This paper reports the outcomes of three vocabulary tests taken by 71 second-year undergraduates, discusses the possible effects of contextualized explanation of new words and Ebbinghaus Forgetting Curve on the vocabu...This paper reports the outcomes of three vocabulary tests taken by 71 second-year undergraduates, discusses the possible effects of contextualized explanation of new words and Ebbinghaus Forgetting Curve on the vocabulary teaching and learning. The authors find that in a short duration there is a significant difference between the effect of bilingual （English ＆ Chinese） explanation and that of monolingual （Chinese） explanation on the students＇ recognition of English new words.展开更多
A new algorithm taking the spatial context of local features into account by utilizing contextualized histograms was proposed to recognize facial expression. The contextualized histograms were extracted fromtwo widely...A new algorithm taking the spatial context of local features into account by utilizing contextualized histograms was proposed to recognize facial expression. The contextualized histograms were extracted fromtwo widely used descriptors—the local binary pattern（ LBP） and weber local descriptor（ WLD）. The LBP and WLD feature histograms were extracted separately fromeach facial image,and contextualized histogram was generated as feature vectors to feed the classifier. In addition,the human face was divided into sub-blocks and each sub-block was assigned different weights by their different contributions to the intensity of facial expressions to improve the recognition rate. With the support vector machine（SVM） as classifier,the experimental results on the 2D texture images fromthe 3D-BU FE dataset indicated that contextualized histograms improved facial expression recognition performance when local features were employed.展开更多
For event analysis,the information from both before and after the event can be crucial in certain scenarios.By incorporating a contextualized perspective in event analysis,analysts can gain deeper insights from the ev...For event analysis,the information from both before and after the event can be crucial in certain scenarios.By incorporating a contextualized perspective in event analysis,analysts can gain deeper insights from the events.We propose a contextualized visual analysis framework which enables the identification and interpretation of temporal patterns within and across multivariate events.The framework consists of a design of visual representation for multivariate event contexts,a data processing workflow to support the visualization,and a context-centered visual analysis system to facilitate the interactive exploration of temporal patterns.To demonstrate the applicability and effectiveness of our framework,we present case studies using real-world datasets from two different domains and an expert study conducted with experienced data analysts.展开更多
1 Introduction Document-level Role Filler Extraction aims to identify those spans of text that denote the role fillers for each event described in the document[1].Despite achieving certain accomplishments,existing met...1 Introduction Document-level Role Filler Extraction aims to identify those spans of text that denote the role fillers for each event described in the document[1].Despite achieving certain accomplishments,existing methods are still not effective due to the following two issues:(1)there are difficulties in contextual modeling of long text,which requires modeling and understanding coherence and connections across sentences and paragraphs;(2)there usually ignore the explicit dependency relationships between event elements displayed in long text.To this end,we propose a novel graph-augmented approach for document-level event role filler extraction,named element relational graph-augmented multi-granularity contextualized encoder(ERGM),whose main idea is to effectively enhance the model's capabilities in capturing deep semantic information of events in long texts and modeling dependency relationships among event elements by incorporating the Event elements relational graph.Specifically,this method first constructs the structural graph by extracting elements from the source document.展开更多
Purpose:The paper aims to enhance Arabic machine translation(MT)by proposing novel approaches:(1)a dimensionality reduction technique for word embeddings tailored for Arabic text,optimizing efficiency while retaining ...Purpose:The paper aims to enhance Arabic machine translation(MT)by proposing novel approaches:(1)a dimensionality reduction technique for word embeddings tailored for Arabic text,optimizing efficiency while retaining semantic information;(2)a comprehensive comparison of meta-embedding techniques to improve translation quality;and(3)a method leveraging self-attention and Gated CNNs to capture token dependencies,including temporal and hierarchical features within sentences,and interactions between different embedding types.These approaches collectively aim to enhance translation quality by combining different embedding schemes and leveraging advanced modeling techniques.Design/methodology/approach:Recent works on MT in general and Arabic MT in particular often pick one type of word embedding model.In this paper,we present a novel approach to enhance Arabic MT by addressing three key aspects.Firstly,we propose a new dimensionality reduction technique for word embeddings,specifically tailored for Arabic text.This technique optimizes the efficiency of embeddings while retaining their semantic information.Secondly,we conduct an extensive comparison of different meta-embedding techniques,exploring the combination of static and contextual embeddings.Through this analysis,we identify the most effective approach to improve translation quality.Lastly,we introduce a novel method that leverages self-attention and Gated convolutional neural networks(CNNs)to capture token dependencies,including temporal and hierarchical features within sentences,as well as interactions between different types of embeddings.Our experimental results demonstrate the effectiveness of our proposed approach in significantly enhancing Arabic MT performance.It outperforms baseline models with a BLEU score increase of 2 points and achieves superior results compared to state-of-the-art approaches,with an average improvement of 4.6 points across all evaluation metrics.Findings:The proposed approaches