In recent years,multi-label zero-shot learning(ML-ZSL)has garnered increasing attention because of its wide range of potential applications,such as image annotation,text classification,and bioinformatics.The central c...In recent years,multi-label zero-shot learning(ML-ZSL)has garnered increasing attention because of its wide range of potential applications,such as image annotation,text classification,and bioinformatics.The central challenge in ML-ZSL lies in predicting multiple labels for unseen classes without requiring any labeled training data,which contrasts with conventional supervised learning paradigms.However,existing methods face several significant challenges.These include the substantial semantic gap between different modalities,which impedes effective knowledge transfer,and the intricate and typically complex relationships among multiple labels,making it difficult to model them in a meaningful and accurate manner.To overcome these challenges,we propose a graph-augmented multimodal chain-of-thought(GMCoT)reasoning approach.The proposed method combines the strengths of multimodal large language models with graph-based structures,significantly enhancing the reasoning process involved in multi-label prediction.First,a novel multimodal chain-of-thought reasoning framework is presented which imitates human-like step-by-step reasoning to produce multi-label predictions.Second,a technique is presented for integrating label graphs into the reasoning process.This technique enables the capture of complex semantic relationships among labels,thereby improving the accuracy and consistency of multi-label generation.Comprehensive experiments on benchmark datasets demonstrate that the proposed GMCoT approach outperforms state-of-the-art methods in ML-ZSL.展开更多
High-dimensional data causes difficulties in machine learning due to high time consumption and large memory requirements.In particular,in amulti-label environment,higher complexity is required asmuch as the number of ...High-dimensional data causes difficulties in machine learning due to high time consumption and large memory requirements.In particular,in amulti-label environment,higher complexity is required asmuch as the number of labels.Moreover,an optimization problem that fully considers all dependencies between features and labels is difficult to solve.In this study,we propose a novel regression-basedmulti-label feature selectionmethod that integrates mutual information to better exploit the underlying data structure.By incorporating mutual information into the regression formulation,the model captures not only linear relationships but also complex non-linear dependencies.The proposed objective function simultaneously considers three types of relationships:(1)feature redundancy,(2)featurelabel relevance,and(3)inter-label dependency.These three quantities are computed usingmutual information,allowing the proposed formulation to capture nonlinear dependencies among variables.These three types of relationships are key factors in multi-label feature selection,and our method expresses them within a unified formulation,enabling efficient optimization while simultaneously accounting for all of them.To efficiently solve the proposed optimization problem under non-negativity constraints,we develop a gradient-based optimization algorithm with fast convergence.Theexperimental results on sevenmulti-label datasets show that the proposed method outperforms existingmulti-label feature selection techniques.展开更多
Predicting the behavior of renewable energy systems requires models capable of generating accurate forecasts from limited historical data,a challenge that becomes especially pronounced when commissioning new facil-iti...Predicting the behavior of renewable energy systems requires models capable of generating accurate forecasts from limited historical data,a challenge that becomes especially pronounced when commissioning new facil-ities where operational records are scarce.This review aims to synthesize recent progress in data-efficient deep learning approaches for addressing such“cold-start”forecasting problems.It primarily covers three interrelated domains—solar photovoltaic(PV),wind power,and electrical load forecasting—where data scarcity and operational variability are most critical,while also including representative studies on hydropower and carbon emission prediction to provide a broader systems perspective.To this end,we examined trends from over 150 predominantly peer-reviewed studies published between 2019 and mid-2025,highlighting advances in zero-shot and few-shot meta-learning frameworks that enable rapid model adaptation with minimal labeled data.Moreover,transfer learning approaches combined with spatiotemporal graph neural networks have been employed to transfer knowledge from existing energy assets to new,data-sparse environments,effectively capturing hidden dependencies among geographic features,meteorological dynamics,and grid structures.Synthetic data generation has further proven valuable for expanding training samples and mitigating overfitting in cold-start scenarios.In addition,large language models and explainable artificial intelligence(XAI)—notably conversational XAI systems—have been used to interpret and communicate complex model behaviors in accessible terms,fostering operator trust from the earliest deployment stages.By consolidating methodological advances,unresolved challenges,and open-source resources,this review provides a coherent overview of deep learning strategies that can shorten the data-sparse ramp-up period of new energy infrastructures and accelerate the transition toward resilient,low-carbon electricity grids.展开更多
In its 2023 global health statistics,the World Health Organization noted that noncommunicable diseases(NCDs)remain the leading cause of disease burden worldwide,with cardiovascular diseases(CVDs)resulting in more deat...In its 2023 global health statistics,the World Health Organization noted that noncommunicable diseases(NCDs)remain the leading cause of disease burden worldwide,with cardiovascular diseases(CVDs)resulting in more deaths than the three other major NCDs combined.In this study,we developed a method that can comprehensively detect which CVDs are present in a patient.Specifically,we propose a multi-label classification method that utilizes photoplethysmography(PPG)signals and physiological characteristics from public datasets to classify four types of CVDs and related conditions:hypertension,diabetes,cerebral infarction,and cerebrovascular disease.Our approach to multi-disease classification of cardiovascular diseases(CVDs)using PPG signals achieves the highest classification performance when encompassing the broadest range of disease categories,thereby offering a more comprehensive assessment of human health.We employ a multi-label classification strategy to simultaneously predict the presence or absence of multiple diseases.Specifically,we first apply the Savitzky-Golay(S-G)filter to the PPG signals to reduce noise and then transform into statistical features.We integrate processed PPG signals with individual physiological features as a multimodal input,thereby expanding the learned feature space.Notably,even with a simple machine learning method,this approach can achieve relatively high accuracy.The proposed method achieved a maximum F1-score of 0.91,minimum Hamming loss of 0.04,and an accuracy of 0.95.Thus,our method represents an effective and rapid solution for detecting multiple diseases simultaneously,which is beneficial for comprehensively managing CVDs.展开更多
