S/HIC: Robust Identification of Soft and Hard Sweeps Using Machine Learning by 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. …”

A blocked staggered-level design for an experiment with two hard-to-change factors by 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 by 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 by 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 by Timothy G. Barraclough (7274534)

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

Supplementary Figure 1 from The evolution and fate of diversity under hard and soft selection by Patrick Chen (4343074)

“…Each point is the mean proportion of resistant individuals from evolved lines undergoing either soft or hard selection (n=12 for each treatment). …”

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

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For the Karate network. by Xi Chen (35903)

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

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

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

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

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For 1,000 random y(0). by 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. by 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]. by 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>). by Xi Chen (35903)

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

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