Aurelia Butler

The future of wireless communications is set to take a giant leap with the advent of sixth-generation (6G) wireless technology. A research team at City University of Hong Kong (CityU) invented a groundbreaking tunable terahertz (THz) meta-device that can control the radiation direction and coverage area of THz beams. By rotating its metasurface, the device can promptly direct the 6G signal only to a designated recipient, minimizing power leakage and enhancing privacy. It is expected to provide a highly adjustable, directional and secure means for future 6G communications systems.
The potential of THz band technology is unlimited, as it has abundant spectrum resources to support 100 Gbps (gigabit per second)- and even Tbps (terabit per second)-level ultrahigh-speed data rate for wireless communications, which is hundreds to thousands of times faster than the 5G transmission data rate. However, conventional THz systems use bulky, heavy dielectric lenses and reflectors, which can guide waves only to a fixed transmitter or detector, or transmit them to a single receiver located at a fixed position or covering a limited area. This hinders the development of future 6G applications, which require precise positioning and concentrated signal strength.
Existing bulky systems hinder 6G applications
With the joint effort of two research teams at CityU, led by Professor Tsai Din-Ping, Chair Professor in the Department of Electrical Engineering, and Professor Chan Chi-hou, Acting Provost and Director of the State Key Laboratory of Terahertz and Millimeter Waves (SKLTMW), a novel, tunable meta-devices that can fully control the THz beam’s propagation direction and coverage area was recently developed to overcome these challenges.
“The advent of a tunable THz meta-device presents exciting prospects for 6G communications systems,” said Professor Tsai, who is an expert in the field of metasurfaces and photonics. “Our meta-device allows for signal delivery to specific users or detectors and has the flexibility to adjust the propagating direction, as needed.”
“Our findings offer a range of benefits for advanced THz communications systems, including security, flexibility, high directivity and signal concentration,” added Professor Chan, who specializes in terahertz technology research.

Rotary metasurface with thousands of micro-antennas
The meta-device consists of two or three rotary metasurfaces (artificial, thin-sheet material with sub-wavelength thickness), which work as efficient projectors to steer the focal spot of THz beams on a two-dimensional plane or in a three-dimensional space. With a diameter of 30 mm, each metasurface has about 11,000 micro-antennas, which are just 0.25mm x 0.25mm in size and different from each other. “The secret to the success of the meta-device lies in the meticulous calculation and design of each micro-antenna,” said Professor Tsai. By simply rotating the metasurfaces without additional space requirements, the THz beam focus can be adjusted and directed to the specified X, Y and Z coordinates of the destination accordingly.
With the highly precise and advanced equipment in the SKLTMW, the research team conducted experiments and verified that the two kinds of varifocal meta-devices they developed — doublet and triplet meta-devices — can project the focusing spot of the THz wave into an arbitrary spot in a 2D plane and a 3D space, respectively, with high precision.
This innovative design has demonstrated the capability of a meta-device to direct a 6G signal towards a specific location in two- and three-dimensional space.
Since only the user or detector in a specific spot can receive the signal, and the highly concentrated signal can be flexibly switched to other users or detectors without wasting power on nearby receivers or impairing privacy, the meta-device can increase directivity, security and flexibility in future 6G communications with lower energy consumption.

Easy to scale up production at low cost
The metasurfaces are fabricated with high-temperature resin and a 3D printing method developed by the team. They are lightweight and small and can be easily produced in large scale at low cost for practical applications.
The novel THz tunable meta-device is expected to have great application potential for 6G communications systems, including wireless power transfer, zoom imaging and remote sensing. The research team plans to design further meta-device applications based on THz varifocal imaging.
The findings were published in the scientific journal Science Advances under the title “A 6G meta-device for 3D varifocal.”
Professor Tsai and Professor Chan are the co-corresponding authors. The co-first authors are Mr Zhang Jingcheng, PhD student under Professor Tsai’s supervision, Dr Wu Gengbo, postdoctoral research fellow in the SKLTMW, and Dr Chen Mu-Ku, Assistant Professor in the Department of Electrical Engineering at CityU. Miss Liu Xiaoyuan, PhD student in the Department of Electrical Engineering, and Dr Chan Ka-fai, from the SKLTMW, also contributed to the research.
The research was supported by the University Grants Committee and the Research Grants Council of HKSAR, the Science, Technology and Innovation Commission of Shenzhen Muncipality, the Department of Science and Technology of Guangdong Province, and CityU. More

