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May 30

Reported by ScienceDaily, May 26 2011.

Physicists have demonstrated a crucial element for a future functioning quantum computer: repetitive error correction. This allows scientists to correct errors occurring in a quantum computer efficiently.

The quantum bit (blue) is entangled with the auxiliary qubits (red). If an error occurs, the state of the defective quantum bit is corrected. (Credit: Harald Ritsch)

A general rule in data processing is that disturbances cause the distortion or deletion of information during data storage or transfer. Methods for conventional computers were developed that automatically identify and correct errors: Data are processed several times and if errors occur, the most likely correct option is chosen. As quantum systems are even more sensitive to environmental disturbances than classical systems, a quantum computer requires a highly efficient algorithm for error correction. The research group of Rainer Blatt from the Institute for Experimental Physics of the University of Innsbruck and the Institute for Quantum Optics and Quantum Information of the Austrian Academy of Sciences (IQOQI) has now demonstrated such an algorithm experimentally.

“The difficulty arises because quantum information cannot be copied,” explains Schindler. “This means that we cannot save information repeatedly and then compare it.” Therefore, the physicists use one of the peculiarities of quantum physics and use quantum mechanical entanglement to perform error correction.

Quick and efficient error correction

The Innsbruck physicists demonstrate the mechanism by storing three calcium ions in an ion trap. All three particles are used as quantum bits (qubits), where one ion represents the system qubit and the other two ions auxiliary qubits. “First we entangle the system qubit with the other qubits, which transfers the quantum information to all three particles,” says Philipp Schindler. “Then a quantum algorithm determines whether an error occurs and if so, which one. Subsequently, the algorithm itself corrects the error.” After having made the correction, the auxiliary qubits are reset using a laser beam. “This last point is the new element in our experiment, which enables repetitive error correction,” says Rainer Blatt. “Some years ago, American colleagues demonstrated the general functioning of quantum error correction. Our new mechanism allows us to repeatedly and efficiently correct errors.”

Leading the field

“For a quantum computer to become reality, we need a quantum processor with many quantum bits,” explains Schindler. “Moreover, we need quantum operations that work nearly error-free. The third crucial element is an efficient error correction.” For many years Rainer Blatt’s research group, which is one of the global leaders in the field, has been working on realizing a quantum computer. Three years ago they presented the first quantum gate with fidelity of more than 99 percent. Now they have realized another key element: repetitive error correction.

This research work is supported by the Austrian Science Fund (FWF), the European Commission, the European Research Council and the Federation of Austrian Industries Tyrol and is published in the scientific journal Science.

Reference: Philipp Schindler, Julio T. Barreiro, Thomas Monz, Volckmar Nebendahl, Daniel Nigg, Michael Chwalla, Markus Hennrich, and Rainer Blatt. Experimental Repetitive Quantum Error Correction. Science, 27 May 2011: Vol. 332 no. 6033 pp. 1059-1061 DOI: 10.1126/science.1203329

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May 29

Reported by University of Buffalo News Center, 26 May 2011.

Theoretical physicist Igor Zutic has been exploring ways to use magnets to revolutionize computing. A new article in Science may show that it is possible.

What causes a magnet to be a magnet, and how can we control a magnet’s behavior? These are the questions that University at Buffalo researcher Igor Zutic, a theoretical physicist, has been exploring over many years.

He is one of many scientists who believe that magnets could revolutionize computing, forming the basis of high-capacity and low-energy memory, data storage and data transfer devices.

Today, in a commentary in Science, Zutic and fellow UB physicist John Cerne, who studies magnetism experimentally, discuss an exciting advancement: A study by Japanese scientists showing that it is possible to turn a material’s magnetism on and off at room temperature.

A material’s magnetism is determined by a property all electrons possess: something called “spin.” Electrons can have an “up” or “down” spin, and a material is magnetic when most of its electrons possess the same spin. Individual spins are akin to tiny bar magnets, which have north and south poles.

In the Japanese study, which also appears in the current issue of Science, a team led by researchers at Tohoku University added cobalt to titanium dioxide, a nonmagnetic semiconductor, to create a new material that, like a chameleon, can transform from a paramagnet (a nonmagnetic material) to a ferromagnet (a magnetic material) at room temperature.

