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DARPA digs into the details of practical quantum computing -- GCN

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Quantum computing promises enough computational power to solve problems far beyond the capabilities of the fastest digital computers, so the Defense Advanced Research Projects Agency is laying the groundwork for applying the technology to real-world problems. In a request for information, DARPA is asking how quantum computing can enable new capabilities when it comes to solving science and technology problems, such as understanding complex physical systems, optimizing artificial intelligence and machine learning and enhancing distributed sensing. Noting that it is not interested in solving cryptology issues, DARPA is asking the research community to help solve challenges of scale, environmental interactions, connectivity and memory and suggest "hard" science and technology problems the technology could be leveraged to solve. Establishing the fundamental limits of quantum computing in terms of how problems should be framed, when a model's scale requires a quantum-based solution, how to manage connectivity and errors, the size of potential speed gains and the ability to break large problems into smaller pieces that can map to several quantum platforms. Improving machine learning by leveraging a hybrid quantum/classical computing approach to decrease the time required to train machine learning models.


DARPA tunes machine learning to radio signals -- GCN

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The Defense Advanced Research Projects Agencies is looking to apply the same kind of machine learning to the radio spectrum as is used by advanced systems for applications ranging from voice recognition to management of internet-of-things devices to autonomous vehicles. DARPA has issued a broad agency announcement for a new Radio Frequency Machine Learning Systems (RFMLS) program that will address the need for enhanced situational awareness regarding the ever-changing composition of RF signals in the IoT and spectrum sharing. Machine learning is widely used to manage data and images, but the similar work in the radio spectrum offers unique challenges, making a more compelling case for developing a native approach. "What I am imagining is the ability of an RF machine learning system to see and understand the composition of the radio frequency spectrum – the kinds of signals occupying it, differentiating those that are'important' from the background, and identifying those that don't follow the rules," said DARPA's Microsystems Technology Office Program Manager Paul Tilghman. An RFMLS would be able to discern subtle differences in the RF signals among identical, mass-manufactured IoT devices and identify signals intended to spoof or hack into these devices.


TSA plans to add machine learning to carry-on baggage scans -- GCN

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The Transportation Security Agency plans to incorporate machine learning into the computer tomography scanners that are starting to be used at airport security checkpoints. To advance the Accessible Property Screening Systems program, TSA is looking for researchers and industry partners to develop algorithms that could improve the automated detection of explosives and prohibited items among carry-on baggage and speed passengers through the checkpoints. Although it has been used to screen checked luggage for explosives since 2001, CT scanning is a relatively new tool for examining carry-on baggage where it would have to identify prohibited items like knives and disassembled weapons. The 3-D imaging and detection software in the CT scanners would increase the speed and accuracy of the scans, flagging the threats operators should manually check. It may eliminate the need for passengers to put their electronics and liquids in separate screening bins, TSA said prior to a June 2017 test of the technology.


Army tests HPC climate model in Azure cloud -- GCN

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The Army Engineer Research and Development Center (ERDC) is working with Microsoft to improve climate modeling and natural disaster resilience planning through the use of predictive analytics-powered, cloud-based tools and artificial intelligence services. Under a new agreement, ERDC will test the scalability of its coastal storm modeling system, CSTORM-MS -- which was previously run on high-performance computers -- inside Microsoft's Azure Government cloud. The CSTORM-MS models provide can give coastal communities a robust, standardized approach for determining the risk of future storm events and for evaluating flood risk reduction measures caused by tropical and extra-tropical storms, as well as wind, wave and water levels. Currently, CSTORM-MS models are run at ERDC's Department of Defense Supercomputing Resource Center. In 2020, ERDC worked with DOD's High Performance Modernization Program's (HPCMP) on a feasibility study testing whether CSTORM-MS could be run in a commercial cloud.


Getting started with intelligent automation -- GCN

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As agencies look to automation to lower costs, improve efficiency and achieve higher customer satisfaction, many wonder where to start. To start them on their journey, ACT-IAC created the Intelligent Automation Primer. The document defines IA as the marriage of automation with artificial intelligence, but not all automation need be so sophisticated. It can range from desktop automation tools that leverage scripts and macros, to robotic process automation that uses simple rules to process structured data, to enhanced RPA, which addresses more-complex tasks using unstructured data from multiple sources. Full-blown IA applications, which can "sense and synthesize vast amounts of information and can automate entire processes or workflows, learning and adapting as they go," range from making decisions about text-based information to guiding autonomous vehicles, according to the primer.