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      in Security

      Tackling counterfeit seeds with “unclonable” labels

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      Average crop yields in Africa are consistently far below those expected, and one significant reason is the prevalence of counterfeit seeds whose germination rates are far lower than those of the genuine ones. The World Bank estimates that as much as half of all seeds sold in some African countries are fake, which could help to account for crop production that is far below potential.

      There have been many attempts to prevent this counterfeiting through tracking labels, but none have proved effective; among other issues, such labels have been vulnerable to hacking because of the deterministic nature of their encoding systems. But now, a team of MIT researchers has come up with a kind of tiny, biodegradable tag that can be applied directly to the seeds themselves, and that provides a unique randomly created code that cannot be duplicated.

      The new system, which uses minuscule dots of silk-based material, each containing a unique combination of different chemical signatures, is described today in the journal Science Advances in a paper by MIT’s dean of engineering Anantha Chandrakasan, professor of civil and environmental engineering Benedetto Marelli, postdoc Hui Sun, and graduate student Saurav Maji.

      The problem of counterfeiting is an enormous one globally, the researchers point out, affecting everything from drugs to luxury goods, and many different systems have been developed to try to combat this. But there has been less attention to the problem in the area of agriculture, even though the consequences can be severe. In sub-Saharan Africa, for example, the World Bank estimates that counterfeit seeds are a significant factor in crop yields that average less than one-fifth of the potential for maize, and less than one-third for rice.

      Marelli explains that a key to the new system is creating a randomly-produced physical object whose exact composition is virtually impossible to duplicate. The labels they create “leverage randomness and uncertainty in the process of application, to generate unique signature features that can be read, and that cannot be replicated,” he says.

      What they’re dealing with, Sun adds, “is the very old job of trying, basically, not to get your stuff stolen. And you can try as much as you can, but eventually somebody is always smart enough to figure out how to do it, so nothing is really unbreakable. But the idea is, it’s almost impossible, if not impossible, to replicate it, or it takes so much effort that it’s not worth it anymore.”

      The idea of an “unclonable” code was originally developed as a way of protecting the authenticity of computer chips, explains Chandrakasan, who is the Vannevar Bush Professor of Electrical Engineering and Computer Science. “In integrated circuits, individual transistors have slightly different properties coined device variations,” he explains, “and you could then use that variability and combine that variability with higher-level circuits to create a unique ID for the device. And once you have that, then you can use that unique ID as a part of a security protocol. Something like transistor variability is hard to replicate from device to device, so that’s what gives it its uniqueness, versus storing a particular fixed ID.” The concept is based on what are known as physically unclonable functions, or PUFs.

      The team decided to try to apply that PUF principle to the problem of fake seeds, and the use of silk proteins was a natural choice because the material is not only harmless to the environment but also classified by the Food and Drug Administration in the “generally recognized as safe” category, so it requires no special approval for use on food products.

      “You could coat it on top of seeds,” Maji says, “and if you synthesize silk in a certain way, it will also have natural random variations. So that’s the idea, that every seed or every bag could have a unique signature.”

      Developing effective secure system solutions has long been one of Chandrakasan’s specialties, while Marelli has spent many years developing systems for applying silk coatings to a variety of fruits, vegetables, and seeds, so their collaboration was a natural for developing such a silk-based coding system toward enhanced security.

      “The challenge was what type of form factor to give to silk,” Sun says, “so that it can be fabricated very easily.” They developed a simple drop-casting approach that produces tags that are less than one-tenth of an inch in diameter. The second challenge was to develop “a way where we can read the uniqueness, in also a very high throughput and easy way.”

      For the unique silk-based codes, Marelli says, “eventually we found a way to add a color to these microparticles so that they assemble in random structures.” The resulting unique patterns can be read out not only by a spectrograph or a portable microscope, but even by an ordinary cellphone camera with a macro lens. This image can be processed locally to generate the PUF code and then sent to the cloud and compared with a secure database to ensure the authenticity of the product. “It’s random so that people cannot easily replicate it,” says Sun. “People cannot predict it without measuring it.”

      And the number of possible permutations that could result from the way they mix four basic types of colored silk nanoparticles is astronomical. “We were able to show that with a minimal amount of silk, we were able to generate 128 random bits of security,” Maji says. “So this gives rise to 2 to the power 128 possible combinations, which is extremely difficult to crack given the computational capabilities of the state-of-the-art computing systems.”

      Marelli says that “for us, it’s a good test bed in order to think out-of-the-box, and how we can have a path that somehow is more democratic.” In this case, that means “something that you can literally read with your phone, and you can fabricate by simply drop casting a solution, without using any advanced manufacturing technique, without going in a clean room.”

      Some additional work will be needed to make this a practical commercial product, Chandrakasan says. “There will have to be a development for at-scale reading” via smartphones. “So, that’s clearly a future opportunity.” But the principle now shows a clear path to the day when “a farmer could at least, maybe not every seed, but could maybe take some random seeds in a particular batch and verify them,” he says.

      The research was partially supported by the U.S. Office of Naval research and the National Science Foundation, Analog Devices Inc., an EECS Mathworks fellowship, and a Paul M. Cook Career Development Professorship. More

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      in Environment

      MIT-led teams win National Science Foundation grants to research sustainable materials

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      Three MIT-led teams are among 16 nationwide to receive funding awards to address sustainable materials for global challenges through the National Science Foundation’s Convergence Accelerator program. Launched in 2019, the program targets solutions to especially compelling societal or scientific challenges at an accelerated pace, by incorporating a multidisciplinary research approach.

