Aurelia Butler

Quantum sensors, which detect the most minute variations in magnetic or electrical fields, have enabled precision measurements in materials science and fundamental physics. But these sensors have only been capable of detecting a few specific frequencies of these fields, limiting their usefulness. Now, researchers at MIT have developed a method to enable such sensors to detect any arbitrary frequency, with no loss of their ability to measure nanometer-scale features.
The new method, for which the team has already applied for patent protection, is described in the journal Physical Review X, in a paper by graduate student Guoqing Wang, professor of nuclear science and engineering and of physics Paola Cappellaro, and four others at MIT and Lincoln Laboratory.
Quantum sensors can take many forms; they’re essentially systems in which some particles are in such a delicately balanced state that they are affected by even tiny variations in the fields they are exposed to. These can take the form of neutral atoms, trapped ions, and solid-state spins, and research using such sensors has grown rapidly. For example, physicists use them to investigate exotic states of matter, including so-called time crystals and topological phases, while other researchers use them to characterize practical devices such as experimental quantum memory or computation devices. But many other phenomena of interest span a much broader frequency range than today’s quantum sensors can detect.
The new system the team devised, which they call a quantum mixer, injects a second frequency into the detector using a beam of microwaves. This converts the frequency of the field being studied into a different frequency — the difference between the original frequency and that of the added signal — which is tuned to the specific frequency that the detector is most sensitive to. This simple process enables the detector to home in on any desired frequency at all, with no loss in the nanoscale spatial resolution of the sensor.
In their experiments, the team used a specific device based on an array of nitrogen-vacancy centers in diamond, a widely used quantum sensing system, and successfully demonstrated detection of a signal with a frequency of 150 megahertz, using a qubit detector with frequency of 2.2 gigahertz — a detection that would be impossible without the quantum multiplexer. They then did detailed analyses of the process by deriving a theoretical framework, based on Floquet theory, and testing the numerical predictions of that theory in a series of experiments.
While their tests used this specific system, Wang says, “the same principle can be also applied to any kind of sensors or quantum devices.” The system would be self-contained, with the detector and the source of the second frequency all packaged in a single device.
Wang says that this system could be used, for example, to characterize in detail the performance of a microwave antenna. “It can characterize the distribution of the field [generated by the antenna] with nanoscale resolution, so it’s very promising in that direction,” he says.
There are other ways of altering the frequency sensitivity of some quantum sensors, but these require the use of large devices and strong magnetic fields that blur out the fine details and make it impossible to achieve the very high resolution that the new system offers. In such systems today, Wang says, “you need to use a strong magnetic field to tune the sensor, but that magnetic field can potentially break the quantum material properties, which can influence the phenomena that you want to measure.”
The system may open up new applications in biomedical fields, according to Cappellaro, because it can make accessible a range of frequencies of electrical or magnetic activity at the level of a single cell. It would be very difficult to get useful resolution of such signals using current quantum sensing systems, she says. It may be possible using this system to detect output signals from a single neuron in response to some stimulus, for example, which typically include a great deal of noise, making such signals hard to isolate.
The system could also be used to characterize in detail the behavior of exotic materials such as 2D materials that are being intensely studied for their electromagnetic, optical, and physical properties.
In ongoing work, the team is exploring the possibility of finding ways to expand the system to be able to probe a range of frequencies at once, rather than the present system’s single frequency targeting. They will also be continuing to define the system’s capabilities using more powerful quantum sensing devices at Lincoln Laboratory, where some members of the research team are based.
The team included Yi-Xiang Liu at MIT and Jennifer Schloss, Scott Alsid and Danielle Braje at Lincoln Laboratory. The work was supported by the Defense Advanced Research Projects Agency (DARPA) and Q-Diamond. More

