KentuckyFC writes: "One of the greatest mysteries in science is why we don't see quantum effects on the macroscopic scale; why Schrodinger's famous cat cannot be both alive and dead at the same time. Now one theorist says the answer is because P is NOT equal to NP. Here's the thinking: The equation that describes the state of any quantum object is called Schrodinger's equation. Physicists have always thought it can be used to describe everything in the universe, even large objects, and perhaps the universe itself. But the new idea is that this requires an additional assumption — that an efficient algorithm exists to solve the equation for complex macroscopic systems. But is this true? The new approach involves showing that the problem of solving Schrodinger's equation is NP-hard. So if macroscopic superpositions exist, there must be an algorithm that can solve this NP-hard problem quickly and efficiently. And because all NP-hard problems are mathematically equivalent, this algorithm must also be capable of solving all other NP-hard problems too, such as the traveling salesman problem. In other words, NP-hard problems are equivalent to the class of much easier problems called P. Or P=NP. But here's the thing: computational complexity theorists have good reason to think that P is not equal to NP (although they haven't yet proven it). If they're right, then macroscopic superpositions cannot exist, which explains why we do not (and cannot) observe them in the real world. Voila!"

KentuckyFC (1144503) writes "The question of what makes puzzles hard for humans is deceptively tricky. One possibility is that puzzles that are hard for computers must also be hard for people. That's undoubtedly true and in recent years computational complexity theorists have spent some time trying to classify the games people play in this way (Pac Man is NP hard, by the way). But humans don't always solve problems in the same way as computers because they don't necessarily pick the best method or even a good way to do it. And that makes it hard to predict the difficulty of a puzzle in advance. Cognitive psychologists have attempted to tease this apart by measuring how long it takes people to solve puzzles and then creating a model of the problem solving process that explains the data.

But the datasets gathered in this way have been tiny — typically 20 people playing a handful of puzzles. Now one researcher has taken a different approach by mining the data from websites in which people can play games such as Sudoku. That's given him data on the way hundreds of players solve over 2000 puzzles, a vast increase over previous datasets and this has allowed him to plot the average time it takes to finish different puzzles. One way to assess the difficulty of Sudoku puzzle is in the complexity of each step required to solve it. But the new work suggests that another factor is important too — whether the steps are independent and so can be attempted in parallel or whether the steps are dependent and so must be tried in sequence, one after the other. A new model of this puzzle-solving process accurately reproduces the time it takes real humans to finish the problems and that makes it possible to accurately predict the difficulty of a puzzle in advance for the first time. It also opens the way for other studies of human problem solving using the vast datasets that have been collected over the web. Indeed work has already begun on the Sudoku-like puzzle game, Nurikabe."

New submitter Mark Buchanan (3595113) writes with a story about research from scientists at MIT, Cornell and elsewhere showing "that big city taxi systems could be made 40% more efficient with device-enabled taxi sharing. We could cut miles driven, costs, and pollution with the right application of just data and algorithms, and do it while introducing no more than a 5 minute delay to any person's trip. " Letting such algorithms compete seems an excellent reason to encourage, rather than reject by law, ride-coordination services like Uber and Lyft.

mdsolar (1045926) writes "Malaysian Prime Minister Najib Razak has announced that, based on satellite data analysis from UK company Inmarsat, Malayian Airlines flight MH370 ended in the southern Indian Ocean, and no one on board survived. 'Effectually we looked at the doppler effect, which is the change in frequency, due to the movement of a satellite in its orbit. What that then gave us was a predicted path for the northerly route and a predicted path the southerly route,' explained Chris McLaughlin, senior vice president of external affairs at Inmarsat. 'What we discovered was a correlation with the southerly route and not with the northern route after the final turn that the aircraft made, so we could be as close to certain as anybody could be in that situation that it went south. Where we then went was to work out where the last ping was, knowing that the aircraft still had some fuel, but that it would have run out before the next automated ping. We don't know what speed the aircraft was flying at, but we assumed about 450 knots.' Inmarsat passed the relevant analysis to the UK Air Accidents Investigation Branch (AAIB) yesterday. The cause of the crash remains a mystery."

