Despite a superficial resemblance to cinema, television both requires and fosters its own methods of character development, plot exposition and audience retention.
Because alternative methods of character state reconstruction can produce conflicting results, both maximum parsimony (MP) and maximum likelihood ML methods of ancestral state reconstruction were employed [ 53, 54].
The choices the player makes in their performance of these tasks thus become methods of character interpretation; a set of dramatic tools establishing the player's newly created self-identity.
Thus, this study was not designed to compare results between alternative methods of character optimization.
Our three methods of character scoring did yield largely compatible results in our analyses.
We implemented Parsimov [ 19], a parsimony-based method to quantify and compare sequence heterochrony, and used the number of timing shifts from the consensus data of the methods of character optimisation as a criterion to support the robustness of a particular branch [ 20- 22].
We demonstrate that our method of character embedding greatly reduces training time and memory use, while significantly improving classification performance.
According to Whitlock (2006, p. 85), "in an age of mediated bodies and avatars, the potential of motion capture in performance suggests a new method of character creation as well as new possibilities for recording and re-using human motion".
In this study, we devised a new method of character embedding to facilitate faster training of deep convolutional neural networks from character-level representations of text data, discuss how it changes network architecture and investigate using padding to building deep networks when using this type of character embedding.
Relationships recovered by Longhorn et al. [30] differed somewhat depending on the method of character coding and analysis, and internodal BPP and BS support was mostly lower than in our trees.
