The End 2016 Mathematics A To Z: Hat


I was hoping to pick a term that was a quick and easy one to dash off. I learned better.

Hat.

This is a simple one. It’s about notation. Notation is never simple. But it’s important. Good symbols organize our thoughts. They tell us what are the common ordinary bits of our problem, and what are the unique bits we need to pay attention to here. We like them to be easy to write. Easy to type is nice, too, but in my experience mathematicians work by hand first. Typing is tidying-up, and we accept that being sluggish. Unique would be nice, so that anyone knows what kind of work we’re doing just by looking at the symbols. I don’t think anything manages that. But at least some notation has alternate uses rare enough we don’t have to worry about it.

“Hat” has two major uses I know of. And we call it “hat”, although our friends in the languages department would point out this is a caret. The little pointy corner that goes above a letter, like so: \hat{i} . \hat{x} . \hat{e} . It’s not something we see on its own. It’s always above some variable.

The first use of the hat like this comes up in statistics. It’s a way of marking that something is an estimate. By “estimate” here we mean what anyone might mean by “estimate”. Statistics is full of uses for this sort of thing. For example, we often want to know what the arithmetic mean of some quantity is. The average height of people. The average temperature for the 18th of November. The average weight of a loaf of bread. We have some letter that we use to mean “the value this has for any one example”. By some letter we mean ‘x’, maybe sometimes ‘y’. We can use any and maybe the problem begs for something. But it’s ‘x’, maybe sometimes ‘y’.

For the arithmetic mean of ‘x’ for the whole population we write the letter with a horizontal bar over it. (The arithmetic mean is the thing everybody in the world except mathematicians calls the average. Also, it’s what mathematicians mean when they say the average. We just get fussy because we know if we don’t say “arithmetic mean” someone will come along and point out there are other averages.) That arithmetic mean is \bar{x} . Maybe \bar{y} if we must. Must be some number. But what is it? If we can’t measure whatever it is for every single example of our group — the whole population — then we have to make an estimate. We do that by taking a sample, ideally one that isn’t biased in some way. (This is so hard to do, or at least be sure you’ve done.) We can find the mean for this sample, though, because that’s how we picked it. The mean of this sample is probably close to the mean of the whole population. It’s an estimate. So we can write \hat{x} and understand. This is not \bar{x} but it does give us a good idea what \hat{x} should be.

(We don’t always use the caret ^ for this. Sometimes we use a tilde ~ instead. ~ has the advantage that it’s often used for “approximately equal to”. So it will carry that suggestion over to its new context.)

The other major use of the hat comes in vectors. Mathematics types do a lot of work with vectors. It turns out a lot of mathematical structures work the way that pointing and moving in directions in ordinary space do. That’s why back when I talked about what vectors were I didn’t say “they’re like arrows pointing some length in some direction”. Arrows pointing some length in some direction are vectors, yes, but there are many more things that are vectors. Thinking of moving in particular directions gives us good intuition for how to work with vectors, and for stuff that turns out to be vectors. But they’re not everything.

If we need to highlight that something is a vector we put a little arrow over its name. \vec{x} . \vec{e} . That sort of thing. (Or if we’re typing, we might put the letter in boldface: x. This was good back before computers let us put in mathematics without giving the typesetters hazard pay.) We don’t always do that. By the time we do a lot of stuff with vectors we don’t always need the reminder. But we will include it if we need a warning. Like if we want to have both \vec{r} telling us where something is and to use a plain old r to tell us how big the vector \vec{r} is. That turns up a lot in physics problems.

Every vector has some length. Even vectors that don’t seem to have anything to do with distances do. We can make a perfectly good vector out of “polynomials defined for the domain of numbers between -2 and +2”. Those polynomials are vectors, and they have lengths.

There’s a special class of vectors, ones that we really like in mathematics. They’re the “unit vectors”. Those are vectors with a length of 1. And we are always glad to see them. They’re usually good choices for a basis. Basis vectors are useful things. They give us, in a way, a representative slate of cases to solve. Then we can use that representative slate to give us whatever our specific problem’s solution is. So mathematicians learn to look instinctively to them. We want basis vectors, and we really like them to have a length of 1. Even if we aren’t putting the arrow over our variables we’ll put the caret over the unit vectors.

