Aurelia Butler

Can intelligence be taught to robots? Advances in physical reservoir computing, a technology that makes sense of brain signals, could contribute to creating artificial intelligence machines that think like us.
In Applied Physics Letters, from AIP Publishing, researchers from the University of Tokyo outline how a robot could be taught to navigate through a maze by electrically stimulating a culture of brain nerve cells connected to the machine.
These nerve cells, or neurons, were grown from living cells and acted as the physical reservoir for the computer to construct coherent signals.
The signals are regarded as homeostatic signals, telling the robot the internal environment was being maintained within a certain range and acting as a baseline as it moved freely through the maze.
Whenever the robot veered in the wrong direction or faced the wrong way, the neurons in the cell culture were disturbed by an electric impulse. Throughout trials, the robot was continually fed the homeostatic signals interrupted by the disturbance signals until it had successfully solved the maze task.
These findings suggest goal-directed behavior can be generated without any additional learning by sending disturbance signals to an embodied system. The robot could not see the environment or obtain other sensory information, so it was entirely dependent on the electrical trial-and-error impulses.
“I, myself, was inspired by our experiments to hypothesize that intelligence in a living system emerges from a mechanism extracting a coherent output from a disorganized state, or a chaotic state,” said co-author Hirokazu Takahashi, an associate professor of mechano-informatics.
Using this principle, the researchers show intelligent task-solving abilities can be produced using physical reservoir computers to extract chaotic neuronal signals and deliver homeostatic or disturbance signals. In doing so, the computer creates a reservoir that understands how to solve the task.
“A brain of [an] elementary school kid is unable to solve mathematical problems in a college admission exam, possibly because the dynamics of the brain or their ‘physical reservoir computer’ is not rich enough,” said Takahashi. “Task-solving ability is determined by how rich a repertoire of spatiotemporal patterns the network can generate.”
The team believes using physical reservoir computing in this context will contribute to a better understanding of the brain’s mechanisms and may lead to the novel development of a neuromorphic computer.
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Climate change and its consequences are becoming increasingly obvious, and solar cells that convert the sun’s energy into electricity will play a key role in the world’s future energy supply.
Common semiconductor materials for solar cells, such as silicon, must be grown via an expensive process to avoid defects within their crystal structure that affect functionality. But metal-halide perovskite semiconductors are emerging as a cheaper, alternative material class, with excellent and tunable functionality as well as easy processability.
In APL Materials, from AIP Publishing, researchers present a road map for organic-inorganic hybrid perovskite semiconductors and devices.
Perovskite semiconductors can be processed from solution, and a semiconductor ink can be coated or simply painted over surfaces to form the desired film. This can be incorporated into semiconductor devices, such as solar cells or light-emitting diodes.
“For many years, solution-processed semiconductors were viewed as unable to deliver the same functionality as specially grown crystalline semiconductors,” said Lukas Schmidt-Mende, a co-author from the University of Konstanz in Germany. “The reason behind this thinking was that simple solution processing will inherently lead to a relative high number of defects within the formed crystal structure, which can negatively affect its functionality.”
It turns out organic-inorganic hybrid perovskites are very defect-tolerant. Defects formed after processing do not dramatically influence device functionality, and for the first time, hybrid perovskites are enabling efficient solution-processed devices. More

A team of Brown University researchers has shown a new method of probing the properties of anyons, strange quasiparticles that could be useful in future quantum computers.
In research published in the journal Physical Review Letters, the team describes a means of probing anyons by measuring subtle properties of the way in which they conduct heat. Whereas other methods probe these particles using electrical charge, this new method enables researchers to probe anyons even in non-conducting materials. That’s critical, the researchers say, because non-conducting systems have far less stringent temperature requirements, making them a more practical option for quantum computing.
“We have beautiful ways of probing anyons using charge, but the question has been how do you detect them in the insulating systems that would be useful in what’s known as topological quantum computing,” said Dima Feldman, a physics professor at Brown and study co-author. “We show that it can be done using heat conductance. Essentially, this is a universal test for anyons that works in any state of matter.”
