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      in Data Management & Statistics

      Hurricane-resistant construction may be undervalued by billions of dollars annually

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      In Florida, June typically marks the beginning of hurricane season. Preparation for a storm may appear as otherworldly as it is routine: businesses and homes board up windows and doors, bottled water is quick to sell out, and public buildings cease operations to serve as emergency shelters.

      What happens next may be unpredictable. If things take a turn for the worse, myriad homes may be leveled. A 2019 Congressional Budget Office report estimated that hurricane-related wind damage causes $14 billion in losses to the residential sector annually. 

      However, new research led by Ipek Bensu Manav, an MIT graduate student in civil and environmental engineering and research assistant at MIT’s Concrete Sustainability Hub, suggests that the value of mitigating this wind damage through stronger construction methods may be significantly underestimated. 

      In fact, the failure of wind loss models to account for neighborhood texture — the density and configuration of surrounding buildings with respect to a building of interest — may result in an over 80 percent undervaluation of these methods in Florida.

      Methodology

      Hazus, a loss estimation tool developed and currently used by the Federal Emergency Management Agency (FEMA), estimates physical and economic damage to buildings due to wind and windborne debris. However, the tool assumes that all buildings in a neighborhood experience the same wind loading.

      Manav notes that this assumption disregards the complexity of neighborhood texture. Buildings of different shapes and sizes can be arranged in innumerable ways. This arrangement can amplify or reduce the wind load on buildings within the neighborhood. 

      Wind load amplifications and reductions result from effects referred to as tunneling and shielding. Densely built-up areas with grid-like layouts are particularly susceptible to wind tunneling effects. You might have experienced these effects yourself walking down a windy street, such as Main Street in Cambridge, Massachusetts, near the MIT campus, only to turn the corner and feel calmer air.

      To address this, Manav and her team sought to create a hurricane loss model that accounts for neighborhood texture. By combining GIS files, census tract data, and models of wind recurrence and structural performance, the researchers constructed a high-resolution estimate of expected wind-related structural losses, as well as the benefits of mitigation to reduce those losses. 

      The model builds on prior research led by Jacob Roxon, a recent CSHub postdoc and co-author of this paper, who developed an empirical relationship that estimates building-specific wind gusts with information about building layout in a given neighborhood. 

      A challenge the researchers had to overcome was the fact that the building footprints that were available for this estimation have little-to-no information on occupancy and building type.

      Manav addressed this by developing a novel statistical model that assigns occupancy and building types to structures based on characteristics of the census tract in which they are located.

      Analysis and cost perspective

      The researchers then estimated the value of stronger construction in a case study of residential buildings in Florida. This involved modeling the impact of several mitigation measures applied to over 9.3 million housing units spread across 6.9 million buildings.

      A map of effective wind speed ratio in Florida. Orange coloration indicates census tracts where, on average, structures experience amplifications in wind loads beyond what current tools estimate. Blue coloration indicates census tracts where, on average, structures experience reductions in wind loads.

      Image courtesy of the MIT Concrete Sustainability Hub.

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      Texture-related loss implications were found to be higher in census tracts along the coast. This occurs because these areas tend to be more dense and ordered, leading to higher wind load amplifications. Also, these loss implications are particularly high for single-family homes, which are more susceptible to damage and have a higher replacement cost per housing unit.

      “Our results sound the alarm that wind loads are more severe than we think,” says Manav. “That is not even accounting for climate change, which might make hurricanes more frequent and their wind speeds more intense over time.”

      The researchers computed expected losses and benefits statewide for hurricane wind damage and its mitigation. They found that $8.1 billion could be saved per year in a scenario where all homes were mitigated with simple measures such as stronger connections between roofs and walls or tighter nail spacing.

      Conventional loss estimation models value these same measures as saving only $4.4 billion per year. This means that conventional models are underestimating the value of stronger construction by over 80 percent.

      “It is important that the benefits of resilient design be quantified so that financial incentives — whether lending, insurance, or otherwise — can be brought to bear to increase mitigation. Manav’s research will move the industry forward toward justifying these benefits,” says structural engineer Evan Reis, who is the executive director of the U.S. Resiliency Council.

      Further implications

      The paper recommends that coastal states enhance their building codes, especially in densely built-up areas, to save dollars and save lives. Manav notes that current building codes do not sufficiently account for texture-induced load amplifications. 

      “Even a building built to code may not be able to protect you and your family,” says Manav. “We need to properly quantify the benefits of mitigating in areas that are exposed to high winds so we promote the right standards of construction where losses can be catastrophic.”

      A goal of Manav’s work is to provide citizens with the information they need before disaster strikes. She has created an online dashboard where you can preview the potential benefits of applying mitigation measures in different communities — perhaps even your own.

      “During my research, I kept hitting a wall. I found that it was difficult to use publicly available information to piece together the bigger picture,” she comments. “We started developing the dashboard to equip homeowners and stakeholders with accessible and actionable information.”

      As a next step, Manav is investigating socioeconomic consequences of hurricane wind damage. 

      “High-resolution analysis, like our case study, allows us to simulate individual household impacts within a geographical context,” adds Manav. “With this, we can capture how differing availability of financial resources may influence how communities cope with the aftermath of natural hazards.” More

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      in Data Management & Statistics

      Building explainability into the components of machine-learning models

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      Explanation methods that help users understand and trust machine-learning models often describe how much certain features used in the model contribute to its prediction. For example, if a model predicts a patient’s risk of developing cardiac disease, a physician might want to know how strongly the patient’s heart rate data influences that prediction.

      But if those features are so complex or convoluted that the user can’t understand them, does the explanation method do any good?

