KentuckyFC writes: Physicists at the Max Planck Institute for Chemistry in Germany have measured sulphur hydride superconducting at 190 Kelvin or -83 degrees Centigrade, albeit at a pressure of 150 gigapascals, about the half that at the Earth's core. If confirmed, that's a significant improvement over the existing high pressure record of 164 kelvin. But that's not why this breakthrough is so important. Until now, all known high temperature superconductors have been ceramic mixes of materials such as copper, oxygen lithium, and so on, in which physicists do not yet understand how superconductivity works. By contrast, sulphur hydride is a conventional superconductor that is described by the BCS theory of superconductivity first proposed in 1957 and now well understood. Most physicists had thought that BCS theory somehow forbids high temperature superconductivity--the current BCS record-holder is magnesium diboride, which superconducts at just 39 Kelvin. Sulphur hydride smashes this record and will focus attention on other hydrogen-bearing materials that might superconduct at even higher temperatures. The team behind this work point to fullerenes, aromatic hydrocarbons and graphane as potential targets. And they suggest that instead of using high pressures to initiate superconductivity, other techniques such as doping, might work instead.
KentuckyFC writes: The global popularity of Twitter allows new words and usages to spread rapidly around the world. And that has raised an interesting question for linguists: is language converging into a global "netspeak" that everyone will end up speaking? Now a new study of linguistic patterns on Twitter gives a definitive answer. By looking at neoligisms in geo-located tweets, computational linguists have been able to study exactly how new words spread in time and space. It turns out that some neoligisms spread like wildfire while others are used only in areas limited by geography and demography, just like ordinary dialects. For example, the word "ard", a shortened version of "alright" cropped up in Philadelphia several years ago but even now is rarely used elsewhere. By contrast, the abbreviation "af" meaning "as fuck", as in "this food is as good as fuck", has spread across the US in just a couple of years. The difference in the way new words spread is the result of the geographic and demographic characteristics of the communities in which the words are used. The work shows that the evolution of language on Twitter is governed by the same cultural fault lines as ordinary communication. So we're safe from a global "netspeak" for now.
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 factorise the number 143 (11 x 13), the largest quantum factorisation 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 factorisation 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.
KentuckyFC writes: Stars in the Milky Way typically travel at a few hundred kilometres per second relative to their peers. But in recent years, astronomers have found a dozen or so "hypervelocity stars" travelling at up to 1000 kilometres per second, fast enough to escape our galaxy entirely. And they have observed stars orbiting the supermassive black hole at the centre of the galaxy travelling at least an order of magnitude faster than this, albeit while gravitationally bound. Now a pair of astrophysicists have discovered a mechanism that would free these stars, sending them rocketing into intergalactic space at speeds in excess of 100,000 kilometres per second. That's more than a third of the speed of light. They calculate that there should be about 100,000 of these stars in every cubic gigaparsec of space and that the next generation of space telescopes will be sensitive to spot them. That's interesting because these stars will be cosmological messengers that can tell us about the conditions in other parts of the universe when they formed. And because these stars can travel across much of the observable universe throughout their lifetimes, they could also be responsible for spreading life throughout the cosmos.
KentuckyFC writes: Single pixel cameras are currently turning photography on its head. They work by recording lots of exposures of a scene through a randomising media such as frosted glass. Although seemingly random, these exposures are correlated because the light all comes from the same scene. So its possible to number crunch the image data looking for this correlation and then use it to reassemble the original image. Physicists have been using this technique, called ghost imaging, for several years to make high resolution images, 3D photos and even 3D movies. Now one group has replaced the randomising medium with breast tissue from a chicken. They've then used the single pixel technique to take clear pictures of an object hidden inside the breast tissue. The potential for medical imaging is clear. Curiously, this technique has a long history dating back to the 19th century when Victorian doctors would look for testicular cancer by holding a candle behind the scrotum and looking for suspicious shadows. The new technique should be more comfortable.