To achieve change, the researchers applied an electric voltage to the material, exposing the material to extra electrons. As Zutic and Cerne explain in their commentary, these additional electrons — called “carriers” — are mobile and convey information between fixed cobalt ions that causes the spins of the cobalt electrons to align in one direction.

In an interview, Zutic calls the ability to switch a magnet “on” or “off” revolutionary. He explains the promise of magnet- or spin-based computing technology — called “spintronics” — by contrasting it with conventional electronics.

Modern, electronic gadgets record and read data as a blueprint of ones and zeros that are represented, in circuits, by the presence or absence of electrons. Processing information requires moving electrons, which consumes energy and produces heat.

Spintronic gadgets, in contrast, store and process data by exploiting electrons’ “up” and “down” spins, which can stand for the ones and zeros devices read. Future energy-saving improvements in data processing could include devices that process information by “flipping” spin instead of shuttling electrons around.

In their Science commentary, Zutic and Cerne write that chameleon magnets could “help us make more versatile transistors and bring us closer to the seamless integration of memory and logic by providing smart hardware that can be dynamically reprogrammed for optimal performance of a specific task.”

“Large applied magnetic fields can enforce the spin alignment in semiconductor transistors,” they write. “With chameleon magnets, such alignment would be tunable and would require no magnetic field and could revolutionize the role ferromagnets play in technology.”

In an interview, Zutic says that applying an electric voltage to a semiconductor injected with cobalt or other magnetic impurities may be just one way of creating a chameleon magnet.

Applying heat or light to such a material could have a similar effect, freeing electrons that can then convey information about spin alignment between ions, he says.

The so-far elusive heat-based chameleon magnets were first proposed by Zutic in 2002. With his colleagues, Andre Petukhov of the South Dakota School of Mines and Technology, and Steven Erwin of the Naval Research Laboratory, he elucidated the behavior of such magnets in a 2007 paper.

The concept of nonmagnetic materials becoming magnetic as they heat up is counterintuitive, Zutic says. Scientists had long assumed that orderly, magnetic materials would lose their neat, spin alignments when heated — just as orderly, crystalline ice melts into disorderly water as temperatures rise.

The carrier electrons, however, are the key. Because heating a material introduces additional carriers that can cause nearby electrons to adopt aligned spins, heating chameleon materials — up to a certain temperature — should actually cause them to become magnetic, Zutic explains. His research on magnetism is funded by the Department of Energy, Office of Naval Research, Air Force Office of Scientific Research and the National Science Foundation.

Reference: I. Zutic, J. Cerne. Chameleon Magnets. Science, 2011; 332 (6033): 1040 DOI: 10.1126/science.1205775

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May 27

Reported by Michael Feldman, 26 May 2011, in HPCwire.

On Wednesday, D-Wave Systems made history by announcing the sale of the world’s first commercial quantum computer. The buyer was Lockheed Martin Corporation, who will use the machine to help solve some of their “most challenging computation problems.” Lockheed purchased the system, known as D-Wave One, as well as maintenance and associated professional services. Terms of the deal were not disclosed.

D-Wave One uses a superconducting 128-qubit (quantum bit) chip, called Rainier, representing the first commercial implementation of a quantum processor. An early prototype, a 16-qubit system called Orion, was demonstrated in February 2007. At the time, D-Wave was talking about future systems based on 512-qubit and 1024-qubit technology, but the 128-qubit Rainier turned out to be the company’s first foray into the commercial market.

According to D-Wave co-founder and CTO Geordie Rose, D-Wave One, the technology uses a method called “quantum annealing”  to solve discrete optimization problems. While that may sound obscure, it applies to all sorts of artificial intelligence-type applications such as natural language processing, computer vision, bioinformatics, financial risk analysis, and other types of highly complex pattern matching.

We asked Rose to describe the D-Wave system and the underlying technology in more detail.

HPCwire: In a nutshell, can you describe the machine and its construction?

Rose: The D-Wave One is built around a superconducting processor. The processor is shielded from noise using specialized filtering and shielding systems that ensure that the processor’s environment is extremely quiet, and is cooled to almost absolute zero during operation. The entire system’s footprint is approximately 100 square feet.