There are some unit vectors we use all the time. One is just the directions in space. That’s \hat{e}_1 and \hat{e}_2 and for that matter \hat{e}_3 and I bet you have an idea what the next one in the set might be. You might be right. These are basis vectors for normal, Euclidean space, which is why they’re labelled “e”. We have as many of them as we have dimensions of space. We have as many dimensions of space as we need for whatever problem we’re working on. If we need a basis vector and aren’t sure which one, we summon one of the letters used as indices all the time. \hat{e}_i , say, or \hat{e}_j . If we have an n-dimensional space, then we have unit vectors all the way up to \hat{e}_n .

We also use the hat a lot if we’re writing quaternions. You remember quaternions, vaguely. They’re complex-valued numbers for people who’re bored with complex-valued numbers and want some thrills again. We build them as a quartet of numbers, each added together. Three of them are multiplied by the mysterious numbers ‘i’, ‘j’, and ‘k’. Each ‘i’, ‘j’, or ‘k’ multiplied by itself is equal to -1. But ‘i’ doesn’t equal ‘j’. Nor does ‘j’ equal ‘k’. Nor does ‘k’ equal ‘i’. And ‘i’ times ‘j’ is ‘k’, while ‘j’ times ‘i’ is minus ‘k’. That sort of thing. Easy to look up. You don’t need to know all the rules just now.

But we often end up writing a quaternion as a number like 4 + 2\hat{i} - 3\hat{j} + 1 \hat{k} . OK, that’s just the one number. But we will write numbers like a + b\hat{i} + c\hat{j} + d\hat{k} . Here a, b, c, and d are all real numbers. This is kind of sloppy; the pieces of a quaternion aren’t in fact vectors added together. But it is hard not to look at a quaternion and see something pointing in some direction, like the first vectors we ever learn about. And there are some problems in pointing-in-a-direction vectors that quaternions handle so well. (Mostly how to rotate one direction around another axis.) So a bit of vector notation seeps in where it isn’t appropriate.

I suppose there’s some value in pointing out that the ‘i’ and ‘j’ and ‘k’ in a quaternion are fixed and set numbers. They’re unlike an ‘a’ or an ‘x’ we might see in the expression. I’m not sure anyone was thinking they were, though. Notation is a tricky thing. It’s as hard to get sensible and consistent and clear as it is to make words and grammar sensible. But the hat is a simple one. It’s good to have something like that to rely on.

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A Leap Day 2016 Mathematics A To Z: Grammar


My next entry for this A To Z was another request, this one from Jacob Kanev, who doesn’t seem to have a WordPress or other blog. (If I’m mistaken, please, let me know.) Kanev’s given me several requests, some of them quite challenging. Some too challenging: I have to step back from describing “both context sensitive and not” kinds of grammar just now. I hope all will forgive me if I just introduce the base idea.

Grammar.

One of the ideals humans hold when writing a mathematical proof is to crush all humanity from the proof. It’s nothing personal. It reflects a desire to be certain we have proved things without letting any unstated assumptions or unnoticed biases interfering. The 19th century was a lousy century for mathematicians and their intuitions. Many ideas that seemed clear enough turned out to be paradoxical. It’s natural to want to not make those mistakes again. We can succeed.

We can do this by stripping out everything but the essentials. We can even do away with words. After all, if I say something is a “square”, that suggests I mean what we mean by “square” in English. Our mathematics might not have proved all the square-ness of the thing. And so we reduce the universe to symbols. Letters will do as symbols, if we want to be kind to our typesetters. We do want to be kind now that, thanks to LaTeX, we do our own typesetting.

This is called building a “formal language”. The “formal” here means “relating to the form” rather than “the way you address people when you can’t just say `heya, gang’.” A formal language has two important components. One is the symbols that can be operated on. The other is the operations you can do on the symbols.

If we’ve set it all up correctly then we get something wonderful. We have “statements”. They’re strings of the various symbols. Some of the statements are axioms; they’re assumed to be true without proof. We can turn a statement into another one by using a statement we have and one of the operations. If the operation requires, we can add in something else we already know to be true. Something we’ve already proven.

Any statement we build this way — starting from an axiom and building with the valid operations — is a new and true statement. It’s a theorem. The proof of the theorem? It’s the full sequence of symbols and operations that we’ve built. The line between advanced mathematics and magic is blurred. To give a theorem its full name is to give its proof. (And now you understand why the biographies of many of the pioneering logicians of the late 19th and early 20th centuries include a period of fascination with the Kabbalah and other forms of occult or gnostic mysticism.)

