Archive for the Kids Category

Le Monde puzzle [open problem]

Posted in Books, Kids with tags , , , , , on October 23, 2017 by xi'an

What should have been the last puzzle in Le Monde competition turned out to be an anticlimactic fizzle on how many yes-no questions are needed to identify an integer between 1 and 1025=2¹⁰+1 and an extension to replies possibly being lies

What is much more exciting is that voting puzzle #1021 got cancelled because the authors of this puzzle thought the cascading majority rule would produce the optimal solution and it does not! (As exhibited by my R code.) So here is an open problem to ponder about! (And another puzzle in the pipeline to complete the competition.)

postdocs positions in Uppsala in computational stats for machine learning

Posted in Kids, pictures, Statistics, Travel, University life with tags , , , , , , , , , , , on October 22, 2017 by xi'an

Lawrence Murray sent me a call for two postdoc positions in computational statistics and machine learning. In Uppsala, Sweden. With deadline November 17. Definitely attractive for a fresh PhD! Here are some of the contemplated themes:

(1) Developing efficient Bayesian inference algorithms for large-scale latent variable models in data rich scenarios.

(2) Finding ways of systematically combining different inference techniques, such as variational inference, sequential Monte Carlo, and deep inference networks, resulting in new methodology that can reap the benefits of these different approaches.

(3) Developing efficient black-box inference algorithms specifically targeted at inference in probabilistic programs. This line of research may include implementation of the new methods in the probabilistic programming language Birch, currently under development at the department.

MUDAM

Posted in Books, Kids, pictures, Travel with tags , , , , , , , on October 22, 2017 by xi'an

As our son is doing an internship in Luxembourg City this semester, we visited him last weekend and took the opportunity to visit the Museum of Modern Art (or MUDAM) there. The building itself is quite impressive, inserted in the walls of the 18th Century Fort Thüngen designed by Vauban, with a very luminous and airy building designed by Ming Pei. The main exhibit at the MUDAM is a coverage of the work on Su-Mei Tse, an artist from Luxembourg I did not know but whom vision I find both original and highly impressive, playing on scales and space, from atoms to planets… With connections to Monet’s nympheas. And an almost raw rendering of rock forms that I appreciate most particularly!

The bottom floor also contains an extensive display of the political drawings of Ad Reinhardt, who is more (?) famous for his black-on-black series…

splitting a field by annealing

Posted in Kids, pictures, R, Statistics with tags , , , , , , , , on October 18, 2017 by xi'an

A recent riddle [from The Riddle] that I pondered about during a [long!] drive to Luxembourg last weekend was about splitting a square field into three lots of identical surface for a minimal length of separating wire… While this led me to conclude that the best solution was a T like separation, I ran a simulated annealing R code on my train trip to AutransValence, seemingly in agreement with this conclusion.I discretised the square into n² units and explored configurations by switching two units with different colours, according to a simulated annealing pattern (although unable to impose connectivity on the three regions!):

partz=matrix(1,n,n)
partz[,1:(n/3)]=2;partz[((n/2)+1):n,((n/3)+1):n]=3
#counting adjacent units of same colour 
nood=hood=matrix(4,n,n)
for (v in 1:n2) hood[v]=bourz(v,partz)
minz=el=sum(4-hood)
for (t in 1:T){
  colz=sample(1:3,2) #picks colours
  a=sample((1:n2)[(partz==colz[1])&(hood<4)],1)
  b=sample((1:n2)[(partz==colz[2])&(hood<4)],1) 
  partt=partz;partt[b]=colz[1];partt[a]=colz[2] 
#collection of squares impacted by switch 
  nood=hood 
  voiz=unique(c(a,a-1,a+1,a+n,a-n,b-1,b,b+1,b+n,b-n)) 
  voiz=voiz[(voiz>0)&(voiz<n2)] 
  for (v in voiz) nood[v]=bourz(v,partt) 
  if (nood[a]*nood[b]>0){
    difz=sum(nood)-sum(hood)
    if (log(runif(1))<difz^3/(n^3)*(1+log(10*rep*t)^3)){
      el=el-difz;partz=partt;hood=nood     
      if (el<minz){ minz=el;cool=partz}
  }}}

(where bourz computes the number of neighbours), which produces completely random patterns at high temperatures (low t) and which returns to the T configuration (more or less):if not always, as shown below:Once the (a?) solution was posted on The Riddler, it appeared that one triangular (Y) version proved better than the T one [if not started from corners], with a gain of 3% and that a curved separation was even better with an extra gain less than 1% [solution that I find quite surprising as straight lines should improve upon curved ones…]

never let me go [book review]

