This paper proposes an active learning accelerated Monte-Carlo simulation method based on the modified K-nearest neighbors algorithm.The core idea of the proposed method is to judge whether or not the output of a rand...This paper proposes an active learning accelerated Monte-Carlo simulation method based on the modified K-nearest neighbors algorithm.The core idea of the proposed method is to judge whether or not the output of a random input point can be postulated through a classifier implemented through the modified K-nearest neighbors algorithm.Compared to other active learning methods resorting to experimental designs,the proposed method is characterized by employing Monte-Carlo simulation for sampling inputs and saving a large portion of the actual evaluations of outputs through an accurate classification,which is applicable for most structural reliability estimation problems.Moreover,the validity,efficiency,and accuracy of the proposed method are demonstrated numerically.In addition,the optimal value of K that maximizes the computational efficiency is studied.Finally,the proposed method is applied to the reliability estimation of the carbon fiber reinforced silicon carbide composite specimens subjected to random displacements,which further validates its practicability.展开更多
This study proposes two metrics using the nearest neighbors method to improve the accuracy of time-series forecasting. These two metrics can be treated as a hybrid forecasting approach to combine linear and non-linear...This study proposes two metrics using the nearest neighbors method to improve the accuracy of time-series forecasting. These two metrics can be treated as a hybrid forecasting approach to combine linear and non-linear forecasting techniques. One metric redefines the distance in k-nearest neighbors based on the coefficients of autoregression (AR) in time series. Meanwhile, an improvement to Kulesh's adaptive metrics in the nearest neighbors is also presented. To evaluate the performance of the two proposed metrics, three types of time-series data, namely deterministic synthetic data, chaotic time-series data and real time-series data, are predicted. Experimental results show the superiority of the proposed AR-enhanced k-nearest neighbors methods to the traditional k-nearest neighbors metric and Kulesh's adaptive metrics.展开更多
The increasing amount of user traffic on Internet discussion forums has led to a huge amount of unstructured natural language data in the form of user comments.Most modern recommendation systems rely on manual tagging...The increasing amount of user traffic on Internet discussion forums has led to a huge amount of unstructured natural language data in the form of user comments.Most modern recommendation systems rely on manual tagging,relying on administrators to label the features of a class,or story,which a user comment corresponds to.Another common approach is to use pre-trained word embeddings to compare class descriptions for textual similarity,then use a distance metric such as cosine similarity or Euclidean distance to find top k neighbors.However,neither approach is able to fully utilize this user-generated unstructured natural language data,reducing the scope of these recommendation systems.This paper studies the application of domain adaptation on a transformer for the set of user comments to be indexed,and the use of simple contrastive learning for the sentence transformer fine-tuning process to generate meaningful semantic embeddings for the various user comments that apply to each class.In order to match a query containing content from multiple user comments belonging to the same class,the construction of a subquery channel for computing class-level similarity is proposed.This channel uses query segmentation of the aggregate query into subqueries,performing k-nearest neighbors(KNN)search on each individual subquery.RecBERT achieves state-of-the-art performance,outperforming other state-of-the-art models in accuracy,precision,recall,and F1 score for classifying comments between four and eight classes,respectively.RecBERT outperforms the most precise state-of-the-art model(distilRoBERTa)in precision by 6.97%for matching comments between eight classes.展开更多
This research explores the use of Fuzzy K-Nearest Neighbor(F-KNN)and Artificial Neural Networks(ANN)for predicting heart stroke incidents,focusing on the impact of feature selection methods,specifically Chi-Square and...This research explores the use of Fuzzy K-Nearest Neighbor(F-KNN)and Artificial Neural Networks(ANN)for predicting heart stroke incidents,focusing on the impact of feature selection methods,specifically Chi-Square and Best First Search(BFS).The study demonstrates that BFS significantly enhances the performance of both classifiers.With BFS preprocessing,the ANN model achieved an impressive accuracy of 97.5%,precision and recall of 97.5%,and an Receiver Operating Characteristics(ROC)area of 97.9%,outperforming the Chi-Square-based ANN,which recorded an accuracy of 91.4%.Similarly,the F-KNN model with BFS achieved an accuracy of 96.3%,precision and recall of 96.3%,and a Receiver Operating Characteristics(ROC)area of 96.2%,surpassing the performance of the Chi-Square F-KNN model,which showed an accuracy of 95%.These results highlight that BFS improves the ability to select the most relevant features,contributing to more reliable and accurate stroke predictions.The findings underscore the importance of using advanced feature selection methods like BFS to enhance the performance of machine learning models in healthcare applications,leading to better stroke risk management and improved patient outcomes.展开更多
