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    Lincoln Laboratory convenes top network scientists for Graph Exploitation Symposium

    As the Covid-19 pandemic has shown, we live in a richly connected world, facilitating not only the efficient spread of a virus but also of information and influence. What can we learn by analyzing these connections? This is a core question of network science, a field of research that models interactions across physical, biological, social, and information systems to solve problems.

    The 2021 Graph Exploitation Symposium (GraphEx), hosted by MIT Lincoln Laboratory, brought together top network science researchers to share the latest advances and applications in the field.

    “We explore and identify how exploitation of graph data can offer key technology enablers to solve the most pressing problems our nation faces today,” says Edward Kao, a symposium organizer and technical staff in Lincoln Laboratory’s AI Software Architectures and Algorithms Group.

    The themes of the virtual event revolved around some of the year’s most relevant issues, such as analyzing disinformation on social media, modeling the pandemic’s spread, and using graph-based machine learning models to speed drug design.

    “The special sessions on influence operations and Covid-19 at GraphEx reflect the relevance of network and graph-based analysis for understanding the phenomenology of these complicated and impactful aspects of modern-day life, and also may suggest paths forward as we learn more and more about graph manipulation,” says William Streilein, who co-chaired the event with Rajmonda Caceres, both of Lincoln Laboratory.

    Social networks

    Several presentations at the symposium focused on the role of network science in analyzing influence operations (IO), or organized attempts by state and/or non-state actors to spread disinformation narratives.  

    Lincoln Laboratory researchers have been developing tools to classify and quantify the influence of social media accounts that are likely IO accounts, such as those willfully spreading false Covid-19 treatments to vulnerable populations.

    “A cluster of IO accounts acts as an echo chamber to amplify the narrative. The vulnerable population is then engaging in these narratives,” says Erika Mackin, a researcher developing the tool, called RIO or Reconnaissance of Influence Operations.

    To classify IO accounts, Mackin and her team trained an algorithm to detect probable IO accounts in Twitter networks based on a specific hashtag or narrative. One example they studied was #MacronLeaks, a disinformation campaign targeting Emmanuel Macron during the 2017 French presidential election. The algorithm is trained to label accounts within this network as being IO on the basis of several factors, such as the number of interactions with foreign news accounts, the number of links tweeted, or number of languages used. Their model then uses a statistical approach to score an account’s level of influence in spreading the narrative within that network.

    The team has found that their classifier outperforms existing detectors of IO accounts, because it can identify both bot accounts and human-operated ones. They’ve also discovered that IO accounts that pushed the 2017 French election disinformation narrative largely overlap with accounts influentially spreading Covid-19 pandemic disinformation today. “This suggests that these accounts will continue to transition to disinformation narratives,” Mackin says.

    Pandemic modeling

    Throughout the Covid-19 pandemic, leaders have been looking to epidemiological models, which predict how disease will spread, to make sound decisions. Alessandro Vespignani, director of the Network Science Institute at Northeastern University, has been leading Covid-19 modeling efforts in the United States, and shared a keynote on this work at the symposium.

    Besides taking into account the biological facts of the disease, such as its incubation period, Vespignani’s model is especially powerful in its inclusion of community behavior. To run realistic simulations of disease spread, he develops “synthetic populations” that are built by using publicly available, highly detailed datasets about U.S. households. “We create a population that is not real, but is statistically real, and generate a map of the interactions of those individuals,” he says. This information feeds back into the model to predict the spread of the disease. 

    Today, Vespignani is considering how to integrate genomic analysis of the virus into this kind of population modeling in order to understand how variants are spreading. “It’s still a work in progress that is extremely interesting,” he says, adding that this approach has been useful in modeling the dispersal of the Delta variant of SARS-CoV-2. 

    As researchers model the virus’ spread, Lucas Laird at Lincoln Laboratory is considering how network science can be used to design effective control strategies. He and his team are developing a model for customizing strategies for different geographic regions. The effort was spurred by the differences in Covid-19 spread across U.S. communities, and what the researchers found to be a gap in intervention modeling to address those differences.

    As examples, they applied their planning algorithm to three counties in Florida, Massachusetts, and California. Taking into account the characteristics of a specific geographic center, such as the number of susceptible individuals and number of infections there, their planner institutes different strategies in those communities throughout the outbreak duration.

    “Our approach eradicates disease in 100 days, but it also is able to do it with much more targeted interventions than any of the global interventions. In other words, you don’t have to shut down a full country.” Laird adds that their planner offers a “sandbox environment” for exploring intervention strategies in the future.

    Machine learning with graphs

    Graph-based machine learning is receiving increasing attention for its potential to “learn” the complex relationships between graphical data, and thus extract new insights or predictions about these relationships. This interest has given rise to a new class of algorithms called graph neural networks. Today, graph neural networks are being applied in areas such as drug discovery and material design, with promising results.

    “We can now apply deep learning much more broadly, not only to medical images and biological sequences. This creates new opportunities in data-rich biology and medicine,” says Marinka Zitnik, an assistant professor at Harvard University who presented her research at GraphEx.

    Zitnik’s research focuses on the rich networks of interactions between proteins, drugs, disease, and patients, at the scale of billions of interactions. One application of this research is discovering drugs to treat diseases with no or few approved drug treatments, such as for Covid-19. In April, Zitnik’s team published a paper on their research that used graph neural networks to rank 6,340 drugs for their expected efficacy against SARS-CoV-2, identifying four that could be repurposed to treat Covid-19.

