Thursday, February 25, 2016

IEEE Conference on Big Data From Health to Brains to Stars

IEEE Conference on Big Data From Health to Brains to Stars

IEEE is the world’s largest professional society in engineering, with more than 400,000 members all over the world. Most of its societies, like the Computational Intelligence Society (CIS), have a significant surplus of cash from their conferences, journals, and magazines. Last year, to “give back” some of this money to the community, CIS held a competition to fund ideas for summer schools or conferences. That is how they funded a “summer school” (a large workshop) last week on Big Data in Computational Intelligence: From  Basic Principles to Large-Scale Applications. The conference was mainly made up of seven 1.5-hour talks spaced over two days, illustrating the key role that big data and computational intelligence tools can play on areas from health care and environment to brains and stars. I gave the first of these, bringing together the big picture both on risks and opportunities and on the underlying mathematics. The organizer, Nian Zhang of the University of the District of Columbia, plans to post the talks on the web, and had important assistance from others in the community. The talks and speakers, coming from great distances, are described at:

Here I will not post my own slides (even though they were quite colorful and took a lot of work).

In fact – I apologize to you. UNLIKE most of my blog posts, this is NOT written to be understandable to anyone except me. These are just my notes to myself after the conference, and preparing for a seminar I will give in Memphis. I usually do not post my personal journal entries, but ... just once, it should do no harm, I hope.


The UDC conference last weekend and the coming Memphis challenge have both been very stimulating to my thoughts – enough to remind me what I have been missing since retirement, and to remind me it would have been better if I had always taken the time to write down the insights I get before I forget them. Robert Kozma’s talk at UDC was “from brain to stars,” and indeed the new thoughts out of my usual daily routine mainly come in those two areas.

But: a lot of it builds on previous hard thinking here at home (easier than at NSF as schedules and culture get more and more hectic), and on the challenge ahead. Bit by bit, I feel called to broaden my attitude on about three things (as noted on page 87 of X2015, which I wrote in “like the old days” yesterday, sitting in a comfortable wooden chair with cushions tilted back, in this study, with new age music – Mannheim Steamroller and Kitaro – playing in the background, through this computer): on brains/minds, to get past “point prototypes” and the early syncretism model, to a full-fledged appreciation of Jungian things from math to noosphere; from Z to richer structure of time tracks (evaluating data of course); and – more today in particular, related to seminar to be given at Memphis – richer understanding of emergent behavior of nonlinear dynamical systems.

To some extent, my core job at Memphis is just to present what I have already done. The challenge is to make it comprehensible. Yesterday and in bed this morning, I tried to do more, to deepen the understanding... but because of limited time, I need to just record the basics of the new stuff and the new questions (incomplete as it is) this morning, and then freeze it, and move on to preparing talk (and reading Yury’s thesis).

Luda this morning reminded me that I need to be very careful with HOW I define my terms for Memphis. For example, I can take time with definitions, so long as I use the right language. Functions, distributions and measures are OK, but I need to be careful not to use the word “field” in a way which conjures up terror. But there is the advantage that I can say “look at this small special case” and not worry about people noticing I have included the whole universe and a highly multidimensional continuum of its relatives.  Are PDE dynamical systems? Sometimes yes, sometimes no. Dynamical systems are allowed objects, and I guess even definable. In fact, I do need to review a lot of ODE (and/or Prigogine space) objects to put it into context anyway, from Mehta to Boltzmann like.

But in the green notebook yesterday... there were ideas I also need to get down on computer, for two reasons: (1) to consolidate the logic a bit; and (2) to make it readable by me, with the major issue of eyestrain now permanent after cataract surgery. (A big ugly red hemmorage removed any doubt that I must be careful now as long as I live!)

My initial goal (when I sent abstract for seminar to Robert) was to explain the new generalized Boltzmann distribution (in a couple of forms) given in my “extended Glauber-Sudarshan” paper in arxiv. presented in 2014 in Scully’s workshop.
In practice, the consequences are almost the same as those reported in my much older cond-mat paper: for continuous states S(t) a probability Pr(S(t)) which is just f(E, C1, ... Cm)dS(t), where f is some function of energy and of the m conserved quantities of the theory. (m=0 is an allowed possibility, but the approach breaks down when there is no conserved energy.) This differs from the old idea that “the universe must go to a disorderly heat bath” (which Harold Szu reiterated at UDC, referring to a debate between Boltzmann and Poincare) because in the PDE case the derivative terms in the energy function do insert some connection/correlation between neighboring points, at infinitesimal separation. Still, that seems to suggest, on the surface, that the emerging order (pdf not well-approximated by the independence assumption across points in space) is mainly at a small scale.