Imagine you’re sitting on a train. You look out the window and see another train on an adjacent track that appears to be moving. But, has your train stopped while the other train is moving, or are you moving while the other train is stopped?
The same sensory experience — viewing a train — can yield two very different perceptions, leading you to feel either a sensation of yourself in motion or a sensation of being stationary while an object moves around you.
Human brains are constantly faced with such ambiguous sensory inputs. In order to resolve the ambiguity and correctly perceive the world, our brains employ a process known as causal inference.
Causal inference is a key to learning, reasoning, and decision making, but researchers currently know little about the neurons involved in the process.
In a new paper published in the journal eLife, researchers at the University of Rochester, including Greg DeAngelis, the George Eastman Professor of Brain and Cognitive Sciences, and his colleagues at Sungkyunkwan University and New York University, describe a novel neural mechanism involved in causal inference that helps the brain detect object motion during self-motion.
The research offers new insights into how the brain interprets sensory information and may have applications in designing artificial intelligence devices and developing treatments and therapies to treat brain disorders.
“While much has been learned previously about how the brain processes visual motion, most laboratory studies of neurons have ignored the complexities introduced by self-motion,” DeAngelis says. “Under natural conditions, identifying how objects move in the world is much more challenging for the brain.”
Now imagine a still, crouching lion waiting to spot prey; it is easy for the lion to spot a moving gazelle. Just like the still lion, when an observer is stationary, it is easy for her to detect when objects move in the world, because motion in the world directly maps to motion on the retina. However, when the observer is also moving, her eyes are taking in motion everywhere on her retina as she moves relative to objects in the scene. This causes a complex pattern of motion that makes it more difficult for the brain to detect when an object is moving in the world and when it is stationary; in this case, the brain has to distinguish between image motion that results from the observer herself versus image motion of other objects around the self.
The researchers discovered a type of neuron in the brain that has a particular combination of response properties, which makes the neuron well-suited to contribute to the task of distinguishing between self-motion and the motion of other objects.
“Although the brain probably uses multiple tricks to solve this problem, this new mechanism has the advantage that it can be performed in parallel at each local region of the visual field, and thus may be faster to implement than more global processes,” DeAngelis says. “This mechanism might also be applicable to autonomous vehicles, which also need to rapidly detect moving objects.”
The research may additionally have important applications in developing treatments and therapies for neural disorders such as autism and schizophrenia, conditions in which casual inference is thought to be impaired.
“While the project is basic science focused on understanding the fundamental mechanisms of causal inference, this knowledge should eventually be applicable to the treatment of these disorders,” DeAngelis says.
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Materials provided by University of Rochester. Original written by Lindsey Valich. Note: Content may be edited for style and length. More

Fireflies that light up dusky backyards on warm summer evenings use their luminescence for communication — to attract a mate, ward off predators, or lure prey.
These glimmering bugs also sparked the inspiration of scientists at MIT. Taking a cue from nature, they built electroluminescent soft artificial muscles for flying, insect-scale robots. The tiny artificial muscles that control the robots’ wings emit colored light during flight.
This electroluminescence could enable the robots to communicate with each other. If sent on a search-and-rescue mission into a collapsed building, for instance, a robot that finds survivors could use lights to signal others and call for help.
The ability to emit light also brings these microscale robots, which weigh barely more than a paper clip, one step closer to flying on their own outside the lab. These robots are so lightweight that they can’t carry sensors, so researchers must track them using bulky infrared cameras that don’t work well outdoors. Now, they’ve shown that they can track the robots precisely using the light they emit and just three smartphone cameras.
“If you think of large-scale robots, they can communicate using a lot of different tools — Bluetooth, wireless, all those sorts of things. But for a tiny, power-constrained robot, we are forced to think about new modes of communication. This is a major step toward flying these robots in outdoor environments where we don’t have a well-tuned, state-of-the-art motion tracking system,” says Kevin Chen, who is the D. Reid Weedon, Jr. Assistant Professor in the Department of Electrical Engineering and Computer Science (EECS), the head of the Soft and Micro Robotics Laboratory in the Research Laboratory of Electronics (RLE), and the senior author of the paper.
He and his collaborators accomplished this by embedding miniscule electroluminescent particles into the artificial muscles. The process adds just 2.5 percent more weight without impacting the flight performance of the robot. More

