Aurelia Butler

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  • 113 Shares119 Views

    in Computers Math

    Adaptive optical neural network connects thousands of artificial neurons

    Scientists headed by physicists Prof. Wolfram Pernice, Prof. Martin Salinga and computer specialist Prof. Benjamin Risse, all from the University of Münster (Germany), developed a so-called event-based architecture, using photonic processors. In a similar way to the brain, this makes possible the continuous adaptation of the connections within the neural network.
    Modern computer models — for example for complex, potent AI applications — push traditional digital computer processes to their limits. New types of computing architecture, which emulate the working principles of biological neural networks, hold the promise of faster, more energy-efficient data processing. A team of researchers has now developed a so-called event-based architecture, using photonic processors with which data are transported and processed by means of light. In a similar way to the brain, this makes possible the continuous adaptation of the connections within the neural network. This changeable connections are the basis for learning processes. For the purposes of the study, a team working at Collaborative Research Centre 1459 (“Intelligent Matter”) — headed by physicists Prof. Wolfram Pernice and Prof. Martin Salinga and computer specialist Prof. Benjamin Risse, all from the University of Münster — joined forces with researchers from the Universities of Exeter and Oxford in the UK. The study has been published in the journal “Science Advances.”
    What is needed for a neural network in machine learning are artificial neurons which are activated by external excitatory signals, and which have connections to other neurons. The connections between these artificial neurons are called synapses — just like the biological original. For their study, the team of researchers in Münster used a network consisting of almost 8,400 optical neurons made of waveguide-coupled phase-change material, and the team showed that the connection between two each of these neurons can indeed become stronger or weaker (synaptic plasticity), and that new connections can be formed, or existing ones eliminated (structural plasticity). In contrast to other similar studies, the synapses were not hardware elements but were coded as a result of the properties of the optical pulses — in other words, as a result of the respective wavelength and of the intensity of the optical pulse. This made it possible to integrate several thousand neurons on one single chip and connect them optically.
    In comparison with traditional electronic processors, light-based processors offer a significantly higher bandwidth, making it possible to carry out complex computing tasks, and with lower energy consumption. This new approach consists of basic research. “Our aim is to develop an optical computing architecture which in the long term will make it possible to compute AI applications in a rapid and energy-efficient way,” says Frank Brückerhoff-Plückelmann, one of the lead authors.
    Methodology: The non-volatile phase-change material can be switched between an amorphous structure and a crystalline structure with a highly ordered atomic lattice. This feature allows permanent data storage even without an energy supply. The researchers tested the performance of the neural network by using an evolutionary algorithm to train it to distinguish between German and English texts. The recognition parameter they used was the number of vowels in the text. More

  • 163 Shares129 Views

    in Computers Math

    To excel at engineering design, generative AI must learn to innovate, study finds

    ChatGPT and other deep generative models are proving to be uncanny mimics. These AI supermodels can churn out poems, finish symphonies, and create new videos and images by automatically learning from millions of examples of previous works. These enormously powerful and versatile tools excel at generating new content that resembles everything they’ve seen before.
    But as MIT engineers say in a new study, similarity isn’t enough if you want to truly innovate in engineering tasks.
    “Deep generative models (DGMs) are very promising, but also inherently flawed,” says study author Lyle Regenwetter, a mechanical engineering graduate student at MIT. “The objective of these models is to mimic a dataset. But as engineers and designers, we often don’t want to create a design that’s already out there.”
    He and his colleagues make the case that if mechanical engineers want help from AI to generate novel ideas and designs, they will have to first refocus those models beyond “statistical similarity.”
    “The performance of a lot of these models is explicitly tied to how statistically similar a generated sample is to what the model has already seen,” says co-author Faez Ahmed, assistant professor of mechanical engineering at MIT. “But in design, being different could be important if you want to innovate.”
    In their study, Ahmed and Regenwetter reveal the pitfalls of deep generative models when they are tasked with solving engineering design problems. In a case study of bicycle frame design, the team shows that these models end up generating new frames that mimic previous designs but falter on engineering performance and requirements.
    When the researchers presented the same bicycle frame problem to DGMs that they specifically designed with engineering-focused objectives, rather than only statistical similarity, these models produced more innovative, higher-performing frames. More