mdsolar (1045926) writes with this excerpt from Slate on the still-missing Malaysian Airline flight "In a case that is swirling with uncertainties, a few pieces of evidence have stood apart for seeming reliability. Among them was the revelation last Saturday by Malaysia Prime Minister Najib Razak that his country's investigators, in collaboration with U.S. authorities, had analyzed an electronic ping that MH370 had broadcast to the Inmarsat satellite at 8:11 a.m. on the morning of the disappearance. Based on this data, the investigators had determined that at that moment MH370 must have been somewhere along one of two broad arcs: one which passed through Central Asia, and the other of which covered a swath of largely empty Indian Ocean, far to the south. The revelation left a burning question unresolved: what about the six earlier pings, which had been exchanged between the aircraft and the satellite about once per hour? Could any position data be deduced from them? Today, Inmarsat revealed some crucial information. 'The ping timings got longer,' Inmarsat spokesman Chris McLaughlin stated via email. That is to say, at each stage of its journey, the aircraft got progressively farther away from the geostationary satellite's position, located over a spot on the equator south of Pakistan, and never changed its heading in a direction that took it closer—at least for very long."

Hugh Pickens DOT Com (2995471) writes "Jake Simpson reports at The Atlantic that Mathematician Tim Chartier, a Davidson College professor who specializes in ranking methods, teaches a math-heavy form of bracketology — the science of predicting the annual NCAA college basketball tournament at Davidson College in North Carolina. Chartier's academic research is in ranking methods where he looks at things like the page-ranking algorithms of Google. 'In 2009, my collaborator Amy Langville said: "You know what? ESPN has this huge online bracket tournament. Let's create brackets with our ranking methods, just to see if it's creating meaningful information."' Chartier's formula, an evolving code-based matrix that ranks each of the 68 tournament teams, has helped several Davidson students score in the 96th percentile (or higher) in ESPN's bracket challenge and this year, Chartier's goal is to help someone win the $1 billion prize offered by Warren Buffett to anyone who correctly predicts all 63 games of the men's tournament.

Chartier uses two methods. One is the Colley Method, named after astrophysicist Wesley Colley who developed a method used by the BCS for college football (PDF). His basketball method only counts wins and losses, not margin of victory. The other method is the Massey method created by sports statistician Kenneth Massey (PDF), which does integrate scores. Chartier has not been banned from any office pools — at least none that he knows of. But as a result of coming pretty darn close to filling out a perfect bracket just by crunching the numbers, brackets have become a labor of love. 'Now that the brackets are actually out, I've had students in and out of my office all week, sharing new ideas,' says Chartier. 'For me, that's more fun than filling out a bracket. They will all be filling out brackets, so it's like I'm doing parallel processing. I know what might work, but watching them figure out the odds, is a thrill.'"

mikejuk writes to let us know that today is Pi Day — 3/14 in American date notation. He writes, "This year, it feels as though we aren't celebrating alone. For the first time, it looks as if momentum has built up to the point where people have heard about Pi Day. There are even attempts to sell you Pi-related items as if it was a real holiday. But there is always some one to spoil the party so what ever you do to celebrate don't miss Vi Hart's Anti-Pi Rant video." Thus begins the yearly debate over Pi Day vs. Tau Day (June 28). Phil Plait has a post defending Pi Day's honor, and MIT isn't holding back their Pi Day celebrations.

theodp writes "'I want to explain why Common Core is among the most important education ideas in years,' wrote Bill Gates in a USA Today op-ed last month that challenged the "dangerous misconceptions" of those who oppose the initiative (pretty confident for a guy who conceded there wasn't much to show for his earlier $5B education reform effort!). 'The Gates Foundation helped fund this process,' acknowledged Gates in quite an understatement of his influence. Receiving $6.5M in Gates Grants was Student Achievement Partners, whose founder David Coleman was dubbed the 'Architect of the Common Core.' So it's not too surprising that at last week's SXSWedu, Coleman — now President and CEO of The College Board (no stranger to Gates money itself) — announced a dramatic overhaul of the SAT that includes a new emphasis on evidence-based reading and writing and evidence analysis, which the AJC's Maureen Downey calls 'reflective of the approach of the Common Core State Standards.'" (Read more, below.)

sciencehabit writes "Think women can't do math? You're wrong — but new research (paywalled) shows you might not change your mind, even if you get evidence to the contrary. A study of how both men and women perceive each other's mathematical ability finds that an unconscious bias against women — by both men and women — could be skewing hiring decisions, widening the gender gap in mathematical professions like engineering."

