Next-to-Leading-Order study on the associate production of J/ψ+γJ/\psi+\gamma at the LHC

The associate $J/\psi+\gamma$ production at the LHC is studied completely at next-to-leading-order (NLO) within the framework of nonrelativistic QCD. By using three sets of color-octet long-distance matrix elements (LDMEs) obtained in previous prompt $J/\psi$ studies, we find that only one of them can result in a positive transverse momentum ($p_t$) distribution of $J/\psi$ production rate at large $p_t$ region. Based on reasonable consideration to cut down background, our estimation is measurable upto $p_t=50$GeV with present data sample collected at $8$TeV LHC. All the color-octet LDMEs in $J/\psi$ production could be fixed sensitively by including this proposed measurement and our calculation, and then confident conclusion on $J/\psi$ polarization puzzle could be achieved.Comment: 5 pages, 2 figure

Incorporating GAN for Negative Sampling in Knowledge Representation Learning

Knowledge representation learning aims at modeling knowledge graph by encoding entities and relations into a low dimensional space. Most of the traditional works for knowledge embedding need negative sampling to minimize a margin-based ranking loss. However, those works construct negative samples through a random mode, by which the samples are often too trivial to fit the model efficiently. In this paper, we propose a novel knowledge representation learning framework based on Generative Adversarial Networks (GAN). In this GAN-based framework, we take advantage of a generator to obtain high-quality negative samples. Meanwhile, the discriminator in GAN learns the embeddings of the entities and relations in knowledge graph. Thus, we can incorporate the proposed GAN-based framework into various traditional models to improve the ability of knowledge representation learning. Experimental results show that our proposed GAN-based framework outperforms baselines on triplets classification and link prediction tasks.Comment: Accepted to AAAI 201

Intertwined order and holography: the case of the parity breaking pair density wave

We present a minimal bottom-up extension of the Chern-Simons bulk action for holographic translational symmetry breaking that naturally gives rise to pair density waves. We construct stationary inhomogeneous black hole solutions in which both the U(1) symmetry and spatially translational symmetry are spontaneously broken at finite temperature and charge density. This novel solution provides a dual description of a superconducting phase intertwined with charge, current and parity orders.Comment: v3: Revised version in which the rules of effective field theory are highlighted, to appear in Phys.Rev.Let

Cell-centric and user-centric multi-user scheduling in visible light communication aided networks

Visible Light Communication (VLC) combined withadvanced illumination has been expected to become an integralpart of next generation heterogeneous networks at the time ofwriting, by inspiring further research interests. From both theCell-Centric (CC) and the User-Centric (UC) perspectives, variousVLC cell formations, ranging from fixed-shape regular cellswith different Frequency Reuse (FR) patterns and merged cellsemploying advanced transmission scheme to amorphous userspecificcells are investigated. Furthermore, different Multi-UserScheduling (MUS) algorithms achieving Proportional Fairness(PF) are implemented according to different cell formations.By analysing some critical and unique characteristics of VLC,our simulation results demonstrate that, the proposed MUSalgorithms are capable of providing a high aggregate throughputand achieving modest fairness with low complexity in most of thescenarios considered.<br/

Learning Two-layer Neural Networks with Symmetric Inputs

We give a new algorithm for learning a two-layer neural network under a general class of input distributions. Assuming there is a ground-truth two-layer network $$y = A \sigma(Wx) + \xi,$$ where $A,W$ are weight matrices, $\xi$ represents noise, and the number of neurons in the hidden layer is no larger than the input or output, our algorithm is guaranteed to recover the parameters $A,W$ of the ground-truth network. The only requirement on the input $x$ is that it is symmetric, which still allows highly complicated and structured input. Our algorithm is based on the method-of-moments framework and extends several results in tensor decompositions. We use spectral algorithms to avoid the complicated non-convex optimization in learning neural networks. Experiments show that our algorithm can robustly learn the ground-truth neural network with a small number of samples for many symmetric input distributions