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    New method for comparing neural networks exposes how artificial intelligence works

    A team at Los Alamos National Laboratory has developed a novel approach for comparing neural networks that looks within the “black box” of artificial intelligence to help researchers understand neural network behavior. Neural networks recognize patterns in datasets; they are used everywhere in society, in applications such as virtual assistants, facial recognition systems and self-driving cars.
    “The artificial intelligence research community doesn’t necessarily have a complete understanding of what neural networks are doing; they give us good results, but we don’t know how or why,” said Haydn Jones, a researcher in the Advanced Research in Cyber Systems group at Los Alamos. “Our new method does a better job of comparing neural networks, which is a crucial step toward better understanding the mathematics behind AI.”
    Jones is the lead author of the paper “If You’ve Trained One You’ve Trained Them All: Inter-Architecture Similarity Increases With Robustness,” which was presented recently at the Conference on Uncertainty in Artificial Intelligence. In addition to studying network similarity, the paper is a crucial step toward characterizing the behavior of robust neural networks.
    Neural networks are high performance, but fragile. For example, self-driving cars use neural networks to detect signs. When conditions are ideal, they do this quite well. However, the smallest aberration — such as a sticker on a stop sign — can cause the neural network to misidentify the sign and never stop.
    To improve neural networks, researchers are looking at ways to improve network robustness. One state-of-the-art approach involves “attacking” networks during their training process. Researchers intentionally introduce aberrations and train the AI to ignore them. This process is called adversarial training and essentially makes it harder to fool the networks.
    Jones, Los Alamos collaborators Jacob Springer and Garrett Kenyon, and Jones’ mentor Juston Moore, applied their new metric of network similarity to adversarially trained neural networks, and found, surprisingly, that adversarial training causes neural networks in the computer vision domain to converge to very similar data representations, regardless of network architecture, as the magnitude of the attack increases.
    “We found that when we train neural networks to be robust against adversarial attacks, they begin to do the same things,” Jones said.
    There has been extensive effort in industry and in the academic community searching for the “right architecture” for neural networks, but the Los Alamos team’s findings indicate that the introduction of adversarial training narrows this search space substantially. As a result, the AI research community may not need to spend as much time exploring new architectures, knowing that adversarial training causes diverse architectures to converge to similar solutions.
    “By finding that robust neural networks are similar to each other, we’re making it easier to understand how robust AI might really work. We might even be uncovering hints as to how perception occurs in humans and other animals,” Jones said.
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    Intelligent cooperation to provide surveillance and epidemic services in smart cities

    The potential of unmanned aerial vehicles (UAVs) to provide a safe environment and epidemic prevention services to people is huge. This potential has been harnessed by the scientists at Incheon National University to design a cooperative infrastructure for artificial intelligence-assisted aerial and ground operations using UAVs and mobile robots. This infrastructure can provide surveillance and epidemic prevention activities to smart cities.
    There has been a lot of interest in mobile robots and unmanned aerial vehicles (UAVs) in recent times, primarily because these technologies have the potential to provide us with immense benefits. With the rise of 5G technology, it is expected that UAVs or drones and mobile robots will efficiently and safely provide a wide range of services in smart cities, including surveillance and epidemic prevention. It is now well established that robots can be deployed in various environments to perform activities like surveillance and rescue operations. But till date, all these operations have been independent of each other, often working in parallel. To realize the full potential of UAVs and mobile robots, we need to use these technologies together so that they can support each other and augment mutual functions.
    To this end, a team of researchers led by Associate Professor Hyunbum Kim from Incheon National University, South Korea, have designed an Artificial Intelligence (AI)-assisted cooperative infrastructure for UAVs and mobile robots. In a paper published in volume 36 issue 3 of IEEE Network on 13 July 2022, the researchers outline the entire structure that can use UAVs and mobile robots in public and private areas for multiple operations like patrolling, accident detection and rescue, and epidemic prevention. According to Dr Kim, “It is critical to look at surveillance and unprecedented epidemic spread such as COVID-19 together. This is why we designed the next generation system to focus on aerial-ground surveillance and epidemic prevention supported by intelligent mobile robots and smart UAVs.”
    The system designed by the team is composed of two subsystems, one for public areas and one for private areas. Both systems comprise of a Centralized Administrator Center (CAC). The CAC is connected to various Unified Rendezvous Stations (URSs) that are situated in public areas. These URSs are where the UAVs and mobile robots receive replenishment and share data. Mobile robots are also equipped with charging facilities to recharge airborne docking UAVs. The public system aims at patrolling public areas, detecting accidents and calamities, providing aid, and performing epidemic prevention activities like transporting medical equipment. The private system can provide rapid medical deliveries and screening tests to homes.
    But what about privacy under such surveillance? Dr Kim allays concerns, saying, “Privacy is indeed a major concern for any surveillance mechanism. Therefore, we have created different privacy settings for different systems. For the public system, there are restricted districts where only authorized public UAVs can enter. For the private system, there are permanent private zones where no UAVs can enter except in emergencies and temporal access zones where permitted UAVs can enter with legal permission from the owners.”
    The authors are optimistic about the potential of this infrastructure to improve people’s lives. The system can provide a vast array of services, from detecting and preventing potential terror in public spaces to detecting and extinguishing fires in private homes. Indeed, two is better than one and we look forward to living in this cooperative and optimistic future!
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    Tiny, caterpillar-like soft robot folds, rolls, grabs and degrades