An anonymous reader writes "I am a middle school math teacher and I also run a programming club. I recent completed my M.Ed in math education and was inspired to try to do the new GT online MS in Computer Science in a couple of years. I have some background in programming: two intro to comp sci courses, Java, C++, Python, the main scripting languages, and a bunch of math background. I also read through this great article on getting these pre-requisites completed through Coursera but unfortunately you need to wait for courses to enroll. I would like to just learn these on my own time, no credit necessary. Suggestions?"

cartechboy writes "Math watch time: For many traffic analysts, INRIX is considered the gold-standard. This week the company says traffic congestion surged in 2013 and grew over three times as fast as the American economy. The bad news: If true, this reverses two consecutive years of traffic declines with a six percent increase in 2013. (GDP, by comparison, grew 1.9 percent last year.) The analysts then theorize links between economic growth and traffic congestion, which makes sense on the surface. (As the economy improves, more jobs are created, so more commuters on the roads) But INRIX's theory creates as many questions as it answers. For example, the U.S. GDP has been steadily growing since 2009. So why did congestion decline in 2011 and 2012?"

An anonymous reader writes "According to the NY Times, 'Saying its college admission exams do not focus enough on the important academic skills, the College Board announced on Wednesday a fundamental rethinking of the SAT, eliminating obligatory essays, ending the longstanding penalty for guessing wrong and cutting obscure vocabulary words. ... The SAT's rarefied vocabulary words will be replaced by words that are common in college courses, such as "empirical" and "synthesis." The math questions, now scattered widely across many topics, will focus more narrowly on linear equations, functions and proportional thinking. The use of a calculator will no longer be allowed on some of the math sections.' The College Board will also be working with Khan Academy to provide students with free, online practice problems and instructional videos. The new version of the SAT will be introduced in 2016."

An anonymous reader writes "Mathematics Ph.D. student Jeremy Kun has an interesting post about how mathematicians approach doing new work and pushing back the boundaries of human knowledge. He says it's immensely important for mathematicians to be comfortable with extended periods of ignorance when working on a new topic. 'The truth is that mathematicians are chronically lost and confused. It's our natural state of being, and I mean that in a good way. ... This is something that has been bred into me after years of studying mathematics. I know how to say, “Well, I understand nothing about anything,” and then constructively answer the question, “What’s next?” Sometimes the answer is to pinpoint one very basic question I don’t understand and try to tackle that first.' He then provides some advice for people learning college level math like calculus or linear algebra: 'I suggest you don't worry too much about verifying every claim and doing every exercise. If it takes you more than 5 or 10 minutes to verify a "trivial" claim in the text, then you can accept it and move on. ... But more often than not you'll find that by the time you revisit a problem you've literally grown so much (mathematically) that it's trivial. What's much more useful is recording what the deep insights are, and storing them for recollection later.'"

Doofus writes "The Atlantic has an interesting story about opening up what we routinely consider 'advanced' areas of mathematics to younger learners. The goals here are to use complex but easy tasks as introductions to more advanced topics in math, rather than the standard, sequential process of counting, arithmetic, sets, geometry, then eventually algebra and finally calculus. Quoting: 'Examples of activities that fall into the "simple but hard" quadrant: Building a trench with a spoon (a military punishment that involves many small, repetitive tasks, akin to doing 100 two-digit addition problems on a typical worksheet, as Droujkova points out), or memorizing multiplication tables as individual facts rather than patterns. Far better, she says, to start by creating rich and social mathematical experiences that are complex (allowing them to be taken in many different directions) yet easy (making them conducive to immediate play). Activities that fall into this quadrant: building a house with LEGO blocks, doing origami or snowflake cut-outs, or using a pretend "function box" that transforms objects (and can also be used in combination with a second machine to compose functions, or backwards to invert a function, and so on).' I plan to get my children learning the 'advanced' topics as soon as possible. How about you?"

