Herself’s Artificial Intelligence

If you are interested in network information theory you might want to check out this pdf of combined lecture notes from several graduate classes.

Network information theory deals with the fundamental limits on information flow in networks and optimal coding techniques and protocols that achieve these limits. It extends Shannon’s point-to-point information theory and the Ford–Fulkerson max-flow min-cut theorem to networks with multiple sources and destinations, broadcasting, interference, relaying, distributed compression and computing. Although a complete theory is yet to be developed, several beautiful results and techniques have been developed over the past forty years with potential applications in wireless communication, the Internet, and other networked systems.
This set of lecture notes, which is a much expanded version of lecture notes used in graduate courses over the past eight years at Stanford, UCSD, CUHK, UC Berkeley, and EPFL, aims to provide a broad coverage of key results, techniques, and open problems in network information theory. The lectures are organized in a “top-down” manner into four parts: background, single-hop networks, multi-hop networks, and extensions. The organization attempts to balance the introduction of new techniques and new models. Extensions (if any) to many users and large networks are discussed throughout. The lectures notes provide a unified, simplified, and formalized treatment of achievability using a few basic lemmas. The proofs in the lecture notes use elementary tools and techniques, which should make them accessible to graduate students in EE, CS, Statistics, and related fields as well as to researchers and practitioners in industry.

Network Information Theory Notes

pdf only

More Information:
First improvement to max flow algorithm in 10 years announced by MIT

Yet another year, yet another dozen languages. Some times it seems as if all my time gets sapped up learning new languages. R is growing rapidly in popularity making news on Slashdot and the NYT late last year.

R provides a graphics package for visualizing your data, a data editor, data manipulation and has C/C++ interfaces. When R is open it provides a set of windows allowing you to interact with your data. The instruction manuals, tutorials, source code for Linux, OSX and Windows are available for free at the R Project site

R is also the name of a popular programming language used by a growing number of data analysts inside corporations and academia. It is becoming their lingua franca partly because data mining has entered a golden age, whether being used to set ad prices, find new drugs more quickly or fine-tune financial models. Companies as diverse as Google, Pfizer, Merck, Bank of America, the InterContinental Hotels Group and Shell use it.

But R has also quickly found a following because statisticians, engineers and scientists without computer programming skills find it easy to use.

“R is really important to the point that it’s hard to overvalue it,” said Daryl Pregibon, a research scientist at Google, which uses the software widely. “It allows statisticians to do very intricate and complicated analyses without knowing the blood and guts of computing systems.”

It is also free. R is an open-source program, and its popularity reflects a shift in the type of software used inside corporations. Open-source software is free for anyone to use and modify. I.B.M., Hewlett-Packard and Dell make billions of dollars a year selling servers that run the open-source Linux operating system, which competes with Windows from Microsoft. Most Web sites are displayed using an open-source application called Apache, and companies increasingly rely on the open-source MySQL database to store their critical information. Many people view the end results of all this technology via the Firefox Web browser, also open-source software. R, the software, finds fans in data analysts read more . . .

See also:
@RStats tips and examples
An Introduction to R
The R Project for Statistical Computing
Revolutions: How R is disrupting a billion dollar market
The iGraph Library for Complex Network Research
SPOT: An R Package for Automatic and Interactive Tuning of Optimization Algorithms by Sequential Parameter Optimization (Download SPOT)

I hadn’t heard anything about wavlets in several years and then this news story caught my eye.

. . .Meningiomas are tumours of the brain and nervous system and they account for 20% of all brain tumours. Doctors have a major problem of discriminating between the four different subtypes of meningiomas but doctors face three key problems in making such a diagnois:

– The work can be painstakingly slow requiring up to two hours of analysis and expert consideration of a full “slide” of information.

– The finest tumour specialists (histopathologists) can at times come up with completely contradictory findings based on slight variations in their method of analysis.

– Currently the slides that specialists examine contain a few million pixels of data and the task of tumour diagnosis is painstakingly slow already. This problem is quite literally growing as medical equipment is coming on stream that can produce slides with hundreds of millions pixel resolution.

. . .

Now researchers in the University of Warwick’s Department of Computer Science have devised a method of using “wavelets” to provide an automated analysis of the varying texture of the tumours and guidance to doctor’s within seconds of being presented the data.

[ read more Wavelets crunch through doctor’s day to long struggle to diagnose brain tumors

Maybe wavelets are about to make a bigger splash in the world of artificial intelligence?

Learn more:
An introduction to wavelets
A really friendly guide to wavelets
Tutorial on continuous wavelet analysis
Wavelet ( Wolfram site )
Wavelets
Wavelets for computer graphics

Code:
WAILI – Wavelets C++ library ( open source )
PyWavelets – Python library ( open source )
Wavelets in Java ( source code provided)