Automated cartoon character recognition is crucial for applications in content indexing,filtering,and copyright protection,yet it faces a significant challenge in animated media due to high intra-class visual variabil...Automated cartoon character recognition is crucial for applications in content indexing,filtering,and copyright protection,yet it faces a significant challenge in animated media due to high intra-class visual variability,where characters frequently alter their appearance.To address this problem,we introduce the novel Kral Sakir dataset,a public benchmark of 16,725 images specifically curated for the task of multi-label cartoon character classification under these varied conditions.This paper conducts a comprehensive benchmark study,evaluating the performance of state-of-the-art pretrained Convolutional Neural Networks(CNNs),including DenseNet,ResNet,and VGG,against a custom baseline model trained from scratch.Our experiments,evaluated using metrics of F1-Score,accuracy,and Area Under the ROC Curve(AUC),demonstrate that fine-tuning pretrained models is a highly effective strategy.The best-performing model,DenseNet121,achieved an F1-Score of 0.9890 and an accuracy of 0.9898,significantly outperforming our baseline CNN(F1-Score of 0.9545).The findings validate the power of transfer learning for this domain and establish a strong performance benchmark.The introduced dataset provides a valuable resource for future research into developing robust and accurate character recognition systems.展开更多
With the availability of high-performance computing technology and the development of advanced numerical simulation methods, Computational Fluid Dynamics (CFD) is becoming more and more practical and efficient in engi...With the availability of high-performance computing technology and the development of advanced numerical simulation methods, Computational Fluid Dynamics (CFD) is becoming more and more practical and efficient in engineering. As one of the high-precision representative algorithms, the high-order Discontinuous Galerkin Method (DGM) has not only attracted widespread attention from scholars in the CFD research community, but also received strong development. However, when DGM is extended to high-speed aerodynamic flow field calculations, non-physical numerical Gibbs oscillations near shock waves often significantly affect the numerical accuracy and even cause calculation failure. Data driven approaches based on machine learning techniques can be used to learn the characteristics of Gibbs noise, which motivates us to use it in high-speed DG applications. To achieve this goal, labeled data need to be generated in order to train the machine learning models. This paper proposes a new method for denoising modeling of Gibbs phenomenon using a machine learning technique, the zero-shot learning strategy, to eliminate acquiring large amounts of CFD data. The model adopts a graph convolutional network combined with graph attention mechanism to learn the denoising paradigm from synthetic Gibbs noise data and generalize to DGM numerical simulation data. Numerical simulation results show that the Gibbs denoising model proposed in this paper can suppress the numerical oscillation near shock waves in the high-order DGM. Our work automates the extension of DGM to high-speed aerodynamic flow field calculations with higher generalization and lower cost.展开更多
The ability of mobile robots to plan and execute a path is foundational to various path-planning challenges,particularly Coverage Path Planning.While this task has been typically tackled with classical algorithms,thes...The ability of mobile robots to plan and execute a path is foundational to various path-planning challenges,particularly Coverage Path Planning.While this task has been typically tackled with classical algorithms,these often struggle with flexibility and adaptability in unknown environments.On the other hand,recent advances in Reinforcement Learning offer promising approaches,yet a significant gap in the literature remains when it comes to generalization over a large number of parameters.This paper presents a unified,generalized framework for coverage path planning that leverages value-based deep reinforcement learning techniques.The novelty of the framework comes from the design of an observation space that accommodates different map sizes,an action masking scheme that guarantees safety and robustness while also serving as a learning-fromdemonstration technique during training,and a unique reward function that yields value functions that are size-invariant.These are coupled with a curriculum learning-based training strategy and parametric environment randomization,enabling the agent to tackle complete or partial coverage path planning with perfect or incomplete knowledge while generalizing to different map sizes,configurations,sensor payloads,and sub-tasks.Our empirical results show that the algorithm can perform zero-shot learning scenarios at a near-optimal level in environments that follow a similar distribution as during training,outperforming a greedy heuristic by sixfold.Furthermore,in out-of-distribution environments,our method surpasses existing state-of-the-art algorithms in most zero-shot and all few-shot scenarios,paving the way for generalizable and adaptable path-planning algorithms.展开更多
Fine-grained image classification, which aims to distinguish images with subtle distinctions, is a challenging task for two main reasons: lack of sufficient training data for every class and difficulty in learning dis...Fine-grained image classification, which aims to distinguish images with subtle distinctions, is a challenging task for two main reasons: lack of sufficient training data for every class and difficulty in learning discriminative features for representation. In this paper, to address the two issues, we propose a two-phase framework for recognizing images from unseen fine-grained classes, i.e., zeroshot fine-grained classification. In the first feature learning phase, we finetune deep convolutional neural networks using hierarchical semantic structure among fine-grained classes to extract discriminative deep visual features. Meanwhile, a domain adaptation structure is induced into deep convolutional neural networks to avoid domain shift from training data to test data. In the second label inference phase, a semantic directed graph is constructed over attributes of fine-grained classes. Based on this graph, we develop a label propagation algorithm to infer the labels of images in the unseen classes. Experimental results on two benchmark datasets demonstrate that our model outperforms the state-of-the-art zero-shot learning models. In addition, the features obtained by our feature learning model also yield significant gains when they are used by other zero-shot learning models, which shows the flexility of our model in zero-shot finegrained classification.展开更多
Multi-label learning is an active research area which plays an important role in machine learning. Traditional learning algorithms, however, have to depend on samples with complete labels. The existing learning algori...Multi-label learning is an active research area which plays an important role in machine learning. Traditional learning algorithms, however, have to depend on samples with complete labels. The existing learning algorithms with missing labels do not consider the relevance of labels, resulting in label estimation errors of new samples. A new multi-label learning algorithm with support vector machine(SVM) based association(SVMA) is proposed to estimate missing labels by constructing the association between different labels. SVMA will establish a mapping function to minimize the number of samples in the margin while ensuring the margin large enough as well as minimizing the misclassification probability. To evaluate the performance of SVMA in the condition of missing labels, four typical data sets are adopted with the integrity of the labels being handled manually. Simulation results show the superiority of SVMA in dealing with the samples with missing labels compared with other models in image classification.展开更多
