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    Research collaboration puts climate-resilient crops in sight

    Any houseplant owner knows that changes in the amount of water or sunlight a plant receives can put it under immense stress. A dying plant brings certain disappointment to anyone with a green thumb. 

    But for farmers who make their living by successfully growing plants, and whose crops may nourish hundreds or thousands of people, the devastation of failing flora is that much greater. As climate change is poised to cause increasingly unpredictable weather patterns globally, crops may be subject to more extreme environmental conditions like droughts, fluctuating temperatures, floods, and wildfire. 

    Climate scientists and food systems researchers worry about the stress climate change may put on crops, and on global food security. In an ambitious interdisciplinary project funded by the Abdul Latif Jameel Water and Food Systems Lab (J-WAFS), David Des Marais, the Gale Assistant Professor in the Department of Civil and Environmental Engineering at MIT, and Caroline Uhler, an associate professor in the MIT Department of Electrical Engineering and Computer Science and the Institute for Data, Systems, and Society, are investigating how plant genes communicate with one another under stress. Their research results can be used to breed plants more resilient to climate change.

    Crops in trouble

    Governing plants’ responses to environmental stress are gene regulatory networks, or GRNs, which guide the development and behaviors of living things. A GRN may be comprised of thousands of genes and proteins that all communicate with one another. GRNs help a particular cell, tissue, or organism respond to environmental changes by signaling certain genes to turn their expression on or off.

    Even seemingly minor or short-term changes in weather patterns can have large effects on crop yield and food security. An environmental trigger, like a lack of water during a crucial phase of plant development, can turn a gene on or off, and is likely to affect many others in the GRN. For example, without water, a gene enabling photosynthesis may switch off. This can create a domino effect, where the genes that rely on those regulating photosynthesis are silenced, and the cycle continues. As a result, when photosynthesis is halted, the plant may experience other detrimental side effects, like no longer being able to reproduce or defend against pathogens. The chain reaction could even kill a plant before it has the chance to be revived by a big rain.

    Des Marais says he wishes there was a way to stop those genes from completely shutting off in such a situation. To do that, scientists would need to better understand how exactly gene networks respond to different environmental triggers. Bringing light to this molecular process is exactly what he aims to do in this collaborative research effort.

    Solving complex problems across disciplines

    Despite their crucial importance, GRNs are difficult to study because of how complex and interconnected they are. Usually, to understand how a particular gene is affecting others, biologists must silence one gene and see how the others in the network respond. 

    For years, scientists have aspired to an algorithm that could synthesize the massive amount of information contained in GRNs to “identify correct regulatory relationships among genes,” according to a 2019 article in the Encyclopedia of Bioinformatics and Computational Biology. 

    “A GRN can be seen as a large causal network, and understanding the effects that silencing one gene has on all other genes requires understanding the causal relationships among the genes,” says Uhler. “These are exactly the kinds of algorithms my group develops.”

    Des Marais and Uhler’s project aims to unravel these complex communication networks and discover how to breed crops that are more resilient to the increased droughts, flooding, and erratic weather patterns that climate change is already causing globally.

    In addition to climate change, by 2050, the world will demand 70 percent more food to feed a booming population. “Food systems challenges cannot be addressed individually in disciplinary or topic area silos,” says Greg Sixt, J-WAFS’ research manager for climate and food systems. “They must be addressed in a systems context that reflects the interconnected nature of the food system.”

    Des Marais’ background is in biology, and Uhler’s in statistics. “Dave’s project with Caroline was essentially experimental,” says Renee J. Robins, J-WAFS’ executive director. “This kind of exploratory research is exactly what the J-WAFS seed grant program is for.”

    Getting inside gene regulatory networks

    Des Marais and Uhler’s work begins in a windowless basement on MIT’s campus, where 300 genetically identical Brachypodium distachyon plants grow in large, temperature-controlled chambers. The plant, which contains more than 30,000 genes, is a good model for studying important cereal crops like wheat, barley, maize, and millet. For three weeks, all plants receive the same temperature, humidity, light, and water. Then, half are slowly tapered off water, simulating drought-like conditions.

    Six days into the forced drought, the plants are clearly suffering. Des Marais’ PhD student Jie Yun takes tissues from 50 hydrated and 50 dry plants, freezes them in liquid nitrogen to immediately halt metabolic activity, grinds them up into a fine powder, and chemically separates the genetic material. The genes from all 100 samples are then sequenced at a lab across the street.

