167 research outputs found
Age structure of the population and the choice of household financial assets
China is undergoing changes in its demographic structure, and
the burden of raising children and caring for the elderly is negatively affecting the welfare of Chinese families. Optimising the
financial structure of households may be an effective solution.
Using data from the 2017 Chinese Household Finance Survey, in
this study, we empirically analyse how the age structure of the
population affects a household’s finances by applying probit and
tobit models. It was found that an increase in the proportion of
infants in a family has a crowding-out effect on its investment in
risky and deposit assets, and an increase in the proportion of elderly family members encourages the replacement of risky assets
with savings. Further mechanism tests showed that the presence
of infants affects the choice of financial assets by crowding a family’s labour or leisure time, increasing expenditures, and changing
a family’s cash demand, while the presence of the elderly changes
household finances because a family’s risk preferences are altered
by the weakened cognitive abilities of the elderly. Finally, in this
article, we advance some policy suggestions, such as regulating
capital market management, increasing birth subsidies, and
improving the financial literacy of the elderly, to ensure future
pension securit
Wavelet-based Fourier Information Interaction with Frequency Diffusion Adjustment for Underwater Image Restoration
Underwater images are subject to intricate and diverse degradation,
inevitably affecting the effectiveness of underwater visual tasks. However,
most approaches primarily operate in the raw pixel space of images, which
limits the exploration of the frequency characteristics of underwater images,
leading to an inadequate utilization of deep models' representational
capabilities in producing high-quality images. In this paper, we introduce a
novel Underwater Image Enhancement (UIE) framework, named WF-Diff, designed to
fully leverage the characteristics of frequency domain information and
diffusion models. WF-Diff consists of two detachable networks: Wavelet-based
Fourier information interaction network (WFI2-net) and Frequency Residual
Diffusion Adjustment Module (FRDAM). With our full exploration of the frequency
domain information, WFI2-net aims to achieve preliminary enhancement of
frequency information in the wavelet space. Our proposed FRDAM can further
refine the high- and low-frequency information of the initial enhanced images,
which can be viewed as a plug-and-play universal module to adjust the detail of
the underwater images. With the above techniques, our algorithm can show SOTA
performance on real-world underwater image datasets, and achieves competitive
performance in visual quality
High-speed photon correlation monitoring of amplified quantum noise by chaos using deep-learning balanced homodyne detection
Precision experimental determination of photon correlation requires the
massive amounts of data and extensive measurement time. We present a technique
to monitor second-order photon correlation of amplified quantum
noise based on wideband balanced homodyne detection and deep-learning
acceleration. The quantum noise is effectively amplified by an injection of
weak chaotic laser and the of the amplified quantum noise is
measured with a real-time sample rate of 1.4 GHz. We also exploit a photon
correlation convolutional neural network accelerating correlation data using a
few quadrature fluctuations to perform a parallel processing of the
for various chaos injection intensities and effective bandwidths.
The deep-learning method accelerates the experimental acquisition
with a high accuracy, estimating 6107 sets of photon correlation data with a
mean square error of 0.002 in 22 seconds and achieving a three orders of
magnitude acceleration in data acquisition time. This technique contributes to
a high-speed and precision coherence evaluation of entropy source in secure
communication and quantum imaging.Comment: 6 pages, 6 figure
Explicit Attention-Enhanced Fusion for RGB-Thermal Perception Tasks
Recently, RGB-Thermal based perception has shown significant advances.
Thermal information provides useful clues when visual cameras suffer from poor
lighting conditions, such as low light and fog. However, how to effectively
fuse RGB images and thermal data remains an open challenge. Previous works
involve naive fusion strategies such as merging them at the input,
concatenating multi-modality features inside models, or applying attention to
each data modality. These fusion strategies are straightforward yet
insufficient. In this paper, we propose a novel fusion method named Explicit
Attention-Enhanced Fusion (EAEF) that fully takes advantage of each type of
data. Specifically, we consider the following cases: i) both RGB data and
thermal data, ii) only one of the types of data, and iii) none of them generate
discriminative features. EAEF uses one branch to enhance feature extraction for
i) and iii) and the other branch to remedy insufficient representations for
ii). The outputs of two branches are fused to form complementary features. As a
result, the proposed fusion method outperforms state-of-the-art by 1.6\% in
mIoU on semantic segmentation, 3.1\% in MAE on salient object detection, 2.3\%
in mAP on object detection, and 8.1\% in MAE on crowd counting. The code is
available at https://github.com/FreeformRobotics/EAEFNet
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