An anonymous reader writes: When we talk about how the NSA operates, it's typically about the policymakers and what the agency should or should not do. It's worth remembering that the NSA is built upon the backs of world-class mathematicians, whom they aggressively recruit to make all their underlying surveillance technology work. A new piece in Science discusses how the relationship between mathematicians and the NSA has changed following the Snowden leaks (PDF). But as Peter Woit points out, these ethical conundrums are not actually spurring any change. This is perhaps due to the NSA's generous funding of mathematics-related research.

The article talks about the American Mathematical Society, which until recently was led by David Vogan: "...after all was said and done, no action was taken. Vogan describes a meeting about the matter last year with an AMS governing committee as 'terrible,' revealing little interest among the rest of the society's leadership in making a public statement about NSA's ethics, let alone cutting ties. Ordinary AMS members, by and large, feel the same way, adds Vogan, who this week is handing over the presidency to Robert Bryant, a mathematician at Duke University in Durham, North Carolina. For now, U.S. mathematicians aren't willing to disown their shadowy but steadfast benefactor."

theodp writes Maybe Bill Gates' Summer Reading this year will include The Art of R Programming. Pushing further into Big Data, Microsoft on Friday announced it's buying Revolution Analytics, the top commercial provider of software and services for the open-source R programming language for statistical computing and predictive analytics. "By leveraging Revolution Analytics technology and services," blogged Microsoft's Joseph Sirosh, "we will empower enterprises, R developers and data scientists to more easily and cost effectively build applications and analytics solutions at scale." Revolution Analytics' David Smith added, "Now, Microsoft might seem like a strange bedfellow for an open-source company [RedHat:Linux as Revolution Analytics:R], but the company continues to make great strides in the open-source arena recently." Now that it has Microsoft's blessing, is it finally time for AP Statistics to switch its computational vehicle to R?

samzenpus (5) writes "Alexander Stepanov is an award winning programmer who designed the C++ Standard Template Library. Daniel E. Rose is a programmer, research scientist, and is the Chief Scientist for Search at A9.com. In addition to working together, the duo have recently written a new book titled, From Mathematics to Generic Programming. Earlier this month you had a chance to ask the pair about their book, their work, or programming in general. Below you'll find the answers to those questions."

dcblogs writes New legislation being pushed by Sen. Orrin Hatch (R-Utah) to hike the H-1B visa cap is drawing criticism and warnings that it will lead to an increase in offshoring of tech jobs. IEEE-USA said the legislation, introduced by a bipartisan group of lawmakers on Tuesday, will "help destroy" the U.S. tech workforce with guest workers. Other critics, including Ron Hira, a professor of public policy at Howard University and a leading researcher on the issue, said the bill gives the tech industry "a huge increase in the supply of lower-cost foreign guest workers so they can undercut and replace American workers." Hira said this bill "will result in an exponential rise of American jobs being shipped overseas." Technically, the bill is a reintroduction of the earlier "I-Square" bill, but it includes enough revisions to be considered new. It increases the H-1B visa cap to 195,000 (instead of an earlier 300,000 cap), and eliminates the cap on people who earn an advanced degree in a STEM (science, technology, education and math) field. Hatch, who is the No. 2 ranking senator in the GOP-controlled chamber, was joined by co-sponsors Amy Klobuchar (D-Minn.), Marco Rubio (R-Fla.), Chris Coons (D-Del.), Jeff Flake (R-Ariz.) and Richard Blumenthal (D-Conn.) in backing the legislation."

