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Lecture 41

Lecture 41. CSE 331 Dec 9, 2011. HW 10 due today. Q1, Q2 and Q3 in separate piles. I will not take any HW after 1:15pm. Finals. Noon- 2:30pm. TALBRT 107. Blog post on the finals up. Fri, Dec 16. Today and Monday. hours-a-thon. Old HW and soins. Atri: Fri, 2:00-3:30 (Davis 319).

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Lecture 41

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  1. Lecture 41 CSE 331 Dec 9, 2011

  2. HW 10 due today Q1, Q2 and Q3 in separate piles I will not take any HW after 1:15pm

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

  4. 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)

  5. Solutions to HW 10 End of the lecture

  6. Reminder Please fill in the feedback forms from the Engineering school

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

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

  9. HW 10 due today Q1, Q2 and Q3 in separate piles I will not take any HW after 1:15pm

  10. Now relax…

  11. Coding Theory

  12. 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

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

  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. 1 1 1 0 0 1 0 0 0 0 1 1 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 ?

  19. Interested in more? CSE 545, Spring 2012

  20. Datastream Algorithms Single pass over the input Poly-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

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

  24. 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?

  25. Compressed Sensing http://www-stat.stanford.edu/~candes/stats330/index.shtml

  26. Moving your data to the cloud http://myhosting.com/blog/2011/06/cloud-storage-vps-vps-remote-fill-storage/ http://1sdiresource.com/pile.jpg

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

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

  29. Fingerprints as Passwords Or making “Forgot password” links obsolete Challenges in Fingerprint Matching Using fingerprints securely? Stored fingerprints can be stolen Fingerprint readings are inconsistent Main idea: obfuscate the fingerprint! Hard Matching Algorithms exist Easy Use “error correcting codes” Team: Relevant UBCSE courses: Supported by: CSE 545 (S ‘12) Sergey Tulyakov Jesse Hartloff CSE 666 (S ‘12) Atri Rudra (co-PI) Venu Govindaraju (PI) even in practice…

  30. Whatever your impression of the 331 IT WAS RIDING

  31. Hopefully it was fun!

  32. Thanks!

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