Author: IMC

Calculating probabilities can be complicated, as this classic “what’s behind the doors” problem shows, says Peter Rowlett.

Calculating probabilities can be tricky, with subtle changes in context giving quite different results. I was reminded of this recently after setting BrainTwister #10 for New Scientist readers, which was about the odds of seating two pairs of people adjacently in a row of 22 chairs.

Several readers wrote to say my solution was wrong. I had figured out all the possible seating arrangements and counted the ones that had the two groups adjacent. The readers, meanwhile, seated one pair first and then counted the ways of seating the second pair adjacently. Neither approach was wrong, depending on how you read the question.

This subtlety with probability is illustrated nicely by the Monty Hall problem, which is based on the long-running US game show Let’s Make a Deal. A contestant tries to guess which of three doors conceals a big prize. They guess at random, with ⅓ probability of finding the prize. In the puzzle, host Monty Hall doesn’t open the chosen door. Instead, he opens one of the other doors to reveal a “zonk”, an item of little value. He then offers the contestant the opportunity to switch to the remaining door or stick with their first choice.

Hall said in 1991 that the game is designed so contestants make the mistaken assumption that, since there are now two choices, their ⅓ probability has increased to ½. This, combined with a psychological preference to avoid giving up a prize already won, means people tend to stick

Marilyn vos Savant published the problem in her column in Parade magazine in 1990 along with the answer that you are much more likely to win if you switch. She received thousands of letters, many from mathematicians and scientists, telling her she was wrong.

Imagine the host opened one of the unchosen doors at random: one-third of the time, they would reveal the prize. But in the remaining cases, the prize would be behind the chosen door half the time, for a probability of ½.

But that isn’t really the problem being solved. The missing piece of information is that the host knows where the prize is, and of course the show must go on. There is a ⅓ probability that the prize is behind the chosen door, and therefore a ⅔ probability that it is behind one of the other two. Being shown a zonk behind one of the other two hasn’t changed this set-up – the door chosen still has a probability of ⅓, so the other door carries a ⅔ probability. You should switch.

Probability problems depend on the precise question more than people realise. This is why it might seem surprising when you run into a friend, because you aren’t considering the number of people you walked past and how many friends you might see. And for scientists, it is why they have to be very careful about what their evidence is really telling them.

For more such insights, log into www.international-maths-challenge.com.

*Credit for article given to Peter Rowlett*

Our laws of nature are written in the language of mathematics. But maths itself is only as dependable as the axioms it is built on, and we have to assume those axioms are true.

You might think that mathematics is the most trustworthy thing humans have ever come up with. It is the basis of scientific rigour and the bedrock of much of our other knowledge too. And you might be right. But be careful: maths isn’t all it seems. “The trustworthiness of mathematics is limited,” says Penelope Maddy, a philosopher of mathematics at the University of California, Irvine.

Maddy is no conspiracy theorist. All mathematicians know her statement to be true because their subject is built on “axioms” – and try as they might, they can never prove these axioms to be true.

An axiom is essentially an assumption based on observations of how things are. Scientists observe a phenomenon, formalise it and write down a law of nature. In a similar way, mathematicians use their observations to create an axiom. One example is the observation that there always seems to be a unique straight line that can be drawn between two points. Assume this to be universally true and you can build up the rules of Euclidean geometry. Another is that 1 + 2 is the same as 2 + 1, an assumption that allows us to do arithmetic. “The fact that maths is built on unprovable axioms is not that surprising,” says mathematician Vera Fischer at the University of Vienna in Austria.

These axioms might seem self-evident, but maths goes a lot further than arithmetic. Mathematicians aim to uncover things like the properties of numbers, the ways in which they are all related to one another and how they can be used to model the real world. These more complex tasks are still worked out through theorems and proofs built on axioms, but the relevant axioms might have to change. Lines between points have different properties on curved surfaces than flat ones, for example, which means the underlying axioms have to be different in different geometries. We always have to be careful that our axioms are reliable and reflect the world we are trying to model with our maths.

Set theory

The gold standard for mathematical reliability is set theory, which describes the properties of collections of things, including numbers themselves. Beginning in the early 1900s, mathematicians developed a set of underpinning axioms for set theory known as ZFC (for “Zermelo-Fraenkel”, from two of its initiators, Ernst Zermelo and Abraham Fraenkel, plus something called the “axiom of choice”).