  • 163 Shares179 Views

    in Computers Math

    Keeping a human in the loop: Managing the ethics of AI in medicine

    Artificial intelligence (AI) — of ChatGPT fame — is increasingly used in medicine to improve diagnosis and treatment of diseases, and to avoid unnecessary screening for patients. But AI medical devices could also harm patients and worsen health inequities if they are not designed, tested, and used with care, according to an international task force that included a University of Rochester Medical Center bioethicist.
    Jonathan Herington, PhD, was a member of the AI Task Force of the Society for Nuclear Medicine and Medical Imaging, which laid out recommendations on how to ethically develop and use AI medical devices in two papers published in the Journal of Nuclear Medicine. In short, the task force called for increased transparency about the accuracy and limits of AI and outlined ways to ensure all people have access to AI medical devices that work for them — regardless of their race, ethnicity, gender, or wealth.
    While the burden of proper design and testing falls to AI developers, health care providers are ultimately responsible for properly using AI and shouldn’t rely too heavily on AI predictions when making patient care decisions.
    “There should always be a human in the loop,” said Herington, who is assistant professor of Health Humanities and Bioethics at URMC and was one of three bioethicists added to the task force in 2021. “Clinicians should use AI as an input into their own decision making, rather than replacing their decision making.”
    This requires that doctors truly understand how a given AI medical device is intended to be used, how well it performs at that task, and any limitations — and they must pass that knowledge on to their patients. Doctors must weigh the relative risks of false positives versus false negatives for a given situation, all while taking structural inequities into account.
    When using an AI system to identify probable tumors in PET scans, for example, health care providers must know how well the system performs at identifying this specific type of tumor in patients of the same sex, race, ethnicity, etc., as the patient in question.
    “What that means for the developers of these systems is that they need to be very transparent,” said Herington. More

  • 113 Shares109 Views

    in Computers Math

    Ensuring fairness of AI in healthcare requires cross-disciplinary collaboration

    Pursuing fair artificial intelligence (AI) for healthcare requires collaboration between experts across disciplines, says a global team of scientists led by Duke-NUS Medical School in a new perspective published in npj Digital Medicine.
    While AI has demonstrated potential for healthcare insights, concerns around bias remain. “A fair model is expected to perform equally well across subgroups like age, gender and race. However, differences in performance may have underlying clinical reasons and may not necessarily indicate unfairness,” explained first author Ms Liu Mingxuan, a PhD candidate in the Quantitative Biology and Medicine (Biostatistics & Health Data Science) Programme and Centre for Quantitative Medicine (CQM) at Duke-NUS.
    “Focusing on equity — that is, recognising factors like race, gender, etc., and adjusting the AI algorithm or its application to make sure more vulnerable groups get the care they need — rather than complete equality, is likely a more reasonable approach for clinical AI,” said Dr Ning Yilin, Research Fellow with CQM and a co-first-author of the paper. “Patient preferences and prognosis are also crucial considerations, as equal treatment does not always mean fair treatment. An example of this is age, which frequently factors into treatment decisions and outcomes.”
    The paper highlights key misalignments between AI fairness research and clinical needs. “Various metrics exist to measure model fairness, but choosing suitable ones for healthcare is difficult as they can conflict. Trade-offs are often inevitable,” commented Associate Professor Liu Nan also from Duke-NUS’ CQM, senior and corresponding author of the paper.
    He added, “Differences detected between groups are frequently treated as biases to be mitigated in AI research. However, in the medical context, we must discern between meaningful differences and true biases requiring correction.”
    The authors emphasise the need to evaluate which attributes are considered ‘sensitive’ for each application. They say that actively engaging clinicians is vital for developing useful and fair AI models.
    “Variables like race and ethnicity need careful handling as they may represent systemic biases or biological differences,” said Assoc Prof Liu. “Clinicians can provide context, determine if differences are justified, and guide models towards equitable decisions.”
    Overall, the authors argue that pursuing fair AI for healthcare requires collaboration between experts in AI, medicine, ethics and beyond. More

  • 175 Shares169 Views

    in Computers Math

    Eyes may be the window to your soul, but the tongue mirrors your health

    A 2000-year-old practice by Chinese herbalists — examining the human tongue for signs of disease — is now being embraced by computer scientists using machine learning and artificial intelligence.
    Tongue diagnostic systems are fast gaining traction due to an increase in remote health monitoring worldwide, and a study by Iraqi and Australian researchers provides more evidence of the increasing accuracy of this technology to detect disease.
    Engineers from Middle Technical University (MTU) in Baghdad and the University of South Australia (UniSA) used a USB web camera and computer to capture tongue images from 50 patients with diabetes, renal failure and anaemia, comparing colours with a data base of 9000 tongue images.
    Using image processing techniques, they correctly diagnosed the diseases in 94 per cent of cases, compared to laboratory results. A voicemail specifying the tongue colour and disease was also sent via a text message to the patient or nominated health provider.
    MTU and UniSA Adjunct Associate Professor Ali Al-Naji and his colleagues have reviewed the worldwide advances in computer-aided disease diagnosis, based on tongue colour, in a new paper in AIP Conference Proceedings.
    “Thousands of years ago, Chinese medicine pioneered the practice of examining the tongue to detect illness,” Assoc Prof Al-Naji says.
    “Conventional medicine has long endorsed this method, demonstrating that the colour, shape, and thickness of the tongue can reveal signs of diabetes, liver issues, circulatory and digestive problems, as well as blood and heart diseases. More

  • 150 Shares199 Views

    in Heart

    10 billion snow crabs have disappeared off the Alaskan coast. Here’s why

    From 2018 to 2021, an estimated 10 billion snow crabs disappeared from the eastern Bering Sea off the coast of Alaska, with the population plummeting to record lows in 2021. Researchers had only speculated as to what happened to the missing crabs. Now, a study in the Oct. 20 Science finds that a marine heat wave probably spurred a mass die-off, in part by causing crabs to starve.

