Aurelia Butler

A mind-controlled wheelchair can help a paralyzed person gain new mobility by translating users’ thoughts into mechanical commands. On November 18 in the journal iScience, researchers demonstrate that tetraplegic users can operate mind-controlled wheelchairs in a natural, cluttered environment after training for an extended period.
“We show that mutual learning of both the user and the brain-machine interface algorithm are both important for users to successfully operate such wheelchairs,” says José del R. Millán, the study’s corresponding author at The University of Texas at Austin. “Our research highlights a potential pathway for improved clinical translation of non-invasive brain-machine interface technology.”
Millán and his colleagues recruited three tetraplegic people for the longitudinal study. Each of the participants underwent training sessions three times per week for 2 to 5 months. The participants wore a skullcap that detected their brain activities through electroencephalography (EEG), which would be converted to mechanical commands for the wheelchairs via a brain-machine interface device. The participants were asked to control the direction of the wheelchair by thinking about moving their body parts. Specifically, they needed to think about moving both hands to turn left and both feet to turn right.
In the first training session, three participants had similar levels of accuracy — when the device’s responses aligned with users’ thoughts — of around 43% to 55%. Over the course of training, the brain-machine interface device team saw significant improvement in accuracy in participant 1, who reached an accuracy of over 95% by the end of his training. The team also observed an increase in accuracy in participant 3 to 98% halfway through his training before the team updated his device with a new algorithm.
The improvement seen in participants 1 and 3 is correlated with improvement in feature discriminancy, which is the algorithm’s ability to discriminate the brain activity pattern encoded for “go left” thoughts from that for “go right.” The team found that the better feature discrimnancy is not only a result of machine learning of the device but also learning in the brain of the participants. The EEG of participants 1 and 3 showed clear shifts in brainwave patterns as they improved accuracy in mind-controlling the device.
“We see from the EEG results that the subject has consolidated a skill of modulating different parts of their brains to generate a pattern for ‘go left’ and a different pattern for ‘go right,'” Millán says. “We believe there is a cortical reorganization that happened as a result of the participants’ learning process.”
Compared with participants 1 and 3, participant 2 had no significant changes in brain activity patterns throughout the training. His accuracy increased only slightly during the first few sessions, which remained stable for the rest of the training period. It suggests machine learning alone is insufficient for successfully maneuvering such a mind-controlled device, Millán says
By the end of the training, all participants were asked to drive their wheelchairs across a cluttered hospital room. They had to go around obstacles such as a room divider and hospital beds, which are set up to simulate the real-world environment. Both participants 1 and 3 finished the task while participant 2 failed to complete it.
“It seems that for someone to acquire good brain-machine interface control that allows them to perform relatively complex daily activity like driving the wheelchair in a natural environment, it requires some neuroplastic reorganization in our cortex,” Millán says.
The study also emphasized the role of long-term training in users. Although participant 1 performed exceptionally at the end, he struggled in the first few training sessions as well, Millán says. The longitudinal study is one of the first to evaluate the clinical translation of non-invasive brain-machine interface technology in tetraplegic people.
Next, the team wants to figure out why participant 2 didn’t experience the learning effect. They hope to conduct a more detailed analysis of all participants’ brain signals to understand their differences and possible interventions for people struggling with the learning process in the future.
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Researchers at the Hong Kong University of Science and Technology (HKUST) have recently developed a novel integration scheme for efficient coupling between III-V compound semiconductor devices and silicon components on silicon photonics (Si-photonics) platform by selective direct epitaxy, unlocking the potential of integrating energy-efficient photonics with cost-effective electronics, as well as enabling the next generation telecommunications with low cost, high speed and large capacity.
Over the past few years, data traffic has been growing exponentially driven by various applications and emerging techniques such as big data, automobiles, cloud applications and sensors. To address the issues, Si-photonics has been widely investigated as a core technology to enable, extend, and increase data transmission through energy-efficient, high-capacity and low-cost optical interconnects. While silicon-based passive components have been well established on Si-photonics platform, the lasers and photodetectors can’t be realized by silicon and necessitate the integration of other materials such as III-V compound semiconductors on silicon.
III-V lasers and photodetectors on silicon has been investigated by two main methods. The first one is the bonding-based method which has yielded devices with impressive performance. However, it requires complicated manufacturing technique that is low yield and high-cost, making mass production very challenging. The other way is direct epitaxy method by growing multiple layers of III-V on silicon. While it provides a solution with lower cost, larger scalability and higher integration density, the micrometers thick III-V buffer layers which are crucial for this method hinders efficient light coupling between III-V and silicon — the key for integrated Si-photonics.
