Rémy Léone

My LaTeX Workflow to avoid spelling mistakes

2015-12-22T00:00:00+00:00

I use several tools and techniques to ensure that my Latex is as good as possible.

LaTeX tools

Nag

Nag is a tool to help you detect old commands and deprecated LaTeX code.

Simply put the following lines in your code:

\RequirePackage[l2tabu, orthodox]{nag}

latexmk

Latexmk is a very good tool to never have to worry anymore about how many time you have to compile to get your bibliography right. A latemkrc is the file you will put inside the project that will help bootstrap latexmk. Here’s mine:

$pdf_mode = "1";
$pdflatex = "pdflatex";
$makeindex = "splitindex";
$makeindex = "makeindex;splitindex;";

Spelling help

Language tool

Language tool is Java grammar checker. It helps detect grammar mistakes such as He play tennis and many others. I usually launch the checker against all my tex folder. If you use TeXstudio you can have Language tool directly integrated inside your editor:

Checking (La)TeX With LanguageTool - LanguageTool Wiki

Pandoc + Microsoft Office

Pandoc is like a swiss-army knife for converting text back and forth in different format.

Suppose that you have a large body of text in LaTeX and you want to convert it to a docx format to spell check it on Microsoft Office. By using pandoc you could have it quickly:

pandoc main.tex -o main.docx

That’s it! Now, you have a main.docx that you can pass along to other grammar checker such as the one embedded in Microsoft Word.

Project layout

I like to have a modular project. Therefore, I use the following layout:

├── myproject.sublime-project
├── myproject.sublime-workspace
├── IEEEtran.cls
├── latexmkrc
├── main.pdf
├── main.tex
├── Makefile
├── readme.md
├── main.bib
└── tex
    ├── abstract.tex
    ├── conclusion.tex
    ├── experiment.tex
    ├── intro.tex
    └── related.tex

A main.tex document that import all other using similar snippets:

\begin{document}

\maketitle

\input{tex/abstract}
\input{tex/intro}
\input{tex/related}
\input{tex/experiment}
\input{tex/conclusion}

\end{document}

Organizing code this way is handy because I only have small files that I can easily get on one screen, share in an email or simply read in one sight. If my code was a monolithic file with thoushands of lines, it would be much harder to see where I am in the structure.

Plus if you use an editor such as Vim (plus the Ctrl-p plugin) or Sublime Text you can open any file you want by simply typing Ctrl-p and going in very few key strokes anywhere in your document.

Makefile

Of course, you don’t want to type commands all the time. Therefore you put them in a makefile like this one:

all:
        latexmk

clean:
        latexmk -C

Useful Commands

2014-05-09T00:00:00+00:00

TODO: Move this cheat sheet to english but google (translate) can help you meanwhile ;)

General commands

Command	Meaning
!!	Commande précédente
7 signal	Liste des signaux
arch	Affiche l’architecture de la machine
arp	Gestion de la table ARP du noyau
basename	Nom du fichier sans l’extension
bashdb	Debugger pour bash
bg/fg	Mettre en fond un processus (lien avec jobs)
builtins	Fonction de base de bash
crontab	Tache planifiée
dd	Copie generique
dmesg	Message du kernel
egrep	grep -E
fgrep	grep -F
find	Trouver des fichiers
fmt	Formatter un texte
fuser	Processus utilisant un fichier
getopt(s)	Prendre les arguments de la ligne de commande
hexdump
hier	Contient une description de l’organisation de l’arborescence Unix
hostname	Nom de l’hôte
iconv	Convertir l’encodage d’un fichier
ifconfig	Configuration du reseau
init	Niveau d’execution (init 0 -> shutdown)
ipcs	Utilisation des ressources IPC System V
jobs	Liste des jobs en cours (A voir de plus près)
join	Joindre des fichiers triés
killall	Envoyer un signal a tous les processus de meme nom
ldconfig	Valider les bibliothèques dynamiques
locate	Trouver des fichiers
logger	syslog
mail	mail tools
mkfifo	Tube nommés
mknod	Fichiers spéciaux
mktemp	Fichier temporaire (Stocker des mots de passe sensibles)
netcat	permet de faire ce que l’on veut avec des paquets TCP et UDP
netstat	Statistiques reseau
nice	Courtoisie d’un processus
nm	Lire les a.out
nohup	Mettre un process comme indépendant du terminal
nslookup	permet d’avoir des infos sur un site
objdump	Information sur un fichier objet
patch	Modification de fichiers existants
pr	Mettre en forme pour l’impression
ps	Processus
pstree	Arbre des processus
regex	POSIX regular expression
rename	Perl tool for rename
renice	Changer la courtoisie d’un processus
rev	Inverser les lignes d’un fichier
route	Gestion de la table de routage
route	permet de voir les routes du système
seq
setuid	Id des processus
socklist	Liste des sockets actives
sort	Trier les lignes d’un fichier texte
source
split	Decouper un fichier en plusieurs lignes
squid	Pour monter un proxy
stty	Configuration du terminal
tcpdump	Information sur le reseau
tcpdump	output PCAP
tr	Eliminer ou convertir des lettres d’un texte
traceroute	Itineraire d’un paquet
tsort
uname	Informations systèmes
uniq	Ote les lignes dupliquées
uptime	Uptime
vmstat
w	Qui est connecté sur la machine
wc
wodim	Graveur en ligne de commande
xargs	Ligne de commandes
xxd

Kernel module:

Command	Meaning
depmod	Verification des dependances
insmod	Insertion d’un module dans le noyau
lsmod	Liste des modules employés
modinfo	Information sur un fichier module
modprobe	Chargement gérant les dépendances
rmmod	Suppression d’un fichier dans le noyau
sysctl	Paramètres du noyau (possible de les reconfigurer a chaud)

Tools:

