Yep, as you acknowledge, your reasoning is irrational. Replacing regular milk with coconut milk is more environmentally friendly, medium-chain fatty acids are better for the liver and heart.
Still, veganism is much more than just a diet and this meaning is what was meant by the vegan-friendly natural latex.
I do assume that people who have never googled "veganism" might have negative opinion of these condoms, but I guess they also won't be aware of the struggles of third world countries and won't care about the goal of this company.
Not that I don't find the fact that veganism had to be mention entertaining.
The author could change the generation of piece by generating a short theme, then fixing that theme at particular points in the note sequence and then generate the gaps, and then repeat the thing as recursively as he wants.
This would provide some recurrence of melodies and would sound infinitely better. Problem with these kinds of models is the window based structure without the notion of the general theme. Theme should then be added by the human, and everything else can be filled using the model.
Why do they need a deep learning model for this? They are obviously targeting signs, product names, menus and similar. Model will obviously fail in translating large texts.
Was there any advantage of using a deep learning model instead of something more computationally simple?
There's also a compiler for Haskell to JavaScript called GHCJS[0]. Many of Haskell libraries can be compiled easily. I think someone compiled Pandoc directly to JS and made a web interface and bundled the app[2].
One interesting things besides web that people have been doing with it is writing Atom editor plugins[1].
This doesn't seem to solve the puzzle. As much as DSL processing part is done, the whole point of the puzzle was to make the operations sublinear in time complexity.
Sampling should at most take linear time in the number of pixels (ignoring the edge detection). Even less if edge detection result is represented as a sparse array.
Microsoft case has nothing to do with this - even the market share numbers (that are obviously way smaller in case of Apple) do not influence the absence of lawsuits.
Apple produces the hardware and the software.
Microsoft imposed software restrictions (or lets say defaults) on hardware sold by vendors which turned out undesirable for EU. If Apple offered iOS on other phones and restricted the browser application to Safari, they would obviously get sued.
Since they "own" the hardware, and bundle it with their own software - there's nothing illegal about it.
Not that I condone of that kind of behavior, just clearing up the reasoning that was not present in the article.
It improves the performance by fusing some of the operations doing them in one pass instead of multiple passes, and it does so generically - operations are composed regardless of the source, and the implementation isn't looking at the type of the source at all.
I believe one of the arguments was also that these couldn't be written in statically typed languages. Although, I do not know if this turned out to be true.
This is only slightly complicated if you're writing your own transducer operation.
What is interesting is that these transducing operations can be written quite generically - code reuse is huge.
But, usage of transducers is similar to Java 8 Streams, or Haskell stream fusion, only that the implementation is fully generic, not dependent on the container/source.
Large dictionaries (set of words) can compactly be represented with tries.
For words with similar prefixes and suffixes directed acyclic word graph is a much better option (reuses prefixes and suffixes, not just the prefix as in trie), it's a little bit slower to build but fast to traverse if done right.
Any problem where there's a lot of suffix/prefix reusage benefits from a proper trie implementation (or suffix array/tree as alternative) - ex. lempel-ziv compression.