Ninth Dedekind number discovered: Scientists solve long-known problem in mathematics

Making history with 42 digits, scientists at Paderborn University and KU Leuven have unlocked a decades-old mystery of mathematics with the so-called ninth Dedekind number.

Experts worldwide have been searching for the value since 1991. The Paderborn scientists arrived at the exact sequence of numbers with the help of the Noctua supercomputer located there. The results will be presented in September at theĀ International Workshop on Boolean Functions and their ApplicationsĀ (BFA) in Norway.

What started as a master’s thesis project by Lennart Van Hirtum, then a computer science student at KU Leuven and now a research associate at the University of Paderborn, has become a huge success. The scientists join an illustrious group with their work. Earlier numbers in the series were found by mathematician Richard Dedekind himself when he defined the problem in 1897, and later by greats of early computer science such as Randolph Church and Morgan Ward. “For 32 years, the calculation of D(9) was an open challenge, and it was questionable whether it would ever be possible to calculate this number at all,” Van Hirtum says.

The previous number in the Dedekind sequence, the 8th Dedekind number, was found in 1991 using a Cray 2, the most powerful supercomputer at the time. “It therefore seemed conceivable to us that it should be possible by now to calculate the 9th number on a large supercomputer,” says Van Hirtum, describing the motivation for the ambitious project, which he initially implemented jointly with the supervisors of his master’s thesis at KU Leuven.

Grains of sand, chess and supercomputers

The main subject of Dedekind numbers are so-called monotone Boolean functions. Van Hirtum explains, “Basically, you can think of a monotone Boolean function in two, three, and infinite dimensions as a game with an n-dimensional cube. You balance the cube on one corner and then color each of the remaining corners either white or red. There is only one rule: you must never place a white corner above a red one. This creates a kind of vertical red-white intersection.

“The object of the game is to count how many different cuts there are. Their number is what is defined as the Dedekind number. Even if it doesn’t seem like it, the numbers quickly become gigantic in the process: the 8th Dedekind number already has 23 digits.”

Comparably largeā€”but incomparably easier to calculateā€”numbers are known from a legend concerning the invention of the game of chess. “According to this legend, the inventor of the chess game asked the king for only a few grains of rice on each square of the chess board as a reward: one grain on the first square, two grains on the second, four on the third, and twice as many on each of the following squares. The king quickly realized that this request was impossible to fulfill, because so much rice does not exist in the whole world.

“The number of grains of rice on the complete board would have 20 digitsā€”an unimaginable amount, but still less than D(8). When you realize these orders of magnitude, it is obvious that both an efficient computational method and a very fast computer would be needed to find D(9),” Van Hirtum said.

Milestone: Years become months

To calculate D(9), the scientists used a technique developed by master’s thesis advisor Patrick De Causmaecker known as the P-coefficient formula. It provides a way to calculate Dedekind numbers not by counting, but by a very large sum. This allows D(8) to be decoded in just eight minutes on a normal laptop. But, “What takes eight minutes for D(8) becomes hundreds of thousands of years for D(9). Even if you used a large supercomputer exclusively for this task, it would still take many years to complete the calculation,” Van Hirtum points out.

The main problem is that the number of terms in this formula grows incredibly fast. “In our case, by exploiting symmetries in the formula, we were able to reduce the number of terms to ‘only’ 5.5×1018ā€”an enormous amount. By comparison, the number of grains of sand on Earth is about 7.5×1018, which is nothing to sneeze at, but for a modern supercomputer, 5.5×1018Ā operations are quite manageable,” the computer scientist said.

The problem: The calculation of these terms on normal processors is slow and also the use of GPUs as currently the fastest hardware accelerator technology for many AI applications is not efficient for this algorithm.

The solution: Application-specific hardware using highly specialized and parallel arithmetic unitsā€”so-called FPGAs (field programmable gate arrays). Van Hirtum developed an initial prototype for the hardware accelerator and began looking for a supercomputer that had the necessary FPGA cards. In the process, he became aware of the Noctua 2 computer at the “Paderborn Center for Parallel Computing (PC2)” at the University of Paderborn, which has one of the world’s most powerful FPGA systems.

Prof. Dr. Christian Plessl, head of PC2, explains, “When Lennart Van Hirtum and Patrick De Causmaeker contacted us, it was immediately clear to us that we wanted to support this moonshot project. Solving hard combinatorial problems with FPGAs is a promising field of application and Noctua 2 is one of the few supercomputers worldwide with which the experiment is feasible at all. The extreme reliability and stability requirements also pose a challenge and test for our infrastructure. The FPGA expert consulting team worked closely with Lennart to adapt and optimize the application for our environment.”

After several years of development, the program ran on the supercomputer for about five months. And then the time had come: on March 8, the scientists found the 9th Dedekind number: 286386577668298411128469151667598498812366.

Today, three years after the start of the Dedekind project, Van Hirtum is working as a fellow of the NHR Graduate School at the Paderborn Center for Parallel Computing to develop the next generation of hardware tools in his Ph.D. The NHR (National High Performance Computing) Graduate School is the jointĀ graduate schoolĀ of the NHR centers. He will report on his extraordinary success together with Patrick De Causmaecker on June 27 at 2 p.m. in Lecture Hall O2 of the University of Paderborn.

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Credit of the article given to UniversitƤt Paderborn


AI can teach math teachers how to improve student skills

When middle school math teachers completed an online professional development program that uses artificial intelligence to improve their math knowledge and teaching skills, their students’ math performance improved.

