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    Using deep learning to image the Earth’s planetary boundary layer

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    Although the troposphere is often thought of as the closest layer of the atmosphere to the Earth’s surface, the planetary boundary layer (PBL) — the lowest layer of the troposphere — is actually the part that most significantly influences weather near the surface. In the 2018 planetary science decadal survey, the PBL was raised as an important scientific issue that has the potential to enhance storm forecasting and improve climate projections.  

    “The PBL is where the surface interacts with the atmosphere, including exchanges of moisture and heat that help lead to severe weather and a changing climate,” says Adam Milstein, a technical staff member in Lincoln Laboratory’s Applied Space Systems Group. “The PBL is also where humans live, and the turbulent movement of aerosols throughout the PBL is important for air quality that influences human health.” 

    Although vital for studying weather and climate, important features of the PBL, such as its height, are difficult to resolve with current technology. In the past four years, Lincoln Laboratory staff have been studying the PBL, focusing on two different tasks: using machine learning to make 3D-scanned profiles of the atmosphere, and resolving the vertical structure of the atmosphere more clearly in order to better predict droughts.  

    This PBL-focused research effort builds on more than a decade of related work on fast, operational neural network algorithms developed by Lincoln Laboratory for NASA missions. These missions include the Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats (TROPICS) mission as well as Aqua, a satellite that collects data about Earth’s water cycle and observes variables such as ocean temperature, precipitation, and water vapor in the atmosphere. These algorithms retrieve temperature and humidity from the satellite instrument data and have been shown to significantly improve the accuracy and usable global coverage of the observations over previous approaches. For TROPICS, the algorithms help retrieve data that are used to characterize a storm’s rapidly evolving structures in near-real time, and for Aqua, it has helped increase forecasting models, drought monitoring, and fire prediction. 

    These operational algorithms for TROPICS and Aqua are based on classic “shallow” neural networks to maximize speed and simplicity, creating a one-dimensional vertical profile for each spectral measurement collected by the instrument over each location. While this approach has improved observations of the atmosphere down to the surface overall, including the PBL, laboratory staff determined that newer “deep” learning techniques that treat the atmosphere over a region of interest as a three-dimensional image are needed to improve PBL details further.

    “We hypothesized that deep learning and artificial intelligence (AI) techniques could improve on current approaches by incorporating a better statistical representation of 3D temperature and humidity imagery of the atmosphere into the solutions,” Milstein says. “But it took a while to figure out how to create the best dataset — a mix of real and simulated data; we needed to prepare to train these techniques.”

    The team collaborated with Joseph Santanello of the NASA Goddard Space Flight Center and William Blackwell, also of the Applied Space Systems Group, in a recent NASA-funded effort showing that these retrieval algorithms can improve PBL detail, including more accurate determination of the PBL height than the previous state of the art. 

    While improved knowledge of the PBL is broadly useful for increasing understanding of climate and weather, one key application is prediction of droughts. According to a Global Drought Snapshot report released last year, droughts are a pressing planetary issue that the global community needs to address. Lack of humidity near the surface, specifically at the level of the PBL, is the leading indicator of drought. While previous studies using remote-sensing techniques have examined the humidity of soil to determine drought risk, studying the atmosphere can help predict when droughts will happen.  

    In an effort funded by Lincoln Laboratory’s Climate Change Initiative, Milstein, along with laboratory staff member Michael Pieper, are working with scientists at NASA’s Jet Propulsion Laboratory (JPL) to use neural network techniques to improve drought prediction over the continental United States. While the work builds off of existing operational work JPL has done incorporating (in part) the laboratory’s operational “shallow” neural network approach for Aqua, the team believes that this work and the PBL-focused deep learning research work can be combined to further improve the accuracy of drought prediction. 

    “Lincoln Laboratory has been working with NASA for more than a decade on neural network algorithms for estimating temperature and humidity in the atmosphere from space-borne infrared and microwave instruments, including those on the Aqua spacecraft,” Milstein says. “Over that time, we have learned a lot about this problem by working with the science community, including learning about what scientific challenges remain. Our long experience working on this type of remote sensing with NASA scientists, as well as our experience with using neural network techniques, gave us a unique perspective.”

    According to Milstein, the next step for this project is to compare the deep learning results to datasets from the National Oceanic and Atmospheric Administration, NASA, and the Department of Energy collected directly in the PBL using radiosondes, a type of instrument flown on a weather balloon. “These direct measurements can be considered a kind of ‘ground truth’ to quantify the accuracy of the techniques we have developed,” Milstein says.

    This improved neural network approach holds promise to demonstrate drought prediction that can exceed the capabilities of existing indicators, Milstein says, and to be a tool that scientists can rely on for decades to come. More

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      A new dataset of Arctic images will spur artificial intelligence research

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      As the U.S. Coast Guard (USCG) icebreaker Healy takes part in a voyage across the North Pole this summer, it is capturing images of the Arctic to further the study of this rapidly changing region. Lincoln Laboratory researchers installed a camera system aboard the Healy while at port in Seattle before it embarked on a three-month science mission on July 11. The resulting dataset, which will be one of the first of its kind, will be used to develop artificial intelligence tools that can analyze Arctic imagery.

      “This dataset not only can help mariners navigate more safely and operate more efficiently, but also help protect our nation by providing critical maritime domain awareness and an improved understanding of how AI analysis can be brought to bear in this challenging and unique environment,” says Jo Kurucar, a researcher in Lincoln Laboratory’s AI Software Architectures and Algorithms Group, which led this project.

      As the planet warms and sea ice melts, Arctic passages are opening up to more traffic, both to military vessels and ships conducting illegal fishing. These movements may pose national security challenges to the United States. The opening Arctic also leaves questions about how its climate, wildlife, and geography are changing.

      Today, very few imagery datasets of the Arctic exist to study these changes. Overhead images from satellites or aircraft can only provide limited information about the environment. An outward-looking camera attached to a ship can capture more details of the setting and different angles of objects, such as other ships, in the scene. These types of images can then be used to train AI computer-vision tools, which can help the USCG plan naval missions and automate analysis. According to Kurucar, USCG assets in the Arctic are spread thin and can benefit greatly from AI tools, which can act as a force multiplier.

      The Healy is the USCG’s largest and most technologically advanced icebreaker. Given its current mission, it was a fitting candidate to be equipped with a new sensor to gather this dataset. The laboratory research team collaborated with the USCG Research and Development Center to determine the sensor requirements. Together, they developed the Cold Region Imaging and Surveillance Platform (CRISP).

      “Lincoln Laboratory has an excellent relationship with the Coast Guard, especially with the Research and Development Center. Over a decade, we’ve established ties that enabled the deployment of the CRISP system,” says Amna Greaves, the CRISP project lead and an assistant leader in the AI Software Architectures and Algorithms Group. “We have strong ties not only because of the USCG veterans working at the laboratory and in our group, but also because our technology missions are complementary. Today it was deploying infrared sensing in the Arctic; tomorrow it could be operating quadruped robot dogs on a fast-response cutter.”