Peripheral immunity forms the foundation of tumor immunity,while tumor immunity represents a more refined adaptation of peripheral immune responses.The tumor microenvironment(TME),a localized niche surrounding tumor c...Peripheral immunity forms the foundation of tumor immunity,while tumor immunity represents a more refined adaptation of peripheral immune responses.The tumor microenvironment(TME),a localized niche surrounding tumor cells,is inherently immunosuppressive(1,2).Effective tumor therapy necessitates the dismantling of this microenvironment,aiming to eradicate tumors from the host system.展开更多
This paper studies the emergent dynamics of a flock with nonlinear inherent dynamics under closest neighbors model.We establish sufficient frameworks for convergence to flocking in terms of initial state and system pa...This paper studies the emergent dynamics of a flock with nonlinear inherent dynamics under closest neighbors model.We establish sufficient frameworks for convergence to flocking in terms of initial state and system parameters.When the number of closest neighbors is at least half of the population,it is shown that convergence to flocking occurs regardless of the initial state provided that the Lipschitz constant of nonlinear dynamics is smaller than the coupling strength.In contrast,when this number of closest neighbors is less than half of the population,we need to impose some restrictive conditions on the initial state to ensure the emergence of flocking based on the disturbed graphs approach.Our results are applicable to both continuous and discrete time cases.Finally,the validity of our theoretical analysis is tested by numerical simulations.展开更多
In this study,a k-nearest neighbor(kNN)method based on nonlinear directional dimension reduction is applied to gas-bearing reservoir prediction.The kNN method can select the most relevant training samples to establish...In this study,a k-nearest neighbor(kNN)method based on nonlinear directional dimension reduction is applied to gas-bearing reservoir prediction.The kNN method can select the most relevant training samples to establish a local model according to feature similarities.However,the kNN method cannot extract gas-sensitive attributes and faces dimension problems.The features important to gas-bearing reservoir prediction could not be the main features of the samples.Thus,linear dimension reduction methods,such as principal component analysis,fail to extract relevant features.We thus implemented dimension reduction using a fully connected artifi cial neural network(ANN)with proper architecture.This not only increased the separability of the samples but also maintained the samples’inherent distribution characteristics.Moreover,using the kNN to classify samples after the ANN dimension reduction is also equivalent to replacing the deep structure of the ANN,which is considered to have a linear classifi cation function.When applied to actual data,our method extracted gas-bearing sensitive features from seismic data to a certain extent.The prediction results can characterize gas-bearing reservoirs accurately in a limited scope.展开更多
The nearest neighbors (NNs) classifiers, especially the k-Nearest Neighbors (kNNs) algorithm, are among the simplest and yet most efficient classification rules and widely used in practice. It is a nonparametric metho...The nearest neighbors (NNs) classifiers, especially the k-Nearest Neighbors (kNNs) algorithm, are among the simplest and yet most efficient classification rules and widely used in practice. It is a nonparametric method of pattern recognition. In this paper, k-Nearest Neighbors, one of the most commonly used machine learning methods, work in automatic classification of multi-wavelength astronomical objects. Through the experiment, we conclude that the running speed of the kNN classier is rather fast and the classification accuracy is up to 97.73%. As a result, it is efficient and applicable to discriminate active objects from stars and normal galaxies with this method. The classifiers trained by the kNN method can be used to solve the automated classification problem faced by astronomy and the virtual observatory (VO).展开更多
Text categorization is a significant technique to manage the surging text data on the Internet.The k-nearest neighbors(kNN) algorithm is an effective,but not efficient,classification model for text categorization.In t...Text categorization is a significant technique to manage the surging text data on the Internet.The k-nearest neighbors(kNN) algorithm is an effective,but not efficient,classification model for text categorization.In this paper,we propose an effective strategy to accelerate the standard kNN,based on a simple principle:usually,near points in space are also near when they are projected into a direction,which means that distant points in the projection direction are also distant in the original space.Using the proposed strategy,most of the irrelevant points can be removed when searching for the k-nearest neighbors of a query point,which greatly decreases the computation cost.Experimental results show that the proposed strategy greatly improves the time performance of the standard kNN,with little degradation in accuracy.Specifically,it is superior in applications that have large and high-dimensional datasets.展开更多
