The US is not the greatest country in the world. Is France?


Yesterday I read this piece by Shaun King. I know a few countries that, viewed from the outside, look obsessed by the idea that they are “the best place in the world”. Even without going that far, many countries like to brag (to outsiders too, but mostly to themselves) that they have the best [insert your answer here] in the world. French people have a reputation for being pretentious, but also for complaining a lot about their homeland. Today I will indulge in the latter. We all know what feels good about our countries, but do we know about what’s really bad about them?

“The 1st step to solving a problem is to recognise that there is a problem.” In this post, I will just go through the same topics as King did and discuss the ranking of France through some indicators. What are we good at, and what should we be really focusing on improving right now?

Disclaimer 1: Some people’s go-to answer to any nation-based criticism is “Leave if you don’t like it here.” A. Criticising does not mean you hate a place, and B. I already left, I don’t live in France anymore. As far as I can tell, that did not make our county’s issues suddenly disappear…

Disclaimer 2: Yes, yes, I am addressing my fellow countrypeople, I should write in French. But I found that international opinions seem to count more for a certain breed of people than criticism from the inside…


Prisons and police

France ranks 147th out of 222 countries for prison population rate.The website prisonstudies has handy tools to navigate prison data. It’s not a terrible score, but it’s nothing to be proud of either. We have been putting more and more people in prison since 1992; in 2011 we had 102 people in prison per 100 000 inhabitants. More than 1 in 5 of these prisoners are not even convicted people; they are just waiting for a trial. But where we have a terrible record is on how we overstuff our prisons, putting people in really bad conditions – our prisons are running at 116.6% capacity, and the state has been repeatedly condemned for mistreatment, and even for not respecting basic human rights by the European Court of Human Rights. Not really what you would expect when you think “France”. Then there is the issue of police violence. It’s nothing comparable to what has been happening in the US, but who would take that as a standard? Tensions between the police and poorer suburbs are no state secret, sometimes culminating in assault in death. The thing is… we have official numbers on violence against police, but none about the violences committed by police. We do have emblematic cases of people, overwhelmingly POC, assaulted or killed by the police. Tensions between the police and the people are never a good sign; no numbers about police violence is not a good sign; and what’s really a bad sign is that we have been criticised by Amnesty International (fr) for the impunity of our policemen when they do commit violent acts on people. About 20 people are killed by the police every year, but there are rarely any consequences for the policemen, and that is what is criticised by Amnesty.

France’s prisons and police: Not outrageous, but lots to improve.


Health care

99.9% of the French have health insurance. In its study of health systems around the world, the World Health Organisation says: “France provides the best overall health care followed among major countries by Italy, Spain, Oman, Austria and Japan”. Wow! That’s something to be happy about. Being sick in France won’t make you bankrupt, although I do find the dentist expensive. French people have been worried for years about the future of their healthcare: “le trou de la secu”, the hole in the health system budget. But things are looking good as this deficit is planned to reach its lowest level in 2017. Issues with health care can rather be found in the working conditions of doctors (in 2005 France was found not to respect the limits on working time fixed by Europe),  in the slowly dwindling numbers of doctors (fr), and in issues linked to gender: no official study here, but recent scandals have shown that women face gender-specific discrimination by medical professionals  (this is unfortunately hardly a French specificity).

French Health Care: I’ve had a number of astonishingly bad experiences, but who am I to argue with the WHO? “Best overall health care”! Congrats, France.



The PISA survey data can be found here. Compared to other countries in the OECD, we’re doing average in Science, and good in reading and gender equity. We’re doing really bad in equity between social backgrounds though, so bad in fact that we were ranked last in 2015. This report by the National Agency that evaluates French school system actually says that school is one of the causes of social inequalities. Don’t be born poor, and if you do, silly you, don’t go to a French school.

French Education System: average, but rife with inequalities.


