Szwabiński J - Search Results

1

Classification of diffusion modes in single-particle tracking data: Feature-based versus deep-learning approach.

Kowalek P, Loch-Olszewska H, Szwabiński J. Kowalek P, et al. Among authors: szwabinski j. Phys Rev E. 2019 Sep;100(3-1):032410. doi: 10.1103/PhysRevE.100.032410. Phys Rev E. 2019. PMID: 31640019

2

Detection of ε-ergodicity breaking in experimental data-A study of the dynamical functional sensibility.

Loch-Olszewska H, Szwabiński J. Loch-Olszewska H, et al. Among authors: szwabinski j. J Chem Phys. 2018 May 28;148(20):204105. doi: 10.1063/1.5025941. J Chem Phys. 2018. PMID: 29865829

3

Classification of particle trajectories in living cells: Machine learning versus statistical testing hypothesis for fractional anomalous diffusion.

Janczura J, Kowalek P, Loch-Olszewska H, Szwabiński J, Weron A. Janczura J, et al. Among authors: szwabinski j. Phys Rev E. 2020 Sep;102(3-1):032402. doi: 10.1103/PhysRevE.102.032402. Phys Rev E. 2020. PMID: 33076015

4

Impact of Feature Choice on Machine Learning Classification of Fractional Anomalous Diffusion.

Loch-Olszewska H, Szwabiński J. Loch-Olszewska H, et al. Among authors: szwabinski j. Entropy (Basel). 2020 Dec 19;22(12):1436. doi: 10.3390/e22121436. Entropy (Basel). 2020. PMID: 33352694 Free PMC article.

5

Objective comparison of methods to decode anomalous diffusion.

Muñoz-Gil G, Volpe G, Garcia-March MA, Aghion E, Argun A, Hong CB, Bland T, Bo S, Conejero JA, Firbas N, Garibo I Orts Ò, Gentili A, Huang Z, Jeon JH, Kabbech H, Kim Y, Kowalek P, Krapf D, Loch-Olszewska H, Lomholt MA, Masson JB, Meyer PG, Park S, Requena B, Smal I, Song T, Szwabiński J, Thapa S, Verdier H, Volpe G, Widera A, Lewenstein M, Metzler R, Manzo C. Muñoz-Gil G, et al. Among authors: szwabinski j. Nat Commun. 2021 Oct 29;12(1):6253. doi: 10.1038/s41467-021-26320-w. Nat Commun. 2021. PMID: 34716305 Free PMC article.

6

Machine-Learning Solutions for the Analysis of Single-Particle Diffusion Trajectories.

Seckler H, Szwabiński J, Metzler R. Seckler H, et al. Among authors: szwabinski j. J Phys Chem Lett. 2023 Sep 7;14(35):7910-7923. doi: 10.1021/acs.jpclett.3c01351. Epub 2023 Aug 30. J Phys Chem Lett. 2023. PMID: 37646323 Review.

7

Detection of Anomalous Diffusion with Deep Residual Networks.

Gajowczyk M, Szwabiński J. Gajowczyk M, et al. Among authors: szwabinski j. Entropy (Basel). 2021 May 22;23(6):649. doi: 10.3390/e23060649. Entropy (Basel). 2021. PMID: 34067344 Free PMC article.

8

Role of detritus in a spatial food web model with diffusion.

Pękalski A, Szwabiński J. Pękalski A, et al. Among authors: szwabinski j. Phys Rev E Stat Nonlin Soft Matter Phys. 2014 May;89(5):052714. doi: 10.1103/PhysRevE.89.052714. Epub 2014 May 28. Phys Rev E Stat Nonlin Soft Matter Phys. 2014. PMID: 25353836

9

Attribution Markers and Data Mining in Art Authentication.

Łydżba-Kopczyńska BI, Szwabiński J. Łydżba-Kopczyńska BI, et al. Among authors: szwabinski j. Molecules. 2021 Dec 23;27(1):70. doi: 10.3390/molecules27010070. Molecules. 2021. PMID: 35011312 Free PMC article.

10

Stability of a model food web.

Szwabiński J, Pekalski A. Szwabiński J, et al. Phys Rev E Stat Nonlin Soft Matter Phys. 2009 Feb;79(2 Pt 1):021915. doi: 10.1103/PhysRevE.79.021915. Epub 2009 Feb 26. Phys Rev E Stat Nonlin Soft Matter Phys. 2009. PMID: 19391786

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