In clustering algorithms,the selection of neighbors significantly affects the quality of the final clustering results.While various neighbor relationships exist,such as K-nearest neighbors,natural neighbors,and shared...In clustering algorithms,the selection of neighbors significantly affects the quality of the final clustering results.While various neighbor relationships exist,such as K-nearest neighbors,natural neighbors,and shared neighbors,most neighbor relationships can only handle single structural relationships,and the identification accuracy is low for datasets with multiple structures.In life,people’s first instinct for complex things is to divide them into multiple parts to complete.Partitioning the dataset into more sub-graphs is a good idea approach to identifying complex structures.Taking inspiration from this,we propose a novel neighbor method:Shared Natural Neighbors(SNaN).To demonstrate the superiority of this neighbor method,we propose a shared natural neighbors-based hierarchical clustering algorithm for discovering arbitrary-shaped clusters(HC-SNaN).Our algorithm excels in identifying both spherical clusters and manifold clusters.Tested on synthetic datasets and real-world datasets,HC-SNaN demonstrates significant advantages over existing clustering algorithms,particularly when dealing with datasets containing arbitrary shapes.展开更多
Imbalanced multiclass datasets pose challenges for machine learning algorithms.They often contain minority classes that are important for accurate predictions.However,when the data is sparsely distributed and overlaps...Imbalanced multiclass datasets pose challenges for machine learning algorithms.They often contain minority classes that are important for accurate predictions.However,when the data is sparsely distributed and overlaps with data points fromother classes,it introduces noise.As a result,existing resamplingmethods may fail to preserve the original data patterns,further disrupting data quality and reducingmodel performance.This paper introduces Neighbor Displacement-based Enhanced Synthetic Oversampling(NDESO),a hybridmethod that integrates a data displacement strategy with a resampling technique to achieve data balance.It begins by computing the average distance of noisy data points to their neighbors and adjusting their positions toward the center before applying random oversampling.Extensive evaluations compare 14 alternatives on nine classifiers across synthetic and 20 real-world datasetswith varying imbalance ratios.This evaluation was structured into two distinct test groups.First,the effects of k-neighbor variations and distance metrics are evaluated,followed by a comparison of resampled data distributions against alternatives,and finally,determining the most suitable oversampling technique for data balancing.Second,the overall performance of the NDESO algorithm was assessed,focusing on G-mean and statistical significance.The results demonstrate that our method is robust to a wide range of variations in these parameters and the overall performance achieves an average G-mean score of 0.90,which is among the highest.Additionally,it attains the lowest mean rank of 2.88,indicating statistically significant improvements over existing approaches.This advantage underscores its potential for effectively handling data imbalance in practical scenarios.展开更多
The electrochemical reduction of nitrate(NO_(3)−)to ammonia(NH_(3))(NO3RR)represents an environmentally sustainable strategy for NH_(3)production while concurrently addressing water pollution challenges.Nevertheless,t...The electrochemical reduction of nitrate(NO_(3)−)to ammonia(NH_(3))(NO3RR)represents an environmentally sustainable strategy for NH_(3)production while concurrently addressing water pollution challenges.Nevertheless,the intrinsic complexity of this multi-step reaction severely constrains both the selectivity and efficiency of NO3RR.Copper-based electrocatalysts have been extensively investigated for NO_(3)RR but often suffer from nitrite(NO_(2)^(−))accumulation,which stems from insufficient NO_(3)^(−)adsorption strength.This limitation often leads to rapid catalyst deactivation,hindered hydrogenation pathways,and reduced overall efficiency.Herein,we report a one-step green chemical reduction method to synthesize PtCuSnCo quarternary alloy nanoparticles with homogeneously distributed elements.Under practical NO3−concentrations,the optimized catalyst exhibited an impressive Faradaic efficiency approaching 100%and an outstanding selectivity of 95.6±2.9%.Mechanistic insights uncovered that SnCo sites robustly facilitated NO_(3)^(−)adsorption,complemented by the proficiency of PtCu sites in NO3−reduction.The synergistic spatial neighborhood effect between SnCo and PtCu sites efficiently stabilizes NO_(3)^(−)deoxygenation and suppresses NO_(2)^(−)accumulation.This tandem architecture achieves a finely tuned balance between adsorption strength and deoxygenation kinetics,enabling highly selective and efficient NO3RR.Our findings emphasize the indispensable role of engineered multi-metallic catalysts in overcoming persistent challenges of NO3RR,paving the way for advanced NH3 synthesis and environmental remediation.展开更多