Income inequality

In 2008, France was one of 5 OECD countries where income inequality was steadily decreasing (pdf). In 2013, we were ranked on a par with Germany and Hungary, still better than average but worse than in 2008. On the other hand, the gender wage gap of 14% had not budged since the 2000s, while it was decreasing from 18 to 15% in the whole OECD. At that rate we will soon be worse than the average, all because we haven’t evolved in 17 years.

French income inequality: Slightly better than average, but overall getting worse.


Quality of life

It’s no secret that French people used to pop up more antidepressant than anyone else in the world, but that’s not the case anymore. Let’s have a look at the Happiness Report: France ranks 31st out of 155 countries. Not bad at all! For a nation self-described as a country of whiners. The report explains French happiness in equal parts by the fact that they live in a rich country and by the strength of their social networks. We also have good life expectancy. However, we are apparently growing less and less happy, with one of the worse progression of the report, 24 places away from Venezuela (the worst progression). We’re still one of the most attractive countries for tourists although we are not the most competitive place for travel (pdf), we’re 2nd behind Spain. To be fair, Spain is where French people go when they need a break! It is worth noting that in terms of traveler safety (see also previous link), we are not even in the top 20. I don’t think a single tourist will be surprised by this news – it’s the most common complaint I hear.

French life: We’re getting less happy, but hey, at least we’re popping less pills. And it’s not that bad overall, especially if you are a traveler (but hold on your wallet).



There topics were not in King’s piece, which makes sense because his was tailored for the US. France ranks as one of the worst European countries for English language proficiency, doing only slightly better than Japan on the world ranking. We rank best at preventing “preventable deaths” thanks to timely and effective care. We’re 39th on the free press ranking, good but still our worse position since 2013.

The weight of the cow: Dealing with bias in datasets

One of the best science books I read this year is “Superforcasters”, by Philip Tetlock. The story of how this book got to be written is just about as fascinating as the book itself, and I strongly recommend it to both scientists and non scientists.

Today I would like to talk about something that I am surprised wasn’t discussed in the book. As many posts on this blog, this is something that may or may not be an original idea: all I know is that it occurred to me and I thought it was worth sharing, and I haven’t heard of it in the forecasting world.

How to pool forecasting data to reduce bias

Early in the book, Tetlock gives an example of the “wisdom of the crowd”. At a fair, people are asked to guess the weight of a cow. Taken individually, some guesses are quite far from the real value. But the average of all the guesses turns out to be almost exactly equal to the real weight of the cow.

He uses that example to illustrate the fact that individual people can be biased, but when you average all the guesses, the biases cancel each other. Imagine each guess as having two components: the signal and the noise. The signal represents all the valid reasons why a person might have a good estimation of the weight of the cow: they know what is a cow and what things weight in general, and maybe this particular person grew up in a farm and knows really well about cows, or maybe they’re a weightlifter and knows really well about weights. The noise represents all the reasons why they might be biased. Maybe they’re a butcher and often overestimate the weight of what they sell to increase the price. Maybe they raise pigs and underestimated the weight of the cow because it’s so much bigger than a pig.

By averaging all the guesses, you are making strong assumptions.
You assume that people’s biases are opposite and equivalent.
Therefore by averaging, the noise components should cancel each other out and you should be able to get only the signal: the wisdom of the crowd.


These are reasonable assumptions that are used by default in many different fields. Noise is supposed to be random, while the signal contains information about something, therefore not is not random. Most people know what a cow is and most people know roughly what things weight. But in the case of human-driven forecasting, these assumptions are not perfect.
1. There is no reason why the bias should be evenly distributed. (In sciency terms: the probability distribution of the noise might not be a uniform distribution). If your crowd is made of 30 cheating butchers (overestimating weights) and 10 greedy clients (underestimating weights), your biases may be opposite but they are not evenly distributed. Even if the clients bias happen to be exactly opposite to the butchers bias, averaging the 40 guesses will not give you the right answer, because you have many more butchers in your population. It will give you an overestimated weight. Instead you should pool the data: average the clients guesses (pool A), average the butchers guesses (pool B), and then take the average of the results of of pool A and B.
2. There is no reason why the biases should be exactly opposite. (The distribution of the noise might not be 0-mean).
Ideally, you would know by how much butchers tend to overestimate (say on average +5% of the total weight) and by how much the clients tend to underestimate (say -10%). If you have this information, you can use it to weight your pooled data before putting them together. In this example, you would want to give less weight to the clients pool because you know that usually, their bias is higher than that of the butchers.