While there is a substantial amount of exotic technology inside the D-Wave One, the system has been built to require very little specialized knowledge to operate. Users interact with the system via an API that allows the D-Wave One to be accessed remotely from a variety of programming environments, including Python, Java, C++, SQL and MATLAB.

HPCwire: What is “quantum annealing?”

Rose: Quantum annealing is a prescription for solving certain types of hard computing problems. In order to run quantum annealing algorithms, hardware that behaves quantum mechanically — such as the Rainier processor in the D-Wave One — is required. Quantum annealing is conceptually similar to simulated annealing and genetic algorithms, but is much more powerful.

HPCwire: Can you prove that quantum computing is actually taking place?

Rose: This was the question we set out to prove with the research published in the recent edition of Nature. The answer was a conclusive “yes.”

HPCwire: How much power is required to run the machine?

Rose: The total wall-plug power consumed by a D-Wave One system is 15 kilowatts. This power requirement will not change as the processors become more powerful over time.

HPCwire: How much does D-Wave One cost?

Rose: Pricing for D-Wave One is consistent with large-scale, high-performance computing systems.

HPCwire: What kinds of problems is it capable of solving? Have you demonstrated any specific algorithms?

Rose: We have used the D-Wave One to run numerous applications. For example, we used the system to solve optimization problems arising from building software that could detect cars in images. This process outputs software that can be deployed anywhere – mobile phones, for example. The software the D-Wave One system wrote, with collaborators from Google and D-Wave, was among the best detectors of cars in images ever built. It is discussed at http://googleresearch.blogspot.com/2009/12/machine-learning-with-quantum.html.

HPCwire: What’s next?

Rose: This is a very significant time in the history of D-Wave. We’ve sold the world’s first commercial quantum computer to a large global security company, Lockheed Martin. That’s a real milestone for us. We are excited to work with Lockheed and future customers to tackle complex problems traditional methods cannot resolve. Last week we were validated on the science side by Nature and this week, on the business side, by the sale of our quantum computer to this Fortune 500 company.

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May 27

Reported by Kate McAlpine, 13 May 2011, in New Scientist.

COULD the structure of space and time be sketched out inside a cousin of plain old pencil lead? The atomic grid of graphene may mimic a lattice underlying reality, two physicists have claimed, an idea that could explain the curious spin of the electron.

Call that a spin? (Image: AlexanderAlUS/GNU Free Documentation License, Version 1.2 or any later)

Graphene is an atom-thick layer of carbon in a hexagonal formation. Depending on its position in this grid, an electron can adopt either of two quantum states – a property called pseudospin which is mathematically akin to the intrinsic spin of an electron.

Most physicists do not think it is true spin, but Chris Regan at the University of California, Los Angeles, disagrees. He cites work with carbon nanotubes (rolled up sheets of graphene) in the late 1990s, in which electrons were found to be reluctant to bounce back off these obstacles. Regan and his colleague Matthew Mecklenburg say this can be explained if a tricky change in spin is required to reverse direction. Their quantum model of graphene backs that up. The spin arises from the way electrons hop between atoms in graphene’s lattice, says Regan.

So how about the electron’s intrinsic spin? It cannot be a rotation in the ordinary sense, as electrons are point particles with no radius and no innards. Instead, like pseudospin, it might come from a lattice pattern in space-time itself, says Regan. This echoes some attempts to unify quantum mechanics with gravity in which space-time is built out of tiny pieces or fundamental networks (Physical Review Letters, vol 106, p 116803).

Sergei Sharapov of the National Academy of Sciences of Ukraine in Kiev says that the work provides an interesting angle on how electrons and other particles acquire spin, but he is doubtful how far the analogy can be pushed. Regan admits that moving from the flatland world of graphene to higher-dimensional space is tricky. “It will be interesting to see if there are other lattices that give emergent spin,” he says.

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May 26

Reported by Shelley Littin, University Communications, University of Arizona, May 20, 2011.

UA researchers have uncovered evidence in ant colonies suggesting that social networks may function differently than previously assumed.

Be it through the Internet, Facebook, the local grapevine or the spread of disease, interaction networks influence nearly every part of our lives.

