Computers Math

You can carry an entire computer in your pocket today because the technological building blocks have been getting smaller and smaller since the 1950s. But in order to create future generations of electronics — such as more powerful phones, more efficient solar cells, or even quantum computers — scientists will need to come up with entirely new technology at the tiniest scales.
One area of interest is nanocrystals. These tiny crystals can assemble themselves into many configurations, but scientists have had trouble figuring out how to make them talk to each other.
A new study introduces a breakthrough in making nanocrystals function together electronically. Published March 25 in Science, the research may open the doors to future devices with new abilities.
“We call these super atomic building blocks, because they can grant new abilities — for example, letting cameras see in the infrared range,” said University of Chicago Prof. Dmitri Talapin, the corresponding author of the paper. “But until now, it has been very difficult to both assemble them into structures and have them talk to each other. Now for the first time, we don’t have to choose. This is a transformative improvement.”
In their paper, the scientists lay out design rules which should allow for the creation of many different types of materials, said Josh Portner, a Ph.D. student in chemistry and one of the first authors of the study.
A tiny problem
Scientists can grow nanocrystals out of many different materials: metals, semiconductors, and magnets will each yield different properties. But the trouble was that whenever they tried to assemble these nanocrystals together into arrays, the new supercrystals would grow with long “hairs” around them. More

Newly discovered Fermi arcs that can be controlled through magnetism could be the future of electronics based on electron spins. These new Fermi arcs were discovered by a team of researchers from Ames Laboratory and Iowa State University, as well as collaborators from the United States, Germany, and the United Kingdom. During their investigation of the rare-earth monopnictide NdBi (neodymium-bismuth), the research team discovered a new type of Fermi arc that appeared at low temperatures when the material became antiferromagnetic, i.e., neighboring spins point in opposite directions.
Fermi surfaces in metals are a boundary between energy states that are occupied and unoccupied by electrons. Fermi surfaces are normally closed contours forming shapes such as spheres, ovoids, etc. Electrons at the Fermi surface control many properties of materials such as electrical and thermal conductivity, optical properties, etc. In extremely rare occasions, the Fermi surface contains disconnected segments that are known as Fermi arcs and often are associated with exotic states like superconductivity.
Adam Kaminski, leader of the research team, explained that newly discovered Fermi arcs are the result of electron band splitting, which results from the magnetic order of Nd atoms that make up 50% of the sample. However, the electron splitting that the team observed in NdBi was not typical band splitting behavior.
There are two established types of band splitting, Zeeman and Rashba. In both cases the bands retain their original shape after splitting. The band splitting that the research team observed resulted in two bands of different shapes. As the temperature of the sample decreased, the separation between these bands increased and the band shapes changed, indicating a change in fermion mass.
“This splitting is very, very unusual, because not only is the separation between those bands increasing, but they also change the curvature,” Kaminski said. “This is very different from anything else that people have observed to date.”
The previously known cases of Fermi arcs in Weyl semimetals persist because they are caused by the crystal structure of the material which is difficult to control. However, the Fermi arcs that the team discovered in NdBi are induced by magnetic ordering of the Nd atoms in the sample. This order can be readily changed by applying a magnetic field, and possibly by changing the Nd ion for another rare earth ion such as Cerium, Praseodymium, or Samarium (Ce, Pr, or Sm). Since Ames Lab is a world leader in rare earth research, such changes in composition can be easily explored. More

Researchers at the University of Bonn have created a gas of light particles that can be extremely compressed. Their results confirm the predictions of central theories of quantum physics. The findings could also point the way to new types of sensors that can measure minute forces. The study is published in the journal Science.
If you plug the outlet of an air pump with your finger, you can still push its piston down. The reason: Gases are fairly easy to compress — unlike liquids, for example. If the pump contained water instead of air, it would be essentially impossible to move the piston, even with the greatest effort.
Gases usually consist of atoms or molecules that swirl more or less quickly through space. It is quite similar with light: Its smallest building blocks are photons, which in some respect behave like particles. And these photons can also be treated as a gas, however, one that behaves somewhat unusually: You can compress it under certain conditions with almost no effort. At least that is what theory predicts.
Photons in the mirror box
Researchers from the Institute of Applied Physics (IAP) at the University of Bonn have now demonstrated this very effect in experiments for the first time. “To do this, we stored light particles in a tiny box made of mirrors,” explains Dr. Julian Schmitt of the IAP, who is a principal investigator in the group of Prof. Dr. Martin Weitz. “The more photons we put in there, the denser the photon gas became.”
The rule is usually: The denser a gas, the harder it is to compress. This is also the case with the plugged air pump — at first the piston can be pushed down very easily, but at some point it can hardly be moved any further, even when applying a lot of force. The Bonn experiments were initially similar: The more photons they put into the mirror box, the more difficult it became to compress the gas. More