My colleagues and I developed this onlineĀ professional developmentĀ program, which relies on a virtual facilitator that canā€”among other thingsā€”present problems to theĀ teacherĀ around teachingĀ mathĀ and provide feedback on the teacher’s answers.

Our goal was to enhance teachers’ mastery ofĀ knowledge and skills required to teach math effectively. These include understanding why the mathematical rules and procedures taught inĀ school work. The program also focuses on common struggles students have as they learn a particular math concept and how to use instructional tools and strategies to help them overcome these struggles.

We thenĀ conducted an experimentĀ in which 53 middle school math teachers were randomly assigned to either this AI-based professional development or no additional training. On average, teachers spent 11 hours to complete the program. We then gave 1,727 of their students a math test. While students of these two groups of teachers started off with no difference in their math performance, the students taught by teachers who completed the program increased their mathematics performance by 0.18 of a standard deviation more on average. This is a statistically significant gain that is equal to the average math performance difference between sixth and seventh graders in the study.

Why it matters

This study demonstrates the potential for using AI technologies to create effective, widely accessible professional development for teachers. This is important because teachers often have limited access to high-quality professional development programs to improve their knowledge and teaching skills.Ā Time conflictsĀ orĀ living in rural areasĀ that are far from in-person professional development programs can prevent teachers from receiving the support they need.

Additionally, many existing in-person professional development programs for teachers have been shown to enhance participants’ teaching knowledge and practices but to haveĀ little impact on student achievement.

Effective professional development programs include opportunities for teachers to solve problems, analyse students’ work and observe teaching practices. Teachers also receive real-time support from the program facilitators. This is often a challenge for asynchronous online programs.

Our program addresses the limitations of asynchronous programs because the AI-supported virtual facilitator acts as a human instructor. It gives teachers authentic teaching activities to work on, asks questions to gauge their understanding and provides real-time feedback and guidance.

What’s next

Advancements in AI technologies will allow researchers to develop more interactive, personalizedĀ learning environmentsĀ for teachers. For example, the language processing systems used in generative AI programs such as ChatGPT can improve the ability of these programs to analyse teachers’ responses more accurately and provide more personalized learning opportunities. Also, AI technologies can be used to develop new learning materials so that programs similar to ours can be developed faster.

More importantly, AI-based professional development programs can collect rich, real-time interaction data. Such data makes it possible to investigate how learning from professional development occurs and therefore how programs can be made more effective. DespiteĀ billions of dollarsĀ being spent each year on professional development for teachers, research suggests thatĀ how teachers learn through professional developmentĀ is not yet well understood.

For more such insights, log into our website https://international-maths-challenge.com

Credit of the article given to Yasemin Copur-Gencturk,Ā The Conversation

 


Pi Day: How To Calculate Pi Using A Cardboard Tube And A Load Of Balls

Grab a few balls and get calculating pi

Pi Day, which occurs every 14 March ā€“ or 3/14, in the US date format ā€“ celebrates the worldā€™s favourite mathematical constant. This year, why not try an experiment to calculate its value? All you will need is a cardboard tube and a series of balls, each 100 times lighter than the next. You have those lying around the house, right?

This experiment was firstĀ formulatedĀ by mathematician Gregory Galperin in 2001. It works because of a mathematical trick involving the masses of a pair of balls and the law of conservation of energy.

First, take the tube and place one end up against a wall. Place two balls of equal mass in the tube. Letā€™s say that the ball closer to the wall is red, and the other is blue.

Next, bounce the blue ball off the red ball. If you have rolled the blue ball hard enough, there should be three collisions: the blue ball hits the red one, the red ball hits the wall, and the red ball bounces back to hit the blue ball once more. Not-so-coincidentally, three is also the first digit of pi.

To calculate pi a little bit more precisely, replace the red ball with one that is 100 times less massive than the blue ball ā€“ a ping pong ball might work, so we will call this the white ball.

When you perform the experiment again, you will find that the blue ball hits the white ball, the white ball hits the wall and then the white ball continues toĀ bounce back and forthĀ between the blue ball and the wall as it slows down. If you count the bounces, youā€™ll find that there are 31 collisions. That gives you the first two digits of pi: 3.1.

Galperin calculated that if you continue the same way, you will keep getting more digits of pi. If you replace the white ball with another one that is 10,000 times less massive than the blue ball, you will find that there are 314 collisions, and so on. If you have enough balls, you can count as many digits of pi as you like.

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

*Credit for article given to Leah Crane*


Air pollution found to impair performance in matriculation exams in mathematical subjects

Researchers from the University of Oulu, Finland, investigated how air pollution affects students’ performance in matriculation exams, particularly in mathematical subjects. The study revealed that performance declines in exams involving thinking and memorization when fine particulate matter (PM2.5) levels in the school’s vicinity increase even slightly.

The research is the first to examine the same student’s performance in a test measuring the same skill in a short time frame. Skills refer to linguistic and mathematical abilities, measured by exams in the Finnish language, writing, reading, mathematics, and physics.

Researchers from the University of Oulu Business School examined the effects of very short-term exposure toĀ air pollutionĀ on students’ performance in matriculation exams in Finland from 2006 to 2016.

According to the study, a one-unit increase in PM2.5 particle concentration (particles smaller than 2.5 micrometers) reduced the average student’s performance in a mathematical exam by approximately 0.13 percentage points compared to performance in a similar exam with lower fine particulate concentrations.

The study found no impact on linguistic skills due to an increase inĀ fine particulate matter, and there were no gender differences observed.

