科学

题目: Greedy Algorithms for Sparse Learning
报告人：Tong Zhang
Department of Statistics
Rutgers University
时间：7月14日，14:00-15:00
地点：南京大学蒙民伟楼404

摘要：Sparse Learning has attracted much attention in recent years. There are two classes of methods: convex relaxation such as L1 regularization and greedy algorithms. Although the former has received more attention in the machine learning community, my opinion is that the latter approach is more flexible and powerful. This talk will discuss variations of greedy algorithms in the context of sparse recovery.

简介: Tong Zhang received a B.A. in mathematics and computer science from Cornell University in 1994 and a Ph.D. in Computer Science from Stanford University in 1998. After graduation, he worked at IBM T.J. Watson Research Center in Yorktown Heights, New York, and Yahoo Research in New York city. He is currently a statistics professor at Rutgers University. His research interests include machine learning, algorithms for statistical computation, their mathematical analysis and applications.

点击下载:Notes for research design and paper writing

作者：黄合来

【核心提示】本文提出科研思维和论文写作的五大原则，包括评判性（critical）、一致性（consistent）、简洁性（concise）、清晰性（clear）和完整性（complete），以期为年轻学者和在读博士生的科研思维训练和规范提供参考。

【前言】

科学研究可以笼统的用胡适先生提出的“大胆假设，小心求证”进行概括，是一个开拓求新与严谨求实的有机结合。求新是一个基于对客观现象或问题的深入思考和探究，挣破旧有理论束缚，大胆创新，对未解决的问题提出新的假设或解决的可能。而求实是一个尊重证据，对新的方法或理论严谨求证的过程。科学的进步离不开两者的相辅相成，“求新”和“求实”两大准则应该贯穿整个科研实践过程。

然而，求新和求实两大准则往往由于其抽象性很难得到严格界定。实际科研工作过程往往要求遵循一些实用性更强的原则。良好的科研思维对于一个科研工作者极为重要，而科研思维的形成需要一个基于一系列具体原则的较为长期的训练。本文提出一个科研思维与论文写作的“5C”原则，力求具体，力求实用，以期为年轻学者和在读博士生的科研思维训练和规范提供参考。

【原则一】 Critical （评判性）

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IEEE CS Nanjing Chapter 5月10日15：30~16：30在南京大学蒙民伟楼404举行学术报告会，信息如下。欢迎参加！

题目：Transcriptome analysis for identifying stress-inducible microRNAs

报告人：Weixiong Zhang

Department of Computer Science and Engineering

Department of Genetics

Washington University in St. Louis

http://www.cse.wustl.edu/~zhang

时间：5月10日15:30-16:30

地点：蒙民伟楼404室

摘要：MicroRNAs (miRNAs) are ~21nt non-coding RNAs that regulate gene expression at the post-transcriptional level. Plant miRNAs regulate many genes that are involved in development and stress response. Although a large number of miRNAs have been identified and studied, most of them remain to be functionally annotated. Experimental functional analysis is laborious and costly. It is, therefore, desirable to develop computational approaches to support and complement experimental approaches for miRNA functional analysis. In this talk I will describe a novel, machine learning/datamining approach for identifying microRNA genes in plants that are responsive to environmental stresses. Our overall approach consists of a new computational method for identifying cis-regulatory DNA sequences (motifs) from the promoters of mRNA genes, a method for predicting core promoters of miRNA genes, a new transcriptome-based gene expression modeling method, and experimental verification of mature miRNAs and miRNA precursors. We applied our approach to study cold-responsive microRNA genes in Arabidospsis thaliana. We predicted nineteen individual microRNAs in twelve miRNA families to be up-regulated in Arabidopsis seedlings under cold stress. Our experimental validation showed that among the twelve microRNA families, eight were differentially induced by cold and three were constantly expressed under cold stimulus. A promoter analysis also showed that these cold-inducible microRNA genes contain many known stress-related cis-regulatory elements in their promoters. I will also discuss putative transcriptional down-regulation pathways triggered by the induction of these microRNA genes. Particularly, our result indicated that auxin signaling pathways in Arabidopsis seedlings may be mediated by many microRNAs.

简介：Weixiong Zhang is a professor of Computer Science and of Genetics at Washington University in St. Louis, Missouri, USA. He received his B.S. and M.S. in computer engineering from Tsinghua University, Beijing, China, and his M.S. and Ph.D. in computer science from University of California at Los Angeles (UCLA). Professor Zhang’s research interests include computational systems biology and genomics, artificial intelligence, data mining, and combinatorial optimization. He has published more than 100 papers in these areas and is the author of a research monograph, State-Space Search: Algorithms, Complexity, Extensions and Applications, published by Springer in 1999. He is currently associate editors of PLoS Computational Biology, J. Alzheimer’s Disease, Artificial Intelligence, and AI Communications – The European Journal on Artificial Intelligence.