    The team is left with a spreadsheet listing the 30,000 genes found in each of the 100 plants at the moment they were frozen, and how many copies there were. Uhler’s PhD student Anastasiya Belyaeva inputs the massive spreadsheet into the computer program she developed and runs her novel algorithm. Within a few hours, the group can see which genes were most active in one condition over another, how the genes were communicating, and which were causing changes in others. 

    The methodology captures important subtleties that could allow researchers to eventually alter gene pathways and breed more resilient crops. “When you expose a plant to drought stress, it’s not like there’s some canonical response,” Des Marais says. “There’s lots of things going on. It’s turning this physiologic process up, this one down, this one didn’t exist before, and now suddenly is turned on.” 

    In addition to Des Marais and Uhler’s research, J-WAFS has funded projects in food and water from researchers in 29 departments across all five MIT schools as well as the MIT Schwarzman College of Computing. J-WAFS seed grants typically fund seven to eight new projects every year.

    “The grants are really aimed at catalyzing new ideas, providing the sort of support [for MIT researchers] to be pushing boundaries, and also bringing in faculty who may have some interesting ideas that they haven’t yet applied to water or food concerns,” Robins says. “It’s an avenue for researchers all over the Institute to apply their ideas to water and food.”

    Alison Gold is a student in MIT’s Graduate Program in Science Writing. More

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    A universal system for decoding any type of data sent across a network

    Every piece of data that travels over the internet — from paragraphs in an email to 3D graphics in a virtual reality environment — can be altered by the noise it encounters along the way, such as electromagnetic interference from a microwave or Bluetooth device. The data are coded so that when they arrive at their destination, a decoding algorithm can undo the negative effects of that noise and retrieve the original data.

    Since the 1950s, most error-correcting codes and decoding algorithms have been designed together. Each code had a structure that corresponded with a particular, highly complex decoding algorithm, which often required the use of dedicated hardware.

    Researchers at MIT, Boston University, and Maynooth University in Ireland have now created the first silicon chip that is able to decode any code, regardless of its structure, with maximum accuracy, using a universal decoding algorithm called Guessing Random Additive Noise Decoding (GRAND). By eliminating the need for multiple, computationally complex decoders, GRAND enables increased efficiency that could have applications in augmented and virtual reality, gaming, 5G networks, and connected devices that rely on processing a high volume of data with minimal delay.

    The research at MIT is led by Muriel Médard, the Cecil H. and Ida Green Professor in the Department of Electrical Engineering and Computer Science, and was co-authored by Amit Solomon and Wei Ann, both graduate students at MIT; Rabia Tugce Yazicigil, assistant professor of electrical and computer engineering at Boston University; Arslan Riaz and Vaibhav Bansal, both graduate students at Boston University; Ken R. Duffy, director of the Hamilton Institute at the National University of Ireland at Maynooth; and Kevin Galligan, a Maynooth graduate student. The research will be presented at the European Solid-States Device Research and Circuits Conference next week.

    Focus on noise

    One way to think of these codes is as redundant hashes (in this case, a series of 1s and 0s) added to the end of the original data. The rules for the creation of that hash are stored in a specific codebook.

    As the encoded data travel over a network, they are affected by noise, or energy that disrupts the signal, which is often generated by other electronic devices. When that coded data and the noise that affected them arrive at their destination, the decoding algorithm consults its codebook and uses the structure of the hash to guess what the stored information is.

    Instead, GRAND works by guessing the noise that affected the message, and uses the noise pattern to deduce the original information. GRAND generates a series of noise sequences in the order they are likely to occur, subtracts them from the received data, and checks to see if the resulting codeword is in a codebook.

    While the noise appears random in nature, it has a probabilistic structure that allows the algorithm to guess what it might be.

    “In a way, it is similar to troubleshooting. If someone brings their car into the shop, the mechanic doesn’t start by mapping the entire car to blueprints. Instead, they start by asking, ‘What is the most likely thing to go wrong?’ Maybe it just needs gas. If that doesn’t work, what’s next? Maybe the battery is dead?” Médard says.

    Novel hardware

    The GRAND chip uses a three-tiered structure, starting with the simplest possible solutions in the first stage and working up to longer and more complex noise patterns in the two subsequent stages. Each stage operates independently, which increases the throughput of the system and saves power.