Trailrunner7 writes In a new article in an academic math journal, the NSA's director of research says that the agency's decision not to withdraw its support of the Dual EC_DRBG random number generator after security researchers found weaknesses in it and questioned its provenance was a "regrettable" choice. Michael Wertheimer, the director of researcher at the National Security Agency, wrote in a short piece in Notices, a publication of the American Mathematical Society, that even during the standards development process for Dual EC many years ago, members of the working group focused on the algorithm raised concerns that it could have a backdoor in it. The algorithm was developed in part by the NSA and cryptographers were suspect of it from the beginning. "With hindsight, NSA should have ceased supporting the dual EC_DRBG algorithm immediately after security researchers discovered the potential for a trapdoor. In truth, I can think of no better way to describe our failure to drop support for the Dual_EC_DRBG algorithm as anything other than regrettable," Wertheimer wrote in a piece in Notices' February issue.

prajendran writes There were a lot of controversies generated at the Indian Science Congress earlier this month, including claims of ancient aircraft in India, the use of plastic surgery there, and ways to divine underground water sources using herbal paste on the feet. One argument that could be tested using some form of evidence was the assertion by Science Minister Harsh Vardhan that the Pythagorean theorem was discovered in India. Manjul Bhargava, a Princeton University professor of mathematics and a Fields Medal winner describes why the question is not defined well.

Jason Koebler writes The best limit Texas Hold'Em poker player in the world is a robot. Given enough hands, it will never, ever lose, regardless of what its opponent does or which cards it is dealt. Researchers at the University of Alberta essentially "brute forced" the game of limit poker, in which there are roughly 3 x 10^14 possible decisions. Cepheus runs through a massive table of all of these possible permutations of the game—the table itself is 11 terabytes of data—and decides what the best move is, regardless of opponent.

jones_supa writes: Siddhesh Poyarekar from Red Hat has taken a professional look into mathematical functions found in Glibc (the GNU C library). He has been able to provide an 8-times performance improvement to slowest path of pow() function. Other transcendentals got similar improvements since the fixes were mostly in the generic multiple precision code. These improvements already went into glibc-2.18 upstream. Siddhesh believes that a lot of the low hanging fruit has now been picked, but that this is definitely not the end of the road for improvements in the multiple precision performance. There are other more complicated improvements, like the limitation of worst case precision for exp() and log() functions, based on the results of the paper Worst Cases for Correct Rounding of the Elementary Functions in Double Precision (PDF). One needs to prove that those results apply to the Glibc multiple precision bits.

theodp writes In addition to The Ghost of Steve Jobs, The Codecracker, a remix of 'The Nutcracker' performed by Silicon Valley's all-girl Castilleja School during Computer Science Education Week earlier this month featured a Bubble Sort Dance. Bubble Sort dancing, it turns out, is more popular than one might imagine. Search YouTube, for example, and you'll find students from the University of Rochester to Osmania University dancing to sort algorithms. Are you a fan of Hungarian folk-dancing? Well there's a very professionally-done Bubble Sort Dance for you! Indeed, well-meaning CS teachers are pushing kids to Bubble Sort Dance to hits like Beauty and a Beat, Roar, Gentleman, Heartbeat, Under the Sea, as well as other music.

Onnimikki writes James Stewart, author of the calculus textbooks many of us either loved or loved to hate, has died. In case you ever wondered what the textbook was funding, this story has the answer: a $32 million dollar home over-looking a ravine in Toronto, Canada.

KentuckyFC writes Statisticians have long thought it impossible to tell cause and effect apart using observational data. The problem is to take two sets of measurements that are correlated, say X and Y, and to find out if X caused Y or Y caused X. That's straightforward with a controlled experiment in which one variable can be held constant to see how this influences the other. Take for example, a correlation between wind speed and the rotation speed of a wind turbine. Observational data gives no clue about cause and effect but an experiment that holds the wind speed constant while measuring the speed of the turbine, and vice versa, would soon give an answer. But in the last couple of years, statisticians have developed a technique that can tease apart cause and effect from the observational data alone. It is based on the idea that any set of measurements always contain noise. However, the noise in the cause variable can influence the effect but not the other way round. So the noise in the effect dataset is always more complex than the noise in the cause dataset. The new statistical test, known as the additive noise model, is designed to find this asymmetry. Now statisticians have tested the model on 88 sets of cause-and-effect data, ranging from altitude and temperature measurements at German weather stations to the correlation between rent and apartment size in student accommodation.The results suggest that the additive noise model can tease apart cause and effect correctly in up to 80 per cent of the cases (provided there are no confounding factors or selection effects). That's a useful new trick in a statistician's armoury, particularly in areas of science where controlled experiments are expensive, unethical or practically impossible.