      MIT researchers are striving to improve the interpretability of features so decision makers will be more comfortable using the outputs of machine-learning models. Drawing on years of field work, they developed a taxonomy to help developers craft features that will be easier for their target audience to understand.

      “We found that out in the real world, even though we were using state-of-the-art ways of explaining machine-learning models, there is still a lot of confusion stemming from the features, not from the model itself,” says Alexandra Zytek, an electrical engineering and computer science PhD student and lead author of a paper introducing the taxonomy.

      To build the taxonomy, the researchers defined properties that make features interpretable for five types of users, from artificial intelligence experts to the people affected by a machine-learning model’s prediction. They also offer instructions for how model creators can transform features into formats that will be easier for a layperson to comprehend.

      They hope their work will inspire model builders to consider using interpretable features from the beginning of the development process, rather than trying to work backward and focus on explainability after the fact.

      MIT co-authors include Dongyu Liu, a postdoc; visiting professor Laure Berti-Équille, research director at IRD; and senior author Kalyan Veeramachaneni, principal research scientist in the Laboratory for Information and Decision Systems (LIDS) and leader of the Data to AI group. They are joined by Ignacio Arnaldo, a principal data scientist at Corelight. The research is published in the June edition of the Association for Computing Machinery Special Interest Group on Knowledge Discovery and Data Mining’s peer-reviewed Explorations Newsletter.

      Real-world lessons

      Features are input variables that are fed to machine-learning models; they are usually drawn from the columns in a dataset. Data scientists typically select and handcraft features for the model, and they mainly focus on ensuring features are developed to improve model accuracy, not on whether a decision-maker can understand them, Veeramachaneni explains.

      For several years, he and his team have worked with decision makers to identify machine-learning usability challenges. These domain experts, most of whom lack machine-learning knowledge, often don’t trust models because they don’t understand the features that influence predictions.

      For one project, they partnered with clinicians in a hospital ICU who used machine learning to predict the risk a patient will face complications after cardiac surgery. Some features were presented as aggregated values, like the trend of a patient’s heart rate over time. While features coded this way were “model ready” (the model could process the data), clinicians didn’t understand how they were computed. They would rather see how these aggregated features relate to original values, so they could identify anomalies in a patient’s heart rate, Liu says.

      By contrast, a group of learning scientists preferred features that were aggregated. Instead of having a feature like “number of posts a student made on discussion forums” they would rather have related features grouped together and labeled with terms they understood, like “participation.”

      “With interpretability, one size doesn’t fit all. When you go from area to area, there are different needs. And interpretability itself has many levels,” Veeramachaneni says.

      The idea that one size doesn’t fit all is key to the researchers’ taxonomy. They define properties that can make features more or less interpretable for different decision makers and outline which properties are likely most important to specific users.

      For instance, machine-learning developers might focus on having features that are compatible with the model and predictive, meaning they are expected to improve the model’s performance.

      On the other hand, decision makers with no machine-learning experience might be better served by features that are human-worded, meaning they are described in a way that is natural for users, and understandable, meaning they refer to real-world metrics users can reason about.

      “The taxonomy says, if you are making interpretable features, to what level are they interpretable? You may not need all levels, depending on the type of domain experts you are working with,” Zytek says.

      Putting interpretability first

      The researchers also outline feature engineering techniques a developer can employ to make features more interpretable for a specific audience.

      Feature engineering is a process in which data scientists transform data into a format machine-learning models can process, using techniques like aggregating data or normalizing values. Most models also can’t process categorical data unless they are converted to a numerical code. These transformations are often nearly impossible for laypeople to unpack.

      Creating interpretable features might involve undoing some of that encoding, Zytek says. For instance, a common feature engineering technique organizes spans of data so they all contain the same number of years. To make these features more interpretable, one could group age ranges using human terms, like infant, toddler, child, and teen. Or rather than using a transformed feature like average pulse rate, an interpretable feature might simply be the actual pulse rate data, Liu adds.

      “In a lot of domains, the tradeoff between interpretable features and model accuracy is actually very small. When we were working with child welfare screeners, for example, we retrained the model using only features that met our definitions for interpretability, and the performance decrease was almost negligible,” Zytek says.

      Building off this work, the researchers are developing a system that enables a model developer to handle complicated feature transformations in a more efficient manner, to create human-centered explanations for machine-learning models. This new system will also convert algorithms designed to explain model-ready datasets into formats that can be understood by decision makers. More

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      in Data Management & Statistics

      Robots play with play dough

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      The inner child in many of us feels an overwhelming sense of joy when stumbling across a pile of the fluorescent, rubbery mixture of water, salt, and flour that put goo on the map: play dough. (Even if this happens rarely in adulthood.)

      While manipulating play dough is fun and easy for 2-year-olds, the shapeless sludge is hard for robots to handle. Machines have become increasingly reliable with rigid objects, but manipulating soft, deformable objects comes with a laundry list of technical challenges, and most importantly, as with most flexible structures, if you move one part, you’re likely affecting everything else. 

      Scientists from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and Stanford University recently let robots take their hand at playing with the modeling compound, but not for nostalgia’s sake. Their new system learns directly from visual inputs to let a robot with a two-fingered gripper see, simulate, and shape doughy objects. “RoboCraft” could reliably plan a robot’s behavior to pinch and release play dough to make various letters, including ones it had never seen. With just 10 minutes of data, the two-finger gripper rivaled human counterparts that teleoperated the machine — performing on-par, and at times even better, on the tested tasks. 

