ACM TechNews


Welcome to the January 19, 2018 edition of ACM TechNews, providing timely information for IT professionals three times a week.

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Two drones flying with sunset in background Energy Neutral Drone Swarms Can Spy on You Without Taking a Break
Motherboard
Michael Byrne
January 17, 2018


Researchers at the University of Cambridge in the U.K. and Koc University in Turkey propose an Energy Neutral Internet of Drones (enIoD) as a swarm of aerial surveillance robots that operates continuously because charging and data transfer tasks are integrated nearly seamlessly into its function. They note the enIoD would use a hybrid energy harvesting system that does not tap the power grid, with each drone equipped with a solar cell or wind turbine, while the drones themselves serve as the grid connecting each charging station. The researchers also say wireless power transfer systems combined with wireless charging docks distributed across a wide area would enable the drone swarm to fly more or less without interruption. The team says the purpose of this network is to keep track of amateur drones, which they see as a vector for terrorists and other malefactors to attack civilians.

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NUS Engineers Invent Tiny Vision Processing Chip for Ultra-Small Smart Vision Systems and IoT Applications
NUS News (Singapore)
January 18, 2018


Researchers at the National University of Singapore (NUS) say they have developed EQSCALE, a microchip that captures visual details from video frames using 20 times less power than existing chips, and runs on a much smaller battery than conventional devices. The team says this could result in technologies that are powered continuously by a millimeters-sized solar cell without the need for battery replacement. They also think EQSCALE could pave the way for cost-effective Internet of Things applications. The video feature extractor captures visual details taken by a smart camera and reduces them into a smaller set of points of interest and edges for further analysis. The researchers note EQSCALE also performs continuous feature extraction using just 0.2 milliwatts of electricity, which they say is a major advancement in the level of miniaturization for smart vision systems. "Energy-quality scaling allows correct object recognition even when a substantial number of points of interests are missed due to the degraded quality of the target," says NUS professor Massimo Alioto.

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Researchers Measure Impact of 'Meltdown' and 'Spectre' Patches on HPC Workloads
HPCwire
Tiffany Trader
January 17, 2018


Researchers at the State University of New York (SUNY), University at Buffalo have analyzed the impact of Meltdown and Spectre security patches on the performance of popular high-performance computing (HPC) applications and benchmarks. The team used the application kernel module of the XD Metrics on Demand tool to run tests prior to and after the installation of the patches, and evaluated their performance on NWChem, NAMD, the HPC Challenge Benchmark suite, IOR, MDTest, and interconnect/MPI benchmarks. Most of the kernels were executed on one or two nodes of a development cluster at the university's Center for Computational Research. The worst-case performance hit went up to 54 percent for select functions, and real-world applications exhibited a 2- percent to 3-percent decline for single-node jobs and a 5-percent to 11-percent decline for parallel two-node workloads. Respective slowdowns of 6.4 percent, 2 percent, and 10 percent also were observed for Fourier transformation, matrix multiplication, and matrix transposition.

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Metal board with drops of colored liquid Programmable Droplets
MIT News
Larry Hardesty
January 19, 2018


Researchers at the Massachusetts Institute of Technology's (MIT) Media Lab have created hardware that applies electric fields to manipulate droplets of chemical or biological solutions around a surface, mixing them to test thousands of reactions in parallel as a low-cost alternative to experimentation. "Traditional microfluidic systems...are mechanical, and they break down all the time," notes MIT's Udayan Umapathi. He says the new system can deposit thousands of droplets on the device surface, and they would automatically move around to conduct biological experiments. The system runs software that enables users to describe the desired experiments, automatically calculates droplets' paths across the surface, and coordinates the timing of successive tasks.

