An anonymous reader writes: Who doesn't love a good conspiracy theory? Well, I don't — they're usually annoying daydreams from annoying people. Fortunately, an Oxford mathematician seems to feel the same way. Dr. David Grimes just published research in PLOS One establishing a formula for determining the likelihood of a failed conspiracy — in other words, how likely some of its participants are to spill the beans. There are three main factors: number of conspirators, the amount of time passed since it started, and how often we can expect conspiracies to intrinsically fail (a value he derived by studying actual conspiracies that were exposed). From the article: "He then applied his equation to four famous conspiracy theories: The belief that the Moon landing was faked, the belief that climate change is a fraud, the belief that vaccines cause autism, and the belief that pharmaceutical companies have suppressed a cure for cancer. Dr. Grimes's analysis suggests that if these four conspiracies were real, most are very likely to have been revealed as such by now. Specifically, the Moon landing 'hoax' would have been revealed in 3.7 years, the climate change 'fraud' in 3.7 to 26.8 years, the vaccine-autism 'conspiracy' in 3.2 to 34.8 years, and the cancer 'conspiracy' in 3.2 years."

peetm writes: I have an above averagely bright nephew, aged 10, who's into maths and whose birthday is coming up soon. I'd like to get him a suitable present – most likely one that's mathematically centred. At Christmas we sat together while I helped him build a few very simple Python programs that 'animated' some simple but interesting maths, e.g., we built a factorial function, investigated the Collatz conjecture (3n + 1 problem) and talked about, but didn't implement Eratosthenes' Sieve – one step too far for him at the moment perhaps. I've looked about for books that might blend computing + maths, but haven't really found anything appropriate for a 10-year-old. I should be indebted to anyone who might suggest either a suitable maths book, or one that brings in some facet of computing. Or, if not a book, then some other present that might pique his interest.

theodp writes: GeekWire reports that Melinda Gates concluded a Davos panel discussion about gender parity with a personal story about her own family, explaining how she originally became interested in computer science, and how she later played Lab Manager to Bill's Mr. Wizard to help pass along their passion for science and math to their kids. "On Saturday mornings," Gates explained, "I wanted to sleep late. So you know what I did? I made sure there were science projects available, and that's what he did with our two daughters and our son. And guess what my two daughters are interested in? Science and math."

mspohr writes: This article makes a fairly persuasive argument for the utility of the blockchain. It discusses a wide variety of companies and government exploring blockchain to maintain secure records which cannot be altered. One interesting application is to use blockchain to maintain property records in many countries where these records are often incomplete and are easily corrupted (intentionally or unintentionally). A linked article in The Economist expands the thought and discusses changes to the blockchain to improve performance, reduce overhead and accommodate different uses. (See also this related poll.)

Reader John Tromp points to an explanation posted at GitHub of a computational challenge Tromp coordinated that makes a nice companion to the recent discovery of a 22 million-digit Mersenne prime. A distributed effort using pooled computers from two centers at Princeton, and more contributed from the HP Helion cloud, after "many hiccups and a few catastrophes" calculated the number of legal positions in a 19x19 game of Go. Simple as Go board layout is, the permutations allowed by the rules are anything but simple to calculate: "For running an L19 job, a beefy server with 15TB of fast scratch diskspace, 8 to 16 cores, and 192GB of RAM, is recommended. Expect a few months of running time." More: Large numbers have a way of popping up in the game of Go. Few people believe that a tiny 2x2 Go board allows for more than a few hundred games. Yet 2x2 games number not in the hundreds, nor in the thousands, nor even in the millions. They number in the hundreds of billions! 386356909593 to be precise. Things only get crazier as you go up in boardsize. A lower bound of 10^{10^48} on the number of 19x19 games, as proved in our paper, was recently improved to a googolplex. (For anyone who wants to double check his work, Tromp has posted as open source the software used.)

New submitter mtapman writes: I'm looking for suggestions on introductory software engineering projects for a high school level student. Assume the student can do basic math (up through Algebra I or Statistics I) but is new to logic and computer science. Each project should take no more than four hours to complete including research, coding, and testing. The intent is to introduce the student to software engineering (and computer science) through practical and fun examples. Classic CS problems are welcome. One of the key criteria is available research/reference material to allow the student to make progress with 30-60 minutes of online research.

Some ideas that came to my mind (not necessarily good ones) are: (1) pick a sorting algorithm and sort a list of ten words alphabetically, (2) write a program to convert characters from lower to upper case, (3) write a program to divide two numbers in two different programming languages and compare the results to determine the differences between the languages.

