Computers Math

The new study found that by tweaking the electrical properties of individual cells in simulations of brain networks, the networks learned faster than simulations with identical cells.
They also found that the networks needed fewer of the tweaked cells to get the same results, and that the method is less energy intensive than models with identical cells.
The authors say that their findings could teach us about why our brains are so good at learning, and might also help us to build better artificially intelligent systems, such as digital assistants that can recognise voices and faces, or self-driving car technology.
First author Nicolas Perez, a PhD student at Imperial College London’s Department of Electrical and Electronic Engineering, said: “The brain needs to be energy efficient while still being able to excel at solving complex tasks. Our work suggests that having a diversity of neurons in both brains and AI systems fulfils both these requirements and could boost learning.”
The research is published in Nature Communications.
Why is a neuron like a snowflake?
The brain is made up of billions of cells called neurons, which are connected by vast ‘neural networks’ that allow us to learn about the world. Neurons are like snowflakes: they look the same from a distance but on further inspection it’s clear that no two are exactly alike. More

Reservoir computing (RC) tackles complex problems by mimicking the way information is processed in animal brains. It relies on a randomly connected network that serves as a reservoir for information and ultimately leads to more efficient outputs. For realizing RC directly in matter (instead of simulating it in a digital computer), numerous reservoir materials have been investigated to date. Now a team including researchers from Osaka University has designed a sulfonated polyaniline network for RC.
Neural networks in the brain use electrochemical signals carried by ions. Therefore, an electrochemical approach is a logical choice when choosing a material system for RC. Organic electrochemical field-effect transistors (OECFETs) are popular in bioelectronics; however, they have not yet been widely used for RC.
The key to the reservoir material is that it has rich (time-dependent) behavior and is disordered, which makes polymer materials an excellent option as they form random networks by themselves.
Polyaniline is a promising polymer for RC applications, because it is easy to polymerize, has good stability in the atmosphere, and has reversible doping/de-doping behavior, which means its conduction can be altered.
The researchers investigated sulfonated polyaniline (SPAN), which, in addition to the advantages of polyaniline, has high water-solubility and self-doping behavior. These make SPAN easier to work with and the doping more uniform.
“Atmospheric protons are injected directly into the polymer chain of SPAN, which causes it to conduct,” explains study lead author Yuki Usami. “This conduction can then be controlled by adjusting the humidity.”
The researchers used a simple drop-casting method to assemble the SPAN on gold electrodes to give an organic electrochemical network device (OEND).
The SPAN OEND was tested for RC by checking the waveform and assessing its performance in short-term memory tasks. Results of a test to see how well speech could be recognized achieved 70% accuracy. This ability of SPAN OEND was comparable with a software simulation of RC.
“We have shown that our SPAN OEND system can be applied in RC,” says study corresponding author Takuya Matsumoto. “Future steps to establish systems that do not rely on humidity will provide more practical options; however, the success of our SPAN-based system is a positive step for material-based reservoir computing, which is expected to have a significant impact on the next generation of artificial intelligence devices.”
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Materials provided by Osaka University. Note: Content may be edited for style and length. More

Imagine a team of autonomous drones equipped with advanced sensing equipment, searching for smoke as they fly high above the Sierra Nevada mountains. Once they spot a wildfire, these leader robots relay directions to a swarm of firefighting drones that speed to the site of the blaze.
But what would happen if one or more leader robots was hacked by a malicious agent and began sending incorrect directions? As follower robots are led farther from the fire, how would they know they had been duped?
The use of blockchain technology as a communication tool for a team of robots could provide security and safeguard against deception, according to a study by researchers at MIT and Polytechnic University of Madrid, which was published today in IEEE Transactions on Robotics. The research may also have applications in cities where multirobot systems of self-driving cars are delivering goods and moving people across town.
A blockchain offers a tamper-proof record of all transactions — in this case, the messages issued by robot team leaders — so follower robots can eventually identify inconsistencies in the information trail.
Leaders use tokens to signal movements and add transactions to the chain, and forfeit their tokens when they are caught in a lie, so this transaction-based communications system limits the number of lies a hacked robot could spread, according to Eduardo Castelló, a Marie Curie Fellow in the MIT Media Lab and lead author of the paper.
“The world of blockchain beyond the discourse about cryptocurrency has many things under the hood that can create new ways of understanding security protocols,” Castelló says. More

