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 Berea


NFL has 77 apparently false positive COVID-19 tests from lab

FOX News

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. NEW YORK (AP) -- The NFL had 77 positive COVID-19 tests from 11 teams re-examined by a New Jersey lab after false positives, and all those tests came back negative. The league asked the New Jersey lab BioReference to investigate the results, and those 77 tests are being re-tested once more to make sure they were false positives. Among teams reporting false positives, the Minnesota Vikings said they had 12, the New York Jets 10 and the Chicago Bears nine.


Predicting Failure of the University

Communications of the ACM

Lucas asserted "... technology-enhanced teaching and learning can dramatically improve the quality and success of higher education ..." His Figure 1 and Figure 2, in outlining traditional versus technology-enhanced courses, suggested traditional teaching methods deliver a low-quality result, while professional (Hollywood) production methods deliver a high-quality result, with, again, no evidence provided. The idea of universities as "content producers" giving students "content" consisting of "course materials and exercises" gave me an analogous idea. Families give food and clothing to their children, but families are inefficient and can involve bloated administrations (parents). Just as parents do more than feed (they try to create an environment where their children can develop and thrive), universities likewise try to create a learning environment for students. Indispensable elements include laboratory work, fieldwork, real essays marked by real scholars (not against a list of bullet points), and project work.


Block Modeling in Large Social Networks with Many Clusters

Biesan, Shawn (Baldwin Wallace University) | Anthony, Adam (Baldwin Wallace University) | desJardins, Marie (University of Maryland Baltimore County)

AAAI Conferences

In this paper, we present an optimized version of the previously developed Block Modularity algorithm (Anthony,2009). The original algorithm was a fast, greedy method that effectively discovered a structured clustering in linked data and scaled very well with the number of nodes and edges. The optimized version is scalable in terms of the model complexity; the technique can now be used effectively to discover thousands of clusters in data sets with hundreds of thousands (and possibly more) nodes and edges. The optimization leads to an improvement of the runtime per iteration from cubic to quadratic with a small increase in the constant factor. The algorithm compares favorably with Karrer and Newman's Degree-Corrected Block Model (DCBM) in both runtime and quality of results.