Facebook of the Planet Science

David Kramer, MSU Hannah Distinguished Professor in Photosynthesis and Bioenergetics, has created the Facebook of plant science. Courtesy of MSU

By building PhotosynQ – a handheld device with sensors and an online data-sharing and analysis platform – a team of Michigan State University researchers is creating the plant-science equivalent of Facebook.

Following the trail blazed by successful social media networks, the team is giving away patentable devices at a nominal fee, building an active global community of plant science enthusiasts and sharing all data collected from around the world.

The goal is to allow even citizen scientists to make research-quality measurements, said David Kramer, MSU Hannah Distinguished Professor in Photosynthesis and Bioenergetics.

“We’ve built a platform that everyone can access through their cell phones,” he said. “We want to create a community that sees a 12-year-old student in China ask a question about a drought-resistant plant. Then we hope that hundreds of people answer, and not only the student in China is able to grow sustainable crops, but also a farmer in Africa could benefit from those insights.”

One component of PhotosynQ is a handheld device that costs about $100, scans plants and collects a handful of key data points. Via a smartphone running Android, the data is transferred from the device to the researcher’s project page on the PhotosynQ platform.

Currently, there are about 20 research projects on the burgeoning network. As new data is collected, community members can observe the projects’ progression.

Projects range from one measuring the robustness and productivity of beans, to another monitoring the efficiency of photosynthesis. Collecting data on how well plants convert sunlight to energy can be derived from satellite images in a very limited way. To improve the data, it’s best to get on-the-ground observations as well. The more handheld devices used in the field to gather the data, the better.

PhotosynQ will enable local scientists, plant breeders and citizens to improve the productivity and security of crops in communities around the world. This low-cost approach of collecting samples from global sites could change how science has traditionally been conducted, said Greg Austic, who is leading the development in the Kramer lab.

“It’s critical that PhotosynQ stays open source,” he said. “We’re changing the model of moving new technology from academia to the world. We’re maximizing the data and building a community rather than maximizing profits.”

If only two people use the network, it’s worthless. If 2 million people join in, it’s priceless. It will be a snapshot of what’s happening in the plant world at this very moment. Successful breeding efforts, rapidly spreading diseases and other trends can be identified quicker, he added.

This nontraditional approach is indicative of Kramer’s unique lab. Soldering irons and circuit boards outnumber plants and petri dishes. Shelves are lined with electronic prototypes. The buzzing hive of nearly 40 students is a blend of biologists, programmers and engineers.

“Many times one of our biology students will come up with an idea and bounces it off some of the other students,” Kramer said. “The computer specialists write a program, and the electronics students build a prototype and a new technique is developed and used – sometimes in a single day.”

His lab is a microcosm of what he hopes he can create on a global scale; empower people with data and easy-to-use scientific instruments, and people will look at their world differently, he said.

Kramer is a professor in the College of Natural Science and the MSU-DOE Plant Research Laboratory. His research is funded in part by MSU AgBioResearch.

Source: MSU

New Stanford research finds computers are better judges of personality than friends and family

New research shows that a computer's analysis of data can better judge a person's psychological traits than family and friends.

Computers can judge personality traits far more precisely than ever believed, according to newly published research.

In fact, they might do so better than one's friends and colleagues. The study, published Jan. 12 and conducted jointly by researchers at Stanford University and the University of Cambridge, compares the ability of computers and people to make accurate judgments about our personalities. People's judgments were based on their familiarity with the judged individual, while the computer used digital signals – Facebook "likes."

The researchers were Michal Kosinski, co-lead author and a postdoctoral fellow at Stanford's Department of Computer Science; Wu Youyou, co-lead author and a doctoral student at the University of Cambridge; and David Stillwell, a researcher at the University of Cambridge.

According to Kosinski, the findings reveal that by mining a person's Facebook "likes," a computer was able to predict a person's personality more accurately than most of their friends and family. Only a person's spouse came close to matching the computer's results.

The computer predictions were based on which articles, videos, artists and other items the person had liked on Facebook. The idea was to see how closely a computer prediction could match the subject's own scores on the five most basic personality dimensions: openness, conscientiousness, extraversion, agreeableness and neuroticism.

The researchers noted, "This is an emphatic demonstration of the ability of a person's psychological traits to be discovered by an analysis of data, not requiring any person-to-person interaction. It shows that machines can get to know us better than we'd previously thought, a crucial step in interactions between people and computers."

Kosinski, a computational social scientist, pointed out that "the findings also suggest that in the future, computers could be able to infer our psychological traits and react accordingly, leading to the emergence of emotionally intelligent and socially skilled machines."

"In this context," he added, "the human-computer interactions depicted in science fiction films such as Her seem not to be beyond our reach."

He said the research advances previous work from the University of Cambridge in 2013 that showed that a variety of psychological and demographic characteristics could be "predicted with startling accuracy" through Facebook likes.

The study's methodology

In the new study, researchers collected personality self-ratings of 86,220 volunteers using a standard, 100-item long personality questionnaire. Human judges, including Facebook friends and family members, expressed their judgment of a subject's personality using a 10-item questionnaire. Computer-based personality judgments, based on their Facebook likes, were obtained for the participants.

The results showed that a computer could more accurately predict the subject's personality than a work colleague by analyzing just 10 likes; more than a friend or a roommate with 70; a family member with 150; and a spouse with 300 likes.

"Given that an average Facebook user has about 227 likes (and this number is growing steadily), artificial intelligence has a potential to know us better than our closest companions do," wrote Kosinski and his colleagues.

Why are machines better in judging personality than human beings?

