Category: Data Representation

Faster fiber

Image by Tomislav Jakupec from Pixabay

Polina Bayvel, Professor of Optical Communications, at UCL, and her team have just set a new speed record for sending data over real-world optical cable. They managed to send about 10 times more data than the best commercial services. Remarkable this was without changing the cable or other core infrastructure: the record was set over existing fiber running through a city centre with all the interference that causes, and with all the grime, wear and tear that comes with real use.

What was the secret? Commercial fibre optics typically use wavelengths of 850, 1300 and 1550 nanometers. That is infrared light, which some animals can see, including some snakes, fish and insects (and vampire bats). However, we need special cameras that convert it to the visible range before we can see infrared. What we can do though is create lasers that send pulses of infrared at these wavelengths. We can also design hardware that turns infrared pulses back into data. Polina’s team developed special hardware that could send data over a much larger range of frequencies of light than the existing commercial systems. It used a range of wavelengths of light between 1264 and 1618 nanometers. By mixing these higher wavelengths together they could send more data at the same time – but that is only useful if their hardware could extract the separate signals from the mixed up mess of them, back at the end. The test showed that their hardware could do that in the real-world conditions of sending data from their lab in central London out to a data centre at Canary Wharf over the existing cables, and back, so around 10 miles in total.

It means that in future we will be able to send far more data over existing cable networks with no need to replace the cables, so avoiding the extra time and costs (never mind the road works). The speed of 450 terabits per second is enough to stream 50 million films at the same time. No one actually needs to do that of course. However, our technologies do seem to voraciously use up whatever capacity we create, and with the ever-increasing use of AI tools and their need for masses of data, it may well be this ability to send more data is needed sooner than we might think.

Paul Curzon, Queen Mary University of London

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Johnny Ball’s ‘Two Wrongs Do Make a Right’ Trick

(or how to stack properly)

A girl holding a 4 of Hearts
Image by Adina Voicu from Pixabay

We present a CS4FN exclusive (from our archive): a mathematical card trick contributed by TV celebrity mathematician, Johnny Ball. The BAFTA-winning father of radio DJ Zoë Ball is famous for his TV shows like Think of a Number, Think Again and Johnny Ball Explains All, plus numerous brilliant books on maths and science.

The trick is as follows, in Johnny’s words…

There is a very simple card trick that I often use, as I can get to the explanation very quickly. In fact, for a mathematically minded audience, they are already some way to working out why it works, as soon as it does work. It’s called the Two Wrongs Do Make a Right Trick.

Just memorize the top card of a pack; let’s assume it’s the 4 of Hearts. Hand the pack to someone and ask them to choose any smallish number (eg between 1 and 10), then to deal that number of cards face down. Then announce the next card dealt will be the 4 of Hearts. Oops – it isn’t? Get them to place all the dealt cards on top of the pack again. Now ask them to choose a number bigger than the first one, but not so big that we run out of cards (eg between 10 and 20). They now deal that number face down, and the next card dealt will be the 4 of Hearts. Oops – wrong again. Let’s now see if we can make two wrongs equal a right.

Place the cards back on top. Now ask for a last chance. Take the smaller number from the larger. They now deal that number face down, and the next card dealt is the 4 of Hearts. Success!

Why does it work?

Hidden in this distracting presentation, the tale of your mistakes played up for laughs, is some clever mathematics. It uses a magic technique called a stack, which is a set up sequence of cards in a known order. Sometimes magicians will put a pre-defined stack of cards, for example four aces, on top of the deck and do a false shuffle, and then be able to deal a wining poker hand because they know exactly what cards will be dealt. In this trick your ‘mistakes’ create the stack for you.

X stacks the deck

Starting with your known card on top (that’s your secret card), your spectator chooses a number from 1 to 10. Let’s call that number X. You deal down X cards so that the first card down is the secret card, next down is the second from the original top of the pile and so on. At the end of your count of X there are X cards on the table, with the secret card on the bottom. Oops, mistake number one. Don’t worry, you can regroup. Just place the mistaken card back on top of the pile of undealt cards, then stack those X dealt cards back on top, and your deck now looks like this from the top down: (X-1) indifferent cards, the secret card, and the rest of the pack.

