New predictions on the mass of the 1−+1^{-+} light hybrid meson from QCD sum rules

We calculate the coefficients of the dimension-8 quark and gluon condensates in the current-current correlator of $1^{-+}$ light hybrid current $g\bar{q}(x)\gamma_{\nu}iG_{\mu\nu}(x)q{(x)}$. With inclusion of these higher-power corrections and updating the input parameters, we re-analyze the mass of the $1^{-+}$ light hybrid meson from Monte-Carlo based QCD sum rules. Considering the possible violation of factorization of higher dimensional condensates and variation of $\langle g^3G^3\rangle$, we obtain a conservative mass range 1.72--2.60\,GeV, which favors $\pi_{1}(2015)$ as a better hybrid candidate compared with $\pi_{1}(1600)$ and $\pi_{1}(1400)$.Comment: 12pages, 2 figures, the version appearing in JHE

Activation of Erk in the anterior cingulate cortex during the induction and expression of chronic pain

The extracellular signal-regulated kinase (Erk) activity contributes to synaptic plasticity, a key mechanism for learning, memory and chronic pain. Although the anterior cingulate cortex (ACC) has been reported as an important cortical region for neuronal mechanisms underlying the induction and expression of chronic pain, it has yet to be investigated whether or not Erk activity in the ACC may be affected by peripheral injury or in chronic pain state. In the present study, we use adult rat animal models of inflammatory and neuropathic pain and demonstrate that Erk signaling pathway in the ACC is potently activated after peripheral tissue or nerve injury. Furthermore, we demonstrate that mechanical allodynia significantly activated Erk activity at synaptic sites at two weeks after the injury. We propose a synaptic model for explaining the roles of Erk activity during different phases of chronic pain. Our findings suggest that cortical activation of Erk may contribute to both induction and expression of chronic pain

Towards Scalable Spectral Clustering via Spectrum-Preserving Sparsification

Eigenvalue decomposition of Laplacian matrices for large nearest-neighbor (NN)graphs is the major computational bottleneck in spectral clustering (SC). To fundamentally address this computational challenge in SC, we propose a scalable spectral sparsification framework that enables to construct nearly-linear-sized ultra-sparse NN graphs with guaranteed preservation of key eigenvalues and eigenvectors of the original Laplacian. The proposed method is based on the latest theoretical results in spectral graph theory and thus can be applied to robustly handle general undirected graphs. By leveraging a nearly-linear time spectral graph topology sparsification phase and a subgraph scaling phase via stochastic gradient descent (SGD) iterations, our approach allows computing tree-like NN graphs that can serve as high-quality proxies of the original NN graphs, leading to highly-scalable and accurate SC of large data sets. Our extensive experimental results on a variety of public domain data sets show dramatically improved performance when compared with state-of-the-art SC methods