So if you have some forecasting data and you want to get the best forecast out of it, there are two things you should do before making averages.
First, identify all possible sources of biases and form pools based on this information. Repeat this step as many times as needed for different repartitions. If you are doing political forecasting, people might be biased in favor of their candidate: divide your data in political parties (repartition A). If women tend to have different political biases than men for some reason, identify that reason and divide your pool in men and women (repartition B). The more (verifiable) causes for bias you can find, the more you will be able to cancel them out.
Second, quantify the bias so you can attribute weights to your pools. For that you will have to rely on previous data and make guesses.
Finally, make your averages per repartitions. You will have as many averages as the number of repartitions you made. You can decide to make a final average out of this data, or you can go meta and give weights to your repartitions. If you are quite sure that repartition A captures a real cause for opposite biases, but less sure about B, give less weight to B.

Now of course, it should be noted that it isn’t always worth doing all this work. If you have data about guessing a cow’s weight, maybe just do a simple average. The result might be good enough and the extra work is not worth your time.
But if you are gathering data about whether country A and country B are going to start a war in the next 3 months, it might be worth putting a little bit more effort into pooling your data. It doesn’t have to be data directly produced by people: it can be governmental data, numbers from different agencies, it can be you trying to predict what people you know will do next… There is always place for bias anyway.
In addition, clearly identifying the source of bias in your data allows you to notice what data may be missing (if all your pools are biased in the same direction), and it allows you to update your forecasts efficiently. When you come into possession of new data, it can be hard to decide how much it should change your original forecast. But if you can readily identify to which pools the data belongs, updating is much easier.

Happy forecasting! The Good Judgement Open project is a good place to start (you don’t have to be a scientist at all, just give your opinion).

Deep Irrationality Cares About Facts


Bansky – Geisha from “Better in than out” – Wikimedia commons


I think all decisions we take are based on irrationality — but this irrationality can appear at different levels.

Paradoxically, when irrationality is at a deep level, opinions are easier to change. When irrationality is at the very last of the decision process, it is very difficult, if not impossible to operate change.

One of my most irrational opinions is that I love living in Japan. It is irrational in the sense that there is no reason for that opinion. I can make up reasons, I can rationalize my decision to live here: It’s a good place to do AI research, it’s full of good people, it has nice scenery.
But I knew none of that when I first visited here at 19 yo, and decided that I wanted to live here. This irrationality drove, from the top, my decision process; it is therefore difficult to convince me of changing my decision by giving me reasons why I should not love Japan.

Take someone who loves pasta. “I don’t think it tastes particularly good, and I know it’s a bit boring, but I just love pasta.”
You could tell them that spaghetti gives cancer and is made from dead skunks, they would have a hard time just starting to hate spaghetti.
Now take someone whose irrationality is much deeper in the decision process. “I love food that is made of wheat, and I heard my great grandpa was Italian, so I love spaghetti.” If you convince them that spaghetti isn’t made of wheat and that their great grandpa was Irish, they might just lose all interest for pasta.
You can change “love” by “hate”, “fear”, or any other emotion in the above examples. The point is that the level at which your emotions guide some of your decision processes determines how much you can be swayed by new facts.

And as much as I don’t understand America’s recent choice of president, I think that might be part of it. No amount of facts about that candidate could convince his followers that he wasn’t to be trusted. Maybe they are just in love with him. He says something and they interprete it as what they want to hear, and that is enough. They say he is actually a good person, a smart man, a competent businessman, that he respects woman and is not racist. Even when he himself says the contrary.

