News Flash (September-October 2020)

SEPTEMBER

UK mathematician wins Breakthrough Prize in mathematics

Martin Hairer, an Austrian-British researcher at Imperial College London, is the winner of the 2021 Breakthrough prize for mathematics. It’s one of the several Breakthrough prizes announced yearly by a foundation set up by the Israeli-Russian investor Yuri Milner and Facebook’s Mark Zuckerberg. He was awarded for his work on stochastic analysis, a field that describes how random effects turn the maths of things like stirring a cup of tea, the growth of a forest fire, or the spread of a water droplet that has fallen on a tissue into a fiendishly complex problem. Read more here

Mathematical proof to show that fall of Indus Valley civilization was due to climate change

Image credit: Chris Sloan.

RIT Assistant Professor Nishant Malik has developed a mathematical method that shows climate change likely caused the rise and fall of the Indus Valley civilization. He developed a method to study paleoclimate time series, sets of data that tell us about past climates using indirect observations. He noted that usually the data received when analyzing paleoclimate is a short time series with noise in it, so it’s hard to apply dynamical systems theory to it. Although there are several theories about why the Indus Valley Civilization declined, climate change appears to be the most likely scenario. His analysis has shown that there was a major shift in monsoon patterns just before the dawn of this civilization and that the pattern reversed course right before it declined.

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A mathematical tool to select the best sensors for aircrafts

Post the Boeing 737 Max crash in 2019, in which the recovered black box hinted that a failed pressure sensor may have caused the accident, there have been debates on sensor selection. Researchers have developed a mathematical framework that can help engineers make informed decisions about which sensors to use and where they must be positioned in aircraft and other machines – the model is fed with information on what needs to be sensed and with what precision, and the model's output will be the fewest sensors needed and their accuracies, thus taking out unnecessary sensors.

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OCTOBER

Mathematicians and Computer Scientists solve a 90-year old geometry problem

John Mackey, a teaching professor in the Computer Science Department (CSD) and Department of Mathematical Sciences, and Marjin Heule, an associate professor of computer science, of Carnegie Mellon University have successfully solved the 90-year old last geometry problem of Keller's conjecture. The researchers structured the puzzle as a satisfiability(SAT) problem, put the problem to rest with four months of frenzied computer programming and just 30 minutes of computation using a cluster of computers. “I was really happy when we solved it, but then I was a little sad that the problem was gone,”, said John Mackey, ”But then I felt happy again. There’s just this feeling of satisfaction.” “The problem has intrigued many people for decades, almost a century,” Heule said of Keller’s conjecture. “This is really a showcase for what can be done now that was not possible previously.”

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Divide and conquer: A new formula to minimize 'mathemaphobia'

In a new study by the University of South Australia in collaboration with the Australian Council for Educational Research, researchers have been exploring the impact of anxiety on learning maths, finding that boosting student confidence, is pivotal to greater engagement with the subject. Lead researcher, Dr. Florence Gabriel says maths anxiety is one of the biggest barriers to students choosing to study it, especially at senior school and tertiary levels. The study assessed the responses of 4295 Australian 15-year-old students that participated in the 2012 cycle of the OECD's Program for International Student Assessment (PISA). It focused on the psychological factors of maths learning: motivation, maths self-concept, maths anxiety, perseverance, maths self-efficacy, and maths literacy. The results of the research state,” By developing a student's ability to reflect on past successes -- before maths anxiety sets in -- we can break through some of the negative and emotional beliefs about maths and, hopefully, pave the way for students to accept and engage with maths in the future."

Mathematician proposes a new technique to drastically increase the amount of testing for COVID-19

Dr. Usama Kadri, a mathematician from Cardiff University, developed a new method for processing large volumes of COVID-19 tests that could allow many more patients to be tested using the same amount of test tubes and with a lower possibility of false negatives occurring. Dr. Kadri's technique uses simple algebraic equations to identify positive samples in tests and takes advantage of a testing technique known as 'pooling' that involves grouping a large number of samples from different patients into one test tube and performing a single test on that tube. If the tube is returned negative then you know that everybody from that group does not have the virus. So far, the method has been assessed using simulations of testing scenarios and Dr. Kadri acknowledges that lab testing will need to be carried out to increase confidence in the proposed method.

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Assessing consistency in meta-analysis: A new measure considers statistical power

Researchers have improved the assessment of consistency in meta-analysis whose measure considers statistical power, and it has the potential to alter the interpretation of meta-analyses. "Repeating a study with high statistical power is more difficult than repeating a study with low statistical power. Our proposed measure of inconsistency emphasizes this aspect," "If a meta-analysis combines studies that differ from each other with respect to statistical power, the meta-analysis is inconsistent, even if within-study variances are low. Moreover, it is important that studies with the highest statistical power have the strongest effect on the results of a meta-analysis. Our proposed measure accentuates the meaning of statistical power also from this viewpoint." says Data Manager Ari Voutilainen from the University of Eastern Finland, one of the four developers of the measure. This improved consistency measure can bring a major breakthrough in the field of research where meta-analyses are used.

Forecasting elections with a model of infectious diseases

Voters can interact both within and between states, thus potentially influencing each other's political opinions. Credit: Figure courtesy of Alexandria Volkening, Daniel F. Linder, Mason A. Porter, and Grzegorz A. Rempala.

In a paper published in SIAM Review, Alexandria Volkening (Northwestern University), Daniel F. Linder (Augusta University), Mason A. Porter (University of California, Los Angeles), and Grzegorz A. Rempala (The Ohio State University) proposed a new model whose findings assume that Americans vote the way that they say they will in publicly available polling data and that voters not accounted for in existing polling data will turn out equally for both candidates. The researchers arrived at their conclusion by applying a modeling framework like the ones experts use to forecast the spread of infectious diseases (such as COVID-19) to the high-stakes challenge of forecasting election outcomes. Despite clear differences between contagion and voting dynamics, this study suggests a valuable approach for describing how political opinions change across states. Volkening said. "The fact that our model does well is exciting since there are many ways to make it more realistic in the future. We hope that our work encourages folks to think more critically about how they judge forecasts and get involved in election forecasting themselves."

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