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A bstract Electroweak baryogenesis (EWBG) offers a compelling narrative for the generation of the baryon asymmetry, however it cannot be realised in the Standard Model, and leads to severe experimental tensions in the Minimal Supersymmetric Standard Model (MSSM). One of the reasons for these experimental tensions is that in traditional approaches to EWBG new physics is required to enter at the electroweak phase transition, which conventionally is fixed near 100 GeV. Here we demonstrate that the addition of sub-TeV fields in supersymmetric extensions of the Standard Model permits TeV-scale strongly first-order electroweak phase transition. While earlier literature suggested no-go arguments with regards to high-temperature symmetry breaking in supersymmetric models, we show these can be evaded by employing a systematic suppression of certain thermal corrections in theories with a large number of states. The models presented push the new physics needed for EWBG to higher scales, hence presenting new parameter regions in which to realize EWBG and evade experimental tensions, however they are not expected to render EWBG completely outside of the foreseeable future experimental reach. 

The goal of text-to-text generation is to make machines express like a human in many applications such as conversation, summarization, and translation. It is one of the most important yet challenging tasks in natural language processing (NLP). Various neural encoder-decoder models have been proposed to achieve the goal by learning to map input text to output text. However, the input text alone often provides limited knowledge to generate the desired output, so the performance of text generation is still far from satisfaction in many real-world scenarios. To address this issue, researchers have considered incorporating (i) internal knowledge embedded in the input text and (ii) external knowledge from outside sources such as knowledge base and knowledge graph into the text generation system. This research topic is known as knowledge-enhanced text generation. In this survey, we present a comprehensive review of the research on this topic over the past five years. The main content includes two parts: (i) general methods and architectures for integrating knowledge into text generation; (ii) specific techniques and applications according to different forms of knowledge data. This survey can have broad audiences, researchers and practitioners, in academia and industry.