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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An example of the algorithm of the ORFanID engine is explained in a UML flow chart diagram. by Richard S. Gunasekera (6752072)

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Width of stripes as a function of time. by Pratik Mullick (12851011)

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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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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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Traditional evaluation metric (<i>x</i>-axis) and weighted accuracy metric (<i>y</i>-axis) scores for the final Challenge entries with the murmur detection task on the hidden test... by Matthew A. Reyna (16965038)

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ADT: A Generalized Algorithm and Program for Beyond Born–Oppenheimer Equations of “<i>N</i>” Dimensional Sub-Hilbert Space by Koushik Naskar (7510592)

“…For the numerical case, user can directly provide <i>ab initio</i> data (adiabatic PESs and NACTs) as input files to this software or can generate those input files through in-built python codes interfacing MOLPRO followed by ADT calculation. …”

Boxplots for the maximising function of pattern matching technique. by Pratik Mullick (12851011)

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Confusion matrix <i>M</i> for murmur detection with three classes: Murmur present, murmur unknown, and murmur absent. by Matthew A. Reyna (16965038)

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Confusion matrix <i>N</i> for clinical outcome detection with two classes: Clinical outcome abnormal and clinical outcome normal. by Matthew A. Reyna (16965038)

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The list of reactions and corresponding epidemiological events simulated by the Gillespie algorithm in our model, and the number of reactions in each category in function of the number of mutable sites <i>U</i>, number of susceptible individuals <i>T</i>, and the number of populations <i>K</i>. by Vladimir Shchur (5758916)

“…<p>The list of reactions and corresponding epidemiological events simulated by the Gillespie algorithm in our model, and the number of reactions in each category in function of the number of mutable sites <i>U</i>, number of susceptible individuals <i>T</i>, and the number of populations <i>K</i>.…”

Fig 1 - by Vlad Ploscaru (13767257)

Subjects: “…artificial intelligence algorithms…”

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

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Globus Compute: Fire-and-Forget Federated Function-as-a-Service by Kyle Chard (5131859)

Algorithm membership function. by Mohamed Raef Smaoui (9865830)

“…<p>(Top) Input Membership Function. The algorithm classifies glucose input into 4 sets: low, medium, high, and ex_high. …”

Summary of the contrasts obtained from the normalized images for both inclusions as a function of the host type and the wavelength. by María Victoria Waks-Serra (21578611)

Constructing the dynamical law. by Robert Marsland (8616483)

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

Subjects: “…unsupervised learning algorithms…”

Datasheet1_Gait analysis comparison between manual marking, 2D pose estimation algorithms, and 3D marker-based system.pdf by Dimitrios Menychtas (16935831)

S1 File - by Yuh-Chin T. Huang (17867207)

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