Computers Math

Unlike fictional laser swords, real laser beams do not interact with each other when they cross — unless the beams meet within a suitable material allowing for nonlinear light-matter interaction. In such a case, wave mixing can give rise to beams with changed colors and directions.
Wave-mixing processes between different light beams are one cornerstone of the field of nonlinear optics, which is firmly established since lasers have become widely available. Within a suitable material such as particular crystals, two laser beams can “feel each other’s presence.” In this process, energy and momentum can be exchanged, giving rise to additional laser beams emerging from the interaction zone in different directions and with different frequencies, in the visible spectral range seen as different colors. These effects are commonly used to design and realize new laser light sources. Just as important, the analysis of the emerging light beams in wave mixing phenomena provides insights into the nature of the material in which the wave mixing process occurs. Such wave-mixing based spectroscopy allows researchers to understand intricacies of the electronic structure of a specimen and how light can excite and interact with the material. So far, however, these approaches have been hardly used outside of the visible or infrared spectral range.
A team of researchers from Max Born Institute (MBI), Berlin, and DESY, Hamburg, has now observed a new kind of such wave mixing process involving soft x-rays. Overlapping ultrashort pulses of soft x-rays and infrared radiation in a single crystal of lithium fluoride (LiF), they see how energy from two infrared photons is transferred to or from the x-ray photon, changing the x-ray “color” in a so-called third-order nonlinear process . Not only do they observe this particular process with x-rays for the first time, they were also able to map out its efficiency when changing the color of the incoming x-rays. It turns out that the mixing signals are only detectable when the process involves an inner-shell electron from a lithium atom being promoted into a state where this electron is tightly bound to the vacancy it left behind — a state known as exciton. Furthermore, comparison with theory shows that an otherwise “optically forbidden” transition of an inner-shell electron contributes to the wave mixing process.
Via analysis of this resonant four-wave mixing process, the researchers get a detailed picture of where the optically excited electron travels in its very short lifetime. “Only if the excited electron is localized in the immediate vicinity of the hole it has left behind do we observe the four-wave mixing signal,” says Robin Engel, a PhD student involved in the work, “and because we have used a specific color of x-rays, we know that this hole is very close to the atomic nucleus of the lithium atom.” Due to the ability of x-rays to excite inner shell electrons selectively at the different atomic species in a material, the demonstrated approach allows researchers to track electrons moving around in molecules or solids after they have been stimulated by an ultrafast laser pulse. Exactly such processes — electrons moving towards different atoms after having been excited by light — are crucial steps in photochemical reactions or applications such as light harvesting, e.g., via photovoltaics or direct solar fuel generation. “As our wave-mixing spectroscopy approach can be scaled to much higher photon energies at x-ray lasers, many different atoms of the periodic table can be selectively excited. In this way we expect that it will be possible to track the transient presence of electrons at many different atoms of a more complex material, giving new insight into these important processes,” explains Daniel Schick, researcher at MBI.
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Materials provided by Max Born Institute for Nonlinear Optics and Short Pulse Spectroscopy (MBI). Note: Content may be edited for style and length. More

