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.
Estimating mutual information
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
background 1
baseline 1
citation-polarity summary
years
2026 3verdicts
UNVERDICTED 3representative citing papers
QD-LLM evolves prompt embeddings via neuroevolution in a quality-diversity framework, delivering 46% higher coverage and 41% higher QD-score than prior methods on coding and writing benchmarks.
On a complete graph the ballistic deposition model falls outside the KPZ universality class, showing saturation roughness that increases with system size and an ultrafast growth regime, while the RSOS model aligns more closely with continuum predictions.
citing papers explorer
-
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.
-
Parameter-Efficient Neuroevolution for Diverse LLM Generation: Quality-Diversity Optimization via Prompt Embedding Evolution
QD-LLM evolves prompt embeddings via neuroevolution in a quality-diversity framework, delivering 46% higher coverage and 41% higher QD-score than prior methods on coding and writing benchmarks.
-
Discrete Lattice Models for Interface Growth on a Complete Graph
On a complete graph the ballistic deposition model falls outside the KPZ universality class, showing saturation roughness that increases with system size and an ultrafast growth regime, while the RSOS model aligns more closely with continuum predictions.