A grammar is what’s required to describe a language like this. It’s defined to be a quartet of properties. The first property is the collection of symbols that can’t be the end of a statement. These are called nonterminal symbols. The second property is the collection of symbols that can end a statement. These are called terminal symbols. (You see why we want to have those as separate lists.) The third property is the collection of rules that let you build new statements from old. The fourth property is the collection of things we take to be true to start. We only have finitely many options for each of these, at least for your typical grammar. I imagine someone has experimented with infinite grammars. But that hasn’t got to be enough of a research field people have to pay attention to them. Not yet, anyway.

Now it’s reasonable to ask if we need mathematicians at all. If building up theorems is just a matter of applying the finitely many rules of inference on finitely many collections of symbols, finitely many times over, then what about this can’t be done by computer? And done better by a computer, since a computer doesn’t need coffee, or bathroom breaks an hour later, or the hope of moving to a tenure-track position?

Well, we do need mathematicians. I don’t say that just because I hope someone will give me money in exchange for doing mathematics. It’s because setting up a computer to just grind out every possible theorem will never turn up what you want to know now. There are several reasons for this.

Here’s a way to see why. It’s drawn from Douglas Hofstadter’s Gödel, Escher, Bach, a copy of which you can find in any college dorm room or student organization office. At least you could back when I was an undergraduate. I don’t know what the kids today use.

Anyway, this scheme has three nonterminal symbols: I, M, and U. As a terminal symbol … oh, let’s just use the space at the end of a string. That way everything looks like words. We will include a couple variables, lowercase letters like x and y and z. They stand for any string of nonterminal symbols. They’re falsework. They help us get work done, but must not appear in our final result.

There’s four rules of inference. The first: if xI is valid, then so is xIM. The second: if Mx is valid, then so is Mxx. The third: if MxIIIy is valid, then so is MxUy. The fourth: if MxUUy is valid, then so is Mxy.

We have one axiom, assumed without proof to be true: MI.

So let’s putter around some. MI is true. So by the second rule, so is MII. That’s a theorem. And since MII is true, by the second rule again, so is MIIII. That’s another theorem. Since MIIII is true, by the first rule, so is MIIIIM. We’ve got another theorem already. Since MIIIIM is true, by the third rule, so is MIUM. We’ve got another theorem. For that matter, since MIIIIM is true, again by the third rule, so is MUIM. Would you like MIUMIUM? That’s waiting there to be proved too.

And that will do. First question: what does any of this even mean? Nobody cares about whether MIUMIUM is a theorem in this system. Nobody cares about figuring out whether MUIUMUIUI might be a theorem. We care about questions like “what’s the smallest odd perfect number?” or “how many equally-strong vortices can be placed in a ring without the system becoming unstable?” With everything reduced to symbol-shuffling like this we’e safe from accidentally assuming something which isn’t justified. But we’re pretty far from understanding what these theorems even mean.

In this case, these strings don’t mean anything. They’re a toy so we can get comfortable with the idea of building theorems this way. We don’t expect them to do any more work than we expect Lincoln Logs to build usable housing. But you can see how we’re starting pretty far from most interesting mathematics questions.

Still, if we started from a system that meant something, we would get there in time, right? … Surely? …

Well, maybe. The thing is, even with this I, M, U scheme and its four rules there are a lot of things to try out. From the first axiom, MI, we can produce either MII or MIM. From MII we can produce MIIM or MIIII. From MIIII we could produce MIIIIM, or MUI, or MIU, or MIIIIIIII. From each of those we can produce … quite a bit of stuff.

All of those are theorems in this scheme and that’s nice. But it’s a lot. Suppose we have set up symbols and axioms and rules that have clear interpretations that relate to something we care about. If we set the computer to produce every possible legitimate result we are going to produce an enormous number of results that we don’t care about. They’re not wrong, they’re just off-point. And there’s a lot more true things that are off-point than there are true things on-point. We need something with judgement to pick out results that have anything to do with what we want to know. And trying out combinations to see if we can produce the pattern we want is hard. Really hard.