      The CRISP system comprises a long-wave infrared camera, manufactured by Teledyne FLIR (for forward-looking infrared), that is designed for harsh maritime environments. The camera can stabilize itself during rough seas and image in complete darkness, fog, and glare. It is paired with a GPS-enabled time-synchronized clock and a network video recorder to record both video and still imagery along with GPS-positional data.  

      The camera is mounted at the front of the ship’s fly bridge, and the electronics are housed in a ruggedized rack on the bridge. The system can be operated manually from the bridge or be placed into an autonomous surveillance mode, in which it slowly pans back and forth, recording 15 minutes of video every three hours and a still image once every 15 seconds.

      “The installation of the equipment was a unique and fun experience. As with any good project, our expectations going into the install did not meet reality,” says Michael Emily, the project’s IT systems administrator who traveled to Seattle for the install. Working with the ship’s crew, the laboratory team had to quickly adjust their route for running cables from the camera to the observation station after they discovered that the expected access points weren’t in fact accessible. “We had 100-foot cables made for this project just in case of this type of scenario, which was a good thing because we only had a few inches to spare,” Emily says.

      The CRISP project team plans to publicly release the dataset, anticipated to be about 4 terabytes in size, once the USCG science mission concludes in the fall.

      The goal in releasing the dataset is to enable the wider research community to develop better tools for those operating in the Arctic, especially as this region becomes more navigable. “Collecting and publishing the data allows for faster and greater progress than what we could accomplish on our own,” Kurucar adds. “It also enables the laboratory to engage in more advanced AI applications while others make more incremental advances using the dataset.”

      On top of providing the dataset, the laboratory team plans to provide a baseline object-detection model, from which others can make progress on their own models. More advanced AI applications planned for development are classifiers for specific objects in the scene and the ability to identify and track objects across images.

      Beyond assisting with USCG missions, this project could create an influential dataset for researchers looking to apply AI to data from the Arctic to help combat climate change, says Paul Metzger, who leads the AI Software Architectures and Algorithms Group.

      Metzger adds that the group was honored to be a part of this project and is excited to see the advances that come from applying AI to novel challenges facing the United States: “I’m extremely proud of how our group applies AI to the highest-priority challenges in our nation, from predicting outbreaks of Covid-19 and assisting the U.S. European Command in their support of Ukraine to now employing AI in the Arctic for maritime awareness.”

      Once the dataset is available, it will be free to download on the Lincoln Laboratory dataset website. More

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      System tracks movement of food through global humanitarian supply chain

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      Although more than enough food is produced to feed everyone in the world, as many as 828 million people face hunger today. Poverty, social inequity, climate change, natural disasters, and political conflicts all contribute to inhibiting access to food. For decades, the U.S. Agency for International Development (USAID) Bureau for Humanitarian Assistance (BHA) has been a leader in global food assistance, supplying millions of metric tons of food to recipients worldwide. Alleviating hunger — and the conflict and instability hunger causes — is critical to U.S. national security.

      But BHA is only one player within a large, complex supply chain in which food gets handed off between more than 100 partner organizations before reaching its final destination. Traditionally, the movement of food through the supply chain has been a black-box operation, with stakeholders largely out of the loop about what happens to the food once it leaves their custody. This lack of direct visibility into operations is due to siloed data repositories, insufficient data sharing among stakeholders, and different data formats that operators must manually sort through and standardize. As a result, accurate, real-time information — such as where food shipments are at any given time, which shipments are affected by delays or food recalls, and when shipments have arrived at their final destination — is lacking. A centralized system capable of tracing food along its entire journey, from manufacture through delivery, would enable a more effective humanitarian response to food-aid needs.

      In 2020, a team from MIT Lincoln Laboratory began engaging with BHA to create an intelligent dashboard for their supply-chain operations. This dashboard brings together the expansive food-aid datasets from BHA’s existing systems into a single platform, with tools for visualizing and analyzing the data. When the team started developing the dashboard, they quickly realized the need for considerably more data than BHA had access to.

      “That’s where traceability comes in, with each handoff partner contributing key pieces of information as food moves through the supply chain,” explains Megan Richardson, a researcher in the laboratory’s Humanitarian Assistance and Disaster Relief Systems Group.

      Richardson and the rest of the team have been working with BHA and their partners to scope, build, and implement such an end-to-end traceability system. This system consists of serialized, unique identifiers (IDs) — akin to fingerprints — that are assigned to individual food items at the time they are produced. These individual IDs remain linked to items as they are aggregated along the supply chain, first domestically and then internationally. For example, individually tagged cans of vegetable oil get packaged into cartons; cartons are placed onto pallets and transported via railway and truck to warehouses; pallets are loaded onto shipping containers at U.S. ports; and pallets are unloaded and cartons are unpackaged overseas.

      With a trace

      Today, visibility at the single-item level doesn’t exist. Most suppliers mark pallets with a lot number (a lot is a batch of items produced in the same run), but this is for internal purposes (i.e., to track issues stemming back to their production supply, like over-enriched ingredients or machinery malfunction), not data sharing. So, organizations know which supplier lot a pallet and carton are associated with, but they can’t track the unique history of an individual carton or item within that pallet. As the lots move further downstream toward their final destination, they are often mixed with lots from other productions, and possibly other commodity types altogether, because of space constraints. On the international side, such mixing and the lack of granularity make it difficult to quickly pull commodities out of the supply chain if food safety concerns arise. Current response times can span several months.

      “Commodities are grouped differently at different stages of the supply chain, so it is logical to track them in those groupings where needed,” Richardson says. “Our item-level granularity serves as a form of Rosetta Stone to enable stakeholders to efficiently communicate throughout these stages. We’re trying to enable a way to track not only the movement of commodities, including through their lot information, but also any problems arising independent of lot, like exposure to high humidity levels in a warehouse. Right now, we have no way to associate commodities with histories that may have resulted in an issue.”

      “You can now track your checked luggage across the world and the fish on your dinner plate,” adds Brice MacLaren, also a researcher in the laboratory’s Humanitarian Assistance and Disaster Relief Systems Group. “So, this technology isn’t new, but it’s new to BHA as they evolve their methodology for commodity tracing. The traceability system needs to be versatile, working across a wide variety of operators who take custody of the commodity along the supply chain and fitting into their existing best practices.”

      As food products make their way through the supply chain, operators at each receiving point would be able to scan these IDs via a Lincoln Laboratory-developed mobile application (app) to indicate a product’s current location and transaction status — for example, that it is en route on a particular shipping container or stored in a certain warehouse. This information would get uploaded to a secure traceability server. By scanning a product, operators would also see its history up until that point.   

      Hitting the mark

      At the laboratory, the team tested the feasibility of their traceability technology, exploring different ways to mark and scan items. In their testing, they considered barcodes and radio-frequency identification (RFID) tags and handheld and fixed scanners. Their analysis revealed 2D barcodes (specifically data matrices) and smartphone-based scanners were the most feasible options in terms of how the technology works and how it fits into existing operations and infrastructure.