Y marks the spot

Second try – this time you ask for a bigger number, a number between 10 and 20. Let’s call that new number Y. Counting and dealing down Y cards, where number Y is bigger than number X, means that once Y cards are on the table you can reveal mistake two, oops again. But let’s ‘think of the numbers’ of the cards on the table. That pile has Y cards, and because we stacked the deck in the first phase the secret card is now at position X from the bottom of these Y cards, meaning it’s at position Y-X. Exactly the position in the stack to be revealed by the finale where you subtract X from Y and count down (oh wait, that’s another mathematical TV programme).

Think again – with some real numbers!

The algebra of Xs and Ys says it works, but to see this is true let’s test it and think of some specific numbers, say X=5 and Y=15. The first deal stacks the secret card at position 5 from the top. We then do the second deal of 15 cards, reversing as we go. Onto the table goes 1, 2, 3, 4, SECRET, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15. Pick up this pile and put it on top of the pack. The secret card is now in a new stack. From the top down it’s at 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, SECRET, rest of pack. Now count down 10 cards from the top of this new stack (that’s X-Y = 15-5 = 10), dealing 10 cards from the final stack gives us 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, and the next card is the SECRET card! Magic.

Proving algorithms work

A magic trick like this is what computer scientists call an ‘algorithm’: steps that if you follow them guarantee some result – here that your secret card is revealed. What we showed above is a logical, mathematical proof that the trick works. Now, programs are just algorithms (written in a programming language) so in the same way as we proved our trick works we can prove logically that a program we write always works too!

Even if you do do such a proof, it is also a good idea with software to do lots of testing too – putting in some values to check it works for specific cases too (lots more than the one example we checked). Doing that first can also help you come up with the proof.

Finale the stack pays off

Stacks, in this case the series of cards in a given order, that you deal from the top of the pack, are also used in computer science, though the meaning is slightly different. It is a data structure, a place to store information, that works like a stack of chairs. You can add a chair or take one from the top of a stack of chairs but not from the middle, and its the same with adding and removing data in a computer science stack.

Computer scientists like to have ways of thinking of data, normally numbers in the computer memory, in such a way that they know where things are so they can use them when they need them. A stack is an example of what computer scientists call an abstract data type, a data structure with rules of how you add and remove data to and from it. The stack is a ‘container’ of data (or nodes if the things stored are more complex than just plain numbers, and link off to other parts of memory). Computer scientists do two basic things to stacks: they ‘push’ and they ‘pop’. In a push a new element is added to the top of the stack, and all those elements under effectively move down a place. It’s like adding those cards on top of the secret card. In a pop the top element is removed from the stack, and up jumps all the elements underneath, like dealing a card from the top of the deck. Your laptop and your mobile phone will have a stack somewhere in them. The software will be pushing and popping and doing lots of other clever computer science tricks to keep track of the information and how it’s moving around. The programmer will probably use even smarter things like stack pointers. These are a note for the computer to tell it what was last changed and where in the stack it was.

One too many?

One of the nastiest (and easiest to make) errors in computer software can come from something called a stack overflow. A program pops from or pushes to the top of the stack. That position in memory is kept track of using the ‘stack pointer’, which is just like a sign pointing to where the top of the stack is. So we push and pop from where the stack pointer is pointing changing the position of the pointer as we do. A stack normally has a fixed maximum size (like hitting the ceiling with a stack of chairs). A stack overflow is where you shoot off the end of the stack, so where you try to push a value on the stack when it is already full. It is like a magician counting past 52 cards. It can only end in tears. Sometimes this nasty trick is used by computer hackers to gain control of a computer system. Once the stack has been overshot, the computer surrenders. The software has literally lost its place, and the hackers can move in and take over.

Fortunately maths can come to the rescue by creating tests to ensure that the stack doesn’t pop its clogs or push up the daisies. Even better you can prove that the program controlling the stack (just an algorithm) never lets you overflow the stack in the same way that we proved our trick always works.

Let’s pop over and give the last word to Johnny

Ever ready to make things simple to understand and easy to do, Johnny points out:

You can reduce the trick to choosing just 1 and then 2 with just three cards, and show that the manoevring moves the top card down by the difference in numbers chosen.

Now why didn’t we think of that?