The distribution of igneous rocks is closely related to hydrocarbon resources.This study utilized high-precision gravity,magnetic,and rock physical property data,employing gravity-magnetic field fusion technology and ...The distribution of igneous rocks is closely related to hydrocarbon resources.This study utilized high-precision gravity,magnetic,and rock physical property data,employing gravity-magnetic field fusion technology and Euler deconvolution technology.The objective was to identify the distribution of igneous rocks in the China Seas and neighboring regions and investigate their relationships with petroliferous basins.Our results reveal that igneous rocks are widely scattered throughout the China Seas and neighboring regions,with the highest concentration in the northwest(NW)and the second highest concentration in the east-northeast(ENE).The largest-scale igneous rocks are those with a north-south(N-S)orientation,followed by those with northeast(NE),NW,and ENE orientations.The depths of igneous rocks within petroliferous basins typically range from 3 km to 9 km and are associated with hydrocarbon resource distributions characterized by deep oil and shallow gas.The proportions of igneous rocks in different types of basins exhibit varying correlations with the total hydrocarbon resources.In particular,the proportion of igneous rocks in rift-type basins in the China Seas exhibits a strong linear correlation with the total hydrocarbon resources.These research findings provide valuable guidance for studying the relationship between igneous rock distribution and petroliferous basins,offering insights that can inform future hydrocarbon exploration endeavors.展开更多
As an important part of China's neighborhood,Southeast Asia has always been a high priority in China's neighborhood diplomacy,playing a benchmark and example-setting role in China's drive to build a commun...As an important part of China's neighborhood,Southeast Asia has always been a high priority in China's neighborhood diplomacy,playing a benchmark and example-setting role in China's drive to build a community with a shared future with neighboring countries.Shortly after the 2025 Central Conference on Work Related to Neighboring Countries,Chinese President Xi Jinping paid state visits to Vietnam,Malaysia,and Cambodia.展开更多
The Central Conference on Work Related to Neighboring Countries held on April 8–9,2025,highlighted the importance of China's neighborhood as“a vital foundation for achieving development and prosperity,a key fron...The Central Conference on Work Related to Neighboring Countries held on April 8–9,2025,highlighted the importance of China's neighborhood as“a vital foundation for achieving development and prosperity,a key front for safeguarding national security,a priority area in managing overall diplomacy,and a crucial link in promoting the building of a community with a shared future for mankind.”展开更多
Neighborhood is an important strategic support for China to take into account both the domestic and international situations and coordinate development and security.It is also a crucial link in building a community wi...Neighborhood is an important strategic support for China to take into account both the domestic and international situations and coordinate development and security.It is also a crucial link in building a community with a shared future for mankind.China adheres to fostering an amicable,secure,and prosperous neighborhood and works with neighboring countries to create a better future.By seeking an amicable,secure,and prosperous neighborhood,following the principles of amity,sincerity,mutual benefit,and inclusiveness,and sharing weal and woe with its neighbors,China remains committed to deepening exchanges and cooperation with neighboring countries in various fields.Facing a complex and unstable international situation,China and neighboring countries jointly advocate the Asian values of peace,cooperation,openness,and inclusiveness and are committed to promoting indivisible security,common development,and shared prosperity in Asia.China has cooperated with neighboring countries to build a high-quality Belt and Road Initiative and promote global economic recovery.In the face of a critical phase where regional dynamics and global transformations are deeply intertwined,China has put forward the Asian security model,proposed the vision of com mon,comprehensive,cooperative,and sustainable security in Asia,stuck to seeking common ground while shelving differences,and advocated equal-footed consultation.China is taking the initiative to shape the regional security pattern with a positive attitude to safeguard peace and development in Asia.展开更多
From April 8 to 9,2025,the Central Conference on Work Related to Neighboring Countries was held in Beijing.General Secretary of the Communist Party of China(CPC)Central Committee Xi Jinping systematically summarized t...From April 8 to 9,2025,the Central Conference on Work Related to Neighboring Countries was held in Beijing.General Secretary of the Communist Party of China(CPC)Central Committee Xi Jinping systematically summarized the achievements and experience of China’s neighborhood work in the new era,scientifically analyzed the situation,clarified the goals,tasks,ideas and measures for neighborhood work in the coming period,and emphasized the need to focus on building a community with a shared future with neighboring countries,striving to break new ground in neighborhood work.Facing the ever-changing international landscape,especially the surrounding environment,China needs to carry out integrated diplomacy,reshape the neighborhood environment,translate the concepts and policies of the CPC neighborhood diplomacy into diplomatic practices and achieve greater results.展开更多