    At Lincoln Laboratory, researchers are similarly applying graph neural networks to the challenge of designing advanced materials, such as those that can withstand extreme radiation or capture carbon dioxide. Like the process of designing drugs, the trial-and-error approach to materials design is time-consuming and costly. The laboratory’s team is developing graph neural networks that can learn relationships between a material’s crystalline structure and its properties. This network can then be used to predict a variety of properties from any new crystal structure, greatly speeding up the process of screening materials with desired properties for specific applications.

    “Graph representation learning has emerged as a rich and thriving research area for incorporating inductive bias and structured priors during the machine learning process, with broad applications such as drug design, accelerated scientific discovery, and personalized recommendation systems,” Caceres says. 

    A vibrant community

    Lincoln Laboratory has hosted the GraphEx Symposium annually since 2010, with the exception of last year’s cancellation due to Covid-19. “One key takeaway is that despite the postponement from last year and the need to be virtual, the GraphEx community is as vibrant and active as it’s ever been,” Streilein says. “Network-based analysis continues to expand its reach and is applied to ever-more important areas of science, society, and defense with increasing impact.”

    In addition to those from Lincoln Laboratory, technical committee members and co-chairs of the GraphEx Symposium included researchers from Harvard University, Arizona State University, Stanford University, Smith College, Duke University, the U.S. Department of Defense, and Sandia National Laboratories. More

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    Lockdowns reveal inequities in opportunities for walking activities

    Lockdowns saved lives during the global SARS-CoV-2 pandemic. But as much as they have slowed the spread of Covid-19, there have been some unintended consequences.

    New MIT research shows that lockdowns in 10 metropolitan areas throughout the United States led to a marked reduction in walking. These decreases were mostly seen among residents living in lower-income areas of the city, effectively reducing access to physical activity for minorities and people suffering from illnesses such as obesity and diabetes.

    “Walking is the cheapest, most accessible physical exercise that you can do,” says Esteban Moro, visiting research scientist in the MIT Connection Science Group and senior author on the Nature Communications paper published on June 16. “Places in which people have lower incomes, less park access, and more obesity prevalence were more affected by this walking reduction — which you can think of as another pandemic, the lack of access to affordable exercise.”

    The research focused on recreational versus utilitarian walking done by residents in the U.S. cities of New York, Los Angeles, Chicago, Boston, Miami, Dallas, San Francisco, Seattle, Philadelphia, and Washington D.C. (Utilitarian walking is defined as having a goal; for example, walking to the store or to public transportation. Recreational walking is a walk meant for leisure or exercise.)

    Comparing cellphone data from February 2020 to different time points throughout 2020 lockdowns, the researchers saw an average 70 percent decrease in the number of walks — which remained down by about 18 percent after loosened restrictions — a 50 percent decrease in distance walked, and a 72 percent decrease in utilitarian walking — which remained down by 39 percent even after restrictions were lifted.

    On their face, these findings may not be surprising. When people couldn’t leave their homes, they walked less. But digging deeper into the data yields troubling insights. For example, people in lower-income regions are more likely to rely on public transportation. Lockdowns cut back on those services, meaning fewer people walking to trains and buses.

    Another statistic showed that people in higher-income areas reduced their number of utilitarian walks but were able to replace some of the lost movement with recreational walks around their neighborhoods or in nearby parks.

    “People in higher-income areas generally not only have a park nearby, but also have jobs that give them a degree of flexibility. Jobs that permit them to take a break and walk,” says Moro. “People in the low-income regions often don’t have the ability, the opportunity or even the facilities to actually do this.”

    How it was done

    The researchers used de-identified mobile data obtained through a partnership within the company Cuebiq’s Data for Good COVID-19 Collaborative program. The completely anonymized dataset consisted of GPS locations gathered from smartphone accelerometers from users who opted into the program. Moro and his collaborators took these data and, using specifically designed algorithms, determined when people walked, for how long, and for what purpose. They compared this information from before the pandemic, at different points throughout lockdown, and at a point when most restrictions had been eased. They matched the GPS-identified locations of the smartphones with census data to understand income level and other demographics.

    To make sure their dataset was robust, they only used information from areas that could reasonably be considered pedestrian. The researchers also acknowledge that the dataset may be incomplete, considering people may have occasionally walked without their phones on them.

    Leisure versus utilitarian walks were separated according to distance and/or destination. Utilitarian walks are usually shorter and involve stops at destinations other than the starting point. Leisure walks are longer and usually happen closer to home or in dedicated outdoor spaces.

    For example, many of the walks recorded pre-Covid-19 were short and occurred at around 7 a.m. and between 3 and 5 p.m., which would indicate a walking commute. These bouts of walking were replaced on weekends by short walks around noon.

    The key takeaway is that most walking in cities occurs with the goal of getting to a place. If people don’t have the opportunity to walk to places they need to go, they will reduce their walking activity overall. But when provided opportunity and access, people can supplement utilitarian activity with leisure walking.

    What can be done about it

    Taking into account the public health implications of physical inactivity, the authors argue a reduction in access to walking should be considered a second pandemic and be addressed with the same rigor as the Covid-19 pandemic.

    They suggest several tactical urbanization strategies (defined as non-permanent but easily accessible measures) to increase safety and appeal for both utilitarian and recreational walkers. Many of these have already been implemented in various cities around the world to ease economic and other hardships of the pandemic. Sections of city streets have been closed off to cars on weekends or other non-busy times to allow for pedestrian walking areas. Restaurants have been given curb space to allow for outdoor dining.

    “But most of these pop-up pedestrian areas happen in downtown, where people are high-income and have easier access to more walking opportunities,” notes Moro.