A new University of Illinois project is using advanced object recognition technology to keep toxin-contaminated wheat kernels out of the food supply and to help researchers make wheat more resistant to fusarium head blight, or scab disease, the crop’s top nemesis.
“Fusarium head blight causes a lot of economic losses in wheat, and the associated toxin, deoxynivalenol (DON), can cause issues for human and animal health. The disease has been a big deterrent for people growing wheat in the Eastern U.S. because they could grow a perfectly nice crop, and then take it to the elevator only to have it get docked or rejected. That’s been painful for people. So it’s a big priority to try to increase resistance and reduce DON risk as much as possible,” says Jessica Rutkoski, assistant professor in the Department of Crop Sciences, part of the College of Agricultural, Consumer and Environmental Sciences (ACES) at Illinois. Rutkoski is a co-author on the new paper in the Plant Phenome Journal.
Increasing resistance to any crop disease traditionally means growing a lot of genotypes of the crop, infecting them with the disease, and looking for symptoms. The process, known in plant breeding as phenotyping, is successful when it identifies resistant genotypes that don’t develop symptoms, or less severe symptoms. When that happens, researchers try to identify the genes related to disease resistance and then put those genes in high-performing hybrids of the crop.
It’s a long, repetitive process, but Rutkoski hoped one step — phenotyping for disease symptoms — could be accelerated. She looked for help from AI experts Junzhe Wu, doctoral student in the Department of Agricultural and Biological Engineering (ABE), and Girish Chowdhary, associate professor in ABE and the Department of Computer Science (CS). ABE is part of ACES and the Grainger College of Engineering, which also houses CS.
“We wanted to test whether we could quantify kernel damage using simple cell phone images of grains. Normally, we look at a petri dish of kernels and then give it a subjective rating. It’s very mind-numbing work. You have to have people specifically trained and it’s slow, difficult, and subjective. A system that could automatically score kernels for damage seemed doable because the symptoms are pretty clear,” Rutkoski says.
Wu and Chowdhary agreed it was possible. They started with algorithms similar to those used by tech giants for object detection and classification. But discerning minute differences in diseased and healthy wheat kernels from cell phone images required Wu and Chowdhary to advance the technology further.

“One of the unique things about this advance is that we trained our network to detect minutely damaged kernels with good enough accuracy using just a few images. We made this possible through meticulous pre-processing of data, transfer learning, and bootstrapping of labeling activities,” Chowdhary says. “This is another nice win for machine learning and AI for agriculture and society.”
He adds, “This project builds on the AIFARMS National AI Institute and the Center for Digital Agriculture here at Illinois to leverage the strength of AI for agriculture.”
Successfully detecting fusarium damage — small, shriveled, gray, or chalky kernels — meant the technology could also foretell the grain’s toxin load; the more external signs of damage, the greater the DON content.
When the team tested the machine learning technology alone, it was able to predict DON levels better than in-field ratings of disease symptoms, which breeders often rely on instead of kernel phenotyping to save time and resources. But when compared to humans rating disease damage on kernels in the lab, the technology was only 60% as accurate.
The researchers are still encouraged, though, as their initial tests didn’t use a large number of samples to train the model. They’re currently adding samples and expect to achieve greater accuracy with additional tweaking.
“While further training is needed to improve the capabilities of our model, initial testing shows promising results and demonstrates the possibility of providing an automated and objective phenotyping method for fusarium damaged kernels that could be widely deployed to support resistance breeding efforts,” Wu says.
Rutkoski says the ultimate goal is to create an online portal where breeders like her could upload cell phone photos of wheat kernels for automatic scoring of fusarium damage.
“A tool like this could save weeks of time in a lab, and that time is critical when you’re trying to analyze the data and prepare the next trial. And ultimately, the more efficiency we can bring to the process, the faster we can improve resistance to the point where scab can be eliminated as a problem,” she says. More