      “We needed to come up with a solution that would be practical and sustainable in the field,” MacLaren says. “While scanners can automatically read any RFID tags in close proximity as someone is walking by, they can’t discriminate exactly where the tags are coming from. RFID is expensive, and it’s hard to read commodities in bulk. On the other hand, a phone can scan a barcode on a particular box and tell you that code goes with that box. The challenge then becomes figuring out how to present the codes for people to easily scan without significantly interrupting their usual processes for handling and moving commodities.” 

      As the team learned from partner representatives in Kenya and Djibouti, offloading at the ports is a chaotic, fast operation. At manual warehouses, porters fling bags over their shoulders or stack cartons atop their heads any which way they can and run them to a drop point; at bagging terminals, commodities come down a conveyor belt and land this way or that way. With this variability comes several questions: How many barcodes do you need on an item? Where should they be placed? What size should they be? What will they cost? The laboratory team is considering these questions, keeping in mind that the answers will vary depending on the type of commodity; vegetable oil cartons will have different specifications than, say, 50-kilogram bags of wheat or peas.

      Leaving a mark

      Leveraging results from their testing and insights from international partners, the team has been running a traceability pilot evaluating how their proposed system meshes with real-world domestic and international operations. The current pilot features a domestic component in Houston, Texas, and an international component in Ethiopia, and focuses on tracking individual cartons of vegetable oil and identifying damaged cans. The Ethiopian team with Catholic Relief Services recently received a container filled with pallets of uniquely barcoded cartons of vegetable oil cans (in the next pilot, the cans will be barcoded, too). They are now scanning items and collecting data on product damage by using smartphones with the laboratory-developed mobile traceability app on which they were trained. 

      “The partners in Ethiopia are comparing a couple lid types to determine whether some are more resilient than others,” Richardson says. “With the app — which is designed to scan commodities, collect transaction data, and keep history — the partners can take pictures of damaged cans and see if a trend with the lid type emerges.”

      Next, the team will run a series of pilots with the World Food Program (WFP), the world’s largest humanitarian organization. The first pilot will focus on data connectivity and interoperability, and the team will engage with suppliers to directly print barcodes on individual commodities instead of applying barcode labels to packaging, as they did in the initial feasibility testing. The WFP will provide input on which of their operations are best suited for testing the traceability system, considering factors like the network bandwidth of WFP staff and local partners, the commodity types being distributed, and the country context for scanning. The BHA will likely also prioritize locations for system testing.

      “Our goal is to provide an infrastructure to enable as close to real-time data exchange as possible between all parties, given intermittent power and connectivity in these environments,” MacLaren says.

      In subsequent pilots, the team will try to integrate their approach with existing systems that partners rely on for tracking procurements, inventory, and movement of commodities under their custody so that this information is automatically pushed to the traceability server. The team also hopes to add a capability for real-time alerting of statuses, like the departure and arrival of commodities at a port or the exposure of unclaimed commodities to the elements. Real-time alerts would enable stakeholders to more efficiently respond to food-safety events. Currently, partners are forced to take a conservative approach, pulling out more commodities from the supply chain than are actually suspect, to reduce risk of harm. Both BHA and WHP are interested in testing out a food-safety event during one of the pilots to see how the traceability system works in enabling rapid communication response.

      To implement this technology at scale will require some standardization for marking different commodity types as well as give and take among the partners on best practices for handling commodities. It will also require an understanding of country regulations and partner interactions with subcontractors, government entities, and other stakeholders.

      “Within several years, I think it’s possible for BHA to use our system to mark and trace all their food procured in the United States and sent internationally,” MacLaren says.

      Once collected, the trove of traceability data could be harnessed for other purposes, among them analyzing historical trends, predicting future demand, and assessing the carbon footprint of commodity transport. In the future, a similar traceability system could scale for nonfood items, including medical supplies distributed to disaster victims, resources like generators and water trucks localized in emergency-response scenarios, and vaccines administered during pandemics. Several groups at the laboratory are also interested in such a system to track items such as tools deployed in space or equipment people carry through different operational environments.

      “When we first started this program, colleagues were asking why the laboratory was involved in simple tasks like making a dashboard, marking items with barcodes, and using hand scanners,” MacLaren says. “Our impact here isn’t about the technology; it’s about providing a strategy for coordinated food-aid response and successfully implementing that strategy. Most importantly, it’s about people getting fed.” More

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      Communications system achieves fastest laser link from space yet

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      In May 2022, the TeraByte InfraRed Delivery (TBIRD) payload onboard a small CubeSat satellite was launched into orbit 300 miles above Earth’s surface. Since then, TBIRD has delivered terabytes of data at record-breaking rates of up to 100 gigabits per second — 100 times faster than the fastest internet speeds in most cities — via an optical communication link to a ground-based receiver in California. This data rate is more than 1,000 times higher than that of the radio-frequency links traditionally used for satellite communication and the highest ever achieved by a laser link from space to ground. And these record-setting speeds were all made possible by a communications payload roughly the size of a tissue box.

      MIT Lincoln Laboratory conceptualized the TBIRD mission in 2014 as a means of providing unprecedented capability to science missions at low cost. Science instruments in space today routinely generate more data than can be returned to Earth over typical space-to-ground communications links. With small, low-cost space and ground terminals, TBIRD can enable scientists from around the world to fully take advantage of laser communications to downlink all the data they could ever dream of.

      Designed and built at Lincoln Laboratory, the TBIRD communications payload was integrated onto a CubeSat manufactured by Terran Orbital as part of NASA’s Pathfinder Technology Demonstrator program. NASA Ames Research Center established this program to develop a CubeSat bus (the “vehicle” that powers and steers the payload) for bringing science and technology demonstrators into orbit more quickly and inexpensively. Weighing approximately 25 pounds and the size of two stacked cereal boxes, the CubeSat was launched into low-Earth orbit (LEO) aboard Space X’s Transporter-5 rideshare mission from Cape Canaveral Space Force Station in Florida in May 2022. The optical ground station is located in Table Mountain, California, where most weather takes place below the mountain’s summit, making this part of the sky relatively clear for laser communication. This ground station leverages the one-meter telescope and adaptive optics (to correct for distortions caused by atmospheric turbulence) at the NASA Jet Propulsion Laboratory Optical Communications Telescope Laboratory, with Lincoln Laboratory providing the TBIRD-specific ground communications hardware.

      “We’ve demonstrated a higher data rate than ever before in a smaller package than ever before,” says Jade Wang, the laboratory’s program manager for the TBIRD payload and ground communications and assistant leader of the Optical and Quantum Communications Technology Group. “While sending data from space using lasers may sound futuristic, the same technical concept is behind the fiber-optic internet we use every day. The difference is that the laser transmissions are taking place in the open atmosphere, rather than in contained fibers.”

