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.
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Schreiber, Measuring information transfer, Physical review letters 85 (2) (2000) 461
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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.
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.
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.
A practical guide that organizes seven IT measures around three questions each—what it answers in AI, suitable estimators, and dangerous misuses—complete with flowchart, table, and worked examples.
The paper reviews existing signal processing strategies for brain-heart interactions, their usability in biomarker development, and key challenges for future work.
citing papers explorer
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Causal Anomaly Detection for Lithium-Ion Battery Degradation
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.
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Prediction and Empowerment: A Theory of Agency through Bridge Interfaces
In deterministic partially observable worlds, perfect prediction requires either identifying the relevant hidden quotient or achieving overwrite control, while high empowerment alone is insufficient.
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Causality in Liquid Water as a Hallmark of Emergent Glassy Dynamics
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.
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M-CaStLe: Uncovering Local Causal Structures in Multivariate Space-Time Gridded Data
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.
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Structural Gating and Effect-aligned Lag-resolved Temporal Causal Discovery Framework with Application to Heat-Pollution Extremes
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.
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Information-Theoretic Measures in AI: A Practical Decision Guide
A practical guide that organizes seven IT measures around three questions each—what it answers in AI, suitable estimators, and dangerous misuses—complete with flowchart, table, and worked examples.
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Measures and Models of Brain-Heart Interactions
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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