Researchers were surprised to find significant effects on matriculation exam performance in Finland, a country with relatively low air pollution levels. This is the first time such effects have been demonstrated in Finland. The researchers emphasize that even in countries like Finland, where air pollution levels generally comply with the World Health Organization’s recommendations, reducing air pollution remains crucial.

“Increasing evidence suggests that exposure to air pollution during exams may have a decisive impact on the progression of students into further studies, especially if matriculation exam results are used as a significant selection criterion,” says University Researcher Marko Korhonen.

The primary data for the study came from Statistics Finland, covering all matriculation exams in Finland from spring 2006 to autumn 2016, including 22 academic terms. The study included over 370,000Ā final examsĀ from Finnish high schools, involving 172,414 students from 253 schools in 54 municipalities.

Student performance was assessed using hourly air quality measurements from monitoring points located near the exam venues. The structure of Finnish highĀ schoolĀ final exams, where students take multiple exams in different courses, allowed the examination of each student’s test results in various final exams. Exams were conducted on different days in the same schools, and air quality was measured during the exams near each school.

The study, titled “The impact of ambient PM2.5 air pollution onĀ student performance: Evidence from Finnish matriculation examinations,” has beenĀ publishedĀ inĀ Economics Letters.

For more such insights, log into our website https://international-maths-challenge.com

Credit of the article given to University of Oulu

 


Millennium Prize: The PoincarƩ Conjecture

The problemā€™s been solved ā€¦ but the sweet treats were declined.Ā Back to the Cutting Board

In 1904, French mathematicianĀ Henri PoincarĆ©Ā asked a key question about three-dimensional spaces (ā€œmanifoldsā€).

Imagine a piece of rope, so that firstly a knot is tied in the rope and then the ends are glued together. This is what mathematicians call aĀ knot. AĀ linkĀ is a collection of knots that are tangled together.

It has been observed that DNA, which is coiled up within cells, occurs in closed knotted form.

Complex molecules such asĀ polymersĀ are tangled in knotted forms. There are deep connections betweenĀ knot theoryĀ and ideas in mathematical physics. The outsides of a knot or link in space give important examples of three-dimensional spaces.

Torus.Ā Fropuff

Back to PoincarĆ© and his conjecture. He asked if theĀ 3-sphereĀ (which can be formed by either adding a point at infinity to ordinary three-dimensionalĀ Euclidean spaceĀ or by gluing two solid three-dimensional balls together along their boundaryĀ 2-spheres) was the only three-dimensional space in which every loop can be continuously shrunk to a point.

PoincarĆ© had introduced important ideas in the structure and classification of surfaces and their higher dimensional analogues (ā€œmanifoldsā€), arising from his work onĀ dynamical systems.

Donuts to go, please

A good way to visualise PoincarĆ©ā€™s conjecture is to examine the boundary of a ball (a two-dimensional sphere) and the boundary of a donut (called aĀ torus). Any loop of string on a 2-sphere can be shrunk to a point while keeping it on the sphere, whereas if a loop goes around the hole in the donut, it cannot be shrunk without leaving the surface of the donut.

Many attempts were made on the PoincarĆ© conjecture, until in 2003 a wonderful solution was announced by a young Russian mathematician,Ā Grigori ā€œGrishaā€ Perelman.

This is a brief account of the ideas used by Perelman, which built on work of two other outstanding mathematicians,Ā Bill ThurstonĀ andĀ Richard Hamilton.

3D spaces

Thurston made enormous strides in our understanding of three-dimensional spaces in the late 1970s. In particular, he realised that essentially all the work that had been done since PoincarƩ fitted into a single theme.

He observed that known three-dimensional spaces could be divided into pieces in a natural way, so that each piece had a uniform geometry, similar to the flat plane and the round sphere. (To see this geometry on a torus, one must embed it into four-dimensional space!).

Thurston made a bold ā€œgeometrisation conjectureā€ that this should be true for all three-dimensional spaces. He had many brilliant students who further developed his theories, not least by producing powerful computer programs that could test any given space to try to find its geometric structure.

Thurston made spectacular progress on the geometrisation conjecture, which includes the PoincarĆ© conjecture as a special case. The geometrisation conjecture predicts that any three-dimensional space in which every loop shrinks to a point should have a round metric ā€“ it would be a 3-sphere and PoincarĆ©ā€™s conjecture would follow.

In 1982, Richard Hamilton published a beautiful paper introducing a new technique in geometric analysis which he calledĀ Ricci flow. Hamilton had been looking for analogues of a flow of functions, so that the energy of the function decreases until it reaches a minimum. This type of flow is closely related to the way heat spreads in a material.

Hamilton reasoned that there should be a similar flow for the geometric shape of a space, rather than a function between spaces. He used theĀ Ricci tensor, a key feature of Einsteinā€™s field equations forĀ general relativity, as the driving force for his flow.

He showed that, for three-dimensional spaces where the Ricci curvature is positive, the flow gradually changes the shape until the metric satisfies Thurstonā€™s geometrisation conjecture.

Hamilton attracted many outstanding young mathematicians to work in this area. Ricci flow and other similar flows have become a huge area of research with applications in areas such as moving interfaces, fluid mechanics and computer graphics.

Ricci flow.Ā CBN

He outlined a marvellous program to use Ricci flow to attack Thurstonā€™s geometrisation conjecture. The idea was to keep evolving the shape of a space under Ricci flow.

Hamilton and his collaborators found the space might form aĀ singularity, where a narrow neck became thinner and thinner until the space splits into two smaller spaces.

Hamilton worked hard to try to fully understand this phenomenon and to allow the pieces to keep evolving under Ricci flow until the geometric structure predicted by Thurston could be found.