    The same attention needs to be paid to lower-income areas, the researchers argue. This study’s data showed that people explored their own neighborhoods in a recreational way more during lockdown than pre-pandemic. Such wanderings, the researcher say, should be encouraged by making any large, multi-lane intersections safer to cross for the elderly, sick, or those with young children. And local parks, usually seen as places for running laps, should be made more attractive destinations by adding amenities like water fountains, shaded pavilions, and hygiene and sanitation spaces.

    This study was unique in that its data came straight from mobile devices, rather than being self-reported in surveys. This more reliable method of tracking made this study more data-driven than other, similar efforts. And the geotagged data allowed the researchers to dig into socioeconomic trends associated with the findings.

    This is the team’s first analysis of physical activity during and just after lockdown. They hope to use lessons learned from this and planned follow-ups to encourage more permanent adoption of pedestrian-friendly pandemic-era changes.

    The Connection Science Group, co-led by faculty member Alex “Sandy” Pentland — who, along with Moro was a co-author on the paper along with six others from the UK, Brazil, and Australia — is part of the MIT Sociotechnical Systems Research Center within the MIT Institute for Data, Systems, and Society. The collaborative research exemplified in this study is core to the mission of the SSRC; in pairing computer science with public health, the group not only observes trends but also contextualizes data and use them to make improvements for everyone.

    “SSRC merges both the social and technological components of the research,” says Moro. “We’re not only building an analysis, but going beyond that to propose new policies and interventions to change what we are seeing for the better.” More

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    Driving commuters toward sustainable options

    When organizations like MIT transitioned to remote work and learning last year in the interest of health and safety, one impact of this change was immediately visible to many people — a large decrease in commuters on roads and public transportation. The change spurred theories of the potential positive impact on emissions, but also created many questions about the future of commuting and commuting benefits programs like those offered at MIT. With concerns over safety and large numbers of employees working remotely, public transportation use dwindled while the delicate balance of permits to parking spots on a dense urban campus like MIT didn’t demand the same level of focus as pre-pandemic.

    But now, more than a year after remote work became the norm for many, MIT is preparing for employees and a full cohort of students to return to campus in the fall. This transition back has come with many challenging questions, one being: How do you manage the commuting choices of thousands of staff to support safety and sustainability while strategically managing limited on-campus parking? For MIT, part of the answer is data.

    Earlier this year, the Office of Sustainability (MITOS) launched “Commuting at the Institute: The Story of Access MIT,” a new data dashboard in the Sustainability DataPool that shares aggregate data on employee commuting choices with data stretching back to 2016 — helping Institute leaders identify patterns and best practices for balancing commuting needs of employees with Institute resources and safety as MIT transitions back to a densely populated campus. 

    Access MIT launched in 2016 with the goal of reducing parking demand on campus by 10 percent over two years. The program itself was designed based on years of collaborative research and testing by the MIT Committee for Transportation and Parking, Transit Lab, Parking and Transportation Office, MITOS, and other partners to determine if commuter behavior could be influenced by incentives like cost-free public transportation — data showed that the answer was “yes.” Access MIT’s pre-pandemic design included cost-free local subway and bus and a flexible pay-per-day parking fee structure (among other benefits) to encourage all benefits-eligible MIT employees to choose sustainable, low-carbon commutes. Between 2016 and 2019, Access MIT drove a nearly 15 percent reduction in on-campus parking in gated lots and increased public transportation adoption by employees.

    The original intent of the data collection and sharing was to understand employee behaviors to continue to adjust the program to support low-carbon commutes; the dashboard displays overall use trends including which days have higher use rates for public transportation or parking. But as the pandemic upended daily life at MIT, the data began to tell a story of that impact.

    One impact was a substantial increase in the number of parking account requests that the Office of Transportation and Parking fielded — suggesting a projected increase of single-occupancy vehicle trips. In response, the office worked to further incentivize safe and sustainable commuting options with expanded subsidies for Bluebike membership, commuter rail passes, MBTA parking, and more, in addition to the free local bus and subway already offered through the Access MIT program, in an effort to return to the pre-pandemic trends illustrated on the transportation dashboard.

    “An unexpected outcome of this data collection and sharing is that we now have data to inform the return to campus,” explains Director of Sustainability Julie Newman, noting that campus growth like the new MIT Schwarzman College of Computing is also expected to impact parking demand and require strategic decision-making. “We now have the ability to look at the data to understand how much parking we do or do not need, or to explore additional opportunities for congestion mitigation strategies.”

    This type of responsive planning is the result of the changes demanded by the pandemic and now key to a successful return to campus and the future of work at MIT. In a recent MIT News article, Vice President for Campus Services and Stewardship Joe Higgins explained, “[T]he pandemic created a forced experiment in MIT’s operations. We learned what our current technology systems and policies can flexibly support, and where improvements could be rapidly applied to support our academic, research, and administrative functions.”

    The dynamic use and socializing of the data behind Access MIT is an example of the living lab culture on campus, where campus programs can be used to inform sustainability policies, research, and decision-making. For example, MITOS is also partnered with the Media Lab’s City Science research group, who are using the data to inform their research around the future of work.

    “Looking forward as we plan for a return in the fall, the transportation data dashboard can be used to look back at our pre-pandemic commuting behavior, inform future planning, and track our return to campus and administrative functions,” says Newman. “It’s really an invaluable tool.” More

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    New directions in real estate practice

    Among the courses taught by Siqi Zheng is one identifying how real estate companies can be profitable while building and operating sustainably. Her class, 11.S949 (Sustainable Real Estate), at the MIT Center for Real Estate (CRE) attracts students from throughout the MIT School of Architecture and Planning (SA+P) and MIT Sloan School of Management. Harvard University students also cross-register to attend her course.

    For Zheng, the Samuel Tak Lee Champion Professor of Urban and Real Estate Sustainability, there is a sense of coming full circle.

