Computers Math

While electrons are well known to carry both charge and spin, only the electric charge portion is utilized as an information carrier in modern electronic devices. However, the limits of modern electronics and the impending end of Moore’s Law have rekindled the interest in the development of “spintronic” devices, which are capable of harnessing the spin of the electrons. It is expected that the widespread adoption of spintronic computing devices can revolutionize information technology similar to the invention of electronics.
One key challenge in spintronics is finding an efficient and sensitive way to electrically detect the electronic spin state. For example, the discovery of giant magnetoresistance (GMR) in the late 1980s, allowed for such functionality. In GMR, a large change in electrical resistance occurs under the magnetic field depending on parallel or antiparallel spin configurations of the ferromagnetic bilayer. The discovery of GMR has led to the development of hard-disk drive technology, which is technically the first-ever mass-produced spintronic device. Since then, discoveries of other related phenomena, including colossal magnetoresistance (CMR) which occurs in the presence of a magnetic field, have advanced our understanding of the interplay between spin and charge degrees of freedom and served as a foundation of emergent spintronic applications.
In the latest issue of the journal Nature, a research team led by Prof. KIM Jun Sung in Center for Artificial Low Dimensional Electron Systems within the Institute for Basic Science (IBS, South Korea) and Physics Department at Pohang University of Science and Technology (POSTECH, South Korea) found a new magnetotransport phenomenon, in the magnetic semiconductor Mn3Si2Te6. The group found that the magnitude of change in resistance can reach as large as a billion-fold under a rotating magnetic field. This unprecedented shift of resistance depending on magnetic field angle is coined as colossal angular magnetoresistance (CAMR). “Unlike the previous magnetotransport phenomena, a huge change in resistance is induced by only rotating the spin direction without altering their configurations. This unusual effect originates from the unique topologically-protected band structure of this magnetic semiconductor,” notes Professor KIM Jun Sung, one of the co-corresponding authors of the study.
Topological materials, a newly discovered class of materials, have become increasingly important in spintronic applications. A topological material refers to a material whose electronic structures are described to be “twisted.” Just as a Mobius strip cannot be unraveled without fundamentally altering its form, the twisted electronic structure in topological materials is preserved unless the system’s symmetry changes. Such topologically protected states can be used to host and control spin information. Along with the recent development of topological materials, topological magnets, where both magnetism and topological electronic states coexist, have been intensively studied. These topological magnets are of great interest with multitudes of potential applications, since their electronic structures are topologically protected but changeable by modulating spin configurations or orientation. This new class of materials offers novel opportunities to couple spin and charge degrees of freedom, which are useful for spin-electronic applications.
In 2018, the research team has reported the discovery of a ferromagnetic semimetal Fe3GeTe2 in Nature Materials. This material was found to have unique nodal-line-shaped band crossing points, and thus classified as a topological magnet. One unique property of this topological magnet is that degeneracy can be lifted in the nodal-line states depending on spin orientation. Extending the idea, the research team has focused on magnetic semiconductors, which possess topological nodal-line states in conduction or valence bands. Again, the band degeneracy of the nodal-line state is sensitive to spin orientation, but in magnetic semiconductors, the lifting of band degeneracy, controlled by spin rotation, can turn the system into either a semiconductor or a metal. Thus charge current flow can be switched on or off by spin rotation, as it is done in conventional semiconductors by applying an electric field.
Identifying the candidate material possessing both ferro- or ferrimagnetism and a topological band degeneracy was the first obstacle. Dr. KIM Kyoo at the Korea Atomic Energy Research Institute (KAERI), used first-principle calculation methods to predict a nodal-line-type band degeneracy in a ferrimagnet Mn3Si2Te6. When he rotated the net magnetic moment of Mn3Si2Te6 in his calculations, the nodal-line degeneracy was lifted, as found in Fe3GeTe2, which is strong enough to induce the bandgap closure. HA Hyunsoo and Prof. YANG Bohm-Jung at the IBS and Seoul National University used the symmetry analysis and found that nodal-line degeneracy of Mn3Si2Te6 is protected by a certain crystalline symmetry, reflecting its topological nature. The constructed Hamiltonian, taking into account both nodal-line states and strong spin-orbit coupling, can capture the calculated changes in the nodal-line states, depending on the spin direction.
Dr. SEO Junho and Dr. De Chandan in Prof. KIM Jun Sung’s research team at the IBS and POTSECH successfully synthesized single crystals of Mn3Si2Te6 and measured their resistance at low temperatures while rotating its spin moments using external magnetic fields. They found that large resistance, reaching gigaohm, turns to tens of milliohm as the magnetic field rotates. This huge change in resistance depending on magnetic field angle has never been observed and is, at least, 100 thousand times larger than previously known magnetic materials that show angular magnetoresistance. LEE Ji Eun and Prof. KIM Jae Hoon in the Department of Physics at Yonsei University in Seoul, South Korea used terahertz absorption measurements to experimentally confirm that the observed huge change in resistance is indeed due to electronic gap closure and the resulting insulator-to-metal transition, as it was theoretically predicted. These theoretical and experimental findings from the close collaboration of the research teams involved proved that the colossal angular magnetoresistance is a direct consequence of spin-polarized nodal-line states and their unique spin-charge coupling.
The newly discovered colossal angular magnetoresistance is expected to be utilized in vector magnetic sensing with high angular sensitivity or efficient electrical readout of the spin state. Furthermore, by exploiting the semiconducting nature of Mn3Si2Te6, a new type of spintronic device can be realized, in which both charge and spin degrees of freedom are modulated by using electric or magnetic fields simultaneously. One of the remaining challenges is how to extend the working temperature range of the colossal angular magnetoresistance up to room temperature. The colossal angular magnetoresistance is considered to be a common property of magnetic topological semiconductors that have a triangular lattice as a structural motif. “In nature, there is a vast possibility of candidate magnetic semiconductors, showing similar or even stronger properties at high temperatures, awaiting theoretical investigation and experimental verification,” noted Professor Yang, one of the co-corresponding authors of the study. More