Multi-label learning deals with objects associated with multiple class labels,and aims to induce a predictive model which can assign a set of relevant class labels for an unseen instance.Since each class might possess...Multi-label learning deals with objects associated with multiple class labels,and aims to induce a predictive model which can assign a set of relevant class labels for an unseen instance.Since each class might possess its own characteristics,the strategy of extracting label-specific features has been widely employed to improve the discrimination process in multi-label learning,where the predictive model is induced based on tailored features specific to each class label instead of the identical instance representations.As a representative approach,LIFT generates label-specific features by conducting clustering analysis.However,its performance may be degraded due to the inherent instability of the single clustering algorithm.To improve this,a novel multi-label learning approach named SENCE(stable label-Specific features gENeration for multi-label learning via mixture-based Clustering Ensemble)is proposed,which stabilizes the generation process of label-specific features via clustering ensemble techniques.Specifically,more stable clustering results are obtained by firstly augmenting the original instance repre-sentation with cluster assignments from base clusters and then fitting a mixture model via the expectation-maximization(EM)algorithm.Extensive experiments on eighteen benchmark data sets show that SENCE performs better than LIFT and other well-established multi-label learning algorithms.展开更多
In recent years,multi-label learning has received a lot of attention.However,most of the existing methods only consider global label correlation or local label correlation.In fact,on the one hand,both global and local...In recent years,multi-label learning has received a lot of attention.However,most of the existing methods only consider global label correlation or local label correlation.In fact,on the one hand,both global and local label correlations can appear in real-world situation at same time.On the other hand,we should not be limited to pairwise labels while ignoring the high-order label correlation.In this paper,we propose a novel and effective method called GLLCBN for multi-label learning.Firstly,we obtain the global label correlation by exploiting label semantic similarity.Then,we analyze the pairwise labels in the label space of the data set to acquire the local correlation.Next,we build the original version of the label dependency model by global and local label correlations.After that,we use graph theory,probability theory and Bayesian networks to eliminate redundant dependency structure in the initial version model,so as to get the optimal label dependent model.Finally,we obtain the feature extraction model by adjusting the Inception V3 model of convolution neural network and combine it with the GLLCBN model to achieve the multi-label learning.The experimental results show that our proposed model has better performance than other multi-label learning methods in performance evaluating.展开更多
The recent worldwide spreading of pneumonia-causing virus, such as Coronavirus, COVID-19, and H1N1, has been endangering the life of human beings all around the world. To provide useful clues for developing antiviral ...The recent worldwide spreading of pneumonia-causing virus, such as Coronavirus, COVID-19, and H1N1, has been endangering the life of human beings all around the world. To provide useful clues for developing antiviral drugs, information of anatomical therapeutic chemicals is vitally important. In view of this, a CNN based predictor called “iATC_Deep-mISF” has been developed. The predictor is particularly useful in dealing with the multi-label systems in which some chemicals may occur in two or more different classes. To maximize the convenience for most experimental scientists, a user-friendly web-server for the new predictor has been established at http://www.jci-bioinfo.cn/iATC_Deep-mISF/, which will become a very powerful tool for developing effective drugs to fight pandemic coronavirus and save the mankind of this planet.展开更多
To reduce the discrepancy between the source and target domains,a new multi-label adaptation network(ML-ANet)based on multiple kernel variants with maximum mean discrepancies is proposed in this paper.The hidden repre...To reduce the discrepancy between the source and target domains,a new multi-label adaptation network(ML-ANet)based on multiple kernel variants with maximum mean discrepancies is proposed in this paper.The hidden representations of the task-specific layers in ML-ANet are embedded in the reproducing kernel Hilbert space(RKHS)so that the mean-embeddings of specific features in different domains could be precisely matched.Multiple kernel functions are used to improve feature distribution efficiency for explicit mean embedding matching,which can further reduce domain discrepancy.Adverse weather and cross-camera adaptation examinations are conducted to verify the effectiveness of our proposed ML-ANet.The results show that our proposed ML-ANet achieves higher accuracies than the compared state-of-the-art methods for multi-label image classification in both the adverse weather adaptation and cross-camera adaptation experiments.These results indicate that ML-ANet can alleviate the reliance on fully labeled training data and improve the accuracy of multi-label image classification in various domain shift scenarios.展开更多
Label correlations are an essential technique for data mining that solves the possible correlation problem between different labels in multi-label classification.Although this technique is widely used in multi-label c...Label correlations are an essential technique for data mining that solves the possible correlation problem between different labels in multi-label classification.Although this technique is widely used in multi-label classification problems,batch learning deals with most issues,which consumes a lot of time and space resources.Unlike traditional batch learning methods,online learning represents a promising family of efficient and scalable machine learning algorithms for large-scale datasets.However,existing online learning research has done little to consider correlations between labels.On the basis of existing research,this paper proposes a multi-label online learning algorithm based on label correlations by maximizing the interval between related labels and unrelated labels in multi-label samples.We evaluate the performance of the proposed algorithm on several public datasets.Experiments show the effectiveness of our algorithm.展开更多
A generalization of supervised single-label learning based on the assumption that each sample in a dataset may belong to more than one class simultaneously is called multi-label learning.The main objective of this wor...A generalization of supervised single-label learning based on the assumption that each sample in a dataset may belong to more than one class simultaneously is called multi-label learning.The main objective of this work is to create a novel framework for learning and classifying imbalancedmulti-label data.This work proposes a framework of two phases.The imbalanced distribution of themulti-label dataset is addressed through the proposed Borderline MLSMOTE resampling method in phase 1.Later,an adaptive weighted l21 norm regularized(Elastic-net)multilabel logistic regression is used to predict unseen samples in phase 2.The proposed Borderline MLSMOTE resampling method focuses on samples with concurrent high labels in contrast to conventional MLSMOTE.The minority labels in these samples are called difficult minority labels and are more prone to penalize classification performance.The concurrentmeasure is considered borderline,and labels associated with samples are regarded as borderline labels in the decision boundary.In phase II,a novel adaptive l21 norm regularized weighted multi-label logistic regression is used to handle balanced data with different weighted synthetic samples.Experimentation on various benchmark datasets shows the outperformance of the proposed method and its powerful predictive performances over existing conventional state-of-the-art multi-label methods.展开更多
