Recently a divergence-sensitive branching bisimilarity has been proposed and studied for the randomized CCS model. In this article, we give an equivalent inductive characterization for the bisimilarity, which is a probabilistic extension of the previous work on the non-probabilistic model. Based on the new characterization, a novel polynomial-time verification algorithm for the divergence-sensitive branching bisimilarity is proposed.

Ocean fronts are significant mesoscale phenomena in oceanography. Ocean front detection has important impacts on fishery, environmental protection, and other fields. However, in multiclass ocean front detection, existing deep learning models struggle to detect small target fronts (STFs) in remote sensing images effectively. To achieve accurate STF detection, we model multiclass ocean front detection as a semantic segmentation problem and propose the dense contextual ensemble network (DCENet). Specifically, DCENet adopts a novel spatially enhanced contextual ensemble (S-CE) architecture based on encoder-decoder to aggregate rich multiscale context information. The dense aggregation block (DA Block) introduced in the encoder and decoder cascades all convolutional features, allowing the deep layers of the network for precise positioning to use STF detailed information. In addition, the designed hybrid loss consisting of balanced cross-entropy (BCE) loss and Dice loss can balance the losses among the front classes and make the model fit the distribution of fronts. Experimental results on the SCSOF dataset show that DCENet can accurately detect various classes of ocean fronts, with mean intersection over union (mIoU) and mean$F1$-score (mF1) reaching 78.97% and 88%, respectively. DCENet significantly outperforms other representative semantic segmentation models in STF detection.

Abstract This paper investigates the interactive power of the higher-order pi-calculus extended with parameterization. We study two kinds of parameterization: name parameterization and process parameterization. We show that each of these kinds of parameterization results in an interactively complete model, in the sense that they can express the elementary interactive model (named C ) with built-in recursive functions.