In recent years, artificial intelligence has become ubiquitous, with applications such as speech interpretation, image recognition, medical diagnosis, and many more. At the same time, quantum technology has been proven capable of computational power well beyond the reach of even the world’s largest supercomputer. Physicists at the University of Vienna have now demonstrated a new device, called quantum memristor, which may allow to combine these two worlds, thus unlocking unprecedented capabilities. The experiment, carried out in collaboration with the National Research Council (CNR) and the Politecnico di Milano in Italy, has been realized on an integrated quantum processor operating on single photons. The work is published in the current issue of the journal Nature Photonics.
At the heart of all artificial intelligence applications are mathematical models called neural networks. These models are inspired by the biological structure of the human brain, made of interconnected nodes. Just like our brain learns by constantly rearranging the connections between neurons, neural networks can be mathematically trained by tuning their internal structure until they become capable of human-level tasks: recognizing our face, interpreting medical images for diagnosis, even driving our cars. Having integrated devices capable of performing the computations involved in neural networks quickly and efficiently has thus become a major research focus, both academic and industrial.
One of the major game changers in the field was the discovery of the memristor, made in 2008. This device changes its resistance depending on a memory of the past current, hence the name memory-resistor, or memristor. Immediately after its discovery, scientists realized that (among many other applications) the peculiar behavior of memristors was surprisingly similar to that of neural synapses. The memristor has thus become a fundamental building block of neuromorphic architectures.
A group of experimental physicists from the University of Vienna, the National Research Council (CNR) and the Politecnico di Milano led by Prof. Philip Walther and Dr. Roberto Osellame, have now demonstrated that it is possible to engineer a device that has the same behavior as a memristor, while acting on quantum states and being able to encode and transmit quantum information. In other words, a quantum memristor. Realizing such device is challenging because the dynamics of a memristor tends to contradict the typical quantum behavior.
By using single photons, i.e. single quantum particles of lights, and exploiting their unique ability to propagate simultaneously in a superposition of two or more paths, the physicists have overcome the challenge. In their experiment, single photons propagate along waveguides laser-written on a glass substrate and are guided on a superposition of several paths. One of these paths is used to measure the flux of photons going through the device and this quantity, through a complex electronic feedback scheme, modulates the transmission on the other output, thus achieving the desired memristive behavior. Besides demonstrating the quantum memristor, the researchers have provided simulations showing that optical networks with quantum memristor can be used to learn on both classical and quantum tasks, hinting at the fact that the quantum memristor may be the missing link between artificial intelligence and quantum computing.
“Unlocking the full potential of quantum resources within artificial intelligence is one of the greatest challenges of the current research in quantum physics and computer science,” says Michele Spagnolo, who is first author of the publication in the journal “Nature Photonics.” The group of Philip Walther of the University of Vienna has also recently demonstrated that robots can learn faster when using quantum resources and borrowing schemes from quantum computation. This new achievement represents one more step towards a future where quantum artificial intelligence become reality.
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Researchers have developed a new analog photonic correlator that can be used to locate an object transmitting a radio signal. Because the new correlator is faster than other methods and works with a wide range of radio frequency signals, it could be useful for locating cell phones, signal jammers or a variety of tracking tags.
“The photonic architecture we developed uses no moving parts and enables real-time signal processing,” said Hugues Guillet de Chatellus from Université Grenoble Alpes-CNRS in France. “Real-time processing helps ensure there isn’t any downtime, which is critical for defense applications, for example.”
In Optica, Optica Publishing Group’s journal for high-impact research, Guillet de Chatellus and colleagues describe the new photonic correlator and demonstrate its ability to identify the location of a radio frequency transmitter. The device is considerably simpler than today’s analog or digital correlators and uses off-the-shelf telecommunications components.
“Many of today’s radio signals have large bandwidths because they carry a great deal of information,” said Guillet de Chatellus. “Our photonic approach offers a simple method for correlating signals with bandwidths of up to a few GHz, a larger bandwidth than is available from commercial approaches based on purely digital techniques.”
Using light to calculate correlation
The new photonic correlator can be used to compute what is known as a cross-correlation function for two signals emitted from one source and detected by two antennas. This measures the similarity of the signals as a function of the displacement of one signal relative to the other and provides information about their relative delay, which can be used to calculate the location of the signal’s source. More

