# Social Media Update

I finally gave up on Twitter. It had been descending into mediocrity and worse for a long time. The provocation that gave me the nudge I needed was dropping in after a few days away and finding my timeline cluttered into uselessness, because their Algorithm (in its ineffable Algorithmhood) had decided to interpret “likes” as retweets. This is a feature they decided the world needed, and they decided that it was so beneficial that there would be no way to turn it off. What’s more, it comes and goes, so one cannot plan around it or adapt one’s habits to it, and when it is present, it is applied stochastically.

Consequently, the meaning of clicking the “like” icon is not constant over time. If you care at all about what your followers experience, you cannot expect taking the same action to have the same result. The software demands, by definition, insanity.

So, now I fill my subway-riding time with paperback books that I’d bought at the Harvard Bookstore warehouse sale and never gotten around to reading.

I’ve also been making a space for myself on the Mastodon decentralized social platform. My primary home in that ecosystem is @bstacey@icosahedron.website. I’m also the Blake Stacey at mastodon.mit.edu (all that tuition had to buy me something), and at the suggestion of Evelyn Lamb, for good measure I claimed Blake Stacey at mathstodon.xyz.

# Social Media Experiment

I decided to give Mastodon a whirl, so a while back I created an account for myself at the icosahedron.website instance. (After all, a big part of my research is to generalize regular icosahedra to higher dimensions and complex coordinates.) There I am: Blake C. Stacey (@bstacey@icosahedron.website). It’s been fun so far.

It seems the best way to explain Mastodon to an old person (like me) is that it’s halfway between social networking, the way big companies do it, and email. You create an account on one server (or “instance”), and from there, you can interact with people who have accounts, even if those accounts are on other servers. Different instances can have different policies about what kinds of content they allow, depending for example on what type of community the administrators of the instance want to cater to.

If I ever administrate a Mastodon instance, I think I’ll make “content warnings” mandatory, but I’ll change the interface so that they’re called “subject lines.”

Google Scholar is seriously borked today. I heard about the problem when Christopher Fuchs emailed me to say that he had his Google Scholar profile open in a browser and happened to click the refresh button, whereupon his total citation count jumped by 700. After the refresh, his profile was full of things he hadn’t even written. Poking around, I found that a lot of publications in the American Institute of Physics’s AIP Conference Proceedings were being wildly misattributed, almost as if everyone who contributed to an issue was getting credit for everything in that issue.

For example, here’s Jan-Åke Larsson getting credit for work by Giacomo D’Ariano:

And here’s Chris picking up 38 bonus points for research on Mutually Unbiased Bases—a topic not far from my own heart!—research done, that is, by Ingemar Bengtsson:

A few years ago, I noticed a glitch in a paper that colleagues of mine had published back in 2002. A less-than sign in an inequality should have been a less-than-or-equals. This might have been a transcription error during the typing-up of the work, or it could have entered during some other phase of the writing process. Happens to the best of us! Algebraically, it was equivalent to solving an equation
$ax^2 + bx + c = 0$ with the quadratic formula,
$x = \frac{-b \pm \sqrt{b^2 – 4ac}}{2a},$ and neglecting the fact that if the expression under the square root sign equals zero, you still get a real solution.

This sort of glitch is usually not worth a lot of breath, though I do tend to write in when I notice them, to keep down the overall confusingness of the scientific literature. In this case, however, there’s a surprise bonus. The extra solutions you pick up turn out to have a very interesting structure to them, and they include mathematical objects that were already interesting for other reasons. So, I wrote a little note explaining this. In order to make it self-contained, I had to lay down a bit of background, and with one thing and another, the little note became more substantial. Too substantial, I learned: The journal that published the original paper wouldn’t take it as a Comment on that paper, because it said too many new things! Eventually, after a little more work, it found a home:

The number of citations that Google Scholar lists for this paper (one officially published in a journal, mind) fluctuates between 5 and 6. I think it wavers on whether to include a paper by Szymusiak and Słomczyński (Phys. Rev. A 94, 012122 = arXiv:1512.01735 [quant-ph]). Also, if you compare against the NASA ADS results, it turns out that Google Scholar is missing other citations, too, including a journal-published item by Bellomo et al. (Int. J. Quant. Info. 13, 2 (2015), 1550015 = arXiv:1504.02077 [quant-ph]).

As I said in 2014, this would be a rather petty thing to care about, if people didn’t rely on these metrics to make decisions! And, as it happens, all the problems I noted then are still true now.