A robot operating with a popular Internet-based artificial intelligence system consistently gravitates to men over women, white people over people of color, and jumps to conclusions about peoples’ jobs after a glance at their face.
The work, led by Johns Hopkins University, Georgia Institute of Technology, and University of Washington researchers, is believed to be the first to show that robots loaded with an accepted and widely-used model operate with significant gender and racial biases. The work is set to be presented and published this week at the 2022 Conference on Fairness, Accountability, and Transparency (ACM FAccT).
“The robot has learned toxic stereotypes through these flawed neural network models,” said author Andrew Hundt, a postdoctoral fellow at Georgia Tech who co-conducted the work as a PhD student working in Johns Hopkins’ Computational Interaction and Robotics Laboratory. “We’re at risk of creating a generation of racist and sexist robots but people and organizations have decided it’s OK to create these products without addressing the issues.”
Those building artificial intelligence models to recognize humans and objects often turn to vast datasets available for free on the Internet. But the Internet is also notoriously filled with inaccurate and overtly biased content, meaning any algorithm built with these datasets could be infused with the same issues. Joy Buolamwini, Timinit Gebru, and Abeba Birhane demonstrated race and gender gaps in facial recognition products, as well as in a neural network that compares images to captions called CLIP.
Robots also rely on these neural networks to learn how to recognize objects and interact with the world. Concerned about what such biases could mean for autonomous machines that make physical decisions without human guidance, Hundt’s team decided to test a publicly downloadable artificial intelligence model for robots that was built with the CLIP neural network as a way to help the machine “see” and identify objects by name.
The robot was tasked to put objects in a box. Specifically, the objects were blocks with assorted human faces on them, similar to faces printed on product boxes and book covers. More

It’s a given that certain bacteria and viruses can cause illness and disease, but the real culprits are the sequences of concern that lie within the genomes of these microbes.
Calling them out is about to get easier.
Years of work by Rice University computer scientists and their colleagues have led to an improved platform for DNA screening and pathogenic sequence characterization, whether naturally occurring or synthetic, before they have the chance to impact public health.
Computer scientist Todd Treangen of Rice’s George R. Brown School of Engineering and genomic specialist Krista Ternus of Signature Science LLC led the study that produced SeqScreen, a program to accurately characterize short DNA sequences, often called oligonucleotides.
Treangen said SeqScreen is intended to improve the detection and tracking of a wide range of pathogenic sequences.
“SeqScreen is the first open-source software toolkit that is available for synthetic DNA screening,” Treangen said. “Our program improves upon the previous state of the art for companies, individuals and government agencies for their DNA screening practices.”
The study, which began as high-risk, high-payoff research funded by the National Intelligence Agency’s IARPAprogram in 2017, appears in the journal Genome Biology. More

Osaka Metropolitan University researchers observed unprecedented collective resonance motion in chiral helimagnets that allow a boost in current frequency bands.
When will 6G be a reality? The race to realize sixth generation (6G) wireless communication systems requires the development of suitable magnetic materials. Scientists from Osaka Metropolitan University and their colleagues detected an unprecedented collective resonance at high frequencies in a magnetic superstructure called a chiral spin soliton lattice (CSL), revealing CSL-hosting chiral helimagnets as a promising material for 6G technology. The study was published in Physical Review Letters.
Future communication technologies require expanding the frequency band from the current few Gigahertz (GHz) to over 100 GHz. Such high frequencies are not yet possible given that existing magnetic materials used in communication equipment can only resonate and absorb microwaves up to approximately 70 GHz with a practical-strength magnetic field. Addressing this gap in knowledge and technology, the research team led by Professor Yoshihiko Togawa from Osaka Metropolitan University delved into the helicoidal spin superstructure CSL. “CSL has a tunable structure in periodicity, meaning it can be continuously modulated by changing the external magnetic field strength,” explained Professor Togawa. “The CSL phonon mode, or collective resonance mode ― when the CSL’s kinks oscillate collectively around their equilibrium position ― allows frequency ranges broader than those for conventional ferromagnetic materials.” This CSL phonon mode has been understood theoretically, but never observed in experiments.
Seeking the CSL phonon mode, the team experimented on CrNb3S6, a typical chiral magnetic crystal that hosts CSL. They first generated CSL in CrNb3S6 and then observed its resonance behavior under changing external magnetic field strengths. A specially designed microwave circuit was used to detect the magnetic resonance signals.
The researchers observed resonance in three modes, namely the “Kittel mode,” the “asymmetric mode,” and the “multiple resonance mode.” In the Kittel mode, similar to what is observed in conventional ferromagnetic materials, the resonance frequency increases only if the magnetic field strength increases, meaning that creating the high frequencies needed for 6G would require an impractically strong magnetic field. The CSL phonon was not found in the asymmetric mode, either.
In the multiple resonance mode, the CSL phonon was detected; in contrast to what is observed with magnetic materials currently in use, the frequency spontaneously increases when the magnetic field strength decreases. This is an unprecedented phenomenon that will possibly enable a boost to over 100 GHz with a relatively weak magnetic field — this boost is a much-needed mechanism for achieving 6G operability.
“We succeeded in observing this resonance motion for the first time,” noted first author Dr. Yusuke Shimamoto. “Due to its excellent structural controllability, the resonance frequency can be controlled over a wide band up to the sub-terahertz band. This wideband and variable frequency characteristic exceeds 5G and is expected to be utilized in research and development of next-generation communication technologies.”
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Materials provided by Osaka Metropolitan University. Note: Content may be edited for style and length. More

