108 research outputs found
Pattern Division Multiple Access with Large-scale Antenna Array
In this paper, pattern division multiple access with large-scale antenna
array (LSA-PDMA) is proposed as a novel non-orthogonal multiple access (NOMA)
scheme. In the proposed scheme, pattern is designed in both beam domain and
power domain in a joint manner. At the transmitter, pattern mapping utilizes
power allocation to improve the system sum rate and beam allocation to enhance
the access connectivity and realize the integration of LSA into multiple access
spontaneously. At the receiver, hybrid detection of spatial filter (SF) and
successive interference cancellation (SIC) is employed to separate the
superposed multiple-domain signals. Furthermore, we formulate the sum rate
maximization problem to obtain the optimal pattern mapping policy, and the
optimization problem is proved to be convex through proper mathematical
manipulations. Simulation results show that the proposed LSA-PDMA scheme
achieves significant performance gain on system sum rate compared to both the
orthogonal multiple access scheme and the power-domain NOMA scheme.Comment: 6 pages, 5 figures, this paper has been accepted by IEEE VTC
2017-Sprin
Tranquilizing and Allaying Excitement Needling Method Affects BDNF and SYP Expression in Hippocampus
Sleep disorder is a state of sleep loss caused by various reasons, which leads to a series of changes, such as emotion, learning and memory, and immune function. “Tranquilizing and allaying excitement” was widely used in clinical treatment of insomnia; however, the mechanism was still not very clear. We randomly divided rats into three groups: control group, sleep deprivation group, and acupuncture treatment group. We observed BDNF and SYP expression in hippocampus in these three groups. Both protein contents and mRNA contents of BDNF and SYP were measured by western blot, immunohistochemistry, and RT-PCR analysis. The sleep deprivation model was established using modified multiple platform sleep deprivation method (MMPM). Our study explored the BDNF and SYP abnormality in hippocampus caused by sleep deprivation and “tranquilizing and allaying excitement” intervention regulated the abnormal expression of BDNF and SYP caused by sleep deprivation on the short run and the long run. Our study provided a molecular evidence that “tranquilizing and allaying excitement” treatment in rats with sleep disorder affects learning and memory ability
Intriguing Property and Counterfactual Explanation of GAN for Remote Sensing Image Generation
Generative adversarial networks (GANs) have achieved remarkable progress in
the natural image field. However, when applying GANs in the remote sensing (RS)
image generation task, an extraordinary phenomenon is observed: the GAN model
is more sensitive to the size of training data for RS image generation than for
natural image generation. In other words, the generation quality of RS images
will change significantly with the number of training categories or samples per
category. In this paper, we first analyze this phenomenon from two kinds of toy
experiments and conclude that the amount of feature information contained in
the GAN model decreases with reduced training data. Then we establish a
structural causal model (SCM) of the data generation process and interpret the
generated data as the counterfactuals. Based on this SCM, we theoretically
prove that the quality of generated images is positively correlated with the
amount of feature information. This provides insights for enriching the feature
information learned by the GAN model during training. Consequently, we propose
two innovative adjustment schemes, namely Uniformity Regularization (UR) and
Entropy Regularization (ER), to increase the information learned by the GAN
model at the distributional and sample levels, respectively. We theoretically
and empirically demonstrate the effectiveness and versatility of our methods.
Extensive experiments on three RS datasets and two natural datasets show that
our methods outperform the well-established models on RS image generation
tasks. The source code is available at https://github.com/rootSue/Causal-RSGAN
A Unified GAN Framework Regarding Manifold Alignment for Remote Sensing Images Generation
Generative Adversarial Networks (GANs) and their variants have achieved
remarkable success on natural images. However, their performance degrades when
applied to remote sensing (RS) images, and the discriminator often suffers from
the overfitting problem. In this paper, we examine the differences between
natural and RS images and find that the intrinsic dimensions of RS images are
much lower than those of natural images. As the discriminator is more
susceptible to overfitting on data with lower intrinsic dimension, it focuses
excessively on local characteristics of RS training data and disregards the
overall structure of the distribution, leading to a faulty generation model. In
respond, we propose a novel approach that leverages the real data manifold to
constrain the discriminator and enhance the model performance. Specifically, we
introduce a learnable information-theoretic measure to capture the real data
manifold. Building upon this measure, we propose manifold alignment
regularization, which mitigates the discriminator's overfitting and improves
the quality of generated samples. Moreover, we establish a unified GAN
framework for manifold alignment, applicable to both supervised and
unsupervised RS image generation tasks
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