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Welcome to the August 8, 2022, edition of ACM TechNews, providing timely information for IT professionals three times a week.

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Cyborg hands like these could one day feature sensors that can identify whatever they touch. Artificial Finger Can Identify What Common Material Things Are Made Of
New Scientist
Alex Wilkins
August 5, 2022


An artificial finger created by researchers at China's Beijing Institute of Nanoenergy and Nanosystems can identify different materials using triboelectric sensors, and can sense their roughness. The device features four square sensors, each composed of a different polymer that possesses different electrical properties. When the sensors move into sufficient proximity to an object's surface, electrons from each square interact with the surface in a unique, quantifiable way. The sensors are connected to a processor and an organic light-emitting diode screen, which displays the name of the material type. When integrated with machine learning-based data analysis, the finger could identify 12 different materials—including wood, glass, plastic, and silicon—with at least 90% accuracy.

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Researchers trained a machine-learning model to monitor and adjust the 3D printing process in real time. Using AI to Control Digital Manufacturing
MIT News
Adam Zewe
August 2, 2022


An international team of researchers led by the Massachusetts Institute of Technology has taught a machine learning system with computer vision to monitor and control three-dimensional (3D) printing in real time. The researchers used simulations to train a neural network to adjust printing parameters to minimize errors, then applied it to a 3D printer. The machine-vision system employs two cameras focused on the printer's nozzle, and calculates the printing material's thickness as it is extruded via light measurement. The system printed objects with greater accuracy than all other 3D printing control methods evaluated by the researchers. "This project is really the first demonstration of building a manufacturing system that uses machine learning to learn a complex control policy," said MIT's Wojciech Matusik.

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Researchers Taught Machines to Follow Lego Instruction Manuals
Gizmodo
Andrew Liszewski
August 3, 2022


Researchers at Stanford University have developed a learning-based framework that enables machines to interpret step-by-step instructions for building Lego models. Lego instruction manuals typically show semi-assembled models that change as bricks are added with each step. The Manual-to-Executable-Plan Network (MEPNet) compares previously generated three-dimensional models to the next iteration to determine where new pieces fit in each step. Because differences are too subtle for the framework to detect from scanned images or printed pages, it must make these determinations on its own. The process is made easier by the fact that MEPNet understands how Lego bricks fit together and their positional limitations, which helps narrow down potential attachment points in the semi-assembled model.

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Nauticus Robotics’ Aquanaut robot can swim to a destination and carry out tasks with minimal supervision, saving money on the offshore operation of oil wells, wind turbines, fish farms, and more. NASA Space Robotics Dive into Deep-Sea Work
NASA
August 2, 2022


Former NASA roboticists who were part of the team that developed Robonaut 2, the first humanoid robot to serve as an astronaut assistant on the International Space Station in 2011, are part of a research team at Nauticus Robotics Inc. that has developed a deep-sea robot. The fully electric, sports-car-sized robot, called Aquanaut, features cameras, sensors, and claw hands that can be equipped with various tools. The goal is for the robot to work with minimal supervision from a remote control center, providing significant savings to offshore oil well and wind turbine operators and even fish farmers. Two Aquanauts have been built so far, with 20 more planned in the next three years.

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All Roads Lead to Big Cities
Hokkaido University (Japan)
August 4, 2022


Researchers at Japan's Hokkaido, Kagawa, and Tohoku universities and the U.K.'s University of Oxford devised a computational model that factors in roads to explain the distribution and expansion of towns in Italy. The researchers based the model on a grid of cells that each feature a terrain, a slope, and populations. The computer assesses in each simulation round how road networks connecting each point on the map to every other point grow or contract depending on the endpoints' popularity, and how each cell's populace changes based on how well connected it is to all other cells. Although the model cannot replicate the distribution of modern towns with total accuracy, the researchers said it still "provides a baseline reference tool to predict the expected population distribution when constrained solely by topography."