And there’s worse. If we set up a formal language that matches real mathematics, then we need a lot of work to prove anything. Even simple statements can take forever. I seem to remember my logic professor needing 27 steps to work out the uncontroversial theorem “if x = y and y = z, then x = z”. (Granting he may have been taking the long way around for demonstration purposes.) We would have to look in theorems of unspeakably many symbols to find the good stuff.

Now it’s reasonable to ask what the point of all this is. Why create a scheme that lets us find everything that can be proved, only to have all we’re interested in buried in garbage?

There are some uses. To make us swear we’ve read Jorge Luis Borges, for one. Another is to study the theory of what we can prove. That is, what are we able to learn by logical deduction? And another is to design systems meant to let us solve particular kinds of problems. That approach makes the subject merge into computer science. Code for a computer is, in a sense, about how to change a string of data into another string of data. What are the legitimate data to start with? What are the rules by which to change the data? And these are the sorts of things grammars, and the study of grammars, are about.

Making A Joke Of Entropy


This entered into my awareness a few weeks back. Of course I’ve lost where I got it from. But the headline is of natural interest to me. Kristy Condon’s “Researchers establish the world’s first mathematical theory of humor” describes the results of an interesting paper studying the phenomenon of funny words.

The original paper is by Chris Westbury, Cyrus Shaoul, Gail Moroschan, and Michael Ramscar, titled “Telling the world’s least funny jokes: On the quantification of humor as entropy”. It appeared in The Journal of Memory and Language. The thing studied was whether it’s possible to predict how funny people are likely to find a made-up non-word.

As anyone who tries to be funny knows, some words just are funnier than others. Or at least they sound funnier. (This brings us into the problem of whether something is actually funny or whether we just think it is.) Westbury, Shaoul, Moroschan, and Ramscar try testing whether a common measure of how unpredictable something is — the entropy, a cornerstone of information theory — can tell us how funny a word might be.

We’ve encountered entropy in these parts before. I used it in that series earlier this year about how interesting a basketball tournament was. Entropy, in this context, is low if something is predictable. It gets higher the more unpredictable the thing being studied is. You see this at work in auto-completion: if you have typed in ‘th’, it’s likely your next letter is going to be an ‘e’. This reflects the low entropy of ‘the’ as an english word. It’s rather unlikely the next letter will be ‘j’, because English has few contexts that need ‘thj’ to be written out. So it will suggest words that start ‘the’ (and ‘tha’, and maybe even ‘thi’), while giving ‘thj’ and ‘thq’ and ‘thd’ a pass.

Westbury, Shaoul, Moroschan, and Ramscar found that the entropy of a word, how unlikely that collection of letters is to appear in an English word, matches quite well how funny people unfamiliar with it find it. This fits well with one of the more respectable theories of comedy, Arthur Schopenhauer’s theory that humor comes about from violating expectations. That matches well with unpredictability.

Of course it isn’t just entropy that makes words funny. Anyone trying to be funny learns that soon enough, since a string of perfect nonsense is also boring. But this is one of the things that can be measured and that does have an influence.

(I doubt there is any one explanation for why things are funny. My sense is that there are many different kinds of humor, not all of them perfectly compatible. It would be bizarre if any one thing could explain them all. But explanations for pieces of them are plausible enough.)

Anyway, I recommend looking at the Kristy Condon report. It explains the paper and the research in some more detail. And if you feel up to reading an academic paper, try Westbury, Shaoul, Moroschan, and Ramscar’s report. I thought it readable, even though so much of it is outside my field. And if all else fails there’s a list of two hundred made-up words used in field tests for funniness. Some of them look pretty solid to me.

The Set Tour, Part 6: One Big One Plus Some Rubble


I have a couple of sets for this installment of the Set Tour. It’s still an unusual installment because only one of the sets is that important for my purposes here. The rest I mention because they appear a lot, even if they aren’t much used in these contexts.

I, or J, or maybe Z

The important set here is the integers. You know the integers: they’re the numbers everyone knows. They’re the numbers we count with. They’re 1 and 2 and 3 and a hundred million billion. As we get older we come to accept 0 as an integer, and even the negative integers like “negative 12” and “minus 40” and all that. The integers might be the easiest mathematical construct to know. The positive integers, anyway. The negative ones are still a little suspicious.

