Data Science newsletter – April 22, 2020

GROUP CURATION: N/A

UNC Charlotte’s School of Data Science, North Carolina’s first undergraduate program in data science, is now enrolling undergraduate students for the fall semester. The program, the first of its kind to incorporate the liberal arts and sciences with technical data skills, allows the School of Data Science to expand its efforts in helping to meet the soaring demand for qualified, dynamic data science professionals throughout the region and the nation.

Current students are able to register for the program’s first two introductory courses available this fall, where they will learn to apply statistical methods, tools and script programming languages to explore the ethical implications of collecting and using tabular data. Incoming freshmen and transfer students will have the opportunity to learn more about the bachelor’s degree program during Student Orientation, Advising, and Registration (SOAR) sessions this summer in preparation for fall 2020 registration.

This is how the Supreme Court embraces technology. Slowly.

It took a worldwide pandemic for the court to agree to hear arguments over the telephone, with audio available live for the first time. C-SPAN plans to carry the arguments.

Just two years ago case filings were made available online, decades after other courts. Other forays into technology, including posting opinions online, have not always gone smoothly.

Chief Justice John Roberts acknowledged in 2014 that courts will always be cautious when it comes to embracing the “next big thing” in technology.

Gov. Kim Reynolds’ administration has denied some requests for information about Iowa’s preparedness and response to the novel coronavirus by citing a broad exemption in the state’s public records law.

Two Iowa agencies have denied Des Moines Register requests this month seeking documentation of the state’s pandemic response plan and daily reports regarding the state’s response to the virus and COVID-19, the respiratory disease caused by the virus.

In their denials, the agencies, which are leading Iowa’s response to COVID-19, cited a broad confidentiality exemption in Iowa’s public records law that says information and records about “physical infrastructure, cyber security, critical infrastructure, security procedures or emergency preparedness” can be denied if “disclosure could reasonably be expected to jeopardize such life or property.”

In a paper published today in Physical Review Letters, University of Arizona engineering and optical sciences researchers, in collaboration with engineers from General Dynamics Mission Systems, demonstrate how a combination of two techniques — radio frequency photonics sensing and quantum metrology — can give sensor networks a previously unheard-of level of precision. The work involves transferring information from electrons to photons, then using quantum entanglement to increase the photons’ sensing capabilities.

“This quantum sensing paradigm could create opportunities to improve GPS systems, astronomy laboratories and biomedical imaging capabilities,” said Zheshen Zhang, assistant professor of materials science and engineering and optical sciences, and principal investigator of the university’s Quantum Information and Materials Group. “It could be used to improve the performance of any application that requires a network of sensors.”

To better understand its importance to public policy, it’s useful to distinguish between two broad (though highly interdependent) trends that define data science. The first is a gradual expansion of the types of data and statistical methods that can be used to glean insights into policy studies, such as predictive analytics, clustering, big data methods, and the analysis of networks, text, and images. The second trend is the emergence of a set of tools and the formalization of standards in the data analysis process. These tools include open-source programming languages, data visualization, cloud computing, reproducible research, as well as data collection and storage infrastructure.

Perhaps not coincidentally, these two trends align reasonably well with the commonly cited data science Venn diagram. In this diagram, data science is defined as the overlap of computer science (the new tools), statistics (the new data and methods), and critically, the pertinent domain knowledge (in our case, economics and public policy).

Researchers are engaged in a fierce debate over the startling estimates in a Stanford study that suggested as many as 81,000 people could already have been infected with coronavirus in Santa Clara County, with some of the world’s top number crunchers calling the study sloppy, biased and an example of “how NOT to do statistics.”

“I think the authors owe us all an apology… not just to us, but to Stanford,” wrote Andrew Gelman, a professor of statistics and political science and director of the Applied Statistics Center at Columbia University.