significantly enhance Arabic MT performance.The dimensionality reduction technique improves the efficiency of word embeddings while preserving semantic information.Comprehensive comparison identifies effective meta-embedding techniques,with the contextualized dynamic meta-embeddings(CDME)model showcasing competitive results.Integration of Gated CNNs with the transformer model surpasses baseline performance,leveraging both architectures’strengths.Overall,these findings demonstrate substantial improvements in translation quality,with a BLEU score increase of 2 points and an average improvement of 4.6 points across all evaluation metrics,outperforming state-of-the-art approaches.Originality/value:The paper’s originality lies in its departure from simply fine-tuning the transformer model for a specific task.Instead,it introduces modifications to the internal architecture of the transformer,integrating Gated CNNs to enhance translation performance.This departure from traditional fine-tuning approaches demonstrates a novel perspective on model enhancement,offering unique insights into improving translation quality without solely relying on pre-existing architectures.The originality in dimensionality reduction lies in the tailored approach for Arabic text.While dimensionality reduction techniques are not new,the paper introduces a specific method optimized for Arabic word embeddings.By employing independent component analysis(ICA)and a post-processing method,the paper effectively reduces the dimensionality of word embeddings while preserving semantic information which has not been investigated before especially for MT task.展开更多
The urban evolution of Gulangyu,a historic international settlement in the city of Xiamen on China’s southeast coast,has long attracted the attention of scholars working in the field of Chinese urban history.Until re...The urban evolution of Gulangyu,a historic international settlement in the city of Xiamen on China’s southeast coast,has long attracted the attention of scholars working in the field of Chinese urban history.Until recently,however,few efforts have been made to theorize the spatial formation of Gulangyu.Drawing upon existing research on the island’s development,particularly since the early 1840s,this study aims to offer a critical reading of the modern transformation of Gulangyu.By retracing the urban evolution of Gulangyu on the basis of historical maps and fieldtrip data,its spatial restructuring up to the early 1940s has been reviewed and analyzed.With GIS and CAD-based tools,conflated results are presented to explain the island’s spatial mixity.The results show that from military fortification to village settlements,through the competition between local inhabitants and earlier imperialists,and then the coopetition of foreign residents and returned overseas Chinese,the making of Gulangyu as a historic international settlement was hardly a Western-dominant process.Instead,it should provide an entry point for a contextualized critique of current research on similar urban space in China’s modern history.展开更多
In global navigation satellite system denial environment,cross-view geo-localization based on image retrieval presents an exceedingly critical visual localization solution for Unmanned Aerial Vehicle(UAV)systems.The e...In global navigation satellite system denial environment,cross-view geo-localization based on image retrieval presents an exceedingly critical visual localization solution for Unmanned Aerial Vehicle(UAV)systems.The essence of cross-view geo-localization resides in matching images containing the same geographical targets from disparate platforms,such as UAV-view and satellite-view images.However,images of the same geographical targets may suffer from occlusions and geometric distortions due to variations in the capturing platform,view,and timing.The existing methods predominantly extract features by segmenting feature maps,which overlook the holistic semantic distribution and structural information of objects,resulting in loss of image information.To address these challenges,dilated neighborhood attention Transformer is employed as the feature extraction backbone,and Multi-feature representations based on Multi-scale Hierarchical Contextual Aggregation(MMHCA)is proposed.In the proposed MMHCA method,the multiscale hierarchical contextual aggregation method is utilized to extract contextual information from local to global across various granularity levels,establishing feature associations of contextual information with global and local information in the image.Subsequently,the multi-feature representations method is utilized to obtain rich discriminative feature information,bolstering the robustness of model in scenarios characterized by positional shifts,varying distances,and scale ambiguities.Comprehensive experiments conducted on the extensively utilized University-1652 and SUES-200 benchmarks indicate that the MMHCA method surpasses the existing techniques.showing outstanding results in UAV localization and navigation.展开更多
Detecting abnormal cervical cells is crucial for early identification and timely treatment of cervical cancer.However,this task is challenging due to the morphological similarities between abnormal and normal cells an...Detecting abnormal cervical cells is crucial for early identification and timely treatment of cervical cancer.However,this task is challenging due to the morphological similarities between abnormal and normal cells and the significant variations in cell size.Pathologists often refer to surrounding cells to identify abnormalities.To emulate this slide examination behavior,this study proposes a Multi-Scale Feature Fusion Network(MSFF-Net)for detecting cervical abnormal cells.MSFF-Net employs a Cross-Scale Pooling Model(CSPM)to effectively capture diverse features and contextual information,ranging from local details to the overall structure.Additionally,a Multi-Scale Fusion Attention(MSFA)module is introduced to mitigate the impact of cell size variations by adaptively fusing local and global information at different scales.To handle the complex environment of cervical cell images,such as cell adhesion and overlapping,the Inner-CIoU loss function is utilized to more precisely measure the overlap between bounding boxes,thereby improving detection accuracy in such scenarios.Experimental results on the Comparison detector dataset demonstrate that MSFF-Net achieves a mean average precision(mAP)of 63.2%,outperforming state-of-the-art methods while maintaining a relatively small number of parameters(26.8 M).This study highlights the effectiveness of multi-scale feature fusion in enhancing the detection of cervical abnormal cells,contributing to more accurate and efficient cervical cancer screening.展开更多