The ability to precisely control and change properties of a photon, including polarization, position in space, and arrival time, gave rise to a wide range of communication technologies we use today, including the Internet. The next generation of photonic technologies, such as photonic quantum networks and computers, will require even more control over the properties of a photon.
One of the hardest properties to change is a photon’s color, otherwise known as its frequency, because changing the frequency of a photon means changing its energy.
Today, most frequency shifters are either too inefficient, losing a lot of light in the conversion process, or they can’t convert light in the gigahertz range, which is where the most important frequencies for communications, computing, and other applications are found.
Now, researchers from the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) have developed highly efficient, on-chip frequency shifters that can convert light in the gigahertz frequency range. The frequency shifters are easily controlled, using continuous and single-tone microwaves.
“Our frequency shifters could become a fundamental building block for high-speed, large-scale classical communication systems as well as emerging photonic quantum computers,” said Marko Lončar, the Tiantsai Lin Professor of Electrical Engineering and senior author of the paper.
The paper outlines two types of on-chip frequency shifter — one that can covert one color to another, using a shift of a few dozen gigahertz, and another that can cascade multiple shifts, a shift of more than 100 gigahertz. More

Scientists seek stable, high-energy batteries designed for the electric grid.
Bringing new sources of renewable energy like wind and solar power onto the electric grid will require specially designed large batteries that can charge when the sun is shining and give energy at night. One type of battery is especially promising for this purpose: the flow battery. Flow batteries contain two tanks of electrically active chemicals that exchange charge and can have large volumes that hold a lot of energy.
For researchers working on flow batteries, their chief concern involves finding target molecules that offer the ability to both store a lot of energy and remain stable for long periods of time.
To find the right flow battery molecules, researchers at the U.S. Department of Energy’s (DOE) Argonne National Laboratory have turned to the power of artificial intelligence (AI) to search through a vast chemical space of over a million molecules. Discovering the right molecules requires optimizing between several different characteristics. “In these batteries, we know that a majority of the molecules that we need will have to satisfy multiple properties,” said Argonne chemist Rajeev Assary. “By optimizing several properties simultaneously, we have a better shot of finding the best possible chemistry for our battery.”
In a new study that follows on from work done last year, Assary and his colleagues in Argonne’s Joint Center for Energy Storage Research modeled anolyte redoxmers, or electrically active molecules in a flow battery. For each redoxmer, the researchers identified three properties that they wanted to optimize. The first two, reduction potential and solvation free energy, relate to how much energy the molecule can store. The third, fluorescence, serves as a kind of self-reporting marker that indicates the overall health of the battery.
Because it is extraordinarily time consuming to calculate the properties of interest for all potential candidates, the researchers turned to a machine learning and AI technique called active learning, in which a model can actually train itself to identify increasingly plausible targets. “We’re essentially looking for needles in haystacks,” said Argonne postdoctoral researcher Hieu Doan. “When our model finds something that looks like a needle, it teaches itself how to find more.”
For the most efficient use of active learning, the researchers started with a fairly small “haystack” — a dataset of 1400 redoxmer candidates whose properties they already knew from quantum mechanical simulations. By using this dataset as practice, they were able to see that the algorithm correctly identified the molecules with the best properties. More