      “Solutions for today’s national-scale societal challenges are hard to solve within a single discipline. Instead, these challenges require convergence to merge ideas, approaches, and technologies from a wide range of diverse sectors, disciplines, and experts,” the NSF explains in its description of the Convergence Accelerator program. Phase 1 of the award involves planning to expand initial concepts, identify new team members, participate in an NSF development curriculum, and create an early prototype.

      Sustainable microchips

      One of the funded projects, “Building a Sustainable, Innovative Ecosystem for Microchip Manufacturing,” will be led by Anuradha Murthy Agarwal, a principal research scientist at the MIT Materials Research Laboratory. The aim of this project is to help transition the manufacturing of microchips to more sustainable processes that, for example, can reduce e-waste landfills by allowing repair of chips, or enable users to swap out a rogue chip in a motherboard rather than tossing out the entire laptop or cellphone.

      “Our goal is to help transition microchip manufacturing towards a sustainable industry,” says Agarwal. “We aim to do that by partnering with industry in a multimodal approach that prototypes technology designs to minimize energy consumption and waste generation, retrains the semiconductor workforce, and creates a roadmap for a new industrial ecology to mitigate materials-critical limitations and supply-chain constraints.”

      Agarwal’s co-principal investigators are Samuel Serna, an MIT visiting professor and assistant professor of physics at Bridgewater State University, and two MIT faculty affiliated with the Materials Research Laboratory: Juejun Hu, the John Elliott Professor of Materials Science and Engineering; and Lionel Kimerling, the Thomas Lord Professor of Materials Science and Engineering.

      The training component of the project will also create curricula for multiple audiences. “At Bridgewater State University, we will create a new undergraduate course on microchip manufacturing sustainability, and eventually adapt it for audiences from K-12, as well as incumbent employees,” says Serna.

      Sajan Saini and Erik Verlage of the MIT Department of Materials Science and Engineering (DMSE), and Randolph Kirchain from the MIT Materials Systems Laboratory, who have led MIT initiatives in virtual reality digital education, materials criticality, and roadmapping, are key contributors. The project also includes DMSE graduate students Drew Weninger and Luigi Ranno, and undergraduate Samuel Bechtold from Bridgewater State University’s Department of Physics.

      Sustainable topological materials

      Under the direction of Mingda Li, the Class of 1947 Career Development Professor and an Associate Professor of Nuclear Science and Engineering, the “Sustainable Topological Energy Materials (STEM) for Energy-efficient Applications” project will accelerate research in sustainable topological quantum materials.

      Topological materials are ones that retain a particular property through all external disturbances. Such materials could potentially be a boon for quantum computing, which has so far been plagued by instability, and would usher in a post-silicon era for microelectronics. Even better, says Li, topological materials can do their job without dissipating energy even at room temperatures.

      Topological materials can find a variety of applications in quantum computing, energy harvesting, and microelectronics. Despite their promise, and a few thousands of potential candidates, discovery and mass production of these materials has been challenging. Topology itself is not a measurable characteristic so researchers have to first develop ways to find hints of it. Synthesis of materials and related process optimization can take months, if not years, Li adds. Machine learning can accelerate the discovery and vetting stage.

      Given that a best-in-class topological quantum material has the potential to disrupt the semiconductor and computing industries, Li and team are paying special attention to the environmental sustainability of prospective materials. For example, some potential candidates include gold, lead, or cadmium, whose scarcity or toxicity does not lend itself to mass production and have been disqualified.

      Co-principal investigators on the project include Liang Fu, associate professor of physics at MIT; Tomas Palacios, professor of electrical engineering and computer science at MIT and director of the Microsystems Technology Laboratories; Susanne Stemmer of the University of California at Santa Barbara; and Qiong Ma of Boston College. The $750,000 one-year Phase 1 grant will focus on three priorities: building a topological materials database; identifying the most environmentally sustainable candidates for energy-efficient topological applications; and building the foundation for a Center for Sustainable Topological Energy Materials at MIT that will encourage industry-academia collaborations.

      At a time when the size of silicon-based electronic circuit boards is reaching its lower limit, the promise of topological materials whose conductivity increases with decreasing size is especially attractive, Li says. In addition, topological materials can harvest wasted heat: Imagine using your body heat to power your phone. “There are different types of application scenarios, and we can go much beyond the capabilities of existing materials,” Li says, “the possibilities of topological materials are endlessly exciting.”

      Socioresilient materials design

      Researchers in the MIT Department of Materials Science and Engineering (DMSE) have been awarded $750,000 in a cross-disciplinary project that aims to fundamentally redirect materials research and development toward more environmentally, socially, and economically sustainable and resilient materials. This “socioresilient materials design” will serve as the foundation for a new research and development framework that takes into account technical, environmental, and social factors from the beginning of the materials design and development process.

      Christine Ortiz, the Morris Cohen Professor of Materials Science and Engineering, and Ellan Spero PhD ’14, an instructor in DMSE, are leading this research effort, which includes Cornell University, the University of Swansea, Citrine Informatics, Station1, and 14 other organizations in academia, industry, venture capital, the social sector, government, and philanthropy.