Of course, my decision to live in Japan is not supposed to destroy the life of millions of people. And maybe all of Trump voters actually made a rational choice, which is both extremely scary and leaves place to hope. But the way that election unfolded suggest that their choice is not based on facts, and therefore as irrational as can be.

Next year are the German and French elections, and a survey already revealed that the majority of French people would have chosen Trump if they could vote in the US elections.
What can be done? Unfortunately I do not have an answer.

For the past few years I have tried to deal with the worst of my irrational opinions, and the news are not good. Facts do not work. Pushing contradictory emotions does not work. Taking a step back has mixed results, but how do you force people to take a step back from politics? Replacing one irrational behaviour by another might work, from what I hear. I have not tested it. But if it is true, does it mean that the Trump crowd could only have been swayed by a candidate that they find more charismatic?
Given what their idea of charisma seems to be, I don’t know what a “more charismatic” candidate would have looked like.

Maybe we will discover it next year. Or maybe we will have to bow to President Le Pen.

New York Public Library’s Fantastic Data

As you know, my passions in life are food and food. This blogpost is therefore about the taxonomy of window panes.

Sorry. This post is about FOOD! NYPL made a heap of information about New York food open access here; it’s the dataset I used to train my crazy twitter bot (@CrazyPoshCook), and you can find more examples of the bot’s output at the end of this post.

In their crowd-driven project, NYPL numerized data about more than 17 000 menus from NY restaurants between 1851 and 2012. There are regular menus, menus for special events, cruise menus… I delved into the data because I’m a serious scientist, not at all because I was hungry and bored. Here we go!

I first looked at which dish appeared more often in New York’s restaurants. To the surprise of no one, the most common menu item is… Coffee, with 8487 apparitions! In second position comes Tea (4769), then more surprisingly, Celery (4247), Olives (4554) and Radish (3349). NY people, you’re officially weird. The followers are somewhat more expected: mashed potatoes, milk, and boiled potatoes. Wait, who orders plain milk at a restaurant??


There are lots of items that appear only once, mostly dishes with really long names. Some dishes have a negative number of citations (that’s human error), like the awesome “Clam Fry (with Bacon)” from a 1914 menu (“-4” citations!).

The same kind of error popped up when I looked for the dish with the shortest name: it seems to be the mysteriously named “&”, which appeared on 4 different menus in 1901.
The dish with the longest “name” is a long ramble about tea that manages to include references to Elizabeth II and Lewis Caroll:

Afternoon Tea- A Great British Tradition- Tea, the most universally consumed of all drinks, is especially popular in Britain where the annual consumption is something in the region of 512 million cups. W. E. Gladstone observed “If you are cold, tea will warm you- if you are heated, it will cool you- if you are depressed, it will cheer you- if you are excited, it will calm you.” First brought to England c. 1559 by Giambattista Rusmusio, tea did not evolve into an afternoon meal until the end of the 18th century. Anna, Duchess of Bedford, invented afternoon tea to fill the long gap between early lunch and dinner which bored many house parties. It became a meal surrounded by etiquette and customs, delicate china, silver, cake stands and doilies- a time when friend and family meet. Famous tea parties include Mad Hatter’s (Alice’s Adventures in Wonderland by Lewis Carroll 1865), the Boston Tea Party, 1773, and not forgetting HM Queen Elizabeth II’s annual garden parties at Buckingham Palace. The Duke of Wellington declared that “Tea cleared my head and left no misapprehensions.” He was right- tea contains small amounts of two B vitamins, and has no calories, artificial flavourings or colourings. It is said to cure gout, apoplexy, epilepsy, gall stones and sleepiness, and one’s longevity is assured. “Thank God for Tea! What would the world do without tea?”- Sydney Smith

But that’s cheating – there’s nothing about the actual tea they serve you. So the real laureate is the famous Tour d’Argent of Paris, with this lengthy but delicious-sounding single dish from 1987:

Fresh Water Prawn Rampant, Baby White Fish, with Timbal of Transylvanian Macadamia Nuts, Sea Scallops, Two Gunkan Rolls of American Sturgeon and Salmon Caviars, served on Wasabi Sauce Rouille- The fresh water prawn is from an American based farm and is split and baked in a hot rouille sauce. One prawn is served along side the baked white fish. It is served under a timbal made with sea scallops and alongside two Chinese rice rolls filled with two caviars. Rouille is similar to a hot Hollandaise. The sauce is made with wasabi powder and cream.

