Distinguishing between inhomogeneous model and ΛCDM\Lambda\textrm{CDM} model with the cosmic age method

Cosmological observables could be used to construct cosmological models, however, a fixed number of observables limited on the light cone is not enough to uniquely determine a certain model. A reconstructed spherically symmetric, inhomogeneous model that share the same angular-diameter-distance-redshift relationship $d_A(z)$ and Hubble parameter $H(z)$ besides $\Lambda\textrm{CDM}$ model (which we call LTB-$\Lambda\textrm{CDM}$ model in this paper), may provide another solution. Cosmic age, which is off the light cone, could be employed to distinguish these two models. We derive the formulae for age calculation with origin conditions. From the data given by 9-year WMAP measurement, we compute the likelihood of the parameters in these two models respectively by using the Distance Prior method and do likelihood analysis by generating Monte Carlo Markov Chain for the purpose of breaking the degeneracy of $\Omega_m$ and $H_0$ (the parameters that we use for calculation). The results yield to be: $t_{\Lambda\textrm{CDM}} =13.76 \pm 0.09 ~\rm Gyr$, $t_{\rm {LTB}-\Lambda\textrm{CDM}} =11.38 \pm 0.15 ~\rm Gyr$, both in $1\sigma$ agreement with the constraint of cosmic age given by metal-deficient stars. The cosmic age method that is set in this paper enables us to distinguish between the inhomogeneous model and $\Lambda\textrm{CDM}$ model.Comment: 10 pages, 2 figures, accepted by Physics Letters B. arXiv admin note: text overlap with arXiv:0911.3852 by other author

Nationalist Allegories in the Post-human Era

As China’s expansion of influence now takes up the spotlight of the world stage, Chinese science fiction, a relatively little known genre, reaches a global audience. In 2015, Liu Cixin received the Hugo Award for Best Novel for his trilogy The Three-Body Problem, as the first Asian science fiction writer to receive the Hugo Award. A year later, Hao Jingfang’s Folding Beijing was awarded the 2016 Hugo Award for Best Novelette. The recent world-wide recognition of Chinese science fiction begins with English translation, U.S. publication and promotion. The New York Times cited The Three-Body Problem as having helped popularize Chinese science fiction internationally, crediting the quality of Ken Liu’s English translation, as well as endorsements by George R. R. Martin, Facebook founder Mark Zuckerberg, and former U.S. president Barack Obama (Alter). In this review essay, I argue that recent Chinese science fiction boom represents both Chinese exceptionalism and universalist concerns for humanities now and future. In what follows, I first offer a brief outline of the two works, highlighting the alterations that occur in translations. Then I try to identify several salient features of these works by situating them within the global political and economic contexts of China rise (or threat), geopolitical conflicts, competition and rivalry in science and technology, particularly AI, 5G technology, especially the global rise of nationalism and populism. Finally, I suggest an allegorical reading of these two works (and other recent Chinese science fiction) as nationalist allegories

TV-GAN: Generative Adversarial Network Based Thermal to Visible Face Recognition

This work tackles the face recognition task on images captured using thermal camera sensors which can operate in the non-light environment. While it can greatly increase the scope and benefits of the current security surveillance systems, performing such a task using thermal images is a challenging problem compared to face recognition task in the Visible Light Domain (VLD). This is partly due to the much smaller amount number of thermal imagery data collected compared to the VLD data. Unfortunately, direct application of the existing very strong face recognition models trained using VLD data into the thermal imagery data will not produce a satisfactory performance. This is due to the existence of the domain gap between the thermal and VLD images. To this end, we propose a Thermal-to-Visible Generative Adversarial Network (TV-GAN) that is able to transform thermal face images into their corresponding VLD images whilst maintaining identity information which is sufficient enough for the existing VLD face recognition models to perform recognition. Some examples are presented in Figure 1. Unlike the previous methods, our proposed TV-GAN uses an explicit closed-set face recognition loss to regularize the discriminator network training. This information will then be conveyed into the generator network in the forms of gradient loss. In the experiment, we show that by using this additional explicit regularization for the discriminator network, the TV-GAN is able to preserve more identity information when translating a thermal image of a person which is not seen before by the TV-GAN

Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model

Recently exciting progress has been made on protein contact prediction, but the predicted contacts for proteins without many sequence homologs is still of low quality and not very useful for de novo structure prediction. This paper presents a new deep learning method that predicts contacts by integrating both evolutionary coupling (EC) and sequence conservation information through an ultra-deep neural network formed by two deep residual networks. This deep neural network allows us to model very complex sequence-contact relationship as well as long-range inter-contact correlation. Our method greatly outperforms existing contact prediction methods and leads to much more accurate contact-assisted protein folding. Tested on three datasets of 579 proteins, the average top L long-range prediction accuracy obtained our method, the representative EC method CCMpred and the CASP11 winner MetaPSICOV is 0.47, 0.21 and 0.30, respectively; the average top L/10 long-range accuracy of our method, CCMpred and MetaPSICOV is 0.77, 0.47 and 0.59, respectively. Ab initio folding using our predicted contacts as restraints can yield correct folds (i.e., TMscore>0.6) for 203 test proteins, while that using MetaPSICOV- and CCMpred-predicted contacts can do so for only 79 and 62 proteins, respectively. Further, our contact-assisted models have much better quality than template-based models. Using our predicted contacts as restraints, we can (ab initio) fold 208 of the 398 membrane proteins with TMscore>0.5. By contrast, when the training proteins of our method are used as templates, homology modeling can only do so for 10 of them. One interesting finding is that even if we do not train our prediction models with any membrane proteins, our method works very well on membrane protein prediction. Finally, in recent blind CAMEO benchmark our method successfully folded 5 test proteins with a novel fold