SaaS Metrics
Theoretical & Applied Causality
Causal Structure
AI Synthesis & Market Narrative
Research is exploring global causality constraints in modified gravity theories, indicating fundamental physics advancements. Concurrently, AI agent development is leveraging cognitive routing and evaluation harnesses, implicitly relying on causal structures for data analytics.
Correlated Linguistic Patterns
["Global causality constraints"
"causal structure of curved spacetimes"
"cognitive routing and evaluation harness for data analytics agents"]
Driving Media Context
Global causality constraints in rotating scalar-tensor spacetimes
Modified gravity is often formulated as an effective field theory (EFT), where higher-order corrections parametrize departures from general relativity (GR). ...
dennice added to PyPI
A cognitive routing and evaluation harness for data analytics agents.
Prenatal exposure to maternal depression and antidepressants and neurodevelopmental outcomes: A population cohort study
In this study of more than 167,000 children in Wales, Saraid McIlvride and colleagues examined how prenatal exposure to maternal depression and antidepressan...
Stuttgart in a posterior: a city simulation you can question
A research demonstrator: 936,597 simulated people, shops chosen by size and distance, nine parameters inferred from the city's published retail turnover, and...
Ritualized experiences and perceived authenticity in shaping tourists’ willingness to support intangible cultural heritage inheritance
Intangible cultural heritage tourism plays an important role in cultural sustainability, yet how tourists’ experiential perceptions are associated with their...
Thermodynamic–molecular interdependencies and causal determinants governing pharmaceutical solubility in SC-CO2 systems
Scientific Reports - Thermodynamic–molecular interdependencies and causal determinants governing pharmaceutical solubility in SC-CO2 systems
gcn-python 2.1.0
GCN Causal Engine — extract, model and reason over causal structure in natural language and code
gcn-python 2.1.1
GCN Causal Engine — extract, model and reason over causal structure in natural language and code
Group-theoretic upper bounds on reconstructability in inverse problems
Reconstructing the causal structure of physical systems from observational data constitutes a fundamental inverse problem. Here we show that the reconstructi...
a-machine 0.3.0
Construct epsilon-machines to generate symbol sequences with ground truth causal structure and information-theoretic complexity for studying neural network l...