theodp writes: Just days after it came under fire for dismissing a call for diversity as "unduly burdensome and not necessary," Apple quietly released its 2015 EEO-1 diversity report (dated 10/6/2015, reflects the 8/1 payroll). Like other tech companies' diversity disclosures, Apple's EEO-1 raw numbers can't really be reconciled to the percentages based on undisclosed raw numbers that grace the infographic-heavy diversity progress narrative CEO Tim Cook spoke to last August. As to why they keep two sets of diversity books, Apple explains, "The EEO-1 has not kept pace with changes in industry or the American workforce over the past half century. We believe the information we report elsewhere on this site is a far more accurate reflection of our progress toward diversity." Taking this stance allows Apple CEO Tim Cook to boast that "in the United States, we hired more than 2,200 Black employees — a 50 percent increase over last year," while ignoring Apple's EEO-1 report, which indicates that Black employees showed a year-over-year net increase of only 1,475 employees and accounted for only 1.9% of the 4,333 YOY net increase in "Professionals" at Apple (White employees accounted for 50.6%, Asian for 42.1%). If you want to check the math, Apple's EEO-1 data (typed in from the content-copy-not-allowed 2015 and 2014 PDFs) and additional charts can be found in this Tableau workbook.

destinyland writes: Tuesday's State of the Union address included a shout-out to Katherine Johnson, the pioneering African American mathematician and physicist who calculated the trajectory of Alan Shepherd's 1961 space trip. "Her reputation was so strong that John Glenn asked her to recheck the calculations made by the new electronic computers before the mission on which he became the first American to orbit the Earth," notes one technology reporter. NASA policy at the time was to not acknowledge the female contributors to scientific papers, though "She literally wrote the textbook on rocket science," according to one NASA official, noting that her impact literally reaches all the way to the moon. At a ceremony in November, Johnson was awarded the Presidential Medal of Freedom, and the 97-year-old pioneer continues to encourage young people to also pursue careers in technology, science, engineering and math.

theodp writes: In A New Chapter for Computer Science Education, the U.S. Department of Education explained earlier this month that the federal STEM Education Act of 2015 'provides an unprecedented opportunity to fully leverage federal resources' to address large gaps in students' participation in Advanced Placement (AP) computer science classes based on gender and race. "In three states," lamented the DOE, "not a single female student took the AP computer science exam" (that only 8 boys took the AP CS exam in those same 3 states was apparently not a concern). And the DOE has good news for those hoping to tap Title I and II funds for CS, but don't have any computer science teachers. "A background in math or science isn't necessarily a requirement to teach CS," explains the Dept. of Ed, "as disciplines like English, history and civics can also provide a solid foundation for teaching CS concepts."

szczys writes: It is surprising to learn how broken the JavaScript Random Number Generator has been for the past six years. The problem is compounded by the fact that Node.js uses the same broken Math.random() module. Learning about why this is broken is interesting, but perhaps even more interesting is how the bad code got there in the first place. It seems that a forum thread from way back in 1999 shared two versions of the code. If you read to the end of the thread you got the working version, if you didn't make it that far (perhaps the case with JavaScript devs) you got the bad version of the code whose fix is just now being rolled out.

dkatana writes: AWS started out defining its virtual CPUs as being composed of EC2 compute units, or ECUs, which it defined as an equivalent to a physical Xeon processor. However, a virtual CPU now looks suspiciously variable... A virtual CPU is whatever Amazon wants to offer in an instance series. The user has no firm measure to go by. From the article: [B]y doing a little math, you could actually compare what you were getting in virtual CPUs in EC2 versus Azure. Also by doing a little math, you knew how to compare one Amazon instance to another based on the ECU count in each virtual CPU. Microsoft didn't look too bad in the comparison. That is one of the casualties of the nomenclature change. I have searched for updated information on how a virtual CPU is measured and found nothing comparable to the definition of the 2012 ECU measure. I have questioned Amazon representatives three times between Oct. 27 and Dec. 21, and don't have much of an answer."

theodp writes: Teaching multivariate statistics to college students, writes AnnMaria De Mars, was a piece of cake compared to her current project — making a game to teach statistics to middle school students who have never been exposed to the idea. In the interest of making a better game, De Mars asks, "Here's my question to you, oh reader people, what resources have you found useful for teaching statistics? I mean, resources you have really watched or used and thought, 'Hey, this would be great for teaching?' There is a lot of mediocre, boring stuff on the interwebz and if any of you could point me to what you think rises above the rest, I'd be super appreciative." Larry Gonick's The Cartoon Guide to Statistics is pretty amazing, but is it a little too advanced for this age group? Anyone have experience with the Khan Academy Data and Statistics offerings? Any other ideas?

An anonymous reader writes: Humanoid robots in space are attractive because their emulation of the human form makes them capable of a huge range of tasks. But remotely controlling such bots is a very different challenge from the math-based methods used to make probe course corrections or plot rover routes. NASA has collaborated with Sony using PlayStation VR to explore methods for controlling humanoid robots in space, and created a virtual reality simulation designed to train operators to compensate for the data delay caused by the vast distances involved in space communication.

theodp writes: The WSJ's Yoree Koh reports that computer science has been recognized as important an academic subject as math and English in the new Every Student Succeeds Act, putting it on equal footing with other subjects when state and local policymakers decide how to dole out federal funds. The law is likely to be a boon for tech companies, Koh adds, which constantly face a shortage of engineers to hire, and have backed Code.org to lobby for computer science teaching in schools. "This legislation will increase access to STEM and computer science learning nationwide and will advance some of the goals outlined in Microsoft's National Talent Strategy," said Microsoft in a blog post. "ESSA makes a number of significant improvements to expand access to computer science education by diverse populations in urban, suburban, and rural areas," explained the ACM. As far as CS and STEM goes, the bill calls for "increasing access for students through grade 12 who are members of groups underrepresented in such subject fields, such as female students, minority students, English learners, children with disabilities, and economically disadvantaged students."