Yet... I feel called to try to stretch further and be more open. So when I read Modesitt’s new novel, The Solar Express, I did take notice of his discussion of systems where there may be more pattern/regularity/order at LARGER distances than at smaller ones. (And of course for years and years I have remembered a paper by Wheeler, where he talks about neutrino temperature and tweaks things to account for the glaring fact that there are these objects called planets and stars, rather fundamental to what we see.) How does that work? Can that kind of patterning be understood/explained within the confines of first-order Hamiltonian systems, using the energy term f and not the invariant measure dS as a source of rich emergent behavior? Precisely how do open systems change things – remembering that there is such a thing as classical chaos in conservative systems (was that chaotic three-arm broom balancer due to Poincare?...)... and that the universe as a whole is presumed to be a closed system? (e.g. If chaos can occur in Hamiltonian systems, why not fractal patterns?)

First observation: I have ALREADY looked into emergent order, without naming it as such! The Boltzmann distribution is a function of conserved charges Ck, as well as E. It is precisely one of those conserved charges, the topological charge(s), which leads to that very important example of patterning or order, the topological soliton – i.e. the true elementary particles, the very foundation of everything we see on earth! Even more interesting... albeit just at vixra and my follow-on internal journal entry... “local minima” in the classical energy function also lie behind the various metastable states of atoms, as was worked out in some detail there! So YES, the Boltzmann term can do it, can generate “macroscopic” order on distance scales as huge as the Angstrom unit, so many orders of magnitude larger than the femtosecond scale we start with for the elementary particles. Two quantum jumps to greater distance already, just based on f(E,C1,...Cm)! Beyond that, what comes next are solid objects, things I know well from quantum electronics and photonics, and they certainly have interesting Boltzmann-based patterning varieties. I remembered Roger Lake... how the same topological soliton kind of pattern distribution can be elevated to the ordinary laboratory level... and the quantum separator (QS) at that scale. (Not to mention some of Von Neumann’s ideas about life.) So already with closed PDE systems (DO I SAY “CLOSED” OR “CONSERVATIVE” AT MEMPHIS???)... the energy term DOES allow all kinds of neat stuff. This week, I seem to be supporting Poincare as much as Von Neumann... maybe I need to look him up more... later...  (though for Memphis, it might help to show links...)

Now of course, the patterning we see with life on the surface of the earth (and the neuron example in the Solar Express,,, which I probably should WRITE DOWN when next I visit Central Library)... is due more to the injection of forwards time free energy, light from the sun. But what specific kinds of boundary conditions count as “forwards time free energy”? It is embarrassing how elusive it is to try to pin that down. In a way, we may not really NEED to formalize the GENERAL idea; we need Pr- and Pr+ boundary conditions at final and initial times, and that’s enough; more may not even be inherently well-defined, as it is ultimately just a rough way to characterize emergent statistics and specific local experiments/systems. But if we can see something more clear and more universal, it could be nice...

As I think more about that... I am intrigued by the possibility of getting more concrete (more specific, useful) results form a couple of special cases of “open” systems, which I think of as the “uniform light” and “cosmic ecology” examples/cases/paradigms. In the “light” example, we still have PDE, which can be represented by spatially uniform additional terms violating time-symmetry and/or energy conservation. (Now that I think of it... we have not proven that uniformity in 3D space would work... a vast ocean of light model ... in generating life as well as our local light does, entering in 2D. So many issues to explore!) Can we get nice equations for that? The cosmic ecology would start more concretely... considering things like rates of stuff ejected from black holes, gross recycling of stuff keeping the universe alive, described as close to ODE as possible... trying to keep it as simple as possible but real. (That reminds me of very important new supernova results from Kozma/Chile, which we will pursue after Memphis and after a brain exercise if all goes well.)

Thinking about the “light” example... it is striking how my previous results (in “Extended..”) really did rely on energy conservation. Yet shouldn’t the concept of equilibrium probability be expressable in a USEFUL closed-form way for density operators in the general case? In my mind... I think of how the basic Hamiltonian dynamics translate into dynamics for rho, with an obvious equilibrium condition. But isn’t that condition in the GENERAL case (e.g. including light systems) about as useless as Wiener’s nonlinear statistics, and in fact quite similar? It reminds me of the inherent problems in reification, and the inherent stuff in Arnold and in chaos.

So: for later. Something probably is there... some ordering... but not this week.

============================ brainy stuff:

Perhaps I should also type some of my thoughts about brain/mind/NN which came during UDC. Much fuzzier, and not part of an active goal beyond my own mind... but... whatever.

