Computers Math

Most snakes get from A to B by bending their bodies into S-shapes and slithering forward headfirst. A few species, however — found in the deserts of North America, Africa and the Middle East — have an odder way of getting around. Known as “sidewinders,” these snakes lead with their mid-sections instead of their heads, slinking sideways across loose sand.
Scientists took a microscopic look at the skin of sidewinders to see if it plays a role in their unique method of movement. They discovered that sidewinders’ bellies are studded with tiny pits and have few, if any, of the tiny spikes found on the bellies of other snakes.
The Proceedings of the National Academy of Sciences published the discovery, which includes a mathematical model linking these distinct structures to function.
“The specialized locomotion of sidewinders evolved independently in different species in different parts of the world, suggesting that sidewinding is a good solution to a problem,” says Jennifer Rieser, assistant professor of physics at Emory University and a first author of the study. “Understanding how and why this example of convergent evolution works may allow us to adapt it for our own needs, such as building robots that can move in challenging environments.”
Co-authors of the paper include Joseph Mendelson, a herpetologist and the director of research at Zoo Atlanta; evolutionary biologist Jessica Tingle (University of California, Riverside); and physicists Daniel Goldman (Georgia Tech) and co-first author Tai-De Li (City University of New York).
Rieser’s research interests bring together the physics of soft matter — flowable materials like sand — and organismal biology. She studies how animals’ surfaces interact with the flowable materials in their environments to get around. Insights from her research may lead to improvements in human technology.

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Snakes, and other limbless locomotors, are particularly interesting to Rieser. “Even though snakes have a relatively simple body plan, they are able to navigate a variety of habitats successfully,” she says. Their long, flexible bodies are inspiring work on “snake” robots for everything from surgical procedures to search-and-rescue missions in collapsed buildings, she adds.
In a previous paper, Rieser and colleagues found that designing robots to move in serpentine ways may help them to avoid catastrophe when they collide with objects in their path.
Sidewinders offered her a chance to dig further into how nature has evolved ways to move across loose sand and other soft matter.
Most snakes tend to keep their bellies largely in contact with the ground as they slide forward, bending their bodies from their heads to their tails. A sidewinder, however, lifts its midsection off the ground, shifting it in a sideways direction.
Previous studies have hypothesized that sidewinding may allow a snake to move better on sandy slopes. “The thought is that sidewinders spread out the forces that their bodies impart to the ground as they move so that they don’t cause a sand dune to avalanche as they move across it,” Rieser explains.

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For the current paper, Rieser and her colleagues investigated whether sidewinders’ skin might also play a role in their unique movement style.
They focused on three species of sidewinders, all of them vipers, in residence at zoos: The sidewinder rattlesnake (Crotalus cerastes), found in the deserts of the Southwestern United States and northern Mexico; and the Saharan horned viper (Cerastes cerastes) and the Saharan sand viper (Cerastes vipera), both from the deserts of north Africa.
Skins shed from the sidewinders were collected and scanned with atomic force microscopy, a technique that provides resolution at the atomic level, on the order of fractions of a nanometer. For comparison, they also scanned snake skins shed from non-sidewinders.
As expected, the microscopy revealed tiny, head-to-tail pointing spikes on the skin of the non-sidewinders. Previous research had identified these micro spikes on a variety of other slithering snakes.
The current study, however, found that the skin of sidewinders is different. The two African sidewinders had micro pits on their bellies and no spikes. The skin of the sidewinder rattlesnake was also studded with tiny pits, along with a few, much smaller, spikes — although far fewer spikes than those of the slithering snakes.
The researchers created a mathematical model to test how these different structures affect frictional interactions with a surface. The model showed that head-to-tail pointing spikes enhance the speed and distance of forward undulation but are detrimental to sidewinding.
“You can think about it like the ridges on corduroy material,” Rieser says. “When you run your fingers along corduroy in the same direction as the ridges there is less friction than when you slide your fingers across the ridges.”
The model also showed that the uniform, non-directional structure of the round pits enhanced sidewinding, but was not as efficient as spikes for forward undulation.
The research provides snapshots at different points in time of convergent evolution — when different species independently evolve similar traits as a result of having to adapt to similar environments.
Rieser notes that American sandy deserts are much younger than those in Africa. The Mojave of North America accumulated sand about 20,000 years ago while sandy conditions appeared in the Sahara region at least seven million years ago.
“That may explain why the sidewinder rattlesnake still has a few micro spikes left on its belly,” she says. “It has not had as much time to evolve specialized locomotion for a sandy environment as the two African species, that have already lost all of their spikes.”
Engineers may also want to adapt their robot designs accordingly, Rieser adds. “Depending on what type of surface you need a robot to move on,” she says, “you may want to consider designing its surface to have a particular texture to enhance its movement.” More

