Welcome to the September 12, 2018 edition of ACM TechNews, providing timely information for IT professionals three times a week.

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Photo of Japan’s supercomputer ATERUI II Japan's New Supercomputer Is the Fastest Ever for Astronomy Research
Extreme Tech
Ryan Whitwam
September 10, 2018

Japanese supercomputer ATERUI II at the National Astronomical Observatory of Japan is the fastest system in the world dedicated entirely to astronomy research. ATERUI II, a Cray XC50 system, is number 83 on the TOP500 ranking of supercomputers. Researchers brought ATERUI II online in June with more than 40,000 total processing cores supporting up to 3 quadrillion operations per second. The supercomputer's Intel Xeon Gold 6148 processors each come with 20 cores with a maximum frequency of 3.7GHz and 27.5MB of cache. ATERUI II also offers 385 terabytes of RAM, and is expected to be one of the world's best multitasking supercomputers, dividing a simulation into sections and working from multiple angles. ATERUI II is powerful enough to model gravitational variables for a galaxy of 100 billion individual stars. More than 150 teams are scheduled to use the supercomputer this year. Because simulations help inform future observations, ATERUI II will help scientists decide on targets and observational methods more rapidly.

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DARPA Announces $2 Billion in Funding for 'AI Next' Campaign
Tajha Chappellet-Lanier
September 7, 2018

The U.S. Defense Advanced Research Projects Agency (DARPA) plans to spend more than $2 billion on research into "third wave" artificial intelligence (AI) capabilities over the next few years. The AI Next initiative aims to advance AI beyond the point where it requires large volumes of training data, and to bring the technology closer to human intelligence in adapting to change. DARPA's Steven Walker says, "We want to explore how machines can acquire human-like communication and reasoning capabilities, with the ability to recognize new situations and environments and adapt to them." AI Next's priority areas include automating U.S. Department of Defense business tasks and improving the security of machine learning technology. The initiative also will utilize DARPA's Artificial Intelligence Exploration funding program, which gives researchers 18 months to prove an AI theory is viable.

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Girl wearing red hoodie looking circuit board throughout magnifying glass Role Models Tell Girls That STEM’s for Them in New Campaign
The New York Times
Jane L. Levere
September 9, 2018

A recently-launched public service campaign, called "She Can STEM," aims to encourage girls ages 11 to 15 to get involved in science, technology, engineering, and math (STEM). The campaign was assembled by the Advertising Council in collaboration with General Electric, Google, IBM, Microsoft, and Verizon. Each company identified a female employee in a STEM field to be featured in the campaign, alongside women who work at Boeing and the Adler Planetarium in Chicago. The campaign features videos in which the seven women discuss with girls what they do professionally and describe opportunities in their fields. Said Lisa Sherman, president and chief executive of the Ad Council, “If we want women at the forefront of the next generation of STEM leaders, we must show young girls that it is possible.”

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Interpretation of Material Spectra Can Be Data-Driven Using Machine Learning
University of Tokyo
September 7, 2018

Using big data techniques, researchers at the University of Tokyo Institute of Industrial Science in Japan have found a way to interpret much larger numbers of material spectra than traditional approaches. The Institute's Teruyasu Mizoguchi said, "We developed a data-driven approach based on machine learning techniques using a combination of the layer clustering and decision tree methods." The researchers used theoretical calculations to construct a spectral database in which each spectrum had a one-to-one correspondence with its atomic structure, with all spectra having the same parameters. With two machine learning techniques, the team developed a spectral interpretation method and a spectral prediction method. University of Tokyo researcher Shin Kiyohara says, "Our approach has the potential to provide information about a material that cannot be determined manually and can predict a spectrum from the material's geometric information alone."