Kosinski said that computers have a couple of key advantages over human beings in the area of personality analysis. Above all, they can retain and access large quantities of information, and analyze all this data through algorithms.

This provides the accuracy that the human mind has a hard time achieving due to a human tendency to give too much weight to one or two examples or to lapse into non-rational ways of thinking, the researchers wrote.

Nevertheless, the authors concede that the detection of some personality traits might be best left to human beings, such as "those (traits) without digital footprints and those depending on subtle cognition."

'Digital footprints'

Wu, co-lead author of the study, explains that the plot behind a movie like Her (released in 2013) becomes increasingly realistic. The film involves a man who strikes up a relationship with an advanced computer operating system that promises to be an intuitive entity in its own right.

"The ability to accurately assess psychological traits and states, using digital footprints of behavior, occupies an important milestone on the path toward more social human-computer interactions," said Wu.

Such data-driven decisions could improve people's lives, the researchers said. For example, recruiters could better match candidates with jobs based on their personality, and companies could better match products and services with consumers' personalities.

"The ability to judge personality is an essential component of social living – from day-to-day decisions to long-term plans such as whom to marry, trust, hire or elect as president," said Stillwell.

Dystopia concerns

The researchers acknowledge that this type of research may conjure up privacy concerns about online data mining and tracking the activities of users.

"A future with our habits being an open book may seem dystopian to those who worry about privacy," they wrote.

Kosinski said, "We hope that consumers, technology developers and policymakers will tackle those challenges by supporting privacy-protecting laws and technologies, and giving the users full control over their digital footprints."

In July, Kosinski will begin a new appointment as an assistant professor at Stanford Graduate School of Business.

Source: Stanford university

Wearable device to track diet under development

A concept of the device with sensor was made through 3-D printing. Credit: The University of Alabama

Sensors and software used to track physical activity are increasingly popular, as smart phones and their apps become more powerful and sophisticated, but, when it comes to food, they all rely on the user to report meals.

Dr. Edward Sazonov, an associate professor of electrical and computer engineering at The University of Alabama, hopes to change that through development of a sensor worn around the ear that would automatically track diet, giving medical professionals and consumers accurate information that can be missed with self-reporting.

"Weight gain comes from an unbalance of the energy we take in versus the energy we expend," Sazonov said. "We can estimate diet and nutrient intake, but the primary method is self-reporting. The sensor could provide objective data, helping us better understand patterns of food intake associated with obesity and eating disorders."

Sazonov is the lead on a $1.8 million, five-year grant from the National Institute of Health to test the practical accuracy of the wearable sensor in tracking diet. Already proven viable, the device will be updated, further miniaturized and validated in a more formal, robust experiment in the community.

Called an Automatic Ingestion Monitor, or AIM, it has potential to monitor eating by automatically detecting and capturing imagery of food intake and to estimate the mass and the energy content of ingested food.

The sensor feels vibrations from movement in the jaw during food intake, and the device is programmed to filter out jaw motions, such as talking, that are not coming from drinking or eating. Estimates of energy intake would be taken from the pictures of food or drink.

More than two-thirds of adults in the United States are clinically overweight or obese, according to estimates from the Center for Disease Control and Prevention.

"Eating may be an unconscious, even automatic behavior for some individuals, and the literature is full of examples of dietary behaviors which increase the risk for overeating," Sazonov said.

In a study, the AIM will be tested against the accuracy of an alternative method, the use of a doubly-labeled water to track energy use by humans. That method measures the body's elimination rate of stable isotopes of hydrogen and oxygen added to the water, a process that can take two weeks. The information can be used to estimate how many calories a person consumes over a period of time.

However, this method is expensive and requires medical specialization, and, unlike the proposed AIM, does not track eating behavior.

The information provided by AIM could be used to improve behavioral weight loss strategies or to develop new kinds of weight-loss interventions. In addition, the AIM could also provide an objective method of assessing the effectiveness of pharmacological and behavioral interventions for eating disorders.

It's likely the technology's first application would be as a medical device, but Sazonov said it's possible it could become a consumer device that would eliminate the need for health-conscious people to keep a record of their diet.

Source: University of Alabama

Smartphone sensors leave trackable fingerprints

Example demonstrating how accelerometer data shared with separate traffic and health applications could indicate Bob's location. Credit: Image courtesy of University of Illinois College of Engineering

Fingerprints -- those swirling residues left on keyboards and doorknobs -- are mostly invisible. They can affirm your onetime presence, but they cannot be used to track your day-to-day activities.

They cannot tell someone in real time that after exercising at the gym, you went to office in a bus and played video games during lunch. But what if our hand-held electronics are leaving real-time fingerprints instead? Fingerprints that are so intrinsic to the device that, like our own, they cannot be removed?

Research by Associate Professor Romit Roy Choudhury and graduate students Sanorita Dey and Nirupam Roy has demonstrated that these fingerprints exist within smartphone sensors, mainly because of imperfections during the hardware manufacturing process.

In some ways, it's like cutting out sugar cookies. Even using the same dinosaur-shaped cutter, each cookie will come out slightly different: a blemish here, a pock there. For smartphone sensors, these imperfections simply occur at the micro- or nanoscale.

Their findings were published at the Network and Distributed System Security Symposium (NDSS), a major conference on wireless and web security, held last February in San Diego. 

The research also won the best poster award at the HotMobile international workshop in 

2013.

The researchers focused specifically on the accelerometer, a sensor that tracks three-dimensional movements of the phone -- essential for countless applications, including pedometers, sleep monitoring, mobile gaming -- but their findings suggest that other sensors could leave equally unique fingerprints.

"When you manufacture the hardware, the factory cannot produce the identical thing in millions," Roy said. "So these imperfections create fingerprints."