Social media is increasingly used to spread fake news. The same problem can be found on the capital market — criminals spread fake news about companies in order to manipulate share prices. Researchers at the Universities of Göttingen and Frankfurt and the Jožef Stefan Institute in Ljubljana have developed an approach that can recognise such fake news, even when the news contents are repeatedly adapted. The results of the study were published in the Journal of the Association for Information Systems.
In order to detect false information — often fictitious data that presents a company in a positive light — the scientists used machine learning methods and created classification models that can be applied to identify suspicious messages based on their content and certain linguistic characteristics. “Here we look at other aspects of the text that makes up the message, such as the comprehensibility of the language and the mood that the text conveys,” says Professor Jan Muntermann from the University of Göttingen.
The approach is already known in principle from its use by spam filters, for example. However, the key problem with the current methods is that to avoid being recognised, fraudsters continuously adapt the content and avoid certain words that are used to identify the fake news. This is where the researchers’ new approach comes in: to identify fake news despite such strategies to evade detection, they combine models recently developed by the researchers in such a way that high detection rates and robustness come together. So even if “suspicious” words disappear from the text, the fake news is still recognised by its linguistic features. “This puts scammers into a dilemma. They can only avoid detection if they change the mood of the text so that it is negative, for instance,” explains Dr Michael Siering. “But then they would miss their target of inducing investors to buy certain stocks.”
The new approach can be used, for example, in market surveillance to temporarily suspend the trading of affected stocks. In addition, it offers investors valuable information to avoid falling for such fraud schemes. It is also possible that it could be used for criminal prosecutions in the future.

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Imagine a rubber band that was capable of snapping itself many times over, or a small robot that could jump up a set of stairs propelled by nothing more than its own energy. Researchers at the University of Massachusetts Amherst have discovered how to make materials that snap and reset themselves, only relying upon energy flow from their environment. The discovery may prove useful for various industries that want to source movement sustainably, from toys to robotics, and is expected to further inform our understanding of how the natural world fuels some types of movement.
Al Crosby, a professor of polymer science and engineering in the College of Natural Sciences at UMass Amherst, and Yongjin Kim, a graduate student in Crosby’s group, along with visiting student researcher Jay Van den Berg from Delft University of Technology in the Netherlands, uncovered the physics during a mundane experiment that involved watching a gel strip dry. The researchers observed that when the long, elastic gel strip lost internal liquid due to evaporation, the strip moved. Most movements were slow, but every so often, they sped up. These faster movements were snap instabilities that continued to occur as the liquid evaporated further. Additional studies revealed that the shape of the material mattered and that the strips could reset themselves to continue their movements.
“Many plants and animals, especially small ones, use special parts that act like springs and latches to help them move really fast, much faster than animals with muscles alone,” says Crosby, when explaining the study. “Plants like the Venus flytraps are good examples of this kind of movement, as are grasshoppers and trap-jaw ants in the animal world. Snap instabilities are one way that nature combines a spring and a latch and are increasingly used to create fast movements in small robots and other devices, as well as toys like rubber poppers. However, most of these snapping devices need a motor or a human hand to keep moving. With this discovery, there could be various applications that won’t require batteries or motors to fuel movement.”
Kim explains that after learning the essential physics from the drying strips, the team experimented with different shapes to find the ones most likely to react in expected ways and that would move repeatedly without any motors or hands resetting them. The team even showed that the reshaped strips could do work, such as climb a set of stairs on their own.
Crosby continues, “These lessons demonstrate how materials can generate powerful movement by harnessing interactions with their environment, such as through evaporation, and they are important for designing new robots, especially at small sizes where it’s difficult to have motors, batteries, or other energy sources.”
These latest results from Crosby and his group are part of a larger multidisciplinary university research initiative funded by the Army Research Office, an element of the U.S. Army Combat Capabilities Development Command’s Army Research Laboratory and led by Sheila Patek, professor of biology at Duke University, that aims to uncover many similar mechanisms from fast-moving biological organisms and translate them into new engineered devices.
“This work is part of a larger multidisciplinary effort that seeks to understand biological and engineered impulsive systems that will lay the foundations for scalable methods for generating forces for mechanical action and energy storing structures and materials,” says Ralph Anthenien, branch chief, Army Research Office, an element of the U.S. Army Combat Capabilities Development Command’s Army Research Laboratory. “The work will have myriad possible future applications in actuation and motive systems for the Army and DoD.”

