Distribution of the True Positive Rate, above, and True Negative Rate, below, for the 50 simulations from a model with <i>K</i> = 3 clusters, i.i.d. random error term. by Susana Conde (5495675)

Supplementary Tables S1 to S14 from Gene Expression Integration into Pathway Modules Reveals a Pan-Cancer Metabolic Landscape by Cankut Cubuk (14932079)

Optimal number of clusters from silhouette statistics. by Muhammad Atif (2575789)

The convergence curves of the test functions. by Ruiyu Zhan (21602031)

“…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)

“…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. …”

Hash function construct used in SKINNY-tk3-hash. by Sonal Arvind Barge (20454967)

“…These devices gather information from their environment and send it across a network. …”

Increasing consensus of context-specific metabolic models by integrating data-inferred cell functions by Anne Richelle (6589178)

“…Models can quantify the activities of diverse pathways and cellular functions. Since some metabolic reactions are only catalyzed in specific environments, several algorithms exist that build context-specific models. …”

List of selected ranges of movements(ROM), from which features were then counted. by Jindřich Adolf (17334216)

Optimization outcome for the Rosenbrock function. by Shikun Chen (14625352)

“…The approach leverages gradient information from neural networks to guide SLSQP optimization while maintaining XGBoost’s prediction precision. …”

Optimization outcome for the Rastrigin function. by Shikun Chen (14625352)

“…The approach leverages gradient information from neural networks to guide SLSQP optimization while maintaining XGBoost’s prediction precision. …”

2D Rastrigin function. by Shikun Chen (14625352)

“…The approach leverages gradient information from neural networks to guide SLSQP optimization while maintaining XGBoost’s prediction precision. …”

2D Levy function. by Shikun Chen (14625352)

“…The approach leverages gradient information from neural networks to guide SLSQP optimization while maintaining XGBoost’s prediction precision. …”

2D Rosenbrock function. by Shikun Chen (14625352)

“…The approach leverages gradient information from neural networks to guide SLSQP optimization while maintaining XGBoost’s prediction precision. …”

Optimization outcome for the Levy function. by Shikun Chen (14625352)

“…The approach leverages gradient information from neural networks to guide SLSQP optimization while maintaining XGBoost’s prediction precision. …”

The profile log-likelihood of data simulated from four distinct MA(1) models, demonstrating a few examples of multimodal likelihood surfaces. by Jesse Wheeler (18462457)

Sigmoid function calculating rain probability. by Niccolo Pescetelli (7000043)

QLIKE loss function results for four models. by Shusheng Ding (11663294)

S1 Dataset - by Hao Wu (65943)

“…In the network structure, the PS-UNet++ network is based on the sub-pixel convolution upsampling module, and the UNet++ network is constructed as the feature extraction sub-network of the optimization algorithm to extract more detailed information from the model. …”

Test results of different training methods. by Hao Wu (65943)

“…In the network structure, the PS-UNet++ network is based on the sub-pixel convolution upsampling module, and the UNet++ network is constructed as the feature extraction sub-network of the optimization algorithm to extract more detailed information from the model. …”

Schematic diagram of subpixel convolution. by Hao Wu (65943)

“…In the network structure, the PS-UNet++ network is based on the sub-pixel convolution upsampling module, and the UNet++ network is constructed as the feature extraction sub-network of the optimization algorithm to extract more detailed information from the model. …”