Abs As a crucial vehicle for young children’s artistic enlightenment,music appreciation holds an irreplaceable value in cognitive development,emotional edification,and the cultivation of aesthetic abilities.Currently...Abs As a crucial vehicle for young children’s artistic enlightenment,music appreciation holds an irreplaceable value in cognitive development,emotional edification,and the cultivation of aesthetic abilities.Currently,in music appreciation activities for senior kindergarten classes,there is a widespread phenomenon of homogenized teaching content and mechanized teaching methods,which results in insufficient enthusiasm for participation among young children and a superficial understanding of music.The situational teaching method,by constructing concrete and immersive learning scenarios,can effectively activate young children’s multi-dimensional sensory experiences.Its characteristics of intuitiveness and interactivity are highly consistent with the traits of young children’s concrete thinking,thus providing a new approach to resolving the current predicament.The research focuses on the practical pain points in music appreciation activities for senior kindergarten classes and proposes targeted solutions from four dimensions:content design,method innovation,resource integration,and teacher training,aiming to reconstruct a child-centered,in-depth music learning model.Practice has shown that the situational teaching method can not only enhance young children’s perceptual sensitivity to musical elements but also guide them to achieve emotional resonance through role-playing and life-related associations,laying a foundation for the sustainable development of young children’s musical literacy.展开更多
Based on the contextual adaptation perspective of Verschueren’s Adaptation Theory,this paper explores the Chinese translation strategies of Japanese quotation sentences in the Yang translation of The Courage of One f...Based on the contextual adaptation perspective of Verschueren’s Adaptation Theory,this paper explores the Chinese translation strategies of Japanese quotation sentences in the Yang translation of The Courage of One from the perspectives of communicative context and linguistic context.The study finds that the Chinese translation of Japanese quotation sentences involves various strategies,including retaining direct quotations,converting direct quotations into statements,transforming direct quotations into attributive+noun forms,and alternating between direct and indirect quotations.This research provides a new perspective for the Chinese translation of Japanese quotation sentences and offers theoretical support for translation practices in cross-cultural communication.展开更多
The primary challenge in weakly supervised semantic segmentation is effectively leveraging weak annotations while minimizing the performance gap compared to fully supervised methods.End-to-end model designs have gaine...The primary challenge in weakly supervised semantic segmentation is effectively leveraging weak annotations while minimizing the performance gap compared to fully supervised methods.End-to-end model designs have gained significant attention for improving training efficiency.Most current algorithms rely on Convolutional Neural Networks(CNNs)for feature extraction.Although CNNs are proficient at capturing local features,they often struggle with global context,leading to incomplete and false Class Activation Mapping(CAM).To address these limitations,this work proposes a Contextual Prototype-Based End-to-End Weakly Supervised Semantic Segmentation(CPEWS)model,which improves feature extraction by utilizing the Vision Transformer(ViT).By incorporating its intermediate feature layers to preserve semantic information,this work introduces the Intermediate Supervised Module(ISM)to supervise the final layer’s output,reducing boundary ambiguity and mitigating issues related to incomplete activation.Additionally,the Contextual Prototype Module(CPM)generates class-specific prototypes,while the proposed Prototype Discrimination Loss and Superclass Suppression Loss guide the network’s training,(LPDL)(LSSL)effectively addressing false activation without the need for extra supervision.The CPEWS model proposed in this paper achieves state-of-the-art performance in end-to-end weakly supervised semantic segmentation without additional supervision.The validation set and test set Mean Intersection over Union(MIoU)of PASCAL VOC 2012 dataset achieved 69.8%and 72.6%,respectively.Compared with ToCo(pre trained weight ImageNet-1k),MIoU on the test set is 2.1%higher.In addition,MIoU reached 41.4%on the validation set of the MS COCO 2014 dataset.展开更多
The posttreatment period is a key part of the management of pediatric cancer.During this time,school and psychological difficulties have been described in childhood cancer survivors(CCS)and can be prognostic for the s...The posttreatment period is a key part of the management of pediatric cancer.During this time,school and psychological difficulties have been described in childhood cancer survivors(CCS)and can be prognostic for the success of social reintegration.This study estimated the influence of the household’s socioeconomic status(SES)on these psychosocial difficulties.This study is based on a prospective multicentric database and focused on children who received a psychosocial evaluation during their follow-up from 2013 to 2020.We retrieved data on school and psychological difficulties.Household SES was estimated by a social deprivation score.Data from1003 patients were analyzed.School difficulties were noted in 22%of CCS.A greater social deprivation was significantly associated with school difficulty.Tumor relapse,treatment with hematopoietic stem cell transplantation,and central nervous system(CNS)tumors remained significant risk factors.In the subgroup of CNS tumors,school difficulties were increased and associated with greater social deprivation.Psychological difficulties were not associated with the deprivation score.There is a link between SES and school difficulties in CCS.Further investigations should be carried out for children with CNS tumors,which is the population of the greatest concern.展开更多