      From radio waves to laser light

      Whether video conferencing, gaming, or streaming movies in high definition, you are using high-data-rate links that run across optical fibers made of glass (or sometimes plastic). About the diameter of a strand of human hair, these fibers are bundled into cables, which transmit data via fast-traveling pulses of light from a laser or other source. Fiber-optic communications are paramount to the internet age, in which large amounts of data must be quickly and reliably distributed across the globe every day.

      For satellites, however, a high-speed internet based on laser communications does not yet exist. Since the beginning of spaceflight in the 1950s, missions have relied on radio frequencies to send data to and from space. Compared to radio waves, the infrared light employed in laser communications has a much higher frequency (or shorter wavelength), which allows more data to be packed into each transmission. Laser communications will enable scientists to send 100 to 1,000 times more data than today’s radio-frequency systems — akin to our terrestrial switch from dial-up to high-speed internet.

      From Earth observation to space exploration, many science missions will benefit from this speedup, especially as instrument capabilities advance to capture larger troves of high-resolution data, experiments involve more remote control, and spacecraft voyage further from Earth into deep space.  

      However, laser-based space communication comes with several engineering challenges. Unlike radio waves, laser light forms a narrow beam. For successful data transmission, this narrow beam must be pointed precisely toward a receiver (e.g., telescope) located on the ground. And though laser light can travel long distances in space, laser beams can be distorted because of atmospheric effects and weather conditions. This distortion causes the beam to experience power loss, which can result in data loss.

      For the past 40 years, Lincoln Laboratory been tackling these and related challenges through various programs. At this point, these challenges have been reliably solved, and laser communications is rapidly becoming widely adopted. Industry has begun a proliferation of LEO cross-links using laser communications, with the intent to enhance the existing terrestrial backbone, as well as to provide a potential internet backbone to serve users in rural locations. Last year, NASA launched the Laser Communications Relay Demonstration (LCRD), a two-way optical communications system based on a laboratory design. In upcoming missions, a laboratory-developed laser communications terminal will be launched to the International Space Station, where the terminal will “talk” to LCRD, and support Artemis II, a crewed program that will fly by the moon in advance of a future crewed lunar landing.

      “With the expanding interest and development in space-based laser communications, Lincoln Laboratory continues to push the envelope of what is possible,” says Wang. “TBIRD heralds a new approach with the potential to further increase data rate capabilities; shrink size, weight, and power; and reduce lasercom mission costs.”

      One way that TBIRD aims to reduce these costs is by utilizing commercial off-the-shelf components originally developed for terrestrial fiber-optic networks. However, terrestrial components are not designed to survive the rigors of space, and their operation can be impacted by atmospheric effects. With TBIRD, the laboratory developed solutions to both challenges.

      Commercial components adapted for space

      The TBIRD payload integrates three key commercial off-the-shelf components: a high-rate optical modem, a large high-speed storage drive, and an optical signal amplifier.

      All these hardware components underwent shock and vibration, thermal-vacuum, and radiation testing to inform how the hardware might fare in space, where it would be subject to powerful forces, extreme temperatures, and high radiation levels. When the team first tested the amplifier through a thermal test simulating the space environment, the fibers melted. As Wang explains, in vacuum, no atmosphere exists, so heat gets trapped and cannot be released by convection. The team worked with the vendor to modify the amplifier to release heat through conduction instead.

      To deal with data loss from atmospheric effects, the laboratory developed its own version of Automatic Repeat Request (ARQ), a protocol for controlling errors in data transmission over a communications link. With ARQ, the receiver (in this case, the ground terminal) alerts the sender (satellite) through a low-rate uplink signal to re-transmit any block of data (frame) that has been lost or damaged.

      “If the signal drops out, data can be re-transmitted, but if done inefficiently — meaning you spend all your time sending repeat data instead of new data — you can lose a lot of throughput,” explains TBIRD system engineer Curt Schieler, a technical staff member in Wang’s group. “With our ARQ protocol, the receiver tells the payload which frames it received correctly, so the payload knows which ones to re-transmit.”

      Another aspect of TBIRD that is new is its lack of a gimbal, a mechanism for pointing the narrow laser beam. Instead, TBIRD relies on a laboratory-developed error-signaling concept for precision body pointing of the spacecraft. Error signals are provided to the CubeSat bus so it knows how exactly to point the body of the entire satellite toward the ground station. Without a gimbal, the payload can be even further miniaturized.

      “We intended to demonstrate a low-cost technology capable of quickly downlinking a large volume of data from LEO to Earth, in support of science missions,” says Wang. “In just a few weeks of operations, we have already accomplished this goal, achieving unprecedented transmission rates of up to 100 gigabits per second. Next, we plan to exercise additional features of the TBIRD system, including increasing rates to 200 gigabits per second, enabling the downlink of more than 2 terabytes of data — equivalent to 1,000 high-definition movies — in a single five-minute pass over a ground station.”

      Lincoln Laboratory developed the TBIRD mission and technology in partnership with NASA Goddard Space Flight Center. More

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      Deep learning with light

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

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

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

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

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

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

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

      Lightening the load

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

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

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

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

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

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

      Trillions per second

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

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

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

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

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

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

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

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

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      Taking a magnifying glass to data center operations

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      When the MIT Lincoln Laboratory Supercomputing Center (LLSC) unveiled its TX-GAIA supercomputer in 2019, it provided the MIT community a powerful new resource for applying artificial intelligence to their research. Anyone at MIT can submit a job to the system, which churns through trillions of operations per second to train models for diverse applications, such as spotting tumors in medical images, discovering new drugs, or modeling climate effects. But with this great power comes the great responsibility of managing and operating it in a sustainable manner — and the team is looking for ways to improve.

      “We have these powerful computational tools that let researchers build intricate models to solve problems, but they can essentially be used as black boxes. What gets lost in there is whether we are actually using the hardware as effectively as we can,” says Siddharth Samsi, a research scientist in the LLSC. 

      To gain insight into this challenge, the LLSC has been collecting detailed data on TX-GAIA usage over the past year. More than a million user jobs later, the team has released the dataset open source to the computing community.

      Their goal is to empower computer scientists and data center operators to better understand avenues for data center optimization — an important task as processing needs continue to grow. They also see potential for leveraging AI in the data center itself, by using the data to develop models for predicting failure points, optimizing job scheduling, and improving energy efficiency. While cloud providers are actively working on optimizing their data centers, they do not often make their data or models available for the broader high-performance computing (HPC) community to leverage. The release of this dataset and associated code seeks to fill this space.

      “Data centers are changing. We have an explosion of hardware platforms, the types of workloads are evolving, and the types of people who are using data centers is changing,” says Vijay Gadepally, a senior researcher at the LLSC. “Until now, there hasn’t been a great way to analyze the impact to data centers. We see this research and dataset as a big step toward coming up with a principled approach to understanding how these variables interact with each other and then applying AI for insights and improvements.”

