duvets? just say no!

We gave up on using a duvet years ago, because it is simply too hot, and with no fine control. (I recall a novel that described an overbearing parent as like a duvet: “soft and sweet, and full of icy draughts at the edges”.)

Glad to know that if we hadn’t, I’d be fatter, itchier, and tireder!

(via Danny Yee’s blog)

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10 years of Cassini

I remember when Saturn was a fairly fuzzy blob with a few rings, and its ten (yes, just ten!) moons were mere dots.  We’ve come far, and gone far.

Phil Plait collects 10 great images of Saturn from Cassini.

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change blindness

I went to a fascinating seminar by Professor Stephen Tipper last week.  It was about how perception (mostly vision described here) and action deeply influence each other.  There were lots of fascinating examples (with predictions backed up by clever experiments):

• Moving your finger clockwise or anticlockwise can change the apparent rotational direction of some flashing spots.

• Merely looking at a picture of a footballer v a tennis player activates your foot or hand mirror neurons respectively (although I personally didn’t even recognise the photos!).

• Watching someone perform a movement fluently v more awkwardly can affect your emotional response to them positively or negatively respectively (does this mean my left-handed self shouldn’t write on a whiteboard in front of my predominantly right-handed students?).

One aspect that caught my attention was the illustration of change blindness: how quite large changes in a scene are completely invisible, until eventually seen, at which point they cannot be unseen.  One of the examples shown was "couple at dinner".

The version above is taken from a webpage on a talk by J. Kevin O’Regan  and Alva Noë.  It seems to be flashing slower than the example in the talk, and I can’t tell if it still works, because now that I know the change, I can’t unsee it!  However, that page has several other examples, which did all work for me.

After talks like this, I never want to trust anything I see again!  But what really caught my attention about the change blindness segment of the talk was a seeming contradiction between what I was witnessing here, and something I’ve experienced before.  A blink comparator is used in astronomy to  to detect planets and other rapidly moving bodies.  Two images of the night sky are switched back and forth, and any object moving against the essentially motionless stars leaps out to the viewer.  How can a blink comparator work if we suffer from change blindness?

I asked Stephen about this after his talk.  The answer: it’s all in the timing.  The blink comparator blinks faster than do the change blindness images.  Blinking fast enough engages our motion detectors; blinking slower does not, and we become change blind.

Amazing.

but I can't recognise people...

Given how bad I am at recognising people, that’s me locked out of computers, then!

never seen you before in my life

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rainbow colour maps

Many many years ago (long before the web was available to give me advice) I discovered a problem with rainbow colour maps that I haven’t seen mentioned elsewhere.  (There are many other problems.)

(a) rainbow, (b) gray-scale, (c) black-body radiation,
and (d) isoluminant green–red color maps

I was using a rainbow colour map to indicate “heat”, so, of course, I used red for the cool end, and blue for the hot end.  I was told by my then-manager that this was counter-intuitive.

Depends on the intuition, I suppose… 

note the direction of correlation between the temperature and the colour bar

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Euclidean geometry

I saw this interactive geometry game on BoingBoing recently.  I’ve just completed the final level 20.  It’s great fun.  The final couple of levels did have me scribbling on pieces of paper and muttering for a bit.  (Well, it has been a while since I left school!)

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visualising algorithms

Mike Bostock has some beautiful algorithm visualisations, not only of the ubiquitous quicksort and mergesort, but also of sampling, shuffling, and maze generation.

progression of a randomised depth-first traversal maze generation algorithm

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cubehelix colour map

cubehelix – a colourmap with a continuous increase in perceived intensity (and prints as greyscale on a B&W PS device)

Example image using the ‘cubehelix’ colour scheme

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thunder-map

Watch thunderstorms as they happen over Europe. (It’s just stopped thundering and raining here.)

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summer solstice

Hail solstice!  Where I live, today sunrise is (was!) 4:36 am, sunset will be 9:24 pm (BST). The sun is above the horizon for nearly 17 hours.  Bliss.  (Mind you, 21 December, it’s above the horizon for less than 8 hours: not so much fun.)

