A Study of English Translation Strategies of Subtitles of the Domestic Animated Film Chang’an from the Perspective of Eco-translatology

This article conducts an analysis and exploration of the English subtitles translation of the domestic animated film Chang’an from the perspective of eco-translatology.Taking three-dimensional transformation as the entry point,it analyzes the adaptation and selection of subtitle translators in the translation process from the dimensions of language,culture,and communication in Chang’an,aiming to enrich the practical analysis of eco-translatology and provide valuable insights and references for the theoretical construction and practical application of eco-translatology.This article enriches the practical analysis of eco-translatology and also validates to a certain extent the applicability of“three-dimensional”transformation.

Solving Multitrip Pickup and Delivery Problem With Time Windows and Manpower Planning Using Multiobjective Algorithms

The multitrip pickup and delivery problem with time windows and manpower planning(MTPDPTW-MP)determines a set of ambulance routes and finds staff assignment for a hospital. It involves different stakeholders with diverse interests and objectives. This study firstly introduces a multiobjective MTPDPTW-MP(MO-MTPDPTWMP) with three objectives to better describe the real-world scenario. A multiobjective iterated local search algorithm with adaptive neighborhood selection(MOILS-ANS) is proposed to solve the problem. MOILS-ANS can generate a diverse set of alternative solutions for decision makers to meet their requirements. To better explore the search space, problem-specific neighborhood structures and an adaptive neighborhood selection strategy are carefully designed in MOILS-ANS. Experimental results show that the proposed MOILS-ANS significantly outperforms the other two multiobjective algorithms. Besides, the nature of objective functions and the properties of the problem are analyzed. Finally, the proposed MOILS-ANS is compared with the previous single-objective algorithm and the benefits of multiobjective optimization are discussed.

Adaptive Neighboring Selection Algorithm Based on Curvature Prediction in Manifold Learning

Recently manifold learning algorithm for dimensionality reduction attracts more and more interests, and various linear and nonlinear,global and local algorithms are proposed. The key step of manifold learning algorithm is the neighboring region selection. However,so far for the references we know,few of which propose a generally accepted algorithm to well select the neighboring region. So in this paper,we propose an adaptive neighboring selection algorithm,which successfully applies the LLE and ISOMAP algorithms in the test. It is an algorithm that can find the optimal K nearest neighbors of the data points on the manifold. And the theoretical basis of the algorithm is the approximated curvature of the data point on the manifold. Based on Riemann Geometry,Jacob matrix is a proper mathematical concept to predict the approximated curvature. By verifying the proposed algorithm on embedding Swiss roll from R3 to R2 based on LLE and ISOMAP algorithm,the simulation results show that the proposed adaptive neighboring selection algorithm is feasible and able to find the optimal value of K,making the residual variance relatively small and better visualization of the results. By quantitative analysis,the embedding quality measured by residual variance is increased 45. 45% after using the proposed algorithm in LLE.

Mark Bender’s Translation and Introduction of Plants and Animals in The Nuosu Book of Origins

Mark Bender and Aku Wuwu translate Hnewo Teyy,a crucial epic for Nuosu people from Yi to English directly and entirely for the first time.Their translation and introduction of the epic are involved in The Nuosu Book of Origins,in which Bender concerns plants and animals.This paper mainly analyses Bender’s adaptation and selection concerning his investigation and translation of plants and animals under the guidance of Eco-translatology.Significantly,Eco-translatology and Bender’s translation and introduction provide enlightenment to the transmission of Chinese culture.

On Subtitle Translation of Game of Thrones from the Perspective of Eco-translatology

HBO TV series–Game of Thrones has gone viral ever since its first release in 2011 and its soaring popularity among Chineseaudience has no doubt benefited from the"appropriate"E-C subtitles attached to it. This paper, through the analysis and discussion ofthe E-C subtitles of Game of Thrones from the perspective of Eco-translatology, demonstrates that the approach of Translation as Adap-tation and Selection(TAS) proves to be operational in subtitle translation, and gives further recommendations in the communicativetransformation of subtitle translation.

On Translation of Figure of Speech in Fortress Besieged From the Perspective of Eco-translatology

On the framework of eco- translatology, this thesis presents an introduction of the novel Fortress Besieged, the author and the translation work, and then gives an analysis of the translator's adaptation and selection of the translational eco-environment.

An Eco-translatological Perspective to Researching Publicity Texts Translation

The importance of an interdisciplinary investigation into the translation of publicity texts cannot be overestimated at a time when the role of international communication grows rapidly,which in turn pushes the development of globalization.Ecotranslatology takes the concept of"adaptation/selection"of Darwin’s Evolution Theory as its theoretical foundation,focusing on the integrity of the translation ecosystem,making a novel description and interpretation to the nature,process,standard,principles,methods of translation and translation phenomena from the perspective of Eco-translatology.Though the construct of the theory keeps expanding,there is still a lack of systematic discussion on translation activities with focus on the integrity and harmony in Eco-translatology.This study therefore aims to promote the need of researching publicity text translation from an Ecotranslatological perspective through the review of the existing literature in this field.

