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which is independent of the bit index n, so that the bit index can be dropped.
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Obviously, having eight neighbors at most, the direction of the next index can be represented by three bits in the worst case.
For a new bus journey record, the index can be efficiently updated by appending a new bit at the end of each bit-vector.
Thus, each frequency index can be coded using ⌈log2(N/2)⌉ bits where N is the STFT length.
The wrong index can be easy to find.
Stocks indexes can be trendy.
But the rewards, accumulating bit by bit, can be great.
A long, but algorithmically simple, bit string can be compressed into a much shorter bit string.
Finally, we are investigating whether bit-patterns can be used to index and manage protein folding simulation data.
By rearranging the bits of the prefixes, memory efficient index tables can be constructed to support IP address lookup.
The main drawback of these indexes is their space requirement of Ω(nlogn) bits, which can be much more than the optimal nlogσ bits to store the text T. This paper addresses an open question asked by Mäkinen and Navarro [LATIN, 2006], which is whether it is possible to design a succinct index answering PRSS queries efficiently.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com