On-chip Spectrometer Formed by a Multi-stage Structure

With apparent size and weight advantages, on-chip spectrometer could be a good choice for the spectrum analysis application which has been widely used in numerous areas such as optical network performance monitoring, materials analysis and medical research. In order to realize the broadband and the high resolution simultaneously, we propose a new on-chip spectrometer structure, which is a two-stage structure. The coarse wavelength division is realized by the cascaded Mach-Zehnder interferometers, which is the first stage of the spectrometer. The output of the Mach-Zehnder interferometers are further dispersed by the second stage structure, which can be realized either by arrayed waveguide gratings or by digital Fourier transform spectrometer structure. We further implemented the thermo-optic modulation for the arrayed waveguide gratings to achieve a higher spectral resolution. The output channel wavelengths of the spectrometer are modulated by the embedded heater to obtain the first order derivative spectra of the input optical signal to obtain a 2nm resolution. With respect to the computer simulation and device characterization results, the 400nm spectral range and the nanoscale resolution have been demonstrated

A Span-Extraction Dataset for Chinese Machine Reading Comprehension

Machine Reading Comprehension (MRC) has become enormously popular recently and has attracted a lot of attention. However, the existing reading comprehension datasets are mostly in English. In this paper, we introduce a Span-Extraction dataset for Chinese machine reading comprehension to add language diversities in this area. The dataset is composed by near 20,000 real questions annotated on Wikipedia paragraphs by human experts. We also annotated a challenge set which contains the questions that need comprehensive understanding and multi-sentence inference throughout the context. We present several baseline systems as well as anonymous submissions for demonstrating the difficulties in this dataset. With the release of the dataset, we hosted the Second Evaluation Workshop on Chinese Machine Reading Comprehension (CMRC 2018). We hope the release of the dataset could further accelerate the Chinese machine reading comprehension research. Resources are available: https://github.com/ymcui/cmrc2018Comment: 6 pages, accepted as a conference paper at EMNLP-IJCNLP 2019 (short paper

Convolutional Spatial Attention Model for Reading Comprehension with Multiple-Choice Questions

Machine Reading Comprehension (MRC) with multiple-choice questions requires the machine to read given passage and select the correct answer among several candidates. In this paper, we propose a novel approach called Convolutional Spatial Attention (CSA) model which can better handle the MRC with multiple-choice questions. The proposed model could fully extract the mutual information among the passage, question, and the candidates, to form the enriched representations. Furthermore, to merge various attention results, we propose to use convolutional operation to dynamically summarize the attention values within the different size of regions. Experimental results show that the proposed model could give substantial improvements over various state-of-the-art systems on both RACE and SemEval-2018 Task11 datasets.Comment: 8 pages. Accepted as a conference paper at AAAI-19 Technical Trac