Price unknown. Oh gods. While we’re on the subject, let’s have a look at the priciest items. Ordering by highest “highest_price” gave ludicrous results (a $2550 grape fruit… Is it from the Hesperides’ Garden?!?) so I ordered the data by highest “lowest_price” instead. The winner is some “Chicken Liver Omelette”, at $1035! Yes, I checked the currency. Here are the 10 priciest dishes:


Lots of champagne, and a… ham sandwich?

Next I liked at old dishes. The oldest entries are from 1851, but most of them appeared only once, so instead I looked for old dishes that lasted for more than a year. I thought they would be more representative of what food was common at the time:

We have some weird ones! My favorite is the stale bread. Next I wondered what were the dishes that had the longest lifespan:

Super boring, but those are some pretty expensive peaches 0_0
I had to Google “Charles Heidsieck”. It’s champagne. Oh, and mashed potatoes can cost you more if you ask it with a capital P.
The menus with the most gigantic number of dishes all come from “Waldof Astoria”, with more than 1000 dishes to choose from for a single occasion! Here is what a page looks like:



So that’s some tidbits about the dataset I used. The bot was super fun to train, here are some screenshots that made me cry laughing (because I’m a bit crazy too I guess).

See you next time!

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Anti-Layer Manifesto

Disclaimer: Deep Learning is giving the best results in pretty much all areas of machine learning right now, and it’s much easier to train a deep network than to train a shallow network. But it has also been shown that in several cases, a shallow network can match a deep one in precision and number of parameters, while requiring less computational power. In this post I leave aside the problem of training/learning.

This started by me buying several books and trying to read all of them at the same time. Strangely, they all treated the same topics in their opening chapters (the unconscious, perception, natural selection, human behaviour, neurobiology…), and all disagreed with each other. Also none of them was talking about layers, but somehow my conclusion about these books is that we might want to give up layered design in AI.


So today I’m going to talk about layers, and how and why we might want to give them up. The “layers” I’m talking about here are like the ones used in Deep Learning (DL). I’m not going to explain the whole DL thing here, but in short it’s an approach to machine learning where you have several layers of “neurons” transmitting data to each other. Each layer takes data from the previous one and does operation on it to reach a “higher level of abstraction”. For example, imagine that Layer 1 is an image made of colored pixels, and Layer 2 is doing operations on these pixels to detect edges of objects in the image. So each neuron is holding a value (or a group of values).

Typically, neurons in the same layer don’t talk to each other (no lateral connections); they only perform operations on data coming from the previous layer and send it to the next layer (bottom-up connections). What would happen if there were lateral connections, it that your layer would stop representing “something calculated from below”. Instead they would hold data made up of lower abstraction and current abstraction data mixed up together. As if instead of calculating edges from pixels, you calculated edges from pixels and from other edges. Layers also usually don’t have top-down connections (equivalent to deciding the color of a pixel based on the result of your edge calculation). These properties are shared by many processing architectures, not only DL. I’m not focusing on DL particularly, but rather trying to find what we might be missing by using layers – and what might be used by real brains.

Example of layering – Feedforward neural network. “Artificial neural network” by en:User:Cburnett – Wikimedia Commons

Layers are good for human designers: you know what level of data is calculated where, or at least you can try to guess it. Also we talk about the human brain cortex in terms of layers – but these are very different from the DL layers, even from a high level point of view. Neurons in the human brain have lateral and top-down connections.