    When you hear the term “robot,” you might think of complicated machinery working in factories or roving on other planets. But “millirobots” might change that. They’re robots about as wide as a finger that someday could deliver drugs or perform minimally invasive surgery. Now, researchers reporting in ACS Applied Polymer Materials have developed a soft, biodegradable, magnetic millirobot inspired by the walking and grabbing capabilities of insects.
    Some soft millirobots are already being developed for a variety of biomedical applications, thanks to their small size and ability to be powered externally, often by a magnetic field. Their unique structures allow them to inch or roll themselves through the bumpy tissues of our gastrointestinal tract, for example. They could someday even be coated in a drug solution and deliver the medicine exactly where it’s needed in the body. However, most millirobots are made of non-degradable materials, such as silicone, which means they’d have to be surgically removed if used in clinical applications. In addition, these materials aren’t that flexible and don’t allow for much fine-tuning of the robot’s properties, limiting their adaptability. So, Wanfeng Shang, Yajing Shen and colleagues wanted to create a millirobot out of soft, biodegradable materials that can grab, roll and climb, but then easily dissolve away after its job is done.
    As a proof of concept, the researchers created a millirobot using a gelatin solution mixed with iron oxide microparticles. Placing the material above a permanent magnet caused the microparticles in the solution to push the gel outward, forming insect-like “legs” along the lines of the magnetic field. Then, the hydrogel was placed in the cold to make it more solid. The final step was to soak the material in ammonium sulfate to cause cross-linking in the hydrogel, making it even stronger. Changing various factors, such as the composition of the ammonium sulfate solution, the thickness of the gel or the strength of the magnetic field allowed the researchers to tune the properties. For example, placing the hydrogel farther away from the magnet resulted in fewer, but longer, legs.
    Because the iron oxide microparticles form magnetic chains within the gel, moving a magnet near the hydrogel caused the legs to bend and produce a claw-like grasping motion. In experiments, the material gripped a 3D-printed cylinder and a rubber band and carried each one to new locations. In addition, the researchers tested the millirobot’s ability to deliver a drug by coating it in a dye solution, then rolling it through a stomach model. Once at its destination, the robot unfurled and released the dye with the strategic use of magnets. Since it’s made using water-soluble gelatin, the millirobot easily degraded in water in two days, leaving behind only the tiny magnetic particles. The researchers say that the new millirobot could open up new possibilities for drug delivery and other biomedical applications.
    The authors acknowledge funding from the National Natural Science Foundation of China, Hong Kong RGC General Research Fund and Shenzhen Key Basic Research Project.
    Video: https://youtu.be/1va-OQvfJDg
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    How Kenyans help themselves and the planet by saving mangrove trees

    On the fringe of Kenya’s Gazi village, 50 kilometers south of Mombasa, Mwatime Hamadi walks barefoot on a path of scorching-hot sand toward a thicket of trees that seem to float where the land meets the Indian Ocean. Behind her moves village life: Mothers carry babies on their backs while they hang laundry between palm trees, women sweep the floors of huts thatched with palm fronds and old men chat idly about bygone days under the shade of mango trees.

