PPT-Tight Bound for the Gap Hamming Distance Problem

Author : sherrill-nordquist | Published Date : 2019-12-13

Tight Bound for the Gap Hamming Distance Problem Oded Regev Tel Aviv University TexPoint fonts used in EMF Read the TexPoint manual before you delete this box A

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Tight Bound for the Gap Hamming Distance Problem: Transcript

Tight Bound for the Gap Hamming Distance Problem Oded Regev Tel Aviv University TexPoint fonts used in EMF Read the TexPoint manual before you delete this box A A A A A A A Based on joint paper with. Nearest Neighbor Search (2). Alex . Andoni. (Columbia University). MADALGO Summer School on Streaming Algorithms 2015. Sketching. . short bit-strings. given . . and . ,. . should be able to estimate some function of . Rachel Ah Chuen. Basic. . concepts. Networks must be able to transfer data from one device to another with complete accuracy.. Data can be corrupted during transmission.. For reliable communication, errors must be detected and corrected.. Xiaoming. Sun and David P. Woodruff. Chinese Academy of Sciences and IBM Research-. Almaden. Streaming Models. Long sequence of items appear one-by-one. numbers, points, edges, …. (usually) . adversarially. . in the previous class (1). Consider an “odd” parity check code . C. whose . codewords. are. . (. x. 1. , …, . x. k. , p. ) with . p . = . x. 1. +…+. x. k. +1. Is . C. a linear code?. No. . By,. B. R. Chandavarkar,. CSE Dept., NITK, Surathkal. Ref: . B. A. Forouzan, 5. th. Edition. This chapter is divided into five sections. .. The . first section introduces . types of errors. , the concept of redundancy, and . Based on an article by . Foto. N. . Afrati. , Anish Das . Sarma. , . Semih. . Salihoglu. , Jeffrey D. Ullman. Images taken from slides by same authors.. Agenda. MapReduce. – a brief overview. Communication / Parallelism Tradeoff Model. CSE, HKUST. March 20. Recap. String declaration. str1=“Hong”. str2=“Kong”. String Operators. strr. =str1+str2. “H” in . strr. String Slicing. strr. [. i. ]. strr. [:. i. ]. strr. [. i. :]. Similarity Search. Alex Andoni. (Columbia University). Find pairs of similar images. 2. how should we measure similarity?. Naïvely: about . comparisons. Can we do better?. . Measuring similarity. Grigory. . Yaroslavtsev. http://grigory.us. Lecture . 2. 3. : . -testing and isotonic regression. . Slides at . http://grigory.us/big-data-class.html. Testing Big Data. Q. : How to make sense of big data?. Xiaoming. Sun and David P. Woodruff. Chinese Academy of Sciences and IBM Research-. Almaden. Streaming Models. Long sequence of items appear one-by-one. numbers, points, edges, …. (usually) . adversarially. dave@create.aau.dk. Source. Chapter 3 of. Cormen. , T. H., . Leiserson. , C. E., . Rivest. , R. L. and Stein, C. (2001). . Introduction to Algorithms. (Second Edition). MIT Press, Cambridge, MA.. Introduction. Xiaoming. Sun and David P. Woodruff. Chinese Academy of Sciences and IBM Research-. Almaden. Streaming Models. Long sequence of items appear one-by-one. numbers, points, edges, …. (usually) . adversarially. David Woodruff. Carnegie Mellon University. Theme: Tight Upper and Lower Bounds. Number of comparisons to sort an array. Number of exchanges to sort an array. Number of comparisons needed to find the largest and second-largest elements in an array. thenIuvVEuvnS011x0000rl1/rtptycntTheroofisbinductiononrTheassertionclearlyholdsforr 1Let k 2andsupposethattheassertionholdsforr k-1 Let G VEbeagraph satisfyingwith r kLet S0be a maximal subset of S

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