An anonymous reader writes: There's often debate amongst modern programmers about how much math a professional developer should know, and to what extent programming is math. Learning to program is often viewed as being on a spectrum between learning math and learning spoken/written languages. But in a new article, Jeremy Kun argues that the spectrum should be formulated another way: Human language -> Mathematics -> Programming. "Having studied all three subjects, I'd argue that mathematics falls between language and programming on the hierarchy of rigor. ... [T]he hierarchy of abstraction is the exact reverse, with programming being the most concrete and language being the most abstract. Perhaps this is why people consider mathematics a bridge between human language and programming. Because it allows you to express more formal ideas in a more concrete language, without making you worry about such specific hardware details like whether your integers are capped at 32 bits or 64. Indeed, if you think that the core of programming is expressing abstract ideas in a concrete language, then this makes a lot of sense. This is precisely why learning mathematics is 'better' at helping you learn the kind of abstract thinking you want for programming than language. Because mathematics is closer to programming on the hierarchy. It helps even more that mathematics and programming readily share topics."

ectoman writes: Opensource.com is running an interview with Jennifer Davidson of ChickTech, a non-profit organization whose mission is to create communities of support for women and girls pursuing (or interested in pursuing) careers in tech. "In the United States, many girls are brought up to believe that 'girls can't do math' and that science and other 'geeky' topics are for boys," Davidson said. "We break down that idea." Portland, OR-based ChickTech is quickly expanding throughout the United States—to cities like Corvallis and San Francisco—thanks to the "ChickTech: High School" initiative, which gathers hundreds of young women for two-day workshops featuring open source technologies. "We fill a university engineering department with 100 high school girls—more girls than many engineering departments have ever seen," Davidson said. "The participants can look around the building and see that girls from all backgrounds are just as excited about tech as they are."

An anonymous reader writes in with a look at a scientist's interesting wave-tracking experiment and the incredible journeys that waves make. His name is Walter Munk, now in his 90s and a professor emeritus at the Scripps Institution of Oceanography in La Jolla, California. About 60 years ago, he was anchored off Guadalupe Island, on Mexico's west coast, watching swells come in, and using an equation that he and others had devised to plot a wave's trajectory backward in time, he plotted the probable origins of those swells. But the answer he got was so startling, so over-the-top improbable, that he thought, "No, there must be something wrong." His equations said that the swells hitting beaches In Mexico began some 9,000 miles away — somewhere in the southern reaches of the Indian Ocean, near Antarctica. "Could it be?" he wrote in an autobiographical sketch. Could a storm half way across the world produce a patch of moving water that traveled from near the South Pole, up past Australia, then past New Zealand, then across the vast expanse of the Pacific, arriving still intact – at a beach off Mexico? He decided to find out for himself. That is why, in 1957, Walter Munk designed a global, real life, wave-watching experiment.

KentuckyFC (1144503) writes "In the 1970 World Cup in Mexico, teams used a new kind of ball called the Telstar made from 12 black pentagonal panels and 20 white hexagonal panels. This ball has icosahedral symmetry and its own molecular analogue in the form of C60, the famous soccer ball-shaped fullerene. In 2006, a new ball called the TeamGeist was introduced at the World Cup in Germany. This was made of 14 curved panels that together gave it tetrahedral symmetry. This also had a molecular analogue with tetrahedral symmetry among the fullerenes. Now teams at the current World Cup in Brazil are playing with yet another design: the Brazuca, a ball constructed from six panels each with a four-leaf clover shape that knit together like a jigsaw to form a sphere. This has octahedral symmetry. But here's question that has been puzzling chemists, topologists and..errr...soccer fans: is there a molecular analogue of the Brazuca? Or put another way, can fullerenes have octahedral symmetry? Now a pair of mathematicians have finally solved this problem. They've shown that fullerenes can indeed have octahedral symmetry just like the Brazuca, although in addition to hexagonal and pentagonal carbon rings, the ball-shaped molecules must also have rings of 4 and 8 carbon atoms. The next stage is to actually synthesis one of these fullerenes, perhaps something to keep chemists occupied until the 2018 World Cup in Russia."

An anonymous reader writes Dr. James Simons received his doctorate at the age of 23. He was breaking codes for the NSA at 26, and was put in charge of Stony Brook University's math department at 30. He received the Veblen Prize in Geometry in 1976. Today, he's a multi-billionaire, using his fortune to set up educational foundations for math and science. "His passion, however, is basic research — the risky, freewheeling type. He recently financed new telescopes in the Chilean Andes that will look for faint ripples of light from the Big Bang, the theorized birth of the universe. The afternoon of the interview, he planned to speak to Stanford physicists eager to detect the axion, a ghostly particle thought to permeate the cosmos but long stuck in theoretical limbo. Their endeavor 'could be very exciting,' he said, his mood palpable, like that of a kid in a candy store." Dr. Simons is quick to say this his persistence, more than his intelligence, is key to his success: "I wasn't the fastest guy in the world. I wouldn't have done well in an Olympiad or a math contest. But I like to ponder. And pondering things, just sort of thinking about it and thinking about it, turns out to be a pretty good approach."