Objective:The relationship between cause-specific mortality and regional socio-economic and environmental indicators remains poorly studied in Russia.The study first aims to study regional differences in cause-specifi...Objective:The relationship between cause-specific mortality and regional socio-economic and environmental indicators remains poorly studied in Russia.The study first aims to study regional differences in cause-specific mortality among the population aged 20 years and older in Russia,and second to investigate the association between regional deprivation and cause-specific mortality.Material and methods:Russian deprivation index was used to measure level of deprivation.The index consists of three components:social,economic and environmental.The index measures general deprivation,and its compo-nents measure social,economic and environmental deprivation.The mortality data by age(five-year groups)and sex in the subjects of Russia from 2006 to 2022 were extracted from the Russian Fertility and Mortality Database of the Center of Demographic Research of the New Economic School.Results:In the most general deprived areas,mortality rate from infectious and parasitic diseases increased by more than twice in the total population,women and men as compared to the least deprived quantile(Q1).Fully adjusted negative binomial regression showed an increase in mortality rate from injuries,poisoning and external causes and infectious and parasitic diseases in more social deprived areas as compared to Q1 in the total population,women and men.In men,there was a significantly higher mortality rate from neoplasms and from infectious and parasitic diseases in more economic deprived areas as compared to Q1.Both in total population and in women,there was a trend towards an increase in mortality from neoplasms depending on the level of environmental deprivation.Conclusions:This is the first study examining the relationship of contextual factors with cause-specific mortality that takes into account sex,age and year of death at the population level in Russia.General,social,economic and environmental deprivation are associated with cause-specific mortality.展开更多
There are five vital signs that healthcare providers assess: temperature, pulse, respiration, blood pressure, and pain. Normal levels for the five vital signs are published by the American Heart Association, and other...There are five vital signs that healthcare providers assess: temperature, pulse, respiration, blood pressure, and pain. Normal levels for the five vital signs are published by the American Heart Association, and other specialty organizations, however, the sixth vital sign (resilience) which adopts the measure of immune resilience is suggested in this paper. Resilience is the ability of the immune system to respond to attacks and defend effectively against infections and inflammatory stressors, and psychological resilience is the capacity to resist, adapt, recover, thrive, and grow from a challenge or a stressor. Individuals with better optimal immune resilience had better health outcomes than those with minimal immune resilience. The purpose of this paper is to conceptualize, contextualize, and operationalize all six vital signs. We suggest measuring resilience subjectively and objectively. Subjectively, use a 5-item guided interview revised from the Connor-Davidson Resilience Scale (CDRC), a scale of 10 items. The revised CDRC scale is a 5-item scale. The scale is rated on a 5-point Likert scale from 0 (not true) to 4 (true all the time). The total score ranges from 0 to 20, with higher total scores indicating greater resilience. The scale demonstrated good construct validity and internal consistency (α = 0.85) during the development of the scale. The CD-RISC had a good Cronbach’s alpha level of 0.85. The Revised CD-RISC can be completed in 2 - 4 minutes. To measure resilience objectively, we suggest using Immune Resilience (IR) levels, the level of resilience to preserve and/or rapidly restore immune resilience functions that promote disease resistance and control inflammation and other inflammatory stress. IR levels are gauged with two peripheral blood metrics that quantify the balance between CD8 and CD4 T-cell levels and gene expression signatures tracking longevity-associated immunocompetence and mortality- or entropy-associated inflammation. IR deregulation is potentially reversible by decreasing inflammatory stress. IR metrics and mechanisms have utility as vital signs and biomarkers for measuring immune health and improving health outcomes.展开更多
This study explores the application of Abrams’Fourfold Model in the classification of Western literary criticism.Abrams’framework categorizes literary criticism into four fundamental elements:text,author,world,and a...This study explores the application of Abrams’Fourfold Model in the classification of Western literary criticism.Abrams’framework categorizes literary criticism into four fundamental elements:text,author,world,and audience.The text is viewed as an independent entity with intrinsic artistic value,necessitating a detailed analysis of its structure,style,themes,and symbols.Author study delves into the creator’s life and socio-cultural context,often to uncover the work’s deeper meanings.Contextual study situates the work within its historical and social milieu,examining its reflection of or response to societal norms and events.Audience response analysis considers the diverse interpretations shaped by readers’backgrounds,emphasizing the reader’s role in constructing the work’s meaning.The study concludes that Abrams’Fourfold Model offers a comprehensive and flexible analytical tool,enabling critics to engage with literary works from multiple perspectives,thereby enriching the understanding of literary complexity and diversity.展开更多
文摘This paper reports the outcomes of three vocabulary tests taken by 71 second-year undergraduates, discusses the possible effects of contextualized explanation of new words and Ebbinghaus Forgetting Curve on the vocabulary teaching and learning. The authors find that in a short duration there is a significant difference between the effect of bilingual （English ＆ Chinese） explanation and that of monolingual （Chinese） explanation on the students＇ recognition of English new words.