DL-like layers are a convenient architecture. It keeps the levels of abstraction separated from each other – your original pixel data is not modified by your edge detection method.Your edge detection is not modified by object detection. But… Why would you want to keep your original data unmodified in the first place? Because you might want to use it for something else? Say that you’re playing a “find the differences” game on two pictures. You don’t want to modify the model picture while looking for the 1st difference; you want to keep the model intact, find difference 1, then use the model again to find difference 2 etc.

But… For example if you could look for all errors in parallel, you wouldn’t care about modifying the images. And if what is being modified is a “layer” of neurons inside your head, you really shouldn’t care about it being modified; after all, the model image is still there on the table, unmodified.

The assumptions behind layers

Let’s analyse that sentence: “you might want to use it for something else.”

It: it is the original unmodified data. Or rather, it is the data that will be transmitted to the next layer. That’s not trivial. How to decide what data should be transmitted? Should you try to find edges and then send that to another layer? Or is it OK to find edges and objects at the same place and then send that to a different layer? All depends on the “something else”.

Something else: If you can calculate everything in a messy bundle of neurons and go directly from perception to action in a single breath, you probably should. Especially if there is no learning needed. But when you have a whole range of behaviors depending on data from the same sensor (eyes for example), you might want to “stop” the processing somewhere to preserve the data from modification and send these results to several different places. You might send the edge detection results to both a sentence-reading module and a face detection module. In that case you want to keep your edge detection clean and unmodified in order to send the results to the different modules.

Might: But actually, you don’t always want to do that. If there are different behaviors using data from the same sensors but relying on different cues, you don’t need to preserve the original data. Just send what your sensor senses to the different modules; each one modifying its own data should not cause any problem. Even if your modules use the same cues but in different ways, sending to each one a copy of the data and letting them modify it can be OK. Especially, if your modules need to function fast and in parallel. Let’s say that module 1 needs to do some contrast detection in the middle of your field of vision (for face detection maybe). Module 2 needs to do contrast detection everywhere in your field of vision (obstacle detection?). If we make the (sometimes true in computers) assumption that contrast detection takes more time for a big field than a small one, it will be faster for module 1 to do its own contrast calculation on partial data instead of waiting for the results calculated in module 2. (but more costly).
Did you know that if the main vision center of your brain is destroyed, you will still be able to unconsciously detect the emotions in human faces… while being blind? You will also be able to avoid obstacles when walking. The parts of your brain for conscious vision, face recognition and obstacle detection are located at different places, and function semi-independently. My hypothesis is that these 3 functions rely on different use of the same cues and need to be running fast, therefore in parallel.

If not layers then what?

I would go for modules – so called “shallow networks”. A network of shallow network. And I suspect that it is also what happens in the brain, although that discussion will require a completely different blog post.

First, I think that the division in layers or in modules need to be less arbitrary. Yes, it is easy to use for human designers. But it can also be a cost for performance. I can see some advantages in using messy shallow networks. First, neurons (data) of the same level of abstraction can directly influence each other. I think it’s great to perform simplifications. If you need to do edge detection, you can just try to inhibit (erase) anything that’s not an edge, right there in the “pixel” layer. You don’t need to send all that non-edge data to the next module – after all, very likely, most of the data is actually not edges. If you actually send all the data to be analyzed (combined, added, subtracted…) in an upper layer, you also need more connections.

Furthermore, it seems justified to calculate edges also from other edges and not just from pixels. Edges are typically continuous both in space and time: using this knowledge might help to calculate edges faster from results that are already available about both pixel and edges than if you just update your “edge layer” after having completely updated your “pixel layer”.

Ideally we should only separate modules when there is a need to do so – not because the human designer has a headache, but because the behavior of the system requires so. If the output of the module is required as is for functionally different behaviors, then division is justified.

I would also allow top-down connections between modules. Yes, it means that your module’s output is modified by the next module, representing a higher level of abstraction. It means that you are “constructing” your low level input from a higher level output. Like deciding the color of pixels based on the result of edge detection… I think it can be justified: sometimes it is faster and more economical to construct a perception than to just calculate it (vast subject…); sometimes accurate calculation is just not possible and construction is necessary. Furthermore if a constructed perception guide your behavior as to make it more successful, then it will stick around thanks to natural selection. I also think that it happens in your brain (just think about that color illusion where two squares look like different colors just because of the surrounding context like shadows).

