a film about Stan [not a film review]

Berni Alder obituary in Nature [and the Metropolis algorithm]

When reading through the 15 October issue of Nature, I came across an obituary by David Ceperley for Berni Alder (1925-2020). With Thomas Wainwright, Alder invented the technique of molecular dynamics, “silencing criticism that the results were the product of inaccurate computer arithmetic.” 

“Berni Alder pioneered computer simulation, in particular of the dynamics of atoms and molecules in condensed matter. To answer fundamental questions, he encouraged the view that computer simulation was a new way of doing science, one that could connect theory with experiment. Alder’s vision transformed the field of statistical mechanics and many other areas of applied science.”

As I was completely unaware of Alder’s contributions to the field, I was most surprised to read the following

“During his PhD, he and the computer scientist Stan Frankel developed an early Monte Carlo algorithm — one in which the spheres are given random displacements — to calculate the properties of the hard-sphere fluid. The advance was scooped by Nicholas Metropolis and his group at the Los Alamos National Laboratory in New Mexico.”

that would imply missing credit is due!, but I could only find the following information on Stan Frankel’s Wikipedia page: Frankel “worked with PhD candidate Berni Alder in 1949–1950 to develop what is now known as Monte Carlo analysis. They used techniques that Enrico Fermi had pioneered in the 1930s. Due to a lack of local computing resources, Frankel travelled to England in 1950 to run Alder’s project on the Manchester Mark 1 computer. Unfortunately, Alder’s thesis advisor [John Kirkwood] was unimpressed, so Alder and Frankel delayed publication of their results until 1955, in the Journal of Chemical Physics. This left the major credit for the technique to a parallel project by a team including Teller and Metropolis who published similar work in the same journal in 1953.” The (short) paper by Alder, Frankel and Lewinson is however totally silent on a potential precursor to the Metropolis et al. algorithm (included in its references)… It also contains a proposal for a completely uniform filling of a box by particles, provided they do not overlap, but the authors had to stop at 98 particles due to its inefficiency.

Le Monde puzzle [#851]

A more unusual Le Monde mathematical puzzle:

Fifty black and white tokens are set on an equilateral triangle of side 9, black on top and white on bottom. If they can only be turned three by three, determine whether it is possible to produce a triangle with all white sides on top, under each of the following constraints:

• the three tokens must stand on a line;
• the three tokens must stand on a line and be contiguous;
• the three tokens must stand on the summits of an equilateral triangle;
• the three tokens must stand on the summits of an equilateral triangle of side one.

I could not think of a quick fix with an R code so leave it to the interested ‘Og reader… In the next issue of the Science&Médecine leaflet (Jan. 29), which appeared while I was in Warwick, there were a few entries of interest. First, the central article was about Big Data (again), but, for a change, the journalist took the pain to include French statisticians and machine learners in the picture, like Stefan Clemençon, Aurélien Garivier, Jean-Michel Loubes, and Nicolas Vayatis. (In a typical French approach, the subtitle was “A challenge for maths”, rather than statistics!) Ignoring the (minor) confusion therein of “small n, large p” with the plague of dimensionality, the article does mention a few important issues like distributed computing, inhomogeneous datasets, overfitting and learning. There are also links to the new masters in data sciences at ENSAE, Telecom-Paritech, and Paris 6-Pierre et Marie Curie. (The one in Paris-Dauphine is still under construction and will not open next year.) As a side column, the journal also wonders about the “end of Science” due to massive data influx and “Big Data” techniques that could predict and explain without requiring theories and deductive or scientific thinking. Somewhat paradoxically, the column ends up by a quote of Jean-Michel Loubes, who states that one could think “our” methods start from effects to end up with causes, but that in fact the models are highly dependent on the data. And on the opinion of experts. Doesn’t that suggest some Bayesian principles at work there?!

Another column is dedicated to Edward Teller‘s “dream” of using nuclear bombs for civil engineering, like in the Chariot project in Alaska. And the last entry is against Kelvin’s “to measure is to know”, with the title “To known is not to measure”, although it does not aim at a general philosophical level but rather objects to the unrestricted intrusion of bibliometrics and other indices brought from marketing. Written by a mathematician, this column is not directed against statistics and the Big Data revolution, but rather the myth that everything can be measured and quantified. (There was also a pointer to a tribune against the pseudo-recruiting of top researchers by Saudi universities in order to improve their Shanghai ranking but I do not have time to discuss it here. And now. Maybe later.)