Percentage totBurden and percentage maxBurden reduced by UnitGreedy, SingletonGreedy, FastGreedy and baselines for = 0.535 in Iowa for FRED (first column) and SafeGraph (second col... by Jeffrey Keithley (14626551)

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Metapopulation model notation. by Jeffrey Keithley (14626551)

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Estimates of for each problem instance. by Jeffrey Keithley (14626551)

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The instance of MaxCasesAverted and MaxPeaksReduced is a graph <i>G</i> constructed from the given instance of Max k-SI. by Jeffrey Keithley (14626551)

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Percentage totBurden and percentage maxBurden reduced by UnitGreedy, FastGreedy and baselines for = 0.525 in Texas for FRED (first column) and SafeGraph (second column). by Jeffrey Keithley (14626551)

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Approximation factors for each problem instance. by Jeffrey Keithley (14626551)

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Percentage totBurden and percentage maxBurden reduced by all approaches for = 0.5345 in New Hampshire for FRED (first column) and SafeGraph (second column). by Jeffrey Keithley (14626551)

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FastGreedy, UnitGreedy, SingletonGreedy, 3-EnumGreedy, and POMS comparison with respect to practical running time (estimated for POMS) to achieve approximation guarantee for New Ha... by Jeffrey Keithley (14626551)

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Pair Potentials as Machine Learning Features by Jun Pei (1765720)

“…Finally, the importance of the RF algorithm and force field parameters were also tested and the comparison results suggest that both the RF algorithm and force field potentials are important with the ML scoring function achieving its best performance only by combining them together. …”

Pair Potentials as Machine Learning Features by Jun Pei (1765720)

“…Finally, the importance of the RF algorithm and force field parameters were also tested and the comparison results suggest that both the RF algorithm and force field potentials are important with the ML scoring function achieving its best performance only by combining them together. …”

Pair Potentials as Machine Learning Features by Jun Pei (1765720)

“…Finally, the importance of the RF algorithm and force field parameters were also tested and the comparison results suggest that both the RF algorithm and force field potentials are important with the ML scoring function achieving its best performance only by combining them together. …”

The phase-field function for three different network configurations considered in this work. by Martina Bukač (20161460)

Idea of the search for a domain/motif within the range from 1 to W positions (in the window) instead of an only predetermined position. by Alexander V. Spirov (11302085)

APSO–CNN algorithm’s minimal fitness values modified over iterations. by Ahmed Bahaa (12887063)

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APSO–WOA–CNN and APSO–CNN algorithms’ accuracy training sets. by Ahmed Bahaa (12887063)

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APSO–WOA–CNN and APSO–CNN algorithms’ accuracy validation sets. by Ahmed Bahaa (12887063)

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Comparison of the WHO and IERWHO algorithms improvement points. by WanRu Zhao (18980374)

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The <i>min</i> obtained by DSRABSQL, S2V-DQN and 2-opt-DRL algorithms for 25 instances. by Jin Zhang (53297)

Sensitivity analysis of the TV-BayesOpt algorithm performance at tracking a periodic temporal drift when the period of the drift is offset from the covariance function period antic... by John E. Fleming (8533956)

Cost function with VPLE. by Ijaz Ahmed (1545961)

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