      Papers describing the dataset and potential applications have been accepted to a number of venues, including the IEEE International Symposium on High-Performance Computer Architecture, the IEEE International Parallel and Distributed Processing Symposium, the Annual Conference of the North American Chapter of the Association for Computational Linguistics, the IEEE High-Performance and Embedded Computing Conference, and International Conference for High Performance Computing, Networking, Storage and Analysis. 

      Workload classification

      Among the world’s TOP500 supercomputers, TX-GAIA combines traditional computing hardware (central processing units, or CPUs) with nearly 900 graphics processing unit (GPU) accelerators. These NVIDIA GPUs are specialized for deep learning, the class of AI that has given rise to speech recognition and computer vision.

      The dataset covers CPU, GPU, and memory usage by job; scheduling logs; and physical monitoring data. Compared to similar datasets, such as those from Google and Microsoft, the LLSC dataset offers “labeled data, a variety of known AI workloads, and more detailed time series data compared with prior datasets. To our knowledge, it’s one of the most comprehensive and fine-grained datasets available,” Gadepally says. 

      Notably, the team collected time-series data at an unprecedented level of detail: 100-millisecond intervals on every GPU and 10-second intervals on every CPU, as the machines processed more than 3,000 known deep-learning jobs. One of the first goals is to use this labeled dataset to characterize the workloads that different types of deep-learning jobs place on the system. This process would extract features that reveal differences in how the hardware processes natural language models versus image classification or materials design models, for example.   

      The team has now launched the MIT Datacenter Challenge to mobilize this research. The challenge invites researchers to use AI techniques to identify with 95 percent accuracy the type of job that was run, using their labeled time-series data as ground truth.

      Such insights could enable data centers to better match a user’s job request with the hardware best suited for it, potentially conserving energy and improving system performance. Classifying workloads could also allow operators to quickly notice discrepancies resulting from hardware failures, inefficient data access patterns, or unauthorized usage.

      Too many choices

      Today, the LLSC offers tools that let users submit their job and select the processors they want to use, “but it’s a lot of guesswork on the part of users,” Samsi says. “Somebody might want to use the latest GPU, but maybe their computation doesn’t actually need it and they could get just as impressive results on CPUs, or lower-powered machines.”

      Professor Devesh Tiwari at Northeastern University is working with the LLSC team to develop techniques that can help users match their workloads to appropriate hardware. Tiwari explains that the emergence of different types of AI accelerators, GPUs, and CPUs has left users suffering from too many choices. Without the right tools to take advantage of this heterogeneity, they are missing out on the benefits: better performance, lower costs, and greater productivity.

      “We are fixing this very capability gap — making users more productive and helping users do science better and faster without worrying about managing heterogeneous hardware,” says Tiwari. “My PhD student, Baolin Li, is building new capabilities and tools to help HPC users leverage heterogeneity near-optimally without user intervention, using techniques grounded in Bayesian optimization and other learning-based optimization methods. But, this is just the beginning. We are looking into ways to introduce heterogeneity in our data centers in a principled approach to help our users achieve the maximum advantage of heterogeneity autonomously and cost-effectively.”

      Workload classification is the first of many problems to be posed through the Datacenter Challenge. Others include developing AI techniques to predict job failures, conserve energy, or create job scheduling approaches that improve data center cooling efficiencies.

      Energy conservation 

      To mobilize research into greener computing, the team is also planning to release an environmental dataset of TX-GAIA operations, containing rack temperature, power consumption, and other relevant data.

      According to the researchers, huge opportunities exist to improve the power efficiency of HPC systems being used for AI processing. As one example, recent work in the LLSC determined that simple hardware tuning, such as limiting the amount of power an individual GPU can draw, could reduce the energy cost of training an AI model by 20 percent, with only modest increases in computing time. “This reduction translates to approximately an entire week’s worth of household energy for a mere three-hour time increase,” Gadepally says.

      They have also been developing techniques to predict model accuracy, so that users can quickly terminate experiments that are unlikely to yield meaningful results, saving energy. The Datacenter Challenge will share relevant data to enable researchers to explore other opportunities to conserve energy.

      The team expects that lessons learned from this research can be applied to the thousands of data centers operated by the U.S. Department of Defense. The U.S. Air Force is a sponsor of this work, which is being conducted under the USAF-MIT AI Accelerator.

      Other collaborators include researchers at MIT Computer Science and Artificial Intelligence Laboratory (CSAIL). Professor Charles Leiserson’s Supertech Research Group is investigating performance-enhancing techniques for parallel computing, and research scientist Neil Thompson is designing studies on ways to nudge data center users toward climate-friendly behavior.

      Samsi presented this work at the inaugural AI for Datacenter Optimization (ADOPT’22) workshop last spring as part of the IEEE International Parallel and Distributed Processing Symposium. The workshop officially introduced their Datacenter Challenge to the HPC community.

      “We hope this research will allow us and others who run supercomputing centers to be more responsive to user needs while also reducing the energy consumption at the center level,” Samsi says. More

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      Cracking the case of Arctic sea ice breakup

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      Despite its below-freezing temperatures, the Arctic is warming twice as fast as the rest of the planet. As Arctic sea ice melts, fewer bright surfaces are available to reflect sunlight back into space. When fractures open in the ice cover, the water underneath gets exposed. Dark, ice-free water absorbs the sun’s energy, heating the ocean and driving further melting — a vicious cycle. This warming in turn melts glacial ice, contributing to rising sea levels.

      Warming climate and rising sea levels endanger the nearly 40 percent of the U.S. population living in coastal areas, the billions of people who depend on the ocean for food and their livelihoods, and species such as polar bears and Artic foxes. Reduced ice coverage is also making the once-impassable region more accessible, opening up new shipping lanes and ports. Interest in using these emerging trans-Arctic routes for product transit, extraction of natural resources (e.g., oil and gas), and military activity is turning an area traditionally marked by low tension and cooperation into one of global geopolitical competition.

      As the Arctic opens up, predicting when and where the sea ice will fracture becomes increasingly important in strategic decision-making. However, huge gaps exist in our understanding of the physical processes contributing to ice breakup. Researchers at MIT Lincoln Laboratory seek to help close these gaps by turning a data-sparse environment into a data-rich one. They envision deploying a distributed set of unattended sensors across the Arctic that will persistently detect and geolocate ice fracturing events. Concurrently, the network will measure various environmental conditions, including water temperature and salinity, wind speed and direction, and ocean currents at different depths. By correlating these fracturing events and environmental conditions, they hope to discover meaningful insights about what is causing the sea ice to break up. Such insights could help predict the future state of Arctic sea ice to inform climate modeling, climate change planning, and policy decision-making at the highest levels.

      “We’re trying to study the relationship between ice cracking, climate change, and heat flow in the ocean,” says Andrew March, an assistant leader of Lincoln Laboratory’s Advanced Undersea Systems and Technology Group. “Do cracks in the ice cause warm water to rise and more ice to melt? Do undersea currents and waves cause cracking? Does cracking cause undersea waves? These are the types of questions we aim to investigate.”

