Comparison of average reward values in various simulation scenarios. by Honglei Pang (22693724)

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HIL test results of split-μ road: (a)Vehicle velocity; (b) Braking distance; (c) Lateral displacement; (d) Lateral acceleration; (e)Vehicle sideslip angle; (f) Yaw rate. by Honglei Pang (22693724)

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

Reverse Designing the Wavelength-Specific Thermally Activation Delayed Fluorescent Molecules Using a Genetic Algorithm Coupled with Cheap QM Methods by Xubin Wang (1861147)

“…The fitness function includes three key parameters, i.e., the emission wavelength, the energy gap (Δ<i>E</i>_ST) between the lowest singlet (S₁)- and triplet (T₁)-excited states, and the oscillator strengths for electron transition from S₀ and S₁. …”

Classification performances of Ensemble, SVM, and kNN algorithms. For each performance metric, the mean value across all runs and standard deviation of the mean value is reported i... by Ayşenur Eser (21713911)

Highly Accurate Prediction of Core Spectra of Molecules at Density Functional Theory Cost: Attaining Sub-electronvolt Error from a Restricted Open-Shell Kohn–Sham Approach by Diptarka Hait (2835971)

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Highly Accurate Prediction of Core Spectra of Molecules at Density Functional Theory Cost: Attaining Sub-electronvolt Error from a Restricted Open-Shell Kohn–Sham Approach by Diptarka Hait (2835971)

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Distributions of |Δθ| for the stable low-|Δθ| (left columns), stable high-|Δθ| (middle columns), and shifting-|Δθ| (right columns) networks for different <i>q</i> ranges in 5-perce... by Melisa Menceloglu (7610213)

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Sensitivity analysis of ASMC <i>σ</i> parameter. by Haoran Cui (7235234)

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Wilcoxon’s test results for EBJADE algorithms and other state-of-the-art CEA-ES algorithms using CEC2014 functions. by Yang Cao (53545)

“…<p>Wilcoxon’s test results for EBJADE algorithms and other state-of-the-art CEA-ES algorithms using CEC2014 functions.…”

The convergence curves of the test 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. …”

Single-peaked 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. …”

Data_Sheet_1_FI-Net: Identification of Cancer Driver Genes by Using Functional Impact Prediction Neural Network.PDF by Hong Gu (68558)

“…Then the estimation of the background distribution and the identification of driver genes were conducted in each cluster obtained by the hierarchical clustering algorithm. We applied FI-net and other 22 state-of-the-art methods to 31 datasets from The Cancer Genome Atlas project. …”

Data_Sheet_1_FI-Net: Identification of Cancer Driver Genes by Using Functional Impact Prediction Neural Network.PDF by Hong Gu (68558)

“…Then the estimation of the background distribution and the identification of driver genes were conducted in each cluster obtained by the hierarchical clustering algorithm. We applied FI-net and other 22 state-of-the-art methods to 31 datasets from The Cancer Genome Atlas project. …”

Benchmark functions. by Wei Liu (20030)

“…The convergence of SGWO was analyzed by mathematical theory, and the optimization ability of SGWO and the prediction performance of SGWO-Elman were examined using comparative experiments. The results show: (1) the global convergence probability of SGWO was 1, and its process was a finite homogeneous Markov chain with an absorption state; (2) SGWO not only has better optimization performance when solving complex functions of different dimensions, but also when applied to Elman for parameter optimization, SGWO can significantly optimize the network structure and SGWO-Elman has accurate prediction performance.…”

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. …”

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

“…We plot whether reconstruction of the patterns better than a random guess is easy (blue) or impossible (white) using the message-passing algorithm as a function of the constant threshold <i>τ</i> and the variance <i>ν</i> of the Gaussian noise appearing in the connectivity structure (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1010813#pcbi.1010813.e013" target="_blank">3</a>). …”

Parameter estimation for <i>L</i>₁. by Zhengbin Li (14259417)

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Parameter estimation for <i>L</i>₂. by Zhengbin Li (14259417)

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Functional Connectivity for Experiment and Simulated Anesthesia and Reversal. by Bolaji P. Eniwaye (12915339)