An anonymous reader writes: Many prominent news organizations, including the BBC, are reporting on a study (PDF) that claims a new blood test is 87% accurate in predicting which patients will develop cognitive impairment. It's hailed as a major step forward in efforts to fight dementia and Alzheimer's disease. Unfortunately, reality isn't quite so impressive. An article at MedPage Today explains all of the statistical facts that the mainstream press glosses over: "Only about 10% of patients of patients with MCI convert to clinical dementia per year. With nearly 30% of positive results false (remember, the specificity was 71%) as well as 15% of negative results false, most of the positive results in such a group will be false. Yes, it's time once again for a tutorial in positive predictive values. If we have 100 MCI patients and a 10% conversion rate, then 10 of them will develop dementia. These are the true positives. There will be 90 true negatives — the ones who don't convert. But with a specificity of 71%, the test will falsely identify 29% of the 90 true negatives, or 26, as positive. Meanwhile, with a false negative rate of 15%, only nine (rounding up from 8.5) of the 10 true positives will be correctly identified. ... It's easy to get a high negative predictive value when the annual event rate is 10%. If I simply predict that no one will convert, I'll be right 90% of the time."

schwit1 (797399) writes A survey of professional academic economists finds that a large percentage are quite willing to cheat or fake data to get the results they want. From the paper's abstract: "This study reports the results of a survey of professional, mostly academic economists about their research norms and scientific misbehavior. Behavior such as data fabrication or plagiarism are (almost) unanimously rejected and admitted by less than 4% of participants. Research practices that are often considered 'questionable,' e.g., strategic behavior while analyzing results or in the publication process, are rejected by at least 60%. Despite their low justifiability, these behaviors are widespread. Ninety-four percent report having engaged in at least one unaccepted research practice."

That less than 4% engage in "data fabrication or plagiarism" might seem low, but it is a terrible statistic . ... 40% admit to doing what they agree are "questionable" research practices, while 94% admit to committing "at least one unaccepted research practice." In other words, almost none of these academic economists can be trusted in the slightest. As the paper notes, "these behaviors are widespread.""

An anonymous reader writes "Many people reading this site probably have a functional understanding of how algorithms work. But whether you know algorithms down to highly mathematical abstractions or simple as a fuzzy series of steps that transform input into output, it can be helpful to visualize what's going on under the hood. That's what Mike Bostock has done in a new article. He walks through algorithms for sampling, shuffling, and maze generation, using beautiful and fascinating visualizations to show how each algorithm works and how it differs from other options.

He says, "I find watching algorithms endlessly fascinating, even mesmerizing. Particularly so when randomness is involved. ... Being able to see what your code is doing can boost productivity. Visualization does not supplant the need for tests, but tests are useful primarily for detecting failure and not explaining it. Visualization can also discover unexpected behavior in your implementation, even when the output looks correct. ...Even if you just want to learn for yourself, visualization can be a great way to gain deep understanding. Teaching is one of the most effective ways of learning, and implementing a visualization is like teaching yourself."

mpicpp (3454017) writes with news that a dump of fare logs from NYC cabs resulted in trip details being leaked thanks to using an MD5 hash on input data with a very small key space and regular format. From the article: City officials released the data in response to a public records request and specifically obscured the drivers' hack license numbers and medallion numbers. ... Presumably, officials used the hashes to preserve the privacy of individual drivers since the records provide a detailed view of their locations and work performance over an extended period of time.

It turns out there's a significant flaw in the approach. Because both the medallion and hack numbers are structured in predictable patterns, it was trivial to run all possible iterations through the same MD5 algorithm and then compare the output to the data contained in the 20GB file. Software developer Vijay Pandurangan did just that, and in less than two hours he had completely de-anonymized all 173 million entries.

theodp writes: "What if learning to code weren't actually the most important thing?" asks Mother Jones' Tasneem Raja. "Rather than increasing the number of kids who can crank out thousands of lines of JavaScript, we first need to boost the number who understand what code can do." Computational thinking, Raja explains, is what really matters. So, while Google is spending another $50 million (on top of an earlier $40 million) and pulling out all the stops in an effort to convince girls that code and AP Computer Science is a big deal, could AP Statistics actually be a better way to teach computational thinking to college credit-seeking high school students? Not only did AP Statistics enrollment surge as AP CS flat-lined, it was embraced equally by girls and boys. Statistics also offers plenty of coding opportunities to boot. And it teaches one how to correctly analyze AP CS enrollment data!

