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    Urbanization: No fast lane to transformation

    Accra, Ghana, “is a city I’ve come to know as well as any place in the U.S,” says Associate Professor Noah Nathan, who has conducted research there over the past 15 years. The booming capital of 4 million is an ideal laboratory for investigating the rapid urbanization of nations in Africa and beyond, believes Nathan, who joined the MIT Department of Political Science in July.

    “Accra is vibrant and exciting, with gleaming glass office buildings, shopping centers, and an emerging middle class,” he says. “But at the same time there is enormous poverty, with slums and a mixing pot of ethnic groups.” Cities like Accra that have emerged in developing countries around the world are “hybrid spaces” that provoke a multitude of questions for Nathan.

    “Rich and poor are in incredibly close proximity and I want to know how this dramatic inequality can be sustainable, and what politics looks like with such ethnic and class diversity living side-by-side,” he says.

    With his singular approach to data collection and deep understanding of Accra, its neighborhoods, and increasingly, its built environment, Nathan is generating a body of scholarship on the political impacts of urbanization throughout the global South.

    A trap in the urban transition

    Nathan’s early studies of Accra challenged common expectations about how urbanization shifts political behavior.

    “Modernization theory states that as people become more ‘modern’ and move to cities, ethnicity fades and class becomes the dominant dynamic in political behavior,” explains Nathan. “It predicts that the process of urbanization transforms the relationship between politicians and voters, and elections become more ideologically and policy oriented,” says Nathan.  

    But in Accra, the heart of one of the fastest-growing economies in the developing world, Nathan found “a type of politics stuck in an old equilibrium, hard to dislodge, and not updated by newly wealthy voters,” he says. Using census data revealing the demographic composition of every neighborhood in Accra, Nathan determined that there were many enclaves in which forms of patronage politics and ethnic competition persist. He conducted sample surveys and collected polling-station level results on residents’ voting across the city. “I was able to merge spatial data on where people lived and their answers to survey questions, and determine how different neighborhoods voted,” says Nathan.

    Among his findings: Ethnic politics were thriving in many parts of Accra, and many middle-class voters were withdrawing from politics entirely in reaction to the well-established practice of patronage rather than pressuring politicians to change their approach. “They decided it was better to look out for themselves,” he explains.

    In Nathan’s 2019 book, “Electoral Politics and Africa’s Urban Transition: Class and Ethnicity in Ghana,” he described this situation as a trap. “As the wealthy exit from the state, politicians double down on patronage politics with poor voters, which the middle class views as further evidence of corruption,” he explains. The wealthier citizens “want more public goods, and big policy reforms, such as changes in the health-care and tax systems, while poor voters focus on immediate needs such as jobs, homes, better schools in their communities.”

    In Ghana and other developing countries where the state’s capacity is limited, politicians can’t deliver on the broad-scale changes desired by the middle class. Motivated by their own political survival, they continue dealing with poor voters as clients, trading services for votes. “I connect urban politics in Ghana to the early 20th-century urban machines in the United States, run by party bosses,” says Nathan.

    This may prove sobering news for many engaged with the developing world. “There’s enormous enthusiasm among foreign aid organizations, in the popular press and policy circles, for the idea that urbanization will usher in big, radical political change,” notes Nathan. “But these kinds of transformations will only come about with structural change such as civil service reforms and nonpartisan welfare programs that can push politicians beyond just delivering targeted services to poor voters.”

    Falling in love with Ghana

    For most of his youth, Nathan was a committed jazz saxophonist, toying with going professional. But he had long cultivated another fascination as well. “I was a huge fan of ‘The West Wing’ in middle school” and got into American politics through that,” he says. He volunteered in Hillary Clinton’s 2008 primary campaign during college, but soon realized work in politics was “both more boring and not as idealistic” as he’d hoped.

    As an undergraduate at Harvard University, where he concentrated in government, he “signed up for African history on a lark — because American high schools didn’t teach anything on the subject — and I loved it,” Nathan says. He took another African history course, and then found his way to classes taught by Harvard political scientist Robert H. Bates PhD ’69 that focused on the political economy of development, ethnic conflict, and state failure in Africa. In the summer before his senior year, he served as a research assistant for one of his professors in Ghana, and then stayed longer, hoping to map out a senior thesis on ethnic conflict.

    “Once I got to Ghana, I was fascinated by the place — the dynamism of this rapidly transforming society,” he recalls. “Growing up in the U.S., there are a lot of stereotypes about the developing world, and I quickly realized how much more complicated everything is.”

    These initial experiences living in Ghana shaped Nathan’s ideas for what became his doctoral dissertation at Harvard and first book on the ethnic and class dynamics driving the nation’s politics. His frequent return visits to that country sparked a wealth of research that built on and branched out from this work.

    One set of studies examines the historical development of Ghana’s rural north in its colonial and post-colonial periods, the center of ethnic conflict in the 1990s. These are communities “where the state delivers few resources, doesn’t seem to do much, yet figures as a central actor in people’s lives,” he says.

    Part of this region had been a German colony, and the other part was originally under British rule, and Nathan compared the political trajectories of these two areas, focusing on differences in early state efforts to impose new forms of local political leadership and gradually build a formal education system.

    “The colonial legacy in the British areas was elite families who came to dominate, entrenching themselves and creating political dynasties and economic inequality,” says Nathan. But similar ethnic groups exposed to different state policies in the original German colony were not riven with the same class inequalities, and enjoy better access to government services today. “This research is changing how we think about state weakness in the developing world, how we tend to see the emergence of inequality where societal elites come into power,” he says. The results of Nathan’s research will be published in a forthcoming book, “The Scarce State: Inequality and Political Power in the Hinterland.”

    Politics of built spaces

    At MIT, Nathan is pivoting to a fresh new framing for questions on urbanization. Wielding a public source map of cities around the world, he is scrutinizing the geometry of street grids in 1,000 of sub-Saharan Africa’s largest cities “to think about urban order,” he says. Digitizing historical street maps of African cities from the Library of Congress’s map collection, he can look at how these cities were built and evolved physically. “When cities emerge based on grids, rather than tangles, they are more legible to governments,” he says. “This means that it’s easier to find people, easier to govern, tax, repress, and politically mobilize them.”  

