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Bulk-type all-solid-state lithium batteries (ASSLBs) are being considered as a promising technology to improve the safety and energy density of today's batteries. However, current bulk-type ASSLBs suffer from low cell-level energy density due to the challenges in reducing the electrolyte thickness. In this work, we report cathode-supported ASSLBs with a thin solid electrolyte layer. Starting from a stainless steel mesh-supported Li 2 S cathode, we are able to build an ASSLB with a ∼100 μm thick Li 3 PS 4 electrolyte reinforced by a Kevlar nonwoven scaffold and with Li metal as the anode. The ASSLB delivers a high capacity with high rate and cycling performances at room temperature. Moreover, the unique cell design also enabled utilization of a thick cathode with a Li 2 S loading of 7.64 mg cm −2 , providing a high cell-level energy density (excluding the current collectors) of 370.6 Wh kg −1 for the first cycle.

A novel high-quality MoS2-doped Li2S–P2S5glass-ceramic electrolyte (Li7P2.9S10.85Mo0.01) is successfully prepared by a facile combined method of high-energy ball milling plus annealing. The Li7P2.9S10.85Mo0.01electrolyte shows a high ionic conductivity and excellent electrochemical stability.

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Lithium-sulfur batteries (LSBs) are considered to be one of the most promising alternatives to the current lithium ion batteries (LIBs) to meet the increasing demand of energy storage due to their high energy density, natural abundance, low cost and environmental-friendliness. Despite great success, LSBs are still suffering from several problems including undermined capacity arising from low utilization of sulfur, unsatisfactory rate performance and poor cycling life due to shuttle effect of polysulfides and poor electrical conductivity of sulfur. Under such circumstances, design/fabrication of porous carbon-sulfur composite cathodes is regarded as an effective solution to overcome the above problems. In this review, we summarize different synthetic methods of porous carbon hosts and corresponding integration ways of carbon-sulfur cathodes. We also address the pore formation mechanism of porous carbon hosts. The pore size effect on electrochemical performance is highlighted and compared. The enhanced mechanism of porous carbon host on the sulfur cathode is systematically reviewed and revealed. Finally, we demonstrate the combination of porous carbon hosts and high-profile solid-state electrolytes nowadays, and discuss the challenges to realize large-scale commercial application of porous carbon-sulfur cathodes and propose their developing trend in the future.

Cross-lingual transfer of word embeddings aims to establish the semantic mappings among words in different languages by learning the transformation functions over the corresponding word embedding spaces. Successfully solving this problem would benefit many downstream tasks such as to translate text classification models from resource-rich languages (e.g. English) to low-resource languages. Supervised methods for this problem rely on the availability of cross-lingual supervision, either using parallel corpora or bilingual lexicons as the labeled data for training, which may not be available for many low resource languages. This paper proposes an unsupervised learning approach that does not require any cross-lingual labeled data. Given two monolingual word embedding spaces for any language pair, our algorithm optimizes the transformation functions in both directions simultaneously based on distributional matching as well as minimizing the backtranslation losses. We use a neural network implementation to calculate the Sinkhorn distance, a well-defined distributional similarity measure, and optimize our objective through back-propagation. Our evaluation on benchmark datasets for bilingual lexicon induction and cross-lingual word similarity prediction shows stronger or competitive performance of the proposed method compared to other stateof-the-art supervised and unsupervised baseline methods over many language pairs.

A lithium superionic conductor of Li7P2.9Mn0.1S10.7I0.3 as solid electrolyte was successfully prepared via high-energy milling, possessing high ionic conductivity and excellent electrochemical stability. The prepared all solid state LSBs shows a large capacity of 796 mA h g−1 with good cycling stability.

With the abundance of automatic meeting transcripts, meeting summarization is of great interest to both participants and other parties. Traditional methods of summarizing meetings depend on complex multi-step pipelines that make joint optimization intractable. Meanwhile, there are a handful of deep neural models for text summarization and dialogue systems. However, the semantic structure and styles of meeting transcripts are quite different from articles and conversations. In this paper, we propose a novel abstractive summary network that adapts to the meeting scenario. We design a hierarchical structure to accommodate long meeting transcripts and a role vector to depict the difference among speakers. Furthermore, due to the inadequacy of meeting summary data, we pretrain the model on largescale news summary data. Empirical results show that our model outperforms previous approaches in both automatic metrics and human evaluation. For example, on ICSI dataset, the ROUGE-1 score increases from 34.66% to 46.28%.