2,345 research outputs found
Distinguishing between inhomogeneous model and 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 and Hubble parameter besides
model (which we call LTB- 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 and (the parameters that we use for calculation). The
results yield to be: ,
, both in
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 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
Linear inviscid damping in the presence of an embedding eigenvalue
In this paper, we investigate the long-time dynamics of the linearized 2-D
Euler equations around a hyperbolic tangent flow . A key
difference compared to previous results is that the linearized operator has an
embedding eigenvalue, which has a significant impact on the dynamics of the
linearized system. For the first mode, the dynamics consists of there parts:
non-decay part related to the eigenspace associated with the embedding
eigenvalue, slow decay part due to the resolvent singularity, and fast decay
part related to the inviscid damping. For higher modes, the dynamics is similar
to the inviscid damping phenomena in the case without embedding eigenvalues.Comment: 57 page
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