    Nathan has begun to demonstrate that in the post-colonial period, “cities that were built under authoritarian regimes tend to be most legible, with even low-capacity regimes trying to impose control and make them gridded.” Democratic governments, he says, “lead to more tangled and chaotic built environments, with people doing what they want.” He also draws comparisons to how state policies shaped urban growth in the United States, with local and federal governments exerting control over neighborhood development, leading to redlining and segregation in many cities.

    Nathan’s interests naturally pull him toward the MIT Governance Lab and Global Diversity Lab. “I’m hoping to dive into both,” he says. “One big attraction of the department is the really interesting research that’s being done on developing countries.”  He also plans to use the stature he has built over many years of research in Africa to help “open doors” to African researchers and students, who may not always get the same kind of access to institutions and data that he has had. “I’m hoping to build connections to researchers in the global South,” he says. More

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    Can your phone tell if a bridge is in good shape?

    Want to know if the Golden Gate Bridge is holding up well? There could be an app for that.

    A new study involving MIT researchers shows that mobile phones placed in vehicles, equipped with special software, can collect useful structural integrity data while crossing bridges. In so doing, they could become a less expensive alternative to sets of sensors attached to bridges themselves.

    “The core finding is that information about structural health of bridges can be extracted from smartphone-collected accelerometer data,” says Carlo Ratti, director of the MIT Sensable City Laboratory and co-author of a new paper summarizing the study’s findings.

    The research was conducted, in part, on the Golden Gate Bridge itself. The study showed that mobile devices can capture the same kind of information about bridge vibrations that stationary sensors compile. The researchers also estimate that, depending on the age of a road bridge, mobile-device monitoring could add from 15 percent to 30 percent more years to the structure’s lifespan.

    “These results suggest that massive and inexpensive datasets collected by smartphones could play an important role in monitoring the health of existing transportation infrastructure,” the authors write in their new paper.

    The study, “Crowdsourcing Bridge Vital Signs with Smartphone Vehicle Trips,” is being published in Communications Engineering.

    The authors are Thomas J. Matarazzo, an assistant professor of civil and mechanical engineering at the United States Military Academy at West Point; Daniel Kondor, a postdoc at the Complexity Science Hub in Vienna; Sebastiano Milardo, a researcher at the Senseable City Lab; Soheil S. Eshkevari, a senior research scientist at DiDi Labs and a former member of Senseable City Lab; Paolo Santi, principal research scientist at the Senseable City Lab and research director at the Italian National Research Council; Shamim N. Pakzad, a professor and chair of the Department of Civil and Environmental Engineering at Lehigh University; Markus J. Buehler, the Jerry McAfee Professor in Engineering and professor of civil and environmental engineering and of mechanical engineering at MIT; and Ratti, who is also professor of the practice in MIT’s Department of Urban Studies and Planning.

    Bridges naturally vibrate, and to study the essential “modal frequencies” of those vibrations in many directions, engineers typically place sensors, such as accelerometers, on bridges themselves. Changes in the modal frequencies over time may indicate changes in a bridge’s structural integrity.

    To conduct the study, the researchers developed an Android-based mobile phone application to collect accelerometer data when the devices were placed in vehicles passing over the bridge. They could then see how well those data matched up with data record by sensors on bridges themselves, to see if the mobile-phone method worked.

    “In our work, we designed a methodology for extracting modal vibration frequencies from noisy data collected from smartphones,” Santi says. “As data from multiple trips over a bridge are recorded, noise generated by engine, suspension and traffic vibrations, [and] asphalt, tend to cancel out, while the underlying dominant frequencies emerge.”

    In the case of the Golden Gate Bridge, the researchers drove over the bridge 102 times with their devices running, and the team used 72 trips by Uber drivers with activated phones as well. The team then compared the resulting data to that from a group of 240 sensors that had been placed on the Golden Gate Bridge for three months.

    The outcome was that the data from the phones converged with that from the bridge’s sensors; for 10 particular types of low-frequency vibrations engineers measure on the bridge, there was a close match, and in five cases, there was no discrepancy between the methods at all.

    “We were able to show that many of these frequencies correspond very accurately to the prominent modal frequencies of the bridge,” Santi says.  

    However, only 1 percent of all bridges in the U.S. are suspension bridges. About 41 percent are much smaller concrete span bridges. So, the researchers also examined how well their method would fare in that setting.

    To do so, they studied a bridge in Ciampino, Italy, comparing 280 vehicle trips over the bridge to six sensors that had been placed on the bridge for seven months. Here, the researchers were also encouraged by the findings, though they found up to a 2.3 percent divergence between methods for certain modal frequencies over all 280 trips, and a 5.5 percent divergence over a smaller sample. That suggests a larger volume of trips could yield more useful data.

    “Our initial results suggest that only a [modest amount] of trips over the span of a few weeks are sufficient to obtain useful information about bridge modal frequencies,” Santi says.

    Looking at the method as a whole, Buehler observes, “Vibrational signatures are emerging as a powerful tool to assess properties of large and complex systems, ranging from viral properties of pathogens to structural integrity of bridges as shown in this study. It’s a universal signal found widely in the natural and built environment that we’re just now beginning to explore as a diagnostic and generative tool in engineering.”

    As Ratti acknowledges, there are ways to refine and expand the research, including accounting for the effects of the smartphone mount in the vehicle, the influence of the vehicle type on the data, and more.

    “We still have work to do, but we believe that our approach could be scaled up easily — all the way to the level of an entire country,” Ratti says. “It might not reach the accuracy that one can get using fixed sensors installed on a bridge, but it could become a very interesting early-warning system. Small anomalies could then suggest when to carry out further analyses.”

    The researchers received support from Anas S.p.A., Allianz, Brose, Cisco, Dover Corporation, Ford, the Amsterdam Institute for Advanced Metropolitan Solutions, the Fraunhofer Institute, the former Kuwait-MIT Center for Natural Resources and the Environment, Lab Campus, RATP, Singapore–MIT Alliance for Research and Technology (SMART), SNCF Gares & Connexions, UBER, and the U.S. Department of Defense High-Performance Computing Modernization Program. More

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    Methane research takes on new urgency at MIT

    One of the most notable climate change provisions in the 2022 Inflation Reduction Act is the first U.S. federal tax on a greenhouse gas (GHG). That the fee targets methane (CH4), rather than carbon dioxide (CO2), emissions is indicative of the urgency the scientific community has placed on reducing this short-lived but powerful gas. Methane persists in the air about 12 years — compared to more than 1,000 years for CO2 — yet it immediately causes about 120 times more warming upon release. The gas is responsible for at least a quarter of today’s gross warming. 