KentuckyFC writes: The halting problem is to determine whether an arbitrary computer program, once started, will ever finish running or whether it will continue forever. In 1936, Alan Turing famously showed that there is no general algorithm that can solve this problem. Now a group of computer scientists and ethicists have used the halting problem to tackle the question of how a weaponised robot could decide to kill a human. Their trick is to reformulate the problem in algorithmic terms by considering an evil computer programmer who writes a piece of software on which human lives depend. The question is whether the software is entirely benign or whether it can ever operate in a way that ends up killing people. In general, a robot could never decide the answer to this question. As a result, autonomous robots should never be designed to kill or harm humans, say the authors, even though various lethal autonomous robots are already available. One curious corollary is that if the human brain is a Turing machine, then humans can never decide this issue either, a point that the authors deliberately steer well clear of.
KentuckyFC writes: During the Chinese New Year earlier this year, some 3.6 billion people travelled across China making it the largest seasonal migration on Earth. These kinds of mass movements have always been hard to study in detail. But the Chinese web services company Baidu has managed it using a mapping app that tracked the location of 200 million smartphone users during the New Year period. The latest analysis of this data shows just how vast this mass migration is. For example, over 2 million people left the Guandong province of China and returned just a few days later--that's equivalent to the entire population of Chicago upping sticks. The work shows how easy it is to track the movement of large numbers of people with current technology--assuming they are willing to allow their data to be used in this way.
KentuckyFC writes: Back in 1947, a pair of physicists demonstrated that when a beam of light reflects off a surface, the point of reflection can shift forward when parts of the beam interfere with each other. 60 years later, another group of physicists discovered that this so-called Goos-Hanchen effect could sometimes be negative so the point of reflection would back towards the source rather than away from it. They even suggested that if the negative effect could be made big enough, it could cancel out the forward movement of the light. In other words, the light would become trapped at a single location. Now, physicists have demonstrated this effect for the first time using light reflected of a sheet of silica. The trick they've employed is to place a silicon diffraction grating in contact with the silica to make the interference effect large enough to counteract the forward motion of the light. And by using several gratings with different spacings, they've trapped an entire rainbow. The light can be easily released by removing the grating. Until now, it has only been possible to trap light efficiently inside Bose Einstein Condensate at temperatures close to absolute zero. The new technique could be used as a cheap optical buffer or memory, making it an enabling technology for purely optical computing.
KentuckyFC writes: Earth's closest white dwarf is called van Maanen 2 and sits 14 light years from here. It was discovered by the Dutch astronomer Adriaan van Maanen in 1917, but it was initially hard to classify. That's because its spectra contains lots of heavy elements alongside hydrogen and helium, the usual components of a white dwarf photosphere. In recent years, astronomers have discovered many white dwarfs with similar spectra and shown that the heavy elements come from asteroids raining down onto the surface of the stars. It turns out that all these white dwarfs are orbited by a large planet and an asteroid belt. As the planet orbits, it perturbs the rocky belt causing asteroids to collide and spiral in towards their parent star. This process is so common that astronomers now use the heavy element spectra as a marker for the presence of extrasolar planets. And a re-analysis of van Maanen's work shows that, in hindsight, he was the first to discover the tell-tale signature of extrasolar planets almost a century ago.
KentuckyFC writes: One way to explore the link between quantum mechanics and general relativity is to study the physics that occurs on a small scale in highly curved spacetimes. However, these conditions only occur in the most extreme environments such as at the edge of black holes or in the instants after the Big Bang. But now one physicist has described how it is possible to create curved spacetime in an ordinary quantum optics lab. The idea is based on optical lattices which form when a pair of lasers interfere to create an eggbox-like interference pattern. When ultracold atoms are dropped into the lattice, they become trapped like ping pong balls in an eggbox. This optical trapping technique is common in labs all over the world. However, the ultracold atoms do not stay at a fixed location in the lattice because they can tunnel from one location to another. This tunnelling is a form of movement through the lattice and can be controlled by changing the laser parameters to make tunneling easier or more difficult. Now one physicists has shown that on a large scale, the tunneling motion of atoms through the lattice is mathematically equivalent to the motion of atoms in a quantum field in a flat spacetime. And that means it is possible to create a formal analogue of a curved spacetime by changing the laser parameters across the lattice. Varying the laser parameters over time even simulates the behaviour of gravitational waves. Creating this kind of curved spacetime in the lab won't reveal any new physics but it will allow researchers to study the behaviour of existing laws under these conditions for the first time. That's not been possible even in theory because the equations that describe these behaviours are so complex that they can only be solved in the simplest circumstances.