Command	Meaning
ab	benchrmarking web servers
bc	calculator
cssh	visual concurrent shell
cut	and paste and join: data manipulation
dd	moving data between files
dmesg	boot and system error messages
dstat	and htop: improved system stats monitors
env	run a command (useful in scripts)
file	identify type of a file
hd	and bvi: dump or edit binary files
iconv	or uconv: conversion for text encodings
iftop	or nethogs: network utilization by socket or process
last	login history
ldd	dynamic library info -> Ce dont on a besoin pour lancer un executable
lsof	process file descriptor and socket info
mtr	better traceroute for network debugging
nc	network debugging and data transfer
nm	symbols from object files
pr	format text
sar	historic system stats
shuf	random selection of lines from a file
split	and csplit: splitting files
stat	file info
strace	system call debugging
strings	extract text from binary files
tac	print files in reverse
tr	character translation or manipulation

Some Shortcuts

2014-05-08T00:00:00+00:00

Here is some commands that you can use inside a terminal and/or when you are typing on a UNIX like terminal.

BOFH Excuses

telnet towel.blinkenlights.nl 666

Shortcuts

Shortcut	Meaning
Ctrl + U	Clean all line
Ctrl + K	Clean [position;end]
Ctrl + w	Clean the word brefore the position
Ctrl + a	Go to begin of line
Ctrl + e	Go to End of line
Ctrl + p	Previous command
Ctrl + b	Move back from a char
Ctrl + f	Move forward a char
Ctrl + k	Delete to EOL
Ctrl + n	Next command
Ctrl + r	Search in previous command (begin research mode)
Ctrl + s	Search in next command
Ctrl + o	Do command researched
Ctrl + l	Clean screen
Ctrl + u	Clean text before cursor + copy it
Ctrl + k	Clean text after cursor + copy it
Ctrl + w	Clean the word before the cursor + copy it
Ctrl + y	Paste the content of the cursor (yank)
Ctrl + d	Terminate the current buffer
Ctrl + c	Cancel a process
Ctrl + z	Background (fg to bring back)

Shortcut	Meaning
Alt + f	move forward one word
Alt + b	move background one word
Alt + del	cuts the word before the cursor
Alt + d	Cut the word after the cursor
Alt + u	UPPER every char from cursor to the end of the cursor word
Alt + l	lower “”
Alt + c	up a char and move to the end of the word
Alt + r	cancel the changes on the current line
Alt + <	Move to the first line in the history
Alt + >	Move to the last line in the history
Alt + ?	Show current completion list
Alt + *	Insert all possible completions
Alt + /	Attempt to complete filename
Alt + .	Yank last argument to previous command
Alt + t	Move words around
Alt + Back-Space	Delete backward from cursor

Funny French idioms.

2014-03-30T00:00:00+00:00

Anger

French expression	English expression	Literal translation
Péter les plombs	to blow one’s top	Bust the fuses (Here the word “plombs” refers to the fuses that melt in order to avoid short-circuit)
Sortir de ses gonds	to fly off the handle	(The image of a door that come off its hinges)
Se fâcher tout rouge	to go up the wall	to be upset all red
J’ai la moutarde qui me monte au nez	I’m beginning to lose my temper	Mustard is winding up in my nose.
Prendre la mouche	to fly off the handle	Take a fly (This expression is close to “quelle mouche vous a piqué ?/What’s with you?”) The idea is that a person’s sudden outburst is the result of having being stung by an insect.
Avoir les nerfs en pelote	To be on the edge, to be nervy	Got my nerves in a ball (like ball of wool)

Criticizing

French expression	English expression	Literal translation
Casser du sucre sur le dos de quelqu’un	To bad-mouth someone	Break sugar on the back of someone
Tailler un costard à quelqu’un	To slag someone off	Cut a suit for someone (The idea here is that once you have a bad reputation it’s difficult to lose it. In the same way that a suit cannot be altered once it has been cut to a certain shape)

Annoyance

French expression	English expression	Literal translation
Casser les pieds de quelqu’un	to do someone’s head in	Break the feet of someone.

Getting along (or not)

French expression	English expression	Literal translation
S’entendre comme larrons en foire	to be as thick as thieves	agreeing as thieves in a fair.
Être comme cul et chemise	to be inseparable	Be like ass and shirt (more for a friendship than a love).
Être comme les doigts de la main	to be very close	Be like fingers of the hand.
Il me sort par les trous de nez	I can’t stand the sight of him	He is getting out of my nostrils.
Entre eux le torchon brule	They’re at loggerheads	The rag is burning between them.

Love

French expression	English expression	Literal translation
Avoir le coup de foudre	To fall in love at first sight	to be stuck by lightning.
Être un bourreau des coeurs	to be a ladykiller	Being a executioner of hearts.
Avoir un coeur d’artichaut	to be a fickle (in love)	To have a artichoke heart (A leaf for everyone).
Avoir quelqu’un dans la peau	to be crazy about someone	having someone in the skin.
Être mordu	to be madly in love	to be beaten.
En pincer pour quelqu’un	to be crazy about someone	To pinch for someone.

Truth and lies

French expression	English expression	Literal translation
Mentir comme un arracheur de dents	to be a compulsive liar	Lie as a teeth puller (no anaesthetic but don’t worry you will not feel pain).
Raconter des salades	to tell fibs	Tell salads.
Faire avaler des couleuvres	to take someone in	Make somebody swallow grass-snake.
Une histoire à dormir debout	A cock and bull story	A story that make you sleep while standing up.
Se mettre sur son 31	to get all dressed up	To get on his/her 31 (31 as December 31th).
Être tiré à 4 épingles	to be dressed up to the nines	To be stretch by 4 pins (4 pins to stretch a piece of fabric and eliminate creases).
Être joli comme un coeur	to be pretty as a picture	Be pretty as an heart.

