X(1576) and the Final State Interaction Effect

We study whether the broad peak X(1576) observed by BES Collaboration arises from the final state interaction effect of $\rho(1450,1700)$ decays. The interference effect could produce an enhancement around 1540 MeV in the $K^+K^-$ spectrum with typical interference phases. However, the branching ratio $B[J/\psi\to \pi^{0}\rho(1450,1700)]\cdot B[\rho(1450,1700)\to K^{+}K^{-}]$ from the final state interaction effect is far less than the experimental data.Comment: 6 pages, 4 figures. Some typos corrected, more discussion and references adde

Adaptive Synchronization of Complex Dynamical Networks with State Predictor

This paper addresses the adaptive synchronization of complex dynamical networks with nonlinear dynamics. Based on the Lyapunov method, it is shown that the network can synchronize to the synchronous state by introducing local adaptive strategy to the coupling strengths. Moreover, it is also proved that the convergence speed of complex dynamical networks can be increased via designing a state predictor. Finally, some numerical simulations are worked out to illustrate the analytical results

Secret key distribution leveraging color shift over visible light channel

Given the widely adoption of screen and camera in many electronic devices, the visible light communication (VLC) over screen-to-camera channel emerges as a novel short range communication technique in recent years. Active research explores various ways to convey messages over screen-camera channel, such as barcode and unobtrusive optical pattern. However, with the prevalence of LED screens of wide viewing angles and mobile devices equipped with high standard cameras, the threat of information leakage over screen-to-camera channel becomes in-negligible. Few studies have discussed how to ensure the security of data transmission over screen-to-camera channel. In this paper, we propose a secret key distribution system leveraging the unique color shift property over visible light channel. To facilitate such design, we develop a practical secret key matching based method to map the secret key into gridded optical patterns on screen, which can only be correctly recognized by the legitimate user through an accessible region and allow regular data stream transmission through valid grids. The proposed system is prototyped with off-the-shelf devices and validated under various experimental scenarios. The results show that our system can achieve high bit-decoding accuracy for the legitimate users while maintaining comparable data throughput as regular unobtrusive VLC systems with very low recovery accuracy of the encrypted data for the attackers

Graph Condensation via Eigenbasis Matching

The increasing amount of graph data places requirements on the efficiency and scalability of graph neural networks (GNNs), despite their effectiveness in various graph-related applications. Recently, the emerging graph condensation (GC) sheds light on reducing the computational cost of GNNs from a data perspective. It aims to replace the real large graph with a significantly smaller synthetic graph so that GNNs trained on both graphs exhibit comparable performance. However, our empirical investigation reveals that existing GC methods suffer from poor generalization, i.e., different GNNs trained on the same synthetic graph have obvious performance gaps. What factors hinder the generalization of GC and how can we mitigate it? To answer this question, we commence with a detailed analysis and observe that GNNs will inject spectrum bias into the synthetic graph, resulting in a distribution shift. To tackle this issue, we propose eigenbasis matching for spectrum-free graph condensation, named GCEM, which has two key steps: First, GCEM matches the eigenbasis of the real and synthetic graphs, rather than the graph structure, which eliminates the spectrum bias of GNNs. Subsequently, GCEM leverages the spectrum of the real graph and the synthetic eigenbasis to construct the synthetic graph, thereby preserving the essential structural information. We theoretically demonstrate that the synthetic graph generated by GCEM maintains the spectral similarity, i.e., total variation, of the real graph. Extensive experiments conducted on five graph datasets verify that GCEM not only achieves state-of-the-art performance over baselines but also significantly narrows the performance gaps between different GNNs.Comment: Under Revie