'Edit distance' presentation slideshows

Lecture 2: Pairwise Sequence Alignment Eric C. Rouchka, D.Sc. eric.rouchka@uofl.edu http://kbrin.kwing.louisville.edu/~

Lecture 2: Pairwise Sequence Alignment Eric C. Rouchka, D.Sc. eric.rouchka@uofl.edu http://kbrin.kwing.louisville.edu/~rouchka/CECS694/. Sequence Alignment. Question: Are two sequences related? Compare the two sequences, see if they are similar Example: pear and tear

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Recall that we have seen an example of feature generation .

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Modern Homology Search

Modern Homology Search. Ming Li Canada Research Chair in Bioinformatics Computer Science. U. Waterloo. I want to show you how one simple theoretical idea can make big impact to a practical field. Outline. Motivation, market, biology, homology search

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Modern Homology Search

Modern Homology Search. Ming Li Canada Research Chair in Bioinformatics Computer Science. U. Waterloo. I want to show you how one simple theoretical idea can make big impact to a practical field. Outline. Motivation, market, biology, homology search

956 views • 82 slides

Input-Driven Pushdown Automata for Edit Distance Neighborhood

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Theory of LSH

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CSCI 5417 Information Retrieval Systems Jim Martin

CSCI 5417 Information Retrieval Systems Jim Martin. Lecture 4 9/1/2011. Today. Finish up spelling correction Realistic indexing Block merge Single-pass in memory Distributed indexing Next HW details. Query correction. Our principal focus here Examples like the query Alanas Morisett

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Outline

Introduction Background Distributed Database Design Database Integration Schema Matching Schema Mapping Semantic Data Control Distributed Query Processing Multimedia Query Processing Distributed Transaction Management Data Replication Parallel Database Systems Distributed Object DBMS

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Sequence Alignment

Sequence Alignment. Lecture 2, Thursday April 3, 2003. Review of Last Lecture. Lecture 2, Thursday April 3, 2003. Structure of a genome. a gene. transcription. pre-mRNA. splicing. mature mRNA. translation. Human 3x10 9 bp Genome: ~30,000 genes ~200,000 exons

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Automatic methods of MT evaluation

Automatic methods of MT evaluation. Practical 18/04/2005 MODL5003 Principles and applications of machine translation slides available at: http://www.comp.leeds.ac.uk/bogdan/. Overview. Aspects of MT evaluation Text Quality evaluation Advantages / disadvantages of automatic techniques

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Space-Constrained Gram-Based Indexing for Efficient Approximate String Search

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An Empirical Study on Language Model Adaptation

An Empirical Study on Language Model Adaptation. Jianfeng Gao , Hisami Suzuki, Microsoft Research Wei Yuan Shanghai Jiao Tong University. Presented by Patty Liu. Outline. Introduction The Language Model and the Task of IME Related Work LM Adaptation Methods Experimental Results

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Algorithms for Large Data Sets

Algorithms for Large Data Sets. Ziv Bar-Yossef. Lecture 14 July 2, 2006. http://www.ee.technion.ac.il/courses/049011. Sketching. Outline. Syntactic clustering of the web Locality sensitive hash functions Resemblance and shingling Min-wise independent permutations The sketching model

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Chapter 7

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CHAPTER 9

CHAPTER 9. Text Searching. Algorithm 9.1.1 Simple Text Search. This algorithm searches for an occurrence of a pattern p in a text t . It returns the smallest index i such that t [ i..i + m- 1] = p , or - 1 if no such index exists. Input Parameters: p , t Output Parameters: None

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CHAPTER 9

CHAPTER 9. Text Searching. Algorithm 9.1.1 Simple Text Search. This algorithm searches for an occurrence of a pattern p in a text t . It returns the smallest index i such that t [ i..i + m- 1] = p , or - 1 if no such index exists. Input Parameters: p , t Output Parameters: None

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Edit Distance and Large Data Sets

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Dual Execution Protocols (for when semi-honest is only semi-good-enough)

Dual Execution Protocols (for when semi-honest is only semi-good-enough). DHOSA MURI Review UVa Falls Church 8 December 2011. David Evans University of Virginia http://www.cs.virginia.edu/evans http://www.MightBeEvil.com. SVA. Cryptographic secure computation.

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