      The team’s project, “Mind Over Matter: Socioresilient Materials Design,” emphasizes that circular design approaches, which aim to minimize waste and maximize the reuse, repair, and recycling of materials, are often insufficient to address negative repercussions for the planet and for human health and safety.

      Too often society understands the unintended negative consequences long after the materials that make up our homes and cities and systems have been in production and use for many years. Examples include disparate and negative public health impacts due to industrial scale manufacturing of materials, water and air contamination with harmful materials, and increased risk of fire in lower-income housing buildings due to flawed materials usage and design. Adverse climate events including drought, flood, extreme temperatures, and hurricanes have accelerated materials degradation, for example in critical infrastructure, leading to amplified environmental damage and social injustice. While classical materials design and selection approaches are insufficient to address these challenges, the new research project aims to do just that.

      “The imagination and technical expertise that goes into materials design is too often separated from the environmental and social realities of extraction, manufacturing, and end-of-life for materials,” says Ortiz. 

      Drawing on materials science and engineering, chemistry, and computer science, the project will develop a framework for materials design and development. It will incorporate powerful computational capabilities — artificial intelligence and machine learning with physics-based materials models — plus rigorous methodologies from the social sciences and the humanities to understand what impacts any new material put into production could have on society. More

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      in Environment

      Exploring the nanoworld of biogenic gems

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      A new research collaboration with The Bahrain Institute for Pearls and Gemstones (DANAT) will seek to develop advanced characterization tools for the analysis of the properties of pearls and to explore technologies to assign unique identifiers to individual pearls.

      The three-year project will be led by Admir Mašić, associate professor of civil and environmental engineering, in collaboration with Vladimir Bulović, the Fariborz Maseeh Chair in Emerging Technology and professor of electrical engineering and computer science.

      “Pearls are extremely complex and fascinating hierarchically ordered biological materials that are formed by a wide range of different species,” says Mašić. “Working with DANAT provides us a unique opportunity to apply our lab’s multi-scale materials characterization tools to identify potentially species-specific pearl fingerprints, while simultaneously addressing scientific research questions regarding the underlying biomineralization processes that could inform advances in sustainable building materials.”

      DANAT is a gemological laboratory specializing in the testing and study of natural pearls as a reflection of Bahrain’s pearling history and desire to protect and advance Bahrain’s pearling heritage. DANAT’s gemologists support clients and students through pearl, gemstone, and diamond identification services, as well as educational courses.

      Like many other precious gemstones, pearls have been human-made through scientific experimentation, says Noora Jamsheer, chief executive officer at DANAT. Over a century ago, cultured pearls entered markets as a competitive product to natural pearls, similar in appearance but different in value.

      “Gemological labs have been innovating scientific testing methods to differentiate between natural pearls and all other pearls that exist because of direct or indirect human intervention. Today the world knows natural pearls and cultured pearls. However, there are also pearls that fall in between these two categories,” says Jamsheer. “DANAT has the responsibility, as the leading gemological laboratory for pearl testing, to take the initiative necessary to ensure that testing methods keep pace with advances in the science of pearl cultivation.”

      Titled “Exploring the Nanoworld of Biogenic Gems,” the project will aim to improve the process of testing and identifying pearls by identifying morphological, micro-structural, optical, and chemical features sufficient to distinguish a pearl’s area of origin, method of growth, or both. MIT.nano, MIT’s open-access center for nanoscience and nanoengineering will be the organizational home for the project, where Mašić and his team will utilize the facility’s state-of-the-art characterization tools.

      In addition to discovering new methodologies for establishing a pearl’s origin, the project aims to utilize machine learning to automate pearl classification. Furthermore, researchers will investigate techniques to create a unique identifier associated with an individual pearl.

      The initial sponsored research project is expected to last three years, with potential for continued collaboration based on key findings or building upon the project’s success to open new avenues for research into the structure, properties, and growth of pearls. More

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      in Environment

      Sensing with purpose

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      Fadel Adib never expected that science would get him into the White House, but in August 2015 the MIT graduate student found himself demonstrating his research to the president of the United States.

      Adib, fellow grad student Zachary Kabelac, and their advisor, Dina Katabi, showcased a wireless device that uses Wi-Fi signals to track an individual’s movements.

      As President Barack Obama looked on, Adib walked back and forth across the floor of the Oval Office, collapsed onto the carpet to demonstrate the device’s ability to monitor falls, and then sat still so Katabi could explain to the president how the device was measuring his breathing and heart rate.

      “Zach started laughing because he could see that my heart rate was 110 as I was demoing the device to the president. I was stressed about it, but it was so exciting. I had poured a lot of blood, sweat, and tears into that project,” Adib recalls.

      For Adib, the White House demo was an unexpected — and unforgettable — culmination of a research project he had launched four years earlier when he began his graduate training at MIT. Now, as a newly tenured associate professor in the Department of Electrical Engineering and Computer Science and the Media Lab, he keeps building off that work. Adib, the Doherty Chair of Ocean Utilization, seeks to develop wireless technology that can sense the physical world in ways that were not possible before.

      In his Signal Kinetics group, Adib and his students apply knowledge and creativity to global problems like climate change and access to health care. They are using wireless devices for contactless physiological sensing, such as measuring someone’s stress level using Wi-Fi signals. The team is also developing battery-free underwater cameras that could explore uncharted regions of the oceans, tracking pollution and the effects of climate change. And they are combining computer vision and radio frequency identification (RFID) technology to build robots that find hidden items, to streamline factory and warehouse operations and, ultimately, alleviate supply chain bottlenecks.

