The History of the Former Han Dynasty:A Critical Translation with Annotations Translated by Homer Hasenpflug Dubs

Translation is an important medium of cultural communication.It is not a mere transfer of two languages,but the interaction of two cultures.Cultural misreading,which results from cultural discrepancy and translator’s subjectivity,truly reflects where the blockade and conflict in the cultural communication is.Cultural misreading is an objective phenomenon that exists in the entire process of translation.This paper intends to make a comprehensive analysis and discussion on The History of the Former Han Dynasty:a Critical Translation with Annotations translated by Homer Hasenpflug Dubs.As for the reasons of cultural misreading,this paper divides them into three types—language,thinking habit,traditional culture.It is to be hoped that this paper will draw more attention from the translation circle to the phenomena,and make contribution to the development of literary translation.

A Study on Annotations Under the Theory of Paratext in Translation

This paper mainly studies the basic types of annotation and the analysis of its effective functional usage,so as to pay more attention to annotation in the translation of poetry and Fu.The annotation of this study belongs to the category of paratext.Annotation is attributed to the paratext,undertakes its special function,enriches and perfects the paratext system.

Acquiring Weak Annotations for Tumor Localization in Temporal and Volumetric Data

Creating large-scale and well-annotated datasets to train AI algorithms is crucial for automated tumor detection and localization.However,with limited resources,it is challenging to determine the best type of annotations when annotating massive amounts of unlabeled data.To address this issue,we focus on polyps in colonoscopy videos and pancreatic tumors in abdominal CT scans;Both applications require significant effort and time for pixel-wise annotation due to the high dimensional nature of the data,involving either temporary or spatial dimensions.In this paper,we develop a new annotation strategy,termed Drag&Drop,which simplifies the annotation process to drag and drop.This annotation strategy is more efficient,particularly for temporal and volumetric imaging,than other types of weak annotations,such as per-pixel,bounding boxes,scribbles,ellipses and points.Furthermore,to exploit our Drag&Drop annotations,we develop a novel weakly supervised learning method based on the watershed algorithm.Experimental results show that our method achieves better detection and localization performance than alternative weak annotations and,more importantly,achieves similar performance to that trained on detailed per-pixel annotations.Interestingly,we find that,with limited resources,allocating weak annotations from a diverse patient population can foster models more robust to unseen images than allocating per-pixel annotations for a small set of images.In summary,this research proposes an efficient annotation strategy for tumor detection and localization that is less accurate than per-pixel annotations but useful for creating large-scale datasets for screening tumors in various medical modalities.

deCS:A Tool for Systematic Cell Type Annotations of Single-cell RNA Sequencing Data among Human Tissues

Single-cell RNA sequencing(scRNA-seq)is revolutionizing the study of complex and dynamic cellular mechanisms.However,cell type annotation remains a main challenge as it largely relies on a priori knowledge and manual curation,which is cumbersome and subjective.The increasing number of scRNA-seq datasets,as well as numerous published genetic studies,has motivated us to build a comprehensive human cell type reference atlas.Here,we present decoding Cell type Specificity(deCS),an automatic cell type annotation method augmented by a comprehensive collection of human cell type expression profiles and marker genes.We used deCS to annotate scRNAseq data from various tissue types and systematically evaluated the annotation accuracy under different conditions,including reference panels,sequencing depth,and feature selection strategies.Our results demonstrate that expanding the references is critical for improving annotation accuracy.Compared to many existing state-of-the-art annotation tools,deCS significantly reduced computation time and increased accuracy.deCS can be integrated into the standard scRNA-seq analytical pipeline to enhance cell type annotation.Finally,we demonstrated the broad utility of deCS to identify trait-cell type associations in 51 human complex traits,providing deep insights into the cellular mechanisms underlying disease pathogenesis.

