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

Sampling theorems in the OLCT and the OLCHT domains by polar coordinates

The sampling theorem for the offset linear canonical transform (OLCT) of bandlimited functions in polar coordinates is an important mathematical tool in many fields of signal processing and medical imaging. This paper investigates two sampling theorems for interpolating \Omega bandlimited and highest frequency bandlimited functions f(r,{\theta}) in the OLCT and the offset linear canonical Hankel transform (OLCHT) domains by polar coordinates. Based on the classical Stark's interpolation formulas, we derive the sampling theorems for \Omega bandlimited functions f(r,{\theta}) in the OLCT and the OLCHT domains, respectively. The first interpolation formula is concise and applicable. Due to the consistency of the OLCHT order, the second interpolation formula is superior to the first interpolation formula in computational complexity.Comment: 24 page

The meson-exchange model for the ΛΛˉ\Lambda\bar{\Lambda} interaction

In the present work, we apply the one-boson-exchange potential (OBEP) model to investigate the possibility of Y(2175) and $\eta(2225)$ as bound states of $\Lambda\bar{\Lambda}(^3S_1)$ and $\Lambda\bar{\Lambda}(^1S_0)$ respectively. We consider the effective potential from the pseudoscalar $\eta$-exchange and $\eta^{'}$-exchange, the scalar $\sigma$-exchange, and the vector $\omega$-exchange and $\phi$-exchange. The $\eta$ and $\eta^{'}$ meson exchange potential is repulsive force for the state $^1S_0$ and attractive for $^3S_1$. The results depend very sensitively on the cutoff parameter of the $\omega$-exchange ($\Lambda_{\omega}$) and least sensitively on that of the $\phi$-exchange ($\Lambda_{\phi}$). Our result suggests the possible interpretation of Y(2175) and $\eta(2225)$ as the bound states of $\Lambda\bar{\Lambda}(^3S_1)$ and $\Lambda\bar{\Lambda}(^1S_0)$ respectively

A Generally Semisupervised Dimensionality Reduction Method with Local and Global Regression Regularizations for Recognition

The insufficiency of labeled data is an important problem in image classification such as face recognition. However, unlabeled data are abundant in the real-world application. Therefore, semisupervised learning methods, which corporate a few labeled data and a large number of unlabeled data into learning, have received more and more attention in the field of face recognition. During the past years, graph-based semisupervised learning has been becoming a popular topic in the area of semisupervised learning. In this chapter, we newly present graph-based semisupervised learning method for face recognition. The presented method is based on local and global regression regularization. The local regression regularization has adopted a set of local classification functions to preserve both local discriminative and geometrical information, as well as to reduce the bias of outliers and handle imbalanced data; while the global regression regularization is to preserve the global discriminative information and to calculate the projection matrix for out-of-sample extrapolation. Extensive simulations based on synthetic and real-world datasets verify the effectiveness of the proposed method