Zero-shot learning enables the recognition of new class samples by migrating models learned from semanticfeatures and existing sample features to things that have never been seen before. The problems of consistencyof ...Zero-shot learning enables the recognition of new class samples by migrating models learned from semanticfeatures and existing sample features to things that have never been seen before. The problems of consistencyof different types of features and domain shift problems are two of the critical issues in zero-shot learning. Toaddress both of these issues, this paper proposes a new modeling structure. The traditional approach mappedsemantic features and visual features into the same feature space;based on this, a dual discriminator approachis used in the proposed model. This dual discriminator approach can further enhance the consistency betweensemantic and visual features. At the same time, this approach can also align unseen class semantic features andtraining set samples, providing a portion of information about the unseen classes. In addition, a new feature fusionmethod is proposed in the model. This method is equivalent to adding perturbation to the seen class features,which can reduce the degree to which the classification results in the model are biased towards the seen classes.At the same time, this feature fusion method can provide part of the information of the unseen classes, improvingits classification accuracy in generalized zero-shot learning and reducing domain bias. The proposed method isvalidated and compared with othermethods on four datasets, and fromthe experimental results, it can be seen thatthe method proposed in this paper achieves promising results.展开更多
Multi-label classification is a challenging problem that has attracted significant attention from researchers, particularly in the domain of image and text attribute annotation. However, multi-label datasets are prone...Multi-label classification is a challenging problem that has attracted significant attention from researchers, particularly in the domain of image and text attribute annotation. However, multi-label datasets are prone to serious intra-class and inter-class imbalance problems, which can significantly degrade the classification performance. To address the above issues, we propose the multi-label weighted broad learning system(MLW-BLS) from the perspective of label imbalance weighting and label correlation mining. Further, we propose the multi-label adaptive weighted broad learning system(MLAW-BLS) to adaptively adjust the specific weights and values of labels of MLW-BLS and construct an efficient imbalanced classifier set. Extensive experiments are conducted on various datasets to evaluate the effectiveness of the proposed model, and the results demonstrate its superiority over other advanced approaches.展开更多
In this paper, we utilize the framework of multi-label learning for face demographic classification. We also attempt t;o explore the suitable classifiers and features for face demographic classification. Three most po...In this paper, we utilize the framework of multi-label learning for face demographic classification. We also attempt t;o explore the suitable classifiers and features for face demographic classification. Three most popular demographic information, gender, ethnicity and age are considered in experiments. Based on the results from demographic classification, we utilize statistic analysis to explore the correlation among various face demographic information. Through the analysis, we draw several conclusions on the correlation and interaction among these high-level face semantic, and the obtained results can be helpful in automatic face semantic annotation and other face analysis tasks.展开更多
Phononic crystals,as artificial composite materials,have sparked significant interest due to their novel characteristics that emerge upon the introduction of nonlinearity.Among these properties,second-harmonic feature...Phononic crystals,as artificial composite materials,have sparked significant interest due to their novel characteristics that emerge upon the introduction of nonlinearity.Among these properties,second-harmonic features exhibit potential applications in acoustic frequency conversion,non-reciprocal wave propagation,and non-destructive testing.Precisely manipulating the harmonic band structure presents a major challenge in the design of nonlinear phononic crystals.Traditional design approaches based on parameter adjustments to meet specific application requirements are inefficient and often yield suboptimal performance.Therefore,this paper develops a design methodology using Softmax logistic regression and multi-label classification learning to inversely design the material distribution of nonlinear phononic crystals by exploiting information from harmonic transmission spectra.The results demonstrate that the neural network-based inverse design method can effectively tailor nonlinear phononic crystals with desired functionalities.This work establishes a mapping relationship between the band structure and the material distribution within phononic crystals,providing valuable insights into the inverse design of metamaterials.展开更多
基金supported by the Key R&D Program of Zhejiang Province(No.2024C01021)the National Regional Innovation and Development Joint Fund of China(No.U24A20254)the Leading Talents of Technological Innovation Program of Zhejiang Province(No.2023R5214)。
文摘In recent years,multi-label zero-shot learning(ML-ZSL)has garnered increasing attention because of its wide range of potential applications,such as image annotation,text classification,and bioinformatics.The central challenge in ML-ZSL lies in predicting multiple labels for unseen classes without requiring any labeled training data,which contrasts with conventional supervised learning paradigms.However,existing methods face several significant challenges.These include the substantial semantic gap between different modalities,which impedes effective knowledge transfer,and the intricate and typically complex relationships among multiple labels,making it difficult to model them in a meaningful and accurate manner.To overcome these challenges,we propose a graph-augmented multimodal chain-of-thought(GMCoT)reasoning approach.The proposed method combines the strengths of multimodal large language models with graph-based structures,significantly enhancing the reasoning process involved in multi-label prediction.First,a novel multimodal chain-of-thought reasoning framework is presented which imitates human-like step-by-step reasoning to produce multi-label predictions.Second,a technique is presented for integrating label graphs into the reasoning process.This technique enables the capture of complex semantic relationships among labels,thereby improving the accuracy and consistency of multi-label generation.Comprehensive experiments on benchmark datasets demonstrate that the proposed GMCoT approach outperforms state-of-the-art methods in ML-ZSL.