    The device is also designed to switch seamlessly between two codebooks. It contains two static random-access memory chips, one that can crack codewords, while the other loads a new codebook and then switches to decoding without any downtime.

    The researchers tested the GRAND chip and found it could effectively decode any moderate redundancy code up to 128 bits in length, with only about a microsecond of latency.

    Médard and her collaborators had previously demonstrated the success of the algorithm, but this new work showcases the effectiveness and efficiency of GRAND in hardware for the first time.

    Developing hardware for the novel decoding algorithm required the researchers to first toss aside their preconceived notions, Médard says.

    “We couldn’t go out and reuse things that had already been done. This was like a complete whiteboard. We had to really think about every single component from scratch. It was a journey of reconsideration. And I think when we do our next chip, there will be things with this first chip that we’ll realize we did out of habit or assumption that we can do better,” she says.

    A chip for the future

    Since GRAND only uses codebooks for verification, the chip not only works with legacy codes but could also be used with codes that haven’t even been introduced yet.

    In the lead-up to 5G implementation, regulators and communications companies struggled to find consensus as to which codes should be used in the new network. Regulators ultimately chose to use two types of traditional codes for 5G infrastructure in different situations. Using GRAND could eliminate the need for that rigid standardization in the future, Médard says.

    The GRAND chip could even open the field of coding to a wave of innovation.

    “For reasons I’m not quite sure of, people approach coding with awe, like it is black magic. The process is mathematically nasty, so people just use codes that already exist. I’m hoping this will recast the discussion so it is not so standards-oriented, enabling people to use codes that already exist and create new codes,” she says.

    Moving forward, Médard and her collaborators plan to tackle the problem of soft detection with a retooled version of the GRAND chip. In soft detection, the received data are less precise.

    They also plan to test the ability of GRAND to crack longer, more complex codes and adjust the structure of the silicon chip to improve its energy efficiency.

    The research was funded by the Battelle Memorial Institute and Science Foundation of Ireland. More

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    Using adversarial attacks to refine molecular energy predictions

    Neural networks (NNs) are increasingly being used to predict new materials, the rate and yield of chemical reactions, and drug-target interactions, among others. For these applications, they are orders of magnitude faster than traditional methods such as quantum mechanical simulations. 

    The price for this agility, however, is reliability. Because machine learning models only interpolate, they may fail when used outside the domain of training data.

    But the part that worried Rafael Gómez-Bombarelli, the Jeffrey Cheah Career Development Professor in the MIT Department of Materials Science and Engineering, and graduate students Daniel Schwalbe-Koda and Aik Rui Tan was that establishing the limits of these machine learning (ML) models is tedious and labor-intensive. 

    This is particularly true for predicting ‘‘potential energy surfaces” (PES), or the map of a molecule’s energy in all its configurations. These surfaces encode the complexities of a molecule into flatlands, valleys, peaks, troughs, and ravines. The most stable configurations of a system are usually in the deep pits — quantum mechanical chasms from which atoms and molecules typically do not escape. 

    In a recent Nature Communications paper, the research team presented a way to demarcate the “safe zone” of a neural network by using “adversarial attacks.” Adversarial attacks have been studied for other classes of problems, such as image classification, but this is the first time that they are being used to sample molecular geometries in a PES. 

    “People have been using uncertainty for active learning for years in ML potentials. The key difference is that they need to run the full ML simulation and evaluate if the NN was reliable, and if it wasn’t, acquire more data, retrain and re-simulate. Meaning that it takes a long time to nail down the right model, and one has to run the ML simulation many times” explains Gómez-Bombarelli.

    The Gómez-Bombarelli lab at MIT works on a synergistic synthesis of first-principles simulation and machine learning that greatly speeds up this process. The actual simulations are run only for a small fraction of these molecules, and all those data are fed into a neural network that learns how to predict the same properties for the rest of the molecules. They have successfully demonstrated these methods for a growing class of novel materials that includes catalysts for producing hydrogen from water, cheaper polymer electrolytes for electric vehicles,  zeolites for molecular sieving, magnetic materials, and more. 

    The challenge, however, is that these neural networks are only as smart as the data they are trained on.  Considering the PES map, 99 percent of the data may fall into one pit, totally missing valleys that are of more interest. 

    Such wrong predictions can have disastrous consequences — think of a self-driving car that fails to identify a person crossing the street.

    One way to find out the uncertainty of a model is to run the same data through multiple versions of it. 