    “It’s a fishery disaster in the truest sense of the word,” says Cody Szuwalski, a fishery biologist at the U.S. National Oceanic and Atmospheric Administration’s Alaska Fisheries Science Center in Seattle.

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    On average, snow crabs bring in about $150 million of annual revenue for Alaskan fisheries. In the 2021-2022 crabbing season, that revenue fell to around $24 million. With marine heat waves becoming more common because of human-caused climate change, the future of such fisheries and arctic marine ecosystems, more broadly, is uncertain, researchers say (SN: 7/13/23).

    The new research can help fishery managers anticipate and prepare for similar events such as the crab collapse in the future, Szuwalski says, particularly when it comes to getting proper disaster aid to affected fishers quickly.

    Usually, cold, arctic water makes ideal habitat for snow crabs (Chionoecetes opilio) and other crustaceans. As winter’s thick sea ice melts, the frigid meltwater settles on the seafloor, creating a cold-water pool with temperatures below 2˚ Celsius. Crabs thrive in this chilly sanctuary on the eastern Bering Sea shelf. But a marine heat wave in the region in 2018 and 2019 prevented the usual amount of sea ice from forming, and, according to yearly temperature and population survey data, the cold pool never appeared and then the crab population collapsed.

    Szuwalski and colleagues used computer models to analyze temperature data combined with population surveys, fishing catch numbers and lab experiments to look for drivers behind the sudden collapse. Two causes stood out: higher water temperatures and an initially dense crab population.

    The water temperature probably didn’t kill the crabs directly, as snow crabs in laboratories can survive in waters up to 12° C. Instead, the crabs might have starved to death, Szuwalski says.

    Data show the crab population initially boomed in 2018 — reaching historic highs — thanks to ideal ocean conditions for newborn crabs around 2010. But the crabs also occupied a smaller area than normal, though Szuwalski and colleagues are uncertain why. That means more crabs were crammed onto less space on the shelf. Then came the heat wave. Higher water temperatures can rev the cold-blooded crabs’ metabolism; previous research has shown that the calorie requirements of snow crabs in labs almost double as water temperature rises from 0° to 3° C. 

    As a result, the crowded crabs probably needed more food, but because of the smaller foraging area they had even fewer resources to sink their claws into. Compared with similar-sized crabs from the previous year, those surveyed in 2018 had lower body weights, another clue starvation played a role in the missing crabs.

    “It’s just yet another example of something we didn’t expect, but now we have to live with,” says Christopher Harley, a marine ecologist at the University of British Columbia in Vancouver, who was not involved with the research. In the eastern Bering Sea, it could take at least four years before more crabs of a fishable size start showing up, meaning fishers there will remain in a lurch.

    Such effects of marine heat waves are likely to extend beyond snow crabs. Ecosystems in northern latitudes, such as Alaska’s, are changing more rapidly in response to climate change than anywhere else (SN: 8/11/22). Scientists can typically use data from the past to help predict and prepare for changes in the future. But the future increasingly holds events that have never happened on record before — like the snow crab population collapse — so they’re harder to prepare for, Harley says.

    That’s especially true, he says, because there hasn’t been enough attention on the secondary effects of marine heat waves on cold-blooded creatures, such as higher calorie needs and the risk of starvation. More