Of course, these fingerprints are only visible when accelerometer data signals are analyzed in detail. Most applications do not require this level of analysis, yet the data shared with all applications -- your favorite game, your pedometer -- bear the mark. Should someone want to perform this analysis, they could do so.

The researchers tested more than 100 devices over the course of nine months: 80 standalone accelerometer chips used in popular smartphones, 25 Android phones, and 2 tablets.

The accelerometers in all permutations were selected from different manufacturers, to ensure that the fingerprints weren't simply defects resulting from a particular production line.

With 96 percent accuracy, the researchers could discriminate one sensor from another.

"We do not need to know any other information about the phone -- no phone number or SIM card number," Dey said. "Just by looking at the data, we can tell you which device it's coming from. It's almost like another identifier."

In the real world, this suggests that even when a smartphone application doesn't have access to location information (by asking "this application would like to use your current location"), there are other means of identifying the user's activities. It could be obtained with an innocuous-seeming game or chatting service, simply by recording and sending accelerometer data. There are no regulations mandating consent.

To collect the data, the researchers -- as with any would-be attacker -- needed to sample the accelerometer data. Each accelerometer was vibrated using a single vibrator motor -- like those that buzz when a text message is received -- for two-second intervals. During those periods, the accelerometer detected the movement and the readings were transmitted to a supervised-learning tool, which decoded the fingerprint.

"Even if you erase the app in the phone, or even erase and reinstall all software," Roy said, "the fingerprint still stays inherent. That's a serious threat."

At this point, however, there is no absolute solution. Smartphone cases made of rubber or plastic do little to mask the signal. Deliberately injecting white noise in the sensor data can smudge the fingerprint, but such noise can also affect the operation of the application, making your pedometer inaccurate and functionally useless.

If accelerometer data were processed directly on the phone or tablet, rather than on the cloud, the fingerprint could be scrubbed before sending information to the application.

That is, the pedometer application might only receive basic information like "300 steps taken," rather than receiving the raw accelerometer data. This, however, imposes a load on the phone's processor and, more importantly, reduces the phone's battery life.

The research also suggests that other sensors in the phone -- gyroscopes, magnetometers, microphones, cameras, and so forth -- could possess the same types of idiosyncratic differences. So even if, at a large scale, the accuracy of accelerometer fingerprints diminishes, when combined with prints from other sensors, an attack could be even more precise.

"Imagine that your right hand fingerprint, by some chance, matches with mine," Roy Choudhury said. "But your left-hand fingerprint also matching with mine is extremely unlikely. So even if accelerometers don't have unique fingerprints across millions of devices, we believe that by combining with other sensors such as the gyroscope, it might still be possible to track a particular device over time and space."

For smartphone users and e-book readers, smartwatch wearers and tablet devotees, perhaps the most critical take-home message, in the short run anyway, is the importance of vigilance.

"Don't share your accelerometer data without thinking about how legitimate or how secure that application is," Dey said. "Even if it's using only the sensor data, still it can attack you in some way. The consumer should be aware."

Source: University of Illinois College of Engineering

Scientists twist radio beams to send data: Transmissions reach speeds of 32 gigibits per second

Graphic showing the intensity of the radio beams after twisting.
Credit: Courtesy of Alan Willner / USC Viterbi

Building on previous research that twisted light to send data at unheard-of speeds, scientists at USC have developed a similar technique with radiowaves, reaching high speeds without some of the hassles that can go with optical systems.

The researchers, led by electrical engineering professor Alan Willner of the USC Viterbi School of Engineering, reached data transmission rates of 32 gigabits per second across 2.5 meters of free space in a basement lab at USC.

For reference, 32 gigabits per second is fast enough to transmit more than 10 hour-and-a-half-long HD movies in one second and is 30 times faster than LTE wireless.

"Not only is this a way to transmit multiple spatially collocated radio data streams through a single aperture, it is also one of the fastest data transmission via radio waves that has been demonstrated," Willner said.

Faster data transmission rates have been achieved -- Willner himself led a team two years ago that twisted light beams to transmit data at a blistering 2.56 terabits per second -- but methods to do so rely on light to carry the data.

"The advantage of radio is that it uses wider, more robust beams. Wider beams are better able to cope with obstacles between the transmitter and the receiver, and radio is not as affected by atmospheric turbulence as optics," Willner said.

Willner is the corresponding author of an article about the research that will be published in Nature Communications on Sept. 16. The study's co-lead authors Yan Yan and Guodong Xie are both graduate students at USC Viterbi, and other contributors came from USC, the University of Glasgow, and Tel Aviv University.

To achieve the high transmission rates, the team took a page from Willner's previous work and twisted radio beams together. They passed each beam -- which carried its own independent stream of data -- through a "spiral phase plate" that twisted each radio beam into a unique and orthogonal DNA-like helical shape. A receiver at the other end of the room then untwisted and recovered the different data streams.

"This technology could have very important applications in ultra-high-speed links for the wireless 'backhaul' that connects base stations of next-generation cellular systems," said Andy Molisch of USC Viterbi. Molisch, whose research focuses on wireless systems, co-designed and co-supervised the study with Willner.

Future research will focus on attempting to extend the transmission's range and capabilities.

The work was supported by Intel Labs University Research Office and the DARPA InPho (Information in a Photon) Program.

Source: University of Southern California

Sensors that improve rail transport safety

Cloud-supported sensor network for the condition-based maintenance of rail vehicles.
Credit: © Fraunhofer IZM

A new kind of human-machine communication is to make it possible to detect damage to rail vehicles before it's too late and service trains only when they need it -- all thanks to a cloud-supported, wireless network of sensors.