Label Recovery and Trajectory Designable Network for Transfer Fault Diagnosis of Machines With Incorrect Annotation

The success of deep transfer learning in fault diagnosis is attributed to the collection of high-quality labeled data from the source domain.However,in engineering scenarios,achieving such high-quality label annotation is difficult and expensive.The incorrect label annotation produces two negative effects:1)the complex decision boundary of diagnosis models lowers the generalization performance on the target domain,and2)the distribution of target domain samples becomes misaligned with the false-labeled samples.To overcome these negative effects,this article proposes a solution called the label recovery and trajectory designable network(LRTDN).LRTDN consists of three parts.First,a residual network with dual classifiers is to learn features from cross-domain samples.Second,an annotation check module is constructed to generate a label anomaly indicator that could modify the abnormal labels of false-labeled samples in the source domain.With the training of relabeled samples,the complexity of diagnosis model is reduced via semi-supervised learning.Third,the adaptation trajectories are designed for sample distributions across domains.This ensures that the target domain samples are only adapted with the pure-labeled samples.The LRTDN is verified by two case studies,in which the diagnosis knowledge of bearings is transferred across different working conditions as well as different yet related machines.The results show that LRTDN offers a high diagnosis accuracy even in the presence of incorrect annotation.

Meibomian glands segmentation in infrared images with limited annotation

●AIM:To investigate a pioneering framework for the segmentation of meibomian glands(MGs),using limited annotations to reduce the workload on ophthalmologists and enhance the efficiency of clinical diagnosis.●METHODS:Totally 203 infrared meibomian images from 138 patients with dry eye disease,accompanied by corresponding annotations,were gathered for the study.A rectified scribble-supervised gland segmentation(RSSGS)model,incorporating temporal ensemble prediction,uncertainty estimation,and a transformation equivariance constraint,was introduced to address constraints imposed by limited supervision information inherent in scribble annotations.The viability and efficacy of the proposed model were assessed based on accuracy,intersection over union(IoU),and dice coefficient.●RESULTS:Using manual labels as the gold standard,RSSGS demonstrated outcomes with an accuracy of 93.54%,a dice coefficient of 78.02%,and an IoU of 64.18%.Notably,these performance metrics exceed the current weakly supervised state-of-the-art methods by 0.76%,2.06%,and 2.69%,respectively.Furthermore,despite achieving a substantial 80%reduction in annotation costs,it only lags behind fully annotated methods by 0.72%,1.51%,and 2.04%.●CONCLUSION:An innovative automatic segmentation model is developed for MGs in infrared eyelid images,using scribble annotation for training.This model maintains an exceptionally high level of segmentation accuracy while substantially reducing training costs.It holds substantial utility for calculating clinical parameters,thereby greatly enhancing the diagnostic efficiency of ophthalmologists in evaluating meibomian gland dysfunction.

gFACs:Gene Filtering,Analysis,and Conversion to Unify Genome Annotations Across Alignment and Gene Prediction Frameworks

Published genomes frequently contain erroneous gene models that represent issues associated with identification of open reading frames,start sites,splice sites,and related structural features.The source of these inconsistencies is often traced back to integration across text file formats designed to describe long read alignments and predicted gene structures.In addition,the majority of gene prediction frameworks do not provide robust downstream filtering to remove problematic gene annotations,nor do they represent these annotations in a format consistent with current file standards.These frameworks also lack consideration for functional attributes,such as the presence or absence of protein domains that can be used for gene model validation.To provide oversight to the increasing number of published genome annotations,we present a software package,the Gene Filtering,Analysis,and Conversion(gFACs),to filter,analyze,and convert predicted gene models and alignments.The software operates across a wide range of alignment,analysis,and gene prediction files with a flexible framework for defining gene models with reliable structural and functional attributes.gFACs supports common downstream applications,including genome browsers,and generates extensive details on the filtering process,including distributions that can be visualized to further assess the proposed gene space.gFACs is freely available and implemented in Perl with support from BioPerl libraries at https://gitlab.com/PlantGenomicsLab/gFACs.

Exploring Social Annotations with the Application to Web Page Recommendation

Collaborative social annotation systems allow users to record and share their original keywords or tag attachments to Web resources such as Web pages, photos, or videos. These annotations are a method for organizing and labeling information. They have the potential to help users navigate the Web and locate the needed resources. However, since annotations axe posted by users under no central control, there exist problems such as spare and synonymous annotations. To efficiently use annotation information to facilitate knowledge discovery from the Web, it is advantageous if we organize social annotations from semantic perspective and embed them into algorithms for knowledge discovery. This inspires the Web page recommendation with annotations, in which users and Web pages are clustered so that semantically similar items can be related. In this paper we propose four graphic models which cluster users, Web pages and annotations and recommend Web pages for given users by assigning items to the right cluster first. The algorithms are then compared to the classical collaborative filtering recommendation method on a real-world data set. Our result indicates that the graphic models provide better recommendation performance and are robust to fit for the real applications.

