An anonymous reader quotes a report from IEEE Spectrum: Deep learning has a DRAM problem. Systems designed to do difficult things in real time, such as telling a cat from a kid in a car's backup camera video stream, are continuously shuttling the data that makes up the neural network's guts from memory to the processor. The problem, according to startup Flex Logix, isn't a lack of storage for that data; it's a lack of bandwidth between the processor and memory. Some systems need four or even eight DRAM chips to sling the 100s of gigabits to the processor, which adds a lot of space and consumes considerable power. Flex Logix says that the interconnect technology and tile-based architecture it developed for reconfigurable chips will lead to AI systems that need the bandwidth of only a single DRAM chip and consume one-tenth the power.

Mountain View-based Flex Logix had started to commercialize a new architecture for embedded field programmable gate arrays (eFPGAs). But after some exploration, one of the founders, Cheng C. Wang, realized the technology could speed neural networks. A neural network is made up of connections and "weights" that denote how strong those connections are. A good AI chip needs two things, explains the other founder Geoff Tate. One is a lot of circuits that do the critical "inferencing" computation, called multiply and accumulate. "But what's even harder is that you have to be very good at bringing in all these weights, so that the multipliers always have the data they need in order to do the math that's required. [Wang] realized that the technology that we have in the interconnect of our FPGA, he could adapt to make an architecture that was extremely good at loading weights rapidly and efficiently, giving high performance and low power."

An anonymous reader shares a report: Factorising numbers into their constituent primes may sound esoteric, but the one-way nature of the problem -- and of some other, closely related mathematical tasks -- is the foundation on which much modern encryption rests. Such encryption has plenty of uses. It defends state secrets, and the corporate sort. It protects financial flows and medical records. And it makes the $2trn e-commerce industry possible. Nobody, however, is certain that the foundation of all this is sound. Though mathematicians have found no quick way to solve the prime-factors problem, neither have they proved that there isn't one. In theory, any of the world's millions of professional or amateur mathematicians could have a stroke of inspiration tomorrow and publish a formula that unravels internet cryptography -- and most internet commerce with it.

In fact, something like this has already happened. In 1994 Peter Shor, a mathematician then working at Bell Laboratories, in America, came up with a quick and efficient way to find a number's prime factors. The only catch was that for large numbers his method -- dubbed Shor's algorithm -- needs a quantum computer to work. Quantum computers rely on the famous weirdness of quantum mechanics to perform certain sorts of calculation far faster than any conceivable classical machine. Their fundamental unit is the "qubit", a quantum analogue of the ones and zeros that classical machines manipulate. By exploiting the quantum-mechanical phenomena of superposition and entanglement, quantum computers can perform some forms of mathematics -- though only some -- far faster than any conceivable classical machine, no matter how beefy.

In a paper published Thursday in the journal Science, Dr. Sergey Bravyi and his team reveal that they've developed a mathematical proof which, in specific cases, illustrates the quantum algorithm's inherent computational advantages over classical. Engadget reports: "It's good to know, because results like this become parts of algorithms," Bob Sutor, vice president of IBM Q Strategy and Ecosystem, told Engadget. "They become part of decisions about how people will start to attack problems. Where will they try classical techniques? Where will they try quantum techniques? How will those interplay? How will they work back and forth together?" What's more, the proof shows that, in these cases, the quantum algorithm can solve the problem in a fixed number of steps, regardless of how many inputs are added. With a classical computer, the more inputs you add, the more steps it needs to take in order to solve. Such are the advantages of parallel processing.

"The main point of this paper is not that somehow we discover some incredibly important quantum algorithm, or some practical, interesting problem," Bravyi told Engadget. "We ask if we can separate a constant depth [between] quantum and classical algorithms. As we increase the problem size, the runtime of the quantum algorithm remains constant, but the total number of operations grows." As Bravyi points out, this new proof doesn't, in and of itself, solve any existing computational issues. Instead, "it gives us insight into what makes a quantum computers more powerful," he continued. "And hopefully in the future it will lead to more practical, useful algorithms."