Since the 18th National Congress of the Communist Party of China(CPC)in 2012,neighborhood diplomacy has been at the top of China’s diplomatic agenda with growing importance.In October 2013,the CPC Central Committee c...Since the 18th National Congress of the Communist Party of China(CPC)in 2012,neighborhood diplomacy has been at the top of China’s diplomatic agenda with growing importance.In October 2013,the CPC Central Committee convened the central conference on work related to neighboring countries,first of its kind since the founding of the People’s Republic of China,stressing“let awareness of a community with a shared future take root in the neighboring countries”.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.12002246 and No.52178301)Knowledge Innovation Program of Wuhan(Grant No.2022010801020357)+2 种基金the Science Research Foundation of Wuhan Institute of Technology(Grant No.K2021030)2020 annual Open Fund of Failure Mechanics&Engineering Disaster Prevention and Mitigation,Key Laboratory of Sichuan Province(Sichuan University)(Grant No.2020JDS0022)Open Research Fund Program of Hubei Provincial Key Laboratory of Chemical Equipment Intensification and Intrinsic Safety(Grant No.2019KA03)。
文摘This paper proposes an active learning accelerated Monte-Carlo simulation method based on the modified K-nearest neighbors algorithm.The core idea of the proposed method is to judge whether or not the output of a random input point can be postulated through a classifier implemented through the modified K-nearest neighbors algorithm.Compared to other active learning methods resorting to experimental designs,the proposed method is characterized by employing Monte-Carlo simulation for sampling inputs and saving a large portion of the actual evaluations of outputs through an accurate classification,which is applicable for most structural reliability estimation problems.Moreover,the validity,efficiency,and accuracy of the proposed method are demonstrated numerically.In addition,the optimal value of K that maximizes the computational efficiency is studied.Finally,the proposed method is applied to the reliability estimation of the carbon fiber reinforced silicon carbide composite specimens subjected to random displacements,which further validates its practicability.
基金the National Natural Science Foundation of China(No.61203337)the Natural Science Foundation of Shanghai(No.12ZR1440200)
文摘This study proposes two metrics using the nearest neighbors method to improve the accuracy of time-series forecasting. These two metrics can be treated as a hybrid forecasting approach to combine linear and non-linear forecasting techniques. One metric redefines the distance in k-nearest neighbors based on the coefficients of autoregression (AR) in time series. Meanwhile, an improvement to Kulesh's adaptive metrics in the nearest neighbors is also presented. To evaluate the performance of the two proposed metrics, three types of time-series data, namely deterministic synthetic data, chaotic time-series data and real time-series data, are predicted. Experimental results show the superiority of the proposed AR-enhanced k-nearest neighbors methods to the traditional k-nearest neighbors metric and Kulesh's adaptive metrics.
文摘The increasing amount of user traffic on Internet discussion forums has led to a huge amount of unstructured natural language data in the form of user comments.Most modern recommendation systems rely on manual tagging,relying on administrators to label the features of a class,or story,which a user comment corresponds to.Another common approach is to use pre-trained word embeddings to compare class descriptions for textual similarity,then use a distance metric such as cosine similarity or Euclidean distance to find top k neighbors.However,neither approach is able to fully utilize this user-generated unstructured natural language data,reducing the scope of these recommendation systems.This paper studies the application of domain adaptation on a transformer for the set of user comments to be indexed,and the use of simple contrastive learning for the sentence transformer fine-tuning process to generate meaningful semantic embeddings for the various user comments that apply to each class.In order to match a query containing content from multiple user comments belonging to the same class,the construction of a subquery channel for computing class-level similarity is proposed.This channel uses query segmentation of the aggregate query into subqueries,performing k-nearest neighbors(KNN)search on each individual subquery.RecBERT achieves state-of-the-art performance,outperforming other state-of-the-art models in accuracy,precision,recall,and F1 score for classifying comments between four and eight classes,respectively.RecBERT outperforms the most precise state-of-the-art model(distilRoBERTa)in precision by 6.97%for matching comments between eight classes.
基金funded by FCT/MECI through national funds and,when applicable,co-funded EU funds under UID/50008:Instituto de Telecomunicacoes.
