Search Results (2)

We consider the problem of assessing the non-equilibrium behavior of a system from the study of time series. In particular, we analyze experimental data from a piezoelectric energy harvester driven by broadband random vibrations where the extracted power and the relative tip displacement can be simultaneously measured. We compute autocorrelation and cross-correlation functions of these quantities in order to investigate the system properties under time reversal. We support our findings with numerical simulations of a linear underdamped Langevin equation, which very well describes the dynamics and fluctuations of the energy harvester. Our study shows that, due to the linearity of the system, from the analysis of a single variable, it is not possible to evidence the non-equilibrium nature of the dynamics. On the other hand, when cross-correlations are considered, the irreversible nature of the dynamics can be revealed. Full article

The inference of causal relations between observable phenomena is paramount across scientific disciplines; however, the means for such enterprise without experimental manipulation are limited. A commonly applied principle is that of the cause preceding and predicting the effect, taking into account other circumstances. Intuitively, when the temporal order of events is reverted, one would expect the cause and effect to apparently switch roles. This was previously demonstrated in bivariate linear systems and used in design of improved causal inference scores, while such behaviour in linear systems has been put in contrast with nonlinear chaotic systems where the inferred causal direction appears unchanged under time reversal. The presented work explores the conditions under which the causal reversal happens—either perfectly, approximately, or not at all—using theoretical analysis, low-dimensional examples, and network simulations, focusing on the simplified yet illustrative linear vector autoregressive process of order one. We start with a theoretical analysis that demonstrates that a perfect coupling reversal under time reversal occurs only under very specific conditions, followed up by constructing low-dimensional examples where indeed the dominant causal direction is even conserved rather than reversed. Finally, simulations of random as well as realistically motivated network coupling patterns from brain and climate show that level of coupling reversal and conservation can be well predicted by asymmetry and anormality indices introduced based on the theoretical analysis of the problem. The consequences for causal inference are discussed. Full article