Anyons are of interest because they don’t follow the same rules as particles in the everyday, three-dimensional world. In three dimensions, there are only two broad kinds of particles: bosons and fermions. Bosons follow what’s known as Bose-Einstein statistics, while fermions follow Fermi-Dirac statistics. Generally speaking, those different sets of statistical rules mean that if one boson orbits around another in a quantum system, the particle’s wave function — the equation that fully describes its quantum state — does not change. On the other hand, if a fermion orbits around another fermion, the phase value of its wave function flips from a positive integer to a negative integer. If it orbits again, the wave function returns to its original state.
Anyons, which emerge only in systems that are confined to two dimensions, don’t follow either rule. When one anyon orbits another, its wave function changes by some fraction of an integer. And another orbit does not necessarily restore the original value of the wave function. Instead, it has a new value — almost as if the particle maintains a “memory” of its interactions with the other particle even though it ended up back where it started.
That memory of past interactions can be used to encode information in a robust way, which is why the particles are interesting tools for quantum computing. Quantum computers promise to perform certain types of calculations that are virtually impossible for today’s computers. A quantum computer using anyons — known as a topological quantum computer — has the potential to operate without elaborate error correction, which is a major stumbling block in the quest for usable quantum computers.
But using anyons for computing requires first being able to identify these particles by probing their quantum statistics. Last year, researchers did that for the first time using a technique known as charge interferometry. Essentially, anyons are spun around each other, causing their wave functions to interfere with each other occasionally. The pattern of interference reveals the particles’ quantum statistics. That technique of probing anyons using charge works beautifully in systems that conduct electricity, the researchers say, but it can’t be used to probe anyons in non-conducting systems. And non-conducting systems have the potential to be useful at higher temperatures than conducting systems, which need to be near absolute zero. That makes them a more practical option of topological quantum computing.
For this new research, Feldman, who in 2017 was part of a team that measured the heat conductance of anyons for the first time, collaborated with Brown graduate student Zezhu Wei and Vesna Mitrovic, a Brown physics professor and experimentalist. Wei, Feldman and Mitrovic showed that comparing properties of heat conductance in two-dimensional solids etched in very specific geometries could reveal the statistics of the anyons in those systems.
“Any difference in the heat conductance in the two geometries would be smoking gun evidence of fractional statistics,” Mitrovic said. “What this study does is show exactly how people should set up experiments in their labs to test for these strange statistics.”
Ultimately, the researchers hope the study is a step toward understanding whether the strange behavior of anyons can indeed be harnessed for topological quantum computing.
The research was supported by the National Science Foundation (DMR-1902356, QLCI-1936854, DMR-1905532).
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Researchers in the health and wellness space have typically relied on people to report their personal health data, like activity levels, heart rate or blood pressure, during brief snapshots in time.
Wearable health devices, such as the popular Apple Watch, have changed the game, surfacing meaningful data that can paint a more complete picture of daily life and resulting health and disease for clinicians.
Early results from a landmark, three-year observational study called MIPACT, short for Michigan Predictive Activity & Clinical Trajectories, provide insight into the baseline health status of a representative group of thousands of people, as reported in a paper published in The Lancet Digital Health.
“From both a research and clinical standpoint, as we design digital health interventions or make recommendations for our patients, it’s important to understand patients’ baseline activity levels,” said Jessica Golbus, M.D., of University of Michigan Health’s Division of Cardiovascular Medicine, and co-investigator on the study.
The University of Michigan Health study is led by Sachin Kheterpal M.D., the associate dean for Research Information Technology and professor of Anesthesiology and launched in 2018 as a collaboration with Apple. The study aims to enroll a diverse set of participants across a range of ages, races, ethnicities and underlying health conditions.
