Reconstruction performance across algorithms, dynamics, cell types, and recording length. by Christian Donner (3663007)

Subjects:

Multimodal reference functions. by Ruiyu Zhan (21602031)

“…For the early-stage diabetes dataset, LGWO-BP achieved an accuracy of 0.92, a recall of 0.93, a precision of 0.88, an F1-score of 0.91, and an AUC of 0.95. Utilizing the diabetes dataset from 130 U.S. hospitals, the LGWO-BP algorithm achieved a precision rate of 0.97, a sensitivity of 1.00, a correct classification rate of 0.99, a harmonic mean of precision and recall (F1-score) of 0.98, and an area under the ROC curve (AUC) of 1.00. …”

Summary statistics for the values of objective function—Phase I. by Fadi K. Dib (5204807)

Subjects:

Summary statistics for the number of function evaluations—Phase I. by Fadi K. Dib (5204807)

Subjects:

Analyses of Immune microenvironment and functions between gene clusters by Jiangli Zhao (2308330)

Convergence curves of F1-F7. by Ting Yang (189879)

Subjects:

Illustration of how (1 + 1)-WEA_v3 improves the target set when solving TSS. by Alexander Andreev (16822333)

Subjects:

Fig 1 - by Yuh-Chin T. Huang (17867207)

Subjects:

Conservation and functional variation across TcdB subtypes. by Michael J. Mansfield (9922934)

The expert screening cost <i>g</i>_expert(<i>x</i>) defined for the Challenge: Mean cost for screening a fraction <i>x</i> of a patient cohort for cardiac abnormalities. by Matthew A. Reyna (16965038)

Subjects:

The expert screening cost <i>g</i>_expert(<i>x</i>) defined for the Challenge: Mean cost for screening a fraction <i>x</i> of a patient cohort for cardiac abnormalities. by Matthew A. Reyna (16965038)

Subjects:

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). …”

Effectiveness of RWOA and other SOTA algorithms. by Junhao Wei (6816803)

“…We also conducted a quantitative analysis of RWOA and compared its performance with other state-of-the-art (SOTA) metaheuristic algorithms. …”

Out-of-sample accuracy vs. weighted <i>F</i>-score (comparison to ground-truth) for various clustering algorithms. by James Anibal (13899712)

Parameters used to train the xgboost final models through the extreme gradient boosting algorithm in this work (n = 11,009 species). by Luis Javier Madrigal-Roca (17743035)

Weighted accuracy metric (1) for the murmur detection task (<i>x</i>-axis) and the cost metric (8) scores for the clinical outcome identification task (<i>y</i>-axis) of the final... by Matthew A. Reyna (16965038)

Subjects:

Reverse engineering information flow using stimulus-encoding and stimulus-transfer estimation algorithms. by Gabriel Matías Lorenz (21094672)

Comparison of PyNoetic’s functionality for BCI design with other existing state-of-the-art frameworks. by Gursimran Singh (575288)

Subjects:

Rank correlation plot for TF-binding algorithms and the peaBrain impact score. by Moustafa Abdalla (12419257)

Traditional evaluation metric (<i>x</i>-axis) and cost metric (<i>y</i>-axis) scores for the final Challenge entries with the clinical outcome identification task on the hidden tes... by Matthew A. Reyna (16965038)

Subjects: