Sentence examples for learned relevance from inspiring English sources

Therefore, our interpretation is that the N1 reflects adaptive type tendencies, similar to those described in the visual stimulus repetition paradigms, and P2 enhancement reflects a consolidation process associated with auditory memory and learned relevance.

Recommendations: Lessons Learned Relevance:  OHTA funding addressed important gaps in demand creation, community-facility linkages and data quality.

Table 2 The percentage of mathematics and biology teachers that mentioned elements of each category   Teaching strategies Meaningful learning Relevance Curriculum CK General pedagogy Teacher's personality Learner's personality Mathematics teachers 92 77 15 31 100 69 85 15 Biology teachers 94 63 31 0 100 0 81199.

Table 1 The percentage of elements in each category that were mentioned by the mathematics and biology teachers   Teaching strategies Meaningful learning Relevance Curriculum CK General pedagogy Teacher's personality Learner's personality Mathematics teachers 25 13 2 3 20 11 25 1 Biology teachers 23 1233 0 35 0 23 3.

The results and discussion provide useful lessons learned with relevance to other areas in the Great Lakes Basin.

Experts can solve difficult problems, explain the result, learn, restructure knowledge, and determine relevance, and they know what they don't know.

CARD is designed to aggregate pairwise distances from multiple relational matrices, partition the data into clusters, and learn a relevance weight for each matrix in each cluster simultaneously.

Mohan, S., Fiorini, N., Kim, S. & Lu, Z. Deep learning for biomedical information retrieval: learning textual relevance from click logs. in Proceedings of the 16th Workshop on Biomedical Natural Language Processing 222 231 (ACL, 2017).

In addition to goodie bags, Grossman devotes 4-5 minutes of each lecture explaining how the material and concepts that the students are learning have relevance and meaning with real-life applications and implications.

This algorithm is designed to give a fuzzy partition and a vector of medoids for each fuzzy cluster as well as to learn a relevance weight for each dissimilarity matrix by optimizing an objective function.

These methods are designed to furnish a partition and a prototype for each cluster as well as to learn a relevance weight for each dissimilarity matrix by optimizing an adequacy criterion that measures the fitting between the clusters and their representatives.