Researchers have reported a nano-sized neuromorphic memory device that emulates neurons and synapses simultaneously in a unit cell, another step toward completing the goal of neuromorphic computing designed to rigorously mimic the human brain with semiconductor devices.
Neuromorphic computing aims to realize artificial intelligence (AI) by mimicking the mechanisms of neurons and synapses that make up the human brain. Inspired by the cognitive functions of the human brain that current computers cannot provide, neuromorphic devices have been widely investigated. However, current Complementary Metal-Oxide Semiconductor (CMOS)-based neuromorphic circuits simply connect artificial neurons and synapses without synergistic interactions, and the concomitant implementation of neurons and synapses still remains a challenge. To address these issues, a research team led by Professor Keon Jae Lee from the Department of Materials Science and Engineering implemented the biological working mechanisms of humans by introducing the neuron-synapse interactions in a single memory cell, rather than the conventional approach of electrically connecting artificial neuronal and synaptic devices.
Similar to commercial graphics cards, the artificial synaptic devices previously studied often used to accelerate parallel computations, which shows clear differences from the operational mechanisms of the human brain. The research team implemented the synergistic interactions between neurons and synapses in the neuromorphic memory device, emulating the mechanisms of the biological neural network. In addition, the developed neuromorphic device can replace complex CMOS neuron circuits with a single device, providing high scalability and cost efficiency.
The human brain consists of a complex network of 100 billion neurons and 100 trillion synapses. The functions and structures of neurons and synapses can flexibly change according to the external stimuli, adapting to the surrounding environment. The research team developed a neuromorphic device in which short-term and long-term memories coexist using volatile and non-volatile memory devices that mimic the characteristics of neurons and synapses, respectively. A threshold switch device is used as volatile memory and phase-change memory is used as a non-volatile device. Two thin-film devices are integrated without intermediate electrodes, implementing the functional adaptability of neurons and synapses in the neuromorphic memory.
Professor Keon Jae Lee explained, “Neurons and synapses interact with each other to establish cognitive functions such as memory and learning, so simulating both is an essential element for brain-inspired artificial intelligence. The developed neuromorphic memory device also mimics the retraining effect that allows quick learning of the forgotten information by implementing a positive feedback effect between neurons and synapses.”
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Materials provided by The Korea Advanced Institute of Science and Technology (KAIST). Note: Content may be edited for style and length. More

Room-temperature superconductors could transform everything from electrical grids to particle accelerators to computers — but before they can be realized, researchers need to better understand how existing high-temperature superconductors work.
Now, researchers from the Department of Energy’s SLAC National Accelerator Laboratory, the University of British Columbia, Yale University and others have taken a step in that direction by studying the fast dynamics of a material called yttrium barium copper oxide, or YBCO.
The team reports May 20 in Science that YBCO’s superconductivity is intertwined in unexpected ways with another phenomenon known as charge density waves (CDWs), or ripples in the density of electrons in the material. As the researchers expected, CDWs get stronger when they turned off YBCO’s superconductivity. However, they were surprised to find the CDWs also suddenly became more spatially organized, suggesting superconductivity somehow fundamentally shapes the form of the CDWs at the nanoscale.
“A big part of what we don’t know is the relationship between charge density waves and superconductivity,” said Giacomo Coslovich, a staff scientist at the Department of Energy’s SLAC National Accelerator Laboratory, who led the study. “As one of the cleanest high-temperature superconductors that can be grown, YBCO offers us the opportunity to understand this physics in a very direct way, minimizing the effects of disorder.”
He added, “If we can better understand these materials, we can make new superconductors that work at higher temperatures, enabling many more applications and potentially addressing a lot of societal challenges — from climate change to energy efficiency to availability of fresh water.”
Observing fast dynamics
The researchers studied YBCO’s dynamics at SLAC’s Linac Coherent Light Source (LCLS) X-ray laser. They switched off superconductivity in the YBCO samples with infrared laser pulses, and then bounced X-ray pulses off those samples. For each shot of X-rays, the team pieced together a kind of snapshot of the CDWs’ electron ripples. By pasting those together, they recreated the CDWs rapid evolution. More