文摘This research explores the use of Fuzzy K-Nearest Neighbor(F-KNN)and Artificial Neural Networks(ANN)for predicting heart stroke incidents,focusing on the impact of feature selection methods,specifically Chi-Square and Best First Search(BFS).The study demonstrates that BFS significantly enhances the performance of both classifiers.With BFS preprocessing,the ANN model achieved an impressive accuracy of 97.5%,precision and recall of 97.5%,and an Receiver Operating Characteristics(ROC)area of 97.9%,outperforming the Chi-Square-based ANN,which recorded an accuracy of 91.4%.Similarly,the F-KNN model with BFS achieved an accuracy of 96.3%,precision and recall of 96.3%,and a Receiver Operating Characteristics(ROC)area of 96.2%,surpassing the performance of the Chi-Square F-KNN model,which showed an accuracy of 95%.These results highlight that BFS improves the ability to select the most relevant features,contributing to more reliable and accurate stroke predictions.The findings underscore the importance of using advanced feature selection methods like BFS to enhance the performance of machine learning models in healthcare applications,leading to better stroke risk management and improved patient outcomes.
文摘Peripheral immunity forms the foundation of tumor immunity,while tumor immunity represents a more refined adaptation of peripheral immune responses.The tumor microenvironment(TME),a localized niche surrounding tumor cells,is inherently immunosuppressive(1,2).Effective tumor therapy necessitates the dismantling of this microenvironment,aiming to eradicate tumors from the host system.
基金supported by the National Key R&D Program of China(2023YFA1009200)the National Natural Science Foundation of China(12171069)the Fundamental Research Funds for the Central Universities.
文摘This paper studies the emergent dynamics of a flock with nonlinear inherent dynamics under closest neighbors model.We establish sufficient frameworks for convergence to flocking in terms of initial state and system parameters.When the number of closest neighbors is at least half of the population,it is shown that convergence to flocking occurs regardless of the initial state provided that the Lipschitz constant of nonlinear dynamics is smaller than the coupling strength.In contrast,when this number of closest neighbors is less than half of the population,we need to impose some restrictive conditions on the initial state to ensure the emergence of flocking based on the disturbed graphs approach.Our results are applicable to both continuous and discrete time cases.Finally,the validity of our theoretical analysis is tested by numerical simulations.
基金supported by the National Key R&D Program of China(No.2018YFA0702504)the National Natural Science Foundation of China(No.42174152 and No.41974140)the Strategic Cooperation Technology Projects of CNPC and CUPB(No.ZLZX2020-03).
文摘In this study,a k-nearest neighbor(kNN)method based on nonlinear directional dimension reduction is applied to gas-bearing reservoir prediction.The kNN method can select the most relevant training samples to establish a local model according to feature similarities.However,the kNN method cannot extract gas-sensitive attributes and faces dimension problems.The features important to gas-bearing reservoir prediction could not be the main features of the samples.Thus,linear dimension reduction methods,such as principal component analysis,fail to extract relevant features.We thus implemented dimension reduction using a fully connected artifi cial neural network(ANN)with proper architecture.This not only increased the separability of the samples but also maintained the samples’inherent distribution characteristics.Moreover,using the kNN to classify samples after the ANN dimension reduction is also equivalent to replacing the deep structure of the ANN,which is considered to have a linear classifi cation function.When applied to actual data,our method extracted gas-bearing sensitive features from seismic data to a certain extent.The prediction results can characterize gas-bearing reservoirs accurately in a limited scope.
基金the National Natural Science Foundation of China (Grant Nos. 10473013, 10778724 and 90412016)
文摘The nearest neighbors (NNs) classifiers, especially the k-Nearest Neighbors (kNNs) algorithm, are among the simplest and yet most efficient classification rules and widely used in practice. It is a nonparametric method of pattern recognition. In this paper, k-Nearest Neighbors, one of the most commonly used machine learning methods, work in automatic classification of multi-wavelength astronomical objects. Through the experiment, we conclude that the running speed of the kNN classier is rather fast and the classification accuracy is up to 97.73%. As a result, it is efficient and applicable to discriminate active objects from stars and normal galaxies with this method. The classifiers trained by the kNN method can be used to solve the automated classification problem faced by astronomy and the virtual observatory (VO).