So far, science has described more than 2 million species, and millions more await discovery. While species have value in themselves, many also deliver important ecosystem services to humanity, such as insects that pollinate our crops.
Meanwhile, as we lack a standardized system to quantify the value of different species, it is too easy to jump to the conclusion that they are practically worthless. As a result, humanity has been quick to justify actions that diminish populations and even imperil biodiversity at large.
In a study, published in the scholarly open-science journal Research Ideas and Outcomes, a team of Estonian and Swedish scientists propose to formalize the value of all species through a conceptual species ‘stock market’ (SSM). Much like the regular stock market, the SSM is to act as a unified basis for instantaneous valuation of all items in its holdings.
However, other aspects of the SSM would be starkly different from the regular stock market. Ownership, transactions, and trading will take new forms. Indeed, species have no owners, and ‘trade’ would not be about transfer of ownership rights among shareholders. Instead, the concept of ‘selling’ would comprise processes that erase species from some specific area — such as war, deforestation, or pollution.
“The SSM would be able to put a price tag on such transactions, and the price could be thought of as an invoice that the seller needs to settle in some way that benefits global biodiversity,” explains the study’s lead author Prof. Urmas Kõljalg (University of Tartu, Estonia).
Conversely, taking some action that benefits biodiversity — as estimated through individuals of species — would be akin to buying on the species stock market. Buying, too, has a price tag on it, but this price should probably be thought of in goodwill terms. Here, ‘money’ represents an investment towards increased biodiversity.
“By rooting such actions in a unified valuation system it is hoped that goodwill actions will become increasingly difficult to dodge and dismiss,” adds Kõljalg.
Interestingly, the SSM revolves around the notion of digital species. These are representations of described and undescribed species concluded to exist based on DNA sequences and elaborated by including all we know about their habitat, ecology, distribution, interactions with other species, and functional traits.
For the SSM to function as described, those DNA sequences and metadata need to be sourced from global scientific and societal resources, including natural history collections, sequence databases, and life science data portals. Digital species might be managed further by incorporating data records of non-sequenced individuals, notably observations, older material in collections, and data from publications.
The study proposes that the SSM is orchestrated by the international associations of taxonomists and economists.
“Non-trivial complications are foreseen when implementing the SSM in practice, but we argue that the most realistic and tangible way out of the looming biodiversity crisis is to put a price tag on species and thereby a cost to actions that compromise them,” says Kõljalg.
“No human being will make direct monetary profit out of the SSM, and yet it’s all Earth’s inhabitants — including humans — that could benefit from its pointers.”
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Materials provided by Pensoft Publishers. Note: Content may be edited for style and length. More

Researchers used mice to develop a mathematical model of a myocardial infarction, popularly known as a heart attack.
The new model predicts several useful new drug combinations that may one day help treat heart attacks, according to researchers at The Ohio State University.
Typically caused by blockages in the coronary arteries — or the vessels that supply blood to the heart — these cardiovascular events are experienced by more than 800,000 Americans every year, and about 30% end up dying. But even for those who survive, the damage these attacks inflict on the muscles of the heart is permanent and can lead to dangerous inflammation in the affected areas of the heart.
Treatment to restore blood flow to these blocked passages of the heart often includes surgery and drugs, or what’s known as reperfusion therapy. Nicolae Moise, lead author of the study and a postdoctoral researcher in biomedical engineering at Ohio State, said the study uses mathematical algorithms to assess the efficacy of the drugs used to combat the potentially lethal inflammation many patients experience in the aftermath of an attack.
“Biology and medicine are starting to become more mathematical,” Moise said. “There’s so much data that you need to start integrating it into some kind of framework.” While Moise has worked on other mathematical models of animal hearts, he said that the framework detailed in the current paper is the most detailed schematic of myocardial infarctions in mice ever made.
The research is published in the Journal of Theoretical Biology.
Represented by a series of differential equations, the model Moise’s team created was made using data from previous animal studies. In medicine, differential equations are often used to monitor the growth of diseases in graph form. More