Electrical engineers at Duke University have discovered that changing the physical shape of a class of materials commonly used in electronics and near- and mid-infrared photonics — chalcogenide glasses — can extend their use into the visible and ultraviolet parts of electromagnetic spectrum. Already commercially used in detectors, lenses and optical fibers, chalcogenide glasses may now find a home in applications such as underwater communications, environmental monitoring and biological imaging.
The results appear online on October 5 in the journal Nature Communications.
As the name implies, chalcogenide glasses contain one or more chalcogens — chemical elements such as sulfur, selenium and tellurium. But there’s one member of the family they leave out: oxygen. Their material properties make them a strong choice for advanced electronic applications such as optical switching, ultra-small direct laser writing (think tiny rewritable CDs) and molecular fingerprinting. But because they strongly absorb wavelengths of light in the visible and ultraviolet parts of electromagnetic spectrum, chalcogenide glasses have long been constrained to the near- and mid-infrared with respect to their applications in photonics.
“Chalcogenides have been used in the near- and mid-IR for a long time, but they’ve always had this fundamental limitation of being lossy at visible and UV wavelengths,” said Natalia Litchinitser, professor of electrical and computer engineering at Duke. “But recent research into how nanostructures affect the way these materials respond to light indicated that there might be a way around these limitations.”
In recent theoretical research into the properties of gallium arsenide (GaAs), a semiconductor commonly used in electronics, Litchinitser’ s collaborators, Michael Scalora of the US Army CCDC Aviation and Missile Center and Maria Vincenti of the University of Brescia predicted that nanostructured GaAs might respond to light differently than its bulk or even thin film counterparts. Because of the way that high intensity optical pulses interact with the nanostructured material, very thin wires of the material lined up next to one another might create higher-order harmonic frequencies (shorter wavelengths) that could travel through them.
Imagine a guitar string that is tuned to resonate at 256 Hertz — otherwise known as middle C. The researchers were proposing that if fabricated just right, this string when plucked might also vibrate at frequencies one or two octaves higher in small amounts. More

Scientists have made a path-breaking discovery in strontium ruthenate — with potential for new applications in quantum electronics.
Since the discovery of superconductivity in Sr2RuO4 in 1994, hundreds of studies have been published on this compound, which have suggested that Sr2RuO4 is a very special system with unique properties. These properties make Sr2RuO4 a material with great potential, for example, for the development of future technologies including superconducting spintronics and quantum electronics by virtue of its ability to carry lossless electrical currents and magnetic information simultaneously. An international research team led by scientists at the University of Konstanz has been now able to answer one of the most interesting open questions on Sr2RuO4: why does the superconducting state of this material exhibit some features that are typically found in materials known as ferromagnets, which are considered being antagonists to superconductors? The team has found that Sr2RuO4 hosts a new form of magnetism, which can coexist with superconductivity and exists independently of superconductivity as well. The results have been published in the current issue of Nature Communications.
After a research study that lasted several years and involved 26 researchers from nine different universities and research institutions, the missing piece of the puzzle seems to have been found. Alongside the University of Konstanz, the universities of Salerno, Cambridge, Seoul, Kyoto and Bar Ilan as well as the Japan Atomic Energy Agency, the Paul Scherrer Institute and the Centro Nazionale delle Ricerche participated in the study.
So far not the right tool to find evidence
“Despite decades of research on Sr2RuO4, there had been no evidence for the existence of this unusual type of magnetism in this material. A few years ago, however, we wondered if the reconstruction that happens in this material on the surface, where the crystal structure exhibits some small changes at the atomic scale level, could also lead to an electronic ordering with magnetic properties. Following this intuition, we realized that this question had probably not been addressed because nobody had used the “right tool” to find evidence for this magnetism, which we thought could be extremely weak and only limited to a few atomic layers from the surface of the material” states the leader of this international research study, Professor Angelo Di Bernardo from the University of Konstanz, whose research focuses on superconducting spintronic and quantum devices based on innovative materials.
To carry out the experiment, the team used high-quality single crystals of Sr2RuO4 prepared by the group of Dr Antonio Vecchione from the Centro Nazionale delle Ricerche (CNR) Spin in Salerno. “Making large crystals of Sr2RuO4 without any impurities was a big challenge albeit crucial for the success of the experiment, since defects would have given a signal similar to the magnetic signal which we were hunting,” says Dr Vecchione. More