    “Methane has a disproportionate effect on near-term warming,” says Desiree Plata, the director of MIT Methane Network. “CH4 does more damage than CO2 no matter how long you run the clock. By removing methane, we could potentially avoid critical climate tipping points.” 

    Because GHGs have a runaway effect on climate, reductions made now will have a far greater impact than the same reductions made in the future. Cutting methane emissions will slow the thawing of permafrost, which could otherwise lead to massive methane releases, as well as reduce increasing emissions from wetlands.  

    “The goal of MIT Methane Network is to reduce methane emissions by 45 percent by 2030, which would save up to 0.5 degree C of warming by 2100,” says Plata, an associate professor of civil and environmental engineering at MIT and director of the Plata Lab. “When you consider that governments are trying for a 1.5-degree reduction of all GHGs by 2100, this is a big deal.” 

    Under normal concentrations, methane, like CO2, poses no health risks. Yet methane assists in the creation of high levels of ozone. In the lower atmosphere, ozone is a key component of air pollution, which leads to “higher rates of asthma and increased emergency room visits,” says Plata. 

    Methane-related projects at the Plata Lab include a filter made of zeolite — the same clay-like material used in cat litter — designed to convert methane into CO2 at dairy farms and coal mines. At first glance, the technology would appear to be a bit of a hard sell, since it converts one GHG into another. Yet the zeolite filter’s low carbon and dollar costs, combined with the disproportionate warming impact of methane, make it a potential game-changer.

    The sense of urgency about methane has been amplified by recent studies that show humans are generating far more methane emissions than previously estimated, and that the rates are rising rapidly. Exactly how much methane is in the air is uncertain. Current methods for measuring atmospheric methane, such as ground, drone, and satellite sensors, “are not readily abundant and do not always agree with each other,” says Plata.  

    The Plata Lab is collaborating with Tim Swager in the MIT Department of Chemistry to develop low-cost methane sensors. “We are developing chemiresisitive sensors that cost about a dollar that you could place near energy infrastructure to back-calculate where leaks are coming from,” says Plata.  

    The researchers are working on improving the accuracy of the sensors using machine learning techniques and are planning to integrate internet-of-things technology to transmit alerts. Plata and Swager are not alone in focusing on data collection: the Inflation Reduction Act adds significant funding for methane sensor research. 

    Other research at the Plata Lab includes the development of nanomaterials and heterogeneous catalysis techniques for environmental applications. The lab also explores mitigation solutions for industrial waste, particularly those related to the energy transition. Plata is the co-founder of an lithium-ion battery recycling startup called Nth Cycle. 

    On a more fundamental level, the Plata Lab is exploring how to develop products with environmental and social sustainability in mind. “Our overarching mission is to change the way that we invent materials and processes so that environmental objectives are incorporated along with traditional performance and cost metrics,” says Plata. “It is important to do that rigorous assessment early in the design process.”

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    MIT amps up methane research 

    The MIT Methane Network brings together 26 researchers from MIT along with representatives of other institutions “that are dedicated to the idea that we can reduce methane levels in our lifetime,” says Plata. The organization supports research such as Plata’s zeolite and sensor projects, as well as designing pipeline-fixing robots, developing methane-based fuels for clean hydrogen, and researching the capture and conversion of methane into liquid chemical precursors for pharmaceuticals and plastics. Other members are researching policies to encourage more sustainable agriculture and land use, as well as methane-related social justice initiatives. 

    “Methane is an especially difficult problem because it comes from all over the place,” says Plata. A recent Global Carbon Project study estimated that half of methane emissions are caused by humans. This is led by waste and agriculture (28 percent), including cow and sheep belching, rice paddies, and landfills.  

    Fossil fuels represent 18 percent of the total budget. Of this, about 63 percent is derived from oil and gas production and pipelines, 33 percent from coal mining activities, and 5 percent from industry and transportation. Human-caused biomass burning, primarily from slash-and-burn agriculture, emits about 4 percent of the global total.  

    The other half of the methane budget includes natural methane emissions from wetlands (20 percent) and other natural sources (30 percent). The latter includes permafrost melting and natural biomass burning, such as forest fires started by lightning.  

    With increases in global warming and population, the line between anthropogenic and natural causes is getting fuzzier. “Human activities are accelerating natural emissions,” says Plata. “Climate change increases the release of methane from wetlands and permafrost and leads to larger forest and peat fires.”  

    The calculations can get complicated. For example, wetlands provide benefits from CO2 capture, biological diversity, and sea level rise resiliency that more than compensate for methane releases. Meanwhile, draining swamps for development increases emissions. 

    Over 100 nations have signed onto the U.N.’s Global Methane Pledge to reduce at least 30 percent of anthropogenic emissions within the next 10 years. The U.N. report estimates that this goal can be achieved using proven technologies and that about 60 percent of these reductions can be accomplished at low cost. 

    Much of the savings would come from greater efficiencies in fossil fuel extraction, processing, and delivery. The methane fees in the Inflation Reduction Act are primarily focused on encouraging fossil fuel companies to accelerate ongoing efforts to cap old wells, flare off excess emissions, and tighten pipeline connections.  

    Fossil fuel companies have already made far greater pledges to reduce methane than they have with CO2, which is central to their business. This is due, in part, to the potential savings, as well as in preparation for methane regulations expected from the Environmental Protection Agency in late 2022. The regulations build upon existing EPA oversight of drilling operations, and will likely be exempt from the U.S. Supreme Court’s ruling that limits the federal government’s ability to regulate GHGs. 

    Zeolite filter targets methane in dairy and coal 

    The “low-hanging fruit” of gas stream mitigation addresses most of the 20 percent of total methane emissions in which the gas is released in sufficiently high concentrations for flaring. Plata’s zeolite filter aims to address the thornier challenge of reducing the 80 percent of non-flammable dilute emissions. 