Perelman

This is when Perelman burst on to the scene. He had produced some brilliant results at a very young age and was a researcher at the famousĀ Steklov InstituteĀ in St Petersburg. Perelman got a Miller fellowship to visit UC Berkeley for three years in the early 1990s.

I met him there around 1992. He then ā€œdisappearedā€ from the mathematical scene for nearly ten years and re-emerged to announce that he had completed Hamiltonā€™s Ricci flow program, in a series of papers he posted on the electronic repository calledĀ ArXiv.

His papers created enormous excitement and within several months a number of groups had started to work through Perelmanā€™s strategy.

Eventually everyone was convinced that Perelman had indeed succeeded and both the geometrisation and PoincarƩ conjecture had been solved.

Perelman was awarded both aĀ Fields medal (the mathematical equivalent of a Nobel prize) and also offered a million dollars for solving one of the Millenium prizes from the Clay Institute.

He turned down both these awards, preferring to live a quiet life in St Petersburg. Mathematicians are still finding new ways to use the solution to the geometrisation conjecture, which is one of the outstanding mathematical results of this era.

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

*Credit for article given to Hyam Rubinstein*

 


Octonions: The Strange Maths That Could Unite The Laws Of Nature

Could a system of eight-dimensional numbers help physicists find a single mathematical framework that describes the entire universe?

Words can be slippery. That is perhaps even more true in physics than it is in the rest of life. Think of aĀ ā€œparticleā€, for instance, and we might conjure an image of a tiny sphere. In truth, ā€œparticleā€ isĀ just a poetic term for something far removed from our everyday experienceĀ ā€“ which is why our best descriptions of reality make use of theĀ cold precision of mathematics.

But just as there are many human languages, so there is more than one type of number system. Most of us deal with only the familiar number line that begins 1, 2, 3. But other, more exotic systems are available. Recently, physicists have been asking a profound question: what if we are trying to describe reality with the wrong type of numbers?

Each mathematical system has its own special disposition, just like languages. Love poems sound better in French. German has that knack of expressing sophisticated concepts ā€“ like schadenfreude ā€“ in a few syllables. Now, in the wake of a fresh breakthrough revealing tantalising connections between models of how matter works at different energy scales, it seems increasingly likely that an exotic set of numbers known as the octonions might have what it takes to capture the truth about reality.

Mathematicians are excited because they reckon that by translating our theories of reality into theĀ language of the octonions, it could tidy up some of the deepest problems in physics and clear a path to a ā€œgrand unified theoryā€ that can describe the universe in one statement. ā€œThis feels like a very promising direction,ā€ saysĀ Latham BoyleĀ at the Perimeter Institute in Waterloo, Canada. ā€œI find it irresistible to think about.ā€

Many physicists dream of finding a grandĀ unified theory, a single mathematical framework that tells us where the forces of nature come from and how they act on matter. Critically, such a theory would also capture how and why these properties changed over the life of the universe, as we know they have.

So far, the closest we have come is the standard model of particle physics, which details the universeā€™s fundamental particles and forces: electrons, quarks, photons and theĀ rest. The trouble is, the standard model hasĀ its shortcomings. To make it work, we mustĀ feed in around 20 measured numbers, such as the masses of particles. We donā€™t know why these numbers are what they are. Worse, the standard model has little to say about space-time, the canvas in which particles live. We seem to live in a four-dimensional space-time, but the standard model doesnā€™t specify that this must be so. ā€œWhy not, say, seven-dimensional space-time?ā€ Boyle wonders.

Real and imaginary numbers

Many think the solution to these woes will come when experiments uncover a missing piece of the standard model. But after years of effort, this hasnā€™t happened, and some are wondering if the problem is the maths itself.

Mathematicians have known for centuries that there areĀ numbers other than the ones we can count on our fingers. Take the square root of -1, known as i. There is no meaningful answer to this expression, as both 1ā€‰Ć—ā€‰1 and -1ā€‰Ć—ā€‰-1 are equal to 1, so i is an ā€œimaginary numberā€. They found that by combining i with real numbers ā€“ which include all the numbers you could place on a number line, including negative numbers and decimals ā€“ they could fashion a new system called the complex numbers.

Think of complex numbers as being two-dimensional; the two parts of each number can record unrelated properties of the same object. This turns out to be extremely handy. All our electronic infrastructure relies on complex numbers. AndĀ quantum theory, our hugely successful description of the small-scale world, doesnā€™t work without them.

In 1843, Irish mathematician William RowanĀ Hamilton took things a step further. Supplementing the real and the imaginary numbers with two more sets of imaginary numbers called j and k, he gave us the quaternions, a set of four-dimensional numbers. Within a few months, Hamiltonā€™s friend John Graves had found another system with eight dimensions called the octonions.

Real numbers, complexĀ numbers, quarternions and octonions are collectively known asĀ the normed division algebras. They are theĀ only sets of numbers with which you can perform addition, subtraction, multiplication and division. Wilder systems are possibleĀ ā€“ theĀ 16-dimensional sedenions, for exampleĀ ā€“ but here the normal rules break down.

Today, physics makes prolific use of three of these systems. The real numbers are ubiquitous. Complex numbers are essential in particle physics as well as quantum physics. The mathematical structure of general relativity,Ā Albert Einsteinā€™s theory of gravity, can be expressed elegantly by the quaternions.

The octonions stand oddly apart as the only system not to tie in with a central physical law. But why would nature map onto only three of these four number systems? ā€œThis makes one suspect that the octonions ā€“ the grandest and least understood of the four ā€“ should turn out to be important too,ā€ says Boyle.

