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Learned boundary maps are known to outperform handcrafted ones as a basis for the watershed algorithm. We show, for the first time, how to train watershed computation jointly with boundary map prediction. The estimator for the merging priorities is cast as a neural network that is convolutional (over space) and recurrent (over iterations). The latter allows learning of complex shape priors. The method...
Little is known about the relationship between the function and structure of materials. Materials (solids with a function) are complex entities and a better knowledge of the parameters that contribute to function is desirable. Here, we present modeling approaches that correlate chemical composition with function of heterogeneous catalysts. The complete composition space of the mixed oxides of Ni–Cr–Mn...
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