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Stark Gender, Racial Gaps Persist in Engineering, Computer Science
Diverse Education
Tiffany Pennamon
January 17, 2018


There is a profound lack of female and minority students pursuing engineering or computer science (ECS) degrees at Texas universities, according to a Society of Women Engineers (SWE) study. SWE researchers found less than 4 percent of female students chose ECS majors versus about 20 percent of men across two- and four-year institutions. Less than 1 percent of black, Hispanic, and white female transfer students earned an ECS baccalaureate degree by fall 2015 across all analyzed first-time-in-college cohorts. Meanwhile, a national overview of the demographics of the engineering and science fields from the U.S. National Science Foundation found Caucasian men and women comprise 62 percent of people in those fields, while Hispanic, black, and Asian women make up only 8 percent, 7 percent, and 3 percent, respectively. SWE's Roberta Rincon cites efforts at diversification as including providing support networks, professional development opportunities, and career resources to raise persistence and completion rates for women and other minority students.

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Toothpaste and toothbrush on counter AI 'Scientist' Finds That Toothpaste Ingredient May Help Fight Drug-Resistant Malaria
University of Cambridge
Craig Brierley
January 18, 2018


Researchers at the University of Cambridge in the U.K. using Eve, an artificially intelligent "robot scientist," discovered that triclosan, a toothpaste ingredient, could be used as a drug against strains of malaria that are resistant to existing drugs. The researchers found, with the help of Eve, that triclosan can affect the malarial parasite's growth by inhibiting an enzyme of the parasite called DHFR. DHFR is the target of an established antimalarial drug, called pyrimethamine, but resistance to the drug among malaria parasites is common, especially in Africa. The researchers demonstrated that triclosan was able to target and act on this enzyme even in pyrimethamine-resistant parasites. "The discovery by our robot 'colleague' Eve that triclosan is effective against malaria targets offers hope that we may be able to use it to develop a new drug," says former University of Cambridge professor Elizabeth Bilsland.

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Piece of paper with Stanford's New Report Card for Artificial Intelligence
Singularity Hub
Thomas Hornigold
January 18, 2018


Stanford University's new AI Index attempts to measure the progress of artificial intelligence (AI) by aggregating data across various regimes, including venture capital investment, academic conference attendance, and published papers. The Index has recorded 10-fold increases in academic activity since 1996, as well as booming growth in AI-related startups and investment. The Index also rates public sentiment, and found people believe AI's benefits outweigh its drawbacks by three to one. In addition, it tries to track the advancement of various algorithms against a series of tasks, but measuring AI's progress toward general intelligence is harder. Such progress is likely to be gradual, but it may be possible to rate an AI's ability to learn and adapt to the work routines of humans in office-based tasks. "The main reason general AI is not captured in the report is that neither I nor anyone else would know how to measure progress," says University of Oxford professor Michael Woodridge.

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Are Programs Better Than People at Predicting Reoffending?
The Economist
January 17, 2018


A Dartmouth College research study found the COMPAS computer program performed about as well as humans at predicting recidivism. The researchers had 400 volunteers at Amazon Mechanical Turk and COMPAS predict who out of 1,000 defendants chosen at random would be arrested for another crime within two years of arraignment. Each volunteer saw only one group of 50 subjects, and each group was seen by 20 volunteers. The volunteers correctly predicted whether someone had been rearrested 62.1 percent of the time, and this climbed to 67 percent when the assessments of the 20 who studied a particular defendant's case were pooled. Meanwhile, COMPAS scored a recidivism rate of 65.2 percent, and a follow-up experiment in which the defendant's race was mentioned made no difference. The implication is that COMPAS is as competent as human common sense at parsing relevant facts to predict who will and will not be rearrested.

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Himawari-8 Data Assimilated Simulation Enables 10-Minute Updates of Rain and Flood Predictions
RIKEN
Jens Wilkinson
January 18, 2018


Researchers at the RIKEN Advanced Institute for Computational Science in Japan have used the K supercomputer to show incorporating satellite data at frequent intervals into weather prediction models can improve rainfall predictions and enable more precise predictions of typhoon development. They used data from Himawari-8, a satellite that can scan the entire area it covers every 10 minutes in both visible and infrared light, at a resolution of up to 500 meters. When studying the behavior of Typhoon Soudelor, a category 5 storm that struck the Pacific region in late July and early August 2015, the researchers found their new simulation more accurately predicted the rapid development of the storm. "It is gratifying to see that supercomputers, along with new satellite data, will allow us to create simulations that will be better at predicting sudden precipitation and other dangerous weather phenomena," says RIKEN's Takemasa Miyoshi.

