Isospin violation in ϕ,J/ψ,ψ′→ωπ0\phi, J/\psi, \psi^\prime \to \omega \pi^0 via hadronic loops

In this work, we study the isospin-violating decay of $\phi\to \omega\pi^0$ and quantify the electromagnetic (EM) transitions and intermediate meson exchanges as two major sources of the decay mechanisms. In the EM decays, the present datum status allows a good constraint on the EM decay form factor in the vector meson dominance (VMD) model, and it turns out that the EM transition can only account for about $1/4\sim 1/3$ of the branching ratio for $\phi\to \omega\pi^0$. The intermediate meson exchanges, $K\bar{K}(K^*)$ (intermediate $K\bar{K}$ interaction via $K^*$ exchanges), $K\bar{K^*}(K)$ (intermediate $K\bar{K^*}$ rescattering via kaon exchanges), and $K\bar{K^*}(K^*)$ (intermediate $K\bar{K^*}$ rescattering via $K^*$ exchanges), which evade the naive Okubo-Zweig-Iizuka (OZI) rule, serve as another important contribution to the isospin violations. They are evaluated with effective Lagrangians where explicit constraints from experiment can be applied. Combining these three contributions, we obtain results in good agreement with the experimental data. This approach is also extended to $J/\psi(\psi^\prime)\to \omega\pi^0$, where we find contributions from the $K\bar{K}(K^*)$, $K\bar{K^*}(K)$ and $K\bar{K^*}(K^*)$ loops are negligibly small, and the isospin violation is likely to be dominated by the EM transition.Comment: Revised version resubmitted to PRD; Additional loop contributions included; Conclusion unchange

Joint Domain Based Massive Access for Small Packets Traffic of Uplink Wireless Channel

The fifth generation (5G) communication scenarios such as the cellular network and the emerging machine type communications will produce massive small packets. To support massive connectivity and avoid signaling overhead caused by the transmission of those small packets, this paper proposes a novel method to improve the transmission efficiency for massive connections of wireless uplink channel. The proposed method combines compressive sensing (CS) with power domain NOMA jointly, especially neither the scheduling nor the centralized power allocation is necessary in the method. Both the analysis and simulation show that the method can support up to two or three times overloading.Comment: 6 pages, 5 figures.submitted to globecom 201

Source and Physical-Layer Network Coding for Correlated Two-Way Relaying

In this paper, we study a half-duplex two-way relay channel (TWRC) with correlated sources exchanging bidirectional information. In the case, when both sources have the knowledge of correlation statistics, a source compression with physical-layer network coding (SCPNC) scheme is proposed to perform the distributed compression at each source node. When only the relay has the knowledge of correlation statistics, we propose a relay compression with physical-layer network coding (RCPNC) scheme to compress the bidirectional messages at the relay. The closed-form block error rate (BLER) expressions of both schemes are derived and verified through simulations. It is shown that the proposed schemes achieve considerable improvements in both error performance and throughput compared with the conventional non-compression scheme in correlated two-way relay networks (CTWRNs).Comment: 15 pages, 6 figures. IET Communications, 201

Ultra accurate collaborative information filtering via directed user similarity

A key challenge of the collaborative filtering (CF) information filtering is how to obtain the reliable and accurate results with the help of peers' recommendation. Since the similarities from small-degree users to large-degree users would be larger than the ones opposite direction, the large-degree users' selections are recommended extensively by the traditional second-order CF algorithms. By considering the users' similarity direction and the second-order correlations to depress the influence of mainstream preferences, we present the directed second-order CF (HDCF) algorithm specifically to address the challenge of accuracy and diversity of the CF algorithm. The numerical results for two benchmark data sets, MovieLens and Netflix, show that the accuracy of the new algorithm outperforms the state-of-the-art CF algorithms. Comparing with the CF algorithm based on random-walks proposed in the Ref.7, the average ranking score could reach 0.0767 and 0.0402, which is enhanced by 27.3\% and 19.1\% for MovieLens and Netflix respectively. In addition, the diversity, precision and recall are also enhanced greatly. Without relying on any context-specific information, tuning the similarity direction of CF algorithms could obtain accurate and diverse recommendations. This work suggests that the user similarity direction is an important factor to improve the personalized recommendation performance.Comment: 6 pages, 4 figure