Convolution kernel of size 5 with arbitrary shape. by Xinwei Wang (352488)

Efficient Sampling From the Watson Distribution in Arbitrary Dimensions by Lukas Sablica (19933752)

Summary of greedy algorithms presented in this section. Details of approximation factors for FastGreedy and UnitGreedy may be found in [26] and [25], respectively. by Jeffrey Keithley (14626551)

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)

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)

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)

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)

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)

Metapopulation model notation. by Jeffrey Keithley (14626551)

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

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

YOLOv5s. by Xinwei Wang (352488)

“…Experimental results on the VisDrone2019 UAV dataset show that the improved algorithm achieves a 5.9%, 4.5%, and 6.1% increase in detection precision, detection recall, and mean average precision, respectively, compared to the original algorithm. …”

Classification from 1D vs. 3D k-Means trained on the pooled scores from Days 5 and 6 of the modeling sample. by Camille Godin (21445879)

Comparison of contingent versus <i>in situ</i> evolution of antennae under 2.5m depth of water. by Callum Gray (2691769)

Constructing Accurate Potential Energy Surfaces with Limited High-Level Data Using Atom-Centered Potentials and Density Functional Theory by Mahsa Nazemi-Ashani (21876936)

Environment ConFIGuration Information. by Xinwei Wang (352488)

“…Experimental results on the VisDrone2019 UAV dataset show that the improved algorithm achieves a 5.9%, 4.5%, and 6.1% increase in detection precision, detection recall, and mean average precision, respectively, compared to the original algorithm. …”

Results of ablation experiment. by Xinwei Wang (352488)

“…Experimental results on the VisDrone2019 UAV dataset show that the improved algorithm achieves a 5.9%, 4.5%, and 6.1% increase in detection precision, detection recall, and mean average precision, respectively, compared to the original algorithm. …”

Comparison diagram of mAP50. by Xinwei Wang (352488)

“…Experimental results on the VisDrone2019 UAV dataset show that the improved algorithm achieves a 5.9%, 4.5%, and 6.1% increase in detection precision, detection recall, and mean average precision, respectively, compared to the original algorithm. …”

Improved YOLOv8s. by Xinwei Wang (352488)

“…Experimental results on the VisDrone2019 UAV dataset show that the improved algorithm achieves a 5.9%, 4.5%, and 6.1% increase in detection precision, detection recall, and mean average precision, respectively, compared to the original algorithm. …”

Partial Training Images of the Dataset. by Xinwei Wang (352488)

“…Experimental results on the VisDrone2019 UAV dataset show that the improved algorithm achieves a 5.9%, 4.5%, and 6.1% increase in detection precision, detection recall, and mean average precision, respectively, compared to the original algorithm. …”