Algorithm detection steps. by José M. Rivera-Arbeláez (12418512)

Search for Correlations Between the Results of the Density Functional Theory and Hartree–Fock Calculations Using Neural Networks and Classical Machine Learning Algorithms by Saadiallakh Normatov (20682324)

Search for Correlations Between the Results of the Density Functional Theory and Hartree–Fock Calculations Using Neural Networks and Classical Machine Learning Algorithms by Saadiallakh Normatov (20682324)

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

“…The panels highlight that the task of identifying a predictive coarsening of an ecosystem (B) is distinct from the task of predicting the function well (A), and for small or noisy datasets, the former is best accomplished by a simpler method. …”

Attack and defense behaviors with the sensory–motor algorithm between multiple predators and prey. by Hiroyuki Ichijo (13891153)

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

“…We show that batched formation of the XC matrix from the density matrix yields the best performance for large (>O(103) basis functions), sparse systems such as glycine chains and water clusters. …”

<b>Gene Catalog and Associated Functional Data</b> for BioProject PRJNA1311452 by Satheeswaran Thangaraj (22187269)

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)

<b>Algorithm Pseudocode</b> by Yibin Zhao (22425801)

Linear-regression-based algorithms succeed at identifying the correct functional groups in synthetic data, and multi-group algorithms recover more information. by Yuanchen Zhao (12905580)

“…<p>(A), (B) Algorithm performance, evaluated over 50 simulated datasets generated as described in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1012590#pcbi.1012590.g001" target="_blank">Fig 1</a> with <i>N</i> = 3 true groups, 900 samples and 10% simulated measurement noise. …”

The benchmark functions. by Shuhui Yu (1535500)

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

CEC2017 basic functions. by Tengfei Ma (597633)

“…Specifically, it achieves faster iteration speeds across four different environments, with the planned path length after escaping local optima being shortened by an average of 7.55175 m (16.291%) compared to other optimization algorithms. These results confirm OP-ZOA’s enhanced optimization capability, significantly improving both escape efficiency from local optima and solution reliability.…”

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

AUC 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. …”

Recall 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. …”

Flowchart of the general algorithm for one trial. by Junying Wang (509009)

Illustration of the unrestricted model and its training algorithm. by Yuru Song (21300760)

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

CEC2017 test function test results. by Tengfei Ma (597633)

“…Specifically, it achieves faster iteration speeds across four different environments, with the planned path length after escaping local optima being shortened by an average of 7.55175 m (16.291%) compared to other optimization algorithms. These results confirm OP-ZOA’s enhanced optimization capability, significantly improving both escape efficiency from local optima and solution reliability.…”