A Cosmic Selection Rule for Glueball Dark Matter Relic Density

We point out a unique mechanism to produce the relic abundance for glueball dark matter from a gauged $SU(N)_d$ hidden sector which is bridged to the standard model sector through heavy vectorlike quarks colored under gauge interactions from both sides. A necessary ingredient of our assumption is that the vectorlike quarks, produced either thermally or non-thermally, are abundant enough to dominate the universe for some time in the early universe. They later undergo dark color confinement and form unstable vectorlike-quarkonium states which annihilate decay and reheat the visible and dark sectors. The ratio of entropy dumped into two sectors and the final energy budget in the dark glueballs is only determined by low energy parameters, including the intrinsic scale of the dark $SU(N)_d$, $\Lambda_d$, and number of dark colors, $N_d$, but depend weakly on parameters in the ultraviolet such as the vectorlike quark mass or the initial condition. We call this a cosmic selection rule for the glueball dark matter relic density.Comment: 7 pages, 3 figures; v2 references added; v3 published versio

Impact of imperfect angle estimation on spatial and directional modulation

In this paper, we investigate the impact of imperfect angle estimation (IAE) on spatial and directional modulation (SDM) systems, assuming that the signal experiences line of sight (LoS) propagation. In SDM systems with IAE, the variation is analyzed in detail, when there is a mismatch between the beamforming and precise channel matrices. Based on the union bound and statistics theory, the average bit error probabilities (ABEPs) for both the legitimate user and eavesdropper are derived. In addition, the ergodic rate is also quantified with IAE. Simulation results are presented to show that the achieved theoretical ABEPs are useful in quantifying the potential performance penalty. We also show that the mismatch between the beamforming and precise channel matrices will become greater with the increase in direction measurement error (DME), which affects the detection for both the legitimate user and eavesdropper. On the other hand, due to the effect of IAE, the SDM requires more signal-to-noise ratio (SNR) gain to achieve a stable ergodic secrecy rate

Open Domain Event Extraction Using Neural Latent Variable Models

We consider open domain event extraction, the task of extracting unconstraint types of events from news clusters. A novel latent variable neural model is constructed, which is scalable to very large corpus. A dataset is collected and manually annotated, with task-specific evaluation metrics being designed. Results show that the proposed unsupervised model gives better performance compared to the state-of-the-art method for event schema induction.Comment: accepted by ACL 201