    Hamadi is on her way to Gazi Forest, a dense patch of mangroves along Gazi Bay that coastal residents see as vital to their future. Mangroves “play a crucial role in safeguarding the marine ecosystem, which in turn is important for fisheries we depend on for our livelihood,” she says as she reaches a boardwalk that snakes through the coastal wetland.

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    Hamadi is a tour guide with Gazi Ecotourism Ventures, a group dedicated to empowering women and their community through mangrove conservation. This group is part of a larger carbon offset project called Mikoko Pamoja that has taken root and is now being copied farther south on Kenya’s coastline and in Mozambique and Madagascar.

    Through Mikoko Pamoja, residents of Gazi and nearby Makongeni are cultivating an economic ecosystem that relies on efforts to preserve and restore the mangrove forests. Revenue from carbon credits sold plus the money Hamadi and others earn from ecotourism are split between salaries, project costs and village improvements to health care, sanitation, schools and more.

    Mikoko Pamoja, launched in 2013, is the world’s first mangrove­-driven carbon credit initiative. It earned the United Nations’ Equator Prize in 2017, awarded for innovative solutions to poverty that involve conservation and sustainable use of biodiversity.

    “The mangrove vegetation was a thriving, healthy ecosystem in precolonial times,” says Ismail Barua, Mikoko Pamoja’s chairperson. During British rule, which stretched from the 1890s to 1963, the colonial government issued licenses to private companies to export mangrove wood. They did this without community involvement, which led to poaching of trees. Even after Kenya gained independence, mangroves were an important source of timber and fuel for industrial processes, main drivers of extensive destruction of the forests.

    Today, mangrove restoration is helping the region enter a new chapter, one where labor and resources are well-managed by local communities instead of being exploited. “The community is now able to run its own affairs,” Barua notes. Through innovative solutions and hard work, he says, “we’re trying to bring back a semblance of that ecosystem.”

    DOLIMAC/ISTOCK/GETTY IMAGES PLUS

    “The mangrove vegetation was a thriving, healthy ecosystem in precolonial times.…We’re trying to bring back a semblance of that ecosystem.”Ismail Barua

    A fragile carbon sponge

    The dominant mangrove species in Gazi Forest is Rhizophora murcronata. With oval, leathery leaves about the size of a child’s palm and spindly branches that reach to the sun, the trees can grow up to 27 meters tall. Their interlaced roots, which grow from the base of the trunk into the salt­water, make these evergreen trees unique.

    Salt kills most plants, but mangrove roots separate freshwater from salt for the tree to use. At low tide, the looping roots act like stilts and buttresses, keeping trunks and branches above the waterline and dry. Speckling these roots are thousands of specialized pores, or lenticels. The lenticels open to absorb gases from the atmosphere when exposed, but seal tight at high tide, keeping the mangrove from drowning.

    The thickets of roots also prevent soil erosion and buffer coastlines against tropical storms. Within these roots and branches, shorebirds and fish — and in some places, manatees and dolphins — thrive.

    Mangrove roots support an ecosystem that stores four times as much carbon as inland forests. That’s because the saltwater slows decomposition of organic matter, says Kipkorir Lang’at, a principal scientist at the Kenya Marine and Fisheries Research Institute, or KMFRI. So when mangrove plants and animals die, their carbon gets trapped in thick soils. As long as mangroves stay standing, the carbon stays in the soil.

    Robust estimates of mangrove forest area in Kenya before 1980 are not available, Lang’at says. However, with the clear-cutting of mangrove forests in Gazi Bay in the 1970s, he says, the area was left with vast expanses of bare, sandy coast.

    Other parts of the country experienced similar losses: Kenya lost up to 20 percent of its mangrove forests between 1985 and 2009 because no mechanism existed for their protection. The losses had a steep price: Just as mangroves absorb more carbon than inland forests, when destroyed, they release more carbon than other forests. And since the mangroves provided habitat and shelter for fish, their destruction meant that fishers were catching less.