ZFC is a powerful foundation. “If it could be guaranteed that ZFC is consistent, all uncertainty about mathematics could be dispelled,” says Maddy. But, brutally, that is impossible. “Alas, it soon became clear that the consistency of those axioms could be proved only by assuming even stronger axioms,” she says, “which obviously defeats the purpose.”

Maddy is untroubled by the limits: “Set theorists have been proving theorems from ZFC for 100 years with no hint of a contradiction.” It has been hugely productive, she says, allowing mathematicians to create no end of interesting results, and they have even been able to develop mathematically precise measures of just how much trust we can put in theories derived from ZFC.

In the end, then, mathematicians might be providing the bedrock on which much scientific knowledge is built, but they can’t offer cast-iron guarantees that it won’t ever shift or change. In general, they don’t worry about it: they shrug their shoulders and turn up to work like everybody else. “The aim of obtaining a perfect axiomatic system is exactly as feasible as the aim of obtaining a perfect understanding of our physical universe,” says Fischer.

At least mathematicians are fully aware of the futility of seeking perfection, thanks to the “incompleteness” theorems laid out by Kurt Gödel in the 1930s. These show that, in any domain of mathematics, a useful theory will generate statements about this domain that can’t be proved true or false. A limit to reliable knowledge is therefore inescapable. “This is a fact of life mathematicians have learned to live with,” says David Aspero at the University of East Anglia, UK.

All in all, maths is in pretty good shape despite this – and nobody is too bothered. “Go to any mathematics department and talk to anyone who’s not a logician, and they’ll say, ‘Oh, the axioms are just there’. That’s it. And that’s how it should be. It’s a very healthy approach,” says Fischer. In fact, the limits are in some ways what makes it fun, she says. “The possibility of development, of getting better, is exactly what makes mathematics an absolutely fascinating subject.”

HOW BIG IS INFINITY?

Infinity is infinitely big, right? Sadly, it isn’t that simple. We have long known that there are different sizes of infinity. In the 19th century, mathematician Georg Cantor showed that there are two types of infinity. The “natural numbers” (1, 2, 3 and so on forever) are a countable infinity. But between each natural number, there is a continuum of “real numbers” (such as 1.234567… with digits that go on forever). Real number infinities turn out not to be countable. And so, overall, Cantor concluded that there are two types of infinity, each of a different size.

In the everyday world, we never encounter anything infinite. We have to content ourselves with saying that the infinite “goes on forever” without truly grasping conceptually what that means. This matters, of course, because infinities crop up all the time in physics equations, most notably in those that describe the big bang and black holes. You might have expected mathematicians to have a better grasp of this concept, then – but it remains tricky.

This is especially true when you consider that Cantor suggested there might be another size of infinity nestled between the two he identified, an idea known as the continuum hypothesis. Traditionally, mathematicians thought that it would be impossible to decide whether this was true, but work on the foundations of mathematics has recently shown that there may be hope of finding out either way after all.

For more such insights, log into www.international-maths-challenge.com.

*Credit for article given to Michael Brooks*

My intention with this article is to give an intuitive and non-technical introduction to the field of evolutionary algorithms, particularly with regards to optimisation.

If I get you interested, I think you’re ready to go down the rabbit hole and simulate evolution on your own computer. If not … well, I’m sure we can still be friends.

Survival of the fittest

According to Charles Darwin, the great evolutionary biologist, the human race owes its existence to the phenomenon of survival of the fittest. And being the fittest doesn’t necessarily mean the biggest physical presence.

Once in high school, my lunchbox was targeted by swooping eagles, and I was reduced to a hapless onlooker. The eagle, though smaller in form, was fitter than me because it could take my lunch and fly away – it knew I couldn’t chase it.

As harsh as it sounds, look around you and you will see many examples of the rule of the jungle – the fitter survive while the rest gradually vanish.

The research area, now broadly referred to as Evolutionary Algorithms, simulates this behaviour on a computer to find the fittest solutions to a number of different classes of problems in science, engineering and economics.

The area in which this area is perhaps most widely used is known as “optimisation”.

Optimisation is everywhere

Your high school maths teacher probably told you the shortest way to go from point A to point B was along the straight line joining A and B. Your mum told you that you should always get the right amount of sleep.

And, if you have lived on your own for any length of time, you’ll be familiar with the ever-increasing cost of living versus the constant income – you always strive to minimise the expenditures, while ensuring you are not malnourished.

Whenever you undertake an activity that seeks to minimise or maximise a well-defined quantity such as distance or the vague notion of the right amount of sleep, you are optimising.