Scientists have been relentlessly working on understanding the fundamental mechanisms at the base of high-temperature superconductivity with the ultimate goal to design and engineer new quantum materials superconducting close to room temperature.
High temperature superconductivity is something of a holy grail for researchers studying quantum materials. Superconductors, which conduct electricity without dissipating energy, promise to revolutionize our energy and telecommunication power systems. However, superconductors typically work at extremely low temperatures, requiring elaborate freezers or expensive coolants. For this reason, scientist have been relentlessly working on understanding the fundamental mechanisms at the base of high-temperature superconductivity with the ultimate goal to design and engineer new quantum materials superconducting close to room temperature.
Fabio Boschini, Professor at the Institut national de la recherche scientifique (INRS), and North American scientists studied the dynamics of the superconductor yttrium barium copper oxide (YBCO), which offers superconductivity at higher-than-normal temperatures, via time-resolved resonant x-ray scattering at the Linac Coherent Light Source (LCLS) free-electron laser, SLAC (US). The research was published on May 19 in the journal Science. In this new study, researchers have been able to track how charge density waves in YBCO react to a sudden “quenching” of the superconductivity, induced by an intense laser pulse.
“We are learning that charge density waves — self-organized electrons behaving like ripples in water — and superconductivity are interacting at the nanoscale on ultrafast timescales. There is a very deep connection between superconductivity emergence and charge density waves,” says Fabio Boschini, co-investigator on this project and affiliate investigator at the Stewart Blusson Quantum Matter Institute (Blusson QMI).
“Up until a few years ago, researchers underestimated the importance of the dynamics inside these materials,” said Giacomo Coslovich, lead investigator and Staff Scientist at the SLAC National Accelerator Laboratory in California. “Until this collaboration came together, we really didn’t have the tools to assess the charge density wave dynamics in these materials. The opportunity to look at the evolution of charge order is only possible thanks to teams like ours sharing resources, and by the use of a free-electron laser to offer new insight into the dynamical properties of matter.”
Owing to a better picture of the dynamical interactions underlying high-temperature superconductors, the researchers are optimistic that they can work with theoretical physicists to develop a framework for a more nuanced understanding of how high-temperature superconductivity emerges.
Collaboration is key
The present work came about from a collaboration of researchers from several leading research centres and beamlines. “We began running our first experiments at the end of 2015 with the first characterization of the material at the Canadian Light Source, says Boschini. Over time, the project came to involve many Blusson QMI researchers, such as MengXing Na who I mentored and introduced to this work. She was integral to the data analysis.”
“This work is meaningful for a number of reasons, but it also really showcases the importance of forming long-lasting, meaningful collaborations and relationships,” said Na. “Some projects take a really long time, and it’s a credit to Giacomo’s leadership and perseverance that we got here.”
The project has linked at least three generations of scientists, following some as they progressed through their postdoctoral careers and into faculty positions. The researchers are excited to expand upon this work, by using light as an optical knob to control the on-off state of superconductivity.
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Materials provided by Institut national de la recherche scientifique – INRS. Original written by Audrey-Maude Vézina. Note: Content may be edited for style and length. More

An article published May 20 in Nature’s npj Digital Medicine provides a step-by-step plan for an international effort to create a digital twin of the human immune system.
“This paper outlines a road map that the scientific community should take in building, developing and applying a digital twin of the immune system,” said Tomas Helikar, a University of Nebraska-Lincoln biochemist who is one of 10 co-authors from six universities from around the world. Earlier this year, the National Institutes of Health renewed a five-year $1.8 million grant for Helikar to continue his work in the area.
“This is an effort that will require the collaboration of computational biologists, immunologists, clinicians, mathematicians and computer scientists,” he said. “Trying to break down this complexity down into measurable and achievable steps has been a challenge. This paper is addressing that.”
A digital twin of the immune system would be a breakthrough that could offer precision medicine for a wide array of ailments, including cancer, autoimmune disease and viral infections like COVID-19.
Helikar’s involvement has been inspired in part by his 7-year-old son, who required a lung transplant as an infant. This has resulted in a life-long careful balancing of his immune system through powerful immunosuppression drugs to prevent organ rejection while keeping infections and other diseases at bay.
While the first step is to create a generic model that reflects common biological mechanisms, the eventual goal is to make virtual models at the individual level. That would enable doctors to deliver treatments precisely designed for the individual. More

