Seabed deposition and erosion change and influence factors in the Yangshan Deepwater Port over the years

The seabed scouring and silting are very important to the construction of port and waterway engineering.Seabed deposition and erosion change is complicated due to the influence of sediment supply,human activities and other factors.The Yangshan Deepwater Port is the new deep water harbor,which is an important part of the Shanghai International Shipping Service Center.Its construction has received much attention.At present,the water depth from the 1st to the 3rd harbor district is currently suitable under regular dredging and tidal current action.The fourth harbor district will be built in the world’s largest fully-automated deep water wharf.In the study,bathymetry change of the entire sea area of the Yangshan Deepwater Port and the 4th harbor district(i.e.,PhaseⅣproject)waters were analyzed quantitatively using multiyear bathymetric,hydrological and sediment data.The results show that from 1998 to 2010,seabed changes are characterized by large volumes of erosion and sedimentation,which the southern part was deposited and the northern part was eroded in the inner harbor waters,but the seabed of the Kezhushan inlet was eroded.Seabed changes of PhaseⅣproject waters generally show a scour tendency in recent few years with the annual scour rate about 0.7 m.Among the many factors,the existence of Kezhushan inlet and its influence of the western water flow play an important positive role in water depth changes under the ebb tide action.

Enhancing low-resource cross-lingual summarization from noisy data with fine-grained reinforcement learning

Cross-lingual summarization(CLS)is the task of generating a summary in a target language from a document in a source language.Recently,end-to-end CLS models have achieved impressive results using large-scale,high-quality datasets typically constructed by translating monolingual summary corpora into CLS corpora.However,due to the limited performance of low-resource language translation models,translation noise can seriously degrade the performance of these models.In this paper,we propose a fine-grained reinforcement learning approach to address low-resource CLS based on noisy data.We introduce the source language summary as a gold signal to alleviate the impact of the translated noisy target summary.Specifically,we design a reinforcement reward by calculating the word correlation and word missing degree between the source language summary and the generated target language summary,and combine it with cross-entropy loss to optimize the CLS model.To validate the performance of our proposed model,we construct Chinese-Vietnamese and Vietnamese-Chinese CLS datasets.Experimental results show that our proposed model outperforms the baselines in terms of both the ROUGE score and BERTScore.

CeO_(2)nanoparticles-decorated CoP nanocubes for accelerating alkaline electrocatalytic oxygen evolution reaction

Constructing heterojunction interface as an active catalyst is an effective strategy to boost electrocatalytic activity of oxygen evolution reaction(OER).Herein,we report an interfacial CoP/CeO_(2)heterostructure catalyst constructed by interface engineering and selective phosphorization procedure.X-ray photoelectron spectroscopy(XPS)suggests that coupling CeO_(2)nanoparticles on the surface of CoP will generate interfacial interaction at the two-phase interface,resulting in electron transfer between CoP and CeO_(2)components at the interface.Benefitting from the interfacial interaction,large exposed interface area,and luxuriant mesopores structure,CoP/CeO_(2)shows fascinating alkaline OER performance.At the current densities of 10 and 50 mA cm^(−2),the optimal CoP/CeO_(2)heterojunction exhibits lower overpotential(257 and 298 mV)than either CoP(288 and 354 mV)or RuO_(2)(305 and 409 mV).This work provides a facile synthetic protocol for constructing heterostructure interfaces to improve OER performance.

Exploiting comments information to improve legal public opinion news abstractive summarization

Automatically generating a brief summary for legal-related public opinion news(LPO-news,which contains legal words or phrases)plays an important role in rapid and effective public opinion disposal.For LPO-news,the critical case elements which are significant parts of the summary may be mentioned several times in the reader comments.Consequently,we investigate the task of comment-aware abstractive text summarization for LPO-news,which can generate salient summary by learning pivotal case elements from the reader comments.In this paper,we present a hierarchical comment-aware encoder(HCAE),which contains four components:1)a traditional sequenceto-sequence framework as our baseline;2)a selective denoising module to filter the noisy of comments and distinguish the case elements;3)a merge module by coupling the source article and comments to yield comment-aware context representation;4)a recoding module to capture the interaction among the source article words conditioned on the comments.Extensive experiments are conducted on a large dataset of legal public opinion news collected from micro-blog,and results show that the proposed model outperforms several existing state-of-the-art baseline models under the ROUGE metrics.

Linguistic feature template integration for Chinese-Vietnamese neural machine translation

1 Introduction and main contributions Template-based approaches have achieved significant progress in low-resource neural machine translation(NMT)recently[1],such as the efficient works,NMT-GTM[2],SoftPrototype[3],etc.However,most previous works only retrieve target sentence as template to generate translation,neglecting the utilization of linguistic feature that contained in the source sentence and template.