A Genetic Algorithm Approach for Compact Wave Function Representations in Spin-Adapted Bases by Maru Song (22593561)

Subjects:

Summary of the contrasts obtained from the normalized images for both inclusions as a function of the host type and the wavelength. by María Victoria Waks-Serra (21578611)

Flowchart for Algorithm 1. by Sagvan Y. Musa (17797611)

The ALO algorithm optimization flowchart. by Wenjing Wang (181404)

“…The average running time of the proposed algorithm in Sphere function and Griebank function was 2.67s and 1.64s, respectively. …”

The IALO algorithm solution flowchart. by Wenjing Wang (181404)

“…The average running time of the proposed algorithm in Sphere function and Griebank function was 2.67s and 1.64s, respectively. …”

Meta data for selected plots of "Optimal sampling of tensor networks targeting wave function’s fast decaying tails" by Marco Ballarin (17780767)

Continuous Probability Distributions generated by the PIPE Algorithm by LUIS G.B. PINHO (14073372)

“…The PIPE algorithm can generate several candidate functions to fit the empirical distribution of data. …”

Study proposed algorithm. by Ainhoa Pérez-Guerrero (21377457)

Subjects: “…assess microvascular function…”

Efficient Algorithms for GPU Accelerated Evaluation of the DFT Exchange-Correlation Functional by Ryan Stocks (16867476)

“…Kohn–Sham density functional theory (KS-DFT) has become a cornerstone for studying the electronic structure of molecules and materials. …”

Animations as supplemental material for "Optimal sampling of tensor networks targeting wave function’s fast decaying tails" by Marco Ballarin (17780767)

Figures and supplemental figures for "Optimal sampling of tensor networks targeting wave function’s fast decaying tails" by Marco Ballarin (17780767)

“…<p dir="ltr">Figures and supplemental figures for "Optimal sampling of tensor networks targeting wave function’s fast decaying tails". The supplemental figures are the data from the "Dibona" machine regarding the energy consumption of the algorithm. …”

F1-scores of anomaly detection algorithms. by GaoXiang Zhao (21499525)

“…Empirical evaluations conducted on multiple benchmark datasets demonstrate that the proposed method outperforms classical anomaly detection algorithms while surpassing conventional model averaging techniques based on minimizing standard loss functions. …”

Prediction performance of different optimization algorithms. by Ali-Kemal Aydin (10968731)

“…<p>(A) 3 algorithms were compared in terms of the residuals of the cost function of the optimized TF on 7 mice datasets (Derivative free algorithm failed in optimizing a TF in a mouse). …”

Standard functions to test. by Kemal Gökhan Nalbant (18858009)

Solid lines present trend curves for normalized rolling algorithmic complexity and dashed lines depict the average rolling algorithmic complexity as a function of experimental cond... by Mikołaj Biesaga (17816940)

Flowchart for Algorithm 2. by Sagvan Y. Musa (17797611)

Gillespie algorithm simulation parameters. by Nicholas H. Vitale (20469289)

“…<div><p>We present a model for the noise and inherent stochasticity of fluorescence signals in both continuous wave (CW) and time-gated (TG) conditions. …”

Algorithm of the brightness scale calibration experiment. by Krzysztof Petelczyc (3954203)

“…<p>In the algorithm, the following variables were used: “I” denotes the current luminous intensity of the reference diode, “inc” denotes the current difference between reference and target diode luminous intensity; “cnt” is the current number of performed trials, while “correct” is a counter of correct answers in cnt trials, both of them are counted separately for every settings of I and inc. …”

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

Subjects:

Flexible CDOCKER: Hybrid Searching Algorithm and Scoring Function with Side Chain Conformational Entropy by Yujin Wu (2901128)

“…We also describe a novel hybrid searching algorithm that combines both molecular dynamics (MD)-based simulated annealing and genetic algorithm crossovers to address the enhanced sampling of the increased search space. …”