When National Science Foundation officials announced in February that three major providers of cloud computing were donating up to $9 million collectively for big data research, they already were looking for ways to broaden the effort to include a wider variety of topics, including cybersecurity. The expansion is intended to benefit both research and education initiatives and is necessary, in part, because the cloud providers now acquire cutting-edge hardware before it is made available to researchers.
The foundation announced nearly $30 million in new funding for research in data science and engineering through a program known as Critical Techniques, Technologies and Methodologies for Advancing Foundations and Applications of Big Data Sciences and Engineering (BIGDATA). The National Science Foundation (NSF) awards are paired with support from Amazon Web Services, Google Cloud Platform and Microsoft Azure, which committed up to $3 million each in cloud resources for relevant BIGDATA projects over a three-year period starting last fiscal year. A key goal of the collaboration is to encourage research projects focusing on large-scale experimentation and scalability studies.
“No human can read 200,000 journal articles, but a machine can,” said Paul Cohen, the school’s founding dean.
Cohen announced the launch of the Modeling and Managing Complicated Systems (MoMaCS) Institute in May during the school’s inaugural Modeling the World’s Systems Conference in Pittsburgh. The two-day conference, which featured panel discussions on the potential for modeling national security challenges, the aging process, the opioid epidemic and more, was designed to bring collaborators to the table and examine how the institute and modeling can help to find new solutions to complicated and age-old questions.
Cohen said the institute will serve as a hub where researchers and stakeholders from across disciplines collaborate on projects that utilize the power of computer modeling and simulation.
For many people, household chores are a dreaded, inescapable part of life that we often put off or do with little care. But what if a robot assistant could help lighten the load?
Recently, computer scientists have been working on teaching machines to do a wider range of tasks around the house. In a new paper spearheaded by MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and the University of Toronto, researchers demonstrate “VirtualHome,” a system that can simulate detailed household tasks and then have artificial “agents” execute them, opening up the possibility of one day teaching robots to do such tasks.
We develop a model for contagion in reinsurance networks by which primary insurers’ losses are spread through the network. Our model handles general reinsurance contracts, such as typical excess of loss contracts. We show that simpler models existing in the literature–namely proportional reinsurance–greatly underestimate contagion risk. We characterize the fixed points of our model and develop efficient algorithms to compute contagion with guarantees on convergence and speed under conditions on network structure. We characterize exotic cases of problematic graph structure and nonlinearities, which cause network effects to dominate the overall payments in the system. We lastly apply our model to data on real world reinsurance networks. Our simulations demonstrate the following: (1) Reinsurance networks face extreme sensitivity to parameters. A firm can be wildly uncertain about its losses even under small network uncertainty. (2) Our sensitivity results reveal a new incentive for firms to cooperate to prevent fraud, as even small cases of fraud can have outsized effect on the losses across the network. (3) Nonlinearities from excess of loss contracts obfuscate risks and can cause excess costs in a real world system.
On the whole, the world loves TV sci-fi series Westworld.
It’s about killer robots in the wild west, features warped old-timey renditions of Radiohead songs, and Christopher Nolan’s brother Jonathan co-created it. What’s not to love?
Westworld also explores one of the biggest topics in tech: AI. Dolores (Evan Rachel Wood) is an android in the Westworld theme park who enacts a handful of storylines everyday before workers hit reset and she forgets. Until one day she doesn’t. What ensues is a sometimes Blade Runner philosophical, Django Unchained blood fest.
Unlike the rest of the world, though, scientists, roboticists and researchers have a degree-backed understanding of the tech served up in the show. So, assuming important people have time to watch TV, I asked them what they thought of Westworld.
For a study published in this week’s issue of Science, UCLA economist Keith Chen and Washington State University economist Ryne Rohla combined smartphone-location data from more than 10 million Americans with precinct-level election results to quantify the impact of partisanship and political advertising on Thanksgiving dinners in 2016, on the heels of the presidential election. Among their findings: Democrats shortened their visits to Republican households by between 20 and 40 minutes; Republicans cut their time with Democratic hosts by 50 to 70 min; and mismatched families from areas with high political ad exposure spent even less time together.