A train running on damaged wheels could easily be heading for serious trouble. This is why German national rail corporation Deutsche Bahn continuously monitors the wheelsets of its intercity express trains -- a process that costs a considerable amount of time and money. 

Researchers at the Berlin-based Fraunhofer Institute for Reliability and Microintegration IZM are collaborating with industry partners to develop a solution that ensures a great safety while reducing effort and cost. "We want to root out any damage early on and move away from maintenance at set intervals in favor of condition-based maintenance," explains Dr. Michael Niedermayer, microsystems engineer and head of the IZM's Technology-Oriented Design Methods working group. He is also project coordinator for "Mobile Sensor Systems for Condition-Based Maintenance," or MoSe for short.

Seamless monitoring

It's all based on a cloud-supported, wireless network of sensors. Every axle and undercarriage on a train is fitted with small radio sensors, which collect data on the condition of wearing parts. These data are then transferred to the online maintenance cloud, where the measurement and analysis data are encrypted and stored ready for use. The sensors can detect even the tiniest scratch on a ball bearing. As Niedermayer says, "Here we have sensor nodes that can capture even the slightest variations in vibration. We call this in-depth diagnosis." As a result, repairs can be made before anything works its way loose and causes damage.

"What's remarkable about this approach is that it allows everything to be monitored with the train in service, rather than having to inspect it at the rail yard. And in any case, visual checks are not 100 percent reliable," says Manfred Deutzer from project partner Deutzer Technische Kohle GmbH. Although there are wired sensors out there that can be used to examine rail vehicle chassis for wear and tear, these fail to match the high diagnostic quality standards the MoSe developers are striving for.

Using the new method, it is possible to get precise data on, say, whether an axle bearing will have to be replaced three months down the line, which avoids the need to replace it prematurely just in case. The latter is just as uneconomical as the custom of overhauling wheels at preset intervals with a view to resolving any wheel flats that could damage rails. 

"Wheels can tolerate such repairs no more than three times before they have to be scrapped," Deutzer reports. "It would make more sense and cost less to grind only those wheels we know actually turn poorly. The problem is that there has never been a suitable way of checking for wheel flats." MoSe is to change all that and much more besides.

"Not only do we intend to improve diagnostics, a top priority is also to process the data collected in as detailed and tailored a manner as possible," says Niedermayer. The idea is to provide train drivers with all relevant data (for instance about critical wheel damage), diagnostic technicians with detailed measurement data so they can assess how fast gear damage is progressing, and designers with measurement statistics covering wear to all parts, enabling them to improve the technical design of the next product generation. Making sure everyone involved receives the data they need in a form they can work with right away involves developing some clever diagnostic algorithms. "Yet another advantage is that wireless sensors can be easily retrofitted," adds Niedermayer.

What's also new is that the system can adapt to the different rotational speeds of the parts being examined -- such as the wheels on a train -- and in doing so, deliver incredibly precise data at whatever speed the train happens to be traveling. It used to be that sensors were designed to work at constant rotational speeds. Although this setup may be easier to manage, it means that the diagnostic quality suffers. Thanks to analysis algorithms, this is set to change. But developing these algorithms is a balancing act: "Since the system is intended to work without batteries, the algorithms mustn't drain unnecessary energy by using up excessive computing power," explains Niedermayer. As MoSe uses energy harvesting, it can tap energy from the vibrations and heat generated as the parts rotate.

Over the next couple of years a prototype will be developed that will be tested in a tram run by the German city of Brandenburg an der Havel. The system could then be used for monitoring purposes in suburban or long-distance trains.

Source: Fraunhofer-Gesellschaft

Toward quantum computing, spintronic memory, better displays: Nuclear spins control current in plastic LED

An organic light-emitting diode, or OLED, glows orange when electrical current flows through it. University of Utah physicists used this kind of OLED – basically a plastic LED instead of a conventional silicon semiconductor LED – to show that they could read the subatomic “spins” in the center or nuclei of hydrogen isotopes and use those spins to control current to the OLED. It is a step toward “spintronic” devices such as faster computers, better data storage and more efficient OLEDS for TV, computer and cell phone displays.  Credit: Andy Brimhall, University of Utah Marketing and Communications

University of Utah physicists read the subatomic "spins" in the centers or nuclei of hydrogen isotopes, and used the data to control current that powered light in a cheap, plastic LED -- at room temperature and without strong magnetic fields.

The study -- published in Friday's issue of the journal Science -- brings physics a step closer to practical machines that work "spintronically" as well as electronically: superfast quantum computers, more compact data storage devices and plastic or organic light-emitting diodes, or OLEDs, more efficient than those used today in display screens for cell phones, computers and televisions.

"We have shown we can use room-temperature, plastic electronic devices that allow us to see the orientation of the tiniest magnets in nature -- the spins in the smallest atomic nuclei," says physics professor Christoph Boehme, one of the study's principal authors. "This is a step that may lead to new ways to store information, produce better displays and make faster computers."

The experiment is a much more practical version of a study Boehme and colleagues published in Science in 2010, when they were able to read nuclear spins from phosphorus atoms in a conventional silicon semiconductor. But they could only do so when the apparatus was chilled to minus 453.9 degrees Fahrenheit (nearly absolute zero), was bombarded with intense microwaves and exposed to superstrong magnetic fields.

In the new experiments, the physicists were able to read the nuclear spins of two isotopes of hydrogen: a single proton and deuterium, which is a proton, neutron and electron. The isotopes were embedded in an inexpensive plastic polymer or organic semiconductor named MEH-PPV, an OLED that glows orange when current flows.

