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Title: The Machine Learning landscape of top taggers
Based on the established task of identifying boosted, hadronicallydecaying top quarks, we compare a wide range of modern machine learningapproaches. Unlike most established methods they rely on low-levelinput, for instance calorimeter output. While their networkarchitectures are vastly different, their performance is comparativelysimilar. In general, we find that these new approaches are extremelypowerful and great fun.
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SciPost Physics
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National Science Foundation
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  1. Abstract
    These data are from four separate projects undertaken between 1997 and 2017. The first of these are two snow manipulation (freeze) projects: 1) In 1997, as part of a study of the relationships between snow depth, soil freezing and nutrient cycling, we established eight 10 x 10-m plots located within four stands; two dominated (80%) by sugar maple (SM1 and SM2) and two dominated by yellow birch(YB1 and YB2), with one snow reduction (shoveling) and one reference plot in each stand. 2) In 2001, we established eight new 10-m x 10-m plots (4 treatment, 4 reference) in four new sites; two high elevation, north facing and (East Kineo and West Kineo) two low elevation, south facing (Upper Valley and Lower Valley) maple-beech-birch stands. To establish plots, we cleared minor amounts of understory vegetation from all (both treatment and reference) plots (to facilitate shoveling). Treatments (keeping plots snow free by shoveling through the end of January) were applied in the winters of 1997/98, 1998/99, 2002/2003 and 2003/2004. The Climate Gradient Project was established in October 2010. Here we evaluated relationships between snow depth, soil freezing and nutrient cycling along an elevation/aspect gradient that created variation in climate with little variationMore>>
  2. Abstract

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