Look around you right now and you’ll see optimisation in play – your Coke can is shaped like that for a reason, a water droplet is spherical for a reason, you wash all your dishes together in the dishwasher for a reason.

Each of these strives to save on something: volume of material of the Coke can, and energy and water, respectively, in the above cases.

So we can safely say optimisation is the act of minimising or maximising a quantity. But that definition misses an important detail: there is always a notion of subject to, or satisfying some conditions.

You must get the right amount of sleep, but you also must do your studies and go for your music lessons. Such conditions, which you also have to adhere to, are known as “constraints”. Optimisation with constraints is then collectively termed “constrained optimisation”.

After constraints comes the notion of “multi-objective optimisation”. You’ll usually have more than one thing to worry about (you must keep your supervisor happy with your work and keep yourself happy and also ensure that you are working on your other projects). In many cases these multiple objectives can be in conflict.

Evolutionary algorithms and optimisation

Imagine your local walking group has arranged a weekend trip for its members and one of the activities is a hill climbing exercise. The problem assigned to your group leader is to identify who among you will reach the hill in the shortest time.

There are two approaches he or she could take to complete this task: ask only one of you to climb up the hill at a time and measure the time needed, or ask all of you to run all at once and see who reaches first.

That second method is known as the “population approach” of solving optimisation problems – and that’s how evolutionary algorithms work. The “population” of solutions are evolved over a number of iterations, with only the fittest solutions making it to the next.

This is analogous to the champion girl from your school making to the next round which was contested among champions from other schools in your state, then your country, and finally winning among all the countries.

Or, in our above scenario, finding who in the walking group reaches the hill top fastest, who would then be denoted as the fittest.

In engineering, optimisation needs are faced at almost every step, so it’s not surprising evolutionary algorithms have been successful in that domain.

Design optimisation of scramjets

At the Multi-disciplinary Design Optimisation Group at the University of New South Wales, my colleagues and I are involved in the design optimisation of scramjets, as part of the SCRAMSPACE program. In this, we’re working with colleagues from the University of Queensland.

Our evolutionary algorithms-based optimisation procedures have been successfully used to obtain the optimal configuration of various components of a scramjet.

Some of these have quite technical names, that in themselves would require quite a bit of explanation but, if you want, you can get a feel for the kind of work we do, and its applications for scramjets, by clicking here.

There are, at the risk of sounding over-zealous, no limits to the application of evolutionary algorithms.

Has this whetted your appetite? Have you learnt something new today?

If so, I’m glad. May the force be with you!

For more such insights, log into www.international-maths-challenge.com.

*Credit for article given to Amit Saha*

The mathematical study of how repeating tiles fit together usually involves pointed shapes like triangles or squares, but these aren’t normally found in the natural world.

The chambers of a nautilus shell are an example of a soft cell in nature

A new class of mathematical shapes called soft cells can be used to describe how a remarkable variety of patterns in living organisms – such as muscle cells and nautilus shells – form and grow.

Mathematicians have long studied how tiles fit together and cover surfaces, but they have largely focused on simple shapes that fit together without gaps, such as squares and triangles, because these are easier to work with.

It is rare, however, for nature to use perfectly straight lines and sharp points. Some natural objects are similar enough to straight-edged tiles, known as polyhedrons, that they can be described by polyhedral models, such as a collection of bubbles in a foam or the cracked surface of Mars. But there are some curved shapes, such as three-dimensional polygons found in the epithelial cells that tile the lining of blood vessels and organs, that are harder to describe.

Now, Gábor Domokos at the Budapest University of Technology, Hungary, and his colleagues have discovered a class of shapes that describe tilings with curved edges, which they call soft cells. The key to these shapes is that they contain as few sharp corners as possible, while also fitting together as snugly as they can.

“These shapes emerge in art, but also in biology,” says Domokos. “If you look at sections of muscle tissue, you’ll see the cells having just two sharp corners, which is one less than the triangle – it is a very special kind of tiling.”

In two dimensions, soft cells have just two sharp points connected by curved edges and can take on an infinite number of different forms. But in three dimensions, these shapes have no sharp points, or corners, at all. It isn’t obvious how many of these 3D soft cells, which Domokos and his team call z-cells, there might be or how to easily make them, he says.

After defining soft cells mathematically, Domokos and his team looked for examples in nature and discovered they were widespread. “We found that architects have found these kinds of shapes intuitively when they wanted to avoid corners,” says Domokos. They also found z-cells were common in biological processes that grow from the tip of an object.

