EPSTE decomposes MEG time series into geometric symbolic tokens and uses an attention RNN to predict surrogate-validated transfer entropy, recovering directed structure more accurately than a standard symbolic baseline on AAL90-parcellated data.
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Measuring Information Transfer
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CausalHealth detects lithium-ion battery degradation with 100% sensitivity and up to 402-cycle lead time using causal anomaly scores from voltage, current, temperature, and resistance time series across seven cells.
In deterministic partially observable worlds, perfect prediction requires either identifying the relevant hidden quotient or achieving overwrite control, while high empowerment alone is insufficient.
Causal analysis of water MD simulations shows translational motions drive orientational dynamics in supercooled HDL but remain decoupled at ambient conditions, revealing an emergent arrow of time in fluctuation couplings.
Information-theoretic analysis of TNG50 simulations finds high mutual information (0.4-0.8) between bar and spiral parameters and comparable transfer entropy (0.33-0.42) in both directions, indicating mutual co-regulation.
M-CaStLe generalizes local stencil-based causal discovery to the multivariate case and decomposes resulting graphs into reaction and spatial components for interpretation in space-time gridded data.
Applies causal inference to PCs from MD trajectories of two proteins to construct directed influence networks complementary to PCA and TICA.
SGED-TCD is a lag-resolved causal discovery framework that uses structural gating and perturbation-effect alignment to infer interpretable weighted causal networks from complex time series, shown on heat-pollution extremes in China.
Soft matter systems are modeled as information channels of increasing complexity, yielding a heuristic thermodynamic ceiling on information processing performance and a performance gap to biology attributed to per-element energy scales.
A synthesis paper offering a practical decision guide, flowchart, and table for choosing among seven established information-theoretic measures in AI and agent applications.
The paper reviews existing signal processing strategies for brain-heart interactions, their usability in biomarker development, and key challenges for future work.
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