Unsupervised multi-modal image translation based on the squeeze-and-excitation mechanism and feature attention module

The unsupervised multi-modal image translation is an emerging domain of computer vision whose goal is to transform an image from the source domain into many diverse styles in the target domain.However,the multi-generator mechanism is employed among the advanced approaches available to model different domain mappings,which results in inefficient training of neural networks and pattern collapse,leading to inefficient generation of image diversity.To address this issue,this paper introduces a multi-modal unsupervised image translation framework that uses a generator to perform multi-modal image translation.Specifically,firstly,the domain code is introduced in this paper to explicitly control the different generation tasks.Secondly,this paper brings in the squeeze-and-excitation(SE)mechanism and feature attention(FA)module.Finally,the model integrates multiple optimization objectives to ensure efficient multi-modal translation.This paper performs qualitative and quantitative experiments on multiple non-paired benchmark image translation datasets while demonstrating the benefits of the proposed method over existing technologies.Overall,experimental results have shown that the proposed method is versatile and scalable.

基于BiLSTM-Attention的F_(10.7)指数预测模型与中国自主数据集的应用

F_(10.7)指数是太阳活动的重要指标,准确预测F_(10.7)指数有助于预防和缓解太阳活动对无线电通信、导航和卫星通信等领域的影响.基于F_(10.7)射电流量的特性,在双向长短时记忆网络(Bidirectional Long Short-Term Memory Network,BiLSTM)基础上融入注意力机制(Attention),提出了一种基于BiLSTM-Attention的F_(10.7)预报模型.在加拿大DRAO数据集上其平均绝对误差(MAE)为5.38,平均绝对百分比误差(MAPE)控制在5%以内,相关系数(R)高达0.987,与其他RNN模型相比拥有优越的预测性能.针对中国廊坊L&S望远镜观测的F_(10.7)数据集,提出了一种转换平均校准(Conversion Average Calibration,CAC)方法进行数据预处理,处理后的数据与DRAO数据集具有较高的相关性.基于该数据集对比分析了RNN系列模型的预报效果,实验结果表明,BiLSTM-Attention和BiLSTM两种模型在预测F_(10.7)指数方面具有较好的优势,表现出较好的预测性能和稳定性.

An Assisted Diagnosis of Alzheimer’s Disease Incorporating Attention Mechanisms Med-3D Transfer Modeling

Alzheimer’s disease(AD)is a complex,progressive neurodegenerative disorder.The subtle and insidious onset of its pathogenesis makes early detection of a formidable challenge in both contemporary neuroscience and clinical practice.In this study,we introduce an advanced diagnostic methodology rooted in theMed-3D transfermodel and enhanced with an attention mechanism.We aim to improve the precision of AD diagnosis and facilitate its early identification.Initially,we employ a spatial normalization technique to address challenges like clarity degradation and unsaturation,which are commonly observed in imaging datasets.Subsequently,an attention mechanism is incorporated to selectively focus on the salient features within the imaging data.Building upon this foundation,we present the novelMed-3D transfermodel,designed to further elucidate and amplify the intricate features associated withADpathogenesis.Our proposedmodel has demonstrated promising results,achieving a classification accuracy of 92%.To emphasize the robustness and practicality of our approach,we introduce an adaptive‘hot-updating’auxiliary diagnostic system.This system not only enables continuous model training and optimization but also provides a dynamic platform to meet the real-time diagnostic and therapeutic demands of AD.

Dependency-Based Local Attention Approach to Neural Machine Translation

Recently dependency information has been used in different ways to improve neural machine translation.For example,add dependency labels to the hidden states of source words.Or the contiguous information of a source word would be found according to the dependency tree and then be learned independently and be added into Neural Machine Translation(NMT)model as a unit in various ways.However,these works are all limited to the use of dependency information to enrich the hidden states of source words.Since many works in Statistical Machine Translation(SMT)and NMT have proven the validity and potential of using dependency information.We believe that there are still many ways to apply dependency information in the NMT structure.In this paper,we explore a new way to use dependency information to improve NMT.Based on the theory of local attention mechanism,we present Dependency-based Local Attention Approach(DLAA),a new attention mechanism that allowed the NMT model to trace the dependency words related to the current translating words.Our work also indicates that dependency information could help to supervise attention mechanism.Experiment results on WMT 17 Chineseto-English translation task shared training datasets show that our model is effective and perform distinctively on long sentence translation.

