But wait. We already had an E, at 5 Hz. If we put these two E’s in the same octave, then one of them is at 80/64 Hz, and the other is at 81/64 Hz. That may not seem like much of a difference, but even untrained listeners will be able to hear that they are out of tune with each other. Furthermore, if we use the E derived from C, then it will be out of tune with A. However, if we use the E derived from A, then it will be out of tune with C. This is going to be a problem.
This story was originally featured on Fortune.com
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一群研究人員決定測試「正向思考」是否能提高人工智慧(AI)聊天機器人的準確率,結果卻出乎意料。他們向不同的聊天機器人提問,嘗試稱讚它們「聰明」,鼓勵它們認真思考,甚至在問題結尾加上一句「這會很有趣!」。然而,這些方法都沒有產生一致的效果,但其中一種方法脫穎而出。當他們讓AI假裝自己身處《星際迷航》場景,它的基礎數學能力竟然有所提升。看來,它真的能把我傳送上去。
If you know what arithmetic coding is, FSE is like that, but for large alphabets.zstd complicates the pre-processing step and uses Finite State Entropy instead of Huffman coding, which effectively allows tokens to be encoded with fractional bit lengths. FSE is simple, but requires large tables, so let’s say ~2000 bytes for storing and parsing them. Adding glue, we should get about 3 KB.On the web, brotli often wins due to a large pre-shared dictionary. It raises the size of the decoder, so in our setup, it’s a hindrance, and I’m not taking it into consideration.brotli keeps Huffman coding, but switches between multiple static Huffman tables on the flight depending on context. I couldn’t find the exact count, but I get 7 tables on my input. That’s a lot of data that we can’t just inline – we’ll need to encode it and parse it. Let’s say ~500 bytes for parser and ~100 bytes per table. Together with the rest of the code, we should get something like 2.2 kB.For bzip decoders, BWT can be handled in ~250 bytes. As for the unique parts,bzip2 compresses the BWT output with MTF + RLE + Huffman. With the default 6 Huffman tables, let’s assign ~1.5 KB to all Huffman-related code and data and ~400 bytes for MTF, RLE, and glue.