Bumblebees are clumsy fliers. It is estimated that a foraging bee bumps into a flower about once per second, which damages its wings over time. Yet despite having many tiny rips or holes in their wings, bumblebees can still fly.
Aerial robots, on the other hand, are not so resilient. Poke holes in the robot’s wing motors or chop off part of its propellor, and odds are pretty good it will be grounded.
Inspired by the hardiness of bumblebees, MIT researchers have developed repair techniques that enable a bug-sized aerial robot to sustain severe damage to the actuators, or artificial muscles, that power its wings — but to still fly effectively.
They optimized these artificial muscles so the robot can better isolate defects and overcome minor damage, like tiny holes in the actuator. In addition, they demonstrated a novel laser repair method that can help the robot recover from severe damage, such as a fire that scorches the device.
Using their techniques, a damaged robot could maintain flight-level performance after one of its artificial muscles was jabbed by 10 needles, and the actuator was still able to operate after a large hole was burnt into it. Their repair methods enabled a robot to keep flying even after the researchers cut off 20 percent of its wing tip.
This could make swarms of tiny robots better able to perform tasks in tough environments, like conducting a search mission through a collapsing building or dense forest.

“We spent a lot of time understanding the dynamics of soft, artificial muscles and, through both a new fabrication method and a new understanding, we can show a level of resilience to damage that is comparable to insects. We’re very excited about this. But the insects are still superior to us, in the sense that they can lose up to 40 percent of their wing and still fly. We still have some catch-up work to do,” says Kevin Chen, 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 on these latest advances.
Chen wrote the paper with co-lead authors and EECS graduate students Suhan Kim and Yi-Hsuan Hsiao; Younghoon Lee, a postdoc; Weikun “Spencer” Zhu, a graduate student in the Department of Chemical Engineering; Zhijian Ren, an EECS graduate student; and Farnaz Niroui, the EE Landsman Career Development Assistant Professor of EECS at MIT and a member of the RLE. The article will appear in Science Robotics.
Robot repair techniques
The tiny, rectangular robots being developed in Chen’s lab are about the same size and shape as a microcassette tape, though one robot weighs barely more than a paper clip. Wings on each corner are powered by dielectric elastomer actuators (DEAs), which are soft artificial muscles that use mechanical forces to rapidly flap the wings. These artificial muscles are made from layers of elastomer that are sandwiched between two razor-thin electrodes and then rolled into a squishy tube. When voltage is applied to the DEA, the electrodes squeeze the elastomer, which flaps the wing.
But microscopic imperfections can cause sparks that burn the elastomer and cause the device to fail. About 15 years ago, researchers found they could prevent DEA failures from one tiny defect using a physical phenomenon known as self-clearing. In this process, applying high voltage to the DEA disconnects the local electrode around a small defect, isolating that failure from the rest of the electrode so the artificial muscle still works.

Chen and his collaborators employed this self-clearing process in their robot repair techniques.
First, they optimized the concentration of carbon nanotubes that comprise the electrodes in the DEA. Carbon nanotubes are super-strong but extremely tiny rolls of carbon. Having fewer carbon nanotubes in the electrode improves self-clearing, since it reaches higher temperatures and burns away more easily. But this also reduces the actuator’s power density.
“At a certain point, you will not be able to get enough energy out of the system, but we need a lot of energy and power to fly the robot. We had to find the optimal point between these two constraints — optimize the self-clearing property under the constraint that we still want the robot to fly,” Chen says.
However, even an optimized DEA will fail if it suffers from severe damage, like a large hole that lets too much air into the device.
Chen and his team used a laser to overcome major defects. They carefully cut along the outer contours of a large defect with a laser, which causes minor damage around the perimeter. Then, they can use self-clearing to burn off the slightly damaged electrode, isolating the larger defect.
“In a way, we are trying to do surgery on muscles. But if we don’t use enough power, then we can’t do enough damage to isolate the defect. On the other hand, if we use too much power, the laser will cause severe damage to the actuator that won’t be clearable,” Chen says.
The team soon realized that, when “operating” on such tiny devices, it is very difficult to observe the electrode to see if they had successfully isolated a defect. Drawing on previous work, they incorporated electroluminescent particles into the actuator. Now, if they see light shining, they know that part of the actuator is operational, but dark patches mean they successfully isolated those areas.
Flight test success
Once they had perfected their techniques, the researchers conducted tests with damaged actuators — some had been jabbed by many needles while other had holes burned into them. They measured how well the robot performed in flapping wing, take-off, and hovering experiments.
Even with damaged DEAs, the repair techniques enabled the robot to maintain its flight performance, with altitude, position, and attitude errors that deviated only very slightly from those of an undamaged robot. With laser surgery, a DEA that would have been broken beyond repair was able to recover 87 percent of its performance.
“I have to hand it to my two students, who did a lot of hard work when they were flying the robot. Flying the robot by itself is very hard, not to mention now that we are intentionally damaging it,” Chen says.
These repair techniques make the tiny robots much more robust, so Chen and his team are now working on teaching them new functions, like landing on flowers or flying in a swarm. They are also developing new control algorithms so the robots can fly better, teaching the robots to control their yaw angle so they can keep a constant heading, and enabling the robots to carry a tiny circuit, with the longer-term goal of carrying its own power source.
This work is funded, in part, by the National Science Foundation (NSF) and a MathWorks Fellowship. More