One of the clearest examples of z-cells was in seashells made from multiple chambers, such as the nautilus shell, which is an object of fascination for mathematicians because its structure follows a logarithmic pattern.

Domokos and his team noticed that the two-dimensional slices of each of the shell’s chambers looked like a soft cell, so they examined nautilus shells with a CT scanner to measure the chambers in three dimensions. “We saw no corners,” says Domokos, which suggested that the chambers were like the z-cells they had described mathematically.

“They’ve come up with a language for describing cellular materials that might be more physically realistic than the strict polyhedral model that mathematicians have been playing with for millennia,” says Chaim Goodman-Strauss at the University of Arkansas. These models could improve our understanding of how the geometry of biological systems, like in soft tissues, affects their material properties, says Goodman-Strauss. “The way that geometry influences the mechanical properties of tissue is really very poorly understood.”

For more such insights, log into www.international-maths-challenge.com.

*Credit for article given to Alex Wilkins*

You ordered a pizza for your party, but the restaurant forgot to slice it – these mathematical tricks can help you cut it evenly, says Katie Steckles.

Fairness is important – in life, and in pizza. If you want to cut a pizza into equal-sized pieces, the difficulty will depend on how many people you need to share it between. Luckily, mathematics has some tricks to keep things equal.

For example, if the number of people you are sharing a pizza between is a power of two – one, two, four, eight, 16 – cutting the pizza into as many slices is easy. For one piece, obviously no cuts are needed. For each larger power of two, a cut across through the centre of the pizza – cutting all of the existing pieces exactly in half – will result in pieces of equal size.

Some numbers will be much harder: prime numbers, by definition, can’t be divided easily. Luckily, geometry can help.

If you need to cut a pizza into five equal pieces, first grab a long, thin, rectangular strip of paper. Tie the paper in an ordinary overhand knot, like you would tie in a piece of string. Then, keeping the ends flat, pull gently to tighten the knot. The whole thing will flatten and come together – stop pulling when you can’t go any further without it wrinkling.

The flat shape you are looking at should now be vaguely familiar, if you ignore the two ends of paper sticking out. Fold these ends into the middle, or cut them off, and you will have a shape with five straight edges, created purely by the shape of the knot. Yes, that is right – you have made a perfect regular pentagon, with five equal-length sides and five equal angles at the corners.

It is possible to prove this mathematically by showing that all the folds you make in the paper strip are at 72 degrees to the parallel edges of the strip. But for simplicity, because the paper is the same width everywhere, and weaves in and out five times in the right way, these will be five equal edges. And more importantly, the pentagon’s corners are equally spread around a circle – making it the perfect guide for pizza slicing.

Place your pentagon in the centre of the pizza, then cut along lines radiating out from the centre of the pentagon and through each corner. And presto: you have a pentagonal pizza party for five. This paper-strip method can be used whenever you are in a pentagon-based emergency.

You can use the same technique to produce a shape with any odd number of sides by creating a more complex knot with the strip passing through the middle more times, although the strip of paper needs to be increasingly thin and it takes a lot more patience to pull the ends through and carefully flatten out the shape.

Combined with our existing halving methods, you can now produce any number of slices you like. The same results can be extended to any other round food – thanks to maths, the world is your cheesecake.

For more such insights, log into www.international-maths-challenge.com.

*Credit for article given to Katie Steckles *

If you want your cup of tea to stay as hot as possible, should you put milk in immediately, or wait until you are ready to drink it? Katie Steckles does the sums.

Picture the scene: you are making a cup of tea for a friend who is on their way and won’t be arriving for a little while. But – disaster – you have already poured hot water onto a teabag! The question is, if you don’t want their tea to be too cold when they come to drink it, do you add the cold milk straight away or wait until your friend arrives?

Luckily, maths has the answer. When a hot object like a cup of tea is exposed to cooler air, it will cool down by losing heat. This is the kind of situation we can describe using a mathematical model – in this case, one that represents cooling. The rate at which heat is lost depends on many factors, but since most have only a small effect, for simplicity we can base our model on the difference in temperature between the cup of tea and the cool air around it.

A bigger difference between these temperatures results in a much faster rate of cooling. So, as the tea and the surrounding air approach the same temperature, the heat transfer between them, and therefore cooling of the tea, slows down. This means that the crucial factor in this situation is the starting condition. In other words, the initial temperature of the tea relative to the temperature of the room will determine exactly how the cooling plays out.

When you put cold milk into the hot tea, it will also cause a drop in temperature. Your instinct might be to hold off putting milk into the tea, because that will cool it down and you want it to stay as hot as possible until your friend comes to drink it. But does this fit with the model?