The set of integers has several shorthand names. I is a popular and common one. As with the real-valued numbers R and the complex-valued numbers C it gets written by hand, and typically typeset, with a double vertical stroke. And we’ll put horizontal serifs on the top and bottom of the symbol. That’s a concession to readability. You see the same effect in comic strip lettering. A capital “I” in the middle of a word will often be written without serifs, while the word by itself needs the extra visual bulk.

The next popular symbol is J, again with a double vertical stroke. This gets used if we want to reserve “I”, or the word “I”, for some other purpose. J probably gets used because it’s so very close to I, and it’s only quite recently (in historic terms) that they’ve even been seen as different letters.

The symbol that seems to come out of nowhere is Z. It comes less from nowhere than it does from German. The symbol derives from “Zahl”, meaning “number”. It seems to have got into mathematics by way of Nicolas Bourbaki, the renowned imaginary French mathematician. The Z gets written with a double diagonal stroke.

Personally, I like Z most of this set, but on trivial grounds. It’s a more fun letter to write, especially since I write it with the middle horizontal stroke that. I’ve got no good cultural or historical reason for this. I just picked it up as a kid and never set it back down.

In these Set Tour essays I’m trying to write about sets that get used often as domains and ranges for functions. The integers get used a fair bit, although not nearly as often as real numbers do. The integers are a natural way to organize sequences of numbers. If the record of a week’s temperatures (in Fahrenheit) are “58, 45, 49, 54, 58, 60, 64”, there’s an almost compelling temperature function here. f(1) = 58, f(2) = 45, f(3) = 49, f(4) = 54, f(5) = 58, f(6) = 60, f(7) = 64. This is a function that has as its domain the integers. It happens that the range here is also integers, although you might be able to imagine a day when the temperature reading was 54.5.

Sequences turn up a lot. We are almost required to measure things we are interested in in discrete samples. So mathematical work with sequences uses integers as the domain almost by default. The use of integers as a domain gets done so often that it often becomes invisible, though. Someone studying my temperature data above might write the data as f1, f2, f3, and so on. One might reasonably never even notice there’s a function there, or a domain.

And that’s fine. A tool can be so useful it disappears. Attend a play; the stage is in light and the audience in darkness. The roles the light and darkness play disappear unless the director chooses to draw attention to this choice.

And to be honest, integers are a lousy domain for functions. It’s achingly hard to prove things for functions defined just on the integers. The easiest way to do anything useful is typically to find an equivalent problem for a related function that’s got the real numbers as a domain. Then show the answer for that gives you your best-possible answer for the original question.

If all we want are the positive integers, we put a little superscript + to our symbol: I+ or J+ or Z+. That’s a popular choice if we’re using the integers as an index. If we just want the negative numbers that’s a little weird, but, change the plus sign to a minus: I.

Now for some trouble.

Sometimes we want the positive numbers and zero, or in the lingo, the “nonnegative numbers”. Good luck with that. Mathematicians haven’t quite settled on what this should be called, or abbreviated. The “Natural numbers” is a common name for the numbers 0, 1, 2, 3, 4, and so on, and this makes perfect sense and gets abbreviated N. You can double-brace the left vertical stroke, or the diagonal stroke, as you like and that will be understood by everybody.

That is, everybody except the people who figure “natural numbers” should be 1, 2, 3, 4, and so on, and that zero has no place in this set. After all, every human culture counts with 1 and 2 and 3, and for that matter crows and raccoons understand the concept of “four”. Yet it took thousands of years for anyone to think of “zero”, so how natural could that be?

So we might resort to speaking of the “whole numbers” instead. More good luck with that. Besides leaving open the question of whether zero should be considered “whole” there’s the linguistic problem. “Whole” number carries, for many, the implication of a number that is an integer with no fractional part. We already have the word “integer” for that, yes. But the fact people will talk about rounding off to a whole number suggests the phrase “whole number” serves some role that the word “integer” doesn’t. Still, W is sitting around not doing anything useful.

Then there’s “counting numbers”. I would be willing to endorse this as a term for the integers 0, 1, 2, 3, 4, and so on, except. Have you ever met anybody who starts counting from zero? Yes, programmers for some — not all! — computer languages. You know which computer languages. They’re the languages which baffle new students because why on earth would we start counting things from zero all of a sudden? And the obvious single-letter abbreviation C is no good because we need that for complex numbers, a set that people actually use for domains a lot.

