An expectation-maximization algorithm for finding noninvadable stationary states. حسب Robert Marsland (8616483)

"…<i>(b)</i> Metabolic byproducts move the relevant unperturbed state from <b>R</b>⁰ (gray ‘x’) to (black ‘x’), which is itself a function of the current environmental conditions. …"

Python implementation of the Trajectory Adaptive Multilevel Sampling algorithm for rare events and improvements حسب Pascal Wang (10130612)

"…<div>This directory contains Python 3 scripts implementing the Trajectory Adaptive Multilevel Sampling algorithm (TAMS), a variant of Adaptive Multilevel Splitting (AMS), for the study of rare events. …"

Python-Based Algorithm for Estimating NRTL Model Parameters with UNIFAC Model Simulation Results حسب Se-Hee Jo (20554623)

"…A major challenge in bioprocess simulation is the lack of physical and chemical property databases for biochemicals. A Python-based algorithm was developed for estimating the nonrandom two-liquid (NRTL) model parameters of aqueous binary systems in a straightforward manner from simplified molecular-input line-entry specification (SMILES) strings of substances in a system. …"

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

<b>Opti2Phase</b>: Python scripts for two-stage focal reducer حسب Morgan Najera (21540776)

"…</li></ul><p dir="ltr">The scripts rely on the following Python packages. Where available, repository links are provided:</p><ol><li><b>NumPy</b>, version 1.22.1</li><li><b>SciPy</b>, version 1.7.3</li><li><b>PyGAD</b>, version 3.0.1 — https://pygad.readthedocs.io/en/latest/#</li><li><b>bees-algorithm</b>, version 1.0.2 — https://pypi.org/project/bees-algorithm</li><li><b>KrakenOS</b>, version 1.0.0.19 — https://github.com/Garchupiter/Kraken-Optical-Simulator</li><li><b>matplotlib</b>, version 3.5.2</li></ol><p dir="ltr">All scripts are modular and organized to reflect the design stages described in the manuscript.…"

Python-based Monte Carlo Simulation Tool حسب Yangfan Jiang (9372782)

Where to Handle an Exception? Recommending Exception Handling Locations from a Global Perspective حسب Xiangyang Jia (10026932)

Results of the application of different clustering algorithms to average functional connectivity from healthy subjects. حسب Francisco Páscoa dos Santos (16510676)

"…<p>A) Resulting cluster inertia from applying the k-means algorithm described in the methods to empirical averaged functional connectivity from healthy subjects, with different numbers of clusters. …"

FAR-1: A Fast Integer Reduction Algorithm Compared to Collatz and Half-Collatz حسب Faizan Ali (21689861)

الموضوعات:

A Python Package for the Localization of Protein Modifications in Mass Spectrometry Data حسب Anthony S. Barente (14035175)

الموضوعات:

Table_1_Clinical Functional Seizure Score (CFSS): a simple algorithm for clinicians to suspect functional seizures.DOCX حسب Mohammad Dashtkoohi (15417886)

ADT: A Generalized Algorithm and Program for Beyond Born–Oppenheimer Equations of “<i>N</i>” Dimensional Sub-Hilbert Space حسب Koushik Naskar (7510592)

"…In order to overcome such shortcoming, we develop a generalized algorithm, “ADT” to generate the nonadiabatic equations through symbolic manipulation and to construct highly accurate diabatic surfaces for molecular processes involving excited electronic states. …"

Flowchart of one iteration of the CE-Symm algorithm. حسب Spencer E. Bliven (6672755)

Listed python codes of our algorithm with explanations (Python 3.9 recommended). حسب Takayuki Niizato (162226)

WGCNA and machine learning algorithms. حسب Shushu Wang (6870191)

Example of the CNF function for off-axis DHM holograms recorded in a non-telecentric configuration. حسب Raul Castañeda (13932971)

Summary of results of naïve Bayes algorithms. حسب Christopher E. Niemczak (8586861)

"…Algorithms trained without auditory variables as features were statistically worse (p < .001) in both the primary measure of area under the curve (0.82/0.78) and the secondary measure of accuracy (72.3%/74.5%) for the Gaussian and kernel algorithms respectively.…"

Cost functions implemented in Neuroptimus. حسب Máté Mohácsi (20469514)

If datasets are small and/or noisy, linear-regression-based algorithms for identifying functional groups outperform more complex versions. حسب Yuanchen Zhao (12905580)

"…Both versions are evaluated on the same synthetic datasets with a 3-group ground truth. Each algorithm return a set of coarsened <i>variables</i> (a grouping of species into three groups) and a <i>model</i> that uses these variables to predict the function. …"

Multi-scale detection of hierarchical community architecture in structural and functional brain networks حسب Arian Ashourvan (6685232)

"…To address this limitation, we exercise a multi-scale extension of a common community detection technique, and we apply the tool to synthetic graphs and to graphs derived from human neuroimaging data, including structural and functional imaging data. …"