Emerging 5G wireless systems are designed to support high-bandwidth and low-latency networks connecting everything from autonomous robots to self-driving cars. But these large and complex communication networks could also pose new security concerns.
Encryption methods now used to secure communications from eavesdroppers can be challenging to scale towards such high-speed and ultra-low latency systems for 5G and beyond. This is because the very nature of encryption requires exchange of information between sender and receiver to encrypt and decrypt a message. This exchange makes the link vulnerable to attacks; it also requires computing that increases latency. Latency, the amount of time between sending instructions on a network and the arrival of the data, is a key measure for tasks like autonomous driving and industrial automation. For networks that support latency-critical systems such as self-driving cars, robots and other cyber-physical systems, minimizing time-to-action is critical.
Seeking to close this security gap, Princeton University researchers have developed a methodology that incorporates security in the physical nature of the signal. In a report published Nov. 22 in Nature Electronics, the researchers describe how they developed a new millimeter-wave wireless microchip that allows secure wireless transmissions to prevent interception without reducing latency, efficiency and speed of the 5G network. According to senior researcher Kaushik Sengupta, the technique should make it very challenging to eavesdrop on such high-frequency wireless transmissions, even with multiple colluding bad actors.
“We are in a new era of wireless — the networks of the future are going to be increasingly complex while serving a large set of different applications that demand very different features,” Sengupta said. “Think low-power smart sensors in your home or in an industry, high-bandwidth augmented reality or virtual reality, and self-driving cars. To serve this and serve this well, we need to think about security holistically and at every level.”
Instead of relying on encryption, the Princeton method shapes the transmission itself to foil would-be eavesdroppers. To explain this, it helps to picture wireless transmissions as they emerge from an array of antennas. With a single antenna, radio waves radiate from the antenna in a wave. When there are multiple antennas working as an array, these waves interfere with each other like waves of water in a pond. The interference increases the size of some wave crests and troughs and smooths out others.
An array of antennas is able to use this interference to direct a transmission along a defined path. But besides the main transmission, there are secondary paths. These secondary paths are weaker than the main transmission, but in a typical system they contain the exact same signal as the main path. By tapping these paths, potential eavesdroppers can compromise the transmission. More

A new “image analysis pipeline” is giving scientists rapid new insight into how disease or injury have changed the body, down to the individual cell.
It’s called TDAExplore, which takes the detailed imaging provided by microscopy, pairs it with a hot area of mathematics called topology, which provides insight on how things are arranged, and the analytical power of artificial intelligence to give, for example, a new perspective on changes in a cell resulting from ALS and where in the cell they happen, says Dr. Eric Vitriol, cell biologist and neuroscientist at the Medical College of Georgia.
It is an “accessible, powerful option” for using a personal computer to generate quantitative — measurable and consequently objective — information from microscopic images that likely could be applied as well to other standard imaging techniques like X-rays and PET scans, they report in the journal Patterns.
“We think this is exciting progress into using computers to give us new information about how image sets are different from each other,” Vitriol says. “What are the actual biological changes that are happening, including ones that I might not be able to see, because they are too minute, or because I have some kind of bias about where I should be looking.”
At least in the analyzing data department, computers have our brains beat, the neuroscientist says, not just in their objectivity but in the amount of data they can assess. Computer vision, which enables computers to pull information from digital images, is a type of machine learning that has been around for decades, so he and his colleague and fellow corresponding author Dr. Peter Bubenik, a mathematician at the University of Florida and an expert on topological data analysis, decided to partner the detail of microscopy with the science of topology and the analytical might of AI. Topology and Bubenik were key, Vitriol says.
Topology is “perfect” for image analysis because images consist of patterns, of objects arranged in space, he says, and topological data analysis (the TDA in TDAExplore) helps the computer also recognize the lay of the land, in this case where actin — a protein and essential building block of the fibers, or filaments, that help give cells shape and movement — has moved or changed density. It’s an efficient system, that instead of taking literally hundreds of images to train the computer how to recognize and classify them, it can learn on 20 to 25 images. More