      “Modeling and manipulating objects with high degrees of freedom are essential capabilities for robots to learn how to enable complex industrial and household interaction tasks, like stuffing dumplings, rolling sushi, and making pottery,” says Yunzhu Li, CSAIL PhD student and author on a new paper about RoboCraft. “While there’s been recent advances in manipulating clothes and ropes, we found that objects with high plasticity, like dough or plasticine — despite ubiquity in those household and industrial settings — was a largely underexplored territory. With RoboCraft, we learn the dynamics models directly from high-dimensional sensory data, which offers a promising data-driven avenue for us to perform effective planning.” 

      Play video

      With undefined, smooth material, the whole structure needs to be accounted for before you can do any type of efficient and effective modeling and planning. By turning the images into graphs of little particles, coupled with algorithms, RoboCraft, using a graph neural network as the dynamics model, makes more accurate predictions about the material’s change of shapes. 

      Typically, researchers have used complex physics simulators to model and understand force and dynamics being applied to objects, but RoboCraft simply uses visual data. The inner-workings of the system relies on three parts to shape soft material into, say, an “R.” 

      The first part — perception — is all about learning to “see.” It uses cameras to collect raw, visual sensor data from the environment, which are then turned into little clouds of particles to represent the shapes. A graph-based neural network then uses said particle data to learn to “simulate” the object’s dynamics, or how it moves. Then, algorithms help plan the robot’s behavior so it learns to “shape” a blob of dough, armed with the training data from the many pinches. While the letters are a bit loose, they’re indubitably representative. 

      Besides cutesy shapes, the team is (actually) working on making dumplings from dough and a prepared filling. Right now, with just a two finger gripper, it’s a big ask. RoboCraft would need additional tools (a baker needs multiple tools to cook; so do robots) — a rolling pin, a stamp, and a mold. 

      A more far in the future domain the scientists envision is using RoboCraft for assistance with household tasks and chores, which could be of particular help to the elderly or those with limited mobility. To accomplish this, given the many obstructions that could take place, a much more adaptive representation of the dough or item would be needed, and as well as exploration into what class of models might be suitable to capture the underlying structural systems. 

      “RoboCraft essentially demonstrates that this predictive model can be learned in very data-efficient ways to plan motion. In the long run, we are thinking about using various tools to manipulate materials,” says Li. “If you think about dumpling or dough making, just one gripper wouldn’t be able to solve it. Helping the model understand and accomplish longer-horizon planning tasks, such as, how the dough will deform given the current tool, movements and actions, is a next step for future work.” 

      Li wrote the paper alongside Haochen Shi, Stanford master’s student; Huazhe Xu, Stanford postdoc; Zhiao Huang, PhD student at the University of California at San Diego; and Jiajun Wu, assistant professor at Stanford. They will present the research at the Robotics: Science and Systems conference in New York City. The work is in part supported by the Stanford Institute for Human-Centered AI (HAI), the Samsung Global Research Outreach (GRO) Program, the Toyota Research Institute (TRI), and Amazon, Autodesk, Salesforce, and Bosch. More

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      Researchers release open-source photorealistic simulator for autonomous driving

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      Hyper-realistic virtual worlds have been heralded as the best driving schools for autonomous vehicles (AVs), since they’ve proven fruitful test beds for safely trying out dangerous driving scenarios. Tesla, Waymo, and other self-driving companies all rely heavily on data to enable expensive and proprietary photorealistic simulators, since testing and gathering nuanced I-almost-crashed data usually isn’t the most easy or desirable to recreate. 

      To that end, scientists from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) created “VISTA 2.0,” a data-driven simulation engine where vehicles can learn to drive in the real world and recover from near-crash scenarios. What’s more, all of the code is being open-sourced to the public. 

      “Today, only companies have software like the type of simulation environments and capabilities of VISTA 2.0, and this software is proprietary. With this release, the research community will have access to a powerful new tool for accelerating the research and development of adaptive robust control for autonomous driving,” says MIT Professor and CSAIL Director Daniela Rus, senior author on a paper about the research. 

      Play video

      VISTA is a data-driven, photorealistic simulator for autonomous driving. It can simulate not just live video but LiDAR data and event cameras, and also incorporate other simulated vehicles to model complex driving situations. VISTA is open source and the code can be found below.

      VISTA 2.0 builds off of the team’s previous model, VISTA, and it’s fundamentally different from existing AV simulators since it’s data-driven — meaning it was built and photorealistically rendered from real-world data — thereby enabling direct transfer to reality. While the initial iteration supported only single car lane-following with one camera sensor, achieving high-fidelity data-driven simulation required rethinking the foundations of how different sensors and behavioral interactions can be synthesized. 

      Enter VISTA 2.0: a data-driven system that can simulate complex sensor types and massively interactive scenarios and intersections at scale. With much less data than previous models, the team was able to train autonomous vehicles that could be substantially more robust than those trained on large amounts of real-world data. 

      “This is a massive jump in capabilities of data-driven simulation for autonomous vehicles, as well as the increase of scale and ability to handle greater driving complexity,” says Alexander Amini, CSAIL PhD student and co-lead author on two new papers, together with fellow PhD student Tsun-Hsuan Wang. “VISTA 2.0 demonstrates the ability to simulate sensor data far beyond 2D RGB cameras, but also extremely high dimensional 3D lidars with millions of points, irregularly timed event-based cameras, and even interactive and dynamic scenarios with other vehicles as well.” 

      The team was able to scale the complexity of the interactive driving tasks for things like overtaking, following, and negotiating, including multiagent scenarios in highly photorealistic environments. 