Singled out by unique color codes, ants provide evidence through their interactions that challenges previous assumptions about how social networks function. (Photo courtesy of Benjamin Blonder)

Scientists previously assumed that interaction networks without central control, known as self-directed networks, have universal properties that make them efficient at spreading information. Just think of the local grapevine: Let something slip, and it seems like no time at all before nearly everyone knows.

By observing interactions in ant colonies, University of Arizona researcher Anna Dornhaus and doctoral candidate Benjamin Blonder have uncovered new evidence that challenges the assumption that all interaction networks have the same properties that maximize their efficiency. The National Science Foundation-funded study was published in the Public Library of Science on May 20.

“Many people who have studied interaction networks in the past have found them to be very efficient at transferring resources,” said Blonder. “The dominant paradigm has been that most self-organized networks tend to have this universal structure and that one should look for this structure and make predictions based on this structure. Our study challenges that and demonstrates that there are some interaction networks that don’t have these properties yet are still clearly functional.”

“There are a huge number of systems that are comprised of interacting parts, and we really don’t have a good sense of how these systems are organized,” said Blonder. “Think of a city with many people or the Internet with many computers. You have all these parts doing their own thing and somehow achieving some greater function.”

The researchers chose to use ant colonies as models for self-directed networks because they are comprised of many individual components – the ants – with no apparent central organization and yet are able to function as a colony.

“We think no individual ant has a sense of purpose,” said Blonder. “It doesn’t go out one day and say: ‘I’m going to move this pebble for the greater good of the society.’ It has a behavioral program where if it sees a pebble, then it’s likely to move it. The reason that contributes to the good of the colony is an evolutionary argument where the ants’ behavior is shaped over thousands or millions of generations.”

Dornhaus and Blonder studied colonies of Temnothorax rugatulus, an ant species that is common in southern Arizona.

“These ants like to live in little rock crevices such as underneath a rock or in a split in the rock,” said Blonder. “The trick is convincing them to go from their nice little home on Mount Lemmon to the lab.”

Which raises an interesting question: How does one collect an ant colony?

“It isn’t easy,” said Blonder. “You get an aspirator, which is a tube with a fine mesh on the end of it so you don’t inhale the ants, and you put the tube down in the colony and you suck. And the ants come up and you blow them out into a container to transport them to the lab.”

“Of course, once you flip the rock over, the ants are upset. You have to get them before they all run off somewhere. And you also have to get the queen because without the queen the colony will die.”

The queen, the mother ultimatum among ants, is the only member of the colony that reproduces. Without her, there would be no new ant workers and the colony would die.

“There is evidence that the queen secretes a chemical that makes the other workers recognize that she is the queen,” said Blonder. “But there’s not much evidence for the queen communicating with the workers in ways beyond that.”

Back in the lab, the ants were placed in artificial nests. “The nice thing about this species is that because they like to live in rock crevices, they’re also completely happy to live between glass slides. All we have to do is take two large glass slides, put a cardboard spacer in between them and the ants happily walk into that very nice thin space and live out their lives in this artificial nest,” said Blonder.

Having secured and relocated several ant colonies, the researchers tackled their second challenge: How to tell two ants apart.

“To understand an interaction network, you need to know who all the individuals are,” said Blonder. “You need to be able to tell any two individuals apart. We accomplished it by painting each ant with a unique color code.”

The researchers filmed the ants with high-definition video and recorded roughly 9,000 interactions between 300 to 400 individual ants. “We watched every single video repeatedly to make sure we didn’t miss any interactions and correctly identified every ant,” said Blonder.

Dornhaus and Blonder recorded every interaction that involved one ant touching another. “We didn’t use visual interactions in this study, and that gave us some ability to standardize,” said Blonder. “There could be many more meaningless visual interactions than meaningless touch interactions because touch definitely conveys some chemical data about the other ant.”

While the ants do have limited vision, it’s thought that most of their sensory input comes through direct chemosensory touch.

Ants antennate, or touch each other with their antennae, for a variety of reasons such as to get another ant to move out of the way, to prod a particularly lazy individual into action or to solicit food. “Not all ants go out and forage for food,” said Blonder. “Often the ants that forage will have whatever they found in their guts and food is transferred from one ant’s stomach through mouth-to-mouth contact to the other ant. It’s called trophallaxis.”