A new study in Frontiers in Psychology examined why people struggle so much to solve statistical problems, particularly why we show a marked preference for complicated solutions over simpler, more intuitive ones. Chalk it up to our resistance to change. From a report: The study concluded that fixed mindsets are to blame: we tend to stick with the familiar methods we learned in school, blinding us to the existence of a simpler solution. Roughly 96 percent of the general population struggles with solving problems relating to statistics and probability. Yet being a well-informed citizen in the 21st century requires us to be able to engage competently with these kinds of tasks, even if we don't encounter them in a professional setting. "As soon as you pick up a newspaper, you're confronted with so many numbers and statistics that you need to interpret correctly," says co-author Patrick Weber, a graduate student in math education at the University of Regensburg in Germany. Most of us fall far short of the mark.

Part of the problem is the counterintuitive way in which such problems are typically presented. Meadows presented his evidence in the so-called "natural frequency format" (for example, 1 in 10 people), rather than in terms of a percentage (10 percent of the population). That was a smart decision, since 1-in-10 a more intuitive, jury-friendly approach. Recent studies have shown that performance rates on many statistical tasks increased from four percent to 24 percent when the problems were presented using the natural frequency format.

Economist Paul Romer, a co-winner of the 2018 Nobel Prize in economics, uses the programming language Python for his research, according to Quartz. Romer reportedly tried using Wolfram Mathematica to make his work transparent, but it didn't work so he converted to a Jupyter notebook instead. From the report: Romer believes in making research transparent. He argues that openness and clarity about methodology is important for scientific research to gain trust. As Romer explained in an April 2018 blog post, in an effort to make his own work transparent, he tried to use Mathematica to share one of his studies in a way that anyone could explore every detail of his data and methods. It didn't work. He says that Mathematica's owner, Wolfram Research, made it too difficult to share his work in a way that didn't require other people to use the proprietary software, too. Readers also could not see all of the code he used for his equations.

Instead of using Mathematica, Romer discovered that he could use a Jupyter notebook for sharing his research. Jupyter notebooks are web applications that allow programmers and researchers to share documents that include code, charts, equations, and data. Jupyter notebooks allow for code written in dozens of programming languages. For his research, Romer used Python -- the most popular language for data science and statistics. Importantly, unlike notebooks made from Mathematica, Jupyter notebooks are open source, which means that anyone can look at all of the code that created them. This allows for truly transparent research. In a compelling story for The Atlantic, James Somers argued that Jupyter notebooks may replace the traditional research paper typically shared as a PDF.

Cloudflare, which is celebrating its eighth birthday has announced yet another service: an at-cost domain registrar. From a report: While Cloudflare had already been handling domain registration through the company's Enterprise Registrar service, that service was intended for some of Cloudflare's high-end customers who wanted extra levels of security for their domain names. The new domain registrar business -- called Cloudflare Registrar -- will eventually be open to anyone, and it will charge exactly what it costs for Cloudflare to register a domain. As Cloudflare CEO Matthew Prince wrote in a blog post this week, "We promise to never charge you anything more than the wholesale price each TLD charges." That includes the small fee assessed by ICANN for each registration.

Prince said that he was motivated to take the company into the registrar business because of Cloudflare's own experience with registrars and by the perception that many registrars are in the business mostly to up-sell things that require no additional effort. "All the registrar does is record you as the owner of a particular domain," Prince said. "That just involves sending some commands to an API. In other words, domain registrars are charging you for being a middle-man and delivering essentially no value to justify their markup." Charging overhead for that sort of service, Prince said, "seemed as nutty to us as certificate authorities charging to run a bit of math." (Cloudflare also provides free SSL certificates.)

A study of school grades of more than 1.6 million students shows that girls and boys perform similarly in science, technology, engineering and math (STEM) subjects. From a report: The research, published today in Nature Communications, also shows that girls do better than boys in non-STEM subjects. Our results provide evidence that large gaps in the representation of women in STEM careers later in life are not due to differences in academic performance. One explanation for gender imbalance in STEM is the "variability hypothesis." This is the idea that gender gaps are much larger at the tails of the distribution -- among the highest and lowest performers -- than in the middle.