    Recognizing this high cost, as well as the eco­system’s other benefits, Kenya’s government ratified the Forest Conservation and Management Act of 2016, a law protecting mangroves and inland forests. Cutting down mangroves is now banned throughout the country, except in very specific areas under very specific circumstances.

    Available data suggest that Kenya’s rate of mangrove loss has declined in the last two decades. The country is now losing about 0.65 percent of its mangrove forest annually, according to unpublished evaluations conducted in 2020 by KMFRI. Since the turn of the millennium, global mangrove deforestation has slowed as well, hovering between a loss of 0.2 and 0.7 percent per year, says a 2020 study in Scientific Reports.

    Mikoko Pamoja offers hope for turning around those declines. The project, whose Swahili name means “mangroves together,” has its roots in a small mangrove restoration effort that started in 1991 in Gazi Bay, spearheaded by KMFRI. The effort evolved into a scientific experiment to see what it would take to restore a degraded ecosystem. It attracted collaborators from Edinburgh Napier University, Europe’s Earthwatch Institute and other organizations across Europe.

    Now, Gazi Forest boasts 615 hectares of mangrove forest, including 56,000 individual seedlings planted by the community. Plans to plant more mangrove trees — at least 2,000 per year — are in the works.

    Creating carbon credits

    Gazi Forest siphons carbon from the atmosphere at a rate of 3,000 metric tons per year, says Rahma Kivugo, the outgoing project coordinator for Mikoko Pamoja. These aren’t merely ballpark numbers: To sell the carbon offsets collected by Mikoko Pamoja, forest managers must calculate the amount of carbon stored by mangroves.

    Volunteers venture into the forest twice a year, checking on 10 selected 10-square-meter plots in the wild forest and five plots in planted forest. Workers measure the diameter of mature trees at an adult’s chest height. They then estimate the trees’ height. Finally, they classify young trees as knee-height, waist-height, chest-height and higher.

    From these observations, researchers estimate the volume of mangrove material above ground in each plot and extrapolate for the whole forest area.

    Once they have an idea of the volume of plant material above ground, team members can estimate root volume below ground using a standardized factor specific to mangrove forests, says Mbatha Anthony, a research assistant at KMFRI in charge of carbon accounting. Even though mangrove forests store a lot of soil carbon, the project calculates carbon stored only by the tree itself because “calculating soil carbon is a resource-intensive undertaking for a small project like Mikoko Pamoja,” Anthony says.

    With an estimate of the total volume of biomass in the forest in hand, “we can then translate that into tons of carbon,” says environmental biologist Mark Huxham of Edinburgh Napier University, who helps Mikoko Pamoja with its calculations. In general, 50 percent of aboveground biomass is carbon. Below ground, 39 percent of biomass is carbon.

    The amount of carbon stored by Gazi Forest is then relayed to the Plan Vivo Foundation, a group based in Scotland that certifies carbon calculations. Once its calculations are certified, Mikoko Pamoja receives Plan Vivo Certificates, or PVCs.

    One PVC is equivalent to one metric ton of carbon dioxide emission reductions. These PVCs are submitted to the Association for Coastal Ecosystem Services — an organization that markets carbon credits for Mikoko Pamoja and similar projects. Through ACES, Mikoko Pamoja’s PVCs can then be purchased by anyone who wishes to offset their carbon emissions.

    Roughly 117 hectares of Gazi Forest have been demarcated for the sale of carbon credits. “Mikoko Pamoja generates approximately $15,000 annually from the sale of carbon credits,” Anthony says. From 2014 to 2018, the project generated 9,880 credits — 9,880 tons of avoided carbon dioxide emissions.

    Ismail Barua, chairperson of Mikoko Pamoja, stands at a water distribution kiosk funded by the organization’s conservation work.G. Kamadi

    A community at work

    Mikoko Pamoja sells carbon credits at more than $7 per ton. Revenues get split in a clearly defined manner, according to what residents decide are pressing needs of Makongeni and Gazi villages. Around 21 percent pays wages of residents involved with Mikoko Pamoja. And “more than half of what is earned goes toward community projects,” Kivugo says.

    In total, about $117,000 has gone to community projects since Mikoko Pamoja was founded. These projects include donating medicine to health clinics and textbooks to schools and digging clean water wells. Plans are under way to revive a windmill in Gazi for pumping water and renovate Makongeni’s primary school.