    “Like these students, I migrated from Harvard to MIT,” Zheng says. “Fifteen years ago, I was one of them. Now I attract Harvard students to my classes.”

    Not only has Zheng progressed from taking courses at CRE while a postdoc at Harvard’s Graduate School of Design to joining the SA+P faculty in 2017, she assumed the role of CRE’s faculty director last summer. Among her goals in this new position is encouraging the center’s culture of sustainability and innovation — the very qualities that brought her to MIT as a student.

    While Zheng’s doctoral studies focused on housing and China’s transition from a centrally planned economy to a market-based system, it was MIT’s focus on urban economics and the “clean air and blue skies” of Cambridge, Massachusetts — in contrast to the polluted air in Beijing — that altered her focus to urban sustainability.

    “Back in 2006, I audited several very good courses at CRE in urban and real estate economics. It opened a window for me to say, ‘I need to study cities instead of just housing — and in a broader way — to understand urban dynamics.’ My research area became the intersection of urban economics and environmental sustainability.”

    Following her postdoc, Zheng returned to Beijing and joined Tsinghua University as an assistant professor and director of its Hang Lung Center for Real Estate.

    Creative urban studies research

    Shortly after arriving at MIT, Zheng founded the China Future City Lab, giving her the opportunity to focus on that country’s rapid economic growth alongside the tension of more sustainable urbanization. Her research shows that Chinese urban households are willing to pay higher real estate prices to live in cities and locations with better environmental quality, and this demand has increased over time. She has also identified a substantial price premium for green buildings, which gives real estate developers a monetary incentive to build energy-efficient structures. Gradually, she says, her research and team expanded along with her interest in other fast-urbanizing countries; she renamed her lab the Sustainable Urbanization Lab.

    Zheng’s research is remarkably varied and prolific, with many collaborators in the United States and overseas. Last year, Zheng was one of six MIT faculty awarded a grant from Harvard Medical School to address the effects of Covid-19. While the other researchers focused on clinical areas, such as vaccine development and diagnostic tools, Zheng’s research explored the role of social distancing in shaping Covid-19’s curve. Currently under review for publication, Zheng’s research compares how people’s sentiment in cities globally responded to the shock of the pandemic and the policies each government mandated to slow the spread of the virus.

    “My overarching goal as a scholar is to build our understanding of the behavioral foundations for urban real estate and environmental actions aimed at sustainable urbanization,” Zheng says. “I look at incentives and how an individual’s behavior gets aggregated into our society and its outcomes. Last year, without a vaccine, we needed to slow the spread of the virus. We had to rely on people in all countries to socially distance. We wanted to understand the interactions between individual sentiments, voluntary behaviors, and government intervention — how they work together, and their outcomes.”

    Currently, Zheng’s team is monitoring social media data to detect behavior changes in the U.S. population before and after vaccination. Their theory is that individuals — once vaccinated against Covid-19 — are happier and take part in riskier behaviors, such as restaurant dining or not wearing a mask.

    “We’ve been monitoring emotional states on social media before the vaccination process began,” she says. “We can measure their emotional status and their activities from their social media posts. People lose their anxiety and fear after vaccination, and they stop taking precautions.”

    Zheng began using social media data as a tool to assess a population’s emotional status several years ago, when she studied emotions in conjunction with levels of air pollution in China. Her paper, “Air pollution lowers Chinese urbanites’ expressed happiness on social media,” appeared in Nature Human Behavior in 2019, and was the journal’s fourth-most popular paper that year.  

    Zheng used the same approach to understand how climate change affects people in China by coupling meteorological conditions with more than 400 million social media posts from 43 million users. Finding that extreme weather worsens emotional expressions on social media allowed the researchers to project the potentially harmful impacts of global warming on subjective well-being.

    CRE’s strategic directions

    Working with CRE Executive Director Professor Kairos Shen, and Associate Director Lisa Thoma, Zheng is mapping out a strategic plan for CRE. One emphasis is expanding interdisciplinary research. She is excited by the new work undertaken by the center’s postdocs and doctoral students, which she sees as fostering synergy with teaching.

    “This is MIT,” says Zheng. “We have excellent teaching — but that’s not enough. We need to have a strong research focus to support teaching because we need to introduce our brilliant students to the field’s frontiers.”

    A parallel strategy is expanding the center’s global perspective. Zheng notes the oft-used expression “location, location, location,” pointing out that, while CRE’s attention has leaned toward the United States and Boston, half of their students are from overseas and the majority of their alumni are based in Asia. As such, she is working to expand collaborations with academic institutions and alumni who are now leaders in the field in Korea, mainland China, Hong Kong, Japan, Singapore, and India. Asia is also the region with the fastest urbanization and real estate growth potential. That’s why Zheng and her colleagues are now developing their “MIT Asia Real Estate Initiative.”

    “I like creating things from scratch,” Zheng says. “The center is small, flexible, and forward-looking, so I have an opportunity to create some new exciting programs and generate new impact.”

    As part of her globalization strategy, Zheng also expects to expand MIT/CRE’s online education offerings. While the center admits only 30 graduate students each year, Zheng sees opportunities for professionals in the global real estate industry to expand their education with an online certificate program. Currently, Zheng is designing six new courses to join the two already online.

    Having begun her new role during the global pandemic, Zheng and her team have only worked remotely. While anxious to get to know her team members “in person and not only over Zoom,” Zheng keeps busy managing various research initiatives, teaching and deepening MIT/CRE’s global connections. She is active on her social media accounts, sharing the Center’s many research activities, industry developments, and student achievements. On weekends however, she posts photos of hiking and exploring with her husband and son.