Fear and worry

French expression	English expression	Literal translation
Serrer les fesses	To have the wind up	Tighten the buttocks.
Avoir une peur bleue	To be scared to death	Having blue fear.

Directness

French expression	English expression	Literal translation
Ne pas y aller par 4 chemins	To go straight to the point	Don’t go by 4 different tracks.
Ne pas y aller avec le dos de la cuillère	Not to go for half-measures	Not to dish things out using the back of a spoon.

Speaking and keeping silent

French expression	English expression	Literal translation
Tenir la jambe à quelqu’un	To bore someone with one’s talk	Hold the leg of someone (picture someone who wants to go away).
Souler quelqu’un	to bore someone silly	Make someone drunk.
Ne pas avoir sa langue dans sa poche	Never to be at a loss for words	Don’t have his tongue in his pocket.

Surprise

French expression	English expression	Literal translation
Couper le sifflet à quelqu’un	To shut someone up	Cut the whistle of somebody.

Happiness

French expression	English expression	Literal translation
Être au anges	To be walking on air	Be with the angels.

Sadness

French expression	English expression	Literal translation
Avoir le cafard	To feel down	To have the cockroach.
Être malheureux comme les pierres	To be utterly miserable	Be as sad as a rock.
Rire comme un bossu/une baleine	to laugh one’s head off	It is believed that “rire comme une baleine” (to laugh like a whale) might have derived from “se tordre comme une baleine de parapluie retourné”. Playing on the words “se tordre”, which means both “to bend” and ‘to laugh’ (in colloquial language) and “baleine” which of course means “whale” but which also means the rib of an umbrella.

Madness

French expression	English expression	Literal translation
Il ne tourne pas rond	he’s not all there	he is not turning around.
Être timbré	to be cracked	To be stamped.
Être tombé sur la tête	To have a screw loose	Have fallen on head.
Être marteau	To be nuts	Be a hammer.

Stupidity

French expression	English expression	Literal translation
Il n’a pas inventé l’eau chaude	He’ll never set the Thames on fire	He didn’t invented warm water.
Être bête à manger du foin	To be as thick as two shorts planks	Be stupid enough to eat hay.
Être bête comme ses pieds	To be unbelievably stupid	Be as stupid as his own feet.
Avoir une case de vide	To have a screw loose	Have an empty shake.
Il est un peu bas de plafond	He’s not the sharpest knife in the drawer	His ceiling is quite low.

Hunger and eating

French expression	English expression	Literal translation
Avoir les crocs	To be famished	Got the fangs.

Drinking

French expression	English expression	Literal translation
Boire comme un trou	To drink like a fish	Drink like a hole.
Avoir la gueule de bois	To be hangover	Having a wooden face.
Avoir du vent dans les voiles	To be three sheets in the wind	Having wind in the sails. This expression likens the teetering drunkard to a ship that is buffeted by the wind.
Lever le coude	To booze	Lift the elbow.
S’en jeter un derrière la cravate	To knock back a drink	To throw one behind the tie.

Doing things easily

French expression	English expression	Literal translation
Ce n’est pas la mer à boire	It’s no big deal	It’s not the sea to drink.
Faire quelque chose les doigts dans le nez	To do something very easily	Do something with fingers in the nose.
Ce n’est pas le bout du monde	It’s no big deal	It’s not the end of the world.

Leaving

French expression	English expression	Literal translation
Prendre ses jambes à son cou	to take one’s heels	take his legs to his neck.
Mettre les voiles	To skedaddle	To set up the sails.
Se faire la malle	To clear off	To make the trunk (big suitcase).

Work and effort

French expression	English expression	Literal translation
Être un bourreau de travail	being a workaholic	to be an work executioner.
Faire des pieds et des mains pour faire quelque chose	to move heaven and earth to do something	Move feet and hands to do something.
Avoir un poil dans la main	to be workshy	Having an hair in the hand.

Success

French expression	English expression	Literal translation
Marcher comme sur des roulettes	to be going very smoothly	work on roulette (little wheels).

Inferiority

French expression	English expression	Literal translation
Ses rivaux ne lui arrivent pas à la cheville	non of his rivals can touch him	his rivals doesn’t even height match his anckles.
ça ne vaut pas un clou	it’s not worth a bean	it doesn’t worth a nail.

Authority and obediance

French expression	English expression	Literal translation
Mener quelqu’un à la baguette	to rule someone with a rod of iron	lead someone with a stick.

Enjoying oneself

French expression	English expression	Literal translation
S’en payer une tranche	to have a whale of a time	to pay one’s slice.
s’éclater comme une bête	to have a wild time	to bust like a beast.

Money

French expression	English expression	Literal translation
Couter la peau des fesses	To cost an arm and a leg	To cost skin of butt.
Jeter l’argent par les fenêtres	To throw money down the drain	To throw money through the windows.
Être plein aux as	To be rolling it	To be full to aces.
Être payé au lance-pierre	To be paid peanuts	To be paid by slingshot.

What’s a french prepa?

2014-01-02T00:00:00+00:00

The ‘classes préparatoires aux grandes écoles’ (CPGE), commonly called ‘classes prépas’ or ‘prépas’, are a two-year curriculum extensible to three-years, acting as a prep course with the main goal of training undergraduate students for enrollment in one of the grandes écoles. The grandes écoles of France are higher education establishments aside the mainstream framework of the public universities. They include sciences & engineering schools, business schools, specific humanities schools, but include neither medical institutes, nor architecture institutes nor law institutes. They are generally focused on a single subject area, such as engineering or business, have a moderate size, and are often quite selective in their admission of students. Due to their competitive entrance exams, they are widely regarded as a prestigious curriculum by students, and traditionally have produced most of France’s scientists and executives.