    Plata found inspiration in decades-old catalysis research for turning methane into methanol. One strategy has been to use an abundant, low-cost aluminosilicate clay called zeolite.  

    “The methanol creation process is challenging because you need to separate a liquid, and it has very low efficiency,” says Plata. “Yet zeolite can be very efficient at converting methane into CO2, and it is much easier because it does not require liquid separation. Converting methane to CO2 sounds like a bad thing, but there is a major anti-warming benefit. And because methane is much more dilute than CO2, the relative CO2 contribution is minuscule.”  

    Using zeolite to create methanol requires highly concentrated methane, high temperatures and pressures, and industrial processing conditions. Yet Plata’s process, which dopes the zeolite with copper, operates in the presence of oxygen at much lower temperatures under typical pressures. “We let the methane proceed the way it wants from a thermodynamic perspective from methane to methanol down to CO2,” says Plata. 

    Researchers around the world are working on other dilute methane removal technologies. Projects include spraying iron salt aerosols into sea air where they react with natural chlorine or bromine radicals, thereby capturing methane. Most of these geoengineering solutions, however, are difficult to measure and would require massive scale to make a difference.  

    Plata is focusing her zeolite filters on environments where concentrations are high, but not so high as to be flammable. “We are trying to scale zeolite into filters that you could snap onto the side of a cross-ventilation fan in a dairy barn or in a ventilation air shaft in a coal mine,” says Plata. “For every packet of air we bring in, we take a lot of methane out, so we get more bang for our buck.”  

    The major challenge is creating a filter that can handle high flow rates without getting clogged or falling apart. Dairy barn air handlers can push air at up to 5,000 cubic feet per minute and coal mine handlers can approach 500,000 CFM. 

    Plata is exploring engineering options including fluidized bed reactors with floating catalyst particles. Another filter solution, based in part on catalytic converters, features “higher-order geometric structures where you have a porous material with a long path length where the gas can interact with the catalyst,” says Plata. “This avoids the challenge with fluidized beds of containing catalyst particles in the reactor. Instead, they are fixed within a structured material.”  

    Competing technologies for removing methane from mine shafts “operate at temperatures of 1,000 to 1,200 degrees C, requiring a lot of energy and risking explosion,” says Plata. “Our technology avoids safety concerns by operating at 300 to 400 degrees C. It reduces energy use and provides more tractable deployment costs.” 

    Potentially, energy and dollar costs could be further reduced in coal mines by capturing the heat generated by the conversion process. “In coal mines, you have enrichments above a half-percent methane, but below the 4 percent flammability threshold,” says Plata. “The excess heat from the process could be used to generate electricity using off-the-shelf converters.” 

    Plata’s dairy barn research is funded by the Gerstner Family Foundation and the coal mining project by the U.S. Department of Energy. “The DOE would like us to spin out the technology for scale-up within three years,” says Plata. “We cannot guarantee we will hit that goal, but we are trying to develop this as quickly as possible. Our society needs to start reducing methane emissions now.”  More

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    Deep learning with light

    Ask a smart home device for the weather forecast, and it takes several seconds for the device to respond. One reason this latency occurs is because connected devices don’t have enough memory or power to store and run the enormous machine-learning models needed for the device to understand what a user is asking of it. The model is stored in a data center that may be hundreds of miles away, where the answer is computed and sent to the device.

    MIT researchers have created a new method for computing directly on these devices, which drastically reduces this latency. Their technique shifts the memory-intensive steps of running a machine-learning model to a central server where components of the model are encoded onto light waves.

    The waves are transmitted to a connected device using fiber optics, which enables tons of data to be sent lightning-fast through a network. The receiver then employs a simple optical device that rapidly performs computations using the parts of a model carried by those light waves.

    This technique leads to more than a hundredfold improvement in energy efficiency when compared to other methods. It could also improve security, since a user’s data do not need to be transferred to a central location for computation.

    This method could enable a self-driving car to make decisions in real-time while using just a tiny percentage of the energy currently required by power-hungry computers. It could also allow a user to have a latency-free conversation with their smart home device, be used for live video processing over cellular networks, or even enable high-speed image classification on a spacecraft millions of miles from Earth.

    “Every time you want to run a neural network, you have to run the program, and how fast you can run the program depends on how fast you can pipe the program in from memory. Our pipe is massive — it corresponds to sending a full feature-length movie over the internet every millisecond or so. That is how fast data comes into our system. And it can compute as fast as that,” says senior author Dirk Englund, an associate professor in the Department of Electrical Engineering and Computer Science (EECS) and member of the MIT Research Laboratory of Electronics.

    Joining Englund on the paper is lead author and EECS grad student Alexander Sludds; EECS grad student Saumil Bandyopadhyay, Research Scientist Ryan Hamerly, as well as others from MIT, the MIT Lincoln Laboratory, and Nokia Corporation. The research is published today in Science.

    Lightening the load

    Neural networks are machine-learning models that use layers of connected nodes, or neurons, to recognize patterns in datasets and perform tasks, like classifying images or recognizing speech. But these models can contain billions of weight parameters, which are numeric values that transform input data as they are processed. These weights must be stored in memory. At the same time, the data transformation process involves billions of algebraic computations, which require a great deal of power to perform.

    The process of fetching data (the weights of the neural network, in this case) from memory and moving them to the parts of a computer that do the actual computation is one of the biggest limiting factors to speed and energy efficiency, says Sludds.

    “So our thought was, why don’t we take all that heavy lifting — the process of fetching billions of weights from memory — move it away from the edge device and put it someplace where we have abundant access to power and memory, which gives us the ability to fetch those weights quickly?” he says.

    The neural network architecture they developed, Netcast, involves storing weights in a central server that is connected to a novel piece of hardware called a smart transceiver. This smart transceiver, a thumb-sized chip that can receive and transmit data, uses technology known as silicon photonics to fetch trillions of weights from memory each second.

    It receives weights as electrical signals and imprints them onto light waves. Since the weight data are encoded as bits (1s and 0s) the transceiver converts them by switching lasers; a laser is turned on for a 1 and off for a 0. It combines these light waves and then periodically transfers them through a fiber optic network so a client device doesn’t need to query the server to receive them.

