A Quantum Calculation: Is Information at the Root of Everything?

From the article “… Vlatko Vedral, an Oxford physicist, examines the claim that bits of information are the universe’s basic units, and the universe as a whole is a giant quantum computer. He argues that all of reality can be explained if readers accept that information is at the root of everything.”

The Age of Quantum Computing? [From Nature]

From the Abstract … “The race is on to build a computer that exploits quantum mechanics. Such a machine could solve problems in physics, mathematics and cryptography that were once thought intractable, revolutionizing information technology and illuminating the foundations of physics. But when?” (Subscription may be required for Access)

"Sink Method" Poster for Conference on Empirical Legal Studies (CELS 2009 @ USC)

Sinks Poster

As we mentioned in previous posts, Seadragon is a really cool product. Please note load times may vary depending upon your specific machine configuration as well as the strength of your internet connection. For those not familiar with how to operate it please see below. In our view, the Full Screen is best the way to go ….

Algorithmic Community Detection in Networks

Communities in Networks

Community detection in networks is an extremely important part of the broader network science literature. For quite a while, we have meant to highlight the extremely useful review article written by Mason Porter (Oxford) Jukka-Pekka Onnela (Harvard/Oxford) and Peter J Mucha (UNC). Rather than offer our description of the article, we thought it best to highlight commentary on the subject provided by the authors.

For example, in describing the paper over at Harvard’s Complexity and Social Networks Blog Jukka-Pekka Onnela posted the following… “Uncovering the “community” structure of social networks has a long history, but communities play a pivotal role in almost all networks across disciplines. Intuitively, one can think of a network community as consisting of a group of nodes that are relatively densely connected to each other but sparsely connected to other dense groups of nodes. Communities are important because they are thought to have a strong bearing on functional units in many networks. So, for example, communities in social networks can correspond to different social groups, such as family, whereas web pages dealing with a given subject tend to form topical communities.  The concept is simple enough, but it turns out that coming up with precise mathematical definitions and algorithms for community detection is one of the most challenging problems in network science. Recently, a lot of the research in this area has been done using ideas from statistical physics, which has an arsenal of tools and concepts to tackle the problem. Unfortunately (but understandably) relatively few non-physicists like to read statistical physics papers.”

These scholars quote Mark Newman noting “[T]he development of methods for finding communities within networks is a thriving sub-area of the field, with an enormous number of different techniques under development. Methods for understanding what the communities mean after you find them are, by contrast, still quite primitive, and much needs to be done if we are to gain real knowledge from the output of our computer programs.”  They later note “the problem of how to validate and use communities once they are identified is almost completely open.”

Anyway, if you are interested in learning more about this important piece of the network science toolkit … we suggest you read this paper!