基金supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(RS-2020-NR049579).
文摘High-dimensional data causes difficulties in machine learning due to high time consumption and large memory requirements.In particular,in amulti-label environment,higher complexity is required asmuch as the number of labels.Moreover,an optimization problem that fully considers all dependencies between features and labels is difficult to solve.In this study,we propose a novel regression-basedmulti-label feature selectionmethod that integrates mutual information to better exploit the underlying data structure.By incorporating mutual information into the regression formulation,the model captures not only linear relationships but also complex non-linear dependencies.The proposed objective function simultaneously considers three types of relationships:(1)feature redundancy,(2)featurelabel relevance,and(3)inter-label dependency.These three quantities are computed usingmutual information,allowing the proposed formulation to capture nonlinear dependencies among variables.These three types of relationships are key factors in multi-label feature selection,and our method expresses them within a unified formulation,enabling efficient optimization while simultaneously accounting for all of them.To efficiently solve the proposed optimization problem under non-negativity constraints,we develop a gradient-based optimization algorithm with fast convergence.Theexperimental results on sevenmulti-label datasets show that the proposed method outperforms existingmulti-label feature selection techniques.
文摘Predicting the behavior of renewable energy systems requires models capable of generating accurate forecasts from limited historical data,a challenge that becomes especially pronounced when commissioning new facil-ities where operational records are scarce.This review aims to synthesize recent progress in data-efficient deep learning approaches for addressing such“cold-start”forecasting problems.It primarily covers three interrelated domains—solar photovoltaic(PV),wind power,and electrical load forecasting—where data scarcity and operational variability are most critical,while also including representative studies on hydropower and carbon emission prediction to provide a broader systems perspective.To this end,we examined trends from over 150 predominantly peer-reviewed studies published between 2019 and mid-2025,highlighting advances in zero-shot and few-shot meta-learning frameworks that enable rapid model adaptation with minimal labeled data.Moreover,transfer learning approaches combined with spatiotemporal graph neural networks have been employed to transfer knowledge from existing energy assets to new,data-sparse environments,effectively capturing hidden dependencies among geographic features,meteorological dynamics,and grid structures.Synthetic data generation has further proven valuable for expanding training samples and mitigating overfitting in cold-start scenarios.In addition,large language models and explainable artificial intelligence(XAI)—notably conversational XAI systems—have been used to interpret and communicate complex model behaviors in accessible terms,fostering operator trust from the earliest deployment stages.By consolidating methodological advances,unresolved challenges,and open-source resources,this review provides a coherent overview of deep learning strategies that can shorten the data-sparse ramp-up period of new energy infrastructures and accelerate the transition toward resilient,low-carbon electricity grids.
基金supporting of the National Science and Technology Council NSTC(grant nos.NSTC 112-2221-E-019-023,NSTC 113-2221-E-019-039)Taiwan University of Science and Technology.
文摘In its 2023 global health statistics,the World Health Organization noted that noncommunicable diseases(NCDs)remain the leading cause of disease burden worldwide,with cardiovascular diseases(CVDs)resulting in more deaths than the three other major NCDs combined.In this study,we developed a method that can comprehensively detect which CVDs are present in a patient.Specifically,we propose a multi-label classification method that utilizes photoplethysmography(PPG)signals and physiological characteristics from public datasets to classify four types of CVDs and related conditions:hypertension,diabetes,cerebral infarction,and cerebrovascular disease.Our approach to multi-disease classification of cardiovascular diseases(CVDs)using PPG signals achieves the highest classification performance when encompassing the broadest range of disease categories,thereby offering a more comprehensive assessment of human health.We employ a multi-label classification strategy to simultaneously predict the presence or absence of multiple diseases.Specifically,we first apply the Savitzky-Golay(S-G)filter to the PPG signals to reduce noise and then transform into statistical features.We integrate processed PPG signals with individual physiological features as a multimodal input,thereby expanding the learned feature space.Notably,even with a simple machine learning method,this approach can achieve relatively high accuracy.The proposed method achieved a maximum F1-score of 0.91,minimum Hamming loss of 0.04,and an accuracy of 0.95.Thus,our method represents an effective and rapid solution for detecting multiple diseases simultaneously,which is beneficial for comprehensively managing CVDs.