A busy commuter is ready to walk out the door, only to realize they’ve misplaced their keys and must search through piles of stuff to find them. Rapidly sifting through clutter, they wish they could figure out which pile was hiding the keys.
Researchers at MIT have created a robotic system that can do just that. The system, RFusion, is a robotic arm with a camera and radio frequency (RF) antenna attached to its gripper. It fuses signals from the antenna with visual input from the camera to locate and retrieve an item, even if the item is buried under a pile and completely out of view.
The RFusion prototype the researchers developed relies on RFID tags, which are cheap, battery-less tags that can be stuck to an item and reflect signals sent by an antenna. Because RF signals can travel through most surfaces (like the mound of dirty laundry that may be obscuring the keys), RFusion is able to locate a tagged item within a pile.
Using machine learning, the robotic arm automatically zeroes-in on the object’s exact location, moves the items on top of it, grasps the object, and verifies that it picked up the right thing. The camera, antenna, robotic arm, and AI are fully integrated, so RFusion can work in any environment without requiring a special set up.
While finding lost keys is helpful, RFusion could have many broader applications in the future, like sorting through piles to fulfill orders in a warehouse, identifying and installing components in an auto manufacturing plant, or helping an elderly individual perform daily tasks in the home, though the current prototype isn’t quite fast enough yet for these uses.
“This idea of being able to find items in a chaotic world is an open problem that we’ve been working on for a few years. Having robots that are able to search for things under a pile is a growing need in industry today. Right now, you can think of this as a Roomba on steroids, but in the near term, this could have a lot of applications in manufacturing and warehouse environments,” said senior author Fadel Adib, associate professor in the Department of Electrical Engineering and Computer Science and director of the Signal Kinetics group in the MIT Media Lab. More

Ultrashort pulses of light are proven indistinguishable from continuous illumination, in terms of controlling the electronic states of atomically-thin material tungsten disulfide (WS2).
A new, Swinburne-led study proves that ultrashort pulses of light can be used to drive transitions to new phases of matter, aiding the search for future Floquet-based, low-energy electronics.
There is significant interest in transiently controlling the band-structure of a monolayer semiconductor by using ultra-short pulses of light to create and control exotic new phases of matter.
The resulting temporary states known as Floquet-Bloch states are interesting from a pure research standpoint as well as for a proposed new class of transistor based on Floquet topological insulators (FTIs).
In an important finding, the ultra-short pulses of light necessary for detecting the formation of Floquet states were shown to be as effective in triggering the state as continuous illumination, an important question that, until now, had been largely ignored.
A CONTINUOUS WAVE OR ULTRASHORT-PULSES: THE PROBLEM WITH TIME
Floquet physics, which has been used to predict how an insulator can be transformed into an FTI, is predicated on a purely sinusoidal field, ie continuous, monochromatic (single wavelength) illumination that has no beginning or end. More

Inequalities in income affect how well children do in maths — but not reading, the most comprehensive study of its kind has found.
Looking at data stretching from 1992 to 2019, the analysis, published in the journal Educational Review, revealed that 10-year-olds in US states with bigger gaps in income did less well in maths than those living in areas of America where earnings were more evenly distributed.
With income inequality in the US the highest in the developed world, researcher Professor Joseph Workman argues that addressing social inequality may do more to boost academic achievement than reforming schools or curricula — favoured methods of policymakers.
Income inequality — a measure of how unevenly income is distributed through a population — has long been associated with a host of health and social problems including mental health issues, lack of trust, higher rates of imprisonment and lower rates of social mobility.
It may also affect academic achievement, through various routes.
For instance, income inequality is linked to higher rates of divorce, substance abuse and child maltreatment, the stresses of which may affect a child’s development. It is also associated with higher odds of babies being of a low weight a birth — something which can raise their risk of developmental delays as they grow up. More