In truth, physicists have been thinking such thoughts since the 1970s, but the octonions have yet to fulfil their promise. Michael Duff at Imperial College London was, and still is, drawn to the octonions, but he knows many have tried and failed to decipher their role in describing reality. ā€œThe octonions became known as the graveyard of theoretical physics,ā€ he says.

That hasnā€™t put off a new generation of octonion wranglers, includingĀ Nichol FureyĀ at Humboldt University of Berlin. She likes to look at questions in physics without making any assumptions. ā€œI try to solve problems right from scratch,ā€ she says. ā€œIn doing so, you can often find alternate paths that earlier authors may have missed.ā€ Now, it seems she and others might be making the beginnings of an octonion breakthrough.

Internal symmetries in quantum mechanics

To get to grips with Fureyā€™s work, it helps to understand a concept in physics called internal symmetry. This isnā€™t the same as the rotational or reflectional symmetry of aĀ snowflake. Instead, it refers to a number of more abstract properties, such as the character of certain forces and the relationships between fundamental particles. All these particles are defined by a series of quantum numbers ā€“ their mass, charge and a quantum property called spin, for instance. If a particle transforms into another particleĀ ā€“ an electron becoming aĀ neutrino, sayĀ ā€“ some of those numbers will change while others wonā€™t. These symmetries define the structure of the standard model.

Internal symmetries are central to the quest for a grand unified theory. Physicists have already found various mathematical models that might explain how reality worked back atĀ the time when the universe had much more energy. At these higher energies, it is thought there would have been more symmetries, meaning that some forces we now experience as distinct would have been one and the same. None of these models have managed to rope gravity into the fold: that would require an even grander ā€œtheory of everythingā€. But they do show, for instance, that the electromagnetic force and weak nuclear force would have been one ā€œelectroweakā€ force until a fraction of a second after the big bang. As the universe cooled, some of the symmetries broke, meaning this particular model would no longer apply.

Each different epoch requires aĀ different mathematical model with a gradually reducing number of symmetries. InĀ aĀ sense, these models all contain each other, like a set of Russian dolls.

One of the most popular candidates forĀ theĀ outermost dollĀ ā€“ the grand unified theoryĀ that contains all the othersĀ ā€“ is known as the spin(10) model. It has a whopping 45Ā symmetries. In one formulation, inside this sits the Pati-Salam model, with 21 symmetries. Then comes the left-right symmetric model, with 15 symmetries, including one known as parity, the kind of left-right symmetry that we encounter when we look in a mirror. Finally, we reach the standard model, with 12 symmetries. The reason we study each of these models is that they work; their symmetries are consistent with experimental evidence. But weĀ have never understood what determines which symmetries fall away at each stage.

In August 2022, Furey, together with Mia Hughes at Imperial College London, showed for the first time that theĀ division algebras, including the octonions, could provide this link. To do so, they drew on ideas Furey had years ago to translate all the mathematical symmetries and particle descriptions of various models into the language of division algebras. ā€œIt took a long time,ā€ says Furey. The task required using the Dixon algebra, a set of numbers that allow you to combine real, complex, quaternion and octonion maths. The result was a system that describes a set of octonions specified by quaternions, which are in turn specified by complex numbers that are specified by a set of real numbers. ā€œItā€™s a fairly crazy beast,ā€ says Hughes.

It is a powerful beast, too. The new formulation exposed an intriguing characteristic of the Russian doll layers. When some numbers involved in the complex, quaternion and octonion formulations are swapped from positive to negative, or vice versa, some of the symmetries change and some donā€™t. Only the ones that donā€™t are found in the next layer down. ā€œIt allowed us to see connections between these well-studied particle models that had not been picked up on before,ā€ says Furey. This ā€œdivision algebraic reflectionā€, as Furey calls it, could be dictating what we encounter in the real physical universe, and ā€“ perhaps ā€“ showing us the symmetry-breaking road up to the long-sought grand unified theory.

The result is new, and Furey and Hughes havenā€™t yet been able to see where it may lead. ā€œIt hints that there might be some physical symmetry-breaking process that somehow depends upon these division algebraic reflections, but so far the nature of that process is fairly mysterious,ā€ says Hughes.

Furey says the result might have implications for experiments. ā€œWe are currently investigating whether the division algebras are telling us what can and cannot be directly measured at different energy scales,ā€ she says. It is a work in progress, but analysis of the reflections seems to suggest that there are certain sets of measurements that physicists should be able to make on particles at low energies ā€“ such as the measurement of an electronā€™s spin ā€“ and certain things that wonā€™t be measurable, such as the colour charge of quarks.

Among those who work on octonions, the research is making waves. Duff says that trying to fit the standard model into octonionic language is a relatively new approach: ā€œIf it paid off, it would be very significant, so itā€™s worth trying.ā€Ā Corinne ManogueĀ at Oregon State University has worked with octonions for decades and has seen interest ebb and flow. ā€œThis moment does seem to be a relative high,ā€ she says, ā€œprimarily, I think, because of Fureyā€™s strong reputation and advocacy.

The insights from the octonions donā€™t stop there. Boyle has been toying with another bit of exotic maths called the ā€œexceptional Jordan algebraā€, which was invented by German physicist Pascual Jordan in the 1930s. Working with two other luminaries of quantum theory, Eugene Wigner and John von Neumann, Jordan found a set of mathematical properties of quantum theory that resisted classification and were closely related to the octonions.

