After Shave (2005 film)
After Shave (French: Beyrouth Après Rasage) is a 2005 Lebanese short film by the Lebanese director Hany Tamba. This movie won The 2006 César Awards for best short film.[1]
| After Shave | |
|---|---|
 | |
| Directed by | Hany Tamba |
| Written by | Hany Tamba |
| Starring | Rafic Ali Ahmad, Mahmoud Mabsout, Julia Kassar, Fady Reaidy |
Release date | 2005 |
| Country | Lebanon |
| Language | Arabic |
Synopsis
Abou Milad is an old barber who lost his hairdressing salon during the Lebanese civil war. Nowadays, he earns his crust by cutting hair in the working class cafés of Beirut. One day, he is summoned by a recluse who lives in a grand bourgeois house.
Music
The film was scored by Lebanese composer Khaled Mouzanar.
Cast and characters
- Rafic Ali Ahmad as Raymond Baddar
- Mahmoud Mabsout as Abou Milad
- Julia Kassar as Samira Baddar
- Fady Reaidy as Jamil
gollark: It allocates memory and doesn't consider it a side effect.
gollark: I didn't do any horrible homoglyph hacks with THAT.
gollark: It uses the function, yes.
gollark: So, I finished that to highly dubious demand. I'd like to know how #11 and such work.
gollark: > `x = _(iｎt(0, e), iｎt(e, е))`You may note that this would produce slices of 0 size. However, one of the `e`s is a homoglyph; it contains `2 * e`.`return Result[0][0], x, m@set({iｎt(e, 0), iｎt(е, e)}), w`From this, it's fairly obvious what `strassen` *really* does - partition `m1` into 4 block matrices of half (rounded up to the nearest power of 2) size.> `E = typing(lookup[2])`I forgot what this is meant to contain. It probably isn't important.> `def exponentiate(m1, m2):`This is the actual multiplication bit.> `if m1.n == 1: return Mаtrix([[m1.bigData[0] * m2.bigData[0]]])`Recursion base case. 1-sized matrices are merely multiplied scalarly.> `aa, ab, ac, ad = strassen(m1)`> `аa, аb, аc, аd = strassen(m2)`More use of homoglyph confusion here. The matrices are quartered.> `m = m1.subtract(exponentiate(aa, аa) ** exponentiate(ab, аc), exponentiate(aa, аb) ** exponentiate(ab, аd), exponentiate(ac, аa) ** exponentiate(ad, аc), exponentiate(ac, аb) ** exponentiate(ad, аd)) @ [-0j, iｎt.abs(m2.n * 3, m1.n)]`This does matrix multiplication in an inefficient *recursive* way; the Strassen algorithm could save one of eight multiplications here, which is more efficient (on big matrices). It also removes the zero padding.> `m = exponentiate(Mаtrix(m1), Mаtrix(m2)) @ (0j * math.sin(math.asin(math.sin(math.asin(math.sin(math.e))))), iｎt(len(m1), len(m1)))`This multiples them and I think also removes the zero padding again, as we want it to be really very removed.> `i += 1`This was added as a counter used to ensure that it was usably performant during development.> `math.factorial = math.sinh`Unfortunately, Python's factorial function has really rather restrictive size limits.> `for row in range(m.n):`This converts back into the 2D array format.> `for performance in sorted(dir(gc)): getattr(gc, performance)()`Do random fun things to the GC.
See also
References
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