# What Would I Buy With $3 Million for Math[s]? Leading off the topic of my previous post, I think it’s a good time to ask what we can do with resources that are already allocated. How can we fine-tune the application of resources already set aside for a certain purpose, and so achieve the best outcome in the current Situation? This post will be a gentle fantasy, because sometimes, in the Situation, we need that, or because that’s all I can do today. Last month, Evelyn Lamb asked, how should we revamp the Breakthrough Prize for mathematics? This is an award with$3 million attached, supported by tech billionaires. A common sentiment about such awards, a feeling that I happen to share, is that they go to people who have indeed accomplished good things, but on the whole it isn’t a good way to spend money. Picking one person out of a pool of roughly comparable candidates and elevating them above their peers doesn’t really advance the cause of mathematics, particularly when the winner already has a stable position. Lamb comments,

$\$3$million a year could generously fund 30 postdoc years (or provide 10 3-year postdocs). I still think that wouldn’t be a terrible idea, especially as jobs in math are hard to come by for fresh PhD graduates. But […] more postdoc funding could just postpone the inevitable. Tenure track jobs are hard to come by in mathematics, and without more of them, the job crunch will still exist. Helping to create permanent tenured or tenure-track positions in math would ease up on the job crisis in math and, ideally, make more space for the many deserving people who want to do math in academia. […] from going to the websites of a few major public universities, it looks like it’s around$2.5 million to permanently endow a chair at that kind of institution.

I like the sound of this, but let’s not forget: If we have $3 million per year, then we don’t have to do the same thing every year! My own first thought was that if you can fund 10 postdocs for three years apiece, you can easily pay for 10 new open-source math textbooks. In rough figures, let us say that it takes about a year to write a textbook on material you know well. Then, the book has to be field-tested for at least a semester. To find errors in technical prose, you need to find people who don’t already know what it’s supposed to say, and have them work through the whole thing. If we look at, say, what MIT expects of undergrad math majors, we can work up a list of courses: Continue reading What Would I Buy With$3 Million for Math[s]?

# On “Invention”

When I was a little younger than Ahmed Mohamed is now, I invented the distance formula for Cartesian coordinates. I wanted to make a simulation of bugs that ran around and ate each other. To implement a rule like “when the predator is near the prey, it will chase the prey,” I needed to compute distances between points given their $x$- and $y$-coordinates. I knew BASIC, and I knew the Pythagorean Theorem. However many people had solved that before me, it wasn’t written down in any book that I had, so I took what I knew and figured it out.

Those few pages of PowerBASIC on MS-DOS never amounted to much by themselves, but simulating ecosystems remained an interest of mine. I returned to the general idea now and then as I learned more.

And then, hey, what’s this? It looks like a PhD thesis.

“I bet every great mathematician started by
rediscovering a bunch of ‘well known’ results.”
—Donald Knuth, Surreal Numbers

Your password must contain a pound of flesh. No blood, nor less nor more, but just a pound of flesh.

Your password must contain any letter of the alphabet save the second. NOT THE BEES! NOT THE BEES!

Google Scholar is definitely missing citations to my papers.

The cited-by results for “Some Negative Remarks on Operational Approaches to Quantum Theory” [arXiv:1401.7254] on Google Scholar and on INSPIRE are completely nonoverlapping. Google Scholar can tell that “An Information-Theoretic Formalism for Multiscale Structure in Complex Systems” [arXiv:1409.4708] cites “Eco-Evolutionary Feedback in Host–Pathogen Spatial Dynamics” [arXiv:1110.3845] but not that it cites My Struggles with the Block Universe [arXiv:1405.2390]. Meanwhile, the SAO/NASA Astrophysics Data System catches both.

This would be a really petty thing to complain about, if people didn’t seemingly rely on such metrics.

EDIT TO ADD (17 November 2014): Google Scholar also misses that David Mermin cites MSwtBU in his “Why QBism is not the Copenhagen interpretation and what John Bell might have thought of it” [arXiv:1409.2454]. This maybe has something to do with being worse at detecting citations in footnotes than in endnotes.

# Can Stephen Wolfram Catch Carmen Sandiego?

Via Chris Granade, I learned we now have an actual implementation of Wolfram Language to play around with. Wolfram lauds the Wolfram Programming Cloud, the first product based on the Wolfram Language:

My goal with the Wolfram Language in general—and Wolfram Programming Cloud in particular—is to redefine the process of programming, and to automate as much as possible, so that once a human can express what they want to do with sufficient clarity, all the details of how it is done should be handled automatically. [my emphasis]

Ah. You mean, like programming?