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Researchers from North Carolina State University and the University of Texas have developed and demonstrated a new approach for designing photonic devices. The advance allows them to control the direction and polarization of light from thin-film LEDs, paving the way for a new generation of virtual reality (VR) and augmented reality (AR) technologies.
“This is a fundamentally new device architecture for photonic devices,” says Franky So, corresponding author of a paper describing the work. “And we’ve demonstrated that, using our approach, directional and polarized emissions from an organic LED or a perovskite LED without external optical elements can be realized.” So is the Walter and Ida Freeman Distinguished Professor of Materials Science and Engineering at NC State.
In practical terms, an approach that allows for directional control of light using thin-film LEDs makes it possible to develop VR and AR headsets that are substantially lighter and less bulky. And the improved efficiency of the devices means that you get more photons out of the display unit for every electron that you put in.
For AR units, it also means that more light from the outside world gets through to the user. In other words, you’ll still be able to see the image being superimposed on your view of the real world, and your view of the real world will be clearer.
“Because the device we’ve demonstrated is simple to fabricate and can be easily scaled-up, our discovery of this strong directional and polarized light emission from OLEDs and perovskite LEDs has important applications for displays, lighting and other photonic applications,” So says.

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Rice University engineers have discovered technology that could slash the cost of semiconductor electron sources, key components in devices ranging from night-vision goggles and low-light cameras to electron microscopes and particle accelerators.
In an open-access Nature Communications paper, Rice researchers and collaborators at Los Alamos National Laboratory (LANL) describe the first process for making electron sources from halide perovskite thin films that efficiently convert light into free electrons.
Manufacturers spend billions of dollars each year on photocathode electron sources made from semiconductors containing rare elements like gallium, selenium, cadmium and tellurium.
“This should be orders of magnitude lower in cost than what exists today in the market,” said study co-corresponding author Aditya Mohite, a Rice materials scientist and chemical engineer. He said the halide perovskites have the potential to outperform existing semiconductor electron sources in several ways.
“First, there’s the combination of quantum efficiency and lifetime,” Mohite said. “Even through this was a proof-of-concept, and the first demonstration of halide perovskites as electron sources, quantum efficiency was only about four times lower than that of commercially available gallium arsenide photocathodes. And we found halide perovskites had a longer lifetime than gallium arsenide.”
Another advantage is that perovskite photocathodes are made by spin coating, a low-cost method that can easily be scaled up, said Mohite, an associate professor of chemical and biomolecular engineering and of materials science and nanoengineering.

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“We also found that degraded perovskite photocathodes can be easily regenerated compared to conventional materials that usually require high-temperature annealing,” he said.
The researchers tested dozens of halide perovskite photocathodes, some with quantum efficiencies as high as 2.2%. They demonstrated their method by creating photocathodes with both inorganic and organic components, and showed they could tune electron emission over both the visible and ultraviolet spectrum.
Quantum efficiency describes how effective a photocathode is at converting light to useable electrons.
“If each incoming photon generates an electron and you collected every electron, you would have 100% quantum efficiency,” said study lead author Fangze Liu, a postdoctoral research associate at LANL. “The best semiconductor photocathodes today have quantum efficiencies around 10-20%, and they are all made of extremely expensive materials using complex fabrication processes. Metals are also sometimes used as electron sources, and the quantum efficiency of copper is very small, about .01%, but it’s still used, and it’s a practical technology.”
The cost savings from halide perovskite photocathodes would come in two forms: the raw materials for making them are abundant and inexpensive, and the manufacturing process is simpler and less expensive than for traditional semiconductors.