theodp writes: Stephen Wolfram received a PhD in particle physics at age 20 (his thesis committee included Richard Feynman). So it's probably not too surprising that Wolfram's new book, An Elementary Introduction to the Wolfram Language (free on the web), aspires to teach those new to programming how to do much more than just move Minecraft and Star Wars characters around. "The goal of the book," explains Wolfram in a blog post, "is to take people from zero to the point where they know enough about the Wolfram Language that they can routinely use it to create programs for things they want to do. And when I say 'zero', I really mean 'zero'. This is a book for everyone. It doesn't assume any knowledge of programming, or math (beyond basic arithmetic), or anything else. It just starts from scratch and explains things. I've tried to make it appropriate for both adults and kids. I think it'll work for typical kids aged about 12 and up."

enFi writes: All dice are slightly unfair; automating 3k rolls x 29 dice allows detailed exploration. For example: GameScience claims their d20s are fairest, and actually has the fairest die in the study. Chessex d20s are consistently mid-range and all favor the same numbers; Wiz Dice d20s are highly variable (some rival GameScience). Shape differences measurable with calipers account for some of the larger observed differences, but not everything. Read the details for graphs, a video of the Arduino-powered rolling machine, and an explanation of using OpenCV to sort die rolls.

(Disclaimer: I'm the author.)

A few weeks ago you had a chance to ask author, entrepreneur, and software developer Jeff Atwood about founding Stack Overflow and the Stack Exchange Network, as well as his new endeavor, the Discourse open-source discussion platform. Below you will find his answers to your questions.

An anonymous reader writes: Over the decades, the Kadison-Singer problem had wormed its way into a dozen distant areas of mathematics and engineering, but no one seemed to be able to crack it. The question "defied the best efforts of some of the most talented mathematicians of the last 50 years," wrote Peter Casazza and Janet Tremain of the University of Missouri in Columbia, in a 2014 survey article.

As a computer scientist, Daniel Spielman knew little of quantum mechanics or the Kadison-Singer problem's allied mathematical field, called C*-algebras. But when Gil Kalai, whose main institution is the Hebrew University of Jerusalem, described one of the problem's many equivalent formulations, Spielman realized that he himself might be in the perfect position to solve it. "It seemed so natural, so central to the kinds of things I think about," he said. "I thought, 'I've got to be able to prove that.'" He guessed that the problem might take him a few weeks.

Instead, it took him five years. In 2013, working with his postdoc Adam Marcus, now at Princeton University, and his graduate student Nikhil Srivastava, now at the University of California, Berkeley, Spielman finally succeeded. Word spread quickly through the mathematics community that one of the paramount problems in C*-algebras and a host of other fields had been solved by three outsiders — computer scientists who had barely a nodding acquaintance with the disciplines at the heart of the problem.

An anonymous reader writes with this story about Michigan State University Professor Cristop Adami and his quest to answer how life arose with mathematics. From the Quanta story: "Christoph Adami does not know how life got started, but he knows a lot of other things. His main expertise is in information theory, a branch of applied mathematics developed in the 1940s for understanding information transmissions over a wire. Since then, the field has found wide application, and few researchers have done more in that regard than Adami, who is a professor of physics and astronomy and also microbiology and molecular genetics at Michigan State University. He takes the analytical perspective provided by information theory and transplants it into a great range of disciplines, including microbiology, genetics, physics, astronomy and neuroscience. Lately, he's been using it to pry open a statistical window onto the circumstances that might have existed at the moment life first clicked into place.

To do this, he begins with a mental leap: Life, he argues, should not be thought of as a chemical event. Instead, it should be thought of as information. The shift in perspective provides a tidy way in which to begin tackling a messy question. In the following interview, Adami defines information as 'the ability to make predictions with a likelihood better than chance,' and he says we should think of the human genome — or the genome of any organism — as a repository of information about the world gathered in small bits over time through the process of evolution. The repository includes information on everything we could possibly need to know, such as how to convert sugar into energy, how to evade a predator on the savannah, and, most critically for evolution, how to reproduce or self-replicate."

rtoz sends word of the discovery of a new algorithm that dramatically reduces the computation time for complex processes. Scientists from MIT say it conceptually resembles a shrinking bull's eye, incrementally narrowing down on its target. "With this method, the researchers were able to arrive at the same answer as a classic computational approaches, but 200 times faster." Their full academic paper is available at the arXiv. "The algorithm can be applied to any complex model to quickly determine the probability distribution, or the most likely values, for an unknown parameter. Like the MCMC analysis, the algorithm runs a given model with various inputs — though sparingly, as this process can be quite time-consuming. To speed the process up, the algorithm also uses relevant data to help narrow in on approximate values for unknown parameters."