TV-BayesOpt algorithm performance for tracking a gradual drift in the optimal stimulation phase for phase-locked stimulation, <i>ψ</i>*. by John E. Fleming (8533956)

“…For each estimated GPR the confidence bounds observed at the predicted optimal phase value are small and become larger for values further away from this value due to the algorithm’s acquisition function prioritizing exploitation of the parameter space during the optimization process.…”

TV-BayesOpt algorithm performance for tracking a periodic drift in the optimal stimulation phase for phase-locked stimulation, <i>ψ</i>*. by John E. Fleming (8533956)

“…For each estimated GPR the confidence bounds observed at the predicted optimal phase value are small and become larger for values further away from this value due to the algorithm’s acquisition function prioritizing exploitation of the parameter space during the optimization process.…”

Relationship between bistability and resettability. by Takashi Naka (272391)

Illustration of how (1 + 1)-WEA_v3 improves the target set when solving TSS. by Alexander Andreev (16822333)

Robotic Prototype with Human Mimicry Abilities - 104 Algorithms (RPHMA^104a): Toward an Integrative Computational-Robotic Theory of Very Complex Skills by Maicon S. Aliaga Machado (20468810)

“…<p dir="ltr"><b>Abstract</b></p><p dir="ltr">This paper introduces the RPHMS^104a model (Robotic Prototype with Human Mimicry Abilities - 104 Algorithms), a framework for the progressive development of artificial systems capable of emulating human cognitive-behavioral functions. …”

CEC2018 DF1,3-8 PSAMOO by Anonymized (8222226)

Optimization Algorithm Applied to Environmental Control in Broiler Houses by MAC Benvenga (10447461)

“…Results indicated that there was a large dispersion of the data collected from the environmental variables. …”

Critical values of membership functions learned from the study cohort by the algorithm. by Yufeng Zhang (195768)

Effects of Nash equilibrium speed changes with the non-sensory motor algorithm on encounters, agent numbers, and payoffs. by Hiroyuki Ichijo (13891153)

Swarm intelligence algorithms for width and length on influence games by Francisco Muñoz (9455441)

“…<br><br><div>usage: Main.py [-h] [-a] [-r] [-d D] [-t T] [-i I] [-q Q] [--shape SHAPE] [--sym] [--folder FOLDER] [--seed SEED] [--sum SUM] [--mh MH] [--tagsfile TAGSFILE] [--notags] [--prune] [--excludenodes EXCLUDENODES]<br><br>Calculates the best Influence Spread set on a Weighted Symmetric Graph using PSO<br></div><div><br></div><div><div>positional arguments:</div><div> file</div><div><br></div><div>optional arguments:</div><div> -h, --help: show this help message and exit</div><div> -a: threat file input contents as an Adjacency Matrix</div><div> -r: reverse order of nodes, from (a,b,w) a -> b will be b -> a</div><div> -d D: line separator to use while parsing</div><div> -t T: number of times to execute</div><div> -i I: number of metaheuristic iterations per execution</div><div> -q Q: fixed quota, use 0 = floor(n/2)+1</div><div> --shape SHAPE: shape functions for binarization - list of implemented shape functions: s2,s2_neg,v2,v4</div><div> --sym: consider graph as symmetric instead of directed</div><div> --folder FOLDER: output folder</div><div> --seed SEED: use custom seed for metaheuristic calcs</div><div> --sum SUM: adds a value to all node labels</div><div> --mh MH: metaheuristic to use - list of implemented metaheuristics: {1: 'Swarm', 2: 'Swarm2', 3: 'Swarm_W', 4:</div><div> 'Swarm_L'}</div><div> --tagsfile TAGSFILE: use first row as node tags instead of using plurality criteria</div><div> --notags: do not use first row as node tags - tags will be calculated</div><div> --prune: nodes with outdegree = 0 and indegree > 0, and with outdegree = 1 and neighbor's outdegree > 0 will be excluded</div><div> --excludenodes EXCLUDENODES: nodes to skip, comma separated</div></div>…”

Variations of loss functions versus epoch number. by Xueliang Guo (4797057)

Dataset information from the GEO database. by Guanghao Xin (17583227)

“…Using the GSEA and GSVA algorithms, we analyzed the biological functions associated with immune-related hub genes. …”

Surface Effect of Small Nanocrystals on the Accuracy of Structural Parameters Obtained by Pair Distribution Function Analysis by Merdan Batyrow (20808619)

“…The effect of the nanocrystal surface on the accuracy of average lattice parameters, crystal sizes, and mean square atom displacements (MSDs) obtained from pair distribution function (PDF) analysis of powder X-ray diffraction is investigated. …”

Optimum feature set for classification using both core and margin information utilizing GLCM, GRLM, and GLSZM texture methods and SVM-RBF classification algorithm. by Laurentius O. Osapoetra (9931759)

Data is generated from ARMA (p, q) models with (p, q) , and the corresponding AIC table is created. by Jesse Wheeler (18462457)

Table_6_Gut microbiota functional profiling in autism spectrum disorders: bacterial VOCs and related metabolic pathways acting as disease biomarkers and predictors.xls by Pamela Vernocchi (791125)

Table_1_Gut microbiota functional profiling in autism spectrum disorders: bacterial VOCs and related metabolic pathways acting as disease biomarkers and predictors.xlsx by Pamela Vernocchi (791125)

Table_7_Gut microbiota functional profiling in autism spectrum disorders: bacterial VOCs and related metabolic pathways acting as disease biomarkers and predictors.xlsx by Pamela Vernocchi (791125)

Table_8_Gut microbiota functional profiling in autism spectrum disorders: bacterial VOCs and related metabolic pathways acting as disease biomarkers and predictors.csv by Pamela Vernocchi (791125)

Table_3_Gut microbiota functional profiling in autism spectrum disorders: bacterial VOCs and related metabolic pathways acting as disease biomarkers and predictors.xlsx by Pamela Vernocchi (791125)