Open Research Online

Sudakow, Ivan and Vakulenko, Sergey A. (2024). New spin models in ecology: Super multi-stationarity and chaos. Chaos, Solitons & Fractals, 184, article no. 114996.

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Sultana, Aqsa; Abouzahra, Shaik N.; Asari, Vijayan K.; Aspiras, Theus H.; Liu, Ruixu; Sudakow, Ivan; Cooper, Lee W.; Agaian, Sos S.; DelMarco, Stephen P. and Asari, Vijayan K. (2024). Diffusion model-based generation of sea ice data. In: Proceedings SPIE 13033: Multimodal Image Exploitation and Learning 2024, SPIE, 130330B p. 12.

Sultana, Aqsa; Asari, Vijayan K.; Aspiras, Theus; Liu, Ruixu; Sudakow, Ivan and Cooper, Lee W. (2024). NABLA-N for meltpond detection. In: Pattern Recognition and Prediction XXXV (Alam, Mohammad S. and Asari, Vijayan K. eds.), SPIE, 13040, article no. 1304006.

Abramova, I. A.; Demchev, D. M.; Kharyutkina, E. V.; Savenkova, E. N. and Sudakow, I. A. (2024). Utilization of the U-Net Convolutional Neural Network and Its Modifications for Segmentation of Tundra Lakes in Satellite Optical Images. Atmospheric and Oceanic Optics, 37(2) pp. 205–210.

Sudakow, Ivan; Reinitz, John; Vakulenko, Sergey A. and Grigoriev, Dima (2024). Evolution of biological cooperation: an algorithmic approach. Scientific Reports, 14, article no. 1468.

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Loginov, Sergey; Moraru, Evgeniia; Kharyutkina, Elena and Sudakow, Ivan (2024). Climatology of Synoptic Non-Gaussian Meteorological Anomalies in the Northern Hemisphere during 1979–2018. Climate, 12(1), article no. 8.

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Savenkova, Elena; Vakulenko, Sergey and Sudakow, Ivan (2024). Slow-Fast Dynamical Systems with a Load Variation. In: Mathematical Modeling in Physical Sciences. ICMSQUARE 2023 (Vlachos, Dimitrios ed.), Springer, Cham, pp. 255–265.

Winkelmann, R.; Steinert, N. J.; Armstrong McKay, D. I.; Brovkin, V.; Kääb, A.; Notz, D.; Aksenov, Y.; Arndt, S.; Bathiany, S.; Burke, E.; Garbe, J.; Gasson, E.; Goelzer, H.; Hugelius, G.; Kristin Klose, A.; Langebroek, P; Marzeion, B.; Maussion, F.; Nitzbon, J.; Robinson, A.; Rynders, S. and Sudakow, I. (2023). Chapter 1.2: Tipping points in the cryosphere. In: Lenton, T. M.; Armstrong McKay, D. I.; Loriani, S.; Abrams, J. F.; Lade, S. J.; Donges, J. F.; Milkoreit, M.; Powell, T.; Smith, S. R.; Zimm, C.; Buxton, J. E.; Bailey, E.; Laybourn, L.; Ghadiali, A. and Dyke, J. G. eds. The Global Tipping Points Report 2023.

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Sudakow, Ivan; Myers, Corinne; Petrovskii, Sergei V.; Sumrall, Colin D. and Witts, James (2023). Mathematical modeling is an efficient research tool to address challenges in mass extinction research: Reply to comments on “Knowledge gaps and missing links in understanding mass extinctions: Can mathematical modeling help?”. Physics of Life Reviews, 46 pp. 5–7.

Demchev, Denis; Sudakow, Ivan; Khodos, Alexander; Abramova, Irina; Lyakhov, Dmitry and Michels, Dominik (2023). Recognizing the Shape and Size of Tundra Lakes in Synthetic Aperture Radar (SAR) Images Using Deep Learning Segmentation. Remote Sensing, 15(5), article no. 1298.

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Sudakow, Ivan; Vakulenko, Sergey A.; Pound, Matthew and Kirievskaya, Dubrava (2023). Biome stability and fragmentation under critical environmental temperature change. Applied Mathematical Modelling, 114 pp. 189–204.

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Sudakow, Ivan; Vakulenko, Sergey A. and Grigoriev, Dmitry (2023). Excitable media store and transfer complicated information via topological defect motion. Communications in Nonlinear Science and Numerical Simulation, 116, article no. 106844.

Reinitz, J.; Vakulenko, S.; Sudakow, I. and Grigoriev, D. (2023). Robust morphogenesis by chaotic dynamics. Scientific Reports, 13(1), article no. 7482.

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Sudakow, Ivan; Myers, Corinne; Petrovskii, Sergei; Sumrall, Colin D. and Witts, James (2022). Knowledge Gaps and Missing Links in Understanding Mass Extinctions: Can Mathematical Modeling Help? Physics of Life Reviews, 41 pp. 22–57.

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Sudakow, Ivan; Asari, Vijayan K.; Liu, Ruixu and Demchev, Denis (2022). MeltPondNet: A Swin Transformer U-Net for Detection of Melt Ponds on Arctic Sea Ice. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15 pp. 8776–8784.