Pseudocode of the BBAC_I Algorithm. by Zezhi Lin (22521663)

Results of test functions (F1-F11) with 50 dimensions. by Dingming Yang (8505990)

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Results of test functions (F1-F11) with 30 dimensions. by Dingming Yang (8505990)

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Results of test functions (F1-F11) with 100 dimensions. by Dingming Yang (8505990)

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Results of unimodal benchmark functions (F1–F7). by Xiaofeng Qu (577153)

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Convergence curve of functions <i>f</i>_<i>1</i><i>-f</i>_<i>10</i> in D = 10. by Yu Li (183836)

Function test results. by Xiaowen Zhang (83669)

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Efficient algorithms to discover alterations with complementary functional association in cancer by Rebecca Sarto Basso (6728921)

“…We show that on two such datasets, one from project Achilles and one from the Genomics of Drug Sensitivity in Cancer project, UNCOVER identifies several significant gene sets with complementary functional associations with targets. …”

Standard benchmark 28 test functions. by Qianhang Du (13410314)

Distribution of FEV1/FVC in pulmonary function tests with discrepant interpretations between the algorithm and the clinicians. by Yuh-Chin T. Huang (17867207)

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An example of our color correction algorithm (with primary surface function <i>f</i>₁). by Guixiang Zhang (383795)

Table_1_Clinical Functional Seizure Score (CFSS): a simple algorithm for clinicians to suspect functional seizures.DOCX by Mohammad Dashtkoohi (15417886)

Solid lines present trend curves for normalized rolling algorithmic complexity and dashed lines depict the average rolling algorithmic complexity as a function of experimental cond... by Mikołaj Biesaga (17816940)

Route for bays29 output by ABSQL algorithm. by Jin Zhang (53297)

“…DSRABSQL builds upon the Q-learning (QL) algorithm. Considering its problems of slow convergence and low accuracy, four strategies within the QL framework are designed first: the weighting function-based reward matrix, the power function-based initial Q-table, a self-adaptive <i>ε-beam</i> search strategy, and a new Q-value update formula. …”

<i>p</i>-values of the Wilcoxon rank-sum test in terms of average of benchmark functions. by Bitanu Chatterjee (15295261)

Recursive attention function and loss function change. by Wang Linfeng (17102068)

Parameters of trained algorithms. by Swathi Anil (17382903)

Performance of algorithms compared to a smaller <i>RNN.</i> by Swathi Anil (17382903)

An expectation-maximization algorithm for finding noninvadable stationary states. by Robert Marsland (8616483)

“…<i>(b)</i> Metabolic byproducts move the relevant unperturbed state from <b>R</b>⁰ (gray ‘x’) to (black ‘x’), which is itself a function of the current environmental conditions. …”

Specific process of one-stage object detection algorithm. by Xutao Liu (13006965)

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