processing of asymmetric tensor fields is proposed restricting our attention to tensors of second-order given by a 2x2 array or matrix with real entries. This is achieved by a transformation resulting in Hermitian matrices that have an eigendecomposition similar to symmetric matrices. With this new idea numerical results for real-world data arising from a deformation of an object by external forces are given. It is shown that the asymmetric part indeed contains valuable information.},
  language  = {en}
}
@inproceedings{BornheimGriegerBialonski2021,
  author    = {Bornheim, Tobias and Grieger, Niklas and Bialonski, Stephan},
  title     = {FHAC at GermEval 2021: Identifying German toxic, engaging, and fact-claiming comments with ensemble learning},
  series = {Proceedings of the GermEval 2021 Workshop on the Identification of Toxic, Engaging, and Fact-Claiming Comments : 17th Conference on Natural Language Processing KONVENS 2021},
  booktitle = {Proceedings of the GermEval 2021 Workshop on the Identification of Toxic, Engaging, and Fact-Claiming Comments : 17th Conference on Natural Language Processing KONVENS 2021},
  publisher = {Heinrich Heine University},
  address   = {D{\"u}sseldorf},
  doi       = {10.48415/2021/fhw5-x128},
  pages     = {105 -- 111},
  year      = {2021},
  language  = {en}
}
@inproceedings{IomdinaKiselevaKotliaretal.2020,
  author    = {Iomdina, Elena N. and Kiseleva, Anna A. and Kotliar, Konstantin and Luzhnov, Petr V.},
  title     = {Quantification of Choroidal Blood Flow Using the OCT-A System Based on Voxel Scan Processing},
  series = {Proceedings of the International Conference on Biomedical Innovations and Applications- BIA 2020},
  booktitle = {Proceedings of the International Conference on Biomedical Innovations and Applications- BIA 2020},
  publisher = {IEEE},
  address   = {New York, NY},
  isbn      = {978-1-7281-7073-2},
  doi       = {10.1109/BIA50171.2020.9244511},
  pages     = {41 -- 44},
  year      = {2020},
  abstract  = {The paper presents a method for the quantitative assessment of choroidal blood flow using an OCT-A system. The developed technique for processing of OCT-A scans is divided into two stages. At the first stage, the identification of the boundaries in the selected portion was performed. At the second stage, each pixel mark on the selected layer was represented as a volume unit, a voxel, which characterizes the region of moving blood. Three geometric shapes were considered to represent the voxel. On the example of one OCT-A scan, this work presents a quantitative assessment of the blood flow index. A possible modification of two-stage algorithm based on voxel scan processing is presented.},
  language  = {en}
}