We propose a novel methodology to generate domain-specific large-scale question answering (QA) datasets by re-purposing existing annotations for other NLP tasks. We demonstrate an instance of this methodology in generating a large-scale QA dataset for electronic medical records by leveraging existing expert annotations on clinical notes for various NLP tasks from the community shared i2b2 datasets § . The resulting corpus (emrQA) has 1 million question-logical form and 400,000+ questionanswer evidence pairs. We characterize the dataset and explore its learning potential by training baseline models for question to logical form and question to answer mapping. § maximum representation of these templates comes from the i2b2 heart disease risk dataset

Recent research proposes syntax-based approaches to address the problem of generating programs from natural language specifications. These approaches typically train a sequence-to-sequence learning model using a syntax-based objective: maximum likelihood estimation (MLE). Such syntax-based approaches do not effectively address the goal of generating semantically correct programs, because these approaches fail to handle Program Aliasing, i.e., semantically equivalent programs may have many syntactically different forms. To address this issue, in this paper, we propose a semantics-based approach named SemRegex. SemRegex provides solutions for a subtask of the program-synthesis problem: generating regular expressions from natural language. Different from the existing syntax-based approaches, SemRegex trains the model by maximizing the expected semantic correctness of the generated regular expressions. The semantic correctness is measured using the DFA-equivalence oracle, random test cases, and distinguishing test cases. The experiments on three public datasets demonstrate the superiority of SemRegex over the existing state-of-the-art approaches.

to biofuels derived from food and feed crops, despite their similar effects on food production and cropland. Unless the EU fixes this problem, the more it restricts fossil carbon, the more it will encourage diversion of cropland to energy crops and outsource its food production.The best solution is to incorporate the 'carbon opportunity cost' of land use into the accounting of emissions from bioenergy in all climate and energy laws. This cost can be measured simply as the carbon that could otherwise be stored by regrowing native vegetation. A superior approach would use carbon opportunity costs, as we have done here, to calculate the average carbon cost to reproduce the same food elsewhere. This approach does not require a switch to consumption-based accounting but recognizes that land use has an opportunity cost, which should be factored into the life-cycle analyses of bioenergy used by the EU.Saving terrestrial carbon and biodiversity starts by reducing, not outsourcing, Europe's land carbon footprint. Adapting Europe's plan can deliver a more beneficial land future.

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Based on a set of formulae for calculating elliptical contact parameters and elastic deformation developed by Houpert and mechanical analysis, a numerical model is established for calculating the roller load distribution and shaft axis orbit of spherical roller bearings with considering off-sized rollers in this paper. A numerical calculation example is carried out to investigate the effects of single off-sized roller and multiple off-sized rollers on spherical roller bearing roller load distribution and axis orbit. Results show that the roller maximum load will increase as its off-sized diameter becomes larger than the nominal diameter, and the roller maximum load will decrease as its off-sized diameter is smaller than the nominal diameter. The maximum loads of the rollers in the two rows neighboring on the off-sized roller/rollers can also be affected. Meanwhile, roller diameter error will largely affect the axis orbit and inner ring radial displacement of spherical roller bearings.