EOF

I have been running the blog for four full years now, never missing a week, posting some 345 entries. While the experiment has been quite enjoyable, I feel it is time for me to move on to something else.

To avoid repeating one self, or doing trivial entries too often, one must always push forward. Some blog entries took half an hour to write, but a lot actually asked for quite a bit o’work. Keeping the pace has been increasingly difficult given all the other things that are going on in my life.

I have thought for a while of merely slowing the pace and posting say, twice a month, because I would have enough material to do so—a lot comes up all the time. For a while, I thought of posting only the occasional entry. But I think it will keep asking for more time, and that my energies are needed elsewhere presently.

I would like to thank my followers (there are quite a few) and all the others for all the good discussions in the (800-something) comments. I will not close the blog, so I will still manage it, at least for a while.

Suggested Reading: The Filter Bubble

Eli Pariser — The Filter Bubble: What the Internet Is Hiding from You — Penguin Press HC, 2011, 304 pp. ISBN 978-1594203008

(Buy at Amazon.com)

Pariser warns us about the danger of (potentially) excessive personalization of search engine queries, such as Google’s. While the search engine’s first goal is to be useful, targeted, and relevant to your personal interests, they pose the very real danger of hiding large segments of the web from search, thus preventing serendipitous discovery of new interests, possibly quite orthogonal to your usual tastes.

However, Pariser goes on a long while on the subject; after a few examples, we quite get the idea, and the book could have been considerably shorter, offering a few examples and then discussing the consequences, without loss. I have sometimes the impression that the author (or maybe his editor) feared a short, direct-to-the-point, book. However, conveying information succinctly is sometimes better.

Suggested Reading: Data Smog

David Shenk — Data Smog: Surviving the Information Glut (Revised and Updated Edition) — Harper One, 1998, 256 pp. ISBN 978-0062515513

(Buy at Amazon.com)

This book, not unrelated to Schwartz’s Paradox of Choice, presents the fundamental paradox of information: the more you get, the less you can understand, the more you get, the narrower the vision you must have; the more you get, the more isolated you are. In a dozen “information glut laws”, Shenk discusses the various problems due to information glut (not only from classical media but also from the Internet) and offers tentative solutions to keep your head out of the water.

What’s the most interesting about this book is, maybe, the fact that it was written in 1997, a bit before the Internet became truly mainstream, and before his conclusions became somewhat self-evident (especially a posteriori). It can be read as a prelude to Pariser’s Filter Bubble.

Fat, Slim Pointers

64 bits address space lets us access tons more memory than 32 bits, but with a catch: the pointers themselves are … well, yes, 64 bits. 8 bytes. Which eventually pile up to make a whole lot of memory devoted to pointers if you use pointer-rich data structures. Can we do something about this?

Well, in ye goode olde dayes of 16 bits/32 bits computing, we had some compilers that could deal with near and far pointers; the near, 16-bit pointers being relative to one of the segments, possibly the stack segment, and the far, 32-bits pointers being absolute or relative to a segment. This, of course, made programming pointlessly complicated as each pointer was to be used in its correct context to point to the right thing.

Glass Tardis

A good friend of mine and his wife just had a beautiful baby girl, and I wanted to give them something different. Turns out, the mom is a big fan of Dr Who and so I had the idea of making a stain-glass Tardis nightlight.

So I looked around the interwebs to find a couple of pictures of the Tardis to get the scale/ratio right, and set to cut glass:

Bad Graphs!

One of the good things of the peer review process is that if you publish, you’re eventually going to have to review papers for conferences or journal in your (perceived) area of expertise. Sometimes you get pearls such as “the resulting results of algorithm X are resulted” (true story), or “the dynamics of the attorney of yes no plasmodium” (also true), but sometimes bad science comes from the bad presentation of results.

This is also a (essentially true) story. So I’m reviewing a paper that proposes some kind of method for predicting the value of (some) parameter that minimizes some error function. The method is fast, but not analytic. The graph in the paper looks something like:

Stemming

A few weeks ago, I went to Québec Ouvert Hackathon 3.3, and I was most interested by Michael Mulley’s Open Parliament. One possible addition to the project is to use cross-referencing of entries based not only on the parliament-supplied subject tags but also on the content of the text itself.

One possibility is to learn embeddings on bags of words but on stemmed words to reduce the dimensionality of the one-hot vector, essentially a bitmap where the bit corresponding to a word is set to 1 if it appears in the bag of words. So, let us start at the beginning, stemming.

Fast Interpolation (Interpolation, part V)

In the last four installments of this series, we have seen linear interpolation, cubic interpolation, Hermite splines, and lastly cardinal splines.

In this installment (which should be the last of the series; at least for a while), let us have a look at how we can implement these interpolation efficient.

Artsy Recycling: follow-up

A while ago I wrote to my mayor to ask for better recycling of electronics and other technological items in my home town. The mayor responded rapidly with good news!

Cardinal Splines (Interpolation, part IV)

In the last installment of this series, we left off Hermite splines asking how we should choose the derivatives at end points so that patches line up nicely, in a visually (or any other context-specific criterion) pleasing way.

Cardinal splines solve part of this problem quite elegantly. I say part of the problem because they address only the problem of the first derivative, ensuring that the curve resulting from neighboring patches are -continuous, that is, the patches line up at the same point, and are -continuous, that is, the first derivatives line up as well. We can imagine splines what are (up to) -continuous, that is, patches lining up, and up to the -th derivatives lining up as well.