199 research outputs found
Characterizations of John spaces
The main purpose of this paper is to study the characterizations of John
spaces. We obtain five equivalence characteristics for length John spaces. As
an application, we establish a dimension-free quasisymmetric invariance of
length John spaces.This result is new also in the case of the Euclidean space.Comment: Monatshefte fur Mathematik;201
Uniformizing Gromov hyperbolic spaces and Busemann functions
By introducing a new metric density via Busemann function, we establish an
unbounded uniformizing Gromov hyperbolic spaces procedure which is an analogue
of a recent work of Bonk, Heinonen and Koskela in \cite{BHK}. Then we show that
there is a one-to-one correspondence between the quasi-isometry classes of
proper geodesic Gromov hyperbolic spaces that are roughly starlike with respect
to the points at the boundaries of infinity and the quasi-similarity classes of
unbounded locally compact uniform spaces.
As applications, we establish Teichm\"{u}ller's displacement theorem for
roughly quasi-isometry in Gromov hyperbolic spaces, and explain the connections
to the bilipschitz extensions of certain Gromov hyperbolic spaces. By using our
uniformizing procedure, we also provide a new proof for
V\"{a}is\"{a}l\"{a}-Heinonen-N\"{a}kki's Theorem in the setting of metric
spaces. Moreover, we obtain the quasisymmetry from local to global on uniform
metric spaces
Gromov hyperbolization of unbounded noncomplete spaces and Hamenst\"adt metric
In this note, we investigate the hyperbolizations of unbounded noncomplete
metric spaces associated to three hyperbolic type metrics: hyperbolization
metric introduced by Ibragimov, -metric and the
quasihyperbolic metric . We show that for such a space , , , are mutually
quasisymmetrically equivalent with respect to the metric and certain
Hamenst\"adt metrics on the boundaries at infinity of these two hyperbolic
spaces, respectively. Moreover, is also
quasisymmetrically equivalent to Gromov boundary equipped with
certain Hamenst\"adt metric whenever is uniform. As an application, we get
a characterization of unbounded uniform domains in Banach spaces
Bidirectional-Convolutional LSTM Based Spectral-Spatial Feature Learning for Hyperspectral Image Classification
This paper proposes a novel deep learning framework named
bidirectional-convolutional long short term memory (Bi-CLSTM) network to
automatically learn the spectral-spatial feature from hyperspectral images
(HSIs). In the network, the issue of spectral feature extraction is considered
as a sequence learning problem, and a recurrent connection operator across the
spectral domain is used to address it. Meanwhile, inspired from the widely used
convolutional neural network (CNN), a convolution operator across the spatial
domain is incorporated into the network to extract the spatial feature.
Besides, to sufficiently capture the spectral information, a bidirectional
recurrent connection is proposed. In the classification phase, the learned
features are concatenated into a vector and fed to a softmax classifier via a
fully-connected operator. To validate the effectiveness of the proposed
Bi-CLSTM framework, we compare it with several state-of-the-art methods,
including the CNN framework, on three widely used HSIs. The obtained results
show that Bi-CLSTM can improve the classification performance as compared to
other methods
- β¦