    “Optics is great because there are many ways to carry data within optics. For instance, you can put data on different colors of light, and that enables a much higher data throughput and greater bandwidth than with electronics,” explains Bandyopadhyay.

    Trillions per second

    Once the light waves arrive at the client device, a simple optical component known as a broadband “Mach-Zehnder” modulator uses them to perform super-fast, analog computation. This involves encoding input data from the device, such as sensor information, onto the weights. Then it sends each individual wavelength to a receiver that detects the light and measures the result of the computation.

    The researchers devised a way to use this modulator to do trillions of multiplications per second, which vastly increases the speed of computation on the device while using only a tiny amount of power.   

    “In order to make something faster, you need to make it more energy efficient. But there is a trade-off. We’ve built a system that can operate with about a milliwatt of power but still do trillions of multiplications per second. In terms of both speed and energy efficiency, that is a gain of orders of magnitude,” Sludds says.

    They tested this architecture by sending weights over an 86-kilometer fiber that connects their lab to MIT Lincoln Laboratory. Netcast enabled machine-learning with high accuracy — 98.7 percent for image classification and 98.8 percent for digit recognition — at rapid speeds.

    “We had to do some calibration, but I was surprised by how little work we had to do to achieve such high accuracy out of the box. We were able to get commercially relevant accuracy,” adds Hamerly.

    Moving forward, the researchers want to iterate on the smart transceiver chip to achieve even better performance. They also want to miniaturize the receiver, which is currently the size of a shoe box, down to the size of a single chip so it could fit onto a smart device like a cell phone.

    “Using photonics and light as a platform for computing is a really exciting area of research with potentially huge implications on the speed and efficiency of our information technology landscape,” says Euan Allen, a Royal Academy of Engineering Research Fellow at the University of Bath, who was not involved with this work. “The work of Sludds et al. is an exciting step toward seeing real-world implementations of such devices, introducing a new and practical edge-computing scheme whilst also exploring some of the fundamental limitations of computation at very low (single-photon) light levels.”

    The research is funded, in part, by NTT Research, the National Science Foundation, the Air Force Office of Scientific Research, the Air Force Research Laboratory, and the Army Research Office. More

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    Ad hoc committee releases report on remote teaching best practices for on-campus education

    The Ad Hoc Committee on Leveraging Best Practices from Remote Teaching for On-Campus Education has released a report that captures how instructors are weaving lessons learned from remote teaching into in-person classes. Despite the challenges imposed by teaching and learning remotely during the Covid-19 pandemic, the report says, “there were seeds planted then that, we hope, will bear fruit in the coming years.”

    “In the long run, one of the best things about having lived through our remote learning experience may be the intense and broad focus on pedagogy that it necessitated,” the report continues. “In a moment when nobody could just teach the way they had always done before, all of us had to go back to first principles and ask ourselves: What are our learning goals for our students? How can we best help them to achieve these goals?”

    The committee’s work is a direct response to one of the Refinement and Implementation Committees (RIC) formed as part of Task Force 2021 and Beyond. Led by co-chairs Krishna Rajagopal, the William A. M. Burden Professor of Physics, and Janet Rankin, director of the MIT Teaching + Learning Lab, the committee engaged with faculty and instructional staff, associate department heads, and undergraduate and graduate officers across MIT.

    The findings are distilled into four broad themes:

    Community, Well-being, and Belonging. Conversations revealed new ways that instructors cultivated these key interrelated concepts, all of which are fundamental to student learning and success. Many instructors focused more on supporting well-being and building community and belonging during the height of the pandemic precisely because the MIT community, and everyone in it, was under such great stress. Some of the resulting practices are continuing, the committee found. Examples include introducing simple gestures, such as start-of-class welcoming practices, and providing extensions and greater flexibility on student assignments. Also, many across MIT felt that the week-long Thanksgiving break offered in 2020 should become a permanent fixture in the academic calendar, because it enhances the well-being of both students and instructors at a time in the fall semester when everyone’s batteries need recharging. 
    Enhancing Engagement. The committee found a variety of practices that have enhanced engagement between students and instructors; among students; and among instructors. For example, many instructors have continued to offer some office hours on Zoom, which seems to reduce barriers to participation for many students, while offering in-person office hours for those who want to take advantage of opportunities for more open-ended conversations. Several departments increased their usage of undergraduate teaching assistants (UTAs) in ways that make students’ learning experience more engaging and give the UTAs a real teaching experience. In addition, many instructors are leveraging out-of-class communication spaces like Slack, Perusall, and Piazza so students can work together, ask questions, and share ideas. 
    Enriching and Augmenting the Learning Environment. The report presents two ways in which instructors have enhanced learning within the classroom: through blended learning and by incorporating authentic experiences. Although blended learning techniques are not new at MIT, after having made it through remote teaching many faculty have found new ways to combine synchronous in-person teaching with asynchronous activities for on-campus students, such as pre-class or pre-lab sequences of videos with exercises interspersed, take-home lab kits, auto-graded online problems that give students immediate feedback, and recorded lab experiences for subsequent review. In addition, instructors found many creative ways to make students’ learning more authentic by going on virtual field trips, using Zoom to bring experts from around the world into MIT classrooms or to enable interactions with students at other universities, and live-streaming experiments that students could not otherwise experience since they cannot be performed in a teaching lab.   
     Assessing Learning. For all its challenges, the report notes, remote teaching prompted instructors to take a step back and think about what they wanted students to learn, how to support it, and how to measure it. The committee found a variety of examples of alternatives to traditional assessments, such as papers or timed, written exams, that instructors tried during the pandemic and are continuing to use. These alternatives include shorter, more frequent, lower-stakes assessments; oral exams or debates; asynchronous, open-book/notes exams; virtual poster sessions; alternate grading schemes; and uploading paper psets and exams into Gradescope to use its logistics and rubrics to improve grading effectiveness and efficiency.
    A large portion of the report is devoted to an extensive, annotated list of best practices from remote instruction that are being used in the classroom. Interestingly, Rankin says, “so many of the strategies and practices developed and used during the pandemic are based on, and supported by, solid educational research.”