The researchers flipped the spins of the hydrogen nuclei to control electrical current flowing though the OLED, making the current stronger or weaker. They did it at room temperature and without powerful light bombardment or magnetic fields -- in other words, at normal operating conditions for most electronic devices, Boehme says.

"This experiment is remarkable because the magnetic forces created by the nuclei are millions of times smaller than the electrostatic forces that usually drive currents," yet they were able to control currents, he says.

Harnessing nuclear spins can increase the efficiency "of electronic materials out of which so much technology is made," Boehme adds. "It also raises the question whether this effect can be used for technological applications such as computer chips that use nuclear spins as memory and our method as a way to read the spins."

The U.S. Department of Energy funded the new study, and the physicists used facilities of the University of Utah's Materials Research Science and Engineering Center, funded by the National Science Foundation.

Boehme conducted the study with fellow University of Utah physicists: first author and postdoctoral fellow Hans Malissa; research professor and co-senior author John Lupton, who also is on the faculty of the University of Regensburg, Germany; distinguished professor Z. Valy Vardeny; professor Brian Saam; graduate students Marzieh Kavand and David Waters; and postdoctoral fellow Kipp van Schooten. Another co-author was Paul Burn of Australia's University of Queensland.

Spintronics: Storing Data in Atomic Nuclei

Electronic devices use electrical current or electrons, which are negatively charged particles orbiting the nuclei or centers of atoms. Modern computers store data electronically: data are stored as binary "bits" in which zero is represented by "off," or no electrical charge, and one is represented by "on" or the presence of electrical charge.

In spintronics, data are stored by the spins of either electrons or, preferably, atomic nuclei. Spin often is compared with a tiny bar magnet like a compass needle, either pointing up or down -- representing one or zero -- in an electron or an atom's nucleus. Nuclear spin orientations live longer, so are better for storing data.

The 2010 study by Boehme and colleagues showed that nuclear spins of phosphorus in a silicon semiconductor could control electrical current, but at impractically low temperatures and strong magnetic fields. They had to use the magnetic fields to align spins of phosphorus electrons in the same direction, and then use intense light to transfer the same alignment to the spins of phosphorus nuclei. Then they bombarded the semiconductor with radio waves to reverse the nuclear spins and control the current.

Boehme says scientists previously have claimed that current in plastic semiconductors -- known formally as pi-conjugated polymers -- can be controlled by the nuclear spins in hydrogen. Until the new study, "nobody has ever shown it directly" at room temperature by turning nuclear spins to change an electrical current, he adds.

The New Study

In the new experiments, the physicists used magnetic resonance to reverse the nuclear spins in hydrogen isotopes embedded in the OLED, and then were able to detect how the reversed 

spins caused a change in the electrical current through the OLED.

In the first two experiments, Boehme says, the physicists made nuclear spins in a proton and deuterium wiggle in characteristic ways, and were able to read corresponding wiggles in the resulting electrical current. In a third experiment, they flipped the spins back and forth at a rate they wanted instead of at the characteristic frequencies.

"It worked," Boehme says. "This shows you can turn a nuclear spin when you want, and only then the current turns around. We can control a current by controlling nuclear spins."

The researchers measured the current change directly, but not resulting changes in the OLED's light output -- changes so small they aren't detectable with the naked eye.

In both the 2010 and the new studies, the physicists did not read the spins of individual nuclei, but the collective spins of more than 1 million nuclei at a time. The ultimate goal is to be able to read the spins of nuclei individually.

"If you want to store information, the highest storage density would be to store information in single nuclear spins," Boehme says. Since the 2010 study, other physicists have achieved that in phosphorus nuclei, he adds.

Benefits of Spintronics

By storing information using both spins and electrical charge, spintronic devices should have greater storage capacity and process data more quickly -- although researchers still have years to go to figure out how to connect and process spintronically stored information in futuristic computers, conventional and quantum.

"We don't know if its five years, 50 years or never," Boehme says.

Yet he says spintronics already resulted in today's terabyte-sized computer hard drives, which use spintronic "read heads" so small that data can be stored more densely.

In 2012, Boehme and colleagues showed the same spintronic OLED in the new study works as a "dirt cheap" magnetic field sensor at room temperature without being compromised by degradation. Such sensors may enable more accurate spacecraft navigation systems, he says.

Because nuclear spin-controlled electrical current regulates output of light by the OLED, it provides a way to study how to make OLEDs more efficient. OLEDs convert far more electricity into light than incandescent light bulbs, which turn most incoming electricity into heat. But there is much more room for improved efficiency.

"Hopefully, OLEDs will become better -- use less electricity and produce more light -- because we learned here how nuclear spins' orientation influences how well the OLED works," Boehme says. "Any sort of efficiency limitation can only be overcome if the mechanism that imposes this limitation is understood."

Source: University of Utah

A new, tunable device for spintronics

Tunable spin Hall angle device based on GaAs through field induced intervalley repopulation.
Credit: Copyright Jairo Sinova

An international team of scientists including physicist Jairo Sinova from the University of Mainz has developed a tunable spin-charge converter made of GaAs.

Spin-charge converters are important devices in spintronics, an electronic which is not only based on the charge of electrons but also on their spin and the spin-related magnetism. Spin-charge converters enable the transformation of electric into magnetic signals and vice versa. Recently, the research group of Professor Jairo Sinova from the Institute of Physics at Johannes Gutenberg University Mainz in collaboration with researchers from the UK, Prague, and Japan, has for the first time realised a new, efficient spin-charge converter based on the common semiconductor material GaAs. Comparable efficiencies had so far only been observed in platinum, a heavy metal. In addition, the physicists demonstrated that the creation or detection efficiency of spin currents is electrically tunable in a certain regime. This is important when it comes to real devices. The underlying mechanism, that was revealed by theoretical works of the Sinova group, opens up a new approach in searching and engineering spintronic materials. These results have recently been published in the journal Nature Materials.

