This page will contain various emails and references discussing the Shnoll work. Thus far we have: Tatanya Zenchenko's description of the beginning of the work John Walker's examination of random (radioactive) data using algorithmic methods Dick Bierman, letter describing Axford confirmation Edwin Pozharski, Re: human judging -> algorithm Dean Radin, correlational analysis (not yet available) Email from Shnoll following Euro-SSE meeting, including abstracts Tatanya Zenchenko's description of the beginning of the work In response to my question about the genesis of Simon Shnoll's quite remarkable procedure, one of his colleagues, Tatanya Zenchenko kindly described the history of his work. This is a slightly edited version of the story. Date: Tue, 11 Jul 2000 20:40:39 +0400 From: Tatyana Zenchenko To: rdnelson Subject: Re: Shnoll method, was Randomness in Amsterdam > I would like to repeat this question. Even though it may be > difficult to explain, I and others would appreciate some > discussion, which should lead to more efficient effort to > develop robust automated analysis, and ultimately to more > valuable insight on the possible cosmophysical influences. > > Best wishes, > Roger Roger, Sorry for delay, I'll try to answer your question (as far as I know the history) but it is actually very long story. About 45 years ago Simon Shnoll was a biochemist dealing with muscle proteins.
He was a very good experimentalist, but during experiment he obtained two distinct values of subsequent measurements of chemical reaction rate. For example, 100 and 120, but almost never - 110. It contradicted theory... He repeated his probes up to 300 times every day, under absolutely equivalent conditions, but situation remained the same. Now we can say that he got one histogram (300 points) per day and these histograms didn't look like smooth normal distributions, but very often they were similar to each other: they had two distinct peaks and deep gaps between them. Moreover, sometimes this "two-peak" distribution kept the same form for two or three days, and after that it became "two-and-half peaks" and so on. (Of course the matching of these histograms requared shifting and rescaling, but the main idea, "the idea of form" remained the same). When all possible artifacts were excluded, he supposed that the point is in a special properties of muscle proteins. Then he asked his teacher, great russian biochemist Vladimir Engelhardt, "why are these repeated probes so different?" and Engelhardt answered: "Don't do so many repetitions and this effect will disappear"... Simon Shnoll wrote in his laboratory journal "[I plan] to find out the origin of strange result distributions and return to main question". After that 45 years passed. He did not return yet [to the biochemistry]. During these years he has examined many systems. If the explanation is special properties of myosin, let us take another protein where the distributions should not be strange, should not have sharp peaks and "inertia" in their form. But other proteins had similar behaviour. If we take a chemical, not a biochemical system? The same. It was very labour-intensive work and he could do only one histogram per day. But these histogram sequences had some logic in their development. But there were plenty of possible explanations of this effect, both scientific and artifactual.
All of them needed to be examined, so it took many years. During this long time Dr. Shnoll suspected that oscillations in homogeneous aqueous system can exist. He was right: under his head in his laboratory the Belousov-Zhabotinskii reaction was discovered. But in any case these oscillations could not explain the effect he was interested in, because around that time he observed the same effect in distributions of radioactivity measurements (radioactivity was his second speciality). Since that discovery (in 1986) Prof. Shnoll and his team use radioactivity measurements as a main object of experiments because of absence of trivial factors affecting this process. So, for scientist who usually asks unusual questions it was not so hard to apply this approach (histogram sequences) to abnormalities of biochemical reaction, [and then to other systems]. After that there was just the search for an answer. The last part of this story you read in our first UFN paper, where an exact description of the phenomenon is given. All the words above are only the "poetry", but I hope it gives the answer for your question. I should note that it was a real (and rare) pleasure to read the interpretation of reader who understood the sense of our articles so correctly. I am sorry for my English, I hope this story is written clearly to understand. But if you want to publish this message in Internet (I guess it is so) I would be grateful for some help in English style.
