I am delighted to join my Thai and Chinese friends in celebrating the 50th anniversary of the establishment of diplomatic relations between our two great nations.It is the“Golden Jubilee”of Thailand-China friendship...I am delighted to join my Thai and Chinese friends in celebrating the 50th anniversary of the establishment of diplomatic relations between our two great nations.It is the“Golden Jubilee”of Thailand-China friendship.Half a century ago when Thailand’s then Prime Minister M.R.Kukrit Pramoj and China’s Premier Zhou Enlai signed a Joint Communique on the Establishment of Diplomatic Relations on July 1,1975,the foundation for a profound and mutually beneficial partnership was laid,fostering enduring friendship,cooperation,and understanding between our two nations.This bond has stood the test of time amidst the ever-changing international landscape and been further enhanced by both countries’commitment to advancing the Comprehensive Strategic Cooperative Partnership and building a Thailand-China community with a shared future for enhanced stability,prosperity,and sustainability through a forwardlooking and people-centered vision.展开更多
The year 2025 marks the 75th anniversary of the establishment of diplomatic relations between China and Indonesia.Over the past 75 years,the bilateral relationship has made significant progress across various fields,l...The year 2025 marks the 75th anniversary of the establishment of diplomatic relations between China and Indonesia.Over the past 75 years,the bilateral relationship has made significant progress across various fields,laying a solid foundation for regional stability.China Report ASEAN interviewed Indonesian Ambassador to China Djauhari Oratmangun to reflect on the remarkable journey of Indonesia-China relations over the past 75 years.展开更多
In recent years,with the rapid development of deep learning technology,relational triplet extraction techniques have also achieved groundbreaking progress.Traditional pipeline models have certain limitations due to er...In recent years,with the rapid development of deep learning technology,relational triplet extraction techniques have also achieved groundbreaking progress.Traditional pipeline models have certain limitations due to error propagation.To overcome the limitations of traditional pipeline models,recent research has focused on jointly modeling the two key subtasks-named entity recognition and relation extraction-within a unified framework.To support future research,this paper provides a comprehensive review of recently published studies in the field of relational triplet extraction.The review examines commonly used public datasets for relational triplet extraction techniques and systematically reviews current mainstream joint extraction methods,including joint decoding methods and parameter sharing methods,with joint decoding methods further divided into table filling,tagging,and sequence-to-sequence approaches.In addition,this paper also conducts small-scale replication experiments on models that have performed well in recent years for each method to verify the reproducibility of the code and to compare the performance of different models under uniform conditions.Each method has its own advantages in terms of model design,task handling,and application scenarios,but also faces challenges such as processing complex sentence structures,cross-sentence relation extraction,and adaptability in low-resource environments.Finally,this paper systematically summarizes each method and discusses the future development prospects of joint extraction of relational triples.展开更多
The year 2025 marks the 50th anniversary of diplomatic relations between China and the Philippines.Over the past half century,despite ups and downs,China-Philippines relations have maintained a steady momentum of deve...The year 2025 marks the 50th anniversary of diplomatic relations between China and the Philippines.Over the past half century,despite ups and downs,China-Philippines relations have maintained a steady momentum of development with continuous progress in political,economic,and cultural exchanges.Since the second half of 2023,the Philippines’freguent provocations in the South China Sea have negatively impacted bilateral relations.展开更多
If there were any doubts about the Trump administration’s inherent obsession with decoupling,polarization and jolting of global trade,they can be laid to rest.A series of statements and measures have been made by the...If there were any doubts about the Trump administration’s inherent obsession with decoupling,polarization and jolting of global trade,they can be laid to rest.A series of statements and measures have been made by the U.S.government on economic relations with China including administrative decision to hit Chinese goods with additional tariffs despite China.展开更多
This year marks the 50th anniversary of the establishment of diplomatic relations between China and the EU.Over half a century,China and the EU have steadily enhanced political mutual trust,deepened mutually beneficia...This year marks the 50th anniversary of the establishment of diplomatic relations between China and the EU.Over half a century,China and the EU have steadily enhanced political mutual trust,deepened mutually beneficial cooperation,and firmly upheld multilateralism,bringing tangible benefits to both peoples and making a significant contribution to global peace and development.展开更多
As Satellite Frequency and Orbit(SFO)constitute scarce natural resources,constructing a Satellite Frequency and Orbit Knowledge Graph(SFO-KG)becomes crucial for optimizing their utilization.In the process of building ...As Satellite Frequency and Orbit(SFO)constitute scarce natural resources,constructing a Satellite Frequency and Orbit Knowledge Graph(SFO-KG)becomes crucial for optimizing their utilization.In the process of building the SFO-KG from Chinese unstructured data,extracting Chinese entity relations is the fundamental step.Although Relation Extraction(RE)methods in the English field have been extensively studied and developed earlier than their Chinese counterparts,their direct application to Chinese texts faces significant challenges due to linguistic distinctions such as unique grammar,pictographic characters,and prevalent polysemy.The absence of comprehensive reviews on Chinese RE research progress necessitates a systematic investigation.A thorough review of Chinese RE has been conducted from four methodological approaches:pipeline RE,joint entityrelation extraction,open domain RE,and multimodal RE techniques.In addition,we further analyze the essential research infrastructure,including specialized datasets,evaluation benchmarks,and competitions within Chinese RE research.Finally,the current research challenges and development trends in the field of Chinese RE were summarized and analyzed from the perspectives of ecological construction methods for datasets,open domain RE,N-ary RE,and RE based on large language models.This comprehensive review aims to facilitate SFO-KG construction and its practical applications in SFO resource management.展开更多
