Aurelia Butler

X-rays can be used like a superfast, atomic-resolution camera, and if researchers shoot a pair of X-ray pulses just moments apart, they get atomic-resolution snapshots of a system at two points in time. Comparing these snapshots shows how a material fluctuates within a tiny fraction of a second, which could help scientists design future generations of super-fast computers, communications, and other technologies.
Resolving the information in these X-ray snapshots, however, is difficult and time intensive, so Joshua Turner, a lead scientist at the Department of Energy’s SLAC National Accelerator Center and Stanford University, and ten other researchers turned to artificial intelligence to automate the process. Their machine learning-aided method, published October 17 in Structural Dynamics, accelerates this X-ray probing technique, and extends it to previously inaccessible materials.
“The most exciting thing to me is that we can now access a different range of measurements, which we couldn’t before,” Turner said.
Handling the blob
When studying materials using this two-pulse technique, the X-rays scatter off a material and are usually detected one photon at a time. A detector measures these scattered photons, which are used to produce a speckle pattern — a blotchy image that represents the precise configuration of the sample at one instant in time. Researchers compare the speckle patterns from each pair of pulses to calculate fluctuations in the sample.
“However, every photon creates an explosion of electrical charge on the detector,” Turner said. “If there are too many photons, these charge clouds merge together to create an unrecognizable blob.” This cloud of noise means the researchers must collect tons of scattering data to yield a clear understanding of the speckle pattern. More

While quantum computing seems like the big-ticket item among the developing technologies based on the behaviour of matter and energy on the atomic and subatomic level, another direction promises to open a new door for scientific research itself — quantum microscopy.
With the advance of quantum technologies, new microscopy modalities are becoming possible — ones that can see electric currents, detect fluctuating magnetic fields, and even see single molecules on a surface.
A prototype of such a microscope, demonstrating high resolution sensitivity, has been developed by an Australian research team headed by Professor Igor Aharonovich of the University of Technology Sydney and Dr Jean-Philippe Tetienne of RMIT University. The team’s findings have now been published in Nature Physics.
The quantum microscope is based on atomic impurities, that following laser illumination, emit light that can be directly related to interesting physical quantities such as magnetic field, electric field or the chemical environment in proximity to the defect.
Professor Aharonovich said the ingenuity of the new approach was that, as opposed to the bulky crystals often employed for quantum sensing, the research team had utilised atomically thin layers, called hexagonal boron nitride (hBN).
“This van der Waals material — that is, made up of strongly bonded two-dimensional layers — can be made to be very thin and can conform to arbitrarily rough surfaces, thus enabling high resolution sensitivity,” Professor Aharonovich said. More

A key question in a number of court cases is whether a speaker on an audio recording is a particular known speaker, e.g., whether a speaker on a recording of an intercepted telephone call is the defendant.
In most English-speaking countries, expert testimony is only admissible in a court of law if it will potentially assist the judge or the jury to make a decision. If the judge or the jury’s speaker identification were equally accurate or more accurate than a forensic scientist’s forensic voice comparison, then the forensic-voice-comparison testimony would not be admissible.
In a research paper “Speaker identification in courtroom contexts — Part I,” recently published in the journal Forensic Science International, a multidisciplinary international team of researchers has reported the first set of results from a comprehensive study that compares the accuracy of speaker-identification by individual listeners (like judges or jury members) with the accuracy of a forensic-voice-comparison system that is based on state-of-the-art automatic-speaker-recognition technology, and that does so using recordings that reflect the conditions of an actual case.
The questioned-speaker recording was of a telephone call with background office noise, and the known-speaker recording was of a police interview conducted in echoey room with background ventilation-system noise.
The forensic-voice-comparison system performed better than all the 226 listeners who were tested.
The research team was made up of forensic data scientists, legal scholars, experimental psychologists, and phoneticians, based in the UK, Australia, and Chile.
Corresponding author Dr Geoffrey Stewart Morrison, director of the Forensic Data Science Laboratory at Aston University, said:
“A few years ago, when I was testifying in a court case, I was asked by a lawyer why the judge couldn’t just listen to the recordings and make a decision. Wouldn’t the judge do better than the forensic-voice-comparison system that I had used? That was the spark that lead to us conducting this research. I was expecting our forensic-voice-comparison system to perform better than most of the listeners, but I was surprized when it actually performed better than all of them. I’m happy that we now have such a clear answer to the question asked by the lawyer.”
Contributing author Dr Kristy A Martire, School of Psychology at the University of New South Wales, said:
“Past experiences where we have successfully recognized familiar speakers, such as family members or friends, can lead us to believe that we are better at identifying unfamiliar voices than we really are. This study shows that whatever ability a listener may have in recognizing familiar speakers, their ability to identify unfamiliar speakers is unlikely to be better than a forensic-voice-comparison system.”
Contributing author Professor Gary Edmond, School of Law at the University of New South Wales, said:
“Unequivocal scientific findings are that identification of unfamiliar speakers by listeners is unexpectedly difficult and much more error-prone than judges and others have appreciated. We should not encourage or enable nonexperts, including judges and jurors, to engage in unduly error-prone speaker identification. Instead, we should seek the services of real experts: specialist forensic scientists who employ empirically validated and demonstrably reliable forensic-voice-comparison systems.”
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Materials provided by Aston University. Note: Content may be edited for style and length. More

