Mathematics;Ramanujan;and

Mathematicians say there’s more to Ramanujan’s 1729 than meets the eye

Emory University researchers say Ramanujan showed how the number is also related to elliptic curves and K3 surfaces, which play key roles in string theory and quantum physics

The story of the number 1729 goes back to 1918 when Indian mathematician Srinivasa Ramanujan lay sick in a clinic near London and his friend and collaborator G.H. Hardy paid him a visit. Hardy said that he had arrived in taxi number 1729 and described the number “as rather a dull one.” Ramanujan replied to that saying, “No, Hardy, it’s a very interesting number! It’s the smallest number expressible as the sum of two cubes in two different ways.”
Ramanujan, in his ailing state saw that 1729 can be represented as:
1³ + 12³ = 1 + 1,728 = 1,729
9³ + 10³ = 729 + 1,000 = 1,729
Because of this incident, 1729 is now known as the Ramanujan-Hardy number. To date, only six taxi-cab numbers have been discovered that share the properties of 1729. These are the smallest numbers which are the sum of cubes in different ways.
In fact, on Ramanujan’s 125th birth anniversary, a Mint columnist paid tribute to the mathematical genius by finding quirky properties of the number 125. (http://www.livemint.com/Opinion/DOdX57BpprR3qbXJSR9uEN/The-1729-Man.html)
But now mathematicians have discovered that there is more to 1729 that a casual conversation between Hardy and Ramanujan. Emory University researchers say that Ramanujan showed how the number is also related to elliptic curves and K3 surfaces—objects which play key roles today in string theory and quantum physics.
“We’ve found that Ramanujan actually discovered a K3 surface more than 30 years before others started studying K3 surfaces and they were even named,” says Ken Ono, a number theorist at Emory in a public release. “It turns out that Ramanujan’s work anticipated deep structures that have become fundamental objects in arithmetic geometry, number theory and physics,” added Ono.
In 2013, Ono searched through the Ramanujan archive at Cambridge and unearthed a page of formulas that Ramanujan wrote a year after the 1729 conversation between him and Hardy. “From the bottom of one of the boxes in the archive, I pulled out one of Ramanujan’s deathbed notes,” Ono recalls. “The page mentioned 1729 along with some notes about it,” said Ono.
Ono and his graduate student Sarah Trebat-Leder are publishing a paper about these new insights in the journal Research in Number Theory. The paper will describe how one of Ramanujan’s formulas associated with the taxi-cab number can unearth secrets of elliptic curves. “We were able to tie the record for finding certain elliptic curves with an unexpected number of points, or solutions, without doing any heavy lifting at all,” Ono explains. “Ramanujan’s formula, which he wrote on his deathbed in 1919, is that ingenious. It’s as though he left a magic key for the mathematicians of the future,” Ono added.
Although Elliptic curves have been studied for many years, in the last 50 years they have been found to have an impact outside mathematics in areas such as Internet cryptography systems that protect information like bank account numbers.
“This paper adds yet another truly beautiful story to the list of spectacular recent discoveries involving Ramanujan’s notebooks,” says Manjul Bhargava, a number theorist at Princeton University. “Elliptic curves and K3 surfaces form an important next frontier in mathematics, and Ramanujan gave remarkable examples illustrating some of their features that we didn’t know before. He identified a very special K3 surface, which we can use to understand a certain special family of elliptic curves. These new examples and insights are certain to spawn further work that will take mathematics forward,” Bhargava added in the public release by Emory University.
Ono had worked with K3 surfaces before and discovered that Ramanujan had found a K3 surface, much before they were officially identified and named by mathematician AndrĂ© Weil during the 1950s. “Ramanujan was using 1729 and elliptic curves to develop formulas for a K3 surface,” Ono said. “Mathematicians today still struggle to manipulate and calculate with K3 surfaces. So it comes as a major surprise that Ramanujan had this intuition all along,” Ono added.

energy from ocean currents

15-Year-Old Girl Becomes 'America's Top Young Scientist'
Washington:  A 15-year-old US girl was crowned "America's Top Young Scientist" for creating an innovative prototype to help developing countries tap , a contest which had five Indian-American teens among the finalists.

Hannah Herbst was named winner of the 2015 Discovery Education 3M Young Scientist Challenge for creating a prototype that seeks to offer a stable power of source to developing countries by using untapped energy from ocean currents, local media reported.

The award includes USD 25,000 and a student adventure trip to a destination such as Costa Rica.

Herbst, a ninth grader from Florida Atlantic University High School, competed alongside nine other middle school finalists on Tuesday during a live competition at the 3M Innovation Centre in St Paul, Minnesota.

