M aster, a friend told me today that I should never use the editor's copy-paste functions when programming , " said the young apprentice. " I thought the whole point of programming tools was to make our lives easier, " he continued. The Master stroked his long grey beard and pressed the busy button on his phone. This was going to be one of those long, important discussions. " Why do you think c...

[1] Recent suggestions to reduce the accumulation of anthropogenic carbon dioxide in the atmosphere have included ocean fertilization by artificial upwelling. Our coupled carbon‐climate model simulations suggest that artificial upwelling may, under most optimistic assumptions, be able to sequester atmospheric CO2 at a rate of about 0.9 PgC/yr. However, the model predicts that about 80% of the c...

rj A WEAK THEORY DOMAIN The paper presents an interactive approach to learning apprentice systems for weak theory domains. The approach consists of a combination of teaming by analogy and learning by generalizing instances. One main point of this approach is that it uses the explanations drawn from an example, both to reduce the version space of me rules to be learned, and to generate new examp...

BACKGROUND Falls remain a serious source of morbidity and mortality in residential construction despite considerable knowledge of risk factors and prevention strategies. While training is universally viewed as positive, we know little about its effectiveness in preventing residential falls. METHODS A series of focus groups were conducted with union apprentice carpenters (n = 36) at varied lev...

Problem 10. Your instructor constructed two polynomials P1(x) and P2(x), each of degree (at most) d and evaluated them both at points a1, . . . , an ∈ R. At this point things got a bit mixed up and he no longer knows which value is coming from which polynomial. You are given n pairs of values: (b1, c1), . . . , (bn, cn). We know that at each point ai, either bi = P1(ai) and ci = P2(ai), or bi =...

Deep learning networks have achieved state-of-the-art accuracies on computer vision workloads like image classification and object detection. The performant systems, however, typically involve big models with numerous parameters. Once trained, a challenging aspect for such top performing models is deployment on resource constrained inference systems — the models (often deep networks or wide net...