Yet after a weekend of attacks on the paper, a study announced Monday out of the University of Southern California on a sampling of residents in Los Angeles reached a very similar conclusion: It found hundreds of thousands of adults there may have already been infected. As of Monday, Los Angeles County had recorded fewer than 13,000 cases.

Students are protesting plans by the Australian National University (ANU) to enforce the use of remote monitoring software on their home systems for exams during the COVID-19 pandemic.

As reported by ABC News, a petition has been launched to fight the imposition of software on the basis that the forced install of such a solution is an invasion of student privacy.

Proctorio is at the heart of the controversy. The platform is touted as a “comprehensive learning integrity platform” and a means to “secure remote exams.”

As acquiring bigger data becomes easier in experimental brain science, computational and statistical brain science must achieve similar advances to fully capitalize on these data. Tackling these problems will benefit from a more explicit and concerted effort to work together. Specifically, brain science can be further democratized by harnessing the power of community-driven tools, which both are built by and benefit from many different people with different backgrounds and expertise. This perspective can be applied across modalities and scales and enables collaborations across previously siloed communities. [full text pdf download]

Draganfly conducted its first series of U.S. pandemic drone test flights in Westport, Connecticut.

Draganfly’s pandemic drone technology is being tested by the Westport Police Department as a new “Flatten the Curve Pilot Program.” According to Draganfly, this initiative is a collaboration of technologies developed by Draganfly, Vital Intelligence, a healthcare data services and deep learning company, and the University of South Australia.

Westport is located in Fairfield County, which has more than 17,550 confirmed cases of COVID-19. Fairfield County is adjacent to New York City, which has the most confirmed cases of coronavirus in the United States.

One of the most interesting projects in the MIT Connection Science group I’m affiliated with is The Atlas of Inequality. The project,— led by visiting professor Esteban Moro and professor Alex ‘Sandy’ Pentland — uses aggregated anonymous geolocation data from digital devices to estimate where different groups of people in U.S. cities spend their time. Over time, the project will be expanded to cities around the world. The data show the significant income inequality among people in those cities, not just by neighborhoods as you’d expect, but also in the restaurants, stores and other places beyond their neighborhoods which they visit every day.

A couple of weeks ago, the Atlas research group started to apply its geolocation data and methods to analyze the effectiveness of the social distancing policies adopted in the New York metropolitan area in response to the Covid-19 pandemic.

All of these reports are flawed in similar ways. Most important, none of them control for race, socioeconomic status, or quality of care—social determinants of health that we know explain the lion’s share of health disparities between groups of people. Structural racism and other forms of inequality in our society have long been linked to worse health outcomes, including higher rates of diabetes and hypertension (two likely Covid-19 risk factors) among people in oppressed groups. Now, those health disparities are on full display in the Covid-19 pandemic, which is disproportionately impacting black communities—not because of biology, but because of systemic inequalities like higher rates of exposure to the virus and less access to medical care.

University or college rankings have almost become an industry of their own, published by US News \& World Report (USNWR) and similar organizations. Most of the rankings use a similar scheme: Rank universities in decreasing score order, where each score is computed using a set of attributes and their weights; the attributes can be objective or subjective while the weights are always subjective. This scheme is general enough to be applied to ranking objects other than universities. As shown in the related work, these rankings have important implications and also many issues. In this paper, we take a fresh look at this ranking scheme using the public College dataset; we both formally and experimentally show in multiple ways that this ranking scheme is not reliable and cannot be trusted as authoritative because it is too sensitive to weight changes and can easily be gamed. For example, we show how to derive reasonable weights programmatically to move multiple universities in our dataset to the top rank; moreover, this task takes a few seconds for over 600 universities on a personal laptop. Our mathematical formulation, methods, and results are applicable to ranking objects other than universities too. We conclude by making the case that all the data and methods used for rankings should be made open for validation and repeatability.