Golbus notes that one of the biggest successes of the study so far was their ability to recruit from groups that have largely been underrepresented or unrepresented in digital health research. For example, 18% of the more than 6,700 participants were 65 or older, 17% were Black, and 17% were Asian. More

Researchers from North Carolina State University have developed a new process that makes use of existing industry standard techniques for making III-nitride semiconductor materials, but results in layered materials that will make LEDs and lasers more efficient.
III-nitride semiconductor materials are wide-bandgap semiconductors that are of particular interest in optic and photonic applications because they can be used to create lasers and LEDs that produce light in the visible bandwidth range. And when it comes to large-scale manufacturing, III-nitride semiconductor materials produced using a technique called metal organic chemical vapor deposition (MOCVD).
Semiconductor devices require two materials, a “p-type” and an “n-type.” Electrons move from the n-type material to the p-type material. This is made possible by creating a p-type material that has “holes,” or spaces that electrons can move into.
A challenge for people who make LEDs and lasers has been that there was a limit on the number of holes that you can make in p-type III-nitride semiconductor materials that are created using MOCVD. But that limit just went up.
“We have developed a process that produces the highest concentration of holes in p-type material in any III-Nitride semiconductor made using MOCVD,” says Salah Bedair, co-author of a paper on the work and a distinguished professor of electrical and computer engineering at NC State. “And this is high quality material — very few defects — making it suitable for use in a variety of devices.”
In practical terms, this means more of the energy input in LEDs is converted into light. For lasers, it means that less of the energy input will be wasted as heat by reducing the metal contact resistance. More

In the past few years, artificial intelligence models of language have become very good at certain tasks. Most notably, they excel at predicting the next word in a string of text; this technology helps search engines and texting apps predict the next word you are going to type.
The most recent generation of predictive language models also appears to learn something about the underlying meaning of language. These models can not only predict the word that comes next, but also perform tasks that seem to require some degree of genuine understanding, such as question answering, document summarization, and story completion.
Such models were designed to optimize performance for the specific function of predicting text, without attempting to mimic anything about how the human brain performs this task or understands language. But a new study from MIT neuroscientists suggests the underlying function of these models resembles the function of language-processing centers in the human brain.
Computer models that perform well on other types of language tasks do not show this similarity to the human brain, offering evidence that the human brain may use next-word prediction to drive language processing.
“The better the model is at predicting the next word, the more closely it fits the human brain,” says Nancy Kanwisher, the Walter A. Rosenblith Professor of Cognitive Neuroscience, a member of MIT’s McGovern Institute for Brain Research and Center for Brains, Minds, and Machines (CBMM), and an author of the new study. “It’s amazing that the models fit so well, and it very indirectly suggests that maybe what the human language system is doing is predicting what’s going to happen next.”
Joshua Tenenbaum, a professor of computational cognitive science at MIT and a member of CBMM and MIT’s Artificial Intelligence Laboratory (CSAIL); and Evelina Fedorenko, the Frederick A. and Carole J. Middleton Career Development Associate Professor of Neuroscience and a member of the McGovern Institute, are the senior authors of the study, which appears this week in the Proceedings of the National Academy of Sciences. Martin Schrimpf, an MIT graduate student who works in CBMM, is the first author of the paper. More

Even after more than 30 years of research, high-temperature superconductivity is still one of the great unsolved mysteries of materials physics. The exact mechanism that causes certain materials to still conduct electric current without any resistance even at relatively high temperatures is still not fully understood.
Two years ago, a new class of promising superconductors was discovered: so-called layered nickelates. For the first time, a research team at TU Wien has now succeeded in determining important parameters of these novel superconductors by comparing theory and experiment. This means that for the first time a theoretical model is now available that can be used to understand the electronic mechanisms of high-temperature superconductivity in these materials.
In search of high-temperature superconductors
Many superconductors are known today, but most of them are only superconducting at extremely low temperatures, close to absolute zero. Materials that remain superconducting at higher temperatures are called “high-temperature superconductors” — even though these “high” temperatures (often in the order of magnitude of less than -200°C) are still extremely cold by human standards.
