Hbfs.wordpress.com Website Review

This is my tenth anniversary of blogging. I started back in 2008, and since then there has been 586 posts (including this one), about 1300 comments, 170 followers, 325000 visitors, and 825000 views, with a few posts well over 9000. Well, that’s not bad, but for now, I must reconsider the time I invested, and will continue to invest in this blog. Posting once a week, even with the summer hiatuses, is becoming a strenuous pace, especially that I also have—many—other things to do. So I was tempted to just end it, and say, well, after ten years, it’s much better than your average two-post blog, let’s call it quits. But the thing is, I enjoy writing about the things I do, the math I work out, and other ideas. But I will likely stop for this month, then be back look at the Rosenberg-Strong function—and we’ll modify it a bit. number—think of a number-theoretical version of std::pair. Cantor was the first (or so I think) to propose one such function. His goal wasn’t data compression but to show that there are as many rationals as natural numbers. Cantor’s function as*ociates pairs (i,j) with a single number: …but that’s not the only way of doing this. A much more fun—and spatially coherent—is the boustrophedonic pairing function. In the last few weeks (of posts, because in real time, I worked on that problem over a week-end) we’ve looked at how to generate well distributed, maximally different colors. The methods were to use well-distributed sequences or lattices to ensure that the colors are equidistant. What if we used physical an*logies to push the colors around so that they are maximally apart? Last week, we’ve had a look at how to distribute maximally different colors on the RGB cube. But I also remarked that we could use some other color space, say HSV. How do we distribute colors uniformly in HSV space? are meaningful in themselves (like a rainbow to encode heat) or maximally different when the image is segmented (like a map showing