Concluding words

Lots of unsubstantiated claims in this blog post! As usual. If I could “substantiate” I’d write papers! But I really think it’s worth considering: are layers the best thing, and if not then why? This thought actually came from considerations about whether or not we are constructing our perceptions – my conclusion was yes, constructed perceptions have many advantages (more on that later…maybe?). But what kind of architecture allows to construct perceptions? The answer: not layers.


Salut ! Un passage en coup de vent, parce que je ne pouvais pas ne pa relayer cette video ! Un vietnamien qui chante du zouk, c’est pas tous les jours  😮

Voici donc vietzoukeur, un vietnamien qui vit apparemment en Guadeloupe. Ca ne casse pas trois pattes a un canard, mais ca me fait quand meme bien  plaisir !

PS* bientot l’article expliquant ma cuisine hi-tech…

Une bougie

Cela n’aura pas echappe a certains d’entre vous, le premier anniversaire de mon arrivee au Japon s’est perdu dans le long silence dans lequel est tombe le blog depuis un peu plus d’un mois…

Ca fait donc un an, que je resumerais en deux mots : “super experiences”. Malheureusement, on ne peut pas en dire autant de mon arrivee a Tohoku (dans le Tohoku ?), d’ou mon silence prolonge. J’ai remarque que quelquefois, il vaut mieux se taire que d’egrenner un longue litanie de plaintes… Sachez donc simplement que si Sendai representait mon premier contact avec le Japon, j’aurais surement deteste ce pays. Ce n’est pas peu dire, car je crois quand meme avoir un peu voyage, eu quelques moments difficiles et tout et tout  ! Mais Sendai me sort par les yeux depuis le tout premier jour. Je sais que certains etrangers aiment bien cette ville, et que certains Japonais font de leur mieux pour accueillir les immigres chaleureusement : disons donc que je n’ai pas eu de chance durant ce premier mois et demi.

Heureusement, j’ai commence a demenager (encore) depuis la semaine derniere, pas pour changer de ville (bien dommage) mais pour changer d’appart : j’echange le vieux dortoir glacial et les douches communes crados pour un sympathique petit bout de maison un peu moins loin de mes activites academiques. Apres avoir evite a grand’peine les ghettos d’expatries, extremement bon marche mais insalubres et loin de tout, j’ai pu trouver un coin ou vivent quelques Japonais. Je n’ai rien contre mes confreres etrangers, mais etre systematiquement parque dans le “coin special etrangers”, ca commencait a bien faire ! Franchement, a quoi ca rime de faire un “dortoir universitaire special non-japonais”, un bout de labo “special non-japonais” etc ? Si je voulais fuir les contacts avec les autochtones j’aurais commence par rester chez moi, enfin il me semble. La question serait plutot qui fuit qui, mais chuuut !

Bref, vous l’aurez compris, je me languis de Tokyo. Faites que ces deux ans a Sendai passent tres vite et que je revienne a la civillisation, la ou on depasse 10°C a l’approche du mois de Juin. Comment ca “rien a voir” ?!  D’accord : ” …la civillisation, la ou IKEA et McDo livrent a domicile”.

Voila donc pour les activites domestiques, mais qu’en est-il de l’universite, du labo, des cours ? Et bien disons que tout va treeees leeeeentement. Me croiriez vous si je vous disais que j’en suis exactement au meme point maintentant qu’il y a un mois et demi ? Je veux dire par la, que je ne sais rien de plus de l’ecole ou de mon futur sujet de recherche que si j’avais passe mon temps a hiberner. Ah misere, rendez moi mon labo chez Honda, avec ses joyeux chercheurs, ses dejeuners conviviaux et ses millions de projets en cours ! Mon royaume pour une occupation ! Mon compte en banque pour une conversation en japonais !