PS-UNet++ model structure. by Hao Wu (65943)

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Comparison of training results of data sets with different sizes. by Hao Wu (65943)

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Network structure diagram of UNet++. by Hao Wu (65943)

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Ablation test results of the data set of electrical equipment nameplate exposure. by Hao Wu (65943)

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Sub-pixel convolution upsampling module. by Hao Wu (65943)

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Comparison between algorithm and expert cell counting. by Adam L. Tyson (10892822)

Efficient Algorithms for GPU Accelerated Evaluation of the DFT Exchange-Correlation Functional by Ryan Stocks (16867476)

“…Kohn–Sham density functional theory (KS-DFT) has become a cornerstone for studying the electronic structure of molecules and materials. …”

Test results of multimodal benchmark functions. by Ruiyu Zhan (21602031)

Fixed-dimensional multimodal reference functions. by Ruiyu Zhan (21602031)

Test results of multimodal benchmark functions. by Ruiyu Zhan (21602031)

The impact of extrinsic noise sources on the measured SNR is plotted for both CW and TG against varying optical filter magnitudes. by Nicholas H. Vitale (20469289)

Schematics for gmxapi operations. by M. Eric Irrgang (4487278)

<i>L</i>^{<i>AB</i>/<i>F</i>} quality assessment results. by Hui Huang (4122)

The pseudocode for the NAFPSO algorithm. by Huichao Guo (14515171)

“…The experimental results show that compared with the traditional particle swarm optimization algorithm, NACFPSO performs well in both convergence speed and scheduling time, with an average convergence speed of 81.17 iterations and an average scheduling time of 200.00 minutes; while the average convergence speed of the particle swarm optimization algorithm is 82.17 iterations and an average scheduling time of 207.49 minutes. …”

PSO algorithm flowchart. by Huichao Guo (14515171)

“…The experimental results show that compared with the traditional particle swarm optimization algorithm, NACFPSO performs well in both convergence speed and scheduling time, with an average convergence speed of 81.17 iterations and an average scheduling time of 200.00 minutes; while the average convergence speed of the particle swarm optimization algorithm is 82.17 iterations and an average scheduling time of 207.49 minutes. …”

Model selection for machine learning algorithm based on microbial abundances. by Elio L. Herzog (11576876)

500 <i>ϕ</i> vectors learned from ISTA. by Ilias Rentzeperis (10215602)

“…Traditionally, to replicate such biological sparsity, generative models have been using the <i>ℓ</i>₁ norm as a penalty due to its convexity, which makes it amenable to fast and simple algorithmic solvers. …”

500 <i>ϕ</i> vectors learned from hard thresholding. by Ilias Rentzeperis (10215602)

“…Traditionally, to replicate such biological sparsity, generative models have been using the <i>ℓ</i>₁ norm as a penalty due to its convexity, which makes it amenable to fast and simple algorithmic solvers. …”

500 <i>ϕ</i> vectors learned from CEL0. by Ilias Rentzeperis (10215602)

“…Traditionally, to replicate such biological sparsity, generative models have been using the <i>ℓ</i>₁ norm as a penalty due to its convexity, which makes it amenable to fast and simple algorithmic solvers. …”

Results of L-SHADE based on our method, MLCCDE, EDEV, MPEDE and CoBiDE when function dimensionality is set 30. by Haiming Du (6691646)