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sequestering carbon, several books at a time XXVI

Quite a large batch this time:

I need to read Stross’ Neptune’s Brood (which takes place in the far future of the Saturn’s Children universe) before Hugo voting closes on 1 August.  Of the other nominees, I’ve already read Ancillary Justice (which is apparently the first in a series, but that’s not obvious when reading it); Parasite (also the first in a series) is on the pile. I won’t, however, be reading either Warbound, Book III of the Grimnoir Chronicles (which would require reading Books I and II first, I presume), or the 15 volumes, 12,000 pages, 4.4M words of the entire Wheel of Time cycle.

And that’s all the Harry Dresdens now (modulo the latest one still in hardback).  Maybe I should start reading them…

book review: Too Big to Know

David Weinberger.

Too Big to Know: rethinking knowledge now that the facts aren't the facts, experts are everywhere, and the smartest person in the room is the room.

Basic Books. 2011

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Knowledge used to be gatekeepered, curated and preserved in serious books. Now we have the Web, and all its diverse chaotic formats and voices. Overload! Crisis! Catastrophe!

Well, no. In this lovely book, Weinberger argues strongly that our conception of knowledge has been blinkered by those very gatekeepers, curators and formats. Books, which he dubs “long-form thinking”, have their problems: they are one-way (author to reader), and closed format (material chosen and packaged into book-length form, implying that is all there is to be siad on the subject). It is only because we are so used to books that we don’t see their disadvantages and limitations very clearly. (And Weinberger is conscious of the irony of making this argument in book form.)

He examines the current state of this “knowledge messiness”, taking the argument a whole stage further than the information messiness covered in his previous long-form work.

The web allows conversations, hyper-linked non-linear presentation, and an unbounded, “bottomless”, format. It allows diversity: anyone can contribute, not just those previously allowed by the gatekeepers. This openness has its benefits, particularly allowing “networked knowledge”, bringing many eyes, viewpoints and skills to bear. It also has its downsides: it allows fools, and trolls, and confusion, and crud. But then Sturgeon’s law applies to books, too.

Weinberger carefully dissects these issues, showing where, when and how web-based knowledge can be superior to book-based. Books have helped hide the fact that knowledge really is messy, and that even when we have access to it, we will often not come to agreement. The rise of the messy web is not to be lamented, as having destroyed the clean, calm, long-form presentation of books; it should be celebrated for exposing the fundamental messiness of reality, and exploited to help us navigate that messiness. He finishes off with a discussion of how to help ensure the web can provide the best support for messy networked knowledge: open access, metadata, links, and, of course, education.

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When does a physical system compute?

Dom Horsman, Susan Stepney, Rob C. Wagner, Viv Kendon
When does a physical system compute?
Proceedings of the Royal Society A, 2014 (accepted)

A version of our recently accepted paper can be found on the arXiv.  Here is a very potted summary for those for whom its 21 pages is tl;dr.  Warning: what follows is necessarily an incomplete and misleading summary: if it wasn’t, we wouldn’t have written 21 pages in the first place!

The issue is this: the subject area of unconventional, or non-standard, computing has people building (or theorising about) computers made of many weird things: bacteria, slime moulds, soap films, black holes.  You name it, someone probably wants to build a computer out of it. Some people also claim that everything computes: a light ray, stone, the entire universe.

We want to pick apart what really constitutes computation with such a physical system (and, of course, traditional computers are physical systems too).  This helps us (a) distinguish between playing with weird materials from computing with them, and (b) distinguish between physical systems just doing their thing, and them computing.

First, we define science. (No wonder it took us 21 pages!)

Below the blue line is the real world of kickable physical stuff. Above is the abstract world of models and calculations.  p is a real world physical thing (not a model of a thing) that does its thing in the world to become real world physical thing p'.  We can represent p in the abstract world, m_p, using a representation relation R.  This is what scientists do all the time when they model bits of reality.  It’s all theory dependent, and there are many possible representations; we are looking at a specific one here.  If we have some scientific theory of things like p, we can use it to infer, or calculate, how our model of p behaves: m_p → m'_p.  We want this result inferred from the theory to be close to the real result m_p', the representation of the physical state p'.  In other words, we want ε to be small: this is the mark of a good theory.

Once we have a good scientific theory, with a sufficiently small ε, we can use it to predict things in the real world.

