COLA-Gen: Active Learning Techniques for Automatic Code Generation of Benchmarks

Benchmarking is crucial in code optimization. It is required to have a set of programs that we consider representative to validate optimization techniques or evaluate predictive performance models. However, there is a shortage of available benchmarks for code optimization, more pronounced when using machine learning techniques. The problem lies in the number of programs for testing because these techniques are sensitive to the quality and quantity of data used for training. Our work aims to address these limitations. We present a methodology to efficiently generate benchmarks for the code optimization domain. It includes an automatic code generator, an associated DSL handling, the high-level specification of the desired code, and a smart strategy for extending the benchmark as needed. The strategy is based on Active Learning techniques and helps to generate the most representative data for our benchmark. We observed that Machine Learning models trained on our benchmark produce better quality predictions and converge faster. The optimization based on the Active Learning method achieved up to 15% more speed-up than the passive learning method using the same amount of data

Sensitization to the storage mites Lepidoglyphus destructor and Tyrophagus putrescentiae (Acari, Sarcoptiformes, Astigmatina) in a suburban population in Southern Poland

Mite infestation of stored products is a serious threat to food safety and public health. These stored product mites are not only serious pests of stored food but also cause allergies in humans. Thirty serum samples from patients living in suburban areas of Upper Silesia (South Poland) were tested for sensitization to two species of storage mites: Lepidoglyphus destructor [LD] and Tyrophagus putrescentiae [TP]. Patient antibodies against particular antigens were identified using anti-human anti-IgE monoclonal antibodies. Fifteen protein fractions from LD gave positive reactions with IgE antibodies and 18 from TP. Seven of the 30 samples showed positive reactions to a protein fraction measuring about 29 kDa from LD and six reacted with a fraction measuring about 25 kDa from TP. These findings may imply the existence of many protein fractions with allergenic properties besides the characterized allergens in the two tested species