Observations suggest a major melting event at the Ross Ice Shelf was connected to atmospheric turbulence.
The Ross Ice Shelf in Antarctica typically melts on its underside as warmer ocean water flows beneath. But in January 2016, an unusual melting episode occurred on its topside.
A team from the Massachusetts Institute of Technology (MIT) Haystack Observatory used data from existing GNSS stations, in conjunction with 13 stations installed on shelf, to examine the turbulent state of the atmosphere. Key were delay differences at each station and between stations that showed the strength (or rockiness) of atmospheric turbulence over the ice shelf.
Wind, water vapor, and temperature variations drawn in by warm and humid air caused the surface to melt, with turbulence four times greater than usual during the 2016 surface melting event.
The study also demonstrated a novel application of the GNSS station data to remotely observe unusual atmospheric conditions.
The open-access study was published Feb. 27 in Geophysical Research Letters.
Contact tracing can help stem the spread of the COVID-19 pandemic. It involves tracking the movement and interactions of infected individuals to identify others at risk.
National and regional responses to the COVID-19 pandemic have included containment through quarantine and restriction of movement. When properly implemented, these solutions limit spread of the contagion to prevent it from overwhelming healthcare and emergency management systems.
According to the World Health Organization, the Centers for Disease Control and Prevention, and virtually all medical professionals, any effective strategy to return the world to normal requires three components: testing, contact tracing and isolation.
While testing to find the people who are infected is the absolute top priority, contact tracing is vital for stopping a disease from spreading out of control. It involves tracking the movement and interactions of infected individuals to identify others at risk. Any positive test without contact tracing is bad public health — it misses an opportunity to reduce the spread of the virus.
While the concept of contact tracing has just entered popular consciousness, it has been a standard public health tool for a century. For example, in the 1930s, Great Britain used it to contain the incidence of sexually transmitted infections. In the 1960s and 1970s, South American, African and Asian countries used it to eradicate smallpox. Additional diseases for which contact tracing is performed include tuberculosis, measles, HIV, Ebola, bloodborne infections, serious bacterial infections and novel infections.
What Is Contact Tracing?
The World Health Organization describes three basic steps:
Contact identification. Those who have been in contact with someone who has been confirmed to be infected are identified, by asking about their activities and those of the people around them.
Contact listing. All persons who have had contact with the infected person are informed of their status and told to receive early care if they develop symptoms.
Follow-up. Contacts are monitored for symptoms and tested for signs of infection.
In some cases, quarantine or isolation is required for high-risk contacts.
The enormous dimensions of the current pandemic, however, challenge traditional models of contact tracing, which are very resource intensive. In search of a technological assist, several Asian countries already have been taking advantage of the functionalities of smartphones to scale up contact tracing to match the pandemic’s rate of growth, such as the Trace Together app built by the Singapore government. Companies and organizations around the world are following suit, including Britain’s National Health Service, a pan-European initiative, and an unprecedented joint venture by Apple and Google.
Automating Contact Tracing
A study published on March 31 in Science concluded that “viral spread is too fast to be contained by manual contact tracing but could be controlled if this process [were] faster, more efficient and happened at scale.” A contact-tracing app that memorizes close contacts and immediately notifies users if they have had contact with infected individuals, prompting them to self-isolate, could control the pandemic without need for mass quarantines if enough people used it, the study argues.
Privacy versus Protection. A similar app has been deployed in China, where people are required to use it to be allowed to move beyond their neighborhood, enter public spaces, or use public transport. A central database collects data on each user’s movement and coronavirus diagnosis, artificial intelligence analyzes these data, then the app displays a red, amber or green code that determines the user’s freedom of movement. This app has been credited with significantly helping China suppress the pandemic, but has been criticized for its disregard for data protection and privacy.