      While these areas may seem quite different, each time they launch a new project, the researchers uncover common threads that tie the disciplines together, Adib says.

      “When we operate in a new field, we get to learn. Every time you are at a new boundary, in a sense you are also like a kid, trying to understand these different languages, bring them together, and invent something,” he says.

      A science-minded child

      A love of learning has driven Adib since he was a young child growing up in Tripoli on the coast of Lebanon. He had been interested in math and science for as long as he could remember, and had boundless energy and insatiable curiosity as a child.

      “When my mother wanted me to slow down, she would give me a puzzle to solve,” he recalls.

      By the time Adib started college at the American University of Beirut, he knew he wanted to study computer engineering and had his sights set on MIT for graduate school.

      Seeking to kick-start his future studies, Adib reached out to several MIT faculty members to ask about summer internships. He received a response from the first person he contacted. Katabi, the Thuan and Nicole Pham Professor in the Department of Electrical Engineering and Computer Science (EECS), and a principal investigator in the Computer Science and Artificial Intelligence Laboratory (CSAIL) and the MIT Jameel Clinic, interviewed him and accepted him for a position. He immersed himself in the lab work and, as the end of summer approached, Katabi encouraged him to apply for grad school at MIT and join her lab.

      “To me, that was a shock because I felt this imposter syndrome. I thought I was moving like a turtle with my research, but I did not realize that with research itself, because you are at the boundary of human knowledge, you are expected to progress iteratively and slowly,” he says.

      As an MIT grad student, he began contributing to a number of projects. But his passion for invention pushed him to embark into unexplored territory. Adib had an idea: Could he use Wi-Fi to see through walls?

      “It was a crazy idea at the time, but my advisor let me work on it, even though it was not something the group had been working on at all before. We both thought it was an exciting idea,” he says.

      As Wi-Fi signals travel in space, a small part of the signal passes through walls — the same way light passes through windows — and is then reflected by whatever is on the other side. Adib wanted to use these signals to “see” what people on the other side of a wall were doing.

      Discovering new applications

      There were a lot of ups and downs (“I’d say many more downs than ups at the beginning”), but Adib made progress. First, he and his teammates were able to detect people on the other side of a wall, then they could determine their exact location. Almost by accident, he discovered that the device could be used to monitor someone’s breathing.

      “I remember we were nearing a deadline and my friend Zach and I were working on the device, using it to track people on the other side of the wall. I asked him to hold still, and then I started to see him appearing and disappearing over and over again. I thought, could this be his breathing?” Adib says.

      Eventually, they enabled their Wi-Fi device to monitor heart rate and other vital signs. The technology was spun out into a startup, which presented Adib with a conundrum once he finished his PhD — whether to join the startup or pursue a career in academia.

      He decided to become a professor because he wanted to dig deeper into the realm of invention. But after living through the winter of 2014-2015, when nearly 109 inches of snow fell on Boston (a record), Adib was ready for a change of scenery and a warmer climate. He applied to universities all over the United States, and while he had some tempting offers, Adib ultimately realized he didn’t want to leave MIT. He joined the MIT faculty as an assistant professor in 2016 and was named associate professor in 2020.

      “When I first came here as an intern, even though I was thousands of miles from Lebanon, I felt at home. And the reason for that was the people. This geekiness — this embrace of intellect — that is something I find to be beautiful about MIT,” he says.

      He’s thrilled to work with brilliant people who are also passionate about problem-solving. The members of his research group are diverse, and they each bring unique perspectives to the table, which Adib says is vital to encourage the intellectual back-and-forth that drives their work.

      Diving into a new project

      For Adib, research is exploration. Take his work on oceans, for instance. He wanted to make an impact on climate change, and after exploring the problem, he and his students decided to build a battery-free underwater camera.

      Adib learned that the ocean, which covers 70 percent of the planet, plays the single largest role in the Earth’s climate system. Yet more than 95 percent of it remains unexplored. That seemed like a problem the Signal Kinetics group could help solve, he says.

      But diving into this research area was no easy task. Adib studies Wi-Fi systems, but Wi-Fi does not work underwater. And it is difficult to recharge a battery once it is deployed in the ocean, making it hard to build an autonomous underwater robot that can do large-scale sensing.

      So, the team borrowed from other disciplines, building an underwater camera that uses acoustics to power its equipment and capture and transmit images.

      “We had to use piezoelectric materials, which come from materials science, to develop transducers, which come from oceanography, and then on top of that we had to marry these things with technology from RF known as backscatter,” he says. “The biggest challenge becomes getting these things to gel together. How do you decode these languages across fields?”

      It’s a challenge that continues to motivate Adib as he and his students tackle problems that are too big for one discipline.

      He’s excited by the possibility of using his undersea wireless imaging technology to explore distant planets. These same tools could also enhance aquaculture, which could help eradicate food insecurity, or support other emerging industries.

      To Adib, the possibilities seem endless.

      “With each project, we discover something new, and that opens up a whole new world to explore. The biggest driver of our work in the future will be what we think is impossible, but that we could make possible,” he says. More

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      in Energy

      Computers that power self-driving cars could be a huge driver of global carbon emissions

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      In the future, the energy needed to run the powerful computers on board a global fleet of autonomous vehicles could generate as many greenhouse gas emissions as all the data centers in the world today.