基金Project (No. 2012BAH18B05) supported by the National Key Technology R&D Program of China
文摘Text categorization is a significant technique to manage the surging text data on the Internet.The k-nearest neighbors(kNN) algorithm is an effective,but not efficient,classification model for text categorization.In this paper,we propose an effective strategy to accelerate the standard kNN,based on a simple principle:usually,near points in space are also near when they are projected into a direction,which means that distant points in the projection direction are also distant in the original space.Using the proposed strategy,most of the irrelevant points can be removed when searching for the k-nearest neighbors of a query point,which greatly decreases the computation cost.Experimental results show that the proposed strategy greatly improves the time performance of the standard kNN,with little degradation in accuracy.Specifically,it is superior in applications that have large and high-dimensional datasets.
基金This work was supported by Science and Technology Research Program of Chongqing Municipal Education Commission(KJZD-M202300502,KJQN201800539).
文摘In clustering algorithms,the selection of neighbors significantly affects the quality of the final clustering results.While various neighbor relationships exist,such as K-nearest neighbors,natural neighbors,and shared neighbors,most neighbor relationships can only handle single structural relationships,and the identification accuracy is low for datasets with multiple structures.In life,people’s first instinct for complex things is to divide them into multiple parts to complete.Partitioning the dataset into more sub-graphs is a good idea approach to identifying complex structures.Taking inspiration from this,we propose a novel neighbor method:Shared Natural Neighbors(SNaN).To demonstrate the superiority of this neighbor method,we propose a shared natural neighbors-based hierarchical clustering algorithm for discovering arbitrary-shaped clusters(HC-SNaN).Our algorithm excels in identifying both spherical clusters and manifold clusters.Tested on synthetic datasets and real-world datasets,HC-SNaN demonstrates significant advantages over existing clustering algorithms,particularly when dealing with datasets containing arbitrary shapes.
文摘Imbalanced multiclass datasets pose challenges for machine learning algorithms.They often contain minority classes that are important for accurate predictions.However,when the data is sparsely distributed and overlaps with data points fromother classes,it introduces noise.As a result,existing resamplingmethods may fail to preserve the original data patterns,further disrupting data quality and reducingmodel performance.This paper introduces Neighbor Displacement-based Enhanced Synthetic Oversampling(NDESO),a hybridmethod that integrates a data displacement strategy with a resampling technique to achieve data balance.It begins by computing the average distance of noisy data points to their neighbors and adjusting their positions toward the center before applying random oversampling.Extensive evaluations compare 14 alternatives on nine classifiers across synthetic and 20 real-world datasetswith varying imbalance ratios.This evaluation was structured into two distinct test groups.First,the effects of k-neighbor variations and distance metrics are evaluated,followed by a comparison of resampled data distributions against alternatives,and finally,determining the most suitable oversampling technique for data balancing.Second,the overall performance of the NDESO algorithm was assessed,focusing on G-mean and statistical significance.The results demonstrate that our method is robust to a wide range of variations in these parameters and the overall performance achieves an average G-mean score of 0.90,which is among the highest.Additionally,it attains the lowest mean rank of 2.88,indicating statistically significant improvements over existing approaches.This advantage underscores its potential for effectively handling data imbalance in practical scenarios.
文摘The electrochemical reduction of nitrate(NO_(3)−)to ammonia(NH_(3))(NO3RR)represents an environmentally sustainable strategy for NH_(3)production while concurrently addressing water pollution challenges.Nevertheless,the intrinsic complexity of this multi-step reaction severely constrains both the selectivity and efficiency of NO3RR.Copper-based electrocatalysts have been extensively investigated for NO_(3)RR but often suffer from nitrite(NO_(2)^(−))accumulation,which stems from insufficient NO_(3)^(−)adsorption strength.This limitation often leads to rapid catalyst deactivation,hindered hydrogenation pathways,and reduced overall efficiency.Herein,we report a one-step green chemical reduction method to synthesize PtCuSnCo quarternary alloy nanoparticles with homogeneously distributed elements.Under practical NO3−concentrations,the optimized catalyst exhibited an impressive Faradaic efficiency approaching 100%and an outstanding selectivity of 95.6±2.9%.Mechanistic insights uncovered that SnCo sites robustly facilitated NO_(3)^(−)adsorption,complemented by the proficiency of PtCu sites in NO3−reduction.The synergistic spatial neighborhood effect between SnCo and PtCu sites efficiently stabilizes NO_(3)^(−)deoxygenation and suppresses NO_(2)^(−)accumulation.This tandem architecture achieves a finely tuned balance between adsorption strength and deoxygenation kinetics,enabling highly selective and efficient NO3RR.Our findings emphasize the indispensable role of engineered multi-metallic catalysts in overcoming persistent challenges of NO3RR,paving the way for advanced NH3 synthesis and environmental remediation.