Here we start with a p, and represent it as m_p.  We can use our theory to infer m'_p, which is now sufficiently close to m_p' because we have a good theory.  We can then use m_p' to instantiate a p', using the reverse of the representation relation.  (This reverse relation is non-trivial to establish, and there might be no suitable p': is is perfectly possible to build models of unphysical systems!  See the paper if you want all the gory details.)  This is the physical prediction cycle, using science.

Computing goes the other way.  We instead use a physical system to predict the result of an abstract inference or calculation.

With physical computation, we start with some abstract problem m_p, and instantiate it in a physical system p (remember, instantiation is non-trivial).  The physical system does its thing, giving p'.  We then represent p' back in the abstract world as m_p'.  Because we have a good theory of the physical system, its m_p' is sufficiently close to our m'_p, the result of our desired calculation.  Hey presto, we are computing!

So: physical computing is the use of a physical system to predict the outcome of an abstract evolution.

The computation has three steps: the instantiation or encoding of the input problem, the physical evolution of the system (which will almost certainly be a highly engineered physical system), and the representation or decoding of the result.

So a physical system just doing its thing isn’t computing: that’s only the bottom line of the figure.  This includes the case where the physical system is a PC whirring away, but its output is never extracted and decoded.  A watched pot never boils; an unwatched computer never computes.  (We are not requiring the “watcher” to be a conscious agent, however; again, see the paper for details, else this blog post will be longer than the paper.)

And using a stone where we calculate its representation post hoc to give the right answer, or a highly experimental physical system where we throw away those evolutions that don’t work, also aren’t computing: there’s no prediction involved, just a comparison with a result actually computed separately.  There is a lot of work going on in those encoding/decoding stages.  (I blogged about something analogous when explaining why a stopped clock is never right: it is telling the time in exactly the same way that a stone is computing: not.)

If there are bits here you don’t follow, that’s because this is highly summarised, with important steps very compressed.  Read the full paper on the arXiv: it should hopefully make more sense!  Also, there’s lots more in the paper, about engineering, and refinement, and using a system to simulate another system, or even itself, and how that all fits in this model.

Where x drives me mad

There is a common style in technical writing, where you state a formula, and then explain what the various terms mean.  For example, for a short formula, you might write this inline as:

E = m c², where m is the mass and c is the speed of light.

If the formula gets too long, you can display the formula on a line by itself, and put the explanatory ‘where’ clause on a new line:

E = m c²
where m is the mass and c is the speed of light.

Aside: I have removed the punctuation after the displayed equation.  People, and style manuals, argue about whether it should be retained or not.  My argument is: extra punctuation in a mathematical formula might be confused as a symbol of the formula; the end of line provides visible punctuation; I leave it out.  Others disagree.  But that is for a different post, probably on a different blog.  Here I have a different peeve.

The above is common usage, but I have been noticing a different form:

E = m c²
Where m is the mass and c is the speed of light.

Where did that capital letter come from?  It’s not the start of a new sentence!  And the where clause isn’t a well-formed sentence, anyway!!

Personally, I blame MSWord and the like, that oh so helpfully auto‘correct’ words that start a new line to start with a capital letter.  (Yes, I know this can be switched off: I have switched it off.  But many people don’t know, and haven’t.)

I have been patiently (most of the time, anyway) correcting my students when they do this. But now I have noticed people doing it in LaTeX documents, which (probably) means they are doing it deliberately.

Aaaargh!

As Bob the Angry Flower would say: No! Wrong! Totally Wrong! Stop doing it!

Francis Matthews, 1927 – 2014

Captain Scarlet not only had the face of Cary Grant, he had the voice of Francis Matthews doing an impression of Cary Grant.

Francis Matthews, 1927 – 2014

And, of course, Matthews was also Paul Temple.

watch light echo

Watch a beautiful time lapse of an astronomical light echo, explained by Phil Plait

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octothorpe

Word of the day: octothorpe

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finding Cinnabar

Sitting on the floor of the garage was a kind of butterfly or moth I don’t remember ever having seen before.

It was quite small, about an inch long.  Now, I identify butterflies as: Red Admiral, Peacock, Small Tortoiseshell, white butterfly, moth (if it’s nighttime), and other.  But this was a different kind of other.

