Welcome to the May 12, 2021 edition of ACM TechNews, providing timely information for IT professionals three times a week.
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Germany to Support Quantum Computing with €2 Billion
U.S. News & World Report
Michael Nienaber
May 11, 2021
Germany's economy and science ministries announced an approximately €2-billion ($2.4-billion) allocation to develop the country's first competitive quantum computer and associated technologies in the next four years. The science ministry will invest €1.1 billion ($1.3 billion) by 2025 to support quantum computing research and development, while the economy ministry will spend €878 million ($1.06 billion) on practical applications. The economy ministry said most subsidies will go to Germany's Aerospace Center, which will partner with industrial companies, midsized enterprises, and startups to establish two consortia. Economy Minister Peter Altmaier cited management of supply and demand in the energy sector, improved traffic control, and faster testing of new active substances as areas that quantum computing could potentially revolutionize.
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Do Humorous Teachable Virtual Agents Enhance Learning, Outcomes?
University of Waterloo Cheriton School of Computer Science (Canada)
May 11, 2021
A study by researchers at the University of Waterloo in Canada considered whether a teachable virtual agent could improve learning and outcomes by employing humor when interacting with students. Using the online learning-by-teaching platform Curiosity Notebook, the researchers evaluated the impact of affiliative and self-defeating humor styles on the effectiveness of such an agent, as well as humor of a self-enhancing nature, and a neutral style in which no verbal humor was expressed. Waterloo's Jessy Ceha said, "We found that participants who interacted with the agent with an affiliative humor style showed an increase in motivation during the teaching task and an increase in the amount of effort they put into teaching the agent. The participants who interacted with the agent with a self-defeating style also showed an increase in effort, but they didn't find it as enjoyable an experience."
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This System Helps Robots Better Navigate Emergency Rooms
UC San Diego News Center
Ioana Patringenaru
May 11, 2021
A navigation system developed by University of California, San Diego (UC San Diego) computer scientists aims to improve the ability of robots to navigate busy clinical environments, especially hospital emergency departments. The Safety Critical Deep Q-Network (SafeDQN) navigation system is built around an algorithm that factors in the number of people clustered in a space and the speed and abruptness with which they are moving and directs robots to move around them. The researchers trained the algorithm using a dataset of more than 700 YouTube videos, mainly from documentaries and reality shows. When tested in a simulated environment and compared to other state-of-the-art robotic navigation systems, the researchers determined that SafeDQN found the most efficient and safest paths in all cases.
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UConn Researchers Study Anti-Vax Facebook Groups in Early Days of COVID-19 Pandemic
UConn Today
Anna Zarra Aldrich
May 11, 2021
A study of anti-vaccine Facebook groups by researchers at the University of Connecticut (UConn) found that, as early as February 2020, these groups considered COVID-19 to be a significant public health threat that likely would require a vaccine. The study looked at four groups, each with tens of thousands of followers, that were highly active on Facebook. The researchers found that anti-vaccine groups capitalized on appeals to emotion and personal anecdotes and used the same messaging and tactics for COVID-19 as for other diseases. UConn's Seth Kalichman said, "Better understanding how anti-vaccine groups formulate their initial communications and start to sow seeds of doubt in vaccines long before there even is a vaccine can help inform public health officials in how they communicate and how they might head off some of the anti-vaccine rhetoric."
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3D Printing Lays Foundation for Range of Diagnostic Tests
KU Leuven News (Belgium)
May 10, 2021
A three-dimensional (3D) printing method developed by researchers at Belgium's KU Leuven paves the way for new lateral flow diagnostic tests. While lateral flow tests like pregnancy tests and COVID-19 self-tests have long been used for yes-no answer tests and self-tests, the new technique aims to make lateral flow tests more useful for those requiring multi-step protocols. The researchers' 3D version of a lateral flow test is built from a small block of porous polymer with a network of printed channels and small locks, which automatically guide the sample through test steps and permit or block the flow as necessary without the need for moving parts. KU Leuven's Cesar Parra said the technique is affordable and scalable; a prototype test manufactured in the lab cost about $1.50, but scaling up could drop the price to under $1.
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96% of U.S. Users Opt Out of App Tracking in iOS 14.5, Analytics Find
Ars Technica
Samuel Axon
May 7, 2021
U.S. users have opted out of application tracking nearly all (96%) of the time following Apple's release of iOS 14.5 in April, according to mobile app analysis platform Flurry Analytics. That release was accompanied by Apple’s launch of enforcement of the App Tracking Transparency policy, which requires iPhone, iPad, and Apple TV apps to request user consent to monitor their activity across multiple apps for data collection and ad targeting. Based on data from roughly 1 million mobile apps, Flurry Analytics said U.S. users agree to be tracked only 4% of the time; globally, the firm found that number reaching 12%.
