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Category Archives: Plectics

My “Worked Physics Homework Problems” book now stands at 372 pages. If you ever wonder what I do instead of meeting people.
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A post today by PZ Myers nicely expresses something which has been frustrating me about people who, in arguing over what can be a legitimate subject of “scientific” study, play the “untestable claim” card.

Their ideal is the experiment that, in one session, shoots down a claim cleanly and neatly. So let’s bring in dowsers who claim to be able to detect water flowing underground, set up control pipes and water-filled pipes, run them through their paces, and see if they meet reasonable statistical criteria. That’s science, it works, it effectively addresses an individual’s very specific claim, and I’m not saying that’s wrong; that’s a perfectly legitimate scientific experiment.

I’m saying that’s not the whole operating paradigm of all of science.

Plenty of scientific ideas are not immediately testable, or directly testable, or testable in isolation. For example: the planets in our solar system aren’t moving the way Newton’s laws say they should. Are Newton’s laws of gravity wrong, or are there other gravitational influences which satisfy the Newtonian equations but which we don’t know about? Once, it turned out to be the latter (the discovery of Neptune), and once, it turned out to be the former (the precession of Mercury’s orbit, which required Einstein’s general relativity to explain).

There are different mathematical formulations of the same subject which give the same predictions for the outcomes of experiments, but which suggest different new ideas for directions to explore. (E.g., Newtonian, Lagrangian and Hamiltonian mechanics; or density matrices and SIC-POVMs.) There are ideas which are proposed for good reason but hang around for decades awaiting a direct experimental test—perhaps one which could barely have been imagined when the idea first came up. Take directed percolation: a simple conceptual model for fluid flow through a randomized porous medium. It was first proposed in 1957. The mathematics necessary to treat it cleverly was invented (or, rather, adapted from a different area of physics) in the 1970s…and then forgotten…and then rediscovered by somebody else…connections with other subjects were made… Experiments were carried out on systems which almost behaved like the idealization, but always turned out to differ in some way… until 2007, when the behaviour was finally caught in the wild. And the experiment which finally observed a directed-percolation-class phase transition with quantitative exactness used a liquid crystal substance which wasn’t synthesized until 1969.

You don’t need to go dashing off to quantum gravity to find examples of ideas which are hard to test in the laboratory, or where mathematics long preceded experiment. (And if you do, don’t forget the other applications being developed for the mathematics invented in that search.) Just think very hard about the water dripping through coffee grounds to make your breakfast.

The following is a selection of interesting papers on the theory of evolutionary dynamics. One issue addressed is that of “levels of selection” in biological evolution. I have tried to arrange them in an order such that the earlier ones provide a good context for the ones listed later.

I’ve met, corresponded with and in a couple cases collaborated with authors of these papers, but I’ve had no input on writing or peer-reviewing any of them.
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T. Biancalani, D. Fanelli and F. Di Patti (2010), “Stochastic Turing patterns in the Brusselator modelPhysical Review E 81, 4: 046215, arXiv:0910.4984 [cond-mat.stat-mech].

Abstract:

A stochastic version of the Brusselator model is proposed and studied via the system size expansion. The mean-field equations are derived and shown to yield to organized Turing patterns within a specific parameters region. When determining the Turing condition for instability, we pay particular attention to the role of cross-diffusive terms, often neglected in the heuristic derivation of reaction-diffusion schemes. Stochastic fluctuations are shown to give rise to spatially ordered solutions, sharing the same quantitative characteristic of the mean-field based Turing scenario, in term of excited wavelengths. Interestingly, the region of parameter yielding to the stochastic self-organization is wider than that determined via the conventional Turing approach, suggesting that the condition for spatial order to appear can be less stringent than customarily believed.

See also the commentary by Mehran Kardar.

A. Franceschini et al. (2011), “Transverse Alignment of Fibers in a Periodically Sheared Suspension: An Absorbing Phase Transition with a Slowly Varying Control Parameter” Physical Review Letters 107, 25: 250603. DOI: 10.1103/PhysRevLett.107.250603.

Abstract: Shearing solutions of fibers or polymers tends to align fiber or polymers in the flow direction. Here, non-Brownian rods subjected to oscillatory shear align perpendicular to the flow while the system undergoes a nonequilibrium absorbing phase transition. The slow alignment of the fibers can drive the system through the critical point and thus promote the transition to an absorbing state. This picture is confirmed by a universal scaling relation that collapses the data with critical exponents that are consistent with conserved directed percolation.

