1,447 research outputs found
The Change of Relationship between Real Estate and Stock Markets in China
The real estate market and stock market, as two major investment channels in China, had experienced dramatically skyrocketing and fluctuations. Especially, after 2008 financial crisis, the price index of these two asset markets tended to be alternately soaring and declining. It seems there is a new principle or new relationship generating between the real estate and stock markets. This study employs VAR model and on the base of data from 2003 January to 2013 December to explore and discuss whether the relationships between the real estate market and stock market changed after financial crisis in 2008 emerged in China. The results show that there is no significant relationship existing between the real estate market and stock market during 2003 to 2008; while there is a significantly negative long-term relationship after the financial crisis in 2008
Gravitational-wave Emission from a Primordial Black Hole Inspiraling inside a Compact Star: a Novel Probe for Dense Matter Equation of State
Primordial black holes of planetary masses captured by compact stars are
widely studied to constrain their composition fraction of dark matter. Such a
capture may lead to an inspiral process and be detected through gravitational
wave signals. In this Letter, we study the post-capture inspiral process by
considering two different kinds of compact stars, i.e., strange stars and
neutron stars. The dynamical equations are numerically solved and the
gravitational wave emission is calculated. It is found that the Advanced LIGO
can detect the inspiraling of a solar mass primordial black hole at a
distance of 10 kpc, while a Jovian-mass case can even be detected at
megaparsecs. Promisingly, the next generation gravitational wave detectors can
detect the cases of solar mass primordial black holes up to
Mpc, and can detect Jovian-mass cases at several hundred megaparsecs. Moreover,
the kilohertz gravitational wave signal shows significant differences for
strange stars and neutron stars, potentially making it a novel probe to the
dense matter equation of state.Comment: 7 figures, 15 pages, match the accepted version, accepted by ApJ
Conv2Former: A Simple Transformer-Style ConvNet for Visual Recognition
This paper does not attempt to design a state-of-the-art method for visual
recognition but investigates a more efficient way to make use of convolutions
to encode spatial features. By comparing the design principles of the recent
convolutional neural networks ConvNets) and Vision Transformers, we propose to
simplify the self-attention by leveraging a convolutional modulation operation.
We show that such a simple approach can better take advantage of the large
kernels (>=7x7) nested in convolutional layers. We build a family of
hierarchical ConvNets using the proposed convolutional modulation, termed
Conv2Former. Our network is simple and easy to follow. Experiments show that
our Conv2Former outperforms existent popular ConvNets and vision Transformers,
like Swin Transformer and ConvNeXt in all ImageNet classification, COCO object
detection and ADE20k semantic segmentation
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