Welcome to the April 4, 2018 edition of ACM TechNews, providing timely information for IT professionals three times a week.
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MIT Professor Who Developed Wi-Fi-like Device That 'Sees Through Walls' to Receive ACM Prize in Computing
Association for Computing Machinery
Jim Ormond
April 4, 2018
ACM on Wednesday announced that Massachusetts Institute of Technology Computer Science and Artificial Intelligence Laboratory (MIT CSAIL) professor Dina Katabi is the recipient of the 2017 ACM Prize in Computing for creative contributions to wireless systems. The ACM Prize in Computing, which carries a $250,000 prize, recognizes early-to-mid-career contributions that have fundamental impact and broad implications. Katabi is being recognized for co-authoring several highly influential papers on overcoming interference in wireless networks to improve the flow of data traffic. "Dina Katabi's work has contributed to a seamless increase in traffic, as well as the ever-increasing volumes of data that are shared over mobile systems," says ACM president Vicki L. Hanson. In addition, Katabi is being honored for her work on wireless network coding, interference mitigation, sensing and wireless signals, and the Sparse Fast Fourier Transform.
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Scientists Use AI to Predict Biological Age Based on Smartphone, Wearables Data
MIPT News (Russia)
March 29, 2018
Moscow Institute of Physics and Technology (MIPT) researchers in Russia have demonstrated that physical activity data obtained from wearable computing devices can be used to generate digital biomarkers of aging and frailty. "Recent promising examples in the field of medicine include neural networks showing cardiologist-level performance in detection of the arrhythmia in ECG data, deriving biomarkers of age from clinical blood biochemistry, and predicting mortality based on electronic medical records," notes MIPT's Peter Fedichev. "Inspired by these examples, we explored AI potential for Health Risks Assessment based on human physical activity." The scientists analyzed physical activity records and clinical data from the U.S. National Health and Nutrition Examination Survey, and trained a neural network to predict biological age and mortality risk of participants in a week-long stream of activity measurements. A convolutional neural network was used to deconstruct the most biologically relevant motion patterns and establish their relation to general health and recorded lifespan.
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DevOps, Machine Learning Dominate Technology Opportunities This Year
ZDNet
Joe McKendrick
April 3, 2018
Programming languages and frameworks associated with DevOps and machine learning are on the rise, and developers working in these areas garner the highest salaries, according to a Stack Overflow survey of more than 100,000 developers from around the world. The survey found DevOps specialists and engineering managers have the highest salaries in the field, averaging between $70,000 and $90,000 a year worldwide. In addition, the survey showed DevOps specialists and developers who code for desktop and enterprise applications have the most experience, with an average of eight years of professional coding under their belts. When asked about their most "loved" and "dreaded" platforms for development, 71 percent of respondents identified their most loved platform as Linux, while 72 percent of respondents said their most dreaded platform was SharePoint. Python was found to be the fastest-growing programming or scripting language currently in use, and JavaScript was the most commonly used programming language for the sixth year in a row.
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What People See in 157 Robot Faces
IEEE Spectrum
Evan Ackerman
April 3, 2018
University of Washington in Seattle researchers conducted a study of robot faces--157 in all--across 76 dimensions, to determine the distinct ways people experience them. Among the study's insights was that robots whose faces were rated less-friendly lacked a mouth and pupils, but had eyelids; eyebrows also were deemed to be indicators of intelligence. Although no robot faces were rated as significantly more likable than the baseline face, the robot that possessed irises was the most liked by survey participants overall. "I think our work helps to elucidate what kind of effect certain design choices have, and while these results are by no means definitive, I think they do help prime our thinking about design consequences," says the University of Washington's Alisa Kalegina. The work was presented in March at the ACM/IEEE International Conference on Human Robot Interaction (HRI 2018) in Chicago, IL.
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Team Uses Blue Waters Supercomputer to Bring Subatomic Resolution to Computational Microscope
Scientific Computing
April 2, 2018
University of Illinois scientists have built a "computational microscope" that can model the atomic and subatomic forces driving molecular interactions, which they think will simplify efforts to understand the chemistry of life, simulate large molecular systems, and develop new pharmaceutical and industrial agents. The team used a nanoscale molecular-dynamics program called NAMD, which applies classical-mechanics methods to model the structure and simulate the behavior of hundreds of millions of individual atoms, in combination with a program that models the interactions of protons, neutrons, and electrons. By implementing a technique for partitioning large molecules into classical- and quantum-mechanics regions, the researchers could concentrate their computational resources on small regions involved in critical interactions. "We set it up so that researchers can easily choose how they will partition their own systems," says University of Illinois professor Zaida Luthey-Schulten.
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Monitor Detects Dangerously Low White Blood Cell Levels
MIT News
Anne Trafton
April 2, 2018
Massachusetts Institute of Technology (MIT) researchers have developed a method for measuring white blood cell levels by imaging the cells as they flow through capillaries at the base of the fingernail. In a study of 11 patients receiving chemotherapy, the device identified an excessively low white blood cell count with 95-percent accuracy. The researchers used a wide-field microscope that emits blue light, which penetrates about 50 to 150 microns under the skin and is reflected back to a video camera. The team imaged the skin at the base of the nail because the capillaries there are located very close to the surface, and are so narrow that white blood cells must squeeze through one at a time, making them easier to visualize. Although a test of the new method involved three blinded human assistants observing video footage of the blood flow, the researchers are developing an algorithm to perform the same task automatically.