Metaheuristic Based Noise Identification and Image Denoising Using Adaptive Block Selection Based Filtering

Image denoising has become one of the major forms of image enhancement methods that form the basis of image processing. Due to the inconsistencies in the machinery producing these signals, medical images tend to require these techniques. In real time, images do not contain a single noise, and instead they contain multiple types of noise distributions in several indistinct regions. This paper presents an image denoising method that uses Metaheuristics to perform noise identification. Adaptive block selection is used to identify and correct the noise contained in these blocks. Though the system uses a block selection scheme, modifications are performed on pixel- to-pixel basis and not on the entire blocks;hence the image accuracy is preserved. PSO is used to identify the noise distribution, and appropriate noise correction techniques are applied to denoise the images. Experiments were conducted using salt and pepper noise, Gaussian noise and a combination of both the noise in the same image. It was observed that the proposed method performed effectively on noise levels up-to 0.5 and was able to produce results with PSNR values ranging from 20 to 30 in most of the cases. Excellent reduction rates were observed on salt and pepper noise and moderate reduction rates were observed on Gaussian noise. Experimental results show that our proposed system has a wide range of applicability in any domain specific image denoising scenario, such as medical imaging, mammogram etc.

Learning to select the recombination operator for derivative-free optimization

Extensive studies on selecting recombination operators adaptively,namely,adaptive operator selection(AOS),during the search process of an evolutionary algorithm(EA),have shown that AOS is promising for improving EA's performance.A variety of heuristic mechanisms for AOS have been proposed in recent decades,which usually contain two main components:the feature extraction and the policy setting.The feature extraction refers to as extracting relevant features from the information collected during the search process.The policy setting means to set a strategy(or policy)on how to select an operator from a pool of operators based on the extracted feature.Both components are designed by hand in existing studies,which may not be efficient for adapting optimization problems.In this paper,a generalized framework is proposed for learning the components of AOS for one of the main streams of EAs,namely,differential evolution(DE).In the framework,the feature extraction is parameterized as a deep neural network(DNN),while a Dirichlet distribution is considered to be the policy.A reinforcement learning method,named policy gradient,is used to train the DNN.As case studies,the proposed framework is applied to two DEs including the classic DE and a recently-proposed DE,which result in two new algorithms named PG-DE and PG-MPEDE,respectively.Experiments on the Congress of Evolutionary Computation(CEC)2018 test suite show that the proposed new algorithms perform significantly better than their counterparts.Finally,we prove theoretically that the considered classic methods are the special cases of the proposed framework.

Population transcriptomics reveals a potentially positive role of expression diversity in adaptation

While it is widely accepted that genetic diversity determines the potential of adaptation,the role that gene expression variation plays in adaptation remains poorly known.Here we show that gene expression diversity could have played a positive role in the adaptation of Miscanthus lutarioriparius.RNA-seq was conducted for 80 individuals of the species,with half planted in the energy crop domestication site and the other half planted in the control site near native habitats.A leaf reference transcriptome consisting of 18,503 high-quality transcripts was obtained using a pipeline developed for de novo assembling with population RNA-seq data.The population structure and genetic diversity of M.lutarioriparius were estimated based on 30,609 genic single nucleotide polymorphisms.Population expression（Ep） and expression diversity（Ed）were defined to measure the average level and the magnitude of variation of a gene expression in the population,respectively.It was found that expression diversity increased while genetic Resediversity decreased after the species was transplanted from the native habitats to the harsh domestication site,especially for genes involved in abiotic stress resistance,histone methylation,and biomass synthesis under water limitation.The increased expression diversity could have enriched phenotypic variation directly subject to selections in the new environment.

Fast Adaptive Wavelet for Remote Sensing Image Compression

Remote sensing images are hard to achieve high compression ratio because of their rich texture. By analyzing the influence of wavelet properties on image compression, this paper proposes wavelet construction rules and builds a new biorthogonal wavelet construction model with parameters. The model parameters are optimized by using genetic algorithm and adopting energy compaction as the optimization object function. In addition, in order to resolve the computation complexity problem of online construction, according to the image classification rule proposed in this paper we construct wavelets for different classes of images and implement the fast adaptive wavelet selection algorithm （FAWS）. Experimental results show wavelet bases of FAWS gain better compression performance than Daubechies9/7.