I was disappointed, of course, that Polikar – who seemed on course to solidify and upgrade syncretism, to address the crucial issue of real-time learning in brains – has drifted away into drifting away. A lot of the questions his new community are asking about nonstationarity are no more puzzling than causality, and perhaps I should have written some of it down earlier. (I have always had too much stuff to be able to publish so much as half of it.) Yet he has some stimulating questions, if the right questions are pulled out and played with.

For example: he asked (with some reification here): “What if we have two sets of variables, Y(t) and X(t), called labels and data? What if we have some initial data on Y(t) and X(t), and then a WHOLE LOT more data on X(t) later, especially in a world where relationships may change?” I urged consideration of two examples, an “auto” example and a linear example. In the auto example... Ford might have millions of data points on Y(t) and X(t) from thousands of engines or batteries, fully instrumented in the lab, followed by later getting billions of data points just from X(t) from cars on the road. (Hypothetical, assume wireless “phone home.”) The obvious approach is to learn just from the laboratory data, but also learn soft sensing to predict Y(t) from history of X(t) for use on the road. (Feldkamp and Prokhorov have an important soft sensing paper in Narendra, which I even repost on my own Later X(t) doesn’t do much! HOWEVER: in principle, one could use X(t) (predicting X(t+1) from X(<=t)) to find proper “hidden nodes,” preferred encoding, which one could then go back and use to REMODEL the laboratory data. ALSO: observing the range of variation of the later data, IF it is larger in some dimensions than the laboratory data, one can use it to put special weight on trying to get accurate measurement of weights which will explain more of the variance in later observations, a possibility for value weighting. (Still, a concept much trickier than Vapnik understands.) The linear example reminds us... if covariance of X to X changes, why not X to Y.... which rather grossly invalidates that kind of exercise.


I worked hard, in preparing my own talk (recorded by UDC, to be posted), to try to avoid being as depressing as I have been lately at home. Probably death of the entire species really is depressing, more so than my cancer, which still bugs me as well. I did not mention climate change hardly at all. But Diego, the final speaker on the first day, got into great detail on the kinds of stratospheric ozone effects which, after H2S upwelling from oceans, seem likely to get worse enough and be the first thing directly fatal to human life from climate as such. He also did a lot of mixture of experts modeling, though he did not remind us of what is or is not known about that general power of such things. Is there any such? I remain skeptical, and skeptical of its relevance to brains. Yet I must admit... that Jungian archetypes are beyond the point prototypes of classical syncretism, and that mixture of models thinking might be adaptable to that general case in a reasonable way, for noospheres if not for mammal brains. Worth thinking about, along with dynamics and drift of prototypes in general.

The final talk by Kozma included example of a solar surface model by Catalina Terra. (Met her to be sure!) Amusing what degree of parallel with Modesitt’s story... different desert, but still...

Wunsch objected to use of PSO to optimize, a comment I certainly found friendly. For general power, we need to get past methods like PSO which do not offer a good ratio of parameters-handled to number of examples explored... yes, use derivative and hence the chain rule for ordered derivatives... but when a dozen or two free parameters can do the job, priority in becoming more scientific involves two different upgrades, as I said to people: (1) technical aspects, related to what they are modeling now; and (2) new questions, the most important.

Re (1) :”Did you train and test?” It is remarkable that one PDE type model (even if with nonlocal field effects) could start form the beginning and track the long history of light emission from any star without new data coming in. (Or did it? Train and test needed.) I have seen cases in chemical engineering (my papers with McAvoy) where such was possible, but others in economics (my 1977/8 SMC paper predicting conflict in Latin America) where it was not, because of something... a general form... of “phase drift,” shown in my slides for UDC. UNLIKE the “nonstationarity” community... we have a compromise method which can address this kind of thing...  one more thing which needs to be translated even now... and a fundamental thing at that! (Some of the other things Polikar talked about can be modelled simply as hidden variables in time-series model, no fundamental significance at all, except insofar as some parameter values need to be thought about...
part of the universal vector intelligence game). Even now, I guess I need to be even more brutally explicit to the world in explaining/defining the basic mathematical concept of vector intelligence.


From UDC, other loose ends... tell Lily about MomLink, Maia about Harold’s earthquake model (communicated and translated better) and Wunsch’s effort to use ADP on iterated prisoner’s dilemma (and his hypothesis about decomposing some types of games into “easy” and “PD-like”.) And Chawla’s health systems... maybe even related to my health! Luis for political cooperation? Whatever... if time allows.. I did look up Gulen, and need to explore loose ends in that area.

Anti-aging also came up, at UDC and in Solar Express, at many levels, from structures to people to ,... well, I always think about noospheres too. It too relates to the Boltzmann world, but has to be managed carefully in the weird environment we are in today.

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