4,391 research outputs found
Fast and Accurate Neural Word Segmentation for Chinese
Neural models with minimal feature engineering have achieved competitive
performance against traditional methods for the task of Chinese word
segmentation. However, both training and working procedures of the current
neural models are computationally inefficient. This paper presents a greedy
neural word segmenter with balanced word and character embedding inputs to
alleviate the existing drawbacks. Our segmenter is truly end-to-end, capable of
performing segmentation much faster and even more accurate than
state-of-the-art neural models on Chinese benchmark datasets.Comment: To appear in ACL201
Simultaneous Spectral-Spatial Feature Selection and Extraction for Hyperspectral Images
In hyperspectral remote sensing data mining, it is important to take into
account of both spectral and spatial information, such as the spectral
signature, texture feature and morphological property, to improve the
performances, e.g., the image classification accuracy. In a feature
representation point of view, a nature approach to handle this situation is to
concatenate the spectral and spatial features into a single but high
dimensional vector and then apply a certain dimension reduction technique
directly on that concatenated vector before feed it into the subsequent
classifier. However, multiple features from various domains definitely have
different physical meanings and statistical properties, and thus such
concatenation hasn't efficiently explore the complementary properties among
different features, which should benefit for boost the feature
discriminability. Furthermore, it is also difficult to interpret the
transformed results of the concatenated vector. Consequently, finding a
physically meaningful consensus low dimensional feature representation of
original multiple features is still a challenging task. In order to address the
these issues, we propose a novel feature learning framework, i.e., the
simultaneous spectral-spatial feature selection and extraction algorithm, for
hyperspectral images spectral-spatial feature representation and
classification. Specifically, the proposed method learns a latent low
dimensional subspace by projecting the spectral-spatial feature into a common
feature space, where the complementary information has been effectively
exploited, and simultaneously, only the most significant original features have
been transformed. Encouraging experimental results on three public available
hyperspectral remote sensing datasets confirm that our proposed method is
effective and efficient
Nonlinear asymptotic stability of compressible vortex sheets with viscosity effects
This paper concerns the stabilizing effect of viscosity on the vortex sheets.
It is found that although a vortex sheet is not a time-asymptotic attractor for
the compressible Navier-Stokes equations, a viscous wave that approximates the
vortex sheet on any finite time interval can be constructed explicitly, which
is shown to be time-asymptotically stable in the -space with small
perturbations, regardless of the amplitude of the vortex sheet. The result
shows that the viscosity has a strong stabilizing effect on the vortex sheets,
which are generally unstable for the ideal compressible Euler equations even
for short time [26,8,1]. The proof is based on the -energy method.In
particular, the asymptotic stability of the vortex sheet under small spatially
periodic perturbations is proved by studying the dynamics of these spatial
oscillations. The first key point in our analysis is to construct an ansatz to
cancel these oscillations. Then using the Galilean transformation, we are able
to find a shift function of the vortex sheet such that an anti-derivative
technique works, which plays an important role in the energy estimates.
Moreover, by introducing a new variable and using the intrinsic properties of
the vortex sheet, we can achieve the optimal decay rates to the viscous wave.Comment: In the second version, a new remark is added behind the Theorems and
some typos in the proof are correcte
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