    “I want to be less intense outside of work; spending time outside surrounded by nature helps me unwind,” she says. More

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    Why the Earth needs a course correction now

    The massive impact of the Covid-19 pandemic on lives and economies underscores that our collective survival and well-being hinges on our willingness to confront environmental threats that have global consequences. Key to protecting lives and making communities more resilient to such threats will be an emphasis on proactive, science-based decision-making at all levels of society. And among the most serious risks that science can help illuminate and alleviate are those resulting from human-induced climate change.

    To minimize those risks, the Paris Agreement aims to commit nearly 200 nations to implement greenhouse gas emissions-reduction policies consistent with keeping the increase in the global average temperature since preindustrial times to well below 2 degrees Celsius — and pursue efforts to further limit that increase to 1.5 C. Recognizing that the first set of submitted near-term Paris pledges, known as Nationally Determined Contributions (NDCs), are inadequate by themselves to put the globe on track to meet those long-term targets and thus avoid the worst consequences of climate change, the accord calls for participating nations to strengthen their NDCs over time. To that end, the United States and a few other nations announced more stringent emissions-reduction goals for 2030 at the virtual climate summit convened by President Joe Biden in April.  

    To support decision-makers now engaged in or impacted by this ongoing, international effort to stabilize the climate, the MIT Joint Program on the Science and Policy of Global Change has released its 2021 Global Change Outlook. Based on a rigorous, integrated analysis of population and economic growth, technological change, NDCs, Covid-19 impacts, and other factors, the report presents the Joint Program’s latest projections for the future of the Earth’s energy, food, water and climate systems, as well as prospects for achieving the Paris Agreement’s short and long-term climate goals.

    Projections are provided for a baseline “Paris Forever” scenario, in which current (as of March 2021) NDCs are maintained in perpetuity; a Paris 2 C scenario that caps global warming at 2 C by 2100; and two scenarios — “Accelerated Actions” (which includes the newly announced U.S. goal for 2030) and Paris 1.5 C — which limit warming to 1.5 C by 2100. Uncertainty is quantified using 400-member ensembles of projections for each scenario. This year’s outlook introduces a visualization tool that enables a higher-resolution exploration of the first three scenarios.

    Energy

    More aggressive emissions-reduction policies would accelerate a shift away from fossil fuels and toward renewable energy sources between now and 2050.

    Under the Paris Forever scenario, the share of fossil fuels in the world’s energy mix drops during this period from about 80 percent to 70 percent, wind and solar expand nearly six-fold and natural gas by 50 percent, and electric vehicles (EVs) account for 38 percent of the light-duty vehicle (LDV) fleet. In the Paris 2 C scenario, the fossil fuel share drops to about 50 percent, wind and solar energy grow almost nine times and natural gas use expands by 25 percent, and EVs account for 50 percent of the global LDV fleet. The Accelerated Actions scenario squeezes out fossil fuels further and makes two-thirds of global LDVs electric.  

    “Electricity generation from renewable sources becomes a dominant source of power by 2050 in all scenarios, providing 70-80 percent of global power generation by mid-century in the climate stabilization scenarios,” says Joint Program Deputy Director Sergey Paltsev, a lead author of the report. “Climate policies essentially eliminate coal-based generation, while natural gas still keeps a sizeable share because of the need to support variable renewables. Resolving long-term energy storage issues are critical to full decarbonization.”

    Food and water

    Under the Paris Forever scenario, agriculture and food production will keep growing. This will increase pressure for land-use change, water use, and use of energy-intensive inputs, which will also lead to higher greenhouse gas (GHG) emissions. The Paris 2 C scenario shows low impacts on agriculture and food production trends by mid-century. Although economic growth tends to shift demand toward more protein-rich food sources, higher carbon costs associated with livestock production drive demand downward, decreasing its prices, and such impacts are transmitted to the food sector.

    The Paris Forever scenario indicates that more than half of the world’s population will undergo stresses on its water supply by 2050, and that three of every 10 people will live in water basins where compounding societal and environmental pressures on water resources will be experienced. The majority of expected increases in population under heightened water stress by mid-century cannot be avoided or reduced by climate mitigation efforts alone. Worldwide increases in population, economic growth, and associated water demands are largely a challenge of sustainability — one that can only be alleviated through widespread transformations of water systems’ storage capacity, conveyance, and water-use efficiencies.

    Climate and Paris goals

    The outlook shows a wide gap between current (as of March 2021) GHG emissions-reduction commitments and those needed to put the world on track to meet the Paris Agreement’s long-term climate goals.

    “Our projected global climate responses under the Paris Forever scenario indicate with near-certainty that the world will surpass critical GHG concentration thresholds and climate targets in the coming decades,” says Joint Program Deputy Director C. Adam Schlosser, a lead author of the report.

    Under Paris Forever, the world is likely to exceed 2 C global climate warming by 2065, 2.8 C by 2100, and 4.1 C by 2150. While many countries have made good progress toward their NDCs and declared more ambitious GHG emissions mitigation goals, financing to assist the least-developed countries in sustainable development is not forthcoming at the levels needed.

    The report’s projections indicate that the long-term climate targets of the Paris Agreement remain achievable, but come with different levels of risk. The Paris 2 C scenario shows negligible likelihood of even the “coolest” trajectories remaining below 1.5 C at the end of the century. The Paris 1.5 C scenario, however, can virtually assure the world of remaining below 2 C of global warming.