Peter W McOwan, Paul Curzon, Queen Mary University of London

but critically of course (and also inspiring us about Maths since we were kids), Johnny Ball

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Bits with Soul (via a puzzle)

Image by Gerd Altmann from Pixabay

In January 2025 computer scientist Simon Peyton Jones gave an inspiring lecture at Darwin College Cambridge on “Bits with Soul” about the joy, beauty, and creativity of computer science … from simple ideas of data representation comes all of virtual reality.

Our universe is built from elementary particles: quarks, electrons and the like. Out of quarks come protons and neutrons. Put those together with electrons in different ways to get different atoms. From atoms are built molecules, and from there on come ever more complexity including the amazing reality of planets and suns, humans, trees, mushrooms and more. From small things ever more complex things are built and ultimately all of creation.

The virtual world of our creation is made of bits combined using binary, but what are bits, and what is binary? Here is a puzzle that Simon Peyton Jones was set by his teacher as a child to solve, to help him think about it. Once you have worked it out then think about how things might be built from bits: numbers, letters, words, novels, sounds, music, images, videos, banking systems, game worlds … and now artificial intelligences?

A bank cashier has a difficult customer. They always arrive in a rush wanting some amount of money, always up to £1000 in whole pounds, but a different amount from day to day. They want it instantly and are always angry at the wait while it is counted out. The cashier hatches a plan. She will have ready each day a set of envelopes that will each contain a different amount of money. By giving the customer the right set of envelope(s) she will be able to hand over the amount asked for immediately. Her first thought had been to have one envelope with £1 in, one envelope with £2 in, one with £3 and so on up to an envelope with £1000 in. However, that takes 1000 envelopes. That’s no good. With a little thought though she realised she could do it with only 10 envelopes if she puts the right amount of money in each. How much does she put in each of the 10 envelopes that allows her to give the customer whatever amount they ask for just by handing over a set of those envelopes?

Simon Peyton Jones gives the answer to the puzzle in the talk and also explores how, from bits, come everything we have built on computers with all their beauty and complexity. Watch the video of Simon’s talk on youtube to find out. [EXTERNAL]

– Paul Curzon, Queen Mary University of London (inspired by Simon’s talk as I hope you will be)

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Ancient Egyptian Numerals

Image of Hey by Jeff Dahl / CC BY-SA (https://creativecommons.org/licenses/by-sa/4.0)
 via Wikimedia 

How data is represented is an important part of computer science. There are lots of ways numbers can be represented. Choosing a good representation can make things easier or harder to do. The Ancient Egyptians had a simple way using hieroglyphs (symbols). It is similar to Roman Numerals but simpler. 

They represented numbers 1 to 9 with a hieroglyph with that number of straight lines. They arranged them into patterns (a bit like we do dots on a dice). The patterns make them easier to recognise. They used an upside down U shape for 10, two of these for 20, and so on. Their symbol for 10 also meant a “cattle hobble”. They then had a new symbols for each power of 10 up to a million. So 100 is the hieroglyph for a coil of rope.

Egyptian Numbers hundreds.jpg
Image by CS4FN

The hieroglyph for the number 1000 was a water lily.

The Ancient Egyptian hieroglyph for a waterlily that also means 1000 with 1000 written next to it
The ancient Egyptian way to write 1000 was a hieroglyph of a waterlily. Image by CS4FN

The hieroglyph for a million, which also rather sensible meant ‘many’, was just the hieroglyph of the god Hey who was the personification of eternity.

To make a number you just combined the hieroglyph for the ones, tens, hundreds and so on.

The Ancient Egyptian number system makes it very easy to write numbers and to add and subtract numbers. Big numbers are fairly compact, though take up more space than our decimals. It is easy to convert a tally representation into this system too. More complicated things like multiplication are harder to do. Computers use binary representation because they make all the main operations easy to do using logic. Ultimately it is all about algorithms. The Egyptians had easy to follow algorithms for addition and subtraction to go with their number representation. We have devised algorithms that allow computers to do all the calculations they do as quickly as possible using a binary representation

Paul Curzon, Queen Mary University of London

To do…

Try doing some sums as an Ancient Egyptian would – without converting to our numbers. What is the algorithm for adding Egyptian numbers? Do multiplication using a repeated addition algorithm – to do 3 x 4 you 4 to zero 3 times.

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An earlier version of this article first appeared on Teaching London Computing.