Slashdot reader OneHundredAndTen writes: Sir Michael Atiyah claims to have proved the Riemann hypothesis. This is not some internet crank, but one the towering figures of mathematics in the second half of the 20th century. The thing is, he's almost 90 years old. According to New Scientist, Atiyah is set to present his "simple proof" of the Riemann hypothesis on Monday at the Heidelberg Laureate Forum in Germany. Atiyah has received two awards often referred to as the Nobel prizes of mathematics, the Fields medal and the Abel Prize; he also served as president of the London Mathematical Society, the Royal Society and the Royal Society of Edinburgh.

"[T]he hypothesis is intimately connected to the distribution of prime numbers, those indivisible by any whole number other than themselves and one," reports New Scientist. "If the hypothesis is proven to be correct, mathematicians would be armed with a map to the location of all such prime numbers, a breakthrough with far-reaching repercussions in the field."

Two mathematicians have found what they say is a hole at the heart of a proof that has convulsed the mathematics community for nearly six years. Quanta Magazine: In a report [PDF] posted online Thursday, Peter Scholze of the University of Bonn and Jakob Stix of Goethe University Frankfurt describe what Stix calls a "serious, unfixable gap" within a mammoth series of papers by Shinichi Mochizuki, a mathematician at Kyoto University who is renowned for his brilliance. Posted online in 2012, Mochizuki's papers supposedly prove the abc conjecture, one of the most far-reaching problems in number theory. Despite multiple conferences dedicated to explicating Mochizuki's proof, number theorists have struggled to come to grips with its underlying ideas. His series of papers, which total more than 500 pages, are written in an impenetrable style, and refer back to a further 500 pages or so of previous work by Mochizuki, creating what one mathematician, Brian Conrad of Stanford University, has called "a sense of infinite regress."

Between 12 and 18 mathematicians who have studied the proof in depth believe it is correct, wrote Ivan Fesenko of the University of Nottingham in an email. But only mathematicians in "Mochizuki's orbit" have vouched for the proof's correctness, Conrad commented in a blog discussion last December. "There is nobody else out there who has been willing to say even off the record that they are confident the proof is complete." Nevertheless, wrote Frank Calegari of the University of Chicago in a December blog post, "mathematicians are very loath to claim that there is a problem with Mochizuki's argument because they can't point to any definitive error." That has now changed. In their report, Scholze and Stix argue that a line of reasoning near the end of the proof of "Corollary 3.12" in Mochizuki's third of four papers is fundamentally flawed. The corollary is central to Mochizuki's proposed abc proof. "I think the abc conjecture is still open," Scholze said. "Anybody has a chance of proving it."

Nvidia's new "Scanner" tool for the company's newest GeForce RTX 2080 graphics cards will provide one-click overclocking. PCWorld reports: Nvidia Scanner isn't actually a tool you can download. Instead, it's an API that developers can implement, similar to how current GeForce overclocking software relies on Nvidia's NVAPI. Tom Peterson, Nvidia's director of technical marketing, says all of the major overclocking programs will implement Scanner. You simply press the Test button, and the software starts walking through your graphics card's volt frequency curve, running arithmetic tests all the while. If the overclock starts pushing too far, Nvidia Scanner will discover a math error before your card crashes. When that happens, Scanner ramps up your card's voltage and starts testing again. After about 20 minutes, Scanner will have a complete understanding of your RTX card's capabilities, and automatically generate an overclocking profile built to squeeze as much performance as possible out of it without crashing. Easy-peasy. PCWorld's Brad Chacos mentions a demonstration where "Nvidia's Tom Peterson showed Nvidia Scanner pushing the GeForce RTX 2080 -- which ships with a 1,710MHz boost clock -- all the way to 2,130MHz at 1,068mV."

