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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.

 

This research illustrates that complex dynamics of gene products enable the creation of any prescribed cellular differentiation patterns. These complex dynamics can take the form of chaotic, stochastic, or noisy chaotic dynamics. Based on this outcome and previous research, it is established that a generic open chemical reactor can generate an exceptionally large number of different cellular patterns. The mechanism of pattern generation is robust under perturbations and it is based on a combination of Turing’s machines, Turing instability and L. Wolpert’s gradients. These results can help us to explain the formidable adaptive capacities of biochemical systems.

 

Abstract Climate emulators are a powerful instrument for climate modeling, especially in terms of reducing the computational load for simulating spatiotemporal processes associated with climate systems. The most important type of emulators are statistical emulators trained on the output of an ensemble of simulations from various climate models. However, such emulators oftentimes fail to capture the “physics” of a system that can be detrimental for unveiling critical processes that lead to climate tipping points. Historically, statistical mechanics emerged as a tool to resolve the constraints on physics using statistics. We discuss how climate emulators rooted in statistical mechanics and machine learning can give rise to new climate models that are more reliable and require less observational and computational resources. Our goal is to stimulate discussion on how statistical climate emulators can further be improved with the help of statistical mechanics which, in turn, may reignite the interest of statistical community in statistical mechanics of complex systems. 

The dataset contains aerial photographs of Arctic sea ice obtained during the Healy-Oden Trans Arctic Expedition (HOTRAX) captured from a helicopter between 5 August and 30 September, 2005. A total of 1013 images were captured, but only 100 images were labeled. The subset of 100 images was created exclusively for the purpose of segmenting sea ice, meltponds, and open water. Original images, labels, and code for segmentation are included in the above files. For dataset, refer site: Ivan Sudakow, Vijayan Asari, Ruixu Liu, & Denis Demchev. (2022). Melt pond from aerial photographs of the Healy–Oden Trans Arctic Expedition (HOTRAX) (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6602409 Manuscript: I. Sudakow, V. K. Asari, R. Liu and D. Demchev, "MeltPondNet: A Swin Transformer U-Net for Detection of Melt Ponds on Arctic Sea Ice," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 15, pp. 8776-8784, 2022, doi: 10.1109/JSTARS.2022.3213192.