基金Supported by the National Natural Science Foundation of China(60772066)
文摘A new algorithm taking the spatial context of local features into account by utilizing contextualized histograms was proposed to recognize facial expression. The contextualized histograms were extracted fromtwo widely used descriptors—the local binary pattern（ LBP） and weber local descriptor（ WLD）. The LBP and WLD feature histograms were extracted separately fromeach facial image,and contextualized histogram was generated as feature vectors to feed the classifier. In addition,the human face was divided into sub-blocks and each sub-block was assigned different weights by their different contributions to the intensity of facial expressions to improve the recognition rate. With the support vector machine（SVM） as classifier,the experimental results on the 2D texture images fromthe 3D-BU FE dataset indicated that contextualized histograms improved facial expression recognition performance when local features were employed.
基金supported by Natural Science Foundation of China(NSFC No.62472099 and No.62202105)Federal Ministry of Education and Research of Germany and the state of North-Rhine Westphalia as part of the Lamarr Institute for Machine Learning and Artificial Intelligence(Lamarr22B)by EU in project CrexData(grant agreement No.101092749).
文摘For event analysis,the information from both before and after the event can be crucial in certain scenarios.By incorporating a contextualized perspective in event analysis,analysts can gain deeper insights from the events.We propose a contextualized visual analysis framework which enables the identification and interpretation of temporal patterns within and across multivariate events.The framework consists of a design of visual representation for multivariate event contexts,a data processing workflow to support the visualization,and a context-centered visual analysis system to facilitate the interactive exploration of temporal patterns.To demonstrate the applicability and effectiveness of our framework,we present case studies using real-world datasets from two different domains and an expert study conducted with experienced data analysts.
基金supported by the National Natural Science Foundation of China(Grant Nos.U21B2027,U23A20388,62266028)the Yunnan Provincial Major Science and Technology Special Plan Projects(202302AD080003,202202AD080003,202303AP140008)+1 种基金the Yunnan Fundamental Research Projects(202301AS070047)the Kunming University of Science and Technology’s”Double First-rate”Construction Joint Project(202201BE070001-021).
文摘1 Introduction Document-level Role Filler Extraction aims to identify those spans of text that denote the role fillers for each event described in the document[1].Despite achieving certain accomplishments,existing methods are still not effective due to the following two issues:(1)there are difficulties in contextual modeling of long text,which requires modeling and understanding coherence and connections across sentences and paragraphs;(2)there usually ignore the explicit dependency relationships between event elements displayed in long text.To this end,we propose a novel graph-augmented approach for document-level event role filler extraction,named element relational graph-augmented multi-granularity contextualized encoder(ERGM),whose main idea is to effectively enhance the model's capabilities in capturing deep semantic information of events in long texts and modeling dependency relationships among event elements by incorporating the Event elements relational graph.Specifically,this method first constructs the structural graph by extracting elements from the source document.
文摘Purpose:The paper aims to enhance Arabic machine translation(MT)by proposing novel approaches:(1)a dimensionality reduction technique for word embeddings tailored for Arabic text,optimizing efficiency while retaining semantic information;(2)a comprehensive comparison of meta-embedding techniques to improve translation quality;and(3)a method leveraging self-attention and Gated CNNs to capture token dependencies,including temporal and hierarchical features within sentences,and interactions between different embedding types.These approaches collectively aim to enhance translation quality by combining different embedding schemes and leveraging advanced modeling techniques.Design/methodology/approach:Recent works on MT in general and Arabic MT in particular often pick one type of word embedding model.In this paper,we present a novel approach to enhance Arabic MT by addressing three key aspects.Firstly,we propose a new dimensionality reduction technique for word embeddings,specifically tailored for Arabic text.This technique optimizes the efficiency of embeddings while retaining their semantic information.Secondly,we conduct an extensive comparison of different meta-embedding techniques,exploring the combination of static and contextual embeddings.Through this analysis,we identify the most effective approach to improve translation quality.Lastly,we introduce a novel method that leverages self-attention and Gated convolutional neural networks(CNNs)to capture token dependencies,including temporal and hierarchical features within sentences,as well as interactions between different types of embeddings.Our experimental results demonstrate the effectiveness of our proposed approach in significantly enhancing Arabic MT performance.It outperforms baseline models with a BLEU score increase of 2 points and achieves superior results compared to state-of-the-art approaches,with an average improvement of 4.6 points across all evaluation metrics.Findings:The proposed approaches significantly enhance Arabic MT performance.The dimensionality reduction technique improves the efficiency of word embeddings while preserving semantic information.Comprehensive comparison identifies effective meta-embedding techniques,with the contextualized dynamic meta-embeddings(CDME)model showcasing competitive results.Integration of Gated CNNs with the transformer model surpasses baseline performance,leveraging both architectures’strengths.Overall,these findings demonstrate substantial improvements in translation quality,with a BLEU score increase of 2 points and an average improvement of 4.6 points across all evaluation metrics,outperforming state-of-the-art approaches.Originality/value:The paper’s originality lies in its departure from simply fine-tuning the transformer model for a specific task.Instead,it introduces modifications to the internal architecture of the transformer,integrating Gated CNNs to enhance translation performance.This departure from traditional fine-tuning approaches demonstrates a novel perspective on model enhancement,offering unique insights into improving translation quality without solely relying on pre-existing architectures.The originality in dimensionality reduction lies in the tailored approach for Arabic text.While dimensionality reduction techniques are not new,the paper introduces a specific method optimized for Arabic word embeddings.By employing independent component analysis(ICA)and a post-processing method,the paper effectively reduces the dimensionality of word embeddings while preserving semantic information which has not been investigated before especially for MT task.