Neural Machine Translation Models with Attention-Based Dropout Layer

In bilingual translation,attention-based Neural Machine Translation(NMT)models are used to achieve synchrony between input and output sequences and the notion of alignment.NMT model has obtained state-of-the-art performance for several language pairs.However,there has been little work exploring useful architectures for Urdu-to-English machine translation.We conducted extensive Urdu-to-English translation experiments using Long short-term memory(LSTM)/Bidirectional recurrent neural networks(Bi-RNN)/Statistical recurrent unit(SRU)/Gated recurrent unit(GRU)/Convolutional neural network(CNN)and Transformer.Experimental results show that Bi-RNN and LSTM with attention mechanism trained iteratively,with a scalable data set,make precise predictions on unseen data.The trained models yielded competitive results by achieving 62.6%and 61%accuracy and 49.67 and 47.14 BLEU scores,respectively.From a qualitative perspective,the translation of the test sets was examined manually,and it was observed that trained models tend to produce repetitive output more frequently.The attention score produced by Bi-RNN and LSTM produced clear alignment,while GRU showed incorrect translation for words,poor alignment and lack of a clear structure.Therefore,we considered refining the attention-based models by defining an additional attention-based dropout layer.Attention dropout fixes alignment errors and minimizes translation errors at the word level.After empirical demonstration and comparison with their counterparts,we found improvement in the quality of the resulting translation system and a decrease in the perplexity and over-translation score.The ability of the proposed model was evaluated using Arabic-English and Persian-English datasets as well.We empirically concluded that adding an attention-based dropout layer helps improve GRU,SRU,and Transformer translation and is considerably more efficient in translation quality and speed.

对The Translator's Invisibility汉译本几处译文的商榷

汉译本《译者的隐形:翻译史论》(The translator's invisibility:A history of translation)对韦努蒂翻译思想和理念的引进和推广起到了很大的作用。然而,其中部分译文存在着对原文词义、搭配、句法结构、跨文化知识背景的理解错误以及遣词造句等方面的问题,与原文的本义有较大出入,容易引起读者误解,值得商榷。本文列出其中几例存疑译文,条分缕析,指出其失误所在,并提出参考译文,以供对比。

On the Gender Difference of Translator's Subjectivity in Literary Translation

Translation is an activity aimed at transferring the source language to the target language,related to many aspects involving culture,society,history and so on.The translator plays a critical role in translation practice.Xu Jun considers"translator’s subjectivity should be reflected to be artistic personal consciousness in translated works."Translator’s differences in age,gender,personal traits,educational level and other aspects make translation artistry completely divergent.Therefore,translator’s subjectivity shoulders great significances on the success of literary translation,which isn’t arbitrary,out of control,must be conducted in line with essence of the original work.This paper focuses on the gender difference of translator’s subjectivity in literary translation,hoping to find a way of eliminating the influence of it in the translation work.

On the Study of the Translator's Position in Traditional Translation Theories

In traditional translation theories, the author and the translator, the original text and the translated text are in the binary oppositions. Both author-centeredness and text-centeredness are in authoritative positions. Moreover, there are some incorrect viewpoints about the translator's position in the traditional translation theories. In this case, this paper will analyze the translator's position in the traditional translation theories comprehensively.

Translator's Subjectivity in Science and Technology Translation

Science and technology translation involves various industries and professions,with its own system of terminology and unique language features.For a translator,who is not a professional scientist or technician,it seems to be difficult to do science and technology translation accurately and properly.Consequently,it is necessary for the translator to give play to his subjectivity when translating science and technology documents or related interpretation:searching for related data to replenish necessary professional knowledge,consulting with professional technicians actively to clarify the jargon and common sayings,and thinking critically to scrutiny the source text.

Cultural Identity and Translator's Style: Case Study of Translation of Tea Relevant Acts in Hong Lou Meng

From the methodological perspective, the thesis summarized leisure tea act, judging-time tea act to map translator's style. Through investigations on all these, the thesis succeeded in confirming the translator's styles and pinpointing cultural identity as essential factors influencing their styles. Hopefully, this could help to provide a new angle of view to make research on translator's style. Moreover, the thesis wishes that with analysis of tea culture in Hong Lou Meng the translation of tea relevant acts receive greater priority in the Chinese Classics Translation.

