So the same way we recognized that adult disorders can be applied to children, we are now, with ADD, noting that those of childhood can be applied to adults. It makes it hard not to imagine a future in which the smallest hardships (trouble studying, stress over a breakup, or perhaps a desire to prevent such nuisances) lead seamlessly to a fully medicated existence starting well before the onset of adulthood.

Maybe predicting the future just wasn't his thing. But I read that his biographer put it as follows: "he spent the first half of his career being right and the second half being wrong."

Don't let the Centre's unwieldy name fool you, they're going to be working on some pretty cool stuff, including cutting-edge optical networking, ad hoc networks, and more. Plus, they're looking for people.

• the manifold algorithms, and
• a bit more on manifold-g

Anyway, from today's New York Times: String Theory, At 20, Explains It All (or not):

"String theory, the Italian physicist Dr. Daniele Amati once said, was a piece of 21st-century physics that had fallen by accident into the 20th century.

And, so the joke went, would require 22nd-century mathematics to solve."

Stephen Hawking: "Not only does God play dice, but... he sometimes throws them where they cannot be seen."

Niels Bohr: "How wonderful that we have met with a paradox. Now we have some hope of making progress."

Exactly.

Anyway, I was saying. Here's a short intro.

Resource Location, Resource Discovery

In essence, Resource Location creates a level of indirection, and therefore a decoupling, between a resource (which can be a person, a machine, a software services or agents, etc.) and its location. This decoupling can then be used for various things: mapping human-readable names to machine names, obtaining related information, autoconfiguration, supporting mobility, load balancing, etc.

Resource discovery, on the other hand, facilitates search for resources that match certain characteristics, allowing then to perform a location request or to use the resulting data set directly.

The canonical example of Resource Location is DNS, while Resource Discovery is what we do with search engines. Sometimes, Resource Discovery will involve a Location step afterwards. Web search is an example of this as well. Other times, discovery on its own will give you what you need, particularly if the result of the query contains enough metadata and what you're looking for is related information.

RLD always involves search, but the lines seemed a bit blurry. When was something one and not the other? What defines it? My answer was to look at usage patterns.

It's all about the user

It's the user's needs that determine what will be used, how. The user isn't necessarily a person: more often than not, RLD happens between systems, at the lower levels of applications. So, I settled on the usage patterns according to two main categories: locality of the (local/global) search, and whether the search was exact or inexact. I use the term "search" as an abstract action, the action of locating something. "Finding a book I might like to read" and "Finding my copy of Neuromancer among my books" and "Finding reviews of a book on the web" are all examples of search as I'm using it here.

Local/Global, defining at a high level the "depth" that the search will have. This means, for the current search action, the context of the user in relation to what they are trying to find.

Exact/Inexact, defining the "fuziness" of the search. Inexact searches will generally return one or more matches; Exact searches identify a single, unique, item or set.

These categories combined define four main types of RLD.

Examples: DNS is Global/Exact. Google is Global/Inexact. Looking up my own printer on the network is Local/Exact. Looking up any available printer on the network is Local/Inexact.

Now, none of these concepts will come as a shock to anybody. But writing them down, clearly identifying them, was useful to define what I was after, served as a way to categorize when a system did one but not the other, and to know the limits of what I was trying to achieve.

The Manifold Algorithms

With the usage patterns in hand, I looked at how to solve one or more of the problems, considering that my goal was to have something where absolutely no servers of any kind would be involved.

Local RLD is comparatively simple, since the size of the search space is going to be limited, and I had already looked at that part of the problem with my Nom system for ad hoc wireless networks. Looking at the state of the art, one thing that was clear was that every one of the systems currently existing or proposed for global RLD depends on infrastructure of some kind. In some of them, the infrastructure is self-organizing to a large degree, one of the best examples of this being the Internet Indirection Infrastructure (i3). So I set about to design an algorithm that would would work at global scales with guaranteed upper time bounds, which later turned out to be an overlay network algorithm (which ended up being based on a hypercube virtual topology), as opposed to the broadcast type that Nom was. For a bit more on overlays vs. broadcast networks, check out my IEEE article on the topic.

Then the question was whether to use one or the other, and it occurred to me that there was no reason I couldn't use both. It is possible to to embed a multicast tree in an overlay and thus use a single network, but there are other advantages to the broadcast algorithm that were pretty important in completely "disconnected" environments such as wireless ad hoc networks.

So Nom became the local component, Manifold-b, and the second algorithm became Manifold-g.

So that's about it for the intro. I know that the algorithms are pretty crucial but I want to take some time to explain them properly, and their implications, so I'll leave that for later.

As usual, comments welcome!

In the latest issue of Wired magazine, James Cameron has a great article, The Drive to Discover. Reminds me of this post I wrote a little over a year ago.

Also in the latest Wired, lots of other great exploration-related articles, such as The New Space Race by Bruce Sterling and Taming the Red Planet, by Kim Stanley Robinson, author of the Mars trilogy (Red Mars, Blue Mars, and Green Mars).

PS: actually, three. Is that a pilot in your pocket?: "Somewhere in Florida, 25,000 disembodied rat neurons are thinking about flying an F-22."

Too bad there isn't some molecular thingamagic to relieve me from this wretched cold/flu right now. Aside from that old medicine called "sleep and rest"...

“ASSUME nothing” is a good motto in science. Even the humble pendulum may spring a surprise on you. In 1954 Maurice Allais, a French economist who would go on to win, in 1988, the Nobel prize in his subject, decided to observe and record the movements of a pendulum over a period of 30 days. Coincidentally, one of his observations took place during a solar eclipse. When the moon passed in front of the sun, the pendulum unexpectedly started moving a bit faster than it should have done.

Since that first observation, the “Allais effect”, as it is now called, has confounded physicists. If the effect is real, it could indicate a hitherto unperceived flaw in General Relativity—the current explanation of how gravity works.

The other was that they've got XML dialects for the genetic information: witness this sequence which is part of our first chromosome. For some reason I can't quite explain, I also find this fascinating. They have different XML dialects which show more information too, with names like TinySeq XML, GBSeq XML, and just "XML" (is this one the standard?), all with DTDs. I imagine they had similar fights as in other fields (such as syndication) over which tags to use, formats, and such--I wonder if there's a mailing list where we can find geneticists and molecular biologists arguing over which tag is best...