Spintronics does not only make use of the electron's charge to transmit and store information but it takes also advantage of the electron's spin. The spin can be regarded as a rotation of the electron around its own axis, and generates a magnetic field like a small magnet. In some materials, electron spins spontaneously align their direction, leading to the phenomenon of ferromagnetism which is well known e.g. in iron. Additionally, "spin-up" or "spin-down" directions can be used to represent two easily distinguishable states -- 0 and 1 -- used in information technology. This is already used for memory applications such as computer hard discs.

Making use of electron spin for information transmission and storage, enables the development of electronic devices with new functionalities and higher efficiency. To make real use of the electron spin, it has to be manipulated precisely: it has to be aligned, transmitted and detected. The work of Sinova and his colleagues shows, that it is possible to do so using electric fields rather than magnetic ones. Thus, the very efficient, simple and precise mechanisms of charge manipulation well established in semiconductor electronics can be transferred to the world of spintronic and thereby combine semiconductor physics with magnetism.

Spin-charge converters are essential tools for that. They can transform charge currents into spin currents, and vice versa. The main principle behind these converters is the so called spin-Hall effect. Jairo Sinova had already been involved in the prediction and discovery of this relativistic phenomenon in 2004.

The spin-Hall effect appears when an electric field drives electrons through a (semi-) conductor plate. Taking a look at the classical Hall effect that is known from undergraduate physics, the interaction of moving electrons and an external magnetic field forces the electrons to move to one side of the plate, perpendicular to their original direction. This leads to the so called Hall voltage between both sides of the plate. For the spin-Hall effect electron-spins are generated by irradiating the sample with circularly polarised light. The electron spins are then parallel or antiparallel, and their direction is perpendicular to the plate and the direction of movement. The moving electron spins are now forced to one or the other side of the plate, depending on the spin orientation. The driving force behind this is the so called spin-orbit coupling, a relativistic electromagnetic effect which influences moving electron spins. This leads to the separation of both spin orientations.

To make practical use of this effect, it is essential to get a highly efficient spin separation. Up to now, platinum has been the most efficient spin-charge converter material, as it is a heavy metal, and the spin-orbit coupling of heavy metals is known to be especially strong due to the large amount of protons (positive charge) in their core.

Now, Sinova and his colleagues have shown that gallium-arsenide (GaAs), a very common and widely used semiconductor material, can be an as efficient spin-charge converter as platinum, even at room temperature, which is important for practical applications. Moreover, the physicists have demonstrated for the first time that the efficiency can be tuned continuously by varying the electric field that drives the electrons.

The reason for this -- as theoretical calculations of the Sinova group have shown -- lies in the existence of certain valleys in the conduction band of the semiconductor material. One can think of the conduction band and its valleys as of a motor highway with different lanes, each one requiring a certain minimum velocity. Applying a higher electric field enables a transition from one lane to the other.

Since the spin-orbit coupling is different in each lane, a transition also affects the strength of the spin-hall effect. By varying the electric field, the scientists can distribute the electron spins on the different lanes, thus varying the efficiency of their spin-charge converter.

By taking into account the valleys in the conduction band, Sinova and his colleagues open up new ways to find and engineer highly efficient materials for spintronics. Especially, since current semiconductor growth technologies are capable of engineering the energy levels of the valleys and the strength of spin-orbit coupling, e.g. by substituting Ga or As with other materials like Aluminum.

Source: Universität Mainz

Wearable tech for battlefield, people at risk for heart attacks

The wearable system, under development by Sentient Science and the University at Buffalo, includes electrodes that relay data to a sensor, which connects with a remote computer network.
Credit: University at Buffalo

Wearable devices can count the steps you take and the calories you burn. But can they help soldiers in the field? Or prevent someone from having a heart attack?

Researchers at Sentient Science and the University at Buffalo say yes.

The sensor and software development company is working with UB engineering professor Albert H. Titus to create wearable technology that fuses real-time medical and physiological data with computer models. The system would then send personalized alerts indicating when the individual's level of stress, fatigue and resilience may put them in danger.

The work is funded by a $150,000 grant from the Office of Naval Research's Small Business Technology Transfer program, which enlists small businesses and research institutions to develop technology with military and commercial applications.

"Whether carrying 100 pounds of gear up a mountain or avoiding makeshift bombs, today's soldiers face incredible physical and mental stress," said Titus, PhD, professor and chair of UB's Department of Biomedical Engineering. "Our wearable system aims to measure how the body reacts to those challenges and combine that information with algorithms designed to help keep soldiers as safe as possible."

The wireless system, which is under development, will feature a series of electrodes that measure heart rate, brain activity and other vital signs. The electrodes may be attached to the skin like a Band-Aid or sewn into clothing that hugs the skin -- researchers are still deciding the best method.

The electrodes will relay information to a sensor (slightly smaller than a dime, attached to the skin like a patch), which will deliver that information to a remote computer network.

"It's like the hospital when you have a bunch of wires and equipment monitoring a patient. We're taking that technology and compressing it into a lightweight, wireless system," said the project's principal investigator Jennifer Haggerty, a research scientist and implementations manager at Sentient. Haggerty is an alumnus of UB.

As the information enters the computer network, it will fuse with Sentient's DigitalClone Live software, which has been validated by NASA and used to test the materials and components in the Hubble telescope, the F-35 fighter jet, wind turbines and other products.