      Training AI models for autonomous vehicles involves hard-to-secure fodder of different varieties of edge cases and strange, dangerous scenarios, because most of our data (thankfully) is just run-of-the-mill, day-to-day driving. Logically, we can’t just crash into other cars just to teach a neural network how to not crash into other cars.

      Recently, there’s been a shift away from more classic, human-designed simulation environments to those built up from real-world data. The latter have immense photorealism, but the former can easily model virtual cameras and lidars. With this paradigm shift, a key question has emerged: Can the richness and complexity of all of the sensors that autonomous vehicles need, such as lidar and event-based cameras that are more sparse, accurately be synthesized? 

      Lidar sensor data is much harder to interpret in a data-driven world — you’re effectively trying to generate brand-new 3D point clouds with millions of points, only from sparse views of the world. To synthesize 3D lidar point clouds, the team used the data that the car collected, projected it into a 3D space coming from the lidar data, and then let a new virtual vehicle drive around locally from where that original vehicle was. Finally, they projected all of that sensory information back into the frame of view of this new virtual vehicle, with the help of neural networks. 

      Together with the simulation of event-based cameras, which operate at speeds greater than thousands of events per second, the simulator was capable of not only simulating this multimodal information, but also doing so all in real time — making it possible to train neural nets offline, but also test online on the car in augmented reality setups for safe evaluations. “The question of if multisensor simulation at this scale of complexity and photorealism was possible in the realm of data-driven simulation was very much an open question,” says Amini. 

      With that, the driving school becomes a party. In the simulation, you can move around, have different types of controllers, simulate different types of events, create interactive scenarios, and just drop in brand new vehicles that weren’t even in the original data. They tested for lane following, lane turning, car following, and more dicey scenarios like static and dynamic overtaking (seeing obstacles and moving around so you don’t collide). With the multi-agency, both real and simulated agents interact, and new agents can be dropped into the scene and controlled any which way. 

      Taking their full-scale car out into the “wild” — a.k.a. Devens, Massachusetts — the team saw  immediate transferability of results, with both failures and successes. They were also able to demonstrate the bodacious, magic word of self-driving car models: “robust.” They showed that AVs, trained entirely in VISTA 2.0, were so robust in the real world that they could handle that elusive tail of challenging failures. 

      Now, one guardrail humans rely on that can’t yet be simulated is human emotion. It’s the friendly wave, nod, or blinker switch of acknowledgement, which are the type of nuances the team wants to implement in future work. 

      “The central algorithm of this research is how we can take a dataset and build a completely synthetic world for learning and autonomy,” says Amini. “It’s a platform that I believe one day could extend in many different axes across robotics. Not just autonomous driving, but many areas that rely on vision and complex behaviors. We’re excited to release VISTA 2.0 to help enable the community to collect their own datasets and convert them into virtual worlds where they can directly simulate their own virtual autonomous vehicles, drive around these virtual terrains, train autonomous vehicles in these worlds, and then can directly transfer them to full-sized, real self-driving cars.” 

      Amini and Wang wrote the paper alongside Zhijian Liu, MIT CSAIL PhD student; Igor Gilitschenski, assistant professor in computer science at the University of Toronto; Wilko Schwarting, AI research scientist and MIT CSAIL PhD ’20; Song Han, associate professor at MIT’s Department of Electrical Engineering and Computer Science; Sertac Karaman, associate professor of aeronautics and astronautics at MIT; and Daniela Rus, MIT professor and CSAIL director. The researchers presented the work at the IEEE International Conference on Robotics and Automation (ICRA) in Philadelphia. 

      This work was supported by the National Science Foundation and Toyota Research Institute. The team acknowledges the support of NVIDIA with the donation of the Drive AGX Pegasus. More

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      Companies use MIT research to identify and respond to supply chain risks

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      In February 2020, MIT professor David Simchi-Levi predicted the future. In an article in Harvard Business Review, he and his colleague warned that the new coronavirus outbreak would throttle supply chains and shutter tens of thousands of businesses across North America and Europe by mid-March.

      For Simchi-Levi, who had developed new models of supply chain resiliency and advised major companies on how to best shield themselves from supply chain woes, the signs of disruption were plain to see. Two years later, the professor of engineering systems at the MIT Schwarzman College of Computing and the Department of Civil and Environmental Engineering, and director of the MIT Data Science Lab has found a “flood of interest” from companies anxious to apply his Risk Exposure Index (REI) research to identify and respond to hidden risks in their own supply chains.

      His work on “stress tests” for critical supply chains and ways to guide global supply chain recovery were included in the 2022 Economic Report of the President presented to the U.S. Congress in April.

      It is rare that data science research can influence policy at the highest levels, Simchi-Levi says, but his models reflect something that business needs now: a new world of continuing global crisis, without relying on historical precedent.

      “What the last two years showed is that you cannot plan just based on what happened last year or the last two years,” Simchi-Levi says.

      He recalled the famous quote, sometimes attributed to hockey great Wayne Gretzsky, that good players don’t skate to where the puck is, but where the puck is going to be. “We are not focusing on the state of the supply chain right now, but what may happen six weeks from now, eight weeks from now, to prepare ourselves today to prevent the problems of the future.”

      Finding hidden risks

      At the heart of REI is a mathematical model of the supply chain that focuses on potential failures at different supply chain nodes — a flood at a supplier’s factory, or a shortage of raw materials at another factory, for instance. By calculating variables such as “time-to-recover” (TTR), which measures how long it will take a particular node to be back at full function, and time-to-survive (TTS), which identifies the maximum duration that the supply chain can match supply with demand after a disruption, the model focuses on the impact of disruption on the supply chain, rather than the cause of disruption.

