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>…”

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

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)

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. …”

Top 15 biological process concepts from GO that are enriched and linked to the TMGs. by Ravinder Sharma (13154079)

MEDOC: A Fast, Scalable, and Mathematically Exact Algorithm for the Site-Specific Prediction of the Protonation Degree in Large Disordered Proteins by Martin J. Fossat (3714079)

“…We show that we can drastically reduce the number of parameters necessary to determine the full, analytical Boltzmann partition function of the charge landscape at both global and site-specific levels. …”

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)

Table_2_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_5_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_4_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)

The results of the ablation experiments in different modules were enhanced with a randomly selected image from the Color-Check7 [34], OceanDark [33] datasets. by Kaibo Liao (18056473)

Vegetation mapping for Feb and Jun 2019 from the year 2015 to 2019. by Gourab Saha (8987405)

Results from starting the inference algorithms from the true partition 10 times for 10 network instances for each <i>k</i>_out = 0, 0.5, …, 16. by Thorben Funke (6623093)

“…Because PAH is not designed to start from a given partition, it is not included, too.</p>…”

The proposed simulation model uses either an experimentally determined or proposed cumulative distribution function to generate a stream length model for the mocked squiggle. by Michael Smith (26855)

Copula Modeling of Serially Correlated Multivariate Data with Hidden Structures by Robert Zimmerman (10457731)

“…We tackle the latter using a theoretically-justified variation of the EM algorithm developed within the framework of inference functions for margins. …”

Tapping on the Black Box: How Is the Scoring Power of a Machine-Learning Scoring Function Dependent on the Training Set? by Minyi Su (1425976)

“…Model scoring functions were derived with these machine-learning algorithms on various training sets selected from over 3700 protein–ligand complexes in the PDBbind refined set (version 2016). …”