There is a good side to this, if you aren’t willing to sit out the 150 years or so mathematicians are going to need to sort this all out. You can set out a symbol that makes sense to you, early on in your writing, and stick with it. If you find you don’t like it, you can switch to something else in your next paper and nobody will protest. If you figure out a good one, people may imitate you. If you figure out a really good one, people will change it just a tiny bit so that their usage drives you crazy. Life is like that.

Eric Weisstein’s Mathworld recommends using Z* for the nonnegative integers. I don’t happen to care for that. I usually associate superscript * symbols with some operations involving complex-valued numbers and with the duals of sets, neither of which is in play here. But it’s not like he’s wrong and I’m right. If I were forced to pick a symbol right now I’d probably give Z0+. And for the nonpositive itself — the negative integers and zero — Z0- presents itself. I fully understand there are people who would be driven stark raving mad by this. Maybe you have a better one. I’d believe that.

Let me close with something non-controversial.

These are some sets that are too important to go unmentioned. But they don’t get used much in the domain-and-range role I’ve been using as basis for these essays. They are, in the terrain of these essays, some rubble.

You know the rational numbers? They’re the things you can write as fractions: 1/2, 5/13, 32/7, -6/7, 0 (think about it). This is a quite useful set, although it doesn’t get used much for the domain or range of functions, at least not in the fields of mathematics I see. It gets abbreviated as Q, though. There’s an extra vertical stroke on the left side of the loop, just as a vertical stroke gets added to the C for complex-valued numbers. Why Q? Well, “R” is already spoken for, as we need it for the real numbers. The key here is that every rational number can be written as the quotient of one integer divided by another. So, this is the set of Quotients. This abbreviation we get thanks to Bourbaki, the same folks who gave us Z for integers. If it strikes you that the imaginary French mathematician Bourbaki used a lot of German words, all I can say is I think that might have been part of the fun of the Bourbaki project. (Well, and German mathematicians gave us many breakthroughs in the understanding of sets in the late 19th and early 20th centuries. We speak with their language because they spoke so well.)

If you’re comfortable with real numbers and with rational numbers, you know of irrational numbers. These are (most) square roots, and pi and e, and the golden ratio and a lot of cosines of angles. Strangely, there really isn’t any common shorthand name or common notation for the irrational numbers. If we need to talk about them, we have the shorthand “R \ Q”. This means “the real numbers except for the rational numbers”. Or we have the shorthand “Qc”. This means “everything except the rational numbers”. That “everything” carries the implication “everything in the real numbers”. The “c” in the superscript stands for “complement”, everything outside the set we’re talking about. These are ungainly, yes. And it’s a bit odd considering that most real numbers are irrational numbers. The rational numbers are a most ineffable cloud of dust the atmosphere of the real numbers.

But, mostly, we don’t need to talk about functions that have an irrational-number domain. We can do our work with a real-number domain instead. So we leave that set with a clumsy symbol. If there’s ever a gold rush of fruitful mathematics to be done with functions on irrational domains then we’ll put in some better notation. Until then, there are better jobs for our letters to do.

Reading the Comics, February 14, 2015: Valentine’s Eve Edition


I haven’t had the chance to read today’s comics, what with it having snowed just enough last night that we have to deal with it instead of waiting for the sun to melt it, so, let me go with what I have. There’s a sad lack of strips I feel justified including the images of, since they’re all Gocomics.com representatives and I’m used to those being reasonably stable links. Too bad.

Eric the Circle has a pair of strips by Griffinetsabine, the first on the 7th of February, and the next on February 13, both returning to “the Shape Single’s Bar” and both working on “complementary angles” for a pun. That all may help folks remember the difference between complementary angles — those add up to a right angle — and supplementary angles — those add up to two right angles, a straight line — although what it makes me wonder is the organization behind the Eric the Circle art collective. It hasn’t got any nominal author, after all, and there’s what appear to be different people writing and often drawing it, so, who does the scheduling so that the same joke doesn’t get repeated too frequently? I suppose there’s some way of finding that out for myself, but this is the Internet, so it’s easier to admit my ignorance and let the answer come up to me.

Mark Anderson’s Andertoons (February 10) surprised me with a joke about the Dewey decimal system that I hadn’t encountered before. I don’t know how that happened; it just did. This is, obviously, a use of decimal that’s distinct from the number system, but it’s so relatively rare to think of decimals as apart from representations of numbers that pointing it out has the power to surprise me at least.

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