J
Janusz Szwabiński
Researcher at Wrocław University of Technology
Publications - 37
Citations - 669
Janusz Szwabiński is an academic researcher from Wrocław University of Technology. The author has contributed to research in topics: Anomalous diffusion & Population. The author has an hindex of 11, co-authored 36 publications receiving 429 citations. Previous affiliations of Janusz Szwabiński include University of Geneva & German National Metrology Institute.
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Classification of diffusion modes in single-particle tracking data: Feature-based versus deep-learning approach.
TL;DR: A deep-learning method known as a convolutional neural network (CNN) is adopted to classify modes of diffusion from given trajectories and it is shown that CNN is usually slightly better than the feature-based methods, but at the cost of much longer processing times.
Journal ArticleDOI
Motion of influential players can support cooperation in Prisoner's Dilemma
TL;DR: It is reported that the motion of the influential payers (type A) can improve remarkably the maintenance of cooperation even for their low densities.
Journal ArticleDOI
Objective comparison of methods to decode anomalous diffusion.
Gorka Muñoz-Gil,Giovanni Volpe,Miguel Ángel García-March,Erez Aghion,Aykut Argun,Chang Beom Hong,Tom Bland,Stefano Bo,J. Alberto Conejero,Nicolas Firbas,Òscar Garibo i Orts,Alessia Gentili,Zihan Huang,Jae-Hyung Jeon,Hélène Kabbech,Yeongjin Kim,Patrycja Kowalek,Diego Krapf,Hanna Loch-Olszewska,Michael A. Lomholt,Jean-Baptiste Masson,Philipp G. Meyer,Seongyu Park,Borja Requena,Ihor Smal,Taegeun Song,Taegeun Song,Taegeun Song,Janusz Szwabiński,Samudrajit Thapa,Samudrajit Thapa,Hippolyte Verdier,Giorgio Volpe,Artur Widera,Maciej Lewenstein,Ralf Metzler,Carlo Manzo,Carlo Manzo +37 more
TL;DR: The Anomalous Diffusion Challenge (AnDi) as mentioned in this paper was an open competition for the characterization of anomalous diffusion from the measurement of an individual trajectory, which traditionally relies on calculating the trajectory mean squared displacement.
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Objective comparison of methods to decode anomalous diffusion
Gorka Muñoz-Gil,Giovanni Volpe,Miguel Ángel García-March,Erez Aghion,Aykut Argun,Chang Beom Hong,Tom Bland,Stefano Bo,J. Alberto Conejero,Nicolas Firbas,Òscar Garibo i Orts,Alessia Gentili,Zihan Huang,Jae-Hyung Jeon,Hélène Kabbech,Yeongjin Kim,Patrycja Kowalek,Diego Krapf,Hanna Loch-Olszewska,Michael A. Lomholt,Jean-Baptiste Masson,Philipp G. Meyer,Seongyu Park,Borja Requena,Ihor Smal,Taegeun Song,Taegeun Song,Taegeun Song,Janusz Szwabiński,Samudrajit Thapa,Samudrajit Thapa,Hippolyte Verdier,Giorgio Volpe,Artur Widera,Maciej Lewenstein,Ralf Metzler,Carlo Manzo,Carlo Manzo +37 more
TL;DR: This paper presents a meta-anatomy of the response of the immune system to chemotherapy, a model derived from the model developed by Carl Friedrich Gauss in 1916.
Journal ArticleDOI
Classification of particle trajectories in living cells: Machine learning versus statistical testing hypothesis for fractional anomalous diffusion.
TL;DR: A new set of features used to transform the raw trajectories data into input vectors required by the classifiers are presented and the resulting models are applied to real data for G protein-coupled receptors and G proteins.