    An important consequence of climate change is altered precipitation levels. Between now and 2050 under Paris Forever, global precipitation will likely increase by about 1.5 centimeters per year — approximately an additional 7,400 cubic kilometers (or nearly 2 quadrillion gallons) each year. By 2100, the total change in precipitation will most likely rise to about 4 cm/year (or 21,200 km3/yr) — nearly triple that of the mid-century change. Paris 2 C halves global precipitation increases, and Paris 1.5 C reduces them to almost a third of the Paris Forever increases. These aggressive mitigation scenarios convey considerable reductions in flood risk and associated adaptation costs.

    Reducing climate risk

    For the first time, the outlook explores two well-known sets of risks posed by climate change. Research highlighted in this report indicates that elevated climate-related physical risks will continue to evolve by mid-century, along with heightened transition risks that arise from shifts in the political, technological, social, and economic landscapes that are likely to occur during the transition to a low-carbon economy.

    “Our outlook shows that we could dramatically reduce overall climate risk through more ambitious and accelerated policy measures and investments aligned with meeting the Paris Agreement’s long-term 1.5 C or 2 C climate targets,” says MIT Joint Program Director Ronald Prinn. “Decision-makers in government, industry, and financial institutions can play a key role in moving us further along this path.” More

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    Could all your digital photos be stored as DNA?

    On Earth right now, there are about 10 trillion gigabytes of digital data, and every day, humans produce emails, photos, tweets, and other digital files that add up to another 2.5 million gigabytes of data. Much of this data is stored in enormous facilities known as exabyte data centers (an exabyte is 1 billion gigabytes), which can be the size of several football fields and cost around $1 billion to build and maintain.

    Many scientists believe that an alternative solution lies in the molecule that contains our genetic information: DNA, which evolved to store massive quantities of information at very high density. A coffee mug full of DNA could theoretically store all of the world’s data, says Mark Bathe, an MIT professor of biological engineering.

    “We need new solutions for storing these massive amounts of data that the world is accumulating, especially the archival data,” says Bathe, who is also an associate member of the Broad Institute of MIT and Harvard. “DNA is a thousandfold denser than even flash memory, and another property that’s interesting is that once you make the DNA polymer, it doesn’t consume any energy. You can write the DNA and then store it forever.”

    Scientists have already demonstrated that they can encode images and pages of text as DNA. However, an easy way to pick out the desired file from a mixture of many pieces of DNA will also be needed. Bathe and his colleagues have now demonstrated one way to do that, by encapsulating each data file into a 6-micrometer particle of silica, which is labeled with short DNA sequences that reveal the contents.

    Using this approach, the researchers demonstrated that they could accurately pull out individual images stored as DNA sequences from a set of 20 images. Given the number of possible labels that could be used, this approach could scale up to 1020 files.

    Bathe is the senior author of the study, which appears today in Nature Materials. The lead authors of the paper are MIT senior postdoc James Banal, former MIT research associate Tyson Shepherd, and MIT graduate student Joseph Berleant.

    Stable storage

    Digital storage systems encode text, photos, or any other kind of information as a series of 0s and 1s. This same information can be encoded in DNA using the four nucleotides that make up the genetic code: A, T, G, and C. For example, G and C could be used to represent 0 while A and T represent 1.

    DNA has several other features that make it desirable as a storage medium: It is extremely stable, and it is fairly easy (but expensive) to synthesize and sequence. Also, because of its high density — each nucleotide, equivalent to up to two bits, is about 1 cubic nanometer — an exabyte of data stored as DNA could fit in the palm of your hand.

    One obstacle to this kind of data storage is the cost of synthesizing such large amounts of DNA. Currently it would cost $1 trillion to write one petabyte of data (1 million gigabytes). To become competitive with magnetic tape, which is often used to store archival data, Bathe estimates that the cost of DNA synthesis would need to drop by about six orders of magnitude. Bathe says he anticipates that will happen within a decade or two, similar to how the cost of storing information on flash drives has dropped dramatically over the past couple of decades.

    Aside from the cost, the other major bottleneck in using DNA to store data is the difficulty in picking out the file you want from all the others.

    “Assuming that the technologies for writing DNA get to a point where it’s cost-effective to write an exabyte or zettabyte of data in DNA, then what? You’re going to have a pile of DNA, which is a gazillion files, images or movies and other stuff, and you need to find the one picture or movie you’re looking for,” Bathe says. “It’s like trying to find a needle in a haystack.”

    Currently, DNA files are conventionally retrieved using PCR (polymerase chain reaction). Each DNA data file includes a sequence that binds to a particular PCR primer. To pull out a specific file, that primer is added to the sample to find and amplify the desired sequence. However, one drawback to this approach is that there can be crosstalk between the primer and off-target DNA sequences, leading unwanted files to be pulled out. Also, the PCR retrieval process requires enzymes and ends up consuming most of the DNA that was in the pool.

    “You’re kind of burning the haystack to find the needle, because all the other DNA is not getting amplified and you’re basically throwing it away,” Bathe says.

    File retrieval

    As an alternative approach, the MIT team developed a new retrieval technique that involves encapsulating each DNA file into a small silica particle. Each capsule is labeled with single-stranded DNA “barcodes” that correspond to the contents of the file. To demonstrate this approach in a cost-effective manner, the researchers encoded 20 different images into pieces of DNA about 3,000 nucleotides long, which is equivalent to about 100 bytes. (They also showed that the capsules could fit DNA files up to a gigabyte in size.)

    Each file was labeled with barcodes corresponding to labels such as “cat” or “airplane.” When the researchers want to pull out a specific image, they remove a sample of the DNA and add primers that correspond to the labels they’re looking for — for example, “cat,” “orange,” and “wild” for an image of a tiger, or “cat,” “orange,” and “domestic” for a housecat.

