Archive for wavelets

chain of lynx and drove of hares

Posted in Books, Kids, pictures, R, Statistics with tags , , , , , , , , , on February 27, 2020 by xi'an

A paper (and an introduction to the paper) in Nature this week seems to have made progress on the existence of indefinite predator-prey cyles. As in the lynx/hare dataset available on R. The paper is focusing on another pair, an invertebrate and its prey, an algae. For which the authors managed a 50 cycle sequence. What I do not get about this experiment is how the cycle can be tested via a rigorous statistical experiment.

“…the predator–prey system showed a strong tendency to return to the dominant dynamical regime with a defined phase relationship. A mathematical model suggests that stochasticity is probably responsible for the reversible shift from coherent to non-coherent oscillations, a notion that was supported by experiments with external forcing by pulsed nutrient supply.”

As I had not renewed my subscription to Nature in time, I could not check the additional material for details, but the modelling seems to involve a wavelet decomposition of the bivariate time series, with correlations between the two series…

machine learning à l’Académie, au Collège, et dans Le Monde

Posted in Books, Statistics, University life with tags , , , , , , , , on January 5, 2018 by xi'an

A back-cover story in Le Monde “Sciences & Médecine” of Stéphane Mallat, professor at École Normale and recently elected at the (French) Academy of Sciences and at the Collège de France, on a newly created Chair of Data Sciences.  With works on wavelets, image compression, and neural networks, Stéphane Mallat will give his first lesson on Data Sciences at Collège de France, downtown Paris, on January 11. Entrance is free and open to everyone. (Collège de France is a unique institution, created by Guillaume Budé and supported by François Ier in 1530 to teach topics not taught (then) at the Sorbonne, as indicated by its motto Docet Omnia, including mathematics! Professors are nominated by the current faculty and the closest to statistics, prior to Stéphane Mallat, was Edmond Malinvaud.)

Paris-Dauphine in Nature

Posted in Statistics with tags , , , , , , , , , on April 25, 2017 by xi'an

Since this is an event unlikely to occur that frequently, let me point out that Université Paris-Dauphine got a nominal mention in Nature of two weeks ago, through an article covering the recent Abel Prize of Yves Meyer and his work on wavelets through a collection of French institutions, including Paris-Dauphine where he was a professor in the maths department (CEREMADE) from 1985 till 1996. (Except for including a somewhat distantly related picture of an oscilloscope and a mention of the Higgs boson, the Nature article is quite nice!)

Abel Prize goes to Yves Meyer

Posted in Books, pictures, University life with tags , , , , , , , , on March 21, 2017 by xi'an


Just heard the great news that the Abel Prize for 2017 goes to Yves Meyer! Yves Meyer is an emeritus professor at École Normale de Cachan and has produced fundamental contributions to number theory, operator theory and harmonic analysis. He is one of the originators of the theory of wavelets and multiresolution analysis. Among other recognitions and prizes, he was an invited speaker at the International Congress of Mathematicians in 1970 (Nice), in 1983 (Warsaw), and in 1990 (Kyoto), and was awarded the Gauß Prize in 2010. Congratulations and total respect to Yves Meyer!!!


reading classics (#8)

Posted in Books, Kids, Statistics, University life with tags , , , on January 23, 2014 by xi'an

La Défense from Paris-Dauphine, Nov. 15, 2012The Reading Classics Seminar today was about (the classic) Donoho and Johnstone’s denoising through wavelets, a 1995 JASA paper entitled Adapting to unknown smoothness via wavelet shrinkage. Two themes (shrinkage and wavelets) I discovered during my PhD years. (Although I did not work on wavelets then, I simply attended seminars where wavelets were the new thing.) My student Anouar Seghir gave a well-prepared presentation, introducing wavelets and Stein’s estimator of the risk. He clearly worked hard on the paper. However, I still feel the talk focussed too much on the maths and not enough on the motivations. For instance I failed to understand why the variance in the white noise was known and where the sparsity indicator came from. (This is anyway a common flaw in those Reading Classics presentations.) The presentation was helped by an on-line demonstration in Matlab, using the resident velvet code. Here are the slides: