Proximal Policy Optimization: Iteratively updates policy to maximize reward while staying close to initial behavior. It uses a reward model to score responses and requires careful tuning of hyperparameters including learning rate, batch size, and PPO clip range. ...
Figures (15) Show 9 more figures Tables (1) Table Extras (2) Download all Video S1. Cryo-electron tomogram of CIA-PspA interacting with EPL vesicles, related to Figures 6D–6G Table S1. PspA cryo-EM structure determination, related to Figures 1 and 5Volume...
Intelligent decision forest (DF) models such as Logistic Model Tree (LMT), Random Forest (RF), Functional Tree (FT), and enhanced variations of LMT, RF, and FT based on weighted soft voting and stacking ensembles are utilized for CCP. DF models generate extremely efficient decision trees (...
Post-disaster reconstruction of the built environment represents a key global challenge that looks set to remain for the foreseeable future, but it also offers significant implications for the future sustainability and resilience of the built environment
CAD models generally store an object’s geometric and kinetic properties, but not its surface properties, defining color and reflective behavior. There are objects with difficult surface properties that hinder the recognition of geometric properties based on optical recordings, i.e., objects made from...
This is based on the random forest voting method used to estimate gene expression response 1 in early testing. The right panel displays the top 17 variables (genes) based on variable importance rankings calculated from the Gini index. The mean decrease Gini is between 0 and 15. For example,...
Attacks on the application layer attempt to take advantage of flaws in a service or application that can lead to instability and make it impossible for authorized users to access the system. Since it takes only a small amount of malicious traffic to mimic the behavior of real consumers, these...
This voting mechanism makes the model robust and less prone to overfitting. Importantly, DF also provides a way to assess variable importance. This is done by randomly shuffling each predictor’s values and measuring how much the prediction error increases. This helps understand the influence of ...