Non-thermal radiation of black hole off canonical typicality

We study the Hawking radiation of black holes by considering the canonical typicality. For the universe consisting of black holes and their outer part, we directly obtain a non-thermal radiation spectrum of an arbitrary black hole from its entropy, which only depends on a few external qualities (known as hairs), such as mass, charge, and angular momentum. Our result shows that the spectrum of the non-thermal radiation is independent of the detailed quantum tunneling dynamics across black hole horizon. We prove that the black hole information paradox is naturally resolved by taking account the correlation between black hole and its radiation in our approach.Comment: 5 pages, 1 figure, pulished on Europhysics Letters, comments are welcome

Physical modeling and validation of porpoises' directional emission via hybrid metamaterials

© The Author(s), 2019. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Dong, E., Zhang, Y., Song, Z., Zhang, T., Cai, C., & Fang, N. X. Physical modeling and validation of porpoises' directional emission via hybrid metamaterials. National Science Review, 6(5), (2019): 921-928, doi:10.1093/nsr/nwz085.In wave physics and engineering, directional emission sets a fundamental limitation on conventional simple sources as their sizes should be sufficiently larger than their wavelength. Artificial metamaterial and animal biosonar both show potential in overcoming this limitation. Existing metamaterials arranged in periodic microstructures face great challenges in realizing complex and multiphase biosonar structures. Here, we proposed a physical directional emission model to bridge the gap between porpoises’ biosonar and artificial metamaterial. Inspired by the anatomical and physical properties of the porpoise's biosonar transmission system, we fabricated a hybrid metamaterial system composed of multiple composite structures. We validated that the hybrid metamaterial significantly increased directivity and main lobe energy over a broad bandwidth both numerically and experimentally. The device displayed efficiency in detecting underwater target and suppressing false target jamming. The metamaterial-based physical model may be helpful to achieve the physical mechanisms of porpoise biosonar detection and has diverse applications in underwater acoustic sensing, ultrasound scanning, and medical ultrasonography.E.D., Y.Z., Z.S., T.Z. and C.C. acknowledge the financial support in part by the National Key Research and Development Program of China (2018YFC1407504), the National Natural Science Foundation of China (41676023, 41276040 and 41422604). N.X.F. acknowledges the support from the MIT Energy Initiative grant. Z.S. thanks the China Scholarship Council for the financial support of his oversea study in Woods Hole Oceanographic Institution

Systematic study of elliptic flow parameter in the relativistic nuclear collisions at RHIC and LHC energies

We employed the new issue of a parton and hadron cascade model PACIAE 2.1 to systematically investigate the charged particle elliptic flow parameter $v_2$ in the relativistic nuclear collisions at RHIC and LHC energies. With randomly sampling the transverse momentum $x$ and $y$ components of the particles generated in string fragmentation on the circumference of an ellipse instead of circle originally, the calculated charged particle $v_2(\eta)$ and $v_2(p_T)$ fairly reproduce the corresponding experimental data in the Au+Au/Pb+Pb collisions at $\sqrt{s_{NN}}$=0.2/2.76 TeV. In addition, the charged particle $v_2(\eta)$ and $v_2(p_T)$ in the p+p collisions at $\sqrt s$=7 TeV as well as in the p+Au/p+Pb collisions at $\sqrt{s_{NN}}$=0.2/5.02 TeV are predicted.Comment: 7 pages, 5 figure

Hepatitis C Virus Network Based Classification of Hepatocellular Cirrhosis and Carcinoma

Hepatitis C virus (HCV) is a main risk factor for liver cirrhosis and hepatocellular carcinoma, particularly to those patients with chronic liver disease or injury. The similar etiology leads to a high correlation of the patients suffering from the disease of liver cirrhosis with those suffering from the disease of hepatocellular carcinoma. However, the biological mechanism for the relationship between these two kinds of diseases is not clear. The present study was initiated in an attempt to investigate into the HCV infection protein network, in hopes to find good biomarkers for diagnosing the two diseases as well as gain insights into their progression mechanisms. To realize this, two potential biomarker pools were defined: (i) the target genes of HCV, and (ii) the between genes on the shortest paths among the target genes of HCV. Meanwhile, a predictor was developed for identifying the liver tissue samples among the following three categories: (i) normal, (ii) cirrhosis, and (iii) hepatocellular carcinoma. Interestingly, it was observed that the identification accuracy was higher with the tissue samples defined by extracting the features from the second biomarker pool than that with the samples defined based on the first biomarker pool. The identification accuracy by the jackknife validation for the between-genes approach was 0.960, indicating that the novel approach holds a quite promising potential in helping find effective biomarkers for diagnosing the liver cirrhosis disease and the hepatocellular carcinoma disease. It may also provide useful insights for in-depth study of the biological mechanisms of HCV-induced cirrhosis and hepatocellular carcinoma

Constraining Multi-scale Pairwise Features between Encoder and Decoder Using Contrastive Learning for Unpaired Image-to-Image Translation

Contrastive learning (CL) has shown great potential in image-to-image translation (I2I). Current CL-based I2I methods usually re-exploit the encoder of the generator to maximize the mutual information between the input and generated images, which does not exert an active effect on the decoder part. In addition, though negative samples play a crucial role in CL, most existing methods adopt a random sampling strategy, which may be less effective. In this paper, we rethink the CL paradigm in the unpaired I2I tasks from two perspectives and propose a new one-sided image translation framework called EnCo. First, we present an explicit constraint on the multi-scale pairwise features between the encoder and decoder of the generator to guarantee the semantic consistency of the input and generated images. Second, we propose a discriminative attention-guided negative sampling strategy to replace the random negative sampling, which significantly improves the performance of the generative model with an almost negligible computational overhead. Compared with existing methods, EnCo acts more effective and efficient. Extensive experiments on several popular I2I datasets demonstrate the effectiveness and advantages of our proposed approach, and we achieve several state-of-the-art compared to previous methods.Comment: 16 pages, 10 figure

Prediction of Body Fluids where Proteins are Secreted into Based on Protein Interaction Network

Determining the body fluids where secreted proteins can be secreted into is important for protein function annotation and disease biomarker discovery. In this study, we developed a network-based method to predict which kind of body fluids human proteins can be secreted into. For a newly constructed benchmark dataset that consists of 529 human-secreted proteins, the prediction accuracy for the most possible body fluid location predicted by our method via the jackknife test was 79.02%, significantly higher than the success rate by a random guess (29.36%). The likelihood that the predicted body fluids of the first four orders contain all the true body fluids where the proteins can be secreted into is 62.94%. Our method was further demonstrated with two independent datasets: one contains 57 proteins that can be secreted into blood; while the other contains 61 proteins that can be secreted into plasma/serum and were possible biomarkers associated with various cancers. For the 57 proteins in first dataset, 55 were correctly predicted as blood-secrete proteins. For the 61 proteins in the second dataset, 58 were predicted to be most possible in plasma/serum. These encouraging results indicate that the network-based prediction method is quite promising. It is anticipated that the method will benefit the relevant areas for both basic research and drug development