Lecture 40 - University at Buffalo

Lecture 40 - University at Buffalo

Lecture 41 CSE 331 Dec 9, 2011 1 HW 10 due today Q1, Q2 and Q3 in separate piles I will not take any HW after 1:15pm 2

Finals Noon- 2:30pm TALBRT 107 Blog post on the finals up Fri, Dec 16 3

Today and Monday hours-a-thon Old HW and soins Atri: Fri, 2:00-3:30 (Davis 319) Jiun-Jie: Fri, 3:00-4:30 (Commons 9) Jesse: Mon, TBA (TBA)

4 Solutions to HW 10 End of the lecture 5 Reminder Please fill in the feedback forms from the Engineering school

6 High level view of CSE 331 Problem Statement Problem Definition Three general techniques Algorithm

Implementation Analysis Data Structures Correctness+Runtime Analysis 7 If you are curious for more CSE431: Algorithms

CSE 396: Theory of Computation 8 HW 10 due today Q1, Q2 and Q3 in separate piles I will not take any HW after 1:15pm 9

Now relax 10 Coding Theory 11 Communicating with my 2 year old C(x) x

y = C(x)+error Code C Akash English C(x) is a codeword x Give up 12

The setup C(x) x y = C(x)+error Mapping C Error-correcting code or just code Encoding: x C(x) Decoding: y x x C(x) is a codeword

Give up 13 Different Channels and Codes Internet Checksum used in multiple layers of TCP/IP stack Cell phones

Satellite broadcast TV Deep space telecommunications Mars Rover 14 Unusual Channels Data Storage

CDs and DVDs RAID ECC memory Paper bar codes UPS (MaxiCode) Codes are all around us 15 Redundancy vs. Error-correction

Repetition code: Repeat every bit say 100 times Good error correcting properties Too much redundancy Parity code: Add a parity bit Minimum amount of redundancy Bad error correcting properties 11100 1 10000 1

Two errors go completely undetected Neither of these codes are satisfactory 16 Two main challenges in coding theory Problem with parity example Messages mapped to codewords which do not differ in many places

Need to pick a lot of codewords that differ a lot from each other Efficient decoding Naive algorithm: check received word with all codewords 17 The fundamental tradeoff Correct as many errors as possible with as little redundancy as possible Can one achieve the optimal tradeoff with

efficient encoding and decoding ? 18 Interested in more? CSE 545, Spring 2012 19 Datastream Algorithms

Single pass over the input Poly-log scratch space 20 Data Streams (another application) Databases are huge Fully reside in disk memory Main memory

Fast, not much of it Disk memory Main memory Slow, lots of it Random access is expensive Sequential scan is reasonably cheap Disk Memory

21 Data Streams (another application) Given a restriction on number of random accesses to disk memory How much main memory is required ? For computations such as join of tables

Main memory Disk memory 22 Group Testing Overview Test soldier for a disease WWII example: syphillis 23

Group Testing Overview Can pool blood samples and check if at least one soldier has the disease Test an army for a disease WWII example: syphillis What if only one

soldier has the disease? 24 Compressed Sensing http://www-stat.stanford.edu/~candes/stats330/index.shtml 25 Moving your data to the cloud http://myhosting.com/blog/2011/06/cloud-storage-vps-vps-remote-fill-storage/

http://1sdiresource.com/pile.jpg 26 What if the cloud was bad? http://area.autodesk.com/userdata/forum/h/harry_potter_clouds_scene.jpg 27

It all comes back to the same thing Coding Theory LIST DECODING Complexity Theory

28 Fingerprints as Passwords Or making Forgot password links obsolete Challenges in Fingerprint Matching Fingerprint readings are inconsistent Using fingerprints securely? Stored fingerprints can be stolen Main idea: obfuscate the fingerprint!

Hard Easy Matching Algorithms exist Use error correcting codes Supported by: Relevant UBCSE courses:

CSE 666 (S 12) even in practice CSE 545 (S 12) Team: Jesse Hartloff Sergey Tulyakov

29 Venu Govindaraju (PI) Atri Rudra (co-PI) Whatever your impression of the 331 IT WAS 30 Hopefully it was fun!

31 Thanks! 32

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