An anonymous reader quotes a report from Bloomberg: Thirty years ago in December, the modern exchange of scholars between the U.S. and China began. Since then, Chinese academics have become the most prolific global contributors to publications in physical sciences, engineering and math. Recent attempts by the U.S. to curtail academic collaboration are unlikely to change this trend. Qingnan Xie of Nanjing University of Science & Technology and Richard Freeman of Harvard University have studied China's contribution to global scientific output. They document a rapid expansion between 2000 and 2016, as the Chinese share of global publications in physical sciences, engineering and math quadrupled. By 2016, the Chinese share exceeded that of the U.S. Furthermore, the authors argue that these metrics -- which are based on the addresses of the authors -- understate China's impact. The data don't count papers written by Chinese researchers located in other countries with addresses outside China and exclude most papers written in Chinese publications. The researchers adjusted for both factors and conclude that Chinese academics now account for more than one-third of global publications in these scientific fields.

An anonymous reader shares a thread [Editor's note: all links in the story will lead you to Twitter]: In the 1970s the cost -- and size -- of calculators tumbled. Business tools became toys; as a result prestige tech companies had to rapidly diversify into other products -- or die! This is the story of the 1970s great calculator race... Compact electronic calculators had been around since the mid-1960s, although 'compact' was a relative term. They were serious, expensive tools for business. So it was quite a breakthrough in 1967 when Texas Instruments presented the Cal-Tech: a prototype battery powered 'pocket' calculator using four integrated circuits. It sparked a wave of interest. Canon was one of the first to launch a pocket calculator in 1970. The Pocketronic used Texas Instruments integrated circuits, with calculations printed on a roll of thermal paper. Sharp was also an early producer of pocket calculators. Unlike Canon they used integrated circuits from Rockwell and showed the calculation on a vacuum fluorescent display. The carrying handle was a nice touch!

The next year brought another big leap: the Hewlet-Packard HP35. Not only did it use a microprocessor it was also the first scientific pocket calculator. Suddenly the slide rule was no longer king; the 35 buttons of the HP35 had taken its crown. The most stylish pocket calculator was undoubtedly the Olivetti Divisumma 18, designed by Mario Bellini. Its smooth look and soft shape has become something of a tech icon and an inspiration for many designers. It even featured in Space:1999! By 1974 Hewlett Packard had created another first: the HP-65 programmable pocket calculator. Programmes were stored on magnetic cards slotted into the unit. It was even used during the Apollo-Soyuz space mission to make manual course corrections. The biggest problem for pocket calculators was the power drain: LED displays ate up batteries. As LCD displays gained popularity in the late 1970s the size of battery needed began to reduce. The 1972 Sinclair Executive had been the first pocket calculator to use small circular watch batteries, allowing the case to be very thin. Once LCD displays took off watch batteries increasingly became the norm for calculators. Solar power was the next innovation for the calculator: Teal introduced the Photon in 1977, no batteries required or supplied!

But the biggest shake-up of the emerging calculator market came in 1975, when Texas Instruments -- who made the chips for most calculator companies -- decided to produce and sell their own models. As a vertically integrated company Texas Instruments could make and sell calculators at a much lower price than its competitors. Commodore almost went out of business trying to compete: it was paying more for its TI chips than TI was selling an entire calculator for. With prices falling the pocket calculator quickly moved from business tool to gizmo: every pupil, every student, every office worker wanted one, especially when they discovered the digital fun they could have! Calculator games suddenly became a 'thing', often combining a calculator with a deck of cards to create new games to play. Another popular pastime was finding numbers that spelt rude words if the calculator was turned upside down; the Samsung Secal even gave you a clue to one!

The calculator was quickly evolving into a lifestyle accessory. Hewlett Packard launched the first calculator watch in 1977... Casio launched the first credit card sized calculator in 1978, and by 1980 the pocket calculator and pocket computer were starting to merge. Peak calculator probably came in 1981, with Kraftwerk's Pocket Calculator released as a cassingle in a calculator-shaped box. Although the heyday of the pocket calculator may be over they are still quite collectable. Older models in good condition with the original packaging can command high prices online. So let's hear it for the pocket calculator: the future in the palm of your hand!

