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We describe the real-time forecasting of September 2024 Arctic sea ice extent using a theory-guided machine learning method based on data-adaptive harmonic decomposition and frequency-based nonlinear stochastic modeling, as part of the Sea Ice Outlook. Compared to standard statistical and machine learning models, this method adeptly accounts for non-linear behavior, effectively incorporates memory effects, and handles a wide range of time scale variations, from synoptic (stochastic-like) weather effects to low-frequency (red-noise like) variability, significantly enhancing the accuracy and reliability of sea ice prediction.more » « less
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Alam, Mohammad S; Asari, Vijayan K (Ed.)
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Alam, Mohammad S; Asari, Vijayan K (Ed.)
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Alam, Mohammad S; Asari, Vijayan K (Ed.)
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Abstract This manuscript presents an algorithmic approach to cooperation in biological systems, drawing on fundamental ideas from statistical mechanics and probability theory. Fisher’s geometric model of adaptation suggests that the evolution of organisms well adapted to multiple constraints comes at a significant complexity cost. By utilizing combinatorial models of fitness, we demonstrate that the probability of adapting to all constraints decreases exponentially with the number of constraints, thereby generalizing Fisher’s result. Our main focus is understanding how cooperation can overcome this adaptivity barrier. Through these combinatorial models, we demonstrate that when an organism needs to adapt to a multitude of environmental variables, division of labor emerges as the only viable evolutionary strategy.more » « less
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Alam, Mohammad S; Asari, Vijayan K (Ed.)
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Agaian, Sos S; DelMarco, Stephen P; Asari, Vijayan K (Ed.)
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Alam, Mohammad S.; Asari, Vijayan K. (Ed.)
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