Let’s say your tea starts off at around 80°C (176°F): if you put milk in straight away, the tea will drop to around 60°C (140°F), which is closer in temperature to the surrounding air. This means the rate of cooling will be much slower for the milky tea when compared with a cup of non-milky tea, which would have continued to lose heat at a faster rate. In either situation, the graph (pictured above) will show exponential decay, but adding milk at different times will lead to differences in the steepness of the curve.

Once your friend arrives, if you didn’t put milk in initially, their tea may well have cooled to about 55°C (131°F) – and now adding milk will cause another temperature drop, to around 45°C (113°F). By contrast, the tea that had milk put in straight away will have cooled much more slowly and will generally be hotter than if the milk had been added at a later stage.

Mathematicians use their knowledge of the rate at which objects cool to study the heat from stars, planets and even the human body, and there are further applications of this in chemistry, geology and architecture. But the same mathematical principles apply to them as to a cup of tea cooling on your table. Listening to the model will mean your friend’s tea stays as hot as possible.

For more such insights, log into www.international-maths-challenge.com.

*Credit for article given to Katie Steckles*

There’s a contradiction between classical and quantum theories.

One of the outstanding discoveries made in the early part of the last century was that of the quantum behaviour of the physical world. At very short distances, such as the size of an atom and smaller, the world behaves very differently to the “classical” world we are used to.

Typical of the quantum world is so-called wave-particle duality: particles such as electrons behave sometimes as if they are point particles with a definite position, and sometimes as if they are spread out like waves.

This strange behaviour is not just of theoretical interest, since it is underpins much of our modern technology. It is fundamental to the behaviour of semiconductors in all our electronic devices, the behaviour of nano-materials, and the current rise of quantum computing.

Quantum theory is fundamental. It must govern not just the very small but also the classical realm. That means physicists and mathematicians have had to develop methods not just for understanding new quantum phenomena, but also for replacing classical theories by their quantum analogues.

This is the process of [quantization.](http://en.wikipedia.org/wiki/Quantization_(physics) When we have a finite number of degrees of freedom, such as for a finite collection of particles, although the quantum behaviour is often counter-intuitive, we have a well-developed mathematical machinery to handle this quantization called quantum mechanics.

This is well understood physically and mathematically. But when we move to study the electric and magnetic fields where we have an infinite number of degrees of freedom, the situation is much more complicated. With the development of so-called quantum field theory, a quantum theory for fields, physics has made progress that mathematically we do not completely understand.

What’s the problem?

Many field theories fall into a class called gauge field theories, where a particular collection of symmetries, called the gauge group, acts on the fields and particles. In the case that these symmetries all commute, so-called abelian gauge theories, we have a reasonable understanding of the quantization.

This includes the case of the electromagnetic field, quantum electrodynamics, for which the theory makes impressively accurate predictions.

The first example of a non-abelian theory that arose historically is the theory of the electro-weak interaction, which requires a mechanism to make the predicted particles massive as we observe them in nature. This involves the so-called Higgs boson, which is currently being searched for with the Large Hadron Collider (LHC) at CERN.

The notable feature of this theory for our present discussion is that the Higgs mechanism is classical and carries over to the quantum theory under the quantization process.

The case of interest in the Millennium Problem “Yang-Mills theory and Mass-Gap” is Yang-Mills gauge theory, a non-abelian theory which we expect to describe quarks and the strong force that binds the nucleus and powers the sun. Here we encounter a contradiction between the classical and quantum theories.

The classical theory predicts massless particles and long-range forces. The quantum theory has to match the real world with short-range forces and massive particles. Physicists expect various mathematical properties such as the “mass gap” and “asymptotic freedom” to explain the non-existence of massless particles in observations of the strong interactions.

As these properties are not visible in the classical theory and arise only in the quantum theory, understanding them means we need a rigorous approach to “quantum Yang-Mills theory”. Currently we do not have the mathematics to do this, although various approximations and simplifications can be done which suggest the quantum theory has the required properties.

The Millennium Problem seeks to establish by rigorous mathematics the existence of the “mass gap” – that is, the non-existence of massless particles in Yang-Mills theory. The solution of the problem would involve an approach to quantum field theory in four dimensions that is sophisticated enough to explain at least this feature of quantum non-abelian Yang-Mills gauge theory.