Green development is vital for bringing about high-quality development,which makes measuring and comparing provincial green development levels essential.This study calculates the comprehensive green development scores...Green development is vital for bringing about high-quality development,which makes measuring and comparing provincial green development levels essential.This study calculates the comprehensive green development scores using panel data from 30 Chinese provinces and autonomous regions(2013-2022)and a combined subjective-objective weighting method.It also innovatively establishes a grey relational degree matrix and a grey improvement sequence to analyze provincial similarities and identify benchmarks for improvement.The results indicate that ecological and environmental protection holds the highest weight among the primary indicators.Beijing,Shanghai,Tianjin,Zhejiang,and Jiangsu lead in green development,with Shanghai,Beijing,and Tianjin exhibiting distinct development trajectories,while Guizhou and Yunnan share a similar trend.Zhejiang and Shaanxi have prominent benchmarks for improvement,while some provinces dynamically adjust their targets.The results suggest that advanced regions should further refine their green development pathways to align with their specific contexts,while less-developed regions should adaptively learn from the appropriate benchmarks and periodically reassess their strategies.This study provides scientific guidance for regional green development planning,policymaking,and benchmarking,thus contributing to sustainable regional development.Furthermore,it lays a foundation for future research to expand into broader datasets,scales,influencing factors,and policy evaluations.展开更多
Existing Transformer-based image captioning models typically rely on the self-attention mechanism to capture long-range dependencies,which effectively extracts and leverages the global correlation of image features.Ho...Existing Transformer-based image captioning models typically rely on the self-attention mechanism to capture long-range dependencies,which effectively extracts and leverages the global correlation of image features.However,these models still face challenges in effectively capturing local associations.Moreover,since the encoder extracts global and local association features that focus on different semantic information,semantic noise may occur during the decoding stage.To address these issues,we propose the Local Relationship Enhanced Gated Transformer(LREGT).In the encoder part,we introduce the Local Relationship Enhanced Encoder(LREE),whose core component is the Local Relationship Enhanced Module(LREM).LREM consists of two novel designs:the Local Correlation Perception Module(LCPM)and the Local-Global Fusion Module(LGFM),which are beneficial for generating a comprehensive feature representation that integrates both global and local information.In the decoder part,we propose the Dual-level Multi-branch Gated Decoder(DMGD).It first creates multiple decoding branches to generate multi-perspective contextual feature representations.Subsequently,it employs the Dual-Level Gating Mechanism(DLGM)to model the multi-level relationships of these multi-perspective contextual features,enhancing their fine-grained semantics and intrinsic relationship representations.This ultimately leads to the generation of high-quality and semantically rich image captions.Experiments on the standard MSCOCO dataset demonstrate that LREGT achieves state-of-the-art performance,with a CIDEr score of 140.8 and BLEU-4 score of 41.3,significantly outperforming existing mainstream methods.These results highlight LREGT’s superiority in capturing complex visual relationships and resolving semantic noise during decoding.展开更多
On May 6,2025,President Xi Jinping exchanged congratulatory messages with European Council President Antonio Costa and European Commission President Ursula von der Leyen to celebrate the 50th anniversary of the establ...On May 6,2025,President Xi Jinping exchanged congratulatory messages with European Council President Antonio Costa and European Commission President Ursula von der Leyen to celebrate the 50th anniversary of the establishment of diplomatic relations between China and the EU.President Xi noted in his message that China and the EU are comprehensive strategic partners,two major forces promoting multi-polarization,two major markets supporting globalization and two major civilizations advocating diversity.展开更多
In the era of exponential growth of digital information,recommender algorithms are vital for helping users navigate vast data to find relevant items.Traditional approaches such as collaborative filtering and contentba...In the era of exponential growth of digital information,recommender algorithms are vital for helping users navigate vast data to find relevant items.Traditional approaches such as collaborative filtering and contentbasedmethods have limitations in capturing complex,multi-faceted relationships in large-scale,sparse datasets.Recent advances in Graph Neural Networks(GNNs)have significantly improved recommendation performance by modeling high-order connection patterns within user-item interaction networks.However,existing GNN-based models like LightGCN and NGCF focus primarily on single-type interactions and often overlook diverse semantic relationships,leading to reduced recommendation diversity and limited generalization.To address these challenges,this paper proposes a dual multi-relational graph neural network recommendation algorithm based on relational interactions.Our approach constructs two complementary graph structures:a User-Item Interaction Graph(UIIG),which explicitly models direct user behaviors such as clicks and purchases,and a Relational Association Graph(RAG),which uncovers latent associations based on user similarities and item attributes.The proposed Dual Multi-relational Graph Neural Network(DMGNN)features two parallel branches that perform multi-layer graph convolutional operations,followed by an adaptive fusion mechanism to effectively integrate information from both graphs.This design enhances the model’s capacity to capture diverse relationship types and complex relational patterns.Extensive experiments conducted on benchmark datasets—including MovieLens-1M,Amazon-Electronics,and Yelp—demonstrate thatDMGNN outperforms state-of-the-art baselines,achieving improvements of up to 12.3%in Precision,9.7%in Recall,and 11.5%in F1 score.Moreover,DMGNN significantly boosts recommendation diversity by 15.2%,balancing accuracy with exploration.These results highlight the effectiveness of leveraging hierarchical multi-relational information,offering a promising solution to the challenges of data sparsity and relation heterogeneity in recommendation systems.Our work advances the theoretical understanding of multi-relational graph modeling and presents practical insights for developing more personalized,diverse,and robust recommender systems.展开更多
Because SQL for querying data from spatial databa se s is ineffective, the query based on natural or visual language becomes an attra ctive research field gradually. However, how to define and represent natural lan gu...Because SQL for querying data from spatial databa se s is ineffective, the query based on natural or visual language becomes an attra ctive research field gradually. However, how to define and represent natural lan guages related to spatial data are still gigantic problems. Because existing mod els of direction relations can’t describe by use of some common concepts. First of all, detailed direction relations are proposed to describe the directions re lated to the interior of spatial objects, such as "east part of a region","ea st boundary of a region", and so on. Secondly, by integrating the detailed dire ctions with exterior direction relations and topological relations, several NLSR s are defined, such as "a road goes across the east part of a lake", "a river goes along the east boundary of a province", etc. Finally, based on the NLSRs abovementioned, a natural spatial query language (NSQL) is formed to retrieve da ta from spatial databases.展开更多