The researchers’ results might not surprise you (2016’s election reportedly led many people to cut their Thanksgivings plans short), but their methods might: The smartphone data came from SafeGraph, a company that uses geolocation data from apps on your phone to maintain anonymized geospatial datasets for more than 10 million US smartphones. These data consist of “pings,” which give the locations of individual smartphones at specific moments in time. For their study, the researchers analyzed some 21 billion pings from November 2016 and 4.5 billion from November 2015.
You might be thinking: OK, that’s one kind of data, but it’s creepy to think that researchers could match my phone’s location with my political affiliation.
Mars colonization? Self-driving cars in the Midstate? Integrated health care databases? All are big topics that require “big data” to address.
And they will be among research areas explored initially by Middle Tennessee State University’s new Data Science Institute, launched earlier this month with a mission to promote funded interdisciplinary research and develop public and private collaborations around the emerging field of “big data.”
“The trend to gather more data has become more prevalent over the last 10 years, whether it be your Twitter feed, Facebook feed or just data that companies are able to collect,” said Charlie Apigian, interim director of the institute and a professor of information systems and analytics in MTSU’s Jones College of Business.
University of California-Berkeley, Digital Humanities at Berkeley
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Berkeley, CA “In conjunction with the inaugural Digital Humanities Summer Minor, DH at Berkeley will be hosting a weekly DH Summer Lecture series, highlighting the distinguished work of faculty and scholars engaged in the digital humanities at UC Berkeley.” June 14th: Adam Anderson, Mellon Postdoctoral Fellow in the Digital Humanities. [free]
Ann Arbor, MI June 6-7. “The 14th Precision Medicine World Conference (PMWC) will take place at the University of Michigan on June 6-7, 2018. This conference coincides with University of Michigan’s launch of a new Precision Health research initiative that integrates U of M’s strengths in Medicine, Engineering, Pharmacy, and Public Health.” [$$$$]
“The five-month Fellowship will run from July through December 2018. Fellows will participate in regular online meetings and trainings, and will receive tips and techniques to effectively train and share knowledge with others. In September 2018 the cohort will come together in Washington, D.C. for a three-day tech camp to sharpen their skills on using GFW and other open source mapping tools, remote sensing and GIS, data collection and visualization, mobile monitoring technologies, storytelling and media engagement, and digital and personal security.” Deadline for applications is June 15.
“Following the announcement of the 2018 China-U.S. Young Maker Competition, we are very excited that there are already over 1000 participants with over a month left before the final submission deadline! Project submissions are open to all makers, developers, and students age 18-40 in the United States.” Deadline for submissions is June 22.
“The opioid epidemic is one of the most profound public health crises facing the United States and the current crisis of opioid overdose deaths requires innovative approaches. The FDA remains committed to addressing this national crisis on all fronts, with a focus on encouraging medical product innovation to prevent new cases of opioid abuse and addiction and to treat those addicted. The agency has already taken significant steps to decrease public exposure to opioids, prevent new cases of opioid use disorder—also known as opioid addiction—and support the treatment of those with the disorder. As part of important efforts to address the epidemic of opioid misuse and abuse, the FDA is launching an innovation challenge to spur the development of medical devices, including diagnostic tests and digital health technologies (mobile medical applications) to help combat the opioid crisis and achieve the goal of preventing and treating opioid use disorder.” Deadline for submissions is September 30.”
I just received a note from Hadley Wickham that a new version of ggplot2 is scheduled to be submitted to CRAN on June 25. Here’s what choroplethr users need to know about this new version of ggplot2.
psyArXiv; Olivier Klein Tom Hardwicke Frederik Aust Johannes Breuer Henrik Danielsson Alicia Hofelich Mohr Hans IJzerman Gustav Nilsonne wolf vanpaemel Michael Frank
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The credibility of scientific claims depends upon the transparency of the research products upon which they are based (e.g., study protocols, data, materials, and analysis scripts). As psychology navigates a period of unprecedented introspection, user-friendly tools and services that support open science have flourished. There has never been a better time to embrace transparent research practices. However, the plethora of decisions and choices involved can be bewildering. Here we provide a practical guide to help researchers navigate the process of preparing and sharing the products of their research. Being an open scientist means adopting a few straightforward research management practices, which lead to less error prone, reproducible research workflows. Further, this adoption can be piecemeal – each incremental step towards complete transparency adds positive value. Transparent research practices not only improve the efficiency of individual researchers, they enhance the credibility of the knowledge generated by the scientific community.