Probe this exceptional Jordan algebra deeply enough and you will find it contains the mathematical structure that we use to describe Einsteinā€™s four-dimensional space-time. Whatā€™s more, we have known for decades that within the exceptional Jordan algebra, you will find a peculiar mathematical structure that we derived through an entirely separate route and process in the early 1970s to describe the standard modelā€™s particles and forces. In other words, this is an octonionic link between our theories of space, time, gravity and quantum theory. ā€œI think this is a very striking, intriguing and suggestive observation,ā€ says Boyle.

Responding to this, Boyle has dug deeper and discovered something intriguing about the way a class of particles called fermions, which includes common particles like electrons and quarks, fits into the octonion-based language. Fermions are ā€œchiralā€, meaning their mirror-image reflections ā€“ the symmetry physicists call parity ā€“ look different. This had created a problem when incorporating fermions into the octonion-based versions of the standard model. But Boyle has now found a way to fix that ā€“ and it has a fascinating spin-off. Restoring the mirror symmetry that is broken in the standard model also enables octonionic fermions to sit comfortably in the left-right symmetric model, one level further up towards the grand unified theory.

Beyond the big bang

This line of thinking might even take us beyond the grand unified theory, towards an explanation of where the universe came from. Boyle has been working with Neil Turok, his colleague at the Perimeter Institute, on what they call a ā€œtwo-sheeted universeā€ that involves a set of symmetries known as charge, parity and time (CPT). ā€œIn this hypothesis, theĀ big bangĀ is a kind of mirror separating our half of the universe from its CPT mirror image on the other side of the bang,ā€ says Boyle. The octonionic properties of fermions that sit in the left-right symmetric model are relevant in developing a coherent theory for this universe, it turns out. ā€œI suspect that combining the octonionic picture with the two-sheeted picture of the cosmos is a further step in the direction of finding the right mathematical framework for describing nature,ā€ says Boyle.

As with all the discoveries linking the octonions to our theories of physics so far, Boyleā€™s work is only suggestive. No one has yet created a fully fledged theory of physics based on octonions that makes new predictions we can test by using particle colliders, say. ā€œThereā€™s still nothing concrete yet: thereā€™s nothing we can tell the experimentalists to go and look for,ā€ says Duff. Furey agrees: ā€œIt is important to say that we are nowhere near being finished.

But Boyle, Furey, Hughes and many others are increasingly absorbed by the possibility that this strange maths really could be our best route to understanding where the laws of nature come from. In fact, Boyle thinks that the octonion-based approach could be just as fruitful as doing new experiments to find new particles. ā€œMost people are imagining that the next bit of progress will be from some new pieces being dropped onto the table,ā€ he says. ā€œThat would be great, but maybe we have not yet finished the process of fitting the current pieces together.ā€

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*Credit for article given to Michael Brooks*


How linguists are unlocking the meanings of Shakespeare’s words using numbers

Today it would seem odd to describe a flower with the word “bastard”ā€”why apply a term of personal abuse to a flower? But in Shakespeare’s time, “bastard” was a technical term describing certain plants.

Similarly, associating the word “bad” with success and talking of a “bad success” would be decidedly odd today. But it was not unusual then, when success meant outcome, which could be good or bad.

Corpus linguisticsĀ is a branch of linguistics which uses computers to explore the use of words in huge collections of language. It can spot nuances that might be overlooked by linguists working manually, or large patterns that a lifetime of studying may not reveal. And numbers, counts of words and keeping track of where the words are occurring, are key.

In my experience at conferences and the like, talk of numbers is not unanimously well received in the world of literary studies. Numbers are sometimes perceived as being reductive, or inappropriate when discussing creative works, or only accessible to specialists.

Yet, describing any pattern involves numbers. In the first paragraph above, I used the words “normal,” “odd” and “unusual” as soft ways of describing frequenciesā€”the numbers of occurrences (think also of, for example, “unique,” “rare,” “common”).

Even talking about “associations” involves numbers. Often associations evolve from an unusually highĀ numberĀ of encounters among two or more things. And numbers help us to see things.

Changing meanings

Along with my team at Lancaster University, I have used computers to examine some 20,000 words gleaned from a million-word corpus (a collection of written texts) of Shakespeare’s plays, resulting in a new kind ofĀ dictionary.

People have created Shakespeare dictionaries before, but this is the first to use the full armory of corpus techniques and the first to be comparative. It not only looks at words inside Shakespeare’s plays, but also compares them with a matching million-word corpus of contemporary early modern plays, along with huge corpus of 320 million words of various writings of the period.

Of course, words in early modern England had lives outside Shakespeare. “Bastard” was generally a term for a hybrid plant, occurring in technical texts on horticulture.

It could be, and very occasionally was, used for personal abuse, as in King Lear, where Edmund is referred to as a “bastard.” But this is no general term of abuse, let alone banter, as you might see it used today. It is a pointed attack on him being of illegitimate parentage, genetically hybrid, suspect at his core.

The word “bad” is not now associated with the word “success,” yet 400 years ago it was, as were other negative words, including “disastrous,” “unfortunate,” “ill,” “unhappy” and “unlucky.”

We can tap into a word’s associations by examining its collocates, that is, words with which it tends to occur (rather like we make judgements about people partly on the basis of the company they keep). In this way we can see that the meaning of “success” was “outcome” and that outcome, given its collocates, could be good or bad.

Highly frequent words

We can use intuition to guess some word patterns. It’s no surprise that in early modern English, the word “wicked” occurred very frequently in religious texts of the time. But less intuitively, so did “ourselves,” a word associated with sermons and plays, both of which have in common a habit of making statements about people on earth.