Posted in Books, Kids, pictures, Travel with tags , , , , , , , , , on October 15, 2017 by xi'an

Another chance occurrence led me to read that not so recent book by Kazuo Ishiguro, taking advantage of my short nights while in Warwick. [I wrote this post before the unexpected Nobelisation of the author.] As in earlier novels of his, the strongest feeling is one of melancholia, of things that had been or had supposed to have been and are no longer. Especially the incomparable The Remains of the Day… In the great tradition of the English [teen] novel, this ideal universe is a boarding school, where a group of students bond and grow up, until they face the real world. The story is told with a lot of flashbacks and personal impressions of the single narrator, which made me uncertain of the reality behind her perception and recasting. And of her role and actions within that group, since they always appear more mature and sensible than the others’. The sinister features of this boarding school and the reasons why these children are treated differently emerge very very slowly through the book and the description of their treatment remains unclear till the end of the book. Purposely so. However, once one understands the very reason for their existence, the novels looses its tension, as the perpetual rotation of their interactions gets inconsequential when faced with their short destinies. While one can get attached to the main characters, the doom awaiting them blurs the relevance of their affairs and disputes. Maybe what got me so quickly distanced from the story is the complacency of these characters and the lack of rebellion against their treatment, unless of course it was the ultimate goal of Ishiguro to show that readers, as the “normal” characters in the story, would come to treat the other ones as not completely human… While the final scene about souvenirs and memories sounding like plastic trash trapped on barbed wires seems an easy line, I appreciated the slow construct of the art pieces of Tommy and the maybe too obvious link with their own destiny.

When searching for reviews about this book, I discovered a movie had been made out this book, in 2011, with the same title. And of which I had never heard either..! [Which made me realise the characters were all very young when they died.]

what is your favorite teacher?

Posted in Kids, Statistics, University life with tags , , , , , , , , on October 14, 2017 by xi'an

When Jean-Louis Foulley pointed out to me this page in the September issue of Amstat News, about nominating a favourite teacher, I told him it had to be an homonym statistician! Or a practical joke! After enquiry, it dawned on me that this completely underserved inclusion came from a former student in my undergraduate Estimation course, who was very enthusiastic about statistics and my insistence on modelling rather than mathematical validation. He may have been the only one in the class, as my students always complain about not seeing the point in slides with no mathematical result. Like earlier this week when after 90mn on introducing the bootstrap method, a student asked me what was new compared with the Glivenko-Cantelli theorem I had presented the week before… (Thanks anyway to David for his vote and his kind words!)

Statistics versus Data Science [or not]

Posted in Books, Kids, Statistics, University life with tags , , , , , , , , on October 13, 2017 by xi'an

Last week a colleague from Warwick forwarded us a short argumentation by Donald Macnaughton (a “Toronto-based statistician”) about switching the name of our field from Statistics to Data Science. This is not the first time I hear of this proposal and this is not the first time I express my strong disagreement with it! Here are the naughtonian arguments

  1. Statistics is (at least in the English language) endowed with several meanings from the compilation of numbers out of a series of observations to the field, to the procedures proposed by the field. This is argued to be confusing for laypeople. And missing the connection with data at the core of our field. As well as the indication that statistics gathers information from the data. Data science seems to convey both ideas… But it is equally vague in that most scientific fields if not all rely on data and observations and the structure exploitation of such data. Actually a lot of so-called “data-scientists” have specialised in the analysis of data from their original field, without voluntarily embarking upon a career of data-scientist. And not necessarily acquiring the proper tools for incorporating uncertainty quantification (aka statistics!).
  2. Statistics sounds old-fashioned and “old-guard” and “inward-looking” and unattractive to young talents, while they flock to Data Science programs. Which is true [that they flock] but does not mean we [as a field] must flock there as well. In five or ten years, who can tell this attraction of data science(s) will still be that strong. We already had to switch our Master names to Data Science or the like, this is surely more than enough.
  3. Data science is encompassing other areas of science, like computer science and operation research, but this is not an issue both in terms of potential collaborations and gaining the upper ground as a “key part” in the field. Which is more wishful thinking than a certainty, given the existing difficulties in being recognised as a major actor in data analysis. (As for instance in a recent grant evaluation in “Big Data” where the evaluation committee involved no statistician. And where we got rejected.)