文摘Automated cartoon character recognition is crucial for applications in content indexing,filtering,and copyright protection,yet it faces a significant challenge in animated media due to high intra-class visual variability,where characters frequently alter their appearance.To address this problem,we introduce the novel Kral Sakir dataset,a public benchmark of 16,725 images specifically curated for the task of multi-label cartoon character classification under these varied conditions.This paper conducts a comprehensive benchmark study,evaluating the performance of state-of-the-art pretrained Convolutional Neural Networks(CNNs),including DenseNet,ResNet,and VGG,against a custom baseline model trained from scratch.Our experiments,evaluated using metrics of F1-Score,accuracy,and Area Under the ROC Curve(AUC),demonstrate that fine-tuning pretrained models is a highly effective strategy.The best-performing model,DenseNet121,achieved an F1-Score of 0.9890 and an accuracy of 0.9898,significantly outperforming our baseline CNN(F1-Score of 0.9545).The findings validate the power of transfer learning for this domain and establish a strong performance benchmark.The introduced dataset provides a valuable resource for future research into developing robust and accurate character recognition systems.
基金co-supported by the Aeronautical Science Foundation of China(Nos.2018ZA52002,2019ZA052011).
文摘With the availability of high-performance computing technology and the development of advanced numerical simulation methods, Computational Fluid Dynamics (CFD) is becoming more and more practical and efficient in engineering. As one of the high-precision representative algorithms, the high-order Discontinuous Galerkin Method (DGM) has not only attracted widespread attention from scholars in the CFD research community, but also received strong development. However, when DGM is extended to high-speed aerodynamic flow field calculations, non-physical numerical Gibbs oscillations near shock waves often significantly affect the numerical accuracy and even cause calculation failure. Data driven approaches based on machine learning techniques can be used to learn the characteristics of Gibbs noise, which motivates us to use it in high-speed DG applications. To achieve this goal, labeled data need to be generated in order to train the machine learning models. This paper proposes a new method for denoising modeling of Gibbs phenomenon using a machine learning technique, the zero-shot learning strategy, to eliminate acquiring large amounts of CFD data. The model adopts a graph convolutional network combined with graph attention mechanism to learn the denoising paradigm from synthetic Gibbs noise data and generalize to DGM numerical simulation data. Numerical simulation results show that the Gibbs denoising model proposed in this paper can suppress the numerical oscillation near shock waves in the high-order DGM. Our work automates the extension of DGM to high-speed aerodynamic flow field calculations with higher generalization and lower cost.
基金supported by project RELIABLE(PTDC/EEI-AUT/3522/2020)R&D Unit SYSTEC-Base(UIDB001472020)+1 种基金Programmatic(UIDP001472020)funds-and Associate Laboratory Advanced Production and Intelligent Systems ARISE-LAP01122020funded by national funds through the FCT/MCTES(PIDDAC).
文摘The ability of mobile robots to plan and execute a path is foundational to various path-planning challenges,particularly Coverage Path Planning.While this task has been typically tackled with classical algorithms,these often struggle with flexibility and adaptability in unknown environments.On the other hand,recent advances in Reinforcement Learning offer promising approaches,yet a significant gap in the literature remains when it comes to generalization over a large number of parameters.This paper presents a unified,generalized framework for coverage path planning that leverages value-based deep reinforcement learning techniques.The novelty of the framework comes from the design of an observation space that accommodates different map sizes,an action masking scheme that guarantees safety and robustness while also serving as a learning-fromdemonstration technique during training,and a unique reward function that yields value functions that are size-invariant.These are coupled with a curriculum learning-based training strategy and parametric environment randomization,enabling the agent to tackle complete or partial coverage path planning with perfect or incomplete knowledge while generalizing to different map sizes,configurations,sensor payloads,and sub-tasks.Our empirical results show that the algorithm can perform zero-shot learning scenarios at a near-optimal level in environments that follow a similar distribution as during training,outperforming a greedy heuristic by sixfold.Furthermore,in out-of-distribution environments,our method surpasses existing state-of-the-art algorithms in most zero-shot and all few-shot scenarios,paving the way for generalizable and adaptable path-planning algorithms.
基金supported by National Basic Research Program of China (973 Program) (No. 2015CB352502)National Nature Science Foundation of China (No. 61573026)Beijing Nature Science Foundation (No. L172037)
文摘Fine-grained image classification, which aims to distinguish images with subtle distinctions, is a challenging task for two main reasons: lack of sufficient training data for every class and difficulty in learning discriminative features for representation. In this paper, to address the two issues, we propose a two-phase framework for recognizing images from unseen fine-grained classes, i.e., zeroshot fine-grained classification. In the first feature learning phase, we finetune deep convolutional neural networks using hierarchical semantic structure among fine-grained classes to extract discriminative deep visual features. Meanwhile, a domain adaptation structure is induced into deep convolutional neural networks to avoid domain shift from training data to test data. In the second label inference phase, a semantic directed graph is constructed over attributes of fine-grained classes. Based on this graph, we develop a label propagation algorithm to infer the labels of images in the unseen classes. Experimental results on two benchmark datasets demonstrate that our model outperforms the state-of-the-art zero-shot learning models. In addition, the features obtained by our feature learning model also yield significant gains when they are used by other zero-shot learning models, which shows the flexility of our model in zero-shot finegrained classification.