基金funded by the National Natural Science Foundation of ChinaGrant Numbers 41030479,41801161,41801163the National Social Science Foundation of China(21AZD034)。
文摘The urban evolution of Gulangyu,a historic international settlement in the city of Xiamen on China’s southeast coast,has long attracted the attention of scholars working in the field of Chinese urban history.Until recently,however,few efforts have been made to theorize the spatial formation of Gulangyu.Drawing upon existing research on the island’s development,particularly since the early 1840s,this study aims to offer a critical reading of the modern transformation of Gulangyu.By retracing the urban evolution of Gulangyu on the basis of historical maps and fieldtrip data,its spatial restructuring up to the early 1940s has been reviewed and analyzed.With GIS and CAD-based tools,conflated results are presented to explain the island’s spatial mixity.The results show that from military fortification to village settlements,through the competition between local inhabitants and earlier imperialists,and then the coopetition of foreign residents and returned overseas Chinese,the making of Gulangyu as a historic international settlement was hardly a Western-dominant process.Instead,it should provide an entry point for a contextualized critique of current research on similar urban space in China’s modern history.
基金supported by the National Natural Science Foundation of China(Nos.12072027,62103052,61603346 and 62103379)the Henan Key Laboratory of General Aviation Technology,China(No.ZHKF-230201)+3 种基金the Funding for the Open Research Project of the Rotor Aerodynamics Key Laboratory,China(No.RAL20200101)the Key Research and Development Program of Henan Province,China(Nos.241111222000 and 241111222900)the Key Science and Technology Program of Henan Province,China(No.232102220067)the Scholarship Funding from the China Scholarship Council(No.202206030079).
文摘In global navigation satellite system denial environment,cross-view geo-localization based on image retrieval presents an exceedingly critical visual localization solution for Unmanned Aerial Vehicle(UAV)systems.The essence of cross-view geo-localization resides in matching images containing the same geographical targets from disparate platforms,such as UAV-view and satellite-view images.However,images of the same geographical targets may suffer from occlusions and geometric distortions due to variations in the capturing platform,view,and timing.The existing methods predominantly extract features by segmenting feature maps,which overlook the holistic semantic distribution and structural information of objects,resulting in loss of image information.To address these challenges,dilated neighborhood attention Transformer is employed as the feature extraction backbone,and Multi-feature representations based on Multi-scale Hierarchical Contextual Aggregation(MMHCA)is proposed.In the proposed MMHCA method,the multiscale hierarchical contextual aggregation method is utilized to extract contextual information from local to global across various granularity levels,establishing feature associations of contextual information with global and local information in the image.Subsequently,the multi-feature representations method is utilized to obtain rich discriminative feature information,bolstering the robustness of model in scenarios characterized by positional shifts,varying distances,and scale ambiguities.Comprehensive experiments conducted on the extensively utilized University-1652 and SUES-200 benchmarks indicate that the MMHCA method surpasses the existing techniques.showing outstanding results in UAV localization and navigation.
基金funded by the China Chongqing Municipal Science and Technology Bureau,grant numbers 2024TIAD-CYKJCXX0121,2024NSCQ-LZX0135Chongqing Municipal Commission of Housing and Urban-Rural Development,grant number CKZ2024-87+3 种基金the Chongqing University of Technology graduate education high-quality development project,grant number gzlsz202401the Chongqing University of Technology-Chongqing LINGLUE Technology Co.,Ltd.,Electronic Information(Artificial Intelligence)graduate joint training basethe Postgraduate Education and Teaching Reform Research Project in Chongqing,grant number yjg213116the Chongqing University of Technology-CISDI Chongqing Information Technology Co.,Ltd.,Computer Technology graduate joint training base.
