Smoothing and analyzing 1D signals

Smoothing and analyzing 1D signals

CS5112: Algorithms and Data Structures for Applications Lecture 3: Hashing Ramin Zabih Some figures from Wikipedia/Google image search Administrivia Web site is: As usual, this is pretty much all you need to know Quiz 1 out today, due Friday 11:59PM

Very high tech! Coverage through Gregs lecture on Tuesday TAs and consultants coming shortly We have a slack channel Today

Clinic this evening (here), Greg on hashing Associative arrays Efficiency: Asymptotic analysis, effects of locality Hashing Additional requirements for cryptographic hashing Fun applications of hashing! Lots of billion-dollar ideas Associative arrays Fundamental data structure in CS

Holds (key,value) pairs, a given key appears at most once API for associative arrays (very informal!) Insert(k,v,A)->A, where A has the new pair (k,v) along with A Lookup(k,A)->v, where v is from the pair (k,v) in A Lots of details we will ignore today Avoiding duplication, raising exceptions, etc. Key question: how to do this fast How computer scientists think about efficiency Two views: asymptotic and practical

Generally give the same result, but math vs engineering Asymptotic analysis, a.k.a. big O Mathematical treatment of algorithms Worst case performance Consider the limit as input gets larger and larger Big O notation: main ideas 2 big ideas: [#1] Think about the worst case (dont assume luck) [#2] Think about all hardware (dont assume Intel/AMD) Example: find duplicates in array of numbers, dumbly

For each element, scan the rest of the array We scan elements [#2] So we examine elements, which is Which is ugly but what happens as ? [#1] Write this as Some canonical complexity classes Constant time algorithms, Running time doesnt depend on input Example: find the first element in an array

Linear time algorithms, Constant work per input item, in the worst case Example: find a particular item in the array Quadratic time algorithms, Linear work per input item, such as find duplicates Clever variants of quadratic time algorithms, A few will be discussed in the clinic tonight Big O notation and its limitations Overall, practical performance correlates very strongly with

asymptotic complexity (= big O) The exceptions to this are actually famous Warning: this does not mean that on a specific input an algorithm will be faster than an one! Linked lists 8 4

1 3 Linked lists as memory arrays Well implement linked lists using a memory array This is very close to what the hardware does 1 2 3

4 5 6 7 8 9

A linked list contains cells A value, and where the next cell is We will represent cells by a pair of adjacent array entries Example 8 4 1

3 1 2 3 4 5

6 7 8 9 8 5

1 7 4 3 3 0

0 Locality and efficiency Locality is important for computation due to physics The amount of information you can pack into a given area The hardware is faster when your memory references are local in terms of time and space Time locality: access the same data repeatedly, then move on Space locality: access nearby data (in memory) Memory hierarchy in a modern CPU

Complexity of associative array algorithms DATA STRUCTURE INSERT LOOKUP Linked list Binary search tree (BST) Balanced BST Hash table

So, why use anything other than a hash table? Hashing in one diagram What makes a good hash function? Almost nobody writes their own hash function Like a random number generator, very easy to get this wrong! Deterministic Uniform With respect to your input!

Technical term for this is entropy (Sometimes) invariant to certain changes Example: punctuation, capitalization, spaces Examples of good and bad hash functions Suppose we want to build a hash table for CS5112 students

Is area code a good hash function? How about zip code? Social security numbers? What is the best and worst hash function you can think of? Cryptographic hashing Sample application: bragging that youve solved HW1 How to show this without sharing your solution? Given the output, hard to compute an input with that output

Given hard to find Sometimes called a 1-way function Given the input, hard to find a matching input Given hard to find Hard to find two inputs with same output: What does hard to find mean? Major topic, center of computational complexity Loosely speaking, we cant absolutely prove this But we can show that if we could solve one problem, we could

solve another problem that is widely believed to be hard Because lots of people have tried to solve it and failed! This proves that one problem is at least as hard as another Problem reduction Handling collisions More common than you think! Birthday paradox Example: 1M buckets and 2,450 keys uniformly distributed 95% chance of a collision

Easiest solution is chaining E.g. with linked lists Now for the fun part What cool stuff can we do with hashing? Rabin-Karp string search Find one string (pattern) in another Naively we repeatedly shift the pattern Example: To find greg in richardandgreg we compare greg against rich, icha, char, etc. (shingles at the word level)

Instead lets use a hash function We first compare (greg) with (rich), then (icha), etc. Only if the hash values are equal do we look at the string Because (but not of course!) Rolling hash functions To make this computationally efficient we need a special kind of hash function As we go through richardandgreg looking for greg we will be computing on consecutive strings of the same length There are clever ways to do this, but to get the flavor of them here is a nave way that mostly works

Take the ASCII values of all the characters and multiply them Reduce this modulo something reasonable Large backups How do we backup all the worlds information? Tape robots! VERY SLOW access Bloom filters Suppose you are processing items, most of them are cheap but a few of them are very expensive. Can we quickly figure out if an item is expensive?

Could store the expensive items in an associative array Or use a binary valued hash table? Efficient way to find out if an item might be expensive We will query set membership but allow false positives I.e. the answer to is either possibly or definitely not Use a few hash functions and bit array To insert we set Bloom filter example Example has 3 hash functions and 18 bit array

are in the set, is not Figure by David Eppstein, Application: web caching CDNs, like Akamai, make the web work (~70% of traffic) About 75% of URLs are one hit wonders Never looked at again by anyone Lets not do the work to put these in the disk cache! Cache on second hit

Use a Bloom filter to record URLs that have been accessed A one hit wonder will not be in the Bloom filter See: Maggs, Bruce M.; Sitaraman, Ramesh K. (July 2015), "Algorithmic nuggets in content delivery" (PDF), SIGCOMM Computer Communication Review, New York, NY, USA,45 (3): 5266 Bloom filters really work!

Figures from: Maggs, Bruce M.; Sitaraman, Ramesh K. (July 2015), "Algorithmic nuggets in content delivery" (PDF), SIGCOMM Computer Communication Review, New York, NY, USA,45 (3): 5266 Cool facts about Bloom filters You dont need to build different hash functions, you can use a single one and divide its output into fields (usually) Can calculate probability of false positives and keep it low Time to add an element to the filter, or check if an element is in the filter, is independent of the size of the element (!) You can estimate the size of the union of two sets from the bitwise OR of their Bloom filters

MinHash Suppose you want to figure out how similar two sets are Jacard similarity measure is This is 0 when disjoint and 1 when identical Define to be the element of with the smallest value of the hash function , i.e. This uses hashing to compute a sets signature Probability that is Do this with a bunch of different hash functions

MinHash applications Plagiarism detection in articles Collaborative filtering! Amazon, NetFlix, etc. Distributed hash tables (DHT) BitTorrent, etc.

Given a file name and its data, store/retrieve it in a network Compute the hash of the file name This maps to a particular processor, which holds the file

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