      That is one key finding of a new study from MIT researchers that explored the potential energy consumption and related carbon emissions if autonomous vehicles are widely adopted.

      The data centers that house the physical computing infrastructure used for running applications are widely known for their large carbon footprint: They currently account for about 0.3 percent of global greenhouse gas emissions, or about as much carbon as the country of Argentina produces annually, according to the International Energy Agency. Realizing that less attention has been paid to the potential footprint of autonomous vehicles, the MIT researchers built a statistical model to study the problem. They determined that 1 billion autonomous vehicles, each driving for one hour per day with a computer consuming 840 watts, would consume enough energy to generate about the same amount of emissions as data centers currently do.

      The researchers also found that in over 90 percent of modeled scenarios, to keep autonomous vehicle emissions from zooming past current data center emissions, each vehicle must use less than 1.2 kilowatts of power for computing, which would require more efficient hardware. In one scenario — where 95 percent of the global fleet of vehicles is autonomous in 2050, computational workloads double every three years, and the world continues to decarbonize at the current rate — they found that hardware efficiency would need to double faster than every 1.1 years to keep emissions under those levels.

      “If we just keep the business-as-usual trends in decarbonization and the current rate of hardware efficiency improvements, it doesn’t seem like it is going to be enough to constrain the emissions from computing onboard autonomous vehicles. This has the potential to become an enormous problem. But if we get ahead of it, we could design more efficient autonomous vehicles that have a smaller carbon footprint from the start,” says first author Soumya Sudhakar, a graduate student in aeronautics and astronautics.

      Sudhakar wrote the paper with her co-advisors Vivienne Sze, associate professor in the Department of Electrical Engineering and Computer Science (EECS) and a member of the Research Laboratory of Electronics (RLE); and Sertac Karaman, associate professor of aeronautics and astronautics and director of the Laboratory for Information and Decision Systems (LIDS). The research appears today in the January-February issue of IEEE Micro.

      Modeling emissions

      The researchers built a framework to explore the operational emissions from computers on board a global fleet of electric vehicles that are fully autonomous, meaning they don’t require a back-up human driver.

      The model is a function of the number of vehicles in the global fleet, the power of each computer on each vehicle, the hours driven by each vehicle, and the carbon intensity of the electricity powering each computer.

      “On its own, that looks like a deceptively simple equation. But each of those variables contains a lot of uncertainty because we are considering an emerging application that is not here yet,” Sudhakar says.

      For instance, some research suggests that the amount of time driven in autonomous vehicles might increase because people can multitask while driving and the young and the elderly could drive more. But other research suggests that time spent driving might decrease because algorithms could find optimal routes that get people to their destinations faster.

      In addition to considering these uncertainties, the researchers also needed to model advanced computing hardware and software that doesn’t exist yet.

      To accomplish that, they modeled the workload of a popular algorithm for autonomous vehicles, known as a multitask deep neural network because it can perform many tasks at once. They explored how much energy this deep neural network would consume if it were processing many high-resolution inputs from many cameras with high frame rates, simultaneously.

      When they used the probabilistic model to explore different scenarios, Sudhakar was surprised by how quickly the algorithms’ workload added up.

      For example, if an autonomous vehicle has 10 deep neural networks processing images from 10 cameras, and that vehicle drives for one hour a day, it will make 21.6 million inferences each day. One billion vehicles would make 21.6 quadrillion inferences. To put that into perspective, all of Facebook’s data centers worldwide make a few trillion inferences each day (1 quadrillion is 1,000 trillion).

      “After seeing the results, this makes a lot of sense, but it is not something that is on a lot of people’s radar. These vehicles could actually be using a ton of computer power. They have a 360-degree view of the world, so while we have two eyes, they may have 20 eyes, looking all over the place and trying to understand all the things that are happening at the same time,” Karaman says.

      Autonomous vehicles would be used for moving goods, as well as people, so there could be a massive amount of computing power distributed along global supply chains, he says. And their model only considers computing — it doesn’t take into account the energy consumed by vehicle sensors or the emissions generated during manufacturing.

      Keeping emissions in check

      To keep emissions from spiraling out of control, the researchers found that each autonomous vehicle needs to consume less than 1.2 kilowatts of energy for computing. For that to be possible, computing hardware must become more efficient at a significantly faster pace, doubling in efficiency about every 1.1 years.

      One way to boost that efficiency could be to use more specialized hardware, which is designed to run specific driving algorithms. Because researchers know the navigation and perception tasks required for autonomous driving, it could be easier to design specialized hardware for those tasks, Sudhakar says. But vehicles tend to have 10- or 20-year lifespans, so one challenge in developing specialized hardware would be to “future-proof” it so it can run new algorithms.

      In the future, researchers could also make the algorithms more efficient, so they would need less computing power. However, this is also challenging because trading off some accuracy for more efficiency could hamper vehicle safety.

      Now that they have demonstrated this framework, the researchers want to continue exploring hardware efficiency and algorithm improvements. In addition, they say their model can be enhanced by characterizing embodied carbon from autonomous vehicles — the carbon emissions generated when a car is manufactured — and emissions from a vehicle’s sensors.

      While there are still many scenarios to explore, the researchers hope that this work sheds light on a potential problem people may not have considered.

      “We are hoping that people will think of emissions and carbon efficiency as important metrics to consider in their designs. The energy consumption of an autonomous vehicle is really critical, not just for extending the battery life, but also for sustainability,” says Sze.