    “The need in the community is great. So carbon trading is unlikely to meet all the needs,” Huxham says. But the funds make a significant contribution to local livelihoods, which primes the community to support conservation, he says.

    The approach seems to be working. On a winding path into the forest, visitors encounter a signboard, with large letters in Swahili declaring, “Take note! This is a Mikoko Pamoja area protected by the community. Littering is prohibited! Trimming trees is prohibited!”

    This sign, written in Swahili, warns visitors to the Gazi Bay mangrove forest against littering and cutting down the trees.G. Kamadi

    Active community participation is central to Mikoko Pamoja’s success. Not only do community members plant mangrove seedlings and survey trees to gauge carbon storage, community scouts monitor the health of this ecosystem.

    Scouts clean up litter within the forests and survey the forest’s biodiversity. From a wooden watchtower above the forest, scouts also track and report illegal logging.

    “Should we spot suspicious activities in the forest, we will call the Kenya Forest Service rangers, who have the authority to detain and arrest any trespasser,” says local scout Shaban Jambia.

    Back at the boardwalk, Hamadi leads a small knot of visitors through the mangroves, pausing occasionally to touch a tree’s waxy leaves. She plucks a propagule — a dark-brown pod longer than her hand — from a tree belonging to the mangrove species Bruguiera gymnorhiza.

    She drops the propagule over the boardwalk’s handrail, into the soft marsh soil about 1.5 meters below. It lands, sticking almost perfectly perpendicular in the ground. “This will soon take root and germinate into a new plant,” she explains to the visitors. “That’s how this species propagates.”

    Hamadi, the tour guide, is one of 27 members of the Gazi Women Mangrove Boardwalk group. Members offer interpretive services to visitors for a fee. The women also prepare Swahili cuisine for sale to groups visiting the area.

    “A dish of coconut rice served with snapper fish is particularly popular, washed down with flavored black tea or tamarind juice,” says Mwanahamisi Bakari, the group’s treasurer.

    These ecotourism efforts have attracted international support. The World Wide Fund for Nature Kenya, for instance, constructed a conference facility, which the women’s group rents to those who want to use the location as a backdrop to discuss sustainability efforts.

    A template for others

    Mikoko Pamoja’s success is spurring conservation efforts throughout Kenya and beyond. For instance, on southern Kenya’s coast is the Vanga Blue Forest, a swath of mangroves five times as large as Gazi Forest. Of Vanga Blue’s more than 3,000 hectares of mangrove forest, a little more than 15 percent — 460 hectares — has been set aside for the sale of carbon credits following Mikoko Pamoja’s example.

    In 2020, with help from KFMRI, a network of scientists from countries along the western Indian Ocean published a blueprint for mangrove restoration. These guidelines are now being customized to suit the restoration plans of individual countries, says Lang’at. The group is also using Mikoko Pamoja’s carbon credit example to set up projects of its own.

    Madagascar’s first community-led mangrove carbon project, known as Tahiry Honko (which means “preserving mangroves” in the local Vezo dialect), was introduced in 2013 and then certified for carbon sale by Plan Vivo in 2019. With Mikoko Pamoja as a guide, Tahiry Honko “is helping tackle climate breakdown and build community resilience by preserving and restoring mangrove forests,” says Lalao Aigrette, an adviser at Blue Ventures, the conservation group coordinating the preservation effort.

    Tahiry Honko is generating carbon credits through the conservation and restoration of over 1,200 hectares of mangroves surrounding the Bay of Assassins on Madagascar’s southwest coast.

    In Mozambique, studies are under way to gauge how much mangrove preservation can protect communities against cyclones, says Célia Macamo, a marine biologist at Eduardo Mondlane University in Maputo, Mozambique.

    In the meantime, the Limpopo estuary and other locations along the Mozambican coast are sites of mangrove restoration efforts. KMFRI is helping local organizers structure their efforts. “We also hope they will assist us when we start working with carbon credits,” Macamo adds.