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Dongarra Awarded 2019 SIAM/ACM Prize in Computational Science and Engineering
Inside HPC
September 7, 2018

The University of Tennessee's Jack Dongarra has been named to receive the 2019 SIAM/ACM Prize in Computational Science and Engineering, to be presented at the Society for Industrial and Applied Mathematics (SIAM) Conference on Computational Science and Engineering (CSE19) in February. Given every two years, the award recognizes research contributions in creating and using mathematical and computational tools and methods for science and engineering challenges. Dongarra, an ACM fellow, founded and runs the Innovative Computing Laboratory (ICL) research group at the University of Tennessee. In addition, he is a distinguished research staff member at Oak Ridge National Laboratory and participates in high-performance computing research. Dongarra specializes in numerical algorithms in linear algebra, parallel computing, advanced-computer architectures, programming methodology, and parallel computer tools. His research includes creating, testing, and documenting mathematical software. Dongarra received the ACM/IEEE Ken Kennedy Award for 2013 for his work on mathematical software standards for high performance computing.

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A Neural Network to Extract Knowledgeable Snippets and Documents
Tech Xplore
Ingrid Fadelli
September 5, 2018

Chinese Academy of Sciences researchers have created a convolutional neural network (CNN)-based model to extract knowledgeable snippets and annotate documents. The model can outperform current analytical tools while undergoing shorter training periods. The model is designed to comprehend the abstract concept of documents in different domains collaboratively and evaluate whether a document is knowledgeable, defined as one "containing multiple knowledgeable snippets, which describe concepts, properties of entities, or the relations among entities." The researchers say the network structure of their SSNN joint CNN-based model is "low-level Sharing, high-level Splitting," in which the low-level layers are shared for different domains while the high-level layers outside the network receive separate training to identify the differences of dissimilar domains. The team assessed SSNN's effectiveness on a dataset of real documents from three content domains on the WeChat messaging/social media/mobile payment platform. The model performed consistently better than other CNN models while saving time and memory usage due to shorter and more efficient training processes. In the future, the model could help build comprehensive knowledge databases and innovative services that answer user queries in real time.

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Cockroach on a wooden floor A Cyborg Cockroach Could Someday Save Your Life
University of Connecticut
Colin Poitras
September 6, 2018

University of Connecticut researchers have created a cyborg cockroach equipped with a novel microcircuit that allows more reliable and precise control of robotic insect motion. The microcircuit incorporates a nine-axis inertial measurement unit that can detect the roach's six degrees of free motion, its linear and rotational acceleration, and its compass heading. In addition, the researchers added a sensor that analyzes the ambient temperature surrounding the insect, because tests have shown that the temperature of the local environment can affect how and where the insect moves. The microcircuit is part of a small electronic "backpack" that can be strapped to the cockroach. Wires from the devices are attached to the insect's antennae lobes, and a tiny Bluetooth transmitter and receiver allows a nearby operator to control the roach's movements via an ordinary cellphone. University of Connecticut researcher Abhishek Dutta says, "The use of insects as platforms for small robots has an incredible number of useful applications, from search and rescue to national defense."

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A human heart. AI Beats Doctors at Predicting Heart Disease Deaths
Francis Crick Institute
September 4, 2018

Scientists at the Francis Crick Institute in the U.K. have used artificial intelligence (AI) to build a machine learning model that outperforms medical expert-designed models at predicting mortality risk in people with coronary artery disease. The researchers designed the model using the digital medical data of more than 80,000 patients compiled as part of routine care on the CALIBER (Clinical research using LInked Bespoke studies and Electronic health Records) platform. They compared the model against an expert-built prognostic model for heart disease that makes predictions based on 27 expert-chosen factors such as age, gender, and chest pains. The Crick AI algorithms are programmed to train themselves, seeking patterns and selecting the most relevant factors from a set of 600. Said Crick’s Andrew Steele, “Machine learning is a hugely powerful tool in medicine and has the ability to revolutionize how we deliver care to patients over the next few years.”