In network science, one can study the dynamics of a network — nodes being added or removed, edges being rewired — or the dynamics on the network — spins flipping from up to down in an Ising model, traffic flow along subway routes, an infection spreading through a susceptible population, etc. These have often been studied separately, on the rationale that they occur at different timescales. For example, the traffic load on the different lines of the Boston subway network changes on an hourly basis, but the plans to extend the Green Line into Medford have been deliberated since World War II.

In the past few years, increasing attention has been focused on adaptive networks, in which the dynamics of and the dynamics on can occur at comparable timescales and feed back on one another. Useful references:
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Consider the Lagrangian density

\[ \mathcal{L} (\tilde{\phi},\phi) = \tilde{\phi}\left((\partial_t + D_A(r_A - \nabla^2)\right)\phi - u\tilde{\phi}(\tilde{\phi} - \phi)\phi + \tau \tilde{\phi}^2\phi^2. \]

Particle physicists of the 1970s would recognize this as the Lagrangian for a Reggeon field theory with triple- and quadruple-Pomeron interaction vertices. In the modern literature on theoretical ecology, it encodes the behaviour of a spatially distributed predator-prey system near the predator extinction threshold.

Such is the perplexing unity of mathematical science: formula X appears in widely separated fields A and Z. Sometimes, this is a sign that a common effect is at work in the phenomena of A and those of Z; or, it could just mean that scientists couldn’t think of anything new and kept doing whatever worked the first time. Wisdom lies in knowing which is the case on any particular day.

[Reposted from the archives, in the light of John Baez's recent writings.]

In the wake of ScienceOnline2011, at which the two sessions I co-moderated went pleasingly well, my Blogohedron-related time and energy has largely gone to doing the LaTeXnical work for this year’s Open Laboratory anthology. I have also made a few small contributions to the Azimuth Project, including a Python implementation of a stochastic Hopf bifurcation model.

I continue to fall behind in writing the book reviews I have promised (to myself, if to nobody else). At ScienceOnline, I scored a free copy of Greg Gbur’s new textbook, Mathematical Methods for Optical Physics and Engineering. Truth be told, at the book-and-author shindig where they had the books written by people attending the conference all laid out and wrapped in anonymizing brown paper, I gauged which one had the proper size and weight for a mathematical-methods textbook and snarfed that. On the logic, you see, that if anyone who was not a physics person drew that book from the pile, they’d probably be sad. (The textbook author was somewhat complicit in this plan.) I am happy to report that I’ve found it a good textbook; it should be useful for advanced undergraduates, procrastinating graduate students and those seeking a clear introduction to techniques used in optics but not commonly addressed in broad-spectrum mathematical-methods books.

A discussion elsewhere on the ‘tubes this morning reminded me of this, so I decided to dig it out of my archives. Short version: people complaining that something sounds silly got it coming right back at them because they have no clue what they’re talking about.

I haven’t yet seen the remake of The Day the Earth Stood Still. Generally speaking, I haven’t been terribly speedy about seeing movies as they come out; sometimes, I just wait until they’re available on mplayer. The reviews have not been kind, but on the flipside, not all the reviews have been particularly insightful. To wit, here is Alonso Duralde at msnbc.com:

The new “Day” can’t be bothered to include the thought-provoking dialogue of the original, choosing instead to bury the audience with special effects that are visually impressive but no substitute for an actual script. And what words do remain are so exquisitely awful that they provide some of the season’s biggest laughs.

OK, bring it.

My personal favorite? Astro-biologist Helen Benson (Jennifer Connelly) takes alien Klaatu (Keanu Reeves) to see a Nobel Prize-winning scientist and notes that her colleague was honored “for his work in biological altruism.” What would that entail, exactly? Helping frogs cross the street?

The sound you hear is my palm hitting my forehead, rather emphatically, followed by a howl from deep within my thorax: “Learn to [expletive deleted] Google, you [anatomically uncomplimentary compound noun]!” Just because Chris Tucker of the Daily Mail can’t do a simple web search doesn’t give you an excuse.

Claudia Puig at USA Today is no better:

What, exactly, would that entail? It sounds like something Cleese and his fellow Monty Python wits might have dreamed up.

You ignorant [epithet derived from SF television show]. Why don’t you go [verb unsuitable for a family blog] with Dave White, who apparently thinks that the mere mention of an actual scientific subject makes a movie instant MST3K fodder.

