We show that skyrmions on the surface of a magnetic topological insulator may experience an attractive interaction that leads to the formation of a skyrmion-skyrmion bound state. This is in contrast to the case of skyrmions in a conventional chiral ferromagnet, for which the intrinsic interaction is repulsive. The origin of skyrmion binding in our model is the molecular hybridization of topologically protected electronic orbitals associated with each skyrmion. Attraction between the skyrmions can therefore be controlled by tuning a chemical potential that populates/depopulates the lowest-energy molecular orbital. We find that the skyrmion-skyrmion bound state can be made stable, unstable, or metastable depending on the chemical potential, magnetic field, and easy-axis anisotropy of the underlying ferromagnet, resulting in a rich phase diagram. Finally, we discuss the possibility to realize this effect in a recently synthesized Cr doped (Bi2−ySby) 2 Te3 heterostructure. arXiv:1907.03887v2 [cond-mat.mes-hall]
We compare the vertical hydrography of the Community Earth System Model Large Ensemble (CESM1‐LE) with observations from two specific periods: the Arctic Ice Dynamics Joint Experiment (AIDJEX; 1975–1976) and Ice‐Tethered Profilers (ITP; 2004–2018). A comparison between simulated and observed salinity and potential temperature profiles highlights two key model biases in all ensemble members: (a) an absence of Pacific Waters in the water column and (b) a slight deepening of the May mixed layer contrary to observations, which show a large reduction in the mixed‐layer depth and an increase in stratification over the same time period. We examine processes controlling the sea ice mass balance using a one‐dimensional vertical heat budget in the light of the model limitations implied by these two biases. Results indicate that remnant solar heat trapped beneath the halocline is mostly ventilated to the surface by mixing before the following melt season. Furthermore, we find that vertical advection associated with Ekman pumping has only a small effect on the vertical heat transport, even in early fall when the winds are strong and the pack ice is weak. Lastly, we estimate the impact of the missing Pacific Waters at 0.40 m of reduced winter ice growth.
The high-dimensional character of most biological systems presents genuine challenges for modeling and prediction. Here we propose a neural network–based approach for dimensionality reduction and analysis of biological gene expression data, using, as a case study, a well-known genetic network in the early
Drosophila
embryo, the gap gene patterning system. We build an autoencoder compressing the dynamics of spatial gap gene expression into a two-dimensional (2D) latent map. The resulting 2D dynamics suggests an almost linear model, with a small bare set of essential interactions. Maternally defined spatial modes control gap genes positioning, without the classically assumed intricate set of repressive gap gene interactions. This, surprisingly, predicts minimal changes of neighboring gap domains when knocking out gap genes, consistent with previous observations. Latent space geometries in maternal mutants are also consistent with the existence of such spatial modes. Finally, we show how positional information is well defined and interpretable as a polar angle in latent space. Our work illustrates how optimization of small neural networks on medium-sized biological datasets is sufficiently informative to capture essential underlying mechanisms of network function.
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