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Herman Hollerith: from punch cards to a special company

Herman Hollerith
Herman Hollerith (Image from wikimedia, Public Domain)

Herman Hollerith, the son of immigrants, struggled early on at school and then later in bookkeeping at college but it didn’t stop him inventing machines that used punch cards to store data. He founded a company to make and sell his machines. It turned into the company now called IBM, which of course helped propel us into the computer age.

Hollerith had worked as a census clerk for a while, and the experience led to his innovation. The United States has been running a national census every 10 years since the American Revolution, aiming to record the details of every person, for tax and national planning purposes. It is not just a count but has recorded information about each person such as male/female, married or not, ethnicity, whether they can read, disabilities, and so on.

As the population expanded it of course became harder to do. It was also made harder as more data about each person was being collected over time. For the 1890 census a competition was held to try and find better ways to compile the data collected. Herman Holerith won it with his punch card based machine. It could process data up to twice as fast as his competitors and with his system data could be prepared 10 times faster.

To use the machine, the census information for each person was recorded by punching holes in special cards at specific positions. It was a binary system with a hole essentially meaning the specific feature was present (eg they were married) and no hole meaning it wasn’t (eg they were single). Holes against numbers could also mean one of several options.

Hollerith punched card from wikimedia
Hollerith punched card (Image from wikimedia, Public Domain)

The machine could read the holes because they allowed a wire to make an electrical connection to a pool of mercury below so the holes just acted as switches. Data could therefore be counted automatically, with each hole adding one to a different counter. It was the first time that a system of machine-readable data had been used and of course binary went on to be the way all computers store information. In processing the census his machines counted the data on around 100 million cards (an early example of Big Data processing!). This contributed to reducing the time it took to compile the data from the whole country by two years. It also saved about $5 million

Holerith patented the machine and was also awarded a PhD for his work on it. He set up a company to sell it called the Tabulating Machine Company. Over time it merged with other companies until eventually in 1924 the resulting company changed its name to International Business Machines or is it is now known, IBM. it is of course one of the most important companies driving the computer age, building early mainframe computers the size of rooms that revolutionised business computing, but later also responsible for the personal computer, leading to the idea that everyone could own a computer.

Not a bad entrepreneurship legacy for someone who early on at school apparently struggled with, and certainly hated, spelling – he jumped out of a window at school to avoid doing it. He also did badly at bookkeeping in college. He was undeterred by what he was poor at though and focussed on what he was good at, He was hard working and developed his idea for a mechanical tabulating machine for 8 years before his first machine went to work. Patience and determination was certainly a strength that paid off for him!

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Join the crowd with swarm intelligence

A shoal of fish crammed into a cylindrical tank.
Image by Paul Sloane from Pixabay

Next time you are in a large crowd, look around you: all those people moving together, and mostly not bumping into each other. How does it happen? Flocks of birds and schools of fish are also examples of this ‘swarm intelligence’. Computer Scientists have been inspired by this behaviour to come up with new solutions to really difficult problems.

Swarming behaviour requires the individuals (birds, fish or people) to have a set of rules about how to interact with the individuals nearest to them. These so-called ‘local’ rules are all that’s needed to give rise to the overall or ‘global’ behaviour of the swarm. We adjust our individual behaviour according to our current state but also the current state of those around us. If I want to turn left then I do it slowly so that others in the crowd can be aware of it and also start to turn. I know what I am doing and I know what they are doing. This is how information makes its way from the edges of the crowd to the centre and vice versa.

A swarm is born

The way a crowd or swarm interacts can be explained with simple maths. This maths is a way of approximating the complex psychological behaviour of all the individuals on the basis of local and global rules. Back in 1995 James Kennedy, a research psychologist, and Computer Scientist Russ Eberhart, having been inspired by the study of bird flocking behaviour by biologist Frank Heppner, realised that this swarm intelligence could be used to solve difficult computer problems. The result was a technique called Particle Swarm Optimisation (PSO).

