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TwoFx

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Maybe I'm just missing the joke, but it feels worth pointing out that almost all of the logos on that page are clearly inspired by the ensō circle from Zen art.

Cloth Simulation 7 months ago

I highly recommend watching the relevant section of that video (4:38 to 8:59) and then implementing it yourself in whatever system you know that can draw lines and circles (I did it in Godot; it took only a few minutes to learn enough Godot to start on the algorithm).

It's absolutely mind-blowing that so little code can produce such a beautiful result. It's also fun to play with the parameters and see how they affect how the cloth feels.

Access has been somewhat reluctant to make BeOS-related material available in the past. For example, they did provide access to the BeOS API documentation (called the Be Book) to the Haiku project, but only under a restrictive no-derivatives license (see [1]). This has caused some pain the past, as extra care must be taken that Haiku's API documentation, the Haiku Book [2], is not a derivative work, so everything had to be rewritten from scratch, even documentation for parts of the API that are largely unchanged.

[1] https://www.haiku-os.org/legacy-docs/bebook/LegalNotice.html

[2] https://www.haiku-os.org/docs/api/index.html

Adding the thing that complains is a one-time cost. It may work on Firefox now, but making sure that it continues working on Firefox is a continuous resource investment.

From the paper: "To investigate the noisiness of using Reddit as a source of self-annotated sarcasm we estimate the proportion of false positives and false negatives induced by our filtering. This is done by having three human evaluators manually check a random subset of 500 comments from SARC-main tagged as sarcastic and 500 tagged as non-sarcastic, with full access to the comment’s context. A comment was labeled a false positive if a majority determined that the “/s” tag was not an annotation but part of the sentence and a false negative if a majority determined that the comment author was clearly being sarcastic. After evaluation, the false positive rate was determined to be 2.0% and the false negative rate 3.0%. Although the false positive rate is reasonable, the false negative rate is significant compared to the sarcasm proportion, indicating large variation in the working definition of sarcasm and the need for methods that can handle noisy data in the unbalanced setting."

Unexecute 10 years ago

Do you happen to have a link to the code in FAKE that implements this for .NET?