      Even before the pandemic, catastrophic events such as the 2010 Iceland volcanic eruption and the 2011 Tohoku earthquake and tsunami in Japan were threatening these nodes. “For many years, companies from a variety of industries focused mostly on efficiency, cutting costs as much as possible, using strategies like outsourcing and offshoring,” Simchi-Levi says. “They were very successful doing this, but it has dramatically increased their exposure to risk.”

      Using their model, Simchi-Levi and colleagues began working with Ford Motor Company in 2013 to improve the company’s supply chain resiliency. The partnership uncovered some surprising hidden risks.

      To begin with, the researchers found out that Ford’s “strategic suppliers” — the nodes of the supply chain where the company spent large amount of money each year — had only moderate exposure to risk. Instead, the biggest risk “tended to come from tiny suppliers that provide Ford with components that cost about 10 cents,” says Simchi-Levi.

      The analysis also found that risky suppliers are everywhere across the globe. “There is this idea that if you just move suppliers closer to market, to demand, to North America or to Mexico, you increase the resiliency of your supply chain. That is not supported by our data,” he says.

      Rewards of resiliency

      By creating a virtual representation, or “digital twin,” of the Ford supply chain, the researchers were able to test out strategies at each node to see what would increase supply chain resiliency. Should the company invest in more warehouses to store a key component? Should it shift production of a component to another factory?

      Companies are sometimes reluctant to invest in supply chain resiliency, Simchi-Levi says, but the analysis isn’t just about risk. “It’s also going to help you identify savings opportunities. The company may be building a lot of misplaced, costly inventory, for instance, and our method helps them to identify these inefficiencies and cut costs.”

      Since working with Ford, Simchi-Levi and colleagues have collaborated with many other companies, including a partnership with Accenture, to scale the REI technology to a variety of industries including high-tech, industrial equipment, home improvement retailers, fashion retailers, and consumer packaged goods.

      Annette Clayton, the CEO of Schneider Electric North America and previously its chief supply chain officer, has worked with Simchi-Levi for 17 years. “When I first went to work for Schneider, I asked David and his team to help us look at resiliency and inventory positioning in order to make the best cost, delivery, flexibility, and speed trade-offs for the North American supply chain,” she says. “As the pandemic unfolded, the very learnings in supply chain resiliency we had worked on before became even more important and we partnered with David and his team again,”

      “We have used TTR and TTS to determine places where we need to develop and duplicate supplier capability, from raw materials to assembled parts. We increased inventories where our time-to-recover because of extended logistics times exceeded our time-to-survive,” Clayton adds. “We have used TTR and TTS to prioritize our workload in supplier development, procurement and expanding our own manufacturing capacity.”

      The REI approach can even be applied to an entire country’s economy, as the U.N. Office for Disaster Risk Reduction has done for developing countries such as Thailand in the wake of disastrous flooding in 2011.

      Simchi-Levi and colleagues have been motivated by the pandemic to enhance the REI model with new features. “Because we have started collaborating with more companies, we have realized some interesting, company-specific business constraints,” he says, which are leading to more efficient ways of calculating hidden risk. More

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      New CRISPR-based map ties every human gene to its function

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      The Human Genome Project was an ambitious initiative to sequence every piece of human DNA. The project drew together collaborators from research institutions around the world, including MIT’s Whitehead Institute for Biomedical Research, and was finally completed in 2003. Now, over two decades later, MIT Professor Jonathan Weissman and colleagues have gone beyond the sequence to present the first comprehensive functional map of genes that are expressed in human cells. The data from this project, published online June 9 in Cell, ties each gene to its job in the cell, and is the culmination of years of collaboration on the single-cell sequencing method Perturb-seq.

      The data are available for other scientists to use. “It’s a big resource in the way the human genome is a big resource, in that you can go in and do discovery-based research,” says Weissman, who is also a member of the Whitehead Institute and an investigator with the Howard Hughes Medical Institute. “Rather than defining ahead of time what biology you’re going to be looking at, you have this map of the genotype-phenotype relationships and you can go in and screen the database without having to do any experiments.”

      The screen allowed the researchers to delve into diverse biological questions. They used it to explore the cellular effects of genes with unknown functions, to investigate the response of mitochondria to stress, and to screen for genes that cause chromosomes to be lost or gained, a phenotype that has proved difficult to study in the past. “I think this dataset is going to enable all sorts of analyses that we haven’t even thought up yet by people who come from other parts of biology, and suddenly they just have this available to draw on,” says former Weissman Lab postdoc Tom Norman, a co-senior author of the paper.

      Pioneering Perturb-seq

      The project takes advantage of the Perturb-seq approach that makes it possible to follow the impact of turning on or off genes with unprecedented depth. This method was first published in 2016 by a group of researchers including Weissman and fellow MIT professor Aviv Regev, but could only be used on small sets of genes and at great expense.

      The massive Perturb-seq map was made possible by foundational work from Joseph Replogle, an MD-PhD student in Weissman’s lab and co-first author of the present paper. Replogle, in collaboration with Norman, who now leads a lab at Memorial Sloan Kettering Cancer Center; Britt Adamson, an assistant professor in the Department of Molecular Biology at Princeton University; and a group at 10x Genomics, set out to create a new version of Perturb-seq that could be scaled up. The researchers published a proof-of-concept paper in Nature Biotechnology in 2020. 

      The Perturb-seq method uses CRISPR-Cas9 genome editing to introduce genetic changes into cells, and then uses single-cell RNA sequencing to capture information about the RNAs that are expressed resulting from a given genetic change. Because RNAs control all aspects of how cells behave, this method can help decode the many cellular effects of genetic changes.