EyeHealer:A large-scale anterior eye segment dataset with eye structure and lesion annotations

Anterior segment eye diseases account for a significant proportion of presentations to eye clinics worldwide,including diseases associated with corneal pathologies,anterior chamber abnormalities(e.g.blood or inflammation),and lens diseases.The construction of an automatic tool for segmentation of anterior segment eye lesions would greatly improve the efficiency of clinical care.With research on artificial intelligence progressing in recent years,deep learning models have shown their superiority in image classification and segmentation.The training and evaluation of deep learning models should be based on a large amount of data annotated with expertise;however,such data are relatively scarce in the domain of medicine.Herein,the authors developed a new medical image annotation system,called EyeHealer.It is a large-scale anterior eye segment dataset with both eye structures and lesions annotated at the pixel level.Comprehensive experiments were conducted to verify its performance in disease classification and eye lesion segmentation.The results showed that semantic segmentation models outperformed medical segmentation models.This paper describes the establishment of the system for automated classification and segmentation tasks.The dataset will be made publicly available to encourage future research in this area.

Ontology-Based News Linking for Semantic Temporal Queries

Daily newspapers publish a tremendous amount of information disseminated through the Internet.Freely available and easily accessible large online repositories are not indexed and are in an un-processable format.The major hindrance in developing and evaluating existing/new monolingual text in an image is that it is not linked and indexed.There is no method to reuse the online news images because of the unavailability of standardized benchmark corpora,especially for South Asian languages.The corpus is a vital resource for developing and evaluating text in an image to reuse local news systems in general and specifically for the Urdu language.Lack of indexing,primarily semantic indexing of the daily news items,makes news items impracticable for any querying.Moreover,the most straightforward search facility does not support these unindexed news resources.Our study addresses this gap by associating and marking the newspaper images with one of the widely spoken but under-resourced languages,i.e.,Urdu.The present work proposed a method to build a benchmark corpus of news in image form by introducing a web crawler.The corpus is then semantically linked and annotated with daily news items.Two techniques are proposed for image annotation,free annotation and fixed cross examination annotation.The second technique got higher accuracy.Build news ontology in protégéusing OntologyWeb Language(OWL)language and indexed the annotations under it.The application is also built and linked with protégéso that the readers and journalists have an interface to query the news items directly.Similarly,news items linked together will provide complete coverage and bring together different opinions at a single location for readers to do the analysis themselves.

Robust Deep Transfer Learning Based Object Detection and Tracking Approach

At present days,object detection and tracking concepts have gained more importance among researchers and business people.Presently,deep learning(DL)approaches have been used for object tracking as it increases the perfor-mance and speed of the tracking process.This paper presents a novel robust DL based object detection and tracking algorithm using Automated Image Anno-tation with ResNet based Faster regional convolutional neural network(R-CNN)named(AIA-FRCNN)model.The AIA-RFRCNN method performs image anno-tation using a Discriminative Correlation Filter(DCF)with Channel and Spatial Reliability tracker(CSR)called DCF-CSRT model.The AIA-RFRCNN model makes use of Faster RCNN as an object detector and tracker,which involves region proposal network(RPN)and Fast R-CNN.The RPN is a full convolution network that concurrently predicts the bounding box and score of different objects.The RPN is a trained model used for the generation of the high-quality region proposals,which are utilized by Fast R-CNN for detection process.Besides,Residual Network(ResNet 101)model is used as a shared convolutional neural network(CNN)for the generation of feature maps.The performance of the ResNet 101 model is further improved by the use of Adam optimizer,which tunes the hyperparameters namely learning rate,batch size,momentum,and weight decay.Finally,softmax layer is applied to classify the images.The performance of the AIA-RFRCNN method has been assessed using a benchmark dataset and a detailed comparative analysis of the results takes place.The outcome of the experiments indicated the superior characteristics of the AIA-RFRCNN model under diverse aspects.