Contrary to predictions that ant networks would spread information efficiently in the same way as other self-directed networks, the researchers found that the ants actually are inefficient at spreading information.

The finding challenges the notion of six degrees of separation, the idea that all individuals in a network are related by six other individuals. For example, I know someone who knows someone who knows someone and so on, and by the sixth person or less I am connected to every person in the world.

This would represent a very efficient network, where it only takes six interactions for information to spread to all of the components. Ant interaction networks apparently function quite differently, indicating that other networks also might not be as efficient as previously thought.

“You could come up with a second simple expectation about how ants might behave,” said Blonder. “They could be just walking around completely randomly bumping into each other. We were able to show that the real ants consistently had rates of information flow that were lower than even that expectation. Not only are they not efficient, they’re also slower than random. They’re actually avoiding each other.”

“So this raises a big question: If you have this ant colony that is presumably very good at surviving and persisting, and there are a lot of good reasons to think it’s optimal to get messages from one part to the other, how come they don’t do it?”

One possible explanation is a concept most of us already are familiar with: “If you spend too much time interacting, then you’re not actually getting anything done,” said Blonder.

Another possibility is that individual ants are responsible for only their region and only need to communicate with other ants in that region.

The research also illustrates the importance of knowing when interactions occur. If two individuals interact and later one of them interacts with a third, then information from the first interaction could be passed to the third individual, but the third individual could not relay information back to the first. “That’s the ordering of events perspective that we’re bringing to this study and we’re hoping is going to catch on with other network studies. We think this is a real opportunity,” said Blonder.

“In some contexts it’s clearly better not to spread information as quickly and then the question becomes understanding in what context it’s good to be efficient and in what context it’s not good to be efficient.”

Understanding how interaction networks function could have applications from allowing us to build self-directed networks to perform specific functions, such as unmanned drones to explore other planets, to preventing the spread of disease.

“Many of these ant species have been on the planet for millions of years, so clearly they’re doing something right,” said Blonder. “Perhaps we could learn from that.”

Doctoral candidate Tuan Cao and undergraduate students Milan Curry, Han Jing, Kayla Lauger and Daniel Wolf assisted with this study.

Reference: Benjamin Blonder, Anna Dornhaus. Time-Ordered Networks Reveal Limitations to Information Flow in Ant Colonies. PLoS ONE, 2011; 6 (5): e20298 DOI: 10.1371/journal.pone.0020298

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May 25

Reported by Mark Brown, Wired UK, May 9, 2011, in Wired Science.

Image: Dave Mosher/Wired.comA new iPhone app called LeafSnap is a field guide for tech-friendly naturalists. It can identify a tree’s species by analyzing a photograph of its leaf.

Point your smartphone’s camera at one of nature’s solar cells (laid out flat on a white piece of paper) and the app will go to work. It separates the leaf from the background, and then analyzes the leaf’s shape.

The algorithm, designed by facial recognition experts at Columbia University and the University of Maryland, gets measurements from numerous points along the leaf’s outline. These are then compared to an encyclopedic database of leaves — kindly donated by the Smithsonian Institution and non-profit nature-photography group Finding Species — to give you a result.

If it isn’t completely sure, it will show you an entire collection of possible leafy matches. You can then look at more information on those trees — finding out where they grow, what time of the year their flowers bloom and pictures of their fruits, seeds and bark — to make a proper decision on what type of leaf you’ve got in front of you.

The app also has a dabble in citizen science. Once you’ve correctly labeled your leaf you can tap “label,” which uploads your data to a community of scientists. Your data will be geo-tagged to your current location, letting flora experts map and monitor the ebb and flow of different trees.

Unfortunately for nature geeks (or shape recognition nerds) in the U.K., you’ll probably have trouble getting the app to identify Britain’s native leaves. LeafSnap currently includes the trees of just New York City and Washington D.C. A full rollout covering the United States is planned, but there are no promises for overseas trees.

Android and iPad versions of the app are planned for this summer. In the meantime, download the free iPhone app.

