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Welcome to the November 1, 2019 edition of ACM TechNews, providing timely information for IT professionals three times a week.

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Volkswagen traffic management system test Volkswagen to Test Quantum Navigation App in Real Traffic
The Wall Street Journal
Sara Castellanos
October 31, 2019

Volkswagen will test a quantum navigation app in Portugal next week, with plans to eventually incorporate its capabilities into automobiles. Nine buses shuttling passengers between Lisbon's Web Summit conference and other metropolitan areas will have iPads loaded with the app, which features technology from Canada’s D-Wave Systems. The app employs cloud-based quantum-computing services to calculate the swiftest route for each bus in near-real time, in order to minimize traffic congestion and shorten travel times. While a predictive analytics tool on a classical computer will identify which of 26 bus stops probably have especially high rider numbers 45 minutes ahead of time, a quantum computer will map out the fastest route for each bus, accounting for millions of real-time data points about congestion and ridership demand in milliseconds. Said Scott Pankin of Los Alamos National Laboratory, “What’s exciting about this work is that it’s being applied in the real world.”

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ACM Recognizes 2019 Distinguished Members for Educational, Engineering, Scientific Contributions to Computing
October 30, 2019

ACM cited 62 of its members as Distinguished Members for their exceptional educational, engineering, and scientific contributions to computing. The 2019 inductees work at prominent universities, corporations, and research institutions across the globe, and their contributions span artificial intelligence, human-computer interaction, computer engineering, computer science education, cybersecurity, graphics, and networking. Said ACM president Cherri M. Pancake, “In everything we do, our overarching goal is to build a community wherein computing professionals can grow professionally and, in turn, contribute to the field and the broader society. We are delighted to recognize these individuals for their contributions to computing, and we hope that the careers of the 2019 ACM Distinguished Members will continue to prosper through their participation with ACM."

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Concordia researchers Elham Mohammadi, Leila Kosseim, and Hessam Amini Concordia Researchers Use Deep Learning to Detect Anorexia in Social Media Posts
Concordia University
Patrick Lejtenyi
October 29, 2019

Researchers at Concordia University in Canada have developed a technique to detect signs of anorexia in individuals via social media analysis. The team reviewed Reddit posts from users tagged as anorexic or non-anorexic, and used deep learning algorithms to search for linguistic patterns in the posts that would identify their authors as anorexic or at risk of anorexia. The system relies on the algorithms differentiating between anorexic and non-anorexic users via analysis of sufficient numbers of posts, and is designed to be complementary to the efforts of mental health professionals. Concordia's Hessam Amini said, “It is becoming more urgent to identify people suffering from anorexia because studies are suggesting that the number of people in need of treatment is much higher than the number of people receiving it.”

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Mouse-Brain Model Maps Spread of Alpha-Synuclein in Parkinson's Disease
National Institute on Aging
October 31, 2019

Scientists at the University of Pennsylvania and its Alzheimer's Disease Research Center have developed a computational mouse model that simulates the spread of the misfolded alpha-synuclein protein found in Parkinson's and other diseases throughout the brain. The team built its network diffusion model from a map of mouse brain areas and their connecting pathways, in order to better understand how the protein alpha-synuclein spreads through the brain and where it tends to accumulate. The researchers said their network brain model offers a new way to “test hypotheses of spreading patterns, timing, directionality, and vulnerability.” The U.S. National Institute on Aging awarded the researchers a grant to support their continued exploration of how alpha-synuclein proteins spread, and how that can lead to neurodegenerative disease.

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burdensome bitcoin, illustration Transaction Fees Change Bitcoin Culture, Study Says
Cornell Chronicle (NY)
Melanie Lefkowitz
October 30, 2019

A study by Cornell University researchers found bitcoin transaction fees may neutralize the cryptocurrency's long-term usefulness and add to energy waste. As the cryptocurrency has grown, so has the wait for transactions to be added to the blockchain ledger, and users pay fees to speed that up. However, Cornell's Maureen O'Hara warns the fees' practicality could be negated by their cost. Said O'Hara, "If everybody's paying a transaction fee now, then you may end up in the same situation that you were in before—the fees got high and you have to wait anyway." The transaction backlog also requires an enormous amount of energy, due to blockchains' massive computing energy requirements. O'Hara said higher fees encourage more blockchain users or miners to compete to solve the mathematical problems that yield bitcoin, causing energy use to spike.