Doing the maths

Clearly this is of interest to physicists, but why is it of importance to mathematicians? It has become apparent in the last few decades that the tools that physicists have developed for doing quantum field theory, in particular path integrals, make precise predictions about geometry and topology, particularly in low dimensions.

But we don’t know mathematically what a path integral is, except in very simple cases. It is as if we are in a pre-Newtonian world – certain calculations can be done with certain tricks but Newton hasn’t developed calculus for us yet.

Analogously, there are calculations in geometry and topology that can be done non-rigorously using methods developed by physicists in quantum field theory which give the right answers. This suggests that there is a set of powerful techniques waiting to be discovered.

A solution to this Millennium Problem would shed light on what these new techniques are.

For more such insights, log into www.international-maths-challenge.com.

*Credit for article given to Michael Murray*

 

The double recurrence relation that defines the higher triangular numbers is a simple one – it is no surprise that they turn up so often.

The geometric interpretation is stacking: For a given dimension d, you get the n+1 d-triangular number by stacking the nth d-1 triangular number (the gnomon) onto the nth d-triangular number.  The zero dimensional triangular numbers are just the sequence: 1, 1, 1, 1,…, presumably counting stacks of nothing. The one-dimensional triangular numbers are the naturals: 1, 2, 3, 4, …, made by stacking the ones of the one-dimensional case. The two dimensional triangular numbers stack the naturals: 1, 3, 6, 10, …, the three dimensional triangular numbers make pyramids of the triangulars: 1, 4, 10, 20, ….

If you write out a difference table for the higher triangular numbers, you end up with Pascal’s triangle. This suggests a nice formula for the triangulars in terms of binomial coefficients:

From this, you can obtain another recursive formula that you can use when working with higher triangular numbers (this is the “another” formula for this post):

If you vary the defining recurrence relation so that the initial “zero dimensional” value is a number other than 1, you get the other polygonal numbers (square, pentagonal, hexagonal, square-based pyramidal, etc.). In particular, if you let the zero-dimensional value be k-2, you obtain the k-polygonal numbers (k-2 corresponding to the number of triangles in your k-sided polygon).

It turns out there is a nice formula for these in terms of binomial coefficients as well:

For more such insights, log into www.international-maths-challenge.com.

*Credit for article given to dan.mackinnon*

Australia faces many big challenges – in the economy, health, energy, water, climate change, infrastructure, sustainable agriculture and the preservation of our precious biodiversity.

To meet these, we need creative scientists and engineers drawn from many disciplines, and a technologically-skilled workforce.

The many world-changing advances and achievements of Australian research and development (R&D) are encouraging. Indeed, the Australian Academy of Science, of which I’m president, believes our country’s scientific potential has never been greater.

But our ability to improve this performance in the future, or even maintain it, is not assured.

Four things threaten our ongoing R&D performance and, as a consequence, our economic security and prosperity, and I’ll address each of these in turn.

1) The level of investment in R&D

Over the past decade, successive Australian governments have recognised the need to properly invest in research and innovation.

Total investment by the current government has increased by almost 43%, and is projected to amount to $9.4 billion dollars over the current financial year. This is very commendable.

It’s heartening to see Australia’s business sector is also increasing its investment – although admittedly this boost is coming off a low base compared to many other OECD nations. (Australia ranks 14th for business expenditure on R&D as a percentage of GDP).

But to remain competitive internationally we need even greater investment.

Australia spends around 2.2% of its GDP (around AU$900 per person per year) on research and development.

Iceland, the next best-ranked country, devotes 2.6% cent of GDP. Top of the list is Israel, with 4.6%, followed by Finland and Sweden, each of which spend 3.6%.

We have around 92,000 full-time equivalent researchers which, again, is only middle order. According to the OECD, in 2008 the proportion of R&D personnel in our total labour force puts Australia 16th, well short of Canada, which ranks ninth.

China has more than 1.6 million people working on research and development, a number that’s increasing rapidly. (China is ranked 33rd, with 2.5 R&D personnel per thousand in the workforce, from a total population of 1.3 billion)

Worryingly, Australia sits well within the bottom half of OECD countries (ranked 20th of 30) when it comes to the number of university graduates emerging with a science or engineering degree per capita.

These are sobering statistics.

The Australian Academy of Science therefore calls on the government to create a Sovereign Fund for Science, to secure the future prosperity of the nation.

The goal should be to increase Australia’s research and development expenditure to at least 3% of GDP by 2020.

2) International collaboration

By its very nature, science is a collaborative enterprise. It transcends generations, individual scientific disciplines and, increasingly, national boundaries. To paraphrase Sir Isaac Newton, we see further by standing on the shoulders of giants.