Travel broadens the mind

An optimisation problem is one where you have a range of options to choose from and you want to know the best solution. One of the classic problems is called ‘The travelling salesperson’ problem. You work for a company and have to deliver packages to say 12 towns. You look at the map. There are many different routes to take, but which is the one that will let you visit all 12 towns using the least petrol? Here the choices are the order in which you visit the towns, and the constraint is that you want to do the least driving. You could have a guess, select the towns in a random order and work out how far you’d have to travel to visit them in this order. You then try another route through all 12 and see if it takes less mileage, and so on. Phew! It could take a long time to work out all the possible routes for 12 towns to see which was best. Now imagine your company grows and you have to deliver to 120 towns or 1200 towns. You would spend all your time with the maps trying to come up with the cheapest solutions. There must be a better way? Well actually simple as this problem seems it’s an example of a set of computational problems known as NP-Complete problems and it’s not easy to solve! You need some guidance to help you through and that’s where swarm optimisation come in. It’s an example of a so-called metaheuristic algorithm: a sort of ‘general rule of thumb’ to help solve these types of problem. It won’t always work unless you have infinite time but it’s better than just trying random solutions. So how does swarm optimisation work here?

State space: the final frontier

First we need to turn the problem into something called a state space. You probably use state spaces all the time but just don’t know it. Think about yourself. What are the characteristic you would use to tell people about your current state: age, height, weight and so on. You can identify yourself by a list of say 3 numbers – one for age, one for height, one for weight. It’s not everything about you of course but it does define your state to some extent. Your numbers will be different to other people’s numbers, if you take all the numbers for your friends you would have a state space. It would be a 3-dimensional space with axes: age, height and weight, and each person would be a point in that space at some coordinate (X, Y, Z).

So state spaces are just a way of representing the possible variations in some problem. With the 12 towns problem, however, you couldn’t draw this space: it would be 12 dimensional! It would have one axis for each town, with the position on the axis an indication of where in the route it was. Every point in that space would be a different route through the 12 towns, so each point in the space would have coordinates (x1, x2, x3, … x11, x12). For each point there would also be a mileage associated with the route, and the task is to find the coordinate point (route) with the lowest mileage.

Where no particle has swarmed before

Enter swarm optimisation. We create a set of ‘particles’ that will be like birds or fish, and will fly and swarm through our state space. Each particle starts with a random starting location in the state space and calculates the mileages involved for the coordinates (route) they are at. The particles remember (store) this coordinate and also the value of the mileage at that position. Each particle therefore has it’s own known ‘local’ best value (where the lowest mileage was) but can compare this with other neighbouring particles to see if they have found any even better solutions. The particles then move onwards randomly in a direction that tends to move them towards their own local best and the best value found by their neighbours. The particles ‘fly’ around the state space in a swarm homing in on even better solutions until they all converge on the best they can find: the answer.

It may be that somewhere in a part of the space where no particle has been there is an even better solution, perhaps even the best solution possible. Our swarm will have missed it! That’s why this algorithm is a heuristic, a best guess to a tough problem. We can always add some more particles at the start to fill more of the state space and reduce the chance of missing a good solution, but we cant ever be 100% sure.

Swarm optimisation has been applied to a whole range of tough problems in computing, electronic engineering, medicinal chemistry and economics. All it needs is for you to create an appropriate state space for your problem and let the particles fly and swarm to a good solution.

It’s yet another example of clever computing based on behaviours found in the natural world. So next time you’re in a crowd, look around and appreciate all that collective interacting swarm intelligence…but make sure you remember to watch where you are stepping.

by Peter W. McOwan, Queen Mary University of London. From the archive.

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Marc Hannah and the graphics pipeline

Film and projectors
Image by Gerd Altmann from Pixabay

What do a Nintendo games console and the films Jurassic Park, Beauty and the Beast and Terminator II have in common? They all used Marc Hannah’s chips and linked programs for their amazing computer effects..It is important that we celebrate the work of Black Computer Scientists and Marc is one who deserves the plaudits as much as anyone as his work has had a massive effect on the leisure time of everyone who watches movies with special effects or plays video games – and that is just about all of us.

In the early 1980s, with six others, Marc founded Silicon Graphics, becoming its principal scientist. Silicon Graphics was a revolutionary company, pioneering fast computers capable of running the kind of graphics programs on special graphics chips that suddenly allowed the film industry to do amazing special effects. Those chips and linked programs were designed by Marc.

Now computers and games consoles have special graphics chips that do fast graphics processing as standard, but it is Marc and his fellow innovators at Silicon Graphics who originally made it happen.

