Microstructure characterization and reconstruction have become indispensable parts of computational materials science. The main contribution of this paper is to introduce a general methodology for practical and efficient characterization and reconstruction of stochastic microstructures based on supervised learning. The methodology is general in that it can be applied to a broad range of microstructures (clustered, porous, and anisotropic). By treating the digitized microstructure image as a set of training data, we generically learn the stochastic nature of the microstructure via fitting a supervised learning model to it (we focus on classification trees). The fitted supervised learning model provides an implicit characterization of the joint distribution of the collection of pixel phases in the image. Based on this characterization, we propose two different approaches to efficiently reconstruct any number of statistically equivalent microstructure samples. We test the approach on five examples and show that the spatial dependencies within the microstructures are well preserved, as evaluated via correlation and lineal-path functions. The main advantages of our approach stem from having a compact empirically-learned model that characterizes the stochastic nature of the microstructure, which not only makes reconstruction more computationally efficient than existing methods, but also provides insight into morphological complexity.

Boundary value problems for the nonlinear Schrödinger equations on the half line with homogeneous Robin boundary conditions are revisited using Bäcklund transformations. In particular: relations are obtained among the norming constants associated with symmetric eigenvalues; a linearizing transformation is derived for the Bäcklund transformation; the reflection‐induced soliton position shift is evaluated and the solution behavior is discussed. The results are illustrated by discussing several exact soliton solutions, which describe the soliton reflection at the boundary with or without the presence of self‐symmetric eigenvalues.

In the original publication of this paper, the concentration of the puromycin was incorrectly reported. In both panel A of Figure 7 and the ''In vivo protein synthesis labeling'' section of the STAR Methods, the amount was listed as 10 mg/kg. The correct concentration should be 10 mg/kg. The authors apologize for this error.

The boundary value problem (BVP) for the Ablowitz-Ladik (AL) system on the natural numbers with linearizable boundary conditions is studied. In particular: (i) a discrete analogue is derived of the Bäcklund transformation that was used to solved similar BVPs for the nonlinear Schrödinger equation; (ii) an explicit proof is given that the Bäcklund-transformed solution of AL remains within the class of solutions that can be studied by the inverse scattering transform; (iii) an explicit linearizing transformation for the Bäcklund transformation is provided; (iv) explicit relations are obtained among the norming constants associated with symmetric eigenvalues; (v) conditions for the existence of self-symmetric eigenvalues are obtained. The results are illustrated by several exact soliton solutions, which describe the soliton reflection at the boundary with or without the presence of self-symmetric eigenvalues.

No abstract