      This research was funded, in part, by the National Science Foundation and the MIT-Accenture Fellowship. More

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      in Energy

      New MIT internships expand research opportunities in Africa

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      With new support from the Office of the Associate Provost for International Activities, MIT International Science and Technology Initiatives (MISTI) and the MIT-Africa program are expanding internship opportunities for MIT students at universities and leading academic research centers in Africa. This past summer, MISTI supported 10 MIT student interns at African universities, significantly more than in any previous year.

      “These internships are an opportunity to better merge the research ecosystem of MIT with academia-based research systems in Africa,” says Evan Lieberman, the Total Professor of Political Science and Contemporary Africa and faculty director for MISTI.

      For decades, MISTI has helped MIT students to learn and explore through international experiential learning opportunities and internships in industries like health care, education, agriculture, and energy. MISTI’s MIT-Africa Seed Fund supports collaborative research between MIT faculty and Africa-based researchers, and the new student research internship opportunities are part of a broader vision for deeper engagement between MIT and research institutions across the African continent.

      While Africa is home to 12.5 percent of the world’s population, it generates less than 1 percent of scientific research output in the form of academic journal publications, according to the African Academy of Sciences. Research internships are one way that MIT can build mutually beneficial partnerships across Africa’s research ecosystem, to advance knowledge and spawn innovation in fields important to MIT and its African counterparts, including health care, biotechnology, urban planning, sustainable energy, and education.

      Ari Jacobovits, managing director of MIT-Africa, notes that the new internships provide additional funding to the lab hosting the MIT intern, enabling them to hire a counterpart student research intern from the local university. This support can make the internships more financially feasible for host institutions and helps to grow the research pipeline.

      With the support of MIT, State University of Zanzibar (SUZA) lecturers Raya Ahmada and Abubakar Bakar were able to hire local students to work alongside MIT graduate students Mel Isidor and Rajan Hoyle. Together the students collaborated over a summer on a mapping project designed to plan and protect Zanzibar’s coastal economy.

      “It’s been really exciting to work with research peers in a setting where we can all learn alongside one another and develop this project together,” says Hoyle.

      Using low-cost drone technology, the students and their local counterparts worked to create detailed maps of Zanzibar to support community planning around resilience projects designed to combat coastal flooding and deforestation and assess climate-related impacts to seaweed farming activities. 

      “I really appreciated learning about how engagement happens in this particular context and how community members understand local environmental challenges and conditions based on research and lived experience,” says Isidor. “This is beneficial for us whether we’re working in an international context or in the United States.”

      For biology major Shaida Nishat, her internship at the University of Cape Town allowed her to work in a vital sphere of public health and provided her with the chance to work with a diverse, international team headed by Associate Professor Salome Maswine, head of the global surgery division and a widely-renowned expert in global surgery, a multidisciplinary field in the sphere of global health focused on improved and equitable surgical outcomes.

      “It broadened my perspective as to how an effort like global surgery ties so many nations together through a common goal that would benefit them all,” says Nishat, who plans to pursue a career in public health.

      For computer science sophomore Antonio L. Ortiz Bigio, the MISTI research internship in Africa was an incomparable experience, culturally and professionally. Bigio interned at the Robotics Autonomous Intelligence and Learning Laboratory at the University of Witwatersrand in Johannesburg, led by Professor Benjamin Rosman, where he developed software to enable a robot to play chess. The experience has inspired Bigio to continue to pursue robotics and machine learning.

      Participating faculty at the host institutions welcomed their MIT interns, and were impressed by their capabilities. Both Rosman and Maswime described their MIT interns as hard-working and valued team members, who had helped to advance their own work.  

      Building strong global partnerships, whether through faculty research, student internships, or other initiatives, takes time and cultivation, explains Jacobovits. Each successful collaboration helps to seed future exchanges and builds interest at MIT and peer institutions in creative partnerships. As MIT continues to deepen its connections to institutions and researchers across Africa, says Jacobovits, “students like Shaida, Rajan, Mel, and Antonio are really effective ambassadors in building those networks.” More

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      in Energy

      Ian Hutchinson: A lifetime probing plasma, on Earth and in space

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      Ordinary folks gazing at the night sky can readily spot Earth’s close neighbors and the light of distant stars. But when Ian Hutchinson scans the cosmos, he takes in a great deal more. There is, for instance, the constant rush of plasma — highly charged ionized gases — from the sun. As this plasma flows by solid bodies such as the moon, it interacts with them electromagnetically, sometimes generating a phenomenon called an electron hole — a perturbation in the gaseous solar tide that forms a solitary, long-lived wave. Hutchinson, a professor in the MIT Department of Nuclear Science and Engineering (NSE), knows they exist because he found a way to measure them.

      “When I look up at the moon with my sweetheart, my wife of 48 years, I imagine that streaming from its dark side are electron holes that my students and I predicted, and that we then discovered,” he says. “It’s quite sentimental to me.”

      Hutchinson’s studies of these wave phenomena, summed up in a paper, “Electron holes in phase space: What they are and why they matter,” recently earned the 2022 Ronald C. Davidson Award for Plasma Physics presented by the American Physical Society’s Division of Plasma Physics.