基于Self-Attention模型的机器翻译系统

近几年来神经机器翻译(Neural Machine Translation,NMT)发展迅速,Seq2Seq框架的提出为机器翻译带来了很大的优势,可以在观测到整个输入句子后生成任意输出序列。但是该模型对于长距离信息的捕获能力仍有很大的局限,循环神经网络(RNN)、LSTM网络都是为了改善这一问题提出的,但是效果并不明显。注意力机制的提出与运用则有效地弥补了该缺陷。Self-Attention模型就是在注意力机制的基础上提出的,本文使用Self-Attention为基础构建编码器-解码器框架。本文通过探讨以往的神经网络翻译模型,分析Self-Attention模型的机制与原理,通过TensorFlow深度学习框架对基于Self-Attention模型的翻译系统进行实现,在英文到中文的翻译实验中与以往的神经网络翻译模型进行对比,表明该模型取得了较好的翻译效果。

Review of the evidence for the management of co-morbid Tic disorders in children and adolescents with attention deficit hyperactivity disorder

Attention deficit hyperactivity disorder(ADHD) is the most common neurodevelopmental disorder in children and adolescents, with prevalence ranging between 5% and 12% in the developed countries. Tic disorders(TD) are common co-morbidities in paediatric ADHD patients with or without pharmacotherapy treatment. There has been conflicting evidence of the role of psychostimulants in either precipitating or exacerbating TDs in ADHD patients. We carried out a literature review relating to the management of TDs in children and adolescents with ADHD through a comprehensive search of MEDLINE, EMBASE, CINAHL and Cochrane databases. No quantitative synthesis(meta-analysis) was deemed appropriate. Metaanalysis of controlled trials does not support an association between new onset or worsening of tics and normal doses of psychostimulant use. Supratherapeutic doses of dextroamphetamine have been shown to exacerbate TD. Most tics are mild or moderate and respond to psychoeducation and behavioural management. Level A evidence support the use of alpha adrenergic agonists, including Clonidine and Guanfacine, reuptake noradrenenaline inhibitors(Atomoxetine) and stimulants(Methylphenidate and Dexamphetamines) for the treatment of Tics and comorbid ADHD. Priority should be given to the management of co-morbid Tourette's syndrome(TS) or severely disabling tics in children and adolescents with ADHD. Severe TDs may require antipsychotic treatment. Antipsychotics, especially Aripiprazole, are safe and effective treatment for TS or severe Tics, but they only moderately control the co-occurring ADHD symptomatology. Short vignettes of different common clinical scenarios are presented to help clinicians determine the most appropriate treatment to consider in each patient presenting with ADHD and co-morbid TDs.

Pervasive Attentive Neural Network for Intelligent Image Classification Based on N-CDE’s

The utilization of visual attention enhances the performance of image classification tasks.Previous attentionbased models have demonstrated notable performance,but many of these models exhibit reduced accuracy when confronted with inter-class and intra-class similarities and differences.Neural-Controlled Differential Equations(N-CDE’s)and Neural Ordinary Differential Equations(NODE’s)are extensively utilized within this context.NCDE’s possesses the capacity to effectively illustrate both inter-class and intra-class similarities and differences with enhanced clarity.To this end,an attentive neural network has been proposed to generate attention maps,which uses two different types of N-CDE’s,one for adopting hidden layers and the other to generate attention values.Two distinct attention techniques are implemented including time-wise attention,also referred to as bottom N-CDE’s;and element-wise attention,called topN-CDE’s.Additionally,a trainingmethodology is proposed to guarantee that the training problem is sufficiently presented.Two classification tasks including fine-grained visual classification andmulti-label classification,are utilized to evaluate the proposedmodel.The proposedmethodology is employed on five publicly available datasets,including CUB-200-2011,ImageNet-1K,PASCAL VOC 2007,PASCAL VOC 2012,and MS COCO.The obtained visualizations have demonstrated that N-CDE’s are better appropriate for attention-based activities in comparison to conventional NODE’s.