    For this project, the researchers had multiple neural networks predict the potential energy surface from the same data. Where the network is fairly sure of the prediction, the variation between the outputs of different networks is minimal and the surfaces largely converge. When the network is uncertain, the predictions of different models vary widely, producing a range of outputs, any of which could be the correct surface. 

    The spread in the predictions of a “committee of neural networks” is the “uncertainty” at that point. A good model should not just indicate the best prediction, but also indicates the uncertainty about each of these predictions. It’s like the neural network says “this property for material A will have a value of X and I’m highly confident about it.”

    This could have been an elegant solution but for the sheer scale of the combinatorial space. “Each simulation (which is ground feed for the neural network) may take from tens to thousands of CPU hours,” explains Schwalbe-Koda. For the results to be meaningful, multiple models must be run over a sufficient number of points in the PES, an extremely time-consuming process. 

    Instead, the new approach only samples data points from regions of low prediction confidence, corresponding to specific geometries of a molecule. These molecules are then stretched or deformed slightly so that the uncertainty of the neural network committee is maximized. Additional data are computed for these molecules through simulations and then added to the initial training pool. 

    The neural networks are trained again, and a new set of uncertainties are calculated. This process is repeated until the uncertainty associated with various points on the surface becomes well-defined and cannot be decreased any further. 

    Gómez-Bombarelli explains, “We aspire to have a model that is perfect in the regions we care about (i.e., the ones that the simulation will visit) without having had to run the full ML simulation, by making sure that we make it very good in high-likelihood regions where it isn’t.”

    The paper presents several examples of this approach, including predicting complex supramolecular interactions in zeolites. These materials are cavernous crystals that act as molecular sieves with high shape selectivity. They find applications in catalysis, gas separation, and ion exchange, among others.

    Because performing simulations of large zeolite structures is very costly, the researchers show how their method can provide significant savings in computational simulations. They used more than 15,000 examples to train a neural network to predict the potential energy surfaces for these systems. Despite the large cost required to generate the dataset, the final results are mediocre, with only around 80 percent of the neural network-based simulations being successful. To improve the performance of the model using traditional active learning methods, the researchers calculated an additional 5,000 data points, which improved the performance of the neural network potentials to 92 percent.

    However, when the adversarial approach is used to retrain the neural networks, the authors saw a performance jump to 97 percent using only 500 extra points. That’s a remarkable result, the researchers say, especially considering that each of these extra points takes hundreds of CPU hours. 

    This could be the most realistic method to probe the limits of models that researchers use to predict the behavior of materials and the progress of chemical reactions. More

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    Last-mile routing research challenge awards $175,000 to three winning teams

    Routing is one of the most studied problems in operations research; even small improvements in routing efficiency can save companies money and result in energy savings and reduced environmental impacts. Now, three teams of researchers from universities around the world have received prize money totaling $175,000 for their innovative route optimization models.

    The three teams were the winners of the Amazon Last-Mile Routing Research Challenge, through which the MIT Center for Transportation & Logistics (MIT CTL) and Amazon engaged with a global community of researchers across a range of disciplines, from computer science to business operations to supply chain management, challenging them to build data-driven route optimization models leveraging massive historical route execution data.

    First announced in February, the research challenge attracted more than 2,000 participants from around the world. Two hundred twenty-nine researcher teams formed during the spring to independently develop solutions that incorporated driver know-how into route optimization models with the intent that they would outperform traditional optimization approaches. Out of the 48 teams whose models qualified for the final round of the challenge, three teams’ work stood out above the rest. Amazon provided real operational training data for the models and evaluated submissions, with technical support from MIT CTL scientists.

    In real life, drivers frequently deviate from planned and mathematically optimized route sequences. Drivers carry information about which roads are hard to navigate when traffic is bad, when and where they can easily find parking, which stops can be conveniently served together, and many other factors that existing optimization models simply don’t capture.

    Each model addressed the challenge data in a unique way. The methodological approaches chosen by the participants frequently combined traditional exact and heuristic optimization approaches with nontraditional machine learning methods. On the machine learning side, the most commonly adopted methods were different variants of artificial neural networks, as well as inverse reinforcement learning approaches.

    There were 45 submissions that reached the finalist phase, with team members hailing from 29 countries. Entrants spanned all levels of higher education from final-year undergraduate students to retired faculty. Entries were assessed in a double-blind review process so that the judges would not know what team was attached to each entry.