When astronauts begin to build a permanent base on the moon, as NASA plans to do in the coming years, they’ll need help. Robots could potentially do the heavy lifting by laying cables, deploying solar panels, erecting communications towers, and building habitats. But if each robot is designed for a specific action or task, a moon base could become overrun by a zoo of machines, each with its own unique parts and protocols.
To avoid a bottleneck of bots, a team of MIT engineers is designing a kit of universal robotic parts that an astronaut could easily mix and match to rapidly configure different robot “species” to fit various missions on the moon. Once a mission is completed, a robot can be disassembled and its parts used to configure a new robot to meet a different task.
The team calls the system WORMS, for the Walking Oligomeric Robotic Mobility System. The system’s parts include worm-inspired robotic limbs that an astronaut can easily snap onto a base, and that work together as a walking robot. Depending on the mission, parts can be configured to build, for instance, large “pack” bots capable of carrying heavy solar panels up a hill. The same parts could be reconfigured into six-legged spider bots that can be lowered into a lava tube to drill for frozen water.
“You could imagine a shed on the moon with shelves of worms,” says team leader George Lordos, a PhD candidate and graduate instructor in MIT’s Department of Aeronautics and Astronautics (AeroAstro), in reference to the independent, articulated robots that carry their own motors, sensors, computer, and battery. “Astronauts could go into the shed, pick the worms they need, along with the right shoes, body, sensors and tools, and they could snap everything together, then disassemble it to make a new one. The design is flexible, sustainable, and cost-effective.”
Lordos’ team has built and demonstrated a six-legged WORMS robot. Last week, they presented their results at IEEE’s Aerospace Conference, where they also received the conference’s Best Paper Award.
MIT team members include Michael J. Brown, Kir Latyshev, Aileen Liao, Sharmi Shah, Cesar Meza, Brooke Bensche, Cynthia Cao, Yang Chen, Alex S. Miller, Aditya Mehrotra, Jacob Rodriguez, Anna Mokkapati, Tomas Cantu, Katherina Sapozhnikov, Jessica Rutledge, David Trumper, Sangbae Kim, Olivier de Weck, Jeffrey Hoffman, along with Aleks Siemenn, Cormac O’Neill, Diego Rivero, Fiona Lin, Hanfei Cui, Isabella Golemme, John Zhang, Jolie Bercow, Prajwal Mahesh, Stephanie Howe, and Zeyad Al Awwad, as well as Chiara Rissola of Carnegie Mellon University and Wendell Chun of the University of Denver.

Animal instincts
WORMS was conceived in 2022 as an answer to NASA’s Breakthrough, Innovative and Game-changing (BIG) Idea Challenge — an annual competition for university students to design, develop, and demonstrate a game-changing idea. In 2022, NASA challenged students to develop robotic systems that can move across extreme terrain, without the use of wheels.
A team from MIT’s Space Resources Workshop took up the challenge, aiming specifically for a lunar robot design that could navigate the extreme terrain of the moon’s South Pole — a landscape that is marked by thick, fluffy dust; steep, rocky slopes; and deep lava tubes. The environment also hosts “permanently shadowed” regions that could contain frozen water, which, if accessible, would be essential for sustaining astronauts.
As they mulled over ways to navigate the moon’s polar terrain, the students took inspiration from animals. In their initial brainstorming, they noted certain animals could conceptually be suited to certain missions: A spider could drop down and explore a lava tube, a line of elephants could carry heavy equipment while supporting each other down a steep slope, and a goat, tethered to an ox, could help lead the larger animal up the side of a hill as it transports an array of solar panels.
“As we were thinking of these animal inspirations, we realized that one of the simplest animals, the worm, makes similar movements as an arm, or a leg, or a backbone, or a tail,” says deputy team leader and AeroAstro graduate student Michael Brown. “And then the lightbulb went off: We could build all these animal-inspired robots using worm-like appendages.'”
Snap on, snap off

