Complementary cumulative distribution functions of in degree distribution and fitted power law, lognormal and truncated power law distributions. by Olga Valba (10531121)

Performance of the three algorithms. by Juanjuan Lin (2096830)

Algorithm results based on FE simulated likelihood functions. by Nicolas Herzig (9230425)

Comparison of site selection results of three algorithms. by Junjie He (700642)

Subjects:

Available functions in the utility package. by Raul Castañeda (13932971)

(Movie for Fig 3C) Dynamics of synaptic clustering for a G-clusteron trained on positive-labeled input patterns from a multivariate Gaussian dataset with multiple groups of positiv... by Toviah Moldwin (10866708)

Empirical cumulative distribution function of RMSE for three sources. by Nauman Ahmed (8086868)

Available functions in the phase-shifting package. by Raul Castañeda (13932971)

Available functions in the numerical propagation package. by Raul Castañeda (13932971)

Available functions in the phase reconstruction package. by Raul Castañeda (13932971)

Algorithm of M3VR. by Qi Xing (5844119)

Benchmark functions. by Xiaowen Zhang (83669)

Subjects:

BOFdat Step 3: Identifying species-specific metabolic end goals. by Jean-Christophe Lachance (6619307)

“…<p>(A) After Step 3, the entire weight of the cell is accounted for by BOFdat. …”

Pseudo-code of DMDDPG algorithm. by Guohua Cao (697580)

“…The simulation results indicate that the proposed algorithm, which models the first three joints and the last three joints of the robotic arm as multi-agents, demonstrates not only enhanced adaptability but also faster and more stable convergence.…”

Swarm intelligence algorithms for multi-objective max-min-ISP by Francisco Muñoz (9455441)

“…<p>Algorithms implemented on article "<strong>On the max-min influence spread problem: A multi-objective optimization approach</strong>"</p> <p><br></p> <p>usage: Main.py [-h] [-a] [-r] [-d D] [-t T] [-i I] [-q Q] [--shape SHAPE] [--sym] [--folder FOLDER] [--seed SEED] [--spread SPREAD] [--sum SUM] [--mh MH] [--tagsfile TAGSFILE] [--notags] [--prune] [--prunelowdeg] [--excludenodes EXCLUDENODES] [--includeindegzero] [--nodepthcriteria] [--memfile MEMFILE] file</p> <p><br></p> <p>Calculates the best Influence Spread set on a Weighted Graph using PSO</p> <p><br></p> <p>positional arguments:</p> <p>file</p> <p><br></p> <p>options:</p> <p>-h, --help: show this help message and exit</p> <p>-a: read file input contents as an Adjacency Matrix</p> <p>-r: reverse nodes order, reads (b,a,w) instead of (a,b,w)</p> <p>-d D: line separator to use on file parsing</p> <p>-t T: number of times to execute this solver</p> <p>-i I: number of metaheuristic iterations per execution</p> <p>-q Q: fixed quota, use 0 = floor(n/2)+1</p> <p>--shape SHAPE: shape functions for binarization - list of implemented shape functions: s2, s2_neg, v2, v4</p> <p>--sym: consider graph as symmetric instead of directed</p> <p>--folder FOLDER: output folder</p> <p>--seed SEED: use custom seeds for metaheuristic pseudo RNG</p> <p>--spread SPREAD: use specific influence spread model funcion - list of implemented models: LT, IC</p> <p>--sum SUM: adds an extra value to all edge's weight</p> <p>--mh MH: metaheuristic to use - list of implemented metaheuristics: 1 (Swarm3), 3 (Swarm3_W), 4 (Swarm3_L), 5 (Swarm4)</p> <p>--tagsfile TAGSFILE: use first row as node tags instead of using plurality criteria</p> <p>--notags: do not use first row as node tags, instead tags will be calculated</p> <p>--prune: nodes with (1) outdegree = 0 and indegree > 0, and (2) with outdegree = 1 and neighbor's outdegree > 0 will be excluded</p> <p>--prunelowdeg: nodes with low outdegree or degree will be excluded</p> <p>--excludenodes EXCLUDENODES: nodes to skip, must be comma separated</p> <p>--includeindegzero: forces to include nodes with indegree = 0 on all executions</p> <p>--nodepthcriteria: particles will not tie off with spread depth in case of having same fitness and spread</p> <p>--memfile MEMFILE: memory dump of other execution</p>…”

Example of landmark registration to align ‘peaks’ of high density in the probability density function (PDF) of CD4 expression on blood T cells for a target distribution and chosen... by Ross J. Burton (10941910)

Objective function values of each algorithm for solving large-scale problems. by Lijun Fan (2007628)

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

Subjects:

Wilcoxon’s test results for EBJADE algorithms and other state-of-the-art CEA-ES algorithms using CEC2014 functions. by Yang Cao (53545)

Wilcoxon’s test results for EBJADE algorithms and other algorithms using CEC2014 functions for D = 30, 50 and 100. by Yang Cao (53545)