Aurelia Butler

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  • 175 Shares189 Views

    in Computers Math

    AI can help write a message to a friend — but don’t do it

    Using artificial intelligence applications to help craft a message to a friend is not a good idea — at least if your friend finds out about the use of AI, a new study suggests.
    Researchers found that people in the study perceived that a fictional friend who used AI assistance to write them a message didn’t put forth as much effort as a friend who wrote a message themselves.
    That perception may be understandable, but the effect goes beyond the message itself, said Bingjie Liu, lead author of the study and assistant professor of communication at The Ohio State University.
    “After they get an AI-assisted message, people feel less satisfied with their relationship with their friend and feel more uncertain about where they stand,” Liu said.
    But to be fair to AI, it wasn’t just the use of technology that turned people off. The study also found negative effects when people learned their friend got help from another person to write a message.
    “People want their partners or friends to put forth the effort to come up with their own message without help — from AI or other people,” Liu said.
    The study was published online recently in the Journal of Social and Personal Relationships. More

  • 75 Shares159 Views

    in Computers Math

    ‘Brainless’ robot can navigate complex obstacles

    Researchers who created a soft robot that could navigate simple mazes without human or computer direction have now built on that work, creating a “brainless” soft robot that can navigate more complex and dynamic environments.
    “In our earlier work, we demonstrated that our soft robot was able to twist and turn its way through a very simple obstacle course,” says Jie Yin, co-corresponding author of a paper on the work and an associate professor of mechanical and aerospace engineering at North Carolina State University. “However, it was unable to turn unless it encountered an obstacle. In practical terms this meant that the robot could sometimes get stuck, bouncing back and forth between parallel obstacles.
    “We’ve developed a new soft robot that is capable of turning on its own, allowing it to make its way through twisty mazes, even negotiating its way around moving obstacles. And it’s all done using physical intelligence, rather than being guided by a computer.”
    Physical intelligence refers to dynamic objects — like soft robots — whose behavior is governed by their structural design and the materials they are made of, rather than being directed by a computer or human intervention.
    As with the earlier version, the new soft robots are made of ribbon-like liquid crystal elastomers. When the robots are placed on a surface that is at least 55 degrees Celsius (131 degrees Fahrenheit), which is hotter than the ambient air, the portion of the ribbon touching the surface contracts, while the portion of the ribbon exposed to the air does not. This induces a rolling motion; the warmer the surface, the faster the robot rolls.
    However, while the previous version of the soft robot had a symmetrical design, the new robot has two distinct halves. One half of the robot is shaped like a twisted ribbon that extends in a straight line, while the other half is shaped like a more tightly twisted ribbon that also twists around itself like a spiral staircase.
    This asymmetrical design means that one end of the robot exerts more force on the ground than the other end. Think of a plastic cup that has a mouth wider than its base. If you roll it across the table, it doesn’t roll in a straight line — it makes an arc as it travels across the table. That’s due to its asymmetrical shape. More

  • 138 Shares179 Views

    in Computers Math

    What do neurons, fireflies and dancing the Nutbush have in common?

    Computer scientists and mathematicians working in complex systems at the University of Sydney and the Max Planck Institute for Mathematics in the Sciences in Germany have developed new methods to describe what many of us take for granted — how easy, or hard, it can be to fall in and out of sync.
    Synchronised phenomena are all around us, whether it is human clapping and dancing, or the way fireflies flash, or how our neurons and heart cells interact. However, it is something not fully understood in engineering and science.
    Associate Professor Joseph Lizier, expert in complex systems at the University of Sydney, said: “We know the feeling of dancing in step to the ‘Nutbush’ in a crowd — or the awkward feeling when people lose time clapping to music. Similar processes occur in nature, and it is vital that we better understand how falling in and out of sync actually works.
    “Being in sync in a system can be very good; you want your heart cells to all beat together rather than fibrillate. But being in sync can also be very bad; you don’t want your brain cells to all fire together in an epileptic seizure.”
    Associate Professor Lizier and colleagues at the Max Planck Institute in Leipzig, Germany have published new research on synchronisation in the Proceedings of the National Academy of Sciences (PNAS).
    The paper sets out the mathematics of how the network structure connecting a set of individual elements controls how well they can synchronise their activity. It is a critical insight into how these systems operate, because in most real-world systems, no one individual element controls all the others. And nor can any individual directly see and react to all the others: they are only connected through a network.
    Associate Professor Lizier, from the Centre of Complex Systems and the School of Computer Science in the Faculty of Engineering, said: “Our results open new opportunities for designing network structures or interventions in networks. This could be super useful in stabilising electricity in power grids, vital for the transition to renewables, or to avoid neural synchronisation in the brain, which can trigger epilepsy.”
    To understand how these systems work, the researchers studied what are known as “walks” through a network in a complex system. Walks are sequences of connected hops between individual elements or nodes in the network. More

