Quantum Energy of Fuzzy Sphere: Gaussian Variational Method

The matrix model with mass term has a nontrivially classical solution which is known to represent a noncommutative fuzzy sphere. The fuzzy sphere has a lower energy then that of the trivial solution. In this letter we investigate the quantum correction of the energy of the fuzzy sphere by using the Gaussian variational technique, in contrast to the other studying in which only the small fluctuation was considered. Our result, which only considering the boson part, shows that the quantum correction does not change the stability of the fuzzy sphere.Comment: Short summary of the Master-thesi

A numerical simulation of the backward Raman amplifying in plasma

This paper describe a numerical simulation method for the interaction between laser pulses and low density plasmas based on hydrodynamic approximation. We investigate Backward Raman Amplifying (BRA) experiments and their variants. The numerical results are in good agreement with experiments.Comment: 11 pages, 4 figur

Joint Frequency Estimation with Two Sub-Nyquist Sampling Sequences

In many applications of frequency estimation, the frequencies of the signals are so high that the data sampled at Nyquist rate are hard to acquire due to hardware limitation. In this paper, we propose a novel method based on subspace techniques to estimate the frequencies by using two sub-Nyquist sample sequences, provided that the two under-sampled ratios are relatively prime integers. We analyze the impact of under-sampling and expand the estimated frequencies which suffer from aliasing. Through jointing the results estimated from these two sequences, the frequencies approximate to the frequency components really contained in the signals are screened. The method requires a small quantity of hardware and calculation. Numerical results show that this method is valid and accurate at quite low sampling rates.Comment: 10 pages, 7 figure

A family of spectral gradient methods for optimization

We propose a family of spectral gradient methods, whose stepsize is determined by a convex combination of the long Barzilai-Borwein (BB) stepsize and the short BB stepsize. Each member of the family is shown to share certain quasi-Newton property in the sense of least squares. The family also includes some other gradient methods as its special cases. We prove that the family of methods is $R$-superlinearly convergent for two-dimensional strictly convex quadratics. Moreover, the family is $R$-linearly convergent in the any-dimensional case. Numerical results of the family with different settings are presented, which demonstrate that the proposed family is promising.Comment: 22 pages, 2figure

A Class of Deterministic Sensing Matrices and Their Application in Harmonic Detection

In this paper, a class of deterministic sensing matrices are constructed by selecting rows from Fourier matrices. These matrices have better performance in sparse recovery than random partial Fourier matrices. The coherence and restricted isometry property of these matrices are given to evaluate their capacity as compressive sensing matrices. In general, compressed sensing requires random sampling in data acquisition, which is difficult to implement in hardware. By using these sensing matrices in harmonic detection, a deterministic sampling method is provided. The frequencies and amplitudes of the harmonic components are estimated from under-sampled data. The simulations show that this under-sampled method is feasible and valid in noisy environments.Comment: 12 pages, 6 figure

An algorithm of frequency estimation for multi-channel coprime sampling

In some applications of frequency estimation, it is challenging to sample at as high as the Nyquist rate due to hardware limitations. An effective solution is to use multiple sub-Nyquist channels with coprime undersampling ratios to jointly sample. In this paper, an algorithm suitable for any number of channels is proposed, which is based on subspace techniques. Numerical simulations show that the proposed algorithm has high accuracy and good robustness.Comment: 2 pages, 3 figures. arXiv admin note: text overlap with arXiv:1508.0572

Constrained and Preconditioned Stochastic Gradient Method

We consider stochastic approximations which arise from such applications as data communications and image processing. We demonstrate why constraints are needed in a stochastic approximation and how a constrained approximation can be incorporated into a preconditioning technique to derive the pre-conditioned stochastic gradient method (PSGM). We perform convergence analysis to show that the PSGM converges to the theoretical best approximation under some simple assumptions on the preconditioner and on the independence of samples drawn from a stochastic process. Simulation results are presented to demonstrate the effectiveness of the constrained and precondi-tioned stochastic gradient method.Comment: 14 pages, 8 figure

Semi-Supervised Graph Classification: A Hierarchical Graph Perspective

Node classification and graph classification are two graph learning problems that predict the class label of a node and the class label of a graph respectively. A node of a graph usually represents a real-world entity, e.g., a user in a social network, or a protein in a protein-protein interaction network. In this work, we consider a more challenging but practically useful setting, in which a node itself is a graph instance. This leads to a hierarchical graph perspective which arises in many domains such as social network, biological network and document collection. For example, in a social network, a group of people with shared interests forms a user group, whereas a number of user groups are interconnected via interactions or common members. We study the node classification problem in the hierarchical graph where a `node' is a graph instance, e.g., a user group in the above example. As labels are usually limited in real-world data, we design two novel semi-supervised solutions named \underline{SE}mi-supervised gr\underline{A}ph c\underline{L}assification via \underline{C}autious/\underline{A}ctive \underline{I}teration (or SEAL-C/AI in short). SEAL-C/AI adopt an iterative framework that takes turns to build or update two classifiers, one working at the graph instance level and the other at the hierarchical graph level. To simplify the representation of the hierarchical graph, we propose a novel supervised, self-attentive graph embedding method called SAGE, which embeds graph instances of arbitrary size into fixed-length vectors. Through experiments on synthetic data and Tencent QQ group data, we demonstrate that SEAL-C/AI not only outperform competing methods by a significant margin in terms of accuracy/Macro-F1, but also generate meaningful interpretations of the learned representations.Comment: 12 pages, WWW-201

On-Line Choice Number of Complete Multipartite Graphs: an Algorithmic Approach

This paper studies the on-line choice number on complete multipartite graphs with independence number $m$. We give a unified strategy for every prescribed $m$. Our main result leads to several interesting consequences comparable to known results. (1) If $k_1-\sum_{p=2}^m(\frac{p^2}{2}-\frac{3p}{2}+1)k_p\geq 0$, where $k_p$ denotes the number of parts of cardinality $p$, then $G$ is on-line chromatic-choosable. (2) If $|V(G)|\leq\frac{m^2-m+2}{m^2-3m+4}\chi(G)$, then $G$ is on-line chromatic-choosable. (3) The on-line choice number of regular complete multipartite graphs $K_{m\star k}$ is at most $(m+\frac{1}{2}-\sqrt{2m-2})k$ for $m\geq 3$

Intrinsic fluctuations of chemical reactions with different approaches

The Brusselator model are used for the study of the intrinsic fluctuations of chemical reactions with different approaches. The equilibrium states of systems are assumed to be spirally stable in mean-field description, and two statistical measures of intrinsic fluctuations are analyzed by different theoretical methods, namely, the master, the Langevin, and the linearized Langevin equation. For systems far away from the Hopf bifurcation line, the discrepancies between the results of different methods are insignificant even for small system size. However, the discrepancies become noticeable even for large system size when systems are closed to the bifurcation line. In particular, the statistical measures possess singular structures for linearized Langevin equation at the bifurcation line, and the singularities are absent from the simulation results of the master and the Langevin equation.Comment: 3 figures, 20 page