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The flag of China China Passes Cryptography Law
Anthony Spadafora
October 29, 2019

China has passed a cryptography statute, which will become law on Jan. 1, that regulates how government, businesses, and private citizens may use the technology. The law classifies three varieties of cryptography (core, common, and commercial) and designates the first two for the protection of confidential government data. Commercial cryptography is to be used to shield non-confidential information, and can be used by citizens, legal staff, and businesses, in accordance with Chinese law. The new law also mandates that institutions that work on cryptography must set up management systems to ensure their encryption's security, and that they cannot ask private developers to share source code or other proprietary information—but any business secrets they acquire must remain confidential.

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Deep Learning Is Making Video Game Characters Move Like Real People
Andrew Liszewski
October 31, 2019

Scientists at the University of Edinburgh in the U.K. and Adobe Research used deep learning neural networks to help digital characters in video games move more realistically. The team trained a neural network on a database of motions by a live performer on a soundstage which they recorded and digitized. The network can adapt what it learned from the database to most scenarios so characters move in natural-looking ways. The network is filling the gaps between a digital character's various poses and motions, intelligently and seamlessly stitching together these elements into a whole.

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A drone on the ground in Iceland surrounded by ice glaciers. Drones Help Map Iceland's Disappearing Glaciers
University of Dundee
Dominic Glasgow
October 25, 2019

Researchers at the University of Dundee (UoD) in Scotland, working with the University of Iceland and the Icelandic Meteorological Office, are using a three-dimensional (3D) process to compare old aerial photos with modern drone photography to track ice loss from Iceland’s largest glaciers. The researchers created composite images by comparing images from 1980s aerial surveys to photos taken by drones, which help to document dramatic ice loss by the glaciers. Said UoD's Kieran Baxter, “We have been working to produce images that are both engaging and easy to understand. ... To do this, we developed a novel process based on principles that are used by glaciologists to measure ice-volume loss."

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Simulating Complexity of Fluids, Strands in the Virtual World
SIGGRAPH Asia 2019
October 31, 2019

Scientists at Canada's University of Waterloo and Columbia Engineering have developed a multi-scale framework to mimic the dynamics of strands interacting with shear-dependent liquids. Columbia's Yun Fei said the simulation must “handle both a large chunk of fluids moving around and the small, detailed motions by strands and their surface flows.” Said Columbia Engineering’s Changxi Zheng, “Our algorithm brings together multiple physical models at both fine and large scales, and enables the simulation to capture highly complex, rich and multi-physics phenomena in fluid-strand interactions.” The new technique can be used to create special effects for the motion picture industry.

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CSL Researchers Develop Platform for Scalable Testing of Autonomous Vehicle Safety
University of Illinois Coordinated Science Lab
Allie Arp
October 24, 2019

Researchers in the University of Illinois at Urbana-Champaign's Coordinated Science Laboratory (CSL) are harnessing artificial intelligence (AI) and machine learning to make autonomous vehicle (AV) technology safer via software and hardware upgrades. The team designed a platform that allows partner companies like Samsung and Nvidia to address safety issues in autonomous technologies faster and at lower cost. The researchers observed that until an autonomous vehicle system has a specific issue, it is difficult to train the software to overcome it. That, explained CSL’s Saurabh Jha, is why “We inject errors in the software and hardware stack of the autonomous vehicles in computer simulations and then collect data on the autonomous vehicle responses to these problems.” Said Nvidia’s Steve Keckler, “We expect that the technologies being developed by the Illinois research team will make it easier for engineers to develop safer automotive systems at lower cost.”

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An example of a model MIT researchers developed using a supercomputer that captures what global Web traffic could look like on any given day. Supercomputer Analyzes Web Traffic Across Entire Internet
MIT News
Rob Matheson
October 27, 2019

Massachusetts Institute of Technology (MIT) researchers used a supercomputing system to design a model that can track global web-traffic patterns on a specific day, which could be used to inform Internet policy, outage management, cybersecurity enhancement, and computing structure efficiency upgrades. The researchers compiled 50 billion data packets exchanged in different locations worldwide over several years, and ran it through a neural network pipeline on the MIT SuperCloud system; the pipeline trained a model that incorporates the relationships for all dataset connections. The model can use any network dataset to produce statistical measurements about the interconnected effects of all network links on each other. Said MIT’s Jeremy Kepner, “We built an accurate model for measuring the background of the virtual universe of the Internet. If you want to detect any variance or anomalies, you have to have a good model of the background.”

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Microsoft 2020 Imagine Cup
Northeastern University Institute for Experiential AI

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