    The report concludes with one broad recommendation: that all faculty and instructors read the findings and experiment with some of the best practices in their own instruction. “Our hope is that the practices shared in the report will continue to be adopted, adapted, and expanded by members of the teaching community at MIT, and that instructors’ openness in sharing and learning from each will continue,” Rankin says.

    Two additional, specific recommendations are included in the report. First, the committee endorses the RIC 16 recommendation that a Classroom Advisory Board be created to provide strategic input grounded in evolving pedagogy about future classroom use and technology needs. In its conversations, the committee found a number of ways that remote teaching and learning have impacted students’ and instructors’ perceptions as they have returned to the classroom. For example, during the pandemic students benefited from being able to see everyone else’s faces on Zoom. As a result, some instructors would prefer classrooms that enable students to face each other, such as semi-circular classrooms instead of rectangular ones.

    More generally, the committee concluded, MIT needs classrooms with seats and tables that can be quickly and flexibly reconfigured to facilitate varying pedagogical objectives. The Classroom Advisory Board could also examine classroom technology; this includes the role of videoconferencing to create authentic engagement between MIT students and people far from campus, and blended learning that allows students to experience more of the in-classroom engagement with their peers and instructors from which the “magic of MIT” originates.

    Second, the committee recommends that an implementation group be formed to investigate the possibility of changing the MIT academic calendar to create a one-week break over Thanksgiving. “Finalizing an implementation plan will require careful consideration of various significant logistical challenges,” the report says. “However, the resulting gains to both well-being and learning from this change to the fall calendar make doing so worthwhile.”

    Rankin notes that the report findings dovetail with the recently released MIT Strategic Action Plan for Belonging, Achievement and Composition. “I believe that one of the most important things that became really apparent during remote teaching was that community, inclusion, and belonging really matter and are necessary for both learning and teaching, and that instructors can and should play a central role in creating structures and processes to support them in their classrooms and other learning environments,” she says.

    Rajagopal finds it inspiring that “during a time of intense stress — that nobody ever wants to relive — there was such an intense focus on how we teach and how our students learn that, today, in essentially every direction we look we see colleagues improving on-campus education for tomorrow. I hope that the report will help instructors across the Institute, and perhaps elsewhere, learn from each other. Its readers will see, as our committee did, new ways in which students and instructors are finding those moments, those interactions, where the magic of MIT is created.”

    In addition to the report, the co-chairs recommend two other valuable remote teaching resources: a video interview series, TLL’s Fresh Perspectives, and Open Learning’s collection of examples of how MIT faculty and instructors leveraged digital technology to support and transform teaching and learning during the heart of the pandemic. More

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    The science of strength: How data analytics is transforming college basketball

    In the 1990s, if you suggested that the corner three-pointer was the best shot in basketball, you might have been laughed out of the gym.

    The game was still dominated largely by a fleet of seven-foot centers, most of whom couldn’t shoot from more than a few feet out from the basket. Even the game’s best player, Michael Jordan, was a mid-range specialist who averaged under two three-point attempts per game for his career.

    Fast forward to today, and the best players average around a dozen long-ball attempts per game — typically favoring shots from the corner.

    What’s changed? Analytics.

    “When I first started in the profession, 10 to 12 years ago, data analytics was almost nonexistent in training rooms,” says Adam Petway, the director of strength and conditioning for men’s basketball at the University of Louisville. “Today, we have force platform technology, we have velocity-based training, we have GPS tracking during games and in training, all to get a more objective analysis to help our athletes. So it’s grown exponentially.”

    Petway, who previously worked on the coaching staffs of the NBA’s Philadelphia 76ers and Washington Wizards, holds a bachelor’s degree in sports science, an MBA with an emphasis in sport management, and a doctorate in sports science. Recently, he extended his education through MIT Professional Education’s Applied Data Science Program (ADSP).

    “The impetus behind enrolling in ADSP was primarily a curiosity to learn and a desire to get better,” Petway says. “In my time in pro and college sports, we’ve had whole departments dedicated to data science, so I know it’s a skill set I’ll need in the future.”

    Applying new skills

    Petway took classes in a live online format. Although he was the only strength and conditioning coach in his cohort — learning alongside lawyers, professors, and business executives — he says that the focus on data gave all of his classmates a common language of sorts.

    “In many people’s minds, the worlds of data science and NCAA strength and conditioning training may not cross. We are finding that there are many other professional and industry sectors that can benefit from data science and analytics, which explains why we are seeing an ever-growing range of professionals from around the globe enroll in our Applied Data Science Program,” says Bhaskar Pant, executive director of MIT Professional Education. “It’s exciting to hear how change-makers like Adam are using the knowledge they gained from the program to tackle their most pressing challenges using data science tools.”

    “Having access to such high-level practitioners within data science was something that I found very, very helpful,” Petway says. “The chance to interact with my classmates, and the chance to interact in small groups with the professionals and the professors, was unbelievable. When you’re writing code in Python you might mess up a semicolon and a comma, and get 200 characters into the code and realize that it’s not going to work. So the ability to stop and ask questions, and really get into the material with a cohort of peers from different industries, that was really helpful.”

    Petway points to his newfound abilities to code in Python, and to run data through artificial intelligence programs that utilize unsupervised learning techniques, as major takeaways from his experience. Sports teams produce a wealth of data, he notes, but coaches need to be able to process that information in ways that lead to actionable insights.

    “Now I’m able to create decision trees, do visualization with data, and run a principal component analysis,” Petway says. “So instead of relying on third-party companies to come in and tell me what to do, I can take all of that data and disseminate the results myself, which not only saves me time, but it saves a lot of money.”

    In addition to giving him new capabilities in his coaching role, the skills were crucial to the research for a paper that Petway and a team of several other authors published in the International Journal of Strength and Conditioning this year. “The data came from my PhD program around five years ago,” Petway notes. “I had the data already, but I couldn’t properly visualize it and analyze it until I took the MIT Professional Education course.”

    “MIT’s motto is ‘mens et manus’ (‘mind and hand’), which translates to experience-based learning. As such, there was great thought put into how the Applied Data Science Program is structured. The expectation is that every participant not only gains foundational skills, but also learns how to apply that knowledge in real-world scenarios. We are thrilled to see learning from our course applied to top-level college basketball,” says Munther Dahleh, director of the Institute for Data, Systems, and Society, the William A. Coolidge Professor of Electrical Engineering and Computer Science at MIT, and one of the instructors of ADSP.

