Lecture 41 CSE 331 Dec 10, 2010

HW 10 due today Q1 in one pile and Q 3+4 in another I will not take any HW after 1:15pm

Finals 3:35-6:05pm KNOX 104 Tue, Dec 14 Blog post on the finals up

On Friday, Dec 10 hours-a-thon Atri: 2:00-3:30 (Bell 123) Jeff: 4:00-5:00 (Bell 224) Alex: 5:00-6:30 (Bell 242)

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New Grading policy Step 1: Compute grade cut-offs using existing scheme (25% mid term+ 40% finals) Step 2: If 65% finals leads to a better grade for you, I’ll go with the new option

High level view of CSE 331 Problem Statement Algorithm Problem Definition “Implementation” Analysis Correctness+Runtime Analysis Data Structures Three general techniques

If you are curious for more CSE431: Algorithms CSE 396: Theory of Computation

Another course of interest (S 11) CSE 443: Compilers Pre-req: 396 Offered infrequently!

HW 10 due today Q1 in one pile and Q 2+3 in another I will not take any HW after 1:15pm

Now relax…

12 Coding Theory

13 The setup C(x) x y = C(x)+error x Give up Mapping C  Error-correcting code or just code  Encoding: x  C(x)  Decoding: y  X  C(x) is a codeword

14 Different Channels and Codes Internet – Checksum used in multiple layers of TCP/IP stack Cell phones Satellite broadcast – TV Deep space telecommunications – Mars Rover

15 “Unusual” Channels Data Storage – CDs and DVDs – RAID – ECC memory Paper bar codes – UPS (MaxiCode) Codes are all around us

16 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 Two errors go completely undetected Neither of these codes are satisfactory

17 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

18 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 ?

Interested in more? CSE 545, Spring 2011

20 Datastream Algorithms Single pass over the inputPoly-log “scratch” space

21 Data Streams (another application) Databases are huge – Fully reside in disk memory Main memory – Fast, not much of it Disk memory – Slow, lots of it – Random access is expensive – Sequential scan is reasonably cheap Main memory Disk Memory

22 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

Group Testing Overview Test soldier for a disease WWII example: syphillis

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

Whatever your impression of the 331 IT WAS

Hopefully it was fun!

Thanks!