It all started with his work with James Clark on a system called the Geometry Engine while they were at Stanford. Their idea was to create chips that do all the maths needed to do sophisticated manipulation of imagery. VLSI (Very Large scale Integration), whereby computers were getting smaller and fitting on a chip was revolutionising computer design. Suddenly a whole microprocessor could be put on a single chip because tens of thousands (now billions) of transistors could be put on a single slice of silicon. They pioneered the idea of using VLSI for creating 3-D computer imagery, rather than just general-purpose computers, and with Silicon Graphics they turned their ideas into an industrial reality that changed both film and games industries for ever.

Silicon Graphics was the first company to create a VLSI chip in this way, not to be a general-purpose computer, but just to manipulate 3-D computer images.

A simple 3D image in a computer might be implemented as the vertices (corners) of a series of polygons. To turn that into an image on a flat screen needs a series of mathematical manipulations of those points’ coordinates to find out where they end up in that flat image. What is in the image depends on the position of the viewer and where light is coming from, for example. If the object is solid you also need to work out what is in front, so seen, and what behind, so not. Each time the object, viewer or light source moves, the calculations need to be redone. It is done as a series of passes doing different geometric manipulations in what is called a geometry pipeline and it is these calculations they focussed on. They started by working out which computations had to be really fast: the ones in the inner most loops of the code that did this image processing, so was executed over and over again. This was the complex code that meant processing images took hours or days because it was doing lots of really complex calculation. Instead of trying to write faster code though, they instead created hardware, ie a VLSI chip, to do the job. Their geometry pipeline did the computation in a lightening fast way as it was avoiding all the overhead of executing programs and instead implementing the calculations that slowed things down directly in logic gates that did all that crucial maths very directly and so really quickly.

The result was that their graphic pipeline chips and programs that worked with them became the way that CGI (computer generated imagery) was done in films allowing realistic imagery, and were incorporated into games consoles too, allowing for ever more realistic looking games.

So if some amazing special effects make some monster appear totally realistic this Halloween, or you get lost in the world of a totally realistic computer game, thank Marc Hannah, as his graphics processing chips originally made it happen.

– Paul Curzon, Queen Mary University of London

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Sonifying zebrafish biology

Zebrafish with the appearance of red white and blue stripes
Image by Petr Kuznetsov from Pixabay

Biologists often analyse data about the cell biology of living animals to understand their development. A large part of this involves looking for patterns in the data to use to refine their understanding of what is going on. The trouble is that patterns can be hard to spot when hidden in the vast amount of data that is typically collected. Humans are very good at spotting patterns in sound though – after all that is all music is. So why not turn the data into sound to find these biological patterns?

In hospitals, the heartbeats of critically ill patients are monitored by turning the data from heart monitors into sounds. Under the sea, in (perhaps yellow) submarines, “golden ear” mariners use their listening talent to help with navigation and detect potential danger for fish and the submarine. They do this by listening to the soundscapes produced by sonar built up from echoes from the objects round about. This way of using sounds to represent other kinds of data is called ‘sonification’. Perhaps similar ideas can help to find patterns in biological data? An interdisciplinary team of researchers from Queen Mary including biologist Rachel Ashworth, Audio experts Mathieu Barthet and Katy Noland and computer scientist William Marsh tried the idea out on the zebrafish. Why zebrafish? Well, they are used lots for the study of the development of vertebrates (animals with backbones). In fact it is what is called a ‘model organism’: a creature that lots of people do research on as a way of building a really detailed understanding of its biology. The hope is that what you learn about zebrafish will help you understand the biology of other vertebrates too. Zebrafish make a good model organism because they mature very quickly. Their embryos are also transparent. That is really useful when doing experiments because it means you can directly see what is going on inside their bodies using special kinds of microscopes.

The particular aspect of zebrafish biology the Queen Mary team has been investigating is the way calcium signals are used by the body. Changes in the concentration of calcium ions are important as they are used inside a cell to regulate its behaviour. These changes can be tracked in zebrafish by injecting fluorescent dyes into cells. Because the zebrafish embryos are transparent whatever has been fluorescently labelled can then be observed.