Herbst said the idea for this innovation dawned upon her during conversations with a nine-year-old friend who lives in Ethiopia in northwest Africa, where infrequent and unstable power supply poses a major challenge, according to the local media.

Among the 10 finalists were five Indian-American - Raghav Ganesh, Krishna Shetty, Sanjana Shah, Iris Gupta and Amulya Garimella.

The finalists are judged on their scientific problem solving, innovation and ingenuity, and communication skills.

Last year, Indian-American Sahil Doshi, a ninth grader from Pittsburg, had won the competition for his innovative design of an eco-friendly device that seeks to reduce carbon footprint while offering power for household usage.

Robo-Car will it work in India ?


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Group of Indian Street Dogs Gathered On Road Around Luxury Car And ...

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Group of Indian Street Dogs Gathered On Road Around Luxury Car And Barking At Car Owner


Google’s Lame Demo Shows Us How Far Its Robo-Car Has Come

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The fact Google invited journalists to ride around a rooftop parking lot didn’t make the car seem any cooler.
The ride was more carefully choreographed than a Taylor Swift concert. I pressed the the big black “Go” button, and the car rolled away with a whir. It made a few turns, and maxed out at around 15 mph. A Google employee stepped in front of me, and the car slowed and let him continue on his way unhindered. A car pulled up alongside me, and the Google Car slowed to ensure we didn’t collide. Then a cyclist made a similar move, and the car responded in a similar fashion. I saw the car make the exact same trip 10 times in all.
Look past the econobox styling and appointments of the car and the boring drive around that parking look, though, and you can see just how far Google has come in its quest to make drivers irrelevant—and how far ahead of the competition it is.

Killing the Driver

Google has been developing this technology for six years, and is taking a distinctly different approach than everyone else. Conventional automakers are rolling out features piecemeal, over the course of many years, starting with active safety features like automatic braking and lane departure warnings.
Google doesn’t give a hoot about anything less than a completely autonomous vehicle, one that reduces “driving” to little more than getting in, typing in a destination, and enjoying the ride. It wants a consumer-ready product ready in four years.
This is how good human drivers think. And the cars have the added advantage of better vision, quicker processing times, and the inability to get distracted, or tired, or drunk, or angry.
The Silicon Valley juggernaut is making rapid progress. Its fleet of modified Lexus SUVs and prototypes has racked up 1.2 million autonomous miles on public roads, and covers 10,000 more each week. Most of that has been done in Mountain View, and Google expanded its testing to Austin last summer.
It’s unclear how this technology will reach consumers, but Google is more likely to sell its software than manufacture its own cars. At the very least, it won’t sell this dinky prototype to the public.

Predicting the Future

As the Google car moves, its laser, camera, and radar systems constantly scan the environment around it, 360 degrees and up to 200 yards away.
“We look at the world around us, and we detect objects in the scene, we categorize them as different types,” says Dmitri Dolgov, the project’s chief engineer. The car knows the difference between people, cyclists, cars, trucks, ambulances, cones, and more. Based on those categories and its surroundings, it anticipates what they’re likely to do.
Making those predictions is likely the most crucial work the team is doing, and it’s based on the huge amount of time the cars have spent dealing with the real world. Anything one car sees is shared with every other car, and nothing is forgotten. From that data, the team builds probabilistic models for the cars to follow.
“All the miles we’ve driven and all the data that we’ve collected allowed us to build very accurate models of how different types of objects behave,” Dolgov says. “We know what to expect from pedestrians, from cyclists, from cars.”
Those are the key learnings the test drive on the roof parking lot was meant to show off. If I may anthropomorphize: The car spotted a person on foot walking near its route and figured, “You’re probably going to jaywalk.” It saw a car coming up quickly from left and thought, “There’s a good chance you’re going to keep going and cut me off.” When the cyclist in front put his left arm out, the car understood that as a turn signal.
This is how good human drivers think. And the cars have the added advantage of better vision, quicker processing times, and the inability to get distracted, or tired, or drunk, or angry.

Detecting Anomalies

The great challenge of making a car without a steering wheel a human can grab is that the car must be able to handle every situation it encounters. Google acknowledges there’s no way to anticipate and model for every situation. So the team created what it calls “anomaly detection.”
If the cars see behavior or an object they can’t categorize, “they understand their own limitations,” Dolgov says. “They understand that there’s something really crazy going on and they might not be able to make really good, confident predictions about the future. So they take a very conservative approach.”
One of Google’s cars once encountered a woman in a wheelchair, armed with a broom, chasing a turkey. Seriously. Unsurprisingly, this was a first for the car. So the car did what a good human driver would have done. It slowed down, Dolgov says, and let the situation play out. Then it went along its way. Unlike a human, though, it did not make a video and post it on Instagram.