Lordos, who is of Greek descent, helped coin WORMS, and chose the letter “O” to stand for “oligomeric,” which in Greek signifies “a few parts.”
“Our idea was that, with just a few parts, combined in different ways, you could mix and match and get all these different robots,” says AeroAstro undergraduate Brooke Bensche.
The system’s main parts include the appendage, or worm, which can be attached to a body, or chassis, via a “universal interface block” that snaps the two parts together through a twist-and-lock mechanism. The parts can be disconnected with a small tool that releases the block’s spring-loaded pins.
Appendages and bodies can also snap into accessories such as a “shoe,” which the team engineered in the shape of a wok, and a LiDAR system that can map the surroundings to help a robot navigate.
“In future iterations we hope to add more snap-on sensors and tools, such as winches, balance sensors, and drills,” says AeroAstro undergraduate Jacob Rodriguez.
The team developed software that can be tailored to coordinate multiple appendages. As a proof of concept, the team built a six-legged robot about the size of a go-cart. In the lab, they showed that once assembled, the robot’s independent limbs worked to walk over level ground. The team also showed that they could quickly assemble and disassemble the robot in the field, on a desert site in California.
In its first generation, each WORMS appendage measures about 1 meter long and weighs about 20 pounds. In the moon’s gravity, which is about one-sixth that of Earth’s, each limb would weigh about 3 pounds, which an astronaut could easily handle to build or disassemble a robot in the field. The team has planned out the specs for a larger generation with longer and slightly heavier appendages. These bigger parts could be snapped together to build “pack” bots, capable of transporting heavy payloads.
“There are many buzz words that are used to describe effective systems for future space exploration: modular, reconfigurable, adaptable, flexible, cross-cutting, et cetera,” says Kevin Kempton, an engineer at NASA’s Langley Research Center, who served as a judge for the 2022 BIG Idea Challenge. “The MIT WORMS concept incorporates all these qualities and more.”
This research was supported, in part, by NASA, MIT, the Massachusetts Space Grant, the National Science Foundation, and the Fannie and John Hertz Foundation.
Video: https://youtu.be/U72lmSXEVkM More

Robots can be useful as mental wellbeing coaches in the workplace — but perception of their effectiveness depends in large part on what the robot looks like.
Researchers from the University of Cambridge carried out a study in a tech consultancy firm using two different robot wellbeing coaches, where 26 employees participated in weekly robot-led wellbeing sessions for four weeks. Although the robots had identical voices, facial expressions, and scripts for the sessions, the robots’ physical appearance affected how participants interacted with it.
Participants who did their wellbeing exercises with a toy-like robot said that they felt more of a connection with their ‘coach’ than participants who worked with a humanoid-like robot. The researchers say that perception of robots is affected by popular culture, where the only limit on what robots can do is the imagination. When faced with a robot in the real world however, it often does not live up to expectations.
Since the toy-like robot looks simpler, participants may have had lower expectations and ended up finding the robot easier to talk connect with. Participants who worked with the humanoid robot found that their expectations didn’t match reality, since the robot was not capable of having interactive conversations.
Despite the differences between expectations and reality, the researchers say that their study shows that robots can be a useful tool to promote mental wellbeing in the workplace. The results will be reported today (15 March) at the ACM/IEEE International Conference on Human-Robot Interaction in Stockholm.
The World Health Organization recommends that employers take action to promote and protect mental wellbeing at work, but the implementation of wellbeing practices is often limited by a lack of resources and personnel. Robots have shown some early promise for helping address this gap, but most studies on robots and wellbeing have been conducted in a laboratory setting.

“We wanted to take the robots out of the lab and study how they might be useful in the real world,” said Dr Micol Spitale, the paper’s first author.
The researchers collaborated with local technology company Cambridge Consultants to design and implement a workplace wellbeing programme using robots. Over the course of four weeks, employees were guided through four different wellbeing exercises by one of two robots: either the QTRobot (QT) or the Misty II robot (Misty).
The QT is a childlike humanoid robot and roughly 90cm tall, while Misty is a 36cm tall toy-like robot. Both robots have screen faces that can be programmed with different facial expressions.
“We interviewed different wellbeing coaches and then we programmed our robots to have a coach-like personality, with high openness and conscientiousness,” said co-author Minja Axelsson. “The robots were programmed to have the same personality, the same facial expressions and the same voice, so the only difference between them was the physical robot form.”
Participants in the experiment were guided through different positive psychology exercises by a robot in an office meeting room. Each session started with the robot asking participants to recall a positive experience or describe something in their lives they were grateful for, and the robot would ask follow-up questions. After the sessions, participants were asked to assess the robot with a questionnaire and an interview. Participants did one session per week for four weeks, and worked with the same robot for each session.