KentuckyFC writes: Machine learning algorithms use a training dataset to learn how to recognise features in images and use this 'knowledge' to spot the same features in new images. The computational complexity of this task is such that the time required to solve it increases in polynomial time with the number of images in the training set and the complexity of the "learned" feature. So it's no surprise that quantum computers ought to be able to rapidly speed up this process. Indeed, a group of theoretical physicists last year designed a quantum algorithm that solves this problem in logarithmic time rather than polynomial, a significant improvement. Now, a Chinese team has successfully implemented this artificial intelligence algorithm on a working quantum computer, for the first time. The information processor is a standard nuclear magnetic resonance quantum computer capable of handling 4 qubits. The team trained it to recognise the difference between the characters '6' and '9' and then asked it to classify a set of handwritten 6s and 9s accordingly, which it did successfully. The team say this is the first time that this kind of artificial intelligence has ever been demonstrated on a quantum computer and opens the way to the more rapid processing of other big data sets--provided, of course, that physicists can build more powerful quantum computers.
KentuckyFC writes: Since 2001, crowdfunding sites have raised almost $3 billion and in 2012 alone, successfully funded more than 1 million projects. But while many projects succeed, far more fail. The reasons for failure are varied and many but one of the most commonly cited is the inability to match a project with suitable investors. Now a group of researchers from Yahoo Labs and the University of Cambridge have mined data from Kickstarter to discover how investors choose projects to back. They studied over 1000 projects in the US funded by over 80,000 investors. They conclude that there are two types of backers: occasional investors who tend to back arts-related projects, probably because of some kind of social connection to the proposers; and frequent investors who have a much more stringent set of criteria. Frequent investors tend to fund projects that are well-managed, have high pledging goals, are global, grow quickly, and match their interests. The team is now working on a website that will create a list of the Twitter handles of potential investors given the URL of a Kickstarter project
KentuckyFC writes: Photons have many properties such as their frequency, momentum, spin and orbital angular momentum. But when it comes to quantum teleportation, physicists have only ever been able to to transmit one of these properties at a time. So the possibility of teleporting a complete quantum object has always seemed a distant dream. Now a team of Chinese physicists has worked out how to teleport more than one quantum property. The team has demonstrated it by teleporting both the spin and orbital angular momentum of single photons simultaneously. They point out that there is no reason in principle why the technique cannot be generalised to include other properties as well, such as a photon's frequency, momentum and so on. That's an important step towards teleporting complex quantum objects in their entirety, such as atoms, molecules and perhaps even small viruses.
KentuckyFC writes: The human visual system has evolved to recognise people in almost any pose under a vast range of lighting conditions. But abstract art pushes this ability to its limits by distorting the human form. In particular, Cubism seeks to represent three-dimensional objects on a two-dimensional plane by juxtaposing snapshots from different angles. The result is that a Cubist picture contains many ‘fragments of perception’ of the same object. That's why it is often hard for people to recognise the human figures that these pictures contain. Now a group of computer scientists have tested how computer vision algorithms fare at the task of spotting human figures in Cubist art. They compared a variety of different algorithms against humans in trying to spot human figures in 218 Cubist paintings by Picasso. Humans easily outperform all the algorithms at this task. But some algorithms were much better than others. The most successful were based on so-called "deformable parts models" that recognise human figures by looking for body parts rather than the entire form. Interestingly, the team says this backs up various studies by neuroscientists suggesting that the human brain works in a similar way.