      Since their initial proof-of-concept paper, Weissman, Regev, and others have used this sequencing method on smaller scales. For example, the researchers used Perturb-seq in 2021 to explore how human and viral genes interact over the course of an infection with HCMV, a common herpesvirus.

      In the new study, Replogle and collaborators including Reuben Saunders, a graduate student in Weissman’s lab and co-first author of the paper, scaled up the method to the entire genome. Using human blood cancer cell lines as well noncancerous cells derived from the retina, he performed Perturb-seq across more than 2.5 million cells, and used the data to build a comprehensive map tying genotypes to phenotypes.

      Delving into the data

      Upon completing the screen, the researchers decided to put their new dataset to use and examine a few biological questions. “The advantage of Perturb-seq is it lets you get a big dataset in an unbiased way,” says Tom Norman. “No one knows entirely what the limits are of what you can get out of that kind of dataset. Now, the question is, what do you actually do with it?”

      The first, most obvious application was to look into genes with unknown functions. Because the screen also read out phenotypes of many known genes, the researchers could use the data to compare unknown genes to known ones and look for similar transcriptional outcomes, which could suggest the gene products worked together as part of a larger complex.

      The mutation of one gene called C7orf26 in particular stood out. Researchers noticed that genes whose removal led to a similar phenotype were part of a protein complex called Integrator that played a role in creating small nuclear RNAs. The Integrator complex is made up of many smaller subunits — previous studies had suggested 14 individual proteins — and the researchers were able to confirm that C7orf26 made up a 15th component of the complex.

      They also discovered that the 15 subunits worked together in smaller modules to perform specific functions within the Integrator complex. “Absent this thousand-foot-high view of the situation, it was not so clear that these different modules were so functionally distinct,” says Saunders.

      Another perk of Perturb-seq is that because the assay focuses on single cells, the researchers could use the data to look at more complex phenotypes that become muddied when they are studied together with data from other cells. “We often take all the cells where ‘gene X’ is knocked down and average them together to look at how they changed,” Weissman says. “But sometimes when you knock down a gene, different cells that are losing that same gene behave differently, and that behavior may be missed by the average.”

      The researchers found that a subset of genes whose removal led to different outcomes from cell to cell were responsible for chromosome segregation. Their removal was causing cells to lose a chromosome or pick up an extra one, a condition known as aneuploidy. “You couldn’t predict what the transcriptional response to losing this gene was because it depended on the secondary effect of what chromosome you gained or lost,” Weissman says. “We realized we could then turn this around and create this composite phenotype looking for signatures of chromosomes being gained and lost. In this way, we’ve done the first genome-wide screen for factors that are required for the correct segregation of DNA.”

      “I think the aneuploidy study is the most interesting application of this data so far,” Norman says. “It captures a phenotype that you can only get using a single-cell readout. You can’t go after it any other way.”

      The researchers also used their dataset to study how mitochondria responded to stress. Mitochondria, which evolved from free-living bacteria, carry 13 genes in their genomes. Within the nuclear DNA, around 1,000 genes are somehow related to mitochondrial function. “People have been interested for a long time in how nuclear and mitochondrial DNA are coordinated and regulated in different cellular conditions, especially when a cell is stressed,” Replogle says.

      The researchers found that when they perturbed different mitochondria-related genes, the nuclear genome responded similarly to many different genetic changes. However, the mitochondrial genome responses were much more variable. 

      “There’s still an open question of why mitochondria still have their own DNA,” said Replogle. “A big-picture takeaway from our work is that one benefit of having a separate mitochondrial genome might be having localized or very specific genetic regulation in response to different stressors.”

      “If you have one mitochondria that’s broken, and another one that is broken in a different way, those mitochondria could be responding differentially,” Weissman says.

      In the future, the researchers hope to use Perturb-seq on different types of cells besides the cancer cell line they started in. They also hope to continue to explore their map of gene functions, and hope others will do the same. “This really is the culmination of many years of work by the authors and other collaborators, and I’m really pleased to see it continue to succeed and expand,” says Norman. More

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      in Data Management & Statistics

      Hallucinating to better text translation

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      As babies, we babble and imitate our way to learning languages. We don’t start off reading raw text, which requires fundamental knowledge and understanding about the world, as well as the advanced ability to interpret and infer descriptions and relationships. Rather, humans begin our language journey slowly, by pointing and interacting with our environment, basing our words and perceiving their meaning through the context of the physical and social world. Eventually, we can craft full sentences to communicate complex ideas.

      Similarly, when humans begin learning and translating into another language, the incorporation of other sensory information, like multimedia, paired with the new and unfamiliar words, like flashcards with images, improves language acquisition and retention. Then, with enough practice, humans can accurately translate new, unseen sentences in context without the accompanying media; however, imagining a picture based on the original text helps.

      This is the basis of a new machine learning model, called VALHALLA, by researchers from MIT, IBM, and the University of California at San Diego, in which a trained neural network sees a source sentence in one language, hallucinates an image of what it looks like, and then uses both to translate into a target language. The team found that their method demonstrates improved accuracy of machine translation over text-only translation. Further, it provided an additional boost for cases with long sentences, under-resourced languages, and instances where part of the source sentence is inaccessible to the machine translator.

      As a core task within the AI field of natural language processing (NLP), machine translation is an “eminently practical technology that’s being used by millions of people every day,” says study co-author Yoon Kim, assistant professor in MIT’s Department of Electrical Engineering and Computer Science with affiliations in the Computer Science and Artificial Intelligence Laboratory (CSAIL) and the MIT-IBM Watson AI Lab. With recent, significant advances in deep learning, “there’s been an interesting development in how one might use non-text information — for example, images, audio, or other grounding information — to tackle practical tasks involving language” says Kim, because “when humans are performing language processing tasks, we’re doing so within a grounded, situated world.” The pairing of hallucinated images and text during inference, the team postulated, imitates that process, providing context for improved performance over current state-of-the-art techniques, which utilize text-only data.