In a given year, 800,000 people are born in France, 480,000 pass the baccalaureat (secondary school diploma), and 37,000 of them are admitted in CPGE. Among those 74,000 undergraduate students, 47,000 of them follow prep courses for entrance exams into sciences & engineering schools, 16,000 of them for entrance exams into business schools, 11,000 of them for entrance exams into humanities schools.

To get into one of the French grandes écoles, most students will take a very competitive national exam at the end of the two-year program in one of the CPGE. This national exam includes written tests during several weeks that will challenge the students on what they have learned for the past two years.

Then, most of these students will be ranked accordingly to their results but each year a certain percentage of students do not make this ranking.

These failing students will generally be allowed to repeat their second year or will continue their studies in one of the local universities.

The successful students from across the country all go to places of examination (usually, but not exclusively, in the capital, Paris) during the summer to participate to a last round of selection. This process consists of oral exams, usually 1h in duration, during which they are given a problem to solve. Generally, after 20 min of preparation, they will expose their solution to a teacher which will then challenge the candidate on their results and the assumptions being made, as well, depending of the cases, to challenge on the fly the candidate on questions not asked explicitly in the subject, especially if the person finishes the problem earlier.

At the end of this stressful selection process, candidates will receive their final ranking which will allow them - if ranked - to finally apply to the grande école of their choice. The national exam is not an exam which applies for exhaustively all the grandes écoles. In order to candidate for all the grandes écoles, one must generally candidate to a large number (around 5) of different examinations (Centrale, CCP, Polytechnique, e3a, mines), leading to a month-long of different written exams and a month-long different oral exams, possibly and likely in different places. The ranking at the end is specific to a group of schools, admission in a prestigious examination like Polytechnique does not automatically allow one to enter a lower-ranked school if one did not pass the group of exams that this school belongs to. Their national ranking will allow them or not to get into the grande école of their choice.

Admission

Admission to the CPGE is usually based on performance during the last two years of high school, called première and terminale. The CPGE are located within high schools due to historical reasons (Napoleon created them at first as fourth to sixth year of high school). but pertain to tertiary education, which means that each student must have passed successfully their Baccalauréat (or equivalent) to be admitted in CPGE. Each CPGE receives the files of hundreds of applicants worldwide every year during April and May, and selects its new students under its own criteria (mostly excellency). A few CPGE programmes, mainly the private CPGEs (which account for 10% of CPGEs), also have an interview process or look at a student’s involvement in the community.

Organization of CPGE

CPGE exist in three different fields of study: Science and Engineering, Business, and Humanities. All CPGE programs have a nominal duration of two years, but the second year is sometimes repeated once.

Scientific CPGE

The oldest CPGEs are the scientific ones, which can only be accessed by scientific Bacheliers. The different tracks are the following : * MPSI (“mathematics, physics, and engineering science”) in the first year, followed either MP (“mathematics and physics”) or PSI (“physics and engineering science”) * PCSI (“physics, chemistry, and engineering science”), followed PC (“physics and chemistry”) or PSI (“physics and engineering science”) * BCPST1 (“biology, chemistery, physics and earth sciences”) followed by BCPST2 * PTSI (“physics, technology, and engineering science”), followed by PT (“physics and technology”) The classes which especially train students for admission to the elite École Normale Supérieure or École Polytechnique have an asterisk added to their name, e.g. MP*, and usually called “MP étoile” (“MP star”). Both the first and second year programmes include as much as sixteen hours of mathematics teaching per week, ten hours of physics, two hours of philosophy, two to four hours of (one or two) foreign languages teaching and two to three hours of minor options: either SI, engineering industrial science, chemistry or theoretical computer science (including some programming using the Pascal or CaML programming languages, as a practical work). With this is added several hours of homework, which can rise as much as the official hours of class.

In scientific CPGE, the first year of CPGE is usually called the ‘math sup’ - or hypotaupe - (sup for “classe de mathématiques supérieures”, superior in French, meaning post-high school), and second year ‘math spé’ - or taupe - (spés for “classe de mathématiques spéciales”, special in French). The students of these classes are called taupins.

Life in a CPGE

The amount of work required of the students is exceptionally high. In addition to class time and homework, students spend several hours each week completing exams and ‘colles’ (very often written ‘khôlles’ to look like a Greek word, this way of writing being initially a khâgneux joke). The so called ‘colles’ are unique to French academic education in CPGEs. They consist of oral examinations twice a week, in math, physics, chemistry, French and the foreign languages, usually English and Spanish. Students, usually in groups of three, spend an hour facing a professor alone in a room, answering questions and solving problems. In CPGE littéraires (humanities), the system of ‘colles’ is a bit different. They are taken every quarter in every subject. Students have one hour to prepare a short presentation that takes the form of a French-style dissertation (a methodologically codified essay, typically structured in 3 parts: thesis, counter-thesis, and synthesis) in history, philosophy, etc. on a given topic, and that of a commentaire composé (a methodologically codified commentary) in literature and foreign languages; as for the Ancient Greek or Latin, they involve a translation and a commentary. The student then has 20 minutes to present his work to the teacher, who ends the session by asking some questions on the presentation and on the corresponding topic. ‘Colles’ are regarded as extremely stressful, particularly due to the high standards expected by the teachers, and the subsequent harshness that may be directed at students who do not perform adequately. But they are important as they prepare the students, from the very first year, to the oral part of the competitive examination, reserved to the happy few who successfully pass the written part.

