Overview of the ligand data preprocessing. by Ubair Aziz (21485738)

Can Coarse-Grained Molecular Dynamics Simulations Predict Pharmaceutical Crystal Growth? by Linghao Shi (20890113)

“…This is followed by applying Particle Swarm Optimization (PSO), a global optimum searching algorithm, to the CG Lennard-Jones intermolecular potentials to fit the radial distribution functions of both the crystalline and melt structures. …”

Hyperparameters for the XGBoost model. by Hoa Thi Trinh (20347834)

“…Therefore, this study exploits machine learning techniques, specifically the hybrid XGBoost model combined with optimization algorithms, to predict the shear strength of RC walls based on model training from available experimental results. …”

Data from Fig 3. by Hoa Thi Trinh (20347834)

“…Therefore, this study exploits machine learning techniques, specifically the hybrid XGBoost model combined with optimization algorithms, to predict the shear strength of RC walls based on model training from available experimental results. …”

Distribution of cross-section stypes. by Hoa Thi Trinh (20347834)

“…Therefore, this study exploits machine learning techniques, specifically the hybrid XGBoost model combined with optimization algorithms, to predict the shear strength of RC walls based on model training from available experimental results. …”

Example of data used in Table 1. by Hoa Thi Trinh (20347834)

“…Therefore, this study exploits machine learning techniques, specifically the hybrid XGBoost model combined with optimization algorithms, to predict the shear strength of RC walls based on model training from available experimental results. …”

Data from Fig 7. by Hoa Thi Trinh (20347834)

“…Therefore, this study exploits machine learning techniques, specifically the hybrid XGBoost model combined with optimization algorithms, to predict the shear strength of RC walls based on model training from available experimental results. …”

Data from Fig 8. by Hoa Thi Trinh (20347834)

“…Therefore, this study exploits machine learning techniques, specifically the hybrid XGBoost model combined with optimization algorithms, to predict the shear strength of RC walls based on model training from available experimental results. …”

Data from Fig 4. by Hoa Thi Trinh (20347834)

“…Therefore, this study exploits machine learning techniques, specifically the hybrid XGBoost model combined with optimization algorithms, to predict the shear strength of RC walls based on model training from available experimental results. …”

Features of shear strength database for RC walls. by Hoa Thi Trinh (20347834)

“…Therefore, this study exploits machine learning techniques, specifically the hybrid XGBoost model combined with optimization algorithms, to predict the shear strength of RC walls based on model training from available experimental results. …”

This Figure shows the flow of information between components when running a simulation. by Till Köster (11287803)

Model output of different pairs of parameters. by Yen-Ju Chen (354876)

“…To investigate how “temporal similarity structures” influence human visual segmentation, we developed a stimulus generation algorithm based on Vision Transformer. …”

Overview of computational workflow employed for interpretation benchmarking. by Justin Reynolds (12625799)

Inferred phylogenies for POP66. by Henri Schmidt (17364844)

“…We derive an algorithm for the regression sub-problem by exploiting the unique, combinatorial structure of the matrices appearing within the problem. …”

Runtime analysis for B-ALL patient phylogenies. by Henri Schmidt (17364844)

“…We derive an algorithm for the regression sub-problem by exploiting the unique, combinatorial structure of the matrices appearing within the problem. …”

ARI and NMI of inferring mutation clusters. by Henri Schmidt (17364844)

“…We derive an algorithm for the regression sub-problem by exploiting the unique, combinatorial structure of the matrices appearing within the problem. …”

Inferred phylogenies for CSC28. by Henri Schmidt (17364844)

“…We derive an algorithm for the regression sub-problem by exploiting the unique, combinatorial structure of the matrices appearing within the problem. …”

<i>ℓ</i>₁ matrix error for B-ALL patient phylogenies. by Henri Schmidt (17364844)

“…We derive an algorithm for the regression sub-problem by exploiting the unique, combinatorial structure of the matrices appearing within the problem. …”

High-Performance, High-Angular-Momentum J Engine on Graphics Processing Units by Elise Palethorpe (18126804)

“…In this Article, we present a high-performance, high-angular-momentum Coulomb-matrix (<b><i>J</i></b>) engine specifically optimized for GPU execution. Our approach introduces a GPU-optimized McMurchie-Davidson recurrence algorithm combined with a tailored integral batching scheme, designed specifically to jointly minimize intermediate storage requirements and redundant computation. …”

Partial MURA dataset for experimental evaluation. by Qifeng Liu (10746428)

“…Additionally, we apply an improved Type-II fuzzy set algorithm to further optimize image sharpness. By simultaneously enhancing contrast and sharpness, the method significantly improves image quality and detail distinguishability. …”