To address these issues, the team led by Prof. Kei-May LAU, Professor Emeritus of the Department of Electronic and Computer Engineering at Hong Kong University of Science and Technology (HKUST), developed lateral aspect ratio trapping (LART) — a novel selective direct epitaxy method that can selectively grow III-V materials on silicon-on-insulator (SOI) in a lateral direction without the need of thick buffers. Furthermore, based on this novel technology, the team devised and demonstrated unique in-plane integration of III-V photodetectors and silicon elements with high coupling efficiency between III-V and silicon. Compared to the commercial ones, the performance of photodetectors by such approach is less noisy, more sensitive, and has wider operation range, with record-high speed of over 112 Gb/s — way faster than existing products. For the first time, the III-V devices can be efficiently coupled with Si elements by direct epitaxy. The integration strategy can be easily applied to the integration of various III-V devices and Si-based components, thereby enabling the ultimate goal of integrating photonics with electronics on silicon photonics platform for data communications.
“This was made possible by our latest development of a novel growth technique named lateral aspect ratio trapping (LART) and our unique design of coupling strategy on the SOI platform. Our team’s combined expertise and insights into both device physics and growth mechanisms allow us to accomplish the challenging task of efficient coupling between III-V and Si and cross-correlated analysis of epitaxial growth and device performance,” said Prof. Lau. “This work will provide practical solutions for photonic integrated circuits and fully integrated Si-photonics, light coupling between III-V lasers and Si components can be realized through this method” said Dr. Ying Xue, first author of the study.
This is a collaborative work with a research team led by Prof. Hon Ki Tsang of Department of Electronic Engineering at Chinese University of Hong Kong (CUHK) and a research team led by Prof. Xinlun Cai of School of Electronics and Information Technology at Sun Yat-sen University (SYSU). The device fabrication technology in the work was developed at HKUST’s Nanosystem Fabrication Facility (NFF) on Clear Water Bay campus. The work is supported by Research Grants Council of Hong Kong and Innovation Technology Fund of Hong Kong. This work has recently been published in Optica.
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Selfless behaviour and cooperation cannot be taken for granted. Mohammad Salahshour of the Max Planck Institute for Mathematics in the Sciences (now at Max Planck Institute of Animal Behavior), has used a game theory-based approach to show why it can be worthwhile for individuals to set self-interests aside.
One of the most fundamental questions facing humanity is: why do we behave morally? Because it is by no means self-evident that under certain circumstances we set our self-interest aside and put ourselves in the service of a group — sometimes to the point of self-sacrifice. Many theories have been developed to get to the bottom of this moral conundrum. There are two well-known proposed solutions: that individuals help their relatives so that the common genes survive (kin selection), and that the principle of “you scratch my back and I’ll scratch yours” applies. If people help each other, everyone benefits in the end (principle of reciprocity).
Prisoner’s dilemma combined with a coordination game
Mathematician Mohammad Salahshour of the Max Planck Institute for Mathematics in the Sciences in Leipzig, Germany, has used the tools of game theory to explain the emergence of moral norms — because game theory studies how people make rational decisions in conflict situations. For Salahshour, the question at the outset was: why do moral norms exist in the first place? And why do we have different, or even contrasting moral norms? For example, while some norms such as “help others,” promote self-sacrificing behaviour, others, such as dress codes, appear not to have much to do with curbing selfishness. To answer these questions, Salahshour coupled two games: first, the classic prisoner’s dilemma, in which two players must decide whether to cooperate for a small reward or betray themselves for a much larger reward (social dilemma). This game can be a typical example of a social dilemma, where success of a group as a whole requires individuals to behave selflessly. In this game everybody loses out if too many members of a group behave selfishly, compared to a scenario in which everybody acts altruistically. However, if only a few individuals behave selfishly, they can receive a better outcome than their altruistic team members. .Second, a game that focuses on typical decisions within groups, such as a coordination task, distribution of resources, choice of a leader, or conflict resolution. Many of these problems can be ultimately categorized as coordination or anticoordination problems.
Without coupling the two games, it is clear that in the Prisoner’s Dilemma, cooperation does not pay off, and self-interested behaviour is the best choice from the individual’s perspective if there are enough people who act selflessly. But individuals who act selfishly are not able to solve coordination problems efficiently and lose a lot of resources due to failing to coordinate their activity. The situation can be completely different when the results of the two games are considered as a whole and there are moral norms at work which favour cooperation: now cooperation in the prisoner’s dilemma can suddenly pay off because the gain in the second game more than compensates for the loss in the first game.