文摘Detecting abnormal cervical cells is crucial for early identification and timely treatment of cervical cancer.However,this task is challenging due to the morphological similarities between abnormal and normal cells and the significant variations in cell size.Pathologists often refer to surrounding cells to identify abnormalities.To emulate this slide examination behavior,this study proposes a Multi-Scale Feature Fusion Network(MSFF-Net)for detecting cervical abnormal cells.MSFF-Net employs a Cross-Scale Pooling Model(CSPM)to effectively capture diverse features and contextual information,ranging from local details to the overall structure.Additionally,a Multi-Scale Fusion Attention(MSFA)module is introduced to mitigate the impact of cell size variations by adaptively fusing local and global information at different scales.To handle the complex environment of cervical cell images,such as cell adhesion and overlapping,the Inner-CIoU loss function is utilized to more precisely measure the overlap between bounding boxes,thereby improving detection accuracy in such scenarios.Experimental results on the Comparison detector dataset demonstrate that MSFF-Net achieves a mean average precision(mAP)of 63.2%,outperforming state-of-the-art methods while maintaining a relatively small number of parameters(26.8 M).This study highlights the effectiveness of multi-scale feature fusion in enhancing the detection of cervical abnormal cells,contributing to more accurate and efficient cervical cancer screening.
文摘Abs As a crucial vehicle for young children’s artistic enlightenment,music appreciation holds an irreplaceable value in cognitive development,emotional edification,and the cultivation of aesthetic abilities.Currently,in music appreciation activities for senior kindergarten classes,there is a widespread phenomenon of homogenized teaching content and mechanized teaching methods,which results in insufficient enthusiasm for participation among young children and a superficial understanding of music.The situational teaching method,by constructing concrete and immersive learning scenarios,can effectively activate young children’s multi-dimensional sensory experiences.Its characteristics of intuitiveness and interactivity are highly consistent with the traits of young children’s concrete thinking,thus providing a new approach to resolving the current predicament.The research focuses on the practical pain points in music appreciation activities for senior kindergarten classes and proposes targeted solutions from four dimensions:content design,method innovation,resource integration,and teacher training,aiming to reconstruct a child-centered,in-depth music learning model.Practice has shown that the situational teaching method can not only enhance young children’s perceptual sensitivity to musical elements but also guide them to achieve emotional resonance through role-playing and life-related associations,laying a foundation for the sustainable development of young children’s musical literacy.
文摘Based on the contextual adaptation perspective of Verschueren’s Adaptation Theory,this paper explores the Chinese translation strategies of Japanese quotation sentences in the Yang translation of The Courage of One from the perspectives of communicative context and linguistic context.The study finds that the Chinese translation of Japanese quotation sentences involves various strategies,including retaining direct quotations,converting direct quotations into statements,transforming direct quotations into attributive+noun forms,and alternating between direct and indirect quotations.This research provides a new perspective for the Chinese translation of Japanese quotation sentences and offers theoretical support for translation practices in cross-cultural communication.
基金funding from the following sources:National Natural Science Foundation of China(U1904119)Research Programs of Henan Science and Technology Department(232102210054)+3 种基金Chongqing Natural Science Foundation(CSTB2023NSCQ-MSX0070)Henan Province Key Research and Development Project(231111212000)Aviation Science Foundation(20230001055002)supported by Henan Center for Outstanding Overseas Scientists(GZS2022011).
文摘The primary challenge in weakly supervised semantic segmentation is effectively leveraging weak annotations while minimizing the performance gap compared to fully supervised methods.End-to-end model designs have gained significant attention for improving training efficiency.Most current algorithms rely on Convolutional Neural Networks(CNNs)for feature extraction.Although CNNs are proficient at capturing local features,they often struggle with global context,leading to incomplete and false Class Activation Mapping(CAM).To address these limitations,this work proposes a Contextual Prototype-Based End-to-End Weakly Supervised Semantic Segmentation(CPEWS)model,which improves feature extraction by utilizing the Vision Transformer(ViT).By incorporating its intermediate feature layers to preserve semantic information,this work introduces the Intermediate Supervised Module(ISM)to supervise the final layer’s output,reducing boundary ambiguity and mitigating issues related to incomplete activation.Additionally,the Contextual Prototype Module(CPM)generates class-specific prototypes,while the proposed Prototype Discrimination Loss and Superclass Suppression Loss guide the network’s training,(LPDL)(LSSL)effectively addressing false activation without the need for extra supervision.The CPEWS model proposed in this paper achieves state-of-the-art performance in end-to-end weakly supervised semantic segmentation without additional supervision.The validation set and test set Mean Intersection over Union(MIoU)of PASCAL VOC 2012 dataset achieved 69.8%and 72.6%,respectively.Compared with ToCo(pre trained weight ImageNet-1k),MIoU on the test set is 2.1%higher.In addition,MIoU reached 41.4%on the validation set of the MS COCO 2014 dataset.
基金supported by a grant from SFCE INCa (Institut National du Cancer)GOCE (Grand Ouest Cancer de l’Enfant).