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“There is a tremendous need for something that is low-cost and that can be scaled up,” Mohite said. “Using solution-processed materials, where you can literally paint a large area, is completely unheard of for making the kind of high-quality semiconductors needed for photocathodes.”
The name ‘perovskite’ refers to both a specific mineral discovered in Russia in 1839 and any compound with the crystal structure of that mineral. Halide perovskites are the latter, and can be made by mixing lead, tin and other metals with bromide or iodide salts.
Research into halide perovskite semiconductors took off worldwide after scientists in the United Kingdom used sheetlike crystals of the material to make high-efficiency solar cells in 2012. Other labs have since shown the materials can be used to make LEDs, photodetectors, photoelectrochemical cells for water-splitting and other devices.
Mohite, an expert in perovskites who worked as a research scientist at LANL prior to joining Rice in 2018, said one reason the halide perovskite photocathode project succeeded is that his collaborators in LANL’s Applied Cathode Enhancement and Robustness Technologies research group are “one of the best teams in the world for exploring new materials and technologies for photocathodes.”
Photocathodes operate according to Einstein’s photoelectric effect, releasing free electrons when they are struck by light of a particular frequency. The reason quantum efficiencies of photocathodes are typically low is because even the slightest defects, like a single atom out of place in the crystal lattice, can create “potential wells” that trap free electrons.
“If you have defects, all your electrons are going to get lost,” Mohite said. “It takes a lot of control. And it took a lot of effort to come up with a process to make a good perovskite material.”
Mohite and Liu used spin-coating, a widely used technique where liquid is dropped onto a rapidly spinning disk and centrifugal force spreads the liquid across the disk’s surface. In Mohite and Liu’s experiments, spin-coating took place in an argon atmosphere to limit impurities. Once spun, the disks were heated and placed in high vacuum to convert the liquid into crystal with a clean surface.
“It took a lot of iterations,” Mohite said. “We tried tuning the material composition and surface treatment in many ways to get the right combination for maximum efficiency. That was the biggest challenge.”
He said the team is already working to improve the quantum efficiency of its photocathodes.
“Their quantum efficiency is still lower than state-of-the-art semiconductors, and we proposed in our paper that this is due to the presence of high surface defects,” he said. “The next step is to fabricate high-quality perovskite crystals with lower surface defect densities.” More

A calculation so complex that it takes twenty years to complete on a powerful desktop computer can now be done in one hour on a regular laptop. Physicist Andreas Ekström at Chalmers University of Technology, together with international research colleagues, has designed a new method to calculate the properties of atomic nuclei incredibly quickly.
The new approach is based on a concept called emulation, where an approximate calculation replaces a complete and more complex calculation. Although the researchers are taking a shortcut, the solution ends up almost exactly the same. It is reminiscent of algorithms from machine learning, but ultimately the researchers have designed a completely new method. It opens up even more possibilities in fundamental research in areas such as nuclear physics.
“Now that we can emulate atomic nuclei using this method, we have a completely new tool to construct and analyse theoretical descriptions of the forces between protons and neutrons inside the atomic nucleus,” says research leader Andreas Ekström, Associate Professor at the Department of Physics at Chalmers.
Fundamental to understanding our existence
The subject may sound niche, but it is in fact fundamental to understanding our existence and the stability and origin of visible matter. Most of the atomic mass resides in the centre of the atom, in a dense region called the atomic nucleus. The constituent particles of the nucleus, the protons and neutrons, are held together by something called the strong force. Although this force is so central to our existence, no one knows exactly how it works. To increase our knowledge and unravel the fundamental properties of visible matter, researchers need to be able to model the properties of atomic nuclei with great accuracy.
The basic research that Andreas Ekström and his colleagues are working on sheds new light on topics ranging from neutron stars and their properties, to the innermost structure and decay of nuclei. Basic research in nuclear physics also provides essential input to astrophysics, atomic physics, and particle physics.
Opening doors to completely new possibilities
“I am incredibly excited to be able to make calculations with such accuracy and efficiency. Compared with our previous methods, it feels like we are now computing at lightning speed. In our ongoing work here at Chalmers, we hope to improve the emulation method further, and perform advanced statistical analyses of our quantum mechanical models. With this emulation method it appears that we can achieve results that were previously considered impossible. This certainly opens doors to completely new possibilities,” says Andreas Ekström.
The project is funded by the European Research Council within the framework of an ERC Starting Grant.
More on the mathematical shortcut
The new emulation method is based on something called eigenvector continuation (EVC). It allows for emulation of many quantum mechanical properties of atomic nuclei with incredible speed and accuracy. Instead of directly solving the time-consuming and complex many-body problem over and over again, researchers have created a mathematical shortcut, using a transformation into a special subspace. This makes it possible to utilise a few exact solutions in order to then obtain approximate solutions much faster.
If the emulator works well, it generates solutions that are almost exactly — circa 99 per cent — similar to the solutions to the original problem. This is in many ways the same principles used in machine learning, but it is not a neural network or a Gaussian process — a completely new method underpins it. The EVC method for emulation is not limited to atomic nuclei, and the researchers are currently looking further into different types of applications.