Finding a material that still remains superconducting at significantly higher temperatures would be a revolutionary discovery that would open the door to many new technologies. For a long time, the so-called cuprates were considered particularly exciting candidates — a class of materials containing copper atoms. Now, however, another class of materials could turn out to be even more promising: Nickelates, which have a similar structure to cuprates, but with nickel instead of copper.
“There has been a lot of research on cuprates, and it has been possible to dramatically increase the critical temperature up to which the material remains superconducting. If similar progress can be made with the newly discovered nickelates, it would be a huge step forward,” says Prof. Jan Kuneš from the Institute of Solid State Physics at TU Wien.
Hard-to-access parameters
Theoretical models describing the behaviour of such superconductors already exist. The problem, however, is that in order to use these models, one must know certain material parameters that are difficult to determine. “The charge transfer energy plays a key role,” explains Jan Kuneš. “This value tells us how much energy you have to add to the system to transfer an electron from a nickel atom to an oxygen atom.”
Unfortunately, this value cannot be measured directly, and theoretical calculations are extremely complicated and imprecise. Therefore, Atsushi Hariki, a member of Jan Kuneš’ research group, developed a method to determine this parameter indirectly: When the material is examined with X-rays, the results also depend on the charge transfer energy. “We calculated details of the X-ray spectrum that are particularly sensitive to this parameter and compared our results with measurements of different X-ray spectroscopy methods,” explains Jan Kuneš. “In this way, we can determine the appropriate value — and this value can now be inserted into the computational models used to describe the superconductivity of the material.”
Important prerequisite for the search for better nickelates
Thus, for the first time, it has now been possible to explain the electronic structure of the material precisely and to set up a parameterised theoretical model for describing superconductivity in nickelates. “With this, we can now get to the bottom of the question of how the mechanics of the effect can be explained at the electronic level,” says Jan Kuneš. “Which orbitals play a decisive role? Which parameters matter in detail? That’s what you need to know if you want to find out how to improve this material further, so that one day you might be able to produce new nickelates whose superconductivity persists up to even significantly higher temperatures.”
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When the COVID-19 pandemic shut down experiments at the Department of Energy’s SLAC National Accelerator Laboratory early last year, Shambhu Ghimire’s research group was forced to find another way to study an intriguing research target: quantum materials known as topological insulators, or TIs, which conduct electric current on their surfaces but not through their interiors.
Denitsa Baykusheva, a Swiss National Science Foundation Fellow, had joined his group at the Stanford PULSE Institute two years earlier with the goal of finding a way to generate high harmonic generation, or HHG, in these materials as a tool for probing their behavior. In HHG, laser light shining through a material shifts to higher energies and higher frequencies, called harmonics, much like pressing on a guitar string produces higher notes. If this could be done in TIs, which are promising building blocks for technologies like spintronics, quantum sensing and quantum computing, it would give scientists a new tool for investigating these and other quantum materials.
With the experiment shut down midway, she and her colleagues turned to theory and computer simulations to come up with a new recipe for generating HHG in topological insulators. The results suggested that circularly polarized light, which spirals along the direction of the laser beam, would produce clear, unique signals from both the conductive surfaces and the interior of the TI they were studying, bismuth selenide — and would in fact enhance the signal coming from the surfaces.
When the lab reopened for experiments with covid safety precautions in place, Baykusheva set out to test that recipe for the first time. In a paper published today in Nano Letters, the research team report that those tests went exactly as predicted, producing the first unique signature from the topological surface.
“This material looks very different than any other material we’ve tried,” said Ghimire, who is a principal investigator at PULSE. “It’s really exciting being able to find a new class of material that has a very different optical response than anything else.”
Over the past dozen years, Ghimire had done a series of experiments with PULSE Director David Reis showing that HHG can be produced in ways that were previously thought unlikely or even impossible: by beaming laser light into a crystal, a frozen argon gas or an atomically thin semiconductor material. Another study described how to use HHG to generate attosecond laser pulses, which can be used to observe and control the movements of electrons, by shining a laser through ordinary glass. More