Testing the anisotropy of the universe using the simulated gravitational wave events from advanced LIGO and Virgo

The detection of gravitational waves (GWs) provides a powerful tool to constrain the cosmological parameters. In this paper, we investigate the possibility of using GWs as standard sirens in testing the anisotropy of the universe. We consider the GW signals produced by the coalescence of binary black hole systems and simulate hundreds of GW events from the advanced Laser Interferometer Gravitational-Wave Observatory (LIGO) and Virgo. It is found that the anisotropy of the universe can be tightly constrained if the redshift of the GW source is precisely known. The anisotropic amplitude can be constrained with an accuracy comparable to the Union2.1 complication of type-Ia supernovae if $\gtrsim 400$ GW events are observed. As for the preferred direction, $\gtrsim 800$ GW events are needed in order to achieve the accuracy of Union2.1. With 800 GW events, the probability of pseudo anisotropic signals with an amplitude comparable to Union2.1 is negligible. These results show that GWs can provide a complementary tool to supernovae in testing the anisotropy of the universe.Comment: 17 pages, 6 figure

Unsupervised Single Image Deraining with Self-supervised Constraints

Most existing single image deraining methods require learning supervised models from a large set of paired synthetic training data, which limits their generality, scalability and practicality in real-world multimedia applications. Besides, due to lack of labeled-supervised constraints, directly applying existing unsupervised frameworks to the image deraining task will suffer from low-quality recovery. Therefore, we propose an Unsupervised Deraining Generative Adversarial Network (UD-GAN) to tackle above problems by introducing self-supervised constraints from the intrinsic statistics of unpaired rainy and clean images. Specifically, we firstly design two collaboratively optimized modules, namely Rain Guidance Module (RGM) and Background Guidance Module (BGM), to take full advantage of rainy image characteristics: The RGM is designed to discriminate real rainy images from fake rainy images which are created based on outputs of the generator with BGM. Simultaneously, the BGM exploits a hierarchical Gaussian-Blur gradient error to ensure background consistency between rainy input and de-rained output. Secondly, a novel luminance-adjusting adversarial loss is integrated into the clean image discriminator considering the built-in luminance difference between real clean images and derained images. Comprehensive experiment results on various benchmarking datasets and different training settings show that UD-GAN outperforms existing image deraining methods in both quantitative and qualitative comparisons.Comment: 10 pages, 8 figure

Recent progress in migraine and cognitive disorder

Migraine is a chronic neurovascular disease characterized by recurrent unilateral headache, which induces incapacity. At present, there are many methods to evaluate cognitive function, and the cognitive function scale is commonly used. Recently, event-related potentials, resting state functional magnetic resonance imaging and other new technologies have been widely used to assess the cognitive function of migraine patients because of their high temporal resolution and high spatial resolution. In this paper, we can overview that the research progress of the relationship between migraine and methods of evaluate cognitive function

Learning Disentangled Feature Representation for Hybrid-distorted Image Restoration

Hybrid-distorted image restoration (HD-IR) is dedicated to restore real distorted image that is degraded by multiple distortions. Existing HD-IR approaches usually ignore the inherent interference among hybrid distortions which compromises the restoration performance. To decompose such interference, we introduce the concept of Disentangled Feature Learning to achieve the feature-level divide-and-conquer of hybrid distortions. Specifically, we propose the feature disentanglement module (FDM) to distribute feature representations of different distortions into different channels by revising gain-control-based normalization. We also propose a feature aggregation module (FAM) with channel-wise attention to adaptively filter out the distortion representations and aggregate useful content information from different channels for the construction of raw image. The effectiveness of the proposed scheme is verified by visualizing the correlation matrix of features and channel responses of different distortions. Extensive experimental results also prove superior performance of our approach compared with the latest HD-IR schemes.Comment: Accepted by ECCV202

Synthesis and evaluation of a novel fluorescent sensor based on hexahomotrioxacalix[3]arene for Zn²+ and Cd²+

A novel type of selective and sensitive fluorescent sensor having triazole rings as the binding sites on the lower rim of a hexahomotrioxacalix[3]arene scaffold in a cone conformation is reported. This sensor has desirable properties for practical applications, including selectivity for detecting Zn²⁺ and Cd²⁺ in the presence of excess competing metal ions at low ion concentration or as a fluorescence enhancement type chemosensor due to the cavity of calixarene changing from a ‘flattened-cone’ to a more-upright form and inhibition of PET. In contrast, the results suggested that receptor 1 is highly sensitive and selective for Cu²⁺ and Fe³⁺ as a fluorescence quenching type chemosensor due to the photoinduced electron transfer (PET) or heavy atom effect