    Data’s growing role in sports

    Analytics are pushing the field of strength and conditioning far beyond the days when trainers would simply tell players to do a certain number of reps in the weight room, Petway says. Wearable devices help to track how much ground athletes cover during practice, as well as their average speed. Data from a force platform helps Petway to analyze the force with which basketball players jump (and land), and even to determine how much force an athlete is generating from each leg. Using a tool called a linear position transducer, Petway can measure how fast athletes are moving a prescribed load during weight-lifting exercises.

    “Instead of telling someone to do 90 percent of their squat max, we’re telling them to squat 200 kilos, and to move it at a rate above one meter per second,” says Petway. “So it’s more power- and velocity-driven than your traditional weight training.”

    The goal is to not only improve athlete’s performance, Petway says, but also to create training programs that minimize the chance of injury. Sometimes, that means deviating from well-worn sports cliches about “giving 110 percent” or “leaving it all on the court.”

    “There’s a misconception that doing more is always better,” Petway says. “One of my mentors would always say, ‘Sometimes you have to have the courage to do less.’ The most important thing for our athletes is being available for competition. We can use data analytics now to forecast the early onset of fatigue. If we see that their power output in the weight room is decreasing, we may need to intervene with rest before things get worse. It’s about using information to make more objective decisions.”

    The ability to create visuals from data, Petway says, has greatly enhanced his ability to communicate with athletes and other coaches about what he’s seeing in the numbers. “It’s a really powerful tool, being able to take a bunch of data points and show that things are trending up or down, along with the intervention we’re going to need to make based on what the data suggests,” he says.

    Ultimately, Petway notes, coaches are primarily interested in just one data point: wins and losses. But as more sports professionals see that data science can lead to more wins, he says, analytics will continue to gain a foothold in the industry. “If you can show that preparing a certain way leads to a higher likelihood that the team will win, that really speaks coaches’ language,” he says. “They just want to see results. And if data science can help deliver those results, they’re going to be bought in.” More

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    Study finds the risks of sharing health care data are low

    In recent years, scientists have made great strides in their ability to develop artificial intelligence algorithms that can analyze patient data and come up with new ways to diagnose disease or predict which treatments work best for different patients.

    The success of those algorithms depends on access to patient health data, which has been stripped of personal information that could be used to identify individuals from the dataset. However, the possibility that individuals could be identified through other means has raised concerns among privacy advocates.

    In a new study, a team of researchers led by MIT Principal Research Scientist Leo Anthony Celi has quantified the potential risk of this kind of patient re-identification and found that it is currently extremely low relative to the risk of data breach. In fact, between 2016 and 2021, the period examined in the study, there were no reports of patient re-identification through publicly available health data.

    The findings suggest that the potential risk to patient privacy is greatly outweighed by the gains for patients, who benefit from better diagnosis and treatment, says Celi. He hopes that in the near future, these datasets will become more widely available and include a more diverse group of patients.

    “We agree that there is some risk to patient privacy, but there is also a risk of not sharing data,” he says. “There is harm when data is not shared, and that needs to be factored into the equation.”

    Celi, who is also an instructor at the Harvard T.H. Chan School of Public Health and an attending physician with the Division of Pulmonary, Critical Care and Sleep Medicine at the Beth Israel Deaconess Medical Center, is the senior author of the new study. Kenneth Seastedt, a thoracic surgery fellow at Beth Israel Deaconess Medical Center, is the lead author of the paper, which appears today in PLOS Digital Health.

    Risk-benefit analysis

    Large health record databases created by hospitals and other institutions contain a wealth of information on diseases such as heart disease, cancer, macular degeneration, and Covid-19, which researchers use to try to discover new ways to diagnose and treat disease.

    Celi and others at MIT’s Laboratory for Computational Physiology have created several publicly available databases, including the Medical Information Mart for Intensive Care (MIMIC), which they recently used to develop algorithms that can help doctors make better medical decisions. Many other research groups have also used the data, and others have created similar databases in countries around the world.

    Typically, when patient data is entered into this kind of database, certain types of identifying information are removed, including patients’ names, addresses, and phone numbers. This is intended to prevent patients from being re-identified and having information about their medical conditions made public.

    However, concerns about privacy have slowed the development of more publicly available databases with this kind of information, Celi says. In the new study, he and his colleagues set out to ask what the actual risk of patient re-identification is. First, they searched PubMed, a database of scientific papers, for any reports of patient re-identification from publicly available health data, but found none.

    To expand the search, the researchers then examined media reports from September 2016 to September 2021, using Media Cloud, an open-source global news database and analysis tool. In a search of more than 10,000 U.S. media publications during that time, they did not find a single instance of patient re-identification from publicly available health data.

    In contrast, they found that during the same time period, health records of nearly 100 million people were stolen through data breaches of information that was supposed to be securely stored.

    “Of course, it’s good to be concerned about patient privacy and the risk of re-identification, but that risk, although it’s not zero, is minuscule compared to the issue of cyber security,” Celi says.

    Better representation

    More widespread sharing of de-identified health data is necessary, Celi says, to help expand the representation of minority groups in the United States, who have traditionally been underrepresented in medical studies. He is also working to encourage the development of more such databases in low- and middle-income countries.

    “We cannot move forward with AI unless we address the biases that lurk in our datasets,” he says. “When we have this debate over privacy, no one hears the voice of the people who are not represented. People are deciding for them that their data need to be protected and should not be shared. But they are the ones whose health is at stake; they’re the ones who would most likely benefit from data-sharing.”

    Instead of asking for patient consent to share data, which he says may exacerbate the exclusion of many people who are now underrepresented in publicly available health data, Celi recommends enhancing the existing safeguards that are in place to protect such datasets. One new strategy that he and his colleagues have begun using is to share the data in a way that it can’t be downloaded, and all queries run on it can be monitored by the administrators of the database. This allows them to flag any user inquiry that seems like it might not be for legitimate research purposes, Celi says.

