Degree 5 algorithm from Recovering the cluster picture of a polynomial over a discretely valued field by Lilybelle Cowland Kellock (20938306)

MEDOC: A Fast, Scalable, and Mathematically Exact Algorithm for the Site-Specific Prediction of the Protonation Degree in Large Disordered Proteins by Martin J. Fossat (3714079)

“…While it is often convenient to assume that these residues follow their model-compound p<i>K</i>_a values, recent work has shown that local charge effects (charge regulation) can upshift or downshift side chain p<i>K</i>_a values with major consequences for molecular function. …”

Assessment of the level of difficulty of the task from subjects (A) and therapists (B) when using the tablet compared to the paper-based support during the item 18, 19 and 22 compl... by Dominique Vincent-Genod (20552594)

Risk Groups Immune Infiltration Analysis by ssGSEA Algorithm. by Yongling Wang (13261656)

Heatmap results from the global sensitivity analysis for the generation of villous vessels within a placentone. by Diana C. de Oliveira (9220729)

Trainable parameters for different algorithms. by Anwar Hossain Efat (19942283)

Diagram of the proposed solution algorithm. by Vytautas Abromavičius (22008573)

R-squared comparison of test function. by Kejia Liu (5699651)

“…The study employed traditional benchmark functions and conducted evaluations versus baselines Standard GEP, NMO-SARA, and MS-GEP-A to assess fitness outcomes, R² values, population diversification, and the avoidance of local optima. …”

Comparative Analysis (Security Functionalities). by Naif Alzahrani (19992885)

Speed limits and gradients from RJ to WYJ. by Yunhu Huang (21402795)

“…On the basis of EITO_E, we propose EITO_P algorithm using the PPO algorithm to optimize multiple objectives by designing reinforcement learning strategies, rewards, and value functions. …”

Speed limits and gradient from SJZ to XHM. by Yunhu Huang (21402795)

“…On the basis of EITO_E, we propose EITO_P algorithm using the PPO algorithm to optimize multiple objectives by designing reinforcement learning strategies, rewards, and value functions. …”

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)

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

Machine learning analysis of 10 hub genes related to SCI was performed using eight algorithms applied to the dataset. by Tianbao Feng (21722233)

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)

Comparison of the set found when solving IM for <i>k</i> = 19 by the greedy algorithm (left) and the one found by (1 + 1)-WEA for a network with <i>n</i> = 50 vertices. by Alexander Andreev (16822333)

Metapopulation model notation. by Jeffrey Keithley (14626551)

“…We provide a theoretical explanation for this effectiveness by showing that the approximation factor (a measure of how well the algorithmic output for a problem instance compares to its theoretical optimum) of these algorithms depends on the <i>submodularity ratio</i> of the objective function <i>g</i>. …”

Estimates of for each problem instance. by Jeffrey Keithley (14626551)

“…We provide a theoretical explanation for this effectiveness by showing that the approximation factor (a measure of how well the algorithmic output for a problem instance compares to its theoretical optimum) of these algorithms depends on the <i>submodularity ratio</i> of the objective function <i>g</i>. …”

Approximation factors for each problem instance. by Jeffrey Keithley (14626551)

“…We provide a theoretical explanation for this effectiveness by showing that the approximation factor (a measure of how well the algorithmic output for a problem instance compares to its theoretical optimum) of these algorithms depends on the <i>submodularity ratio</i> of the objective function <i>g</i>. …”

Functions in nhppp. by Thomas A. Trikalinos (5785877)

“…We developed it to facilitate the sampling of event times in discrete event and statistical simulations. The package’s functions are based on three algorithms that provably sample from a target NHPPP: the time-transformation of a homogeneous Poisson process (of intensity one) via the inverse of the integrated intensity function; the generation of a Poisson number of order statistics from a fixed density function; and the thinning of a majorizing NHPPP via an acceptance-rejection scheme. …”