Tatyana P.S. Thank for your care, all your emails were received. There was mistake in Prof. Shnoll's address, but he got your message from me. John Walker's algorithmic methods Date: Wed, 12 Jul 2000 02:25:25 +0200 From: John Walker To: rdnelson Cc: Dick J Bierman , nick herbert , Jack Sarfatti , Dean Radin , [email protected] Subject: Statistical pitfalls in interpreting Shnoll et al replications Please excuse the unusual format of this message. Rather than including figures as attachments, I've provided URLs which download them so as to avoid burdening you with images you may not be interested in viewing. For the last two weeks, I've been operating a radioactive decay based random event generator and tabulating the data to test for the effect reported in Shnoll et al [1]. Having accumulated several days' data, I reduced them by computing histograms for 10 minute experiments, each consisting of 100 measurements synchronised to the start of a minute. From these I generated smoothed histograms using an exponentially smoothed moving average with a smoothing constant of 0.2. These histograms were read by an analysis program which aligned them by their mean values and then computed a chi-square goodness of fit between the aligned curves. I did not normalise maximum values, adjust variance, or test matching of mirrored curves--all of these are easy to implement. All pairs of histograms were evaluated for closeness of fit and the results sorted from the closest match to most distant. One can then plot the time interval for histograms with the closest match (in this case, the closest 100,000 of a total of 1,738,442 pairs of histograms). (NOTE: Use your browser's back button to return here after viewing these figures.) Closest 100,000 pairs Yaaar!!! Look at that spike at zero! How similar it is to figures 3 and 4 of [2]. Indeed.... As a control, let's make the same plot for the the worst 100,000 matches of our experiment pairs: Worst 100,000 pairs Hmmm...pretty similar. If the effect were real, we'd expect the peak to be attenuated as we broaden the similarity criterion. Okay...let's make a plot of the *entire* data set--certainly this should suppress any "Cosmophysical" influence, as we're comparing every pair of experiments regardless of their closeness, however defined: All pairs Well, hello! Look at those textbook peaks at 0, 24, and 48 hours. What do they mean? Well, it's all really rather simple. Note that the 0 hour peak in all of the graphs, as well as figures 3 and 4 of [2] is almost always about twice the value of the neighbouring bars. This is a simple consequence of binning.
If one computes the number of hours difference between two experiments conducted at Unix time_t second values t1 and t2 as: hbin = (t1 - t2) / (60 * 60); then the zero bin will encompass all values between -3599 and +3599, while the rest of the bins will span only one hour instead of two. Consequently, the zero bin will have on the average twice as many samples, as shown in these examples and figured 3 and 4 in [2]. Periodicities at the 24 and 48 hour intervals are clearly apparent in the above-mentioned chart. These are readily explained by daily cycles of system administration. Histogram time distance statistics are sensitively dependent on regular cycles which interrupt the collection of data. In this case, I tended to shut down the data collection to run other programs around 18:00 UTC every day, and the histogram analysis fingered it. Finally, as a control (and to illustrate the effect of double-counting zero hour histograms), I prepared pseudorandom data for 10 days of experiments and processed it in the same manner yielding the plot: Pseudorandom data This make it clear how the delta-T = 0 spike is purely an artefact of double-counting assignments to the zero bin. With this in mind, take a look at: Shnoll assessment of GCP data Note how closely it resembles these plots--in particular how the central peak is close to twice that of the adjacent values. The crucial thing in reducing data from experiments of this type is to insist on absolute sign symmetry--binning ±0 into one slot will result in spikes around the zero point which are not present in the raw data. Having developed a modular toolkit for analysing experiments of this type, I'll try various other measures of closeness over the next few days. All of this, including source code, will eventually be posted once I beat it into something others can understand. If anybody on this list wants a copy of the code as it stands, let me know and I'll make a copy available to you. But beware...this is "ad hack" SGI/IRIX and Sun/Solaris code written without the slightest thought of portability. It'll probably work on most POSIX-like platforms, but if it doesn't, you're on your own; I typically budget three to six months for portability testing of Unix code I publish--we're working in real-time here, so I'll lighten up if you're willing to debug on your own. References: 1. http://ufn.ioc.ac.ru/ufn98/ufn98_10/ufn9810d.pdf 2. http://www.ufn.ru/ufn2000/ufn00_2/ufn002h.pdf Bierman letter describing Axford confirmation Date: Wed, 12 Jul 2000 12:43:59 +0200 From: Dick J Bierman To: John Walker , rdnelson Cc: nick herbert , Jack Sarfatti , Dean Radin , [email protected] Subject: Shnoll et al replications Hi to all, We just recieved a confirmation by W.I. Axford of the Max-Plank Aeronomy Institute, Lindau, Germany, that he has done an independent replication of the Schnoll effect. If I understand him correctly he just produced sets of histograms from two (random?) sources, removed all time information, randomized the order and then send them to Tatiana for human judgement. She then returned to them the pairs that were simlar and these turned out to be from simultaneous measurements.