      Measuring perturbations in plasma

      Hutchinson’s exploration of electron holes was sparked by his work over many decades in fusion energy, another branch of plasma physics. He has made many contributions to the design, operation, and experimental investigation of tokamaks — a toroidal magnetic confinement device — intended to replicate and harness the fiery thermonuclear reactions in the plasma of stars for carbon-free energy on Earth. Hutchinson took a particular interest in how to measure the plasma, notably the flow at the edges of tokamaks.

      Heat generated from fusion reactions may escape magnetic confinement and build up along these edges, leading to potential temperature spikes that impact the performance of the confinement device. Hutchinson discovered how to interpret signals from small probes to measure and track plasma velocity at the tokamak’s edge.

      “My theoretical work also showed that these probes quite likely induce electron holes,” he says. But proving this contention required experiments at resolutions in time and space beyond what tokamaks allow. That’s when Hutchinson had an important insight.

      “I realized that the phenomena we were trying to investigate can actually be measured with exquisite accuracy by satellites that travel through plasma surrounding Earth and other solid bodies,” he says. Although plasmas in space are at a much larger scale than the plasmas generated in the laboratory, measurements of these gases by a satellite is analogous “to a situation where we fly a tiny micron-sized spacecraft through the wakes of probes at the edge of tokamaks,” says Hutchinson.

      Using satellite data provided by NASA, Hutchinson set about analyzing solar plasma as it whips by the moon. “We predicted instabilities and the generation of electron holes,” he recounts. “Our theory passed with flying colors: We saw lots of holes in the wake of the moon, and few elsewhere.”

      Developing tokamaks

      Hutchinson grew up in the English midlands and attended Cambridge University, where he became “intrigued by plasma physics in a course taught by an entertaining and effective teacher,” he says.

      Hutchinson headed for doctoral studies at Australian National University on fellowship. The experience afforded him his first opportunity for research on plasma confinement. “There I was at the ends of the Earth, and I was one of very few scientists worldwide with a tokamak almost to myself,” he says. “It was a device that had risen to the top of everyone’s agenda in fusion research as something we really needed to understand.”

      His dissertation, which examined instabilities in plasma, and his hands-on experience with the device, brought him to the attention of Ronald Parker SM ’63, PhD ’67, now emeritus professor of nuclear science and engineering and electrical engineering and computer science, who was building MIT’s Alcator tokamak program.

      In 1976, Hutchinson joined this group, spending three years as a research scientist. After an interval in Britain, he returned to MIT with a faculty position in NSE, and soon, a leadership role in developing the next phase of the Institute’s fusion experiment, the Alcator-C Mod tokamak.

      “This was a major development of the high-magnetic field approach to fusion,” says Hutchinson. Powerful magnets are essential for containing the superhot plasma; the MIT group developed an experiment with a magnetic field more than 150,000 times the strength of the Earth’s magnetic field. “We were in the business of determining whether tokamaks had sufficiently good confinement to function as fusion reactors,” he says.

      Hutchinson oversaw the nearly six-year construction of the device, which was funded by the U.S. Department of Energy. He then led its operation starting in 1993, creating a national facility for experiments that drew scientists and students from around the world. At the time, it was the largest research group on campus at MIT.

      In their studies, scientists employed novel heating and sustainment techniques using radio waves and microwaves. They also discovered new methods for performing diagnostics inside the tokamak. “Alcator C-Mod demonstrated excellent confinement in a more compact and cost-effective device,” says Hutchinson. “It was unique in the world.”

      Hutchinson is proud of Alcator C-Mod’s technological achievements, including its record for highest plasma pressure for a magnetic confinement device. But this large-scale project holds even greater significance for him. “Alcator C-Mod helped beat a new path in fusion research, and has become the basis for the SPARC tokamak now under construction,” he says.

      SPARC is a compact, high-magnetic field fusion energy device under development through a collaboration between MIT’s Plasma Science and Fusion Center and startup Commonwealth Fusions Systems. Its goal is to demonstrate net energy gain from fusion, prove the viability of fusion as a source of carbon-free energy, and tip the scales in the race against climate change. A number of SPARC’s leaders are students Hutchinson taught. “This is a source of considerable satisfaction,” he says. “Some of their down-to-Earth realism comes from me, and perhaps some of their aspirations have been molded by their work with me.” 

      A new phase

      After leading Alcator C-Mod for 15 years and generating hundreds of journal articles, Hutchinson served as NSE’s department head from 2003 to 2009. He wrote the standard textbook on measuring plasmas, and has more recently written “A Student’s Guide to Numerical Methods” (2015), which evolved from a course he taught to introduce graduate students to computational problem-solving in physics and engineering.

      After this, his 40th year on the MIT faculty, Hutchinson will be stepping back from teaching. “It’s important for new generations of students to be taught by people at the pinnacle of their mental and intellectual capacity, and when you reach my age, you’re aware of the fact that you’re slowing down,” he says.

      Hutchinson’s at no loss for ways to spend his time. As a devout Christian, he speaks and writes about the relationship between religion and science, trying to help skeptics on both sides find common ground. He sings in two choral groups, and is very busy grandparenting four grandsons. For a complete change of pace, Hutchinson goes fly fishing.

      But he still has plans to explore new frontiers in plasma physics. “I’m gratified to say I still do important research,” he says. “I’ve solved most of the problems in electron holes, and now I need to say something about ion holes!” More

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      in Energy

      Machine learning facilitates “turbulence tracking” in fusion reactors

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      Fusion, which promises practically unlimited, carbon-free energy using the same processes that power the sun, is at the heart of a worldwide research effort that could help mitigate climate change.