Want to know if the Golden Gate Bridge is holding up well? There could be an app for that.
A new study involving MIT researchers shows that mobile phones placed in vehicles, equipped with special software, can collect useful structural integrity data while crossing bridges. In so doing, they could become a less expensive alternative to sets of sensors attached to bridges themselves.
“The core finding is that information about structural health of bridges can be extracted from smartphone-collected accelerometer data,” says Carlo Ratti, director of the MIT Sensable City Laboratory and co-author of a new paper summarizing the study’s findings.
The research was conducted, in part, on the Golden Gate Bridge itself. The study showed that mobile devices can capture the same kind of information about bridge vibrations that stationary sensors compile. The researchers also estimate that, depending on the age of a road bridge, mobile-device monitoring could add from 15 percent to 30 percent more years to the structure’s lifespan.
“These results suggest that massive and inexpensive datasets collected by smartphones could play an important role in monitoring the health of existing transportation infrastructure,” the authors write in their new paper.
The study, “Crowdsourcing Bridge Vital Signs with Smartphone Vehicle Trips,” is being published in Nature Communications Engineering. More

While many parents and caregivers believe teens spend too much time on smartphones, video games and social media, a Michigan State University researcher says not to worry about screen time.
Keith Hampton, a professor in the Department of Media and Information and director of academic research in the Quello Center, says he doesn’t worry about screen time — he worries about adolescents who are disconnected because they have limited access to the internet.
“Teens who are disconnected from today’s technologies are more isolated from their peers, which can lead to problems,” Hampton said. “Many young people are struggling with their mental health. While adolescents often grapple with self-esteem issues related to body image, peers, family and school, disconnection is a much greater threat than screen time. Social media and video games are deeply integrated into youth culture, and they do more than entertain. They help kids to socialize, they contribute to identity formation and provide a channel for social support.”
Hampton and his colleagues study disconnection. For most teens, internet access is a part of their everyday life. These teens only experience disconnection when they choose to limit their device use or when their parents step in to control the time they spend online.
However, a large pocket of teens, living primarily in rural America, is disconnected for a very different reason. They live in households where there is an extremely weak infrastructure for broadband connectivity. These teens often have no internet access outside of school, very slow access at home or spotty data coverage using a smartphone.
“Rural teens are the last remaining natural control group if we want insight into the mental health of adolescents who have no choice but to be disconnected from screens,” Hampton said. More

The challenge of fabricating nanowires directly on silicon substrates for the creation of the next generation of electronics has finally been solved by researchers from Tokyo Tech. Next-generation spintronics will lead to better memory storage mechanisms in computers, making them faster and more efficient.
As our world modernizes faster than ever before, there is an ever-growing need for better and faster electronics and computers. Spintronics is a new system which uses the spin of an electron, in addition to the charge state, to encode data, making the entire system faster and more efficient. Ferromagnetic nanowires with high coercivity (resistance to changes in magnetization) are required to realize the potential of spintronics. Especially L10-ordered (a type of crystal structure) cobalt-platinum (CoPt) nanowires.
Conventional fabrication processes for L10-ordered nanowires involve heat treatment to improve the physical and chemical properties of the material, a process called annealing on the crystal substrate; the transfer of a pattern onto the substrate through lithography; and finally the chemical removal of layers through a process called etching. Eliminating the etching process by directly fabricating nanowires onto the silicon substrate would lead to a marked improvement in the fabrication of spintronic devices. However, when directly fabricated nanowires are subjected to annealing, they tend to transform into droplets as a result of the internal stresses in the wire.
Recently, a team of researchers led by Professor Yutaka Majima from the Tokyo Institute of Technology have found a solution to the problem. The team reported a new fabrication process to make L10-ordered CoPt nanowires on silicon/silicon dioxide (Si/SiO2) substrates. Talking about their research, published in Nanoscale Advances, Prof. Majima says, “Our nanostructure-induced ordering method allows the direct fabrication of ultrafine L10-ordered CoPt nanowires with the narrow widths of 30nm scale required for spintronics. This fabrication method could further be applied to other L10-ordered ferromagnetic materials such as iron-platinum and iron-palladium compounds.”
In this study, the researchers first coated a Si/SiO2 substrate with a material called a ‘resist’ and subjected it to electron beam lithography and evaporation to create a stencil for the nanowires. Then then deposited a multilayer of CoPt on the substrate. The deposited sampled were then ‘lifted-off’, leaving behind CoPt nanowires. These nanowires were then subjected to high temperature annealing. The researchers also examined the fabricated nanowires using several characterization techniques.
They found that the nanowires took on L10-ordering during the annealing process. This transformation was induced by atomic interdiffusion, surface diffusion, and extremely large internal stress at the ultrasmall 10 nm scale curvature radii of the nanowires. They also found that the nanowires exhibited a large coercivity of 10 kiloOersteds (kOe).
According to Prof. Majima, “The internal stresses on the nanostructure here induce the L10-ordering. This is a different mechanism than in previous studies. We are hopeful that this discovery will open up a new field of research called ‘nanostructure-induced materials science and engineering.'”
The wide applicability and convenience of the novel fabrication technique is sure to make a significant contribution to the field of spintronics research.
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Materials provided by Tokyo Institute of Technology. Note: Content may be edited for style and length. More