Relying on fundamental epidemiological principles and common smartphone functionality, the Science study authors designed a simple algorithm to replace manual contact tracing. “Coronavirus diagnoses are communicated to the server, enabling recommendation of risk-stratified quarantine and physical distancing measures in those now known to be possible contacts, while preserving the anonymity of the infected individual.” Symptomatic individuals could use the app to request testing, and everyone could use it to access COVID-19-related health services, information and instructions, or even to request deliveries of food or medicine during self-isolation.
Public trust in the app and how the gathered data are used would be critical to its success. The study’s authors lay out a series of requirements for its ethical implementation, then point out that “the algorithmic approach we propose avoids the need for coercive surveillance, since the system can have very large impacts and achieve sustained epidemic suppression, even with partial uptake.”
The authors of a similar article in the journal JMIR mHealth and uHealth write that a contact-tracing system can limit any central coordination to notifying users who have been in contact with an infected person. Their core idea is that it does not matter where someone contacts an infected person, only that they were in close enough contact to risk infection. Particularly sensitive location data, such as GPS or phone cell data, “is actually neither necessary nor useful.” No one learns who the user is because the app is not linked to an identity, and it neither records nor stores location data.
The authors argue their proposed app is the most effective epidemiologically because it would determine which people were in close proximity, and it would receive user cooperation. “Only if people trust a system — because it does not spy on them — will the system find broad support in the population.”
GPS, Bluetooth or Both?
Technologically, the concepts of location and proximity are embodied in two standard smartphone components: GPS receivers and Bluetooth transceivers. GPS-derived location data makes it possible to map and analyze the movements of individuals and of large numbers of people; for example, it is how we know that many in this country have begun relaxing social distancing rules ahead of the lifting of legal mandates. Bluetooth’s use of low energy, generally a drawback, becomes an advantage here because it can tell us whether two or more people have been within 1.5 to 2 meters of an infected person for at least 10 to 15 minutes — when the risk of infection is highest. This knowledge can enable newly infected, pre-symptomatic people to self-isolate and not infect others.
Numerous companies are developing and proposing smartphone-based contact tracing apps.
Vesedia Mobile Technologies proposes that people who test positive to COVID-19 be asked to provide information about public places they visited in the preceding days and at what times, using their phone location history for verification. The information would be anonymized by healthcare officials, and entered into a database that would be publicly accessible via a website and mobile app.
Ramesh Raskar. (Photo: MIT/John Werner)
Intersections. The COVID Safe Paths phone app and the Safe Places browser tool for contact tracers were created by Ramesh Raskar and other researchers at the MIT Media Lab. If a user tests positive and consents, his or her data is uploaded, redacted by healthcare authorities to remove any personally identifiable information, and downloaded by the app.
The app then performs “intersections” — it identifies and notifies people with whom the infected person has crossed paths. By clicking on intersections, users can display their timeline for the past 14 days, in a calendar view, which tells them how many intersections have occurred each day.
The app also provides news reports from authorized local news channels, based on each user’s position or if they tap the URL for their local healthcare authority, said Abhishek Singh, the program’s tech lead, who is helping with the app’s development.
“We are also building an interoperable architecture,” Singh said. “Because there are many contact tracing apps already in the wild, we want to make sure that they have some common standards and guidelines that enable them to utilize data from other apps securely and through consent.” More than 1,200 people are voluntarily contributing to the project. “It is being led by the open-source community, and all our source code is out there and anybody can contribute,” Singh said.
Safe Places is helping health authorities by making the data and insights visible, enabling them to make the right decisions such as targeting resources to areas that need them most, helping them impose restrictions such as lockdowns, or reopen the economy. “The economy will not reopen in a single burst, but step by step.” Singh said. “A dashboard that allows them to monitor where the infection is spreading and where it has been contained helps them decide where to take which steps.”
The GPS Advantage. The uptake required for GPS-enabled contact tracing to be successful is generally lower than for Bluetooth-based contact tracing, Singh argues, citing an Oxford University simulation. “With GPS, you do not need people to have the app already downloaded for it to be effective,” he points out. A person who tests positive for the virus can use the Safe Place web tool to manually create a GPS trail and help healthy people. This is one of the biggest advantages of GPS compared to Bluetooth, because the latter requires exchanging information directly through the hardware, which cannot be done after the fact.