      This research will be presented at the IEEE / CVF Computer Vision and Pattern Recognition Conference this month. Kim’s co-authors are UC San Diego graduate student Yi Li and Professor Nuno Vasconcelos, along with research staff members Rameswar Panda, Chun-fu “Richard” Chen, Rogerio Feris, and IBM Director David Cox of IBM Research and the MIT-IBM Watson AI Lab.

      Learning to hallucinate from images

      When we learn new languages and to translate, we’re often provided with examples and practice before venturing out on our own. The same is true for machine-translation systems; however, if images are used during training, these AI methods also require visual aids for testing, limiting their applicability, says Panda.

      “In real-world scenarios, you might not have an image with respect to the source sentence. So, our motivation was basically: Instead of using an external image during inference as input, can we use visual hallucination — the ability to imagine visual scenes — to improve machine translation systems?” says Panda.

      To do this, the team used an encoder-decoder architecture with two transformers, a type of neural network model that’s suited for sequence-dependent data, like language, that can pay attention key words and semantics of a sentence. One transformer generates a visual hallucination, and the other performs multimodal translation using outputs from the first transformer.

      During training, there are two streams of translation: a source sentence and a ground-truth image that is paired with it, and the same source sentence that is visually hallucinated to make a text-image pair. First the ground-truth image and sentence are tokenized into representations that can be handled by transformers; for the case of the sentence, each word is a token. The source sentence is tokenized again, but this time passed through the visual hallucination transformer, outputting a hallucination, a discrete image representation of the sentence. The researchers incorporated an autoregression that compares the ground-truth and hallucinated representations for congruency — e.g., homonyms: a reference to an animal “bat” isn’t hallucinated as a baseball bat. The hallucination transformer then uses the difference between them to optimize its predictions and visual output, making sure the context is consistent.

      The two sets of tokens are then simultaneously passed through the multimodal translation transformer, each containing the sentence representation and either the hallucinated or ground-truth image. The tokenized text translation outputs are compared with the goal of being similar to each other and to the target sentence in another language. Any differences are then relayed back to the translation transformer for further optimization.

      For testing, the ground-truth image stream drops off, since images likely wouldn’t be available in everyday scenarios.

      “To the best of our knowledge, we haven’t seen any work which actually uses a hallucination transformer jointly with a multimodal translation system to improve machine translation performance,” says Panda.

      Visualizing the target text

      To test their method, the team put VALHALLA up against other state-of-the-art multimodal and text-only translation methods. They used public benchmark datasets containing ground-truth images with source sentences, and a dataset for translating text-only news articles. The researchers measured its performance over 13 tasks, ranging from translation on well-resourced languages (like English, German, and French), under-resourced languages (like English to Romanian) and non-English (like Spanish to French). The group also tested varying transformer model sizes, how accuracy changes with the sentence length, and translation under limited textual context, where portions of the text were hidden from the machine translators.

      The team observed significant improvements over text-only translation methods, improving data efficiency, and that smaller models performed better than the larger base model. As sentences became longer, VALHALLA’s performance over other methods grew, which the researchers attributed to the addition of more ambiguous words. In cases where part of the sentence was masked, VALHALLA could recover and translate the original text, which the team found surprising.

      Further unexpected findings arose: “Where there weren’t as many training [image and] text pairs, [like for under-resourced languages], improvements were more significant, which indicates that grounding in images helps in low-data regimes,” says Kim. “Another thing that was quite surprising to me was this improved performance, even on types of text that aren’t necessarily easily connectable to images. For example, maybe it’s not so surprising if this helps in translating visually salient sentences, like the ‘there is a red car in front of the house.’ [However], even in text-only [news article] domains, the approach was able to improve upon text-only systems.”

      While VALHALLA performs well, the researchers note that it does have limitations, requiring pairs of sentences to be annotated with an image, which could make it more expensive to obtain. It also performs better in its ground domain and not the text-only news articles. Moreover, Kim and Panda note, a technique like VALHALLA is still a black box, with the assumption that hallucinated images are providing helpful information, and the team plans to investigate what and how the model is learning in order to validate their methods.

      In the future, the team plans to explore other means of improving translation. “Here, we only focus on images, but there are other types of a multimodal information — for example, speech, video or touch, or other sensory modalities,” says Panda. “We believe such multimodal grounding can lead to even more efficient machine translation models, potentially benefiting translation across many low-resource languages spoken in the world.”

      This research was supported, in part, by the MIT-IBM Watson AI Lab and the National Science Foundation. More

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      in Data Management & Statistics

      Making data visualization more accessible for blind and low-vision individuals

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      Data visualizations on the web are largely inaccessible for blind and low-vision individuals who use screen readers, an assistive technology that reads on-screen elements as text-to-speech. This excludes millions of people from the opportunity to probe and interpret insights that are often presented through charts, such as election results, health statistics, and economic indicators. 

      When a designer attempts to make a visualization accessible, best practices call for including a few sentences of text that describe the chart and a link to the underlying data table — a far cry from the rich reading experience available to sighted users.

      An interdisciplinary team of researchers from MIT and elsewhere is striving to create screen-reader-friendly data visualizations that offer a similarly rich experience. They prototyped several visualization structures that provide text descriptions at varying levels of detail, enabling a screen-reader user to drill down from high-level data to more detailed information using just a few keystrokes.

