Computers Math

Scientists have long looked to the brain as an inspiration for designing computing systems. Some researchers have recently gone even further by making computer hardware with a brainlike structure. These “neuromorphic chips” have already shown great promise, but they have used conventional digital electronics, limiting their complexity and speed. As the chips become larger and more complex, the signals between their individual components become backed up like cars on a gridlocked highway and reduce computation to a crawl.
Now, a team at the National Institute of Standards and Technology (NIST) has demonstrated a solution to these communication challenges that may someday allow artificial neural systems to operate 100,000 times faster than the human brain.
The human brain is a network of about 86 billion cells called neurons, each of which can have thousands of connections (known as synapses) with its neighbors. The neurons communicate with each other using short electrical pulses called spikes to create rich, time-varying activity patterns that form the basis of cognition. In neuromorphic chips, electronic components act as artificial neurons, routing spiking signals through a brainlike network.
Doing away with conventional electronic communication infrastructure, researchers have designed networks with tiny light sources at each neuron that broadcast optical signals to thousands of connections. This scheme can be especially energy-efficient if superconducting devices are used to detect single particles of light known as photons — the smallest possible optical signal that could be used to represent a spike.
In a new Nature Electronics paper, NIST researchers have achieved for the first time a circuit that behaves much like a biological synapse yet uses just single photons to transmit and receive signals. Such a feat is possible using superconducting single-photon detectors. The computation in the NIST circuit occurs where a single-photon detector meets a superconducting circuit element called a Josephson junction. A Josephson junction is a sandwich of superconducting materials separated by a thin insulating film. If the current through the sandwich exceeds a certain threshold value, the Josephson junction begins to produce small voltage pulses called fluxons. Upon detecting a photon, the single-photon detector pushes the Josephson junction over this threshold and fluxons are accumulated as current in a superconducting loop. Researchers can tune the amount of current added to the loop per photon by applying a bias (an external current source powering the circuits) to one of the junctions. This is called the synaptic weight.
This behavior is similar to that of biological synapses. The stored current serves as a form of short-term memory, as it provides a record of how many times the neuron produced a spike in the near past. The duration of this memory is set by the time it takes for the electric current to decay in the superconducting loops, which the NIST team demonstrated can vary from hundreds of nanoseconds to milliseconds, and likely beyond. This means the hardware could be matched to problems occurring at many different time scales — from high-speed industrial control systems to more leisurely conversations with humans. The ability to set different weights by changing the bias to the Josephson junctions permits a longer-term memory that can be used to make the networks programmable so that the same network could solve many different problems.
Synapses are a crucial computational component of the brain, so this demonstration of superconducting single-photon synapses is an important milestone on the path to realizing the team’s full vision of superconducting optoelectronic networks. Yet the pursuit is far from complete. The team’s next milestone will be to combine these synapses with on-chip sources of light to demonstrate full superconducting optoelectronic neurons.
“We could use what we’ve demonstrated here to solve computational problems, but the scale would be limited,” NIST project leader Jeff Shainline said. “Our next goal is to combine this advance in superconducting electronics with semiconductor light sources. That will allow us to achieve communication between many more elements and solve large, consequential problems.”
The team has already demonstrated light sources that could be used in a full system, but further work is required to integrate all the components on a single chip. The synapses themselves could be improved by using detector materials that operate at higher temperatures than the present system, and the team is also exploring techniques to implement synaptic weighting in larger-scale neuromorphic chips.
The work was funded in part by the Defense Advanced Research Projects Agency.
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Materials provided by National Institute of Standards and Technology (NIST). Note: Content may be edited for style and length. More

MSU researchers are using big data and AI to identify current drugs that could be applied to treat new COVID-19 variants.
Finding new ways to treat the novel coronavirus and its ever-changing variants has been a challenge for researchers, especially when the traditional drug development and discovery process can take years. A Michigan State University researcher and his team are taking a hi-tech approach to determine whether drugs already on the market can pull double duty in treating new COVID variants.
“The COVID-19 virus is a challenge because it continues to evolve,” said Bin Chen, an associate professor in the College of Human Medicine. “By using artificial intelligence and really large data sets, we can repurpose old drugs for new uses.”