Australia produces only 2% of the world’s knowledge. To gain access to the other 98%, we must ensure our scientists are well-connected internationally.

Getting involved with major international projects at inception allows Australia to stay abreast of new scientific developments, to have a say in their direction, to take the knowledge further, and to apply it.

International collaborations also attract scientists from overseas to spend time in Australia, bringing us new skills and knowledge. Importantly, many return and become part of our scientific workforce.

Work arising from such collaborations often attracts great attention and gets cited more frequently. Take the recently announced kangaroo genome sequence, which garnered international media attention.

This work was done by a consortium of more than 100 researchers from Australia, the US, the UK, Germany and Japan, headed by my friend and Academy colleague Professor Marilyn Renfree. The “kangaroo” was in fact the Tammar wallaby.

Its genome is yielding many unexpected insights that may have significance for humans as well as for wallabies – for example the genes that make antibiotics in the mother’s milk to protect the tiny newborns from harmful bacteria.

There are many such examples.

We hope to bring international astronomers to Australia by winning the bid to build a giant collection of radio telescopes in the Western Australian desert. Known as the Square Kilometre Array, or SKA, this international project – which could go to either South Africa or Australia – will give astronomers huge insights into the formation and evolution of the first stars and galaxies after the Big Bang.

Barriers that have impeded the use of Australian research grants for international collaborations are being dismantled.

Today many grants and fellowships provided by the Australian Research Council, National Health and Medical Research Council and CSIRO support projects that include international partners.

Many of these linkages were initially catalysed by the federal government’s International Science Linkages (or ISL) program.

With funding of about $10 million per year, the ISL program has supported bilateral and multilateral relations with many other countries.

Regrettably, the ten-year program ended in June this year as funding was not renewed in the 2011-2012 Budget.

Put simply, it would be a grave blow if our ability to compete on the international stage were to be diminished.

I strongly urge the Federal Government to fund in its next Budget a new program to provide strategic support for Australia’s International Science Linkages.

3) Science capability in the workforce

We are a lucky nation: we have access to immense mineral wealth. But resources are finite. Even the minerals sector acknowledges that we cannot ride the current boom indefinitely.

Further, the Minerals Council of Australia warns skills shortages and structural weaknesses in the Australian economy have been masked by the boom.

And so, when the end of the mining boom comes, where will Australia be?

There is broad consensus among minds more economically astute than mine that our future prosperity will depend upon:

• a skilled workforce
• innovation
• entrepreneurship
• high productivity
• the creation of the kind of knowledge-intensive goods and services that can only result from robust research and development.

Certain skills are already in short supply in Australia.

In fact, the No More Excuses report issued by the Industry Skills Council earlier this year points to an alarming deficit in even basic skills.

According to that report, “millions of Australians have insufficient language, literacy and numeracy skills to benefit fully from training or to participate effectively at work”.

A recent project looking at the maths skills of bricklaying apprentices at a regional TAFE showed:

• 75% could not do basic arithmetic.
• 80% could not calculate the area of a rectangle, or the pay owed for working four-and-a-half hours.

Such figures are particularly worrying at a time when the demand for higher-level skills is increasing.

It’s essential we act now to ease the bottleneck and put in place measures that will create the technologically-competent workforce we need for the future.

We can, and should, be “the clever country”. But this will only happen if we place appropriate emphasis on properly educating our young people.

4) Science and maths education

Without a robust and inspiring science and maths education system, it’s impossible to create an internationally-competitive workforce.

Myriad jobs – apart from the obvious research, engineering and technology careers – require a basic understanding of science and maths.

And, as a parent, a mentor of young scientists and a passionate advocate for quality education, I know that all children are natural born scientists.

“Why?”, “How?”, and “What happens if …?” are questions asked frequently by young children, whose natural spirit of inquiry is crucial to understanding the big exciting world around them.

We need to harness this natural curiosity and nurture it with inspiring education.

Australian public expenditure on education as a percentage of GDP is just 4.2% – significantly below the OECD average of 5.4%.

A decade ago, a review of Australian science education, revealed many students were disappointed with their high school science.

Today, this disenchantment continues, as evidenced by the declining number of students choosing to study science in senior secondary school. Consider the following:

• In 1991, more than a third of Year 12 students chose to study biology. That now sits at less than a quarter.
• 23% of Year 12 students studied chemistry ten years ago, compared with 18% now.
• In the same period, physics has fallen from 21% to 14%.

