Month: March 2015

Atavachron

Posted on by John

In the Star Trek episode “All Our Yesterdays” the people of the planet Sarpeidon have escaped into their past because their sun is about to become a supernova. They did this via a time machine called the Atavachron.

One detail of the episode has stuck with me since I first saw it many years ago: although people can go back to any period in history, they have to be prepared somehow, and once prepared they cannot go back. Kirk, Spock, and McCoy only have hours to live because they traveled back in time via the Atavachron without being properly prepared. (Kirk is in a period analogous to Renaissance England while Spock and McCoy are in an ice age.)

If such time travel were possible, I expect you would indeed need to be prepared. Life in Renaissance England or the last ice age would be miserable for someone with contemporary expectations, habits, fitness, etc., though things weren’t as bad for the people at the time. Neither would life be entirely pleasant for someone thrust into our time from the past. Cultures work out their own solutions to life’s problems, and these solutions form a package. It may not be possible to swap components in and out à la carte and maintain a working solution.

Why isn’t everything normally distributed?

Posted on by John

Adult heights follow a Gaussian, a.k.a. normal, distribution [1]. The usual explanation is that many factors go into determining one’s height, and the net effect of many separate causes is approximately normal because of the central limit theorem.

If that’s the case, why aren’t more phenomena normally distributed? Someone asked me this morning specifically about phenotypes with many genetic inputs.

The central limit theorem says that the sum of many independent, additive effects is approximately normally distributed [2]. Genes are more digital than analog, and do not produce independent, additive effects. For example, the effects of dominant and recessive genes act more like max and min than addition. Genes do not appear independently—if you have some genes, you’re more likely to have certain other genes—nor do they act independently—some genes determine how other genes are expressed.

Height is influenced by environmental effects as well as genetic effects, such as nutrition, and these environmental effects may be more additive or independent than genetic effects.

Incidentally, if effects are independent but multiplicative rather than additive, the result may be approximately log-normal rather than normal.

* * *

Fine print:

[1] Men’s heights follow a normal distribution, and so do women’s. Adults not sorted by sex follow a mixture distribution as described here and so the distribution is flatter on top than a normal. It gets even more complicated when you considered that there are slightly more women than men in the world. And as with many phenomena, the normal distribution is a better description near the middle than at the extremes.

[2] There are many variations on the central limit theorem. The classical CLT requires that the random variables in the sum be identically distributed as well, though that isn’t so important here.

Machine learning and magic

Posted on by John

When I first heard about a lie detector as a child, I was puzzled. How could a machine detect lies? If it could, why couldn’t you use it to predict the future? For example, you could say “IBM stock will go up tomorrow” and let the machine tell you whether you’re lying.

Of course lie detectors can’t tell whether someone is lying. They can only tell whether someone is exhibiting physiological behavior believed to be associated with lying. How well the latter predicts the former is a matter of debate.

I saw a presentation of a machine learning package the other day. Some of the questions implied that the audience had a magical understanding of machine learning, as if an algorithm could extract answers from data that do not contain the answer. The software simply searches for patterns in data by seeing how well various possible patterns fit, but there may be no pattern to be found. Machine learning algorithms cannot generate information that isn’t there any more than a polygraph machine can predict the future.

Quaternions in Paradise Lost

Posted on by John

Last night I checked a few books out from a library. One was Milton’s Paradise Lost and another was Kuipers’ Quaternions and Rotation Sequences. I didn’t expect any connection between these two books, but there is one.

photo of books mentioned here

The following lines from Book V of Paradise Lost, starting at line 180, are quoted in Kuipers’ book:

Air and ye elements, the eldest birth
Of nature’s womb, that in quaternion run
Perpetual circle, multiform, and mix
And nourish all things, let your ceaseless change
Vary to our great maker still new praise.

When I see quaternion I naturally think of Hamilton’s extension of the complex numbers, discovered in 1843. Paradise Lost, however, was published in 1667.

Milton uses quaternion to refer to the four elements of antiquity: air, earth, water, and fire. The last three are “the eldest birth of nature’s womb” because they are mentioned in Genesis before air is mentioned.

 

Technical notes

Posted on by John

For the last fifteen Wednesdays I’ve been posting links to technical notes. This is the end of the series.

You can find most of the links from previous Wednesday posts on one page by going to technical notes from the navigation menu at the top of the site.

Graphemes

Posted on by John

Here’s something amusing I ran across in the glossary of Programming Perl:

grapheme A graphene is an allotrope of carbon arranged in a hexagonal crystal lattice one atom thick. Grapheme, or more fully, a grapheme cluster string is a single user-visible character, which in turn may be several characters (codepoints) long. For example … a “ȫ” is a single grapheme but one, two, or even three characters, depending on normalization.

In case the character ȫ doesn’t display correctly for you, here it is:

Unicode character U_022B

First, graphene has little to do with grapheme, but it’s geeky fun to include it anyway. (Both are related to writing. A grapheme has to do with how characters are written, and the word graphene comes from graphite, the “lead” in pencils. The origin of grapheme has nothing to do with graphene but was an analogy to phoneme.)

Second, the example shows how complicated the details of Unicode can get. The Perl code below expands on the details of the comment about ways to represent ȫ.

This demonstrates that the character . in regular expressions matches any single character, but \X matches any single grapheme. (Well, almost. The character . usually matches any character except a newline, though this can be modified via optional switches. But \X matches any grapheme including newline characters.)

   
# U+0226, o with diaeresis and macron 
my $a = "\x{22B}"; 

# U+00F6 U+0304, (o with diaeresis) + macron 
my $b = "\x{F6}\x{304}";    
     
# o U+0308 U+0304, o + diaeresis + macron   
my $c = "o\x{308}\x{304}"; 

my @versions = ($a, $b, $c);

# All versions display the same.
say @versions;

# The versions have length 1, 2, and 3.
# Only $a contains one character and so matches .
say map {length $_ if /^.$/} @versions;

# All versions consist of one grapheme.
say map {length $_ if /^\X$/} @versions;