Chen built an international team of researchers with expertise on topics ranging from biology to computer science to tackle this challenge. First, Chen and his team turned to publicly available databases to mine for the unique coronavirus gene expression signatures from 1,700 host transcriptomic profiles that came from patient tissues, cell cultures and mouse models. These signatures revealed the biology shared by COVID-19 and its variants.
With the virus’s signature and knowing which genes need to be suppressed and which genes need to be activated, the team was able to use a computer program to screen a drug library consisting of FDA-approved or investigational drugs to find candidates that could correct the expression of signature genes and further inhibit the coronavirus from replicating. Chen and his team discovered one novel candidate, IMD-0354, a drug that passed phase I clinical trials for the treatment of atopic dermatitis. A group in Korea later observed that it was 90-fold more effective against six COVID-19 variants than remdesivir, the first drug approved to treat COVID-19. The team further found that IMD-0354 inhibited the virus from copying itself by boosting the immune response pathways in the host cells. Based on the information learned, the researchers studied a prodrug of IMD-0354 called IMD-1041. A prodrug is an inactive substance that is metabolized within the body to create an active drug.
“IMD-1041 is even more promising as it is orally available and has been investigated for chronic obstructive pulmonary disease, a group of lung diseases that block airflow and make it difficult to breathe,” Chen said. “Because the structure of IMD-1041 is undisclosed, we are developing a new artificial intelligence platform to design novel compounds that hopefully could be tested and evaluated in more advanced animal models.”
The research was published in the journal iScience.
This project was led by two senior postdoctoral scholars in the Chen lab: Jing Xing, who recently became a young investigator at the Chinese Academy of Sciences, and Rama Shankar, with the support from researchers from Institute Pasteur Korea, Shanghai Institute of Materia Medica, University of Texas Medical Branch, Spectrum Health in Grand Rapids and Stanford University.
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Materials provided by Michigan State University. Original written by Emilie Lorditch. Note: Content may be edited for style and length. More

This year, about 240,000 people in the U.S. will discover they have lung cancer. Some 200,000 of them will be diagnosed with non-small-cell lung cancer, which is the second leading cause of death after cardiovascular disease.
Georgia Tech researcher Ahmet Coskun is working to improve the odds for these patients in two recently published studies that are essentially focused on understanding why and how patients respond differently to disease and treatments.
“What we have learned is connectivity and communication between molecules and between cells is what really controls everything, regarding whether or not patients get healthy, or how they will respond to drugs,” said Coskun, an assistant professor in the Wallace H. Coulter Department of Biomedical Engineering at Georgia Tech and Emory University.
Published in the journals npj Precision Oncology and iScience, the studies detail the development of tools and techniques to deeply explore the tumor microenvironment at the subcellular level, utilizing the Coskun lab’s expertise in combining multiplex cellular imaging methods with artificial intelligence.
“We are developing a better grasp of cellular signaling and decision making, and how it is coordinated in the tumor microenvironment, which can lead to better personalized, precision treatments for these patients,” said Coskun, who is keenly interested in why some patients respond to groundbreaking immunotherapy drugs, and some don’t.
With that in mind, his team developed SpatialVizScore, a new method they describe in npj Precision Oncology, to deeply study tumor immunology in cancer tissues and help identify which patients are more likely to respond to an immunotherapy. It’s a significant upgrade to the current standard methodology used by cancer physicians and researchers, Immunoscore. More

Algorithms developed in Cornell’s Laboratory for Intelligent Systems and Controls can predict the in-game actions of volleyball players with more than 80% accuracy, and now the lab is collaborating with the Big Red hockey team to expand the research project’s applications.
The algorithms are unique in that they take a holistic approach to action anticipation, combining visual data — for example, where an athlete is located on the court — with information that is more implicit, like an athlete’s specific role on the team.
“Computer vision can interpret visual information such as jersey color and a player’s position or body posture,” said Silvia Ferrari, the John Brancaccio Professor of Mechanical and Aerospace Engineering, who led the research. “We still use that real-time information, but integrate hidden variables such as team strategy and player roles, things we as humans are able to infer because we’re experts at that particular context.”
Ferrari and doctoral students Junyi Dong and Qingze Huo trained the algorithms to infer hidden variables the same way humans gain their sports knowledge — by watching games. The algorithms used machine learning to extract data from videos of volleyball games, and then used that data to help make predictions when shown a new set of games.