When a student repeats his second year, he gets then the status of cinq demi (“five halves”), for he was only a trois demi (“three halves”) during his first second year, and un demi (“one half”) in his first year. The explanation behind those names is that the most coveted engineering school is the Ecole Polytechnique, nicknamed the X (as the mathematical unknown). In French, a student is said to integrate a school when they are allowed to enroll in it. A student is called a 3/2 if he integrates the Ecole Polytechnique between his first and second year of preparatory class since the integral of x from 1 to 2 is 3/2. The same idea is valid for “cinq demi”, since the integral of x from 2 to 3 is 5/2. Students in their first year are also called “bizhuts”, and in their second year, “carrés” (“squares”). Students enrolled in their second second-year are also called “cubes” (or Khûbes), and a few turn to “bicarrés” for a third and final second-year. These terms probably stem from repeated attempts at applying to “X” (Polytechnique), yielding x2 and x3. Some ambitious professors encourage their top students to eschew admittance to other prestigious schools in order to try their hand at X one more time… Despite this high standard, the 47 000 students in scientific CPGE must face the fact that they won’t all go to the Ecole Polytechnique. The renowned engineering schools are Centrale Paris, Supélec, École nationale supérieure des mines de Paris, École nationale des ponts et chaussées, École nationale de la statistique et de l’administration économique, École nationale supérieure de techniques avancées, École nationale supérieure des télécommunications, École supérieure de physique et de chimie industrielles de la ville de Paris, Institut Supérieur de l’Aéronautique et de l’Espace, École nationale de l’aviation civile or École nationale supérieure d’arts et métiers, are also a proud goal to obtain for these students.

The students of CPGE are usually matriculated concurrently in universities, and can rejoin college in case of failure of their grandes écoles ambitions or if they just do not wish to become engineers and feel not able to pass the Écoles Normales Supérieures competitive examinations. The ratio of students who failed to enter grandes écoles is low in the scientific and economics CPGE, but high in humanities, for the only grande école aimed at in these classes is the École Normale Supérieure.

Services lourds de windows.

2013-07-13T00:00:00+00:00

Services très lourds

SuperFetch
WindowsSearch

Services à virer si Windows Media Center n’est pas utilisé

Carte à puce
Enumerateur de bus IP PnP-X
Expérience audio video haute qualité windows
Propagation des certificats
Services de planification Windows Media Center
Services de reception Windows Media Center
Service Media Center extender
Service Partage reseau du Lecteur Windows Media
Strategie de retrait de la carte à puce

Services peu utiles

Assistance IP
Agent de protection d’accès reseau
BranchCache
Configuration automatique de réseau cablé
Detection de services interactifs
Fichiers hors connexion
Gestion des clefs et certificats d’intégrité
Intérruption SNMP
Localisation d’appels de procédure distante
Netlogon
Parental Controls
Registre à distance
Service de strétégie de diagnostique
Service initiateur iSCSI de Microsoft
Service de panneau de saisie TabletPC
Service de base de module de plateforme sécurisé
Télécopie (Fax)
Windows CardSpace

Les services sans doute inutiles

Acquisition d’image Windows
Brillance Adaptative (A desactiver sans ordinateurs portables)
Service découverte automatique de proxy web (Inutile sans portable)
Programme d’installation d’ActiveX (Inutile si on utilise pas Internet Explorer)
Service de prise en charge Bluetooth
Service énumérateur d’appareil mobiles (Appreil photos lecteurs video)
Service de biométrie Windows (Stockage des données biométriques (empreintes)
Service de configuration automatique WWAN (reseau 3G data)

A desactiver si vous n’utilisez pas les groupes residentiels (reseau local entre ordinateurs windows7 uniquement)

Decouverte SSDP
Ecouteur HomeGroup
Fournisseur HomeGroup
Gestionnaire d’identité de reseau homologue
Groupement de mise en reseau de pairs
Hote du fournisseur de decouverte de fonctions
Protocol PNRP
Publication des ressources de decouverte de fonctions
Service de publication des noms d’ordinateurs PNRP

A desactiver si vous n’avez pas d’imprimante meme virtuelle

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A desactiver sans le WIFI

Isolation de clé CNG
Protocol EAP
Service de configuration automatique WLAN
Windows connect now - Registre de configuration

A desactiver avec un seul PC et pas de reseau local

Assistant Netbios sur TCP/IP
Client de suivi de lien distribué
Configuration des services bureau a distance
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Redirecteur de port du mode utilisateur des services bureau a distance
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Service bureau a distance
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Station de travail

ssh trick for heavy github user

2013-06-30T00:00:00+00:00

If like me you use github, bitbucket and more generally git a lot, here is a trick you might be interested in.

A typical git clone command look like this:

git clone git@github.com:sieben/dotfiles.git

Even if this is very straightforward, there is something simpler that can be done. Just open your .ssh/config and add the following lines:

Host github
HostName github.com
User git

Congratulations! Now, you can type:

git clone github:sieben/dotfiles.git

Of course, you can adapt this trick to bitbucket and your own git servers.

Hash in Python

2012-09-17T00:00:00+00:00

Python 3 is shipped with a hash library. To use it in a script just do this :

import hashlib
message = "Bonjour"
h = hashlib.sha512()
h.update(message.encode("utf-8"))
print(h.hexdigest())

Don’t forget encoding ;)

What Is It Like to Have an Understanding of Very Advanced Mathematics?

2012-01-23T00:00:00+00:00

from link

You can answer many seemingly difficult questions quickly.

But you are not very impressed by what can look like magic, because you know the trick. The trick is that your brain can quickly decide if question is answerable by one of a few powerful general purpose “machines” (e.g., continuity arguments, the correspondences between geometric and algebraic objects, linear algebra, ways to reduce the infinite to the finite through various forms of compactness) combined with specific facts you have learned about your area. The number of fundamental ideas and techniques that people use to solve problems is, perhaps surprisingly, pretty small — see http://www.tricki.org/tricki/map for a partial list, maintained by Timothy Gowers.

You are often confident that something is true long before you have an airtight proof for it (this happens especially often in geometry).

The main reason is that you have a large catalogue of connections between concepts, and you can quickly intuit that if X were to be false, that would create tensions with other things you know to be true, so you are inclined to believe X is probably true to maintain the harmony of the conceptual space. It’s not so much that you can imagine the situation perfectly, but you can quickly imagine many other things that are logically connected to it.