Calcium ions are used inside a cell to regulate its behaviour

The Queen Mary team developed software that detects calcium changes by automatically spotting the peaks of activity over time. They relied on a technique that is used in music signal processing to detect the start of notes in musical sequences. Finding the peaks in a zebrafish calcium signal and the notes from the Beatles’ Day Tripper riff may seem to be light years apart, but from a signal processing point of view, the problems are similar. Both involve detecting sudden burst of energy in the signals. Once the positions of the calcium peaks have been found they can then be monitored by sonifying the data.

What the team found using this approach is that the calcium activity in the muscle cells of zebrafish varies a lot between early developmental stages of the embryo and the late ones. You can have a go at hearing the difference yourself – listen to the sonified versions of the data.

by the CS4FN team (from the archive)

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Solving Railway Timetabling Problems with Data Visualisation

by Daniel Gill, Queen Mary University of London

Steam train on a bridge, looking back down the side of the carriages
Image by Laurent from Pixabay

Train timetables are complex. When designing a timetable for railways you have to think about the physical capabilities of the actual train, what stops it needs to make, whether it is carrying passengers or freight, the number of platforms at a station, the gradient of the track, and the placement of passing loops on single-track sections, amongst many other things. Data visualisation can help with timetabling and make sure our railways continue to run on track!

Data visualisation is an important area in computer science. If you had a huge amount of complex data in a spreadsheet, your first thought wouldn’t be to sit down with a cup of tea and spend hours reading through it – instead you might graph it or create an infographic to get a better picture. Humans are very bad at understanding and processing raw data, so we speed up the process by converting it to something easier to understand.

Timetabling is like this – we need to consider the arrival and departure times from all stations for each train. You might have used a (perhaps now) old fashioned paper timetable, with each train as a column, and the times at each station along the rows, like the one below. This is great if you’re a passenger… you can see clearly when your train leaves, and when it gets to your desired destination. If you’re unlucky enough to miss a train, you can also easily scan along to find the next one.

A traditional timetable with stopping times of different trains in columns and rows for each station
Image by Daniel Gill for CS4FN

Unfortunately, this kind of presentation might be more challenging for timetable designers. In this timetable, there’s a mix of stopping and fast services. You can see which of them are fast based on the number of stations they skip (marked with a vertical line), but, because they travel at different speeds it’s difficult to imagine where they are on the railway line at any one time. 

One of the main challenges in railway timetabling, and perhaps the most obvious, is that trains can’t easily overtake slower ones in front of them. it’s this quirk that causes lots of problems. So, if you needed to insert another train into this timetable you would need to consider all the departure times of the trains around it, to make sure there is no conflicts – this is a lot of data to juggle.

 But there’s an easier way to visualise these timetables: introducing Marey charts! They represent a railway on a graph, with stations listed vertically, time along the top, and each train represented by a single (bumpy) line. If we take our original timetable from above and convert it to a Marey chart, we get something that looks like this:

A Marey chart of the same timetable now with lines showing the path of the train through time (which is now the x-axis
Image by Daniel Gill for CS4FN


Though thought to have been invented by a lesser-known railway engineer called Charles Ibry, these charts were popularised by Étienne-Jules Marey, and (perhaps unfairly) take his name. 

How does it work?

There are a few things that you might notice immediately from this diagram. The stations along the side aren’t equally spaced, like you might expect from other types of graph, instead they are spaced relative to the distance between the stations on the actual railway. This means we can estimate when a fast train will pass each of the stations. This is an estimation, of course, because the train won’t be travelling at a constant speed throughout – but it’s better than our table from before which is no help at all!

Given this relative spacing, we can also estimate how fast a train is going. The steepness of the line, in this diagram, directly reflects the speed of the train*. Look at the dark blue and purple trains – they both leave Coventry really close together, but the purple train is a bit slower, so the gap widens near Birmingham International. We can also see that trains that do lots of stopping (when the line is horizontal) travel at a much slower average speed than the fast ones: though that shouldn’t be a surprise! 

*There’s a fun reason that this is the case. The gradient (the steepness of the line) is calculated as the change in y divided by the change in x. In this case, the change in the y dimension is the distance the train has travelled, and the change in x is the time it has taken. If you have studied physics, you might immediately recognise that distance divided by time is speed (or velocity). Therefore, the steepness in a Marey chart is proportional to the speed of the train. 