      The MIT team embarked on an iterative co-design process with collaborator Daniel Hajas, a researcher at University College London who works with the Global Disability Innovation Hub and lost his sight at age 16. They collaborated to develop prototypes and ran a detailed user study with blind and low-vision individuals to gather feedback.

      “Researchers might see some connections between problems and be aware of potential solutions, but very often they miss it by a little bit. Insights from people who have the lived experience of a certain specific, measurable problem are really important for a lot of disability-related solutions. I think we found a really nice fit,” says Hajas.

      They created a framework to help designers think systematically about how to develop accessible visualizations. In the future, they plan to use their prototypes and design framework to build a user-friendly tool that could convert visualizations into accessible formats.

      MIT collaborators include co-lead authors and Computer Science and Artificial Intelligence Laboratory (CSAIL) graduate students Jonathan Zong, Crystal Lee, and Alan Lundgard, as well as JiWoong Jang, an undergraduate at Carnegie Mellon University who worked on this project during MIT’s Summer Research Program (MSRP), and senior author Arvind Satyanarayan, assistant professor of computer science who leads the Visualization Group in CSAIL. The research paper, which will be presented at the Eurographics Conference on Visualization, won a best paper honorable mention award.

      “Push what is possible”

      The researchers defined three design dimensions as key to making accessible visualizations: structure, navigation, and description. Structure involves arranging the information into a hierarchy. Navigation refers to how the user moves through different levels of detail. Description is how the information is spoken, including how much information is conveyed.

      Using these design dimensions, they developed several visualization prototypes that emphasized ease-of-navigation for screen-reader users. One prototype, known as multiview, enabled individuals to use the up and down arrows to navigate between different levels of information (like the chart title as the top level, the legend as the second level, etc.), and the right and left arrow keys to cycle through information on the same level (such as adjacent scatterplots). Another prototype, known as target, included the same arrow key navigation but also a drop-down menu of key chart locations so the user could quickly jump to an area of interest.

      “Our goal is not just to work within existing standards to make them serviceable. We really set out to do grounded speculation and imagine where we can push what is possible with these existing standards. We didn’t want to limit ourselves to refitting tools that were designed for images,” says Zong.

      They tested these prototypes and an accessible data table, the existing best practice for accessible visualizations, with 13 blind and visually impaired screen-reader users. They asked users to rate each tool on several criteria, including how easy it was to learn and how easy it was to locate data or answer questions.

      “One thing I thought was really interesting was how much people were constantly testing their own hypotheses or trying to make specific patterns as they moved through the visualization. The implication for navigation is that you want to be able to orient yourself within the visualization so you know where the limits are,” says Lee. “Can you accurately and easily know where the walls are in the room you are exploring?”

      Improved insights

      Users said both prototypes enabled them to more rapidly identify patterns in the data. Scrolling from a high level to deeper levels of information helped them gain insights more easily than when browsing the data table, they said. They also enjoyed faster navigation using the menu in the target prototype.

      But the data table got top marks for ease of use.

      “I expected people to be disappointed with the everyday tools when compared to the new prototypes, but they still clung to the data table a bit, likely because of their familiarity with it. That shows that principles like familiarity, learnability, and usability still matter. No matter how ‘good’ our new invention is, if it is not easy enough to learn, people might stick with an older version,” Hajas says.

      Drawing on these insights, the researchers are refining the prototypes and using them to build a software package that can be used with existing design tools to give visualizations an accessible, navigable structure.

      They also want to explore multimodal solutions. Some study participants used different devices together, like screen readers and braille displays, or data sonification tools that convey information using non-speech audio. How these tools can complement each other when applied to a visualization is still an open question, Zong says.

      In the long-run, they hope their work might lead to careful rethinking of web accessibility standards.

      “There is no one-size-fits-all solution for accessibility. While existing standards don’t presume that, they only offer simple approaches, like data tables and alt text. One of the key benefits of our research contribution is that we are proposing a framework — different preferences and data representations are situated at different points in this design space,” says Lundgard.

      “We have been working hard toward reducing the inequities that screen-reader users face when extracting information from online data visualizations for the past few years. So, we are really appreciative of this work and the knowledge that it adds to the existing literature,” says Ather Sharif, a graduate student who researches accessibility and visualization in the labs of professors Jacob Wobbrock and Katharina Reinecke at the Paul G. Allen School of Computer Science and Engineering of the University of Washington at Seattle, and who was not involved with this work.

      “I like to think of it as a movement where we’re all finally coming together and improving the experiences of a demographic that has been largely ignored, especially when presenting data through visualizations. Kudos to Jonathan, Arvind, and their team for this insightful and timely work! I am looking forward to what’s next,” adds Sharif, who is lead author of several recent papers related to accessible data visualizations.

      Amy Bower, a senior scientist in the Department of Physical Oceanography at the Woods Hole Oceanographic Institution who suffers from a degenerative retinal disease and uses a screen reader extensively in her work as a researcher and also for basic living tasks, found the researchers’ explanations of the importance of co-design to be powerful and compelling.  

      “As a blind scientist, I’m constantly searching for effective tools that will allow me to access the information conveyed in data visualizations. The layered approach taken by these researchers, which provides the option to get the ‘big picture’ from the data as well as drill down into the data points themselves, allows the user to choose how they want to explore the data,” says Bower, who also was not involved with this work. “I think the ability to freely explore the data is necessary not just to learn the ‘story’ that the data are telling, but to allow a blind researcher such as myself to formulate the next questions that need to be tackled to advance understanding in any field of study.”

      This work was supported, in part, by the National Science Foundation.   More