      Arctic access

      In March 2022, Ben Evans and Dave Whelihan, both researchers in March’s group, traveled for 16 hours across three flights to Prudhoe Bay, located on the North Slope of Alaska. From there, they boarded a small specialized aircraft and flew another 90 minutes to a three-and-a-half-mile-long sheet of ice floating 160 nautical miles offshore in the Arctic Ocean. In the weeks before their arrival, the U.S. Navy’s Arctic Submarine Laboratory had transformed this inhospitable ice floe into a temporary operating base called Ice Camp Queenfish, named after the first Sturgeon-class submarine to operate under the ice and the fourth to reach the North Pole. The ice camp featured a 2,500-foot-long runway, a command center, sleeping quarters to accommodate up to 60 personnel, a dining tent, and an extremely limited internet connection.

      At Queenfish, for the next four days, Evans and Whelihan joined U.S. Navy, Army, Air Force, Marine Corps, and Coast Guard members, and members of the Royal Canadian Air Force and Navy and United Kingdom Royal Navy, who were participating in Ice Exercise (ICEX) 2022. Over the course of about three weeks, more than 200 personnel stationed at Queenfish, Prudhoe Bay, and aboard two U.S. Navy submarines participated in this biennial exercise. The goals of ICEX 2022 were to assess U.S. operational readiness in the Arctic; increase our country’s experience in the region; advance our understanding of the Arctic environment; and continue building relationships with other services, allies, and partner organizations to ensure a free and peaceful Arctic. The infrastructure provided for ICEX concurrently enables scientists to conduct research in an environment — either in person or by sending their research equipment for exercise organizers to deploy on their behalf — that would be otherwise extremely difficult and expensive to access.

      In the Arctic, windchill temperatures can plummet to as low as 60 degrees Fahrenheit below zero, cold enough to freeze exposed skin within minutes. Winds and ocean currents can drift the entire camp beyond the reach of nearby emergency rescue aircraft, and the ice can crack at any moment. To ensure the safety of participants, a team of Navy meteorological specialists continually monitors the ever-changing conditions. The original camp location for ICEX 2022 had to be evacuated and relocated after a massive crack formed in the ice, delaying Evans’ and Whelihan’s trip. Even the newly selected site had a large crack form behind the camp and another crack that necessitated moving a number of tents.

      “Such cracking events are only going to increase as the climate warms, so it’s more critical now than ever to understand the physical processes behind them,” Whelihan says. “Such an understanding will require building technology that can persist in the environment despite these incredibly harsh conditions. So, it’s a challenge not only from a scientific perspective but also an engineering one.”

      “The weather always gets a vote, dictating what you’re able to do out here,” adds Evans. “The Arctic Submarine Laboratory does a lot of work to construct the camp and make it a safe environment where researchers like us can come to do good science. ICEX is really the only opportunity we have to go onto the sea ice in a place this remote to collect data.”

      A legacy of sea ice experiments

      Though this trip was Whelihan’s and Evans’ first to the Arctic region, staff from the laboratory’s Advanced Undersea Systems and Technology Group have been conducting experiments at ICEX since 2018. However, because of the Arctic’s remote location and extreme conditions, data collection has rarely been continuous over long periods of time or widespread across large areas. The team now hopes to change that by building low-cost, expendable sensing platforms consisting of co-located devices that can be left unattended for automated, persistent, near-real-time monitoring. 

      “The laboratory’s extensive expertise in rapid prototyping, seismo-acoustic signal processing, remote sensing, and oceanography make us a natural fit to build this sensor network,” says Evans.

      In the months leading up to the Arctic trip, the team collected seismometer data at Firepond, part of the laboratory’s Haystack Observatory site in Westford, Massachusetts. Through this local data collection, they aimed to gain a sense of what anthropogenic (human-induced) noise would look like so they could begin to anticipate the kinds of signatures they might see in the Arctic. They also collected ice melting/fracturing data during a thaw cycle and correlated these data with the weather conditions (air temperature, humidity, and pressure). Through this analysis, they detected an increase in seismic signals as the temperature rose above 32 F — an indication that air temperature and ice cracking may be related.

      A sensing network

      At ICEX, the team deployed various commercial off-the-shelf sensors and new sensors developed by the laboratory and University of New Hampshire (UNH) to assess their resiliency in the frigid environment and to collect an initial dataset.

      “One aspect that differentiates these experiments from those of the past is that we concurrently collected seismo-acoustic data and environmental parameters,” says Evans.

      The commercial technologies were seismometers to detect the vibrational energy released when sea ice fractures or collides with other ice floes; a hydrophone (underwater microphone) array to record the acoustic energy created by ice-fracturing events; a sound speed profiler to measure the speed of sound through the water column; and a conductivity, temperature, and depth (CTD) profiler to measure the salinity (related to conductivity), temperature, and pressure (related to depth) throughout the water column. The speed of sound in the ocean primarily depends on these three quantities. 

      To precisely measure the temperature across the entire water column at one location, they deployed an array of transistor-based temperature sensors developed by the laboratory’s Advanced Materials and Microsystems Group in collaboration with the Advanced Functional Fabrics of America Manufacturing Innovation Institute. The small temperature sensors run along the length of a thread-like polymer fiber embedded with multiple conductors. This fiber platform, which can support a broad range of sensors, can be unspooled hundreds of feet below the water’s surface to concurrently measure temperature or other water properties — the fiber deployed in the Arctic also contained accelerometers to measure depth — at many points in the water column. Traditionally, temperature profiling has required moving a device up and down through the water column.

      The team also deployed a high-frequency echosounder supplied by Anthony Lyons and Larry Mayer, collaborators at UNH’s Center for Coastal and Ocean Mapping. This active sonar uses acoustic energy to detect internal waves, or waves occurring beneath the ocean’s surface.

      “You may think of the ocean as a homogenous body of water, but it’s not,” Evans explains. “Different currents can exist as you go down in depth, much like how you can get different winds when you go up in altitude. The UNH echosounder allows us to see the different currents in the water column, as well as ice roughness when we turn the sensor to look upward.”

      “The reason we care about currents is that we believe they will tell us something about how warmer water from the Atlantic Ocean is coming into contact with sea ice,” adds Whelihan. “Not only is that water melting ice but it also has lower salt content, resulting in oceanic layers and affecting how long ice lasts and where it lasts.”

      Back home, the team has begun analyzing their data. For the seismic data, this analysis involves distinguishing any ice events from various sources of anthropogenic noise, including generators, snowmobiles, footsteps, and aircraft. Similarly, the researchers know their hydrophone array acoustic data are contaminated by energy from a sound source that another research team participating in ICEX placed in the water. Based on their physics, icequakes — the seismic events that occur when ice cracks — have characteristic signatures that can be used to identify them. One approach is to manually find an icequake and use that signature as a guide for finding other icequakes in the dataset.