The ancient arts of origami, the art of paper-folding, and kirigami, the art of paper-cutting, have gained popularity in recent years among researchers building mechanical metamaterials. Folding and cutting 2D thin-film materials transforms them into complex 3D structures and shapes with unique and programmable mechanical properties.
In Applied Physics Reviews, by AIP Publishing, researchers in the United States and China categorize origami- and kirigami-based mechanical metamaterials, artificially engineered materials with unusual mechanical properties, into six groups based on two different criteria.
“Origami and kirigami are, by nature, mechanical metamaterials, because their properties are mainly determined by how the crease patterns and/or cuts are made and just slightly depend on the material that folds the origami or kiragami,” said author Hanqing Jiang.
The researchers divided the mechanical metamaterials into three categories that include origami-based metamaterials (folding only), kirigami-based metamaterials (cutting only), and hybrid origami-kirigami metamaterials (both folding and cutting). The hybrid origami-kirigami metamaterials, in particular, offer great potential in shape morphing.
Each group was subdivided into a rigid or deformable category based on the elastic energy landscape. Metamaterials were classified as rigid if energy was stored in the creases or linkages only. Metamaterials were put in the deformable category if energy was stored in both creases or linkages and panels.
The researchers want to discover new origami and kirigami designs, especially curved origami designs, hybrid origami-kirigami designs, modular designs, and hierarchical designs.
They plan to focus on the selection of new materials for origami- and kirigami-based mechanical metamaterials. Traditionally paper is used to prototype metamaterials but there are limits based on the fragility and plasticity of paper. To design for real-world applications, it will be helpful to explore materials with different properties such as thin or thick, soft or hard, and elastic or plastic.
They want to use the energy landscape and energy distribution as two powerful tools to analyze mechanical performances of origami and kirigami and will seek to carefully design the actuation method of origami- and kirigami-based mechanical metamaterials.
“Origami- and kiragami-based mechanical metamaterials can be applied in many fields, including flexible electronics, medical devices, robotics, civil engineering and aerospace engineering,” said Jiang.
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Materials provided by American Institute of Physics. Note: Content may be edited for style and length. More

Mathematicians have analysed global COVID-19 data to identify two constants which can drastically change a country’s rate of infection.
An international team of researchers led by Professor Alexander Gorban from the University of Leicester used available data from 13 countries to determine the rate of stress response, or ‘mobilisation’ and the rate of spontaneous exhaustion, or ‘demobilisation’.
Their findings, published in Scientific Reports, show that social stress — which varied broadly across the countries studied — drives the multi-wave dynamics of COVID-19 outbreaks.
The study analysed data from China, the USA, UK, Germany, Colombia, Italy, Spain, Israel, Russia, France, Brazil, India, and Iran — and contributed to the research team’s proposed new system of models, which combine the dynamics of the established concept of social stress with classical epidemic models.
Alexander Gorban is a Professor of Applied Mathematics at the University of Leicester, and Director of the Centre for Artificial Intelligence, Data Analysis and Modelling. Professor Gorban said:
“We tried to use the pandemic for research and quantify the social and cultural differences between countries. We measured how variable countries are in two processes: mobilisation of people for rational protective behaviour and exhaustion of this mobilisation with destroying of rational behaviour. More

Science has the technology to measure the activity of every gene within a single individual cell, and just one experiment can generate thousands of cells worth of data. Researchers at Lund University in Sweden have now revolutionised the way this data is analysed — by using 3D video gaming technology. The study is published in the journal iScience.
Advanced techniques in DNA and RNA sequencing have opened up the possibility of studying individual cells in tissue in a more comprehensive way than was previously possible. The big challenge with these sequencing techniques is that they lead to large amounts of data.
“When you want to distinguish cancer cells from normal cells, for example, you need to examine thousands of cells to get a proper understanding, which translates into enormous amounts of numerical data,” says Shamit Soneji, researcher in computational biology at Lund University.
To make this data comprehensible, each cell is mathematically positioned in three-dimensional space to form a “roadmap” of the cells, and how they relate to each other. However, these maps can be difficult to navigate using a regular desktop computer.
“To be able to walk around your own data and manipulate it intuitively and efficiently gives it a whole new understanding. I would actually go so far as to say that one thinks differently in VR, thanks to the technique’s ability to involve your body in the analysis process,” explains Mattias Wallergård. researcher in interaction design and virtual reality at Lund University.
The Lund University team have developed the software CellexalVR; a virtual reality environment that enables researchers to use intuitive tools to explore all their data in one place. 3D maps of cells that have been calculated from gene activity and other information captured from individual cells can be displayed, and the researcher can clearly see which genes are active when certain cell types are formed.
Using a VR headset, the user has a complete universe of cell populations in front of them, and can more accurately determine how cells relate to one another. Using two hand controllers, they can select cells of interest for further analysis with simple hand gestures as if they were physical objects.
Since space is not an issue, it is possible to have several cellular maps in the same “room” and compare them side by side, something that is difficult on a traditional computer screen. Researchers can also meet in this VR world to analyze data together, despite being in different places geographically.
“Even if you are not familiar with computer programming, this type of analysis is open to everyone. A virtual world is a fast developing area of research that has enormous potential for scientists that need to access and process big-data in a more interactive and collaborative way,” concludes Shamit Soneji.
The software can be downloaded for free at https://www.cellexalvr.med.lu.se/
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Materials provided by Lund University. Note: Content may be edited for style and length. More