Monthly Archives: March 2017

Diamond optical circuits could work at large scales

Quantum computers are experimental devices that offer large speedups on some computational problems. One promising approach to building them involves harnessing nanometer-scale atomic defects in diamond materials.

But practical, diamond-based quantum computing devices will require the ability to position those defects at precise locations in complex diamond structures, where the defects can function as qubits, the basic units of information in quantum computing. In today’s of Nature Communications, a team of researchers from MIT, Harvard University, and Sandia National Laboratories reports a new technique for creating targeted defects, which is simpler and more precise than its predecessors.

In experiments, the defects produced by the technique were, on average, within 50 nanometers of their ideal locations.

“The dream scenario in quantum information processing is to make an optical circuit to shuttle photonic qubits and then position a quantum memory wherever you need it,” says Dirk Englund, an associate professor of electrical engineering and computer science who led the MIT team. “We’re almost there with this. These emitters are almost perfect.”

The new paper has 15 co-authors. Seven are from MIT, including Englund and first author Tim Schröder, who was a postdoc in Englund’s lab when the work was done and is now an assistant professor at the University of Copenhagen’s Niels Bohr Institute. Edward Bielejec led the Sandia team, and physics professor Mikhail Lukin led the Harvard team.

Initial size enables speedy analysis of laparoscopic procedures

Laparoscopy is a surgical technique in which a fiber-optic camera is inserted into a patient’s abdominal cavity to provide a video feed that guides the surgeon through a minimally invasive procedure.

Laparoscopic surgeries can take hours, and the video generated by the camera — the laparoscope — is often recorded. Those recordings contain a wealth of information that could be useful for training both medical providers and computer systems that would aid with surgery, but because reviewing them is so time consuming, they mostly sit idle.

Researchers at MIT and Massachusetts General Hospital hope to change that, with a new system that can efficiently search through hundreds of hours of video for events and visual features that correspond to a few training examples.

In work they presented at the International Conference on Robotics and Automation this month, the researchers trained their system to recognize different stages of an operation, such as biopsy, tissue removal, stapling, and wound cleansing.

But the system could be applied to any analytical question that doctors deem worthwhile. It could, for instance, be trained to predict when particular medical instruments — such as additional staple cartridges — should be prepared for the surgeon’s use, or it could sound an alert if a surgeon encounters rare, aberrant anatomy.

“Surgeons are thrilled by all the features that our work enables,” says Daniela Rus, an Andrew and Erna Viterbi Professor of Electrical Engineering and Computer Science and senior author on the paper. “They are thrilled to have the surgical tapes automatically segmented and indexed, because now those tapes can be used for training. If we want to learn about phase two of a surgery, we know exactly where to go to look for that segment. We don’t have to watch every minute before that. The other thing that is extraordinarily exciting to the surgeons is that in the future, we should be able to monitor the progression of the operation in real-time.”

Joining Rus on the paper are first author Mikhail Volkov, who was a postdoc in Rus’ group when the work was done and is now a quantitative analyst at SMBC Nikko Securities in Tokyo; Guy Rosman, another postdoc in Rus’ group; and Daniel Hashimoto and Ozanan Meireles of Massachusetts General Hospital (MGH).

Report warns of hacking risk to electric grid

In a world where hackers can sabotage power plants and impact elections, there has never been a more crucial time to examine cybersecurity for critical infrastructure, most of which is privately owned.

According to MIT experts, over the last 25 years presidents from both parties have paid lip service to the topic while doing little about it, leading to a series of short-term fixes they liken to a losing game of “Whac-a-Mole.” This scattershot approach, they say, endangers national security.

In a new report based on a year of workshops with leaders from industry and government, the MIT team has made a series of recommendations for the Trump administration to develop a coherent cybersecurity plan that coordinates efforts across departments, encourages investment, and removes parts of key infrastructure like the electric grid from the internet.

Coming on the heels of a leak of the new administration’s proposed executive order on cybersecurity, the report also recommends changes in tax law and regulations to incentivize private companies to improve the security of their critical infrastructure. While the administration is focused on federal systems, the MIT team aimed to address what’s left out of that effort: privately-owned critical infrastructure.

“The nation will require a coordinated, multi-year effort to address deep strategic weaknesses in the architecture of critical systems, in how those systems are operated, and in the devices that connect to them,” the authors write. “But we must begin now. Our goal is action, both immediate and long-term.”

Entitled “Making America Safer: Toward a More Secure Network Environment for Critical Sectors,” the 50-page report outlines seven strategic challenges that would greatly reduce the risks from cyber attacks in the sectors of electricity, finance, communications and oil/natural gas. The workshops included representatives from major companies from each sector, and focused on recommendations related to immediate incentives, long-term research and streamlined regulation.

Vehicle systems in ongoing collaboration with Toyota

The MIT AgeLab will build and analyze new deep-learning-based perception and motion planning technologies for automated vehicles in partnership with the Toyota Collaborative Safety Research Center (CSRC). The new research initiative, called CSRC Next, is part of a five-year-old ongoing relationship with Toyota.

The first phase of projects with Toyota CSRC has been led by Bryan Reimer, a research scientist at MIT AgeLab, which is part of the MIT Center for Transportation and Logistics. Reimer manages a multidisciplinary team of researchers, and students focused on understanding how drivers respond to the increasing complexity of the modern operating environment. He and his team studied the demands of modern in-vehicle voice interfaces and found that they draw drivers’ eyes away from the road to a greater degree than expected, and that the demands of these interfaces need to be considered in the time course optimization of systems. Reimer’s study eventually contributed to the redesign of the instrumentation of the current Toyota Corolla and the forthcoming 2018 Toyota Camry. (Read more in the 2017 Toyota CSRC report.)

Reimer and his team are also building and developing prototypes of hardware and software systems that can be integrated into cars in order to detect everything about the state of the driver and the external environment. These prototypes are designed to work both with cars with minimal levels of autonomy and with cars that are fully autonomous.

Computer scientist and team member Lex Fridman is leading a group of seven computer engineers who are working on computer vision, deep learning, and planning algorithms for semi-autonomous vehicles. The application of deep learning is being used for understanding both the world around the car and human behavior inside it.

“The vehicle must first gain awareness of all entities in the driving scene, including pedestrians, cyclists, cars, traffic signals, and road markings,” Fridman says. “We use a learning-based approach for this perception task and also for the subsequent task of planning a safe trajectory around those entities.”