Abstract: The automatic generation of synthetic training data that can be used to train a language model to generate code examples following a code language based on a natural language input. Thus, new language models may be created, or existing language models may be fine-tuned, to adapt to automatically generate code without having to manually generate bulk quantities of training data. Rather, a many-to-many grammar mapping is navigated to generate training data. Specifically, the many-to-many grammar mapping maps code grammar to natural grammar. Then, each training data is generated by navigating the many-to-many grammar mapping definition to generate a mapping of a respective code expression to a respective natural language expression.

Abstract: The invention relates to a computer implemented method and systems for cell level fish dot counting. FISH (fluorescence in situ hybridization) dot counting is the process of enumerating chromosomal abnormalities in the cells which can be used in areas of diagnosis and cancer research. The method comprises in part overlaying images of a biological sample comprising a nuclear counterstain mask and a FISH binary mask. The FISH binary mask is extracted using a multi-level extended h-maxima or h-minima.

Abstract: The invention relates to a computer implemented method and systems for cell level fish dot counting. FISH (fluorescence in situ hybridization) dot counting is the process of enumerating chromosomal abnormalities in the cells which can be used in areas of diagnosis and cancer research. The method comprises in part overlaying images of a biological sample comprising a nuclear counterstain mask and a FISH binary mask. The FISH binary mask is extracted using a multi-level extended h-maxima or h-minima.