Characteristics of optical multi-peak solitons induced by higher-order effects in an erbium-doped fiber system

We study multi-peak solitons \textit{on a plane-wave background} in an erbium-doped fiber system with some higher-order effects, which is governed by a coupled Hirota and Maxwel-Bloch (H-MB) model. The important characteristics of multi-peak solitons induced by the higher-order effects, such as the velocity changes, localization or periodicity attenuation, and state transitions, are revealed in detail. In particular, our results demonstrate explicitly that a multi-peak soliton can be converted to an anti-dark soliton when the periodicity vanishes; on the other hand, a multi-peak soliton is transformed to a periodic wave when the localization vanishes. Numerical simulations are performed to confirm the propagation stability of multi-peak solitons riding on a plane-wave background. Finally, we compare and discuss the similarity and difference of multi-peak solitons in special degenerate cases of the H-MB system with general existence conditions.Comment: 7 pages, 4 figure

Joint RNN Model for Argument Component Boundary Detection

Argument Component Boundary Detection (ACBD) is an important sub-task in argumentation mining; it aims at identifying the word sequences that constitute argument components, and is usually considered as the first sub-task in the argumentation mining pipeline. Existing ACBD methods heavily depend on task-specific knowledge, and require considerable human efforts on feature-engineering. To tackle these problems, in this work, we formulate ACBD as a sequence labeling problem and propose a variety of Recurrent Neural Network (RNN) based methods, which do not use domain specific or handcrafted features beyond the relative position of the sentence in the document. In particular, we propose a novel joint RNN model that can predict whether sentences are argumentative or not, and use the predicted results to more precisely detect the argument component boundaries. We evaluate our techniques on two corpora from two different genres; results suggest that our joint RNN model obtain the state-of-the-art performance on both datasets.Comment: 6 pages, 3 figures, submitted to IEEE SMC 201