This data catalogue contains the generated results for "Generative AI for Epidemiological Information Extraction," comparing in-context learning and fine-tuning with large language models to enhance outbreak data extraction. It includes the generated outputs and plots for evaluation and analysis.
Also available via: Open MIND
Final reproducible workflow for the manuscript "Prediction of Microemulsion Phase Classes from Formulation Variables Using Machine Learning and Feature Importance". This release contains the raw dataset, cleaned dataset, data dictionary, Python workflow, package requirements, gen…
HomoeoGWAS is a GWAS framework for allopolyploid crops (wheat AABBDD, cotton AADD, rapeseed AACC, oat AACCDD, strawberry octoploid). It combines subgenome-partitioned random-effect LMMs with per-logical-chromosome LOCO, an optional scope-conditional homoeolog Hadamard kernel, and…
Software-Defined Stochastic Inference Engine (SDSIE) Initial public release and technical research specification for the Software-Defined Stochastic Inference Engine (SDSIE). Overview This specification outlines an adaptive virtual runtime architecture designed to execute generat…
Large language models frequently possess the knowledge needed to answer a question correctly yet commit to the wrong response. This paper presents friction-guided inference, a calibrated inference-time pipeline that uses the model's own logprob distribution — available at zero co…
🔗 Reproducible code: github.com/jpbronsard/syntonic-portfolio v 3.0 V2.0 measured one channel of financial adaptation: the return channel, where \(\tau^\star=1/\sqrt{2}\) is the structural signature of the random-walk limit. Markets have a second channel with the same structure…
The exponential growth of heterogeneous digital information across structured and unstructured repositories presents a critical challenge for large language models (LLMs): the inability to access and reason over dynamically evolving knowledge without costly model retraining. This…