189 research outputs found
Rogue waves of the Fokas-Lenells equation
The Fokas-Lenells (FL) equation arises as a model eqution which describes for
nonlinear pulse propagation in optical fibers by retaining terms up to the next
leading asymptotic order (in the leading asymptotic order the nonlinear
Schr\"odinger (NLS) equation results). Here we present an explicit analytical
representation for the rogue waves of the FL equation. This representation is
constructed by deriving an appropriate Darboux transformation (DT) and
utilizing a Taylor series expansion of the associated breather solution. when
certain higher-order nonlinear effects are considered, the propagation of rogue
waves in optical fibers is given.Comment: 7 pages, 3 figure
The hierarchy of higher order solutions of the derivative nonlinear Schr\"odinger equation
In this paper, we provide a simple method to generate higher order position
solutions and rogue wave solutions for the derivative nonlinear Schr\"odinger
equation. The formulae of these higher order solutions are given in terms of
determinants. The dynamics and structures of solutions generated by this method
are studied
The higher order Rogue Wave solutions of the Gerdjikov-Ivanov equation
We construct higher order rogue wave solutions for the Gerdjikov-Ivanov
equation explicitly in term of determinant expression. Dynamics of both soliton
and non-soliton solutions is discussed. A family of solutions with distinct
structures are presented, which are new to the Gerdjikov-Ivanov equation
Compare More Nuanced:Pairwise Alignment Bilinear Network For Few-shot Fine-grained Learning
The recognition ability of human beings is developed in a progressive way.
Usually, children learn to discriminate various objects from coarse to
fine-grained with limited supervision. Inspired by this learning process, we
propose a simple yet effective model for the Few-Shot Fine-Grained (FSFG)
recognition, which tries to tackle the challenging fine-grained recognition
task using meta-learning. The proposed method, named Pairwise Alignment
Bilinear Network (PABN), is an end-to-end deep neural network. Unlike
traditional deep bilinear networks for fine-grained classification, which adopt
the self-bilinear pooling to capture the subtle features of images, the
proposed model uses a novel pairwise bilinear pooling to compare the nuanced
differences between base images and query images for learning a deep distance
metric. In order to match base image features with query image features, we
design feature alignment losses before the proposed pairwise bilinear pooling.
Experiment results on four fine-grained classification datasets and one generic
few-shot dataset demonstrate that the proposed model outperforms both the
state-ofthe-art few-shot fine-grained and general few-shot methods.Comment: ICME 2019 Ora
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