Given the scarcity of Satellite Frequency and Orbit(SFO)resources,it holds paramount importance to establish a comprehensive knowledge graph of SFO field(SFO-KG)and employ knowledge reasoning technology to automatical...Given the scarcity of Satellite Frequency and Orbit(SFO)resources,it holds paramount importance to establish a comprehensive knowledge graph of SFO field(SFO-KG)and employ knowledge reasoning technology to automatically mine available SFO resources.An essential aspect of constructing SFO-KG is the extraction of Chinese entity relations.Unfortunately,there is currently no publicly available Chinese SFO entity Relation Extraction(RE)dataset.Moreover,publicly available SFO text data contain numerous NA(representing for“No Answer”)relation category sentences that resemble other relation sentences and pose challenges in accurate classification,resulting in low recall and precision for the NA relation category in entity RE.Consequently,this issue adversely affects both the accuracy of constructing the knowledge graph and the efficiency of RE processes.To address these challenges,this paper proposes a method for extracting Chinese SFO text entity relations based on dynamic integrated learning.This method includes the construction of a manually annotated Chinese SFO entity RE dataset and a classifier combining features of SFO resource data.The proposed approach combines integrated learning and pre-training models,specifically utilizing Bidirectional Encoder Representation from Transformers(BERT).In addition,it incorporates one-class classification,attention mechanisms,and dynamic feedback mechanisms to improve the performance of the RE model.Experimental results show that the proposed method outperforms the traditional methods in terms of F1 value when extracting entity relations from both balanced and long-tailed datasets.展开更多
During Donald Trump’s first term,the“Trump Shock”brought world politics into an era of uncertainties and pulled the transatlantic alliance down to its lowest point in history.The Trump 2.0 tsunami brewed by the 202...During Donald Trump’s first term,the“Trump Shock”brought world politics into an era of uncertainties and pulled the transatlantic alliance down to its lowest point in history.The Trump 2.0 tsunami brewed by the 2024 presidential election of the United States has plunged the U.S.-Europe relations into more gloomy waters,ushering in a more complex and turbulent period of adjustment.展开更多
Multi-view clustering is a critical research area in computer science aimed at effectively extracting meaningful patterns from complex,high-dimensional data that single-view methods cannot capture.Traditional fuzzy cl...Multi-view clustering is a critical research area in computer science aimed at effectively extracting meaningful patterns from complex,high-dimensional data that single-view methods cannot capture.Traditional fuzzy clustering techniques,such as Fuzzy C-Means(FCM),face significant challenges in handling uncertainty and the dependencies between different views.To overcome these limitations,we introduce a new multi-view fuzzy clustering approach that integrates picture fuzzy sets with a dual-anchor graph method for multi-view data,aiming to enhance clustering accuracy and robustness,termed Multi-view Picture Fuzzy Clustering(MPFC).In particular,the picture fuzzy set theory extends the capability to represent uncertainty by modeling three membership levels:membership degrees,neutral degrees,and refusal degrees.This allows for a more flexible representation of uncertain and conflicting data than traditional fuzzy models.Meanwhile,dual-anchor graphs exploit the similarity relationships between data points and integrate information across views.This combination improves stability,scalability,and robustness when handling noisy and heterogeneous data.Experimental results on several benchmark datasets demonstrate significant improvements in clustering accuracy and efficiency,outperforming traditional methods.Specifically,the MPFC algorithm demonstrates outstanding clustering performance on a variety of datasets,attaining a Purity(PUR)score of 0.6440 and an Accuracy(ACC)score of 0.6213 for the 3 Sources dataset,underscoring its robustness and efficiency.The proposed approach significantly contributes to fields such as pattern recognition,multi-view relational data analysis,and large-scale clustering problems.Future work will focus on extending the method for semi-supervised multi-view clustering,aiming to enhance adaptability,scalability,and performance in real-world applications.展开更多
Collecting amounts of distorted/clean image pairs in the real world is non-trivial,which severely limits the practical application of these supervised learning-based methods to real-world image super-resolution(RealSR...Collecting amounts of distorted/clean image pairs in the real world is non-trivial,which severely limits the practical application of these supervised learning-based methods to real-world image super-resolution(RealSR).Previous works usually address this problem by leveraging unsupervised learning-based technologies to alleviate the dependency on paired training samples.However,these methods typically suffer from unsatisfactory texture synthesis due to the lack of supervision of clean images.To overcome this problem,we are the first to take a close look at the under-explored direction for RealSR,i.e.,few-shot real-world image super-resolution,which aims to tackle the challenging RealSR problem with few-shot distorted/clean image pairs.Under this brand-new scenario,we propose distortion relation guided transfer learning(DRTL)for the few-shot RealSR by transferring the rich restoration knowledge from auxiliary distortions(i.e.,synthetic distortions)to the target RealSR under the guidance of the distortion relation.Concretely,DRTL builds a knowledge graph to capture the distortion relation between auxiliary distortions and target distortion(i.e.,real distortions in RealSR).Based on the distortion relation,DRTL adopts a gradient reweighting strategy to guide the knowledge transfer process between auxiliary distortions and target distortions.In this way,DRTL is able to quickly learn the most relevant knowledge from the synthetic distortions for the target distortion.We instantiate DRTL with two commonly-used transfer learning paradigms,including pretraining and meta-learning pipelines,to realize a distortion relation-aware few-shot RealSR.Extensive experiments on multiple benchmarks and thorough ablation studies demonstrate the effectiveness of our DRTL.展开更多
Metastable β titanium alloy is an ideal material for lightweight and high strength due to its excellent comprehensive mechanical properties.However,overcoming the trade-off relation between strength and ductility rem...Metastable β titanium alloy is an ideal material for lightweight and high strength due to its excellent comprehensive mechanical properties.However,overcoming the trade-off relation between strength and ductility remains a significant challenge.In this study,the mechanical properties of Ti-38644 alloy were optimized by introducing a heterogeneous bi-grain bi-lamella(BG-BL)structure through a well-designed combination of rolling,drawing and heat treatment.The results demonstrate that the present BG-BL Ti-38644 alloy shows a tensile strength of~1500 MPa and a total elongation of 18%.In particular,the high strength-elongation combination of the BG-BL Ti-38644 alloy breakthroughs the trade-off relation in all the titanium alloys available.The recrystallized grains with low dislocation enhance the ductility of the Ti-38644 alloy,while the highly distorted elongated grains mainly contribute to the high strength.The present study provides a new principle for designing Ti alloys with superior strength and ductility.展开更多