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College in the Metaverse Is Here. Is Higher Ed Ready?
Inside Higher Ed
Susan D'Agostino
August 3, 2022


This fall, students at 10 U.S. universities will attend metaversities, virtual reality (VR) platforms where educators and students wear VR headsets and interact synchronously. Advocates claim VR increases student engagement, achievement, and satisfaction, but some scholars worry about metaversity technology licensors putting revenues above academic freedom, exploiting students' data, or replicating biased narratives in VR. Many such challenges can be addressed by aligning educational best practices, commercial incentives, and political resolution, while students may find metaversities better than engagement via remote, two-dimensional screens. Last year, Morehouse College tested a proof-of-concept metaversity with courses in world history, biology, and chemistry, and participating world history students improved their grade point averages 10% compared to grades in the same class facilitated through Zoom and face-to-face. The University of Massachusetts' Nir Eisikovits believes on-campus education will ultimately supplement metaversities, not vice-versa.

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Zooming In to Get the Full Picture
European Molecular Biology Laboratory
Ivy Kupec
August 4, 2022


Scientists at the European Molecular Biology Laboratory and the University of Washington (UW) have built the most comprehensive single-cell map of embryo development in any animal to date, using the fruit fly (Drosophila melanogaster) as its model. The researchers profiled open chromatin from nearly 1 million cells and RNA from 500,000 cells from overlapping time-points encompassing fruit fly embryogenesis. Said UW's Diego Calderon, who taught a neural network to predict the developmental time for each cell, "This method allows you to zoom in to any part of this embryogenesis timeline at a scale of minutes."

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Sihong Wang shows a single neuromorphic device with three electrodes, which can implement artificial intelligence (AI) to process massive amounts of health information in real time. Stretchy Computing Device Analyzes Health Data
University of Chicago Pritzker School of Molecular Engineering
Sarah C.P. Williams
August 4, 2022


The University of Chicago's Sihong Wang said his team has engineered a stretchable computing chip that mimics the human brain to "analyze health data right on our own bodies." The neuromorphic chip is designed to be worn on the skin, collect data from biosensors, and infer conclusions about the subject's health via machine learning. The researchers combined polymers into a device that enables artificial intelligence-based health data analysis, storing and analyzing data in an integrated manner. The team trained the device to categorize electrocardiogram (ECG) data into five types of signals (one healthy, four abnormal), and showed it could accurately classify new ECGs whether the chip was stretched or bent.

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Western Team Achieves International Holographic Teleportation
Western News (Canada)
Justin Zadorsky
August 4, 2022


A team of researchers at the Institute for Earth and Space Exploration of Canada's Western University conducted what they described as the first international two-way holographic teleportation (holoportation) demonstration. "We transported one person from Alabama to London, Ontario, and then each of the students here on the project were able to instantly holoport themselves in holographic form down to Huntsville, Alabama," explained Western's Adam Sirek. The technology integrates a camera that generates a holographic image of a subject, which is transmitted to the destination of choice. The user on the receiving end wears a hololens to see the subject within their environment. Both participants can interact in their environments as if they are really there if they are wearing a hololens.

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Streaming from the Future
Research at Osaka University (Japan)
July 28, 2022


A machine learning system developed by researchers at Japan's Osaka University can remove buildings from a live view on a mobile device, which could speed up the process of urban renewal when various community stakeholders must come to an agreement. The researchers used semantic segmentation on the input video to classify images pixel by pixel, speeding up the generative adversarial network algorithm to provide real-time augmented video. In field tests, the researchers could stream virtual demolition video at an average of 5.71 frames per second. Said Osaka University's Takuya Kikuchi, "Our method enables users to intuitively understand what the future landscape will look like, which can contribute to reducing the time and cost for forming a consensus."

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To help make use of smart meters in the U.S. like this one, a Lawrence Livermore National Laboratory team developed an open-source data-science toolkit for power and data engineers. An Open-Source Data-Science Toolkit for Energy GridDS
Lawrence Livermore National Laboratory
August 2, 2022


GridDS, an open-source data-science toolkit, was designed to provide an integrated energy data storage and augmentation infrastructure, as well as a flexible and wide-ranging set of cutting-edge machine learning (ML) models. Researchers at the U.S. Department of Energy's Lawrence Livermore National Laboratory (LLNL) developed GridDS to train and validate ML models to help enhance the efficiency of distributed energy resources. The toolkit also is engineered to harness advanced metering infrastructure, outage management systems data, supervisory control data acquisition, and geographic information systems to predict energy requirements and to detect nascent grid failures. LLNL's Vaibhav Donde said, "GridDS can take general approaches, apply them to highly specific energy tasks, and evaluate and validate their performance."

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Weaving Fire into Form: Aspirations for Tangible and Embodied Interaction
 
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