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    in Computers Math

    International team develops novel DNA nano engine

    An international team of scientists has recently developed a novel type of nano engine made of DNA. It is driven by a clever mechanism and can perform pulsing movements. The researchers are now planning to fit it with a coupling and install it as a drive in complex nano machines. Their results were just published today in the journal Nature Nanotechnology.
    Petr Šulc, an assistant professor at Arizona State University’s School of Molecular Sciences and the Biodesign Center for Molecular Design and Biomimetics, has collaborated with professor Famulok (project lead) from the University of Bonn, Germany and professor Walter from the University of Michigan on this project.
    Šulc has used his group’s computer modeling tools to gain insights into design and operation of this leaf-spring nano engine. The structure is comprised of almost 14,000 nucleotides, which form the basic structural units of DNA.
    “Being able to simulate motion in such a large nanostructure would be impossible without oxDNA, the computer model that our group uses for design and design of DNA nanostructures,” explains Šulc. ” It is the first time that a chemically powered DNA nanotechnology motor has been successfully engineered. We are very excited that our research methods could help with studying it, and are looking forward to building even more complex nanodevices in the future.”
    This novel type of engine is similar to a hand grip strength trainer that strengthens your grip when used regularly. However, the motor is around one million times smaller. Two handles are connected by a spring in a V-shaped structure.
    In a hand grip strength trainer, you squeeze the handles together against the resistance of the spring. Once you release your grip, the spring pushes the handles back to their original position. “Our motor uses a very similar principle,” says professor Michael Famulok from the Life and Medical Sciences (LIMES) Institute at the University of Bonn. “But the handles are not pressed together but rather pulled together.”
    The researchers have repurposed a mechanism without which there would be no plants or animals on Earth. Every cell is equipped with a sort of library. It contains the blueprints for all types of proteins that each cell needs to perform its function. If the cell wants to produce a certain type of protein, it orders a copy from the respective blueprint. This transcript is produced by the enzymes called RNA polymerases. More

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    in Computers Math

    Physical theory improves protein folding prediction

    Proteins are important molecules that perform a variety of functions essential to life. To function properly, many proteins must fold into specific structures. However, the way proteins fold into specific structures is still largely unknown. Researchers from the University of Tokyo developed a novel physical theory that can accurately predict how proteins fold. Their model can predict things previous models cannot. Improved knowledge of protein folding could offer huge benefits to medical research, as well as to various industrial processes.
    You are literally made of proteins. These chainlike molecules, made from tens to thousands of smaller molecules called amino acids, form things like hair, bones, muscles, enzymes for digestion, antibodies to fight diseases, and more. Proteins make these things by folding into various structures that in turn build up these larger tissues and biological components. And by knowing more about this folding process, researchers can better understand more about the processes that constitute life itself. Such knowledge is also essential to medicine, not only for the development of new treatments and industrial processes to produce medicines, but also for knowledge of how certain diseases work, as some are examples of protein folding gone wrong. So, to say proteins are important is putting it mildly. Proteins are the stuff of life.
    Encouraged by the importance of protein folding, Project Assistant Professor Koji Ooka from the College of Arts and Sciences and Professor Munehito Arai from the Department of Life Sciences and Department of Physics embarked on the hard task of improving upon the prediction methods of protein folding. This task is formidable for many reasons. In particular, the computational requirements to simulate the dynamics of molecules necessitate a powerful supercomputer. Recently, the artificial intelligence-based program AlphaFold 2 accurately predicts structures resulting from a given amino acid sequence; but it cannot give details of the way proteins fold, making it a black box. This is problematic, as the forms and behaviors of proteins vary such that two similar ones may fold in radically different ways. So, instead of AI, the duo needed a different approach: statistical mechanics, a branch of physical theory.
    “For over 20 years, a theory called the Wako-Saitô-Muñoz-Eaton (WSME) model has successfully predicted the folding processes for proteins comprising around 100 amino acids or fewer, based on the native protein structures,” said Arai. “WSME can only evaluate small sections of proteins at a time, missing potential connections between sections farther apart. To overcome this issue, we produced a new model, WSME-L, where the L stands for ‘linker.’ Our linkers correspond to these nonlocal interactions and allow WSME-L to elucidate the folding process without the limitations of protein size and shape, which AlphaFold 2 cannot.”
    But it doesn’t end there. There are other limitations of existing protein folding models that Ooka and Arai set their sights on. Proteins can exist inside or outside of living cells; those within are in some ways protected by the cell, but those outside cells, such as antibodies, require additional bonds during folding, called disulfide bonds, which help to stabilize them. Conventional models cannot factor in these bonds, but an extension to WSME-L called WSME-L(SS), where each S stands for sulfide, can. To further complicate things, some proteins have disulfide bonds before folding starts, so the researchers made a further enhancement called WSME-L(SSintact), which factors in that situation at the expense of extra computation time.
    “Our theory allows us to draw a kind of map of protein folding pathways in a relatively short time; mere seconds on a desktop computer for short proteins, and about an hour on a supercomputer for large proteins, assuming the native protein structure is available by experiments or AlphaFold 2 prediction,” said Arai. “The resulting landscape allows a comprehensive understanding of multiple potential folding pathways a long protein might take. And crucially, we can scrutinize structures of transient states. This might be helpful for those researching diseases like Alzheimer’s and Parkinson’s — both are caused by proteins which fail to fold correctly. Also, our method may be useful for designing novel proteins and enzymes which can efficiently fold into stable functional structures, for medical and industrial use.”
    While the models produced here accurately reflect experimental observations, Ooka and Arai hope they can be used to elucidate the folding processes of many proteins that have not yet been studied experimentally. Humans have about 20,000 different proteins, but only around 100 have had their folding processes thoroughly studied. More