In Scientific American, Michael Shermer gives the movie a mostly positive review, and indicates that “biological altruism” is a real subject. Kenneth Turan of the LA Times is also mostly happy with the film, and he doesn’t crack wise about the “biological altruism” business, though I’m not sure about his grasp of it:

Aside from Klaatu and Gort, the “Day” team claims to have retained the original’s snappy catchphrase, “Klaatu barada nikto,” but it’s so hard to hear that viewers will be forgiven if they miss it. Also still around is the charming blackboard scene, in which Klaatu solves an equation for Professor Barnhardt (John Cleese), a man smart enough to have won the nonexistent but indisputably high-minded Nobel Prize for biological altruism.

Supposing that Barnhardt did work in the field of kin recognition, evolutionary ecology or some such topic which was honoured with a Nobel Prize, it wouldn’t be “the Nobel Prize for biological altruism”, but rather the Nobel Prize in Physiology or Medicine or, possibly, Economics (if Barnhardt’s research focused on, say, evolutionary game theory).

MTV’s Kurt Loder calls the film “boldly mediocre” but says that “biological altruism” is “a very Pythonian name for an actual subject of scientific inquiry”. Stephen D. Greydanus has a similar attitude. The recapper at the Agony Booth was also underwhelmed, by this part and by the rest of the movie:

Helen explains that Karl won the Nobel “for his work in biological altruism.” This sounds like something goofy they made up to make Karl sound noble, but in fact it’s a real field of philosophic study that investigates why, in times of limited resources, individual organisms throughout the animal kingdom occasionally produce fewer offspring (which, in Darwinian terms, is self-abnegation) for the good of the community. Which is great, but since it’s not explained, most of the audience is left to think that it’s something goofy the filmmakers made up.

So, I guess you can still dislike the movie after you’ve looked up the relevant science.

I might be going to this, because it’s in the neighbourhood and I suppose I ought to see what colourful examples other people use in these situations, having given similar talks a couple times myself.

MIT Physics Department Colloquium: Jennifer Chayes

“Interdisciplinarity in the Age of Networks”

Everywhere we turn these days, we find that dynamical random networks have become increasingly appropriate descriptions of relevant interactions. In the high tech world, we see mobile networks, the Internet, the World Wide Web, and a variety of online social networks. In economics, we are increasingly experiencing both the positive and negative effects of a global networked economy. In epidemiology, we find disease spreading over our ever growing social networks, complicated by mutation of the disease agents. In problems of world health, distribution of limited resources, such as water, quickly becomes a problem of finding the optimal network for resource allocation. In biomedical research, we are beginning to understand the structure of gene regulatory networks, with the prospect of using this understanding to manage the many diseases caused by gene mis-regulation. In this talk, I look quite generally at some of the models we are using to describe these networks, and at some of the methods we are developing to indirectly infer network structure from measured data. In particular, I will discuss models and techniques which cut across many disciplinary boundaries.

9 September 2010, 16:15 o’clock, Room 10-250.

By Gad, the future is an amazing place to live.

Where else could you buy this?

Self-Organized Criticality:  Now on a Mug!

Or this?

The Zachary Karate Club network:  If your method doesn\'t work on this, then go home.

(Via Clauset and Shalizi, naturally.)

I have a confession to make: Once, when I had to give a talk on network theory to a seminar full of management people, I wrote a genetic algorithm to optimize the Newman-Girvan Q index and divide the Zachary Karate Club network into modules before their very eyes. I made Movie Science happen in the real world; peccavi.

Copied from my old ScienceBlogs site to test out the mathcache JavaScript tool.

Ah, complex networks: manufacturing centre for the textbook cardboard of tomorrow!

When you work in the corner of science where I do, you hear a lot of “sales talk” — claims that, thanks to the innovative research of so-and-so, the paradigms are shifting under the feet of the orthodox. It’s sort of a genre convention. To stay sane, it helps to have an antidote at hand (“The paradigm works fast, Dr. Jones!”).

For example, everybody loves “scale-free networks”: collections of nodes and links in which the probability that a node has $k$ connections falls off as a power-law function of $k$. In the jargon, the “degree” of a node is the number of links it has, so a “scale-free” network has a power-law degree distribution.
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I noticed this one when it first hit the arXivotubes a while back; now that it’s been officially published, it caught my eye again.

G. Rozhnova and A. Nunes, “Population dynamics on random networks: simulations and analytical models” Eur. Phys. J. B 74, 2 (2010): 235–42. arXiv:0907.0335.