Thyroid nodules,a common disorder in the endocrine system,require accurate segmentation in ultrasound images for effective diagnosis and treatment.However,achieving precise segmentation remains a challenge due to vari...Thyroid nodules,a common disorder in the endocrine system,require accurate segmentation in ultrasound images for effective diagnosis and treatment.However,achieving precise segmentation remains a challenge due to various factors,including scattering noise,low contrast,and limited resolution in ultrasound images.Although existing segmentation models have made progress,they still suffer from several limitations,such as high error rates,low generalizability,overfitting,limited feature learning capability,etc.To address these challenges,this paper proposes a Multi-level Relation Transformer-based U-Net(MLRT-UNet)to improve thyroid nodule segmentation.The MLRTUNet leverages a novel Relation Transformer,which processes images at multiple scales,overcoming the limitations of traditional encoding methods.This transformer integrates both local and global features effectively through selfattention and cross-attention units,capturing intricate relationships within the data.The approach also introduces a Co-operative Transformer Fusion(CTF)module to combine multi-scale features from different encoding layers,enhancing the model’s ability to capture complex patterns in the data.Furthermore,the Relation Transformer block enhances long-distance dependencies during the decoding process,improving segmentation accuracy.Experimental results showthat the MLRT-UNet achieves high segmentation accuracy,reaching 98.2% on the Digital Database Thyroid Image(DDT)dataset,97.8% on the Thyroid Nodule 3493(TG3K)dataset,and 98.2% on the Thyroid Nodule3K(TN3K)dataset.These findings demonstrate that the proposed method significantly enhances the accuracy of thyroid nodule segmentation,addressing the limitations of existing models.展开更多
Understanding the relationship between sediment and discharge is crucial for effective river management and water-sediment modeling,especially in the Brahmaputra River,one of the large transboundary rivers with high s...Understanding the relationship between sediment and discharge is crucial for effective river management and water-sediment modeling,especially in the Brahmaputra River,one of the large transboundary rivers with high sediment and discharge variability in South Asia.Current knowledge of sediment-water relations is constrained by limited data,hindering effective transboundary river management.Using multivariate linear regression,climate elasticity coefficient,and traditional sediment rating curve,this study is designed to compare the sediment-water relations of the upstream(Nuxia)and the downstream(Bahadurabad).The results reveal significant variability between the two stations.In the upstream Nuxia,the simulation strongly correlates with observed suspended sediment load(SSL)and discharge(Q)(Pearson's r of 0.62 and 0.68,respectively).Conversely,at downstream Bahadurabad,weaker correlations(r=0.31 for sediment and r=0.51 for discharge simulation)indicate a reduced relation.This contrast reflects the non-linear nature of sediment-discharge coupling along the river continuum,shaped by both climatic and anthropogenic influences.Elasticity(ε)analysis highlights the dominant role of precipitation in shaping sediment-water dynamics(εP-SSL=2.53,εP-Q=1.01)at Nuxia,while Bahadurabad(εP-SSL=0.41,εP-Q=0.82)reflects a reduced sensitivity,possibly due to sediment retention along the floodplain.Air temperature elasticity(εT-SSL,-0.15&-3.06 at Nuxia and Bahadurabad,respectively)reveals contrasting impacts,strongly negatively influencing sediment transport at Bahadurabad.These findings highlight the significance of spatial variability and climatic influences on sediment dynamics,underscoring the necessity for site-specific management strategies.The sediment rating curve(SRC)analysis reveals a strong relationship between sediment and discharge(R^(2)=0.88)at Nuxia and a relatively weaker relationship(R^(2)=0.14)at Bahadurabad,which demonstrates a lower sedimentdischarge coupling that could be affected by downstream factors such as sediment deposition,channel morphology,and anthropogenic activities.This study offers valuable insights into sediment-water dynamics,highlighting the importance of understanding nonlinear relationships in the Brahmaputra River.展开更多
Entity relation extraction,a fundamental and essential task in natural language processing(NLP),has garnered significant attention over an extended period.,aiming to extract the core of semantic knowledge from unstruc...Entity relation extraction,a fundamental and essential task in natural language processing(NLP),has garnered significant attention over an extended period.,aiming to extract the core of semantic knowledge from unstructured text,i.e.,entities and the relations between them.At present,the main dilemma of Chinese entity relation extraction research lies in nested entities,relation overlap,and lack of entity relation interaction.This dilemma is particularly prominent in complex knowledge extraction tasks with high-density knowledge,imprecise syntactic structure,and lack of semantic roles.To address these challenges,this paper presents an innovative“character-level”Chinese part-of-speech(CN-POS)tagging approach and incorporates part-of-speech(POS)information into the pre-trained model,aiming to improve its semantic understanding and syntactic information processing capabilities.Additionally,A relation reference filling mechanism(RF)is proposed to enhance the semantic interaction between relations and entities,utilize relations to guide entity modeling,improve the boundary prediction ability of entity models for nested entity phenomena,and increase the cascading accuracy of entity-relation triples.Meanwhile,the“Queue”sub-task connection strategy is adopted to alleviate triplet cascading errors caused by overlapping relations,and a Syntax-enhanced entity relation extraction model(SE-RE)is constructed.The model showed excellent performance on the self-constructed E-commerce Product Information dataset(EPI)in this article.The results demonstrate that integrating POS enhancement into the pre-trained encoding model significantly boosts the performance of entity relation extraction models compared to baseline methods.Specifically,the F1-score fluctuation in subtasks caused by error accumulation was reduced by 3.21%,while the F1-score for entity-relation triplet extraction improved by 1.91%.展开更多
文摘I am delighted to join my Thai and Chinese friends in celebrating the 50th anniversary of the establishment of diplomatic relations between our two great nations.It is the“Golden Jubilee”of Thailand-China friendship.Half a century ago when Thailand’s then Prime Minister M.R.Kukrit Pramoj and China’s Premier Zhou Enlai signed a Joint Communique on the Establishment of Diplomatic Relations on July 1,1975,the foundation for a profound and mutually beneficial partnership was laid,fostering enduring friendship,cooperation,and understanding between our two nations.This bond has stood the test of time amidst the ever-changing international landscape and been further enhanced by both countries’commitment to advancing the Comprehensive Strategic Cooperative Partnership and building a Thailand-China community with a shared future for enhanced stability,prosperity,and sustainability through a forwardlooking and people-centered vision.
