The aim of this project is to trace back the scientific lineage of “all” mathematicians throughout history. The main link considered is the “student – advisor” relationship, though sometimes looser relations are taken into account like “student – professor” (for mere lectures or mail correspondence, even when a PhD or a specific degree is not at stake) or even brotherhood (as for the Bernoulli brothers — Jacob and Johann -  who actually worked hand in hand during their studies)

Here, I tried to follow my scientific legacy starting with my PhD advisor Dominque Jeulin, his own advisor Jean Serra, himself having had Georges Matheron as advisor, and so on…

Mathematical lineage (click on image to get the full version)

Lévy, Hermite, Chasles, Poisson, Laplace, Lagrange, Euler… not so bad :) . On the  generated tree, I stopped at the Bernoulli brothers but happily a big part of this tree is common with John D. Cook’s which goes further back, notably passing by Erasmus and Mersenne.

Along with the links  also displayed the corresponding years and countries to get a grasp of what kind journey into time and space the whole thing amounted to. The “looser links” are also represented with dashed arrows and a “~” prefixed to the year.

Interestingly, this kind of genealogical info is also available for other scientific domain for instance on academictree.org

 Do it yourself !

For those who would like to do the same:

• the Python script generating the full graph (in SVG) taking a “simple” text file as input
• the text file with my genealogical data that gives an idea of the format expected by the script¹

1. and yes the data was collected by hand, which is probably one of the most interesting part of the experiment with all the wikipedia checking that curiosity requires along the way

Whatever its (lame) scientific significance, this will remain the very first book I’ve ever written, and, what’s more one that took me 3 years of hard work to gather its content.

After waiting 3 more years for it to come out the “confidential” closet in which it had been put, this is afterall a nice occasion to rejoice.

Hail the beast: Modelisation and prediction of the topography of flat carbon steel[fr] (abstract in english)

And while we’re at it some more details about my past crimes are also available on my former lab’s website.

For the celebration to be even more special I’ve also made available online a description of an image correction technique I’ve developed during my PhD. Half-way between research work¹ and an engineering work² this technique hardly found any place for publication in scientific reviews.

So, I decided to give it its chance by setting it free in the wild territory of reproducible research. That’s why, at the other end of the following link you’ll find not only  detailed explanations about the techniques, not only examples of its application on real life images but also and above all (Python) scripts making it possible to test it on just about any image:
Polynomial Form Correction with Autocorrelation

1. because the purpose was to find a problem that has proved difficult to solve with modern techniques and still had no solution at the time
2. because  it worked (sorry :) ) the solution consisted in a tiny piece of heuristics wrapped around some well-known and old-school mathematical tools

I’m trying something new today: a small experiment falling in between programming and scientific litterature.

I’ve been interested in “literate programming” for quite some time now, without going any further though. But I recently found the tools that would make it actually practicable and decided to dive in.

So, I’ve implemented a very small Python module that illustrates the notion of autocovariance and autocorrelation and the way to compute them via Fourier transforms:

Literate Musing > Autocorrelations

The subject is not new at all, but such measures are something that I had to re-implement (for image processing puproses) more than I’d like to in past years, and each possible implementation having its own statistical biases, it always takes time to remember all the tricky details. So, this new document should at least help (me) quickly find all the relevant info.

This was also a subject underpinning a big part of my PhD work… a PhD that I defended 2 years ago, is there some “causation” here ?

:)

For the technical side of things, the “tools” that helped me setup this “experiment” are essentially:

• Python
• pylit
• sphinx

But first of all a great thanx to Miro’s developer that did a great job in tackling the two major problems I had with the software: impossibility to navigate the list of feeds, while playing a video (or audio) and performance. The only remaining thing on my whishlist would be that Miro handles image feeds (ala CoolIris for instance).

Now for the list of podcasts I listen to, most of them are in french, so here is a quick selection among the english speaking ones:

• ESOCast some info on the southern observatory in Chile
• Martin Bailey Photography Podcast: nice podcast about Martin’s work

• Jeff Curto’s Camera Position : about the “creative side” of photography

• TED Theme: Tales of Invention : high-tech fancy conferences

For a more exhaustive list see my OPML subscription file: miro_subscriptions

Du coup, je suis tombé sur quelques ressources assez concises qui valent le coup d’être partagées:

• le lien (obligatoire) vers Wikipedia à propos de la polarisation de la lumière (une bonne intro mais qui n’explique pas trop ce qu’il se passe en pratique pour la photographie)
  
• la vidéo d’une expérience de TP de lycée sur la lumière polarisée (assez succinct mais qui illustre très bien les phénomènes qui m’intéressent ici)
• une page qui complète bien les précédentes avec des explications plus ciblées autour des propriétés de la lumière et des matériaux exploitées en photo (grâce à cet article assez bien fichu ma mémoire est complètement rafraichie :) )

Pour aller plus loin, il y a aussi un bouquin que j’ai eu brièvement entre les mains et qui m’a semblé à la fois très complet et tout de même assez accessible (avec un prérequis sensiblement plus élevé que les trucs précédents toutefois) :

La couleur – Lumière, vision et matériaux — Mady Elias , Jacques Lafait, et al.
(Coll. Echelles, Ed. Belin)

Franchement dans quel monde vit-on ?

… à dessiner

•  oL(Ga) s’est mis à faire des “pas à pas” pour apprendre à dessiner avec un crayon dans une main et un ordi dans l’autre.
• Rolf Steinort vous montre comment retoucher des images avec Gimp. Moi ça m’apprend plein de truc sur Gimp déjà, mais aussi, au passage, quelques trucs sur la photo¹ : que du bon !
•  Les “screencasters” vous apprennent à faire des purs dessins avec Inkscape, un logiciel de dessin vectoriel particulièrement performant et simple à utiliser.

… un peu de tout

L’ENS (aka Ulm) diffuse son savoir en retransmettant plusieurs conférences sous forme de podcast. Ça couvre plein de domaines² et c’est un bon moyen de se mettre au courant des dernières découvertes, avancées, etc

Le collège de France n’est pas en reste et retransmet ses cours. C’est évidemment très intéressant, et le meilleur c’est quand des cours relevant de plusieurs disciplines viennent aborder un même sujet comme récemment à propos de la notion d’autorité³.

1. bon ça c’est vrai que c’est pas très dur vu qu’à la base j’y connais rien
2. vous saviez ce que c’était, vous, l’ethnopoiétique ? Ben moi maintenant je sais… enfin je crois… bah je sais que ça existe en tout cas, na !
3. aller savoir pourquoi ils ont choisi ce thème, c’est pas comme si c’était à la mode, s’pas ? ;)