Unsupervised Nonlinear Adaptive Manifold Learning for Global and Local Information

In this paper,we propose an Unsupervised Nonlinear Adaptive Manifold Learning method(UNAML)that considers both global and local information.In this approach,we apply unlabeled training samples to study nonlinear manifold features,while considering global pairwise distances and maintaining local topology structure.Our method aims at minimizing global pairwise data distance errors as well as local structural errors.In order to enable our UNAML to be more efficient and to extract manifold features from the external source of new data,we add a feature approximate error that can be used to learn a linear extractor.Also,we add a feature approximate error that can be used to learn a linear extractor.In addition,we use a method of adaptive neighbor selection to calculate local structural errors.This paper uses the kernel matrix method to optimize the original algorithm.Our algorithm proves to be more effective when compared with the experimental results of other feature extraction methods on real face-data sets and object data sets.

A Two-Level Detection Algorithm for Optical Fiber Vibration

Optical fiber vibration is detected by the coherent optical time domain reflection technique. In addition to the vibration signals, the reflected signals include clutters and noises, which lead to a high false alarm rate. The ＂cell averaging＂ constant false alarm rate algorithm has a high computing speed, but its detection performance will be declined in nonhomogeneous environments such as multiple targets. The ＂order statistics＂ constant false alarm rate algorithm has a distinct advantage in multiple target environments, but it has a lower computing speed. An intelligent two-level detection algorithm is presented based on ＂cell averaging＂ constant false alarm rate and ＂order statistics＂ constant false alarm rate which work in serial way, and the detection speed of ＂cell averaging＂ constant false alarm rate and performance of ＂order statistics＂ constant false alarm rate are conserved, respectively. Through the adaptive selection, the ＂cell averaging＂ is applied in homogeneous environments, and the two-level detection algorithm is employed in nonhomogeneous environments. Our Monte Carlo simulation results demonstrate that considering different signal noise ratios, the proposed algorithm gives better detection probability than that of ＂order statistics＂.

Genomic insights into the origin, adaptive evolution, and herbicide resistance of Leptochloa chinensis, a devastating tetraploid weedy grass in rice fields

Chinese sprangletop (Leptochloa chinensis), belonging to the grass subfamily Chloridoideae, is one of the most notorious weeds in rice ecosystems. Here, we report a chromosome-scale reference genome assembly and a genomic variation map of the tetraploid L. chinensis. The L. chinensis genome is derived from two diploid progenitors that diverged ∼10.9 million years ago, and its two subgenomes display neither fractionation bias nor overall gene expression dominance. Comparative genomic analyses reveal substantial genome rearrangements in L. chinensis after its divergence from the common ancestor of Chloridoideae and, together with transcriptome profiling, demonstrate the important contribution of tetraploidization to the gene sources for the herbicide resistance of L. chinensis. Population genomic analyses of 89 accessions from China reveal that L. chinensis accessions collected from southern/southwestern provinces have substantially higher nucleotide diversity than those from the middle and lower reaches of the Yangtze River, suggesting that L. chinensis spread in China from the southern/southwestern provinces to the middle and lower reaches of the Yangtze River. During this spread, L. chinensis developed significantly increased herbicide resistance, accompanied by the selection of numerous genes involved in herbicide resistance. Taken together, our study generated valuable genomic resources for future fundamental research and agricultural management of L. chinensis, and provides significant new insights into the herbicide resistance as well as the origin and adaptive evolution of L. chinensis.

Adaptive IF selection and IQ mismatch compensation in a low-IF GSM receiver

This paper presents an algorithm that can adaptively select the intermediate frequency（IF） and compensate the IQ mismatch according to the power ratio of the adjacent channel interference to the desired signal in a low-IF GSM receiver.The IF can be adaptively selected between 100 and 130 kHz.Test result shows an improvement of phase error from 6.78°to 3.23°.Also a least mean squares（LMS） based IQ mismatch compensation algorithm is applied to improve image rejection ratio（IRR） for the desired signal along with strong adjacent channel interference.The IRR is improved from 29.1 to 44.3 dB in measurement.The design is verified in a low-IF GSM receiver fabricated in SMIC 0.13μm RF CMOS process with a working voltage of 1.2 V.

Kernel Quantile Estimator with ICI Adaptive Bandwidth Selection Technique

In this article, we consider a class of kernel quantile estimators which is the linear combi- nation of order statistics. This class of kernel quantile estimators can be regarded as an extension of some existing estimators. The exact mean square error expression for this class of estimators will be provided when data are uniformly distributed. The implementation of these estimators depends mostly on the bandwidth selection. We then develop an adaptive method for bandwidth selection based on the intersection confidence intervals （ICI） principle. Monte Carlo studies demonstrate that our proposed approach is comparatively remarkable. We illustrate our method with a real data set.