Dark energy imprints on the kinematic Sunyaev-Zel'dovich signal

We investigate the imprint of dark energy on the kinetic Sunyaev-Zel'dovich (kSZ) angular power spectrum on scales of $\ell=1000$ to $10000$, and find that the kSZ signal is sensitive to the dark energy parameter. For example, varying the constant $w$ by 20\% around $w=-1$ results in a $\gtrsim10\%$ change on the kSZ spectrum; changing the dark energy dynamics parametrized by $w_a$ by $\pm0.5$, a 30\% change on the kSZ spectrum is expected. We discuss the observational aspects and develop a fitting formula for the kSZ power spectrum. Finally, we discuss how the precise modeling of the post-reionization signal would help the constraints on patchy reionization signal, which is crucial for measuring the duration of reionization.Comment: 12 pages, 9 figures, 2 table

Effect of sea quarks on the single-spin asymmetries ALW±A^{W^{\pm}}_{L} in polarized pp collisions at RHIC

We calculate the single-spin asymmetries $A^{W^{\pm}}_{L}$ of $W^{\pm}$ bosons produced in polarized pp collisions with the valence part of the up and down quark helicity distributions modeled by the light-cone quark-spectator-diquark model while the sea part helicity distributions of the up and down quarks treated as parametrization. Comparing our results with those from experimental data at RHIC, we find that the helicity distributions of sea quarks play an important role in the determination of the shapes of $A^{W^{\pm}}_{L}$. It is shown that $A^{W^{-}}_{L}$ is sensitive to $\Delta \bar u$, while $A^{W^{+}}_{L}$ to $\Delta \bar d$ intuitively. The experimental data of the polarized structure functions and the sum of helicities are also important to constrain the sizes of quark helicity distributions both for the sea part and the valence part of the nucleon.Comment: 19 latex pages, 5 figures, final version for publicatio

Generating Text Sequence Images for Recognition

Recently, methods based on deep learning have dominated the field of text recognition. With a large number of training data, most of them can achieve the state-of-the-art performances. However, it is hard to harvest and label sufficient text sequence images from the real scenes. To mitigate this issue, several methods to synthesize text sequence images were proposed, yet they usually need complicated preceding or follow-up steps. In this work, we present a method which is able to generate infinite training data without any auxiliary pre/post-process. We tackle the generation task as an image-to-image translation one and utilize conditional adversarial networks to produce realistic text sequence images in the light of the semantic ones. Some evaluation metrics are involved to assess our method and the results demonstrate that the caliber of the data is satisfactory. The code and dataset will be publicly available soon

Cosmological Implications of 5-dimensional Brans-Dicke Theory

The five dimensional Brans-Dicke theory naturally provides two scalar fields by the Killing reduction mechanism. These two scalar fields could account for the accelerated expansion of the universe. We test this model and constrain its parameter by using the type Ia supernova (SN Ia) data. We find that the best fit value of the 5-dimensional Brans-Dicke coupling contant is $\omega = -1.9$. This result is also consistent with other observations such as the baryon acoustic oscillation (BAO).Comment: 5 pages, 4 figures, PLB accepte