Modular Science is something Tim Lord spotted at last month's O'Reilly Solid Conference in San Francisco. Its founder, Peter Sand, has a Ph.D. in Computer Science from MIT. He's scheduled to speak at this year's OSCON, and his speaker blurb for that conference says, "He is the founder of ManyLabs, a nonprofit focused on teaching math and science using sensors and simulations. Peter also founded Modular Science, a company working on hardware and software tools for science labs. He has given talks at Science Hack Day, Launch Edu, and multiple academic conferences, including SIGGRAPH." And now he's also been interviewed on Slashdot. Note that there are plenty of lab automation systems out there. Peter is working on one that is not only "an order of magnitude cheaper" than similar devices, but is also easy to modify and expand. It's the sort of system that would fit well not just in a college-level lab, but in a high school lab or a local makerspace. (Alternate Video Link)

An anonymous reader writes In what appears to be the first study of its kind, computer scientists report that an algorithm discovered more than 50 years ago in game theory and now widely used in machine learning is mathematically identical to the equations used to describe the distribution of genes within a population of organisms. Researchers may be able to use the algorithm, which is surprisingly simple and powerful, to better understand how natural selection works and how populations maintain their genetic diversity.

theodp (442580) writes AP Computer Science is taught in just 10% of our high schools," lamented The White House last December as President Obama kicked off CSEdWeek. "China teaches all of its students one year of computer science." And the U.S. Dept. of Education has made the AP CS exam its Poster Child for inequity in education (citing a viral-but-misinterpreted study). But ignored in all the hand-wringing over low AP CS enrollment is one huge barrier to the goal of AP-CS-for-all: College Board materials indicate that the average 11th grader's combined PSAT/NMSQT score of 96 in reading and math gives him/her only a 20%-30% probability of getting a score of '3' on the AP CS exam (a score '4' or '5' may be required for college credit). The College Board suggests schools tap a pool of students with a "60-100% likelihood of scoring 3 or higher", so it's probably no surprise that CS teachers are advised to turn to the College Board's AP Potential tool to identify students who are likely to succeed (sample Student Detail for an "average" kid) and send their parents recruitment letters — Georgia Tech even offers some gender-specific examples — to help fill class rosters.

An anonymous reader writes Ars Technica reports that for the first time in Bitcoin's five-year history, a single entity has repeatedly provided more than half of the total computational power required to mine new digital coins, in some cases for sustained periods of time. It's an event that, if it persists, signals the end of crypto currency's decentralized structure."

A while ago you had the chance to ask programmer and open source advocate Bruce Perens about the future of open source, its role in government, and a number of other questions. Below you'll find his answers and an update on what he's doing now.

A while ago you had the chance to ask amateur scientist, and author of the Getting Started in Electronics and the Engineer's Mini-Notebook series, Forrest Mims, a number of questions about science, engineering, and a lifetime of educating and experimenting. Below you'll find his detailed answers to those questions.

An anonymous reader writes "Computer science professor Daniel Lemire explains why spreadsheets shouldn't be used for important work, especially where dedicated software could do a better job. His post comes in response to evaluations of a new economics tome by Thomas Piketty, a book that is likely to be influential for years to come. Lemire writes, 'Unfortunately, like too many people, Piketty used spreadsheets instead of writing sane software. On the plus side, he published his code ... on the negative side, it appears that Piketty's code contains mistakes, fudging and other problems. ... Simply put, spreadsheets are good for quick and dirty work, but they are not designed for serious and reliable work. ... Spreadsheets make code review difficult. The code is hidden away in dozens if not hundreds of little cells If you are not reviewing your code carefully and if you make it difficult for others to review it, how do expect it to be reliable?'"

theodp (442580) writes "'R beats Python!' screams the headline at Prof. Norm Matloff's Mad (Data) Scientist blog. 'R beats Julia! Anyone else wanna challenge R?' Not that he has anything against Python, Matloff adds, but he just doesn't believe that Python or Julia will become 'the new R' anytime soon, or ever. Why? 'R is written by statisticians, for statisticians,' explains Matloff. 'It matters. An Argentinian chef, say, who wants to make Japanese sushi may get all the ingredients right, but likely it just won't work out quite the same. Similarly, a Pythonista could certainly cook up some code for some statistical procedure by reading a statistics book, but it wouldn't be quite same. It would likely be missing some things of interest to the practicing statistician. And R is Statistically Correct.'"

thephydes (727739) writes "The maths skills of teenagers in parts of the deep south of the United States are worse than in countries such as Turkey and barely above South American countries such as Chile and Mexico. From the article: '"There is a denial phenomenon," says Prof Peterson. He said the tendency to make internal comparisons between different groups within the US had shielded the country from recognising how much they are being overtaken by international rivals. "The American public has been trained to think about white versus minority, urban versus suburban, rich versus poor," he said.'"

An anonymous reader points out this Science Daily report: "Researchers ... have solved one aspect of the discrete logarithm problem. This is considered to be one of the 'holy grails' of algorithmic number theory, on which the security of many cryptographic systems used today is based. They have devised a new algorithm that calls into question the security of one variant of this problem, which has been closely studied since 1976. The result ... discredits several cryptographic systems that until now were assumed to provide sufficient security safeguards. Although this work is still theoretical, it is likely to have repercussions especially on the cryptographic applications of smart cards, RFID chips , etc."