Highly frequent words, so often excluded by historical dictionaries and reference works, are often short words that seem insignificant. They have a wood-for-trees problem.

Yet corpus techniques highlight the interesting patterns. It turns out that a frequent sense of the humble preposition “by” is religious: to reinforce the sincerity of a statement by invoking the divine (for example, “by God”).

Numbers can also reveal what is happening inside Shakespeare’s works. Frequent words such as “alas” or “ah” are revealed to be heavily used by Shakespeare’s female characters, showing that they do the emotional work of lamentation in the plays, especially his histories.

Infrequent words

What of the infrequent? Words that occur only once in Shakespeareā€”so-called hapax legomenaā€”are nuggets of interest. The single case of “bone-ache” in Troilus and Cressida evokes the horrifying torture that syphilis, which it applies to, would have been. In contrast, “ear-kissing” in King Lear is Shakespeare’s rather more pleasant and creative metaphor for whispering (interestingly, other writers used it for the notion of flattering).

Another group of interesting infrequent words concerns words that seem to have their earliest occurrence in Shakespeare. Corpus techniques allowed us to navigate the troubled waters of spelling variation. Before spelling standardization, searching for the word “sweet,” for instance, would miss cases spelt “sweete,” “swete” or “svveet.”

In this way, we can better establish whether a word written by a writer really is the earliest instance. Shakespearean firsts include the rather boring “branchless” (Antony and Cleopatra), a word probably not coined by Shakespeare but merely first recorded in his text. But there is also the more creative “ear-piercing” (Othello) and the distinctly modern-sounding “self-harming” (The Comedy of Errors and Richard II).

Why are these advances in historical corpus linguistics happening now? Much of the technology to produce these findings was not in place until relatively recently.

Programs to deal with spelling variation (such asĀ Vard) or to analyse vast collections of electronic texts in sophisticated ways (such asĀ CQPweb), to say nothing of the vast quantities of computer-readable early modern language data (such asĀ EEBO-TCP), have only been widely used in the last 10 or so years. We are therefore on the cusp of a significant increase in our understanding and appreciation of major writers such as Shakespeare.

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Credit of the article given to Jonathan Culpeper,Ā The Conversation

 

 


Crowds Beat Computers in Answer to Wikipedia-Sized Maths Problem

A maths problem previously tackled with the help of a computer, which produced a proof the size of Wikipedia, has now been cut down to size by a human. Although it is unlikely to have practical applications, the result highlights the differences between two modern approaches to mathematics: crowdsourcing and computers.

Terence TaoĀ of the University of California, Los Angeles, has published a proof of the Erdős discrepancy problem, a puzzle about the properties of an infinite, random sequence of +1s and -1s. In the 1930s, Hungarian mathematicianĀ Paul ErdősĀ wondered whether such a sequence would always contain patterns and structure within the randomness.

One way to measure this is by calculating a value known as the discrepancy. This involves adding up all the +1s and -1s within every possible sub-sequence. You might think the pluses and minuses would cancel out to make zero, but Erdős said that as your sub-sequences got longer, this sum would have to go up, revealing an unavoidable structure. In fact, he said the discrepancy would be infinite, meaning you would have to add forever, so mathematicians started by looking at smaller cases in the hopes of finding clues to attack the problem in a different way.

Last year,Ā Alexei LisitsaĀ andĀ Boris KonevĀ of the University of Liverpool, UK used a computer to prove that the discrepancy will always be larger than two. The resulting proof was a 13 gigabyte file ā€“ around the size of the entire text of Wikipedia ā€“ thatĀ no human could ever hope to check.

Helping hands

Tao has used more traditional mathematics to prove that Erdős was right, and the discrepancy is infinite no matter the sequence you choose. He did it by combining recent results in number theory with some earlier, crowdsourced work.

In 2010, a group of mathematicians, including Tao, decided to work on the problem as theĀ fifth Polymath project, an initiative that allows professionals and amateurs alike toĀ contribute ideas through SaiBlogs and wikisĀ as part of mathematical super-brain. They made some progress, but ultimately had to give up.

ā€œWe had figured out an interesting reduction of the Erdős discrepancy problem to a seemingly simpler problem involving a special type of sequence called a completely multiplicative function,ā€ says Tao.

Then, in January this year, a new development in the study of these functions made Tao look again at the Erdős discrepancy problem, after a commenter on his SaiBlog pointed out a possible link to the Polymath project and another problem called the Elliot conjecture.

Not just conjecture

ā€œAt first I thought the similarity was only superficial, but after thinking about it more carefully, and revisiting some of the previous partial results from Polymath5, I realised there was a link: if one could prove the Elliott conjecture completely, then one could also resolve the Erdős discrepancy problem,ā€ says Tao.

ā€œI have always felt that that project, despite not solving the problem, was a distinct success,ā€Ā writesĀ University of Cambridge mathematician Tim Gowers, who started the Polymath project and hopes that others will be encouraged to participate in future. ā€œWe now know that Polymath5 has accelerated the solution of a famous open problem.ā€

Lisitsa praises Tao for doing what his algorithm couldnā€™t. ā€œIt is a typical example of high-class human mathematics,ā€ he says. But mathematicians are increasinglyĀ turning to machines for help, a trend that seems likely to continue. ā€œComputers are not needed for this problem to be solved, but I believe they may be useful in other problems,ā€ Lisitsa says.