文摘The year 2025 marks the 75th anniversary of the establishment of diplomatic relations between China and Indonesia.Over the past 75 years,the bilateral relationship has made significant progress across various fields,laying a solid foundation for regional stability.China Report ASEAN interviewed Indonesian Ambassador to China Djauhari Oratmangun to reflect on the remarkable journey of Indonesia-China relations over the past 75 years.
基金funding from Key Areas Science and Technology Research Plan of Xinjiang Production And Construction Corps Financial Science and Technology Plan Project under Grant Agreement No.2023AB048 for the project:Research and Application Demonstration of Data-driven Elderly Care System.
文摘In recent years,with the rapid development of deep learning technology,relational triplet extraction techniques have also achieved groundbreaking progress.Traditional pipeline models have certain limitations due to error propagation.To overcome the limitations of traditional pipeline models,recent research has focused on jointly modeling the two key subtasks-named entity recognition and relation extraction-within a unified framework.To support future research,this paper provides a comprehensive review of recently published studies in the field of relational triplet extraction.The review examines commonly used public datasets for relational triplet extraction techniques and systematically reviews current mainstream joint extraction methods,including joint decoding methods and parameter sharing methods,with joint decoding methods further divided into table filling,tagging,and sequence-to-sequence approaches.In addition,this paper also conducts small-scale replication experiments on models that have performed well in recent years for each method to verify the reproducibility of the code and to compare the performance of different models under uniform conditions.Each method has its own advantages in terms of model design,task handling,and application scenarios,but also faces challenges such as processing complex sentence structures,cross-sentence relation extraction,and adaptability in low-resource environments.Finally,this paper systematically summarizes each method and discusses the future development prospects of joint extraction of relational triples.
文摘The year 2025 marks the 50th anniversary of diplomatic relations between China and the Philippines.Over the past half century,despite ups and downs,China-Philippines relations have maintained a steady momentum of development with continuous progress in political,economic,and cultural exchanges.Since the second half of 2023,the Philippines’freguent provocations in the South China Sea have negatively impacted bilateral relations.
文摘If there were any doubts about the Trump administration’s inherent obsession with decoupling,polarization and jolting of global trade,they can be laid to rest.A series of statements and measures have been made by the U.S.government on economic relations with China including administrative decision to hit Chinese goods with additional tariffs despite China.
文摘This year marks the 50th anniversary of the establishment of diplomatic relations between China and the EU.Over half a century,China and the EU have steadily enhanced political mutual trust,deepened mutually beneficial cooperation,and firmly upheld multilateralism,bringing tangible benefits to both peoples and making a significant contribution to global peace and development.
文摘As Satellite Frequency and Orbit(SFO)constitute scarce natural resources,constructing a Satellite Frequency and Orbit Knowledge Graph(SFO-KG)becomes crucial for optimizing their utilization.In the process of building the SFO-KG from Chinese unstructured data,extracting Chinese entity relations is the fundamental step.Although Relation Extraction(RE)methods in the English field have been extensively studied and developed earlier than their Chinese counterparts,their direct application to Chinese texts faces significant challenges due to linguistic distinctions such as unique grammar,pictographic characters,and prevalent polysemy.The absence of comprehensive reviews on Chinese RE research progress necessitates a systematic investigation.A thorough review of Chinese RE has been conducted from four methodological approaches:pipeline RE,joint entityrelation extraction,open domain RE,and multimodal RE techniques.In addition,we further analyze the essential research infrastructure,including specialized datasets,evaluation benchmarks,and competitions within Chinese RE research.Finally,the current research challenges and development trends in the field of Chinese RE were summarized and analyzed from the perspectives of ecological construction methods for datasets,open domain RE,N-ary RE,and RE based on large language models.This comprehensive review aims to facilitate SFO-KG construction and its practical applications in SFO resource management.