    “What we are advocating for is performing data analysis in a very secure environment so that we weed out any nefarious players trying to use the data for some other reasons apart from improving population health,” he says. “We’re not saying that we should disregard patient privacy. What we’re saying is that we have to also balance that with the value of data sharing.”

    The research was funded by the National Institutes of Health through the National Institute of Biomedical Imaging and Bioengineering. More

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    Learning on the edge

    Microcontrollers, miniature computers that can run simple commands, are the basis for billions of connected devices, from internet-of-things (IoT) devices to sensors in automobiles. But cheap, low-power microcontrollers have extremely limited memory and no operating system, making it challenging to train artificial intelligence models on “edge devices” that work independently from central computing resources.

    Training a machine-learning model on an intelligent edge device allows it to adapt to new data and make better predictions. For instance, training a model on a smart keyboard could enable the keyboard to continually learn from the user’s writing. However, the training process requires so much memory that it is typically done using powerful computers at a data center, before the model is deployed on a device. This is more costly and raises privacy issues since user data must be sent to a central server.

    To address this problem, researchers at MIT and the MIT-IBM Watson AI Lab developed a new technique that enables on-device training using less than a quarter of a megabyte of memory. Other training solutions designed for connected devices can use more than 500 megabytes of memory, greatly exceeding the 256-kilobyte capacity of most microcontrollers (there are 1,024 kilobytes in one megabyte).

    The intelligent algorithms and framework the researchers developed reduce the amount of computation required to train a model, which makes the process faster and more memory efficient. Their technique can be used to train a machine-learning model on a microcontroller in a matter of minutes.

    This technique also preserves privacy by keeping data on the device, which could be especially beneficial when data are sensitive, such as in medical applications. It also could enable customization of a model based on the needs of users. Moreover, the framework preserves or improves the accuracy of the model when compared to other training approaches.

    “Our study enables IoT devices to not only perform inference but also continuously update the AI models to newly collected data, paving the way for lifelong on-device learning. The low resource utilization makes deep learning more accessible and can have a broader reach, especially for low-power edge devices,” says Song Han, an associate professor in the Department of Electrical Engineering and Computer Science (EECS), a member of the MIT-IBM Watson AI Lab, and senior author of the paper describing this innovation.

    Joining Han on the paper are co-lead authors and EECS PhD students Ji Lin and Ligeng Zhu, as well as MIT postdocs Wei-Ming Chen and Wei-Chen Wang, and Chuang Gan, a principal research staff member at the MIT-IBM Watson AI Lab. The research will be presented at the Conference on Neural Information Processing Systems.

    Han and his team previously addressed the memory and computational bottlenecks that exist when trying to run machine-learning models on tiny edge devices, as part of their TinyML initiative.

    Lightweight training

    A common type of machine-learning model is known as a neural network. Loosely based on the human brain, these models contain layers of interconnected nodes, or neurons, that process data to complete a task, such as recognizing people in photos. The model must be trained first, which involves showing it millions of examples so it can learn the task. As it learns, the model increases or decreases the strength of the connections between neurons, which are known as weights.

    The model may undergo hundreds of updates as it learns, and the intermediate activations must be stored during each round. In a neural network, activation is the middle layer’s intermediate results. Because there may be millions of weights and activations, training a model requires much more memory than running a pre-trained model, Han explains.

    Han and his collaborators employed two algorithmic solutions to make the training process more efficient and less memory-intensive. The first, known as sparse update, uses an algorithm that identifies the most important weights to update at each round of training. The algorithm starts freezing the weights one at a time until it sees the accuracy dip to a set threshold, then it stops. The remaining weights are updated, while the activations corresponding to the frozen weights don’t need to be stored in memory.

    “Updating the whole model is very expensive because there are a lot of activations, so people tend to update only the last layer, but as you can imagine, this hurts the accuracy. For our method, we selectively update those important weights and make sure the accuracy is fully preserved,” Han says.

    Their second solution involves quantized training and simplifying the weights, which are typically 32 bits. An algorithm rounds the weights so they are only eight bits, through a process known as quantization, which cuts the amount of memory for both training and inference. Inference is the process of applying a model to a dataset and generating a prediction. Then the algorithm applies a technique called quantization-aware scaling (QAS), which acts like a multiplier to adjust the ratio between weight and gradient, to avoid any drop in accuracy that may come from quantized training.

    The researchers developed a system, called a tiny training engine, that can run these algorithmic innovations on a simple microcontroller that lacks an operating system. This system changes the order of steps in the training process so more work is completed in the compilation stage, before the model is deployed on the edge device.

    “We push a lot of the computation, such as auto-differentiation and graph optimization, to compile time. We also aggressively prune the redundant operators to support sparse updates. Once at runtime, we have much less workload to do on the device,” Han explains.

    A successful speedup

    Their optimization only required 157 kilobytes of memory to train a machine-learning model on a microcontroller, whereas other techniques designed for lightweight training would still need between 300 and 600 megabytes.

    They tested their framework by training a computer vision model to detect people in images. After only 10 minutes of training, it learned to complete the task successfully. Their method was able to train a model more than 20 times faster than other approaches.

    Now that they have demonstrated the success of these techniques for computer vision models, the researchers want to apply them to language models and different types of data, such as time-series data. At the same time, they want to use what they’ve learned to shrink the size of larger models without sacrificing accuracy, which could help reduce the carbon footprint of training large-scale machine-learning models.

    “AI model adaptation/training on a device, especially on embedded controllers, is an open challenge. This research from MIT has not only successfully demonstrated the capabilities, but also opened up new possibilities for privacy-preserving device personalization in real-time,” says Nilesh Jain, a principal engineer at Intel who was not involved with this work. “Innovations in the publication have broader applicability and will ignite new systems-algorithm co-design research.”

    “On-device learning is the next major advance we are working toward for the connected intelligent edge. Professor Song Han’s group has shown great progress in demonstrating the effectiveness of edge devices for training,” adds Jilei Hou, vice president and head of AI research at Qualcomm. “Qualcomm has awarded his team an Innovation Fellowship for further innovation and advancement in this area.”

    This work is funded by the National Science Foundation, the MIT-IBM Watson AI Lab, the MIT AI Hardware Program, Amazon, Intel, Qualcomm, Ford Motor Company, and Google. More