An anonymous reader writes: I graduated with a degree in the liberal arts (English) in 2010 after having transferred from a Microbiology program (not for lack of ability, but for an enlightening class wherein we read Portrait of the Artist). Now, a couple years on, I'm 25, and though I very much appreciate my education for having taught me a great deal about abstraction, critical thinking, research, communication, and cheesily enough, humanity, I realize that I should have stuck with the STEM field. I've found that the jobs available to me are not exactly up my alley, and that I can better impact the world, and make myself happier, doing something STEM-related (preferably within the space industry — so not really something that's easy to just jump into). With a decent amount of student debt already amassed, how can I best break into the STEM world? I'm already taking online courses where I can, and enjoy doing entry-level programming, maths, etc.

Should I continue picking things up where and when I can? Would it be wiser for me to go deeper into debt and get a second undergrad degree? Or should I try to go into grad school after doing some of my own studying up? Would the military be a better choice? Would it behoove me to just start trying to find STEM jobs and learn on the go (I know many times experience speaks louder to employers than a college degree might)? Or perhaps I should find a non-STEM job with a company that would allow me to transfer into that company's STEM work? I'd be particularly interested in hearing from people who have been in my position and from employers who have experience with employees who were in my position, but any insight would be welcome.

An anonymous reader sends this excerpt from Quanta Magazine: "Using the latest deep-learning protocols, computer models consisting of networks of artificial neurons are becoming increasingly adept at image, speech and pattern recognition — core technologies in robotic personal assistants, complex data analysis and self-driving cars. But for all their progress training computers to pick out salient features from other, irrelevant bits of data, researchers have never fully understood why the algorithms or biological learning work.

Now, two physicists have shown that one form of deep learning works exactly like one of the most important and ubiquitous mathematical techniques in physics, a procedure for calculating the large-scale behavior of physical systems such as elementary particles, fluids and the cosmos. The new work, completed by Pankaj Mehta of Boston University and David Schwab of Northwestern University, demonstrates that a statistical technique called "renormalization," which allows physicists to accurately describe systems without knowing the exact state of all their component parts, also enables the artificial neural networks to categorize data as, say, "a cat" regardless of its color, size or posture in a given video.

"They actually wrote down on paper, with exact proofs, something that people only dreamed existed," said Ilya Nemenman, a biophysicist at Emory University.

KentuckyFC writes: One of the big applications for quantum computers is finding the prime factors of large numbers, a technique that can help break most modern cryptographic codes. Back in 2012, a team of Chinese physicists used a nuclear magnetic resonance quantum computer with 4 qubits to factor the number 143 (11 x 13), the largest quantum factorization ever performed. Now a pair of mathematicians say the technique used by the Chinese team is more powerful than originally thought. Their approach is to show that the same quantum algorithm factors an entire class of numbers with factors that differ by 2 bits (like 11 and 13). They've already discovered various examples of these numbers, the largest so far being 56153. So instead of just factoring 143, the Chinese team actually quantum factored the number 56153 (233 x 241, which differ by two bits when written in binary). That's the largest quantum factorization by some margin. The mathematicians point out that their discovery will not help code breakers since they'd need to know in advance that the factors differ by 2 bits, which seems unlikely. What's more, the technique relies on only 4 qubits and so can be easily reproduced on a classical computer.

Recently, you had a chance to ask child prodigy, author and activist, Adora Svitak, about education and women In STEM and politics. Below you'll find her answers to your questions.