No stats were given but from his words it seems the result is robust. (He is a bit worried about the stretching operation but that can't explain the results; it is worrying from a physics perspective though). So this is a good reason (at least for me) to become more optimistic and to invest a bit more time in getting the human judgement replaced by computerized judgement. The results produced by John suggest to me that the chi-2 isn't the measure that corresponds very well to their human scored similarity. I have been thinking about a bit different approach. The idea is that two patterns are more alike if the program that you have to write in order to transform one into the other is shorter. One possible instantiation of this idea might be to fit both histograms with a polynomial and then derive the transformation formula. Not simple but doable. There might be other and probably better ways to fit the histograms. Wavelet analysis comes to mind. Anybody ideas? Dick Pozharski: Notes on algorithmic efforts Date: Fri, 4 Aug 2000 09:11:11 +0200 From: Dick J Bierman To: Edwin Pozharski Subject: Re: human judging -> algorithm Thanks Edwin, I was considering neural nets too, so this mail saves me lots of time! Anyway, at this point I think we should go for presenting the algorithmic failures to the human judge and ask to do them again while thinking aloud. Also I would like to see test-retest reliability for the human procedure. As you noted in an earlier mail it not too difficult to get algorithmic similarity where we see it easily. We should therefore focus on the cases where the similarity is not obvious. My present feeling is that the human guess procedure introduces some paranormal effects. These can easily be sorted out because of their notorious lack of test-retest reliability. The problem of the latter approach is that we need the original human judge and much time. So we need to find somebody who is willing to spend his time on this. Or to find considerable amounts of money to hire such a person. Dick PS It might be a good idea to summarize somewhere what everybody tried so that we don't repeat the same 'mistakes'.
I did a.o. Fourier and Wavelet analysis on untransformed histograms. Wavelet could be tried again after transformation (stretching/shifting). Fourier analysis is rather unsensitive to these transformations. >Dick, > >once again sorry about the delay with response. Below is the same >description I sent to Dean Radin: I could write another one, but it >would basically be the same. > > >> Dean, >> >> I will describe briefly what I tried to do and then you will ask >> about particular details in case you will find it necessary. >> >> 1. Different measures of the correlation between histogram vectors >> itself. >> >> This is, probably, the very first thing which comes to everybody's >> mind. Of course, it doesn't work if you simply calculate correlation >> coefficient between the unperturbed histograms, since they are >> shifted, stretched and probably mirrored. The crucial improvement >> came when I optimized the scalar product (Dean of the university) by shift and stretch. >> >> 2. Neural nets. >> >> I tried backpropagation and Kohonen networks. Backpropagation failed >> completely being applied simply to the original histograms. It >> definitely needs some conversion of the histogram to the the set of >> parameters describing its shape, but I didn't find right one yet. Of >> course, this learning uses sets of pairs of similar histograms, >> pre-judged by human. The key point of Kohonen learning is how to find >> the winning pattern - I used the same optimized scalar product for it. >> Actually, it works as good as optimized scalar product itself - >> recognizes similar histograms pretty well but produces a little bit >> too much wrong pairing. But it gives one unteresting things - the >> set of patterns it finds in the whole dataset. >> >> 3. Converting histogram into a peak sequence. >> >> This approach is based on what we think is how human makes the >> judgement. What seems to be the most important is relative positions >> and heights of peaks which forms the general shape, or pattern, of the >> histogram. Dr Konradov is the one who spent more time trying this as >> I did, so in this particular case it makes sense to ask him. >> >> There was one guy in the past, who tried to fit histograms by >> polynomials ans then made cluster analysis on polynomial >> coefficients. >> >Ed. > >-- >Edwin Pozharski, PhD >Postdoctoral Fellow, Department of Biochemistry, >Northwestern University, Evanston, IL, 60208 >Email: [email protected] Radin analysis (not yet available) -------------------------------------------------------------------------------- Recent briefs by Shnoll and Kirillov From [email protected] Sat Nov 11 11:07:43 2000 Date: Sat, 28 Oct 2000 19:44:58 +0300 From: Simon Shnoll To: rdnelson Cc: Dick J Bierman , Jack Sarfatti , nick herbert , neil slade , Amit Goswami , C Levit , Faustin Bray , Lyle Fuller , Mark Comings , Marcello Truzzi , Paul Zielinski , Russell Targ , Saul-Paul Sirag , Shipi Shtrang , Tony Smith , Vladimir Poponin , zenchenko , [email protected], [email protected], John Walker , Hal Puthoff , John Alexander Subject: Re: Shnoll method, was Randomness in Amsterdam Dear Dr. Sarfatti, Dr. Nelson, and all colleagues, participating in summer 2000 Internet disscussion on Shnoll effect. 1. Continuation of the discussion initiated by Dr. Sarfatti on effects which we have found was delayed up to the end of SSE Meeting in Amsterdam 20-23 October. Now the Meeting is over. An important result of contacts in Amsterdam is the understanding that classic statistic "criteria of agreement of hypotheses" are inappropriate indeed for evaluation of similarity of thin structure of histograms because this is determined by certain cosmogonic influences but is not of probability nature. We have demonstrated before and during the Meeting that human judging of randomized histogram series gives quite objective and valid results. However this work is labour-consuming. Therefore the elaboration of first computer programs which can substitute human judging are most promising results obtained in our laboratory by M.Fedorov and A.Konradov recently.