Automatic Image Annotation Using Adaptive Convolutional Deep Learning Model

Every day,websites and personal archives create more and more photos.The size of these archives is immeasurable.The comfort of use of these huge digital image gatherings donates to their admiration.However,not all of these folders deliver relevant indexing information.From the outcomes,it is dif-ficult to discover data that the user can be absorbed in.Therefore,in order to determine the significance of the data,it is important to identify the contents in an informative manner.Image annotation can be one of the greatest problematic domains in multimedia research and computer vision.Hence,in this paper,Adap-tive Convolutional Deep Learning Model(ACDLM)is developed for automatic image annotation.Initially,the databases are collected from the open-source system which consists of some labelled images(for training phase)and some unlabeled images{Corel 5 K,MSRC v2}.After that,the images are sent to the pre-processing step such as colour space quantization and texture color class map.The pre-processed images are sent to the segmentation approach for efficient labelling technique using J-image segmentation(JSEG).Thefinal step is an auto-matic annotation using ACDLM which is a combination of Convolutional Neural Network(CNN)and Honey Badger Algorithm(HBA).Based on the proposed classifier,the unlabeled images are labelled.The proposed methodology is imple-mented in MATLAB and performance is evaluated by performance metrics such as accuracy,precision,recall and F1_Measure.With the assistance of the pro-posed methodology,the unlabeled images are labelled.

基于Annotation的Spring AOP系统设计

简要介绍AOP(Aspect Oriented Programming)技术和Annotation技术,讨论了Spring中的AOP的两种实现。通过同一个案例阐述了在Spring中通过XML配置来实现AOP和使用了Annotation在Spring中实现AOP,比较了两种实现方式的优缺点。对在Spring使用Annotation实现AOP作了总结,从而体现了Annotation在设计系统时其简洁性和良好的扩展性。

SSH架构中方法级缓存依赖的解决方案

缓存是提高系统性能的重要手段之一。实现缓存依赖就是要在被依赖对象与缓存对象之间建立一个有效关联,使数据得到合理的刷新。通过对EhCache、Map、An-notation以及AOP等相关技术的研究,提出了SSH架构中方法级依赖缓存的解决方案:使用EhCache结合Concur-rentHashMap实现缓存的细粒度化,使用Annotation存储依赖信息,通过AOP实现缓存服务代码与业务方法的分离。这种方案解决了实际应用中不能合理刷新数据、细粒度化较难等问题。实践表明,这种方案具有容易实现、便于维护和扩展、使用方便等特点。

调制域及脉冲分析仪主控软件设计

重点探讨了基于面向对象设计思想,利用CVI控件设计调制域及脉冲分析仪主控软件的若干方法。文章主要以CVI图形控件的应用为例,介绍了如何设置Marker和坐标,捕获并动态显示Marker线与波形交点坐标值,以及正确运用库函数绘制直方图等关键技术。仿真和实验结果证明这些技术方便灵活,切实有效,具有很强的针对性和通用性。

基于XML和Annotation的异构系统数据集成方式的研究

论文运用XML的强大数据表达能力以及J2EE的反射以及Annotation编程来实现异构系统数据集成方式。该方式通过对原始结构代码进行简单的标记,将不同的数据结构串联起来。同时在转换的过程中将细节屏蔽起来并封装成标准接口,用户只需要将提供输入以及所需要的数据结构类型就可以方便地进行转换。最后论文给出集成系统详细的设计方法。

论杜预《春秋经传集解》(下)

Du Yu’s Annotation of Classics and Elucidations in "Spring and Autumn Annals" is considered as a milestone for the studies of Spring and Autumn Annals, In Du’s book, the Preface to the Spring and Autumn Annals is a theoretical summary and revision for the previous studies on the Spring and Autumn Annals and the Tso Chuan. Its combination of classics and elucidations makes Tso Chuan a real elucidating study on Spring and Autumn Annals. Du expounds his philosophy and political ideas in this book.

基于Annotation的Spring AOP日志处理的设计与实现

系统维护和错误排查需要程序能自动的输出日志信息,将用户的操作记录下来。而日志本身不太可能是你开发的主程序的主要任务。如果能将"不可见的"、通用的日志代码注入主程序中,而保证自身业务逻辑的纯洁性,降低代码之间的耦合度,增加程序的灵活性和可重用性。本文使用Spring AOP的Annotation方式,写一个切面,并且指定切入的范围(切入点),当系统运行后,日志信息将可以自动输出或记录在指定的文件及数据库中。

翻译中实现社会语用等效(英文)

As a medium between two different languages, between peoples of two countries, translation is not only a matter of language, but also of cross-cultural transference. According to Professor He Ziran, socio-pragmatic translation is the kind of translation which examines the conditions on language use that stem from the social and cultural situations to serve cross-cultural communication. This paper focuses on the ways used to achieve socio-pragmatic equivalence in translation practice.