Abstract: We study the phase diagram of the standard pair approximation equations for two different models in population dynamics, the susceptible-infective-recovered-susceptible model of infection spread and a predator-prey interaction model, on a network of homogeneous degree [tex]k[/tex]. These models have similar phase diagrams and represent two classes of systems for which noisy oscillations, still largely unexplained, are observed in nature. We show that for a certain range of the parameter [tex]k[/tex] both models exhibit an oscillatory phase in a region of parameter space that corresponds to weak driving. This oscillatory phase, however, disappears when [tex]k[/tex] is large. For [tex]k=3, 4[/tex], we compare the phase diagram of the standard pair approximation equations of both models with the results of simulations on regular random graphs of the same degree. We show that for parameter values in the oscillatory phase, and even for large system sizes, the simulations either die out or exhibit damped oscillations, depending on the initial conditions. We discuss this failure of the standard pair approximation model to capture even the qualitative behavior of the simulations on large regular random graphs and the relevance of the oscillatory phase in the pair approximation diagrams to explain the cycling behavior found in real populations.

Random fun items currently floating up through the arXivotubes include the following. Exercise: find the shortest science-fiction story which can connect all these e-prints, visiting each node only once.

Robert H. Brandenberger, “String Gas Cosmology” (arXiv:0808.0746).

String gas cosmology is a string theory-based approach to early universe cosmology which is based on making use of robust features of string theory such as the existence of new states and new symmetries. A first goal of string gas cosmology is to understand how string theory can effect the earliest moments of cosmology before the effective field theory approach which underlies standard and inflationary cosmology becomes valid. String gas cosmology may also provide an alternative to the current standard paradigm of cosmology, the inflationary universe scenario. Here, the current status of string gas cosmology is reviewed.

Dimitri Skliros, Mark Hindmarsh, “Large Radius Hagedorn Regime in String Gas Cosmology” (arXiv:0712.1254, to be published in Phys. Rev. D).
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And now, a brief break from non-blogging:

Today, I’d like to start with a specific example and move on to a general point. The specific example is a way to approximate the squares of numbers and then refine those approximations to get exact answers, and the general point concerns the place such techniques should have in mathematics education.

My last calculator broke years ago, so when I have to do a spot of ciphering, I have to work the answer out in my head or push a pencil. (If the calculation involves more numbers than can fit on the back of an envelope, then it’s probably a data-analysis job which is being done on a computer anyway.) Every once in a while, the numbers teach you a lesson, in their own sneaky way.

It’s easy to square a smallish multiple of 10. We all learned our times tables, so squaring a number from 1 to 9 is a doddle, and the two factors of 10 just shift the decimal point over twice. Thus, 502 is 2500, no thinking needed.

Now, what if we want to square an integer which is near 50? We have a trick for this, a stunt which first yields an answer “close enough for government work,” and upon refinement gives the exact value. (I use the “close enough for government” line advisedly, as this was a trick Richard Feynman learned from Hans Bethe while they were calculating the explosive power of the first atomic bomb, at Los Alamos.) To get your first approximation, find the difference between your number and 50, and add that many hundreds to 2500. The correction, if you need it, is to add the difference squared. Thus, 482 is roughly 2300 and exactly 2304, while 532 is roughly 2800 and exactly 2809.

I wouldn’t advise teaching this as “the way to multiply,” first because its applicability is limited and second because it’s, well, arcane. What a goofy sequence of steps! Surely, if we’re drilling our children on an algorithm, it should be one which works on any numbers you give it. The situation changes, though, after you’ve seen a little algebra, and you realize where this trick comes from. It’s just squaring a binomial:
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BPSDBRichard Dawkins and PZ Myers had a lengthy, informal chat during the 2008 American Atheists conference in Minneapolis, and a recording of their conversation is now available on DVD and in the video tubes. They discuss the fight against pseudoscience as well as several interesting topics in good science.

I did my best to summarize the kin-vs.-group business in this book review. Among the “glimmerings” which suggest there’s a better way to think about some evolutionary processes (name for that better way still to be defined) are, I think, the epidemiological simulations in which fitness of a genotype is clearly a function of ecology and thus strongly time-dependent, and consequently existing analysis techniques are likely to fail. Assuming this kind of thing happens in the real world, it might be better to speak of “extending the evolutionary stable strategies concept” or “temporally extended phenotypes” than to have yet another largely semantic argument over “group selection.”

Also of note:

When Dawkins spoke at the first artificial life conference in Los Alamos, New Mexico, in 1987, he delivered a paper on “The Evolution of Evolvability.” This essay argues that evolvability is a trait that can be (and has been) selected for in evolution. The ability to be genetically responsive to the environment through such a mechanism as, say, sex, has an enormous impact on one’s evolutionary fitness. Dawkins’s paper has become essential reading in the artificial life community.

Anyway, on with the show.

P-Zed wrote an introduction to allometry a little over a year ago.
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