      From their water column profiling sensors, they identified an interesting evolution in the sound speed profile 30 to 40 meters below the ocean surface, related to a mass of colder water moving in later in the day. The group’s physical oceanographer believes this change in the profile is due to water coming up from the Bering Sea, water that initially comes from the Atlantic Ocean. The UNH-supplied echosounder also generated an interesting signal at a similar depth.

      “Our supposition is that this result has something to do with the large sound speed variation we detected, either directly because of reflections off that layer or because of plankton, which tend to rise on top of that layer,” explains Evans.  

      A future predictive capability

      Going forward, the team will continue mining their collected data and use these data to begin building algorithms capable of automatically detecting and localizing — and ultimately predicting — ice events correlated with changes in environmental conditions. To complement their experimental data, they have initiated conversations with organizations that model the physical behavior of sea ice, including the National Oceanic and Atmospheric Administration and the National Ice Center. Merging the laboratory’s expertise in sensor design and signal processing with their expertise in ice physics would provide a more complete understanding of how the Arctic is changing.

      The laboratory team will also start exploring cost-effective engineering approaches for integrating the sensors into packages hardened for deployment in the harsh environment of the Arctic.

      “Until these sensors are truly unattended, the human factor of usability is front and center,” says Whelihan. “Because it’s so cold, equipment can break accidentally. For example, at ICEX 2022, our waterproof enclosure for the seismometers survived, but the enclosure for its power supply, which was made out of a cheaper plastic, shattered in my hand when I went to pick it up.”

      The sensor packages will not only need to withstand the frigid environment but also be able to “phone home” over some sort of satellite data link and sustain their power. The team plans to investigate whether waste heat from processing can keep the instruments warm and how energy could be harvested from the Arctic environment.

      Before the next ICEX scheduled for 2024, they hope to perform preliminary testing of their sensor packages and concepts in Arctic-like environments. While attending ICEX 2022, they engaged with several other attendees — including the U.S. Navy, Arctic Submarine Laboratory, National Ice Center, and University of Alaska Fairbanks (UAF) — and identified cold room experimentation as one area of potential collaboration. Testing can also be performed at outdoor locations a bit closer to home and more easily accessible, such as the Great Lakes in Michigan and a UAF-maintained site in Barrow, Alaska. In the future, the laboratory team may have an opportunity to accompany U.S. Coast Guard personnel on ice-breaking vessels traveling from Alaska to Greenland. The team is also thinking about possible venues for collecting data far removed from human noise sources.

      “Since I’ve told colleagues, friends, and family I was going to the Arctic, I’ve had a lot of interesting conversations about climate change and what we’re doing there and why we’re doing it,” Whelihan says. “People don’t have an intrinsic, automatic understanding of this environment and its impact because it’s so far removed from us. But the Arctic plays a crucial role in helping to keep the global climate in balance, so it’s imperative we understand the processes leading to sea ice fractures.”

      This work is funded through Lincoln Laboratory’s internally administered R&D portfolio on climate. More

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      Zero-trust architecture may hold the answer to cybersecurity insider threats

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      For years, organizations have taken a defensive “castle-and-moat” approach to cybersecurity, seeking to secure the perimeters of their networks to block out any malicious actors. Individuals with the right credentials were assumed to be trustworthy and allowed access to a network’s systems and data without having to reauthorize themselves at each access attempt. However, organizations today increasingly store data in the cloud and allow employees to connect to the network remotely, both of which create vulnerabilities to this traditional approach. A more secure future may require a “zero-trust architecture,” in which users must prove their authenticity each time they access a network application or data.

      In May 2021, President Joe Biden’s Executive Order on Improving the Nation’s Cybersecurity outlined a goal for federal agencies to implement zero-trust security. Since then, MIT Lincoln Laboratory has been performing a study on zero-trust architectures, with the goals of reviewing their implementation in government and industry, identifying technical gaps and opportunities, and developing a set of recommendations for the United States’ approach to a zero-trust system.

      The study team’s first step was to define the term “zero trust” and understand the misperceptions in the field surrounding the concept. Some of these misperceptions suggest that a zero-trust architecture requires entirely new equipment to implement, or that it makes systems so “locked down” they’re not usable. 

      “Part of the reason why there is a lot of confusion about what zero trust is, is because it takes what the cybersecurity world has known about for many years and applies it in a different way,” says Jeffrey Gottschalk, the assistant head of Lincoln Laboratory’s Cyber Security and Information Sciences Division and study’s co-lead. “It is a paradigm shift in terms of how to think about security, but holistically it takes a lot of things that we already know how to do — such as multi-factor authentication, encryption, and software-defined networking­ — and combines them in different ways.”

      Play video

      Presentation: Overview of Zero Trust Architectures

      Recent high-profile cybersecurity incidents — such as those involving the National Security Agency, the U.S. Office of Personnel Management, Colonial Pipeline, SolarWinds, and Sony Pictures — highlight the vulnerability of systems and the need to rethink cybersecurity approaches.

      The study team reviewed recent, impactful cybersecurity incidents to identify which security principles were most responsible for the scale and impact of the attack. “We noticed that while a number of these attacks exploited previously unknown implementation vulnerabilities (also known as ‘zero-days’), the vast majority actually were due to the exploitation of operational security principles,” says Christopher Roeser, study co-lead and the assistant head of the Homeland Protection and Air Traffic Control Division, “that is, the gaining of individuals’ credentials, and the movement within a well-connected network that allows users to gather a significant amount of information or have very widespread effects.”

      In other words, the malicious actor had “breached the moat” and effectively became an insider.

      Zero-trust security principles could protect against this type of insider threat by treating every component, service, and user of a system as continuously exposed to and potentially compromised by a malicious actor. A user’s identity is verified each time that they request to access a new resource, and every access is mediated, logged, and analyzed. It’s like putting trip wires all over the inside of a network system, says Gottschalk. “So, when an adversary trips over that trip wire, you’ll get a signal and can validate that signal and see what’s going on.”

      In practice, a zero-trust approach could look like replacing a single-sign-on system, which lets users sign in just once for access to multiple applications, with a cloud-based identity that is known and verified. “Today, a lot of organizations have different ways that people authenticate and log onto systems, and many of those have been aggregated for expediency into single-sign-on capabilities, just to make it easier for people to log onto their systems. But we envision a future state that embraces zero trust, where identity verification is enabled by cloud-based identity that’s portable and ubiquitous, and very secure itself.”

      While conducting their study, the team spoke to approximately 10 companies and government organizations that have adopted zero-trust implementations — either through cloud services, in-house management, or a combination of both. They found the hybrid approach to be a good model for government organizations to adopt. They also found that the implementation could take from three to five years. “We talked to organizations that have actually done implementations of zero trust, and all of them have indicated that significant organizational commitment and change was required to be able to implement them,” Gottschalk says.