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May 22

Reported by Brandon Keim , May 4, 2011, in Wired Science.

Robots in a Swiss laboratory have evolved to help each other, just as predicted by a classic analysis of how self-sacrifice might emerge in the biological world.

“Over hundreds of generations … we show that Hamilton’s rule always accurately predicts the minimum relatedness necessary for altruism to evolve,” wrote researchers led by evolutionary biologist Laurent Keller of Switzerland’s University of Lausanne in Public Library of Science Biology. The findings were published May 3.

Hamilton’s rule is named after biologist W.D. Hamilton who in 1964 attempted to explain how ostensibly selfish organisms could evolve to share their time and resources, even sacrificing themselves for the good of others. His rule codified the dynamics — degrees of genetic relatedness between organisms, costs and benefits of sharing — by which altruism made evolutionary sense. According to Hamilton, relatedness was key: Altruism’s cost to an individual would be outweighed by its benefit to a shared set of genes.

In some ways, the rule and its accompanying theory of kin selection is contested. Some scientists have used it to extrapolate too easily from insects to people, and some researchers think it overstates the importance of relatedness. But a more fundamental issue with Hamilton’s rule is the difficulty of testing it in natural systems, where animals evolve at a far slower pace than any research grant cycle.

Simulations of evolution in robots, which can “reproduce” in mere minutes or hours, have thus become a potentially useful system for studying evolutionary dynamics. And though simple in comparison to animals, Keller’s group says robot models are not too different from the insects that originally inspired Hamilton.

In the new study, inch-long wheeled robots equipped with infrared sensors were programmed to search for discs representing food, then push those discs into a designated area. At the end of each foraging round, the computerized “genes” of successful individuals were mixed up and copied into a fresh generation of robots, while less-successful robots disappeared from the gene pool.

Each robot was also given a choice between sharing points awarded for finding food, thus giving other robots’ genes a chance of surviving, or hoarding. In different iterations of the experiment, the researchers altered the costs and benefits of sharing; they found that, again and again, the robots evolved to share at the levels predicted by Hamilton’s equations.

“A fundamental principle of natural selection also applies to synthetic organisms,” wrote the researchers. “These experiments demonstrate the wide applicability of kin selection theory.”

Reference: “A Quantitative Test of Hamilton’s Rule for the Evolution of Altruism.” By Waibel M, Floreano D, Keller L. PLoS Biology, Vol. 9 No. 5, May 3, 2011

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May 21

Reported by Brandon Keim , April 25, 2011, (Technology Review).

Theoretical physicists have proposed an explanation for how bacteria might transmit electromagnetic signals: Chromosomes could act like antennae, with electrons traveling gene circuits to produce species-specific wavelengths.

E. coli (Center for Molecular Biology of Inflammation)

It’s just a hypothesis, and the notion that bacteria can generate radio waves is controversial. But according to Northeastern University physicist Allan Widom, calculations based on the properties of DNA and electrons square with what’s been measured.

“For a long time, there have been signals in water. Something is happening around a kilohertz,” said Widom, lead author of a paper posted April 15 on the preprint website arXiv. “You have to look for natural energy levels in the system that would give you a kilohertz frequency. With the lengths of DNA and the mass of the electron, you get the right frequency range for these signals.”

Frequency analyses of recordings made from purified water (above) and water enriched with E. coli (below). Image courtesy Interdisciplinary Sciences: Computational Life Sciences

The original report of bacterial radio transmissions was made by French virologist Luc Montagnier, who in 2009 described how inductor coils wrapped around flasks of bacteria-enriched water and hooked to an amplifier detected signals in the 1-kilohertz range.

Montagnier’s findings were greeted with considerable skepticism. Though his work linking HIV and AIDS had earned Montagnier a Nobel Prize, his observations of bacterial radio waves — on their own a novel, never-before-seen finding — were followed by even-more-radical descriptions of signals causing loose pieces of DNA to assemble into bacterialike structures. He also speculated about related “nanostructures” in water, which he linked to neurodegenerative diseases.

The claims were embraced by homeopaths, and Montagnier himself became involved in a dubiously designed clinical trial of autistic children. Eventually he left France to head a research institute at Jiaotong University in Shanghai, telling Science that he sought to escape the constrictions of intellectually fearful European scientists. “It’s not pseudoscience. It’s not quackery. These are real phenomena which deserve further study,” he said.

