Reconstruction of the primordial power spectra with Planck and BICEP2

By using the cubic spline interpolation method, we reconstruct the shape of the primordial scalar and tensor power spectra from the recently released {\it Planck} temperature and BICEP2 polarization cosmic microwave background data. We find that the vanishing scalar index running (\dd n_s/\dd\ln k) model is strongly disfavored at more than $3\sigma$ confidence level on the $k=0.0002$ Mpc$^{-1}$ scale. Furthermore, the power-law parameterization gives a blue-tilt tensor spectrum, no matter using only the first 5 bandpowers $n_t = 1.20^{+0.56}_{-0.64} (95$ or the full 9 bandpowers $n_t = 1.24^{+0.51}_{-0.58} (95$ of BICEP2 data sets. Unlike the large tensor-to-scalar ratio value ($r\sim0.20$) under the scale-invariant tensor spectrum assumption, our interpolation approach gives $r_{0.002} < 0.060 (95{\rm CL})$ by using the first 5 bandpowers of BICEP2 data. After comparing the results with/without BICEP2 data, we find that {\it Planck} temperature with small tensor amplitude signals and BICEP2 polarization data with large tensor amplitude signals dominate the tensor spectrum reconstruction on the large and small scales, respectively. Hence, the resulting blue tensor tilt actually reflects the tension between {\it Planck} and BICEP2 data.Comment: complementary results without BICEP2 added, references add, typos corrected, 10 figures, 5 tables, 11 page

Cosmological parameter estimation from CMB and X-ray clusters after Planck

We update the cosmological parameter estimation for three non-vanilla models by a joint analysis of \CCCP\ X-ray cluster, the newly released \Planck\ CMB data as well as some external data sets, such as baryon acoustic oscillation measurements from the 6dFGS, SDSS DR7 and BOSS DR9 surveys, and Hubble Space Telescope $H_0$ measurement. First of all, we find that X-ray cluster data sets strongly favor a non-zero summed neutrino mass at more than 3$\sigma$ confidence level in these non-vanilla models. And then, we reveal some tensions between X-ray cluster and {\it Planck} data in some cosmological parameters. For the matter power spectrum amplitude $\sigma_8$, X-ray cluster data favor a lower value compared with {\it Planck}. Because of the strong $\sigma_8-\sum m_{\nu}$ degeneracy, this tension could beyond 2$\sigma$ confidence level when the summed neutrino mass $\sum m_{\nu}$ is allowed to vary. For the CMB lensing amplitude $A_L$, the addition of X-ray cluster data results in a 3$\sigma$ deviation from the vanilla model. Furthermore, {\it Planck}+X-ray data prefer a large Hubble constant and phantom-like dark energy equation of state, which are in $2\sigma$ tension with those from WMAP7+X-ray data. Finally, we find that these tensions/descrepencies could be relaxed in some sense by adding a $9\%$ systematic shift in the cluster mass functions.Comment: Table update

Inflation coupled to a Gauss-Bonnet term

The newly released Planck CMB data place tight constraints on slow-roll inflationary models. Some of commonly discussed inflationary potentials are disfavored due mainly to the large tensor-to-scalar ratio. In this paper we show that these potentials may be in good agreement with the Planck data when the inflaton has a non-minimal coupling to the Gauss-Bonnet term. Moreover, such a coupling violates the consistency relation between the tensor spectral index and tensor-to-scalar ratio. If the tensor spectral index is allowed to vary freely, the Planck constraints on the tensor-to-scalar ratio are slightly improved.Comment: 7 pages, 2 figures, references adde

Latent Embeddings for Collective Activity Recognition

Rather than simply recognizing the action of a person individually, collective activity recognition aims to find out what a group of people is acting in a collective scene. Previ- ous state-of-the-art methods using hand-crafted potentials in conventional graphical model which can only define a limited range of relations. Thus, the complex structural de- pendencies among individuals involved in a collective sce- nario cannot be fully modeled. In this paper, we overcome these limitations by embedding latent variables into feature space and learning the feature mapping functions in a deep learning framework. The embeddings of latent variables build a global relation containing person-group interac- tions and richer contextual information by jointly modeling broader range of individuals. Besides, we assemble atten- tion mechanism during embedding for achieving more com- pact representations. We evaluate our method on three col- lective activity datasets, where we contribute a much larger dataset in this work. The proposed model has achieved clearly better performance as compared to the state-of-the- art methods in our experiments.Comment: 6pages, accepted by IEEE-AVSS201