Computers Math

Anyone entering the world of quantum physics must prepare themself for quite a few things unknown in the everyday world: Noble gases form compounds, atoms behave like particles and waves at the same time and events that in the macroscopic world exclude each other occur simultaneously.
In the world of quantum physics, Reinhard Dörner and his team are working with molecules which — in the sense of most textbooks — ought not to exist: Helium compounds with two atoms, known as helium dimers. Helium is called a noble gase precisely because it does not form any compounds. However, if the gas is cooled down to just 10 degrees above absolute zero (minus 273 °C) and then pumped through a small nozzle into a vacuum chamber, which makes it even colder, then — very rarely — such helium dimers form. These are unrivaledly the weakest bound stable molecules in the Universe, and the two atoms in the molecule are correspondingly extremely far apart from each other. While a chemical compound of two atoms commonly measures about 1 angstrom (0.1 nanometres), helium dimers on average measure 50 times as much, i.e. 52 angstrom.
The scientists in Frankfurt irradiated such helium dimers with an extremely powerful laser flash, which slightly twisted the bond between the two helium atoms. This was enough to make the two atoms fly apart. They then saw — for the very first time — the helium atom flying away as a wave and record it on film.
According to quantum physics, objects behave like a particle and a wave at the same time, something that is best known from light particles (photons), which on the one hand superimpose like waves where they can pile upor extinguish each other (interference), but on the other hand as “solar wind” can propel spacecraft via their solar sails, for example.
That the researchers were able to observe and film the helium atom flying away as a wave at all in their laser experiment was due to the fact that the helium atom only flew away with a certain probability: With 98 per cent probability it was still bound to its second helium partner, with 2 per cent probability it flew away. These two helium atom waves — Here it comes! Quantum physics! — superimpose and their interference could be measured.
The measurement of such “quantum waves” can be extended to quantum systems with several partners, such as the helium trimer composed of three helium atoms. The helium trimer is interesting because it can form what is referred to as an “exotic Efimov state,” says Maksim Kunitski, first author of the study: “Such three-particle systems were predicted by Russian theorist Vitaly Efimov in 1970 and first corroborated on caesium atoms. Five years ago, we discovered the Efimov state in the helium trimer. The laser pulse irradiation method we’ve now developed might allow us in future to observe the formation and decay of Efimov systems and thus better understand quantum physical systems that are difficult to access experimentally.”

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Materials provided by Goethe University Frankfurt. Note: Content may be edited for style and length. More

Researchers have developed a new microscopy method that allows scientists to see the building blocks of ‘smart’ materials being formed at the nanoscale. More

Chemists at the University of Bonn (Germany) have synthesized extremely unusual compounds. Their central building block is a silicon atom. Different from usual, however, is the arrangement of the four bonding partners of the atom, which are not in the form of a tetrahedron around it, but flat like a trapezoid. This arrangement is usually energetically extremely unfavorable, yet the molecules are very stable. Their properties are completely unknown so far; researchers now want to explore them. The results will be published in the Journal of the American Chemical Society, but are already available online.
Like its relative carbon, silicon generally forms four bonds with other atoms. When it does, the result is usually a tetrahedron. The silicon atom is located in the center, its bonding partners (the so-called ligands) at the tetrahedral corners. This arrangement is most favorable energetically. It therefore arises quasi automatically, just as a soap bubble is usually spherical.
Researchers led by Prof. Dr. Alexander C. Filippou of the Institute for Inorganic Chemistry at the University of Bonn have now constructed silicon-containing molecules that are as unusual as a cube-shaped soap bubble. In these, the four ligands do not form a tetrahedron, but a distorted square, a trapezoid. They lie in one plane together with the silicon. “Despite this, the compounds are so stable that they can be filled into bottles and stored for weeks without any problems,” explains Dr. Priyabrata Ghana, a former doctoral student who has since moved to RWTH Aachen University.
Molecular exotics are unusually stable
The researchers themselves were surprised by this unusual stability. They discovered the reason by modeling the molecules on the computer. The ligands also form bonds with each other. In the process, they form a solid framework. This appears to be so strong that it completely prevents the trapezoidal arrangement from “snapping” into a tetrahedron. “Our computer calculations indicate that there is no structure for the molecules that would be more energetically favorable than the planar trapezoidal shape,” emphasizes Jens Rump, a doctoral student at the Institute for Inorganic Chemistry.
The researchers grew crystals of the substances and then blasted them with X-rays. The X-ray light is scattered by the atoms and changes its direction. These deviations can therefore be used to calculate the spatial structure of the molecules in the crystal. Together with spectroscopic measurements, this method confirmed that ligands and silicon are indeed in the same plane in the new molecules.
Although the synthesis of the exotic compounds must be carried out under inert gas, it is otherwise comparatively simple. Producing the starting materials, on the other hand, is complex; one of them was first synthesized only just over ten years ago and has already been the source for the synthesis of several novel classes of silicon compounds.
The influence of the unusual structure on the properties of silicon, an important element for the electronics industry, is completely unclear at the moment. At any rate, for a long time it was considered completely impossible to produce such compounds.