The results were published Sept. 22 in the journal ACM Transactions on Intelligent Systems and Technology, and show the algorithms can infer players’ roles — for example, distinguishing a defense-passer from a blocker — with an average accuracy of nearly 85%, and can predict multiple actions over a sequence of up to 44 frames with an average accuracy of more than 80%. The actions included spiking, setting, blocking, digging, running, squatting, falling, standing and jumping.
Ferrari envisions teams using the algorithms to better prepare for competition by training them with existing game footage of an opponent and using their predictive abilities to practice specific plays and game scenarios.
Ferrari has filed for a patent and is now working with the Big Red men’s hockey team to further develop the software. Using game footage provided by the team, Ferrari and her graduate students, led by Frank Kim, are designing algorithms that autonomously identify players, actions and game scenarios. One goal of the project is to help annotate game film, which is a tedious task when performed manually by team staff members.
“Our program places a major emphasis on video analysis and data technology,” said Ben Russell, director of hockey operations for the Cornell men’s team. “We are constantly looking for ways to evolve as a coaching staff in order to better serve our players. I was very impressed with the research Professor Ferrari and her students have conducted thus far. I believe that this project has the potential to dramatically influence the way teams study and prepare for competition.”
Beyond sports, the ability to anticipate human actions bears great potential for the future of human-machine interaction, according to Ferrari, who said improved software can help autonomous vehicles make better decisions, bring robots and humans closer together in warehouses, and can even make video games more enjoyable by enhancing the computer’s artificial intelligence.
“Humans are not as unpredictable as the machine learning algorithms are making them out to be right now,” said Ferrari, who is also associate dean for cross-campus engineering research, “because if you actually take into account all of the content, all of the contextual clues, and you observe a group of people, you can do a lot better at predicting what they’re going to do.”
The research was supported by the Office of Naval Research Code 311 and Code 351, and commercialization efforts are being supported by the Cornell Office of Technology Licensing.
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Materials provided by Cornell University. Original written by Syl Kacapyr, courtesy of the Cornell Chronicle. Note: Content may be edited for style and length. More

Tiny particles that are interconnected despite sometimes being thousands of kilometres apart — Albert Einstein called this ‘spooky action at a distance’. Something that would be inexplicable by the laws of classical physics is a fundamental part of quantum physics. Entanglement like this can occur between multiple quantum particles, meaning that certain properties of the particles are intimately linked with each other. Entangled systems containing multiple quantum particles offer significant benefits in implementing quantum algorithms, which have the potential to be used in communications, data security or quantum computing.
Researchers from Paderborn University have been working with colleagues from Ulm University to develop the first programmable optical quantum memory. The study was published as an ‘editor’s suggestion’ in the Physical Review Letters journal.
Entangled light particles
The ‘Integrated Quantum Optics’ group led by Prof. Christine Silberhorn from the Department of Physics and Institute for Photonic Quantum Systems (PhoQS) at Paderborn University is using minuscule light particles, or photons, as quantum systems. The researchers are seeking to entangle as many as possible in large states. Working together with researchers from the Institute of Theoretical Physics at Ulm University, they have now presented a new approach.
Previously, attempts to entangle more than two particles only resulted in very inefficient entanglement generation. If researchers wanted to link two particles with others, in some cases this involved a long wait, as the interconnections that promote this entanglement only operate with limited probability rather than at the touch of a button. This meant that the photons were no longer a part of the experiment once the next suitable particle arrived — as storing qubit states represents a major experimental challenge.
Gradually achieving greater entanglement
“We have now developed a programmable, optical, buffer quantum memory that can switch dynamically back and forth between different modes — storage mode, interference mode and the final release,” Silberhorn explains. In the experimental setup, a small quantum state can be stored until another state is generated, and then the two can be entangled. This enables a large, entangled quantum state to ‘grow’ particle by particle. Silberhorn’s team has already used this method to entangle six particles, making it much more efficient than any previous experiments. By comparison, the largest ever entanglement of photon pairs, performed by Chinese researchers, consisted of twelve individual particles. However, creating this state took significantly more time, by orders of magnitude.
