Schematics for gmxapi operations. by M. Eric Irrgang (4487278)

Eight commonly used benchmark functions. by Guangwei Liu (181992)

“…The proposed algorithm was evaluated on eight standard benchmark functions, CEC2019 benchmark functions, four engineering design problems, and a PID parameter optimization problem. …”

Image_1_Usefulness of Random Forest Algorithm in Predicting Severe Acute Pancreatitis.tif by Wandong Hong (6683141)

Reconstructing <i>sparse</i>, binary patterns using message passing algorithms and PCA. by Sebastian Goldt (14522594)

“…Lines depict the result of the state evolution, while crosses denote the performance of the AMP algorithm on an instance of the problem. While AMP performs the same starting from both initialisations for <i>ρ</i> = 0.1 and <i>ρ</i> = 0.3, there is a gap in performance for <i>ρ</i> = 0.05, which might hint at the existence of a hard phase (see main text). …”

10-fold cross-validation evaluation of accuracy of animal tracking algorithms. by Catalin Mitelut (5587493)

“…<p>We labeled 1,000 frames per cohort under both day and night conditions (total 6,000 human labeled frames across three cohorts), and studied the accuracy of the animal tracking algorithm (SLEAP) as a function of the number of frames (<i>x</i>-axis) trained. …”

Discovery of Protein Modifications Using Differential Tandem Mass Spectrometry Proteomics by Paolo Cifani (1575613)

“…Termed SAMPEI for spectral alignment-based modified peptide identification, this open-source algorithm is designed for the discovery of functional protein and peptide signaling modifications, without prior knowledge of their identities. …”

Discovery of Protein Modifications Using Differential Tandem Mass Spectrometry Proteomics by Paolo Cifani (1575613)

“…Termed SAMPEI for spectral alignment-based modified peptide identification, this open-source algorithm is designed for the discovery of functional protein and peptide signaling modifications, without prior knowledge of their identities. …”

Model hyperparameters used for the XGBoost model. by Aastha Vatsyayan (9981916)

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Primers used in site-directed mutagenesis. by Aastha Vatsyayan (9981916)

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Flowchart of proposed fitness function algorithm. by Ferhat Yuna (21519851)

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The run time for each algorithm in seconds. by Edward Antonian (21453161)

“…<div><p>In this paper, we study a class of non-parametric regression models for predicting graph signals as a function of explanatory variables . Recently, Kernel Graph Regression (KGR) and Gaussian Processes over Graph (GPoG) have emerged as promising techniques for this task. …”

Linear relationship between ln(methadone concentration) and ln(methadone dose) by enantiomer and <i>CYP2B6</i> genotype function. by Andrew H. Talal (8727396)

Linear relationship between ln(methadone metabolism) and ln(methadone dose) by enantiomer and <i>CYP2B6</i> genotype function. by Andrew H. Talal (8727396)

Data used in series-parallel systems. by Heba Askr (15572851)

“…First, MaAVOA was applied to the DTLZ functions, and its performance was compared to that of several popular many-objective algorithms and according to the results, MaAVOA outperforms the competitor algorithms in terms of inverted generational distance and hypervolume performance measures and has a beneficial adaptation ability in terms of both convergence and diversity performance measures. …”

Objective function for pressure control system. by Fazal E. Nasir (14992506)

Example of use of the slightly off-axis strategies. by Raul Castañeda (13932971)

Occurrence probabilities of behavioral switching in multi-agent predator–prey interactions when both <i>D</i>_<i>w</i> and <i>D</i>_<i>s</i> are 2. by Hiroyuki Ichijo (13891153)

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Attack and defense behaviors with the sensory–motor algorithm between a single predator and a single prey. by Hiroyuki Ichijo (13891153)

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Design and implementation of DelaySSA. The input data for DelaySSA are reaction information, initial states, parameters and delays. Combining these into the simulation function, th... by Ziyan Jin (21021976)

Bayesian-frequentist Hybrid Inference in Applications with Small Sample Sizes by Gang Han (127634)

“…This paper develops a computation algorithm for hybrid inference under any general loss functions. …”