On Translation of Children’s Literature Based on Reception Aesthetics:A Case Study of The Secret Garden

Grounded in reader-centered theories,Reception Aesthetics prioritizes the active engagement and acceptance of target readers.This study examines the translation of children’s literature through the lens of Reception Aesthetics,using Li Wenjun’s rendition of The Secret Garden as a case study.The objective is to scrutinize various aspects of the translation-including vocabulary,syntax,rhetorical devices,and cultural nuances-and to discern how the adaptation and application of diverse translation strategies cater to the aesthetic expectations.

China's Largest Granite-Type Gas Field was Discovered in Qinghai——The Inorganic Theory has Aroused Attention again

A growing number of oil and gas reservoirs have been discovered in granite and metamorphic crystallized rock areas. Statistics show that, about 157 oil and gas fields were found in crystallized bedrocks, with oil reserves of 5048x 10^8 t, and gas reserves of 2681x10^8m3. Among the discovered industrial oil and gas fields hosted in crystallized rocks, most occurred in granite rocks, occupying 40% in quantity and 75% in reserves, followed by those hosted in mafic and ultra-mafic rocks （about 3%）, and then tbllowed by those in volcanic rocks and metamorphic rocks.

Functions and Benefits of SDL Translator's Workbench 2007

Nowadays more and more translators are trying to use machine to help their translation work.Translator's Workbench is a tool of machine translation.In this essay,I will explore functions and benefits of Translator's workbench 2007 with the theory of MT and TM.

On Translator＇s Subjectivity in the Chinese Version of Pride and Prejudice Translated by Wang Keyi

As the center and subject of the translation, translator＇ s subjectivity plays a great role in translation research and more and more attention has been aroused. Since the first version of Pride and Prejudice came into being, Chinese translators have begun to show great interest in translating this novel, and there are more than ten translated Chinese versions now. Scholars have conducted a lot of research about this novel and its various translated versions. Among all these Chinese versions, Wang Keyi＇ s version is relatively earlier and is also the most popular one. Thus, it attracts many readers＇ eyes and there are increasing comments and studies on this version. There are also studies analyzing or comparing several versions from the perspective of translator＇ s subjectivity. While, few articles are published to analyze this version from the three elements of translator＇ s subjectivity： initiatives, passiveness, and purposiveness. They are also three basic features of translator＇ s subjectivity, in which translator＇ s subjectivity reflects. It is also the three features that consist of the translator＇ s subjectivity. Therefore, the comprehensive and intensive research of translator＇ s subjectivity in the Chinese version of Pride and Prejudice has great realistic meaning and practical values.

The Translator's Subjectivity Reflected in the English Translation of "The Lane"

The translator's subjectivity plays an indispensible role in the translating activity. As a senior translator, Prof. Zhang Peiji brings his subjectivity into full play in his prose translation, and the English version of "The Lane" can be regarded as one of his representatives. An examination of the translator's subjectivity reflected in this version helps to better understand Prof. Zhang's art of prose translation.

Harmonious Relationship between Authors and Translators under the Feminist Translation Theory

"Androgyny" is an important concept of feminist criticism. The translators have their own voice under the guidance of"androgyny". The relationship between the translator and the author should be a harmonious dialogue.

FSA-Net:A Cost-efficient Face Swapping Attention Network with Occlusion-Aware Normalization

The main challenges in face swapping are the preservation and adaptive superimposition of attributes of two images.In this study,the Face Swapping Attention Network(FSA-Net)is proposed to generate photoreal-istic face swapping.The existing face-swapping methods ignore the blending attributes or mismatch the facial keypoint(cheek,mouth,eye,nose,etc.),which causes artifacts and makes the generated face silhouette non-realistic.To address this problem,a novel reinforced multi-aware attention module,referred to as RMAA,is proposed for handling facial fusion and expression occlusion flaws.The framework includes two stages.In the first stage,a novel attribute encoder is proposed to extract multiple levels of target face attributes and integrate identities and attributes when synthesizing swapped faces.In the second stage,a novel Stochastic Error Refinement(SRE)module is designed to solve the problem of facial occlusion,which is used to repair occlusion regions in a semi-supervised way without any post-processing.The proposed method is then compared with the current state-of-the-art methods.The obtained results demonstrate the qualitative and quantitative outperformance of the proposed method.More details are provided at the footnote link and at https://sites.google.com/view/fsa-net-official.