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Researchers from Mayo Clinic and AliveCor Inc. have been using artificial intelligence (AI) to develop a mobile device that can identify certain patients at risk of sudden cardiac death. This research has yielded a breakthrough in determining the health of the electrical recharging system in a patient’s heart. The researchers determined that a smartphone-enabled mobile EKG device can rapidly and accurately determine a patient’s QTc, thereby identifying patients at risk of sudden cardiac death from congenital long QT syndrome (LQTS) or drug-induced QT prolongation.
The heart beats by a complex system of electrical signals triggering regular and necessary contractions. Clinicians evaluate the heart’s rate-corrected QT interval, or QTc, as a vital health barometer of the heart’s electrical recharging system. A potentially dangerous prolonged QTc, which is equal to or longer than 50 milliseconds, can be caused by:
More than 100 drugs approved by the Food and Drug Administration (FDA).
Genetics, including congenital long QT syndrome.
Many systemic diseases, including even SARS-CoV-2-mediated COVID-19.
Such a prolonged QTc can predispose people to dangerously fast and chaotic heartbeats, and even sudden cardiac death. For over 100 years, QTc assessment and monitoring has relied heavily on the 12-lead electrocardiogram (EKG). But that could be about to change, according to this research.
Under the direction of Michael Ackerman, M.D., Ph.D., a genetic cardiologist at Mayo Clinic, researchers trained and validated an AI-based deep neural network to detect QTc prolongation using AliveCor’s KardiaMobile 6L EKG device. The findings, which were published in Circulation, compared the ability of an AI-enabled mobile EKG to a traditional 12-lead EKG in detecting QT prolongation.
“This collaborative effort with investigators from academia and industry has yielded what I call a ‘pivot’ discovery,” says Dr. Ackerman, who is director of Mayo Clinic’s Windland Smith Rice Comprehensive Sudden Cardiac Death Program. “Whereby, we will pivot from the old way that we have been obtaining the QTc to this new way. Since Einthoven’s first major EKG paper in 1903, 2021 will mark the new beginning for the QT interval.”
The team used more than 1.6 million 12-lead EKGs from over a half-million patients to train and validate an AI-based deep neural network to recognize and accurately measure the QTc. Next this newly developed AI-based QTc assessment ?the “QT meter” ? was tested prospectively on nearly 700 patients evaluated by Dr. Ackerman in Mayo Clinic’s Windland Smith Rice Genetic Heart Rhythm Clinic. Half of these patients had congenital long QT syndrome.