The software includes complex algorithms that consider things like the terrain, weather and other environmental information, as well as the soldier's activity level. It will analyze the data and send personalized health alerts to soldiers and, if necessary, emergency medical facilities in the field. The idea is to improve soldiers' cognitive and physical abilities, making them more resilient and less prone to physical and psychological injuries.

In addition to serving the armed forces, the technology has commercial applications as a health-monitoring device. Sentient is exploring how the sensor can be applied to everyday items such as baseball caps. The individual wearing the cap would receive personalized health alerts regarding their risk of suffering a heart attack and other potential danger.

Source: University at Buffalo

Together, humans and computers can figure out plant world

A Web-based system was built for palynologists to interact with stored data and search for pollen images. This screen shows search capabilities by morphology semantics. From Han et al., part of the special issue 'Bioinformatic and Biometric Methods in Plant Morphology' in Applications in Plant Sciences. Credit: Image credit Han et al. Han, J. G., H. Cao, A. Barb, S. W. Punyasena, C. Jaramillo, and C.-R. Shyu. 2014. A neotropical Miocene pollen database employing image-based search and semantic modeling. Applications in Plant Sciences 2(8): 1400030. doi:10.3732/apps.1400030.

As technology advances, science has become increasingly about data -- how to gather it, organize it, and analyze it. The creation of key databases to analyze and share data lies at the heart of bioinformatics, or the collection, classification, storage, and analysis of biochemical and biological information using computers and software. The tools and methods used in bioinformatics have been instrumental in the development of fields such as molecular genetics and genomics. But, in the plant sciences, bioinformatics and biometrics are employed in all fields -- not just genomics -- to enable researchers to grapple with the rich and varied data sources at their disposal.

In July 2013, Surangi Punyasena of the University of Illinois at Urbana-Champaign and Selena Smith of the University of Michigan organized a special session at Botany 2013, the annual meeting of the Botanical Society of America in New Orleans, Louisiana. They invited plant morphologists, systematists, and paleobotanists, as well as computer scientists, applied mathematicians, and informaticians -- all of whom were united in their interest in developing or applying novel biometric or bioinformatic methods to the form and function of plants. The goal: to provide a forum for a cross-disciplinary exchange of ideas and methods on the theme of the quantitative analysis of plant morphology.

As Punyasena explains, "The quantitative analysis of morphology is the next frontier of bioinformatics. Humans are very good at learning to recognize shape and texture, but there are many problems where accuracy and consistency are difficult to achieve with only expert-derived, qualitative data, and in many fields there are often a limited number of experts trained in these visual assessments."

The results of that session, along with invited papers, are published in the August issue of Applications in Plant Sciences as a special issue on Bioinformatic and Biometric Methods in Plant Morphology. Morphology is, of course, the study of form, and form as represented in this collection of articles has a broad scope -- from microscopic pollen grains and charcoal particles, to macroscopic leaves and whole root systems. The methods presented in the issue, both recent and emerging, are varied as well, including automated classification and identification, geometric morphometrics, and skeleton networks, as well as tests of the limits of human assessment.

Three articles in the issue look at the application of biometric and bioinformatic methods in palynology: Han et al. (2014) introduce an online Miocene pollen database with semantic image search capabilities; Holt and Bebbington (2014) test the applications of an automated pollen classifier; and Mander et al. (2014) analyze differences in human and automated classification of grass pollen based on surface textures. Other papers highlight how biometric and bioinformatic methods apply to plants more broadly, including using skeleton networks to examine plant morphology such as roots (Bucksch, 2014), improving the quantification of geometric leaf shape metrics with a new protocol to measure leaf circularity (Krieger, 2014), comparing human and automated methods of quantifying aspects of leaf venation (Green et al., 2014), and applying morphometrics to charcoalified plant remains (Crawford and Belcher, 2014).

Taken as a whole, the issue presents a compelling argument for the importance of both computational and morphometric approaches.

"I think that there's been a renaissance in morphometric approaches," notes Punyasena. 

"New techniques are using easy access to high-quality digital imaging, powerful computers, and advances in computational analyses like machine learning to rethink the way we gather and analyze morphological data."

As advances in technology allow researchers to gather more and more morphological and image-based data, it has become increasingly important to be able to analyze and interpret those data quickly, accurately, consistently, and objectively. Biometric and bioinformatic methods make this possible, and reveal the potential of data collected from the shape and form of plants to be as rich of a data source as genetic data.

Access to specific articles can be found online at: http://www.bioone.org/toc/apps/2/8

Source: American Journal of Botany

How widespread is tax evasion? Cost of 'round-tripping,' a method investors use to avoid the tax collector

A new study puts a cost on "round-tripping," a method investors use to avoid the tax collector.
Credit: Illustration: Jose-Luis Olivares/MIT

Tax evasion is widely assumed to be an eternal problem for governments -- but how widespread is it? For the first time, a new study, co-authored by an MIT professor, has put a cost on a particular kind of tax evasion, known as "round-tripping," that the U.S. government has been trying to thwart.

In round-tripping, U.S. investors move funds to offshore tax havens, then invest in U.S. equity and debt markets with these "foreign" funds. In essence, the U.S. investors are disguising themselves as foreign investors, who are not subject to the same tax rates on capital gains and interest income. The money is said to have made a "round trip" since it originates in the U.S., and winds up back in U.S. markets.

According to the study, published in the Journal of Finance, every 1 percent increase in the top U.S. tax rate leads to an increase of 2.1 percent to 2.8 percent in foreign portfolio investment (FPI) from tax havens. As of 2008, some $34 billion to $109 billion of FPI from those havens appears to have been invested in the U.S. via round-tripping, leading to a loss of $8 billion to $27 billion in tax revenue.