      But a key takeaway from the study is that there isn’t a one-size-fits-all approach to zero trust. “It’s why we think that having test-bed and pilot efforts are going to be very important to balance out zero-trust security with the mission needs of those systems,” Gottschalk says. The team also recognizes the importance of conducting ongoing research and development beyond initial zero-trust implementations, to continue to address evolving threats.

      Lincoln Laboratory will present further findings from the study at its upcoming Cyber Technology for National Security conference, which will be held June 28-29. The conference will also offer a short course for attendees to learn more about the benefits and implementations of zero-trust architectures.  More

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      MIT announces five flagship projects in first-ever Climate Grand Challenges competition

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      MIT today announced the five flagship projects selected in its first-ever Climate Grand Challenges competition. These multiyear projects will define a dynamic research agenda focused on unraveling some of the toughest unsolved climate problems and bringing high-impact, science-based solutions to the world on an accelerated basis.

      Representing the most promising concepts to emerge from the two-year competition, the five flagship projects will receive additional funding and resources from MIT and others to develop their ideas and swiftly transform them into practical solutions at scale.

      “Climate Grand Challenges represents a whole-of-MIT drive to develop game-changing advances to confront the escalating climate crisis, in time to make a difference,” says MIT President L. Rafael Reif. “We are inspired by the creativity and boldness of the flagship ideas and by their potential to make a significant contribution to the global climate response. But given the planet-wide scale of the challenge, success depends on partnership. We are eager to work with visionary leaders in every sector to accelerate this impact-oriented research, implement serious solutions at scale, and inspire others to join us in confronting this urgent challenge for humankind.”

      Brief descriptions of the five Climate Grand Challenges flagship projects are provided below.

      Bringing Computation to the Climate Challenge

      This project leverages advances in artificial intelligence, machine learning, and data sciences to improve the accuracy of climate models and make them more useful to a variety of stakeholders — from communities to industry. The team is developing a digital twin of the Earth that harnesses more data than ever before to reduce and quantify uncertainties in climate projections.

      Research leads: Raffaele Ferrari, the Cecil and Ida Green Professor of Oceanography in the Department of Earth, Atmospheric and Planetary Sciences, and director of the Program in Atmospheres, Oceans, and Climate; and Noelle Eckley Selin, director of the Technology and Policy Program and professor with a joint appointment in the Institute for Data, Systems, and Society and the Department of Earth, Atmospheric and Planetary Sciences

      Center for Electrification and Decarbonization of Industry

      This project seeks to reinvent and electrify the processes and materials behind hard-to-decarbonize industries like steel, cement, ammonia, and ethylene production. A new innovation hub will perform targeted fundamental research and engineering with urgency, pushing the technological envelope on electricity-driven chemical transformations.

      Research leads: Yet-Ming Chiang, the Kyocera Professor of Materials Science and Engineering, and Bilge Yıldız, the Breene M. Kerr Professor in the Department of Nuclear Science and Engineering and professor in the Department of Materials Science and Engineering

      Preparing for a new world of weather and climate extremes

      This project addresses key gaps in knowledge about intensifying extreme events such as floods, hurricanes, and heat waves, and quantifies their long-term risk in a changing climate. The team is developing a scalable climate-change adaptation toolkit to help vulnerable communities and low-carbon energy providers prepare for these extreme weather events.

      Research leads: Kerry Emanuel, the Cecil and Ida Green Professor of Atmospheric Science in the Department of Earth, Atmospheric and Planetary Sciences and co-director of the MIT Lorenz Center; Miho Mazereeuw, associate professor of architecture and urbanism in the Department of Architecture and director of the Urban Risk Lab; and Paul O’Gorman, professor in the Program in Atmospheres, Oceans, and Climate in the Department of Earth, Atmospheric and Planetary Sciences

      The Climate Resilience Early Warning System

      The CREWSnet project seeks to reinvent climate change adaptation with a novel forecasting system that empowers underserved communities to interpret local climate risk, proactively plan for their futures incorporating resilience strategies, and minimize losses. CREWSnet will initially be demonstrated in southwestern Bangladesh, serving as a model for similarly threatened regions around the world.

      Research leads: John Aldridge, assistant leader of the Humanitarian Assistance and Disaster Relief Systems Group at MIT Lincoln Laboratory, and Elfatih Eltahir, the H.M. King Bhumibol Professor of Hydrology and Climate in the Department of Civil and Environmental Engineering

      Revolutionizing agriculture with low-emissions, resilient crops

      This project works to revolutionize the agricultural sector with climate-resilient crops and fertilizers that have the ability to dramatically reduce greenhouse gas emissions from food production.

      Research lead: Christopher Voigt, the Daniel I.C. Wang Professor in the Department of Biological Engineering

      “As one of the world’s leading institutions of research and innovation, it is incumbent upon MIT to draw on our depth of knowledge, ingenuity, and ambition to tackle the hard climate problems now confronting the world,” says Richard Lester, MIT associate provost for international activities. “Together with collaborators across industry, finance, community, and government, the Climate Grand Challenges teams are looking to develop and implement high-impact, path-breaking climate solutions rapidly and at a grand scale.”

      The initial call for ideas in 2020 yielded nearly 100 letters of interest from almost 400 faculty members and senior researchers, representing 90 percent of MIT departments. After an extensive evaluation, 27 finalist teams received a total of $2.7 million to develop comprehensive research and innovation plans. The projects address four broad research themes:

      To select the winning projects, research plans were reviewed by panels of international experts representing relevant scientific and technical domains as well as experts in processes and policies for innovation and scalability.

      “In response to climate change, the world really needs to do two things quickly: deploy the solutions we already have much more widely, and develop new solutions that are urgently needed to tackle this intensifying threat,” says Maria Zuber, MIT vice president for research. “These five flagship projects exemplify MIT’s strong determination to bring its knowledge and expertise to bear in generating new ideas and solutions that will help solve the climate problem.”

      “The Climate Grand Challenges flagship projects set a new standard for inclusive climate solutions that can be adapted and implemented across the globe,” says MIT Chancellor Melissa Nobles. “This competition propels the entire MIT research community — faculty, students, postdocs, and staff — to act with urgency around a worsening climate crisis, and I look forward to seeing the difference these projects can make.”

      “MIT’s efforts on climate research amid the climate crisis was a primary reason that I chose to attend MIT, and remains a reason that I view the Institute favorably. MIT has a clear opportunity to be a thought leader in the climate space in our own MIT way, which is why CGC fits in so well,” says senior Megan Xu, who served on the Climate Grand Challenges student committee and is studying ways to make the food system more sustainable.

      The Climate Grand Challenges competition is a key initiative of “Fast Forward: MIT’s Climate Action Plan for the Decade,” which the Institute published in May 2021. Fast Forward outlines MIT’s comprehensive plan for helping the world address the climate crisis. It consists of five broad areas of action: sparking innovation, educating future generations, informing and leveraging government action, reducing MIT’s own climate impact, and uniting and coordinating all of MIT’s climate efforts. More