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

*Credit for article given to Jacob Aron*

 


A mathematical understanding of project schedules

Complex projects are made up of many activities, the duration of which vary according to a power law; this model can be used to predict overall project duration and delay.

We have all been frustrated when a project is delayed because one sub-task cannot begin before another ends. It is less well known that the process of scheduling projects efficiently can be described in mathematical terms.

Now, Alexei Vazquez, of technology company Nodes & Links and based in Cambridge, U.K., has shown that the distribution of activity lengths in a project follows the mathematical relationship ofĀ power lawĀ scaling. He hasĀ publishedĀ his findings inĀ The European Physical Journal B.

Any relationship in which one quantity varies as a power of another (such as squared or cubed) is known as a power law. These can be applied to a wide range of physical (e.g., cloud sizes orĀ solar flares), biological (e.g. species frequencies in a habitat) and man-made (e.g.Ā income distribution) phenomena.

In Vazquez’ analysis of projects, the quantities that depend on power laws were the duration of each of the activities that make up the project and the slack times between each activity, or “floats.”

Vazquez analysed data on 118Ā construction projects, together comprising more than 1,000 activities, that was stored in a database belonging to his company. The activity durations in a given project fitted a power law with a negative exponent (i.e., there were more short-duration activities, and a “tail” of small numbers of longer ones); the value of the exponent varied from project to project. The distribution of float times for the activities in a project can be expressed in a similar but independent power law.

He explained that these power law scalings arise from different processes: in the case of the activities, from a historical process in which a generic activity fragments over time into a number of more specialized ones. Furthermore, he showed that estimation of delays associated with a whole project depends on the power law scaling of the activities but not of the floats. This analysis has the potential to forecast delays in planned projects accurately, minimizing the annoyance caused by those long waits.

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Credit of the article given to Clare Sansom,Ā SciencePOD

 


UK hobbyist stuns math world with ‘amazing’ new shapes

David Smith, a retired print technician from the north of England, was pursuing his hobby of looking for interesting shapes when he stumbled onto one unlike any other in November.

When Smith shared hisĀ shapeĀ with the world in March, excited fans printed it onto T-shirts, sewed it into quilts, crafted cookie cutters or used it to replace the hexagons on aĀ soccer ballā€”some even made plans for tattoos.

The 13-sided polygon, which 64-year-old Smith called “the hat”, is the first single shape ever found that can completely cover an infinitely large flat surface without ever repeating the same pattern.

That makes it the first “einstein”ā€”named after the German for “one stone” (ein stein), not the famed physicistā€”and solves a problem posed 60 years ago that someĀ mathematicianshad thought impossible.

After stunning the mathematics world, Smithā€”a hobbyist with no training who told AFP that he wasn’t great at math at schoolā€”then did it again.

While all agreed “the hat” was the first einstein, itsĀ mirror imageĀ was required one in seven times to ensure that a pattern never repeated.

But in a preprint study published online late last month, Smith and the three mathematicians who helped him confirm the discovery revealed a new shapeā€””the specter.”

It requires no mirror image, making it an even purer einstein.

‘It can be that easy’

Craig Kaplan, a computer scientist at Canada’s Waterloo University, told AFP that it was “an amusing and almost ridiculous storyā€”but wonderful”.

He said that Smith, a retired print technician who lives in Yorkshire’s East Riding, emailed him “out of the blue” in November.

Smith had found something “which did not play by his normal expectations for how shapes behave”, Kaplan said.

If you slotted a bunch of these cardboard shapes together on a table, you could keep building outwards without them ever settling into a regular pattern.

Using computer programs, Kaplan and two other mathematicians showed that the shape continued to do this across an infinite plane, making it the first einstein, or “aperiodic monotile”.

When they published their first preprint in March, among those inspired was Yoshiaki Araki. The Japanese tiling enthusiast made art using the hat and another aperiodic shape created by the team called “the turtle”, sometimes using flipped versions.

Smith was inspired back, and started playing around with ways to avoid needing to flip his hat.

Less than a week after their first paper came out, Smith emailed Kaplan a new shape.

Kaplan refused to believe it at first. “There’s no way it can be that easy,” he said.

But analysis confirmed that Tile (1,1) was a “non-reflective einstein”, Kaplan said.

Something still bugged themā€”while this tile could go on forever without repeating a pattern, this required an “artificial prohibition” against using a flipped shape, he said.

So they added little notches or curves to the edges, ensuring that only the non-flipped version could be used, creating “the specter”.

‘Hatfest’

Kaplan said both their papers had been submitted to peer-reviewed journals. But the world of mathematics did not wait to express its astonishment.

Marjorie Senechal, a mathematician at Smith College in the United States, told AFP the discoveries were “exciting, surprising and amazing”.

She said she expects the specter and its relatives “will lead to a deeper understanding of order in nature and the nature of order.”

Doris Schattschneider, a mathematician at Moravian College in the US, said both shapes were “stunning”.

Even Nobel-winning mathematician Roger Penrose, whose previous best effort had narrowed the number of aperiodic tiles down to two in the 1970s, had not been sure such a thing was possible, Schattschneider said.

Penrose, 91, will be among those celebrating the new shapes during the two-day “Hatfest” event at Oxford University next month.

All involved expressed amazement that the breakthrough was achieved by someone without training in math.

“The answer fell out of the sky and into the hands of an amateurā€”and I mean that in the best possible way, a lover of the subject who explores it outside of professional practice,” Kaplan said.

“This is the kind of thing that ought not to happen, but very happily for the history of science does happen occasionally, where a flash brings us the answer all at once.”

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Credit of the article given to Daniel Lawler