The quantum physicist explains: “Our system allows entangled states of increasing size to be gradually built up — which is much more reliable, faster, and more efficient than any previous method. For us, this represents a milestone that puts us in striking distance of practical applications of large, entangled states for useful quantum technologies.” The new approach can be combined with all common photon-pair sources, meaning that other scientists will also be able to use the method.
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Gold, a precious metal, is arguably the most widely used metal across jewelry and coinage due to its physical properties that are unique to the world of metals. Not only is it a good conductor of heat and electricity, it is unaffected by air and most reagents. It is also used in a wide range of industrial, scientific, and medical applications. For example, it has been used as the template for molecular self-assembly, the supporting material for two-dimensional materials growth, and especially for the synthesis of carbon nanoribbons. More than half a century ago, researchers unveiled the fancy textures on gold surfaces at the nanoscale. Efforts for a better understanding of the surface structures on the atomic scale have been continually paid for from then on.
Au(111) surface, the most stable gold surface, has a periodic herringbone texture on it that can be observed by sophisticated microscopes. A long-term puzzle is why this strange herringbone forms on this gold surface. Extensive studies have been performed for decades but a thorough description of structure details is still missing and thus the underlying mechanism has never been properly understood. The difficulties in this issue lie in the fact that even though the size of the texture is at the nanoscale, its periodic unit still contains more than 100,000 atoms. To quantitatively study this system, one needs a very efficient and also very accurate computational method. In traditional approaches, however, these two requirements cannot be satisfied simultaneously.
Recently, Distinguished Professor Feng Ding (Department of Materials Science and Engineering) and his colleagues from the Center for Multidimensional Carbon Materials (CMCM), within the Institute for Basic Science (IBS) at UNIST, utilized the state-of-the-art neural network method to train a gold force field from an accurate but slow computational method.
Due to the powerful learning ability of neural networks, this new force field acquires almost the same accuracy, and more importantly, it is many orders of magnitude faster than the original method. Using this force field, the authors successfully simulated the experimentally observed herringbone texture on Au(111) surface and revealed that there is non-negligible deformation underneath the surface. This deformation is critical for the formation of the herringbone texture because it allows an effective relaxation of the rearranged surface atoms. If the deformation is suppressed (take a thin model for instance), the texture will form stripes.
Meanwhile, the authors also verified that the herringbone texture is sensitive to applied strains. On a strain-free surface, the herringbone texture is mirror-symmetric. However, if a slight strain is introduced, the texture becomes tilted. Above a critical strain, it thoroughly transforms into a stripe texture.
“This important work extends the application of the machine learning method in material science and opens a new avenue to study complex surface systems,” noted the research team.
Led by Distinguished Professor Feng Ding, this study was first authored by Dr. Pai Li. The findings of this research have been published in the October 2022 issue of Science Advances.
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Materials provided by Ulsan National Institute of Science and Technology(UNIST). Original written by JooHyeon Heo. Note: Content may be edited for style and length. More

From superfast magnetic levitation trains and computer chips to magnetic resonance imaging (MRI) machines and particle accelerators, superconductors are electrifying various aspects of our life. Superconductivity is an interesting property that allows materials to transfer moving charges without any resistance, below a certain critical point. This implies that superconducting materials can transfer electrical energy in a highly efficient manner without loss in the form of heat, unlike many conventional conductors.
Almost two decades ago scientists discovered superconductivity in a new material — magnesium diboride, or MgB2. There has been a resurgence in the of popularity MgB2 due to its low cost, superior superconducting abilities, high critical current density (which means that compared to other materials, MgB2 remains a semiconductor even when larger amounts of electric current is passed through it), and trapped magnetic fields arising from strong pinning of the vortices — which are cylindrical current loops or tubes of magnetic flux that penetrate a superconductor. The intermetallic MgB2 also allows adjustability of its properties. For instance, the critical current density values (Jc) of MgB2 can be improved by decreasing the grain size and increasing the number of grain boundaries. Such adjustability is not observed in conventional layered superconductors.
