Objective functions (i.e., RMSEs) and time of optimization for different population sizes of the used algorithms. by Yao Peng (1928524)

“…<p>Objective functions (i.e., RMSEs) and time of optimization for different population sizes of the used algorithms.…”

Forward and inverse problems and misfit function. by Rutinaldo Aguiar Nascimento (14259594)

Subjects:

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

Face matching performance as a function of test condition for Study 1 (threshold = 0.5). by Daniela Barragan (13146762)

Algorithm pseudocode. by Cheng-jie Chen (22272090)

“…The experimental results show that the proposed method enhances the image with a PCQI of 1.033, an IQE of 0.610, an IQM of 1.830, and an information entropy higher than 0.7. …”

Overview of algorithm. by Takayuki Niizato (162226)

Dropping functions <i>d</i>₁-<i>d</i>₈. by Marek Barczyk (12029714)

Function <i>χ</i>_<i>opt</i> superimposed with the phase-field function indicating the channel geometry. by Martina Bukač (20161460)

Function test results. by Xiaowen Zhang (83669)

Subjects:

Convergence curves of the mean function error values for six test functions at <i>D</i> = 30. by Meijun Duan (7461293)

Convergence curve of unimodal functions compared with the PSO family algorithms. by Yong Gao (29929)

Convergence curve of unimodal functions compared with the meta-heuristic algorithms. by Yong Gao (29929)

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

“…We provide analytic evidence of the effectiveness of UNCOVER in finding high-quality solutions and show experimentally that UNCOVER finds sets of alterations significantly associated with functional targets in a variety of scenarios. In particular, we show that our algorithms find sets which are better than the ones obtained by the state-of-the-art method, even when sets are evaluated using the statistical score employed by the latter. …”

Revisiting the “satisfaction of spatial restraints” approach of MODELLER for protein homology modeling by Giacomo Janson (8138517)

“…This program implements the “modeling by satisfaction of spatial restraints” strategy and its core algorithm has not been altered significantly since the early 1990s. …”

Autocorrelation functions <i>C</i>(<i>r</i>, <i>t</i>) as functions of the rescaled distance <i>r</i>/<i>R</i>(<i>t</i>) for several time points, where <i>R</i>(<i>t</i>) is determ... by Marc Durand (556746)

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

Execution Steps of the EOF Algorithm. by Zezhi Lin (22521663)

Comparison of maximum error in finding global optimum of functions <i>f</i>₁-<i>f</i>₁₄. by Panagiotis Aivaliotis-Apostolopoulos (13851171)

Comparison of average error in finding the global optimum of functions <i>f</i>₁-<i>f</i>₁₄. by Panagiotis Aivaliotis-Apostolopoulos (13851171)

Performance of tpopt function for finding <i>T</i>-optimal designs. by Ray-Bing Chen (700703)