The figures showed a comparison of the balanced and imbalanced sequences and performance of classifiers. by Solomon Shiferaw Beyene (9143178)

DFTB Simulation of Charged Clusters Using Machine Learning Charge Inference by Paul Guibourg (18473585)

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Table1_nPCA: a linear dimensionality reduction method using a multilayer perceptron.DOCX by Juzeng Li (17748507)

Comparison of PyNoetic’s functionality for BCI design with other existing state-of-the-art frameworks. by Gursimran Singh (575288)

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The algorithm for the same-day surgery protocol. by Magnus Tveit (11442200)

Markov transition kernel <i>Q</i> for the controlled PDMP. by Benoîte de Saporta (20454662)

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Example of patient follow-up data, PDMP model. by Benoîte de Saporta (20454662)

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Flow Φ of the controlled continuous time PDMP. by Benoîte de Saporta (20454662)

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Fig 4 - by Benoîte de Saporta (20454662)

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Practice of patient follow-up. by Benoîte de Saporta (20454662)

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Scores of municipalities projected on the first and second functional PC (A), and first and third functional PC (B). by Manuel Ribeiro (17889313)

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Overview of functionality supported by PyNoetic and its various modules, including the live analysis and programmable flowchart. by Gursimran Singh (575288)

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Similarity between the hierarchical community structure of structural and functional brain graphs. by Arian Ashourvan (6685232)

The expert screening cost <i>g</i>_expert(<i>x</i>) defined for the Challenge: Mean cost for screening a fraction <i>x</i> of a patient cohort for cardiac abnormalities. by Matthew A. Reyna (16965038)

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Demographic, murmur, and clinical outcome information in the Challenge training, validation, and/or test sets; nan values indicate unknown or missing values. by Matthew A. Reyna (16965038)

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Demographic, murmur, and clinical outcome distributions across the Challenge training, validation, and test data. by Matthew A. Reyna (16965038)

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Weighted accuracy metric (1) for the murmur detection task (<i>x</i>-axis) and the cost metric (8) scores for the clinical outcome identification task (<i>y</i>-axis) of the final... by Matthew A. Reyna (16965038)

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Scores of the officially ranked methods on the test set for the clinical outcome identification task. by Matthew A. Reyna (16965038)

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Auscultation locations for the CirCor DigiScope dataset [6], which was used for the Challenge: Pulmonary valve (PV), aortic valve (AV), mitral valve (MV), and tricuspid valve (TV). by Matthew A. Reyna (16965038)

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Traditional evaluation metric (<i>x</i>-axis) and cost metric (<i>y</i>-axis) scores for the final Challenge entries with the clinical outcome identification task on the hidden tes... by Matthew A. Reyna (16965038)

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