      A multidisciplinary team of researchers is now bringing tools and insights from machine learning to aid this effort. Scientists from MIT and elsewhere have used computer-vision models to identify and track turbulent structures that appear under the conditions needed to facilitate fusion reactions.

      Monitoring the formation and movements of these structures, called filaments or “blobs,” is important for understanding the heat and particle flows exiting from the reacting fuel, which ultimately determines the engineering requirements for the reactor walls to meet those flows. However, scientists typically study blobs using averaging techniques, which trade details of individual structures in favor of aggregate statistics. Individual blob information must be tracked by marking them manually in video data. 

      The researchers built a synthetic video dataset of plasma turbulence to make this process more effective and efficient. They used it to train four computer vision models, each of which identifies and tracks blobs. They trained the models to pinpoint blobs in the same ways that humans would.

      When the researchers tested the trained models using real video clips, the models could identify blobs with high accuracy — more than 80 percent in some cases. The models were also able to effectively estimate the size of blobs and the speeds at which they moved.

      Because millions of video frames are captured during just one fusion experiment, using machine-learning models to track blobs could give scientists much more detailed information.

      “Before, we could get a macroscopic picture of what these structures are doing on average. Now, we have a microscope and the computational power to analyze one event at a time. If we take a step back, what this reveals is the power available from these machine-learning techniques, and ways to use these computational resources to make progress,” says Theodore Golfinopoulos, a research scientist at the MIT Plasma Science and Fusion Center and co-author of a paper detailing these approaches.

      His fellow co-authors include lead author Woonghee “Harry” Han, a physics PhD candidate; senior author Iddo Drori, a visiting professor in the Computer Science and Artificial Intelligence Laboratory (CSAIL), faculty associate professor at Boston University, and adjunct at Columbia University; as well as others from the MIT Plasma Science and Fusion Center, the MIT Department of Civil and Environmental Engineering, and the Swiss Federal Institute of Technology at Lausanne in Switzerland. The research appears today in Nature Scientific Reports.

      Heating things up

      For more than 70 years, scientists have sought to use controlled thermonuclear fusion reactions to develop an energy source. To reach the conditions necessary for a fusion reaction, fuel must be heated to temperatures above 100 million degrees Celsius. (The core of the sun is about 15 million degrees Celsius.)

      A common method for containing this super-hot fuel, called plasma, is to use a tokamak. These devices utilize extremely powerful magnetic fields to hold the plasma in place and control the interaction between the exhaust heat from the plasma and the reactor walls.

      However, blobs appear like filaments falling out of the plasma at the very edge, between the plasma and the reactor walls. These random, turbulent structures affect how energy flows between the plasma and the reactor.

      “Knowing what the blobs are doing strongly constrains the engineering performance that your tokamak power plant needs at the edge,” adds Golfinopoulos.

      Researchers use a unique imaging technique to capture video of the plasma’s turbulent edge during experiments. An experimental campaign may last months; a typical day will produce about 30 seconds of data, corresponding to roughly 60 million video frames, with thousands of blobs appearing each second. This makes it impossible to track all blobs manually, so researchers rely on average sampling techniques that only provide broad characteristics of blob size, speed, and frequency.

      “On the other hand, machine learning provides a solution to this by blob-by-blob tracking for every frame, not just average quantities. This gives us much more knowledge about what is happening at the boundary of the plasma,” Han says.

      He and his co-authors took four well-established computer vision models, which are commonly used for applications like autonomous driving, and trained them to tackle this problem.

      Simulating blobs

      To train these models, they created a vast dataset of synthetic video clips that captured the blobs’ random and unpredictable nature.

      “Sometimes they change direction or speed, sometimes multiple blobs merge, or they split apart. These kinds of events were not considered before with traditional approaches, but we could freely simulate those behaviors in the synthetic data,” Han says.

      Creating synthetic data also allowed them to label each blob, which made the training process more effective, Drori adds.

      Using these synthetic data, they trained the models to draw boundaries around blobs, teaching them to closely mimic what a human scientist would draw.

      Then they tested the models using real video data from experiments. First, they measured how closely the boundaries the models drew matched up with actual blob contours.

      But they also wanted to see if the models predicted objects that humans would identify. They asked three human experts to pinpoint the centers of blobs in video frames and checked to see if the models predicted blobs in those same locations.

      The models were able to draw accurate blob boundaries, overlapping with brightness contours which are considered ground-truth, about 80 percent of the time. Their evaluations were similar to those of human experts, and successfully predicted the theory-defined regime of the blob, which agrees with the results from a traditional method.

      Now that they have shown the success of using synthetic data and computer vision models for tracking blobs, the researchers plan to apply these techniques to other problems in fusion research, such as estimating particle transport at the boundary of a plasma, Han says.

      They also made the dataset and models publicly available, and look forward to seeing how other research groups apply these tools to study the dynamics of blobs, says Drori.

      “Prior to this, there was a barrier to entry that mostly the only people working on this problem were plasma physicists, who had the datasets and were using their methods. There is a huge machine-learning and computer-vision community. One goal of this work is to encourage participation in fusion research from the broader machine-learning community toward the broader goal of helping solve the critical problem of climate change,” he adds.

      This research is supported, in part, by the U.S. Department of Energy and the Swiss National Science Foundation. More