An anonymous reader quotes a report from Gizmodo: A team of scientists in China are reporting that they have now performed the most precise measurement of gravity's strength yet by measuring G, the Newtonian or universal gravitational constant. G relates the gravitational attraction between two objects to their masses and the distance between them. The new measurement is important both for high-powered atomic clocks as well as the study of the universe, earth science, or any kind of science that relies on gravity in some way. The values measured by the team "have the smallest uncertainties reported until now," according to the paper published in Nature.

In the new study, scientists performed two independent calculations of G using a pair of pendulums in a vacuum, one pendulum setup for each test. Each pendulum swings back and forth between a pair of massive objects whose positions can be adjusted. The pendulums measure the force of gravity in two ways. First, they measure the difference between how quickly the pendulum swings to the "near," or parallel position, versus the "far," or horizontal position. They also measure how the direction of the pendulum's swing changes based on the pull of the test masses. The researchers ended up measuring 6.674184 and 6.674484 hundred billionths (10-11) for the time-of-swinging and angular acceleration methods, respectively. These measures were both very precise, but are still different from one another for unknown reasons. This might have had something to do with the string used for the pendulum. The paper's reviewer, Stephan Schlamminger from the National Institute of Standards and Technology, wrote in a commentary: "Li et al. carried out their experiments with great care and gave a detailed description of their work. The study is an example of excellent craftsmanship in precision measurements. However, the true value of G remains unclear. Various determinations of G that have been made over the past 40 years have a wide spread of values. Although some of the individual relative uncertainties are of the order of 10 parts per million, the difference between the smallest and largest values is about 500 parts per million."

Sportsbooks have closed 50,000 betting accounts just in the U.K. -- and placed strict limits on 50,000 more, according to gaming experts contacted by ESPN. "Bookmakers from London to Las Vegas are refusing to take bets from a growing number of customers whose only offense might be trying to win." Banning or limiting sophisticated players has been a regular part of Las Vegas sports betting for decades, and, like in the U.K., there's absolutely nothing illegal about it. Bettors say the practice is increasing and has even occurred in some of the new states (such as New Jersey) that have entered into the now-legal bookmaking game in recent months. "Americans should be worried," said Brian Chappell, a founder for the U.K. bettor advocacy group Justice for Punters. "It's coming."

In Nevada, refusing to take bets from any customer, from card counters to wise-guy sports bettors, is completely within any casino's legal rights. From Caesars Palace to the Venetian to more local spots like Station Casinos, every bookmaker in town will tell you -- albeit somewhat quietly -- that they've 86'd customers for one reason or another. Seasoned bettors are concerned, though, that the practice of banning or limiting accounts is not only increasing, but the reasoning behind the decisions is becoming more and more suspect. Many believe that the only thing betting intelligently will get you at some shops is a one-way ticket to being thrown out...

In shooting for commercial success, should bookmakers be allowed to refuse to take bets from customers who take steps to try to win? On the other hand, should a business be forced to take on a customer they fear will repeatedly damage its bottom line? The debate is getting ready to play out in state legislatures across the U.S. In May, the Supreme Court struck down the federal ban on state-sponsored sports betting. Full-scale, legal sportsbooks have since opened in Delaware, Mississippi and New Jersey, and many more states are expected to pass sports betting laws and set up regulations in the coming months and years.
"In the end, you have two professions, each trying to increase profits, but only one side gets to make the rules," concludes ESPN.

One London-based veteran of the international sports betting industry even suggests a peer-to-peer betting exchange which simply pairs people betting on opposing outcomes -- thus taking a commission, but not facing any risk.