文摘Green development is vital for bringing about high-quality development,which makes measuring and comparing provincial green development levels essential.This study calculates the comprehensive green development scores using panel data from 30 Chinese provinces and autonomous regions(2013-2022)and a combined subjective-objective weighting method.It also innovatively establishes a grey relational degree matrix and a grey improvement sequence to analyze provincial similarities and identify benchmarks for improvement.The results indicate that ecological and environmental protection holds the highest weight among the primary indicators.Beijing,Shanghai,Tianjin,Zhejiang,and Jiangsu lead in green development,with Shanghai,Beijing,and Tianjin exhibiting distinct development trajectories,while Guizhou and Yunnan share a similar trend.Zhejiang and Shaanxi have prominent benchmarks for improvement,while some provinces dynamically adjust their targets.The results suggest that advanced regions should further refine their green development pathways to align with their specific contexts,while less-developed regions should adaptively learn from the appropriate benchmarks and periodically reassess their strategies.This study provides scientific guidance for regional green development planning,policymaking,and benchmarking,thus contributing to sustainable regional development.Furthermore,it lays a foundation for future research to expand into broader datasets,scales,influencing factors,and policy evaluations.
基金supported by the Natural Science Foundation of China(62473105,62172118)Nature Science Key Foundation of Guangxi(2021GXNSFDA196002)+1 种基金in part by the Guangxi Key Laboratory of Image and Graphic Intelligent Processing under Grants(GIIP2302,GIIP2303,GIIP2304)Innovation Project of Guang Xi Graduate Education(2024YCXB09,2024YCXS039).
文摘Existing Transformer-based image captioning models typically rely on the self-attention mechanism to capture long-range dependencies,which effectively extracts and leverages the global correlation of image features.However,these models still face challenges in effectively capturing local associations.Moreover,since the encoder extracts global and local association features that focus on different semantic information,semantic noise may occur during the decoding stage.To address these issues,we propose the Local Relationship Enhanced Gated Transformer(LREGT).In the encoder part,we introduce the Local Relationship Enhanced Encoder(LREE),whose core component is the Local Relationship Enhanced Module(LREM).LREM consists of two novel designs:the Local Correlation Perception Module(LCPM)and the Local-Global Fusion Module(LGFM),which are beneficial for generating a comprehensive feature representation that integrates both global and local information.In the decoder part,we propose the Dual-level Multi-branch Gated Decoder(DMGD).It first creates multiple decoding branches to generate multi-perspective contextual feature representations.Subsequently,it employs the Dual-Level Gating Mechanism(DLGM)to model the multi-level relationships of these multi-perspective contextual features,enhancing their fine-grained semantics and intrinsic relationship representations.This ultimately leads to the generation of high-quality and semantically rich image captions.Experiments on the standard MSCOCO dataset demonstrate that LREGT achieves state-of-the-art performance,with a CIDEr score of 140.8 and BLEU-4 score of 41.3,significantly outperforming existing mainstream methods.These results highlight LREGT’s superiority in capturing complex visual relationships and resolving semantic noise during decoding.
文摘On May 6,2025,President Xi Jinping exchanged congratulatory messages with European Council President Antonio Costa and European Commission President Ursula von der Leyen to celebrate the 50th anniversary of the establishment of diplomatic relations between China and the EU.President Xi noted in his message that China and the EU are comprehensive strategic partners,two major forces promoting multi-polarization,two major markets supporting globalization and two major civilizations advocating diversity.
文摘In the era of exponential growth of digital information,recommender algorithms are vital for helping users navigate vast data to find relevant items.Traditional approaches such as collaborative filtering and contentbasedmethods have limitations in capturing complex,multi-faceted relationships in large-scale,sparse datasets.Recent advances in Graph Neural Networks(GNNs)have significantly improved recommendation performance by modeling high-order connection patterns within user-item interaction networks.However,existing GNN-based models like LightGCN and NGCF focus primarily on single-type interactions and often overlook diverse semantic relationships,leading to reduced recommendation diversity and limited generalization.To address these challenges,this paper proposes a dual multi-relational graph neural network recommendation algorithm based on relational interactions.Our approach constructs two complementary graph structures:a User-Item Interaction Graph(UIIG),which explicitly models direct user behaviors such as clicks and purchases,and a Relational Association Graph(RAG),which uncovers latent associations based on user similarities and item attributes.The proposed Dual Multi-relational Graph Neural Network(DMGNN)features two parallel branches that perform multi-layer graph convolutional operations,followed by an adaptive fusion mechanism to effectively integrate information from both graphs.This design enhances the model’s capacity to capture diverse relationship types and complex relational patterns.Extensive experiments conducted on benchmark datasets—including MovieLens-1M,Amazon-Electronics,and Yelp—demonstrate thatDMGNN outperforms state-of-the-art baselines,achieving improvements of up to 12.3%in Precision,9.7%in Recall,and 11.5%in F1 score.Moreover,DMGNN significantly boosts recommendation diversity by 15.2%,balancing accuracy with exploration.These results highlight the effectiveness of leveraging hierarchical multi-relational information,offering a promising solution to the challenges of data sparsity and relation heterogeneity in recommendation systems.Our work advances the theoretical understanding of multi-relational graph modeling and presents practical insights for developing more personalized,diverse,and robust recommender systems.
文摘Because SQL for querying data from spatial databa se s is ineffective, the query based on natural or visual language becomes an attra ctive research field gradually. However, how to define and represent natural lan guages related to spatial data are still gigantic problems. Because existing mod els of direction relations can’t describe by use of some common concepts. First of all, detailed direction relations are proposed to describe the directions re lated to the interior of spatial objects, such as "east part of a region","ea st boundary of a region", and so on. Secondly, by integrating the detailed dire ctions with exterior direction relations and topological relations, several NLSR s are defined, such as "a road goes across the east part of a lake", "a river goes along the east boundary of a province", etc. Finally, based on the NLSRs abovementioned, a natural spatial query language (NSQL) is formed to retrieve da ta from spatial databases.
