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1. Soft Robotics: If You Can Dream It, Can You Print It?

   https://doi.org/10.3389/frym.2024.1401789  

   Williamson, John Garrett; Schell, Caroline; Keller, Michael; Schultz, Joshua (August 2024, Frontiers for Young Minds)

   You can print anything... or can you? 3D printing is an exciting new technology that promises to very quickly create anything people can design. Scientists who want to make soft robots, like Baymax from Big Hero 6^TM, are excited about 3D printers. Our team uses 3D printing to make molds to produce soft robots. Molding is like using a muffin tin to make cupcakes. But can you make anything with 3D printing or are there times when 3D-printed molds do not work? Just like a cupcake liner, 3D-printed molds leave ridges, like a Ruffles potato chip, in soft robots. These ridges are a weak point where cracks can form, causing the robot to pop like a balloon. To prevent this, we sometimes need to make our robots using very smooth molds made from metal. This article talks about when and how 3D printing is useful in making soft robots. 

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2. It Can Understand the Logs, Literally

   https://doi.org/10.1109/IPDPSW.2019.00084  

   Pi, Aidi; Chen, Wei; Zeller, Will; Zhou, Xiaobo (July 2019, 2019 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW))

   Workflow reconstruction through logs is crucial for troubleshooting targeted distributed systems. It is also challenging to extract enough information from logs and keep a concise view, which makes manual log analysis hard to practice. However, currently popular tools rely on identifier-based log parsing, leaving a large amount of workflow information unexploited. In this paper, we propose a log extraction approach NLog, which utilizes a natural language processing based approach to obtain the key information from log messages and identify the same object in logs generated by different statements without any domain knowledge. We propose to use keyed message, a new log storage structure to store the parsed logs. We implement NLog and apply it to distributed data analytics frameworks Spark and MapReduce. Evaluation results show that NLog can accurately identify the objects in log messages even without explicit identifiers. By using keyed messages, users can have a concise as well as flexible view of the workflows. 

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3. Geomagnetic field intensity: How high can it get? How fast can it change? Constraints from Iron Age copper slag (Dataset)

   https://doi.org/10.7288/V4/MAGIC/19214  

   Shaar, Ron; Ben-Yosef, Erez; Ron, Hagai; Tauxe, Lisa; Agnon, Amotz; Kessel, Ronit (June 2021, Magnetics Information Consortium (MagIC))

   Paleomagnetic, rock magnetic, or geomagnetic data found in the MagIC data repository from a paper titled: Geomagnetic field intensity: How high can it get? How fast can it change? Constraints from Iron Age copper slag 

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4. You Can Have Your Cake and Redistrict It Too

   Gerdus Benadè, Ariel D. (January 2023, EC)
5. Can Invalid Information Be Ignored When It Is Detected?

   https://doi.org/10.1177/09567976241231571  

   Ramsey, Adam_T; Liu, Yanjun; Trueblood, Jennifer_S (March 2024, Psychological Science)

   With the rapid spread of information via social media, individuals are prone to misinformation exposure that they may utilize when forming beliefs. Over five experiments (total N = 815 adults, recruited through Amazon Mechanical Turk in the United States), we investigated whether people could ignore quantitative information when they judged for themselves that it was misreported. Participants recruited online viewed sets of values sampled from Gaussian distributions to estimate the underlying means. They attempted to ignore invalid information, which were outlier values inserted into the value sequences. Results indicated participants were able to detect outliers. Nevertheless, participants’ estimates were still biased in the direction of the outlier, even when they were most certain that they detected invalid information. The addition of visual warning cues and different task scenarios did not fully eliminate systematic over- and underestimation. These findings suggest that individuals may incorporate invalid information they meant to ignore when forming beliefs. 

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