文摘The posttreatment period is a key part of the management of pediatric cancer.During this time,school and psychological difficulties have been described in childhood cancer survivors(CCS)and can be prognostic for the success of social reintegration.This study estimated the influence of the household’s socioeconomic status(SES)on these psychosocial difficulties.This study is based on a prospective multicentric database and focused on children who received a psychosocial evaluation during their follow-up from 2013 to 2020.We retrieved data on school and psychological difficulties.Household SES was estimated by a social deprivation score.Data from1003 patients were analyzed.School difficulties were noted in 22%of CCS.A greater social deprivation was significantly associated with school difficulty.Tumor relapse,treatment with hematopoietic stem cell transplantation,and central nervous system(CNS)tumors remained significant risk factors.In the subgroup of CNS tumors,school difficulties were increased and associated with greater social deprivation.Psychological difficulties were not associated with the deprivation score.There is a link between SES and school difficulties in CCS.Further investigations should be carried out for children with CNS tumors,which is the population of the greatest concern.
文摘Objective:The relationship between cause-specific mortality and regional socio-economic and environmental indicators remains poorly studied in Russia.The study first aims to study regional differences in cause-specific mortality among the population aged 20 years and older in Russia,and second to investigate the association between regional deprivation and cause-specific mortality.Material and methods:Russian deprivation index was used to measure level of deprivation.The index consists of three components:social,economic and environmental.The index measures general deprivation,and its compo-nents measure social,economic and environmental deprivation.The mortality data by age(five-year groups)and sex in the subjects of Russia from 2006 to 2022 were extracted from the Russian Fertility and Mortality Database of the Center of Demographic Research of the New Economic School.Results:In the most general deprived areas,mortality rate from infectious and parasitic diseases increased by more than twice in the total population,women and men as compared to the least deprived quantile(Q1).Fully adjusted negative binomial regression showed an increase in mortality rate from injuries,poisoning and external causes and infectious and parasitic diseases in more social deprived areas as compared to Q1 in the total population,women and men.In men,there was a significantly higher mortality rate from neoplasms and from infectious and parasitic diseases in more economic deprived areas as compared to Q1.Both in total population and in women,there was a trend towards an increase in mortality from neoplasms depending on the level of environmental deprivation.Conclusions:This is the first study examining the relationship of contextual factors with cause-specific mortality that takes into account sex,age and year of death at the population level in Russia.General,social,economic and environmental deprivation are associated with cause-specific mortality.
文摘There are five vital signs that healthcare providers assess: temperature, pulse, respiration, blood pressure, and pain. Normal levels for the five vital signs are published by the American Heart Association, and other specialty organizations, however, the sixth vital sign (resilience) which adopts the measure of immune resilience is suggested in this paper. Resilience is the ability of the immune system to respond to attacks and defend effectively against infections and inflammatory stressors, and psychological resilience is the capacity to resist, adapt, recover, thrive, and grow from a challenge or a stressor. Individuals with better optimal immune resilience had better health outcomes than those with minimal immune resilience. The purpose of this paper is to conceptualize, contextualize, and operationalize all six vital signs. We suggest measuring resilience subjectively and objectively. Subjectively, use a 5-item guided interview revised from the Connor-Davidson Resilience Scale (CDRC), a scale of 10 items. The revised CDRC scale is a 5-item scale. The scale is rated on a 5-point Likert scale from 0 (not true) to 4 (true all the time). The total score ranges from 0 to 20, with higher total scores indicating greater resilience. The scale demonstrated good construct validity and internal consistency (α = 0.85) during the development of the scale. The CD-RISC had a good Cronbach’s alpha level of 0.85. The Revised CD-RISC can be completed in 2 - 4 minutes. To measure resilience objectively, we suggest using Immune Resilience (IR) levels, the level of resilience to preserve and/or rapidly restore immune resilience functions that promote disease resistance and control inflammation and other inflammatory stress. IR levels are gauged with two peripheral blood metrics that quantify the balance between CD8 and CD4 T-cell levels and gene expression signatures tracking longevity-associated immunocompetence and mortality- or entropy-associated inflammation. IR deregulation is potentially reversible by decreasing inflammatory stress. IR metrics and mechanisms have utility as vital signs and biomarkers for measuring immune health and improving health outcomes.
基金The paper was supported by Henan Province Teaching Reform and Practice Project(Project Fund No.135)-Research on the Reform of Literary Theory Courses for English Majors in Universities.
文摘This study explores the application of Abrams’Fourfold Model in the classification of Western literary criticism.Abrams’framework categorizes literary criticism into four fundamental elements:text,author,world,and audience.The text is viewed as an independent entity with intrinsic artistic value,necessitating a detailed analysis of its structure,style,themes,and symbols.Author study delves into the creator’s life and socio-cultural context,often to uncover the work’s deeper meanings.Contextual study situates the work within its historical and social milieu,examining its reflection of or response to societal norms and events.Audience response analysis considers the diverse interpretations shaped by readers’backgrounds,emphasizing the reader’s role in constructing the work’s meaning.The study concludes that Abrams’Fourfold Model offers a comprehensive and flexible analytical tool,enabling critics to engage with literary works from multiple perspectives,thereby enriching the understanding of literary complexity and diversity.
