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We propose a simple and efficient multi-hop dense retrieval approach for answering complex open-domain questions, which achieves state-of-the-art performance on two multi-hop datasets, HotpotQA and multi-evidence FEVER. Contrary to previous work, our method does not require access to any corpus-specific information, such as inter-document hyperlinks or human-annotated entity markers, and can be applied to any unstructured text corpus. Our system also yields a much better efficiency-accuracy trade-off, matching the best published accuracy on HotpotQA while being 10 times faster at inference time.

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In order to search for the line-like signals in the Fermi-LAT data, we have analyzed totally 49152 regions of interest (ROIs) that cover the whole sky. No ROI displays a line signal with test statistic (TS) value above 25, while for 50 ROIs weak line-like excesses with TS > 16 are presented. The intrinsic significances of these potential signals are further reduced by the large trial factor introduced in such kind of analysis. For the largest TS value of 24.3 derived in our analysis, the corresponding global significance is only 0.54σ. We thus do not find any significant line-like signal and set up constraints on the cross section of dark matter annihilating to gamma-ray lines, σv γγ .PACS numbers: 95.35.+d, 95.85.Pw

The field of combinatorial synthesis and “artificial intelligence” in materials science is still in its infancy. In order to develop and accelerated strategy in the discovery of new materials and processes, requires the need to integrate both the experimental aspects of combinatorial synthesis with the computational aspects of information based design of materials. In biology and organic chemistry, this has been accomplished by developing descriptors which help to specify “quantitative structure- activity relationships” at the molecular level. If materials science is to adopt these strategies as well, a similar framework of “QSARs” is required. In this paper, we outline some approaches that can lay the foundations for QSARs in materials science.