First time accepted submitter campingman777 writes "I am being asked by students to develop an associates of applied science in modern web development at my community college. I proposed the curriculum to some other web forums and they were absolutely against it. Their argument was that students would not learn enough higher math, algorithms, and data structures to be viable employees when their industry changes every five years. As part of our mission is to turn out employees immediately ready for the work force, is teaching knowledge-based careers as a vocation appropriate?"

camperdave writes "I was recently going through a pile of receipts and other papers to put them into order by date. Lacking one of those fancy sorting sticks they have at the office, I wound up with all sorts of piles and I was getting confused as to which pile was for what. Finally, it struck me: Why don't I use one of the many sorting algorithms I learned back in my computer science classes? So I swept all the papers back into the box and did a radix sort on them. It worked like a charm. Since then, I've had occasion to try quicksorts and merge sorts. So, when you have to physically sort things, what algorithm (if any) do you use?"

theodp writes "The devil will be in the details, but if you were stoked about last November's announcement of the Wolfram programming language, you'll be pleased to know that a just-released dry-but-insanely-great demo delivered by Stephen Wolfram does not disappoint. Even if you're not in love with the syntax or are a FOSS devotee, you'll find it hard not to be impressed by Wolfram's 4-line solution to a traveling salesman tour of the capitals of Western Europe, 6-line camera-capture-to-image-manipulation demo, or 2-line web crawling and data visualization example. And that's just for starters. So, start your Raspberry Pi engines, kids!"

New pweidema writes "Michael Teitelbaum, a senior research associate in the Labor and Worklife Program at Harvard Law School who has been writing a book on the subject of the current state of employment in science and technology fields, recently spoke at an Education Writers Association Conference about the 'STEM Worker Shortage: Does It Exist and Is Education to Blame?' The National Science Board's biennial book, Science and Engineering Indicators , consistently finds that the U.S. produces many more STEM graduates than the workforce can absorb. Meanwhile, employers say managers are struggling to find qualified workers in STEM fields. What explains these apparently contradictory trends? And as the shortage debate rages, what do we know about the pipeline of STEM-talented students from kindergarten to college, and what happens to them in the job market? An article LA Times summarizes his findings of his findings on the STEM hype: '...some of it comes from the country’s longtime cycle of waxing and waning interest in science; attention seems to focus on science every 10 to 15 years before slacking off. The only forces pushing the idea of STEM doom, he said, are those that have something to gain from it. Mostly those are STEM employers ... that want to pack the labor force with people to suppress wages ... Joining the chorus are universities that want more funding for science programs...'"

bfwebster writes "During the past few years, I served as an IT expert witness in BanxCorp v. Costco et al., in which BanxCorp sued Costco and Capital One for citing (with credit) its web-published national averages for CD and money market rates in their advertising. Judge Kenneth M. Karas issued his summary judgment opinion last fall, finding that BanxCorp's published averages are 'uncopyrightable facts' due to the simple calculation involved and the lack of ongoing human judgment in what banks were involved. Here is my summary of his findings, along with a link to the actual ruling."

Hugh Pickens DOT Com writes "Chris Parnin has an interesting read about an international team of scientists lead by Dr. Janet Siegmund using brain imaging with fMRI to understand the programmer's mind and to compare and contrast different cognitive tasks used in programming by analyzing differences in brain locations that are activated by different tasks. One recent debate illuminated by their studies is recent legislation that considers offering foreign-language credits for students learning programming languages. There have been many strong reactions across the software-developer community. Some developers consider the effort laudable but misguided and proclaim programming is not at all like human language and is much closer to mathematics. Siegmund observed 17 participants inside an fMRI scanner while they were comprehending short source-code snippets and found a clear, distinct activation pattern of five brain regions, which are related to language processing, working memory, and attention. The programmers in the study recruited parts of the brain typically associated with language processing and verbal oriented processing (ventral lateral prefrontal cortex). At least for the simple code snippets presented, programmers could use existing language regions of the brain to understand code without requiring more complex mental models to be constructed and manipulated." (Read on for more.)