基金Support by the National High Technology Research and Development Program of China(No.2012AA120802)National Natural Science Foundation of China(No.61771186)+1 种基金Postdoctoral Research Project of Heilongjiang Province(No.LBH-Q15121)Undergraduate University Project of Young Scientist Creative Talent of Heilongjiang Province(No.UNPYSCT-2017125)
文摘Multi-label learning is an active research area which plays an important role in machine learning. Traditional learning algorithms, however, have to depend on samples with complete labels. The existing learning algorithms with missing labels do not consider the relevance of labels, resulting in label estimation errors of new samples. A new multi-label learning algorithm with support vector machine(SVM) based association(SVMA) is proposed to estimate missing labels by constructing the association between different labels. SVMA will establish a mapping function to minimize the number of samples in the margin while ensuring the margin large enough as well as minimizing the misclassification probability. To evaluate the performance of SVMA in the condition of missing labels, four typical data sets are adopted with the integrity of the labels being handled manually. Simulation results show the superiority of SVMA in dealing with the samples with missing labels compared with other models in image classification.
基金This work was supported by the National Science Foundation of China(62176055)the China University S&T Innovation Plan Guided by the Ministry of Education.
文摘Multi-label learning deals with objects associated with multiple class labels,and aims to induce a predictive model which can assign a set of relevant class labels for an unseen instance.Since each class might possess its own characteristics,the strategy of extracting label-specific features has been widely employed to improve the discrimination process in multi-label learning,where the predictive model is induced based on tailored features specific to each class label instead of the identical instance representations.As a representative approach,LIFT generates label-specific features by conducting clustering analysis.However,its performance may be degraded due to the inherent instability of the single clustering algorithm.To improve this,a novel multi-label learning approach named SENCE(stable label-Specific features gENeration for multi-label learning via mixture-based Clustering Ensemble)is proposed,which stabilizes the generation process of label-specific features via clustering ensemble techniques.Specifically,more stable clustering results are obtained by firstly augmenting the original instance repre-sentation with cluster assignments from base clusters and then fitting a mixture model via the expectation-maximization(EM)algorithm.Extensive experiments on eighteen benchmark data sets show that SENCE performs better than LIFT and other well-established multi-label learning algorithms.
文摘In recent years,multi-label learning has received a lot of attention.However,most of the existing methods only consider global label correlation or local label correlation.In fact,on the one hand,both global and local label correlations can appear in real-world situation at same time.On the other hand,we should not be limited to pairwise labels while ignoring the high-order label correlation.In this paper,we propose a novel and effective method called GLLCBN for multi-label learning.Firstly,we obtain the global label correlation by exploiting label semantic similarity.Then,we analyze the pairwise labels in the label space of the data set to acquire the local correlation.Next,we build the original version of the label dependency model by global and local label correlations.After that,we use graph theory,probability theory and Bayesian networks to eliminate redundant dependency structure in the initial version model,so as to get the optimal label dependent model.Finally,we obtain the feature extraction model by adjusting the Inception V3 model of convolution neural network and combine it with the GLLCBN model to achieve the multi-label learning.The experimental results show that our proposed model has better performance than other multi-label learning methods in performance evaluating.
文摘The recent worldwide spreading of pneumonia-causing virus, such as Coronavirus, COVID-19, and H1N1, has been endangering the life of human beings all around the world. To provide useful clues for developing antiviral drugs, information of anatomical therapeutic chemicals is vitally important. In view of this, a CNN based predictor called “iATC_Deep-mISF” has been developed. The predictor is particularly useful in dealing with the multi-label systems in which some chemicals may occur in two or more different classes. To maximize the convenience for most experimental scientists, a user-friendly web-server for the new predictor has been established at http://www.jci-bioinfo.cn/iATC_Deep-mISF/, which will become a very powerful tool for developing effective drugs to fight pandemic coronavirus and save the mankind of this planet.
基金Supported by Shenzhen Fundamental Research Fund of China(Grant No.JCYJ20190808142613246)National Natural Science Foundation of China(Grant No.51805332),and Young Elite Scientists Sponsorship Program funded by the China Society of Automotive Engineers.
文摘To reduce the discrepancy between the source and target domains,a new multi-label adaptation network(ML-ANet)based on multiple kernel variants with maximum mean discrepancies is proposed in this paper.The hidden representations of the task-specific layers in ML-ANet are embedded in the reproducing kernel Hilbert space(RKHS)so that the mean-embeddings of specific features in different domains could be precisely matched.Multiple kernel functions are used to improve feature distribution efficiency for explicit mean embedding matching,which can further reduce domain discrepancy.Adverse weather and cross-camera adaptation examinations are conducted to verify the effectiveness of our proposed ML-ANet.The results show that our proposed ML-ANet achieves higher accuracies than the compared state-of-the-art methods for multi-label image classification in both the adverse weather adaptation and cross-camera adaptation experiments.These results indicate that ML-ANet can alleviate the reliance on fully labeled training data and improve the accuracy of multi-label image classification in various domain shift scenarios.
基金Supported by the State Grid Technology Item(52460D230002)。
文摘Label correlations are an essential technique for data mining that solves the possible correlation problem between different labels in multi-label classification.Although this technique is widely used in multi-label classification problems,batch learning deals with most issues,which consumes a lot of time and space resources.Unlike traditional batch learning methods,online learning represents a promising family of efficient and scalable machine learning algorithms for large-scale datasets.However,existing online learning research has done little to consider correlations between labels.On the basis of existing research,this paper proposes a multi-label online learning algorithm based on label correlations by maximizing the interval between related labels and unrelated labels in multi-label samples.We evaluate the performance of the proposed algorithm on several public datasets.Experiments show the effectiveness of our algorithm.
