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This paper studies the effects of heterogeneity in DO networks and proposes measures for reducing or removing them. In particular, two types of imbalance, i.e., f-Imbalance and OH-Imbalance are identified and methods for compensating them, i.e., RL padding and AT overhead (OH) channel boost, are proposed. The effectiveness of the proposed methods is demonstrated through both, developing an analytic...
This paper studies the effects of heterogeneity in DO networks and proposes measures for reducing or removing them. In particular, two types of imbalance, i.e., f-Imbalance and OH-Imbalance are identified and methods for compensating them, i.e., RL padding and AT overhead (OH) channel boost, are proposed. The effectiveness of the proposed methods is demonstrated through both, developing an analytic...
Commonly used classification algorithms are not capable of incremental learning. When a new pattern is presented to such a computational model, it can either classify the unknown pattern based on its legacy training or declare the pattern as an outlier if such a provision is built into the associated algorithm. In the case of the pattern being an outlier to the existing training model, it is desirable...
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