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December 28, 2020
Michael Bailey, Drew Johnston, Martin Koenen, Theresa Kuchler, Dominic Russel, Johannes Stroebel

We explore how social network exposure to COVID-19 cases shapes individuals’ social distancing behavior during the early months of the ongoing pandemic. We work with de-identified data from Facebook to show that U.S. users whose friends live in areas with worse coronavirus outbreaks reduce their mobility more than otherwise similar users whose friends live in areas with smaller outbreaks.

December 23, 2020
Duncan C. McElfresh, Christian Kroer, Sergey Pupyrev, Eric Sodomka, Karthik Abinav Sankararaman, Zack Chauvin, Neil Dexter, John P. Dickerson

Using the recently deployed Facebook Blood Donation tool, we conduct the first large-scale algorithmic matching of blood donors with donation opportunities. In both simulations and real experiments we match potential donors with opportunities, guided by a machine learning model trained on prior observations of donor behavior.

December 21, 2020
May Yen, Francesco Colella, Harri Kytomaa, Boyd Allin, Alex Ockfen

This paper is the second of a two-part series that discusses a numerical methodology that relies on the concept of cumulative equivalent exposure to evaluate contact burn injury thresholds. In Part I, the effect of a finite thermal mass is analyzed for an infinite plate of several finite thicknesses. In Part II, the sensitivities to object shape, size, thickness, contact resistance and applied heat flux are considered.

December 18, 2020
Yang Liu, Zhengxing Chen, Kittipat Virochsiri, Juan Wang, Jiahao Wu, Feng Liang

We demonstrate statistically significant improvement in daily metrics and resource efficiency by our method in several notification applications at a scale of billions of users. To our best knowledge, our study represents the first deep RL application on the frequency control problem at such an industrial scale.

December 16, 2020
Matteo Castiglioni, Andrea Celli, Alberto Marchesi, Nicola Gatti

In Bayesian persuasion, an informed sender has to design a signaling scheme that discloses the right amount of information so as to influence the behavior of a self-interested receiver. This kind of strategic interaction is ubiquitous in real-world economic scenarios. However, the seminal model by Kamenica and Gentzkow makes some stringent assumptions that limit its applicability in practice.

December 15, 2020
Stéphane d'Ascoli, Levent Sagun, Giulio Biroli

A recent line of research has highlighted the existence of a “double descent” phenomenon in deep learning, whereby increasing the number of training examples N causes the generalization error of neural networks to peak when N is of the same order as the number of parameters P. In earlier works, a similar phenomenon was shown to exist in simpler models such as linear regression, where the peak instead occurs when N is equal to the input dimension D. Since both peaks coincide with the interpolation threshold, they are often conflated in the literature. In this paper, we show that despite their apparent similarity, these two scenarios are inherently different.