It's a well-documented, often criticized phenomenon that women's pockets are too small to fit a smartphone, but "there's been very little data to back up a wealth of anecdotal evidence," writes Megan Farokhmanesh via The Verge. Now, The Pudding has used scientific findings to fill this absence. From the report: According to The Pudding's findings, pockets in women's jeans are, on average, 48 percent shorter and 6.5 percent narrower than those of men's. To put this into a perspective we all care about, the site says that only 40 percent of women's front pockets can completely fit a iPhone X. The number only goes down for the Samsung Galaxy or Google Pixel (20 percent and 5 percent, respectively, though the report doesn't specify which model) of the flagships). As for men's pockets? The Pudding marks a 100 percent success rate for the iPhone X, 95 percent for the Samsung Galaxy, and 85 percent for the Google Pixel. "If you're thinking 'But men are bigger than women,' then sure, on average that's true," the site adds. "But here we measured 80 pairs of jeans that all boasted a 32 inch waistband, meaning that these jeans were all made to fit the same size person."

If you happen to have a box of spaghetti in your pantry, try this experiment: Pull out a single spaghetti stick and hold it at both ends. Now bend it until it breaks. How many fragments did you make? If the answer is three or more, pull out another stick and try again. Can you break the noodle in two? If not, you're in very good company. From a report: The spaghetti challenge has flummoxed even the likes of famed physicist Richard Feynman '39, who once spent a good portion of an evening breaking pasta and looking for a theoretical explanation for why the sticks refused to snap in two. Feynman's kitchen experiment remained unresolved until 2005, when physicists from France pieced together a theory to describe the forces at work when spaghetti -- and any long, thin rod -- is bent. They found that when a stick is bent evenly from both ends, it will break near the center, where it is most curved. This initial break triggers a "snap-back" effect and a bending wave, or vibration, that further fractures the stick. Their theory, which won the 2006 Ig Nobel Prize, seemed to solve Feynman's puzzle. But a question remained: Could spaghetti ever be coerced to break in two?

The answer, according to a new MIT study, is yes -- with a twist. In a paper published this week in the Proceedings of the National Academy of Sciences, researchers report that they have found a way to break spaghetti in two, by both bending and twisting the dry noodles. They carried out experiments with hundreds of spaghetti sticks, bending and twisting them with an apparatus they built specifically for the task. The team found that if a stick is twisted past a certain critical degree, then slowly bent in half, it will, against all odds, break in two. The researchers say the results may have applications beyond culinary curiosities, such as enhancing the understanding of crack formation and how to control fractures in other rod-like materials such as multifiber structures, engineered nanotubes, or even microtubules in cells.

Yesterday, during his quarterly earnings call, Tesla CEO Elon Musk revealed a new piece of hardware that the company is working on to perform all the calculations required to advance the self-driving capabilities of its vehicles. The specialized chip, known as "Hardware 3," will be "swapped into the Model S, X, and 3," reports TechCrunch. From the report: Tesla has thus far relied on Nvidia's Drive platform. So why switch now? By building things in-house, Tesla say it's able to focus on its own needs for the sake of efficiency. "We had the benefit [...] of knowing what our neural networks look like, and what they'll look like in the future," said Pete Bannon, director of the Hardware 3 project. Bannon also noted that the hardware upgrade should start rolling out next year. "The key," adds Elon "is to be able to run the neural network at a fundamental, bare metal level. You have to do these calculations in the circuit itself, not in some sort of emulation mode, which is how a GPU or CPU would operate. You want to do a massive amount of [calculations] with the memory right there." The final outcome, according to Elon, is pretty dramatic: He says that whereas Tesla's computer vision software running on Nvidia's hardware was handling about 200 frames per second, its specialized chip is able to crunch out 2,000 frames per second "with full redundancy and failover." Plus, as AI analyst James Wang points out, it gives Tesla more control over its own future.

