The persistence features of electrochemical noise signals were explained also by correlation coefficients calculated between signals obtained by discrete wavelet multiresolution decomposition.
Distance, inclusion, and persistence features are extracted in this method from the continuous wavelet transform of EEG recordings to discriminate between pre-ictal and inter-ictal states.
While our results confirm that structural (embeddedness) and intensity (number of calls) features are correlated with tie persistence, temporal features of communication events are better and more efficient predictors for tie persistence.
Note that targets can also be specified, and complementarity evaluated in relation to various targets, including those specified for persistence of features (Faith & Walker, 1996 a, b ; Reyers, Van Jaarsveld & Kruger, 2000; Faith et al., 2003; Davis, Costello & Stoms, 2006; Williams et al., 2006).
For the PT-antirealist it is a mistake to suppose that change, persistence and succession feature in our immediate experience.
Interestingly, representation of features in reserves is not permanent and differs from the long-term persistence of those features in the landscape (Margules, Nicholls & Usher, 1994; Pressey & Logan, 1998; Knight et al., 2006 b ; Cabeza et al., 2010).
In contrast, the asymmetric offset of target and non-target items during mask trials elicits non-uniformity of decay; that is, re-entrant processing of non-target features at low-level visual processing areas (i.e., V1) conflicts with a "visible persistence" of target features maintained by high-level visual processing areas.
CI values indicate genetic relationships (common causality) among some landforms and unexpected persistence of some features on older surfaces.
Although this is the traditional setting for tie persistence prediction, the features calculated during Ω might be impacted by the fact that the tie decayed early in the interval Ω (see for example tie C in Figure 1).
More importantly, it seems that temporal properties of ties are better and more efficient descriptions of tie persistence than structural features, which will allow faster and simpler detection of changing events in the topology of social networks.