    The third-place prize of $25,000 was awarded to Okan Arslan and Rasit Abay. Okan is a professor at HEC Montréal, and Rasit is a doctoral student at the University of New South Wales in Australia. The runner-up prize at $50,000 was awarded to MIT’s own Xiaotong Guo, Qingyi Wang, and Baichuan Mo, all doctoral students. The top prize of $100,000 was awarded to Professor William Cook of the University of Waterloo in Canada, Professor Stephan Held of the University of Bonn in Germany, and Professor Emeritus Keld Helsgaun of Roskilde University in Denmark. Congratulations to all winners and contestants were held via webinar on July 30.

    Top-performing teams may be interviewed by Amazon for research roles in the company’s Last Mile organization. MIT CTL will publish and promote short technical papers written by all finalists and might invite top-performing teams to present at MIT. Further, a team led by Matthias Winkenbach, director of the MIT Megacity Logistics Lab, will guest-edit a special issue of Transportation Science, one of the most renowned academic journals in this field, featuring academic papers on topics related to the problem tackled by the research challenge. More

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    Exact symbolic artificial intelligence for faster, better assessment of AI fairness

    The justice system, banks, and private companies use algorithms to make decisions that have profound impacts on people’s lives. Unfortunately, those algorithms are sometimes biased — disproportionately impacting people of color as well as individuals in lower income classes when they apply for loans or jobs, or even when courts decide what bail should be set while a person awaits trial.

    MIT researchers have developed a new artificial intelligence programming language that can assess the fairness of algorithms more exactly, and more quickly, than available alternatives.

    Their Sum-Product Probabilistic Language (SPPL) is a probabilistic programming system. Probabilistic programming is an emerging field at the intersection of programming languages and artificial intelligence that aims to make AI systems much easier to develop, with early successes in computer vision, common-sense data cleaning, and automated data modeling. Probabilistic programming languages make it much easier for programmers to define probabilistic models and carry out probabilistic inference — that is, work backward to infer probable explanations for observed data.

    “There are previous systems that can solve various fairness questions. Our system is not the first; but because our system is specialized and optimized for a certain class of models, it can deliver solutions thousands of times faster,” says Feras Saad, a PhD student in electrical engineering and computer science (EECS) and first author on a recent paper describing the work. Saad adds that the speedups are not insignificant: The system can be up to 3,000 times faster than previous approaches.

    SPPL gives fast, exact solutions to probabilistic inference questions such as “How likely is the model to recommend a loan to someone over age 40?” or “Generate 1,000 synthetic loan applicants, all under age 30, whose loans will be approved.” These inference results are based on SPPL programs that encode probabilistic models of what kinds of applicants are likely, a priori, and also how to classify them. Fairness questions that SPPL can answer include “Is there a difference between the probability of recommending a loan to an immigrant and nonimmigrant applicant with the same socioeconomic status?” or “What’s the probability of a hire, given that the candidate is qualified for the job and from an underrepresented group?”

    SPPL is different from most probabilistic programming languages, as SPPL only allows users to write probabilistic programs for which it can automatically deliver exact probabilistic inference results. SPPL also makes it possible for users to check how fast inference will be, and therefore avoid writing slow programs. In contrast, other probabilistic programming languages such as Gen and Pyro allow users to write down probabilistic programs where the only known ways to do inference are approximate — that is, the results include errors whose nature and magnitude can be hard to characterize.

    Error from approximate probabilistic inference is tolerable in many AI applications. But it is undesirable to have inference errors corrupting results in socially impactful applications of AI, such as automated decision-making, and especially in fairness analysis.

    Jean-Baptiste Tristan, associate professor at Boston College and former research scientist at Oracle Labs, who was not involved in the new research, says, “I’ve worked on fairness analysis in academia and in real-world, large-scale industry settings. SPPL offers improved flexibility and trustworthiness over other PPLs on this challenging and important class of problems due to the expressiveness of the language, its precise and simple semantics, and the speed and soundness of the exact symbolic inference engine.”

    SPPL avoids errors by restricting to a carefully designed class of models that still includes a broad class of AI algorithms, including the decision tree classifiers that are widely used for algorithmic decision-making. SPPL works by compiling probabilistic programs into a specialized data structure called a “sum-product expression.” SPPL further builds on the emerging theme of using probabilistic circuits as a representation that enables efficient probabilistic inference. This approach extends prior work on sum-product networks to models and queries expressed via a probabilistic programming language. However, Saad notes that this approach comes with limitations: “SPPL is substantially faster for analyzing the fairness of a decision tree, for example, but it can’t analyze models like neural networks. Other systems can analyze both neural networks and decision trees, but they tend to be slower and give inexact answers.”