While Australian students have been losing interest in science, their international peers have been taking it up with great enthusiasm.

The OECD Program for International Student Assessment (PISA) examines the scientific literacy of teenagers in 57 different countries.

In 2000, the only nations that performed better than Australia were Korea and Japan. In 2009 – the most recent figures available – Australia ranked behind Shanghai, Finland, Hong Kong, Singapore, Japan and Korea.

What happened? The Assessment indicated that the performance of other countries has improved while Australia’s has remained stationary.

Maths

Australia’s early secondary mathematical literacy scores have significantly declined over the last decade. Our Year 4 and Year 8 students ranked 14th internationally in the most recent Trends International Mathematics and Science Study, conducted in 2007.

The decline in Australia’s mathematical literacy is of grave concern because mathematics is an enabling science, without which it’s not possible to make use of other sciences – either in the lab or in the workforce.

A recent survey conducted by Science and Technology Australia and the Academy of Science showed Australians clearly value science – 80% of respondents acknowledged science education is absolutely essential or very important to the national economy.

But it also revealed some alarming holes in the basic science understanding of the average Australian.

• Three in ten believe humans were around at the time of dinosaurs.
• More than a fifth of our university graduates think that it takes just one day for the Earth to travel around the sun.
• Almost a third of Australians do not think evolution is currently occurring.
• About a quarter say human activity is not influencing the evolution of other species: a worrying statistic given the impact that human activity is having on the environment.

In other words, many of us do not understand even the most basic science.

How can we halt this slide in science and maths in our schools and attain an internationally enviable position?

Thankfully, our government is already investing significantly in school infrastructure and in rolling out a national high-speed internet network.

Last December, education ministers approved the content for new national curricula in English, history, maths and science. In coming months, they’ll be asked to sign off on the standards for these curricula. This is an important initiative and the Academy of Science applauds it.

But we also need investment in teachers, and in inspiring curriculum programs.

This is a responsibility for both the Commonwealth and the States, who must work together rather than reverting to the blame game.

Inspired (and inspiring) teachers will be the most important agents for improving educational outcomes.

We must place a much higher societal value on teachers and do everything we can to recruit some of our brightest and best into teaching.

We must support these educators with the best tools and resources available and provide them with stimulating opportunities for ongoing training.

I agree with Prime Minister Julia Gillard that science is one of the fundamental platforms upon which our conception of a modern advanced society is based.

I agree with the prime minister that we live in a crucial time for science in Australia and around the world.

In fact, I could not agree more.

For more such insights, log into www.international-maths-challenge.com.

*Credit for article given to Suzanne Coryter *

The triangular numbers (and higher triangular numbers) can be generated using this recurrence relationship:

These will form Pascal’s triangle (if we shift the variables n->n+d-1 and d->r, we get the familiar C(n,r) indexing for the Pascal Triangle). The d=2 case gives the usual “flat” 2d triangular numbers, and other d values provide triangular numbers of different dimensions.

 

It turns out that recurrence relation can be generalized to generate a family of sequences and triangles. Consider this more general relation:

Doing some initial exploring reveals four interesting cases:

The triangular numbers 

With all these additional parameters set to 1, we get our original relation, the familiar triangular numbers, and Pascal’s triangle.

The k-polygonal numbers 

If we set the “zero dimension” to k-2, we end up with the k-polygonal numbers. The triangular numbers arise in the special case where k=3. Except in the k=3 case, the triangles that are generated are not symmetrical.

 

Below is the triangle generated by setting k=5.

The symmetrically shifted k-polygonal numbers

As far as I know, there is not a standard name for these.  Each k value will generate a triangle that is symmetrical about its center and whose edge values are equal to k-2. For a given k value, if you enter sequences generated by particular values of d, you’ll find that some are well known. The codes in the diagrams correspond to the sequence ids from the Encyclopedia.

 

Here is the triangle generated by k=4:

And here is the triangle generated for k=5:

The Eulerian numbers (Euler’s number triangle)

This is a particularly nice way to generate the Eulerian numbers, which have a nice connection to the triangular numbers. There is a little inconsistency in the way the Eulerian numbers are indexed, however. For this formula to work, it should be altered slightly so that d>0. The resulting formula looks like this:

And the triangle looks like this:

It is surprising that so many interesting and well known sequences and triangles can be generated from such a simple formula, and that they can be interpreted as being part of a single family.

For more such insights, log into www.international-maths-challenge.com.

*Credit for article given to dan.mackinnon*