An Analysis of Translating Techniques of Long and Complex English Sentences

Long and complex English sentences, just as the name implies, are long sentences with complicated structures. The most typical features of this kind of sentences are the comprehensive use of different subordinate clauses and the flexible use of all kinds of prepositional phrases and participle phrases, which lead to complex sentence structures and varied constituent relationships, and bring great difficulties to English-Chinese translation practice. The paper concludes that, in the translation of long and complex English sentences, the translator should first figure out the logic relationship of the original text, and then, according to the Chinese expression customs, make a flexible use of various translation methods and techniques, and finally he can get a faithful and smooth translated text.

To Compare English Long Sentences And Chinese Long Sentences & Discuss How To Translate The Two Kinds of Long Sentences

In my paper, I compare the characteristics of English long sentences and Chinese long sentences, and I also talk about how to translate long sentences. There are at least two or three modifiers involving no less than one subordinate clause in an English long complex sentence. There are at least twenty words and three or four comparatively independent phrases that are connected together by meaning in a Chinese sentence. The differences between them are the rules of making sentences and the structure. In my paper I also talk about how to translate long sentences, and here are the rules: a) break the formal structure; b) reorganize every part according to the habits of the receptor language. If we can master the rules, we can translate the English and Chinese long sentences better.

On Translation of Long Sentences Based on the Novel-Of Time and the River

Of Time and the River is a famous novel written by American novelist–Thomas Wolfe who was a major American novelist of the early 20th century.His books are well received among Chinese readers.In the novel—Of Time and the River,we can find Wolfe’s frequent use of long sentences.In this paper,the skills of translating long sentences and the ways of analyzing sentence structures are discussed by several examples selected from the novel.Based on my own translation,we can find the importance of sentence structure analysis in translating long sentences.

Translation of Long Sentences from English into Chinese Sense Groups as Units

随着经济全球化的发展,这个世界越来越需要文化交流.翻译是促进不同国家间文化交流的重要手段.一般而言,翻译是双语言之间的交换活动.翻译理论家奈达认为＂所谓的翻译是指在译语中使用自然而又最确切的对等语再现原语信息,首先在语义上,其次在文体上.＂因为英汉两种语言存在差异,所以这两种语言的转化不太容易.尽管关于翻译方面的书籍不少,但仍有深入研究的空间.这篇论文的目的就是拓展英语长句汉译的方法.这篇论文中的翻译法是在以意群为翻译单位的基础上发展而来的.

Translation Strategies of Long and Difficult Journalistic Sentences From the Perspective of Semantic Translation and Communicative Translation Theory

Peter Newmark’s semantic translation and communicative translation theories play a guiding role in translation practice.Translating long and difficult English sentences into Chinese has been a focus of study among translators.The paper explores how to translate long and difficult journalistic English sentences into Chinese from the perspective of semantic translation and communicative translation theories,advancing four effective translating strategies for handling long and difficult journalistic English sentences:sequential translation,splitting translation,reversing translation,and recasting translation,so as to improve the readability and faithfulness of the Chinese version.

Functional Equivalence and Translation of English Long Sentences

Since English long possess a lot of modifiers and their syntax structures are complicated, it is difficult for the Chinese readers to understand them, not to mention translating them. The paper adopts Nida's theory of functional equivalence as the guideline in the process of translation, since it bears the merit of facilitating the communication of information. In terms of concrete methods, the long sentence should be decomposed into kernel sentences and reconstructed according to the expression of the standard Chinese language. Only by doing that, the translation version can be faithful, correct and elegant.

一种基于long short-term memory的唇语识别方法

唇动视觉信息是说话内容的重要载体。受嘴唇外观、背景信息和说话习惯等影响,即使说话者说相同的内容,唇动视觉信息也会相差很大。为解决唇语视觉信息多样性的问题,提出一种基于long short-term memory(LSTM)的新的唇语识别方法。以往大多数的方法从嘴唇外表信息入手。本方法用嘴唇关键点坐标描述嘴唇形变信息作为唇语视频的特征,它具有类内一致性和类间区分性的特点。然后利用LSTM对特征进行时序编码,它能学习具有区分性和泛化性的空间-时序特征。在公开的唇语数据集GRID、MIRACL-VC和Oulu VS上对本方法做了针对分割的单词或短语的说话者独立的唇语识别评估。在GRID和MIRACL-VC上,本方法的准确率比传统方法至少高30%;在Oulu VS上,本方法的准确率接近于最优结果。以上实验结果表明,本文提出的基于LSTM的唇语识别方法有效地解决了唇语视觉信息多样性的问题。

