Key gene identification based on two machine learning algorithms. by Lijun Guo (605276)

Comparison between simulations for all algorithms and for all cases (1, 2 and 3). by Rutinaldo Aguiar Nascimento (14259594)

Subjects:

Convergence curves of the mean function error values for six test functions at <i>D</i> = 30. by Meijun Duan (7461293)

(<i>Left</i>) Reconstruction and performance of the message-passing algorithm for binary patterns. by Sebastian Goldt (14522594)

“…<i>(Center)</i> <b>Phase diagram for the rectified Hopfield channel with</b> <i>P</i> = 1. …”

Genetic Algorithm (GA) and CAGE-based personalization block diagrams. by Dmitrii Smirnov (8822324)

“…<p>(<b>A</b>) Genetic algorithm schematic diagram. Initially a set of organisms is generated, each of which is determined by a random vector of scaling factors for optimized model parameters (step 1). …”

Algorithm description and the effects of replay and forgetting on model performance. by Georgy Antonov (11938961)

“…Right: with MB forgetting (controlled by MB forgetting rate, <i>ϕ</i>^<i>MB</i>), the algorithm’s estimate of reward becomes an expectation of the reward function under its state-transition model. …”

Defocus blur with <i>r</i> = 4 for the “monarch.bmp” image. by Haoyuan Yang (8630391)

Evaluation of the optimization algorithm using <i>in vitro</i> microbiome reference values. by Juan Ricardo Velasco-Álvarez (16859181)

“…<p>A: Evolution of biomass fraction distribution during the cycles of adaptation enforced by the gradient descent algorithm using a value of <i>α</i> = 0.25 and <i>in vitro</i> abundance data obtained from [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0290082#pone.0290082.ref021" target="_blank">21</a>]; B: Comparison of pie charts for the <i>in vitro</i> reference biomass percentage distributions (left) and the obtained with the modeling strategy based on the gradient descent algorithm (right); C: Performance functional (<i>J</i>) evolution for <i>in vitro</i> microbiome simulation.…”

Comparison of algorithms in two cases. by Yi Tao (178829)

Subjects: “…evolutionary genetic algorithm…”

Flow of the NSGA-II algorithm. by Yi Tao (178829)

Subjects: “…evolutionary genetic algorithm…”

Signal detection algorithm adapted from [1] yields exponential distributions and unrealistic mean durations of percepts. by Quynh-Anh Nguyen (847240)

“…(Bottom) Trial-by-trial applications of the signal detection algorithm from [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1008152#pcbi.1008152.ref001" target="_blank">1</a>] with A: <i>C</i>_<i>th</i> = 4.01 and B: <i>C</i>_<i>th</i> = 4.21 yield exponentially distributed subsequent percept durations for <i>I</i> (blue) and <i>S</i> (red). …”

Convergence curve of shift function. by Yong Gao (29929)

Comparison between simulations for all algorithms (success rate <i>versus</i> number of swarm particles). by Rutinaldo Aguiar Nascimento (14259594)

Subjects:

<b>（代码）</b><b>一种新的VMD优化策略ASSA-WFEE及其在滚动轴承故障诊断中的应用</b> by Xiaohan Xu (17426229)

Atmospheric turbulence blur with <i>σ</i> = 3.2 for the “plane.bmp” image. by Haoyuan Yang (8630391)

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

Subjects:

Python implementation of the Trajectory Adaptive Multilevel Sampling algorithm for rare events and improvements by 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. …”

The collective motion, collision avoidance, and CMCA score of the VCMCA algorithm, VCMCA algorithm without CA, and random walk over the simulation steps for <i>n</i> = 30 repetitio... by David L. Krongauz (15447260)

The signal detection algorithm for constructing a neurometric function (the probability of segregation as a function of time) generates acceptable buildup fits at <i>DF</i> = 1, 3, 6, 9. by Quynh-Anh Nguyen (847240)

“…<p>For comparison, see Micheyl et al (2005) [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1008152#pcbi.1008152.ref001" target="_blank">1</a>]. Upper panel: mean spike counts <i>m</i>_{<i>t</i>;<i>DF</i>} (scatter points) at <i>A</i>-tone selective neurons in A1 during tone <i>B</i> were extracted from [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1008152#pcbi.1008152.ref001" target="_blank">1</a>, (Fig.3A in Ref)]. …”

<b>Optimization of the whole life capacity configuration of the hydrogen production system based on improved whale optimization algorithm</b> by Fan Jiang (21178691)