文摘Given the scarcity of Satellite Frequency and Orbit(SFO)resources,it holds paramount importance to establish a comprehensive knowledge graph of SFO field(SFO-KG)and employ knowledge reasoning technology to automatically mine available SFO resources.An essential aspect of constructing SFO-KG is the extraction of Chinese entity relations.Unfortunately,there is currently no publicly available Chinese SFO entity Relation Extraction(RE)dataset.Moreover,publicly available SFO text data contain numerous NA(representing for“No Answer”)relation category sentences that resemble other relation sentences and pose challenges in accurate classification,resulting in low recall and precision for the NA relation category in entity RE.Consequently,this issue adversely affects both the accuracy of constructing the knowledge graph and the efficiency of RE processes.To address these challenges,this paper proposes a method for extracting Chinese SFO text entity relations based on dynamic integrated learning.This method includes the construction of a manually annotated Chinese SFO entity RE dataset and a classifier combining features of SFO resource data.The proposed approach combines integrated learning and pre-training models,specifically utilizing Bidirectional Encoder Representation from Transformers(BERT).In addition,it incorporates one-class classification,attention mechanisms,and dynamic feedback mechanisms to improve the performance of the RE model.Experimental results show that the proposed method outperforms the traditional methods in terms of F1 value when extracting entity relations from both balanced and long-tailed datasets.
文摘During Donald Trump’s first term,the“Trump Shock”brought world politics into an era of uncertainties and pulled the transatlantic alliance down to its lowest point in history.The Trump 2.0 tsunami brewed by the 2024 presidential election of the United States has plunged the U.S.-Europe relations into more gloomy waters,ushering in a more complex and turbulent period of adjustment.
基金funded by the Research Project:THTETN.05/24-25,VietnamAcademy of Science and Technology.
文摘Multi-view clustering is a critical research area in computer science aimed at effectively extracting meaningful patterns from complex,high-dimensional data that single-view methods cannot capture.Traditional fuzzy clustering techniques,such as Fuzzy C-Means(FCM),face significant challenges in handling uncertainty and the dependencies between different views.To overcome these limitations,we introduce a new multi-view fuzzy clustering approach that integrates picture fuzzy sets with a dual-anchor graph method for multi-view data,aiming to enhance clustering accuracy and robustness,termed Multi-view Picture Fuzzy Clustering(MPFC).In particular,the picture fuzzy set theory extends the capability to represent uncertainty by modeling three membership levels:membership degrees,neutral degrees,and refusal degrees.This allows for a more flexible representation of uncertain and conflicting data than traditional fuzzy models.Meanwhile,dual-anchor graphs exploit the similarity relationships between data points and integrate information across views.This combination improves stability,scalability,and robustness when handling noisy and heterogeneous data.Experimental results on several benchmark datasets demonstrate significant improvements in clustering accuracy and efficiency,outperforming traditional methods.Specifically,the MPFC algorithm demonstrates outstanding clustering performance on a variety of datasets,attaining a Purity(PUR)score of 0.6440 and an Accuracy(ACC)score of 0.6213 for the 3 Sources dataset,underscoring its robustness and efficiency.The proposed approach significantly contributes to fields such as pattern recognition,multi-view relational data analysis,and large-scale clustering problems.Future work will focus on extending the method for semi-supervised multi-view clustering,aiming to enhance adaptability,scalability,and performance in real-world applications.
基金supported by the National Natural Science Foundation of China(623B2098,62021001,62371434)the Postdoctoral Fellowship Program of CPSF(GZC20252293)+1 种基金the China Postdoctoral Science Foundation–Anhui Joint Support Program(2024T017AH)Anhui Postdoctoral Scientific Research Program Foundation(2025A1015).
文摘Collecting amounts of distorted/clean image pairs in the real world is non-trivial,which severely limits the practical application of these supervised learning-based methods to real-world image super-resolution(RealSR).Previous works usually address this problem by leveraging unsupervised learning-based technologies to alleviate the dependency on paired training samples.However,these methods typically suffer from unsatisfactory texture synthesis due to the lack of supervision of clean images.To overcome this problem,we are the first to take a close look at the under-explored direction for RealSR,i.e.,few-shot real-world image super-resolution,which aims to tackle the challenging RealSR problem with few-shot distorted/clean image pairs.Under this brand-new scenario,we propose distortion relation guided transfer learning(DRTL)for the few-shot RealSR by transferring the rich restoration knowledge from auxiliary distortions(i.e.,synthetic distortions)to the target RealSR under the guidance of the distortion relation.Concretely,DRTL builds a knowledge graph to capture the distortion relation between auxiliary distortions and target distortion(i.e.,real distortions in RealSR).Based on the distortion relation,DRTL adopts a gradient reweighting strategy to guide the knowledge transfer process between auxiliary distortions and target distortions.In this way,DRTL is able to quickly learn the most relevant knowledge from the synthetic distortions for the target distortion.We instantiate DRTL with two commonly-used transfer learning paradigms,including pretraining and meta-learning pipelines,to realize a distortion relation-aware few-shot RealSR.Extensive experiments on multiple benchmarks and thorough ablation studies demonstrate the effectiveness of our DRTL.
基金financially supported by the National Natural Science Foundation of China(Nos.52321001,52322105,52130002,U2241245,52261135634 and 52371084)the Youth Innovation Promotion Association(CAS,No.2021192)the IMR Innovation Fund(No.2023-ZD01).
文摘Metastable β titanium alloy is an ideal material for lightweight and high strength due to its excellent comprehensive mechanical properties.However,overcoming the trade-off relation between strength and ductility remains a significant challenge.In this study,the mechanical properties of Ti-38644 alloy were optimized by introducing a heterogeneous bi-grain bi-lamella(BG-BL)structure through a well-designed combination of rolling,drawing and heat treatment.The results demonstrate that the present BG-BL Ti-38644 alloy shows a tensile strength of~1500 MPa and a total elongation of 18%.In particular,the high strength-elongation combination of the BG-BL Ti-38644 alloy breakthroughs the trade-off relation in all the titanium alloys available.The recrystallized grains with low dislocation enhance the ductility of the Ti-38644 alloy,while the highly distorted elongated grains mainly contribute to the high strength.The present study provides a new principle for designing Ti alloys with superior strength and ductility.