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Technique Predicts Response of Brain Tumors to Chemoradiation
University of Texas at Austin News
May 10, 2021
A new technique for predicting brain tumors' response to chemoradiation (a combination of chemotherapy and radiation therapy) could help personalize cancer treatment. Researchers at the University of Texas at Austin (UT Austin), the Texas Advanced Computing Center (TACC), and the University of Texas MD Anderson Cancer Center combined anatomical and structural imaging to inform a computational mechanistic model that forecasts high-grade glioma tumor progression. UT Austin's David Hormuth said, "We had roughly 6,000 different calibrations or forecast scenarios that would take years to run on a standard laptop.” Hormuth said using TACC’s Lonestar 5 high-performance computing system to run the model calibration and forecasting approach in parallel allowed the researchers “to evaluate all of these scenarios in a matter of days."
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Self-Learning Robots Go Full Steam Ahead
AMOLF (Netherlands)
May 10, 2021
AMOLF researchers in the Netherlands demonstrated that a group of small autonomous self-learning robots can easily change what they are doing in response to changing conditions. The team induced individual robotic carts that are interlinked and move on a track to maximize their speed in a certain direction without a programmed route or knowing what the others were doing. The system is comprised of a microcontroller, a motion sensor, a pump that pumps air into a bellows, and a needle for deflation; linking a second robot to a robot's bellows causes the robots to push each other away, driving the robotic train. Each robot is fed a set of rules via a short algorithm, while a chip continuously measures speed. The AMOLF researchers found the robots could better adapt to changing situations with an algorithm that only uses the last speed measurement to decide the best moment for the pump to be switched on in each cycle.
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'Unmaking' a Move: Correcting Motion Blur in Single-Photon Images
Tokyo University of Science (Japan)
May 10, 2021
A deblurring method for single-photon imaging proposed by researchers at Japan's Tokyo University of Science (TUS) can be applied even to images in which multiple objects move independently of each other. Rather than adjusting the entire image based on the estimated motion of a single object or on spatial regions where the object is moving, the technique first monitors the movement of individual pixels through statistical assessments on how bit values change over time. A deblurring algorithm then uses the motion estimation results to group pixels with a similar motion together, and to identify separate objects moving at different speeds in each bit plane. TUS' Takayuki Hamamoto said, "Our approach will hopefully lead to new technology for high-quality imaging in dark environments, like outer space, and super-slow recording that will far exceed the capabilities of today's fastest cameras."
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Smart Finger Ring with Integrated RFID Chip
Fraunhofer-Gesellschaft (Germany)
May 3, 2021
Researchers at Germany's Fraunhofer Institute for Casting, Composite, and Processing Technology (IGCV) have developed a three-dimensionally (3D) printed smart finger ring that potentially could replace house keys, wallets, health insurance cards, and more. The smart ring contains an integrated radio-frequency identification (RFID) chip that enables it to make purchases, open smart doors, and store medical data, among other things. Produced using the 3D printing process known as powder bed-based additive manufacturing, the ring is built layer by layer, stopping midstream to place the RFID chip into a cavity in the ring, then continuing. IGCV's Maximilian Binder said, "Converting the hardware technology to allow electronic components to be integrated during the manufacturing process is unique."
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AI Consumes a Lot of Energy. Hackers Could Make It Consume More.
MIT Technology Review
Karen Hao
May 6, 2021
Maryland Cybersecurity Center (MC2) researchers have outlined an attack that could boost the energy consumption of artificial intelligence (AI) systems by forcing a deep neural network to overuse computational resources. The team added small amounts of noise to the inputs of an input-adaptive multi-exit neural network, which were perceived as more difficult, increasing computation that required more energy to complete. In assuming the attacker had full data about the network, the researchers could max out its energy draw; in assuming the attacker had little to no data, they could still slow processing and increase energy consumption 20% to 80%. This hack remains somewhat theoretical, but MC2's Tudor Dumitras said, "What's important to me is to bring to people's attention the fact that this is a new threat model, and these kinds of attacks can be done."
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Graphene Key for Novel Hardware Security
Penn State College of Engineering News
Gabrielle Stewart
May 10, 2021
Researchers at Pennsylvania State University (Penn State) have demonstrated the first graphene-based physically unclonable function (PUF), a hardware security device resistant to the use of artificial intelligence (AI) techniques to crack encrypted keys. The researchers said graphene's physical and electrical properties ensure the novel PUF is more energy-efficient, scalable, and secure than silicon PUFs. The researchers tested the PUF's security by using a simulation of 64 million graphene-based PUFs to train an AI to determine whether it could make predictions about the encrypted data and identify system insecurities. Penn State's Saptarshi Das said, "We found that AI could not develop a model, and it was not possible for the encryption process to be learned."
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