    Mangrove restoration projects have spread outside of Kenya’s Gazi Bay to places such as Limpopo estuary in Mozambique (shown), where residents collect and transport young seedlings.HENRIQUES BALIDY

    Blue economies

    Less than 1 percent of Earth’s surface is covered by mangroves, equivalent to 14.8 million hectares. “Because this area is minuscule compared to terrestrial forests, mangroves have been neglected throughout the world,” says James Kairo, chief scientist at KMFRI.

    At Gazi Bay, a 2011 assessment by the United Nations Environment Programme estimated that the mangrove forests are worth about $1,092 per hectare per year, thanks in part to the potential of fisheries, aquaculture, carbon sequestration and damages averted by the coastal protection that mangroves provide. Assuming that numbers in Gazi Bay hold for the rest of the world, mangroves could provide more than $16 billion in economic benefits planetwide.

    Toward the end of 2020, Kenya’s government included mangroves and seagrasses for the first time in its Nationally Determined Contributions, or NDCs — the greenhouse gas emission reduction commitments for countries that ratified the Paris Agreement. The agreement seeks to limit global warming to below 2 degrees Celsius above preindustrial levels.

    This inclusion commits Kenya to conserving mangroves to balance its emissions. Kenya’s government now “recognizes the potential and importance of the mangrove and seagrass resources that Kenya has,” Huxham says.

    “This is a great commitment on the part of the government. The next challenge is the implementation of these commitments,” says Kairo, who sits on the advisory board of the U.N. Decade of Ocean Science for Sustainable Development (2021–2030), which aims to support efforts to reverse the cycle of decline in ocean health.

    Now, scientists and community managers for that effort need to determine how mangroves can adapt to rising sea levels. “How can communities next to the sea live in harmony with this system, without impacting on their resiliency and productivity?” Kairo asks.

    Mikoko Pamoja is helping provide answers, Kairo adds. Thanks in large part to that small project that began in a secluded corner on the Kenya coast, those answers are now spreading to the rest of the world. More

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    New laser-based instrument designed to boost hydrogen research

    Researchers have developed an analytical instrument that uses an ultrafast laser for precise temperature and concentration measurements of hydrogen. Their new approach could help advance the study of greener hydrogen-based fuels for use in spacecraft and airplanes.
    “This instrument will provide powerful capabilities to probe dynamical processes such as diffusion, mixing, energy transfer and chemical reactions,” said research team leader Alexis Bohlin from Luleå University of Technology in Sweden. “Understanding these processes is fundamental to developing more environmentally friendly propulsion engines.”
    In the Optica Publishing Group journal Optics Express, Bohlin and colleagues from Delft University of Technology and Vrije Universiteit Amsterdam, both in the Netherlands, describe their new coherent Raman spectroscopy instrument for studying hydrogen. It was made possible due to a setup that converts broadband light from a laser with short (femtosecond) pulses into extremely short supercontinuum pulses, which contain a wide range of wavelengths.
    The researchers demonstrated that this supercontinuum generation could be performed behind the same type of thick optical window found on high-pressure chambers used to study a hydrogen-based engine. This is important because other methods for generating ultrabroadband excitation don’t work when these types of optical windows are present.
    “Hydrogen-rich fuel, when made from renewable resources, could have a huge impact on reducing emissions and make a significant contribution to alleviating anthropogenic climate change,” said Bohlin. “Our new method could be used to study these fuels under conditions that closely resemble those in rocket and aerospace engines.”
    Getting light in
    There is much interest in developing aerospace engines that run on renewable hydrogen-rich fuels. In addition to their sustainability appeal, these fuels have among the highest achievable specific impulse — a measure of how efficiently the chemical reaction in an engine creates thrust. However, it has been very challenging to make hydrogen-based chemical propulsion systems reliable. This is because the increased reactivity of hydrogen-rich fuels substantially changes the fuel mixture combustion properties, which increases the flame temperature and decreases ignition delay times. Also, combustion in rocket engines is generally very challenging to control because of the extremely high pressures and high temperatures encountered when traveling to space.
    “The advancement of technology for sustainable launch and aerospace propulsion systems relies on a coherent interplay between experiments and modeling,” said Bohlin. “However, several challenges still exist in terms of producing reliable quantitative data for validating the models.”
    One of the hurdles is that the experiments are usually run in an enclosed space with limited transmission of optical signals in-and-out through optical windows. This window can cause the supercontinuum pulses needed for coherent Raman spectroscopy to become stretched out as they go through the glass. To overcome this problem, the researchers developed a way to transmit femtosecond pulsed laser through a thick optical window and then used a process called laser induced filamentation to transform it into supercontinuum pulses that remain coherent on the other side.
    Studying a hydrogen flame
    To demonstrate the new instrument, the researchers set up a femtosecond laser beam with the ideal properties for supercontinuum generation. They then used it to perform coherent Raman spectroscopy by exciting hydrogen molecules and measuring their rotational transitions. They were able to demonstrate robust measurements of hydrogen gas over a wide range of temperatures and concentrations and also analyzed a hydrogen/air diffusion flame similar to what would be seen when a hydrogen-rich fuel is burned.
    The researchers are now using their instrument to perform a detailed analysis in a turbulent hydrogen flame in hopes of making new discoveries about the combustion process. With a goal of adopting the method for research and testing of rocket engines, the scientists are exploring the limitations of the technique and would like to test it with hydrogen flames in an enclosed slightly pressurized housing.
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    AI helps detect pancreatic cancer