Fortunately, the web is wonderful for this sort of thing, and I rapidly discovered it is a Cinnabar moth, Tyria jacobaeae.  And I’ve seen lots of its caterpillars, munching though our plants.  Now the cycle is complete.

Beautiful.

bad infographic

A great evisceration of a bad infographic (scroll down past the first chart).

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surfing difficulty

Feedly seems to be down still, victim of a DDoS attack. This means I now have to remember the names of all the websites I regularly surf.  Either that, or go back to marking...

 (Fortunately Evernote is now okay, otherwise my world would collapse!) 

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sequestering carbon, several books at a time XXV

The latest batch:

“Process metaphysics” is an obscure phrase I came cross when writing a review of emergence. I recently mentioned this to our local Complexity Reading Group, so last month we read the Stanford Encyclopedia of Philosophy’s entry on Process Philosophy.  It quickly became clear there is a substantial body of very obscure work on the subject; I’ve bought Rescher’s book as, hopefully, the least obscure entry point.  Who knows, I may even read it at some point…

sarcasm

Read Charlie Stross’ take on the sarcasm detector bid.  Charlie’s post should be an item in the acceptance test suite!

Well now, here’s the thing: automating sarcasm detection is easy. It’s so easy they teach it in first year computer science courses; it’s an obvious application of AI. (You just get your Turing-test-passing AI that understands all the shared assumptions and social conventions that human-human conversation rely on to identify those statements that explicitly contradict beliefs that the conversationalist implicitly holds. So if I say “it’s easy to earn a living as a novelist” and the AI knows that most novelists don’t believe this and that I am a member of the set of all novelists, the AI can infer that I am being sarcastic. Or I’m an outlier. Or I’m trying to impress a date. Or I’m secretly plotting to assassinate the POTUS.)

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a long felt want

Copper nails are made of copper, steel nails are made of steel, so felt nails are …?

just bought, for fixing the garden shed roof

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they

I’m a fan of singular “they”, and could briefly tell you why, but this article goes into marvelous detail!

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April showers were in May

May is out, and there are some new solar power stats to talk about.  Here is the usual plot of the sunniest day (as determined by total power generation) of each month so far:

The horizontal time axis runs from 3:00am to 9:00pm GMT (no correction for summer time: this is all sun time!). The vertical axis runs from zero to 8kW.  The black line is the generation on the particular day.  The orange regions indicate the minimum, lower quartile, median, upper quartile, and maximum generation at that time, over the respective month.

The total power generated on the sunniest day each month (essentially sunny all day) was 26.5 kWh on 13th January, 41.4 kWh on 16th February, 52.0 kWh on 24th March, 55.9 kWh on 16th April, and 54.5 kWh on 15 May.

The May plot is noticeably broader than the April plot, thanks to the longer days.  Despite this, on the sunniest day in May, we generated less power than on the sunniest day in April: May was a pretty rainy month, and even the sunniest day had a partially cloudy afternoon.

We can visualise the distribution of daily power generation for each month, using a violin plot for each month.  A “violin” has two parts:

First is a notched box and whisker plot: the box shows the inter-quartile range, with the horizontal midline line showing the median, and the notches showing an approximate 95% confidence interval for the true median; the whiskers show all the data that is within 1.5 × IQR above or below the relevant quartile; any outliers are shown as spot markers.  In general:

Secondly, this is combined with a kernel density plot, which is essentially a smoothed probability distribution plot.

These show that our power generation distribution is unimodal for each month: there are some dull days, some sunny days, but mostly intermediate days.  Median and mean generation increase significantly from January until April, but April is not significantly different from May.

This is in contrast to the rainfall data we have been gathering: there several of the monthly figures are bimodal.

Violin plots are excellent for showing a lot of statistical information in one go: specific quartiles, plus overall distribution.  They are also easy to plot in Python: I slightly modified the function posted by Flavio Coelho to draw these plots, making some cosmetic changes, and including the mean.

So now we are in June, with the longest day coming soon.  Will this result in the maximum generation, or will we get the sunniest day in some other month?

is there in physics no beauty?

Here is a nice rant about beauty in physics.

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