Underneath all the controversy, however, are the original recordings of bacteria-enriched water. Widom considers them sound. The next question, then, is how bacteria produce electromagnetic waves around a 1-kilohertz frequency. In Widom’s arXiv paper, he and other physicists calculate that as electrons flowed through loops of DNA in E. coli and Mycoplasma pirum, the species tested by Montagnier, they should generate wavelengths similar to what was recorded.

“Different species have different lengths of DNA” in their chromosomes, he said. “These lengths probably determine frequency.”

Widom noted that electromagnetic radio transmissions were not in principle so different from electron transmission between bacteria connected by nanowires. Such bacteria have been described in recent years. Their nanowire-enabled transmissions may allow networked microbes to communicate.

“This could be a wireless version,” said Widom. “Bacteria that set up nanowires are, on an evolutionary scale, fairly old. It’s occurred to me that more modern bacteria may use wireless.”

Widom is especially curious about whether cells in higher life forms might also use electromagnetic signaling, perhaps in coordinating DNA code with protein-making cellular machinery. But as a theoretical physicist, he doesn’t plan to investigate the phenomenon himself. That’s for other researchers to do, Widom said.

“We’re just saying that this gets you to the right frequencies,” he said. “We’re right at the very beginning.”

Reference: Electromagnetic Signals from Bacterial DNA, by A. Widom, J. Swain, Y.N. Srivastava, S. Sivasubramanian, arXiv, April 15, 2011.

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May 20

Reported by By Lisa Grossman, April 25, 2011, in Wired Science.

In the first serious study of the physics of fire-ant rafts, researchers have described how the insects form floating, waterproof islands.

In nature, the rafts allow fire ants to survive epic rainstorms in their native Brazil. In the lab, they could help inspire designs for small, swarming robots that might someday be used to explore inaccessible areas or even clean up oil spills.

“The ant raft, up to this point, has been little more than just categorized and documented,” said mechanical engineer Nathan Mlot of the Georgia Institute of Technology, lead author of a paper in the April 25 Proceedings of the National Academy of Sciences. “We were coming at it from an engineering perspective.”

Ant droplet.

Even though ants’ exoskeletons naturally repel water, a lone ant dropped in a bucket will flounder. But whole colonies of fire ants can float downstream for weeks at a time when flushed from their underground nests. Mlot and his graduate advisor, David Hu, wondered what held the dense mass afloat — and whether it could be harnessed for other applications.

“How are the ants actually linking in the raft?” Mlot said. “We could speculate all we wanted, but the only way to know for sure was to get visual data.”

Mlot’s team collected thousands of fire ants (Solenopsis invicta) by roadsides in Atlanta, where the stinging pests are an invasive species. They immediately noticed that clumps of ants take on the consistency of soft playdough. Ant masses flow like honey or ketchup, and can be described using equations usually found in fluid dynamics.

Antpour

“You could pick up a cluster of these ants and mold it in your hand. You could form it into a ball and toss it up in the air, and all the ants would stay together in one ball,” Mlot said. “They’re almost like a material.”

To set up a reproducible experiment, the team molded ants into balls by swirling them in a beaker. The ants’ natural tendency to stick together made them clump into near-perfect spheres.

Then the researchers placed balls of 500 to 8,000 ants into a water-filled filled container. Th ant sphere almost immediately relaxed into a flat, pancake-shaped raft, with ants on bottom forming a stable layer for the rest of the colony to rest on.

Surprisingly, the whole swarming mass remained delicately balanced atop the water’s surface. When the researchers tried to submerge the raft, water underneath deformed like a stretchy fabric, conforming to the raft’s underside contours.

Focusing on the details of this phenomenon, the researchers subjected their ants to a battery of bizarre tests. To measure how much force one ant could apply to another, they glued live ants to the bottom of a glass slide, then harnessed other ants to them with elastic bands. They painted ants with identification marks and charted their path across rafts. To investigate how the mechanics of raft formation in high resolution, they froze an entire ant raft in liquid nitrogen, then looked at it under a scanning electron microscope.