Because the app is open source, any government can deploy it using its own IT infrastructure. However, a government that wants to adopt Safe Paths must sign a letter that commits it to complying with privacy and ethical guidelines. Preventing authoritarian governments and nosy employers from requiring people to use this app and reveal their data requires stringent guidelines as to how it is deployed and who can access the data, Singh said.
Apple and Google Join Forces
Apple and Google have joined in an unprecedented alliance to develop a system for notifying people who have been near others who have tested positive for COVID-19. Eight out of 10 people in the United States own smartphones, and the two companies’ operating systems run more than 99% of them. Apps built directly into iOS and Android, especially if interoperable, could dramatically increase the reach of public health authorities (the only organizations that would receive the data). To avoid fragmentation and encourage wider adoption, Apple and Google will allow only one app per country to use their system, but will allow U.S. states to use it and support countries that opt for a state or regional approach.
The system will use Bluetooth signals from phones to detect encounters rather than GPS location data. It will not run ads, will require users to opt-in, be decentralized, and use randomized IDs not tied to a user’s actual identity to communicate potential contacts with individuals with a confirmed positive COVID-19 diagnosis.
With GPS, you do not need people to have the app already downloaded for it to be effective.
API Coming. On May 20, Apple and Google released an API to developers. Next, they will issue a system update to build in contact tracing at the OS level. Should a user’s phone notify them of a possible contact, they will be prompted to download and install a public health app from their local health authority to obtain trusted instructions.
Developers of coronavirus-related apps for several U.S. states have argued that GPS location data is vital to identify infection hotspots and track outbreaks. However, for various technical reasons, workarounds designed to bypass the decision by Apple and Google and collect GPS data in connection with their contact tracing system would work poorly.
Ethical and Equity Concerns
“The work that we are doing for COVID-19 is pretty similar to work that we do on a routine basis with other reportable communicable diseases,” said Lisa Ferguson, nursing supervisor for Communicable Disease Investigations and Case Management for Multnomah County, Oregon, which includes the city of Portland. Most commonly, her unit is notified of illnesses by the state database, which receives electronic lab reports. “We assign that as a case to somebody on our team, and they call the person, interview them, ask some questions about their illness, their symptoms and where they could have possibly been exposed,” Ferguson explained. “Then, they talk about who that person might have exposed and where they were from two days before they became sick up until the time of the interview or the time that their symptoms were resolved.”
The Multnomah County, Oregon, Disease Detection Team. (Photo: Multnomah County, Oregon)
How could technology — such as smartphone location data — best help Ferguson’s team conduct contact tracing for COVID-19? “In the public health world, we are not used to having access to technology in that way,” she said. “We need to have some ethical discussions before we are prepared to utilize something like a technology that can track people.” Also, unlike tracking measles, which requires knowing whether someone was in an airspace and who was there after them, “We do not automatically consider someone to have been exposed if they were in the same airspace as someone who tested positive.”
If the privacy concerns could be adequately addressed, receiving a list of all the people who were less than six feet away for at least 10 minutes from someone who had tested positive could help her team scale up, Ferguson said. Her team would then reach out to those people, using such language as “You may have been exposed,” and “Please watch yourself closely.”
Ferguson’s team always has “equity concerns,” fearing they might under-identify groups that do not have access to the technology. “It is a supplemental tool, but it certainly would not replace the work that we are doing,” she said.
Help Wanted
Safely reopening the United States will require a new workforce of at least 100,000 contact tracers, according to a report from the Johns Hopkins Center for Health Security and other experts. Any technological assist to contact tracing does not diminish this need. For example, smartphone alerts can help filter out those at low or no risk so that human tracers can focus on genuine cases, people at higher risk, or those who are harder to contact.