Participants who worked with the toy-like Misty robot reported that they had a better working connection with the robot than participants who worked with the child-like QT robot. Participants also had a more positive perception of Misty overall.
“It could be that since the Misty robot is more toy-like, it matched their expectations,” said Spitale. “But since QT is more humanoid, they expected it to behave like a human, which may be why participants who worked with QT were slightly underwhelmed.”
“The most common response we had from participants was that their expectations of the robot didn’t match with reality,” said Professor Hatice Gunes from Cambridge’s Department of Computer Science and Technology, who led the research. “We programmed the robots with a script, but participants were hoping there would be more interactivity. It’s incredibly difficult to create a robot that’s capable of natural conversation. New developments in large language models could really be beneficial in this respect.”
“Our perceptions of how robots should look or behave might be holding back the uptake of robotics in areas where they can be useful,” said Axelsson.
Although the robots used in the experiment are not as advanced as C-3PO or other fictional robots, participants still said they found the wellbeing exercises helpful, and that they were open to the idea of talking to a robot in future.
“The robot can serve as a physical reminder to commit to the practice of wellbeing exercises,” said Gunes. “And just saying things out loud, even to a robot, can be helpful when you’re trying to improve mental wellbeing.”
The team is now working to enhance the robot coaches’ responsiveness during the coaching practices and interactions.
The research was supported by the Engineering and Physical Sciences Research Council (EPSRC), part of UK Research and Innovation (UKRI). Hatice Gunes is a Staff Fellow of Trinity Hall, Cambridge. More

Since the development of functional magnetic resonance imaging in the 1990s, the reliance on neuroimaging has skyrocketed as researchers investigate how fMRI data from the brain at rest, and anatomical brain structure itself, can be used to predict individual traits, such as depression, cognitive decline, and brain disorders.
Brain imaging has the potential to reveal the neural underpinnings of many traits, from disorders like depression and chronic widespread pain to why one person has a better memory than another, and why some people’s memories are resilient as they age. But how reliable brain imaging is for detecting traits has been a subject of wide debate.
Prior research on brain-wide associated studies (termed ‘BWAS’) has shown that links between brain function and structure and traits are so weak that thousands of participants are needed to detect replicable effects. Research of this scale requires millions of dollars in investment in each study, limiting which traits and brain disorders can be studied.
However, according to a new commentary published in Nature, stronger links between brain measures and traits can be obtained when state-of-the-art pattern recognition (or ‘machine learning’) algorithms are utilized, which can garner high-powered results from moderate sample sizes.
In their article, researchers from Dartmouth and University Medicine Essen provide a response to an earlier analysis of brain-wide association studies led by Scott Marek at Washington University School of Medicine in St. Louis, Brenden Tervo-Clemmens at Massachusetts General Hospital/Harvard Medical School, and colleagues. The earlier study found very weak associations across a range of traits in several large brain imaging studies, concluding that thousands of participants would be needed to detect these associations.
The new article explains that the very weak effects found in the earlier paper do not apply to all brain images and all traits, but rather are limited to specific cases. It outlines how fMRI data from hundreds of participants, as opposed to thousands, can be better leveraged to yield important diagnostic information about individuals.