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Materials provided by University of Bonn. Note: Content may be edited for style and length. More

A new study by neuroscientists demonstrates that the brain does not remap itself even with long-term bionic limb use, posing challenges for the development of realistic prosthetic limbs. More

Atomic force microscopy (AFM) allows to obtain images and movies showing proteins at work, however with limited resolution. The developed BioAFMviewer software opens the opportunity to use the enormous amount of available high-resolution protein data to better understand experiments. Within an interactive interface with rich functionality, the BioAFMviewer computationally emulates tip-scanning of any biomolecular structure to generate simulated AFM graphics and movies. They greatly help in the interpretation of e.g., high-speed AFM observations. More

Berkeley — Imagine typing on a computer without a keyboard, playing a video game without a controller or driving a car without a wheel.
That’s one of the goals of a new device developed by engineers at the University of California, Berkeley, that can recognize hand gestures based on electrical signals detected in the forearm. The system, which couples wearable biosensors with artificial intelligence (AI), could one day be used to control prosthetics or to interact with almost any type of electronic device.
“Prosthetics are one important application of this technology, but besides that, it also offers a very intuitive way of communicating with computers.” said Ali Moin, who helped design the device as a doctoral student in UC Berkeley’s Department of Electrical Engineering and Computer Sciences. “Reading hand gestures is one way of improving human-computer interaction. And, while there are other ways of doing that, by, for instance, using cameras and computer vision, this is a good solution that also maintains an individual’s privacy.”
Moin is co-first author of a new paper describing the device, which appeared online Dec. 21 in the journal Nature Electronics.
To create the hand gesture recognition system, the team collaborated with Ana Arias, a professor of electrical engineering at UC Berkeley, to design a flexible armband that can read the electrical signals at 64 different points on the forearm. The electrical signals are then fed into an electrical chip, which is programmed with an AI algorithm capable of associating these signal patterns in the forearm with specific hand gestures.
The team succeeded in teaching the algorithm to recognize 21 individual hand gestures, including a thumbs-up, a fist, a flat hand, holding up individual fingers and counting numbers.

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“When you want your hand muscles to contract, your brain sends electrical signals through neurons in your neck and shoulders to muscle fibers in your arms and hands,” Moin said. “Essentially, what the electrodes in the cuff are sensing is this electrical field. It’s not that precise, in the sense that we can’t pinpoint which exact fibers were triggered, but with the high density of electrodes, it can still learn to recognize certain patterns.”
Like other AI software, the algorithm has to first “learn” how electrical signals in the arm correspond with individual hand gestures. To do this, each user has to wear the cuff while making the hand gestures one by one.
However, the new device uses a type of advanced AI called a hyperdimensional computing algorithm, which is capable of updating itself with new information.
For instance, if the electrical signals associated with a specific hand gesture change because a user’s arm gets sweaty, or they raise their arm above their head, the algorithm can incorporate this new information into its model.
“In gesture recognition, your signals are going to change over time, and that can affect the performance of your model,” Moin said. “We were able to greatly improve the classification accuracy by updating the model on the device.”
Another advantage of the new device is that all of the computing occurs locally on the chip: No personal data are transmitted to a nearby computer or device. Not only does this speed up the computing time, but it also ensures that personal biological data remain private.
“When Amazon or Apple creates their algorithms, they run a bunch of software in the cloud that creates the model, and then the model gets downloaded onto your device,” said Jan Rabaey, the Donald O. Pedersen Distinguished Professor of Electrical Engineering at UC Berkeley and senior author of the paper. “The problem is that then you’re stuck with that particular model. In our approach, we implemented a process where the learning is done on the device itself. And it is extremely quick: You only have to do it one time, and it starts doing the job. But if you do it more times, it can get better. So, it is continuously learning, which is how humans do it.”
While the device is not ready to be a commercial product yet, Rabaey said that it could likely get there with a few tweaks.
“Most of these technologies already exist elsewhere, but what’s unique about this device is that it integrates the biosensing, signal processing and interpretation, and artificial intelligence into one system that is relatively small and flexible and has a low power budget,” Rabaey said. More

