Correspondence of grayscale values between brightness <i>l</i>₀ and brightness <i>l</i>₂. by Hui Huang (4122)

Correspondence of grayscale values between brightness <i>l</i>₀ and brightness <i>l</i>₁. by Hui Huang (4122)

Correspondence of grayscale values between brightness <i>l</i>₀ and brightness <i>l</i>₅. by Hui Huang (4122)

Correspondence of grayscale values between brightness <i>l</i>₀ and brightness <i>l</i>₃. by Hui Huang (4122)

Correspondence of grayscale values between brightness <i>l</i>₀ and brightness <i>l</i>₄. by Hui Huang (4122)

Benchmark test functions. by Meijun Duan (7461293)

A Genetic Algorithm Approach for Compact Wave Function Representations in Spin-Adapted Bases by Maru Song (22593561)

Subjects:

Pathway activity and single-cell level functional states of CEDGs in cancer. by Yuxi Gu (11386081)

PATH has state-of-the-art performance versus previous binding affinity prediction algorithms. by Yuxi Long (11024307)

“…^<b>b</b>ROC curves of scoring functions benchmarked on the DUD-E dataset show PATH⁻ has state-of-the-art performance in discriminating decoys in the DUD-E dataset. …”

Comparison to state of the art. by Nicholas A. Rondoni (21573261)

Piecewise linear charging function with 2 pieces. by Fengjie Fu (7314347)

Subjects:

V-shaped transfer functions. by Nebojsa Bacanin (13932723)

Correspondence of grayscale values between brightness <i>l</i>₀ and brightness <i>l</i>_{<i>average</i>}. by Hui Huang (4122)

Training test results of different algorithms on natural scene exposure dataset. by Hao Wu (65943)

Subjects:

The flow of the genetic algorithm with the nested Frank-Wolfe algorithm. by Zhipeng Huang (1759759)

Subjects:

Mean training time of different algorithms. by Wei Liu (20030)

“…The results show: (1) the global convergence probability of SGWO was 1, and its process was a finite homogeneous Markov chain with an absorption state; (2) SGWO not only has better optimization performance when solving complex functions of different dimensions, but also when applied to Elman for parameter optimization, SGWO can significantly optimize the network structure and SGWO-Elman has accurate prediction performance.…”

Total ranking of algorithms in different dimensions. by Wei Liu (20030)

Algorithm ranking under different dimensions. by Wei Liu (20030)

“…The results show: (1) the global convergence probability of SGWO was 1, and its process was a finite homogeneous Markov chain with an absorption state; (2) SGWO not only has better optimization performance when solving complex functions of different dimensions, but also when applied to Elman for parameter optimization, SGWO can significantly optimize the network structure and SGWO-Elman has accurate prediction performance.…”

State Preparation in Quantum Algorithms for Fragment-Based Quantum Chemistry by Ruhee D’Cunha (8921372)

“…State preparation for quantum algorithms is crucial for achieving high accuracy in quantum chemistry and competing with classical algorithms. …”

Constructing the dynamical law. by Robert Marsland (8616483)