Self-interacting dark matter implied by nano-Hertz gravitational waves

The self-interacting dark matter (SIDM) paradigm provides a potential solution to the challenge faced by the cold dark matter model in explaining small-scale structure problems. This paradigm incorporates self-interactions among DM particles, typically mediated by a particle with a mass around MeV. The recent evidences of nano-Hertz gravitational waves from NANOGrav, EPTA, PPTA, and CPTA collaborations indicate a first-order phase transition (FOPT) occurring at a temperature of the MeV scale. Considering the close proximity between these two scales, we postulate that the mediator mass in the SIDM model originates from the spontaneous breaking of a $U(1)'$ symmetry, which is driven by the FOPT indicated by pulsar time array data. Consequently, the alignment of these two scales is believed to be deeply connected by the same underlying physics. Through a comprehensive survey of the parameter space, we identify the viable region favored by SIDM and simultaneously provide an explanation for the pulsar timing array data.Comment: 5 pages, 1 figur

local fractional fourier series solutions for nonhomogeneous heat equations arising in fractal heat flow with local fractional derivative

The fractal heat flow within local fractional derivative is investigated. The nonhomogeneous heat equations arising in fractal heat flow are discussed. The local fractional Fourier series solutions for one-dimensional nonhomogeneous heat equations are obtained. The nondifferentiable series solutions are given to show the efficiency and implementation of the present method

Joint Learning of Answer Selection and Answer Summary Generation in Community Question Answering

Community question answering (CQA) gains increasing popularity in both academy and industry recently. However, the redundancy and lengthiness issues of crowdsourced answers limit the performance of answer selection and lead to reading difficulties and misunderstandings for community users. To solve these problems, we tackle the tasks of answer selection and answer summary generation in CQA with a novel joint learning model. Specifically, we design a question-driven pointer-generator network, which exploits the correlation information between question-answer pairs to aid in attending the essential information when generating answer summaries. Meanwhile, we leverage the answer summaries to alleviate noise in original lengthy answers when ranking the relevancy degrees of question-answer pairs. In addition, we construct a new large-scale CQA corpus, WikiHowQA, which contains long answers for answer selection as well as reference summaries for answer summarization. The experimental results show that the joint learning method can effectively address the answer redundancy issue in CQA and achieves state-of-the-art results on both answer selection and text summarization tasks. Furthermore, the proposed model is shown to be of great transferring ability and applicability for resource-poor CQA tasks, which lack of reference answer summaries.Comment: Accepted by AAAI 2020 (oral