You are comfortable with feeling like you have no deep understanding of the problem you are studying.

Indeed, when you do have a deep understanding, you have solved the problem and it is time to do something else. This makes the total time you spend in life reveling in your mastery of something quite brief. One of the main skills of research scientists of any type is knowing how to work comfortably and productively in a state of confusion. More on this in the next few bullets.

Your intuitive thinking about a problem is productive and usefully structured, wasting little time on being aimlessly puzzled.

For example, when answering a question about a high-dimensional space (e.g., whether a certain kind of rotation of a five-dimensional object has a “fixed point” which does not move during the rotation), you do not spend much time straining to visualize those things that do not have obvious analogues in two and three dimensions. (Violating this principle is a huge source of frustration for beginning maths students who don’t know that they shouldn’t be straining to visualize things for which they don’t seem to have the visualizing machinery.) Instead…

When trying to understand a new thing, you automatically focus on very simple examples that are easy to think about, and then you leverage intuition about the examples into more impressive insights.

For example, you might imagine two- and three-dimensional rotations that are analogous to the one you really care about, and think about whether they clearly do or don’t have the desired property. Then you think about what was important to the examples and try to distill those ideas into symbols. Often, you see that the key idea in the symbolic manipulations doesn’t depend on anything about two or three dimensions, and you know how to answer your hard question.As you get more mathematically advanced, the examples you consider easy are actually complex insights built up from many easier examples; the “simple case” you think about now took you two years to become comfortable with. But at any given stage, you do not strain to obtain a magical illumination about something intractable; you work to reduce it to the things that feel friendly.

The biggest misconception that non-mathematicians have about how mathematicians think is that there is some mysterious mental faculty that is used to crack a problem all at once.

In reality, one can ever think only a few moves ahead, trying out possible attacks from one’s arsenal on simple examples relating to the problem, or pushing at small parts of the big problem, or looking to make analogies with other ideas one understands. This is the same way that one solves problems in one’s first real maths courses in university and in competitions. What happens as you get more advanced is simply that the arsenal grows larger, the thinking gets somewhat faster due to practice, and you have more examples to try, perhaps making better guesses about what is likely to yield progress.Indeed, most of the bullet points here summarize feelings familiar to many serious students of mathematics who are in the middle of their undergraduate careers; as you learn more mathematics, these experiences apply to “bigger” things but have the same fundamental flavor.

You go up in abstraction, “higher and higher”. The main object of study yesterday becomes just an example or a tiny part of what you are considering today.

For example, in calculus classes you think about functions or curves. In functional analysis or algebraic geometry, you think of spaces whose points are functions or curves — that is, you “zoom out” so that every function is just a point in a space, surrounded by many other “nearby” functions. Using this kind of zooming out technique, you can say very complex things in short sentences — things that, if unpacked and said at the zoomed-in level, would take up pages. Abstracting and compressing in this way allows you to consider extremely complicated issues while using your limited memory and processing power.

The particularly “abstract” or “technical” parts of many other subjects seem quite accessible because they boil down to maths you already know. You generally feel confident about your ability to learn most quantitative ideas and techniques.

A theoretical physicist friend likes to say, only partly in jest, that there should be books titled “X for Mathematicians”, where X is something generally believed to be difficult (quantum chemistry, general relativity, securities pricing, formal epistemology). Those books would be short and pithy, because many key concepts in those subjects are ones that mathematicians are well equipped to understand. Often, those parts can be explained more briefly and elegantly than they usually are if the explanation can assume a knowledge of maths and a facility with abstraction.Learning the domain-specific elements of a different field can still be hard — for instance, physical intuition and economic intuition seem to rely on tricks of the brain that are not learned through mathematical training alone. But the quantitative and logical techniques you sharpen as a mathematician allow you to take many shortcuts that make learning other fields easier, as long as you are willing to be humble and modify those mathematical habits that are not useful in the new field.

You move easily between multiple seemingly very different ways of representing a problem.

For example, most problems and concepts have more algebraic representations (closer in spirit to an algorithm) and more geometric ones (closer in spirit to a picture). You go back and forth between them naturally, using whichever one is more helpful at the moment.Indeed, some of the most powerful ideas in mathematics (e.g., duality, Galois theory, algebraic geometry) provide “dictionaries” for moving between “worlds” in ways that, ex ante, are very surprising. For example, Galois theory allows us to use our understanding of symmetries of shapes (e.g., rigid motions of an octagon) to understand why you can solve any fourth-degree polynomial equation in closed form, but not any fifth-degree polynomial equation. Once you know these threads between different parts of the universe, you can use them like wormholes to extricate yourself from a place where you would otherwise be stuck. The next two bullets expand on this.

Spoiled by the power of your best tools, you tend to shy away from messy calculations or long, case-by-case arguments unless they are absolutely unavoidable.

