题目一：Non-uniform random sampling and reconstruction in sparse multivate trigonometric polynomial spaces

内容简介：In this talk, we will intruduce the problem of random sampling and reconstruction in trigonometric polynomials signal sapces. Also, we take sparse condition into account. It turns out that with overwhelming probability, randomly samples drawn from non-uniform distribution on a bounded interval form a stable sampling set. We also consider the reconstruction of sparse signals by RIP condition.

题目二：Relevant sampling in finitely generated shift-invariant spaces (I)

内容简介：We consider random sampling in finitely generated shift-invariant spaces $V(/Phi) /subset {/rm L}^2(/mathbb{R}^n)$ generated by a vector $/Phi = (/varphi_1,/ldots,/varphi_r) /in ( {/rm L}^2(/mathbb{R}^n))^r$. Following the approach introduced by Bass and Gr/"ochenig, we consider certain relatively compact subsets $V_{R,/delta}(/Phi)$ of such a space, defined in terms of a concentration inequality with respect to a cube with side lengths $R$. Under very mild assumptions on the generators, we show that for $R$ sufficiently large, taking $O(R^n log(R))$ many  random samples (taken independently uniformly distributed within $C_R$) yields  a sampling set for $V_{R,/delta}(/Phi)$ with high probability. We give explicit estimates of all involved constants in terms of the generators $/varphi_1, /ldots, /varphi_r$.

报告人：中山大学  冼军  教授

报告人简介：博士生导师、广东省千百十人才工程入选者、国家优秀青年基金获得者。2004年毕业于中山大学数学系获理学博士学位，同年进入浙江大学博士后流动站，2006年返回中山大学数学学院任副教授、教授、硕士研究生导师、博士研究生导师。主要研究方向为应用调和分析、采样理论及其在信号处理中的应用。2004年至今访问过美国耶鲁大学、美国中佛罗里达大学、加拿大Alberta大学，德国亚琛工业大学、法国马赛大学、新加坡国立大学、香港城市大学等高校，相关论文发表在APPLIED AND COMPUTATIONAL HARMONIC ANALYSIS，JOURNAL OF FOURIER ANALYSIS AND APPLICATIONS，BMC BIOINFORMATICS，SIGNAL PROCESSING，PROCEEDINGS OF THE AMERICAN MATHEMATICAL SOCIETY，JOURNAL OF APPROXIMATION THEORY等国内外核心期刊。

题目三：Recovery algorithms design for generalized linear models via approximate standard Bayesian inference algorithms

内容简介：In this talk, designing recovery algorithms for generalized linear models (GLMs) using approximate standard Bayesian inference algorithms (approximate message passing (AMP), vector approximate message passing (VAMP), sparse Bayesian learning (SBL), variational Bayesian inference (VBI)) will be presented. Substantial examples such as image classification, parameter estimation from quantized data and phase retrieval can be formulated as a GLM problem. Compared to the standard linear models (SLMs), solving the GLMs is more challenging because of the coupling of the linear and nonlinear transforms. Although the generalized approximate message passing (GAMP) algorithm has been proposed to solve the GLMs, it does not provide any insight into the relationship between the SLMs and GLMs. According to expectation propagation (EP), the GLM can be iteratively approximated as a sequence of SLM subproblems, and thus the standard Bayesian algorithm can be easily extended to solve the GLMs.This talk is based on joint work with Xiangming Meng and Sheng Wu.

报告人：浙江大学  朱江  讲师

报告人简介：朱江博士分别于2011年和2016年获得哈尔滨工程大学电子科学与技术学士学位和信息与通信工程博士学位。博士期间以访问学生身份在美国里海大学交流半年。从2016年6月开始担任浙江大学海洋学院讲师。IEEE和中国电子学会会员。目前感兴趣的方向主要包括：广义线性模型下的贝叶斯算法设计、线谱估计问题、检测估计、无标签感知等信号处理问题。

时  间：2018年11月16日（周五）上午10：00始

地  点：南海楼330室

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