Close-Up tells the story of Hossain Sabzian, a con-man (or just troubled individual) who leads a family to believe he is Mohsen Makhmalbaf, famous Iranian director. The film uses a mix of documentary footage with reenactments, where all characters are played by their real-life counterparts.
Like Certified Copy, the film has you guessing which footage is "real" and which is fabricated, which is a fun guessing game, but each time you play it you realize you're missing the point: it's all a movie and "truth" isn't as important as "Truth". Still, watching a reenactment of a bus ride where Sabzian first dupes a middle-age woman into believing he is a director is mesmerizing. First, it's hard to believe both the perpetrator and the victim would agree to appear together to reenact the crime in which they were involved. Second, despite the fact that everyone in the film are non-actors, they all play themselves believably, with no self-aware winks, even when their parts do not always paint them in the best light.
On a side note, the movie shows what I presume to be real court proceedings of Iran, which is fascinating in its own right. It plays out more like a group therapy session, where all in attendance can speak up, and there's no real "prosecutor" or "defense". Hearsay seems to be allowed, and the victim gets a say in the sentencing ("forgiving" the defendant can lessen the sentence). While it seemed to be therapeutic for all parties involved here, I find it hard to take without a grain of salt. Iran has a pretty tight lock on what media gets out of Iran (or even what media gets made in the first place), so you have to imagine this is a best-case scenario. And maybe there are different rules for more serious crimes, but asking the victim point-blank several times if they forgive the defendant--in the defendant's presence--for violent crimes could be pretty traumatic, if not outright dangerous.
Anyway, highly recommended. Don't let the subtitles scare you away.
I use meld as my git merge tool, and the latest version of Ubuntu (13.10) changed its interface, breaking my merge workflow.
Previously, I had a .gitconfig containing:
[merge] tool = meld-autoresolve defaultToUpstream = true [mergetool "meld-autoresolve"] cmd = meld --diff $LOCAL $BASE $REMOTE $MERGED
Which shows a 3-way diff, automatically picks all nonconflicting changes, and writes the resolved file to the correct location.
Meld 1.7 changed its CLI UI (thanks, GNOME), so to get a 3-way diff which writes to the correct file and also auto-picks nonconflicting changes, the proper invocation is now:
[mergetool "meld-autoresolve"] cmd = meld --output $MERGED $LOCAL $BASE $REMOTE --auto-merge
Rhythm Thief was pretty much a disappointment. As a fan of Phoenix Wright, Professor Layton, and Elite Beat Agents, it seemed like a perfect mix of the three. Unfortunately, it has none of the humor and wit of Phoenix Wright, none of the charm of Professor Layton, and the game mechanics themselves were not as fun as Elite Beat.
In particular, Where Elite Beat, Guitar Hero, and Rock Band make it feel like you are "playing" the music, Rhythm Heaven just layers calls and responses on top of the music. While technically it's timed to the rhythm of the music (mostly), the gameplay and the music don't feel very connected. The one exception is the violin minigame--which also happen to be the easiest.
One redeeming factor is the graphics and 3D cutscenes. While the story itself is full of "anime bullshit", from a technical and visual aesthetic level, it's very nice to look at.
If you want to lose faith in humanity, I highly recommend watching Compliance. Even with knowledge of the Milgram experiment and the Stanford prison expiriment exploring how easily normal people can be coerced into compromising their morals, it is quite shocking to see how far people will go in this film based on a true story.
About 10 minutes in to the movie, I thought to myself, "Ok, this is where I'd bail out", and yet the movie goes for another 80 minutes of increasingly unbelievable behavior inflicted on a girl by her coworkers at the request of an authority figure on the other end of a phone line. At the end, I was pretty incredulous and did some brief research on the real events, believing they must've been embellished. To the contrary, every hard-to-believe event really did happen basically as presented. You can read up on it yourself if you want, but I recommend going in to the movie as cold as possible.
One of the common criticisms of the movie I see is that "all the characters are unbelievably dumb." I think the cast does as good a job as possible with the material, and the feeling of disbelief is exactly what they're going for. Recognizing how low we can sink is the first step to prevention.
One year ago I went to a Python meetup where William Bert made a presentation on gensim, a "topic modelling" library for Python. One of the most practical uses of topic modelling is finding similar documents from a large corpus. For example, where Google search takes a query as an argument and returns documents based on that query, Google News tries to organize stories from multiple sources into clusters based on the same topic or event.
In memory of Google Reader shutting down tomorrow, I present rsscluster, a small script which demonstrates the usage of the gensim library. One of the problems I ran into with wanting to play with gensim was finding a large corpus of documents with which to populate the database. When Google Reader announced it was closing and I looked for a new home for my feeds, I found a great corpus to play with.
My typical set of feeds contains about 3000 stories, which isn't a huge corpus, but enough to play with. Crucially, there are a number of feeds I subscribe to which are likely to produce "similar" documents. For example, Ars Technica and The Verge will probably both have stories about the latest Apple keynote, while The Washington Post and NPR will both cover recent Supreme Court decisions.
You can see how well rsscluster did in finding stories similar to those in my feeds published today (June 30th) here. For as little effort I put in and considering how small the corpus is, it did a pretty good job of detecting clusters around recent events (Edward Snowden and the NSA, a presidential tour of Africa, John Kerry speaking on the Middle East). It did a slightly worse job categorizing Supreme Court stories, as they've been pretty active lately and ruled on a lot of disparate issues. Gensim decided to cluster those up more than it should have. Finally, some of my feeds are just not very semantically rich, and it decided to cluster a bunch of Steam sales together. One feed is particularly degenerate in that it only contains the word "NO" in each entry. Gensim dutifully clustered all of those documents together, but those documents probably don't belong in the corpus at all.
Again, this is a really naive use of gensim (I spend more time parsing command line arguments than actually exercising the library), but it let me get gensim testing out of my system and it demonstrates how easy it is to set up a pretty powerful document similarity search engine. Hopefully someone else can also be inspired to play with it, and will find a better scenario with which to exercise it.
See it on github.
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