Cloze-style reading comprehension is a representative problem in mining relationship between document and query. In this paper, we present a simple but novel model called attention-over-attention reader for better solving cloze-style reading comprehension task. The proposed model aims to place another attention mechanism over the document-level attention and induces "attended attention" for final answer predictions. One advantage of our model is that it is simpler than related works while giving excellent performance. In addition to the primary model, we also propose an N-best re-ranking strategy to double check the validity of the candidates and further improve the performance. Experimental results show that the proposed methods significantly outperform various state-ofthe-art systems by a large margin in public datasets, such as CNN and Children's Book Test.

Developing effective catalysts based on earth abundant elements is critical for CO 2 electroreduction. However, simultaneously achieving a high Faradaic efficiency (FE) and high current density of CO (j CO) remains a challenge. Herein, we prepare a Mn single-atom catalyst (SAC) with a Mn-N 3 site embedded in graphitic carbon nitride. The prepared catalyst exhibits a 98.8% CO FE with a j CO of 14.0 mA cm −2 at a low overpotential of 0.44 V in aqueous electrolyte, outperforming all reported Mn SACs. Moreover, a higher j CO of 29.7 mA cm −2 is obtained in an ionic liquid electrolyte at 0.62 V overpotential. In situ X-ray absorption spectra and density functional theory calculations demonstrate that the remarkable performance of the catalyst is attributed to the Mn-N 3 site, which facilitates the formation of the key intermediate COOH * through a lowered free energy barrier.

Large numbers of catalysts have been developed for the electrochemical reduction of CO to value-added liquid fuels. However, it remains a challenge to maintain a high current efficiency in a wide negative potential range for achieving a high production rate of the target products. Herein, we report a 2D/0D composite catalyst composed of bismuth oxide nanosheets and nitrogen-doped graphene quantum dots (Bi O -NGQDs) for highly efficient electrochemical reduction of CO to formate. Bi O -NGQDs demonstrates a nearly 100 % formate Faraday efficiency (FE) at a moderate overpotential of 0.7 V with a good stability. Strikingly, Bi O -NGQDs exhibit a high activity (average formate FE of 95.6 %) from -0.9 V to -1.2 V vs. RHE. Additionally, DFT calculations reveal that the origin of enhanced activity in this wide negative potential range can be attributed to the increased adsorption energy of CO (ads) and OCHO* intermediate after combination with NGQDs.

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To estimate the potential of the state-of-the-art proteomics technologies on full coverage of the encoding gene products, the Chinese Human Chromosome Proteome Consortium (CCPC) applied a multiomics strategy to systematically analyze the transciptome, translatome, and proteome of the same cultured hepatoma cells with varied metastatic potential qualitatively and quantitatively. The results provide a global view of gene expression profiles. The 9064 identified high confident proteins covered 50.2% of all gene products in the translatome. Those proteins with function of adhesion, development, reproduction, and so on are low abundant in transcriptome and translatome but absent in proteome. Taking the translatome as the background of protein expression, we found that the protein abundance plays a decisive role and hydrophobicity has a greater influence than molecular weight and isoelectric point on protein detectability. Thus, the enrichment strategy used for low-abundant transcription factors helped to identify missing proteins. In addition, those peptides with single amino acid polymorphisms played a significant role for the disease research, although they might negligibly contribute to new protein identification. The proteome raw and metadata of proteome were collected using the iProX submission system and submitted to ProteomeXchange (PXD000529, PXD000533, and PXD000535). All detailed information in this study can be accessed from the Chinese Chromosome-Centric Human Proteome Database.

Although Faraday efficiency (FE) for CO production of single-atom catalysts immobilized on nitrogen-doped carbon supports (M-N/C) for CO2 electrocatalytic reduction reaction (CO2RR) is generally over 90%, M-N/C catalysts demonstrate a...

Owing to the continuous contributions by the Chinese NLP community, more and more Chinese machine reading comprehension datasets become available, and they have been pushing Chinese MRC research forward. To add diversity in this area, in this paper, we propose a new task called Sentence Cloze-style Machine Reading Comprehension (SC-MRC). The proposed task aims to fill the right candidate sentence into the passage that has several blanks. Moreover, to add more difficulties, we also made fake candidates that are similar to the correct ones, which requires the machine to judge their correctness in the context. The proposed dataset contains over 100K blanks (questions) within over 10K passages, which was originated from Chinese narrative stories. To evaluate the dataset, we implement several baseline systems based on pre-trained models, and the results show that the state-of-the-art model still underperforms human performance by a large margin. We hope the release of the dataset could further accelerate the machine reading comprehension research. 1

process, desired products can be easily adjusted by changing the type of electrocatalysts and the applied potentials. [3] Unfortunately, there are still many challenges for CO 2 electroreduction due to the highly stable chemical bound of CO 2 molecule and high energy barrier of proton-coupled multielectron transfer process. [4] Thus, it is critical to develop efficient and stable catalyst materials for CO 2 electroreduction.At present, many catalysts have been reported, including metal, metal oxide, MXenes (transition-metal carbides, nitrides, and carbonitrides), carbon-based materials (e.g., graphene, carbon nanotube (CNT), and carbon quantum dots (CQD)). [5] The ideal heterogeneous catalyst of CO 2 electroreduction should contain highly dispersed active species and sufficient accessible surface to prevent the problem of the mass transfer. [6] Recently, single atom catalysts (SACs) have drawn much attention for CO 2 RR, with advantages of unique electronic structure, unsaturated coordination configuration of active centers, maximum atom-utilization efficiency, quantum size effect, strong interactions between metal and support as well as foreign atom effect. [7] SACs materials afford high selectivity and stability toward desired products, exhibiting great potential to bridge the gap between the homogeneous and heterogeneous catalysis.To date, researchers have successfully developed efficient M-N-C-based (M = Ni, Fe, Co, Mn, Zn, Sn) electrocatalysts with Construction of single atom catalysts (SACs) with high activity toward electroreduction of CO 2 still remains a great challenge. A very simple and truly cost-effective synthetic strategy is proposed to prepare SACs via a impregnation-pyrolysis method, through one-step pyrolysis of graphene oxide aerogel. Compared with other traditional methods, this process is fast and free of repeated acid etching, and thus it has great potential for facile operation and large-scale manufacturing. Both X-ray absorption fine structure and high-angle annular dark-field scanning transmission electron microscopy images confirm the presence of isolated nickel atoms, with a high Ni loading of ≈2.6 wt%. The obtained 3D porous Ni-and N-codoped graphene aerogel exhibits excellent activity toward electroreduction of CO 2 to CO, in particular exhibiting a remarkable CO Faradaic efficiency of 90.2%. Density functional theory calculations reveal that free energies for the formation of intermediate *COOH on coordinatively unsaturated NiN sites are significantly lower than that on NiN 4 site, suggesting the outstanding activities of CO 2 electroreduction originate from coordinatively unsaturated NiN sites in catalysts.