Science stories you've missed

So-called "deep learning techniques", such as what are called convolutional neural networks (CNNs), have been exciting ecologists lately. In a paper published this week in Methods in Ecology and Evolution with the British Ecological Society, researchers show how such tools can automatize the analysis of various types of bird data, ranging from species abundance to behaviors, and from sources such as pictures or audio recordings (reviewed in Christin, Hervet, & Lecomte, 2019). Such identification, the authors contend, is crucial when trying to answer questions related to evolutionary biology and is mostly performed by marking animals with tags. "Such methods are well-established, but often make data collection and analyses time-consuming, or limit the contexts in which data can be collected," the researchers write. Computational advances, in particular regarding deep learning -- part of machine learning methods based on artificial neural networks -- can help r

"Many people don't realize that Albert Einstein was at the forefront of racial equality in the United States. It pained him to see the plight of African Americans." -Michio Kaku, Prof of Physics, City College NY Theoretical physicist and Nobelist Albert Einstein, famous for the equation E = mc2 and developing the theory of relativity, was greeted like a rock star when he appeared in public. He was a modest, unassuming celebrity and yet clearly the limelight suited him. In a new docudrama available for purchase on DVD, producer-director Julia Newman paints an elegant if at times frustrating portrait of the enigma scientist from Germany. He was Jewish and to hear her narrators tell it, gradually more fascinated with his own Jewishness. He had two wives (his physics student Mileva and then his cousin, the more matronly Elsa) but was a notorious womanizer, more or less sugarcoated in the film. He is shown to be a jerk in his treatment of wife one with a list of rules r

A new paper published as part of the Cato Working Papers (www.cato.org/workingpapers) posits that travel restrictions imposed from China did not halt transmission of the novel Coronavirus into the United States. The authors write that, "Historically, travel restrictions to prevent the spread of pandemic influenzas have been ineffective at halting or significantly delaying the spread (WHO Writing Group, 2006)." They use what is called the synthetic control method (SCM) to estimate a "counterfactual" (meaning, something has not happened or is not the case) number of COVID-19 cases for the U.S. in the absence of the February 2, 2020 travel restriction (Abadie, 2019; Abadie et al., 2010, 2015; Abadie et al., 2003; McClelland et al., 2017). "We use four different outcome variables to measure [the] number of COVID-19 cases: the cumulative number of new COVID-19 cases, the cumulative number of COVID-19 cases per million, the number of new cases, and the number of n