Wolfram’s example of the Wolfram Programming Cloud is “a piece of code that takes text, figures out what language it’s in, then shows an image based on the flag of the largest country where it’s spoken.” The demo shows how the WPC maps the string good afternoon to the English language, the United States and thence to the modern US flag.

English is an official language of India, which exceeds the US in population size, and of Canada, which exceeds the US in total enclosed area.

The Wolfram Language documentation indicates that “LargestCountry” means “place with most speakers”; by this standard, the US comes out on top (roughly 300 million speakers, versus 125 million for India and 28 million for Canada). But that’s not the problem we were supposed to solve: “place with most speakers” is not the same as “largest country where the language is spoken.”

Even the programming languages which are sold as doing what you mean still just do what you say.

# Wolfram Language…

…because nothing says “stable platform for mission-critical applications” like “from the makers of Mathematica!”

Carl Zimmer linked to this VentureBeat piece on Wolfram Language with the remark, “Always interesting to hear what Stephen Wolfram is up to. But this single-source style of tech reporting? Ugh.” I’d go further: the software may well eventually provide an advance in some respect, but the reporting is so bad, we’d never know.

We’re told “a developer can use some natural language.” What, like the GOTO command? That’s English. Shakespearean, even. (“Go to, I’ll no more on’t; it hath made me mad.” Hamlet, act 3, scene 1.) We’re told that “literally anything” will be “usable and malleable as a symbolic expression”—wasn’t that the idea behind LISP? We’re told, awkwardly, that “Questions in a search engine have many answers,” with the implication that this is a bad thing (and that Wolfram Alpha solved that problem). We are informed that “instead of programs being tens of thousands of lines of code, they’re 20 or 200.” Visual Basic could claim much the same. We don’t push functionality “out to libraries and modules”; we use the Wolfram Cloud. It’s very different!

(Mark Chu-Carroll points out, “What’s scary is that he thinks that not pushing things to libraries is good!”)

The “wink, wink, we’re not not comparing Wolfram to Einstein” got old within a sentence, too.

I have actual footage of Wolfram from the Q&A session of that presentation:

“I am my own reality check.”Stephen Wolfram (1997)

# Citing Tweets in LaTeX

Need to cite Twitter posts in your LaTeX documents? Of course you do! Want someone else to modify the utphys BibTeX style to add a “@TWEET” option so you don’t have to do it yourself? Of course you do!

Style file:

Example document:

\documentclass[aps,amsmath,amssymb]{revtex4}
\usepackage{amsmath,amssymb,hyperref}

\begin{document}
\bibliographystyle{utphystw}

\title{Test}
\author{Blake C. Stacey}
\date{\today}

\begin{abstract}
Only a test!
\end{abstract}

\maketitle

As indicated, this is only
a test.\cite{stacey2011,sfi2011}

\bibliography{twtest.bib}

\end{document}


And the example bibliography file:

@TWEET{stacey2011,
author={Blake Stacey},
authorid={blakestacey},
year={2011},
month={July},
day={25},
tweetid={95521600597786624},
tweetcontent={I find it hard to tell, in some
areas of science, whether I am

@TWEET{sfi2011,
author={anon},
authorid={OverheardAtSFI},
year={2011},
month={June},
day={23},
tweetid={84018131441422336},
tweetcontent={The brilliance of the word
Complexity'' is that it
to anybody.}}


PDF output:

# Interactivelearn

A few complaints about the place of computers in physics classrooms.

Every once in a while, I see an enthusiastic discussion somewhere on the Intertubes about bringing new technological toys into physics classrooms. Instead of having one professor lecture at a room of unengaged, unresponsive bodies, why not put tools into the students’ hands and create a new environment full of interactivity and feedback? Put generically like that, it does sound intriguing, and new digital toys are always shiny, aren’t they?

Prototypical among these schemes is MIT’s “Technology Enabled Active Learning” (traditionally and henceforth TEAL), which, again, you’d think I’d love for the whole alma mater patriotism thing. (“Bright college days, O carefree days that fly…”) I went through introductory physics at MIT a few years too early to get the TEAL deal (I didn’t have Walter Lewin as a professor, either, as it happens). For myself, I couldn’t see the point of buying all those computers and then using them in ways which did not reflect the ways working physicists actually use computers. Watching animations? Answering multiple-choice questions? Where was the model-building, the hypothesis-testing through numerical investigation? In 1963, Feynman was able to explain to Caltech undergraduates how one used a numerical simulation to get predictions out of a hypothesis when one didn’t know the advanced mathematics necessary to do so by hand, or if nobody had yet developed the mathematics in question. Surely, forty years and umpteen revolutions in computer technology later, we wouldn’t be moving backward, would we?