Mathematicians develop a powerful attachment to elegance and depth, which are in tension with, if not directly opposed to, mechanical calculation. Mathematicians will often spend days thinking of a clean argument that completely avoids numbers and strings of elementary deductions in favor of seeing why what they want to show follows easily from some very deep and general pattern that is already well-understood. Indeed, you tend to choose problems motivated by how likely it is that there will be some “clean” insight in them, as opposed to a detailed but ultimately unenlightening proof by exhaustively enumerating a bunch of possibilities. (Nevertheless, detailed calculation of an example is often an crucial part of beginning to see what is really going on in a problem; and, depending on the field, somecalculation often plays an essential role even in the best proof of a result.)In A Mathematician’s Apology https://www.math.ualberta.ca/~mss/misc/A%20Mathematician’s%20Apology.pdf, the most poetic book I know on what it is “like” to be a mathematician], G.H. Hardy wrote:”In both these example theorems (and in the theorems, of course, I include the proofs) there is a very high degree of unexpectedness, combined with inevitability and economy. The arguments take so odd and surprising a form; the weapons used seem so childishly simple when compared with the far-reaching results; but there is no escape from the conclusions. There are no complications of detail—one line of attack is enough in each case; and this is true too of the proofs of many much more difficult theorems, the full appreciation of which demands quite a high degree of technical proficiency. We do not want many ‘variations’ in the proof of a mathematical theorem: ‘enumeration of cases’, indeed, is one of the duller forms of mathematical argument. A mathematical proof should resemble a simple and clear-cut constellation, not a scattered cluster in the Milky Way.”A solution to a difficult chess problem is quite genuine mathematics, and has its merits; but it is just that ‘proof by enumeration of cases’ (and of cases which do not, at bottom, differ at all profoundly) which a real mathematician tends to despise.

You develop a strong aesthetic preference for powerful and general ideas that connect hundreds of difficult questions, as opposed to resolutions of particular puzzles.

Mathematicians don’t really care about “the answer” to any particular question; even the most sought-after theorems, like Fermat’s Last Theorem, are only tantalizing because their difficulty tells us that we have to develop very good tools and understand very new things to have a shot at proving them. It is what we get in the process, and not the answer per se, that is the valuable thing. The accomplishment a mathematician seeks is finding a new dictionary or wormhole between different parts of the conceptual universe. As a result, many mathematicians do not focus on deriving the practical or computational implications of their studies (which can be a drawback of the hyper-abstract approach!); instead, they simply want to find the most powerful and general connections. Timothy Gowers has some interesting comments on this issue, and disagreements within the mathematical community about it https://www.dpmms.cam.ac.uk/~wtg10/2cultures.pdf.

Understanding something abstract or proving that something is true becomes a task a lot like building something.

You think: “First I will lay this foundation, then I will build this framework using these familiar pieces, but leave the walls to fill in later, then I will test the beams…” All these steps have mathematical analogues, and structuring things in a modular way allows you to spend several days thinking about something you do not understand without feeling lost or frustrated. (I should say, “without feeling unbearably lost and frustrated; some amount of these feelings is inevitable, but the key is to reduce them to a tolerable degree.)Andrew Wiles, who proved Fermat’s Last Theorem, used an “exploring” metaphor: “Perhaps I can best describe my experience of doing mathematics in terms of a journey through a dark unexplored mansion. You enter the first room of the mansion and it’s completely dark. You stumble around bumping into the furniture, but gradually you learn where each piece of furniture is. Finally, after six months or so, you find the light switch, you turn it on, and suddenly it’s all illuminated. You can see exactly where you were. Then you move into the next room and spend another six months in the dark. So each of these breakthroughs, while sometimes they’re momentary, sometimes over a period of a day or two, they are the culmination of—and couldn’t exist without—the many months of stumbling around in the dark that proceed them.” http://www.pbs.org/wgbh/nova/physics/andrew-wiles-fermat.html

In listening to a seminar or while reading a paper, you don’t get stuck as much as you used to

You are good at modularizing a conceptual space, taking certain calculations or arguments you don’t understand as “black boxes”, and considering their implications anyway. You can sometimes make statements you know are true and have good intuition for, without understanding all the details. You can often detect where the delicate or interesting part of something is based on only a very high-level explanation. (I first saw these phenomena highlighted by Ravi Vakil, who offers insightful advice on being a mathematics student: http://math.stanford.edu/~vakil/potentialstudents.html.)

You are good at generating your own definitions and your own questions in thinking about some new kind of abstraction.

One of the things one learns fairly late in a typical mathematical education (often only at the stage of starting to do research) is how to make good, useful definitions. Something I’ve reliably heard from people who know parts of mathematics well but never went on to be professional mathematicians (i.e., write articles about new mathematics for a living) is that they were good at proving difficult propositions that were stated in a textbook exercise, but would be lost if presented with a mathematical structure and asked to find and prove some interesting facts about it. Concretely, the ability to do this amounts to being good at making definitions and, using the newly defined concepts, formulating precise conjectures that other mathematicians find intriguing or enlightening.

This kind of challenge is like being given a world and asked to find events in it that come together to form a good detective story. Unlike a more standard detective, you have to figure out what the “crime” (interesting question) might be; you’ll you have to generate your own “clues” by building up deductively from the basic axioms. To do these things, you use analogies with other detective stories (mathematical theories) that you know and a taste for what is surprising or deep. How this process works is perhaps the most difficult aspect of mathematical work to describe precisely but also the thing that I would guess is the strongest thing that mathematicians have in common.

You are easily annoyed by imprecision in talking about the quantitative or logical.

This is mostly because you are trained to quickly think about counterexamples that make an imprecise claim seem obviously false.

On the other hand, you are very comfortable with intentional imprecision or “hand-waving” in areas you know, because you know how to fill in the details.

Terence Tao is very eloquent about this here http://terrytao.wordpress.com/career-advice/there%E2%80%99s-more-to-mathematics-than-rigour-and-proofs/: After learning to think rigorously, comes the ‘post-rigorous’ stage, in which one has grown comfortable with all the rigorous foundations of one’s chosen field, and is now ready to revisit and refine one’s pre-rigorous intuition on the subject, but this time with the intuition solidly buttressed by rigorous theory. (For instance, in this stage one would be able to quickly and accurately perform computations in vector calculus by using analogies with scalar calculus, or informal and semi-rigorous use of infinitesimals, big-O notation, and so forth, and be able to convert all such calculations into a rigorous argument whenever required.) The emphasis is now on applications, intuition, and the ‘big picture’. This stage usually occupies the late graduate years and beyond.”In particular, an idea that took hours to understand correctly the first time (“for any arbitrarily small epsilon I can find a small delta so that this statement is true”) becomes such a basic element of your later thinking that you don’t give it conscious thought.