Two out of 10 people in the United States do not own a smartphone, and only 42% of those above the age of 65 — who suffer 80% of the deaths from COVID-19 — do, according to a 2017 Pew Research Center poll. Hardly any homeless people own a smartphone. Among those most vulnerable to the pandemic are immigrants who do not speak English and are fearful of efforts to collect their personal information, strengthening the need for this to be done in person by trusted community members.
Finally, even if Google and Apple’s automated service is widely adopted and works well, it will require many thousands of health workers to conduct tests and follow-ups.
Creating detailed street maps and keeping them updated is an expensive and time-consuming task performed mostly by large companies. They ignore the many parts of the world where this task is not profitable, even though the need is high due to rapid growth and change in the street network, such as in Thailand.
To automate the process and make accurate digital maps available in any country, researchers at the Massachusetts Institute of Technology (MIT) and the Qatar Computing Research Institute have developed an artificial intelligence (AI) model called RoadTagger. It uses satellite imagery to tag road features in digital maps, such as lane counts, which are essential for reliable navigation.
Satellite imagery companies are constantly expanding their coverage and increasing their refresh rate, so this source of mapping data is more readily available and up to date than the data collected on the ground, such as by Google’s fleet of mapping cars. However, satellite imagery often suffers from occlusion from trees, buildings, overpasses and other obstacles.
RoadTagger gets around this problem by using a combination of neural network architectures to predict hidden features. Testing of the model with digital maps of 20 U.S. cities showed that it predicted the number of lanes with 77% accuracy and the road type with 93% accuracy.
An AI model developed at MIT and Qatar Computing Research Institute that uses only satellite imagery to automatically tag road features in digital maps could improve GPS navigation, especially in countries with limited map data. (Map data: Google/MIT News)
RoadTagger, which combines a convolutional neural network (CNN) and a graph neural network (GNN) is fed only raw data and automatically produces output, without human intervention. The CNN, commonly used for image-processing tasks, takes as input raw satellite images of target roads. The GNN — widely used to model relationships between connected nodes in a graph — breaks the road into roughly 20-meter “tiles,” each of which is a separate graph node.
For each node, the CNN extracts road features and shares that information with its immediate neighbors, thereby propagating road information along the whole graph. For example, if only two lanes of a four-lane road are visible in an image, the model uses information from nearby tiles, such as road width, to conclude that the road has four lanes.
The researchers trained and tested RoadTagger using the OpenStreetMap data set. First, they collected confirmed road attributes from 688 square kilometers of maps of 20 U.S. cities, then they gathered the corresponding satellite images from a Google Maps dataset. The training taught the model what weight to assign to various features and node connections, and it now automatically learns which image features are useful and how to propagate those features along the graph.
The researchers hope that RoadTagger will help humans validate the constant stream of changes in OpenStreetMap and similar datasets as well as enrich them with details that they do not already contain, such as whether a road is paved.
Citation. He, S., Bastani, F., Jagwani, S., Park, E., Abbar, S., Alizadeh, M., Balakrishnan, H., Chawla, S., Madden, S., & Sadeghi, M. A. (Dec. 28, 2019). “RoadTagger: Robust Road Attribute Inference with Graph Neural Networks.” arXiv:1912.12408v1.
Researchers at the Massachusetts Institute of Technology (MIT) presented a project at the International Symposium on Experimental Robotics involving an autonomous drone fleet system that collaboratively mapped an environment under dense forest canopy.
Designed with search and rescue in mind, the drones used lidar, onboard computation and wireless communication, with no requirement for GPS positioning.
Each drone carries laser-range finders for position estimation, localization and path planning. As it flies, each drone creates its own 3-D map of the terrain. A ground station uses simultaneous localization and mapping (SLAM) technology to combine individual maps from multiple drones into a global 3-D map that can be monitored by operators.
The MIT team tested its concept via simulations of randomly generated forests, and world-tested two drones in a forested area at NASA’s Langley Research Center. In both experiments, each drone mapped a roughly 20-square-meter area in about two to five minutes, while the control system integrated their maps together in real-time.