Respective Roles of Short-and Long-Range Interactions in Protein Folding

A new method was presented to discuss the respective roles of short- and long-range interactions in protein folding. It's based on an off-lattice model, which is also being called as toy model. Simulated annealing algorithm was used to search its native conformation. When it is applied to analysis proteins 1agt and 1aho, we find that helical segment cannot fold into native conformation without the influence of long-range interactions. That's to say that long-range interactions are the main determinants in protein folding. Key words toy model - protein folding - simulated annealing algorithm - short and long range interactions CLC number O 242.28 - Q71 Foundation item: Supported by the National Natural Science Foundation of China((60301009)Biography: WANG Long-hui (1976-), female, Ph. D candidate, research direction: machine learning, bioinformatics.

Tool Health Condition Recognition Method for High Speed Milling of Titanium Alloy Based on Principal Component Analysis (PCA) and Long Short Term Memory (LSTM)

The healthy condition of the milling tool has a very high impact on the machining quality of the titanium components.Therefore,it is important to recognize the healthy condition of the tool and replace the damaged cutter at the right time.In order to recognize the health condition of the milling cutter,a method based on the long short term memory(LSTM)was proposed to recognize tool health state in this paper.The various signals collected in the tool wear experiments were analyzed by time-domain statistics,and then the extracted data were generated by principal component analysis(PCA)method.The preprocessed data extracted by PCA is transmitted to the LSTM model for recognition.Compared with back propagation neural network(BPNN)and support vector machine(SVM),the proposed method can effectively utilize the time-domain regulation in the data to achieve higher recognition speed and accuracy.

Conditional Random Field Tracking Model Based on a Visual Long Short Term Memory Network

In dense pedestrian tracking,frequent object occlusions and close distances between objects cause difficulty when accurately estimating object trajectories.In this study,a conditional random field tracking model is established by using a visual long short term memory network in the three-dimensional(3D)space and the motion estimations jointly performed on object trajectory segments.Object visual field information is added to the long short term memory network to improve the accuracy of the motion related object pair selection and motion estimation.To address the uncertainty of the length and interval of trajectory segments,a multimode long short term memory network is proposed for the object motion estimation.The tracking performance is evaluated using the PETS2009 dataset.The experimental results show that the proposed method achieves better performance than the tracking methods based on the independent motion estimation.

Long Short-Term Memory Recurrent Neural Network-Based Acoustic Model Using Connectionist Temporal Classification on a Large-Scale Training Corpus

A Long Short-Term Memory(LSTM) Recurrent Neural Network(RNN) has driven tremendous improvements on an acoustic model based on Gaussian Mixture Model(GMM). However, these models based on a hybrid method require a forced aligned Hidden Markov Model(HMM) state sequence obtained from the GMM-based acoustic model. Therefore, it requires a long computation time for training both the GMM-based acoustic model and a deep learning-based acoustic model. In order to solve this problem, an acoustic model using CTC algorithm is proposed. CTC algorithm does not require the GMM-based acoustic model because it does not use the forced aligned HMM state sequence. However, previous works on a LSTM RNN-based acoustic model using CTC used a small-scale training corpus. In this paper, the LSTM RNN-based acoustic model using CTC is trained on a large-scale training corpus and its performance is evaluated. The implemented acoustic model has a performance of 6.18% and 15.01% in terms of Word Error Rate(WER) for clean speech and noisy speech, respectively. This is similar to a performance of the acoustic model based on the hybrid method.