Everything I heard about TEAL from the students younger than I — every statement without exception, mind — was that it was a dreadful experience, technological glitz with no substance. Now, I’ll freely admit there was probably a heckuva sampling bias involved here: the people I had a chance to speak with about TEAL were, by and large, other physics majors. That is, they were the ones who survived the first-year classes and dove on in to the rest of the programme. So, (a) one would expect they had a more solid grasp of the essential concepts covered in the first year, all else being equal, and (b) they may have had more prior interest and experience with physics than students who declared other majors. But, if the students who liked physics the most and were the best at it couldn’t find a single good thing to say about TEAL, then TEAL needed work.

If your wonderful new education scheme makes things somewhat better for an “average” student but also makes them significantly worse for a sizeable fraction of students, you’re doing something wrong. The map is not the territory, and the average is not the population.

It’s easy to dismiss such complaints. Here, let me give you a running start: “Those kids are just too accustomed to lectures. They find lecture classes fun, so fun they’re fooled into thinking they’re learning.” (We knew dull lecturers when we had them.) “Look at the improvement in attendance rates!” (Not the most controlled of experiments. At a university where everyone has far too many demands made of their time and absolutely no one can fit everything they ought to do into a day, you learn to slack where you can. If attendance is mandated in one spot, it’ll suffer elsewhere.)

Or, perhaps, one could take the fact that physics majors at MIT loathed the entire TEAL experience as a sign that what TEAL did was not the best for every student involved. If interactivity within the classroom is such a wonderful thing, then is it so hard to wonder if interactivity at a larger scale, at the curricular level, might be advisable, too?

It’s not just a matter of doing one thing for the serious physics enthusiasts and another for the non-majors (to use a scandalously pejorative term).

What I had expected the Technological Enabling of Active Learning to look like is actually more like another project from MIT, StarLogo. Unfortunately, the efforts to build science curricula with StarLogo have been going on mostly at the middle- and high-school level. Their accomplishments and philosophy have not been applied to filling the gaps or shoring up the weak spots in MIT’s own curricula. For example, statistical techniques for data analysis aren’t taught to physics majors until junior year, and then they’re stuffed into Junior Lab, one of the most demanding courses offered at the Institute. To recycle part of an earlier rant:

Now, there’s a great deal to be said for stress-testing your students (putting them through Degree Absolute, as it were). The real problem was that it was hard for all the wrong reasons. Not only were the experiments tricky and the concepts on which they were based abstruse, but also we students had to pick up a variety of skills we’d never needed before, none of them connected to any particular experiment but all of them necessary to get the overall job done. What’s more, all these skills required becoming competent and comfortable with one or more technological tools, mostly of the software persuasion. For example: we had to pick up statistical data analysis, curve fitting and all that pretty much by osmosis: “Here’s a MATLAB script, kids — have at it!” This is the sort of poor training which leads to sinful behaviour on log-log plots in later life. Likewise, we’d never had to write up an experiment in formal journal style, or give a technical presentation. (The few experiences with laboratory work provided in freshman and sophomore years were, to put it simply, a joke.) All this on top of the scientific theory and experimental methods we were ostensibly learning!

Sure, it’s great to throw the kids in the pool to force them to swim, but the water is deep enough already! To my way of thinking, it would make more sense to offload those accessory skills like data description, simulation-building, technical writing and oral presentation to an earlier class, where the scientific content being presented is easier. Own up to the fact that you’re the most intimidating major at an elite technical university: make the sophomore-year classes a little tougher, and junior year can remain just as rough, but be so in a more useful way. We might as well go insane and start hallucinating for the right reason.

Better yet, we might end up teaching these skills to a larger fraction of the students who need them. Why should education from which all scientists could benefit be the exclusive province of experimental physicists? I haven’t the foggiest idea. We have all these topics which ought to go into first- or second-year classes — everyone needs them, they don’t require advanced knowledge in physics itself — but the ways we’ve chosen to rework those introductory classes aren’t helping.

To put it another way: if you’re taking “freshman physics for non-majors,” which will you use more often in life: Lenz’s Law or the concept of an error bar?