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In this paper, the structures of the discrete-time cellular neural network (DTCNN) are modified for the purpose of implementation of binary mathematical morphology operations. The structure of a DTCNN for implementation of the four basic mathematical morphology operations: dilation, erosion, opening and closing are given. Two single layer DTCNNs are used to implement binary dilation and binary erosion, and two 2-layer DTCNNs are used to implement binary opening and binary closing, respectively. Simulation results are given.
Peer support works in complex ways that are affected by personal and social contexts. Providers, commissioners and evaluators can use this review to understand and maximise the valuable benefits of peer support, to minimise potential risks, and to devise ways of reaching mothers who do not currently engage with it.
Abstract Theories of perfectly flexible elastic curves and surfaces are frequently used to describe diverse phenomena ranging from bioelasticity and fluid capillarity to rubber elasticity and the mechanics of structural networks. It is our aim here to present a treatment of the coupled response of such continua accounting for three-dimensional interactions in the presence of finite deformations and strains. A more expansive discussion of the subject of the present paper may be found in [1].
Machine learning models have recently emerged to predict whether hypothetical solid-state materials can be synthesized. These models aim to circumvent direct first-principles modeling of solid-state phase transformations, instead learning from large databases of successfully synthesized materials. Here, we assess the alignment of several recently introduced synthesis prediction models with material and reaction thermodynamics, quantified by the energy with respect to the convex hull and a metric accounting for thermodynamic selectivity of enumerated synthesis reactions. A dataset of successful synthesis recipes was used to determine the likely bounds on both quantities beyond which materials can be deemed unlikely to be synthesized. With these bounds as context, thermodynamic quantities were computed using the CHGNet foundation potential for thousands of new hypothetical materials generated using the Chemeleon generative model. Four recently published machine learning models for synthesizability prediction were applied to this same dataset, and the resultant predictions were considered against computed thermodynamics. We find these models generally overpredict the likelihood of synthesis, but some model scores do trend with thermodynamic heuristics, assigning lower scores to materials that are less stable or do not have an available synthesis recipe that is calculated to be thermodynamically selective. In total, this work identifies existing gaps in machine learning models for materials synthesis and introduces a new approach to assess their quality in the absence of extensive negative examples (failed syntheses).
We demonstrate that shape contexts can be used to quickly prune a search for similar shapes. We present two algorithms for rapid shape retrieval: representative shape contexts, performing comparisons based on a small number of shape contexts, and shapemes, using vector quantization in the space of shape contexts to obtain prototypical shape pieces.