Every four years, at an international gathering of mathematicians, the subject's youngest and brightest are honored with the Fields Medal, often described as the Nobel Prize of mathematics. The New York Times: This year's recipients, announced on Wednesday at the International Congress of Mathematicians in Rio de Janeiro, include one of the youngest ever: Peter Scholze, a professor of mathematics at the University of Bonn who is 30 years old. Two weeks ago, Peter Woit, a professor at Columbia University who blogs about mathematics and physics, was among those who anticipated that Dr. Scholze would receive the medal. Dr. Woit said Dr. Scholze was "by far the most talented arithmetic geometer of his generation." By custom, Fields medals are bestowed to mathematicians 40 years old or younger. That means Dr. Scholze would have still been eligible for another two rounds of medals. The medal, first awarded in 1936, was conceived by John Charles Fields, a Canadian mathematician. The youngest winner, Jean-Pierre Serre in 1954, was 27. The other Fields medalists this year are Caucher Birkar, 40, of the University of Cambridge in England; Alessio Figalli, 34, of the Swiss Federal Institute of Technology in Zurich; and Akshay Venkatesh, 36, of the Institute for Advanced Study in Princeton and Stanford University in California. Peter Scholze's award cites "the revolution that he launched in arithmetic geometry," the study of shapes that arise from the rational-number solutions to polynomial equations (like xy3 + x2 = 1 or x2 â" y3z = 3). More about him here. As a mathematician, Caucher Birkar has helped bring order to the infinite variety of polynomial equations -- those equations that consist of different variables raised to various powers. No two equations are exactly alike, but Birkar has helped reveal that many can be neatly categorized into a small number of families. [As a reader pointed out, Birkar's award was stolen within minutes of him receiving it.] UPDATE (8/4/18): Organizers have announced they'll provide an identical replacement medal.

Once a classics student with no particular affinity for mathematics, Alessio Figalli has gone on to shake the venerable mathematical discipline of analysis, which concerns the properties of certain types of equations. Figalli's results have provided a refined mathematical understanding of everything from the shape of crystals to weather patterns, to the way ice melts in water. Akshay Venkatesh, a former prodigy who struggled with the genius stereotype, has won a Fields Medal for his "profound contributions to an exceptionally broad range of subjects in mathematics."

NBA superstar LeBron James is opening a new school that many are calling a "game changer." It extends the length of a traditional school day and focuses on teaching a STEM curriculum to students who have a higher probability of failing academically or dropping out of school. An anonymous Slashdot reader shares a report from SB Nation: LeBron James' I Promise School opened Monday to serve low-income and at-risk students in his hometown, and the public school could be an agent of change in the eastern Ohio city. The institution is the intersection of James' philanthropic Family Foundation and the I Promise Network he helped kickstart. I Promise began as an Akron-based non-profit aimed at boosting achievement for younger students from disadvantaged backgrounds. Now the movement has the means to educate these students year-round. I Promise will feature longer school days, a non-traditional school year, and greater access to the school, its facilities, and its teachers during down time for students. That's a formula aimed at replicating some of the at-home support children may be missing when it comes to schoolwork. The school has also anchored its curriculum in math and science-based teaching, dipping into the STEM -- science, technology, engineering, and math -- curriculum that prepares students for the jobs of the future.

xanthos writes: A fascinating article in Quanta magazine introduces us to Cohl Furey and the eight dimensional mathematics called octonions that she is using to model the interactions of strong and electromagnetic forces.

"Proof surfaced in 1898 that the reals, complex numbers, quaternions and octonions are the only kinds of numbers that can be added, subtracted, multiplied and divided. The first three of these "division algebras" would soon lay the mathematical foundation for 20th-century physics, with real numbers appearing ubiquitously, complex numbers providing the math of quantum mechanics, and quaternions underlying Albert Einstein's special theory of relativity. This has led many researchers to wonder about the last and least-understood division algebra. Might the octonions hold secrets of the universe?"

"In her most recent published paper she consolidated several findings to construct the full Standard Model symmetry group for a single generation of particles, with the math producing the correct array of electric charges and other attributes for an electron, neutrino, three up quarks, three down quarks and their anti-particles. The math also suggests a reason why electric charge is quantized in discrete units -- essentially, because whole numbers are."