    “SPPL shows that exact probabilistic inference is practical, not just theoretically possible, for a broad class of probabilistic programs,” says Vikash Mansinghka, an MIT principal research scientist and senior author on the paper. “In my lab, we’ve seen symbolic inference driving speed and accuracy improvements in other inference tasks that we previously approached via approximate Monte Carlo and deep learning algorithms. We’ve also been applying SPPL to probabilistic programs learned from real-world databases, to quantify the probability of rare events, generate synthetic proxy data given constraints, and automatically screen data for probable anomalies.”

    The new SPPL probabilistic programming language was presented in June at the ACM SIGPLAN International Conference on Programming Language Design and Implementation (PLDI), in a paper that Saad co-authored with MIT EECS Professor Martin Rinard and Mansinghka. SPPL is implemented in Python and is available open source. More

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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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    Contact-aware robot design

    Adequate biomimicry in robotics necessitates a delicate balance between design and control, an integral part of making our machines more like us. Advanced dexterity in humans is wrapped up in a long evolutionary tale of how our fists of fury evolved to accomplish complex tasks. With machines, designing a new robotic manipulator could mean long, manual iteration cycles of designing, fabricating, and evaluating guided by human intuition. 

    Most robotic hands are designed for general purposes, as it’s very tedious to make task-specific hands. Existing methods battle trade-offs between the complexity of designs critical for contact-rich tasks, and the practical constraints of manufacturing, and contact handling. 

    This led researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) to create a new method to computationally optimize the shape and control of a robotic manipulator for a specific task. Their system uses software to manipulate the design, simulate the robot doing a task, and then provide an optimization score to assess the design and control. 

    Such task-driven manipulator optimization has potential for a wide range of applications in manufacturing and warehouse robot systems, where each task needs to be performed repeatedly, but different manipulators would be suitable for individual tasks. 

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    A new method to represent robotic manipulators helps optimize complex and organic shapes for future machines.

    Seeking to test the functionality of the system, the team first created a single robotic finger design to flip over a box on the ground. The fingertip structure, which looked something like Captain Hook’s left hand, was automatically optimized by an algorithm to hook onto the box’s back surface and flip it. They also developed a model for an assembly task, where a two-finger design put a small cube into a larger, movable mount. Since the fingers were two different lengths, they could reach two objects of different sizes, and the larger and flatter surfaces of the fingers helped stably push the object. 

    Traditionally, this joint optimization process consists of using simple, more primitive shapes to approximate each component of a robot design. When creating a three-segment robotic finger, for example, it would likely be approximated by three connected cylinders, where the algorithm optimizes the length and radius to achieve the desired design and shape. While this would simplify the optimization problem, oversimplifying the shape would be limiting for more complex designs, and ultimately complex tasks. 

    To create more involved manipulators, the team’s method used a technique called “cage-based deformation,” which essentially lets the user change or deform the geometry of a shape in real-time.

    Using the software, you’d put something that looks like a cage around the robotic finger, for example. The algorithm can automatically change the cage dimensions to make more sophisticated, natural shapes. The different variations of designs still keep their integrity, so they can be easily fabricated.

    A simulator was developed by the team to simulate the manipulator design and control on a task, which then provides a performance score.

    “Using these simulation tools, we don’t need to evaluate the design by manufacturing and testing it in the real world,” says Jie Xu, MIT PhD student and lead author on a new paper about the research. “In contrast to reinforcement learning algorithms that are popular for manipulation, but are data-inefficient, the proposed cage-based representation and the simulator allows for the use of powerful gradient-based methods. We not only find better solutions, but also find them faster. As a result we can quickly score the design, thus significantly shortening the design cycle.”

    In the future, the team plans to extend the software to optimize the manipulators concurrently for multiple tasks.

    Xu wrote the paper alongside MIT PhD student Tao Chen, MIT graduate student Lara Zlokapa, MIT research scientist Michael Foshey, MIT Professor Wojciech Matusik, Texas A&M University Assistant professor Shinjiro Sueda, and MIT Professor Pulkit Agrawal. They presented the paper virtually at the 2021 Robotic Science and Systems conference last week. The work is supported by the Toyota Research Institute. More