Specific Algorithm Steps. by Na Wang (193263)

The immunogenetics annotation of porcine bnAbs and the evolution of bnAbs-producing B cells lineages. by Fengjuan Li (6429356)

“…Basis on the difference in the V gene segment usage of light chain, 10 bnAbs were colored with blue, light blue, and pink backgrounds, respectively. …”

values scored by the different regression algorithms. Linear Regression (LR), Random Forest (RF), Decision Tree (DT), X Gradient Boosting(XGB), Support Vector Machine (SVM), Gradient Boosting (GB) and AdaBoost (AB). by Itxasne Antúnez Sáenz (22794696)

“…<p> values scored by the different regression algorithms. Linear Regression (LR), Random Forest (RF), Decision Tree (DT), X Gradient Boosting(XGB), Support Vector Machine (SVM), Gradient Boosting (GB) and AdaBoost (AB).…”

Effects of Nash equilibrium attack and defense behaviors with the sensory–motor algorithm on encounters, agent numbers, and payoffs. by Hiroyuki Ichijo (13891153)

Attack and defense behaviors between a single predator and a single prey using the sensory–motor algorithm. by Hiroyuki Ichijo (13891153)

(a) B-Scan skin image damage analysis procedure; (b) B-Scan skin image segmentation algorithm. by Xiyu Zheng (21703063)

Effects of prey’s detection distance (<i>D</i>_<i>s</i>) on encounter frequency (<i>E</i>), agent numbers (<i>N</i>_<i>i</i>), and Nash equilibrium payoffs (<su... by Hiroyuki Ichijo (13891153)

Effects of prey’s speed change probability (<i>K</i>_<i>s</i>) on encounter frequency (<i>E</i>), agent numbers (<i>N</i>_<i>i</i>), and Nash equilibrium payoff... by Hiroyuki Ichijo (13891153)

Effects of predators’ speed change probability (<i>K</i>_<i>w</i>) on encounter frequency (<i>E</i>), agent numbers (<i>N</i>_<i>i</i>), and Nash equilibrium pa... by Hiroyuki Ichijo (13891153)

Effects of predators’ detection distance (<i>D</i>_<i>w</i>) on encounter frequency (<i>E</i>), agent numbers (<i>N</i>_<i>i</i>), and Nash equilibrium payoffs... by Hiroyuki Ichijo (13891153)

Underlying Data for '<b>Detection of False Position Attacks in VANETs through Bagging Ensemble Learning</b>' by Bekan Mekonen (21544637)

Temporal models accuracy changes with aggregating villages. by Micanaldo Ernesto Francisco (18723706)

Reconstructed weekly MODIS-Terra daytime LST. by Micanaldo Ernesto Francisco (18723706)

Accuracy of the temporal models at each village. by Micanaldo Ernesto Francisco (18723706)

Illustrative example of optimal daily itineraries for two CHWs to visit every household in their catchment. by Mauricianot Randriamihaja (9091488)

Background heterogeneity and sequencing depth improve WheresWalker SNPindex. by McKenna Feltes (11242085)

Fig 2 - Accurate Bayesian phylogenetic point estimation using a tree distribution parameterized by clade probabilities by Lars Berling (20715518)

Combination of X‑ray Absorption Spectroscopy and Atomistic Simulations Reveals Features of Correlated Disorder in Quartz-like Germanium Oxide by Harshini Venkata Annadata (21622696)

“…By integrating experimental X-ray absorption spectroscopy with reverse Monte Carlo/evolutionary algorithm and ab initio molecular dynamics simulations, we could pinpoint correlated disorder, which is different from the disorder due to thermal motion. …”

Overtuning in Hyperparameter Optimization - Artifacts by Lennart Schneider (20131899)

“…<p dir="ltr">Trajectories of Hyperparameter Optimization (HPO) runs as analyzed in the paper <a href="https://www.arxiv.org/abs/2506.19540" rel="noreferrer" target="_blank">Overtuning in Hyperparameter Optimization.…”

Image 1_Impact of ITH on PRAD patients and feasibility analysis of the positive correlation gene MYLK2 applied to PRAD treatment.tif by Chuanyu Ma (6701108)

“…The ITH-score of PRAD samples was evaluated using the DEPTH algorithm. The optimal cut-off value of RiskScore was calculated based on the difference in survival curves, and PRAD patients were classified into high ITH and low ITH groups based on the optimal cut-off value. …”