    An artificial intelligence (AI) tool is highly effective at detecting pancreatic cancer on CT, according to a study published in Radiology, a journal of the Radiological Society of North America (RSNA).
    Pancreatic cancer has the lowest five-year survival rate among cancers. It is projected to become the second leading cause of cancer death in the United States by 2030. Early detection is the best way to improve the dismal outlook, as prognosis worsens significantly once the tumor grows beyond 2 centimeters.
    CT is the key imaging method for detection of pancreatic cancer, but it misses about 40% of tumors under 2 centimeters. There is an urgent need for an effective tool to help radiologists in improving pancreatic cancer detection.
    Researchers in Taiwan have been studying a computer-aided detection (CAD) tool that uses a type of AI called deep learning to detect pancreatic cancer. They previously showed that the tool could accurately distinguish pancreatic cancer from noncancerous pancreas. However, that study relied on radiologists manually identifying the pancreas on imaging — a labor-intensive process known as segmentation. In the new study, the AI tool identified the pancreas automatically. This is an important advance considering that the pancreas borders multiple organs and structures and varies widely in shape and size.
    The researchers developed the tool with an internal test set consisting of 546 patients with pancreatic cancer and 733 control participants. The tool achieved 90% sensitivity and 96% specificity in the internal test set.
    Validation followed with a set of 1,473 individual CT exams from institutions throughout Taiwan. The tool achieved 90% sensitivity and 93% specificity in distinguishing pancreatic cancer from controls in that set. Sensitivity for detecting pancreatic cancers less than 2 centimeters was 75%.
    “The performance of the deep learning tool seemed on par with that of radiologists,” said study senior author Weichung Wang, Ph.D., professor at National Taiwan University and director of the university’s MeDA Lab. “Specifically, in this study, the sensitivity of the deep learning computer-aided detection tool for pancreatic cancer was comparable with that of radiologists in a tertiary referral center regardless of tumor size and stage.”
    The CAD tool has the potential to provide a wealth of information to assist clinicians, Dr. Wang said. It could indicate the region of suspicion to speed radiologist interpretation.
    “The CAD tool may serve as a supplement for radiologists to enhance the detection of pancreatic cancer,” said the study’s co-senior author, Wei-Chi Liao, M.D., Ph.D., from National Taiwan University and National Taiwan University Hospital.
    The researchers are planning further studies. In particular, they want to look at the tool’s performance in more diverse populations. Since the current study was retrospective, they want to see how it performs going forward in real-world clinical settings.
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    Healthcare researchers must be wary of misusing AI

