Modeling the GeV emission of HESS J0632+057

The binary system HESS J0632+057 was recently detected by {Fermi} to possess orbital modulated GeV emission. In this paper, we study the possibility that the compact companion of HESS J0632+057 is a pulsar. Under such a presumption, we focus on the high energy emission mechanism of this system, which is as follows. The pulsar companion travels through the circumstellar disc of the main sequence star twice in each orbit, when some of the matter is gravity-captured. The captured matter develops an accretion disc around the pulsar, and the soft photons from which are inverse Compton scattered by the pulsar wind as the GeV emission from the system. With proper choice of parameters, SED and light curve which are in accordance with observations can be produced. We predict that the light curve of GeV emission has two peaks, the larger one is at around 0.4 after the periastron (or 0.1 after the X-ray maximum), while the smaller one is between phases 0 and 0.1, with integrated flux one forth of the larger one.Comment: 7pages, 7 figures. Accepted for publication in MNRA

Inclusive hch_c production at BB factories

Within the nonrelativistic QCD (NRQCD) factorization framework, we investigate the inclusive production of the $h_c$ meson associated with either light hadrons or charmed hadrons at $B$ factory energy $\sqrt{s}=10.58$ GeV. Both the leading color-singlet and color-octet channels are included. For the $h_c$ production associated with light hadrons, the total production rate is dominated by the color-octet channel, thus the future measurement of this process may impose useful constraint on the value of the color-octet matrix element $$; for the $h_c$ production associated with charmed hadrons, the total production rate is about one order of magnitude smaller, and dominated by the color-singlet channel.Comment: v2, 23 pages, 1 table, 6 figures. Minor corrections, and a note added, accepted for publication in PR

AAANE: Attention-based Adversarial Autoencoder for Multi-scale Network Embedding

Network embedding represents nodes in a continuous vector space and preserves structure information from the Network. Existing methods usually adopt a "one-size-fits-all" approach when concerning multi-scale structure information, such as first- and second-order proximity of nodes, ignoring the fact that different scales play different roles in the embedding learning. In this paper, we propose an Attention-based Adversarial Autoencoder Network Embedding(AAANE) framework, which promotes the collaboration of different scales and lets them vote for robust representations. The proposed AAANE consists of two components: 1) Attention-based autoencoder effectively capture the highly non-linear network structure, which can de-emphasize irrelevant scales during training. 2) An adversarial regularization guides the autoencoder learn robust representations by matching the posterior distribution of the latent embeddings to given prior distribution. This is the first attempt to introduce attention mechanisms to multi-scale network embedding. Experimental results on real-world networks show that our learned attention parameters are different for every network and the proposed approach outperforms existing state-of-the-art approaches for network embedding.Comment: 8 pages, 5 figure

Note on a non-critical holographic model with a magnetic field

We consider a noncritical holographic model constructed from an intersecting brane configuration D4/$\bar{\rm{D4}}$-D4 with an external magnetic field. We investigate the influences of this magnetic field on strongly coupled dynamics by the gauge/gravity correspondence.Comment: 18 pages, references added and typos revise