Mathematicians are not immune to the limitations faced by most others.

They are not typically intellectual superheroes. For instance, they often become resistant to new ideas and uncomfortable with ways of thinking (even about mathematics) that are not their own. They can be defensive about intellectual turf, dismissive of others, or petty in their disputes. Above, I have tried to summarize how the mathematical way of thinking feels and works at its best, without focusing on personality flaws of mathematicians or on the politics of various mathematical fields. These issues are worthy of their own long answers!

You are humble about your knowledge because you are aware of how weak maths is, and you are comfortable with the fact that you can say nothing intelligent about most problems.

There are only very few mathematical questions to which we have reasonably insightful answers. There are even fewer questions, obviously, to which any given mathematician can give a good answer. After two or three years of a standard university curriculum, a good maths undergraduate can effortlessly write down hundreds of mathematical questions to which the very best mathematicians could not venture even a tentative answer. (The theoretical computer scientist Richard Lipton lists some examples of potentially “deep” ignorance here: https://rjlipton.wordpress.com/2009/12/26/mathematical-embarrassments/) This makes it more comfortable to be stumped by most problems; a sense that you know roughly what questions are tractable and which are currently far beyond our abilities is humbling, but also frees you from being very intimidated, because you do know you are familiar with the most powerful apparatus we have for dealing with these kinds of problems.

The importance of stupidity in scientific research

2011-11-05T00:00:00+00:00

by Martin A. Schwartz (Original link)

I recently saw an old friend for the first time in many years. We had been Ph.D. students at the same time, both studying science, although in different areas. She later dropped out of graduate school, went to Harvard Law School and is now a senior lawyer for a major environmental organization. At some point, the conversation turned to why she had left graduate school. To my utter astonishment, she said it was because it made her feel stupid. After a couple of years of feeling stupid every day, she was ready to do something else.

I had thought of her as one of the brightest people I knew and her subsequent career supports that view. What she said bothered me. I kept thinking about it; sometime the next day, it hit me. Science makes me feel stupid too. It’s just that I’ve gotten used to it. So used to it, in fact, that I actively seek out new opportunities to feel stupid. I wouldn’t know what to do without that feeling. I even think it’s supposed to be this way. Let me explain.

For almost all of us, one of the reasons that we liked science in high school and college is that we were good at it. That can’t be the only reason – fascination with understanding the physical world and an emotional need to discover new things has to enter into it too. But high-school and college science means taking courses, and doing well in courses means getting the right answers on tests. If you know those answers, you do well and get to feel smart.

A Ph.D., in which you have to do a research project, is a whole different thing. For me, it was a daunting task. How could I possibly frame the questions that would lead to significant discoveries; design and interpret an experiment so that the conclusions were absolutely convincing; foresee difficulties and see ways around them, or, failing that, solve them when they occurred? My Ph.D. project was somewhat interdisciplinary and, for a while, whenever I ran into a problem, I pestered the faculty in my department who were experts in the various disciplines that I needed. I remember the day when Henry Taube (who won the Nobel Prize two years later) told me he didn’t know how to solve the problem I was having in his area. I was a third-year graduate student and I figured that Taube knew about 1000 times more than I did (conservative estimate). If he didn’t have the answer, nobody did.

That’s when it hit me: nobody did. That’s why it was a research problem. And being my research problem, it was up to me to solve. Once I faced that fact, I solved the problem in a couple of days. (It wasn’t really very hard; I just had to try a few things.) The crucial lesson was that the scope of things I didn’t know wasn’t merely vast; it was, for all practical purposes, infinite. That realization, instead of being discouraging, was liberating. If our ignorance is infinite, the only possible course of action is to muddle through as best we can.

I’d like to suggest that our Ph.D. programs often do students a disservice in two ways. First, I don’t think students are made to understand how hard it is to do research. And how very, very hard it is to do important research. It’s a lot harder than taking even very demanding courses. What makes it difficult is that research is immersion in the unknown. We just don’t know what we’re doing. We can’t be sure whether we’re asking the right question or doing the right experiment until we get the answer or the result. Admittedly, science is made harder by competition for grants and space in top journals. But apart from all of that, doing significant research is intrinsically hard and changing departmental, institutional or national policies will not succeed in lessening its intrinsic difficulty.

Second, we don’t do a good enough job of teaching our students how to be productively stupid – that is, if we don’t feel stupid it means we’re not really trying. I’m not talking about ‘relative stupidity’, in which the other students in the class actually read the material, think about it and ace the exam, whereas you don’t. I’m also not talking about bright people who might be working in areas that don’t match their talents. Science involves confronting our “absolute stupidity”. That kind of stupidity is an existential fact, inherent in our efforts to push our way into the unknown. Preliminary and thesis exams have the right idea when the faculty committee pushes until the student starts getting the answers wrong or gives up and says, “I don’t know”. The point of the exam isn’t to see if the student gets all the answers right. If they do, it’s the faculty who failed the exam. The point is to identify the student’s weaknesses, partly to see where they need to invest some effort and partly to see whether the student’s knowledge fails at a sufficiently high level that they are ready to take on a research project.

Productive stupidity means being ignorant by choice. Focusing on important questions puts us in the awkward position of being ignorant. One of the beautiful things about science is that it allows us to bumble along, getting it wrong time after time, and feel perfectly fine as long as we learn something each time. No doubt, this can be difficult for students who are accustomed to getting the answers right. No doubt, reasonable levels of confidence and emotional resilience help, but I think scientific education might do more to ease what is a very big transition: from learning what other people once discovered to making your own discoveries. The more comfortable we become with being stupid, the deeper we will wade into the unknown and the more likely we are to make big discoveries.

Accepted April 9, 2008.