correct typo in about

Author: toddpress

about/index.html

@@ -179,7 +179,7 @@ <h2 id="how-does-roaring-compares-with-the-alternatives">How does Roaring compar
 
 <p>There is a big problem with these formats however that can hurt you badly in some cases: there is no random access. If you want to check whether a given value is present in the set, you have to start from the beginning and &ldquo;uncompress&rdquo; the whole thing. This means that if you want to intersect a big set with a large set, you still have to uncompress the whole big set in the worst case&hellip;</p>
 
-<p>Roaring solves this problem. It works in the following manner. It divides the data into chunks of 2<sup>16</sup> integers (e.g., [0, 2<sup>16</sup>), [2<sup>16</sup>, 2 x 2<sup>16</sup>), &hellip;). Within a chunk, it can use an uncompressed bitmap, a simple list of integers, or a list of runs. Whatever format it uses, they all allow you to check for the present of any one value quickly (e.g., with a binary search). The net result is that Roaring can compute many operations much faster that run-length-encoded formats like WAH, EWAH, Concise&hellip; Maybe surprisingly, Roaring also generally offers better compression ratios.</p>
+<p>Roaring solves this problem. It works in the following manner. It divides the data into chunks of 2<sup>16</sup> integers (e.g., [0, 2<sup>16</sup>), [2<sup>16</sup>, 2 x 2<sup>16</sup>), &hellip;). Within a chunk, it can use an uncompressed bitmap, a simple list of integers, or a list of runs. Whatever format it uses, they all allow you to check for the present of any one value quickly (e.g., with a binary search). The net result is that Roaring can compute many operations much faster than run-length-encoded formats like WAH, EWAH, Concise&hellip; Maybe surprisingly, Roaring also generally offers better compression ratios.</p>
 
 <h2 id="funding">Funding</h2>