A research team based out of the University of Waterloo has developed a drone-powered device that can use WiFi networks to see through walls.
The device, nicknamed Wi-Peep, can fly near a building and then use the inhabitants’ WiFi network to identify and locate all WiFi-enabled devices inside in a matter of seconds.
The Wi-Peep exploits a loophole the researchers call polite WiFi. Even if a network is password protected, smart devices will automatically respond to contact attempts from any device within range. The Wi-Peep sends several messages to a device as it flies and then measures the response time on each, enabling it to identify the device’s location to within a metre.
Dr. Ali Abedi, an adjunct professor of computer science at Waterloo, explains the significance of this discovery.
“The Wi-Peep devices are like lights in the visible spectrum, and the walls are like glass,” Abedi said. “Using similar technology, one could track the movements of security guards inside a bank by following the location of their phones or smartwatches. Likewise, a thief could identify the location and type of smart devices in a home, including security cameras, laptops, and smart TVs, to find a good candidate for a break-in. In addition, the device’s operation via drone means that it can be used quickly and remotely without much chance of the user being detected.”
While scientists have explored WiFi security vulnerability in the past using bulky, expensive devices, the Wi-Peep is notable because of its accessibility and ease of transportation. Abedi’s team built it using a store-bought drone and $20 of easily purchased hardware.
“As soon as the Polite WiFi loophole was discovered, we realized this kind of attack was possible,” Abedi said.
The team built the Wi-Peep to test their theory and quickly realized that anyone with the right expertise could easily create a similar device.
“On a fundamental level, we need to fix the Polite WiFi loophole so that our devices do not respond to strangers,” Abedi said. “We hope our work will inform the design of next-generation protocols.”
In the meantime, he urges WiFi chip manufacturers to introduce an artificial, randomized variation in device response time, which will make calculations like the ones the Wi-Peep uses wildly inaccurate.
The paper summarizing this research, Non-cooperative wi-fi localization & its privacy implications, was presented at the 28th Annual International Conference on Mobile Computing and Networking.
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Three QUT researchers are part of an international research team that have identified new ways for retailers to use Artificial Intelligence in concert with in-store cameras to better service consumer behaviour and tailor store layouts to maximise sales.
In research published in Artificial Intelligence Review, the team propose an AI-powered store layout design framework for retailers to best take advantage of recent advances in AI techniques, and its sub-fields in computer vision and deep learning to monitor the physical shopping behaviours of their customers.
Any shopper who has retrieved milk from the farthest corner of a shop knows well that an efficient store layout presents its merchandise to both attract customer attention to items they had not intended to buy, increase browsing time, and easily find related or viable alternative products grouped together.
A well thought out layout has been shown to positively correlate with increased sales and customer satisfaction. It is one of the most effective in-store marketing tactics which can directly influence customer decisions to boost profitability.
QUT researchers Dr Kien Nguyen and Professor Clinton Fookes from the School of Electrical Engineering & Robotics and Professor Brett Martin, QUT Business Schoolteamed up with researchers Dr Minh Le, from the University of Economics, Ho Chi Minh city, Vietnam, and Professor Ibrahim Cil from Sakarya University, Serdivan, Turkey, to conduct a comprehensive review on existing approaches to in store layout design.
Dr Nguyen says improving supermarket layout design — through understanding and prediction — is a vital tactic to improve customer satisfaction and increase sales. More