基金partly supported by the Technology Development Program of MSS(No.S3033853)by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(No.2021R1A4A1031509).
文摘A generalization of supervised single-label learning based on the assumption that each sample in a dataset may belong to more than one class simultaneously is called multi-label learning.The main objective of this work is to create a novel framework for learning and classifying imbalancedmulti-label data.This work proposes a framework of two phases.The imbalanced distribution of themulti-label dataset is addressed through the proposed Borderline MLSMOTE resampling method in phase 1.Later,an adaptive weighted l21 norm regularized(Elastic-net)multilabel logistic regression is used to predict unseen samples in phase 2.The proposed Borderline MLSMOTE resampling method focuses on samples with concurrent high labels in contrast to conventional MLSMOTE.The minority labels in these samples are called difficult minority labels and are more prone to penalize classification performance.The concurrentmeasure is considered borderline,and labels associated with samples are regarded as borderline labels in the decision boundary.In phase II,a novel adaptive l21 norm regularized weighted multi-label logistic regression is used to handle balanced data with different weighted synthetic samples.Experimentation on various benchmark datasets shows the outperformance of the proposed method and its powerful predictive performances over existing conventional state-of-the-art multi-label methods.
文摘Zero-shot learning enables the recognition of new class samples by migrating models learned from semanticfeatures and existing sample features to things that have never been seen before. The problems of consistencyof different types of features and domain shift problems are two of the critical issues in zero-shot learning. Toaddress both of these issues, this paper proposes a new modeling structure. The traditional approach mappedsemantic features and visual features into the same feature space;based on this, a dual discriminator approachis used in the proposed model. This dual discriminator approach can further enhance the consistency betweensemantic and visual features. At the same time, this approach can also align unseen class semantic features andtraining set samples, providing a portion of information about the unseen classes. In addition, a new feature fusionmethod is proposed in the model. This method is equivalent to adding perturbation to the seen class features,which can reduce the degree to which the classification results in the model are biased towards the seen classes.At the same time, this feature fusion method can provide part of the information of the unseen classes, improvingits classification accuracy in generalized zero-shot learning and reducing domain bias. The proposed method isvalidated and compared with othermethods on four datasets, and fromthe experimental results, it can be seen thatthe method proposed in this paper achieves promising results.
基金supported in part by the National Key R&D Program of China (2023YFA1011601)the Major Key Project of PCL, China (PCL2023AS7-1)+3 种基金in part by the National Natural Science Foundation of China (U21A20478, 62106224, 92267203)in part by the Science and Technology Major Project of Guangzhou (202007030006)in part by the Major Key Project of PCL (PCL2021A09)in part by the Guangzhou Science and Technology Plan Project (2024A04J3749)。
文摘Multi-label classification is a challenging problem that has attracted significant attention from researchers, particularly in the domain of image and text attribute annotation. However, multi-label datasets are prone to serious intra-class and inter-class imbalance problems, which can significantly degrade the classification performance. To address the above issues, we propose the multi-label weighted broad learning system(MLW-BLS) from the perspective of label imbalance weighting and label correlation mining. Further, we propose the multi-label adaptive weighted broad learning system(MLAW-BLS) to adaptively adjust the specific weights and values of labels of MLW-BLS and construct an efficient imbalanced classifier set. Extensive experiments are conducted on various datasets to evaluate the effectiveness of the proposed model, and the results demonstrate its superiority over other advanced approaches.
基金Project supported by the National Natural Science Foundation of China(Grant No.60605012)the Natural Science Foundation of Shanghai(Grant No.08ZR1408200)+1 种基金the Open Project Program of the National Laboratory of Pattern Recognition of China(Grant No.08-2-16)the Shanghai Leading Academic Discipline Project(Grant No.J50103)
文摘In this paper, we utilize the framework of multi-label learning for face demographic classification. We also attempt t;o explore the suitable classifiers and features for face demographic classification. Three most popular demographic information, gender, ethnicity and age are considered in experiments. Based on the results from demographic classification, we utilize statistic analysis to explore the correlation among various face demographic information. Through the analysis, we draw several conclusions on the correlation and interaction among these high-level face semantic, and the obtained results can be helpful in automatic face semantic annotation and other face analysis tasks.
基金supported by the National Key Research and Development Program of China(Grant No.2020YFA0211400)the State Key Program of the National Natural Science of China(Grant No.11834008)+2 种基金the National Natural Science Foundation of China(Grant Nos.12174192,12174188,and 11974176)the State Key Laboratory of Acoustics,Chinese Academy of Sciences(Grant No.SKLA202410)the Fund from the Key Laboratory of Underwater Acoustic Environment,Chinese Academy of Sciences(Grant No.SSHJ-KFKT-1701).
文摘Phononic crystals,as artificial composite materials,have sparked significant interest due to their novel characteristics that emerge upon the introduction of nonlinearity.Among these properties,second-harmonic features exhibit potential applications in acoustic frequency conversion,non-reciprocal wave propagation,and non-destructive testing.Precisely manipulating the harmonic band structure presents a major challenge in the design of nonlinear phononic crystals.Traditional design approaches based on parameter adjustments to meet specific application requirements are inefficient and often yield suboptimal performance.Therefore,this paper develops a design methodology using Softmax logistic regression and multi-label classification learning to inversely design the material distribution of nonlinear phononic crystals by exploiting information from harmonic transmission spectra.The results demonstrate that the neural network-based inverse design method can effectively tailor nonlinear phononic crystals with desired functionalities.This work establishes a mapping relationship between the band structure and the material distribution within phononic crystals,providing valuable insights into the inverse design of metamaterials.