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PhD student Lucas Manuelli standing next to a Kuka robot Robots Can Now Pick Up Any Object After Inspecting It
MIT News
Adam Conner-Simons; Rachel Gordon
September 10, 2018

Researchers at the Massachusetts Institute of Technology's (MIT) Computer Science and Artificial Intelligence Laboratory (CSAIL) have created Dense Object Nets (DON), a system that lets robots inspect random objects, and visually understand them enough to accomplish specific tasks without having seen them previously. The DON system looks at objects as collections of points that serve as a kind of visual roadmap. This approach allows robots to better understand and manipulate items; they can even pick up a specific object among a clutter of similar ones. The system creates a series of coordinates on a given object to give the robot a better understanding of what is needed to grasp it, and where. MIT researcher Pete Florence said, "A system like this that can understand objects' orientations could just take a picture and be able to grasp and adjust the object accordingly." DON has potential applications in manufacturing settings performing tasks such as picking items off a shelf, and in homes completing chores such as cleaning.

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Algorithm Decides on Chemical Compromises When Optimizing Self-Driving Experiments
Chemistry World
Hannah Kerr
September 3, 2018

Researchers at the University of Toronto in Canada have developed a multi-target optimization algorithm that can handle complicated chemical reaction problems. They say the Chimera algorithm does not require any prior knowledge of the process and it can prioritize the most important objectives, as designated by the user. The algorithm constructs a single scalarized objective function from a set of different targets using hierarchical methods with a priori multi-objective optimization. The researchers tested Chimera against another leading optimization approach, c-ASF, on an auto-calibration procedure in a virtual self-driving laboratory in an effort to maximize the response of a high-performance liquid chromatography experiment while minimizing the amount of sample used. Chimera was able to achieve its goal quickly and with a smaller number of experiments than c-ASF. The University of Toronto's Florian Hase says, "We are eager to apply the algorithm to more complex systems in various areas of chemistry and beyond."

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UTEP to Facilitate National Effort to Increase Number of Hispanics in Computing With $10M Grant
KRWG.org (NM)
September 9, 2018

The University of Texas at El Paso (UTEP) is leading a national effort to bring more Hispanic students into computing, as part of a consortium of more than 40 institutions and organizations. The effort will be led by UTEP's Ann Gates, who recently received a $9.9-million National Science Foundation (NSF) grant. Gates participates in the Computing Alliance of Hispanic-Serving Institutions (CAHSI), which will serve as the lead partner in a collaboration through NSF's Inclusion across the Nation of Communities of Learners of Underrepresented Discoverers in Engineering and Science (INCLUDES) program. CAHSI's goal is for Hispanics to represent 20% of U.S. graduates in computing disciplines by 2030. The INCLUDES Alliance Program will build regional hubs throughout the country with stakeholders from two-year colleges, four-year universities, K-12 schools, nonprofits, industry, governmental agencies, and others who work with students in computing fields. UTEP will organize support systems within these hubs to help students succeed in computing.

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Does Technology Really Enhance Our Decision-Making Ability?
U.S. Army Research Laboratory
September 7, 2018

Researchers at the U.S. Army Research Laboratory (ARL) and the University of California, Santa Barbara have discovered that most people cannot distinguish between liking a user interface and making sound choices. The researchers tested whether technology, such as recommender systems, enhances decision-making ability. Previous assumptions said that in recommender systems, users form very complex mental models of user interfaces. However, the researchers' results contradict this assumption and demonstrate that a person's subjective satisfaction with their decisions is strongly influenced by their cognitive state and traits. ARL's James Schaffer says, "User experience and choice satisfaction can easily be conflated when good system design creates positive feelings about an experience, artificially leading participants to think good decisions have been made." The research helps form the basis for evaluation strategies that can help the Army distinguish between technology that boosts performance and technology that is simply innovative. Schaffer received an award for his paper on the work at ACM's 26th Conference on User Modeling, Adaptation and Personalization in Singapore in July.

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