One key to stronger associations between brain images and traits such as memory and intelligence is the use of state-of-the-art pattern recognition algorithms. “Given that there’s virtually no mental function performed entirely by one area of the brain, we recommend using pattern recognition to develop models of how multiple brain areas contribute to predicting traits, rather than testing brain areas individually,” says senior author Tor Wager, the Diana L. Taylor Distinguished Professor of Psychological and Brain Sciences and director of the Brain Imaging Center at Dartmouth.
“If models of multiple brain areas working together rather than in isolation are applied, this provides for a much more powerful approach in neuroimaging studies, yielding predictive effects that are four times larger than when testing brain areas in isolation,” says lead author Tamas Spisak, head of the Predictive Neuroimaging Lab at the Institute of Diagnostic and Interventional Radiology and Neuroradiology at University Medicine Essen.
However, not all pattern recognition algorithms are equal and finding the algorithms that work best for specific types of brain imaging data is an active area of research. The earlier paper by Marek, Tervo-Clemmens et al. also tested whether pattern recognition can be used to predict traits from brain images, but Spisak and colleagues found that the algorithm they used is suboptimal.
When the researchers applied a more powerful algorithm, the effects got even larger and reliable associations could be detected in much smaller samples. “When you do the power calculations on how many participants are needed to detect replicable effects, the number drops to below 500 people,” Spisak says.
“This opens the field to studies of many traits and clinical conditions for which obtaining thousands of patients is not possible, including rare brain disorders,” says co-author Ulrike Bingel at University Medicine Essen, who is the head of the University Centre for Pain Medicine. “Identifying markers, including those involving the central nervous system, are urgently needed, as they are critical to improve diagnostics and individually tailored treatment approaches. We need to move towards a personalized medicine approach grounded in neuroscience. The potential for multivariate BWAS to move us towards this goal should not be underestimated.”
The team explains that the weak associations found in the earlier analysis, particularly through brain images, were collected while people were simply resting in the scanner, rather than performing tasks. But fMRI can also capture brain activity linked to specific moment-by-moment thoughts and experiences.
Wager believes that linking brain patterns to these experiences may be a key to understanding and predicting differences among individuals. “One of the challenges associated with using brain imaging to predict traits is that many traits aren’t stable or reliable. If we use brain imaging to focus on studying mental states and experiences, such as pain, empathy, and drug craving, the effects can be much larger and more reliable,” says Wager. “The key is finding the right task to capture the state.”
“For example, showing images of drugs to people with substance use disorders can elicit drug cravings, according to an earlier study revealing a neuromarker for cravings,” says Wager.
“Identifying which approaches to understanding the brain and mind are most likely to succeed is important, as this affects how stakeholders view and ultimately fund translational research in neuroimaging,” says Bingel. “Finding the limitations and working together to overcome them is key to developing new ways of diagnosing and caring for patients with brain and mental health disorders.” More

To understand how driverless vehicles can navigate the complexities of the road, researchers often use game theory — mathematical models representing the way rational agents behave strategically to meet their goals.
Dejan Milutinovic, professor of electrical and computer engineering at UC Santa Cruz, has long worked with colleagues on the complex subset of game theory called differential games, which have to do with game players in motion. One of these games is called the wall pursuit game, a relatively simple model for a situation in which a faster pursuer has the goal to catch a slower evader who is confined to moving along a wall.
Since this game was first described nearly 60 years ago, there has been a dilemma within the game — a set of positions where it was thought that no game optimal solution existed. But now, Milutinovic and his colleagues have proved in a new paper published in the journal IEEE Transactions on Automatic Control that this long-standing dilemma does not actually exist, and introduced a new method of analysis that proves there is always a deterministic solution to the wall pursuit game. This discovery opens the door to resolving other similar challenges that exist within the field of differential games, and enables better reasoning about autonomous systems such as driverless vehicles.
Game theory is used to reason about behavior across a wide range of fields, such as economics, political science, computer science and engineering. Within game theory, the Nash equilibrium is one of the most commonly recognized concepts. The concept was introduced by mathematician John Nash and it defines game optimal strategies for all players in the game to finish the game with the least regret. Any player who chooses not to play their game optimal strategy will end up with more regret, therefore, rational players are all motivated to play their equilibrium strategy.
This concept applies to the wall pursuit game — a classical Nash equilibrium strategy pair for the two players, the pursuer and evader, that describes their best strategy in almost all of their positions. However, there are a set of positions between the pursuer and evader for which the classical analysis fails to yield the game optimal strategies and concludes with the existence of the dilemma. This set of positions are known as a singular surface — and for years, the research community has accepted the dilemma as fact.
But Milutinovic and his co-authors were unwilling to accept this.