    The primers are labeled with fluorescent or magnetic particles, making it easy to pull out and identify any matches from the sample. This allows the desired file to be removed while leaving the rest of the DNA intact to be put back into storage. Their retrieval process allows Boolean logic statements such as “president AND 18th century” to generate George Washington as a result, similar to what is retrieved with a Google image search.

    “At the current state of our proof-of-concept, we’re at the 1 kilobyte per second search rate. Our file system’s search rate is determined by the data size per capsule, which is currently limited by the prohibitive cost to write even 100 megabytes worth of data on DNA, and the number of sorters we can use in parallel. If DNA synthesis becomes cheap enough, we would be able to maximize the data size we can store per file with our approach,” Banal says.

    For their barcodes, the researchers used single-stranded DNA sequences from a library of 100,000 sequences, each about 25 nucleotides long, developed by Stephen Elledge, a professor of genetics and medicine at Harvard Medical School. If you put two of these labels on each file, you can uniquely label 1010 (10 billion) different files, and with four labels on each, you can uniquely label 1020 files.

    George Church, a professor of genetics at Harvard Medical School, describes the technique as “a giant leap for knowledge management and search tech.”

    “The rapid progress in writing, copying, reading, and low-energy archival data storage in DNA form has left poorly explored opportunities for precise retrieval of data files from huge (1021 byte, zetta-scale) databases,” says Church, who was not involved in the study. “The new study spectacularly addresses this using a completely independent outer layer of DNA and leveraging different properties of DNA (hybridization rather than sequencing), and moreover, using existing instruments and chemistries.”

    Bathe envisions that this kind of DNA encapsulation could be useful for storing “cold” data, that is, data that is kept in an archive and not accessed very often. His lab is spinning out a startup, Cache DNA, that is now developing technology for long-term storage of DNA, both for DNA data storage in the long-term, and clinical and other preexisting DNA samples in the near-term.

    “While it may be a while before DNA is viable as a data storage medium, there already exists a pressing need today for low-cost, massive storage solutions for preexisting DNA and RNA samples from Covid-19 testing, human genomic sequencing, and other areas of genomics,” Bathe says.

    The research was funded by the Office of Naval Research, the National Science Foundation, and the U.S. Army Research Office. More

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    Taking an indirect path into a bright future

    Matthew Johnston was a physics senior looking to postpone his entry into adulting. He had an intense four years at MIT; when he wasn’t in class, he was playing baseball and working various tech development gigs.

    Johnston had led the MIT Engineers baseball team to a conference championship, becoming the first player in his team’s history to be named a three-time Google Cloud Academic All-American. He put an exclamation mark on his career by hitting four home runs in his final game. 

    Johnston also developed a novel method of producing solar devices as a researcher with GridEdge Solar at MIT, and worked on a tax-loss harvesting research project as an intern at Impact Labs in San Francisco, California. As he contemplated post-graduation life, he liked the idea of gaining new experiences before committing to a company.

    Remotely Down Under

    MISTI-Australia matched him with an internship at Sydney-based Okra Solar, which manufactures smart solar charge controllers in Shenzhen, China, to help power off-the-grid remote villages in Southeast Asian countries such as Cambodia and the Philippines, as well as in Nigeria. 

    “I felt that I had so much more to learn before committing to a full-time job, and I wanted to see the world,” he says. “Working an internship for Okra in Sydney seemed like it would be the perfect buffer between university life and life in the real world. If all went well, maybe I would end up living in Sydney a while longer.”

    After graduating in May 2020 with a BS in physics, a minor in computer science, and a concentration in philosophy, he prepared to live in Sydney, with the possibility of travel to Shenzhen, when he received a familiar pitch: a curveball. 

    Like everyone else, he had hoped that the pandemic would wind down before his Down Under move, but when that didn’t happen, he pivoted to sharing a place with friends in Southern California, where they could hike and camp in nearby Sequoia National Park when they weren’t working remotely.

    On Okra’s software team, he focused on data science to streamline the maintenance and improve the reliability of Okra’s solar energy systems. However, his remote status didn’t mesh with an ongoing project to identify remote villages without grid access. So, he launched his own data project: designing a model to identify shaded solar panels based on their daily power output. That project was placed on hold until they could get more reliable data, but he gained experience setting up machine-learning problems as he developed a pipeline to retrieve, process, and load the data to train the model.

    “This project helped me understand that most of the effort in a data science problem goes into sourcing and processing the data. Unfortunately, it seemed that it was just a bit too early for the model to perform accurately.”

    Team-powered engine

    Coordinating with a team of 23 people from more than 10 unique cultures, scattered across 11 countries in different time zones, presented yet another challenge. He responded by developing a productive workflow by leaving questions in his code reviews that would be answered by the next morning.

    “Working remotely is ultimately a bigger barrier to team cohesion than productivity,” he says. He overcame that hurdle as well; the Aussie team took a liking to him and nicknamed him Jonno. “They’re an awesome group to be around and aren’t afraid to laugh at themselves.”   

    Soon, Jonno was helping the service delivery team efficiently diagnose and resolve real issues in the field using sensor data. By automating the maintenance process in this way, Okra makes it possible for energy companies to deploy and manage last-mile energy projects at scale. Several months later, when he began contributing to the firmware team, he also took on the project of calculating a battery’s state of charge, with the goal to open-source a robust and reliable algorithm.

    “Matt excelled despite the circumstance,” says Okra Solar co-founder and CEO Afnan Hannan. “Matt contributed to developing Okra’s automated field alerts system that monitors the health and performance of Okra’s solar systems, which are deployed across Southeast Asia and Africa. Additionally, Matt led the development of a state-of-the-art Kalman filter-based online state-of-charge (SoC) algorithm. This included research, prototyping, developing back-testing infrastructure, and finally implementing and deploying the solution on Okra’s microcontroller. An accurate and stable SoC has been a vital part of Okra’s cutting-edge Battery Sharing feature, for which we have Matt to thank.” 

