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Suboptimal LaTeX #4: mathematics

In the previous episodes: generic mistakes and suggestions, and how to fix the spacing and math environments in your papers. Continuing along these lines, in this post we will see some common mistakes done when writing mathematics.
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Posted in Typography.

Tagged with AmsMath, English, LaTeX, Math, MathTools, Mistakes, Typography.


Suboptimal LaTeX #3: mathematical environments

We have seen some generic mistakes and suggestions, and how to correctly use spacing in your papers. Today we will try to shine some light on how to choose the proper mathematical environment, which is in itself a complicate issue.
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Posted in Typography.

Tagged with AmsMath, English, LaTeX, Math, MathTools, Mistakes, Typography.


Suboptimal LaTeX #2: spacing

In the first post of the series I gave some high level suggestions on writing a paper with LaTeX. Instead, from now on I will focus on specific typographical areas. The first I want to explore is spacing, which often goes unnoticed (because, well, you cannot see spaces) but is nonetheless one of the most important aspects in ensuring that a paper flows correctly.
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Posted in Typography.

Tagged with English, LaTeX, Mistakes, Spacing, Typography.


Suboptimal LaTeX #1: intro

In one of my previous careers, I have been a typesetter for a journal in high energy physics (JHEP), for which I typeset several hundreds of papers. In case you do not know, this means applying the journal’s style and conventions to the LaTeX source provided by the author, and fixing many errors in the process (both coming from the author and from LaTeX). During this job, I could observe a rich bestiary of LaTeX mistakes, and in this series I will guide you through the most common.
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Posted in Typography.

Tagged with English, LaTeX, Mistakes, Typography.


How much is time wrong around the world?

A few years ago I went to Spain for the first time, and like many I was surprised by how late is dinner. The first night I dined almost alone in a restaurant at 8pm, going away just as people were starting to come in. Of course this can be mostly explained by cultural reasons, but the clearly later-than-usual summer sunsets must also have played a role in shaping the Spanish days.

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Posted in Statistics.

Tagged with English, Map, Projection, Solar time, Spain, Standard time, Statistics, Time, Time zone.


Cycling in the rain

Cycling in London is not the most pleasurable ride, one could say for the drivers’ attitude against cyclists, but mostly because I hate riding in the rain. And, you know, rain-wise London is nowhere as bad as people like to complain, but nevertheless it’s bad.

I started commuting to and from work with my bike, but it seems that it’s always raining, or worse that it’s not raining but the bike is in the other place because I failed to bring it with me last time. Are these feelings real?

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Posted in Statistics.

Tagged with Cycling, English, London, Probability, Rain, Statistics.


Facebook, Gramellini predice quando morirai

Ora che ho l’attenzione del pubblico, devo dire che a me questa storia di Gramellini non deprime più di tanto, la vedo più che altro come uno splendido esempio di internet senza fili: ricordate il telefono senza fili, quel gioco in cui una frase veniva detta all’orecchio della persona successiva e dopo qualche passaggio diventava qualcosa di completamente diverso? Questa è la versione digitale.

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Posted in Politics.

Tagged with Facebook, Gramellini, Italian, Italy.


Give Python a bit of type safety with Pydoc Checker

We all love Python: it is easy to learn, expressive, powerful and with impressive libraries to perform complicate operations with only a few keystrokes. We love it so much that it ends up being used in projects big enough for its limitations and drawbacks to appear. Not that I am not guilty of this. And for this exact reason, I wrote Pydoc Checker, a small module that addresses one of these issues: the (too) dynamic type system.

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Posted in Software.

Tagged with CMS, English, Pydoc, pydocchecker, Python, Software, Type system.


Cosa vuol dire “rappresentativo”?

Recentemente l’Italia ha eletto il suo nuovo Presidente della Repubblica. Insieme ad essere il primo Presidente al secondo mandato, l’altra novità è stata la richiesta di uno dei partiti coinvolti di fornire una preferenza per il nome da votare in Parlamento. Questa scelta è stata lodata da alcuni e criticata da altri, in particolare perché i parlamentari del Movimento 5 Stelle professano di essere portavoce dei loro elettori, ma gli ammessi al voto erano non più di 50 mila persone, a fronte di 8,5 milioni di votanti per il Movimento 5 Stelle alle scorse elezioni.

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Posted in Politics, Statistics.

Tagged with Italian, Italy, M5S, Politics, Statistics.


Quanti sono i laureati, tra eletti ed elettori

In uno dei suoi post post-elettorali, Beppe Grillo presenta alcuni numeri per indicare il miglioramento del livello dei parlamentari tra la scorsa legislatura e la presente. Tra questi numeri, la percentuale di donne, l’età media e la percentuale di laureati. Il problema è che queste distribuzioni (sesso, età, educazione) sono correlate nella popolazione italiana; in particolare, non è un mistero che ci siano più laureati tra i giovani che non tra gli anziani. Quindi sorge la domanda: il primato del Movimento 5 Stelle per percentuale di laureati è un effettivo merito o è possibile giustificarlo semplicemente grazie alla distribuzione delle età dei loro eletti?

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Posted in Politics, Statistics.

Tagged with Age, Binomial distribution, Education, Elections, Italian, Italy, M5S, Normal distribution, Politics, Statistics.