“This bothered us because we thought, if the evader knows there is a singular surface, there is a threat that the evader can go to the singular surface and misuse it,” Milutinovic said. “The evader can force you to go to the singular surface where you don’t know how to act optimally — and then we just don’t know what the implication of that would be in much more complicated games.”
So Milutinovic and his coauthors came up with a new way to approach the problem, using a mathematical concept that was not in existence when the wall pursuit game was originally conceived. By using the viscosity solution of the Hamilton-Jacobi-Isaacs equation and introducing a rate of loss analysis for solving the singular surface they were able to find that a game optimal solution can be determined in all circumstances of the game and resolve the dilemma.
The viscosity solution of partial differential equations is a mathematical concept that was non-existent until the 1980s and offers a unique line of reasoning about the solution of the Hamilton-Jacobi-Isaacs equation. It is now well known that the concept is relevant for reasoning about optimal control and game theory problems.
Using viscosity solutions, which are functions, to solve game theory problems involves using calculus to find the derivatives of these functions. It is relatively easy to find game optimal solutions when the viscosity solution associated with a game has well-defined derivatives. This is not the case for the wall-pursuit game, and this lack of well-defined derivatives creates the dilemma.
Typically when a dilemma exists, a practical approach is that players randomly choose one of possible actions and accept losses resulting from these decisions. But here lies the catch: if there is a loss, each rational player will want to minimize it.

So to find how players might minimize their losses, the authors analyzed the viscosity solution of the Hamilton-Jacobi-Isaacs equation around the singular surface where the derivatives are not well-defined. Then, they introduced a rate of loss analysis across these singular surface states of the equation. They found that when each actor minimizes its rate of losses, there are well-defined game strategies for their actions on the singular surface.
The authors found that not only does this rate of loss minimization define the game optimal actions for the singular surface, but it is also in agreement with the game optimal actions in every possible state where the classical analysis is also able to find these actions.
“When we take the rate of loss analysis and apply it elsewhere, the game optimal actions from the classical analysis are not impacted ,” Milutinovic said. “We take the classical theory and we augment it with the rate of loss analysis, so a solution exists everywhere. This is an important result showing that the augmentation is not just a fix to find a solution on the singular surface, but a fundamental contribution to game theory.
Milutinovic and his coauthors are interested in exploring other game theory problems with singular surfaces where their new method could be applied. The paper is also an open call to the research community to similarly examine other dilemmas.
“Now the question is, what kind of other dilemmas can we solve?” Milutinovic said. More

Practical carbon capture technologies are still in the early stages of development, with the most promising involving a class of compounds called amines that can chemically bind with carbon dioxide. In AVS Quantum Science, researchers deploy an algorithm to study amine reactions through quantum computing. An existing quantum computer cab run the algorithm to find useful amine compounds for carbon capture more quickly, analyzing larger molecules and more complex reactions than a traditional computer can.
The amount of carbon dioxide in the atmosphere increases daily with no sign of stopping or slowing. Too much of civilization depends on the burning of fossil fuels, and even if we can develop a replacement energy source, much of the damage has already been done. Without removal, the carbon dioxide already in the atmosphere will continue to wreak havoc for centuries.
Atmospheric carbon capture is a potential remedy to this problem. It would pull carbon dioxide out of the air and store it permanently to reverse the effects of climate change. Practical carbon capture technologies are still in the early stages of development, with the most promising involving a class of compounds called amines that can chemically bind with carbon dioxide. Efficiency is paramount in these designs, and identifying even slightly better compounds could lead to the capture of billions of tons of additional carbon dioxide.
In AVS Quantum Science, by AIP Publishing, researchers from the National Energy Technology Laboratory and the University of Kentucky deployed an algorithm to study amine reactions through quantum computing. The algorithm can be run on an existing quantum computer to find useful amine compounds for carbon capture more quickly.
“We are not satisfied with the current amine molecules that we use for this [carbon capture] process,” said author Qing Shao. “We can try to find a new molecule to do it, but if we want to test it using classical computing resources, it will be a very expensive calculation. Our hope is to have a fast algorithm that can screen thousands of new molecules and structures.”
Any computer algorithm that simulates a chemical reaction needs to account for the interactions between every pair of atoms involved. Even a simple three-atom molecule like carbon dioxide bonding with the simplest amine, ammonia, which has four atoms, results in hundreds of atomic interactions. This problem vexes traditional computers but is exactly the sort of question at which quantum computers excel.
However, quantum computers are still a developing technology and are not powerful enough to handle these kinds of simulations directly. This is where the group’s algorithm comes in: It allows existing quantum computers to analyze larger molecules and more complex reactions, which is vital for practical applications in fields like carbon capture.
“We are trying to use the current quantum computing technology to solve a practical environmental problem,” said author Yuhua Duan. More