“The goal is to bring more edge AI to the marketplace,” said Brainchip’s COO Roger Levinson. “The traditional IT architecture that is data center-centric doesn’t support edge computing that well,” Levinson said. “Consequently, many organizations find themselves with a dearth of IT resources at the edge of their enterprises, where the Internet of Things (IoT) and AI are now working. These organizations find themselves with limited power, compute, and memory at the edge.”

In these circumstances, it’s difficult to scale out the many edge IoT applications that are entering the marketplace and that can help operational improvements.

A solution like Brainchip-Socionext can plug into an edge device through USB or PCIe ports because it uses standard interfaces. It can provide up to 24 core processors in a box. Edge computing packages like this offer real promise for offloading processing and data loads from a central data center. They also reduce bandwidth and throughput requirements and minimize latency, because processing can occur at the edge.

Both California numbers show a far higher rate of infection than some other, similar efforts. In China, Wuhan’s Zhongnan Hospital, for example, found that among 3,600 of its employees — a group particularly at risk of catching the virus — 2.4% had developed antibodies, doctors there said in a press conference earlier this month.

Shifting Rates

Other, similar studies have produced far different rates, adding to the ongoing debate about the virus’s spread. A much smaller one in the Boston suburb of Chelsea found that of 200 randomly sampled residents where were stopped on the street and asked to give some drops of blood, 64 had antibodies. The study was conducted by physicians at Massachusetts General Hospital.

John Iafrate, a pathologist at Harvard University and the study’s principal investigator, said the high numbers may be caused by high levels of community spread in Chelsea’s dense living conditions, as well as socioeconomic challenges.

Pamella Oliver, provost of California State University at Fullerton, said on Monday that the university planned to start the fall semester online and, should governmental and health authorities allow, gradually move back to on-campus operations.

On Tuesday, Mitchell E. Daniels Jr., president of Purdue University, said that his university intended to bring students back to campus in August. He wrote in an email that Purdue was “determined not to surrender helplessly” to the difficulties of the virus.

Shutting down campus, he wrote, “has come at extraordinary costs, as much human as economic, and at some point, clearly before next fall, those will begin to vastly outweigh the benefits of its continuance.”

Online April 24, starting at 2 p.m. EDT. ” Researchers will have an opportunity to hear from multiple generalist repositories about the ways each repository is supporting discoverability and reusability of COVID-19 data and associated code. The [National Library of Medicine] will also provide an overview of available COVID-19 literature.”

Online “Due to the unfortunate circumstances we can not come together in person, but we can virtually! For the next 4 weeks we will be posting online content for you to enjoy.” [free]

Online May 4, starting at 9:30 a.m. PDT. “Panelists on the frontlines will discuss the new normal of schooling and supporting our students, teachers, and parents in a COVID19 reality” [registration required]

Online April 25-26. “A hackathon pairing developers and designers with experts in the medical community and others on the front lines fighting The Coronavirus.” [rsvp required]

The best part of researching my new book, Teachers vs Tech, was getting to read a lot more about memory. Not just the academic research on memory – although this was fascinating – but the practical attempts by real people to come up with systems to improve their long-term memory and remember things forever.

In the first installment of Read Like A Writer, we discussed how to write an ending that is surprising yet inevitable. In this installment, we’re going to talk about another way to build momentum in narrative by thinking about how that “surprise” element can be turned into suspense. Alfred Hitchcock illustrated the difference between surprise and suspense by inviting you to imagine a bomb under the table. If neither the characters nor the audience knows about the bomb, and it goes off, that’s a surprise. If the audience knows about the bomb, but the characters do not, and the audience anticipates the bomb going off, that is the suspense. The key difference is dramatic irony, that old dusty literary concept we all learned in high school.

“Due to the COVID-19 pandemic crisis, the university implemented emergency remote teaching starting on March 23, 2020. Students are now back home—some in different time zones—and expected to continue learning, with their teachers quickly adopting new technologies and methods to keep courses going. It is a tremendous challenge for all of us! This guide aims to help you as you navigate these difficult times.”