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Woman having face scanned Computer-Aided Facial Analysis Helps Diagnosis
University of Bonn (Germany)
January 15, 2018


Researchers at the University of Bonn in Germany say they have demonstrated that the computer-aided image analysis of patient portraits can facilitate and significantly improve diagnoses. The researchers say they used artificial intelligence methods to analyze data on genetic material, cell surface texture, and typical facial features in order to simulate disease models. The team used photographs of the faces of 91 patients suffering from Mabry syndrome, a rare disease that causes mental retardation and is part of a group of diseases known as GPI anchor deficiencies. The researchers say cell surface changes characterized by GPI anchor deficiencies were detected in some of the patients, and genetic analysis revealed gene mutations that are typical for this group of diseases. Alexej Knaus of the Institute for Genome Statistics and Bioinformatics of the University Hospital Bonn says the research shows that "computer-aided evaluation of patients' portraits can facilitate and improve the diagnosis of GPI anchor deficiencies, which is significant progress."

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Stanford's AI Predicts Death for Better End-of-Life Care
IEEE Spectrum
Jeremy Hsu
January 16, 2018


Researchers at Stanford University are testing the concept of using artificial intelligence (AI) to predict mortality as an opportunity to help prompt doctors and patients to engage in end-of-life discussions earlier. Their test involves employing AI to help physicians screen for newly admitted patients who could be positively affected by talking about palliative care options. The team trained a deep-learning AI algorithm on the electronic health records of about 2 million patients admitted to hospitals to predict the death of a given patient within the next three to 12 months. "The scale of data available allowed us to build an all-cause mortality prediction model, instead of being disease- or demographic-specific," says Stanford's Anand Avati. The team wants to evaluate the pilot study's success based on results such as how physicians on both the palliative care team and the first-line team caring for the patients behave differently.

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Ultra-Thin Memory Storage Device Paves Way for More Powerful Computing
UT News
Adrienne Lee
January 17, 2018


Researchers at the University of Texas at Austin (UT Austin), in collaboration with colleagues at Peking University in China, have developed the thinnest memory storage device with dense memory capacity, which they say could lead to faster, smaller, and smarter computer chips for a range of applications. The new devices, dubbed atomristors, are made from two-dimensional nanomaterials and improve upon memristors by permitting the advancement of Moore's Law at the system level. UT Austin professor Deji Akinwande says atomristors enable three-dimensional integration of nanoscale memory with nanoscale transistors on the same chip for advanced computing systems. The researchers used metallic atomic sheets of graphene as electrodes and semiconducting atomic sheets of molybdenum sulfide as the active layer, creating a memory cell sandwich that is about 1.5 nanometers thick. The researchers note the atomristors are the smallest radio frequency memory switches to be demonstrated with no DC battery consumption.

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STEM Majors on Rise Even as College Enrollment Shrinks
Campus Technology
Dian Schaffhauser
January 10, 2018


Despite across-the-board enrollment contraction for the fall 2017 term, the number of college students opting to major in science, technology, engineering, or math (STEM) has increased, according to the National Student Clearinghouse Research Center. The general trend among U.S. four-year institutions was of 2.2-percent growth in STEM majors, and majors related to computer and information sciences and support services expanded by 24,919 enrollments from fall 2016 to fall 2017. The most growth in two-year institutions was seen in programs related to science technologies/technicians, which rose by 2,391 enrollments. The most robust growth was observed in programs associated with science technologies/technicians, while year-over-year enrollment in computer and information sciences and support services grew 3.9 percent. Women outnumber men across all types of institutions, and in the latest term, 10.8 million women were enrolled, down 0.7 percent from a year ago, versus 8 million men, down 1.5 percent from fall 2016.

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