The drones were programmed to identify multiple trees’ orientations, as recognizing individual trees in impossible for the technology, and individual trees’ orientation very difficult. When the lidar signal returns a cluster of trees, an algorithm calculates the angles and distances between trees to identify the cluster and determine if it has already been identified and mapped, or is a new mini-environment.
The technique also aids in merging maps from the separate drones. When two drones scan the same cluster of trees, the ground station merges the maps by calculating the relative transformation between the drones, and then fusing the individual maps to maintain consistent orientations.
A team from Draper and the Massachusetts Institute of Technology (MIT) has developed advanced vision-aided navigation techniques for UAVs that do not rely on external infrastructure, such as GPS, detailed maps of the environment or motion capture systems.
When a firefighter, first responder or soldier operates a small, lightweight flight vehicle inside a building, in urban canyons, underground or under the forest canopy, the GPS-denied environment presents unique navigation challenges.
In many cases, loss of GPS signals can cause these vehicles to become inoperable and, in the worst case, unstable, potentially putting operators, bystanders and property in danger.
Attempts have been made to close this information gap and give UAVs alternative ways to navigate their environments without GPS. But those attempts have resulted in further information gaps, especially on UAVs whose speeds can outpace the capabilities of their onboard technologies.
For instance, scanning lidar routinely fails to achieve its location-matching with accuracy when the UAV is flying through environments that lack buildings, trees and other orienting structures.
Finding a Solution
DARPA awarded contracts to Draper and two other industry teams to create UAVs that autonomously sense and maneuver through unknown environments without external communications or GPS under the Fast Lightweight Autonomy (FLA) program. (Photo: Draper)
Working together under a contract with the Defense Advanced Research Projects Agency (DARPA), Draper and MIT created a UAV that can autonomously sense and maneuver through unknown environments without external communications or GPS under the Fast Lightweight Autonomy (FLA) program.
The team developed and implemented unique sensor and algorithm configurations, and has conducted time-trials and performance evaluations in indoor and outdoor venues.
“The biggest challenge with unmanned aerial vehicles is balancing power, flight time and capability due to the weight of the technology required to power the UAVs,” said Robert Truax, senior member of technical staff at Draper. “What makes the Draper and MIT team’s approach so valuable is finding the sweet spot of a small size, weight and power for an air vehicle with limited onboard computing power to perform a complex mission completely autonomously.”
Draper and MIT’s sensor- and camera-loaded UAV was tested in a number of environments ranging between cluttered warehouses and mixed open and tree filled outdoor environments with speeds up to 10 m/s in cluttered areas and 20 m/s in open areas.
The UAV’s missions were composed of many challenging elements, including tree dodging followed by building entry and exit and long traverses to find a building entry point, all while maintaining precise position estimates.
“A faster, more agile and autonomous UAV means that you’re able to quickly navigate a labyrinth of rooms, stairways and corridors or other obstacle-filled environments without a remote pilot,” said Ted Steiner, senior member of Draper’s technical staff. “Our sensing and algorithm configurations and unique monocular camera with IMU-centric navigation gives the vehicle agile maneuvering and improved reliability and safety — the capabilities most in demand by first responders, commercial users, military personnel and anyone designing and building UAVs.”
Draper’s contribution to the DARPA FLA program — documented in a recent research paper for the 2017 IEE Aerospace Conference — was a novel approach to state estimation (the vehicle’s position, orientation and velocity) called SAMWISE — Smoothing And Mapping With Inertial State Estimation.
SAMWISE is a fused vision and inertial navigation system that combines the advantages of both sensing approaches and accumulates error more slowly over time than either technique on its own, producing a full position, attitude and velocity state estimate throughout the vehicle trajectory.
The result is a navigation solution that enables a UAV to retain all six degrees of freedom and allows it to fly autonomously without the use of GPS or any communication with vehicle speeds of up to 45 miles per hour.
The team’s focus on the FLA program has been on UAVs, but advances made through the program could potentially be applied to ground, marine and underwater systems, which could be especially useful in GPS-degraded or denied environments.