The main manifestations of our phenomenon is replicated by new programs. 2. We had a fruitful discussion with Dr.J. Walker near computer with demonstration of our expert program, Histogram Manager and handed this to him. Unfortunately Prof. D.Bierman could not discuss our disagreements because pressing organizing obligations. 3. I have presented (See Add.#1) new results of our investigations carried out in our laboratory and simultaneously with : 1) prof. Axford and dr. Wilken in Max-Plank Institute fur Aeronomy in Lindau; 2) prof. L.Belousov in International Institute of Biophysics headed by prof. F. Popp in Neuss (Dusseldorf) and dr.V. Voeikov in Moscow State University. In the last case the precision of time resolution during histogram comparison was 1 min. As previously similar histograms in different places were appeared at the same local time. This allows to conclude that during Earth rotation each geographic point passes through heterogeneous space ( wjthin the range of middle latitudes) with scale of heterogeneity no more than 20 km. It can be also concluded that forces causing histogram patterns are outside solar system because cycle of repeated appearance of similar histograms is 23 h 56 min, i.e. «star day». 4. It was important that dr. A.Kirillov presented his theory of Space-Time which explains our phenomenon : Space-Time Fluctuations as a possible explanation of the «Shnoll Effect» (See Shannon). 5. We did not present our initial results of investigation of temporary rows, obtained generators in GCP before discussion with main authors who elaborated this system. As before we are ready for collaboration, Simon S. Shnoll Add.#1 Macroscopic fluctuations in processes of different nature as a result of cosmophysical (cosmogonic) causes. Possible heterogeneity (discretness) of space-time. S.E.Shnoll, T.A.Zenchenko, K.I.Zenchenko, M.V.Fedorov, E.V.Pozharskii, A.A.Konradov,I.M.Zvereva, V.A.Kolombet Moscow State University, Physics Department, Moscow; Institute of Theoretical and Experimental Biophysics, Russian Academy of Sciences, 142290 Puschino, Russia, E-mail: [email protected] 50-year study of dispersion in measurements the rates of different processes shows that this is not experimental error but manifestation of fluctuations caused by cosmophysical factors. Our original tool for investigation of dispersion in temporary rows is comparison the fine structure of histograms obtained from experimental time series. Histograms were obtained according to small non-overlapping successive segments of time series. The fine structure of histograms distinctly changes in time. The similar histograms are observed with high probability simultaneously in different processes and even at a great distances between points of measurements. This effect evidences cosmogonic phenomenon determining fluctuations in any process irrespective of its characteristic scale of energy. The phenomenon can be the result of fluctuations of four-dimensional space-time, related to non-uniformity (heterogeneity) of gravitational structure of the World.
During Earth rotation around its axis and along near solar orbit particular parts of earth surface are regularly exposed to different gravitational heterogeneity's and this is manifested in respective forms of histograms. The histogram patterns are like interferencional pictures and may be the result of interferention of coherent cosmogonic waves. The statements above are based on many year, long-term investigations (the first publication was in 1958) of different processes with careful discriminations of possible artifacts. Reviews of main results have been regularly published in Russian and English (see references in Physics-Uspekhi 41 (10) 1025-1035 (1998) ; 43 (2) 205-209 (2000)). The investigation of the phenomenon was started from biochemical reaction rates in the 50's, was continued in chemical reaction rates in the 70's and during last 20 years are carried out preferably with radioactive decay. The latter allows to exclude trivial "earth's" explanations of the observed effects. The general conclusions are based on the following experimental results. 1. High probability (p<10 -7 - 10 -8 ) of fine structure histogram similarity in the nearest, neigbouring time intervals : "near zone effect". 2. High probability of repeated appearance of similar histograms with periods near 24 hours, 27 days and a year. 3. High probability of histogram similarity at any given time in independent measurements of different processes in the same geographic point. 4. High probability of histogram similarity at the same LOCAL time in measurements of different processes in different geographic points. 5. Recent data showing that ascertained period of repeated appearance of similar histograms is 23 h. 56 min, i.e. star but not solar days. Most recent previous data will be also reported on our study of histograms obtained in time series in "egg-generators" of GCP - net. Add.#2 Space-Time Fluctuations as a Possible Explanation of the "Shnoll-Effect". A.Kirillov Inst. Of Applied Mathematics and Cybernetics Nighnii Novgorod, Russia [email protected] In gravitation theory it is assumed that at Planck scales spacetime acquires a foamlike structure as a result of quantum fluctuations. If we believe in the fact that our Universe had a quantum period of evolution in the past, then we should expect the existence of traces (relicts) of such fluctuations at macroscopic or even cosmological scales. In this report we show that a nontrivial quantum structure of our space at macroscopic scales (wich may be the result of the fluctuations we just pointed out) gives rise to a new fundamental phenomenon: spontaneous origin of an interference picture in every physical processes. This gives a possible explanation of the fine structure of histograms observed in radioactivity measurements (Shnoll Effect) wich, therefore, can possible serve as a test of the real structure of space.