A mathematical model that can help project the contagiousness and spread of infectious diseases like the seasonal flu may not be the best way to predict the continuing spread of the novel coronavirus, especially during lockdowns that alter the normal mix of the population, researchers report.
Called the R-naught, or basic reproductive number, the model predicts the average number of susceptible people who will be infected by one infectious person. It’s calculated using three main factors — the infectious period of the disease, how the disease spreads and how many people an infected individual will likely come into contact with.
Historically, if the R-naught is larger than one, infections can become rampant and an epidemic or more widespread pandemic is likely. The COVID-19 pandemic had an early R-naught between two and three.
In a letter published in Infection Control and Hospital Epidemiology, corresponding author Dr. Arni S.R. Srinivasa Rao, a mathematical modeler at the Medical College of Georgia at Augusta University, argues that while it’s never possible to track down every single case of an infectious disease, the lockdowns that have become necessary to help mitigate the COVID-19 pandemic have complicated predicting the disease’s spread.
Rao and his co-authors instead suggest more of a dynamic, moment in time approach using a model called the geometric mean. That model uses today’s number to predict tomorrow’s numbers. Current number of infections — in Augusta today, for example — is divided by the number of predicted infections for tomorrow to develop a more accurate and current reproductive rate.
While this geometric method can’t predict long term trends, it can more accurately predict likely numbers for the short term.
“The R-naught model can’t be changed to account for contact rates that can change from day to day when lockdowns are imposed,” Rao explains. “In the initial days of the pandemic, we depended on these traditional methods to predict the spread, but lockdowns change the way people have contact with each other.”
A uniform R-naught is also not possible since the COVID-19 pandemic has varied widely in different areas of the country and world. Places have different rates of infection, on different timelines — hotspots like New York and California would have higher R-naughts. The R-naught also did not predict the current third wave of the COVID-19 pandemic.
“Different factors continuously alter ground-level basic reproductive numbers, which is why we need a better model,” Rao says. Better models have implications for mitigating the spread of COVID-19 and for future planning, the authors say.
“Mathematical models must be used with care and their accuracy must be carefully monitored and quantified,” the authors write. “Any alternative course of action could lead to wrong interpretation and mismanagement of the disease with disastrous consequences.”
Rao’s co-authors include Dr. Steven Krantz, a professor of mathematics and statistics at Washington University and Dr. Michael Bonsall, a professor in the Mathematical Ecology Research Group, at the University of Oxford.

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Materials provided by Medical College of Georgia at Augusta University. Original written by Jennifer Hilliard Scott. Note: Content may be edited for style and length. More

Access to the Internet is essential for economic development, education, global communications, and countless other applications. For all its benefits, however, the Internet has a darker side. It has emerged as a conduit for spreading misinformation, stoking tensions, and promoting extremist ideologies. Yet there is hope.
In the latest issue of Psychological Science in the Public Interest, a team of researchers recommend ways that psychological and behavioral sciences can help decrease the negative consequences of Internet use. These recommendations emphasize helping people gain greater control over their digital environments.
“Psychological science can help to inform policy interventions in the digital world,” said Anastasia Kozyreva, a researcher at the Center for Adaptive Rationality at the Max Planck Institute for Human Development in Germany and author on the paper. “It is crucial that psychological and behavioral sciences are employed to ensure users are not manipulated for financial gain and are empowered to detect and resist manipulation.”
Specifically, psychological and cognitive sciences can complement interventions by other fields, such as law and ethics, which develops guidelines and regulations; education, which can provide curricula for digital information literacy; and technology, which can provide automated detection of harmful materials and help implement more ethically designed online choice architectures.
Although there is no silver bullet that could solve all the problems of the digital world, Kozyreva and her colleagues describe three approaches to help mitigate the negative consequences. The first is to design Internet infrastructures that “nudge” people’s behavior toward more positive outcomes, such as systems with default privacy-respecting settings. The second is relying more on “technocognition,” which are technological solutions informed by psychological principles, such as creating obstacles to sharing offensive material online.
The final approach is improving people’s cognitive and motivational competencies through “boosts,” which are tactics that enhance people’s agency in their digital environments and improve reasoning and resilience to manipulation. Examples of a boost include preventively inoculating users against most common manipulative practices and providing easy-to-use rules for digital literacy.
“These cognitive tools are designed to foster the civility of online discourse and protect reason and human autonomy against manipulative choice architectures, attention-grabbing techniques, and the spread of false information,” said Kozyreva.

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Materials provided by Association for Psychological Science. Note: Content may be edited for style and length. More