First time accepted submitter Ugmug (1495847) writes Last year, University of Pennsylvania researchers Alexander J. Stewart and Joshua B. Plotkin published a mathematical explanation for why cooperation and generosity have evolved in nature. Using the classical game theory match-up known as the Prisoner's Dilemma, they found that generous strategies were the only ones that could persist and succeed in a multi-player, iterated version of the game over the long term. But now they've come out with a somewhat less rosy view of evolution. With a new analysis of the Prisoner's Dilemma played in a large, evolving population, they found that adding more flexibility to the game can allow selfish strategies to be more successful. The work paints a dimmer but likely more realistic view of how cooperation and selfishness balance one another in nature."

We've mentioned several times over the years the Antikythera Mechanism, the astounding early analog computer recovered from a Greek shipwreck in shape good enough to allow modern recreations. The device has been attributed to different Greek mathemeticians and thinkers, such as Archimedes, Hipparchus, and Posidonius, but as reader puddingebola writes, "Current research suggests its origin may be much earlier, and its working based on Babylonian arithmetical methods rather than Greek Trigonometry, which did not exist at the time. Puddingebola excerpts from the NYT article: Writing this month in the journal Archive for History of Exact Sciences, Dr. Carman and Dr. Evans took a different tack. Starting with the ways the device's eclipse patterns fit Babylonian eclipse records, the two scientists used a process of elimination to reach a conclusion that the "epoch date," or starting point, of the Antikythera Mechanism's calendar was 50 years to a century earlier than had been generally believed.

HughPickens.com writes Gerrymandering is the practice of establishing a political advantage for a particular party by manipulating district boundaries to concentrate all your opponents' votes in a few districts while keeping your party's supporters as a majority in the remaining districts. For example, in North Carolina in 2012 Republicans ended up winning nine out of 13 congressional seats even though more North Carolinians voted for Democrats than Republicans statewide. Now Jessica Jones reports that researchers at Duke are studying the mathematical explanation for the discrepancy. Mathematicians Jonathan Mattingly and Christy Vaughn created a series of district maps using the same vote totals from 2012, but with different borders. Their work was governed by two principles of redistricting: a federal rule requires each district have roughly the same population and a state rule requires congressional districts to be compact. Using those principles as a guide, they created a mathematical algorithm to randomly redraw the boundaries of the state's 13 congressional districts. "We just used the actual vote counts from 2012 and just retabulated them under the different districtings," says Vaughn. "If someone voted for a particular candidate in the 2012 election and one of our redrawn maps assigned where they live to a new congressional district, we assumed that they would still vote for the same political party."

The results were startling. After re-running the election 100 times with a randomly drawn nonpartisan map each time, the average simulated election result was 7 or 8 U.S. House seats for the Democrats and 5 or 6 for Republicans. The maximum number of Republican seats that emerged from any of the simulations was eight. The actual outcome of the election — four Democratic representatives and nine Republicans – did not occur in any of the simulations. "If we really want our elections to reflect the will of the people, then I think we have to put in safeguards to protect our democracy so redistrictings don't end up so biased that they essentially fix the elections before they get started," says Mattingly. But North Carolina State Senator Bob Rucho is unimpressed. "I'm saying these maps aren't gerrymandered," says Rucho. "It was a matter of what the candidates actually was able to tell the voters and if the voters agreed with them. Why would you call that uncompetitive?"

An anonymous reader writes Last week, Riecoin – a project that doubles as decentralized virtual currency and a distributed computing system — quietly broke the record for the largest prime number sextuplet. This happened on November 17, 2014 at 19:50 GMT and the calculation took only 70 minutes using the massive distributed computing power of its network. This week the feat was outdone and the project beat its own record on November 24, 2014 at 20:28 GMT achieving numbers 654 digits long, 21 more than its previous record.

A few days ago you had a chance to ask the people at Hampton Creek about about their products and the science of food. Below you'll find the answers to your questions from a number of Hampton Creek employees.