Algorithm and its parameter settings. by Lin Li (28817)

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One-step trajectory prediction results for the X-coordinate: (a) one-step prediction error for the basic single algorithm; (b) one-step prediction error for each single algorithm i... by Lin Li (28817)

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One-step trajectory prediction results for the Z-coordinate: (a) one-step prediction error for the basic single algorithm; (b) one-step prediction error for each single algorithm i... by Lin Li (28817)

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One-step trajectory prediction results for the Y-coordinate: (a) one-step prediction error for the basic single algorithm; (b) one-step prediction error for each single algorithm i... by Lin Li (28817)

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Flow chart of ADIBAS-Volterra aircraft trajectory prediction algorithm. by Lin Li (28817)

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Top-rated mode of transport based on the SEU algorithm (SEU scores from 0 to 40). by Johannes Weyer (13392175)

Numbers of iterations (#Ite) and eigenvalue calculations (#Eig) in the DC algorithm on the random and chain graph datasets. by Tomokaze Shiratori (9635271)

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A framework for improving localisation prediction algorithms. by Sven B. Gould (12237287)

“…Proteins with non-canonical internal motifs, or those dually targeted need to be taken into account (as they help to better distinguish between pNTS and mNTS features) and validated data could be sorted according to whether it is part of a core- or pan-proteome. …”

One-step prediction results for Y-coordinate. by Lin Li (28817)

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One-step prediction results for the X-coordinate. by Lin Li (28817)

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One-step prediction results for the trajectory prediction dataset with added noise. by Lin Li (28817)

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Comparison of different temporal recognition methods on a single aircraft flight manoeuvre dataset, segmentation and recognition of a long-range flight manoeuvre dataset containing... by Lin Li (28817)

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One-step prediction results for Z-coordinate. by Lin Li (28817)

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Segmentation results for one flight of the aircraft. by Lin Li (28817)

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Aircraft manoeuvre recognition rules. by Lin Li (28817)

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Computation time as a function of the sample size on the chain graph dataset. by Tomokaze Shiratori (9635271)

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Log-likelihood as a function of the number of selected edges on the chain graph dataset (black dashed line: The true number of edges). by Tomokaze Shiratori (9635271)

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Computation time as a function of the sample size on the random graph dataset. by Tomokaze Shiratori (9635271)

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F1 score of edges selected through cross-validation on the chain graph dataset. by Tomokaze Shiratori (9635271)

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Log-likelihood as a function of the number of selected edges on the random graph dataset (black dashed line: The true number of edges). by Tomokaze Shiratori (9635271)

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