    Full power

    After six months, Johnston joined Okra full time in January, moving to Phnom Penh, Cambodia, to join some of the team in person and immerse himself into firmware and data science. In the short term, the goal is to electrify villages to provide access to much cheaper and more accessible energy.

    “Previously, the only way many of these villages could access electricity was by charging a car battery using a diesel generator,” he says. “This process is very expensive, and it is impossible to charge many batteries simultaneously. In contrast, Okra provides, cheap, accessible, and renewable energy for the entire village.”

    For Johnston to see an Okra project firsthand, some villages are a 30-minute boat ride from their nearest town. He and others travel there to demonstrate small appliances that many in the world take for granted, such as using an electric blender to make a smoothie.

    “It’s really amazing to see how hard-to-reach these villages are and how much electricity can help them,” says Johnston. “Something as simple as using a rice cooker instead of a wood fire can save a family countless hours of chopping wood. It also helps us think about how we can improve our product, both for the users and the energy companies.”   

    “In the long term, the vision is that by providing electricity, we can introduce the possibility of online education and more productive uses of power, allowing these communities to join the modern economy.”

    While getting to Phnom Penh was a challenge, he credits MIT for hitting yet another home run.

    “I think two of the biggest things I learned from both baseball and physics were how to learn challenging things and how to overcome failure. It takes persistence to keep digging for more information and practicing what you’ve already failed, and this same way of thinking has helped me to develop my professional skills. At the same time, I am grateful for the time I spent studying philosophy. Thinking deeply about what might lead to a meaningful life for myself and for others has led me to stumble upon opportunities like this one.” More

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    MIT baseball coach uses sensors, motion capture technology to teach pitching

    The field of sports analytics is most known for assessing player and team performance during competition, but MIT Baseball’s pitching coach, Todd Carroll, is bringing a different kind of analytics to the practice field for his student athletes.

    “A baseball player might practice a pitch 10,000 times before it becomes natural. Through technology, we can speed that process up,” Carroll said in a recent seminar organized by the MIT.nano Immersion Lab. “To help players improve athletically, without taking up that much time, and keep them healthy — that’s the goal.”

    The virtual talk — “Pitching in baseball: Using scientific tools to visualize what we know and learn what we don’t” — grew out of a new research collaboration between MIT Baseball, the MIT Clinical Research Center (CRC), and the Immersion Lab.

    Carroll started with an explanation of how pitching has evolved over time and what specific skills coaches measure to help players perfect their throw. Then, he and Research Laboratory of Electronics (RLE) postdoc Praneeth Namburi used the Immersion Lab’s motion capture platform and wireless physiological sensors from the CRC to explore how biomechanical feedback and interactive visualization tools could change the future of sports.

    Namburi stepped up to the (hypothetical) mound, with Carroll as his coach. By interfacing the physical and digital in real time, the two were able to assess Namburi’s pitches and make immediate adjustments that improved his athletic performance in one session.

    Visualizing sports data

    Stride length, pitcher extension, hip-shoulder separation, and ground force production are all measurable aspects of pitching, explained Carroll. The capabilities of the Immersion Lab allow for digital tracking and visualization of these skills. Wearing wireless sensors on his body, Namburi threw several pitches inside the lab. The sensors plot Namburi’s position and track his movements through space, as shown in the first part of the video below. Adding in the physiological measurements, the second clip shows the activity of his rotation muscles (in green), his acceleration through space (in blue), and the pressure, or ground force, produced by his foot (in red).

    Play video

    Pitching at the Immersion Lab

    By reviewing the motion capture frames together, Carroll could show Namburi how to modify his posture to increase stride length and extend his hip-shoulder separation by holding his back foot on the ground. In this example, the technology betters the communication between coach and player, leading to more efficient improvements.

    Assessing physiological measurements alongside the motion capture can also help decrease injuries. Carroll emphasized how this technology can help rehabbing players, teaching them to trust their body again. “That’s a big part of injury recovery, trusting the process. These students find comfort in the data and that allows them to push through.”

    Following the training session, Namburi overlayed the motion capture from his first and last throw, comparing his posture, spine position, stride length, and feet position. A visual compilation of all his throws compared the trajectory of his wrist, showing that, over time, his movement became more consistent and more natural.

    The seminar concluded with a live demonstration of a novice pitcher in the Immersion Lab following the advice of Coach Carroll via Zoom. “Two people who have never thrown a baseball before today, and we’re able to teach them remotely during a pandemic,” reflected Carroll. “That’s pretty cool.”

    Afterward, Namburi answered questions about the ease of taking the physiological monitoring tools to the field and of being able to capture and measure the movements of multiple athletes at once.

    Play video

    IMMERSED IN: Athletics—Pitching in baseball

    Immersed in collaboration

    The MIT.nano Immersion Lab’s new seminar series, IMMERSED, explores the possibilities enabled by technologies such as motion capture, virtual and augmented reality, photogrammetry, and related computational advances to gather, process, and interact with data from multiple modalities. The series highlights the capabilities available at the Immersion Lab, and the wide range of disciplines to which the tools and space can be applied.

    “IMMERSED offers another avenue for any individual — scientists, artists, engineers, performers — to consider collaborative projects,” says Brian W. Anthony, MIT.nano associate director. “The series combines lectures with demonstrations and tutorials so more people can see the wide breadth of research possible at the lab.”

    As a shared-access facility, MIT.nano’s Immersion Lab is open to researchers from any department, lab, or center at MIT, as well as external partners. Learn more about the Immersion Lab and how to become a user. More