Engineers at the University of California San Diego have developed a low cost, low power technology to help robots accurately map their way indoors, even in poor lighting and without recognizable landmarks or features.
The technology consists of sensors that use WiFi signals to help the robot map where it’s going. It’s a new approach to indoor robot navigation. Most systems rely on optical light sensors such as cameras and LiDARs. In this case, the so-called “WiFi sensors” use radio frequency signals rather than light or visual cues to see, so they can work in conditions where cameras and LiDARs struggle — in low light, changing light, and repetitive environments such as long corridors and warehouses.
And by using WiFi, the technology could offer an economical alternative to expensive and power hungry LiDARs, the researchers noted.
A team of researchers from the Wireless Communication Sensing and Networking Group, led by UC San Diego electrical and computer engineering professor Dinesh Bharadia, will present their work at the 2022 International Conference on Robotics and Automation (ICRA), which will take place from May 23 to 27 in Philadelphia.
“We are surrounded by wireless signals almost everywhere we go. The beauty of this work is that we can use these everyday signals to do indoor localization and mapping with robots,” said Bharadia.
“Using WiFi, we have built a new kind of sensing modality that fills in the gaps left behind by today’s light-based sensors, and it can enable robots to navigate in scenarios where they currently cannot,” added Aditya Arun, who is an electrical and computer engineering Ph.D. student in Bharadia’s lab and the first author of the study. More

What will it take to make our electronics smarter, faster, and more resilient? One idea is to build them from materials that are topological.
Topology stems from a branch of mathematics that studies shapes that can be manipulated or deformed without losing certain core properties. A donut is a common example: If it were made of rubber, a donut could be twisted and squeezed into a completely new shape, such as a coffee mug, while retaining a key trait — namely, its center hole, which takes the form of the cup’s handle. The hole, in this case, is a topological trait, robust against certain deformations.
In recent years, scientists have applied concepts of topology to the discovery of materials with similarly robust electronic properties. In 2007, researchers predicted the first electronic topological insulators — materials in which electrons that behave in ways that are “topologically protected,” or persistent in the face of certain disruptions.
Since then, scientists have searched for more topological materials with the aim of building better, more robust electronic devices. Until recently, only a handful of such materials were identified, and were therefore assumed to be a rarity.
Now researchers at MIT and elsewhere have discovered that, in fact, topological materials are everywhere, if you know how to look for them.
In a paper published in Science, the team, led by Nicolas Regnault of Princeton University and the École Normale Supérieure Paris, reports harnessing the power of multiple supercomputers to map the electronic structure of more than 96,000 natural and synthetic crystalline materials. They applied sophisticated filters to determine whether and what kind of topological traits exist in each structure. More

From extreme weather to another wave of COVID-19, forecasts give decision-makers valuable time to prepare. When it comes to COVID, though, long-term forecasting is a challenge, because it involves human behavior.
While it can sometimes seem like there is no logic to human behavior, new research is working to improve COVID forecasts by incorporating that behavior into prediction models.
UConn College of Agriculture, Health and Natural Resources Allied Health researcher Ran Xu, along with collaborators Hazhir Rahmandad from the Massachusetts Institute of Technology, and Navid Ghaffarzadegan from Virginia Tech, have a paper out today in PLOS Computational Biology where they detail how they applied relatively simple but nuanced variables to enhance modelling capabilities, with the result that their approach out-performed a majority of the models currently used to inform decisions made by the federal Centers for Disease Control and Prevention (CDC).
Xu explains that he and his collaborators are methodologists, and they were interested in examining which parameters impacted the forecasting accuracy of the COVID prediction models. To begin, they turned to the CDC prediction hub, which serves as a repository of models from across the United States.
“Currently there are over 70 different models, mostly from universities and some from companies, that are updated weekly,” says Xu. “Each week, these models give predictions for cases and number of deaths in the next couple of weeks. The CDC uses this information to inform their decisions; for example, where to strategically focus their efforts or whether to advise people to do social distancing.”
The Human Factor
The data was a culmination of over 490,000 point forecasts for weekly death incidents across 57 US locations over the course of one year. The researchers analyzed the length of prediction and how relatively accurate the predictions were across a period of 14 weeks. On further analysis, Xu says they noticed something interesting when they categorized the models based on their methodologies: More