To widen the applications of MgB2, however, there is a need to simplify the method of its preparation. Recently, a team of researchers embarked on a journey to do so. They fabricated a novel bulk MgB2 via a process called spark plasma sintering (SPS). In their recent article, published first on 27 July 2022 in Nanomaterials, Prof. Muralidhar Miryala from Shibaura Institute of Technology (SIT), Japan, who led the group, explains “Spark plasma sintering (SPS) is a very interesting technique — it is a rapid consolidation method, where powder is turned into a dense solid. The heat source in this procedure is not external but is an electric current that flows across the die, causing the powder to sinter into a bulk material. The sintering kinetics can be understood and controlled better with SPS. Unlike other similar techniques, it allows grain growth control. What’s more, it also has a shorter processing time!”
Prof. Miryala and Prof. Jacques G. Noudem (from the University of Normandie, France) had used this unconventional method to prepare bulk samples of MgB2. The resultant material had excellent superconducting properties and a density that reached 95% of what was theoretically predicted for the material. The study team included Prof. Pierre Bernstein and Yiteng Xing, who is a double degree Ph.D. student at SIT and the University of Normandie.
To synthesize the bulk MgB2, the team loaded two powders — magnesium and amorphous boron — into a tungsten carbide (WC) mold and sintered them using SPS at different temperatures ranging from 500-750°C, and pressure ranging from 260-300 megapascal (MPa), then cooled the formed material. The total processing time was about 100 minutes. The team then analyzed the density and the structural properties of the prepared material, using various imaging and testing methods.
Their experiments revealed that the material had a very high density of 2.46 g/cm3 and a high packing factor of 95% (indicating that the atoms in the bulk material were situated very close to each other). It also showed the presence of nano-grains and a large number of grain boundaries. Moreover, it did not exhibit Mg-depleted phases like MgB4. Electromagnetic characterization of the material showed that it exhibited an extremely high Jc of up to 6.75 105 ampere/cm2 at about -253°C. This means that even at that high a current density, the bulk MgB2 made by the team would act as a superconductor. “Its Jc was quite remarkable for pure, undoped MgB2,” commented Prof. Miryala.
Curious as to how the material exhibited such excellent properties, the team dug deeper. They concluded that the prepared MgB2’s superconducting properties were due to its high density, excellent grain connectivity (due to no Mg-depleted phases), and the strong pinning of vortices availed by the presence of nano-grains and grain boundaries.
This study provided a new way to improve the properties of superconducting materials like MgB2. Given this material’s high Jc, it can be used in liquid hydrogen-cooled technology. It is also emerging as a promising candidate for liquid hydrogen-based transportation, storage, and fuel systems. “Global warming is one of the major threats humanity is facing today and shifting to a renewable energy economy is one of the most effective solutions to this problem. Given the material’s potential use in liquid hydrogen systems and its excellent structural and superconducting properties, our work is a positive step towards the realization of greener technology,” concludes Prof. Miryala.
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Discarded electronic devices, such as cell phones, are a fast-growing source of waste. One way to mitigate the problem could be to use components that are made with renewable resources and that are easy to dispose of responsibly. Now, researchers reporting in ACS Applied Materials & Interfaces have created a prototype circuit board that is made of a sheet paper with fully integrated electrical components, and that can be burned or left to degrade.
Most small electronic devices contain circuit boards that are made from glass fibers, resins and metal wiring. These boards are not easy to recycle and are relatively bulky, making them undesirable for use in point-of-care medical devices, environmental monitors or personal wearable devices. One alternative is to use paper-based circuit boards, which should be easier to dispose of, less expensive and more flexible. However, current options require specialized paper, or they simply have traditional metal circuitry components mounted onto a sheet of paper. Instead, Choi and colleagues wanted to develop circuitry that would be simple to manufacture and that had all the electronic components fully integrated into the sheet.
The team designed a paper-based amplifier-type circuit that incorporated resistors, capacitors and a transistor. They first used wax to print channels onto a sheet of paper in a simple pattern. After melting the wax so that it soaked into the paper, the team printed semi-conductive and conductive inks, which soaked into the areas not blocked by wax. Then, the researchers screen-printed additional conductive metal components and casted a gel-based electrolyte onto the sheet.
Tests confirmed that the resistor, capacitor and transistor designs performed properly. The final circuit was very flexible and thin, just like paper, even after adding the components. To demonstrate the degradability of the circuit, the team showed that the entire unit quickly burned to ash after being lit on fire. The researchers say this represents a step toward producing completely disposable electronic devices.
The authors acknowledge funding from the National Science Foundation.
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