Out of self-interest to coordination and cooperation
As a result of this process, not only cooperative behaviour emerges, but also a social order. All individuals benefit from it — and for this reason, moral behaviour pay off for them. “In my evolutionary model, there were no selfless behaviours at the beginning, but more and more moral norms emerged as a result of the coupling of the two games,” Salahshour reports. “Then I observed a sudden transition to a system where there is a lot of cooperation.” In this “moral state,” a set of norms of coordination evolve which help individuals to better coordinate their activity, and it is precisely through this that social norms and moral standards can emerge. However, coordination norms favour cooperation: cooperation turns out to be a rewarding behaviour for the individual as well. Mahammad Salahshour: “A moral system behaves like a Trojan horse: once established out of the individuals’ self-interest to promote order and organization, it also brings self-sacrificing cooperation.”
Through his work, Salahshour hopes to better understand social systems. “This can help improve people’s lives in the future,” he explains. “But you can also use my game-theoretic approach to explain the emergence of social norms in social media. There, people exchange information and make strategic decisions at the same time — for example, who to support or what cause to support.” Again, he said, two dynamics are at work at once: the exchange of information and the emergence of cooperative strategies. Their interplay is not yet well understood — but perhaps game theory will soon shed new light on this topical issue as well.
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Your entire medical journey lives in digital health records, but how do you know if those records are wrong, incomplete or missing important information? That’s the focus of research done by Varadraj Gurupur, associate professor in UCF’s School of Global Health Management and Informatics.
His latest project created an algorithm that can predict and measure the incompleteness of electronic health records — in everything from your lab results to disease diagnoses, medical history to prescription records.
Missing information in the electronic health records (EHR) that hospitals and doctor’s offices keep is like a leaking pipe, he says. If you don’t know where the leak is, you can’t fix it, and soon the house can flood. The same dangers can happen in healthcare. A recent study by Gurupur revealed that a critical percent of digital health records contained missing information.
His algorithm uses mathematics and computer science to answer, “Where is the water leaking?” he says. The analysis performed by Gurupur and his team found that the level of incompleteness per year varies and there is not a pattern of where missing data happens. His algorithm helps identify attributes that have a higher tendency to be incomplete — the areas of the water pipe that are more vulnerable and can break more frequently.
His previous studies have documented that the biggest reasons for missing health information are communication and education. Communication between patients and their providers isn’t always clear — especially if the patient is interacting with a healthcare professional who does not speak their native language. Cultural barriers may keep patients from sharing important information with their providers. Digital technology also creates its own challenges. Providers may not fill out electronic records until day’s end — and forget what the patient said or not have it accurately in their notes. Hospitals and clinics switch electronic health record systems, requiring extensive new retraining which results in a learning curve for providers. Some healthcare workers, especially those who did not grow up with technology, may not be adept at using EHRs.
“Missing health information can sometimes be as simple as a person who isn’t sure what button to push in the new system,” Gurupur says. More

Researchers at the National Institute of Standards and Technology (NIST) have created grids of tiny clumps of atoms known as quantum dots and studied what happens when electrons dive into these archipelagos of atomic islands. Measuring the behavior of electrons in these relatively simple setups promises deep insights into how electrons behave in complex real-world materials and could help researchers engineer devices that make possible powerful quantum computers and other innovative technologies.
In work published in Nature Communications, the researchers made multiple 3-by-3 grids of precisely spaced quantum dots, each comprising one to three phosphorus atoms. Attached to the grids were electrical leads and other components that enabled electrons to flow through them. The grids provided playing fields in which electrons could behave in nearly ideal, textbook-like conditions, free of the confounding effects of real-world materials.
The researchers injected electrons into the grids and observed how they behaved as the researchers varied conditions such as the spacing between the dots. For grids in which the dots were close, the electrons tended to spread out and act like waves, essentially existing in several places at one time. When the dots were far apart, they would sometimes get trapped in individual dots, like electrons in materials with insulating properties.
Advanced versions of the grid would allow researchers to study the behavior of electrons in controllable environments with a level of detail that would be impossible for the world’s most powerful conventional computers to simulate accurately. It would open the door to full-fledged “analog quantum simulators” that unlock the secrets of exotic materials such as high-temperature superconductors. It could also provide hints about how to create materials, such as topological insulators, by controlling the geometry of the quantum dot array.
In related work just published in ACS Nano, the same NIST researchers improved their fabrication method so they can now reliably create an array of identical, equally spaced dots with exactly one atom each, leading to even more ideal environments necessary for a fully accurate quantum simulator. The researchers have set their sights on making such a simulator with a larger grid of quantum dots: A 5×5 array of dots can produce rich electron behavior that is impossible to simulate in even the most advanced supercomputers.
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A research team consisting of NIMS, Hokkaido University and Yamaguchi University has developed a method for easily synthesizing a self-healing polymer gel made of ultrahigh molecular weight (UHMW) polymers (polymers with a molecular weight greater than 106 g/mol) and non-volatile ionic liquids. This recyclable and self-healable polymer gel is compatible with circular economy principles. In addition, it may potentially be used as a durable, ionically conductive material for flexible IoT devices.