文摘Thyroid nodules,a common disorder in the endocrine system,require accurate segmentation in ultrasound images for effective diagnosis and treatment.However,achieving precise segmentation remains a challenge due to various factors,including scattering noise,low contrast,and limited resolution in ultrasound images.Although existing segmentation models have made progress,they still suffer from several limitations,such as high error rates,low generalizability,overfitting,limited feature learning capability,etc.To address these challenges,this paper proposes a Multi-level Relation Transformer-based U-Net(MLRT-UNet)to improve thyroid nodule segmentation.The MLRTUNet leverages a novel Relation Transformer,which processes images at multiple scales,overcoming the limitations of traditional encoding methods.This transformer integrates both local and global features effectively through selfattention and cross-attention units,capturing intricate relationships within the data.The approach also introduces a Co-operative Transformer Fusion(CTF)module to combine multi-scale features from different encoding layers,enhancing the model’s ability to capture complex patterns in the data.Furthermore,the Relation Transformer block enhances long-distance dependencies during the decoding process,improving segmentation accuracy.Experimental results showthat the MLRT-UNet achieves high segmentation accuracy,reaching 98.2% on the Digital Database Thyroid Image(DDT)dataset,97.8% on the Thyroid Nodule 3493(TG3K)dataset,and 98.2% on the Thyroid Nodule3K(TN3K)dataset.These findings demonstrate that the proposed method significantly enhances the accuracy of thyroid nodule segmentation,addressing the limitations of existing models.
基金jointly supported by the Second Tibetan Plateau Scientific Expedition and Research Program(Grant No.2019QZKK0902)National Natural Science Foundation of China(NSFC)project(Grant No.42305178)+1 种基金the Key R&D Program of Tibet Autonomous Region(Grant No.XZ202301ZY0039G)the Science and Technology Research Program of Institute of Mountain Hazards and Environment,Chinese Academy of Sciences(Grant No.IMHEZDRW-01)。
文摘Understanding the relationship between sediment and discharge is crucial for effective river management and water-sediment modeling,especially in the Brahmaputra River,one of the large transboundary rivers with high sediment and discharge variability in South Asia.Current knowledge of sediment-water relations is constrained by limited data,hindering effective transboundary river management.Using multivariate linear regression,climate elasticity coefficient,and traditional sediment rating curve,this study is designed to compare the sediment-water relations of the upstream(Nuxia)and the downstream(Bahadurabad).The results reveal significant variability between the two stations.In the upstream Nuxia,the simulation strongly correlates with observed suspended sediment load(SSL)and discharge(Q)(Pearson's r of 0.62 and 0.68,respectively).Conversely,at downstream Bahadurabad,weaker correlations(r=0.31 for sediment and r=0.51 for discharge simulation)indicate a reduced relation.This contrast reflects the non-linear nature of sediment-discharge coupling along the river continuum,shaped by both climatic and anthropogenic influences.Elasticity(ε)analysis highlights the dominant role of precipitation in shaping sediment-water dynamics(εP-SSL=2.53,εP-Q=1.01)at Nuxia,while Bahadurabad(εP-SSL=0.41,εP-Q=0.82)reflects a reduced sensitivity,possibly due to sediment retention along the floodplain.Air temperature elasticity(εT-SSL,-0.15&-3.06 at Nuxia and Bahadurabad,respectively)reveals contrasting impacts,strongly negatively influencing sediment transport at Bahadurabad.These findings highlight the significance of spatial variability and climatic influences on sediment dynamics,underscoring the necessity for site-specific management strategies.The sediment rating curve(SRC)analysis reveals a strong relationship between sediment and discharge(R^(2)=0.88)at Nuxia and a relatively weaker relationship(R^(2)=0.14)at Bahadurabad,which demonstrates a lower sedimentdischarge coupling that could be affected by downstream factors such as sediment deposition,channel morphology,and anthropogenic activities.This study offers valuable insights into sediment-water dynamics,highlighting the importance of understanding nonlinear relationships in the Brahmaputra River.
基金funded by the National Key Technology R&D Program of China under Grant No.2021YFD2100605the National Natural Science Foundation of China under Grant No.62433002+1 种基金the Project of Construction and Support for High-Level Innovative Teams of Beijing Municipal Institutions under Grant No.BPHR20220104Beijing Scholars Program under Grant No.099.
文摘Entity relation extraction,a fundamental and essential task in natural language processing(NLP),has garnered significant attention over an extended period.,aiming to extract the core of semantic knowledge from unstructured text,i.e.,entities and the relations between them.At present,the main dilemma of Chinese entity relation extraction research lies in nested entities,relation overlap,and lack of entity relation interaction.This dilemma is particularly prominent in complex knowledge extraction tasks with high-density knowledge,imprecise syntactic structure,and lack of semantic roles.To address these challenges,this paper presents an innovative“character-level”Chinese part-of-speech(CN-POS)tagging approach and incorporates part-of-speech(POS)information into the pre-trained model,aiming to improve its semantic understanding and syntactic information processing capabilities.Additionally,A relation reference filling mechanism(RF)is proposed to enhance the semantic interaction between relations and entities,utilize relations to guide entity modeling,improve the boundary prediction ability of entity models for nested entity phenomena,and increase the cascading accuracy of entity-relation triples.Meanwhile,the“Queue”sub-task connection strategy is adopted to alleviate triplet cascading errors caused by overlapping relations,and a Syntax-enhanced entity relation extraction model(SE-RE)is constructed.The model showed excellent performance on the self-constructed E-commerce Product Information dataset(EPI)in this article.The results demonstrate that integrating POS enhancement into the pre-trained encoding model significantly boosts the performance of entity relation extraction models compared to baseline methods.Specifically,the F1-score fluctuation in subtasks caused by error accumulation was reduced by 3.21%,while the F1-score for entity-relation triplet extraction improved by 1.91%.