If a sticker on a banana can make it show up as a toaster, how might strategic vandalism warp how an autonomous vehicle perceives a stop sign? Now, an immune-inspired defense system for neural networks can ward off such attacks, designed by engineers, biologists and mathematicians at the University of Michigan.
Deep neural networks are a subset of machine learning algorithms used for a wide variety of classification problems. These include image identification and machine vision (used by autonomous vehicles and other robots), natural language processing, language translation and fraud detection. However, it is possible for a nefarious person or group to adjust the input slightly and send the algorithm down the wrong train of thought, so to speak. To protect algorithms against such attacks, the Michigan team developed the Robust Adversarial Immune-inspired Learning System.
“RAILS represents the very first approach to adversarial learning that is modeled after the adaptive immune system, which operates differently than the innate immune system,” said Alfred Hero, the John H. Holland Distinguished University Professor, who co-led the work published in IEEE Access.
While the innate immune system mounts a general attack on pathogens, the mammalian immune system can generate new cells designed to defend against specific pathogens. It turns out that deep neural networks, already inspired by the brain’s system of information processing, can take advantage of this biological process, too.
“The immune system is built for surprises,” said Indika Rajapakse, associate professor of computational medicine and bioinformatics and co-leader of the study. “It has an amazing design and will always find a solution.”
RAILS works by mimicking the natural defenses of the immune system to identify and ultimately take care of suspicious inputs to the neural network. To begin developing it, the biological team studied how the adaptive immune systems of mice responded to an antigen. The experiment used the tissues of genetically modified mice that express fluorescent markers on their B cells. More

Scientists at Osaka University have simulated heat transport at the smallest scales using a molecular dynamics computer simulation. By studying the motions of the individual particles that make up the boundary between a solid and a liquid, they have been able to calculate heat flux with unprecedented precision. This work may lead to significant improvements in our ability to fabricate nanoscale devices, as well as functional surfaces and nanofluidic devices.
The process by which heat is transferred at the point where a solid meets a liquid may seem to be a simple physics problem. Traditionally, macroscopic quantities — such as density, pressure, temperature, and heat capacity — were used to compute the rate at which thermal energy moves between materials. However, properly accounting for the motion of individual molecules, while observing the laws of conservation of energy and momentum, adds a great deal of complexity. Improved atomic-scale computer simulations would be invaluable to more accurately understanding a wide array of real-world applications, especially within the field of nanotechnology.
Now, a team of researchers at Osaka University has developed a new numerical technique to visualize a modeled heat flux at the atomic scale for the first time. “To fundamentally understand thermal transport through a solid-liquid interface, the transport properties of atoms and molecules must be considered,” first author of the study Kunio Fujiwara explains. “We modeled the heat flux near a solid-liquid interface region with sub-atomic spatial resolution by using classical molecular dynamics simulations. This allowed us to create images of the three-dimensional structure of the energy flow while heat was being transferred between the layers.”
Using the popular Lennard-Jones potential to calculate the interactions between adjacent atoms, the team found that the direction of heat flux strongly depends on the sub-atomic stresses in the structures of the solids or liquids.
“Before, there was no good way to visualize heat flux at atomic scale,” senior author Masahiko Shibahara says. “These findings should allow us to elucidate and modify the thermal transport based on the 3D heat flux configuration.”
This may allow for customized nanoscale manufacturing to be carried out more efficiently.
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Scientists are using cutting-edge artificial intelligence to help extract complex information from large collections of museum specimens.
A team from Cardiff University is using state-of-the-art techniques to automatically segment and capture information from museum specimens and perform important data quality improvement without the need of human input.
They have been working with museums from across Europe, including the Natural History Museum, London, to refine and validate their new methods and contribute to the mammoth task of digitising hundreds of millions of specimens.
With more than 3 billion biological and geological specimens curated in natural history museums around the world, the digitization of museum specimens, in which physical information from a particular specimen is transformed into a digital format, has become an increasingly important task for museums as they adapt to an increasingly digital world.
A treasure trove of digital information is invaluable for scientists trying to model the past, present and future of organisms and our planet, and could be key to tackling some of the biggest societal challenges our world faces today, from conserving biodiversity and tackling climate change to finding new ways to cope with emerging diseases like COVID-19.
The digitization process also helps to reduce the amount of manual handling of specimens, many of which are very delicate and prone to damage. Having suitable data and images available online can reduce the risk to the physical collection and protect specimens for future generations. More