11,531 research outputs found
On the Common Envelope Efficiency
In this work, we try to use the apparent luminosity versus displacement
(i.e., vs. ) correlation of high mass X-ray binaries (HMXBs) to
constrain the common envelope (CE) efficiency , which is a key
parameter affecting the evolution of the binary orbit during the CE phase. The
major updates that crucial for the CE evolution include a variable
parameter and a new CE criterion for Hertzsprung gap donor stars, both of which
are recently developed. We find that, within the framework of the standard
energy formula for CE and core definition at mass \%, a high value of
, i.e., around 0.8-1.0, is more preferable, while likely can not reconstruct the observed vs.
distribution. However due to an ambiguous definition for the core boundary in
the literature, the used here still carries almost two order of
magnitude uncertainty, which may translate directly to the expected value of
. We present the detailed components of current HMXBs and
their spatial offsets from star clusters, which may be further testified by
future observations of HMXB populations in nearby star-forming galaxies.Comment: 14 pages, 10 figures, 7 tables, accepted for publication in MNRA
Exemplar Based Deep Discriminative and Shareable Feature Learning for Scene Image Classification
In order to encode the class correlation and class specific information in
image representation, we propose a new local feature learning approach named
Deep Discriminative and Shareable Feature Learning (DDSFL). DDSFL aims to
hierarchically learn feature transformation filter banks to transform raw pixel
image patches to features. The learned filter banks are expected to: (1) encode
common visual patterns of a flexible number of categories; (2) encode
discriminative information; and (3) hierarchically extract patterns at
different visual levels. Particularly, in each single layer of DDSFL, shareable
filters are jointly learned for classes which share the similar patterns.
Discriminative power of the filters is achieved by enforcing the features from
the same category to be close, while features from different categories to be
far away from each other. Furthermore, we also propose two exemplar selection
methods to iteratively select training data for more efficient and effective
learning. Based on the experimental results, DDSFL can achieve very promising
performance, and it also shows great complementary effect to the
state-of-the-art Caffe features.Comment: Pattern Recognition, Elsevier, 201
Learning Convolutional Networks for Content-weighted Image Compression
Lossy image compression is generally formulated as a joint rate-distortion
optimization to learn encoder, quantizer, and decoder. However, the quantizer
is non-differentiable, and discrete entropy estimation usually is required for
rate control. These make it very challenging to develop a convolutional network
(CNN)-based image compression system. In this paper, motivated by that the
local information content is spatially variant in an image, we suggest that the
bit rate of the different parts of the image should be adapted to local
content. And the content aware bit rate is allocated under the guidance of a
content-weighted importance map. Thus, the sum of the importance map can serve
as a continuous alternative of discrete entropy estimation to control
compression rate. And binarizer is adopted to quantize the output of encoder
due to the binarization scheme is also directly defined by the importance map.
Furthermore, a proxy function is introduced for binary operation in backward
propagation to make it differentiable. Therefore, the encoder, decoder,
binarizer and importance map can be jointly optimized in an end-to-end manner
by using a subset of the ImageNet database. In low bit rate image compression,
experiments show that our system significantly outperforms JPEG and JPEG 2000
by structural similarity (SSIM) index, and can produce the much better visual
result with sharp edges, rich textures, and fewer artifacts
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