Lecture 41

CSE 331Dec 9, 2011

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HW 10 due today

Q1, Q2 and Q3 in separate piles

I will not take any HW after 1:15pm

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Finals

Noon- 2:30pm

TALBRT 107

Fri, Dec 16

Blog post on the

finals up

Blog post on the

finals up

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Today and Monday

hours-a-thon

Atri: Fri, 2:00-3:30 (Davis 319)

Jiun-Jie: Fri, 3:00-4:30 (Commons 9)

Jesse: Mon, TBA (TBA)4

Old HW and

soins

Old HW and

soins

Solutions to HW 10

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End of the lecture

Reminder

Please fill in the feedback forms from the Engineering school

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High level view of CSE 331Problem StatementProblem Statement

AlgorithmAlgorithm

Problem DefinitionProblem Definition

“Implementation”“Implementation”

AnalysisAnalysis Correctness+Runtime Analysis

Data Structures

Three general techniques

Three general techniques

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If you are curious for more

CSE431: Algorithms

CSE 396: Theory of Computation

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HW 10 due today

Q1, Q2 and Q3 in separate piles

I will not take any HW after 1:15pm

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Now relax…

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Coding Theory

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Communicating with my 2 year oldC(x)

x

y = C(x)+error

x Give up

“Code” C“Akash English”

C(x) is a “codeword”

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The setupC(x)

x

y = C(x)+error

x Give up

Mapping CError-correcting code or just code

Encoding: x C(x)

Decoding: y x

C(x) is a codeword

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Different Channels and Codes• Internet

– Checksum used in multiple layers of TCP/IP stack

• Cell phones• Satellite broadcast

– TV• Deep space

telecommunications– Mars Rover

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“Unusual” Channels

• Data Storage– CDs and DVDs– RAID– ECC memory

• Paper bar codes– UPS (MaxiCode)

Codes are all around us

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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

1 1 1 0 0 1

1 0 0 0 0 1

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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

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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 2012

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Datastream AlgorithmsSingle pass over the input Poly-log “scratch” space

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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

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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

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Group Testing Overview

Test an army for a disease

WWII example: syphillis

What if only one soldier has the

disease?

What if only one soldier has the

disease?

Can pool blood samples and

check if at least one soldier has

the disease

Can pool blood samples and

check if at least one soldier has

the disease

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Compressed Sensing

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

Moving your data to the cloud

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http://1sdiresource.com/pile.jpg

http://myhosting.com/blog/2011/06/cloud-storage-vps-vps-remote-fill-storage/

What if the cloud was bad?

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http://area.autodesk.com/userdata/forum/h/harry_potter_clouds_scene.jpg

It all comes back to the same thing

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CodingTheory

ComplexityTheory

LISTDECODING

Fingerprints as PasswordsOr making “Forgot password” links obsolete

Challenges in Fingerprint Matching

Fingerprint readings are inconsistent

Matching Algorithms exist

even in practice…

Using fingerprints securely?

Stored fingerprints can be stolen

Easy Hard

Main idea: obfuscate the fingerprint!

Use “error correcting codes”

Supported by:

Jesse Hartloff Sergey Tulyakov

Venu Govindaraju (PI) Atri Rudra (co-PI)

Team:

Relevant UBCSE courses:

CSE 666 (S ‘12) CSE 545 (S ‘12)

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Whatever your impression of the 331

IT WASIT WAS

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Hopefully it was fun!

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Thanks!

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