Short-and long-term behaviors of drifts in the Callovo-Oxfordian claystone at the Meuse/Haute-Marne Underground Research Laboratory

Since 2000, the French National Radioactive Waste Management Agency （ANDRA） has been constructing an Underground Research Laboratory （URL） at Bure （east of the Paris Basin） to perform experiments in order to obtain in situ data necessary to demonstrate the feasibility of geological repository in the Callovo- Oxfordian claystone. An important experimental program is planned to characterize the response of the rock to different drift construction methods, Before 2008, at the main level of the laboratory, most of the drifts were excavated using pneumatic hammer and supported with rock bolts, sliding steel arches and fiber shotcrete. Other techniques, such as road header techniques, stiff and flexible supports, have also been used to characterize their impacts. The drift network is developed following the in situ major stresses. The parallel drifts are separated enough so as they can be considered independently when their hydromechanical （HM） behaviors are compared. Mine-by experiments have been performed to measure the HM response of the rock and the mechanical loading applied to the support system due to the digging and after excavation. Drifts exhibit extensional （mode I） and shear fractures （modes II and III） induced by excavation works. The extent of the induced fracture networks depends on the drift orientation versus the in situ stress field. This paper describes the drift convergence and deformation in the surrounding rock walls as function of time and the impact of different support methods on the rock mass behavior. An observation based method is finally applied to distinguish the instantaneous and time-dependent parts of the rock mass deformation around the drifts.

Comparison between the preparation,structure and mechanical properties of long fiber reinforced thermoplastics and short fiber reinforced thermoplastic

This article summarizes the comparison between the preparation, structure and mechanical properties of long fiber reinforced thermoplastics （LFT） and short fiber reinforced thermoplastics （SFT）. Both of the experiment and theory results showed that the mechanical properties of long glass fiber reinforced thermoplastics pellets （LGFRT） have been enhanced better than that of short glass fiber reinforced thermoplastics pellets （SGFRT） manufactured by molding procession. After regulation of the relative humidity by 50 % , the mechanical properties of 30 % （ weight percent） short glass fiber content in SFT （ SFT-PA6-SGF30 ） are similar to that of 40 % long glass fiber content in LFT. Howev- er, the density of the latter is about 17 % lower than that of the former. Thus, the corresponding weight of products is reduced by 13 % ;output rate is increased by 21% , and the cost is therefore significantly lowered. And it has the fol- lowing advantages： impact strength is increased by 87 % ; the proportion is reduced by 20 % ; molding cycle is short- ened by 10 % ;materials cost is saved by 20 % -30 % and the final total cost is saved by 30 % -40 %. So LFT （LFT-PP-LGF40） can replace SFT （SFT-PA6-SGF30） with the similar basic mechanical properties under normal tem- perature or 160 ℃ lower.

Binaural Speech Separation Algorithm Based on Long and Short Time Memory Networks

Speaker separation in complex acoustic environment is one of challenging tasks in speech separation.In practice,speakers are very often unmoving or moving slowly in normal communication.In this case,the spatial features among the consecutive speech frames become highly correlated such that it is helpful for speaker separation by providing additional spatial information.To fully exploit this information,we design a separation system on Recurrent Neural Network(RNN)with long short-term memory(LSTM)which effectively learns the temporal dynamics of spatial features.In detail,a LSTM-based speaker separation algorithm is proposed to extract the spatial features in each time-frequency(TF)unit and form the corresponding feature vector.Then,we treat speaker separation as a supervised learning problem,where a modified ideal ratio mask(IRM)is defined as the training function during LSTM learning.Simulations show that the proposed system achieves attractive separation performance in noisy and reverberant environments.Specifically,during the untrained acoustic test with limited priors,e.g.,unmatched signal to noise ratio(SNR)and reverberation,the proposed LSTM based algorithm can still outperforms the existing DNN based method in the measures of PESQ and STOI.It indicates our method is more robust in untrained conditions.

On the exact solutions to the long short-wave interaction system

The complete discrimination system for polynomial method is applied to the long-short-wave interaction system to obtain the classifications of single traveling wave solutions. Compared with the solutions given by the （G~/G）-expansion method, we gain some new solutions.

Short-range action and long-range action of the electrostatic forces within atomic nuclei

We study the interaction forces in atomic nuclei based on our expressions for the electrostatic interaction between spheres of arbitrary radii and charges. We prove that at small distances the proton-neutron electrostatic attraction forces are short-range-acting and the proton-proton electrostatic repulsion forces are long-range-acting. We obtain that these forces are commensurate with the nuclear forces. The protonneutron electrostatic attraction forces and the proton-proton electrostatic repulsion forces at the same distance between nucleons differ in absolute value by about an order of magnitude. It follows that based on electromagnetic interactions the neutrons are the binding building blocks in nuclear structures.