基金The National Key Research and Development Program of China under contract No.2017YFC0602202.
文摘The distribution of igneous rocks is closely related to hydrocarbon resources.This study utilized high-precision gravity,magnetic,and rock physical property data,employing gravity-magnetic field fusion technology and Euler deconvolution technology.The objective was to identify the distribution of igneous rocks in the China Seas and neighboring regions and investigate their relationships with petroliferous basins.Our results reveal that igneous rocks are widely scattered throughout the China Seas and neighboring regions,with the highest concentration in the northwest(NW)and the second highest concentration in the east-northeast(ENE).The largest-scale igneous rocks are those with a north-south(N-S)orientation,followed by those with northeast(NE),NW,and ENE orientations.The depths of igneous rocks within petroliferous basins typically range from 3 km to 9 km and are associated with hydrocarbon resource distributions characterized by deep oil and shallow gas.The proportions of igneous rocks in different types of basins exhibit varying correlations with the total hydrocarbon resources.In particular,the proportion of igneous rocks in rift-type basins in the China Seas exhibits a strong linear correlation with the total hydrocarbon resources.These research findings provide valuable guidance for studying the relationship between igneous rock distribution and petroliferous basins,offering insights that can inform future hydrocarbon exploration endeavors.
文摘As an important part of China's neighborhood,Southeast Asia has always been a high priority in China's neighborhood diplomacy,playing a benchmark and example-setting role in China's drive to build a community with a shared future with neighboring countries.Shortly after the 2025 Central Conference on Work Related to Neighboring Countries,Chinese President Xi Jinping paid state visits to Vietnam,Malaysia,and Cambodia.
文摘The Central Conference on Work Related to Neighboring Countries held on April 8–9,2025,highlighted the importance of China's neighborhood as“a vital foundation for achieving development and prosperity,a key front for safeguarding national security,a priority area in managing overall diplomacy,and a crucial link in promoting the building of a community with a shared future for mankind.”
文摘Neighborhood is an important strategic support for China to take into account both the domestic and international situations and coordinate development and security.It is also a crucial link in building a community with a shared future for mankind.China adheres to fostering an amicable,secure,and prosperous neighborhood and works with neighboring countries to create a better future.By seeking an amicable,secure,and prosperous neighborhood,following the principles of amity,sincerity,mutual benefit,and inclusiveness,and sharing weal and woe with its neighbors,China remains committed to deepening exchanges and cooperation with neighboring countries in various fields.Facing a complex and unstable international situation,China and neighboring countries jointly advocate the Asian values of peace,cooperation,openness,and inclusiveness and are committed to promoting indivisible security,common development,and shared prosperity in Asia.China has cooperated with neighboring countries to build a high-quality Belt and Road Initiative and promote global economic recovery.In the face of a critical phase where regional dynamics and global transformations are deeply intertwined,China has put forward the Asian security model,proposed the vision of com mon,comprehensive,cooperative,and sustainable security in Asia,stuck to seeking common ground while shelving differences,and advocated equal-footed consultation.China is taking the initiative to shape the regional security pattern with a positive attitude to safeguard peace and development in Asia.
文摘From April 8 to 9,2025,the Central Conference on Work Related to Neighboring Countries was held in Beijing.General Secretary of the Communist Party of China(CPC)Central Committee Xi Jinping systematically summarized the achievements and experience of China’s neighborhood work in the new era,scientifically analyzed the situation,clarified the goals,tasks,ideas and measures for neighborhood work in the coming period,and emphasized the need to focus on building a community with a shared future with neighboring countries,striving to break new ground in neighborhood work.Facing the ever-changing international landscape,especially the surrounding environment,China needs to carry out integrated diplomacy,reshape the neighborhood environment,translate the concepts and policies of the CPC neighborhood diplomacy into diplomatic practices and achieve greater results.
文摘Since the 18th National Congress of the Communist Party of China(CPC)in 2012,neighborhood diplomacy has been at the top of China’s diplomatic agenda with growing importance.In October 2013,the CPC Central Committee convened the central conference on work related to neighboring countries,first of its kind since the founding of the People’s Republic of China,stressing“let awareness of a community with a shared future take root in the neighboring countries”.