  • 150 Shares169 Views

    in Computers Math

    Valleytronics: Innovative way to store and process information up to room temperature

    Researchers at the Center for Functional Nanomaterials (CFN), a U.S. Department of Energy (DOE) Office of Science User Facility at DOE’s Brookhaven National Laboratory, and Northrop Grumman, a multinational aerospace and defense technology company, have found a way to maintain valley polarization at room temperature using novel materials and techniques. This discovery could lead to devices that store and process information in novel ways using this technology without the need to keep them at ultra-low temperatures. Their research was recently published in Nature Communications.
    One of the paths being explored to achieve these devices is a relatively new field called “valleytronics.” A material’s electronic band structure — the range of energy levels in each atom’s electron configurations — can dip up or down. These peaks and troughs are known as “valleys.” Some materials have multiple valleys with the same energy. An electron in a system like this can occupy any one of these valleys, presenting a unique way to store and process information based on which valley the electron occupies. One challenge, however, has been the effort and expense of maintaining the low temperatures needed to keep valley polarization stable. Without this stability, devices would begin to lose information. In order to make a technology like this feasible for practical, affordable applications, experts would need to find a way to around this constraint.
    Exploring 2D Landscapes for the Perfect Valleys
    Transition metal dichalcogenides (TMDs) are interesting, layered materials that can be, at their thinnest, only few atoms thick. Each layer in the material consists of a two-dimensional (2D) sheet of transition metal atoms sandwiched between chalcogen atoms. While the metal and the chalcogen are strongly bound by covalent bonds in a layer, adjacent layers are only weakly bound by van der Waal’s interactions. The weak bonds that hold these layers together enable TMDs to be exfoliated down to a monolayer that’s only one “molecule” thick. These are often referred to as 2D materials.
    The team at CFN synthesized single crystals of chiral lead halide perovskites (R/S-NEAPbI3). Chirality describes a set of objects, like molecules, that are a mirror image of each other but can’t be superimposed. It is derived from the Greek word for “hands,” a perfect example of chirality. The two shapes are identical, but if you put one hand on top of the other, they will not align. This asymmetry is important for controlling valley polarization.
    Flakes of this material, roughly 500 nanometers thick or five-thousandths the thickness of a human hair, were layered onto a monolayer of molybdenum disulfide (MoS2) TMD to create what is known as a heterostructure. By combining different 2D materials with properties that affect the charge transfer at the interface between the two materials, these heterostructures open up a world of possibility.
    After creating and characterizing this heterostructure, the team was eager to see how it behaved. More