The images revealed that fire ants grip each other with their mandibles, claws and sticky pads at the end of their feet. Together, they form a tight weave similar to waterproof fabrics like Gore-Tex, which enhances the natural water-repelling properties of their bodies.

The team also built a simple mathematical model of raft formation. It might be used to inspire programs guiding cooperative robots.

“Robotics has often looked at insect communities for inspiration,” said roboticist James McLurkin of Rice University, who designs and builds robot swarms.

Roboticist Seth Goldstein of Carnegie Mellon University suggests that groups of small robots forming antlike rafts could be used to explore sewer lines or waterlogged caves. McClurkin even floated the idea, so to speak, of cleaning oil spills in the Gulf of Mexico.

As for Mlot and his ants, he didn’t lose any sleep over their fate. “After you get bit a couple of times, you lose your sympathy for them,” he said, adding that the experiments are simple enough that anyone can try them at home, “if they’re brave enough.”

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May 19

Reported by Brandon Keim, May 16, 2011, in Wired Science.

he Euronext Amsterdam floor (Perpetualtourist2000/Flickr).

When people can learn what others think, the wisdom of crowds may veer towards ignorance.

In a new study of crowd wisdom — the statistical phenomenon by which individual biases cancel each other out, distilling hundreds or thousands of individual guesses into uncannily accurate average answers — researchers told test participants about their peers’ guesses. As a result, their group insight went awry.

“Although groups are initially ‘wise,’ knowledge about estimates of others narrows the diversity of opinions to such an extent that it undermines” collective wisdom, wrote researchers led by mathematician Jan Lorenz and sociologist Heiko Rahut of Switzerland’s ETH Zurich, in Proceedings of the National Academy of Sciences on May 16. “Even mild social influence can undermine the wisdom of crowd effect.”

The effect — perhaps better described as the accuracy of crowds, since it best applies to questions involving quantifiable estimates — has been described for decades, beginning with Francis Galton’s 1907 account of fairgoers guessing an ox’s weight. It reached mainstream prominence with economist James Surowiecki’s 2004 bestseller, The Wisdom of Crowds.

Study participants were asked how many murders occurred in Switzerland in 2006. At the end of each round of questioning, they were given small payments for coming close to the actual answer (signified by the gray bar). At left is the range of responses among participants who received no information about others.

Study participants were asked how many murders occurred in Switzerland in 2006. At the end of each round of questioning, they were given small payments for coming close to the actual answer (signified by the gray bar). At left is the range of responses among participants who received no information about others.

As Surowiecki explained, certain conditions must be met for crowd wisdom to emerge. Members of the crowd ought to have a variety of opinions, and to arrive at those opinions independently.

Take those away, and crowd intelligence fails, as evidenced in some market bubbles. Computer modeling of crowd behavior also hints at dynamics underlying crowd breakdowns, with he balance between information flow and diverse opinions becoming skewed.

Lorenz and Rahut’s experiment fits between large-scale, real-world messiness and theoretical investigation. They recruited 144 students from ETH Zurich, sitting them in isolated cubicles and asking them to guess Switzerland’s population density, the length of its border with Italy, the number of new immigrants to Zurich and how many crimes were committed in 2006.

After answering, test subjects were given a small monetary reward based on their answer’s accuracy, then asked again. This proceeded for four more rounds; and while some students didn’t learn what their peers guessed, others were told.

As testing progressed, the average answers of independent test subjects became more accurate, in keeping with the wisdom-of-crowds phenomenon. Socially influenced test subjects, however, actually became less accurate.

The researchers attributed this to three effects. The first they called “social influence”: Opinions became less diverse. The second effect was “range reduction”: In mathematical terms, correct answers became clustered at the group’s edges. Exacerbating it all was the “confidence effect,” in which students became more certain about their guesses.

“The truth becomes less central if social influence is allowed,” wrote Lorenz and Rahut, who think this problem could be intensified in markets and politics — systems that rely on collective assessment.

“Opinion polls and the mass media largely promote information feedback and therefore trigger convergence of how we judge the facts,” they wrote. The wisdom of crowds is valuable, but used improperly it “creates overconfidence in possibly false beliefs.”

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