the easier it is to recall the consequences of something, the greater those consequences are often perceived to be. Most notably, people often rely on the content of their recall if its implications are not called into question by the difficulty they...
The main goal of active learning is to encourage students to make connections between their prior knowledge and new information. It asks students to engage in high-order analysis of content through the articulation of knowledge rather than the passive recall and transmission of facts and ideas. Ac...
Active recall works incredibly well. You just have to make sure you're using it correctly. Learn two rules that make it work flawlessly now.
useofconcepts/methodsinnewsituations.Questioncues:apply,demonstrate,illustrate,examine,solve Comprehension understandingofmeaning.Questioncues:summarize,describe,interpret,predict Knowledge recallofinformation.Questioncues:define,identify,list,matchMay23-24,201918TheresaMooreBloom’sTaxonomyofLearningEvTheThreeCirclesof...
Five steps to active listening are: paying attention; showing that you're listening; providing feedback; deferring judgment; and responding appropriately. Listening is one of the most important skills you can have. How well you listen has a major impact on your job effectiveness and on the qua...
EasIFA outperforms BLASTp with a 10-fold speed increase and improved recall, precision, f1 score, and MCC by 7.57%, 13.08%, 9.68%, and 0.1012, respectively. It also surpasses empirical-rule-based algorithm and other state-of-the-art deep learning annotation method based on PSSM features, ...
In this study, muTOX-AL was trained using the TOXRIC dataset, and the performance of the proposed method was verified. TOXRIC dataset was collected and collated from the TOXRIC website (https://toxric.bioinforai.tech/home) and includes a total of 7495 compounds33. We used a randomize...
steps are repeated until the number of iterations reaches a predefined value. The flow chart shows in Fig.1. The method proposed used an active learning strategy based on subsequence sampling to alleviate the labeling pressure on the medical text relational extraction dataset and remote supervision ...
Biological activity is often highly concentrated on surfaces, across the scales from molecular motors and ciliary arrays to sessile and motile organisms. These ‘active carpets’ locally inject energy into their surrounding fluid. Whereas Fick’s laws of
Training a neural network from scratch requires an extensive training corpus, but by fine-tuning the pre-trained BERT model with additional augmentation steps (described in the “Methods” section), e.g., sampling-training-sampling iteration, data augmentation, and prediction augmentation, we can ...