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The object was to compare the QTc values from a 12-lead EKG to those from the prototype hand-held EKG device used with a smartphone. Both sets of EKGs were given at the same clinical visit, typically within five minutes of each other.
The AI algorithm’s ability to recognize clinically meaningful QTc prolongation on a mobile EKG device was similar to the EKG assessments made by a trained QT expert and a commercial laboratory specializing in QTc measurements for drug studies. The mobile device effectively detected a QTc value of greater than or equal to 500 milliseconds, performing with:
80% sensitivity This means that fewer cases of QTc prolongation were missed.
94.4% specificity
This means that it was highly accurate in predicting who did not have a prolonged QTc.
“The ability to equip mobile EKG devices with accurate AI-powered approaches capable of calculating accurately the QTc represents a potential paradigm shift regarding how and where the QT interval can be assessed,” says John Giudicessi, M.D., Ph.D., a Mayo Clinic cardiology fellow and first author of the study.
“Currently, AliveCor’s KardiaMobile 6L EKG device is FDA-cleared for detection of atrial fibrillation, bradycardia, and tachycardia. Once FDA clearance is received for this AI-based QTc assessment, we will have a true QT meter that can enable this emerging vital sign to be obtained easily and accurately,” says Dr. Ackerman. “Akin to a glucose meter for diabetics, for example, this QT meter will provide an early warning system, enabling patients with congenital or acquired LQTS to be identified and potentially lifesaving adjustments to their medications and electrolytes to be made.”
“This point-of-care application of artificial intelligence is massively scalable, since it is linked to a smartphone. It can save lives by telling a person that a specific medication may be harmful before he or she takes the first pill,” says Paul Friedman, M.D., chair of the Department of Cardiovascular Medicine at Mayo Clinic in Rochester. “This allows a potentially life threatening condition to be detected before symptoms are manifest.”
“Regularly monitoring for LQTS using KardiaMobile 6L allows for accurate, real-time data collection outside the walls of a hospital,” says David Albert, M.D., founder and chief medical officer at AliveCor Inc. “Because LQTS can be intermittent and elusive, the ability to detect this rhythm abnormality without a 12-lead EKG — which requires the patient be in-hospital — can improve patient outcomes and save on hospital resources, while still providing the reliable and timely data physicians and their patients need.”
This research was sponsored by the Mayo Clinic Windland Smith Rice Comprehensive Sudden Cardiac Death Program. Mayo Clinic; Zachi Attia, Ph.D.; Peter Noseworthy, M.D.; Dr. Ackerman; and Dr. Friedman have a financial interest with AliveCor, Inc. related to this research.

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Scientists have given a fascinating new insight into the next steps to develop fast, energy-efficient, future computing systems that use light instead of electrons to process and store information — incorporating hardware inspired directly by the functioning of the human brain.
A team of scientists, including Professor C. David Wright from the University of Exeter, has explored the future potential for computer systems by using photonics in place of conventional electronics.
The article is published today (January 29th 2021) in the journal Nature Photonics.
The study focuses on potential solutions to one of the world’s most pressing computing problems — how to develop computing technologies to process this data in a fast and energy efficient way.
Contemporary computers are based on the von Neumann architecture in which the fast Central Processing Unit (CPU) is physically separated from the much slower program and data memory.
This means computing speed is limited and power is wasted by the need to continuously transfer data to and from the memory and processor over bandwidth-limited and energy-inefficient electrical interconnects — known as the von Neumann bottleneck.
As a result, it has been estimated that more than 50 % of the power of modern computing systems is wasted simply in this moving around of data.
Professor C David Wright, from the University of Exeter’s Department of Engineering, and one of the co-authors of the study explains “Clearly, a new approach is needed — one that can fuse together the core information processing tasks of computing and memory, one that can incorporate directly in hardware the ability to learn, adapt and evolve, and one that does away with energy-sapping and speed-limiting electrical interconnects.”
Photonic neuromorphic computing is one such approach. Here, signals are communicated and processed using light rather than electrons, giving access to much higher bandwidths (processor speeds) and vastly reducing energy losses.
Moreover, the researchers try to make the computing hardware itself isomorphic with biological processing system (brains), by developing devices to directly mimic the basic functions of brain neurons and synapses, then connecting these together in networks that can offer fast, parallelised, adaptive processing for artificial intelligence and machine learning applications.

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