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Guo, Y.-R. et al. Structural signature and transition dynamics of Sb2Te3 melt upon fast cooling.
We investigate the problem of learning the transition dynamics of deterministic, discrete-state environments.
The algorithm learns models of the transition dynamics of a domain using random forests.
In this work a logical specification is used to define the transition dynamics of the discrete subsystem.
In previous work we successfully used a stable Dominican amber to investigate the sub-glass transition dynamics of an organic glass with extremely low fictive temperature.
This observation supports our assertion that it is fundamentally important to investigate how the transition dynamics of learning toward a goal affects the finally acquired structure of the learner.
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For example the linearized Navier-Stokes equations at high Reynolds number provide an accurate model of the transition dynamics in the problem of turbulence suppression in channel flow.
Recognizing this uncertainty in model distinction impacts interpretation of transition dynamics (self-organizing versus probabilistic), and the generation of predictive models for clinical classification of normal and pathological sleep architecture.
Crucial advantages of the proposed approach include its ability to provide insights into key scientific queries related to global and local influences of the exogenous predictors on the transition dynamics via automated tests of hypotheses.
Connecting the rapidly growing basic science understanding of sleep- and wake- promoting brain centers to the behavioral manifestation of arousal state transitions requires improved understanding of transition dynamics.
Thirdly, it encompasses not only greater emphasis but also an opportunity to connect to a body of literature geared to understanding the international, trans-local nature of transition dynamics.
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