Influence of long and short guide vanes on hydraulic performance of water-jet pump

Different guide vane structures will affect the flow inside the pump,and then affect the transformation of the pressure energy and kinetic energy,and change the velocity distribution of the pump outlet.In order to study the influence of long and short guide vanes on the water-jet pump,on the basis of conventional design,eight schemes of guide vane with different vertical heights were designed in the method of computational fluid dynamics for numerical calculation,the performance curve of water-jet pumps with different long and short guide vanes was obtained,and finally the influence of different guide vanes on hydraulic performance and internal flow was analyzed.The results show that all of schemes reducing the height of blade can improve the head and efficiency.In the schemes reducing the height on the shroud,the guide vanes that the height of the blade is equal to the height difference between hub and shroud in impeller have the highest head and efficiency.In all schemes decreasing the blade height,with the increase of the height difference,the velocity increases gradually and the distribution of turbulence kinetic energy becomes more reasonable in the guide vane outlet.The schemes reducing the height on the hub have more reasonable distribution of velocity and turbulence kinetic energy according to schemes reducing the height on the shroud.The guide vanes of long and short blades can be used to stagger the position of the diffusion flow generated by adjacent blades,which can reduce the effect of the velocity circulation and make the flow of the outlet position more stable.

Short-Term Relay Quality Prediction Algorithm Based on Long and Short-Term Memory

The fraction defective of semi-finished products is predicted to optimize the process of relay production lines, by which production quality and productivity are increased, and the costs are decreased. The process parameters of relay production lines are studied based on the long-and-short-term memory network. Then, the Keras deep learning framework is utilized to build up a short-term relay quality prediction algorithm for the semi-finished product. A simulation model is used to study prediction algorithm. The simulation results show that the average prediction absolute error of the fraction is less than 5%. This work displays great application potential in the relay production lines.

Predicting and Curing Depression Using Long Short Term Memory and Global Vector

In today’s world, there are many people suffering from mentalhealth problems such as depression and anxiety. If these conditions are notidentified and treated early, they can get worse quickly and have far-reachingnegative effects. Unfortunately, many people suffering from these conditions,especially depression and hypertension, are unaware of their existence until theconditions become chronic. Thus, this paper proposes a novel approach usingBi-directional Long Short-Term Memory (Bi-LSTM) algorithm and GlobalVector (GloVe) algorithm for the prediction and treatment of these conditions.Smartwatches and fitness bands can be equipped with these algorithms whichcan share data with a variety of IoT devices and smart systems to betterunderstand and analyze the user’s condition. We compared the accuracy andloss of the training dataset and the validation dataset of the two modelsnamely, Bi-LSTM without a global vector layer and with a global vector layer.It was observed that the model of Bi-LSTM without a global vector layer hadan accuracy of 83%,while Bi-LSTMwith a global vector layer had an accuracyof 86% with a precision of 86.4%, and an F1 score of 0.861. In addition toproviding basic therapies for the treatment of identified cases, our model alsohelps prevent the deterioration of associated conditions, making our methoda real-world solution.

Interest Points Analysis for Internet Forum Based on Long-Short Windows Similarity

For Internet forum Points of Interest(PoI),existing analysis methods are usually lack of usability analysis under different conditions and ignore the long-term variation,which lead to blindness in method selection.To address this problem,this paper proposed a PoI variation prediction framework based on similarity analysis between long and short windows.Based on the framework,this paper presented 5 PoI analysis algorithms which can be categorized into 2 types,i.e.,the traditional sequence analysis methods such as autoregressive integrated moving average model(ARIMA),support vector regressor(SVR),and the deep learning methods such as convolutional neural network(CNN),long-short term memory network(LSTM),Transformer(TRM).Specifically,this paper firstly divides observed data into long and short windows,and extracts key words as PoI of each window.Then,the PoI similarities between long and short windows are calculated for training and prediction.Finally,series of experiments is conducted based on real Internet forum datasets.The results show that,all the 5 algorithms could predict PoI variations well,which indicate effectiveness of the proposed framework.When the length of long window is small,traditional methods perform better,and SVR is the best.On the contrary,the deep learning methods show superiority,and LSTM performs best.The results could provide beneficial references for PoI variation analysis and prediction algorithms selection under different parameter configurations.