  • 125 Shares169 Views

    in Computers Math

    Online AI-based test for Parkinson’s disease severity shows promising results

    An artificial intelligence tool developed by researchers at the University of Rochester can help people with Parkinson’s disease remotely assess the severity of their symptoms within minutes. A study in npj Digital Medicine describes the new tool, which has users tap their fingers 10 times in front of a webcam to assess motor performance on a scale of 0-4.
    Doctors often have patients perform simple motor tasks to assess movement disorders and rate the severity using guidelines such as the Movement Disorder Society Unified Parkinson’s Disease Rating Scale (MDS-UPDRS). The AI model provides a rapid assessment using the MDS-UPDRS guidelines, automatically generating computational metrics such as speed, amplitude, frequency, and period that are interpretable, standardized, repeatable, and consistent with medical guidebooks. It uses those attributes to classify the severity of tremors.
    The finger-tapping task was performed by 250 global participants with Parkinson’s disease and the AI system’s ratings were compared with those by three neurologists and three primary care physicians. While expert neurologists performed slightly better than the AI model, the AI model outperformed the primary care physicians with UPDRS certification.
    The AI-based Parkinson’s disease severity test generates computational metrics such as speed, amplitude, frequency, and period, and uses those attributes to classify the severity of tremors. 
    “These findings could have huge implications for patients who have difficulty gaining access to neurologists, getting appointments, and traveling to the hospital,” says Ehsan Hoque, an associate professor in Rochester’s Department of Computer Science and co-director of the Rochester Human-Computer Interaction Laboratory. “It’s an example of how AI is being gradually introduced into health care to serve people outside of the clinic and improve health equity and access.”
    The study was led by Md. Saiful Islam, a Google PhD fellow and a graduate student in computer science advised by Hoque. The team of computer scientists collaborated with several members of the Medical Center’s Department of Neurology, including associate professor Jamie Adams; Ray Dorsey, the David M. Levy Professor of Neurology; and associate professor Ruth Schneider.
    The researchers say their method can be applied to other motor tasks, which opens the door to evaluating other types of movement disorders such as ataxia and Huntington’s disease. The new Parkinson’s disease assessment is available online, though the researchers caution that it reflects an emerging technology and at this early stage should not be considered, on its own and without a physician’s input, as a definitive measure of the presence or severity of the disease. More

  • 100 Shares199 Views

    in Computers Math

    Machine learning contributes to better quantum error correction

    Researchers from the RIKEN Center for Quantum Computing have used machine learning to perform error correction for quantum computers — a crucial step for making these devices practical — using an autonomous correction system that despite being approximate, can efficiently determine how best to make the necessary corrections.
    In contrast to classical computers, which operate on bits that can only take the basic values 0 and 1, quantum computers operate on “qubits,” which can assume any superposition of the computational basis states. In combination with quantum entanglement, another quantum characteristic that connects different qubits beyond classical means, this enables quantum computers to perform entirely new operations, giving rise to potential advantages in some computational tasks, such as large-scale searches, optimization problems, and cryptography.
    The main challenge towards putting quantum computers into practice stems from the extremely fragile nature of quantum superpositions. Indeed, tiny perturbations induced, for instance, by the ubiquitous presence of an environment give rise to errors that rapidly destroy quantum superpositions and, as a consequence, quantum computers lose their edge.
    To overcome this obstacle, sophisticated methods for quantum error correction have been developed. While they can, in theory, successfully neutralize the effect of errors, they often come with a massive overhead in device complexity, which itself is error-prone and thus potentially even increases the exposure to errors. As a consequence, full-fledged error correction has remained elusive.
    In this work, the researchers leveraged machine learning in a search for error correction schemes that minimize the device overhead while maintaining good error correcting performance. To this end, they focused on an autonomous approach to quantum error correction, where a cleverly designed, artificial environment replaces the necessity to perform frequent error-detecting measurements. They also looked at “bosonic qubit encodings,” which are, for instance, available and utilized in some of the currently most promising and widespread quantum computing machines based on superconducting circuits.
    Finding high-performing candidates in the vast search space of bosonic qubit encodings represents a complex optimization task, which the researchers address with reinforcement learning, an advanced machine learning method, where an agent explores a possibly abstract environment to learn and optimize its action policy. With this, the group found that a surprisingly simple, approximate qubit encoding could not only greatly reduce the device complexity compared to other proposed encodings, but also outperformed its competitors in terms of its capability to correct errors.
    Yexiong Zeng, the first author of the paper, says, “Our work not only demonstrates the potential for deploying machine learning towards quantum error correction, but it may also bring us a step closer to the successful implementation of quantum error correction in experiments.”
    According to Franco Nori, “Machine learning can play a pivotal role in addressing large-scale quantum computation and optimization challenges. Currently, we are actively involved in a number of projects that integrate machine learning, artificial neural networks, quantum error correction, and quantum fault tolerance.” More