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App for Early Autism Detection Launched on World Autism Awareness Day
University at Buffalo News
Cory Nealon
April 2, 2018
Researchers at the University at Buffalo (UB) have developed EarlySee, an application that gives parents an easy-to-use tool that can help detect autism spectrum disorder in children as young as 12 months. Traditionally, the decision whether or not to screen for autism is based on subjective observations made by parents or primary care doctors, which often leads to delays for the actual diagnosis. "EarlySee addresses the diagnosis delay by giving parents a widely accessible and quantitative tool they can use in their homes to help spot autism earlier," says UB professor Wenyao Xu. "This is critical because the earlier autism is identified, the more effective the benefits of treatment will be." Xu notes EarlySee tracks behavioral information such as facial expression and gaze attention to infer the neurocognitive responses of children looking at pictures of social scenes, and it can inform parents whether they should seek further medical attention for a conventional diagnosis.
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Digital Life Team Creates Animated 3D Models of Sea Turtles From Live Specimens
University of Massachusetts Amherst
April 2, 2018
The Digital Life team at the University of Massachusetts-Amherst (UMass Amherst) has unveiled three dimensional (3D) models of a loggerhead and a green sea turtle, created in a joint effort with sea turtle rescue and research institutions. Digital Life's photographer captured images of live sea turtles using a custom multi-camera system created in biologist Duncan Irschick's laboratory at UMass Amherst. To create the models, the team used specialized software and a process called photogrammetry, in which multiple still images are integrated to create lifelike 3D meshes with photographic colors. Irschick says the researchers believe their project marks the first time 3D models of sea turtles have been created from live animals. The team says the models can be downloaded and 3D-printed for classrooms, and used by scientists in a computer-modeling environment, for example, to test migration models.
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Twisting Graphene Into Spirals
Kyoto University
March 29, 2018
Researchers at Kyoto and Osaka universities in Japan have successfully synthesized helical nanographene, a breakthrough that could lead to nanoscale induction coils and molecular springs for use in nanomechanics. The team processed some basic chemical compounds through step-by-step reactions, such as McMurry coupling, followed by stepwise photocyclodehydrogenation and aromatization; the researchers found they had synthesized the foundational backbone of helical graphene, says Kyoto University's Yusuke Nakakuki. The team confirmed the helicoid nature of the structure using x-ray crystallography, which also revealed both clockwise and counter-clockwise nanographenes. Additional tests determined the electronic structure and photoabsorption properties of the compound differ significantly from previous ones. "This helical nanographene is the first of its kind," says Kyoto University's Kenji Matsuda. "We will try to expand their surface area and make the helices longer. I expect to find many new physical properties as well."
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Deep Learning, Artificial Intelligence Leading the Way to Smart Houses
Baylor Lariat (TX)
Samantha Amaro
March 28, 2018
Baylor University researchers are studying deep learning, with a focus on improving medical imaging and advancing the future of truly smart houses that will perform all manual labor for occupants. The research is divided into two categories: distributed deep learning and energy-efficient deep learning. Distributed deep learning involves investigating how to use several local machines to compute different parts of the main neural network, while energy-efficient deep learning focuses on the problem of being able to provide a constant source of energy for continuous projects. The researchers are using deep learning to analyze medical images, including positron emission tomography (PET) scans and computed tomography (CT) scans. The team is also leading a smart home project to determine whether a house can measure a person's overall health; sensors throughout the house would read a person’s biorhythms and send alerts to the home's occupants if needed.
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Researchers Study Perceptions of Anxiety and Women in STEM
The Dartmouth
Wally Joe Cook
March 27, 2018
A team of researchers from Dartmouth College, the University at Buffalo, and Carnegie Mellon University have determined gender impacts an individual's perception of women's anxiety in science, technology, engineering, and math (STEM) fields. Men were found to be more likely than women to blame this anxiety and self-doubt on internal factors, while women usually attribute such emotions to external factors. Dartmouth's Gili Freedman says the researchers used narratives about women's actual STEM experiences in the study, noting it was ambiguous whether the instructor had any biases against women. "Women were more likely than men to think that [the character] was experiencing anxiety or self-doubt because of factors like instructor bias or being aware of stereotypes about women in STEM," Freedman says. "Men were more likely than women to think that her anxiety stemmed from just not being prepared enough for the class." Freedman stresses it is vital to understand how people think about the presence of women in STEM in order to boost female representation in its fields.
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Quantum Computing Can Go Chemical With Molecular Qubits
Chemistry World
Katrina Krämer
March 27, 2018
Northwestern University researchers are making qubits out of organometallic molecules, hoping to build extended networks of such molecules that could advance quantum computers. Northwestern's Danna Freedman works with qubit molecules that are vanadium (IV) complexes. The transition metal's unpaired electron is the information-carrying unit, while the rest of the molecule is less relevant in quantum terms. "We're trying to eliminate sources of decoherence, that is the things that collapse the superposition state," says Freedman. The team is using IBM's 20-qubit processor at ultra-low temperatures to avoid thermal movement that can destroy the superconducting circuits' superposition. To eliminate decoherence stemming from nuclear spin, the researchers surround the vanadium center with low nuclear spin atoms such as sulfur, carbon, or oxygen. The longest-lived qubit stays in superposition for almost 1ms at 10K, which the researchers say is a good amount of time to execute an operation.
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