We can also see that the lines don’t intersect at all. This is good, because, quite famously, trains can’t really overtake. If there was an intersection it would mean that at some point, two trains would need to be at the same location at the same time. Unless you’ve invented some amazing quantum train (more about the weirdness of quantum technology in this CS4FN article), this isn’t possible!

Putting it to the Test

Put yourself in the shoes of a railway timetable designer! We have just heard that there is a freight train that needs to run through our little section of railway. The driver needs to head through sometime between 10:45 and 12:15 – how very convenient: we’ve already graphed that period of time.

The difficulty is, though, that their freight train is going to take a very slow 45 minutes to go through our section of railway – how are we going to make it fit? Let’s use the Marey chart to solve this problem visually. Firstly, we’ll put a line on that matches the requirements of the freight train:

A Marey chart showing the freight train as a single line passing through time and stations
Image by Daniel Gill for CS4FN

And then let’s re-enable all the other services.

With all the other trains included the new train crosses their paths - it would be stuck behind them. or vice versa
Image by Daniel Gill for CS4FN

Well, that’s not going to work. We can see from this, though, how slow this freight train actually is, especially compared to the express trains its overlaps with. So, to fix this, we can shift it over. We want to aim for a placement where there are no overlaps at all.

A Marey chart showing a position where the new train does not clash
Image by Daniel Gill for CS4FN

Perfect, now it’s not going to be able to make the journey without interfering with our other services at all.

Solving Problems

When we’re given a difficult problem, it’s often a good idea to find a way to visualise it (or as my A-Level physics teacher often reminded me: “draw a diagram!”). This kind of visualisation is used regularly in computer science. From students learning the craft, all the way to programmers and academics at the top of their field – they all use diagrams to help understand a problem.

More on …

Magazines …

Front cover of CS4FN issue 29 - Diversity in Computing

EPSRC supports this blog through research grant EP/W033615/1,

Happy #WorldEmojiDay 2024 – here’s an emoji film quiz & some computer science history

Emoji! 💻 😁

World Emoji Day is celebrated on the 17th of July every year (why?) and so we’ve put together a ‘Can you guess the film from the emoji’ quiz and added some emoji-themed articles about computer science and the history of computing.

  1. An emoji film quiz
  2. Emoji accessibility, and a ‘text version’ of the quiz
  3. Computer science articles about emoji

Emoji are small digital pictures that behave like text – you can slot them easily them in sentences (you don’t have to ‘insert an image’ from a file or worry about the picture pushing the text out of the way). You can even make them bigger or smaller with the text (🎬 – compare the one in the section title below). People use them as a quick way of sharing a thought or emotion, or adding a comment like a thumbs up so they’re (sort of) a form of data representation. Even so, communication with emoji can be just as tricky, in terms of being misunderstood, just as with using words alone. Different age groups might read the same emoji and understand something quite different from it. What do you think 🙂 (‘slightly smiling face’ emoji) means? What do people older or younger than you think it means? Lots of people think it means “I’m quite happy about this” but others use it in a more sarcastic way.

1. An emoji film quiz 🎬

You can view the quiz online or download and print from Word or PDF versions. If you’re in a classroom with a projector the PowerPoint file is the one you want.

More Computational Thinking Puzzles

2. Emoji accessibility, and a text version of the quiz

We’ve included a text version for blind or visually impaired people which can either be read out by someone or by a screen reader. Use the ‘Text quiz’ files in Word or PDF above.

More generally, when people share photographs and other images on social media it’s helpful if they add some information about the image to the ‘Alt Text’ (alternative text) box. This tells people who can’t easily see the image what’s in the picture. Screenreaders will also tell people what the emojis are in a tweet or text message, but if you use too many… it might sound like this 😬.

https://twitter.com/RNIB/status/1813489201842663723

3. Computer science articles about emoji

Happy World Emoji Day – 📅 17 July 2023 – how people use emoji to communicate and what it tells us about them 😀
The Emoji Crystal Ball

This next article is about the history of computing and the development of the graphical icons for apps that started life being drawn on gridded paper by Susan Kare. You could print some graph / grid paper and design your own!

Susan Kare: Icon Draw

A copy of this post can also be found as a permanent page at https://cs4fn.blog/emoji/

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This blog is funded by EPSRC on research agreement EP/W033615/1.

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