Self-healing polymeric materials are capable of spontaneously repairing damaged areas, thereby increasing their material lifetimes, thus being expected to promote a circular economy. Most reported self-healing polymeric materials in recent years has taken a chemical approach, in which functional groups capable of reversible dissociation and reformation (e.g., hydrogen bonding) were integrated into polymeric networks. However, this approach often requires precise synthetic techniques and complex manufacturing processes. On the other hand, an alternative physical approach (i.e., the use of physical entanglement of polymer chains) to synthesizing versatile polymeric materials with self-healing capabilities had rarely been explored.
This research team recently developed a technique for easily synthesizing UHMW gels composed of entangled UHMW polymers using ionic liquids. The mechanical properties of UHMW gels were found to be superior to those of conventional, chemically crosslinked gels. In addition, they can be recycled via thermal processing, and exhibit high self-healing capabilities at room temperature.
The use of the newly developed recyclable, self-healing, easy-to-synthesize UHMW gel material is expected to promote a circular economy. In addition, because this material is synthesized using non-volatile, flammable ionic liquids, it may be used as a safe, ionically conductive soft material in flexible electronics.
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Scientists used the Advanced Photon Source to watch a nonliving material mimic behavior associated with learning, paving the way for better artificial intelligence.
Scientists looking to create a new generation of supercomputers are looking for inspiration from the most complex and energy-efficient computer ever built: the human brain.
In some of their initial forays into making brain-inspired computers, researchers are looking at different nonbiological materials whose properties could be tailored to show evidence of learning-like behaviors. These materials could form the basis for hardware that could be paired with new software algorithms to enable more potent, useful and energy-efficient artificial intelligence (AI).
In a new study led by scientists from Purdue University, researchers have exposed oxygen deficient nickel oxide to brief electrical pulses and elicited two different electrical responses that are similar to learning. The result is an all-electrically-driven system that shows these learning behaviors, said Rutgers University professor Shriram Ramanathan. (Ramanathan was a professor at Purdue University at the time of this work.) The research team used the resources of the Advanced Photon Source (APS), a U.S. Department of Energy (DOE) Office of Science user facility at DOE’s Argonne National Laboratory.
The first response, habituation, occurs when the material “gets used to” being slightly zapped. The scientists noticed that although the material’s resistance increases after an initial jolt, it soon becomes accustomed to the electric stimulus. “Habituation is like what happens when you live near an airport,” said Fanny Rodolakis, a physicist and beamline scientist at the APS. “The day you move in, you think ‘what a racket,’ but eventually you hardly notice anymore.”
The other response shown by the material, sensitization, occurs when a larger dose of electricity is administered. “With a larger stimulus, the material’s response grows instead of diminishing over time,” Rodolakis said. “It’s akin to watching a scary movie, and then having someone say ‘boo!’ from behind a corner — you see it really jump.”
“Pretty much all living organisms demonstrate these two characteristics,” Ramanathan said. “They really are a foundational aspect of intelligence.” More

An artificial intelligence tool developed by researchers at UCL and Queen Mary University of London could help governments decide whether or not to bail out a bank in crisis by predicting if the intervention will save money for taxpayers in the long term.
The AI tool, described in a new paper in Nature Communications, assesses not only if a bailout is the best strategy for taxpayers, but also suggests how much should be invested in the bank, and which bank or banks should be bailed out at any given time.
The algorithm was tested by the authors using data from the European Banking Authority on a network of 35 European financial institutions judged to be the most important to the global financial system, but it can also be used and calibrated by national banks using detailed proprietary data unavailable to the public.
Dr Neofytos Rodosthenous (UCL Mathematics), corresponding author of the paper, said: “Government bank bailouts are complex decisions that have financial, social and political implications. We believe the AI approach we have developed can be an important tool for governments, helping officials assess specifically financial implications — this means checking if a bailout is in the best interest of taxpayers, or whether it would be better value for money to let the bank fail. Our techniques are freely available for banking authorities to use as tools in their decision-making process.”
Co-author Professor Vito Latora (Queen Mary University of London) added: “Governments and banking authorities can also use our approach to retrospectively review past crises and gain valuable learnings to inform future actions. One could, for example, review the UK government bailout of the Royal Bank of Scotland (RBS) during the financial crisis of 2007-9 and reflect on how this could potentially be improved (from a financial standpoint) in the future in order to primarily benefit taxpayers.”
In a bank bailout, a government investment in a bank increases the bank’s equity and reduces its risk of defaulting. This cost in the short term may be justified to the taxpayer if it leads to lower taxpayer losses in the long term — i.e., it prevents bank defaults that are more damaging to government finances. More