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Moreover, we can compute graph transition energy between images of different subcellular organelles.
In our approach, graph transition energy is defined to quantify the similarity between collections of images.
We define graph transition energy to quantify morphological differences between image sets.
We defined graph transition energy to quantify morphological difference of two cell-image collections under different perturbations.
We found that the values of graph transition energy measured against DMSO for both muricin A and squamocin decrease over time as shown in Figure 9.
Therefore, given two sets of cell images as feature vectors, if their graph transition energy is low than they are quite different, and vice versa.
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Finally, the damping effects to T* and the graph transitions are studied and discussed with comparisons.
We next investigate how T* is affected as one graph transitions to another when some links between the agents weaken and eventually vanish.
In the first five graphs, five synchronization indices are plotted (after block averaging of size 1000) for different synchronized pairs, whereas in the last graph transitions between synchronized clusters are illustrated.
The statistical dependence between the nodes, indicating the connectivity and traffic patterns of the camera network, is represented by a weighted directed graph and transition times that may have multimodal distributions.
This branching point gives rise to two embedded cycles in the graph of transitions representing the two oscillatory regimes in the phase space (Figure 5B).
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