  • 100 Shares99 Views

    in Heart

    When discussing flora and fauna, don’t forget ‘funga’

    Fungi. They grow between toes, on bread and in the shower. But the organisms also produce food and medicine and act as ecosystem maids by decomposing dead matter — benefits that are sometimes overlooked (SN: 11/17/20). That’s why the Fungi Foundation, a nonprofit dedicated to fungi education and conservation, advocates for adding “funga” to the popular phrase “flora and fauna.”

    The mushrooming movement is also backed by the United Nations Convention on Biological Diversity, which in August called for the addition of “a third ‘F’ — funga — to address the planetary challenges of climate change and biodiversity loss.” More than 20 countries already use the term, including Australia, Iceland and Brazil.

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    Historically, fungi have been left out of most conservation discussions and plans, says mycologist Giuliana Furci, founder of the Fungi Foundation, which was created in Chile and is now based in the United States. While flora refers to an area’s plant diversity and fauna its animal diversity, fungi don’t fit into either category. “Fungi didn’t have a way in,” Furci says. “It’s about time they get this recognition.”

    Whether a soil mold or a mushroom on a log, fungi face the same threats as other kingdoms of life, including habitat loss and climate change. The International Union for the Conservation of Nature’s Red List includes more than 200 species of fungi that are either threatened or endangered. Fungi also form essential relationships with other organisms, like gut bacteria or the roots of plants (SN: 5/25/23). That means it is paramount that fungi are considered in conservation policies, Furci says. She and two other mycologists coined the term funga in 2018 in IMA Fungus. Mycota, the ancient Greek word for mushroom, would have been more accurate, but funga seemed catchier, Furci says.

    The phrase has the potential to take off widely, says mycologist Catherine Gehring of Northern Arizona University in Flagstaff. Using funga along with flora and fauna will be particularly powerful in nonscientist circles where the phrase could encourage interest in fungi among policymakers and the public, she says.

    “[Fungi] are super valuable,” Gehring says. “It’s great to see the movement is gaining traction.” More

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    in Computers Math

    New material offers more durable, sustainable multi-level non-volatile phase change memory

    Scientists have achieved a breakthrough in the development of non-volatile phase change memory−−a type of electronic memory that can store data even when the power is turned off−−in a material that has never displayed the sort of characteristics that such memory requires.
    Until now, phase change memory has primarily been developed using chalcogenides−−a group of materials known to exhibit reversible electrical changes when they transition between their crystalline and amorphous states.
    But what if there’s an even better material out there?
    In a recently published study, researchers report a thermally reversible switching of room-temperature electrical resistivity in a layered nickelate−−potentially offering better performance and superior sustainability.
    The study was published in the journal Advanced Science on September 3, 2023.
    Layered nickelates are a class of complex oxide materials composed of nickel ions. They exhibit a layered structure, where planes of nickel and oxygen atoms are interspersed with layers containing other elements, often alkaline-earth or rare-earth elements. Their unique layered arrangement has drawn the interest of researchers due to the intriguing properties of their electrons, with potential applications in fields such as superconductivity and, in this case, electronics.
    The researchers’ particular layered nickelate is composed of layers of of strontium, bismuth and oxygen atoms in a ‘rock salt’ structural arrangement, interleaved with layers of molecules of strontium, nickel and oxygen atoms in a perovskite structure. Perovskites are defined by a specific crystal structure of two positively charged atoms and one negatively charged one, and have a number of intriguing properties, from superconductivity to ferroelectricity−−a spontaneous electric polarization that can be reversed by the application of an electric field. More