"The higher the tax rate, the more securities appear to be purchased from tax haven jurisdictions," says Michelle Hanlon, a professor of accounting at MIT. "This seems to indicate that U.S. individuals are pretending to be foreigners who then invest in the U.S. markets."

The paper, "Taking the Long Way Home: U.S. Tax Evasion and Offshore Investments in U.S. Equity and Debt Markets," is co-authored by Hanlon, Edward L. Maydew of the University of North Carolina, and Jacob R. Thornock of the University of Washington.

Hanlon and her co-authors have presented their findings to the staff of the U.S. Senate's Permanent Subcommittee on Investigations, among other groups interested in the results.

A subtle strategy for identifying evaders

To be sure, not all investments from tax havens are dubious, so the study employed a multiprong strategy. First, it looked at changes in investment levels from tax havens after changes in U.S. tax rates. Second, the study evaluated these changes in investment with an eye to whether or not the U.S. has a bilateral Tax Information Exchange Agreement (TIEA) with the offshore sovereignty in question. TIEAs potentially allow the U.S. to find out considerably more information about the investments being made from those locations.

Sure enough, the researchers found that there is a decrease of up to 32 percent, in both equity and debt investments, when the U.S. creates a TIEA with other sovereign parties.

"The reverse effect we see is that when the U.S. enters into an exchange agreement, we see less investment from tax havens," adds Hanlon, who is the Howard W. Johnson Professor of Accounting at the MIT Sloan School of Management.

To be clear, Hanlon says, "It's very hard to identify tax evasion, because obviously people are trying to hide it." However, she adds, "Once we started seeing the data, we realized we could try to tackle this problem. We had to do a lot of tests to try to isolate the effect we're looking for, [and] we think it's a big step to try to put some numbers around this phenomenon."

The conclusions come from data collected by both the U.S. Federal Reserve and the U.S. Treasury, which allowed the researchers to piece together monthly flows of foreign investment into U.S. equity and debt markets.

Policy changes: What can be done?

Hanlon suggests that greater international cooperation will at least make this type of tax evasion more difficult and riskier. Additional TIEAs, for instance, would force some investors to go to greater lengths to engage in round-tripping.

"People always try to evade taxes, but [more TIEAs] will make it harder," Hanlon suggests. "And the more costly and risky it becomes, the costs will outweigh the benefits, at least on the margin, and the less likely people are to do it."

On the academic front, Hanlon recognizes that the study's findings present a wide range for the total cost of this tax evasion, but hopes the paper will be a spur to other scholars who may want to delve into the same topic.

"We felt it was important enough that someone try to do research like this, to get people thinking about other data sources and other ways to examine [tax evasion]," Hanlon says. 

"Our hope was that it would lead to more research and that people would take more risks to look at things like this."

Source: Massachusetts Institute of Technology

Don't get hacked! Research shows how much we ignore online warnings

For their study, BYU researchers created this screen to simulate hacking into participants' laptops. Credit: Image courtesy of Brigham Young University

Say you ignored one of those "this website is not trusted" warnings and it led to your computer being hacked. How would you react? Would you:

A. Quickly shut down your computer?

B. Yank out the cables?

C. Scream in cyber terror?

For a group of college students participating in a research experiment, all of the above were true. These gut reactions (and more) happened when a trio of Brigham Young University researchers simulated hacking into study participants' personal laptops.

"A lot of them freaked out -- you could hear them audibly make noises from our observation rooms," said Anthony Vance, assistant professor of Information Systems. "Several rushed in to say something bad had happened."

Fortunately for the students, nothing bad had really happened. What they saw -- a message from an "Algerian hacker" with a laughing skull and crossbones, a 10-second countdown timer and the words "Say goodbye to your computer" -- wasn't real. What was real was that all of the participants got the message by ignoring web security warnings.

Vance and BYU colleagues Bonnie Anderson and Brock Kirwan carried out the experiment to better understand how people deal with online security risks, such as malware. They found that people say they care about keeping their computers secure, but behave otherwise -- in this case, they plowed through malware warnings.

"We see these messages so much that we stop thinking about them," Vance said. "In a sense, we don't even see them anymore, and so we often ignore them and proceed anyway."

For the study, researchers first asked participants how they felt about online security. Then, in a seemingly unrelated task, participants were told to use their own laptops to log on to a website to categorize pictures of Batman as animated or photographed. (Students were told their image classification project was being used to check the accuracy of a computer algorithm to do the same task.)

As participants clicked through the image pages, warning signs would randomly pop up indicating malware issues with the site they were accessing. If they ignored the message enough times, they were "hacked."

"A lot of people don't realize that they are the weakest link in their computer security," said Kirwan, assistant professor of Psychology and Neuroscience at BYU. "The operating systems we use have a lot of built-in security and the way for a hacker to get control of your computer is to get you to do something."

Kirwan's role in the research added another fascinating layer: Using his expertise in neuroscience, Kirwan carried out an additional experiment on subjects using EEG machines to measure brain responses to risk.

While results showed that people say they care about web security but behave like they don't; they do behave in-line with what their brains say. In other words, people's brainwaves better predict how risky they are with online security.

"We learned that brain data is a better predictor of security behavior than a person's own response," Vance said. "With neuroscience, we're trying to understand this weakest link and understand how we can fortify it."

Anderson, an associate professor of Information Systems, echoed the need to do so, quoting security expert Bruce Schneier: "Only amateurs attack machines; professionals target people."

Source: Brigham Young University