S/HIC: Robust Identification of Soft and Hard Sweeps Using Machine Learning por Daniel R. Schrider (201333)

“...Here we introduce a new method, S/HIC, which uses supervised machine learning to precisely infer the location of both hard and soft selective sweeps. We show that S/HIC has unrivaled accuracy for detecting sweeps under demographic histories that are relevant to human populations, and distinguishing sweeps from linked as well as neutrally evolving regions. ...”

Hard questions for soft skills: Carpentries community collaboration in Aotearoa & Australia por eRNZ Admin (6438486)

“...We seek to strengthen our networks by asking the hard questions of how and why we foster community. ...”

A blocked staggered-level design for an experiment with two hard-to-change factors por Peter Goos (805576)

“...The experiment was run in blocks and involved one quantitative hard-to-change factor, one two-level categorical hard-to-change factor, and three quantitative easy-to-change factors. ...”

Nuclear Translocation of hARD1 Contributes to Proper Cell Cycle Progression por Ji-Hyeon Park (554738)

“...The high homology and widespread expression of ARD1 across multiple species and tissues signify that it serves a fundamental role in cells. Human ARD1 (hARD1) has been suggested to be involved in diverse biological processes, and its role in cell proliferation and cancer development has been recently drawing attention. ...”

Evaluation of stiffness feedback for hard nodule identification on a phantom silicone model por Min Li (12799)

“...<div><p>Haptic information in robotic surgery can significantly improve clinical outcomes and help detect hard soft-tissue inclusions that indicate potential abnormalities. ...”

Species matter for predicting the functioning of evolving microbial communities – An eco-evolutionary model por Timothy G. Barraclough (7274534)

“...Yet predicting dynamics and functioning of these complex systems is hard, making interventions to enhance functioning harder still. ...”

Delete one edge on Karate network to change the consensus opinion—<i>β</i> = 1. por Xi Chen (35903)

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For the Karate network. por Xi Chen (35903)

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Influence of edge edition on consensus. por Xi Chen (35903)

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Graphical illustration of Case 2 from Theorem 1 (i.e. <i>p</i> < 0 and <i>β</i> ≥ −1/<i>p</i>). por Xi Chen (35903)

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Opinion formation on the star graph. por Xi Chen (35903)

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Based on one ER model (<i>n</i> = 100, <i>ρ</i> = 0.0606), one WS model (<i>n</i> = 100, <i>K</i> = 6, <i>σ</i> = 0.2), and one BA model (<i>n</i> = 100, <i>M</i>₀ = 4,... por Xi Chen (35903)

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For 1,000 random y(0) on Karate network. por Xi Chen (35903)

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Based on three WS models with different rewiring probabilities (<i>n</i> = 100, <i>K</i> = 6, <i>σ</i> = 0.1, 0.3, 0.8). por Xi Chen (35903)

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<i>β</i>^<i>P</i> for real-world twitter datasets. por Xi Chen (35903)

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For 1,000 random y(0). por Xi Chen (35903)

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For a random opinion vector y(0) with mean −0.0395, on 1,000 ER models with <i>n</i> = 100 and <i>ρ</i> = 0.4. por Xi Chen (35903)

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For a random opinion vector y(0), on ER models with <i>n</i> = 100 and <i>ρ</i> ∈ (0,1]. por Xi Chen (35903)

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<i>x</i>₁(<i>t</i> + 1) as a function of <i>x</i>_2,3,4,5(<i>t</i>). por Xi Chen (35903)

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Add one edge on Karate network to change the consensus opinion—<i>β</i> = 1. por Xi Chen (35903)

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