    An international team of researchers, writing in the journal Nature Medicine, advises that strong care needs to be taken not to misuse or overuse machine learning (ML) in healthcare research.
    “I absolutely believe in the power of ML but it has to be a relevant addition,” said neurosurgeon-in-training and statistics editor Dr Victor Volovici, first author of the comment, from Erasmus MC University Medical Center, The Netherlands. “Sometimes ML algorithms do not perform better than traditional statistical methods, leading to the publication of papers that lack clinical or scientific value.”
    Real world examples have shown that the misuse of algorithms in healthcare could perpetuate human prejudices or inadvertently cause harm when the machines are trained on biased datasets.
    “Many believe ML will revolutionise healthcare because machines make choices more objectively than humans. But without proper oversight, ML models may do more harm than good,” said Associate Professor Nan Liu, senior author of the comment, from the Centre for Quantitative Medicine and Health Services & Systems Research Programme at Duke-NUS Medical School, Singapore.
    “If, through ML, we uncover patterns that we otherwise would not see — like in radiology and pathology images — we should be able to explain how the algorithms got there, to allow for checks and balances.”
    Together with a group of scientists from the UK and Singapore, the researchers highlight that although guidelines have been formulated to regulate the use of ML in clinical research, these guidelines are only applicable once a decision to use ML has been made and do not ask whether or when its use is appropriate in the first place.
    For example, companies have successfully trained ML algorithms to recognise faces and road objects using billions of images and videos. But when it comes to their use in healthcare settings, they are often trained on data in the tens, hundreds or thousands. “This underscores the relative poverty of big data in healthcare and the importance of working towards achieving sample sizes that have been attained in other industries, as well as the importance of a concerted, international big data sharing effort for health data,” the researchers write.
    Another issue is that most ML and deep learning algorithms (that do not receive explicit instructions regarding the outcome) are often still regarded as a ‘black box’. For example, at the start of the COVID-19 pandemic, scientists published an algorithm that could predict coronavirus infections from lung photos. Afterwards, it turned out that the algorithm had drawn conclusions based on the imprint of the letter ‘R’ (for ‘Right Lung’) in the photos, which was always found in a slightly different spot on the scans.
    “We have to get rid of the idea that ML can discover patterns in data that we cannot understand,” said Dr Volovici about the incident. “ML can very well discover patterns that we cannot see directly, but then you have to be able to explain how you came to that conclusion. In order to do that, the algorithm has to be able to show what steps it took, and that requires innovation.”
    The researchers advise that ML algorithms should be evaluated against traditional statistical approaches (when applicable) before they are used in clinical research. And when deemed appropriate, they should complement clinician decision-making, rather than replace it. “ML researchers should recognise the limits of their algorithms and models in order to prevent their overuse and misuse, which could otherwise sow distrust and cause patient harm,” the researchers write.
    The team is working on organising an international effort to provide guidance on the use of ML and traditional statistics, and also to set up a large database of anonymised clinical data that can harness the power of ML algorithms.
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    New method to identify symmetries in data using Bayesian statistics

    Symmetries in nature make things beautiful; symmetries in data make data handling efficient. However, the complexity of identifying such patterns in data has always bedeviled researchers. Scientists from Osaka Metropolitan University and their colleagues have taken a major step towards detecting symmetries in multi-dimensional data by utilizing Bayesian statistics. Their findings were published in The Annals of Statistics.
    Bayesian statistics has been in the spotlight in recent years due to improvements in computer performance and its potential applications in artificial intelligence. Bayesian statistics is a statistical approach that, even when data are insufficient, derives the probability of an event occurring by first setting a prior probability and then, whenever new information is obtained, calculating a posterior probability — an update to the prior probability — that the event will occur. The calculation of posterior probabilities requires complex calculations of integrals and therefore is often considered an approximation only.
    The international team including Professor Hideyuki Ishi from Osaka Metropolitan University, Professor Piotr Graczyk from the University of Angers, Professor Bartosz Kołodziejek from Warsaw University of Technology, and the late Professor Hélène Massam from York University (Toronto) has succeeded in deriving new exact integral formulas, and in developing a method to search for symmetries in multi-dimensional data using Bayesian statistical techniques.
    When the amount of data to be handled increases, the optimal pattern must be selected from a vast number of patterns, making it difficult to solve the problem precisely. Addressing this challenge, the team has also developed an efficient algorithm for obtaining an approximate solution even in such cases.
    In the words of Professor Ishi, “Symmetries in data are ubiquitous in a wide variety of models. Once symmetries are identified, the number of parameters required to display the structure of the data, and the number of samples required to determine the parameters, can be significantly reduced. In the future, the results of this research are expected to contribute to genetic analysis, discovering chromosomes that have the same function in different locations.”
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